From c132d638fece5ec793a39bc97badacc43755101d Mon Sep 17 00:00:00 2001 From: mjames-upc Date: Wed, 5 Sep 2018 15:52:38 -0600 Subject: [PATCH] initial commit --- .gitignore | 3 + .travis.yml | 50 + LICENSE | 30 + README.rst | 71 + awips/AlertVizHandler.py | 52 + awips/ConfigFileUtil.py | 39 + awips/DateTimeConverter.py | 90 + awips/NotificationMessage.py | 166 ++ awips/QpidSubscriber.py | 105 ++ awips/ThriftClient.py | 84 + awips/TimeUtil.py | 91 ++ awips/UsageArgumentParser.py | 64 + awips/UsageOptionParser.py | 21 + awips/__init__.py | 20 + awips/dataaccess/CombinedTimeQuery.py | 83 + awips/dataaccess/DataAccessLayer.py | 266 +++ awips/dataaccess/DataNotificationLayer.py | 140 ++ awips/dataaccess/DataQueue.py | 196 +++ awips/dataaccess/PyData.py | 40 + awips/dataaccess/PyGeometryData.py | 63 + awips/dataaccess/PyGeometryNotification.py | 37 + awips/dataaccess/PyGridData.py | 64 + awips/dataaccess/PyGridNotification.py | 42 + awips/dataaccess/PyNotification.py | 93 ++ 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"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" + - WHEELHOUSE="https://unidata-python.s3.amazonaws.com/wheelhouse/index.html" + - WHEELDIR="wheelhouse/" + - EXTRA_INSTALLS="test,cdm" + - MPLLOCALFREETYPE="1" + +matrix: + include: + - python: 2.7 + env: + - VERSIONS="numpy==1.9.1" + - python: 3.4 + env: + - python: 3.5 + env: + - python: "3.6-dev" + env: PRE="--pre" + - python: nightly + env: PRE="--pre" + allow_failures: + - python: "3.6-dev" + - python: nightly + +before_install: + # Shapely dependency needed to keep from using Shapely's manylinux wheels + # which use a different geos that what we build cartopy with on Travis + - pip install --upgrade pip; + - mkdir $WHEELDIR; + - pip download -d $WHEELDIR ".[$EXTRA_INSTALLS]" $EXTRA_PACKAGES -f $WHEELHOUSE $PRE $VERSIONS; + - touch $WHEELDIR/download_marker && ls -lrt $WHEELDIR; + - travis_wait pip wheel -w $WHEELDIR $EXTRA_PACKAGES -f $WHEELHOUSE $PRE $VERSIONS; + - pip install $EXTRA_PACKAGES --upgrade --no-index -f file://$PWD/$WHEELDIR $VERSIONS; + - travis_wait pip wheel -w $WHEELDIR ".[$EXTRA_INSTALLS]" $EXTRA_PACKAGES -f $WHEELHOUSE $PRE $VERSIONS; + - rm -f $WHEELDIR/python-awips*.whl; + +install: + - pip install ".[$EXTRA_INSTALLS]" --upgrade --no-index $PRE -f file://$PWD/$WHEELDIR $VERSIONS; + +script: true diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..2c68773 --- /dev/null +++ b/LICENSE @@ -0,0 +1,30 @@ +Copyright (c) 2017, Unidata Python AWIPS Developers. +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + + * Neither the name of the MetPy Developers nor the names of any + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/README.rst b/README.rst new file mode 100644 index 0000000..fa1781b --- /dev/null +++ b/README.rst @@ -0,0 +1,71 @@ +AWIPS Python Data Access Framework +================================== + +|License| |PyPI| |LatestDocs| + +|Travis| |Codacy| + +.. |License| image:: https://img.shields.io/pypi/l/python-awips.svg + :target: https://pypi.python.org/pypi/python-awips/ + :alt: License + +.. |PyPI| image:: https://img.shields.io/pypi/v/python-awips.svg + :target: https://pypi.python.org/pypi/python-awips/ + :alt: PyPI Package + +.. |PyPIDownloads| image:: https://img.shields.io/pypi/dm/python-awips.svg + :target: https://pypi.python.org/pypi/python-awips/ + :alt: PyPI Downloads + +.. |LatestDocs| image:: https://readthedocs.org/projects/pip/badge/?version=latest + :target: http://python-awips.readthedocs.org/en/latest/ + :alt: Latest Doc Build Status + +.. |Travis| image:: https://travis-ci.org/Unidata/python-awips.svg?branch=master + :target: https://travis-ci.org/Unidata/python-awips + :alt: Travis Build Status + +.. |Codacy| image:: https://api.codacy.com/project/badge/Grade/560b27db294449ed9484da1aadeaee91 + :target: https://www.codacy.com/app/mjames/python-awips + :alt: Codacy issues + + +Install +------- + +- pip install python-awips + +Conda Environment +----------------- + +- git clone https://github.com/Unidata/python-awips.git +- cd python-awips +- conda env create -f environment.yml +- source activate python-awips +- jupyter notebook examples + +Requirements +------------ + +- Python 2.7+ +- pip install numpy shapely six +- pip install metpy enum34 - to run Jupyter Notebook examples + +Documentation +------------- + +* http://python-awips.readthedocs.org/en/latest/ +* http://nbviewer.jupyter.org/github/Unidata/python-awips/tree/master/examples/notebooks + +Install from Github +------------------- + +- git clone https://github.com/Unidata/python-awips.git +- cd python-awips +- python setup.py install + + +License +------- + +Unidata AWIPS source code and binaries (RPMs) are considered to be in the public domain, meaning there are no restrictions on any download, modification, or distribution in any form (original or modified). The Python AWIPS package contains no proprietery content and is therefore not subject to export controls as stated in the Master Rights licensing file and source code headers. diff --git a/awips/AlertVizHandler.py b/awips/AlertVizHandler.py new file mode 100644 index 0000000..85d0756 --- /dev/null +++ b/awips/AlertVizHandler.py @@ -0,0 +1,52 @@ +## +## + + +# +# Pure python logging mechanism for logging to AlertViz from +# pure python (ie not JEP). DO NOT USE IN PYTHON CALLED +# FROM JAVA. +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 08/18/10 njensen Initial Creation. +# +# +# + +import logging +import NotificationMessage + +class AlertVizHandler(logging.Handler): + + def __init__(self, host='localhost', port=61999, category='LOCAL', source='ANNOUNCER', level=logging.NOTSET): + logging.Handler.__init__(self, level) + self._category = category + self._host = host + self._port = port + self._source = source + + + def emit(self, record): + "Implements logging.Handler's interface. Record argument is a logging.LogRecord." + priority = None + if record.levelno >= 50: + priority = 'CRITICAL' + elif record.levelno >= 40: + priority = 'SIGNIFICANT' + elif record.levelno >= 30: + priority = 'PROBLEM' + elif record.levelno >= 20: + priority = 'EVENTA' + elif record.levelno >= 10: + priority = 'EVENTB' + else: + priority = 'VERBOSE' + + msg = self.format(record) + + notify = NotificationMessage.NotificationMessage(self._host, self._port, msg, priority, self._category, self._source) + notify.send() diff --git a/awips/ConfigFileUtil.py b/awips/ConfigFileUtil.py new file mode 100644 index 0000000..83c6733 --- /dev/null +++ b/awips/ConfigFileUtil.py @@ -0,0 +1,39 @@ +## +## + +# +# A set of utility functions for dealing with configuration files. +# +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/27/10 dgilling Initial Creation. +# +# +# + + +def parseKeyValueFile(fileName): + propDict= dict() + + try: + propFile= open(fileName, "rU") + for propLine in propFile: + propDef= propLine.strip() + if len(propDef) == 0: + continue + if propDef[0] in ( '#' ): + continue + punctuation= [ propDef.find(c) for c in ':= ' ] + [ len(propDef) ] + found= min( [ pos for pos in punctuation if pos != -1 ] ) + name= propDef[:found].rstrip() + value= propDef[found:].lstrip(":= ").rstrip() + propDict[name]= value + propFile.close() + except: + pass + + return propDict \ No newline at end of file diff --git a/awips/DateTimeConverter.py b/awips/DateTimeConverter.py new file mode 100644 index 0000000..1a4ba61 --- /dev/null +++ b/awips/DateTimeConverter.py @@ -0,0 +1,90 @@ +# # +# # + +# +# Functions for converting between the various "Java" dynamic serialize types +# used by EDEX to the native python time datetime. +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/24/15 #4480 dgilling Initial Creation. +# + +import datetime +import time + +from dynamicserialize.dstypes.java.util import Date +from dynamicserialize.dstypes.java.sql import Timestamp +from dynamicserialize.dstypes.com.raytheon.uf.common.time import TimeRange + + +MAX_TIME = pow(2, 31) - 1 +MICROS_IN_SECOND = 1000000 + + +def convertToDateTime(timeArg): + """ + Converts the given object to a python datetime object. Supports native + python representations like datetime and struct_time, but also + the dynamicserialize types like Date and Timestamp. Raises TypeError + if no conversion can be performed. + + Args: + timeArg: a python object representing a date and time. Supported + types include datetime, struct_time, float, int, long and the + dynamicserialize types Date and Timestamp. + + Returns: + A datetime that represents the same date/time as the passed in object. + """ + if isinstance(timeArg, datetime.datetime): + return timeArg + elif isinstance(timeArg, time.struct_time): + return datetime.datetime(*timeArg[:6]) + elif isinstance(timeArg, float): + # seconds as float, should be avoided due to floating point errors + totalSecs = long(timeArg) + micros = int((timeArg - totalSecs) * MICROS_IN_SECOND) + return _convertSecsAndMicros(totalSecs, micros) + elif isinstance(timeArg, (int, long)): + # seconds as integer + totalSecs = timeArg + return _convertSecsAndMicros(totalSecs, 0) + elif isinstance(timeArg, (Date, Timestamp)): + totalSecs = timeArg.getTime() + return _convertSecsAndMicros(totalSecs, 0) + else: + objType = str(type(timeArg)) + raise TypeError("Cannot convert object of type " + objType + " to datetime.") + +def _convertSecsAndMicros(seconds, micros): + if seconds < MAX_TIME: + rval = datetime.datetime.utcfromtimestamp(seconds) + else: + extraTime = datetime.timedelta(seconds=(seconds - MAX_TIME)) + rval = datetime.datetime.utcfromtimestamp(MAX_TIME) + extraTime + return rval.replace(microsecond=micros) + +def constructTimeRange(*args): + """ + Builds a python dynamicserialize TimeRange object from the given + arguments. + + Args: + args*: must be a TimeRange or a pair of objects that can be + converted to a datetime via convertToDateTime(). + + Returns: + A TimeRange. + """ + + if len(args) == 1 and isinstance(args[0], TimeRange): + return args[0] + if len(args) != 2: + raise TypeError("constructTimeRange takes exactly 2 arguments, " + str(len(args)) + " provided.") + startTime = convertToDateTime(args[0]) + endTime = convertToDateTime(args[1]) + return TimeRange(startTime, endTime) diff --git a/awips/NotificationMessage.py b/awips/NotificationMessage.py new file mode 100755 index 0000000..d61ceb0 --- /dev/null +++ b/awips/NotificationMessage.py @@ -0,0 +1,166 @@ +## +## + +from string import Template + +import ctypes +import stomp +import socket +import sys +import time +import threading +import xml.etree.ElementTree as ET + +import ThriftClient +from dynamicserialize.dstypes.com.raytheon.uf.common.alertviz import AlertVizRequest +from dynamicserialize import DynamicSerializationManager + +# +# Provides a capability of constructing notification messages and sending +# them to a STOMP data source. +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/30/08 chammack Initial Creation. +# 11/03/10 5849 cjeanbap Moved to awips package from +# com.raytheon.uf.tools.cli +# 01/07/11 5645 cjeanbap Added audio file to Status Message. +# 05/27/11 3050 cjeanbap Added if-statement to check Priority +# value +# 07/27/15 4654 skorolev Added filters +# 11/11/15 5120 rferrel Cannot serialize empty filters. +# +class NotificationMessage: + + priorityMap = { + 0: 'CRITICAL', + 1: 'SIGNIFICANT', + 2: 'PROBLEM', + 3: 'EVENTA', + 4: 'EVENTB', + 5: 'VERBOSE'} + + def __init__(self, host='localhost', port=61999, message='', priority='PROBLEM', category="LOCAL", source="ANNOUNCER", audioFile="NONE", filters=None): + self.host = host + self.port = port + self.message = message + self.audioFile = audioFile + self.source = source + self.category = category + self.filters = filters + + priorityInt = None + + try: + priorityInt = int(priority) + except: + pass + + if priorityInt is None: + #UFStatus.java contains mapping of Priority to Logging level mapping + if priority == 'CRITICAL' or priority == 'FATAL': + priorityInt = int(0) + elif priority == 'SIGNIFICANT' or priority == 'ERROR': + priorityInt = int(1) + elif priority == 'PROBLEM' or priority == 'WARN': + priorityInt = int(2) + elif priority == 'EVENTA' or priority == 'INFO': + priorityInt = int(3) + elif priority == 'EVENTB': + priorityInt = int(4) + elif priority == 'VERBOSE' or priority == 'DEBUG': + priorityInt = int(5) + + if (priorityInt < 0 or priorityInt > 5): + print "Error occurred, supplied an invalid Priority value: " + str(priorityInt) + print "Priority values are 0, 1, 2, 3, 4 and 5." + sys.exit(1) + + if priorityInt is not None: + self.priority = self.priorityMap[priorityInt] + else: + self.priority = priority + + def connection_timeout(self, connection): + if (connection is not None and not connection.is_connected()): + print "Connection Retry Timeout" + for tid, tobj in threading._active.items(): + if tobj.name is "MainThread": + res = ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, ctypes.py_object(SystemExit)) + if res != 0 and res != 1: + # problem, reset state + ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, 0) + + def send(self): + # depending on the value of the port number indicates the distribution + # of the message to AlertViz + # 9581 is global distribution thru ThriftClient to Edex + # 61999 is local distribution + if (int(self.port) == 61999): + # use stomp.py + conn = stomp.Connection(host_and_ports=[(self.host, 61999)]) + timeout = threading.Timer(5.0, self.connection_timeout, [conn]) + + try: + timeout.start(); + conn.start() + finally: + timeout.cancel() + + conn.connect() + + sm = ET.Element("statusMessage") + sm.set("machine", socket.gethostname()) + sm.set("priority", self.priority) + sm.set("category", self.category) + sm.set("sourceKey", self.source) + sm.set("audioFile", self.audioFile) + if self.filters is not None and len(self.filters) > 0: + sm.set("filters", self.filters) + msg = ET.SubElement(sm, "message") + msg.text = self.message + details = ET.SubElement(sm, "details") + msg = ET.tostring(sm, "UTF-8") + + try : + conn.send(msg, destination='/queue/messages') + time.sleep(2) + finally: + conn.stop() + else: + # use ThriftClient + alertVizRequest = createRequest(self.message, self.priority, self.source, self.category, self.audioFile, self.filters) + thriftClient = ThriftClient.ThriftClient(self.host, self.port, "/services") + + serverResponse = None + try: + serverResponse = thriftClient.sendRequest(alertVizRequest) + except Exception, ex: + print "Caught exception submitting AlertVizRequest: ", str(ex) + + if (serverResponse != "None"): + print "Error occurred submitting Notification Message to AlertViz receiver: ", serverResponse + sys.exit(1) + else: + print "Response: " + str(serverResponse) + +def createRequest(message, priority, source, category, audioFile, filters): + obj = AlertVizRequest() + + obj.setMachine(socket.gethostname()) + obj.setPriority(priority) + obj.setCategory(category) + obj.setSourceKey(source) + obj.setMessage(message) + if (audioFile is not None): + obj.setAudioFile(audioFile) + else: + obj.setAudioFile('\0') + obj.setFilters(filters) + return obj + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/awips/QpidSubscriber.py b/awips/QpidSubscriber.py new file mode 100644 index 0000000..695189a --- /dev/null +++ b/awips/QpidSubscriber.py @@ -0,0 +1,105 @@ +## +## + +# +# Provides a Python-based interface for subscribing to qpid queues and topics. +# +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 11/17/10 njensen Initial Creation. +# 08/15/13 2169 bkowal Optionally gzip decompress any data that is read. +# 08/04/16 2416 tgurney Add queueStarted property +# 02/16/17 6084 bsteffen Support ssl connections +# 09/07/17 6175 tgurney Remove "decompressing" log message +# +# + +import os +import os.path +import qpid +import zlib + +from Queue import Empty +from qpid.exceptions import Closed + +class QpidSubscriber: + + def __init__(self, host='127.0.0.1', port=5672, decompress=False, ssl=None): + self.host = host + self.port = port + self.decompress = decompress; + socket = qpid.util.connect(host, port) + if "QPID_SSL_CERT_DB" in os.environ: + certdb = os.environ["QPID_SSL_CERT_DB"] + else: + certdb = os.path.expanduser("~/.qpid/") + if "QPID_SSL_CERT_NAME" in os.environ: + certname = os.environ["QPID_SSL_CERT_NAME"] + else: + certname = "guest" + certfile = os.path.join(certdb, certname + ".crt") + if ssl or (ssl is None and os.path.exists(certfile)): + keyfile = os.path.join(certdb, certname + ".key") + trustfile = os.path.join(certdb, "root.crt") + socket = qpid.util.ssl(socket, keyfile=keyfile, certfile=certfile, ca_certs=trustfile) + self.__connection = qpid.connection.Connection(sock=socket, username='guest', password='guest') + self.__connection.start() + self.__session = self.__connection.session(str(qpid.datatypes.uuid4())) + self.subscribed = True + self.__queueStarted = False + + def topicSubscribe(self, topicName, callback): + # if the queue is edex.alerts, set decompress to true always for now to + # maintain compatibility with existing python scripts. + if (topicName == 'edex.alerts'): + self.decompress = True + + print "Establishing connection to broker on", self.host + queueName = topicName + self.__session.name + self.__session.queue_declare(queue=queueName, exclusive=True, auto_delete=True, arguments={'qpid.max_count':100, 'qpid.policy_type':'ring'}) + self.__session.exchange_bind(exchange='amq.topic', queue=queueName, binding_key=topicName) + self.__innerSubscribe(queueName, callback) + + def __innerSubscribe(self, serverQueueName, callback): + local_queue_name = 'local_queue_' + serverQueueName + queue = self.__session.incoming(local_queue_name) + self.__session.message_subscribe(serverQueueName, destination=local_queue_name) + queue.start() + print "Connection complete to broker on", self.host + self.__queueStarted = True + + while self.subscribed: + try: + message = queue.get(timeout=10) + content = message.body + self.__session.message_accept(qpid.datatypes.RangedSet(message.id)) + if (self.decompress): + try: + # http://stackoverflow.com/questions/2423866/python-decompressing-gzip-chunk-by-chunk + d = zlib.decompressobj(16+zlib.MAX_WBITS) + content = d.decompress(content) + except Exception: + # decompression failed, return the original content + pass + callback(content) + except Empty: + pass + except Closed: + self.close() + + def close(self): + self.__queueStarted = False + self.subscribed = False + try: + self.__session.close(timeout=10) + except Exception: + pass + + @property + def queueStarted(self): + return self.__queueStarted + diff --git a/awips/ThriftClient.py b/awips/ThriftClient.py new file mode 100644 index 0000000..272527a --- /dev/null +++ b/awips/ThriftClient.py @@ -0,0 +1,84 @@ +## +## + +import httplib +from dynamicserialize import DynamicSerializationManager +from dynamicserialize.dstypes.com.raytheon.uf.common.serialization.comm.response import ServerErrorResponse +from dynamicserialize.dstypes.com.raytheon.uf.common.serialization import SerializableExceptionWrapper + +# +# Provides a Python-based interface for executing Thrift requests. +# +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/20/10 dgilling Initial Creation. +# +# +# + + +class ThriftClient: + + # How to call this constructor: + # 1. Pass in all arguments separately (e.g., + # ThriftClient.ThriftClient("localhost", 9581, "/services")) + # will return a Thrift client pointed at http://localhost:9581/services. + # 2. Pass in all arguments through the host string (e.g., + # ThriftClient.ThriftClient("localhost:9581/services")) + # will return a Thrift client pointed at http://localhost:9581/services. + # 3. Pass in host/port arguments through the host string (e.g., + # ThriftClient.ThriftClient("localhost:9581", "/services")) + # will return a Thrift client pointed at http://localhost:9581/services. + def __init__(self, host, port=9581, uri="/services"): + hostParts = host.split("/", 1) + if (len(hostParts) > 1): + hostString = hostParts[0] + self.__uri = "/" + hostParts[1] + self.__httpConn = httplib.HTTPConnection(hostString) + else: + if (port is None): + self.__httpConn = httplib.HTTPConnection(host) + else: + self.__httpConn = httplib.HTTPConnection(host, port) + + self.__uri = uri + + self.__dsm = DynamicSerializationManager.DynamicSerializationManager() + + def sendRequest(self, request, uri="/thrift"): + message = self.__dsm.serializeObject(request) + + self.__httpConn.connect() + self.__httpConn.request("POST", self.__uri + uri, message) + + response = self.__httpConn.getresponse() + if (response.status != 200): + raise ThriftRequestException("Unable to post request to server") + + rval = self.__dsm.deserializeBytes(response.read()) + self.__httpConn.close() + + # let's verify we have an instance of ServerErrorResponse + # IF we do, through an exception up to the caller along + # with the original Java stack trace + # ELSE: we have a valid response and pass it back + try: + forceError = rval.getException() + raise ThriftRequestException(forceError) + except AttributeError: + pass + + return rval + + +class ThriftRequestException(Exception): + def __init__(self, value): + self.parameter = value + + def __str__(self): + return repr(self.parameter) + diff --git a/awips/TimeUtil.py b/awips/TimeUtil.py new file mode 100644 index 0000000..4094dfa --- /dev/null +++ b/awips/TimeUtil.py @@ -0,0 +1,91 @@ +## +## +# ---------------------------------------------------------------------------- +# This software is in the public domain, furnished "as is", without technical +# support, and with no warranty, express or implied, as to its usefulness for +# any purpose. +# +# offsetTime.py +# Handles Displaced Real Time for various applications +# +# Author: hansen/romberg +# ---------------------------------------------------------------------------- + +import string +import time + +# Given the timeStr, return the offset (in seconds) +# from the current time. +# Also return the launchStr i.e. Programs launched from this +# offset application will use the launchStr as the -z argument. +# The offset will be positive for time in the future, +# negative for time in the past. +# +# May still want it to be normalized to the most recent midnight. +# +# NOTES about synchronizing: +# --With synchronizing on, the "current time" for all processes started +# within a given hour will be the same. +# This guarantees that GFE's have the same current time and ISC grid +# time stamps are syncrhonized and can be exchanged. +# Formatters launched from the GFE in this mode will be synchronized as +# well by setting the launchStr to use the time difference format +# (YYYYMMDD_HHMM,YYYYMMDD_HHMM). +# --This does not solve the problem in the general case. +# For example, if someone starts the GFE at 12:59 and someone +# else starts it at 1:01, they will have different offsets and +# current times. +# --With synchronizing off, when the process starts, the current time +# matches the drtTime in the command line. However, with synchronizing +# on, the current time will be offset by the fraction of the hour at +# which the process was started. Examples: +# Actual Starting time: 20040617_1230 +# drtTime 20040616_0000 +# Synchronizing off: +# GFE Spatial Editor at StartUp: 20040616_0000 +# Synchronizing on: +# GFE Spatial Editor at StartUp: 20040616_0030 +# +def determineDrtOffset(timeStr): + launchStr = timeStr + # Check for time difference + if timeStr.find(",") >=0: + times = timeStr.split(",") + t1 = makeTime(times[0]) + t2 = makeTime(times[1]) + #print "time offset", t1-t2, (t1-t2)/3600 + return t1-t2, launchStr + # Check for synchronized mode + synch = 0 + if timeStr[0] == "S": + timeStr = timeStr[1:] + synch = 1 + drt_t = makeTime(timeStr) + #print "input", year, month, day, hour, minute + gm = time.gmtime() + cur_t = time.mktime(gm) + + # Synchronize to most recent hour + # i.e. "truncate" cur_t to most recent hour. + #print "gmtime", gm + if synch: + cur_t = time.mktime((gm[0], gm[1], gm[2], gm[3], 0, 0, 0, 0, 0)) + curStr = '%4s%2s%2s_%2s00\n' % (`gm[0]`,`gm[1]`,`gm[2]`,`gm[3]`) + curStr = curStr.replace(' ','0') + launchStr = timeStr + "," + curStr + + #print "drt, cur", drt_t, cur_t + offset = drt_t - cur_t + #print "offset", offset, offset/3600, launchStr + return int(offset), launchStr + +def makeTime(timeStr): + year = string.atoi(timeStr[0:4]) + month = string.atoi(timeStr[4:6]) + day = string.atoi(timeStr[6:8]) + hour = string.atoi(timeStr[9:11]) + minute = string.atoi(timeStr[11:13]) + # Do not use daylight savings because gmtime is not in daylight + # savings time. + return time.mktime((year, month, day, hour, minute, 0, 0, 0, 0)) + diff --git a/awips/UsageArgumentParser.py b/awips/UsageArgumentParser.py new file mode 100644 index 0000000..ddc07ed --- /dev/null +++ b/awips/UsageArgumentParser.py @@ -0,0 +1,64 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------- -------- --------- --------------------------------------------- +# Feb 13, 2017 6092 randerso Added StoreTimeAction +# +## + +import argparse +import sys +import time + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.db.objects import DatabaseID +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.db.objects import ParmID + +TIME_FORMAT = "%Y%m%d_%H%M" + +class UsageArgumentParser(argparse.ArgumentParser): + """ + A subclass of ArgumentParser that overrides error() to print the + whole help text, rather than just the usage string. + """ + def error(self, message): + sys.stderr.write('%s: error: %s\n' % (self.prog, message)) + self.print_help() + sys.exit(2) + +## Custom actions for ArgumentParser objects ## +class StoreDatabaseIDAction(argparse.Action): + def __call__(self, parser, namespace, values, option_string=None): + did = DatabaseID(values) + if did.isValid(): + setattr(namespace, self.dest, did) + else: + parser.error("DatabaseID [" + values + "] not a valid identifier") + +class AppendParmNameAndLevelAction(argparse.Action): + def __call__(self, parser, namespace, values, option_string=None): + tx = ParmID.parmNameAndLevel(values) + comp = tx[0] + '_' + tx[1] + if (hasattr(namespace, self.dest)) and \ + (getattr(namespace, self.dest) is not None): + currentValues = getattr(namespace, self.dest) + currentValues.append(comp) + setattr(namespace, self.dest, currentValues) + else: + setattr(namespace, self.dest, [comp]) + +class StoreTimeAction(argparse.Action): + """ + argparse.Action subclass to validate GFE formatted time strings + and parse them to time.struct_time + """ + def __call__(self, parser, namespace, values, option_string=None): + try: + timeStruct = time.strptime(values, TIME_FORMAT) + except: + parser.error(str(values) + " is not a valid time string of the format YYYYMMDD_hhmm") + + setattr(namespace, self.dest, timeStruct) + diff --git a/awips/UsageOptionParser.py b/awips/UsageOptionParser.py new file mode 100644 index 0000000..55fc3f1 --- /dev/null +++ b/awips/UsageOptionParser.py @@ -0,0 +1,21 @@ +## +## + +import sys +from optparse import OptionParser + +class UsageOptionParser(OptionParser): + """ + A subclass of OptionParser that prints that overrides error() to print the + whole help text, rather than just the usage string. + """ + def error(self, msg): + """ + Print the help text and exit. + """ + self.print_help(sys.stderr) + sys.stderr.write("\n") + sys.stderr.write(msg) + sys.stderr.write("\n") + sys.exit(2) + diff --git a/awips/__init__.py b/awips/__init__.py new file mode 100644 index 0000000..636de3c --- /dev/null +++ b/awips/__init__.py @@ -0,0 +1,20 @@ +## +## + + +# +# __init__.py for awips package +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/21/10 dgilling Initial Creation. +# +# +# + + +__all__ = [ + ] diff --git a/awips/dataaccess/CombinedTimeQuery.py b/awips/dataaccess/CombinedTimeQuery.py new file mode 100644 index 0000000..947bc38 --- /dev/null +++ b/awips/dataaccess/CombinedTimeQuery.py @@ -0,0 +1,83 @@ +# # +# # + +# +# Method for performing a DAF time query where all parameter/level/location +# combinations must be available at the same time. +# +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/22/16 #5591 bsteffen Initial Creation. +# + +from awips.dataaccess import DataAccessLayer + +def getAvailableTimes(request, refTimeOnly=False): + return __getAvailableTimesForEachParameter(request, refTimeOnly) + +def __getAvailableTimesForEachParameter(request, refTimeOnly=False): + parameters = request.getParameters() + if parameters: + times = None + for parameter in parameters: + specificRequest = __cloneRequest(request) + specificRequest.setParameters(parameter) + specificTimes = __getAvailableTimesForEachLevel(specificRequest, refTimeOnly) + if times is None: + times = specificTimes + else: + times.intersection_update(specificTimes) + if not times: + break + return times + else: + return __getAvailableTimesForEachLevel(request, refTimeOnly) + +def __getAvailableTimesForEachLevel(request, refTimeOnly=False): + levels = request.getLevels() + if levels: + times = None + for level in levels: + specificRequest = __cloneRequest(request) + specificRequest.setLevels(level) + specificTimes = __getAvailableTimesForEachLocation(specificRequest, refTimeOnly) + if times is None: + times = specificTimes + else: + times.intersection_update(specificTimes) + if not times: + break + return times + else: + return __getAvailableTimesForEachLocation(request, refTimeOnly) + +def __getAvailableTimesForEachLocation(request, refTimeOnly=False): + locations = request.getLocationNames() + if locations: + times = None + for location in locations: + specificRequest = __cloneRequest(request) + specificRequest.setLocationNames(location) + specificTimes = DataAccessLayer.getAvailableTimes(specificRequest, refTimeOnly) + if times is None: + times = set(specificTimes) + else: + times.intersection_update(specificTimes) + if not times: + break + return times + else: + return DataAccessLayer.getAvailableTimes(request, refTimeOnly) + + +def __cloneRequest(request): + return DataAccessLayer.newDataRequest(datatype = request.getDatatype(), + parameters = request.getParameters(), + levels = request.getLevels(), + locationNames = request.getLocationNames(), + envelope = request.getEnvelope(), + **request.getIdentifiers()) \ No newline at end of file diff --git a/awips/dataaccess/DataAccessLayer.py b/awips/dataaccess/DataAccessLayer.py new file mode 100644 index 0000000..fca9816 --- /dev/null +++ b/awips/dataaccess/DataAccessLayer.py @@ -0,0 +1,266 @@ +# # +# # + + +# +# Published interface for awips.dataaccess package +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 12/10/12 njensen Initial Creation. +# Feb 14, 2013 1614 bsteffen refactor data access framework +# to use single request. +# 04/10/13 1871 mnash move getLatLonCoords to JGridData and add default args +# 05/29/13 2023 dgilling Hook up ThriftClientRouter. +# 03/03/14 2673 bsteffen Add ability to query only ref times. +# 07/22/14 3185 njensen Added optional/default args to newDataRequest +# 07/30/14 3185 njensen Renamed valid identifiers to optional +# Apr 26, 2015 4259 njensen Updated for new JEP API +# Apr 13, 2016 5379 tgurney Add getIdentifierValues() +# Jun 01, 2016 5587 tgurney Add new signatures for +# getRequiredIdentifiers() and +# getOptionalIdentifiers() +# Oct 18, 2016 5916 bsteffen Add setLazyLoadGridLatLon +# +# + + +import sys +import subprocess +import warnings + +THRIFT_HOST = "edex" + +USING_NATIVE_THRIFT = False + + +if 'jep' in sys.modules: + # intentionally do not catch if this fails to import, we want it to + # be obvious that something is configured wrong when running from within + # Java instead of allowing false confidence and fallback behavior + import JepRouter + router = JepRouter +else: + from awips.dataaccess import ThriftClientRouter + router = ThriftClientRouter.ThriftClientRouter(THRIFT_HOST) + USING_NATIVE_THRIFT = True + +def getForecastRun(cycle, times): + """ + :param cycle: Forecast cycle reference time + :param times: All available times/cycles + :return: DataTime array for a single forecast run + """ + fcstRun = [] + for t in times: + if str(t)[:19] == str(cycle): + fcstRun.append(t) + return fcstRun + +def getAvailableTimes(request, refTimeOnly=False): + """ + Get the times of available data to the request. + + Args: + request: the IDataRequest to get data for + refTimeOnly: optional, use True if only unique refTimes should be + returned (without a forecastHr) + + Returns: + a list of DataTimes + """ + return router.getAvailableTimes(request, refTimeOnly) + + +def getGridData(request, times=[]): + """ + Gets the grid data that matches the request at the specified times. Each + combination of parameter, level, and dataTime will be returned as a + separate IGridData. + + Args: + request: the IDataRequest to get data for + times: a list of DataTimes, a TimeRange, or None if the data is time + agnostic + + Returns: + a list of IGridData + """ + return router.getGridData(request, times) + + +def getGeometryData(request, times=[]): + """ + Gets the geometry data that matches the request at the specified times. + Each combination of geometry, level, and dataTime will be returned as a + separate IGeometryData. + + Args: + request: the IDataRequest to get data for + times: a list of DataTimes, a TimeRange, or None if the data is time + agnostic + + Returns: + a list of IGeometryData + """ + return router.getGeometryData(request, times) + + +def getAvailableLocationNames(request): + """ + Gets the available location names that match the request without actually + requesting the data. + + Args: + request: the request to find matching location names for + + Returns: + a list of strings of available location names. + """ + return router.getAvailableLocationNames(request) + + +def getAvailableParameters(request): + """ + Gets the available parameters names that match the request without actually + requesting the data. + + Args: + request: the request to find matching parameter names for + + Returns: + a list of strings of available parameter names. + """ + return router.getAvailableParameters(request) + + +def getAvailableLevels(request): + """ + Gets the available levels that match the request without actually + requesting the data. + + Args: + request: the request to find matching levels for + + Returns: + a list of strings of available levels. + """ + return router.getAvailableLevels(request) + + +def getRequiredIdentifiers(request): + """ + Gets the required identifiers for this request. These identifiers + must be set on a request for the request of this datatype to succeed. + + Args: + request: the request to find required identifiers for + + Returns: + a list of strings of required identifiers + """ + if str(request) == request: + warnings.warn("Use getRequiredIdentifiers(IDataRequest) instead", + DeprecationWarning) + return router.getRequiredIdentifiers(request) + + +def getOptionalIdentifiers(request): + """ + Gets the optional identifiers for this request. + + Args: + request: the request to find optional identifiers for + + Returns: + a list of strings of optional identifiers + """ + if str(request) == request: + warnings.warn("Use getOptionalIdentifiers(IDataRequest) instead", + DeprecationWarning) + return router.getOptionalIdentifiers(request) + + +def getIdentifierValues(request, identifierKey): + """ + Gets the allowed values for a particular identifier on this datatype. + + Args: + request: the request to find identifier values for + identifierKey: the identifier to find values for + + Returns: + a list of strings of allowed values for the specified identifier + """ + return router.getIdentifierValues(request, identifierKey) + +def newDataRequest(datatype=None, **kwargs): + """" + Creates a new instance of IDataRequest suitable for the runtime environment. + All args are optional and exist solely for convenience. + + Args: + datatype: the datatype to create a request for + parameters: a list of parameters to set on the request + levels: a list of levels to set on the request + locationNames: a list of locationNames to set on the request + envelope: an envelope to limit the request + **kwargs: any leftover kwargs will be set as identifiers + + Returns: + a new IDataRequest + """ + return router.newDataRequest(datatype, **kwargs) + +def getSupportedDatatypes(): + """ + Gets the datatypes that are supported by the framework + + Returns: + a list of strings of supported datatypes + """ + return router.getSupportedDatatypes() + + +def changeEDEXHost(newHostName): + """ + Changes the EDEX host the Data Access Framework is communicating with. Only + works if using the native Python client implementation, otherwise, this + method will throw a TypeError. + + Args: + newHostHame: the EDEX host to connect to + """ + if USING_NATIVE_THRIFT: + global THRIFT_HOST + THRIFT_HOST = newHostName + global router + router = ThriftClientRouter.ThriftClientRouter(THRIFT_HOST) + else: + raise TypeError("Cannot call changeEDEXHost when using JepRouter.") + +def setLazyLoadGridLatLon(lazyLoadGridLatLon): + """ + Provide a hint to the Data Access Framework indicating whether to load the + lat/lon data for a grid immediately or wait until it is needed. This is + provided as a performance tuning hint and should not affect the way the + Data Access Framework is used. Depending on the internal implementation of + the Data Access Framework this hint might be ignored. Examples of when this + should be set to True are when the lat/lon information is not used or when + it is used only if certain conditions within the data are met. It could be + set to False if it is guaranteed that all lat/lon information is needed and + it would be better to get any performance overhead for generating the + lat/lon data out of the way during the initial request. + + + Args: + lazyLoadGridLatLon: Boolean value indicating whether to lazy load. + """ + try: + router.setLazyLoadGridLatLon(lazyLoadGridLatLon) + except AttributeError: + # The router is not required to support this capability. + pass diff --git a/awips/dataaccess/DataNotificationLayer.py b/awips/dataaccess/DataNotificationLayer.py new file mode 100644 index 0000000..2c130c5 --- /dev/null +++ b/awips/dataaccess/DataNotificationLayer.py @@ -0,0 +1,140 @@ +# # +# # + +# +# Published interface for retrieving data updates via awips.dataaccess package +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# May 26, 2016 2416 rjpeter Initial Creation. +# Aug 1, 2016 2416 tgurney Finish implementation +# +# + +""" +Interface for the DAF's data notification feature, which allows continuous +retrieval of new data as it is coming into the system. + +There are two ways to access this feature: + +1. The DataQueue module (awips.dataaccess.DataQueue) offers a collection that +automatically fills up with new data as it receives notifications. See that +module for more information. + +2. Depending on the type of data you want, use either getGridDataUpdates() or +getGeometryDataUpdates() in this module. Either one will give you back an +object that will retrieve new data for you and will call a function you specify +each time new data is received. + +Example code follows. This example prints temperature as observed from KOMA +each time a METAR is received from there. + + from awips.dataaccess import DataAccessLayer as DAL + from awips.dataaccess import DataNotificationLayer as DNL + + def process_obs(list_of_data): + for item in list_of_data: + print(item.getNumber('temperature')) + + request = DAL.newDataRequest('obs') + request.setParameters('temperature') + request.setLocationNames('KOMA') + + notifier = DNL.getGeometryDataUpdates(request) + notifier.subscribe(process_obs) + # process_obs will called with a list of data each time new data comes in + +""" + +import re +import sys +import subprocess +from awips.dataaccess.PyGeometryNotification import PyGeometryNotification +from awips.dataaccess.PyGridNotification import PyGridNotification + + +THRIFT_HOST = subprocess.check_output( + "source /awips2/fxa/bin/setup.env; echo $DEFAULT_HOST", + shell=True).strip() + + +USING_NATIVE_THRIFT = False + +JMS_HOST_PATTERN=re.compile('tcp://([^:]+):([0-9]+)') + +if sys.modules.has_key('jep'): + # intentionally do not catch if this fails to import, we want it to + # be obvious that something is configured wrong when running from within + # Java instead of allowing false confidence and fallback behavior + import JepRouter + router = JepRouter +else: + from awips.dataaccess import ThriftClientRouter + router = ThriftClientRouter.ThriftClientRouter(THRIFT_HOST) + USING_NATIVE_THRIFT = True + + +def _getJmsConnectionInfo(notifFilterResponse): + serverString = notifFilterResponse.getJmsConnectionInfo() + try: + host, port = JMS_HOST_PATTERN.match(serverString).groups() + except AttributeError as e: + raise RuntimeError('Got bad JMS connection info from server: ' + serverString) + return {'host': host, 'port': port} + + +def getGridDataUpdates(request): + """ + Get a notification object that receives updates to grid data. + + Args: + request: the IDataRequest specifying the data you want to receive + + Returns: + an update request object that you can listen for updates to by + calling its subscribe() method + """ + response = router.getNotificationFilter(request) + filter = response.getNotificationFilter() + jmsInfo = _getJmsConnectionInfo(response) + notifier = PyGridNotification(request, filter, requestHost=THRIFT_HOST, **jmsInfo) + return notifier + + +def getGeometryDataUpdates(request): + """ + Get a notification object that receives updates to geometry data. + + Args: + request: the IDataRequest specifying the data you want to receive + + Returns: + an update request object that you can listen for updates to by + calling its subscribe() method + """ + response = router.getNotificationFilter(request) + filter = response.getNotificationFilter() + jmsInfo = _getJmsConnectionInfo(response) + notifier = PyGeometryNotification(request, filter, requestHost=THRIFT_HOST, **jmsInfo) + return notifier + + +def changeEDEXHost(newHostName): + """ + Changes the EDEX host the Data Access Framework is communicating with. Only + works if using the native Python client implementation, otherwise, this + method will throw a TypeError. + + Args: + newHostHame: the EDEX host to connect to + """ + if USING_NATIVE_THRIFT: + global THRIFT_HOST + THRIFT_HOST = newHostName + global router + router = ThriftClientRouter.ThriftClientRouter(THRIFT_HOST) + else: + raise TypeError("Cannot call changeEDEXHost when using JepRouter.") diff --git a/awips/dataaccess/DataQueue.py b/awips/dataaccess/DataQueue.py new file mode 100644 index 0000000..a5325f0 --- /dev/null +++ b/awips/dataaccess/DataQueue.py @@ -0,0 +1,196 @@ +# # +# # + +# +# Convenience class for using the DAF's notifications feature. This is a +# collection that, once connected to EDEX by calling start(), fills with +# data as notifications come in. Runs on a separate thread to allow +# non-blocking data retrieval. +# +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 07/29/16 2416 tgurney Initial creation +# + +from awips.dataaccess import DataNotificationLayer as DNL + +import time +from threading import Thread +import sys + + +if sys.version_info.major == 2: + from Queue import Queue, Empty +else: # Python 3 module renamed to 'queue' + from queue import Queue, Empty + + +"""Used to indicate a DataQueue that will produce geometry data.""" +GEOMETRY = object() + + +"""Used to indicate a DataQueue that will produce grid data.""" +GRID = object() + + +"""Default maximum queue size.""" +_DEFAULT_MAXSIZE = 100 + + +class Closed(Exception): + """Raised when attempting to get data from a closed queue.""" + pass + + +class DataQueue(object): + + """ + Convenience class for using the DAF's notifications feature. This is a + collection that, once connected to EDEX by calling start(), fills with + data as notifications come in. + + Example for getting obs data: + + from DataQueue import DataQueue, GEOMETRY + request = DataAccessLayer.newDataRequest('obs') + request.setParameters('temperature') + request.setLocationNames('KOMA') + q = DataQueue(GEOMETRY, request) + q.start() + for item in q: + print(item.getNumber('temperature')) + """ + + def __init__(self, dtype, request, maxsize=_DEFAULT_MAXSIZE): + """ + Create a new DataQueue. + + Args: + dtype: Either GRID or GEOMETRY; must match the type of data + requested. + request: IDataRequest describing the data you want. It must at + least have datatype set. All data produced will satisfy the + constraints you specify. + maxsize: Maximum number of data objects the queue can hold at + one time. If the limit is reached, any data coming in after + that will not appear until one or more items are removed using + DataQueue.get(). + """ + assert maxsize > 0 + assert dtype in (GEOMETRY, GRID) + self._maxsize = maxsize + self._queue = Queue(maxsize=maxsize) + self._thread = None + if dtype is GEOMETRY: + self._notifier = DNL.getGeometryDataUpdates(request) + elif dtype is GRID: + self._notifier = DNL.getGridDataUpdates(request) + + def start(self): + """Start listening for notifications and requesting data.""" + if self._thread is not None: + # Already started + return + kwargs = {'callback': self._data_received} + self._thread = Thread(target=self._notifier.subscribe, kwargs=kwargs) + self._thread.daemon = True + self._thread.start() + timer = 0 + while not self._notifier.subscribed: + time.sleep(0.1) + timer += 1 + if timer >= 100: # ten seconds + raise RuntimeError('timed out when attempting to subscribe') + + def _data_received(self, data): + for d in data: + if not isinstance(d, list): + d = [d] + for item in d: + self._queue.put(item) + + def get(self, block=True, timeout=None): + """ + Get and return the next available data object. By default, if there is + no data yet available, this method will not return until data becomes + available. + + Args: + block: Specifies behavior when the queue is empty. If True, wait + until an item is available before returning (the default). If + False, return None immediately if the queue is empty. + timeout: If block is True, wait this many seconds, and return None + if data is not received in that time. + Returns: + IData + """ + if self.closed: + raise Closed + try: + return self._queue.get(block, timeout) + except Empty: + return None + + def get_all(self): + """ + Get all data waiting for processing, in a single list. Always returns + immediately. Returns an empty list if no data has arrived yet. + + Returns: + List of IData + """ + data = [] + for _ in range(self._maxsize): + next_item = self.get(False) + if next_item is None: + break + data.append(next_item) + return data + + def close(self): + """Close the queue. May not be re-opened after closing.""" + if not self.closed: + self._notifier.close() + self._thread.join() + + def qsize(self): + """Return number of items in the queue.""" + return self._queue.qsize() + + def empty(self): + """Return True if the queue is empty.""" + return self._queue.empty() + + def full(self): + """Return True if the queue is full.""" + return self._queue.full() + + @property + def closed(self): + """True if the queue has been closed.""" + return not self._notifier.subscribed + + @property + def maxsize(self): + """ + Maximum number of data objects the queue can hold at one time. + If this limit is reached, any data coming in after that will not appear + until one or more items are removed using get(). + """ + return self._maxsize + + def __iter__(self): + if self._thread is not None: + while not self.closed: + yield self.get() + + def __enter__(self): + self.start() + return self + + def __exit__(self, *unused): + self.close() \ No newline at end of file diff --git a/awips/dataaccess/PyData.py b/awips/dataaccess/PyData.py new file mode 100644 index 0000000..acfb4d2 --- /dev/null +++ b/awips/dataaccess/PyData.py @@ -0,0 +1,40 @@ +## +## + +# +# Implements IData for use by native Python clients to the Data Access +# Framework. +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/03/13 dgilling Initial Creation. +# +# + +from awips.dataaccess import IData + +class PyData(IData): + + def __init__(self, dataRecord): + self.__time = dataRecord.getTime() + self.__level = dataRecord.getLevel() + self.__locationName = dataRecord.getLocationName() + self.__attributes = dataRecord.getAttributes() + + def getAttribute(self, key): + return self.__attributes[key] + + def getAttributes(self): + return self.__attributes.keys() + + def getDataTime(self): + return self.__time + + def getLevel(self): + return self.__level + + def getLocationName(self): + return self.__locationName diff --git a/awips/dataaccess/PyGeometryData.py b/awips/dataaccess/PyGeometryData.py new file mode 100644 index 0000000..62c54ee --- /dev/null +++ b/awips/dataaccess/PyGeometryData.py @@ -0,0 +1,63 @@ +## +## + +# +# Implements IGeometryData for use by native Python clients to the Data Access +# Framework. +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/03/13 dgilling Initial Creation. +# 01/06/14 2537 bsteffen Share geometry WKT. +# 03/19/14 2882 dgilling Raise an exception when getNumber() +# is called for data that is not a +# numeric Type. +# 06/09/16 5574 mapeters Handle 'SHORT' type in getNumber(). +# +# + +from awips.dataaccess import IGeometryData +from awips.dataaccess import PyData + +class PyGeometryData(IGeometryData, PyData.PyData): + + def __init__(self, geoDataRecord, geometry): + PyData.PyData.__init__(self, geoDataRecord) + self.__geometry = geometry + self.__dataMap = {} + tempDataMap = geoDataRecord.getDataMap() + for key, value in tempDataMap.items(): + self.__dataMap[key] = (value[0], value[1], value[2]) + + def getGeometry(self): + return self.__geometry + + def getParameters(self): + return self.__dataMap.keys() + + def getString(self, param): + value = self.__dataMap[param][0] + return str(value) + + def getNumber(self, param): + value = self.__dataMap[param][0] + t = self.getType(param) + if t == 'INT' or t == 'SHORT': + return int(value) + elif t == 'LONG': + return long(value) + elif t == 'FLOAT': + return float(value) + elif t == 'DOUBLE': + return float(value) + else: + raise TypeError("Data for parameter " + param + " is not a numeric type.") + + def getUnit(self, param): + return self.__dataMap[param][2] + + def getType(self, param): + return self.__dataMap[param][1] diff --git a/awips/dataaccess/PyGeometryNotification.py b/awips/dataaccess/PyGeometryNotification.py new file mode 100644 index 0000000..7e66d06 --- /dev/null +++ b/awips/dataaccess/PyGeometryNotification.py @@ -0,0 +1,37 @@ +# # +# # + +# +# Notification object that produces geometry data +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 07/22/16 2416 tgurney Initial creation +# 09/07/17 6175 tgurney Override messageReceived +# + +import dynamicserialize +from awips.dataaccess.PyNotification import PyNotification +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint + +class PyGeometryNotification(PyNotification): + + def messageReceived(self, msg): + dataUriMsg = dynamicserialize.deserialize(msg) + dataUris = dataUriMsg.getDataURIs() + dataTimes = set() + for dataUri in dataUris: + if self.notificationFilter.accept(dataUri): + dataTimes.add(self.getDataTime(dataUri)) + if dataTimes: + try: + data = self.getData(self.request, list(dataTimes)) + self.callback(data) + except Exception as e: + traceback.print_exc() + + def getData(self, request, dataTimes): + return self.DAL.getGeometryData(request, dataTimes) diff --git a/awips/dataaccess/PyGridData.py b/awips/dataaccess/PyGridData.py new file mode 100644 index 0000000..0a7ac15 --- /dev/null +++ b/awips/dataaccess/PyGridData.py @@ -0,0 +1,64 @@ +# # +# # + +# +# Implements IGridData for use by native Python clients to the Data Access +# Framework. +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/03/13 #2023 dgilling Initial Creation. +# 10/13/16 #5916 bsteffen Correct grid shape, allow lat/lon +# 11/10/16 #5900 bsteffen Correct grid shape +# to be requested by a delegate +# +# + + +import numpy +import warnings + +from awips.dataaccess import IGridData +from awips.dataaccess import PyData + +NO_UNIT_CONVERT_WARNING = """ +The ability to unit convert grid data is not currently available in this version of the Data Access Framework. +""" + + +class PyGridData(IGridData, PyData.PyData): + + def __init__(self, gridDataRecord, nx, ny, latLonGrid = None, latLonDelegate = None): + PyData.PyData.__init__(self, gridDataRecord) + nx = nx + ny = ny + self.__parameter = gridDataRecord.getParameter() + self.__unit = gridDataRecord.getUnit() + self.__gridData = numpy.reshape(numpy.array(gridDataRecord.getGridData()), (ny, nx)) + self.__latLonGrid = latLonGrid + self.__latLonDelegate = latLonDelegate + + + def getParameter(self): + return self.__parameter + + def getUnit(self): + return self.__unit + + def getRawData(self, unit=None): + # TODO: Find a proper python library that deals will with numpy and + # javax.measure style unit strings and hook it in to this method to + # allow end-users to perform unit conversion for grid data. + if unit is not None: + warnings.warn(NO_UNIT_CONVERT_WARNING, stacklevel=2) + return self.__gridData + + def getLatLonCoords(self): + if self.__latLonGrid is not None: + return self.__latLonGrid + elif self.__latLonDelegate is not None: + return self.__latLonDelegate() + return self.__latLonGrid diff --git a/awips/dataaccess/PyGridNotification.py b/awips/dataaccess/PyGridNotification.py new file mode 100644 index 0000000..cee3ca0 --- /dev/null +++ b/awips/dataaccess/PyGridNotification.py @@ -0,0 +1,42 @@ +# # +# # + +# +# Notification object that produces grid data +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/03/16 2416 rjpeter Initial Creation. +# 09/06/17 6175 tgurney Override messageReceived +# + +import dynamicserialize +from awips.dataaccess.PyNotification import PyNotification +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint + +class PyGridNotification(PyNotification): + + def messageReceived(self, msg): + dataUriMsg = dynamicserialize.deserialize(msg) + dataUris = dataUriMsg.getDataURIs() + for dataUri in dataUris: + if not self.notificationFilter.accept(dataUri): + continue + try: + # This improves performance over requesting by datatime since it requests only the + # parameter that the notification was received for (instead of this and all previous + # parameters for the same forecast hour) + # TODO: This utterly fails for derived requests + newReq = self.DAL.newDataRequest(self.request.getDatatype()) + newReq.addIdentifier("dataURI", dataUri) + newReq.setParameters(self.request.getParameters()) + data = self.getData(newReq, []) + self.callback(data) + except Exception as e: + traceback.print_exc() + + def getData(self, request, dataTimes): + return self.DAL.getGridData(request, dataTimes) diff --git a/awips/dataaccess/PyNotification.py b/awips/dataaccess/PyNotification.py new file mode 100644 index 0000000..163e441 --- /dev/null +++ b/awips/dataaccess/PyNotification.py @@ -0,0 +1,93 @@ +## +## + +# +# Implements IData for use by native Python clients to the Data Access +# Framework. +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Jun 22, 2016 2416 rjpeter Initial creation +# Jul 22, 2016 2416 tgurney Finish implementation +# Sep 07, 2017 6175 tgurney Override messageReceived in subclasses +# + + +import abc +import time +import traceback + +import dynamicserialize +from awips.dataaccess import DataAccessLayer +from awips.dataaccess import INotificationSubscriber +from awips.QpidSubscriber import QpidSubscriber +from awips.ThriftClient import ThriftRequestException +from dynamicserialize.dstypes.com.raytheon.uf.common.time import DataTime + + +class PyNotification(INotificationSubscriber): + """ + Receives notifications for new data and retrieves the data that meets + specified filtering criteria. + """ + + __metaclass__ = abc.ABCMeta + + def __init__(self, request, filter, host='localhost', port=5672, requestHost='localhost'): + self.DAL = DataAccessLayer + self.DAL.changeEDEXHost(requestHost) + self.request = request + self.notificationFilter = filter + self.__topicSubscriber = QpidSubscriber(host, port, decompress=True) + self.__topicName = "edex.alerts" + self.callback = None + + def subscribe(self, callback): + """ + Start listening for notifications. + + Args: + callback: Function to call with a list of received data objects. + Will be called once for each request made for data. + """ + assert hasattr(callback, '__call__'), 'callback arg must be callable' + self.callback = callback + self.__topicSubscriber.topicSubscribe(self.__topicName, self.messageReceived) + # Blocks here + + def close(self): + if self.__topicSubscriber.subscribed: + self.__topicSubscriber.close() + + def getDataTime(self, dataURI): + dataTimeStr = dataURI.split('/')[2] + return DataTime(dataTimeStr) + + @abc.abstractmethod + def messageReceived(self, msg): + """Called when a message is received from QpidSubscriber. + + This method must call self.callback once for each request made for data + """ + pass + + @abc.abstractmethod + def getData(self, request, dataTimes): + """ + Retrieve and return data + + Args: + request: IDataRequest to send to the server + dataTimes: list of data times + Returns: + list of IData + """ + pass + + @property + def subscribed(self): + """True if currently subscribed to notifications.""" + return self.__topicSubscriber.queueStarted diff --git a/awips/dataaccess/SoundingsSupport.py b/awips/dataaccess/SoundingsSupport.py new file mode 100644 index 0000000..db58fa4 --- /dev/null +++ b/awips/dataaccess/SoundingsSupport.py @@ -0,0 +1,266 @@ +# # +# # + +# +# Classes for retrieving soundings based on gridded data from the Data Access +# Framework +# +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/24/15 #4480 dgilling Initial Creation. +# + +from collections import defaultdict +from shapely.geometry import Point + +from awips import DateTimeConverter +from awips.dataaccess import DataAccessLayer + +from dynamicserialize.dstypes.com.raytheon.uf.common.time import DataTime +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.level import Level + + +def getSounding(modelName, weatherElements, levels, samplePoint, refTime=None, timeRange=None): + """" + Performs a series of Data Access Framework requests to retrieve a sounding object + based on the specified request parameters. + + Args: + modelName: the grid model datasetid to use as the basis of the sounding. + weatherElements: a list of parameters to return in the sounding. + levels: a list of levels to sample the given weather elements at + samplePoint: a lat/lon pair to perform the sampling of data at. + refTime: (optional) the grid model reference time to use for the sounding. + If not specified, the latest ref time in the system will be used. + timeRange: (optional) a TimeRange to specify which forecast hours to use. + If not specified, will default to all forecast hours. + + Returns: + A _SoundingCube instance, which acts a 3-tiered dictionary, keyed + by DataTime, then by level and finally by weather element. If no + data is available for the given request parameters, None is returned. + """ + + (locationNames, parameters, levels, envelope, refTime, timeRange) = \ + __sanitizeInputs(modelName, weatherElements, levels, samplePoint, refTime, timeRange) + + requestArgs = { 'datatype' : 'grid', + 'locationNames' : locationNames, + 'parameters' : parameters, + 'levels' : levels, + 'envelope' : envelope, + } + + req = DataAccessLayer.newDataRequest(**requestArgs) + + forecastHours = __determineForecastHours(req, refTime, timeRange) + if not forecastHours: + return None + + response = DataAccessLayer.getGeometryData(req, forecastHours) + soundingObject = _SoundingCube(response) + + return soundingObject + +def setEDEXHost(host): + """ + Changes the EDEX host the Data Access Framework is communicating with. + + Args: + host: the EDEX host to connect to + """ + + if host: + DataAccessLayer.changeEDEXHost(str(host)) + +def __sanitizeInputs(modelName, weatherElements, levels, samplePoint, refTime, timeRange): + locationNames = [str(modelName)] + parameters = __buildStringList(weatherElements) + levels = __buildStringList(levels) + envelope = Point(samplePoint) + if refTime is not None: + refTime = DataTime(refTime=DateTimeConverter.convertToDateTime(refTime)) + if timeRange is not None: + timeRange = DateTimeConverter.constructTimeRange(*timeRange) + return (locationNames, parameters, levels, envelope, refTime, timeRange) + +def __determineForecastHours(request, refTime, timeRange): + dataTimes = DataAccessLayer.getAvailableTimes(request, False) + timesGen = [(DataTime(refTime=dataTime.getRefTime()), dataTime) for dataTime in dataTimes] + dataTimesMap = defaultdict(list) + for baseTime, dataTime in timesGen: + dataTimesMap[baseTime].append(dataTime) + + if refTime is None: + refTime = max(dataTimesMap.keys()) + + forecastHours = dataTimesMap[refTime] + if timeRange is None: + return forecastHours + else: + return [forecastHour for forecastHour in forecastHours if timeRange.contains(forecastHour.getValidPeriod())] + +def __buildStringList(param): + if __notStringIter(param): + return [str(item) for item in param] + else: + return [str(param)] + +def __notStringIter(iterable): + if not isinstance(iterable, basestring): + try: + iter(iterable) + return True + except TypeError: + return False + + + +class _SoundingCube(object): + """ + The top-level sounding object returned when calling SoundingsSupport.getSounding. + + This object acts as a 3-tiered dict which is keyed by time then level + then parameter name. Calling times() will return all valid keys into this + object. + """ + + def __init__(self, geometryDataObjects): + self._dataDict = {} + self._sortedTimes = [] + if geometryDataObjects: + for geometryData in geometryDataObjects: + dataTime = geometryData.getDataTime() + level = geometryData.getLevel() + for parameter in geometryData.getParameters(): + self.__addItem(parameter, dataTime, level, geometryData.getNumber(parameter)) + + def __addItem(self, parameter, dataTime, level, value): + timeLayer = self._dataDict.get(dataTime, _SoundingTimeLayer(dataTime)) + self._dataDict[dataTime] = timeLayer + timeLayer._addItem(parameter, level, value) + if dataTime not in self._sortedTimes: + self._sortedTimes.append(dataTime) + self._sortedTimes.sort() + + def __getitem__(self, key): + return self._dataDict[key] + + def __len__(self): + return len(self._dataDict) + + def times(self): + """ + Returns the valid times for this sounding. + + Returns: + A list containing the valid DataTimes for this sounding in order. + """ + return self._sortedTimes + + +class _SoundingTimeLayer(object): + """ + The second-level sounding object returned when calling SoundingsSupport.getSounding. + + This object acts as a 2-tiered dict which is keyed by level then parameter + name. Calling levels() will return all valid keys into this + object. Calling time() will return the DataTime for this particular layer. + """ + + def __init__(self, dataTime): + self._dataTime = dataTime + self._dataDict = {} + + def _addItem(self, parameter, level, value): + asString = str(level) + levelLayer = self._dataDict.get(asString, _SoundingTimeAndLevelLayer(self._dataTime, asString)) + levelLayer._addItem(parameter, value) + self._dataDict[asString] = levelLayer + + def __getitem__(self, key): + asString = str(key) + if asString in self._dataDict: + return self._dataDict[asString] + else: + raise KeyError("Level " + str(key) + " is not a valid level for this sounding.") + + def __len__(self): + return len(self._dataDict) + + def time(self): + """ + Returns the DataTime for this sounding cube layer. + + Returns: + The DataTime for this sounding layer. + """ + return self._dataTime + + def levels(self): + """ + Returns the valid levels for this sounding. + + Returns: + A list containing the valid levels for this sounding in order of + closest to surface to highest from surface. + """ + sortedLevels = [Level(level) for level in self._dataDict.keys()] + sortedLevels.sort() + return [str(level) for level in sortedLevels] + + +class _SoundingTimeAndLevelLayer(object): + """ + The bottom-level sounding object returned when calling SoundingsSupport.getSounding. + + This object acts as a dict which is keyed by parameter name. Calling + parameters() will return all valid keys into this object. Calling time() + will return the DataTime for this particular layer. Calling level() will + return the level for this layer. + """ + + def __init__(self, time, level): + self._time = time + self._level = level + self._parameters = {} + + def _addItem(self, parameter, value): + self._parameters[parameter] = value + + def __getitem__(self, key): + return self._parameters[key] + + def __len__(self): + return len(self._parameters) + + def level(self): + """ + Returns the level for this sounding cube layer. + + Returns: + The level for this sounding layer. + """ + return self._level + + def parameters(self): + """ + Returns the valid parameters for this sounding. + + Returns: + A list containing the valid parameter names. + """ + return list(self._parameters.keys()) + + def time(self): + """ + Returns the DataTime for this sounding cube layer. + + Returns: + The DataTime for this sounding layer. + """ + return self._time diff --git a/awips/dataaccess/ThriftClientRouter.py b/awips/dataaccess/ThriftClientRouter.py new file mode 100644 index 0000000..afe04f9 --- /dev/null +++ b/awips/dataaccess/ThriftClientRouter.py @@ -0,0 +1,230 @@ +# # +# # + +# +# Routes requests to the Data Access Framework through Python Thrift. +# +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 05/21/13 2023 dgilling Initial Creation. +# 01/06/14 2537 bsteffen Share geometry WKT. +# 03/03/14 2673 bsteffen Add ability to query only ref times. +# 07/22/14 3185 njensen Added optional/default args to newDataRequest +# 07/23/14 3185 njensen Added new methods +# 07/30/14 3185 njensen Renamed valid identifiers to optional +# 06/30/15 4569 nabowle Use hex WKB for geometries. +# 04/13/15 5379 tgurney Add getIdentifierValues() +# 06/01/16 5587 tgurney Add new signatures for +# getRequiredIdentifiers() and +# getOptionalIdentifiers() +# 08/01/16 2416 tgurney Add getNotificationFilter() +# 10/13/16 5916 bsteffen Correct grid shape, allow lazy grid lat/lon +# 10/26/16 5919 njensen Speed up geometry creation in getGeometryData() +# + + +import numpy +import shapely.wkb + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.impl import DefaultDataRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import GetAvailableLocationNamesRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import GetAvailableTimesRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import GetGeometryDataRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import GetGridDataRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import GetGridLatLonRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import GetAvailableParametersRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import GetAvailableLevelsRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import GetRequiredIdentifiersRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import GetOptionalIdentifiersRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import GetIdentifierValuesRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import GetSupportedDatatypesRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import GetNotificationFilterRequest + +from awips import ThriftClient +from awips.dataaccess import PyGeometryData +from awips.dataaccess import PyGridData + + +class LazyGridLatLon(object): + + def __init__(self, client, nx, ny, envelope, crsWkt): + self._latLonGrid = None + self._client = client + self._request = GetGridLatLonRequest() + self._request.setNx(nx) + self._request.setNy(ny) + self._request.setEnvelope(envelope) + self._request.setCrsWkt(crsWkt) + + def __call__(self): + # Its important that the data is cached internally so that if multiple + # GridData are sharing the same delegate then they can also share a + # single request for the LatLon information. + if self._latLonGrid is None: + response = self._client.sendRequest(self._request) + nx = response.getNx() + ny = response.getNy() + latData = numpy.reshape(numpy.array(response.getLats()), (ny, nx)) + lonData = numpy.reshape(numpy.array(response.getLons()), (ny, nx)) + self._latLonGrid = (lonData, latData) + return self._latLonGrid + + +class ThriftClientRouter(object): + + def __init__(self, host='localhost'): + self._client = ThriftClient.ThriftClient(host) + self._lazyLoadGridLatLon = False + + def setLazyLoadGridLatLon(self, lazyLoadGridLatLon): + self._lazyLoadGridLatLon = lazyLoadGridLatLon + + def getAvailableTimes(self, request, refTimeOnly): + timesRequest = GetAvailableTimesRequest() + timesRequest.setRequestParameters(request) + timesRequest.setRefTimeOnly(refTimeOnly) + response = self._client.sendRequest(timesRequest) + return response + + def getGridData(self, request, times): + gridDataRequest = GetGridDataRequest() + gridDataRequest.setIncludeLatLonData(not self._lazyLoadGridLatLon) + gridDataRequest.setRequestParameters(request) + # if we have an iterable times instance, then the user must have asked + # for grid data with the List of DataTime objects + # else, we assume it was a single TimeRange that was meant for the + # request + try: + iter(times) + gridDataRequest.setRequestedTimes(times) + except TypeError: + gridDataRequest.setRequestedPeriod(times) + response = self._client.sendRequest(gridDataRequest) + + locSpecificData = {} + locNames = response.getSiteNxValues().keys() + for location in locNames: + nx = response.getSiteNxValues()[location] + ny = response.getSiteNyValues()[location] + if self._lazyLoadGridLatLon: + envelope = response.getSiteEnvelopes()[location] + crsWkt = response.getSiteCrsWkt()[location] + delegate = LazyGridLatLon( + self._client, nx, ny, envelope, crsWkt) + locSpecificData[location] = (nx, ny, delegate) + else: + latData = numpy.reshape(numpy.array( + response.getSiteLatGrids()[location]), (ny, nx)) + lonData = numpy.reshape(numpy.array( + response.getSiteLonGrids()[location]), (ny, nx)) + locSpecificData[location] = (nx, ny, (lonData, latData)) + retVal = [] + for gridDataRecord in response.getGridData(): + locationName = gridDataRecord.getLocationName() + locData = locSpecificData[locationName] + if self._lazyLoadGridLatLon: + retVal.append(PyGridData.PyGridData(gridDataRecord, locData[ + 0], locData[1], latLonDelegate=locData[2])) + else: + retVal.append(PyGridData.PyGridData( + gridDataRecord, locData[0], locData[1], locData[2])) + return retVal + + def getGeometryData(self, request, times): + geoDataRequest = GetGeometryDataRequest() + geoDataRequest.setRequestParameters(request) + # if we have an iterable times instance, then the user must have asked + # for geometry data with the List of DataTime objects + # else, we assume it was a single TimeRange that was meant for the + # request + try: + iter(times) + geoDataRequest.setRequestedTimes(times) + except TypeError: + geoDataRequest.setRequestedPeriod(times) + response = self._client.sendRequest(geoDataRequest) + geometries = [] + for wkb in response.getGeometryWKBs(): + # the wkb is a numpy.ndarray of dtype int8 + # convert the bytearray to a byte string and load it + geometries.append(shapely.wkb.loads(wkb.tostring())) + + retVal = [] + for geoDataRecord in response.getGeoData(): + geom = geometries[geoDataRecord.getGeometryWKBindex()] + retVal.append(PyGeometryData.PyGeometryData(geoDataRecord, geom)) + return retVal + + def getAvailableLocationNames(self, request): + locNamesRequest = GetAvailableLocationNamesRequest() + locNamesRequest.setRequestParameters(request) + response = self._client.sendRequest(locNamesRequest) + return response + + def getAvailableParameters(self, request): + paramReq = GetAvailableParametersRequest() + paramReq.setRequestParameters(request) + response = self._client.sendRequest(paramReq) + return response + + def getAvailableLevels(self, request): + levelReq = GetAvailableLevelsRequest() + levelReq.setRequestParameters(request) + response = self._client.sendRequest(levelReq) + return response + + def getRequiredIdentifiers(self, request): + if str(request) == request: + # Handle old version getRequiredIdentifiers(str) + request = self.newDataRequest(request) + idReq = GetRequiredIdentifiersRequest() + idReq.setRequest(request) + response = self._client.sendRequest(idReq) + return response + + def getOptionalIdentifiers(self, request): + if str(request) == request: + # Handle old version getOptionalIdentifiers(str) + request = self.newDataRequest(request) + idReq = GetOptionalIdentifiersRequest() + idReq.setRequest(request) + response = self._client.sendRequest(idReq) + return response + + def getIdentifierValues(self, request, identifierKey): + idValReq = GetIdentifierValuesRequest() + idValReq.setIdentifierKey(identifierKey) + idValReq.setRequestParameters(request) + response = self._client.sendRequest(idValReq) + return response + + def newDataRequest(self, datatype, parameters=[], levels=[], locationNames=[], envelope=None, **kwargs): + req = DefaultDataRequest() + if datatype: + req.setDatatype(datatype) + if parameters: + req.setParameters(*parameters) + if levels: + req.setLevels(*levels) + if locationNames: + req.setLocationNames(*locationNames) + if envelope: + req.setEnvelope(envelope) + if kwargs: + # any args leftover are assumed to be identifiers + req.identifiers = kwargs + return req + + def getSupportedDatatypes(self): + response = self._client.sendRequest(GetSupportedDatatypesRequest()) + return response + + def getNotificationFilter(self, request): + notifReq = GetNotificationFilterRequest() + notifReq.setRequestParameters(request) + response = self._client.sendRequest(notifReq) + return response diff --git a/awips/dataaccess/__init__.py b/awips/dataaccess/__init__.py new file mode 100644 index 0000000..f28f178 --- /dev/null +++ b/awips/dataaccess/__init__.py @@ -0,0 +1,372 @@ +## +## + + +# +# __init__.py for awips.dataaccess package +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 12/10/12 njensen Initial Creation. +# Feb 14, 2013 1614 bsteffen refactor data access framework +# to use single request. +# Apr 09, 2013 1871 njensen Add doc strings +# Jun 03, 2013 2023 dgilling Add getAttributes to IData, add +# getLatLonGrids() to IGridData. +# Aug 01, 2016 2416 tgurney Add INotificationSubscriber +# and INotificationFilter +# +# + +__all__ = [ + + ] + +import abc + +class IDataRequest(object): + """ + An IDataRequest to be submitted to the DataAccessLayer to retrieve data. + """ + __metaclass__ = abc.ABCMeta + + @abc.abstractmethod + def setDatatype(self, datatype): + """ + Sets the datatype of the request. + + Args: + datatype: A string of the datatype, such as "grid", "radar", "gfe", "obs" + """ + return + + @abc.abstractmethod + def addIdentifier(self, key, value): + """ + Adds an identifier to the request. Identifiers are specific to the + datatype being requested. + + Args: + key: the string key of the identifier + value: the value of the identifier + """ + return + + @abc.abstractmethod + def setParameters(self, params): + """ + Sets the parameters of data to request. + + Args: + params: a list of strings of parameters to request + """ + return + + @abc.abstractmethod + def setLevels(self, levels): + """ + Sets the levels of data to request. Not all datatypes support levels. + + Args: + levels: a list of strings of level abbreviations to request + """ + return + + @abc.abstractmethod + def setEnvelope(self, env): + """ + Sets the envelope of the request. If supported by the datatype factory, + the data returned for the request will be constrained to only the data + within the envelope. + + Args: + env: a shapely geometry + """ + return + + @abc.abstractmethod + def setLocationNames(self, locationNames): + """ + Sets the location names of the request. + + Args: + locationNames: a list of strings of location names to request + """ + return + + @abc.abstractmethod + def getDatatype(self): + """ + Gets the datatype of the request + + Returns: + the datatype set on the request + """ + return + + @abc.abstractmethod + def getIdentifiers(self): + """ + Gets the identifiers on the request + + Returns: + a dictionary of the identifiers + """ + return + + @abc.abstractmethod + def getLevels(self): + """ + Gets the levels on the request + + Returns: + a list of strings of the levels + """ + return + + @abc.abstractmethod + def getLocationNames(self): + """ + Gets the location names on the request + + Returns: + a list of strings of the location names + """ + return + + @abc.abstractmethod + def getEnvelope(self): + """ + Gets the envelope on the request + + Returns: + a rectangular shapely geometry + """ + return + + + +class IData(object): + """ + An IData representing data returned from the DataAccessLayer. + """ + __metaclass__ = abc.ABCMeta + + @abc.abstractmethod + def getAttribute(self, key): + """ + Gets an attribute of the data. + + Args: + key: the key of the attribute + + Returns: + the value of the attribute + """ + return + + @abc.abstractmethod + def getAttributes(self): + """ + Gets the valid attributes for the data. + + Returns: + a list of strings of the attribute names + """ + return + + @abc.abstractmethod + def getDataTime(self): + """ + Gets the data time of the data. + + Returns: + the data time of the data, or None if no time is associated + """ + return + + @abc.abstractmethod + def getLevel(self): + """ + Gets the level of the data. + + Returns: + the level of the data, or None if no level is associated + """ + return + + @abc.abstractmethod + def getLocationName(self, param): + """ + Gets the location name of the data. + + Returns: + the location name of the data, or None if no location name is + associated + """ + return + + + +class IGridData(IData): + """ + An IData representing grid data that is returned by the DataAccessLayer. + """ + + @abc.abstractmethod + def getParameter(self): + """ + Gets the parameter of the data. + + Returns: + the parameter of the data + """ + return + + @abc.abstractmethod + def getUnit(self): + """ + Gets the unit of the data. + + Returns: + the string abbreviation of the unit, or None if no unit is associated + """ + return + + @abc.abstractmethod + def getRawData(self): + """ + Gets the grid data as a numpy array. + + Returns: + a numpy array of the data + """ + return + + @abc.abstractmethod + def getLatLonCoords(self): + """ + Gets the lat/lon coordinates of the grid data. + + Returns: + a tuple where the first element is a numpy array of lons, and the + second element is a numpy array of lats + """ + return + + + +class IGeometryData(IData): + """ + An IData representing geometry data that is returned by the DataAccessLayer. + """ + + @abc.abstractmethod + def getGeometry(self): + """ + Gets the geometry of the data. + + Returns: + a shapely geometry + """ + return + + @abc.abstractmethod + def getParameters(self): + """Gets the parameters of the data. + + Returns: + a list of strings of the parameter names + """ + return + + @abc.abstractmethod + def getString(self, param): + """ + Gets the string value of the specified param. + + Args: + param: the string name of the param + + Returns: + the string value of the param + """ + return + + @abc.abstractmethod + def getNumber(self, param): + """ + Gets the number value of the specified param. + + Args: + param: the string name of the param + + Returns: + the number value of the param + """ + return + + @abc.abstractmethod + def getUnit(self, param): + """ + Gets the unit of the specified param. + + Args: + param: the string name of the param + + Returns: + the string abbreviation of the unit of the param + """ + return + + @abc.abstractmethod + def getType(self, param): + """ + Gets the type of the param. + + Args: + param: the string name of the param + + Returns: + a string of the type of the parameter, such as + "STRING", "INT", "LONG", "FLOAT", or "DOUBLE" + """ + return + + +class INotificationSubscriber(object): + """ + An INotificationSubscriber representing a notification filter returned from + the DataNotificationLayer. + """ + __metaclass__ = abc.ABCMeta + + @abc.abstractmethod + def subscribe(self, callback): + """ + Subscribes to the requested data. Method will not return until close is + called in a separate thread. + + Args: + callback: the method to call with the IGridData/IGeometryData + + """ + pass + + @abc.abstractmethod + def close(self): + """Closes the notification subscriber""" + pass + +class INotificationFilter(object): + """ + Represents data required to filter a set of URIs and + return a corresponding list of IDataRequest to retrieve data for. + """ + __metaclass__ = abc.ABCMeta + @abc.abstractmethod + def accept(dataUri): + pass diff --git a/awips/gfe/IFPClient.py b/awips/gfe/IFPClient.py new file mode 100644 index 0000000..828abf7 --- /dev/null +++ b/awips/gfe/IFPClient.py @@ -0,0 +1,156 @@ +## +## + +from awips import ThriftClient + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.db.objects import DatabaseID +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.db.objects import ParmID +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.request import CommitGridsRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.request import GetGridInventoryRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.request import GetParmListRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.request import GetSelectTimeRangeRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.server.request import CommitGridRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.message import WsId +from dynamicserialize.dstypes.com.raytheon.uf.common.site.requests import GetActiveSitesRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.server.message import ServerResponse + + +# +# Provides a Python-based interface for executing GFE requests. +# +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 07/26/12 dgilling Initial Creation. +# +# +# + + +class IFPClient(object): + def __init__(self, host, port, user, site=None, progName=None): + self.__thrift = ThriftClient.ThriftClient(host, port) + self.__wsId = WsId(userName=user, progName=progName) + # retrieve default site + if site is None: + sr = self.getSiteID() + if len(sr.getPayload()) > 0: + site = sr.getPayload()[0] + self.__siteId = site + + def commitGrid(self, request): + if type(request) is CommitGridRequest: + return self.__commitGrid([request]) + elif self.__isHomogenousIterable(request, CommitGridRequest): + return self.__commitGrid([cgr for cgr in request]) + raise TypeError("Invalid type: " + str(type(request)) + " specified to commitGrid(). Only accepts CommitGridRequest or lists of CommitGridRequest.") + + def __commitGrid(self, requests): + ssr = ServerResponse() + request = CommitGridsRequest() + request.setCommits(requests) + sr = self.__makeRequest(request) + ssr.setMessages(sr.getMessages()) + return ssr + + def getParmList(self, id): + argType = type(id) + if argType is DatabaseID: + return self.__getParmList([id]) + elif self.__isHomogenousIterable(id, DatabaseID): + return self.__getParmList([dbid for dbid in id]) + raise TypeError("Invalid type: " + str(argType) + " specified to getParmList(). Only accepts DatabaseID or lists of DatabaseID.") + + def __getParmList(self, ids): + ssr = ServerResponse() + request = GetParmListRequest() + request.setDbIds(ids) + sr = self.__makeRequest(request) + ssr.setMessages(sr.getMessages()) + list = sr.getPayload() if sr.getPayload() is not None else [] + ssr.setPayload(list) + return ssr + + def __isHomogenousIterable(self, iterable, classType): + try: + iterator = iter(iterable) + for item in iterator: + if not isinstance(item, classType): + return False + except TypeError: + return False + return True + + def getGridInventory(self, parmID): + if type(parmID) is ParmID: + sr = self.__getGridInventory([parmID]) + list = [] + try: + list = sr.getPayload()[parmID] + except KeyError: + # no-op, we've already default the TimeRange list to empty + pass + sr.setPayload(list) + return sr + elif self.__isHomogenousIterable(parmID, ParmID): + return self.__getGridInventory([id for id in parmID]) + raise TypeError("Invalid type: " + str(type(parmID)) + " specified to getGridInventory(). Only accepts ParmID or lists of ParmID.") + + def __getGridInventory(self, parmIDs): + ssr = ServerResponse() + request = GetGridInventoryRequest() + request.setParmIds(parmIDs) + sr = self.__makeRequest(request) + ssr.setMessages(sr.getMessages()) + trs = sr.getPayload() if sr.getPayload() is not None else {} + ssr.setPayload(trs) + return ssr + + def getSelectTR(self, name): + request = GetSelectTimeRangeRequest() + request.setName(name) + sr = self.__makeRequest(request) + ssr = ServerResponse() + ssr.setMessages(sr.getMessages()) + ssr.setPayload(sr.getPayload()) + return ssr + + def getSiteID(self): + ssr = ServerResponse() + request = GetActiveSitesRequest() + sr = self.__makeRequest(request) + ssr.setMessages(sr.getMessages()) + ids = sr.getPayload() if sr.getPayload() is not None else [] + sr.setPayload(ids) + return sr + + def __makeRequest(self, request): + try: + request.setSiteID(self.__siteId) + except AttributeError: + pass + try: + request.setWorkstationID(self.__wsId) + except AttributeError: + pass + + sr = ServerResponse() + response = None + try: + response = self.__thrift.sendRequest(request) + except ThriftClient.ThriftRequestException as e: + sr.setMessages([str(e)]) + try: + sr.setPayload(response.getPayload()) + except AttributeError: + sr.setPayload(response) + try: + sr.setMessages(response.getMessages()) + except AttributeError: + # not a server response, nothing else to do + pass + + return sr diff --git a/awips/gfe/__init__.py b/awips/gfe/__init__.py new file mode 100644 index 0000000..f0f5feb --- /dev/null +++ b/awips/gfe/__init__.py @@ -0,0 +1,20 @@ +## +## + + +# +# __init__.py for awips.gfe package +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 07/26/12 dgilling Initial Creation. +# +# +# + + +__all__ = [ + ] diff --git a/awips/localization/LocalizationFileManager.py b/awips/localization/LocalizationFileManager.py new file mode 100644 index 0000000..ac0d425 --- /dev/null +++ b/awips/localization/LocalizationFileManager.py @@ -0,0 +1,453 @@ +## +## + +# +# Library for accessing localization files from python. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# --------- -------- --------- -------------------------- +# 08/09/17 5731 bsteffen Initial Creation. + + +import urllib2 +from json import load as loadjson +from xml.etree.ElementTree import parse as parseXml +from base64 import b64encode +from StringIO import StringIO +from getpass import getuser +import dateutil.parser +import contextlib +import os +from urlparse import urlunparse, urljoin + +NON_EXISTENT_CHECKSUM = 'NON_EXISTENT_CHECKSUM' +DIRECTORY_CHECKSUM = 'DIRECTORY_CHECKSUM' + +class LocalizationFileVersionConflictException(Exception): + pass + +class LocalizationFileDoesNotExistException(Exception): + pass + +class LocalizationFileIsNotDirectoryException(Exception): + pass + +class LocalizationContext(object): + """A localization context defines the scope of a localization file. + + For example the base localization context includes all the default files + installed with EDEX, while a particular user context has custom files for + that user. + + A localization context consists of a level and name. The level defines what + kind of entity this context is valid for, such as 'base', 'site', or 'user'. + The name identifies the specific entity, for example the name of a 'user' + level context is usually the username. The 'base' level does not have a name + because there cannot be only one 'base' context. + + Attributes: + level: the localization level + name: the context name + """ + def __init__(self, level="base", name=None, type="common_static"): + if level != "base": + assert name is not None + self.level = level + self.name = name + self.type = type + def isBase(self): + return self.level == "base" + def _getUrlComponent(self): + if self.isBase(): + return self.type + '/' + "base/" + else: + return self.type + '/' + self.level + '/' + self.name + '/' + def __str__(self): + if self.isBase(): + return self.type + ".base" + else: + return self.type + "." + self.level + "." + self.name + def __eq__(self, other): + return self.level == other.level and \ + self.name == other.name and \ + self.type == other.type + def __hash__(self): + return hash((self.level, self.name, self.type)) + +class _LocalizationOutput(StringIO): + """A file-like object for writing a localization file. + + The contents being written are stored in memory and written to a + localization server only when the writing is finished. + + This object should be used as a context manager, a save operation will be + executed if the context exits with no errors. If errors occur the partial + contents are abandoned and the server is unchanged. + + It is also possible to save the contents to the server with the save() + method. + """ + def __init__(self, manager, file): + StringIO.__init__(self) + self._manager = manager + self._file = file + def save(self): + """Send the currently written contents to the server.""" + request = self._manager._buildRequest(self._file.context, self._file.path, method="PUT") + + request.add_data(self.getvalue()) + request.add_header("If-Match", self._file.checksum) + try: + urllib2.urlopen(request) + except urllib2.HTTPError as e: + if e.code == 409: + raise LocalizationFileVersionConflictException, e.read() + else: + raise e + def __enter__(self): + return self + def __exit__(self, exc_type, exc_value, traceback): + if exc_type is None: + self.save() + def __str__(self): + return '<' + self.__class__.__name__ + " for " + str(self._file) + '>' + +class LocalizationFile(object): + """A specific file stored in localization. + + A localization file is uniquely defined by the context and path. There can + only be one valid file for that path and localization at a time. To access + the contents of the file use the open method. + + Attributes: + context: A LocalizationContext + path: A path to this file + checksum: A string representation of a checksum generated from the file contents. + timnestamp: A datetime.datetime object indicating when the file was last modified. + """ + def __init__(self, manager, context, path, checksum, timestamp): + """Initialize a LocalizationFile with the given manager and attributes. + + Args: + manager: A LocalizationFileManager to assist with server communication + context: A LocalizationContext + path: A path to this file + checksum: A string representation of a checksum generated from the file contents. + timnestamp: A datetime.datetime object indicating when the file was last modified. + """ + self._manager = manager + self.context = context + self.path = path + self.checksum = checksum + self.timestamp = timestamp + def open(self, mode='r'): + """Open the file. + + This should always be called as as part of a with statement. When + writing the content is not saved on the server until leaving the with + statement normally, if an error occurs the server is left unchanged. + + Example: + with locFile.open('w') as output: + output.write('some content') + + Args: + mode: 'r' for reading the file, 'w' for writing + + Returns: + A file like object that can be used for reads or writes. + """ + if mode == 'r': + request = self._manager._buildRequest(self.context, self.path) + response = urllib2.urlopen(request) + # Not the recommended way of reading directories. + if not(self.isDirectory()): + checksum = response.headers["Content-MD5"] + if self.checksum != checksum: + raise RuntimeError, "Localization checksum mismatch " + self.checksum + " " + checksum + return contextlib.closing(response) + elif mode == 'w': + return _LocalizationOutput(self._manager, self) + else: + raise ValueError, "mode string must be 'r' or 'w' not " + str(r) + def delete(self): + """Delete this file from the server""" + request = self._manager._buildRequest(self.context, self.path, method='DELETE') + request.add_header("If-Match", self.checksum) + try: + urllib2.urlopen(request) + except urllib2.HTTPError as e: + if e.code == 409: + raise LocalizationFileVersionConflictException, e.read() + else: + raise e + def exists(self): + """Check if this file actually exists. + + Returns: + boolean indicating existence of this file + """ + return self.checksum != NON_EXISTENT_CHECKSUM + def isDirectory(self): + """Check if this file is a directory. + + A file must exist to be considered a directory. + + Returns: + boolean indicating directorocity of this file + """ + return self.checksum == DIRECTORY_CHECKSUM + def getCheckSum(self): + return self.checksum + def getContext(self): + return self.context + def getPath(self): + return self.path + def getTimeStamp(self): + return self.timestamp + def __str__(self): + return str(self.context) + "/" + self.path + def __eq__(self, other): + return self.context == other.context and \ + self.path == other.path and \ + self.checksum == other.checksum \ + and self.timestamp == other.timestamp + def __hash__(self): + return hash((self.context, self.path, self.checksum, self.timestamp)) + +def _getHost(): + import subprocess + host = subprocess.check_output( + "source /awips2/fxa/bin/setup.env; echo $DEFAULT_HOST", + shell=True).strip() + if host: + return host + return 'localhost' + +def _getSiteFromServer(host): + try: + from awips import ThriftClient + from dynamicserialize.dstypes.com.raytheon.uf.common.site.requests import GetPrimarySiteRequest + client = ThriftClient.ThriftClient(host) + return client.sendRequest(GetPrimarySiteRequest()) + except: + # Servers that don't have GFE installed will not return a site + pass + +def _getSiteFromEnv(): + site = os.environ.get('FXA_LOCAL_SITE') + if site is None: + site = os.environ.get('SITE_IDENTIFIER'); + return site + +def _getSite(host): + site = _getSiteFromEnv() + if not(site): + site = _getSiteFromServer(host) + return site + +def _parseJsonList(manager, response, context, path): + fileList = [] + jsonResponse = loadjson(response) + for name, jsonData in jsonResponse.items(): + checksum = jsonData["checksum"] + timestampString = jsonData["timestamp"] + timestamp = dateutil.parser.parse(timestampString) + newpath = urljoin(path, name) + fileList.append(LocalizationFile(manager, context, newpath, checksum, timestamp)) + return fileList + +def _parseXmlList(manager, response, context, path): + fileList = [] + for xmlData in parseXml(response).getroot().findall('file'): + name = xmlData.get("name") + checksum = xmlData.get("checksum") + timestampString = xmlData.get("timestamp") + timestamp = dateutil.parser.parse(timestampString) + newpath = urljoin(path, name) + fileList.append(LocalizationFile(manager, context, newpath, checksum, timestamp)) + return fileList + +class LocalizationFileManager(object): + """Connects to a server and retrieves LocalizationFiles.""" + def __init__(self, host=None, port=9581, path="/services/localization/", contexts=None, site=None, type="common_static"): + """Initializes a LocalizationFileManager with connection parameters and context information + + All arguments are optional and will use defaults or attempt to figure out appropriate values form the environment. + + Args: + host: A hostname of the localization server, such as 'ec'. + port: A port to use to connect to the localization server, usually 9581. + path: A path to reach the localization file service on the server. + contexts: A list of contexts to check for files, the order of the contexts will be used + for the order of incremental results and the priority of absolute results. + site: A site identifier to use for site specific contexts. This is only used if the contexts arg is None. + type: A localization type for contexts. This is only used if the contexts arg is None. + + """ + if host is None: + host = _getHost() + if contexts is None: + if site is None : + site = _getSite(host) + contexts = [LocalizationContext("base", None, type)] + if site: + contexts.append(LocalizationContext("configured", site, type)) + contexts.append(LocalizationContext("site", site, type)) + contexts.append(LocalizationContext("user", getuser(), type)) + netloc = host + ':' + str(port) + self._baseUrl = urlunparse(('http', netloc, path, None, None, None)) + self._contexts = contexts + def _buildRequest(self, context, path, method='GET'): + url = urljoin(self._baseUrl, context._getUrlComponent()) + url = urljoin(url, path) + request = urllib2.Request(url) + username = getuser() + # Currently password is ignored in the server + # this is the defacto standard for not providing one to this service. + password = username + base64string = b64encode('%s:%s' % (username, password)) + request.add_header("Authorization", "Basic %s" % base64string) + if method != 'GET': + request.get_method = lambda: method + return request + def _normalizePath(self, path): + if path == '' or path == '/': + path = '.' + if path[0] == '/': + path = path[1:] + return path + def _list(self, path): + path = self._normalizePath(path) + if path[-1] != '/': + path += '/' + fileList = [] + exists = False + for context in self._contexts: + try: + request = self._buildRequest(context, path) + request.add_header("Accept", "application/json, application/xml") + response = urllib2.urlopen(request) + exists = True + if not(response.geturl().endswith("/")): + # For ordinary files the server sends a redirect to remove the slash. + raise LocalizationFileIsNotDirectoryException, "Not a directory: " + path + elif response.headers["Content-Type"] == "application/xml": + fileList += _parseXmlList(self, response, context, path) + else: + fileList += _parseJsonList(self, response, context, path) + except urllib2.HTTPError as e: + if e.code != 404: + raise e + if not(exists): + raise LocalizationFileDoesNotExistException, "No such file or directory: " + path + return fileList + def _get(self, context, path): + path = self._normalizePath(path) + try: + request = self._buildRequest(context, path, method='HEAD') + resp = urllib2.urlopen(request) + if (resp.geturl().endswith("/")): + checksum = DIRECTORY_CHECKSUM; + else: + if "Content-MD5" not in resp.headers: + raise RuntimeError, "Missing Content-MD5 header in response from " + resp.geturl() + checksum = resp.headers["Content-MD5"] + if "Last-Modified" not in resp.headers: + raise RuntimeError, "Missing Last-Modified header in response from " + resp.geturl() + timestamp = dateutil.parser.parse(resp.headers["Last-Modified"]) + return LocalizationFile(self, context, path, checksum, timestamp) + except urllib2.HTTPError as e: + if e.code != 404: + raise e + else: + return LocalizationFile(self, context, path, NON_EXISTENT_CHECKSUM, None) + def listAbsolute(self, path): + """List the files in a localization directory, only a single file is returned for each unique path. + + If a file exists in more than one context then the highest level(furthest from base) is used. + + Args: + path: A path to a directory that should be the root of the listing + + Returns: + A list of LocalizationFiles + """ + merged = dict() + for file in self._list(path): + merged[file.path] = file + return sorted(merged.values(), key=lambda file: file.path) + def listIncremental(self, path): + """List the files in a localization directory, this includes all files for all contexts. + + Args: + path: A path to a directory that should be the root of the listing + + Returns: + A list of tuples, each tuple will contain one or more files for the + same paths but different contexts. Each tuple will be ordered the + same as the contexts in this manager, generally with 'base' first + and 'user' last. + """ + merged = dict() + for file in self._list(path): + if file.path in merged: + merged[file.path] += (file,) + else: + merged[file.path] = (file, ) + return sorted(merged.values(), key=lambda t: t[0].path) + def getAbsolute(self, path): + """Get a single localization file from the highest level context where it exists. + + Args: + path: A path to a localization file + + Returns: + A Localization File with the specified path or None if the file does not exist in any context. + + """ + for context in reversed(self._contexts): + f = self._get(context, path) + if f.exists(): + return f + def getIncremental(self, path): + """Get all the localization files that exist in any context for the provided path. + + Args: + path: A path to a localization file + + Returns: + A tuple containing all the files that exist for this path in any context. The tuple + will be ordered the same as the contexts in this manager, generally with 'base' first + and 'user' last. + """ + result = () + for context in self._contexts: + f = self._get(context, path) + if f.exists(): + result += (f,) + return result + def getSpecific(self, level, path): + """Get a specific localization file at a given level, the file may not exist. + + The file is returned for whichever context is valid for the provided level in this manager. + + For writing new files this is the only way to get access to a file that + does not exist in order to create it. + + Args: + level: the name of a localization level, such as "base", "site", "user" + path: A path to a localization file + + Returns: + A Localization File with the specified path and a context for the specified level. + """ + for context in self._contexts: + if context.level == level: + return self._get(context, path) + raise ValueError, "No context defined for level " + level + def __str__(self): + contextsStr = '[' + ' '.join((str(c) for c in self._contexts)) + ']' + return '<' + self.__class__.__name__ + " for " + self._baseUrl + ' ' + contextsStr + '>' diff --git a/awips/localization/__init__.py b/awips/localization/__init__.py new file mode 100644 index 0000000..0473217 --- /dev/null +++ b/awips/localization/__init__.py @@ -0,0 +1,15 @@ +## +## + +# +# __init__.py for awips.localization package +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# --------- -------- --------- -------------------------- +# 08/10/17 5731 bsteffen Initial Creation. + +__all__ = [ + ] \ No newline at end of file diff --git a/awips/qpidingest.py b/awips/qpidingest.py new file mode 100644 index 0000000..0a9dc1b --- /dev/null +++ b/awips/qpidingest.py @@ -0,0 +1,129 @@ +#=============================================================================== +# qpidingest.py +# +# @author: Aaron Anderson +# @organization: NOAA/WDTB OU/CIMMS +# @version: 1.0 02/19/2010 +# @requires: QPID Python Client available from http://qpid.apache.org/download.html +# The Python Client is located under Single Component Package/Client +# +# From the README.txt Installation Instructions +# = INSTALLATION = +# Extract the release archive into a directory of your choice and set +# your PYTHONPATH accordingly: +# +# tar -xzf qpid-python-.tar.gz -C +# export PYTHONPATH=/qpid-/python +# +# ***EDEX and QPID must be running for this module to work*** +# +# DESCRIPTION: +# This module is used to connect to QPID and send messages to the external.dropbox queue +# which tells EDEX to ingest a data file from a specified path. This avoids having to copy +# a data file into an endpoint. Each message also contains a header which is used to determine +# which plugin should be used to decode the file. Each plugin has an xml file located in +# $EDEX_HOME/data/utility/edex_static/base/distribution that contains regular expressions +# that the header is compared to. When the header matches one of these regular expressions +# the file is decoded with that plugin. If you make changes to one of these xml files you +# must restart EDEX for the changes to take effect. +# +# NOTE: If the message is being sent but you do not see it being ingested in the EDEX log +# check the xml files to make sure the header you are passing matches one of the regular +# expressions. Beware of spaces, some regular expressions require spaces while others use +# a wildcard character so a space is optional. It seems you are better off having the space +# as this will be matched to both patterns. For the file in the example below, +# 20100218_185755_SAUS46KLOX.metar, I use SAUS46 KLOX as the header to make sure it matches. +# +# +# EXAMPLE: +# Simple example program: +# +#------------------------------------------------------------------------------ +# import qpidingest +# #Tell EDEX to ingest a metar file from data_store. The filepath is +# #/data_store/20100218/metar/00/standard/20100218_005920_SAUS46KSEW.metar +# +# conn=qpidingest.IngestViaQPID() #defaults to localhost port 5672 +# +# #If EDEX is not on the local machine you can make the connection as follows +# #conn=qpidingest.IngestViaQPID(host='',port=) +# +# conn.sendmessage('/data_store/20100218/metar/18/standard/20100218_185755_SAUS46KLOX.metar','SAUS46 KLOX') +# conn.close() +#------------------------------------------------------------------------------- +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# .... +# 06/13/2013 DR 16242 D. Friedman Add Qpid authentication info +# 03/06/2014 DR 17907 D. Friedman Workaround for issue QPID-5569 +# 02/16/2017 DR 6084 bsteffen Support ssl connections +# +#=============================================================================== + +import os +import os.path + +import qpid +from qpid.util import connect +from qpid.connection import Connection +from qpid.datatypes import Message, uuid4 + +QPID_USERNAME = 'guest' +QPID_PASSWORD = 'guest' + +class IngestViaQPID: + def __init__(self, host='localhost', port=5672, ssl=None): + ''' + Connect to QPID and make bindings to route message to external.dropbox queue + @param host: string hostname of computer running EDEX and QPID (default localhost) + @param port: integer port used to connect to QPID (default 5672) + @param ssl: boolean to determine whether ssl is used, default value of None will use ssl only if a client certificate is found. + ''' + + try: + # + socket = connect(host, port) + if "QPID_SSL_CERT_DB" in os.environ: + certdb = os.environ["QPID_SSL_CERT_DB"] + else: + certdb = os.path.expanduser("~/.qpid/") + if "QPID_SSL_CERT_NAME" in os.environ: + certname = os.environ["QPID_SSL_CERT_NAME"] + else: + certname = QPID_USERNAME + certfile = os.path.join(certdb, certname + ".crt") + if ssl or (ssl is None and os.path.exists(certfile)): + keyfile = os.path.join(certdb, certname + ".key") + trustfile = os.path.join(certdb, "root.crt") + socket = qpid.util.ssl(socket, keyfile=keyfile, certfile=certfile, ca_certs=trustfile) + self.connection = Connection (sock=socket, username=QPID_USERNAME, password=QPID_PASSWORD) + self.connection.start() + self.session = self.connection.session(str(uuid4())) + self.session.exchange_bind(exchange='amq.direct', queue='external.dropbox', binding_key='external.dropbox') + print 'Connected to Qpid' + except: + print 'Unable to connect to Qpid' + + def sendmessage(self, filepath, header): + ''' + This function sends a message to the external.dropbox queue providing the path + to the file to be ingested and a header to determine the plugin to be used to + decode the file. + @param filepath: string full path to file to be ingested + @param header: string header used to determine plugin decoder to use + ''' + props = self.session.delivery_properties(routing_key='external.dropbox') + head = self.session.message_properties(application_headers={'header':header}, + user_id=QPID_USERNAME) # For issue QPID-5569. Fixed in Qpid 0.27 + self.session.message_transfer(destination='amq.direct', message=Message(props, head, filepath)) + + def close(self): + ''' + After all messages are sent call this function to close connection and make sure + there are no threads left open + ''' + self.session.close(timeout=10) + print 'Connection to Qpid closed' diff --git a/awips/stomp.py b/awips/stomp.py new file mode 100644 index 0000000..5eefa78 --- /dev/null +++ b/awips/stomp.py @@ -0,0 +1,917 @@ +#!/usr/bin/env python +## +## +"""Stomp Protocol Connectivity + + This provides basic connectivity to a message broker supporting the 'stomp' protocol. + At the moment ACK, SEND, SUBSCRIBE, UNSUBSCRIBE, BEGIN, ABORT, COMMIT, CONNECT and DISCONNECT operations + are supported. + + This changes the previous version which required a listener per subscription -- now a listener object + just calls the 'addlistener' method and will receive all messages sent in response to all/any subscriptions. + (The reason for the change is that the handling of an 'ack' becomes problematic unless the listener mechanism + is decoupled from subscriptions). + + Note that you must 'start' an instance of Connection to begin receiving messages. For example: + + conn = stomp.Connection([('localhost', 62003)], 'myuser', 'mypass') + conn.start() + + Meta-Data + --------- + Author: Jason R Briggs + License: http://www.apache.org/licenses/LICENSE-2.0 + Start Date: 2005/12/01 + Last Revision Date: $Date: 2008/09/11 00:16 $ + + Notes/Attribution + ----------------- + * uuid method courtesy of Carl Free Jr: + http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/213761 + * patch from Andreas Schobel + * patches from Julian Scheid of Rising Sun Pictures (http://open.rsp.com.au) + * patch from Fernando + * patches from Eugene Strulyov + + Updates + ------- + * 2007/03/31 : (Andreas Schobel) patch to fix newlines problem in ActiveMQ 4.1 + * 2007/09 : (JRB) updated to get stomp.py working in Jython as well as Python + * 2007/09/05 : (Julian Scheid) patch to allow sending custom headers + * 2007/09/18 : (JRB) changed code to use logging instead of just print. added logger for jython to work + * 2007/09/18 : (Julian Scheid) various updates, including: + - change incoming message handling so that callbacks are invoked on the listener not only for MESSAGE, but also for + CONNECTED, RECEIPT and ERROR frames. + - callbacks now get not only the payload but any headers specified by the server + - all outgoing messages now sent via a single method + - only one connection used + - change to use thread instead of threading + - sends performed on the calling thread + - receiver loop now deals with multiple messages in one received chunk of data + - added reconnection attempts and connection fail-over + - changed defaults for "user" and "passcode" to None instead of empty string (fixed transmission of those values) + - added readline support + * 2008/03/26 : (Fernando) added cStringIO for faster performance on large messages + * 2008/09/10 : (Eugene) remove lower() on headers to support case-sensitive header names + * 2008/09/11 : (JRB) fix incompatibilities with RabbitMQ, add wait for socket-connect + * 2008/10/28 : (Eugene) add jms map (from stomp1.1 ideas) + * 2008/11/25 : (Eugene) remove superfluous (incorrect) locking code + * 2009/02/05 : (JRB) remove code to replace underscores with dashes in header names (causes a problem in rabbit-mq) + * 2009/03/29 : (JRB) minor change to add logging config file + (JRB) minor change to add socket timeout, suggested by Israel + * 2009/04/01 : (Gavin) patch to change md5 to hashlib (for 2.6 compatibility) + * 2009/04/02 : (Fernando Ciciliati) fix overflow bug when waiting too long to connect to the broker + +""" + +import hashlib +import math +import random +import re +import socket +import sys +import thread +import threading +import time +import types +import xml.dom.minidom +from cStringIO import StringIO + +# +# stomp.py version number +# +_version = 1.8 + + +def _uuid( *args ): + """ + uuid courtesy of Carl Free Jr: + (http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/213761) + """ + + t = long( time.time() * 1000 ) + r = long( random.random() * 100000000000000000L ) + + try: + a = socket.gethostbyname( socket.gethostname() ) + except: + # if we can't get a network address, just imagine one + a = random.random() * 100000000000000000L + data = str(t) + ' ' + str(r) + ' ' + str(a) + ' ' + str(args) + md5 = hashlib.md5() + md5.update(data) + data = md5.hexdigest() + return data + + +class DevNullLogger(object): + """ + dummy logging class for environments without the logging module + """ + def log(self, msg): + print msg + + def devnull(self, msg): + pass + + debug = devnull + info = devnull + warning = log + error = log + critical = log + exception = log + + def isEnabledFor(self, lvl): + return False + + +# +# add logging if available +# +try: + import logging + import logging.config + logging.config.fileConfig("stomp.log.conf") + log = logging.getLogger('root') +except: + log = DevNullLogger() + + +class ConnectionClosedException(Exception): + """ + Raised in the receiver thread when the connection has been closed + by the server. + """ + pass + + +class NotConnectedException(Exception): + """ + Raised by Connection.__send_frame when there is currently no server + connection. + """ + pass + + +class ConnectionListener(object): + """ + This class should be used as a base class for objects registered + using Connection.add_listener(). + """ + def on_connecting(self, host_and_port): + """ + Called by the STOMP connection once a TCP/IP connection to the + STOMP server has been established or re-established. Note that + at this point, no connection has been established on the STOMP + protocol level. For this, you need to invoke the "connect" + method on the connection. + + \param host_and_port a tuple containing the host name and port + number to which the connection has been established. + """ + pass + + def on_connected(self, headers, body): + """ + Called by the STOMP connection when a CONNECTED frame is + received, that is after a connection has been established or + re-established. + + \param headers a dictionary containing all headers sent by the + server as key/value pairs. + + \param body the frame's payload. This is usually empty for + CONNECTED frames. + """ + pass + + def on_disconnected(self): + """ + Called by the STOMP connection when a TCP/IP connection to the + STOMP server has been lost. No messages should be sent via + the connection until it has been reestablished. + """ + pass + + def on_message(self, headers, body): + """ + Called by the STOMP connection when a MESSAGE frame is + received. + + \param headers a dictionary containing all headers sent by the + server as key/value pairs. + + \param body the frame's payload - the message body. + """ + pass + + def on_receipt(self, headers, body): + """ + Called by the STOMP connection when a RECEIPT frame is + received, sent by the server if requested by the client using + the 'receipt' header. + + \param headers a dictionary containing all headers sent by the + server as key/value pairs. + + \param body the frame's payload. This is usually empty for + RECEIPT frames. + """ + pass + + def on_error(self, headers, body): + """ + Called by the STOMP connection when an ERROR frame is + received. + + \param headers a dictionary containing all headers sent by the + server as key/value pairs. + + \param body the frame's payload - usually a detailed error + description. + """ + pass + + +class Connection(object): + """ + Represents a STOMP client connection. + """ + + def __init__(self, + host_and_ports = [ ('localhost', 61613) ], + user = None, + passcode = None, + prefer_localhost = True, + try_loopback_connect = True, + reconnect_sleep_initial = 0.1, + reconnect_sleep_increase = 0.5, + reconnect_sleep_jitter = 0.1, + reconnect_sleep_max = 60.0): + """ + Initialize and start this connection. + + \param host_and_ports + a list of (host, port) tuples. + + \param prefer_localhost + if True and the local host is mentioned in the (host, + port) tuples, try to connect to this first + + \param try_loopback_connect + if True and the local host is found in the host + tuples, try connecting to it using loopback interface + (127.0.0.1) + + \param reconnect_sleep_initial + + initial delay in seconds to wait before reattempting + to establish a connection if connection to any of the + hosts fails. + + \param reconnect_sleep_increase + + factor by which the sleep delay is increased after + each connection attempt. For example, 0.5 means + to wait 50% longer than before the previous attempt, + 1.0 means wait twice as long, and 0.0 means keep + the delay constant. + + \param reconnect_sleep_max + + maximum delay between connection attempts, regardless + of the reconnect_sleep_increase. + + \param reconnect_sleep_jitter + + random additional time to wait (as a percentage of + the time determined using the previous parameters) + between connection attempts in order to avoid + stampeding. For example, a value of 0.1 means to wait + an extra 0%-10% (randomly determined) of the delay + calculated using the previous three parameters. + """ + + sorted_host_and_ports = [] + sorted_host_and_ports.extend(host_and_ports) + + # If localhost is preferred, make sure all (host, port) tuples + # that refer to the local host come first in the list + if prefer_localhost: + def is_local_host(host): + return host in Connection.__localhost_names + + sorted_host_and_ports.sort(lambda x, y: (int(is_local_host(y[0])) + - int(is_local_host(x[0])))) + + # If the user wishes to attempt connecting to local ports + # using the loopback interface, for each (host, port) tuple + # referring to a local host, add an entry with the host name + # replaced by 127.0.0.1 if it doesn't exist already + loopback_host_and_ports = [] + if try_loopback_connect: + for host_and_port in sorted_host_and_ports: + if is_local_host(host_and_port[0]): + port = host_and_port[1] + if (not ("127.0.0.1", port) in sorted_host_and_ports + and not ("localhost", port) in sorted_host_and_ports): + loopback_host_and_ports.append(("127.0.0.1", port)) + + # Assemble the final, possibly sorted list of (host, port) tuples + self.__host_and_ports = [] + self.__host_and_ports.extend(loopback_host_and_ports) + self.__host_and_ports.extend(sorted_host_and_ports) + + self.__recvbuf = '' + + self.__listeners = [ ] + + self.__reconnect_sleep_initial = reconnect_sleep_initial + self.__reconnect_sleep_increase = reconnect_sleep_increase + self.__reconnect_sleep_jitter = reconnect_sleep_jitter + self.__reconnect_sleep_max = reconnect_sleep_max + + self.__connect_headers = {} + if user is not None and passcode is not None: + self.__connect_headers['login'] = user + self.__connect_headers['passcode'] = passcode + + self.__socket = None + self.__current_host_and_port = None + + self.__receiver_thread_exit_condition = threading.Condition() + self.__receiver_thread_exited = False + + # + # Manage the connection + # + + def start(self): + """ + Start the connection. This should be called after all + listeners have been registered. If this method is not called, + no frames will be received by the connection. + """ + self.__running = True + self.__attempt_connection() + thread.start_new_thread(self.__receiver_loop, ()) + + def stop(self): + """ + Stop the connection. This is equivalent to calling + disconnect() but will do a clean shutdown by waiting for the + receiver thread to exit. + """ + self.disconnect() + + self.__receiver_thread_exit_condition.acquire() + if not self.__receiver_thread_exited: + self.__receiver_thread_exit_condition.wait() + self.__receiver_thread_exit_condition.release() + + def get_host_and_port(self): + """ + Return a (host, port) tuple indicating which STOMP host and + port is currently connected, or None if there is currently no + connection. + """ + return self.__current_host_and_port + + def is_connected(self): + try: + return self.__socket is not None and self.__socket.getsockname()[1] != 0 + except socket.error: + return False + + # + # Manage objects listening to incoming frames + # + + def add_listener(self, listener): + self.__listeners.append(listener) + + def remove_listener(self, listener): + self.__listeners.remove(listener) + + # + # STOMP transmissions + # + + def subscribe(self, headers={}, **keyword_headers): + self.__send_frame_helper('SUBSCRIBE', '', self.__merge_headers([headers, keyword_headers]), [ 'destination' ]) + + def unsubscribe(self, headers={}, **keyword_headers): + self.__send_frame_helper('UNSUBSCRIBE', '', self.__merge_headers([headers, keyword_headers]), [ ('destination', 'id') ]) + + def send(self, message='', headers={}, **keyword_headers): + if '\x00' in message: + content_length_headers = {'content-length': len(message)} + else: + content_length_headers = {} + self.__send_frame_helper('SEND', message, self.__merge_headers([headers, + keyword_headers, + content_length_headers]), [ 'destination' ]) + + def ack(self, headers={}, **keyword_headers): + self.__send_frame_helper('ACK', '', self.__merge_headers([headers, keyword_headers]), [ 'message-id' ]) + + def begin(self, headers={}, **keyword_headers): + use_headers = self.__merge_headers([headers, keyword_headers]) + if not 'transaction' in use_headers.keys(): + use_headers['transaction'] = _uuid() + self.__send_frame_helper('BEGIN', '', use_headers, [ 'transaction' ]) + return use_headers['transaction'] + + def abort(self, headers={}, **keyword_headers): + self.__send_frame_helper('ABORT', '', self.__merge_headers([headers, keyword_headers]), [ 'transaction' ]) + + def commit(self, headers={}, **keyword_headers): + self.__send_frame_helper('COMMIT', '', self.__merge_headers([headers, keyword_headers]), [ 'transaction' ]) + + def connect(self, headers={}, **keyword_headers): + if keyword_headers.has_key('wait') and keyword_headers['wait']: + while not self.is_connected(): time.sleep(0.1) + del keyword_headers['wait'] + self.__send_frame_helper('CONNECT', '', self.__merge_headers([self.__connect_headers, headers, keyword_headers]), [ ]) + + def disconnect(self, headers={}, **keyword_headers): + self.__send_frame_helper('DISCONNECT', '', self.__merge_headers([self.__connect_headers, headers, keyword_headers]), [ ]) + self.__running = False + if hasattr(socket, 'SHUT_RDWR'): + self.__socket.shutdown(socket.SHUT_RDWR) + if self.__socket: + self.__socket.close() + self.__current_host_and_port = None + + # ========= PRIVATE MEMBERS ========= + + + # List of all host names (unqualified, fully-qualified, and IP + # addresses) that refer to the local host (both loopback interface + # and external interfaces). This is used for determining + # preferred targets. + __localhost_names = [ "localhost", + "127.0.0.1", + socket.gethostbyname(socket.gethostname()), + socket.gethostname(), + socket.getfqdn(socket.gethostname()) ] + # + # Used to parse STOMP header lines in the format "key:value", + # + __header_line_re = re.compile('(?P[^:]+)[:](?P.*)') + + # + # Used to parse the STOMP "content-length" header lines, + # + __content_length_re = re.compile('^content-length[:]\\s*(?P[0-9]+)', re.MULTILINE) + + def __merge_headers(self, header_map_list): + """ + Helper function for combining multiple header maps into one. + + Any underscores ('_') in header names (keys) will be replaced by dashes ('-'). + """ + headers = {} + for header_map in header_map_list: + for header_key in header_map.keys(): + headers[header_key] = header_map[header_key] + return headers + + def __convert_dict(self, payload): + """ + Encode python dictionary as ... structure. + """ + + xmlStr = "\n" + for key in payload: + xmlStr += "\n" + xmlStr += "%s" % key + xmlStr += "%s" % payload[key] + xmlStr += "\n" + xmlStr += "" + + return xmlStr + + def __send_frame_helper(self, command, payload, headers, required_header_keys): + """ + Helper function for sending a frame after verifying that a + given set of headers are present. + + \param command the command to send + + \param payload the frame's payload + + \param headers a dictionary containing the frame's headers + + \param required_header_keys a sequence enumerating all + required header keys. If an element in this sequence is itself + a tuple, that tuple is taken as a list of alternatives, one of + which must be present. + + \throws ArgumentError if one of the required header keys is + not present in the header map. + """ + for required_header_key in required_header_keys: + if type(required_header_key) == tuple: + found_alternative = False + for alternative in required_header_key: + if alternative in headers.keys(): + found_alternative = True + if not found_alternative: + raise KeyError("Command %s requires one of the following headers: %s" % (command, str(required_header_key))) + elif not required_header_key in headers.keys(): + raise KeyError("Command %s requires header %r" % (command, required_header_key)) + self.__send_frame(command, headers, payload) + + def __send_frame(self, command, headers={}, payload=''): + """ + Send a STOMP frame. + """ + if type(payload) == dict: + headers["transformation"] = "jms-map-xml" + payload = self.__convert_dict(payload) + + if self.__socket is not None: + frame = '%s\n%s\n%s\x00' % (command, + reduce(lambda accu, key: accu + ('%s:%s\n' % (key, headers[key])), headers.keys(), ''), + payload) + self.__socket.sendall(frame) + log.debug("Sent frame: type=%s, headers=%r, body=%r" % (command, headers, payload)) + else: + raise NotConnectedException() + + def __receiver_loop(self): + """ + Main loop listening for incoming data. + """ + try: + try: + threading.currentThread().setName("StompReceiver") + while self.__running: + log.debug('starting receiver loop') + + if self.__socket is None: + break + + try: + try: + for listener in self.__listeners: + if hasattr(listener, 'on_connecting'): + listener.on_connecting(self.__current_host_and_port) + + while self.__running: + frames = self.__read() + + for frame in frames: + (frame_type, headers, body) = self.__parse_frame(frame) + log.debug("Received frame: result=%r, headers=%r, body=%r" % (frame_type, headers, body)) + frame_type = frame_type.lower() + if frame_type in [ 'connected', + 'message', + 'receipt', + 'error' ]: + for listener in self.__listeners: + if hasattr(listener, 'on_%s' % frame_type): + eval('listener.on_%s(headers, body)' % frame_type) + else: + log.debug('listener %s has no such method on_%s' % (listener, frame_type)) + else: + log.warning('Unknown response frame type: "%s" (frame length was %d)' % (frame_type, len(frame))) + finally: + try: + self.__socket.close() + except: + pass # ignore errors when attempting to close socket + self.__socket = None + self.__current_host_and_port = None + except ConnectionClosedException: + if self.__running: + log.error("Lost connection") + # Notify listeners + for listener in self.__listeners: + if hasattr(listener, 'on_disconnected'): + listener.on_disconnected() + # Clear out any half-received messages after losing connection + self.__recvbuf = '' + continue + else: + break + except: + log.exception("An unhandled exception was encountered in the stomp receiver loop") + + finally: + self.__receiver_thread_exit_condition.acquire() + self.__receiver_thread_exited = True + self.__receiver_thread_exit_condition.notifyAll() + self.__receiver_thread_exit_condition.release() + + def __read(self): + """ + Read the next frame(s) from the socket. + """ + fastbuf = StringIO() + while self.__running: + try: + c = self.__socket.recv(1024) + except: + c = '' + if len(c) == 0: + raise ConnectionClosedException + fastbuf.write(c) + if '\x00' in c: + break + self.__recvbuf += fastbuf.getvalue() + fastbuf.close() + result = [] + + if len(self.__recvbuf) > 0 and self.__running: + while True: + pos = self.__recvbuf.find('\x00') + if pos >= 0: + frame = self.__recvbuf[0:pos] + preamble_end = frame.find('\n\n') + if preamble_end >= 0: + content_length_match = Connection.__content_length_re.search(frame[0:preamble_end]) + if content_length_match: + content_length = int(content_length_match.group('value')) + content_offset = preamble_end + 2 + frame_size = content_offset + content_length + if frame_size > len(frame): + # Frame contains NUL bytes, need to + # read more + if frame_size < len(self.__recvbuf): + pos = frame_size + frame = self.__recvbuf[0:pos] + else: + # Haven't read enough data yet, + # exit loop and wait for more to + # arrive + break + result.append(frame) + self.__recvbuf = self.__recvbuf[pos+1:] + else: + break + return result + + + def __transform(self, body, transType): + """ + Perform body transformation. Currently, the only supported transformation is + 'jms-map-xml', which converts a map into python dictionary. This can be extended + to support other transformation types. + + The body has the following format: + + + name + Dejan + + + city + Belgrade + + + + (see http://docs.codehaus.org/display/STOMP/Stomp+v1.1+Ideas) + """ + + if transType != 'jms-map-xml': + return body + + try: + entries = {} + doc = xml.dom.minidom.parseString(body) + rootElem = doc.documentElement + for entryElem in rootElem.getElementsByTagName("entry"): + pair = [] + for node in entryElem.childNodes: + if not isinstance(node, xml.dom.minidom.Element): continue + pair.append(node.firstChild.nodeValue) + assert len(pair) == 2 + entries[pair[0]] = pair[1] + return entries + except Exception, ex: + # unable to parse message. return original + return body + + + def __parse_frame(self, frame): + """ + Parse a STOMP frame into a (frame_type, headers, body) tuple, + where frame_type is the frame type as a string (e.g. MESSAGE), + headers is a map containing all header key/value pairs, and + body is a string containing the frame's payload. + """ + preamble_end = frame.find('\n\n') + preamble = frame[0:preamble_end] + preamble_lines = preamble.split('\n') + body = frame[preamble_end+2:] + + # Skip any leading newlines + first_line = 0 + while first_line < len(preamble_lines) and len(preamble_lines[first_line]) == 0: + first_line += 1 + + # Extract frame type + frame_type = preamble_lines[first_line] + + # Put headers into a key/value map + headers = {} + for header_line in preamble_lines[first_line+1:]: + header_match = Connection.__header_line_re.match(header_line) + if header_match: + headers[header_match.group('key')] = header_match.group('value') + + if 'transformation' in headers: + body = self.__transform(body, headers['transformation']) + + return (frame_type, headers, body) + + def __attempt_connection(self): + """ + Try connecting to the (host, port) tuples specified at construction time. + """ + + sleep_exp = 1 + while self.__running and self.__socket is None: + for host_and_port in self.__host_and_ports: + try: + log.debug("Attempting connection to host %s, port %s" % host_and_port) + self.__socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + self.__socket.settimeout(None) + self.__socket.connect(host_and_port) + self.__current_host_and_port = host_and_port + log.info("Established connection to host %s, port %s" % host_and_port) + break + except socket.error: + self.__socket = None + if type(sys.exc_info()[1]) == types.TupleType: + exc = sys.exc_info()[1][1] + else: + exc = sys.exc_info()[1] + log.warning("Could not connect to host %s, port %s: %s" % (host_and_port[0], host_and_port[1], exc)) + + if self.__socket is None: + sleep_duration = (min(self.__reconnect_sleep_max, + ((self.__reconnect_sleep_initial / (1.0 + self.__reconnect_sleep_increase)) + * math.pow(1.0 + self.__reconnect_sleep_increase, sleep_exp))) + * (1.0 + random.random() * self.__reconnect_sleep_jitter)) + sleep_end = time.time() + sleep_duration + log.debug("Sleeping for %.1f seconds before attempting reconnect" % sleep_duration) + while self.__running and time.time() < sleep_end: + time.sleep(0.2) + + if sleep_duration < self.__reconnect_sleep_max: + sleep_exp += 1 + +# +# command line testing +# +if __name__ == '__main__': + + # If the readline module is available, make command input easier + try: + import readline + def stomp_completer(text, state): + commands = [ 'subscribe', 'unsubscribe', + 'send', 'ack', + 'begin', 'abort', 'commit', + 'connect', 'disconnect' + ] + for command in commands[state:]: + if command.startswith(text): + return "%s " % command + return None + + readline.parse_and_bind("tab: complete") + readline.set_completer(stomp_completer) + readline.set_completer_delims("") + except ImportError: + pass # ignore unavailable readline module + + class StompTester(object): + def __init__(self, host='localhost', port=61613, user='', passcode=''): + self.c = Connection([(host, port)], user, passcode) + self.c.add_listener(self) + self.c.start() + + def __print_async(self, frame_type, headers, body): + print "\r \r", + print frame_type + for header_key in headers.keys(): + print '%s: %s' % (header_key, headers[header_key]) + print + print body + print '> ', + sys.stdout.flush() + + def on_connecting(self, host_and_port): + self.c.connect(wait=True) + + def on_disconnected(self): + print "lost connection" + + def on_message(self, headers, body): + self.__print_async("MESSAGE", headers, body) + + def on_error(self, headers, body): + self.__print_async("ERROR", headers, body) + + def on_receipt(self, headers, body): + self.__print_async("RECEIPT", headers, body) + + def on_connected(self, headers, body): + self.__print_async("CONNECTED", headers, body) + + def ack(self, args): + if len(args) < 3: + self.c.ack(message_id=args[1]) + else: + self.c.ack(message_id=args[1], transaction=args[2]) + + def abort(self, args): + self.c.abort(transaction=args[1]) + + def begin(self, args): + print 'transaction id: %s' % self.c.begin() + + def commit(self, args): + if len(args) < 2: + print 'expecting: commit ' + else: + print 'committing %s' % args[1] + self.c.commit(transaction=args[1]) + + def disconnect(self, args): + try: + self.c.disconnect() + except NotConnectedException: + pass # ignore if no longer connected + + def send(self, args): + if len(args) < 3: + print 'expecting: send ' + else: + self.c.send(destination=args[1], message=' '.join(args[2:])) + + def sendtrans(self, args): + if len(args) < 3: + print 'expecting: sendtrans ' + else: + self.c.send(destination=args[1], message="%s\n" % ' '.join(args[3:]), transaction=args[2]) + + def subscribe(self, args): + if len(args) < 2: + print 'expecting: subscribe [ack]' + elif len(args) > 2: + print 'subscribing to "%s" with acknowledge set to "%s"' % (args[1], args[2]) + self.c.subscribe(destination=args[1], ack=args[2]) + else: + print 'subscribing to "%s" with auto acknowledge' % args[1] + self.c.subscribe(destination=args[1], ack='auto') + + def unsubscribe(self, args): + if len(args) < 2: + print 'expecting: unsubscribe ' + else: + print 'unsubscribing from "%s"' % args[1] + self.c.unsubscribe(destination=args[1]) + + if len(sys.argv) > 5: + print 'USAGE: stomp.py [host] [port] [user] [passcode]' + sys.exit(1) + + if len(sys.argv) >= 2: + host = sys.argv[1] + else: + host = "localhost" + if len(sys.argv) >= 3: + port = int(sys.argv[2]) + else: + port = 61613 + + if len(sys.argv) >= 5: + user = sys.argv[3] + passcode = sys.argv[4] + else: + user = None + passcode = None + + st = StompTester(host, port, user, passcode) + try: + while True: + line = raw_input("\r> ") + if not line or line.lstrip().rstrip() == '': + continue + elif 'quit' in line or 'disconnect' in line: + break + split = line.split() + command = split[0] + if not command.startswith("on_") and hasattr(st, command): + getattr(st, command)(split) + else: + print 'unrecognized command' + finally: + st.disconnect(None) + + diff --git a/awips/test/Record.py b/awips/test/Record.py new file mode 100644 index 0000000..784474a --- /dev/null +++ b/awips/test/Record.py @@ -0,0 +1,31 @@ +## +## + + +# +# Pure python logging mechanism for logging to AlertViz from +# pure python (ie not JEP). DO NOT USE IN PYTHON CALLED +# FROM JAVA. +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 11/03/10 5849 cjeanbap Initial Creation. +# +# +# + +import os +import sys + +class Record(): + def __init__(self, level=0, msg='Test Message'): + self.levelno=level + self.message=msg + self.exc_info=sys.exc_info() + self.exc_text="TEST" + + def getMessage(self): + return self.message \ No newline at end of file diff --git a/awips/test/Test b/awips/test/Test new file mode 100644 index 0000000..24232f1 --- /dev/null +++ b/awips/test/Test @@ -0,0 +1,30 @@ +## +## + + +# +# Pure python logging mechanism for logging to AlertViz from +# pure python (ie not JEP). DO NOT USE IN PYTHON CALLED +# FROM JAVA. +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 11/03/10 5849 cjeanbap Initial Creation. +# +# +# + +## to execute type python Test + + +import os +import logging +from awips import AlertVizHandler +import Record + +avh = AlertVizHandler.AlertVizHandler(host=os.getenv("BROKER_ADDR","localhost"), port=9581, category='LOCAL', source='ANNOUNCER', level=logging.NOTSET) +record = Record.Record(10) +avh.emit(record) diff --git a/awips/test/__init__.py b/awips/test/__init__.py new file mode 100644 index 0000000..54f92eb --- /dev/null +++ b/awips/test/__init__.py @@ -0,0 +1,17 @@ +## +## + + +# +# __init__.py for awips package +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 11/03/10 5489 cjeanbap Initial Creation. +# +# +# + diff --git a/awips/test/dafTests/__init__.py b/awips/test/dafTests/__init__.py new file mode 100644 index 0000000..189c306 --- /dev/null +++ b/awips/test/dafTests/__init__.py @@ -0,0 +1,19 @@ +## +## + + +# +# __init__.py for awips.test.dafTests package +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 02/09/2016 4795 mapeters Initial creation. +# 04/12/2016 5548 tgurney Cleanup +# +# +# + +__all__ = [] diff --git a/awips/test/dafTests/baseBufrMosTestCase.py b/awips/test/dafTests/baseBufrMosTestCase.py new file mode 100644 index 0000000..aa55fa0 --- /dev/null +++ b/awips/test/dafTests/baseBufrMosTestCase.py @@ -0,0 +1,56 @@ +## +## + +from awips.dataaccess import DataAccessLayer as DAL +from shapely.geometry import box + +import baseDafTestCase +import params +import unittest + +# +# Base TestCase for BufrMos* tests. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 12/07/16 5981 tgurney Parameterize +# 12/15/16 5981 tgurney Add envelope test +# +# + + +class BufrMosTestCase(baseDafTestCase.DafTestCase): + """Base class for testing DAF support of bufrmos data""" + + data_params = "temperature", "dewpoint" + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + self.runLocationsTest(req) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + req.setLocationNames(params.OBS_STATION) + self.runTimesTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.setLocationNames(params.OBS_STATION) + req.setParameters(*self.data_params) + self.runGeometryDataTest(req) + + def testGetGeometryDataWithEnvelope(self): + req = DAL.newDataRequest(self.datatype) + req.setParameters(*self.data_params) + req.setEnvelope(params.ENVELOPE) + data = self.runGeometryDataTest(req) + for item in data: + self.assertTrue(params.ENVELOPE.contains(item.getGeometry())) diff --git a/awips/test/dafTests/baseDafTestCase.py b/awips/test/dafTests/baseDafTestCase.py new file mode 100644 index 0000000..3cf5de4 --- /dev/null +++ b/awips/test/dafTests/baseDafTestCase.py @@ -0,0 +1,214 @@ +## +## + +from __future__ import print_function + +from awips.dataaccess import DataAccessLayer as DAL +from awips.ThriftClient import ThriftRequestException + +import os +import unittest + +# +# Base TestCase for DAF tests. This class provides helper methods and +# tests common to all DAF test cases. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/13/16 5379 tgurney Add identifier values tests +# 04/18/16 5548 tgurney More cleanup, plus new tests +# 04/26/16 5587 tgurney Move identifier values tests +# to subclasses +# 06/01/16 5587 tgurney Add testGet*Identifiers +# 06/07/16 5574 tgurney Make geometry/grid data tests +# return the retrieved data +# 06/10/16 5548 tgurney Make testDatatypeIsSupported +# case-insensitive +# 08/10/16 2416 tgurney Don't test identifier values +# for dataURI +# 10/05/16 5926 dgilling Better checks in runGeometryDataTest. +# 11/08/16 5985 tgurney Do not check data times on +# time-agnostic data +# 03/13/17 5981 tgurney Do not check valid period on +# data time +# +# + + +class DafTestCase(unittest.TestCase): + + sampleDataLimit = 5 + """ + Maximum number of levels, locations, times, and geometry/grid data to + display + """ + + numTimesToLimit = 3 + """ + When limiting geometry/grid data requests with times, only retrieve data + for this many times + """ + + datatype = None + """Name of the datatype""" + + @classmethod + def setUpClass(cls): + host = os.environ.get('DAF_TEST_HOST') + if host is None: + host = 'localhost' + DAL.changeEDEXHost(host) + + @staticmethod + def getTimesIfSupported(req): + """Return available times for req. If req refers to a time-agnostic + datatype, return an empty list instead. + """ + times = [] + try: + times = DAL.getAvailableTimes(req) + except ThriftRequestException as e: + if not 'TimeAgnosticDataException' in str(e): + raise + return times + + def testDatatypeIsSupported(self): + allSupported = (item.lower() for item in DAL.getSupportedDatatypes()) + self.assertIn(self.datatype.lower(), allSupported) + + def testGetRequiredIdentifiers(self): + req = DAL.newDataRequest(self.datatype) + required = DAL.getRequiredIdentifiers(req) + self.assertIsNotNone(required) + print("Required identifiers:", required) + + def testGetOptionalIdentifiers(self): + req = DAL.newDataRequest(self.datatype) + optional = DAL.getOptionalIdentifiers(req) + self.assertIsNotNone(optional) + print("Optional identifiers:", optional) + + def runGetIdValuesTest(self, identifiers): + for id in identifiers: + if id.lower() == 'datauri': + continue + req = DAL.newDataRequest(self.datatype) + idValues = DAL.getIdentifierValues(req, id) + self.assertTrue(hasattr(idValues, '__iter__')) + + def runInvalidIdValuesTest(self): + badString = 'id from ' + self.datatype + '; select 1;' + with self.assertRaises(ThriftRequestException) as cm: + req = DAL.newDataRequest(self.datatype) + idValues = DAL.getIdentifierValues(req, badString) + + def runNonexistentIdValuesTest(self): + with self.assertRaises(ThriftRequestException) as cm: + req = DAL.newDataRequest(self.datatype) + idValues = DAL.getIdentifierValues(req, 'idthatdoesnotexist') + + def runParametersTest(self, req): + params = DAL.getAvailableParameters(req) + self.assertIsNotNone(params) + print(params) + + def runLevelsTest(self, req): + levels = DAL.getAvailableLevels(req) + self.assertIsNotNone(levels) + print("Number of levels: " + str(len(levels))) + strLevels = [str(t) for t in levels[:self.sampleDataLimit]] + print("Sample levels:\n" + str(strLevels)) + + def runLocationsTest(self, req): + locs = DAL.getAvailableLocationNames(req) + self.assertIsNotNone(locs) + print("Number of location names: " + str(len(locs))) + print("Sample location names:\n" + str(locs[:self.sampleDataLimit])) + + def runTimesTest(self, req): + times = DAL.getAvailableTimes(req) + self.assertIsNotNone(times) + print("Number of times: " + str(len(times))) + strTimes = [str(t) for t in times[:self.sampleDataLimit]] + print("Sample times:\n" + str(strTimes)) + + def runTimeAgnosticTest(self, req): + with self.assertRaises(ThriftRequestException) as cm: + times = DAL.getAvailableTimes(req) + self.assertIn('TimeAgnosticDataException', str(cm.exception)) + + def runGeometryDataTest(self, req, checkDataTimes=True): + """ + Test that we are able to successfully retrieve geometry data for the + given request. + """ + times = DafTestCase.getTimesIfSupported(req) + geomData = DAL.getGeometryData(req, times[:self.numTimesToLimit]) + self.assertIsNotNone(geomData) + if times: + self.assertNotEqual(len(geomData), 0) + if not geomData: + raise unittest.SkipTest("No data available") + print("Number of geometry records: " + str(len(geomData))) + print("Sample geometry data:") + for record in geomData[:self.sampleDataLimit]: + if (checkDataTimes and times and + "PERIOD_USED" not in record.getDataTime().getUtilityFlags()): + self.assertIn(record.getDataTime(), times[:self.numTimesToLimit]) + print("geometry=" + str(record.getGeometry()), end="") + for p in req.getParameters(): + print(" " + p + "=" + record.getString(p), end="") + print() + return geomData + + def runGeometryDataTestWithTimeRange(self, req, timeRange): + """ + Test that we are able to successfully retrieve geometry data for the + given request. + """ + geomData = DAL.getGeometryData(req, timeRange) + self.assertIsNotNone(geomData) + if not geomData: + raise unittest.SkipTest("No data available") + print("Number of geometry records: " + str(len(geomData))) + print("Sample geometry data:") + for record in geomData[:self.sampleDataLimit]: + self.assertGreaterEqual(record.getDataTime().getRefTime().getTime(), timeRange.getStartInMillis()) + self.assertLessEqual(record.getDataTime().getRefTime().getTime(), timeRange.getEndInMillis()) + print("geometry=" + str(record.getGeometry()), end="") + for p in req.getParameters(): + print(" " + p + "=" + record.getString(p), end="") + print() + return geomData + + def runGridDataTest(self, req, testSameShape=True): + """ + Test that we are able to successfully retrieve grid data for the given + request. + + Args: + testSameShape: whether or not to verify that all the retrieved data + have the same shape (most data don't change shape) + """ + times = DafTestCase.getTimesIfSupported(req) + gridData = DAL.getGridData(req, times[:self.numTimesToLimit]) + self.assertIsNotNone(gridData) + if not gridData: + raise unittest.SkipTest("No data available") + print("Number of grid records: " + str(len(gridData))) + if len(gridData) > 0: + print("Sample grid data shape:\n" + str(gridData[0].getRawData().shape) + "\n") + print("Sample grid data:\n" + str(gridData[0].getRawData()) + "\n") + print("Sample lat-lon data:\n" + str(gridData[0].getLatLonCoords()) + "\n") + + if testSameShape: + correctGridShape = gridData[0].getLatLonCoords()[0].shape + for record in gridData: + rawData = record.getRawData() + self.assertIsNotNone(rawData) + self.assertEqual(rawData.shape, correctGridShape) + return gridData diff --git a/awips/test/dafTests/baseRadarTestCase.py b/awips/test/dafTests/baseRadarTestCase.py new file mode 100644 index 0000000..b109014 --- /dev/null +++ b/awips/test/dafTests/baseRadarTestCase.py @@ -0,0 +1,177 @@ +## +## + +from __future__ import print_function +from shapely.geometry import box +from awips.dataaccess import DataAccessLayer as DAL +from awips.ThriftClient import ThriftRequestException + +import baseDafTestCase +import params +import unittest + +# +# Tests common to all radar factories +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 04/26/16 5587 tgurney Move identifier values tests +# out of base class +# 06/01/16 5587 tgurney Update testGetIdentifierValues +# 06/08/16 5574 mapeters Add advanced query tests +# 06/13/16 5574 tgurney Fix checks for None +# 06/14/16 5548 tgurney Undo previous change (broke +# test) +# 06/30/16 5725 tgurney Add test for NOT IN +# 08/25/16 2671 tgurney Rename to baseRadarTestCase +# and move factory-specific +# tests +# 12/07/16 5981 tgurney Parameterize +# +# + + +class BaseRadarTestCase(baseDafTestCase.DafTestCase): + """Tests common to all radar factories""" + + # datatype is specified by subclass + datatype = None + + radarLoc = params.RADAR.lower() + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + self.runLocationsTest(req) + + def testGetAvailableLevels(self): + req = DAL.newDataRequest(self.datatype) + self.runLevelsTest(req) + + def testGetAvailableLevelsWithInvalidLevelIdentifierThrowsException(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('level.one.field', 'invalidLevelField') + with self.assertRaises(ThriftRequestException) as cm: + self.runLevelsTest(req) + self.assertIn('IncompatibleRequestException', str(cm.exception)) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + req.setEnvelope(params.ENVELOPE) + self.runTimesTest(req) + + def testGetIdentifierValues(self): + req = DAL.newDataRequest(self.datatype) + optionalIds = set(DAL.getOptionalIdentifiers(req)) + requiredIds = set(DAL.getRequiredIdentifiers(req)) + self.runGetIdValuesTest(optionalIds | requiredIds) + + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() + + def runConstraintTest(self, key, operator, value): + raise NotImplementedError + + def testGetDataWithEqualsString(self): + gridData = self.runConstraintTest('icao', '=', self.radarLoc) + for record in gridData: + self.assertEqual(record.getAttribute('icao'), self.radarLoc) + + def testGetDataWithEqualsUnicode(self): + gridData = self.runConstraintTest('icao', '=', unicode(self.radarLoc)) + for record in gridData: + self.assertEqual(record.getAttribute('icao'), self.radarLoc) + + def testGetDataWithEqualsInt(self): + gridData = self.runConstraintTest('icao', '=', 1000) + for record in gridData: + self.assertEqual(record.getAttribute('icao'), 1000) + + def testGetDataWithEqualsLong(self): + gridData = self.runConstraintTest('icao', '=', 1000L) + for record in gridData: + self.assertEqual(record.getAttribute('icao'), 1000) + + def testGetDataWithEqualsFloat(self): + gridData = self.runConstraintTest('icao', '=', 1.0) + for record in gridData: + self.assertEqual(round(record.getAttribute('icao'), 1), 1.0) + + def testGetDataWithEqualsNone(self): + gridData = self.runConstraintTest('icao', '=', None) + for record in gridData: + self.assertIsNone(record.getAttribute('icao')) + + def testGetDataWithNotEquals(self): + gridData = self.runConstraintTest('icao', '!=', self.radarLoc) + for record in gridData: + self.assertNotEqual(record.getAttribute('icao'), self.radarLoc) + + def testGetDataWithNotEqualsNone(self): + gridData = self.runConstraintTest('icao', '!=', None) + for record in gridData: + self.assertIsNotNone(record.getAttribute('icao')) + + def testGetDataWithGreaterThan(self): + gridData = self.runConstraintTest('icao', '>', self.radarLoc) + for record in gridData: + self.assertGreater(record.getAttribute('icao'), self.radarLoc) + + def testGetDataWithLessThan(self): + gridData = self.runConstraintTest('icao', '<', self.radarLoc) + for record in gridData: + self.assertLess(record.getAttribute('icao'), self.radarLoc) + + def testGetDataWithGreaterThanEquals(self): + gridData = self.runConstraintTest('icao', '>=', self.radarLoc) + for record in gridData: + self.assertGreaterEqual(record.getAttribute('icao'), self.radarLoc) + + def testGetDataWithLessThanEquals(self): + gridData = self.runConstraintTest('icao', '<=', self.radarLoc) + for record in gridData: + self.assertLessEqual(record.getAttribute('icao'), self.radarLoc) + + def testGetDataWithInTuple(self): + gridData = self.runConstraintTest('icao', 'in', (self.radarLoc, 'tpbi')) + for record in gridData: + self.assertIn(record.getAttribute('icao'), (self.radarLoc, 'tpbi')) + + def testGetDataWithInList(self): + gridData = self.runConstraintTest('icao', 'in', [self.radarLoc, 'tpbi']) + for record in gridData: + self.assertIn(record.getAttribute('icao'), (self.radarLoc, 'tpbi')) + + def testGetDataWithInGenerator(self): + generator = (item for item in (self.radarLoc, 'tpbi')) + gridData = self.runConstraintTest('icao', 'in', generator) + for record in gridData: + self.assertIn(record.getAttribute('icao'), (self.radarLoc, 'tpbi')) + + def testGetDataWithNotInList(self): + gridData = self.runConstraintTest('icao', 'not in', ['zzzz', self.radarLoc]) + for record in gridData: + self.assertNotIn(record.getAttribute('icao'), ('zzzz', self.radarLoc)) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self.runConstraintTest('icao', 'junk', self.radarLoc) + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self.runConstraintTest('icao', '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self.runConstraintTest('icao', 'in', []) diff --git a/awips/test/dafTests/params.py b/awips/test/dafTests/params.py new file mode 100644 index 0000000..8afe38d --- /dev/null +++ b/awips/test/dafTests/params.py @@ -0,0 +1,26 @@ +## +## + + +# +# Site-specific parameters for DAF tests +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 12/07/16 5981 tgurney Initial creation +# 12/15/16 5981 tgurney Add ENVELOPE +# +# + +from shapely.geometry import box + +AIRPORT = 'OMA' +OBS_STATION = 'KOMA' +SITE_ID = 'OAX' +STATION_ID = '72558' +RADAR = 'KOAX' +SAMPLE_AREA = (-97.0, 41.0, -96.0, 42.0) + +ENVELOPE = box(*SAMPLE_AREA) \ No newline at end of file diff --git a/awips/test/dafTests/testAcars.py b/awips/test/dafTests/testAcars.py new file mode 100644 index 0000000..28d63d3 --- /dev/null +++ b/awips/test/dafTests/testAcars.py @@ -0,0 +1,44 @@ +## +## + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL + +import baseDafTestCase +import unittest + +# +# Test DAF support for ACARS data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# +# + + +class AcarsTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for ACARS data""" + + datatype = "acars" + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + self.runLocationsTest(req) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + self.runTimesTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.setParameters("flightLevel", "tailNumber") + self.runGeometryDataTest(req) diff --git a/awips/test/dafTests/testAirep.py b/awips/test/dafTests/testAirep.py new file mode 100644 index 0000000..94c525f --- /dev/null +++ b/awips/test/dafTests/testAirep.py @@ -0,0 +1,155 @@ +## +## + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint +import baseDafTestCase +import unittest + +# +# Test DAF support for airep data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 06/09/16 5587 bsteffen Add getIdentifierValues tests +# 06/13/16 5574 tgurney Add advanced query tests +# 06/30/16 5725 tgurney Add test for NOT IN +# +# + + +class AirepTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for airep data""" + + datatype = "airep" + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + self.runLocationsTest(req) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + self.runTimesTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.setParameters("flightLevel", "reportType") + self.runGeometryDataTest(req) + + def testGetIdentifierValues(self): + req = DAL.newDataRequest(self.datatype) + optionalIds = set(DAL.getOptionalIdentifiers(req)) + self.runGetIdValuesTest(optionalIds) + + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() + + def _runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + constraint = RequestConstraint.new(operator, value) + req.setParameters("flightLevel", "reportType") + req.addIdentifier(key, constraint) + return self.runGeometryDataTest(req) + + def testGetDataWithEqualsString(self): + geometryData = self._runConstraintTest('reportType', '=', 'AIREP') + for record in geometryData: + self.assertEqual(record.getString('reportType'), 'AIREP') + + def testGetDataWithEqualsUnicode(self): + geometryData = self._runConstraintTest('reportType', '=', u'AIREP') + for record in geometryData: + self.assertEqual(record.getString('reportType'), 'AIREP') + + # No numeric tests since no numeric identifiers are available. + + def testGetDataWithEqualsNone(self): + geometryData = self._runConstraintTest('reportType', '=', None) + for record in geometryData: + self.assertEqual(record.getType('reportType'), 'NULL') + + def testGetDataWithNotEquals(self): + geometryData = self._runConstraintTest('reportType', '!=', 'AIREP') + for record in geometryData: + self.assertNotEqual(record.getString('reportType'), 'AIREP') + + def testGetDataWithNotEqualsNone(self): + geometryData = self._runConstraintTest('reportType', '!=', None) + for record in geometryData: + self.assertNotEqual(record.getType('reportType'), 'NULL') + + def testGetDataWithGreaterThan(self): + geometryData = self._runConstraintTest('reportType', '>', 'AIREP') + for record in geometryData: + self.assertGreater(record.getString('reportType'), 'AIREP') + + def testGetDataWithLessThan(self): + geometryData = self._runConstraintTest('reportType', '<', 'AIREP') + for record in geometryData: + self.assertLess(record.getString('reportType'), 'AIREP') + + def testGetDataWithGreaterThanEquals(self): + geometryData = self._runConstraintTest('reportType', '>=', 'AIREP') + for record in geometryData: + self.assertGreaterEqual(record.getString('reportType'), 'AIREP') + + def testGetDataWithLessThanEquals(self): + geometryData = self._runConstraintTest('reportType', '<=', 'AIREP') + for record in geometryData: + self.assertLessEqual(record.getString('reportType'), 'AIREP') + + def testGetDataWithInTuple(self): + collection = ('AIREP', 'AMDAR') + geometryData = self._runConstraintTest('reportType', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('reportType'), collection) + + def testGetDataWithInList(self): + collection = ['AIREP', 'AMDAR'] + geometryData = self._runConstraintTest('reportType', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('reportType'), collection) + + def testGetDataWithInGenerator(self): + collection = ('AIREP', 'AMDAR') + generator = (item for item in collection) + geometryData = self._runConstraintTest('reportType', 'in', generator) + for record in geometryData: + self.assertIn(record.getString('reportType'), collection) + + def testGetDataWithNotInList(self): + collection = ['AMDAR'] + geometryData = self._runConstraintTest('reportType', 'not in', collection) + for record in geometryData: + self.assertNotIn(record.getString('reportType'), collection) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('reportType', 'junk', 'AIREP') + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self._runConstraintTest('reportType', '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('reportType', 'in', []) + + def testGetDataWithNestedInConstraintThrowsException(self): + collection = ('AIREP', 'AMDAR', ()) + with self.assertRaises(TypeError): + self._runConstraintTest('reportType', 'in', collection) diff --git a/awips/test/dafTests/testBinLightning.py b/awips/test/dafTests/testBinLightning.py new file mode 100644 index 0000000..830d774 --- /dev/null +++ b/awips/test/dafTests/testBinLightning.py @@ -0,0 +1,181 @@ +## +## + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL +from awips.ThriftClient import ThriftRequestException +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint + + +import baseDafTestCase +import unittest + +# +# Test DAF support for binlightning data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 04/21/16 5551 tgurney Add tests to verify #5551 +# 04/25/16 5587 tgurney Enable skipped test added in +# #5551 +# 04/26/16 5587 tgurney Move identifier values tests +# out of base class +# 06/01/16 5587 tgurney Update testGetIdentifierValues +# 06/03/16 5574 tgurney Add advanced query tests +# 06/13/16 5574 tgurney Typo +# 06/30/16 5725 tgurney Add test for NOT IN +# 11/08/16 5985 tgurney Do not check data times +# +# + + +class BinLightningTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for binlightning data""" + + datatype = "binlightning" + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + self.runParametersTest(req) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier("source", "NLDN") + self.runTimesTest(req) + + def testGetGeometryDataSingleSourceSingleParameter(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier("source", "NLDN") + req.setParameters('intensity') + self.runGeometryDataTest(req, checkDataTimes=False) + + def testGetGeometryDataInvalidParamRaisesIncompatibleRequestException(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier("source", "NLDN") + req.setParameters('blahblahblah') + with self.assertRaises(ThriftRequestException) as cm: + self.runGeometryDataTest(req) + self.assertIn('IncompatibleRequestException', str(cm.exception)) + + def testGetGeometryDataSingleSourceAllParameters(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier("source", "NLDN") + req.setParameters(*DAL.getAvailableParameters(req)) + self.runGeometryDataTest(req, checkDataTimes=False) + + def testGetIdentifierValues(self): + req = DAL.newDataRequest(self.datatype) + optionalIds = set(DAL.getOptionalIdentifiers(req)) + requiredIds = set(DAL.getRequiredIdentifiers(req)) + self.runGetIdValuesTest(optionalIds | requiredIds) + + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() + + def _runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + constraint = RequestConstraint.new(operator, value) + req.addIdentifier(key, constraint) + req.setParameters('intensity') + return self.runGeometryDataTest(req, checkDataTimes=False) + + def testGetDataWithEqualsString(self): + geomData = self._runConstraintTest('source', '=', 'NLDN') + for record in geomData: + self.assertEqual(record.getAttribute('source'), 'NLDN') + + def testGetDataWithEqualsUnicode(self): + geomData = self._runConstraintTest('source', '=', u'NLDN') + for record in geomData: + self.assertEqual(record.getAttribute('source'), 'NLDN') + + def testGetDataWithEqualsInt(self): + geomData = self._runConstraintTest('source', '=', 1000) + for record in geomData: + self.assertEqual(record.getAttribute('source'), 1000) + + def testGetDataWithEqualsLong(self): + geomData = self._runConstraintTest('source', '=', 1000L) + for record in geomData: + self.assertEqual(record.getAttribute('source'), 1000) + + def testGetDataWithEqualsFloat(self): + geomData = self._runConstraintTest('source', '=', 1.0) + for record in geomData: + self.assertEqual(round(record.getAttribute('source'), 1), 1.0) + + def testGetDataWithEqualsNone(self): + geomData = self._runConstraintTest('source', '=', None) + for record in geomData: + self.assertIsNone(record.getAttribute('source')) + + def testGetDataWithNotEquals(self): + geomData = self._runConstraintTest('source', '!=', 'NLDN') + for record in geomData: + self.assertNotEqual(record.getAttribute('source'), 'NLDN') + + def testGetDataWithNotEqualsNone(self): + geomData = self._runConstraintTest('source', '!=', None) + for record in geomData: + self.assertIsNotNone(record.getAttribute('source')) + + def testGetDataWithGreaterThan(self): + geomData = self._runConstraintTest('source', '>', 'NLDN') + for record in geomData: + self.assertGreater(record.getAttribute('source'), 'NLDN') + + def testGetDataWithLessThan(self): + geomData = self._runConstraintTest('source', '<', 'NLDN') + for record in geomData: + self.assertLess(record.getAttribute('source'), 'NLDN') + + def testGetDataWithGreaterThanEquals(self): + geomData = self._runConstraintTest('source', '>=', 'NLDN') + for record in geomData: + self.assertGreaterEqual(record.getAttribute('source'), 'NLDN') + + def testGetDataWithLessThanEquals(self): + geomData = self._runConstraintTest('source', '<=', 'NLDN') + for record in geomData: + self.assertLessEqual(record.getAttribute('source'), 'NLDN') + + def testGetDataWithInTuple(self): + geomData = self._runConstraintTest('source', 'in', ('NLDN', 'ENTLN')) + for record in geomData: + self.assertIn(record.getAttribute('source'), ('NLDN', 'ENTLN')) + + def testGetDataWithInList(self): + geomData = self._runConstraintTest('source', 'in', ['NLDN', 'ENTLN']) + for record in geomData: + self.assertIn(record.getAttribute('source'), ('NLDN', 'ENTLN')) + + def testGetDataWithInGenerator(self): + generator = (item for item in ('NLDN', 'ENTLN')) + geomData = self._runConstraintTest('source', 'in', generator) + for record in geomData: + self.assertIn(record.getAttribute('source'), ('NLDN', 'ENTLN')) + + def testGetDataWithNotInList(self): + geomData = self._runConstraintTest('source', 'not in', ['NLDN', 'blah']) + for record in geomData: + self.assertNotIn(record.getAttribute('source'), ('NLDN', 'blah')) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('source', 'junk', 'NLDN') + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self._runConstraintTest('source', '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('source', 'in', []) diff --git a/awips/test/dafTests/testBufrMosAvn.py b/awips/test/dafTests/testBufrMosAvn.py new file mode 100644 index 0000000..cf05db4 --- /dev/null +++ b/awips/test/dafTests/testBufrMosAvn.py @@ -0,0 +1,28 @@ +## +## + +from __future__ import print_function + +import baseBufrMosTestCase +import unittest + +# +# Test DAF support for bufrmosAVN data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# +# + + +class BufrMosAvnTestCase(baseBufrMosTestCase.BufrMosTestCase): + """Test DAF support for bufrmosAVN data""" + + datatype = "bufrmosAVN" + + # All tests inherited from superclass diff --git a/awips/test/dafTests/testBufrMosEta.py b/awips/test/dafTests/testBufrMosEta.py new file mode 100644 index 0000000..adc1aa1 --- /dev/null +++ b/awips/test/dafTests/testBufrMosEta.py @@ -0,0 +1,28 @@ +## +## + +from __future__ import print_function + +import baseBufrMosTestCase +import unittest + +# +# Test DAF support for bufrmosETA data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# +# + + +class BufrMosEtaTestCase(baseBufrMosTestCase.BufrMosTestCase): + """Test DAF support for bufrmosETA data""" + + datatype = "bufrmosETA" + + # All tests inherited from superclass diff --git a/awips/test/dafTests/testBufrMosGfs.py b/awips/test/dafTests/testBufrMosGfs.py new file mode 100644 index 0000000..146ec2a --- /dev/null +++ b/awips/test/dafTests/testBufrMosGfs.py @@ -0,0 +1,28 @@ +## +## + +from __future__ import print_function + +import baseBufrMosTestCase +import unittest + +# +# Test DAF support for bufrmosGFS data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# +# + + +class BufrMosGfsTestCase(baseBufrMosTestCase.BufrMosTestCase): + """Test DAF support for bufrmosGFS data""" + + datatype = "bufrmosGFS" + + # All tests inherited from superclass diff --git a/awips/test/dafTests/testBufrMosHpc.py b/awips/test/dafTests/testBufrMosHpc.py new file mode 100644 index 0000000..4e375c0 --- /dev/null +++ b/awips/test/dafTests/testBufrMosHpc.py @@ -0,0 +1,33 @@ +## +## + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL + +import baseBufrMosTestCase +import params +import unittest + +# +# Test DAF support for bufrmosHPC data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 12/07/16 5981 tgurney Parameterize +# 12/20/16 5981 tgurney Inherit all tests +# +# + + +class BufrMosHpcTestCase(baseBufrMosTestCase.BufrMosTestCase): + """Test DAF support for bufrmosHPC data""" + + datatype = "bufrmosHPC" + data_params = "forecastHr", "maxTemp24Hour" + + # All tests inherited from superclass \ No newline at end of file diff --git a/awips/test/dafTests/testBufrMosLamp.py b/awips/test/dafTests/testBufrMosLamp.py new file mode 100644 index 0000000..4d79d46 --- /dev/null +++ b/awips/test/dafTests/testBufrMosLamp.py @@ -0,0 +1,28 @@ +## +## + +from __future__ import print_function + +import baseBufrMosTestCase +import unittest + +# +# Test DAF support for bufrmosLAMP data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# +# + + +class BufrMosLampTestCase(baseBufrMosTestCase.BufrMosTestCase): + """Test DAF support for bufrmosLAMP data""" + + datatype = "bufrmosLAMP" + + # All tests inherited from superclass diff --git a/awips/test/dafTests/testBufrMosMrf.py b/awips/test/dafTests/testBufrMosMrf.py new file mode 100644 index 0000000..c92c0f9 --- /dev/null +++ b/awips/test/dafTests/testBufrMosMrf.py @@ -0,0 +1,33 @@ +## +## + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL + +import baseBufrMosTestCase +import params +import unittest + +# +# Test DAF support for bufrmosMRF data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 12/07/16 5981 tgurney Parameterize +# 12/20/16 5981 tgurney Inherit all tests +# +# + + +class BufrMosMrfTestCase(baseBufrMosTestCase.BufrMosTestCase): + """Test DAF support for bufrmosMRF data""" + + datatype = "bufrmosMRF" + data_params = "forecastHr", "maxTempDay" + + # All tests inherited from superclass diff --git a/awips/test/dafTests/testBufrUa.py b/awips/test/dafTests/testBufrUa.py new file mode 100644 index 0000000..baefa85 --- /dev/null +++ b/awips/test/dafTests/testBufrUa.py @@ -0,0 +1,204 @@ +# # +# # + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint +import baseDafTestCase +import params +import unittest + +# +# Test DAF support for bufrua data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 06/09/16 5587 bsteffen Add getIdentifierValues tests +# 06/13/16 5574 tgurney Add advanced query tests +# 06/30/16 5725 tgurney Add test for NOT IN +# 12/07/16 5981 tgurney Parameterize +# 12/15/16 5981 tgurney Add envelope test +# +# + + +class BufrUaTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for bufrua data""" + + datatype = "bufrua" + + location = params.STATION_ID + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier("reportType", "2020") + self.runLocationsTest(req) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + req.setLocationNames(self.location) + req.addIdentifier("reportType", "2020") + self.runTimesTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.setLocationNames(self.location) + req.addIdentifier("reportType", "2020") + req.setParameters("sfcPressure", "staName", "rptType", "tdMan") + + print("Testing getGeometryData()") + + geomData = DAL.getGeometryData(req) + self.assertIsNotNone(geomData) + print("Number of geometry records: " + str(len(geomData))) + print("Sample geometry data:") + for record in geomData[:self.sampleDataLimit]: + print("level=", record.getLevel(), end="") + # One dimensional parameters are reported on the 0.0UNKNOWN level. + # 2D parameters are reported on MB levels from pressure. + if record.getLevel() == "0.0UNKNOWN": + print(" sfcPressure=" + record.getString("sfcPressure") + record.getUnit("sfcPressure"), end="") + print(" staName=" + record.getString("staName"), end="") + print(" rptType=" + record.getString("rptType") + record.getUnit("rptType"), end="") + else: + print(" tdMan=" + str(record.getNumber("tdMan")) + record.getUnit("tdMan"), end="") + print(" geometry=", record.getGeometry()) + + print("getGeometryData() complete\n\n") + + def testGetGeometryDataWithEnvelope(self): + req = DAL.newDataRequest(self.datatype) + req.setParameters("staName", "rptType") + req.setEnvelope(params.ENVELOPE) + data = self.runGeometryDataTest(req) + for item in data: + self.assertTrue(params.ENVELOPE.contains(item.getGeometry())) + + def testGetIdentifierValues(self): + req = DAL.newDataRequest(self.datatype) + optionalIds = set(DAL.getOptionalIdentifiers(req)) + self.runGetIdValuesTest(optionalIds) + + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() + + def _runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + constraint = RequestConstraint.new(operator, value) + req.addIdentifier(key, constraint) + # As an identifier it is "reportType" but as a parameter it is + # "rptType"... this is weird... + req.setParameters("staName", "rptType") + return self.runGeometryDataTest(req) + + def testGetDataWithEqualsString(self): + geometryData = self._runConstraintTest('reportType', '=', '2022') + for record in geometryData: + self.assertEqual(record.getString('rptType'), '2022') + + def testGetDataWithEqualsUnicode(self): + geometryData = self._runConstraintTest('reportType', '=', u'2022') + for record in geometryData: + self.assertEqual(record.getString('rptType'), '2022') + + def testGetDataWithEqualsInt(self): + geometryData = self._runConstraintTest('reportType', '=', 2022) + for record in geometryData: + self.assertEqual(record.getString('rptType'), '2022') + + def testGetDataWithEqualsLong(self): + geometryData = self._runConstraintTest('reportType', '=', 2022L) + for record in geometryData: + self.assertEqual(record.getString('rptType'), '2022') + + # No float test because no float identifiers are available + + def testGetDataWithEqualsNone(self): + geometryData = self._runConstraintTest('reportType', '=', None) + for record in geometryData: + self.assertEqual(record.getType('rptType'), 'NULL') + + def testGetDataWithNotEquals(self): + geometryData = self._runConstraintTest('reportType', '!=', 2022) + for record in geometryData: + self.assertNotEqual(record.getString('rptType'), '2022') + + def testGetDataWithNotEqualsNone(self): + geometryData = self._runConstraintTest('reportType', '!=', None) + for record in geometryData: + self.assertNotEqual(record.getType('rptType'), 'NULL') + + def testGetDataWithGreaterThan(self): + geometryData = self._runConstraintTest('reportType', '>', 2022) + for record in geometryData: + self.assertGreater(record.getString('rptType'), '2022') + + def testGetDataWithLessThan(self): + geometryData = self._runConstraintTest('reportType', '<', 2022) + for record in geometryData: + self.assertLess(record.getString('rptType'), '2022') + + def testGetDataWithGreaterThanEquals(self): + geometryData = self._runConstraintTest('reportType', '>=', 2022) + for record in geometryData: + self.assertGreaterEqual(record.getString('rptType'), '2022') + + def testGetDataWithLessThanEquals(self): + geometryData = self._runConstraintTest('reportType', '<=', 2022) + for record in geometryData: + self.assertLessEqual(record.getString('rptType'), '2022') + + def testGetDataWithInTuple(self): + collection = ('2022', '2032') + geometryData = self._runConstraintTest('reportType', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('rptType'), collection) + + def testGetDataWithInList(self): + collection = ['2022', '2032'] + geometryData = self._runConstraintTest('reportType', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('rptType'), collection) + + def testGetDataWithInGenerator(self): + collection = ('2022', '2032') + generator = (item for item in collection) + geometryData = self._runConstraintTest('reportType', 'in', generator) + for record in geometryData: + self.assertIn(record.getString('rptType'), collection) + + def testGetDataWithNotInList(self): + collection = ('2022', '2032') + geometryData = self._runConstraintTest('reportType', 'not in', collection) + for record in geometryData: + self.assertNotIn(record.getString('rptType'), collection) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('reportType', 'junk', '2022') + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self._runConstraintTest('reportType', '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('rptType', 'in', []) + + def testGetDataWithNestedInConstraintThrowsException(self): + collection = ('2022', '2032', ()) + with self.assertRaises(TypeError): + self._runConstraintTest('rptType', 'in', collection) diff --git a/awips/test/dafTests/testClimate.py b/awips/test/dafTests/testClimate.py new file mode 100644 index 0000000..da40aad --- /dev/null +++ b/awips/test/dafTests/testClimate.py @@ -0,0 +1,427 @@ +## +## + +from __future__ import print_function +import datetime +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint +from dynamicserialize.dstypes.com.raytheon.uf.common.time import TimeRange +from awips.dataaccess import DataAccessLayer as DAL +from awips.ThriftClient import ThriftRequestException + +import baseDafTestCase +import params +import unittest + +# +# Test DAF support for climate data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 04/26/16 5587 tgurney Add identifier values tests +# 06/09/16 5574 mapeters Add advanced query tests, Short parameter test +# 06/13/16 5574 tgurney Fix checks for None +# 06/21/16 5548 tgurney Skip tests that cause errors +# 06/30/16 5725 tgurney Add test for NOT IN +# 10/06/16 5926 dgilling Add additional time and location tests. +# 12/07/16 5981 tgurney Parameterize +# 12/20/16 5981 tgurney Add envelope test +# 08/16/17 6388 tgurney Test for duplicate data +# +# + + +class ClimateTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for climate data""" + + datatype = 'climate' + obsStation = params.OBS_STATION + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_monthly') + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_monthly') + self.runLocationsTest(req) + + def testGetAvailableLocationsForRptTable(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.rpt') + self.runLocationsTest(req) + + def testGetAvailableLocationsForStationId(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.day_climate_norm') + self.runLocationsTest(req) + + def testGetAvailableLocationsForInformId(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_mon_season_yr') + self.runLocationsTest(req) + + def testGetAvailableLocationsWithConstraints(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_monthly') + req.addIdentifier('maxtemp_mon', RequestConstraint.new('>', 95)) + self.runLocationsTest(req) + + def testGetAvailableLocationsWithInvalidTable(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.boolean_values') + with self.assertRaises(ThriftRequestException) as cm: + DAL.getAvailableLocationNames(req) + self.assertIn('IncompatibleRequestException', str(cm.exception)) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_monthly') + req.setParameters('maxtemp_mon', 'min_sea_press') + self.runTimesTest(req) + + def testGetAvailableTimesWithLocationNamesForYearMonth(self): + """ + Test retrieval of times for a climo table that uses year and + month columns to build DataTimes. + """ + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_monthly') + req.setLocationNames(self.obsStation, 'KABR', 'KDMO') + req.setParameters('maxtemp_mon', 'min_sea_press') + self.runTimesTest(req) + + def testGetAvailableTimesWithLocationNamesForYearDayOfYear(self): + """ + Test retrieval of times for a climo table that uses year and + day_of_year columns to build DataTimes. + """ + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_daily') + req.setLocationNames(self.obsStation, 'KABR', 'KDMO') + req.setParameters('maxtemp_cal', 'min_press') + self.runTimesTest(req) + + def testGetAvailableTimesWithLocationNamesForPeriod(self): + """ + Test retrieval of times for a climo table that uses + period_start and period_end columns to build DataTimes. + """ + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_mon_season_yr') + req.setLocationNames(self.obsStation, 'KABR', 'KDMO') + req.setParameters('max_temp', 'precip_total') + self.runTimesTest(req) + + def testGetAvailableTimesWithLocationNamesForDate(self): + """ + Test retrieval of times for a climo table that uses a date + column to build DataTimes. + """ + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.daily_climate') + req.setLocationNames(self.obsStation, 'KABR', 'KDMO') + req.setParameters('max_temp', 'precip', 'avg_wind_speed') + self.runTimesTest(req) + + def testGetAvailableTimesWithConstraint(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_monthly') + req.addIdentifier('maxtemp_mon', RequestConstraint.new('<', 75)) + req.setParameters('maxtemp_mon', 'min_sea_press') + self.runTimesTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_monthly') + req.setLocationNames('KFNB') + req.setParameters('maxtemp_mon', 'min_sea_press') + self.runGeometryDataTest(req) + + def testGetGeometryDataWithEnvelopeThrowsException(self): + # Envelope is not used + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_monthly') + req.setParameters('maxtemp_mon', 'min_sea_press') + req.setEnvelope(params.ENVELOPE) + with self.assertRaises(Exception): + data = self.runGeometryDataTest(req) + + def testGetGeometryDataForYearAndDayOfYearTable(self): + """ + Test retrieval of data for a climo table that uses year and + day_of_year columns to build DataTimes. + """ + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_daily') + req.setLocationNames('KFNB') + req.setParameters('maxtemp_cal', 'min_press') + self.runGeometryDataTest(req) + + def testGetGeometryDataForPeriodTable(self): + """ + Test retrieval of data for a climo table that uses a period_start and + period_end columns to build DataTimes. + """ + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_mon_season_yr') + req.setLocationNames('KFNB') + req.setParameters('max_temp', 'precip_total') + self.runGeometryDataTest(req) + + def testGetGeometryDataForDateTable(self): + """ + Test retrieval of data for a climo table that uses a date column to + build DataTimes. + """ + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.daily_climate') + req.setLocationNames('KFNB') + req.setParameters('max_temp', 'precip', 'avg_wind_speed') + self.runGeometryDataTest(req) + + def testGetGeometryDataWithShortParameter(self): + """ + Test that a parameter that is stored in Java as a Short is correctly + retrieved as a number. + """ + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'cli_asos_monthly') + req.setParameters('month') + geometryData = self.runGeometryDataTest(req) + for record in geometryData: + self.assertIsNotNone(record.getNumber('month')) + + def testGetTableIdentifierValues(self): + self.runGetIdValuesTest(['table']) + + def testGetColumnIdValuesWithTable(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_monthly') + idValues = DAL.getIdentifierValues(req, 'year') + self.assertTrue(hasattr(idValues, '__iter__')) + + def testGetColumnIdValuesWithoutTableThrowsException(self): + req = DAL.newDataRequest(self.datatype) + with self.assertRaises(ThriftRequestException): + idValues = DAL.getIdentifierValues(req, 'year') + + @unittest.skip('avoid EDEX error') + def testGetColumnIdValuesWithNonexistentTableThrowsException(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'nonexistentjunk') + with self.assertRaises(ThriftRequestException): + idValues = DAL.getIdentifierValues(req, 'year') + + @unittest.skip('avoid EDEX error') + def testGetNonexistentColumnIdValuesThrowsException(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_monthly') + with self.assertRaises(ThriftRequestException): + idValues = DAL.getIdentifierValues(req, 'nonexistentjunk') + + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() + + def _runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'cli_asos_monthly') + constraint = RequestConstraint.new(operator, value) + req.addIdentifier(key, constraint) + req.setParameters('station_code', 'avg_daily_max') + return self.runGeometryDataTest(req) + + def testGetDataWithEqualsString(self): + geometryData = self._runConstraintTest('station_code', '=', self.obsStation) + for record in geometryData: + self.assertEqual(record.getString('station_code'), self.obsStation) + + def testGetDataWithEqualsUnicode(self): + geometryData = self._runConstraintTest('station_code', '=', unicode(self.obsStation)) + for record in geometryData: + self.assertEqual(record.getString('station_code'), self.obsStation) + + def testGetDataWithEqualsInt(self): + geometryData = self._runConstraintTest('avg_daily_max', '=', 70) + for record in geometryData: + self.assertEqual(record.getNumber('avg_daily_max'), 70) + + def testGetDataWithEqualsLong(self): + geometryData = self._runConstraintTest('avg_daily_max', '=', 70L) + for record in geometryData: + self.assertEqual(record.getNumber('avg_daily_max'), 70) + + def testGetDataWithEqualsFloat(self): + geometryData = self._runConstraintTest('avg_daily_max', '=', 69.2) + for record in geometryData: + self.assertEqual(round(record.getNumber('avg_daily_max'), 1), 69.2) + + def testGetDataWithEqualsNone(self): + geometryData = self._runConstraintTest('station_code', '=', None) + self.assertEqual(len(geometryData), 0) + + def testGetDataWithNotEquals(self): + geometryData = self._runConstraintTest('station_code', '!=', self.obsStation) + for record in geometryData: + self.assertNotEqual(record.getString('station_code'), self.obsStation) + + def testGetDataWithNotEqualsNone(self): + geometryData = self._runConstraintTest('station_code', '!=', None) + for record in geometryData: + self.assertNotEqual(record.getType('station_code'), 'NULL') + + def testGetDataWithGreaterThan(self): + geometryData = self._runConstraintTest('avg_daily_max', '>', 70) + for record in geometryData: + self.assertGreater(record.getNumber('avg_daily_max'), 70) + + def testGetDataWithLessThan(self): + geometryData = self._runConstraintTest('avg_daily_max', '<', 70) + for record in geometryData: + self.assertLess(record.getNumber('avg_daily_max'), 70) + + def testGetDataWithGreaterThanEquals(self): + geometryData = self._runConstraintTest('avg_daily_max', '>=', 70) + for record in geometryData: + self.assertGreaterEqual(record.getNumber('avg_daily_max'), 70) + + def testGetDataWithLessThanEquals(self): + geometryData = self._runConstraintTest('avg_daily_max', '<=', 70) + for record in geometryData: + self.assertLessEqual(record.getNumber('avg_daily_max'), 70) + + def testGetDataWithInTuple(self): + collection = (self.obsStation, 'KABR') + geometryData = self._runConstraintTest('station_code', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('station_code'), collection) + + def testGetDataWithInList(self): + collection = [self.obsStation, 'KABR'] + geometryData = self._runConstraintTest('station_code', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('station_code'), collection) + + def testGetDataWithInGenerator(self): + collection = (self.obsStation, 'KABR') + generator = (item for item in collection) + geometryData = self._runConstraintTest('station_code', 'in', generator) + for record in geometryData: + self.assertIn(record.getString('station_code'), collection) + + def testGetDataWithNotInList(self): + collection = ['KORD', 'KABR'] + geometryData = self._runConstraintTest('station_code', 'not in', collection) + for record in geometryData: + self.assertNotIn(record.getString('station_code'), collection) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('station_code', 'junk', self.obsStation) + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self._runConstraintTest('station_code', '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('station_code', 'in', []) + + def testGetDataWithTimeRangeWithYearAndMonth1(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_monthly') + req.setLocationNames('KFNB') + req.setParameters('maxtemp_mon', 'min_sea_press') + startTime = datetime.datetime(2009, 1, 1) + endTime = datetime.datetime(2009, 12, 31) + tr = TimeRange(startTime, endTime) + self.runGeometryDataTestWithTimeRange(req, tr) + + def testGetDataWithTimeRangeWithYearAndMonth2(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_monthly') + req.setLocationNames('KFNB') + req.setParameters('maxtemp_mon', 'min_sea_press') + startTime = datetime.datetime(2008, 1, 1) + endTime = datetime.datetime(2009, 3, 31) + tr = TimeRange(startTime, endTime) + self.runGeometryDataTestWithTimeRange(req, tr) + + def testGetDataWithTimeRangeWithYearAndMonth3(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_monthly') + req.setLocationNames('KFNB') + req.setParameters('maxtemp_mon', 'min_sea_press') + startTime = datetime.datetime(2007, 7, 1) + endTime = datetime.datetime(2009, 3, 31) + tr = TimeRange(startTime, endTime) + self.runGeometryDataTestWithTimeRange(req, tr) + + def testGetDataWithTimeRangeWithYearAndDayOfYear1(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_daily') + req.setLocationNames('KFNB') + req.setParameters('maxtemp_cal', 'min_press') + startTime = datetime.datetime(2009, 1, 1) + endTime = datetime.datetime(2009, 7, 31) + tr = TimeRange(startTime, endTime) + self.runGeometryDataTestWithTimeRange(req, tr) + + def testGetDataWithTimeRangeWithYearAndDayOfYear2(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_daily') + req.setLocationNames('KFNB') + req.setParameters('maxtemp_cal', 'min_press') + startTime = datetime.datetime(2008, 7, 1) + endTime = datetime.datetime(2009, 3, 31) + tr = TimeRange(startTime, endTime) + self.runGeometryDataTestWithTimeRange(req, tr) + + def testGetDataWithTimeRangeWithYearAndDayOfYear3(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_daily') + req.setLocationNames('KFNB') + req.setParameters('maxtemp_cal', 'min_press') + startTime = datetime.datetime(2007, 7, 1) + endTime = datetime.datetime(2009, 3, 31) + tr = TimeRange(startTime, endTime) + self.runGeometryDataTestWithTimeRange(req, tr) + + def testGetDataWithTimeRangeWithPeriodTable(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_mon_season_yr') + req.setLocationNames('KFNB') + req.setParameters('max_temp', 'precip_total') + startTime = datetime.datetime(2007, 7, 1) + endTime = datetime.datetime(2009, 3, 31) + tr = TimeRange(startTime, endTime) + self.runGeometryDataTestWithTimeRange(req, tr) + + def testGetDataWithTimeRangeWithForDateTable(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.daily_climate') + req.setLocationNames('KFNB') + req.setParameters('max_temp', 'precip', 'avg_wind_speed') + startTime = datetime.datetime(2007, 7, 1) + endTime = datetime.datetime(2009, 3, 31) + tr = TimeRange(startTime, endTime) + self.runGeometryDataTestWithTimeRange(req, tr) + + def testNoDuplicateData(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.cli_asos_monthly') + req.setLocationNames('KOMA') + req.setParameters('maxtemp_day1') + rows = DAL.getGeometryData(req, DAL.getAvailableTimes(req)[0:5]) + for i in range(len(rows)): + for j in range(len(rows)): + if i != j: + self.assertNotEqual(rows[i].__dict__, rows[j].__dict__) diff --git a/awips/test/dafTests/testCombinedTimeQuery.py b/awips/test/dafTests/testCombinedTimeQuery.py new file mode 100644 index 0000000..83ae0f4 --- /dev/null +++ b/awips/test/dafTests/testCombinedTimeQuery.py @@ -0,0 +1,50 @@ +## +## + +from awips.dataaccess import DataAccessLayer as DAL + +from awips.dataaccess import CombinedTimeQuery as CTQ + +import unittest +import os + +# +# Test the CombinedTimedQuery module +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/24/16 5591 bsteffen Initial Creation. +# 11/08/16 5895 tgurney Change grid model +# +# +# + +class CombinedTimeQueryTestCase(unittest.TestCase): + + @classmethod + def setUp(cls): + host = os.environ.get('DAF_TEST_HOST') + if host is None: + host = 'localhost' + DAL.changeEDEXHost(host) + + def testSuccessfulQuery(self): + req = DAL.newDataRequest('grid') + req.setLocationNames('RUC130') + req.setParameters('T','GH') + req.setLevels('300MB', '500MB','700MB') + times = CTQ.getAvailableTimes(req); + self.assertNotEqual(len(times), 0) + + def testNonIntersectingQuery(self): + """ + Test that when a parameter is only available on one of the levels that no times are returned. + """ + req = DAL.newDataRequest('grid') + req.setLocationNames('RUC130') + req.setParameters('T','GH', 'LgSP1hr') + req.setLevels('300MB', '500MB','700MB','0.0SFC') + times = CTQ.getAvailableTimes(req); + self.assertEqual(len(times), 0) diff --git a/awips/test/dafTests/testCommonObsSpatial.py b/awips/test/dafTests/testCommonObsSpatial.py new file mode 100644 index 0000000..81a9ef4 --- /dev/null +++ b/awips/test/dafTests/testCommonObsSpatial.py @@ -0,0 +1,161 @@ +## +## + +from __future__ import print_function +from shapely.geometry import box +from awips.dataaccess import DataAccessLayer as DAL + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint +import baseDafTestCase +import params +import unittest + +# +# Test DAF support for common_obs_spatial data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 05/26/16 5587 njensen Added testGetIdentifierValues() +# 06/01/16 5587 tgurney Move testIdentifiers() to +# superclass +# 06/13/16 5574 tgurney Add advanced query tests +# 06/21/16 5548 tgurney Skip tests that cause errors +# 06/30/16 5725 tgurney Add test for NOT IN +# 12/07/16 5981 tgurney Parameterize +# 01/06/17 5981 tgurney Do not check data times +# + + +class CommonObsSpatialTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for common_obs_spatial data""" + + datatype = "common_obs_spatial" + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier("country", ["US", "CN"]) + self.runLocationsTest(req) + + def testGetIdentifierValues(self): + self.runGetIdValuesTest(['country']) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.setEnvelope(params.ENVELOPE) + req.setParameters("name", "stationid") + self.runGeometryDataTest(req, checkDataTimes=False) + + def testRequestingTimesThrowsTimeAgnosticDataException(self): + req = DAL.newDataRequest(self.datatype) + self.runTimeAgnosticTest(req) + + def _runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + constraint = RequestConstraint.new(operator, value) + req.addIdentifier(key, constraint) + req.setParameters('catalogtype', 'elevation', 'state') + return self.runGeometryDataTest(req, checkDataTimes=False) + + def testGetDataWithEqualsString(self): + geometryData = self._runConstraintTest('state', '=', 'NE') + for record in geometryData: + self.assertEqual(record.getString('state'), 'NE') + + def testGetDataWithEqualsUnicode(self): + geometryData = self._runConstraintTest('state', '=', u'NE') + for record in geometryData: + self.assertEqual(record.getString('state'), 'NE') + + def testGetDataWithEqualsInt(self): + geometryData = self._runConstraintTest('catalogtype', '=', 32) + for record in geometryData: + self.assertEqual(record.getNumber('catalogtype'), 32) + + def testGetDataWithEqualsLong(self): + geometryData = self._runConstraintTest('elevation', '=', 0L) + for record in geometryData: + self.assertEqual(record.getNumber('elevation'), 0) + + # No float test since there are no float identifiers available. Attempting + # to filter a non-float identifier on a float value raises an exception. + + def testGetDataWithEqualsNone(self): + geometryData = self._runConstraintTest('state', '=', None) + for record in geometryData: + self.assertEqual(record.getType('state'), 'NULL') + + def testGetDataWithNotEquals(self): + geometryData = self._runConstraintTest('state', '!=', 'NE') + for record in geometryData: + self.assertNotEqual(record.getString('state'), 'NE') + + def testGetDataWithNotEqualsNone(self): + geometryData = self._runConstraintTest('state', '!=', None) + for record in geometryData: + self.assertNotEqual(record.getType('state'), 'NULL') + + def testGetDataWithGreaterThan(self): + geometryData = self._runConstraintTest('elevation', '>', 500) + for record in geometryData: + self.assertGreater(record.getNumber('elevation'), 500) + + def testGetDataWithLessThan(self): + geometryData = self._runConstraintTest('elevation', '<', 100) + for record in geometryData: + self.assertLess(record.getNumber('elevation'), 100) + + def testGetDataWithGreaterThanEquals(self): + geometryData = self._runConstraintTest('elevation', '>=', 500) + for record in geometryData: + self.assertGreaterEqual(record.getNumber('elevation'), 500) + + def testGetDataWithLessThanEquals(self): + geometryData = self._runConstraintTest('elevation', '<=', 100) + for record in geometryData: + self.assertLessEqual(record.getNumber('elevation'), 100) + + def testGetDataWithInTuple(self): + collection = ('NE', 'TX') + geometryData = self._runConstraintTest('state', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('state'), collection) + + def testGetDataWithInList(self): + collection = ['NE', 'TX'] + geometryData = self._runConstraintTest('state', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('state'), collection) + + def testGetDataWithInGenerator(self): + collection = ('NE', 'TX') + generator = (item for item in collection) + geometryData = self._runConstraintTest('state', 'in', generator) + for record in geometryData: + self.assertIn(record.getString('state'), collection) + + def testGetDataWithNotInList(self): + collection = ('NE', 'TX') + geometryData = self._runConstraintTest('state', 'not in', collection) + for record in geometryData: + self.assertNotIn(record.getString('state'), collection) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('state', 'junk', 'NE') + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self._runConstraintTest('state', '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('state', 'in', []) diff --git a/awips/test/dafTests/testDataTime.py b/awips/test/dafTests/testDataTime.py new file mode 100644 index 0000000..9f104c5 --- /dev/null +++ b/awips/test/dafTests/testDataTime.py @@ -0,0 +1,117 @@ +## +## + +from dynamicserialize.dstypes.com.raytheon.uf.common.time import DataTime + +import unittest + +# +# Unit tests for Python implementation of RequestConstraint +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 08/02/16 2416 tgurney Initial creation +# +# + + +class DataTimeTestCase(unittest.TestCase): + + def testFromStrRefTimeOnly(self): + s = '2016-08-02 01:23:45' + expected = s + self.assertEqual(expected, str(DataTime(s))) + s = s.replace(' ', '_') + self.assertEqual(expected, str(DataTime(s))) + + def testFromStrRefTimeOnlyZeroMillis(self): + s = '2016-08-02 01:23:45.0' + # result of str() will always drop trailing .0 milliseconds + expected = '2016-08-02 01:23:45' + self.assertEqual(expected, str(DataTime(s))) + s = s.replace(' ', '_') + self.assertEqual(expected, str(DataTime(s))) + + def testFromStrRefTimeOnlyWithMillis(self): + s = '2016-08-02 01:23:45.1' + expected = '2016-08-02 01:23:45.001000' + self.assertEqual(expected, str(DataTime(s))) + s = s.replace(' ', '_') + self.assertEqual(expected, str(DataTime(s))) + + def testFromStrWithFcstTimeHr(self): + s = '2016-08-02 01:23:45 (17)' + expected = s + self.assertEqual(expected, str(DataTime(s))) + s = s.replace(' ', '_') + self.assertEqual(expected, str(DataTime(s))) + + def testFromStrWithFcstTimeHrZeroMillis(self): + s = '2016-08-02 01:23:45.0 (17)' + expected = '2016-08-02 01:23:45 (17)' + self.assertEqual(expected, str(DataTime(s))) + s = s.replace(' ', '_') + self.assertEqual(expected, str(DataTime(s))) + + def testFromStrWithFcstTimeHrAndMillis(self): + s = '2016-08-02 01:23:45.1 (17)' + expected = '2016-08-02 01:23:45.001000 (17)' + self.assertEqual(expected, str(DataTime(s))) + s = s.replace(' ', '_') + self.assertEqual(expected, str(DataTime(s))) + + def testFromStrWithFcstTimeHrMin(self): + s = '2016-08-02 01:23:45 (17:34)' + expected = s + self.assertEqual(expected, str(DataTime(s))) + s = s.replace(' ', '_') + self.assertEqual(expected, str(DataTime(s))) + + def testFromStrWithFcstTimeHrMinZeroMillis(self): + s = '2016-08-02 01:23:45.0 (17:34)' + expected = '2016-08-02 01:23:45 (17:34)' + self.assertEqual(expected, str(DataTime(s))) + s = s.replace(' ', '_') + self.assertEqual(expected, str(DataTime(s))) + + def testFromStrWithPeriod(self): + s = '2016-08-02 01:23:45[2016-08-02 02:34:45--2016-08-02 03:45:56]' + expected = s + self.assertEqual(expected, str(DataTime(s))) + s = s.replace(' ', '_') + self.assertEqual(expected, str(DataTime(s))) + + def testFromStrWithPeriodZeroMillis(self): + s = '2016-08-02 01:23:45.0[2016-08-02 02:34:45.0--2016-08-02 03:45:56.0]' + expected = '2016-08-02 01:23:45[2016-08-02 02:34:45--2016-08-02 03:45:56]' + self.assertEqual(expected, str(DataTime(s))) + s = s.replace(' ', '_') + self.assertEqual(expected, str(DataTime(s))) + + def testFromStrWithEverything(self): + s = '2016-08-02 01:23:45.0_(17:34)[2016-08-02 02:34:45.0--2016-08-02 03:45:56.0]' + expected = '2016-08-02 01:23:45 (17:34)[2016-08-02 02:34:45--2016-08-02 03:45:56]' + self.assertEqual(expected, str(DataTime(s))) + s = s.replace(' ', '_') + self.assertEqual(expected, str(DataTime(s))) + + def testDataTimeReconstructItselfFromString(self): + times = [ + '2016-08-02 01:23:45', + '2016-08-02 01:23:45.0', + '2016-08-02 01:23:45.1', + '2016-08-02 01:23:45.123000', + '2016-08-02 01:23:45 (17)', + '2016-08-02 01:23:45.0 (17)', + '2016-08-02 01:23:45.1 (17)', + '2016-08-02 01:23:45 (17:34)', + '2016-08-02 01:23:45.0 (17:34)', + '2016-08-02 01:23:45.1 (17:34)', + '2016-08-02 01:23:45.0[2016-08-02_02:34:45.0--2016-08-02_03:45:56.0]', + '2016-08-02 01:23:45.0[2016-08-02_02:34:45.123--2016-08-02_03:45:56.456]', + '2016-08-02 01:23:45.456_(17:34)[2016-08-02_02:34:45.0--2016-08-02_03:45:56.0]' + ] + for time in times: + self.assertEqual(DataTime(time), DataTime(str(DataTime(time))), time) \ No newline at end of file diff --git a/awips/test/dafTests/testFfmp.py b/awips/test/dafTests/testFfmp.py new file mode 100644 index 0000000..a4c1769 --- /dev/null +++ b/awips/test/dafTests/testFfmp.py @@ -0,0 +1,211 @@ +## +## + +from __future__ import print_function +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint +from awips.dataaccess import DataAccessLayer as DAL + +import baseDafTestCase +import params +import unittest + +# +# Test DAF support for ffmp data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 04/18/16 5587 tgurney Add test for sane handling of +# zero records returned +# 06/20/16 5587 tgurney Add identifier values tests +# 07/01/16 5728 mapeters Add advanced query tests, +# include huc and accumHrs in +# id values tests, test that +# accumHrs id is never required +# 08/03/16 5728 mapeters Fixed minor bugs, replaced +# PRTM parameter since it isn't +# configured for ec-oma +# 11/08/16 5985 tgurney Do not check data times +# 12/07/16 5981 tgurney Parameterize +# 12/20/16 5981 tgurney Do not check data times +# +# + + +class FfmpTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for ffmp data""" + + datatype = 'ffmp' + location = params.RADAR.lower() + + @staticmethod + def addIdentifiers(req): + req.addIdentifier('wfo', params.SITE_ID) + req.addIdentifier('siteKey', 'hpe') + req.addIdentifier('dataKey', 'hpe') + req.addIdentifier('huc', 'ALL') + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + self.addIdentifiers(req) + self.runLocationsTest(req) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + self.addIdentifiers(req) + req.setParameters('DHRMOSAIC') + self.runTimesTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + self.addIdentifiers(req) + req.setParameters('DHRMOSAIC') + self.runGeometryDataTest(req, checkDataTimes=False) + + def testGetGeometryDataEmptyResult(self): + req = DAL.newDataRequest(self.datatype) + self.addIdentifiers(req) + req.setParameters('blah blah blah') # force 0 records returned + result = self.runGeometryDataTest(req, checkDataTimes=False) + self.assertEqual(len(result), 0) + + def testGetIdentifierValues(self): + req = DAL.newDataRequest(self.datatype) + optionalIds = set(DAL.getOptionalIdentifiers(req)) + requiredIds = set(DAL.getRequiredIdentifiers(req)) + ids = requiredIds | optionalIds + for id in ids: + req = DAL.newDataRequest(self.datatype) + if id == 'accumHrs': + req.setParameters('ARI6H2YR') + req.addIdentifier('wfo', params.SITE_ID) + req.addIdentifier('siteKey', self.location) + req.addIdentifier('huc', 'ALL') + idValues = DAL.getIdentifierValues(req, id) + self.assertTrue(hasattr(idValues, '__iter__')) + print(id + " values: " + str(idValues)) + + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() + + def _runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + constraint = RequestConstraint.new(operator, value) + req.addIdentifier(key, constraint) + req.addIdentifier('wfo', params.SITE_ID) + req.addIdentifier('huc', 'ALL') + req.setParameters('QPFSCAN') + return self.runGeometryDataTest(req, checkDataTimes=False) + + def testGetDataWithEqualsString(self): + geometryData = self._runConstraintTest('siteKey', '=', self.location) + for record in geometryData: + self.assertEqual(record.getAttribute('siteKey'), self.location) + + def testGetDataWithEqualsUnicode(self): + geometryData = self._runConstraintTest('siteKey', '=', unicode(self.location)) + for record in geometryData: + self.assertEqual(record.getAttribute('siteKey'), self.location) + + # No numeric tests since no numeric identifiers are available that support + # RequestConstraints. + + def testGetDataWithEqualsNone(self): + geometryData = self._runConstraintTest('siteKey', '=', None) + for record in geometryData: + self.assertIsNone(record.getAttribute('siteKey')) + + def testGetDataWithNotEquals(self): + geometryData = self._runConstraintTest('siteKey', '!=', self.location) + for record in geometryData: + self.assertNotEqual(record.getAttribute('siteKey'), self.location) + + def testGetDataWithNotEqualsNone(self): + geometryData = self._runConstraintTest('siteKey', '!=', None) + for record in geometryData: + self.assertIsNotNone(record.getAttribute('siteKey')) + + def testGetDataWithGreaterThan(self): + geometryData = self._runConstraintTest('siteKey', '>', self.location) + for record in geometryData: + self.assertGreater(record.getAttribute('siteKey'), self.location) + + def testGetDataWithLessThan(self): + geometryData = self._runConstraintTest('siteKey', '<', self.location) + for record in geometryData: + self.assertLess(record.getAttribute('siteKey'), self.location) + + def testGetDataWithGreaterThanEquals(self): + geometryData = self._runConstraintTest('siteKey', '>=', self.location) + for record in geometryData: + self.assertGreaterEqual(record.getAttribute('siteKey'), self.location) + + def testGetDataWithLessThanEquals(self): + geometryData = self._runConstraintTest('siteKey', '<=', self.location) + for record in geometryData: + self.assertLessEqual(record.getAttribute('siteKey'), self.location) + + def testGetDataWithInList(self): + collection = [self.location, 'kuex'] + geometryData = self._runConstraintTest('siteKey', 'in', collection) + for record in geometryData: + self.assertIn(record.getAttribute('siteKey'), collection) + + def testGetDataWithNotInList(self): + collection = [self.location, 'kuex'] + geometryData = self._runConstraintTest('siteKey', 'not in', collection) + for record in geometryData: + self.assertNotIn(record.getAttribute('siteKey'), collection) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('siteKey', 'junk', self.location) + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self._runConstraintTest('siteKey', '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('siteKey', 'in', []) + + def testGetDataWithSiteKeyAndDataKeyConstraints(self): + siteKeys = [self.location, 'hpe'] + dataKeys = ['kuex', 'kdmx'] + + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('wfo', params.SITE_ID) + req.addIdentifier('huc', 'ALL') + + siteKeysConstraint = RequestConstraint.new('in', siteKeys) + req.addIdentifier('siteKey', siteKeysConstraint) + dataKeysConstraint = RequestConstraint.new('in', dataKeys) + req.addIdentifier('dataKey', dataKeysConstraint) + + req.setParameters('QPFSCAN') + geometryData = self.runGeometryDataTest(req, checkDataTimes=False) + for record in geometryData: + self.assertIn(record.getAttribute('siteKey'), siteKeys) + # dataKey attr. is comma-separated list of dataKeys that had data + for dataKey in record.getAttribute('dataKey').split(','): + self.assertIn(dataKey, dataKeys) + + def testGetGuidanceDataWithoutAccumHrsIdentifierSet(self): + # Test that accumHrs identifier is not required for guidance data + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('wfo', params.SITE_ID) + req.addIdentifier('siteKey', self.location) + req.addIdentifier('huc', 'ALL') + req.setParameters('FFG0124hr') + self.runGeometryDataTest(req, checkDataTimes=False) \ No newline at end of file diff --git a/awips/test/dafTests/testGfe.py b/awips/test/dafTests/testGfe.py new file mode 100644 index 0000000..3d3f296 --- /dev/null +++ b/awips/test/dafTests/testGfe.py @@ -0,0 +1,203 @@ +## +## + +from __future__ import print_function +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint +from awips.dataaccess import DataAccessLayer as DAL +from shapely.geometry import box, Point + +import baseDafTestCase +import params +import unittest + +# +# Test DAF support for GFE data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 05/23/16 5637 bsteffen Test vectors +# 05/31/16 5587 tgurney Add getIdentifierValues tests +# 06/01/16 5587 tgurney Update testGetIdentifierValues +# 06/17/16 5574 mapeters Add advanced query tests +# 06/30/16 5725 tgurney Add test for NOT IN +# 11/07/16 5991 bsteffen Improve vector tests +# 12/07/16 5981 tgurney Parameterize +# 12/15/16 6040 tgurney Add testGetGridDataWithDbType +# 12/20/16 5981 tgurney Add envelope test +# 10/19/17 6491 tgurney Add test for dbtype identifier +# 11/10/17 6491 tgurney Replace modelName with +# parmId.dbId.modelName +# +# + + +class GfeTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for GFE data""" + + datatype = 'gfe' + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('parmId.dbId.modelName', 'Fcst') + self.runLocationsTest(req) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('parmId.dbId.modelName', 'Fcst') + req.addIdentifier('parmId.dbId.siteId', params.SITE_ID) + self.runTimesTest(req) + + def testGetGridData(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('parmId.dbId.modelName', 'Fcst') + req.addIdentifier('parmId.dbId.siteId', params.SITE_ID) + req.setParameters('T') + gridDatas = self.runGridDataTest(req) + for gridData in gridDatas: + self.assertEqual(gridData.getAttribute('parmId.dbId.dbType'), '') + + def testGetGridDataWithEnvelope(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('parmId.dbId.modelName', 'Fcst') + req.addIdentifier('parmId.dbId.siteId', params.SITE_ID) + req.setParameters('T') + req.setEnvelope(params.ENVELOPE) + gridData = self.runGridDataTest(req) + if not gridData: + raise unittest.SkipTest('no data available') + lons, lats = gridData[0].getLatLonCoords() + lons = lons.reshape(-1) + lats = lats.reshape(-1) + + # Ensure all points are within one degree of the original box + # to allow slight margin of error for reprojection distortion. + testEnv = box(params.ENVELOPE.bounds[0] - 1, params.ENVELOPE.bounds[1] - 1, + params.ENVELOPE.bounds[2] + 1, params.ENVELOPE.bounds[3] + 1 ) + + for i in range(len(lons)): + self.assertTrue(testEnv.contains(Point(lons[i], lats[i]))) + + def testGetVectorGridData(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('parmId.dbId.modelName', 'Fcst') + req.addIdentifier('parmId.dbId.siteId', params.SITE_ID) + req.setParameters('Wind') + times = DAL.getAvailableTimes(req) + if not(times): + raise unittest.SkipTest('No Wind Data available for testing') + gridData = DAL.getGridData(req, [times[0]]) + rawWind = None + rawDir = None + for grid in gridData: + if grid.getParameter() == 'Wind': + self.assertEqual(grid.getUnit(),'kts') + rawWind = grid.getRawData() + elif grid.getParameter() == 'WindDirection': + self.assertEqual(grid.getUnit(),'deg') + rawDir = grid.getRawData() + self.assertIsNotNone(rawWind, 'Wind Magnitude grid is not present') + self.assertIsNotNone(rawDir, 'Wind Direction grid is not present') + # rawWind and rawDir are numpy.ndarrays so comparison will result in boolean ndarrays. + self.assertTrue((rawWind >= 0).all(), 'Wind Speed should not contain negative values') + self.assertTrue((rawDir >= 0).all(), 'Wind Direction should not contain negative values') + self.assertTrue((rawDir <= 360).all(), 'Wind Direction should be less than or equal to 360') + self.assertFalse((rawDir == rawWind).all(), 'Wind Direction should be different from Wind Speed') + + def testGetIdentifierValues(self): + req = DAL.newDataRequest(self.datatype) + optionalIds = set(DAL.getOptionalIdentifiers(req)) + requiredIds = set(DAL.getRequiredIdentifiers(req)) + self.runGetIdValuesTest(optionalIds | requiredIds) + + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() + + def _runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + constraint = RequestConstraint.new(operator, value) + req.addIdentifier(key, constraint) + req.setLocationNames(params.SITE_ID) + req.setParameters('T') + return self.runGridDataTest(req) + + def testGetDataWithModelNameEqualsString(self): + gridData = self._runConstraintTest('parmId.dbId.modelName', '=', 'Fcst') + for record in gridData: + self.assertEqual(record.getAttribute('parmId.dbId.modelName'), 'Fcst') + + def testGetDataWithDbTypeEqualsString(self): + gridData = self._runConstraintTest('parmId.dbId.dbType', '=', 'Prac') + for record in gridData: + self.assertEqual(record.getAttribute('parmId.dbId.dbType'), 'Prac') + + def testGetDataWithEqualsUnicode(self): + gridData = self._runConstraintTest('parmId.dbId.modelName', '=', u'Fcst') + for record in gridData: + self.assertEqual(record.getAttribute('parmId.dbId.modelName'), 'Fcst') + + # No numeric tests since no numeric identifiers are available. + + def testGetDataWithEqualsNone(self): + gridData = self._runConstraintTest('parmId.dbId.modelName', '=', None) + for record in gridData: + self.assertIsNone(record.getAttribute('parmId.dbId.modelName')) + + def testGetDataWithNotEquals(self): + gridData = self._runConstraintTest('parmId.dbId.modelName', '!=', 'Fcst') + for record in gridData: + self.assertNotEqual(record.getAttribute('parmId.dbId.modelName'), 'Fcst') + + def testGetDataWithNotEqualsNone(self): + gridData = self._runConstraintTest('parmId.dbId.modelName', '!=', None) + for record in gridData: + self.assertIsNotNone(record.getAttribute('parmId.dbId.modelName')) + + def testGetDataWithInTuple(self): + collection = ('Fcst', 'SAT') + gridData = self._runConstraintTest('parmId.dbId.modelName', 'in', collection) + for record in gridData: + self.assertIn(record.getAttribute('parmId.dbId.modelName'), collection) + + def testGetDataWithInList(self): + collection = ['Fcst', 'SAT'] + gridData = self._runConstraintTest('parmId.dbId.modelName', 'in', collection) + for record in gridData: + self.assertIn(record.getAttribute('parmId.dbId.modelName'), collection) + + def testGetDataWithInGenerator(self): + collection = ('Fcst', 'SAT') + generator = (item for item in collection) + gridData = self._runConstraintTest('parmId.dbId.modelName', 'in', generator) + for record in gridData: + self.assertIn(record.getAttribute('parmId.dbId.modelName'), collection) + + def testGetDataWithNotInList(self): + collection = ('Fcst', 'SAT') + gridData = self._runConstraintTest('parmId.dbId.modelName', 'not in', collection) + for record in gridData: + self.assertNotIn(record.getAttribute('parmId.dbId.modelName'), collection) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('parmId.dbId.modelName', 'junk', 'Fcst') + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self._runConstraintTest('parmId.dbId.modelName', '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('parmId.dbId.modelName', 'in', []) + diff --git a/awips/test/dafTests/testGfeEditArea.py b/awips/test/dafTests/testGfeEditArea.py new file mode 100644 index 0000000..7fd9c66 --- /dev/null +++ b/awips/test/dafTests/testGfeEditArea.py @@ -0,0 +1,203 @@ +## +## + +from __future__ import print_function +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint +from awips.dataaccess import DataAccessLayer as DAL +from awips.ThriftClient import ThriftRequestException + +import baseDafTestCase +import params + +# +# Test DAF support for GFE edit area data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/08/17 6298 mapeters Initial Creation. +# 09/27/17 6463 tgurney Remove GID site identifier +# +# + + +class GfeEditAreaTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for GFE edit area data""" + + datatype = 'gfeEditArea' + + siteIdKey = 'siteId' + + editAreaNames = ['ISC_NHA', 'SDZ066', 'StormSurgeWW_EditArea'] + + groupKey = 'group' + + groups = ['ISC', 'WFOs', 'FIPS_' + params.SITE_ID] + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier(self.siteIdKey, params.SITE_ID) + with self.assertRaises(ThriftRequestException): + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier(self.siteIdKey, params.SITE_ID) + self.runLocationsTest(req) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier(self.siteIdKey, params.SITE_ID) + with self.assertRaises(ThriftRequestException): + self.runTimesTest(req) + + def testGetGeometryDataWithoutSiteIdThrowsException(self): + req = DAL.newDataRequest(self.datatype) + with self.assertRaises(ThriftRequestException): + self.runGeometryDataTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier(self.siteIdKey, params.SITE_ID) + data = self.runGeometryDataTest(req) + for item in data: + self.assertEqual(params.SITE_ID, item.getAttribute(self.siteIdKey)) + + def testGetGeometryDataWithLocNames(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier(self.siteIdKey, params.SITE_ID) + req.setLocationNames(*self.editAreaNames) + data = self.runGeometryDataTest(req) + for item in data: + self.assertEqual(params.SITE_ID, item.getAttribute(self.siteIdKey)) + self.assertIn(item.getLocationName(), self.editAreaNames) + + def testGetGeometryDataWithGroups(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier(self.siteIdKey, params.SITE_ID) + req.addIdentifier(self.groupKey, RequestConstraint.new('in', self.groups)) + data = self.runGeometryDataTest(req) + for item in data: + self.assertEqual(params.SITE_ID, item.getAttribute(self.siteIdKey)) + self.assertIn(item.getAttribute(self.groupKey), self.groups) + + def testGetGeometryDataWithLocNamesAndGroupsThrowException(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier(self.siteIdKey, params.SITE_ID) + req.setLocationNames(*self.editAreaNames) + req.addIdentifier(self.groupKey, RequestConstraint.new('in', self.groups)) + with self.assertRaises(ThriftRequestException): + self.runGeometryDataTest(req) + + def testGetGeometryDataWithEnvelope(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier(self.siteIdKey, params.SITE_ID) + req.setEnvelope(params.ENVELOPE) + data = self.runGeometryDataTest(req) + for item in data: + self.assertEqual(params.SITE_ID, item.getAttribute(self.siteIdKey)) + self.assertTrue(params.ENVELOPE.intersects(item.getGeometry())) + + def testGetIdentifierValues(self): + req = DAL.newDataRequest(self.datatype) + optionalIds = set(DAL.getOptionalIdentifiers(req)) + requiredIds = set(DAL.getRequiredIdentifiers(req)) + self.runGetIdValuesTest(optionalIds | requiredIds) + + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() + + def _runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + constraint = RequestConstraint.new(operator, value) + req.addIdentifier(key, constraint) + req.setLocationNames(*self.editAreaNames) + return self.runGeometryDataTest(req) + + def testGetDataWithEqualsString(self): + geomData = self._runConstraintTest(self.siteIdKey, '=', params.SITE_ID) + for record in geomData: + self.assertEqual(record.getAttribute(self.siteIdKey), params.SITE_ID) + + def testGetDataWithEqualsUnicode(self): + geomData = self._runConstraintTest(self.siteIdKey, '=', params.SITE_ID.decode('unicode-escape')) + for record in geomData: + self.assertEqual(record.getAttribute(self.siteIdKey), params.SITE_ID) + + # No numeric tests since no numeric identifiers are available. + + def testGetDataWithEqualsNone(self): + geomData = self._runConstraintTest(self.siteIdKey, '=', None) + for record in geomData: + self.assertIsNone(record.getAttribute(self.siteIdKey)) + + def testGetDataWithNotEquals(self): + geomData = self._runConstraintTest(self.siteIdKey, '!=', params.SITE_ID) + for record in geomData: + self.assertNotEqual(record.getAttribute(self.siteIdKey), params.SITE_ID) + + def testGetDataWithNotEqualsNone(self): + geomData = self._runConstraintTest(self.siteIdKey, '!=', None) + for record in geomData: + self.assertIsNotNone(record.getAttribute(self.siteIdKey)) + + def testGetDataWithGreaterThan(self): + geomData = self._runConstraintTest(self.siteIdKey, '>', params.SITE_ID) + for record in geomData: + self.assertGreater(record.getAttribute(self.siteIdKey), params.SITE_ID) + + def testGetDataWithLessThan(self): + geomData = self._runConstraintTest(self.siteIdKey, '<', params.SITE_ID) + for record in geomData: + self.assertLess(record.getAttribute(self.siteIdKey), params.SITE_ID) + + def testGetDataWithGreaterThanEquals(self): + geomData = self._runConstraintTest(self.siteIdKey, '>=', params.SITE_ID) + for record in geomData: + self.assertGreaterEqual(record.getAttribute(self.siteIdKey), params.SITE_ID) + + def testGetDataWithLessThanEquals(self): + geomData = self._runConstraintTest(self.siteIdKey, '<=', params.SITE_ID) + for record in geomData: + self.assertLessEqual(record.getAttribute(self.siteIdKey), params.SITE_ID) + + def testGetDataWithInTuple(self): + collection = (params.SITE_ID,) + geomData = self._runConstraintTest(self.siteIdKey, 'in', collection) + for record in geomData: + self.assertIn(record.getAttribute(self.siteIdKey), collection) + + def testGetDataWithInList(self): + collection = [params.SITE_ID,] + geomData = self._runConstraintTest(self.siteIdKey, 'in', collection) + for record in geomData: + self.assertIn(record.getAttribute(self.siteIdKey), collection) + + def testGetDataWithInGenerator(self): + collection = (params.SITE_ID,) + generator = (item for item in collection) + geomData = self._runConstraintTest(self.siteIdKey, 'in', generator) + for record in geomData: + self.assertIn(record.getAttribute(self.siteIdKey), collection) + + def testGetDataWithNotInList(self): + collection = [params.SITE_ID,] + geomData = self._runConstraintTest(self.siteIdKey, 'not in', collection) + for record in geomData: + self.assertNotIn(record.getAttribute(self.siteIdKey), collection) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest(self.siteIdKey, 'junk', params.SITE_ID) + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self._runConstraintTest(self.siteIdKey, '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest(self.siteIdKey, 'in', []) diff --git a/awips/test/dafTests/testGrid.py b/awips/test/dafTests/testGrid.py new file mode 100644 index 0000000..15e6922 --- /dev/null +++ b/awips/test/dafTests/testGrid.py @@ -0,0 +1,271 @@ +## +## + +from __future__ import print_function +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint +from shapely.geometry import box, Point +from awips.dataaccess import DataAccessLayer as DAL +from awips.ThriftClient import ThriftRequestException + +import baseDafTestCase +import params +import unittest + +# +# Test DAF support for grid data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 06/09/16 5587 tgurney Typo in id values test +# 07/06/16 5728 mapeters Add advanced query tests +# 08/03/16 5728 mapeters Add additional identifiers to testGetDataWith* +# tests to shorten run time and prevent EOFError +# 10/13/16 5942 bsteffen Test envelopes +# 11/08/16 5985 tgurney Skip certain tests when no +# data is available +# 12/07/16 5981 tgurney Parameterize +# 01/06/17 5981 tgurney Skip envelope test when no +# data is available +# + + +class GridTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for grid data""" + + datatype = 'grid' + + model = 'GFS160' + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('info.datasetId', self.model) + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('info.datasetId', self.model) + self.runLocationsTest(req) + + def testGetAvailableLevels(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('info.datasetId', self.model) + self.runLevelsTest(req) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('info.datasetId', self.model) + req.setLevels('2FHAG') + self.runTimesTest(req) + + def testGetGridData(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('info.datasetId', self.model) + req.setLevels('2FHAG') + req.setParameters('T') + self.runGridDataTest(req) + + def testGetIdentifierValues(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('info.datasetId', 'ENSEMBLE') + req.setLevels('2FHAG') + req.setParameters('T') + idValues = DAL.getIdentifierValues(req, 'info.ensembleId') + self.assertTrue(hasattr(idValues, '__iter__')) + if idValues: + self.assertIn('ctl1', idValues) + self.assertIn('p1', idValues) + self.assertIn('n1', idValues) + else: + raise unittest.SkipTest("no data available") + + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() + + + def testGetDataWithEnvelope(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('info.datasetId', self.model) + req.setLevels('2FHAG') + req.setParameters('T') + req.setEnvelope(params.ENVELOPE) + gridData = self.runGridDataTest(req) + if len(gridData) == 0: + raise unittest.SkipTest("No data available") + lons, lats = gridData[0].getLatLonCoords() + lons = lons.reshape(-1) + lats = lats.reshape(-1) + + # Ensure all points are within one degree of the original box + # to allow slight margin of error for reprojection distortion. + testEnv = box(params.ENVELOPE.bounds[0] - 1, params.ENVELOPE.bounds[1] - 1, + params.ENVELOPE.bounds[2] + 1, params.ENVELOPE.bounds[3] + 1 ) + + for i in range(len(lons)): + self.assertTrue(testEnv.contains(Point(lons[i], lats[i]))) + + + def _runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + constraint = RequestConstraint.new(operator, value) + req.addIdentifier(key, constraint) + req.addIdentifier('info.datasetId', self.model) + req.addIdentifier('info.level.masterLevel.name', 'FHAG') + req.addIdentifier('info.level.leveltwovalue', 3000.0) + req.setParameters('T') + return self.runGridDataTest(req) + + def testGetDataWithEqualsString(self): + gridData = self._runConstraintTest('info.level.levelonevalue', '=', '2000.0') + for record in gridData: + self.assertEqual(record.getAttribute('info.level.levelonevalue'), 2000.0) + + def testGetDataWithEqualsUnicode(self): + gridData = self._runConstraintTest('info.level.levelonevalue', '=', u'2000.0') + for record in gridData: + self.assertEqual(record.getAttribute('info.level.levelonevalue'), 2000.0) + + def testGetDataWithEqualsInt(self): + gridData = self._runConstraintTest('info.level.levelonevalue', '=', 2000) + for record in gridData: + self.assertEqual(record.getAttribute('info.level.levelonevalue'), 2000) + + def testGetDataWithEqualsLong(self): + gridData = self._runConstraintTest('info.level.levelonevalue', '=', 2000L) + for record in gridData: + self.assertEqual(record.getAttribute('info.level.levelonevalue'), 2000) + + def testGetDataWithEqualsFloat(self): + gridData = self._runConstraintTest('info.level.levelonevalue', '=', 2000.0) + for record in gridData: + self.assertEqual(round(record.getAttribute('info.level.levelonevalue'), 1), 2000.0) + + def testGetDataWithEqualsNone(self): + gridData = self._runConstraintTest('info.level.levelonevalue', '=', None) + for record in gridData: + self.assertIsNone(record.getAttribute('info.level.levelonevalue')) + + def testGetDataWithNotEquals(self): + gridData = self._runConstraintTest('info.level.levelonevalue', '!=', 2000.0) + for record in gridData: + self.assertNotEqual(record.getAttribute('info.level.levelonevalue'), 2000.0) + + def testGetDataWithNotEqualsNone(self): + gridData = self._runConstraintTest('info.level.levelonevalue', '!=', None) + for record in gridData: + self.assertIsNotNone(record.getAttribute('info.level.levelonevalue')) + + def testGetDataWithGreaterThan(self): + gridData = self._runConstraintTest('info.level.levelonevalue', '>', 2000.0) + for record in gridData: + self.assertGreater(record.getAttribute('info.level.levelonevalue'), 2000.0) + + def testGetDataWithLessThan(self): + gridData = self._runConstraintTest('info.level.levelonevalue', '<', 2000.0) + for record in gridData: + self.assertLess(record.getAttribute('info.level.levelonevalue'), 2000.0) + + def testGetDataWithGreaterThanEquals(self): + gridData = self._runConstraintTest('info.level.levelonevalue', '>=', 2000.0) + for record in gridData: + self.assertGreaterEqual(record.getAttribute('info.level.levelonevalue'), 2000.0) + + def testGetDataWithLessThanEquals(self): + gridData = self._runConstraintTest('info.level.levelonevalue', '<=', 2000.0) + for record in gridData: + self.assertLessEqual(record.getAttribute('info.level.levelonevalue'), 2000.0) + + def testGetDataWithInList(self): + collection = [2000.0, 1000.0] + gridData = self._runConstraintTest('info.level.levelonevalue', 'in', collection) + for record in gridData: + self.assertIn(record.getAttribute('info.level.levelonevalue'), collection) + + def testGetDataWithNotInList(self): + collection = [2000.0, 1000.0] + gridData = self._runConstraintTest('info.level.levelonevalue', 'not in', collection) + for record in gridData: + self.assertNotIn(record.getAttribute('info.level.levelonevalue'), collection) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('info.level.levelonevalue', 'junk', '2000.0') + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self._runConstraintTest('info.level.levelonevalue', '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('info.level.levelonevalue', 'in', []) + + def testGetDataWithLevelOneAndLevelTwoConstraints(self): + req = DAL.newDataRequest(self.datatype) + levelOneConstraint = RequestConstraint.new('>=', 2000.0) + req.addIdentifier('info.level.levelonevalue', levelOneConstraint) + levelTwoConstraint = RequestConstraint.new('in', (4000.0, 5000.0)) + req.addIdentifier('info.level.leveltwovalue', levelTwoConstraint) + req.addIdentifier('info.datasetId', self.model) + req.addIdentifier('info.level.masterLevel.name', 'FHAG') + req.setParameters('T') + gridData = self.runGridDataTest(req) + for record in gridData: + self.assertGreaterEqual(record.getAttribute('info.level.levelonevalue'), 2000.0) + self.assertIn(record.getAttribute('info.level.leveltwovalue'), (4000.0, 5000.0)) + + def testGetDataWithMasterLevelNameInConstraint(self): + req = DAL.newDataRequest(self.datatype) + masterLevelConstraint = RequestConstraint.new('in', ('FHAG', 'K')) + req.addIdentifier('info.level.masterLevel.name', masterLevelConstraint) + req.addIdentifier('info.level.levelonevalue', 2000.0) + req.addIdentifier('info.level.leveltwovalue', 3000.0) + req.addIdentifier('info.datasetId', 'GFS160') + req.setParameters('T') + gridData = self.runGridDataTest(req) + for record in gridData: + self.assertIn(record.getAttribute('info.level.masterLevel.name'), ('FHAG', 'K')) + + def testGetDataWithDatasetIdInConstraint(self): + req = DAL.newDataRequest(self.datatype) + # gfs160 is alias for GFS160 in this namespace + req.addIdentifier('namespace', 'gfeParamInfo') + datasetIdConstraint = RequestConstraint.new('in', ('gfs160', 'HRRR')) + req.addIdentifier('info.datasetId', datasetIdConstraint) + req.addIdentifier('info.level.masterLevel.name', 'FHAG') + req.addIdentifier('info.level.levelonevalue', 2000.0) + req.addIdentifier('info.level.leveltwovalue', 3000.0) + req.setParameters('T') + gridData = self.runGridDataTest(req, testSameShape=False) + for record in gridData: + self.assertIn(record.getAttribute('info.datasetId'), ('gfs160', 'HRRR')) + + def testGetDataWithMasterLevelNameLessThanEqualsConstraint(self): + req = DAL.newDataRequest(self.datatype) + masterLevelConstraint = RequestConstraint.new('<=', 'K') + req.addIdentifier('info.level.masterLevel.name', masterLevelConstraint) + req.addIdentifier('info.level.levelonevalue', 2000.0) + req.addIdentifier('info.level.leveltwovalue', 3000.0) + req.addIdentifier('info.datasetId', 'GFS160') + req.setParameters('T') + gridData = self.runGridDataTest(req) + for record in gridData: + self.assertLessEqual(record.getAttribute('info.level.masterLevel.name'), 'K') + + def testGetDataWithComplexConstraintAndNamespaceThrowsException(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('namespace', 'grib') + masterLevelConstraint = RequestConstraint.new('<=', 'K') + req.addIdentifier('info.level.masterLevel.name', masterLevelConstraint) + req.addIdentifier('info.datasetId', 'GFS160') + req.setParameters('T') + with self.assertRaises(ThriftRequestException) as cm: + self.runGridDataTest(req) + self.assertIn('IncompatibleRequestException', str(cm.exception)) + self.assertIn('info.level.masterLevel.name', str(cm.exception)) diff --git a/awips/test/dafTests/testHydro.py b/awips/test/dafTests/testHydro.py new file mode 100644 index 0000000..fd655ed --- /dev/null +++ b/awips/test/dafTests/testHydro.py @@ -0,0 +1,251 @@ +## +## + +from __future__ import print_function +import datetime +from awips.dataaccess import DataAccessLayer as DAL +from awips.ThriftClient import ThriftRequestException + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint +from dynamicserialize.dstypes.com.raytheon.uf.common.time import TimeRange +import baseDafTestCase +import unittest + +# +# Test DAF support for hydro data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 04/21/16 5596 tgurney Add tests to verify #5596 +# 04/26/16 5587 tgurney Add identifier values tests +# 06/09/16 5574 tgurney Add advanced query tests +# 06/13/16 5574 tgurney Fix checks for None +# 06/21/16 5548 tgurney Skip tests that cause errors +# 06/30/16 5725 tgurney Add test for NOT IN +# 10/06/16 5926 dgilling Add additional location tests. +# +# + + +class HydroTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for hydro data""" + + datatype = 'hydro' + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'height') + self.runParametersTest(req) + + def testGetAvailableParametersFullyQualifiedTable(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'public.height') + self.runParametersTest(req) + + def testGetAvailableParamsNoTableThrowsInvalidIdentifiersException(self): + req = DAL.newDataRequest(self.datatype) + with self.assertRaises(ThriftRequestException) as cm: + self.runParametersTest(req) + self.assertIn('InvalidIdentifiersException', str(cm.exception)) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'height') + self.runLocationsTest(req) + + def testGetAvailableLocationsWithConstraint(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'height') + req.addIdentifier('value', RequestConstraint.new('>', 5.0)) + self.runLocationsTest(req) + + def testGetAvailableLocationsWithInvalidTable(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'city') + with self.assertRaises(ThriftRequestException) as cm: + DAL.getAvailableLocationNames(req) + self.assertIn('IncompatibleRequestException', str(cm.exception)) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'height') + req.setParameters('lid', 'quality_code') + self.runTimesTest(req) + + def testGetGeometryDataWithoutLocationSpecified(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'height') + req.setParameters('lid', 'quality_code') + self.runGeometryDataTest(req) + + def testGetGeometryDataWithLocationSpecified(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'fcstheight') + locs = DAL.getAvailableLocationNames(req) + if locs: + req.setLocationNames(locs[0]) + req.setParameters('probability', 'value') + data = self.runGeometryDataTest(req) + self.assertNotEqual(len(data), 0) + + def testGetTableIdentifierValues(self): + self.runGetIdValuesTest(['table']) + + def testGetColumnIdValuesWithTable(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'height') + idValues = DAL.getIdentifierValues(req, 'lid') + self.assertTrue(hasattr(idValues, '__iter__')) + + @unittest.skip('avoid EDEX error') + def testGetColumnIdValuesWithNonexistentTableThrowsException(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'nonexistentjunk') + with self.assertRaises(ThriftRequestException): + idValues = DAL.getIdentifierValues(req, 'lid') + + def testGetColumnIdValuesWithoutTableThrowsException(self): + req = DAL.newDataRequest(self.datatype) + with self.assertRaises(ThriftRequestException): + idValues = DAL.getIdentifierValues(req, 'lid') + + @unittest.skip('avoid EDEX error') + def testGetNonexistentColumnIdValuesThrowsException(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'height') + with self.assertRaises(ThriftRequestException): + idValues = DAL.getIdentifierValues(req, 'nonexistentjunk') + + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() + + def _runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + constraint = RequestConstraint.new(operator, value) + req.addIdentifier(key, constraint) + req.addIdentifier('table', 'height') + req.addIdentifier('ts', 'RG') + req.setParameters('value', 'lid', 'quality_code') + return self.runGeometryDataTest(req) + + def testGetDataWithEqualsString(self): + geometryData = self._runConstraintTest('value', '=', '3') + for record in geometryData: + self.assertEqual(record.getNumber('value'), 3) + + def testGetDataWithEqualsUnicode(self): + geometryData = self._runConstraintTest('value', '=', u'3') + for record in geometryData: + self.assertEqual(record.getNumber('value'), 3) + + def testGetDataWithEqualsInt(self): + geometryData = self._runConstraintTest('value', '=', 3) + for record in geometryData: + self.assertEqual(record.getNumber('value'), 3) + + def testGetDataWithEqualsLong(self): + geometryData = self._runConstraintTest('value', '=', 3L) + for record in geometryData: + self.assertEqual(record.getNumber('value'), 3L) + + def testGetDataWithEqualsFloat(self): + geometryData = self._runConstraintTest('value', '=', 3.0) + for record in geometryData: + self.assertEqual(round(record.getNumber('value'), 1), 3.0) + + def testGetDataWithEqualsNone(self): + geometryData = self._runConstraintTest('value', '=', None) + self.assertEqual(len(geometryData), 0) + + def testGetDataWithNotEquals(self): + geometryData = self._runConstraintTest('value', '!=', 3) + for record in geometryData: + self.assertNotEqual(record.getNumber('value'), '3') + + def testGetDataWithNotEqualsNone(self): + geometryData = self._runConstraintTest('value', '!=', None) + self.assertNotEqual(len(geometryData), 0) + for record in geometryData: + self.assertNotEqual(record.getType('value'), 'NULL') + + def testGetDataWithGreaterThan(self): + geometryData = self._runConstraintTest('value', '>', 3) + for record in geometryData: + self.assertGreater(record.getNumber('value'), 3) + + def testGetDataWithLessThan(self): + geometryData = self._runConstraintTest('value', '<', 3) + for record in geometryData: + self.assertLess(record.getNumber('value'), 3) + + def testGetDataWithGreaterThanEquals(self): + geometryData = self._runConstraintTest('value', '>=', 3) + for record in geometryData: + self.assertGreaterEqual(record.getNumber('value'), 3) + + def testGetDataWithLessThanEquals(self): + geometryData = self._runConstraintTest('value', '<=', 3) + for record in geometryData: + self.assertLessEqual(record.getNumber('value'), 3) + + def testGetDataWithInTuple(self): + collection = (3, 4) + geometryData = self._runConstraintTest('value', 'in', collection) + for record in geometryData: + self.assertIn(record.getNumber('value'), collection) + + def testGetDataWithInList(self): + collection = [3, 4] + geometryData = self._runConstraintTest('value', 'in', collection) + for record in geometryData: + self.assertIn(record.getNumber('value'), collection) + + def testGetDataWithInGenerator(self): + collection = (3, 4) + generator = (item for item in collection) + geometryData = self._runConstraintTest('value', 'in', generator) + for record in geometryData: + self.assertIn(record.getNumber('value'), collection) + + def testGetDataWithNotInList(self): + collection = [3, 4] + geometryData = self._runConstraintTest('value', 'not in', collection) + for record in geometryData: + self.assertNotIn(record.getNumber('value'), collection) + + def testGetDataWithTimeRange(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'height') + req.addIdentifier('ts', 'RG') + req.setParameters('value', 'lid', 'quality_code') + times = DAL.getAvailableTimes(req) + limitTimes = times[-self.numTimesToLimit:] + startTime = datetime.datetime.utcfromtimestamp(limitTimes[0].getRefTime().getTime()/1000) + endTime = datetime.datetime.utcnow() + tr = TimeRange(startTime, endTime) + self.runGeometryDataTestWithTimeRange(req, tr) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('value', 'junk', 3) + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self._runConstraintTest('value', '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('value', 'in', []) + + def testGetDataWithNestedInConstraintThrowsException(self): + collection = ('3', '4', ()) + with self.assertRaises(TypeError): + self._runConstraintTest('value', 'in', collection) diff --git a/awips/test/dafTests/testLdadMesonet.py b/awips/test/dafTests/testLdadMesonet.py new file mode 100644 index 0000000..1045bcd --- /dev/null +++ b/awips/test/dafTests/testLdadMesonet.py @@ -0,0 +1,68 @@ +## +## + +from __future__ import print_function +from shapely.geometry import Polygon +from awips.dataaccess import DataAccessLayer as DAL + +import baseDafTestCase +import unittest + +# +# Test DAF support for ldadmesonet data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 01/20/17 6095 tgurney Add null identifiers test +# +# + + +class LdadMesonetTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for ldadmesonet data""" + + datatype = "ldadmesonet" + + envelope = None + + @classmethod + def getReqEnvelope(cls): + # Restrict the output to only records with latitude and + # longitude between -30 and 30. + if not cls.envelope: + vertices = [(-30, -30), (-30, 30), (30, 30), (30, -30)] + polygon = Polygon(vertices) + cls.envelope = polygon.envelope + return cls.envelope + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + req.setEnvelope(self.getReqEnvelope()) + self.runLocationsTest(req) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + req.setEnvelope(self.getReqEnvelope()) + self.runTimesTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.setParameters("highLevelCloud", "pressure") + req.setEnvelope(self.getReqEnvelope()) + self.runGeometryDataTest(req) + + def testGetGeometryDataNullIdentifiers(self): + req = DAL.newDataRequest(self.datatype) + req.setParameters("highLevelCloud", "pressure") + req.setEnvelope(self.getReqEnvelope()) + req.identifiers = None + self.runGeometryDataTest(req) diff --git a/awips/test/dafTests/testMaps.py b/awips/test/dafTests/testMaps.py new file mode 100644 index 0000000..14ad3c2 --- /dev/null +++ b/awips/test/dafTests/testMaps.py @@ -0,0 +1,202 @@ +## +## + +from __future__ import print_function +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint +from awips.dataaccess import DataAccessLayer as DAL +from awips.ThriftClient import ThriftRequestException + +import baseDafTestCase +import unittest + +# +# Test DAF support for maps data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 04/26/16 5587 tgurney Add identifier values tests +# 06/13/16 5574 mapeters Add advanced query tests +# 06/21/16 5548 tgurney Skip tests that cause errors +# 06/30/16 5725 tgurney Add test for NOT IN +# 01/06/17 5981 tgurney Do not check data times +# +# + + +class MapsTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for maps data""" + + datatype = 'maps' + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'mapdata.county') + req.addIdentifier('geomField', 'the_geom') + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'mapdata.county') + req.addIdentifier('geomField', 'the_geom') + req.addIdentifier('locationField', 'cwa') + self.runLocationsTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'mapdata.county') + req.addIdentifier('geomField', 'the_geom') + req.addIdentifier('inLocation', 'true') + req.addIdentifier('locationField', 'cwa') + req.setLocationNames('OAX') + req.addIdentifier('cwa', 'OAX') + req.setParameters('countyname', 'state', 'fips') + self.runGeometryDataTest(req, checkDataTimes=False) + + def testRequestingTimesThrowsTimeAgnosticDataException(self): + req = DAL.newDataRequest(self.datatype) + self.runTimeAgnosticTest(req) + + def testGetTableIdentifierValues(self): + self.runGetIdValuesTest(['table']) + + def testGetGeomFieldIdentifierValues(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'mapdata.county') + idValues = DAL.getIdentifierValues(req, 'geomField') + for idValue in idValues: + self.assertTrue(idValue.startswith('the_geom')) + + def testGetGeomFieldIdValuesWithoutTableThrowsException(self): + with self.assertRaises(ThriftRequestException): + self.runGetIdValuesTest(['geomField']) + + def testGetColumnIdValuesWithTable(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'mapdata.county') + req.addIdentifier('geomField', 'the_geom') + idValues = DAL.getIdentifierValues(req, 'state') + self.assertIn('NE', idValues) + + def testGetColumnIdValuesWithoutTableThrowsException(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('geomField', 'the_geom') + with self.assertRaises(ThriftRequestException): + idValues = DAL.getIdentifierValues(req, 'state') + + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() + + def _runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'mapdata.ffmp_basins') + req.addIdentifier('geomField', 'the_geom') + req.addIdentifier('cwa', 'OAX') + constraint = RequestConstraint.new(operator, value) + req.addIdentifier(key, constraint) + req.setParameters('state', 'reservoir', 'area_sq_mi') + return self.runGeometryDataTest(req, checkDataTimes=False) + + def testGetDataWithEqualsString(self): + geometryData = self._runConstraintTest('state', '=', 'NE') + for record in geometryData: + self.assertEqual(record.getString('state'), 'NE') + + def testGetDataWithEqualsUnicode(self): + geometryData = self._runConstraintTest('state', '=', u'NE') + for record in geometryData: + self.assertEqual(record.getString('state'), 'NE') + + def testGetDataWithEqualsInt(self): + geometryData = self._runConstraintTest('reservoir', '=', 1) + for record in geometryData: + self.assertEqual(record.getNumber('reservoir'), 1) + + def testGetDataWithEqualsLong(self): + geometryData = self._runConstraintTest('reservoir', '=', 1L) + for record in geometryData: + self.assertEqual(record.getNumber('reservoir'), 1) + + def testGetDataWithEqualsFloat(self): + geometryData = self._runConstraintTest('area_sq_mi', '=', 5.00) + for record in geometryData: + self.assertEqual(round(record.getNumber('area_sq_mi'), 2), 5.00) + + def testGetDataWithEqualsNone(self): + geometryData = self._runConstraintTest('state', '=', None) + for record in geometryData: + self.assertEqual(record.getType('state'), 'NULL') + + def testGetDataWithNotEquals(self): + geometryData = self._runConstraintTest('state', '!=', 'NE') + for record in geometryData: + self.assertNotEqual(record.getString('state'), 'NE') + + def testGetDataWithNotEqualsNone(self): + geometryData = self._runConstraintTest('state', '!=', None) + for record in geometryData: + self.assertNotEqual(record.getType('state'), 'NULL') + + def testGetDataWithGreaterThan(self): + geometryData = self._runConstraintTest('area_sq_mi', '>', 5) + for record in geometryData: + self.assertGreater(record.getNumber('area_sq_mi'), 5) + + def testGetDataWithLessThan(self): + geometryData = self._runConstraintTest('area_sq_mi', '<', 5) + for record in geometryData: + self.assertLess(record.getNumber('area_sq_mi'), 5) + + def testGetDataWithGreaterThanEquals(self): + geometryData = self._runConstraintTest('area_sq_mi', '>=', 5) + for record in geometryData: + self.assertGreaterEqual(record.getNumber('area_sq_mi'), 5) + + def testGetDataWithLessThanEquals(self): + geometryData = self._runConstraintTest('area_sq_mi', '<=', 5) + for record in geometryData: + self.assertLessEqual(record.getNumber('area_sq_mi'), 5) + + def testGetDataWithInTuple(self): + collection = ('NE', 'TX') + geometryData = self._runConstraintTest('state', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('state'), collection) + + def testGetDataWithInList(self): + collection = ['NE', 'TX'] + geometryData = self._runConstraintTest('state', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('state'), collection) + + def testGetDataWithInGenerator(self): + collection = ('NE', 'TX') + generator = (item for item in collection) + geometryData = self._runConstraintTest('state', 'in', generator) + for record in geometryData: + self.assertIn(record.getString('state'), collection) + + def testGetDataWithNotInList(self): + collection = ['IA', 'TX'] + geometryData = self._runConstraintTest('state', 'not in', collection) + for record in geometryData: + self.assertNotIn(record.getString('state'), collection) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('state', 'junk', 'NE') + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self._runConstraintTest('state', '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('state', 'in', []) diff --git a/awips/test/dafTests/testModelSounding.py b/awips/test/dafTests/testModelSounding.py new file mode 100644 index 0000000..50fae9c --- /dev/null +++ b/awips/test/dafTests/testModelSounding.py @@ -0,0 +1,200 @@ +## +## + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint + +import baseDafTestCase +import params +import unittest + +# +# Test DAF support for modelsounding data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 06/09/16 5587 bsteffen Add getIdentifierValues tests +# 06/13/16 5574 tgurney Add advanced query tests +# 06/30/16 5725 tgurney Add test for NOT IN +# 11/10/16 5985 tgurney Mark expected failures prior +# to 17.3.1 +# 12/07/16 5981 tgurney Parameterize +# 12/19/16 5981 tgurney Remove pre-17.3 expected fails +# 12/20/16 5981 tgurney Add envelope test +# +# + + +class ModelSoundingTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for modelsounding data""" + + datatype = "modelsounding" + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier("reportType", "ETA") + self.runLocationsTest(req) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier("reportType", "ETA") + req.setLocationNames(params.OBS_STATION) + self.runTimesTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier("reportType", "ETA") + req.setLocationNames(params.OBS_STATION) + req.setParameters("temperature", "pressure", "specHum", "sfcPress", "temp2", "q2") + print("Testing getGeometryData()") + geomData = DAL.getGeometryData(req) + print("Number of geometry records: " + str(len(geomData))) + print("Sample geometry data:") + for record in geomData[:self.sampleDataLimit]: + print("level=" + record.getLevel(), end="") + # One dimensional parameters are reported on the 0.0UNKNOWN level. + # 2D parameters are reported on MB levels from pressure. + if record.getLevel() == "0.0UNKNOWN": + print(" sfcPress=" + record.getString("sfcPress") + + record.getUnit("sfcPress"), end="") + print(" temp2=" + record.getString("temp2") + + record.getUnit("temp2"), end="") + print(" q2=" + record.getString("q2") + + record.getUnit("q2"), end="") + else: + print(" pressure=" + record.getString("pressure") + + record.getUnit("pressure"), end="") + print(" temperature=" + record.getString("temperature") + + record.getUnit("temperature"), end="") + print(" specHum=" + record.getString("specHum") + + record.getUnit("specHum"), end="") + print(" geometry=" + str(record.getGeometry())) + print("getGeometryData() complete\n\n") + + def testGetGeometryDataWithEnvelope(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier("reportType", "ETA") + req.setEnvelope(params.ENVELOPE) + req.setParameters("temperature", "pressure", "specHum", "sfcPress", "temp2", "q2") + print("Testing getGeometryData()") + data = DAL.getGeometryData(req) + for item in data: + self.assertTrue(params.ENVELOPE.contains(item.getGeometry())) + + def testGetIdentifierValues(self): + req = DAL.newDataRequest(self.datatype) + optionalIds = set(DAL.getOptionalIdentifiers(req)) + self.runGetIdValuesTest(optionalIds) + + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() + + def _runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + constraint = RequestConstraint.new(operator, value) + req.setParameters('dataURI') + req.setLocationNames(params.OBS_STATION, 'KORD', 'KOFK', 'KLNK') + req.addIdentifier(key, constraint) + return self.runGeometryDataTest(req) + + # We can filter on reportType but it is not possible to retrieve the value + # of reportType directly. We can look inside the dataURI instead. + # + # For cases like '<=' and '>' the best we can do is send the request and + # see if it throws back an exception. + # + # Can also eyeball the number of returned records. + + def testGetDataWithEqualsString(self): + geometryData = self._runConstraintTest('reportType', '=', 'ETA') + for record in geometryData: + self.assertIn('/ETA/', record.getString('dataURI')) + + def testGetDataWithEqualsUnicode(self): + geometryData = self._runConstraintTest('reportType', '=', u'ETA') + for record in geometryData: + self.assertIn('/ETA/', record.getString('dataURI')) + + # No numeric tests since no numeric identifiers are available. + + def testGetDataWithEqualsNone(self): + geometryData = self._runConstraintTest('reportType', '=', None) + + def testGetDataWithNotEquals(self): + geometryData = self._runConstraintTest('reportType', '!=', 'ETA') + for record in geometryData: + self.assertNotIn('/ETA/', record.getString('dataURI')) + + def testGetDataWithNotEqualsNone(self): + geometryData = self._runConstraintTest('reportType', '!=', None) + + def testGetDataWithGreaterThan(self): + geometryData = self._runConstraintTest('reportType', '>', 'ETA') + + def testGetDataWithLessThan(self): + geometryData = self._runConstraintTest('reportType', '<', 'ETA') + + def testGetDataWithGreaterThanEquals(self): + geometryData = self._runConstraintTest('reportType', '>=', 'ETA') + + def testGetDataWithLessThanEquals(self): + geometryData = self._runConstraintTest('reportType', '<=', 'ETA') + + def testGetDataWithInTuple(self): + collection = ('ETA', 'GFS') + geometryData = self._runConstraintTest('reportType', 'in', collection) + for record in geometryData: + dataURI = record.getString('dataURI') + self.assertTrue('/ETA/' in dataURI or '/GFS/' in dataURI) + + def testGetDataWithInList(self): + collection = ['ETA', 'GFS'] + geometryData = self._runConstraintTest('reportType', 'in', collection) + for record in geometryData: + dataURI = record.getString('dataURI') + self.assertTrue('/ETA/' in dataURI or '/GFS/' in dataURI) + + def testGetDataWithInGenerator(self): + collection = ('ETA', 'GFS') + generator = (item for item in collection) + geometryData = self._runConstraintTest('reportType', 'in', generator) + for record in geometryData: + dataURI = record.getString('dataURI') + self.assertTrue('/ETA/' in dataURI or '/GFS/' in dataURI) + + def testGetDataWithNotInList(self): + collection = ['ETA', 'GFS'] + geometryData = self._runConstraintTest('reportType', 'not in', collection) + for record in geometryData: + dataURI = record.getString('dataURI') + self.assertTrue('/ETA/' not in dataURI and '/GFS/' not in dataURI) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('reportType', 'junk', 'ETA') + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self._runConstraintTest('reportType', '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('reportType', 'in', []) + + def testGetDataWithNestedInConstraintThrowsException(self): + collection = ('ETA', 'GFS', ()) + with self.assertRaises(TypeError): + self._runConstraintTest('reportType', 'in', collection) diff --git a/awips/test/dafTests/testObs.py b/awips/test/dafTests/testObs.py new file mode 100644 index 0000000..125e0db --- /dev/null +++ b/awips/test/dafTests/testObs.py @@ -0,0 +1,169 @@ +## +## + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint + +import baseDafTestCase +import params +import unittest + +# +# Test DAF support for obs data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 06/09/16 5587 bsteffen Add getIdentifierValues tests +# 06/13/16 5574 tgurney Add advanced query tests +# 06/30/16 5725 tgurney Add test for NOT IN +# 12/07/16 5981 tgurney Parameterize +# 12/20/16 5981 tgurney Add envelope test +# +# + + +class ObsTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for obs data""" + + datatype = "obs" + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + self.runLocationsTest(req) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + req.setLocationNames(params.OBS_STATION) + self.runTimesTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.setLocationNames(params.OBS_STATION) + req.setParameters("temperature", "seaLevelPress", "dewpoint") + data = self.runGeometryDataTest(req) + + def testGetGeometryDataWithEnvelope(self): + req = DAL.newDataRequest(self.datatype) + req.setEnvelope(params.ENVELOPE) + req.setParameters("temperature", "seaLevelPress", "dewpoint") + data = self.runGeometryDataTest(req) + for item in data: + self.assertTrue(params.ENVELOPE.contains(item.getGeometry())) + + def testGetIdentifierValues(self): + req = DAL.newDataRequest(self.datatype) + optionalIds = set(DAL.getOptionalIdentifiers(req)) + self.runGetIdValuesTest(optionalIds) + + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() + + def _runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + constraint = RequestConstraint.new(operator, value) + req.setParameters("temperature", "reportType") + req.setLocationNames(params.OBS_STATION) + req.addIdentifier(key, constraint) + return self.runGeometryDataTest(req) + + def testGetDataWithEqualsString(self): + geometryData = self._runConstraintTest('reportType', '=', 'METAR') + for record in geometryData: + self.assertEqual(record.getString('reportType'), 'METAR') + + def testGetDataWithEqualsUnicode(self): + geometryData = self._runConstraintTest('reportType', '=', u'METAR') + for record in geometryData: + self.assertEqual(record.getString('reportType'), 'METAR') + + # No numeric tests since no numeric identifiers are available. + + def testGetDataWithEqualsNone(self): + geometryData = self._runConstraintTest('reportType', '=', None) + for record in geometryData: + self.assertEqual(record.getType('reportType'), 'NULL') + + def testGetDataWithNotEquals(self): + geometryData = self._runConstraintTest('reportType', '!=', 'METAR') + for record in geometryData: + self.assertNotEqual(record.getString('reportType'), 'METAR') + + def testGetDataWithNotEqualsNone(self): + geometryData = self._runConstraintTest('reportType', '!=', None) + for record in geometryData: + self.assertNotEqual(record.getType('reportType'), 'NULL') + + def testGetDataWithGreaterThan(self): + geometryData = self._runConstraintTest('reportType', '>', 'METAR') + for record in geometryData: + self.assertGreater(record.getString('reportType'), 'METAR') + + def testGetDataWithLessThan(self): + geometryData = self._runConstraintTest('reportType', '<', 'METAR') + for record in geometryData: + self.assertLess(record.getString('reportType'), 'METAR') + + def testGetDataWithGreaterThanEquals(self): + geometryData = self._runConstraintTest('reportType', '>=', 'METAR') + for record in geometryData: + self.assertGreaterEqual(record.getString('reportType'), 'METAR') + + def testGetDataWithLessThanEquals(self): + geometryData = self._runConstraintTest('reportType', '<=', 'METAR') + for record in geometryData: + self.assertLessEqual(record.getString('reportType'), 'METAR') + + def testGetDataWithInTuple(self): + collection = ('METAR', 'SPECI') + geometryData = self._runConstraintTest('reportType', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('reportType'), collection) + + def testGetDataWithInList(self): + collection = ['METAR', 'SPECI'] + geometryData = self._runConstraintTest('reportType', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('reportType'), collection) + + def testGetDataWithInGenerator(self): + collection = ('METAR', 'SPECI') + generator = (item for item in collection) + geometryData = self._runConstraintTest('reportType', 'in', generator) + for record in geometryData: + self.assertIn(record.getString('reportType'), collection) + + def testGetDataWithNotInList(self): + collection = ['METAR', 'SPECI'] + geometryData = self._runConstraintTest('reportType', 'not in', collection) + for record in geometryData: + self.assertNotIn(record.getString('reportType'), collection) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('reportType', 'junk', 'METAR') + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self._runConstraintTest('reportType', '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('reportType', 'in', []) + + def testGetDataWithNestedInConstraintThrowsException(self): + collection = ('METAR', 'SPECI', ()) + with self.assertRaises(TypeError): + self._runConstraintTest('reportType', 'in', collection) diff --git a/awips/test/dafTests/testPirep.py b/awips/test/dafTests/testPirep.py new file mode 100644 index 0000000..8d50d02 --- /dev/null +++ b/awips/test/dafTests/testPirep.py @@ -0,0 +1,74 @@ +## +## + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL + +import baseDafTestCase +import params +import unittest + +# +# Test DAF support for pirep data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 12/07/16 5981 tgurney Parameterize +# 12/20/16 5981 tgurney Add envelope test +# +# + + +class PirepTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for pirep data""" + + datatype = "pirep" + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + self.runLocationsTest(req) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + req.setLocationNames(params.AIRPORT) + self.runTimesTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.setLocationNames(params.AIRPORT) + req.setParameters("temperature", "windSpeed", "hazardType", "turbType") + print("Testing getGeometryData()") + geomData = DAL.getGeometryData(req) + self.assertIsNotNone(geomData) + print("Number of geometry records: " + str(len(geomData))) + print("Sample geometry data:") + for record in geomData[:self.sampleDataLimit]: + print("level=", record.getLevel(), end="") + # One dimensional parameters are reported on the 0.0UNKNOWN level. + # 2D parameters are reported on MB levels from pressure. + if record.getLevel() == "0.0UNKNOWN": + print(" temperature=" + record.getString("temperature") + record.getUnit("temperature"), end="") + print(" windSpeed=" + record.getString("windSpeed") + record.getUnit("windSpeed"), end="") + else: + print(" hazardType=" + record.getString("hazardType"), end="") + print(" turbType=" + record.getString("turbType"), end="") + print(" geometry=", record.getGeometry()) + print("getGeometryData() complete\n") + + def testGetGeometryDataWithEnvelope(self): + req = DAL.newDataRequest(self.datatype) + req.setParameters("temperature", "windSpeed", "hazardType", "turbType") + req.setEnvelope(params.ENVELOPE) + print("Testing getGeometryData()") + data = DAL.getGeometryData(req) + for item in data: + self.assertTrue(params.ENVELOPE.contains(item.getGeometry())) diff --git a/awips/test/dafTests/testPracticeWarning.py b/awips/test/dafTests/testPracticeWarning.py new file mode 100644 index 0000000..d9dcde2 --- /dev/null +++ b/awips/test/dafTests/testPracticeWarning.py @@ -0,0 +1,32 @@ +## +## + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL + +import baseDafTestCase +import testWarning + +import unittest + +# +# Test DAF support for practicewarning data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 06/10/16 5548 tgurney Inherit all tests from +# warning +# + + +class PracticeWarningTestCase(testWarning.WarningTestCase): + """Test DAF support for practicewarning data""" + + datatype = "practicewarning" + + # All tests taken from testWarning diff --git a/awips/test/dafTests/testProfiler.py b/awips/test/dafTests/testProfiler.py new file mode 100644 index 0000000..2323e05 --- /dev/null +++ b/awips/test/dafTests/testProfiler.py @@ -0,0 +1,63 @@ +## +## + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL + +import baseDafTestCase +import unittest + +# +# Test DAF support for profiler data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# +# + + +class ProfilerTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for profiler data""" + + datatype = "profiler" + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + self.runLocationsTest(req) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + self.runTimesTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.setParameters("temperature", "pressure", "uComponent", "vComponent") + + print("Testing getGeometryData()") + + geomData = DAL.getGeometryData(req) + self.assertIsNotNone(geomData) + print("Number of geometry records: " + str(len(geomData))) + print("Sample geometry data:") + for record in geomData[:self.sampleDataLimit]: + print("level:", record.getLevel(), end="") + # One dimensional parameters are reported on the 0.0UNKNOWN level. + # 2D parameters are reported on MB levels from pressure. + if record.getLevel() == "0.0UNKNOWN": + print(" temperature=" + record.getString("temperature") + record.getUnit("temperature"), end="") + print(" pressure=" + record.getString("pressure") + record.getUnit("pressure"), end="") + else: + print(" uComponent=" + record.getString("uComponent") + record.getUnit("uComponent"), end="") + print(" vComponent=" + record.getString("vComponent") + record.getUnit("vComponent"), end="") + print(" geometry:", record.getGeometry()) + + print("getGeometryData() complete\n\n") diff --git a/awips/test/dafTests/testRadarGraphics.py b/awips/test/dafTests/testRadarGraphics.py new file mode 100644 index 0000000..6c028e8 --- /dev/null +++ b/awips/test/dafTests/testRadarGraphics.py @@ -0,0 +1,78 @@ +## +## + +import unittest + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint +from awips.dataaccess import DataAccessLayer as DAL + +import baseRadarTestCase +import params + + +# +# Test DAF support for radar graphics data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 08/25/16 2671 tgurney Initial creation. +# 08/31/16 2671 tgurney Add mesocyclone +# 09/08/16 2671 tgurney Add storm track +# 09/27/16 2671 tgurney Add hail index +# 09/30/16 2671 tgurney Add TVS +# 12/07/16 5981 tgurney Parameterize +# 12/19/16 5981 tgurney Do not check data times on +# returned data +# +# +class RadarGraphicsTestCase(baseRadarTestCase.BaseRadarTestCase): + """Test DAF support for radar data""" + + datatype = 'radar' + + def runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + constraint = RequestConstraint.new(operator, value) + req.addIdentifier(key, constraint) + req.setParameters('166') + # TODO: Cannot check datatimes on the result because the times returned + # by getAvailableTimes have level = -1.0, while the time on the actual + # data has the correct level set (>= 0.0). + return self.runGeometryDataTest(req, checkDataTimes=False) + + def testGetGeometryDataMeltingLayer(self): + req = DAL.newDataRequest(self.datatype) + req.setEnvelope(params.ENVELOPE) + req.setLocationNames(self.radarLoc) + req.setParameters('166') + self.runGeometryDataTest(req, checkDataTimes=False) + + def testGetGeometryDataMesocyclone(self): + req = DAL.newDataRequest(self.datatype) + req.setEnvelope(params.ENVELOPE) + req.setLocationNames(self.radarLoc) + req.setParameters('141') + self.runGeometryDataTest(req, checkDataTimes=False) + + def testGetGeometryDataStormTrack(self): + req = DAL.newDataRequest(self.datatype) + req.setEnvelope(params.ENVELOPE) + req.setLocationNames(self.radarLoc) + req.setParameters('58') + self.runGeometryDataTest(req, checkDataTimes=False) + + def testGetGeometryDataHailIndex(self): + req = DAL.newDataRequest(self.datatype) + req.setEnvelope(params.ENVELOPE) + req.setLocationNames(self.radarLoc) + req.setParameters('59') + self.runGeometryDataTest(req, checkDataTimes=False) + + def testGetGeometryDataTVS(self): + req = DAL.newDataRequest(self.datatype) + req.setEnvelope(params.ENVELOPE) + req.setLocationNames(self.radarLoc) + req.setParameters('61') + self.runGeometryDataTest(req, checkDataTimes=False) diff --git a/awips/test/dafTests/testRadarGrid.py b/awips/test/dafTests/testRadarGrid.py new file mode 100644 index 0000000..ba0e4bd --- /dev/null +++ b/awips/test/dafTests/testRadarGrid.py @@ -0,0 +1,44 @@ +## +## + +from awips.dataaccess import DataAccessLayer as DAL +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint + +import baseRadarTestCase +import params +import unittest + +# +# Test DAF support for radar grid data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 08/25/16 2671 tgurney Initial creation +# +# + + +class RadarTestCase(baseRadarTestCase.BaseRadarTestCase): + """Test DAF support for radar data""" + + datatype = 'radar' + + parameterList = ['94'] + + def runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + constraint = RequestConstraint.new(operator, value) + req.addIdentifier(key, constraint) + req.setParameters(*self.parameterList) + # Don't test shapes since they may differ. + return self.runGridDataTest(req, testSameShape=False) + + def testGetGridData(self): + req = DAL.newDataRequest(self.datatype) + req.setEnvelope(params.ENVELOPE) + req.setLocationNames(self.radarLoc) + req.setParameters(*self.parameterList) + # Don't test shapes since they may differ. + self.runGridDataTest(req, testSameShape=False) diff --git a/awips/test/dafTests/testRadarSpatial.py b/awips/test/dafTests/testRadarSpatial.py new file mode 100644 index 0000000..3ef28df --- /dev/null +++ b/awips/test/dafTests/testRadarSpatial.py @@ -0,0 +1,163 @@ +## +## + +from __future__ import print_function +from shapely.geometry import box +from awips.dataaccess import DataAccessLayer as DAL + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint +import baseDafTestCase +import params +import unittest + +# +# Test DAF support for radar_spatial data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 05/26/16 5587 njensen Added testGetIdentifierValues() +# 06/01/16 5587 tgurney Move testIdentifiers() to +# superclass +# 06/13/16 5574 tgurney Add advanced query tests +# 06/30/16 5725 tgurney Add test for NOT IN +# 12/07/16 5981 tgurney Parameterize +# 01/06/17 5981 tgurney Do not check data times +# +# + + +class RadarSpatialTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for radar_spatial data""" + + datatype = "radar_spatial" + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + req.setEnvelope(params.ENVELOPE) + self.runLocationsTest(req) + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + self.runParametersTest(req) + + def testGetIdentifierValues(self): + self.runGetIdValuesTest(['wfo_id']) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.setLocationNames("TORD", "TMDW") + req.setParameters("wfo_id", "name", "elevmeter") + self.runGeometryDataTest(req, checkDataTimes=False) + + def testRequestingTimesThrowsTimeAgnosticDataException(self): + req = DAL.newDataRequest(self.datatype) + self.runTimeAgnosticTest(req) + + def _runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + constraint = RequestConstraint.new(operator, value) + req.addIdentifier(key, constraint) + req.setParameters('elevmeter', 'eqp_elv', 'wfo_id', 'immutablex') + return self.runGeometryDataTest(req, checkDataTimes=False) + + def testGetDataWithEqualsString(self): + geometryData = self._runConstraintTest('wfo_id', '=', params.SITE_ID) + for record in geometryData: + self.assertEqual(record.getString('wfo_id'), params.SITE_ID) + + def testGetDataWithEqualsUnicode(self): + geometryData = self._runConstraintTest('wfo_id', '=', unicode(params.SITE_ID)) + for record in geometryData: + self.assertEqual(record.getString('wfo_id'), params.SITE_ID) + + def testGetDataWithEqualsInt(self): + geometryData = self._runConstraintTest('immutablex', '=', 57) + for record in geometryData: + self.assertEqual(record.getNumber('immutablex'), 57) + + def testGetDataWithEqualsLong(self): + geometryData = self._runConstraintTest('immutablex', '=', 57L) + for record in geometryData: + self.assertEqual(record.getNumber('immutablex'), 57) + + def testGetDataWithEqualsFloat(self): + geometryData = self._runConstraintTest('immutablex', '=', 57.0) + for record in geometryData: + self.assertEqual(round(record.getNumber('immutablex'), 1), 57.0) + + def testGetDataWithEqualsNone(self): + geometryData = self._runConstraintTest('wfo_id', '=', None) + for record in geometryData: + self.assertEqual(record.getType('wfo_id'), 'NULL') + + def testGetDataWithNotEquals(self): + geometryData = self._runConstraintTest('wfo_id', '!=', params.SITE_ID) + for record in geometryData: + self.assertNotEquals(record.getString('wfo_id'), params.SITE_ID) + + def testGetDataWithNotEqualsNone(self): + geometryData = self._runConstraintTest('wfo_id', '!=', None) + for record in geometryData: + self.assertNotEqual(record.getType('wfo_id'), 'NULL') + + def testGetDataWithGreaterThan(self): + geometryData = self._runConstraintTest('elevmeter', '>', 1000) + for record in geometryData: + self.assertGreater(record.getNumber('elevmeter'), 1000) + + def testGetDataWithLessThan(self): + geometryData = self._runConstraintTest('elevmeter', '<', 1000) + for record in geometryData: + self.assertLess(record.getNumber('elevmeter'), 1000) + + def testGetDataWithGreaterThanEquals(self): + geometryData = self._runConstraintTest('eqp_elv', '>=', 1295) + for record in geometryData: + self.assertGreaterEqual(record.getNumber('eqp_elv'), 1295) + + def testGetDataWithLessThanEquals(self): + geometryData = self._runConstraintTest('eqp_elv', '<=', 138) + for record in geometryData: + self.assertLessEqual(record.getNumber('eqp_elv'), 138) + + def testGetDataWithInTuple(self): + collection = (params.SITE_ID, 'GID') + geometryData = self._runConstraintTest('wfo_id', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('wfo_id'), collection) + + def testGetDataWithInList(self): + collection = [params.SITE_ID, 'GID'] + geometryData = self._runConstraintTest('wfo_id', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('wfo_id'), collection) + + def testGetDataWithInGenerator(self): + collection = (params.SITE_ID, 'GID') + generator = (item for item in collection) + geometryData = self._runConstraintTest('wfo_id', 'in', generator) + for record in geometryData: + self.assertIn(record.getString('wfo_id'), collection) + + def testGetDataWithNotInList(self): + collection = [params.SITE_ID, 'GID'] + geometryData = self._runConstraintTest('wfo_id', 'not in', collection) + for record in geometryData: + self.assertNotIn(record.getString('wfo_id'), collection) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('wfo_id', 'junk', params.SITE_ID) + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self._runConstraintTest('wfo_id', '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('wfo_id', 'in', []) diff --git a/awips/test/dafTests/testRequestConstraint.py b/awips/test/dafTests/testRequestConstraint.py new file mode 100644 index 0000000..e7841b8 --- /dev/null +++ b/awips/test/dafTests/testRequestConstraint.py @@ -0,0 +1,228 @@ +## +## + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint + +import unittest + +# +# Unit tests for Python implementation of RequestConstraint +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 07/22/16 2416 tgurney Initial creation +# +# + + +class RequestConstraintTestCase(unittest.TestCase): + + def _newRequestConstraint(self, constraintType, constraintValue): + constraint = RequestConstraint() + constraint.constraintType = constraintType + constraint.constraintValue = constraintValue + return constraint + + def testEvaluateEquals(self): + new = RequestConstraint.new + self.assertTrue(new('=', 3).evaluate(3)) + self.assertTrue(new('=', 3).evaluate('3')) + self.assertTrue(new('=', '3').evaluate(3)) + self.assertTrue(new('=', 12345).evaluate(12345L)) + self.assertTrue(new('=', 'a').evaluate('a')) + self.assertTrue(new('=', 'a').evaluate(u'a')) + self.assertTrue(new('=', 1.0001).evaluate(2.0 - 0.999999)) + self.assertTrue(new('=', 1.00001).evaluate(1)) + self.assertFalse(new('=', 'a').evaluate(['a'])) + self.assertFalse(new('=', 'a').evaluate(['b'])) + self.assertFalse(new('=', 3).evaluate(4)) + self.assertFalse(new('=', 4).evaluate(3)) + self.assertFalse(new('=', 'a').evaluate('z')) + + def testEvaluateNotEquals(self): + new = RequestConstraint.new + self.assertTrue(new('!=', 'a').evaluate(['a'])) + self.assertTrue(new('!=', 'a').evaluate(['b'])) + self.assertTrue(new('!=', 3).evaluate(4)) + self.assertTrue(new('!=', 4).evaluate(3)) + self.assertTrue(new('!=', 'a').evaluate('z')) + self.assertFalse(new('!=', 3).evaluate('3')) + self.assertFalse(new('!=', '3').evaluate(3)) + self.assertFalse(new('!=', 3).evaluate(3)) + self.assertFalse(new('!=', 12345).evaluate(12345L)) + self.assertFalse(new('!=', 'a').evaluate('a')) + self.assertFalse(new('!=', 'a').evaluate(u'a')) + self.assertFalse(new('!=', 1.0001).evaluate(2.0 - 0.9999)) + + def testEvaluateGreaterThan(self): + new = RequestConstraint.new + self.assertTrue(new('>', 1.0001).evaluate(1.0002)) + self.assertTrue(new('>', 'a').evaluate('b')) + self.assertTrue(new('>', 3).evaluate(4)) + self.assertFalse(new('>', 20).evaluate(3)) + self.assertFalse(new('>', 12345).evaluate(12345L)) + self.assertFalse(new('>', 'a').evaluate('a')) + self.assertFalse(new('>', 'z').evaluate('a')) + self.assertFalse(new('>', 4).evaluate(3)) + + def testEvaluateGreaterThanEquals(self): + new = RequestConstraint.new + self.assertTrue(new('>=', 3).evaluate(3)) + self.assertTrue(new('>=', 12345).evaluate(12345L)) + self.assertTrue(new('>=', 'a').evaluate('a')) + self.assertTrue(new('>=', 1.0001).evaluate(1.0002)) + self.assertTrue(new('>=', 'a').evaluate('b')) + self.assertTrue(new('>=', 3).evaluate(20)) + self.assertFalse(new('>=', 1.0001).evaluate(1.0)) + self.assertFalse(new('>=', 'z').evaluate('a')) + self.assertFalse(new('>=', 40).evaluate(3)) + + def testEvaluateLessThan(self): + new = RequestConstraint.new + self.assertTrue(new('<', 'z').evaluate('a')) + self.assertTrue(new('<', 30).evaluate(4)) + self.assertFalse(new('<', 3).evaluate(3)) + self.assertFalse(new('<', 12345).evaluate(12345L)) + self.assertFalse(new('<', 'a').evaluate('a')) + self.assertFalse(new('<', 1.0001).evaluate(1.0002)) + self.assertFalse(new('<', 'a').evaluate('b')) + self.assertFalse(new('<', 3).evaluate(40)) + + def testEvaluateLessThanEquals(self): + new = RequestConstraint.new + self.assertTrue(new('<=', 'z').evaluate('a')) + self.assertTrue(new('<=', 20).evaluate(3)) + self.assertTrue(new('<=', 3).evaluate(3)) + self.assertTrue(new('<=', 12345).evaluate(12345L)) + self.assertTrue(new('<=', 'a').evaluate('a')) + self.assertFalse(new('<=', 1.0001).evaluate(1.0002)) + self.assertFalse(new('<=', 'a').evaluate('b')) + self.assertFalse(new('<=', 4).evaluate(30)) + + def testEvaluateIsNull(self): + new = RequestConstraint.new + self.assertTrue(new('=', None).evaluate(None)) + self.assertTrue(new('=', None).evaluate('null')) + self.assertFalse(new('=', None).evaluate(())) + self.assertFalse(new('=', None).evaluate(0)) + self.assertFalse(new('=', None).evaluate(False)) + + def testEvaluateIsNotNull(self): + new = RequestConstraint.new + self.assertTrue(new('!=', None).evaluate(())) + self.assertTrue(new('!=', None).evaluate(0)) + self.assertTrue(new('!=', None).evaluate(False)) + self.assertFalse(new('!=', None).evaluate(None)) + self.assertFalse(new('!=', None).evaluate('null')) + + def testEvaluateIn(self): + new = RequestConstraint.new + self.assertTrue(new('in', [3]).evaluate(3)) + self.assertTrue(new('in', ['a', 'b', 3]).evaluate(3)) + self.assertTrue(new('in', 'a').evaluate('a')) + self.assertTrue(new('in', [3, 4, 5]).evaluate('5')) + self.assertTrue(new('in', [1.0001, 2, 3]).evaluate(2.0 - 0.9999)) + self.assertFalse(new('in', ['a', 'b', 'c']).evaluate('d')) + self.assertFalse(new('in', 'a').evaluate('b')) + + def testEvaluateNotIn(self): + new = RequestConstraint.new + self.assertTrue(new('not in', ['a', 'b', 'c']).evaluate('d')) + self.assertTrue(new('not in', [3, 4, 5]).evaluate(6)) + self.assertTrue(new('not in', 'a').evaluate('b')) + self.assertFalse(new('not in', [3]).evaluate(3)) + self.assertFalse(new('not in', ['a', 'b', 3]).evaluate(3)) + self.assertFalse(new('not in', 'a').evaluate('a')) + self.assertFalse(new('not in', [1.0001, 2, 3]).evaluate(2.0 - 0.9999)) + + def testEvaluateLike(self): + # cannot make "like" with RequestConstraint.new() + new = self._newRequestConstraint + self.assertTrue(new('LIKE', 'a').evaluate('a')) + self.assertTrue(new('LIKE', 'a%').evaluate('a')) + self.assertTrue(new('LIKE', 'a%').evaluate('abcd')) + self.assertTrue(new('LIKE', '%a').evaluate('a')) + self.assertTrue(new('LIKE', '%a').evaluate('bcda')) + self.assertTrue(new('LIKE', '%').evaluate('')) + self.assertTrue(new('LIKE', '%').evaluate('anything')) + self.assertTrue(new('LIKE', 'a%d').evaluate('ad')) + self.assertTrue(new('LIKE', 'a%d').evaluate('abcd')) + self.assertTrue(new('LIKE', 'aa.()!{[]^%$').evaluate('aa.()!{[]^zzz$')) + self.assertTrue(new('LIKE', 'a__d%').evaluate('abcdefg')) + self.assertFalse(new('LIKE', 'a%').evaluate('b')) + self.assertFalse(new('LIKE', 'a%').evaluate('ba')) + self.assertFalse(new('LIKE', '%a').evaluate('b')) + self.assertFalse(new('LIKE', '%a').evaluate('ab')) + self.assertFalse(new('LIKE', 'a%').evaluate('A')) + self.assertFalse(new('LIKE', 'A%').evaluate('a')) + self.assertFalse(new('LIKE', 'a%d').evaluate('da')) + self.assertFalse(new('LIKE', 'a__d%').evaluate('abccdefg')) + self.assertFalse(new('LIKE', '....').evaluate('aaaa')) + self.assertFalse(new('LIKE', '.*').evaluate('anything')) + + def testEvaluateILike(self): + # cannot make "ilike" with RequestConstraint.new() + new = self._newRequestConstraint + self.assertTrue(new('ILIKE', 'a').evaluate('a')) + self.assertTrue(new('ILIKE', 'a%').evaluate('a')) + self.assertTrue(new('ILIKE', 'a%').evaluate('abcd')) + self.assertTrue(new('ILIKE', '%a').evaluate('a')) + self.assertTrue(new('ILIKE', '%a').evaluate('bcda')) + self.assertTrue(new('ILIKE', '%').evaluate('')) + self.assertTrue(new('ILIKE', '%').evaluate('anything')) + self.assertTrue(new('ILIKE', 'a%d').evaluate('ad')) + self.assertTrue(new('ILIKE', 'a%d').evaluate('abcd')) + self.assertTrue(new('ILIKE', 'a').evaluate('A')) + self.assertTrue(new('ILIKE', 'a%').evaluate('A')) + self.assertTrue(new('ILIKE', 'a%').evaluate('ABCD')) + self.assertTrue(new('ILIKE', '%a').evaluate('A')) + self.assertTrue(new('ILIKE', '%a').evaluate('BCDA')) + self.assertTrue(new('ILIKE', '%').evaluate('')) + self.assertTrue(new('ILIKE', '%').evaluate('anything')) + self.assertTrue(new('ILIKE', 'a%d').evaluate('AD')) + self.assertTrue(new('ILIKE', 'a%d').evaluate('ABCD')) + self.assertTrue(new('ILIKE', 'A').evaluate('a')) + self.assertTrue(new('ILIKE', 'A%').evaluate('a')) + self.assertTrue(new('ILIKE', 'A%').evaluate('abcd')) + self.assertTrue(new('ILIKE', '%A').evaluate('a')) + self.assertTrue(new('ILIKE', '%A').evaluate('bcda')) + self.assertTrue(new('ILIKE', '%').evaluate('')) + self.assertTrue(new('ILIKE', '%').evaluate('anything')) + self.assertTrue(new('ILIKE', 'A%D').evaluate('ad')) + self.assertTrue(new('ILIKE', 'A%D').evaluate('abcd')) + self.assertTrue(new('ILIKE', 'aa.()!{[]^%$').evaluate('AA.()!{[]^zzz$')) + self.assertTrue(new('ILIKE', 'a__d%').evaluate('abcdefg')) + self.assertTrue(new('ILIKE', 'a__d%').evaluate('ABCDEFG')) + self.assertFalse(new('ILIKE', 'a%').evaluate('b')) + self.assertFalse(new('ILIKE', 'a%').evaluate('ba')) + self.assertFalse(new('ILIKE', '%a').evaluate('b')) + self.assertFalse(new('ILIKE', '%a').evaluate('ab')) + self.assertFalse(new('ILIKE', 'a%d').evaluate('da')) + self.assertFalse(new('ILIKE', 'a__d%').evaluate('abccdefg')) + self.assertFalse(new('ILIKE', '....').evaluate('aaaa')) + self.assertFalse(new('ILIKE', '.*').evaluate('anything')) + + def testEvaluateBetween(self): + # cannot make "between" with RequestConstraint.new() + new = self._newRequestConstraint + self.assertTrue(new('BETWEEN', '1--1').evaluate(1)) + self.assertTrue(new('BETWEEN', '1--10').evaluate(1)) + self.assertTrue(new('BETWEEN', '1--10').evaluate(5)) + self.assertTrue(new('BETWEEN', '1--10').evaluate(10)) + self.assertTrue(new('BETWEEN', '1.0--1.1').evaluate(1.0)) + self.assertTrue(new('BETWEEN', '1.0--1.1').evaluate(1.05)) + self.assertTrue(new('BETWEEN', '1.0--1.1').evaluate(1.1)) + self.assertTrue(new('BETWEEN', 'a--x').evaluate('a')) + self.assertTrue(new('BETWEEN', 'a--x').evaluate('j')) + self.assertTrue(new('BETWEEN', 'a--x').evaluate('x')) + self.assertFalse(new('BETWEEN', '1--1').evaluate(2)) + self.assertFalse(new('BETWEEN', '1--2').evaluate(10)) + self.assertFalse(new('BETWEEN', '1--10').evaluate(0)) + self.assertFalse(new('BETWEEN', '1--10').evaluate(11)) + self.assertFalse(new('BETWEEN', '1.0--1.1').evaluate(0.99)) + self.assertFalse(new('BETWEEN', '1.0--1.1').evaluate(1.11)) + self.assertFalse(new('BETWEEN', 'a--x').evaluate(' ')) + self.assertFalse(new('BETWEEN', 'a--x').evaluate('z')) + diff --git a/awips/test/dafTests/testSatellite.py b/awips/test/dafTests/testSatellite.py new file mode 100644 index 0000000..a69cce6 --- /dev/null +++ b/awips/test/dafTests/testSatellite.py @@ -0,0 +1,176 @@ +#!/usr/bin/env python +## +## + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint + +import baseDafTestCase +import unittest + +# +# Test DAF support for satellite data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 04/26/16 5587 tgurney Move identifier values tests +# out of base class +# 06/01/16 5587 tgurney Update testGetIdentifierValues +# 06/07/16 5574 tgurney Add advanced query tests +# 06/13/16 5574 tgurney Typo +# 06/30/16 5725 tgurney Add test for NOT IN +# +# + + +class SatelliteTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for satellite data""" + + datatype = "satellite" + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + self.runLocationsTest(req) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + req.setLocationNames("West CONUS") + self.runTimesTest(req) + + def testGetGridData(self): + req = DAL.newDataRequest(self.datatype) + req.setParameters("Imager 11 micron IR") + req.setLocationNames("West CONUS") + self.runGridDataTest(req) + + def testGetIdentifierValues(self): + req = DAL.newDataRequest(self.datatype) + optionalIds = set(DAL.getOptionalIdentifiers(req)) + requiredIds = set(DAL.getRequiredIdentifiers(req)) + self.runGetIdValuesTest(optionalIds | requiredIds) + + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() + + def _runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + constraint = RequestConstraint.new(operator, value) + req.addIdentifier(key, constraint) + req.setParameters("Imager 11 micron IR") + req.setLocationNames("West CONUS") + return self.runGridDataTest(req) + + def testGetDataWithEqualsString(self): + gridData = self._runConstraintTest('creatingEntity', '=', 'Composite') + for record in gridData: + self.assertEqual(record.getAttribute('creatingEntity'), 'Composite') + + def testGetDataWithEqualsUnicode(self): + gridData = self._runConstraintTest('creatingEntity', '=', u'Composite') + for record in gridData: + self.assertEqual(record.getAttribute('creatingEntity'), 'Composite') + + def testGetDataWithEqualsInt(self): + gridData = self._runConstraintTest('creatingEntity', '=', 1000) + for record in gridData: + self.assertEqual(record.getAttribute('creatingEntity'), 1000) + + def testGetDataWithEqualsLong(self): + gridData = self._runConstraintTest('creatingEntity', '=', 1000L) + for record in gridData: + self.assertEqual(record.getAttribute('creatingEntity'), 1000) + + def testGetDataWithEqualsFloat(self): + gridData = self._runConstraintTest('creatingEntity', '=', 1.0) + for record in gridData: + self.assertEqual(round(record.getAttribute('creatingEntity'), 1), 1.0) + + def testGetDataWithEqualsNone(self): + gridData = self._runConstraintTest('creatingEntity', '=', None) + for record in gridData: + self.assertIsNone(record.getAttribute('creatingEntity')) + + def testGetDataWithNotEquals(self): + gridData = self._runConstraintTest('creatingEntity', '!=', 'Composite') + for record in gridData: + self.assertNotEqual(record.getAttribute('creatingEntity'), 'Composite') + + def testGetDataWithNotEqualsNone(self): + gridData = self._runConstraintTest('creatingEntity', '!=', None) + for record in gridData: + self.assertIsNotNone(record.getAttribute('creatingEntity')) + + def testGetDataWithGreaterThan(self): + gridData = self._runConstraintTest('creatingEntity', '>', 'Composite') + for record in gridData: + self.assertGreater(record.getAttribute('creatingEntity'), 'Composite') + + def testGetDataWithLessThan(self): + gridData = self._runConstraintTest('creatingEntity', '<', 'Composite') + for record in gridData: + self.assertLess(record.getAttribute('creatingEntity'), 'Composite') + + def testGetDataWithGreaterThanEquals(self): + gridData = self._runConstraintTest('creatingEntity', '>=', 'Composite') + for record in gridData: + self.assertGreaterEqual(record.getAttribute('creatingEntity'), 'Composite') + + def testGetDataWithLessThanEquals(self): + gridData = self._runConstraintTest('creatingEntity', '<=', 'Composite') + for record in gridData: + self.assertLessEqual(record.getAttribute('creatingEntity'), 'Composite') + + def testGetDataWithInTuple(self): + collection = ('Composite', 'Miscellaneous') + gridData = self._runConstraintTest('creatingEntity', 'in', collection) + for record in gridData: + self.assertIn(record.getAttribute('creatingEntity'), collection) + + def testGetDataWithInList(self): + collection = ('Composite', 'Miscellaneous') + gridData = self._runConstraintTest('creatingEntity', 'in', collection) + for record in gridData: + self.assertIn(record.getAttribute('creatingEntity'), collection) + + def testGetDataWithInGenerator(self): + collection = ('Composite', 'Miscellaneous') + generator = (item for item in collection) + gridData = self._runConstraintTest('creatingEntity', 'in', generator) + for record in gridData: + self.assertIn(record.getAttribute('creatingEntity'), collection) + + def testGetDataWithNotInList(self): + collection = ('Composite', 'Miscellaneous') + gridData = self._runConstraintTest('creatingEntity', 'not in', collection) + for record in gridData: + self.assertNotIn(record.getAttribute('creatingEntity'), collection) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('creatingEntity', 'junk', 'Composite') + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self._runConstraintTest('creatingEntity', '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('creatingEntity', 'in', []) + + def testGetDataWithNestedInConstraintThrowsException(self): + collection = ('Composite', 'Miscellaneous', ()) + with self.assertRaises(TypeError): + self._runConstraintTest('creatingEntity', 'in', collection) diff --git a/awips/test/dafTests/testSfcObs.py b/awips/test/dafTests/testSfcObs.py new file mode 100644 index 0000000..7be2d90 --- /dev/null +++ b/awips/test/dafTests/testSfcObs.py @@ -0,0 +1,175 @@ +## +## + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint +import baseDafTestCase +import unittest + +# +# Test DAF support for sfcobs data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 06/09/16 5587 bsteffen Add getIdentifierValues tests +# 06/13/16 5574 tgurney Add advanced query tests +# 06/30/16 5725 tgurney Add test for NOT IN +# 01/20/17 6095 tgurney Add null identifiers test +# +# + + +class SfcObsTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for sfcobs data""" + + datatype = "sfcobs" + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + self.runLocationsTest(req) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + req.setLocationNames("14547") + self.runTimesTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.setLocationNames("14547") + req.setParameters("temperature", "seaLevelPress", "dewpoint") + self.runGeometryDataTest(req) + + def testGetGeometryDataNullIdentifiers(self): + req = DAL.newDataRequest(self.datatype) + req.setLocationNames("14547") + req.setParameters("temperature", "seaLevelPress", "dewpoint") + req.identifiers = None + self.runGeometryDataTest(req) + + def testGetIdentifierValues(self): + req = DAL.newDataRequest(self.datatype) + optionalIds = set(DAL.getOptionalIdentifiers(req)) + self.runGetIdValuesTest(optionalIds) + + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() + + def _runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + constraint = RequestConstraint.new(operator, value) + req.addIdentifier(key, constraint) + req.setParameters("temperature", "reportType") + return self.runGeometryDataTest(req) + + def testGetDataWithEqualsString(self): + geometryData = self._runConstraintTest('reportType', '=', '1004') + for record in geometryData: + self.assertEqual(record.getString('reportType'), '1004') + + def testGetDataWithEqualsUnicode(self): + geometryData = self._runConstraintTest('reportType', '=', u'1004') + for record in geometryData: + self.assertEqual(record.getString('reportType'), '1004') + + def testGetDataWithEqualsInt(self): + geometryData = self._runConstraintTest('reportType', '=', 1004) + for record in geometryData: + self.assertEqual(record.getString('reportType'), '1004') + + def testGetDataWithEqualsLong(self): + geometryData = self._runConstraintTest('reportType', '=', 1004L) + for record in geometryData: + self.assertEqual(record.getString('reportType'), '1004') + + # No float test because no float identifiers are available + + def testGetDataWithEqualsNone(self): + geometryData = self._runConstraintTest('reportType', '=', None) + for record in geometryData: + self.assertEqual(record.getType('reportType'), 'NULL') + + def testGetDataWithNotEquals(self): + geometryData = self._runConstraintTest('reportType', '!=', 1004) + for record in geometryData: + self.assertNotEqual(record.getString('reportType'), '1004') + + def testGetDataWithNotEqualsNone(self): + geometryData = self._runConstraintTest('reportType', '!=', None) + for record in geometryData: + self.assertNotEqual(record.getType('reportType'), 'NULL') + + def testGetDataWithGreaterThan(self): + geometryData = self._runConstraintTest('reportType', '>', 1004) + for record in geometryData: + self.assertGreater(record.getString('reportType'), '1004') + + def testGetDataWithLessThan(self): + geometryData = self._runConstraintTest('reportType', '<', 1004) + for record in geometryData: + self.assertLess(record.getString('reportType'), '1004') + + def testGetDataWithGreaterThanEquals(self): + geometryData = self._runConstraintTest('reportType', '>=', 1004) + for record in geometryData: + self.assertGreaterEqual(record.getString('reportType'), '1004') + + def testGetDataWithLessThanEquals(self): + geometryData = self._runConstraintTest('reportType', '<=', 1004) + for record in geometryData: + self.assertLessEqual(record.getString('reportType'), '1004') + + def testGetDataWithInTuple(self): + collection = ('1004', '1005') + geometryData = self._runConstraintTest('reportType', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('reportType'), collection) + + def testGetDataWithInList(self): + collection = ['1004', '1005'] + geometryData = self._runConstraintTest('reportType', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('reportType'), collection) + + def testGetDataWithInGenerator(self): + collection = ('1004', '1005') + generator = (item for item in collection) + geometryData = self._runConstraintTest('reportType', 'in', generator) + for record in geometryData: + self.assertIn(record.getString('reportType'), collection) + + def testGetDataWithNotInList(self): + collection = ['1004', '1005'] + geometryData = self._runConstraintTest('reportType', 'not in', collection) + for record in geometryData: + self.assertNotIn(record.getString('reportType'), collection) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('reportType', 'junk', '1004') + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self._runConstraintTest('reportType', '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('reportType', 'in', []) + + def testGetDataWithNestedInConstraintThrowsException(self): + collection = ('1004', '1005', ()) + with self.assertRaises(TypeError): + self._runConstraintTest('reportType', 'in', collection) diff --git a/awips/test/dafTests/testTopo.py b/awips/test/dafTests/testTopo.py new file mode 100644 index 0000000..8a7032d --- /dev/null +++ b/awips/test/dafTests/testTopo.py @@ -0,0 +1,79 @@ +## +## + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL +from awips.ThriftClient import ThriftRequestException + +import baseDafTestCase +import shapely.geometry +import unittest + +# +# Test DAF support for topo data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 05/26/16 5587 tgurney Add test for +# getIdentifierValues() +# 06/01/16 5587 tgurney Update testGetIdentifierValues +# 07/18/17 6253 randerso Removed referenced to GMTED +# + + +class TopoTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for topo data""" + + datatype = "topo" + + def testGetGridData(self): + print("defaultTopo") + req = DAL.newDataRequest(self.datatype) + req.addIdentifier("group", "/") + req.addIdentifier("dataset", "full") + poly = shapely.geometry.LinearRing(((-70, 40), (-71, 40), (-71, 42), (-70, 42))) + req.setEnvelope(poly) + gridData = DAL.getGridData(req) + self.assertIsNotNone(gridData) + print("Number of grid records: " + str(len(gridData))) + print("Sample grid data shape:\n" + str(gridData[0].getRawData().shape) + "\n") + print("Sample grid data:\n" + str(gridData[0].getRawData()) + "\n") + + for topoFile in ["gtopo30"]: + print("\n" + topoFile) + req.addIdentifier("topoFile", topoFile) + gridData = DAL.getGridData(req) + self.assertIsNotNone(gridData) + print("Number of grid records: " + str(len(gridData))) + print("Sample grid data shape:\n" + str(gridData[0].getRawData().shape) + "\n") + print("Sample grid data:\n" + str(gridData[0].getRawData()) + "\n") + + + def testRequestingTooMuchDataThrowsResponseTooLargeException(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier("group", "/") + req.addIdentifier("dataset", "full") + points = ((-180, 90), (180, 90), (180, -90), (-180, -90)) + poly = shapely.geometry.LinearRing(points) + req.setEnvelope(poly) + + with self.assertRaises(ThriftRequestException) as cm: + DAL.getGridData(req) + self.assertIn('ResponseTooLargeException', str(cm.exception)) + + def testGetIdentifierValues(self): + req = DAL.newDataRequest(self.datatype) + optionalIds = set(DAL.getOptionalIdentifiers(req)) + requiredIds = set(DAL.getRequiredIdentifiers(req)) + self.runGetIdValuesTest(optionalIds | requiredIds) + + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() diff --git a/awips/test/dafTests/testWarning.py b/awips/test/dafTests/testWarning.py new file mode 100644 index 0000000..914178e --- /dev/null +++ b/awips/test/dafTests/testWarning.py @@ -0,0 +1,216 @@ +## +## + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint +import baseDafTestCase +import unittest + +# +# Test DAF support for warning data +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# 04/18/16 5548 tgurney More cleanup +# 04/26/16 5587 tgurney Add identifier values tests +# 06/08/16 5574 tgurney Add advanced query tests +# 06/10/16 5548 tgurney Clean up references to name +# of data type +# 06/13/16 5574 tgurney Fix checks for None +# 06/21/16 5548 tgurney Skip tests that cause errors +# 06/30/16 5725 tgurney Add test for NOT IN +# 12/12/16 5981 tgurney Improve test performance +# +# + + +class WarningTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for warning data""" + + datatype = "warning" + + def _getLocationNames(self): + req = DAL.newDataRequest() + req.setDatatype(self.datatype) + return DAL.getAvailableLocationNames(req) + + def _getAllRecords(self): + req = DAL.newDataRequest() + req.setDatatype(self.datatype) + req.setParameters('id') + return DAL.getGeometryData(req) + + def testGetAvailableParameters(self): + req = DAL.newDataRequest(self.datatype) + self.runParametersTest(req) + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + self.runLocationsTest(req) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + req.setParameters("etn", "wmoid") + self.runTimesTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.setParameters("etn", "wmoid") + self.runGeometryDataTest(req) + + def testFilterOnLocationName(self): + allLocationNames = self._getLocationNames() + if len(allLocationNames) == 0: + errmsg = "No {0} data exists on {1}. Try again with {0} data." + raise unittest.SkipTest(errmsg.format(self.datatype, DAL.THRIFT_HOST)) + testCount = 3 # number of different location names to test + for locationName in allLocationNames[:testCount]: + req = DAL.newDataRequest() + req.setDatatype(self.datatype) + req.setParameters('id') + req.setLocationNames(locationName) + geomData = DAL.getGeometryData(req) + for geom in geomData: + self.assertEqual(geom.getLocationName(), locationName) + + def testFilterOnNonexistentLocationReturnsEmpty(self): + req = DAL.newDataRequest() + req.setDatatype(self.datatype) + req.setParameters('id') + req.setLocationNames('ZZZZ') + self.assertEqual(len(DAL.getGeometryData(req)), 0) + + def testFilterOnInvalidLocationThrowsIncompatibleRequestException(self): + req = DAL.newDataRequest() + req.setDatatype(self.datatype) + req.setParameters('id') + req.setLocationNames(') and 0=1') + with self.assertRaises(Exception) as cm: + DAL.getGeometryData(req) + self.assertIn('IncompatibleRequestException', str(cm.exception)) + + def testGetColumnIdentifierValues(self): + self.runGetIdValuesTest(['act']) + + @unittest.skip('avoid EDEX error') + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + @unittest.skip('avoid EDEX error') + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() + + def _runConstraintTest(self, key, operator, value): + req = DAL.newDataRequest(self.datatype) + constraint = RequestConstraint.new(operator, value) + req.addIdentifier(key, constraint) + req.setParameters("etn", "wmoid", "sig") + return self.runGeometryDataTest(req) + + def testGetDataWithEqualsString(self): + geometryData = self._runConstraintTest('sig', '=', 'Y') + for record in geometryData: + self.assertEqual(record.getString('sig'), 'Y') + + def testGetDataWithEqualsUnicode(self): + geometryData = self._runConstraintTest('sig', '=', u'Y') + for record in geometryData: + self.assertEqual(record.getString('sig'), 'Y') + + def testGetDataWithEqualsInt(self): + geometryData = self._runConstraintTest('etn', '=', 1000) + for record in geometryData: + self.assertEqual(record.getString('etn'), '1000') + + def testGetDataWithEqualsLong(self): + geometryData = self._runConstraintTest('etn', '=', 1000L) + for record in geometryData: + self.assertEqual(record.getString('etn'), '1000') + + def testGetDataWithEqualsFloat(self): + geometryData = self._runConstraintTest('etn', '=', 1.0) + for record in geometryData: + self.assertEqual(round(float(record.getString('etn')), 1), 1.0) + + def testGetDataWithEqualsNone(self): + geometryData = self._runConstraintTest('sig', '=', None) + for record in geometryData: + self.assertEqual(record.getType('sig'), 'NULL') + + def testGetDataWithNotEquals(self): + geometryData = self._runConstraintTest('sig', '!=', 'Y') + for record in geometryData: + self.assertNotEqual(record.getString('sig'), 'Y') + + def testGetDataWithNotEqualsNone(self): + geometryData = self._runConstraintTest('sig', '!=', None) + for record in geometryData: + self.assertNotEqual(record.getType('sig'), 'NULL') + + def testGetDataWithGreaterThan(self): + geometryData = self._runConstraintTest('sig', '>', 'Y') + for record in geometryData: + self.assertGreater(record.getString('sig'), 'Y') + + def testGetDataWithLessThan(self): + geometryData = self._runConstraintTest('sig', '<', 'Y') + for record in geometryData: + self.assertLess(record.getString('sig'), 'Y') + + def testGetDataWithGreaterThanEquals(self): + geometryData = self._runConstraintTest('sig', '>=', 'Y') + for record in geometryData: + self.assertGreaterEqual(record.getString('sig'), 'Y') + + def testGetDataWithLessThanEquals(self): + geometryData = self._runConstraintTest('sig', '<=', 'Y') + for record in geometryData: + self.assertLessEqual(record.getString('sig'), 'Y') + + def testGetDataWithInTuple(self): + collection = ('Y', 'A') + geometryData = self._runConstraintTest('sig', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('sig'), collection) + + def testGetDataWithInList(self): + collection = ['Y', 'A'] + geometryData = self._runConstraintTest('sig', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('sig'), collection) + + def testGetDataWithInGenerator(self): + collection = ('Y', 'A') + generator = (item for item in collection) + geometryData = self._runConstraintTest('sig', 'in', generator) + for record in geometryData: + self.assertIn(record.getString('sig'), collection) + + def testGetDataWithNotInList(self): + collection = ['Y', 'W'] + geometryData = self._runConstraintTest('sig', 'not in', collection) + for record in geometryData: + self.assertNotIn(record.getString('sig'), collection) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('sig', 'junk', 'Y') + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self._runConstraintTest('sig', '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('sig', 'in', []) + + def testGetDataWithNestedInConstraintThrowsException(self): + collection = ('Y', 'A', ()) + with self.assertRaises(TypeError): + self._runConstraintTest('sig', 'in', collection) diff --git a/awips/test/localization/__init__.py b/awips/test/localization/__init__.py new file mode 100644 index 0000000..8df8897 --- /dev/null +++ b/awips/test/localization/__init__.py @@ -0,0 +1,15 @@ +## +## + + +# +# __init__.py for awips.test.localization package +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# --------- -------- --------- -------------------------- +# 08/07/17 5731 bsteffen Initial Creation. + +__all__ = [] diff --git a/awips/test/localization/testLocalizationFileManager.py b/awips/test/localization/testLocalizationFileManager.py new file mode 100644 index 0000000..06cd2f0 --- /dev/null +++ b/awips/test/localization/testLocalizationFileManager.py @@ -0,0 +1,155 @@ +## +## + +# +# Tests for the LocalizationFileManager +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# --------- -------- --------- -------------------------- +# 08/09/17 5731 bsteffen Initial Creation. + +import unittest + +from awips.localization.LocalizationFileManager import (LocalizationFileManager, + LocalizationFileVersionConflictException, + LocalizationContext, + LocalizationFileIsNotDirectoryException, + LocalizationFileDoesNotExistException) + +testFile = "purge/defaultPurgeRules.xml" +testContent = "05-05:05:05" +testDir = "purge/" +testNewFile = "purge/testPurgeRules.xml" + +class ContextTestCase(unittest.TestCase): + def test_eq(self): + c1 = LocalizationContext() + c2 = LocalizationContext() + self.assertEqual(c1,c2) + c3 = LocalizationContext("site", "test") + c4 = LocalizationContext("site", "test") + self.assertEqual(c3,c4) + self.assertNotEqual(c1,c3) + + def test_hash(self): + c1 = LocalizationContext() + c2 = LocalizationContext() + self.assertEqual(hash(c1),hash(c2)) + c3 = LocalizationContext("site", "test") + c4 = LocalizationContext("site", "test") + self.assertEqual(hash(c3),hash(c4)) + +class LFMTestCase(unittest.TestCase): + def setUp(self): + self.manager = LocalizationFileManager() + userFile = self.manager.getSpecific("user", testFile) + if userFile.exists(): + userFile.delete() + newFile = self.manager.getSpecific("user", testNewFile) + if newFile.exists(): + newFile.delete() + def test_gets(self): + startingIncremental = self.manager.getIncremental(testFile) + baseFile = self.manager.getSpecific("base", testFile) + self.assertEqual(baseFile, startingIncremental[0]) + self.assertTrue(baseFile.exists()) + self.assertFalse(baseFile.isDirectory()) + userFile = self.manager.getSpecific("user", testFile) + self.assertFalse(userFile.exists()) + with userFile.open("w") as stream: + stream.write(testContent) + userFile = self.manager.getSpecific("user", testFile) + self.assertTrue(userFile.exists()) + with userFile.open('r') as stream: + self.assertEqual(stream.read(), testContent) + absFile = self.manager.getAbsolute(testFile) + self.assertEqual(absFile, userFile) + endingIncremental = self.manager.getIncremental(testFile) + self.assertEqual(len(startingIncremental) + 1, len(endingIncremental)) + self.assertEqual(userFile, endingIncremental[-1]) + self.assertEqual(baseFile, endingIncremental[0]) + + + userFile.delete() + userFile = self.manager.getSpecific("user", testFile) + self.assertFalse(userFile.exists()) + + def test_concurrent_edit(self): + userFile1 = self.manager.getSpecific("user", testFile) + userFile2 = self.manager.getSpecific("user", testFile) + self.assertFalse(userFile1.exists()) + self.assertFalse(userFile2.exists()) + with self.assertRaises(LocalizationFileVersionConflictException): + with userFile1.open("w") as stream1: + stream1.write(testContent) + with userFile2.open("w") as stream2: + stream2.write(testContent) + + userFile = self.manager.getSpecific("user", testFile) + userFile.delete() + + def test_dir(self): + dir = self.manager.getAbsolute(testDir) + self.assertTrue(dir.isDirectory()) + with self.assertRaises(Exception): + dir.delete() + + def test_list(self): + abs1 = self.manager.listAbsolute(testDir) + inc1 = self.manager.listIncremental(testDir) + self.assertEqual(len(abs1), len(inc1)) + for i in range(len(abs1)): + self.assertEquals(abs1[i], inc1[i][-1]) + + userFile = self.manager.getSpecific("user", testNewFile) + self.assertNotIn(userFile, abs1) + + with userFile.open("w") as stream: + stream.write(testContent) + userFile = self.manager.getSpecific("user", testNewFile) + + + abs2 = self.manager.listAbsolute(testDir) + inc2 = self.manager.listIncremental(testDir) + self.assertEqual(len(abs2), len(inc2)) + for i in range(len(abs2)): + self.assertEquals(abs2[i], inc2[i][-1]) + + self.assertEquals(len(abs1) + 1, len(abs2)) + self.assertIn(userFile, abs2) + + userFile.delete() + + def test_list_file(self): + with self.assertRaises(LocalizationFileIsNotDirectoryException): + self.manager.listIncremental(testFile) + + def test_list_nonexistant(self): + with self.assertRaises(LocalizationFileDoesNotExistException): + self.manager.listIncremental('dontNameYourDirectoryThis') + + def test_root_variants(self): + list1 = self.manager.listAbsolute(".") + list2 = self.manager.listAbsolute("") + list3 = self.manager.listAbsolute("/") + self.assertEquals(list1,list2) + self.assertEquals(list2,list3) + + def test_slashiness(self): + raw = testDir + if raw[0] == '/': + raw = raw[1:] + if raw[-1] == '/': + raw = raw[:-1] + list1 = self.manager.listAbsolute(raw) + list2 = self.manager.listAbsolute(raw + "/") + list3 = self.manager.listAbsolute("/" + raw) + self.assertEquals(list1,list2) + self.assertEquals(list2,list3) + + + +if __name__ == '__main__': + unittest.main() \ No newline at end of file diff --git a/awips/test/localization/testLocalizationRest.py b/awips/test/localization/testLocalizationRest.py new file mode 100644 index 0000000..29c0e8b --- /dev/null +++ b/awips/test/localization/testLocalizationRest.py @@ -0,0 +1,342 @@ +## +## + +import unittest +import urllib2 + +from HTMLParser import HTMLParser +from xml.etree.ElementTree import parse as parseXml +from json import load as loadjson +from urlparse import urljoin +from base64 import b64encode + +# +# Test the localizaiton REST service. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# --------- -------- --------- -------------------------- +# 08/07/17 5731 bsteffen Initial Creation. + +baseURL = "http://localhost:9581/services/localization/" +testSite = "OAX" +testDir = "menus" +testFile = "test.xml" +username = "test" +password = username + +base64string = b64encode('%s:%s' % (username, password)) +authString = "Basic %s" % base64string + +class ValidHTMLParser(HTMLParser): + """Simple HTML parser that performs very minimal validation. + + This ensures that all start and end tags match, and also that there are + some tags. It also accumulates the text of all links in the html file + in the link_texts attribute, which can be used for further validation. + """ + + def __init__(self, testcase): + HTMLParser.__init__(self) + self._testcase = testcase + self._tags = [] + self._any = False + self.link_texts = [] + + def handle_starttag(self, tag, attrs): + self._tags.append(tag) + self._any = True + + def handle_endtag(self, tag): + self._testcase.assertNotEquals([], self._tags, "Unstarted end tag " + tag) + self._testcase.assertEquals(tag, self._tags.pop()) + + def handle_data(self, data): + if self._tags[-1] == "a": + self.link_texts.append(data) + + def close(self): + HTMLParser.close(self) + self._testcase.assertTrue(self._any) + self._testcase.assertEquals([], self._tags) + + + +class AbstractListingTestCase(): + """Base test case for testing listings, retrieves data as html, xml, and json. + + Sub classes should implement assertValidHtml, assertValidXml, and + assertValidJson to ensure that the content returned matches what was + expected. + """ + + def assertRequestGetsHtml(self, request): + response = urllib2.urlopen(request) + self.assertEquals(response.headers["Content-Type"], "text/html") + body = response.read() + parser = ValidHTMLParser(self) + parser.feed(body) + parser.close() + self.assertValidHtml(parser) + + def assertValidHtml(self, parser): + """Intended to be overriden by subclasses to validate HTML content. + + The argument is a populated instance of ValidHTMLParser. + """ + pass + + def test_default(self): + request = urllib2.Request(self.url) + self.assertRequestGetsHtml(request) + + def test_last_slash(self): + if self.url.endswith("/"): + request = urllib2.Request(self.url[:-1]) + else: + request = urllib2.Request(self.url + "/") + self.assertRequestGetsHtml(request) + + def test_wild_mime(self): + request = urllib2.Request(self.url) + request.add_header("Accept", "*/*") + self.assertRequestGetsHtml(request) + request.add_header("Accept", "text/*") + self.assertRequestGetsHtml(request) + + def test_html(self): + request = urllib2.Request(self.url) + request.add_header("Accept", "text/html") + self.assertRequestGetsHtml(request) + + def test_json(self): + request = urllib2.Request(self.url) + request.add_header("Accept", "application/json") + response = urllib2.urlopen(request) + self.assertEquals(response.headers["Content-Type"], "application/json") + jsonData = loadjson(response) + self.assertValidJson(jsonData) + + + def assertValidJson(self, jsonData): + """Intended to be overriden by subclasses to validate JSON content. + + The argument is a python object as returned from json.load + """ + pass + + def test_xml(self): + request = urllib2.Request(self.url) + request.add_header("Accept", "application/xml") + response = urllib2.urlopen(request) + self.assertEquals(response.headers["Content-Type"], "application/xml") + xmlData = parseXml(response) + self.assertValidXml(xmlData) + + def assertValidXml(self, xmlData): + """Intended to be overriden by subclasses to validate XML content. + + The argument is an ElementTree + """ + pass + + def test_delete(self): + request = urllib2.Request(self.url) + request.get_method = lambda: "DELETE" + with self.assertRaises(urllib2.HTTPError) as cm: + response = urllib2.urlopen(request) + self.assertEqual(405, cm.exception.code) + + def test_put(self): + request = urllib2.Request(self.url) + request.get_method = lambda: "PUT" + request.add_data("Test Data") + with self.assertRaises(urllib2.HTTPError) as cm: + response = urllib2.urlopen(request) + self.assertEqual(405, cm.exception.code) + + def test_unacceptable(self): + request = urllib2.Request(self.url) + request.add_header("Accept", "application/fakemimetype") + with self.assertRaises(urllib2.HTTPError) as cm: + response = urllib2.urlopen(request) + self.assertEqual(406, cm.exception.code) + request.add_header("Accept", "fakemimetype/*") + with self.assertRaises(urllib2.HTTPError) as cm: + response = urllib2.urlopen(request) + self.assertEqual(406, cm.exception.code) + + def test_accept_quality_factor(self): + request = urllib2.Request(self.url) + request.add_header("Accept", "application/xml; q=0.8, application/json; q=0.2") + response = urllib2.urlopen(request) + self.assertEquals(response.headers["Content-Type"], "application/xml") + xmlData = parseXml(response) + self.assertValidXml(xmlData) + + request.add_header("Accept", "application/xml; q=0.2, application/json; q=0.8") + response = urllib2.urlopen(request) + self.assertEquals(response.headers["Content-Type"], "application/json") + jsonData = loadjson(response) + self.assertValidJson(jsonData) + + request.add_header("Accept", "application/xml, application/json; q=0.8") + response = urllib2.urlopen(request) + self.assertEquals(response.headers["Content-Type"], "application/xml") + xmlData = parseXml(response) + self.assertValidXml(xmlData) + + request.add_header("Accept", "application/fakemimetype, application/json; q=0.8") + response = urllib2.urlopen(request) + self.assertEquals(response.headers["Content-Type"], "application/json") + jsonData = loadjson(response) + self.assertValidJson(jsonData) + +class RootTestCase(AbstractListingTestCase, unittest.TestCase): + """Test that the root of the localization service returns listing of localization types.""" + def setUp(self): + self.url = baseURL + def assertValidHtml(self, parser): + self.assertIn("common_static/", parser.link_texts) + def assertValidJson(self, jsonData): + self.assertIn("common_static/", jsonData) + def assertValidXml(self, xmlData): + root = xmlData.getroot() + self.assertEquals(root.tag, "entries") + names = [e.text for e in root.findall("entry")] + self.assertIn("common_static/", names) + +class TypeTestCase(AbstractListingTestCase, unittest.TestCase): + """Test that common_static will list context levels.""" + def setUp(self): + self.url = urljoin(baseURL, "common_static/") + def assertValidHtml(self, parser): + self.assertIn("base/", parser.link_texts) + self.assertIn("site/", parser.link_texts) + def assertValidJson(self, jsonData): + self.assertIn("base/", jsonData) + self.assertIn("site/", jsonData) + def assertValidXml(self, xmlData): + root = xmlData.getroot() + self.assertEquals(root.tag, "entries") + names = [e.text for e in root.findall("entry")] + self.assertIn("base/", names) + self.assertIn("site/", names) + +class LevelTestCase(AbstractListingTestCase, unittest.TestCase): + """Test that common_static/site will list sites.""" + def setUp(self): + self.url = urljoin(baseURL, "common_static/site/") + def assertValidHtml(self, parser): + self.assertIn(testSite +"/", parser.link_texts) + def assertValidJson(self, jsonData): + self.assertIn(testSite +"/", jsonData) + def assertValidXml(self, xmlData): + root = xmlData.getroot() + self.assertEquals(root.tag, "entries") + names = [e.text for e in root.findall("entry")] + self.assertIn(testSite +"/", names) + +class AbstractFileListingTestCase(AbstractListingTestCase): + """Base test case for a file listing""" + + def assertValidHtml(self, parser): + self.assertIn(testDir +"/", parser.link_texts) + self.assertEquals(parser.link_texts, sorted(parser.link_texts)) + def assertValidJson(self, jsonData): + self.assertIn(testDir +"/", jsonData) + def assertValidXml(self, xmlData): + root = xmlData.getroot() + self.assertEquals(root.tag, "files") + names = [e.get("name") for e in root.findall("file")] + self.assertIn(testDir +"/", names) + self.assertEquals(names, sorted(names)) + +class BaseFileListingTestCase(AbstractFileListingTestCase, unittest.TestCase): + """Test that common_static/base lists files""" + def setUp(self): + self.url = urljoin(baseURL, "common_static/base/") + +class SiteFileListingTestCase(AbstractFileListingTestCase, unittest.TestCase): + """Test that common_static/site// lists files""" + def setUp(self): + self.url = urljoin(baseURL, "common_static/site/" + testSite + "/") + +class FileTestCase(unittest.TestCase): + """Test retrieval, modification and deletion of an individual.""" + def setUp(self): + self.url = urljoin(baseURL, "common_static/user/" + username + "/" + testFile) + # The file should not exist before the test, but if it does then delete it + # This is some of the same functionality we are testing so if setup fails + # then the test would probably fail anyway + try: + request = urllib2.Request(self.url) + response = urllib2.urlopen(request) + request = urllib2.Request(self.url) + request.get_method = lambda: "DELETE" + request.add_header("Authorization", authString) + request.add_header("If-Match", response.headers["Content-MD5"]) + response = urllib2.urlopen(request) + except urllib2.HTTPError as e: + if e.code != 404: + raise e + def test_file_operations(self): + """Run through a typical set of file interactions and verify everything works correctly.""" + request = urllib2.Request(self.url) + request.get_method = lambda: "PUT" + request.add_data("Test Data") + with self.assertRaises(urllib2.HTTPError) as cm: + response = urllib2.urlopen(request) + self.assertEqual(401, cm.exception.code) + + request.add_header("Authorization", authString) + with self.assertRaises(urllib2.HTTPError) as cm: + response = urllib2.urlopen(request) + self.assertEqual(409, cm.exception.code) + + request.add_header("If-Match", "NON_EXISTENT_CHECKSUM") + response = urllib2.urlopen(request) + + + request = urllib2.Request(self.url) + response = urllib2.urlopen(request) + self.assertEquals(response.read(), "Test Data") + + request = urllib2.Request(self.url + "/") + response = urllib2.urlopen(request) + self.assertEquals(response.read(), "Test Data") + + request = urllib2.Request(self.url) + request.get_method = lambda: "PUT" + request.add_data("Test Data2") + request.add_header("If-Match", response.headers["Content-MD5"]) + request.add_header("Authorization", authString) + response = urllib2.urlopen(request) + checksum = response.headers["Content-MD5"] + + request = urllib2.Request(self.url) + response = urllib2.urlopen(request) + self.assertEquals(response.read(), "Test Data2") + + request = urllib2.Request(self.url) + request.get_method = lambda: "DELETE" + with self.assertRaises(urllib2.HTTPError) as cm: + response = urllib2.urlopen(request) + self.assertEqual(401, cm.exception.code) + + request.add_header("Authorization", authString) + with self.assertRaises(urllib2.HTTPError) as cm: + response = urllib2.urlopen(request) + self.assertEqual(409, cm.exception.code) + + request.add_header("If-Match", checksum) + response = urllib2.urlopen(request) + + request = urllib2.Request(self.url) + with self.assertRaises(urllib2.HTTPError) as cm: + response = urllib2.urlopen(request) + self.assertEqual(404, cm.exception.code) + +if __name__ == '__main__': + unittest.main() diff --git a/awips/test/testQpidTimeToLive.py b/awips/test/testQpidTimeToLive.py new file mode 100644 index 0000000..20e0266 --- /dev/null +++ b/awips/test/testQpidTimeToLive.py @@ -0,0 +1,87 @@ +## +## + +# +# +# +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 03/09/11 njensen Initial Creation. +# 08/15/13 2169 bkowal Decompress data read from the queue +# +# +# + +import time, sys +import threading + +import dynamicserialize + +TIME_TO_SLEEP = 300 + +class ListenThread(threading.Thread): + + def __init__(self, hostname, portNumber, topicName): + self.hostname = hostname + self.portNumber = portNumber + self.topicName = topicName + self.nMessagesReceived = 0 + self.waitSecond = 0 + self.stopped = False + threading.Thread.__init__(self) + + def run(self): + from awips import QpidSubscriber + self.qs = QpidSubscriber.QpidSubscriber(self.hostname, self.portNumber, True) + self.qs.topicSubscribe(self.topicName, self.receivedMessage) + + def receivedMessage(self, msg): + print "Received message" + self.nMessagesReceived += 1 + if self.waitSecond == 0: + fmsg = open('/tmp/rawMessage', 'w') + fmsg.write(msg) + fmsg.close() + + while self.waitSecond < TIME_TO_SLEEP and not self.stopped: + if self.waitSecond % 60 == 0: + print time.strftime('%H:%M:%S'), "Sleeping and stuck in not so infinite while loop" + self.waitSecond += 1 + time.sleep(1) + + print time.strftime('%H:%M:%S'), "Received", self.nMessagesReceived, "messages" + + def stop(self): + print "Stopping" + self.stopped = True + self.qs.close() + + + +def main(): + print "Starting up at", time.strftime('%H:%M:%S') + + topic = 'edex.alerts' + host = 'localhost' + port = 5672 + + thread = ListenThread(host, port, topic) + try: + thread.start() + while True: + time.sleep(3) + except KeyboardInterrupt: + pass + finally: + thread.stop() + + +if __name__ == '__main__': + main() + + + diff --git a/dataaccess/acars-common-dataaccess.xml b/dataaccess/acars-common-dataaccess.xml new file mode 100644 index 0000000..2a0d6bd --- /dev/null +++ b/dataaccess/acars-common-dataaccess.xml @@ -0,0 +1,12 @@ + + + + + + + + + + \ No newline at end of file diff --git a/dataaccess/binlightning-common-dataaccess.xml b/dataaccess/binlightning-common-dataaccess.xml new file mode 100644 index 0000000..a116b2a --- /dev/null +++ b/dataaccess/binlightning-common-dataaccess.xml @@ -0,0 +1,12 @@ + + + + + + + + + + \ No newline at end of file diff --git a/dataaccess/bufrmos-common-dataaccess.xml b/dataaccess/bufrmos-common-dataaccess.xml new file mode 100644 index 0000000..0004cfb --- /dev/null +++ b/dataaccess/bufrmos-common-dataaccess.xml @@ -0,0 +1,41 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/dataaccess/bufrua-common-dataaccess.xml b/dataaccess/bufrua-common-dataaccess.xml new file mode 100644 index 0000000..ed742ab --- /dev/null +++ b/dataaccess/bufrua-common-dataaccess.xml @@ -0,0 +1,82 @@ + + + + + + + + + + + prMan + htMan + tpMan + tdMan + wdMan + wsMan + + + + + + + + + + + prTrop + tpTrop + tdTrop + wdTrop + wsTrop + + + + + + + + + + + prMaxW + wdMaxW + wsMaxW + + + + + + + + + + + prSigT + tpSigT + tdSigT + + + + + + + + + + + htSigW + wdSigW + wsSigW + + + + + + + + + + \ No newline at end of file diff --git a/dataaccess/climate-common-dataaccess.xml b/dataaccess/climate-common-dataaccess.xml new file mode 100644 index 0000000..3bdff14 --- /dev/null +++ b/dataaccess/climate-common-dataaccess.xml @@ -0,0 +1,11 @@ + + + + + + + + + \ No newline at end of file diff --git a/dataaccess/ldadmesonet-common-dataaccess.xml b/dataaccess/ldadmesonet-common-dataaccess.xml new file mode 100644 index 0000000..8d28ae6 --- /dev/null +++ b/dataaccess/ldadmesonet-common-dataaccess.xml @@ -0,0 +1,12 @@ + + + + + + + + + + \ No newline at end of file diff --git a/dataaccess/modelsounding-common-dataaccess.xml b/dataaccess/modelsounding-common-dataaccess.xml new file mode 100644 index 0000000..b7bee9e --- /dev/null +++ b/dataaccess/modelsounding-common-dataaccess.xml @@ -0,0 +1,29 @@ + + + + + + + + + + + + + + + + pressure + temperature + specHum + omega + uComp + vComp + cldCvr + + + + + \ No newline at end of file diff --git a/dataaccess/obs-common-dataaccess.xml b/dataaccess/obs-common-dataaccess.xml new file mode 100644 index 0000000..0900973 --- /dev/null +++ b/dataaccess/obs-common-dataaccess.xml @@ -0,0 +1,39 @@ + + + + + + + + + + + + skyCover + skyLayerBase + skyCoverType + skyCoverGenus + + + + + + + + + + + + presWeather + + + + + + + + + + diff --git a/dataaccess/profiler-common-dataaccess.xml b/dataaccess/profiler-common-dataaccess.xml new file mode 100644 index 0000000..e123267 --- /dev/null +++ b/dataaccess/profiler-common-dataaccess.xml @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + height + uComponent + vComponent + HorizSpStdDev + wComponent + VertSpStdDev + peakPower + levelMode + uvQualityCode + consensusNum + + + + + \ No newline at end of file diff --git a/dataaccess/sfcobs-common-dataaccess.xml b/dataaccess/sfcobs-common-dataaccess.xml new file mode 100644 index 0000000..795f932 --- /dev/null +++ b/dataaccess/sfcobs-common-dataaccess.xml @@ -0,0 +1,12 @@ + + + + + + + + + + \ No newline at end of file diff --git a/dataaccess/warning-common-dataaccess.xml b/dataaccess/warning-common-dataaccess.xml new file mode 100644 index 0000000..352b72b --- /dev/null +++ b/dataaccess/warning-common-dataaccess.xml @@ -0,0 +1,17 @@ + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..8a7dac0 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,218 @@ +# Makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +PAPER = +BUILDDIR = build +NBCONVERT = ipython nbconvert + +# User-friendly check for sphinx-build +ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) +$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/) +endif + +# Internal variables. +PAPEROPT_a4 = -D latex_paper_size=a4 +PAPEROPT_letter = -D latex_paper_size=letter +ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source +# the i18n builder cannot share the environment and doctrees with the others +I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source + +.PHONY: help +help: + @echo "Please use \`make ' where is one of" + @echo " html to make standalone HTML files" + @echo " dirhtml to make HTML files named index.html in directories" + @echo " singlehtml to make a single large HTML file" + @echo " pickle to make pickle files" + @echo " json to make JSON files" + @echo " htmlhelp to make HTML files and a HTML help project" + @echo " qthelp to make HTML files and a qthelp project" + @echo " applehelp to make an Apple Help Book" + @echo " devhelp to make HTML files and a Devhelp project" + @echo " epub to make an epub" + @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" + @echo " latexpdf to make LaTeX files and run them through pdflatex" + @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx" + @echo " text to make text files" + @echo " man to make manual pages" + @echo " texinfo to make Texinfo files" + @echo " info to make Texinfo files and run them through makeinfo" + @echo " gettext to make PO message catalogs" + @echo " changes to make an overview of all changed/added/deprecated items" + @echo " xml to make Docutils-native XML files" + @echo " pseudoxml to make pseudoxml-XML files for display purposes" + @echo " linkcheck to check all external links for integrity" + @echo " doctest to run all doctests embedded in the documentation (if enabled)" + @echo " coverage to run coverage check of the documentation (if enabled)" + +.PHONY: clean +clean: + rm -rf $(BUILDDIR)/* source/examples/generated/* + +.PHONY: html +html: + make clean + $(SPHINXBUILD) -vb html $(ALLSPHINXOPTS) $(BUILDDIR)/html + @echo + @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." + +.PHONY: dirhtml +dirhtml: + $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml + @echo + @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." + +.PHONY: singlehtml +singlehtml: + $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml + @echo + @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." + +.PHONY: pickle +pickle: + $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle + @echo + @echo "Build finished; now you can process the pickle files." + +.PHONY: json +json: + $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json + @echo + @echo "Build finished; now you can process the JSON files." + +.PHONY: htmlhelp +htmlhelp: + $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp + @echo + @echo "Build finished; now you can run HTML Help Workshop with the" \ + ".hhp project file in $(BUILDDIR)/htmlhelp." + +.PHONY: qthelp +qthelp: + $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp + @echo + @echo "Build finished; now you can run "qcollectiongenerator" with the" \ + ".qhcp project file in $(BUILDDIR)/qthelp, like this:" + @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/python-awips.qhcp" + @echo "To view the help file:" + @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/python-awips.qhc" + +.PHONY: applehelp +applehelp: + $(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp + @echo + @echo "Build finished. The help book is in $(BUILDDIR)/applehelp." + @echo "N.B. You won't be able to view it unless you put it in" \ + "~/Library/Documentation/Help or install it in your application" \ + "bundle." + +.PHONY: devhelp +devhelp: + $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp + @echo + @echo "Build finished." + @echo "To view the help file:" + @echo "# mkdir -p $$HOME/.local/share/devhelp/python-awips" + @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/python-awips" + @echo "# devhelp" + +.PHONY: epub +epub: + $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub + @echo + @echo "Build finished. The epub file is in $(BUILDDIR)/epub." + +.PHONY: latex +latex: + $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex + @echo + @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." + @echo "Run \`make' in that directory to run these through (pdf)latex" \ + "(use \`make latexpdf' here to do that automatically)." + +.PHONY: latexpdf +latexpdf: + $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex + @echo "Running LaTeX files through pdflatex..." + $(MAKE) -C $(BUILDDIR)/latex all-pdf + @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." + +.PHONY: latexpdfja +latexpdfja: + $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex + @echo "Running LaTeX files through platex and dvipdfmx..." + $(MAKE) -C $(BUILDDIR)/latex all-pdf-ja + @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." + +.PHONY: text +text: + $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text + @echo + @echo "Build finished. The text files are in $(BUILDDIR)/text." + +.PHONY: man +man: + $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man + @echo + @echo "Build finished. The manual pages are in $(BUILDDIR)/man." + +.PHONY: texinfo +texinfo: + $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo + @echo + @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." + @echo "Run \`make' in that directory to run these through makeinfo" \ + "(use \`make info' here to do that automatically)." + +.PHONY: info +info: + $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo + @echo "Running Texinfo files through makeinfo..." + make -C $(BUILDDIR)/texinfo info + @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." + +.PHONY: gettext +gettext: + $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale + @echo + @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." + +.PHONY: changes +changes: + $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes + @echo + @echo "The overview file is in $(BUILDDIR)/changes." + +.PHONY: linkcheck +linkcheck: + $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck + @echo + @echo "Link check complete; look for any errors in the above output " \ + "or in $(BUILDDIR)/linkcheck/output.txt." + +.PHONY: doctest +doctest: + $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest + @echo "Testing of doctests in the sources finished, look at the " \ + "results in $(BUILDDIR)/doctest/output.txt." + +.PHONY: coverage +coverage: + $(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage + @echo "Testing of coverage in the sources finished, look at the " \ + "results in $(BUILDDIR)/coverage/python.txt." + +.PHONY: xml +xml: + $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml + @echo + @echo "Build finished. The XML files are in $(BUILDDIR)/xml." + +.PHONY: pseudoxml +pseudoxml: + $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml + @echo + @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..f0361d4 --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,263 @@ +@ECHO OFF + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set BUILDDIR=build +set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% source +set I18NSPHINXOPTS=%SPHINXOPTS% source +if NOT "%PAPER%" == "" ( + set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% + set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS% +) + +if "%1" == "" goto help + +if "%1" == "help" ( + :help + echo.Please use `make ^` where ^ is one of + echo. html to make standalone HTML files + echo. dirhtml to make HTML files named index.html in directories + echo. singlehtml to make a single large HTML file + echo. pickle to make pickle files + echo. json to make JSON files + echo. htmlhelp to make HTML files and a HTML help project + echo. qthelp to make HTML files and a qthelp project + echo. devhelp to make HTML files and a Devhelp project + echo. epub to make an epub + echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter + echo. text to make text files + echo. man to make manual pages + echo. texinfo to make Texinfo files + echo. gettext to make PO message catalogs + echo. changes to make an overview over all changed/added/deprecated items + echo. xml to make Docutils-native XML files + echo. pseudoxml to make pseudoxml-XML files for display purposes + echo. linkcheck to check all external links for integrity + echo. doctest to run all doctests embedded in the documentation if enabled + echo. coverage to run coverage check of the documentation if enabled + goto end +) + +if "%1" == "clean" ( + for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i + del /q /s %BUILDDIR%\* + goto end +) + + +REM Check if sphinx-build is available and fallback to Python version if any +%SPHINXBUILD% 1>NUL 2>NUL +if errorlevel 9009 goto sphinx_python +goto sphinx_ok + +:sphinx_python + +set SPHINXBUILD=python -m sphinx.__init__ +%SPHINXBUILD% 2> nul +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +:sphinx_ok + + +if "%1" == "html" ( + %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The HTML pages are in %BUILDDIR%/html. + goto end +) + +if "%1" == "dirhtml" ( + %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. + goto end +) + +if "%1" == "singlehtml" ( + %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml. + goto end +) + +if "%1" == "pickle" ( + %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; now you can process the pickle files. + goto end +) + +if "%1" == "json" ( + %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; now you can process the JSON files. + goto end +) + +if "%1" == "htmlhelp" ( + %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; now you can run HTML Help Workshop with the ^ +.hhp project file in %BUILDDIR%/htmlhelp. + goto end +) + +if "%1" == "qthelp" ( + %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; now you can run "qcollectiongenerator" with the ^ +.qhcp project file in %BUILDDIR%/qthelp, like this: + echo.^> qcollectiongenerator %BUILDDIR%\qthelp\python-awips.qhcp + echo.To view the help file: + echo.^> assistant -collectionFile %BUILDDIR%\qthelp\python-awips.ghc + goto end +) + +if "%1" == "devhelp" ( + %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. + goto end +) + +if "%1" == "epub" ( + %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The epub file is in %BUILDDIR%/epub. + goto end +) + +if "%1" == "latex" ( + %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. + goto end +) + +if "%1" == "latexpdf" ( + %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex + cd %BUILDDIR%/latex + make all-pdf + cd %~dp0 + echo. + echo.Build finished; the PDF files are in %BUILDDIR%/latex. + goto end +) + +if "%1" == "latexpdfja" ( + %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex + cd %BUILDDIR%/latex + make all-pdf-ja + cd %~dp0 + echo. + echo.Build finished; the PDF files are in %BUILDDIR%/latex. + goto end +) + +if "%1" == "text" ( + %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The text files are in %BUILDDIR%/text. + goto end +) + +if "%1" == "man" ( + %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The manual pages are in %BUILDDIR%/man. + goto end +) + +if "%1" == "texinfo" ( + %SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo. + goto end +) + +if "%1" == "gettext" ( + %SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The message catalogs are in %BUILDDIR%/locale. + goto end +) + +if "%1" == "changes" ( + %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes + if errorlevel 1 exit /b 1 + echo. + echo.The overview file is in %BUILDDIR%/changes. + goto end +) + +if "%1" == "linkcheck" ( + %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck + if errorlevel 1 exit /b 1 + echo. + echo.Link check complete; look for any errors in the above output ^ +or in %BUILDDIR%/linkcheck/output.txt. + goto end +) + +if "%1" == "doctest" ( + %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest + if errorlevel 1 exit /b 1 + echo. + echo.Testing of doctests in the sources finished, look at the ^ +results in %BUILDDIR%/doctest/output.txt. + goto end +) + +if "%1" == "coverage" ( + %SPHINXBUILD% -b coverage %ALLSPHINXOPTS% %BUILDDIR%/coverage + if errorlevel 1 exit /b 1 + echo. + echo.Testing of coverage in the sources finished, look at the ^ +results in %BUILDDIR%/coverage/python.txt. + goto end +) + +if "%1" == "xml" ( + %SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The XML files are in %BUILDDIR%/xml. + goto end +) + +if "%1" == "pseudoxml" ( + %SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml. + goto end +) + +:end diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000..cc6ca2a --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,4 @@ +sphinx>=1.3 +nbconvert>=4.1 +enum34 +jupyter diff --git a/docs/source/about.rst b/docs/source/about.rst new file mode 100644 index 0000000..835373f --- /dev/null +++ b/docs/source/about.rst @@ -0,0 +1,181 @@ +=================== +About Unidata AWIPS +=================== + +AWIPS is a weather forecasting display and analysis package being +developed by the National Weather Service and Raytheon. AWIPS is a +Java application consisting of a data-rendering client (CAVE, which runs +on Red Hat/CentOS Linux and Mac OS X) and a backend data server (EDEX, +which runs only on Linux) + +AWIPS takes a unified approach to data ingest, and most data types +follow a standard path through the system. At a high level, data flow +describes the path taken by a piece of data from its source to its +display by a client system. This path starts with data requested and +stored by an `LDM <#ldm>`_ client and includes the decoding of the data +and storing of decoded data in a form readable and displayable by the +end user. + +The AWIPS ingest and request processes are a highly distributed +system, and the messaging broken `Qpid <#qpid>`_ is used for +inter-process communication. + +.. figure:: http://www.unidata.ucar.edu/software/awips2/images/awips2_coms.png + :align: center + :alt: image + + image + +License +------- + +The AWIPS software package released by the Unidata Program Center is considered to +be in the public domain since it is released without proprietary code. As such, export +controls do not apply.  Any person is free to download, modify, distribute, or share +Unidata AWIPS in any form. Entities who modify or re-distribute Unidata AWIPS +software are encouraged to conduct their own FOSS/COTS entitlement/license review +to ensure that they remain compatible with the associated terms (see +FOSS_COTS_License.pdf at `https://github.com/Unidata/awips2 `_). + + +About AWIPS +----------- + +The primary AWIPS application for data ingest, processing, and +storage is the Environmental Data EXchange (**EDEX**) server; the +primary AWIPS application for visualization/data manipulation is the +Common AWIPS Visualization Environment (**CAVE**) client, which is +typically installed on a workstation separate from other AWIPS +components. + +In addition to programs developed specifically for AWIPS, AWIPS uses +several commercial off-the-shelf (COTS) and Free or Open Source software +(FOSS) products to assist in its operation. The following components, +working together and communicating, compose the entire AWIPS system. + +EDEX +---- + +The main server for AWIPS. Qpid sends alerts to EDEX when data stored +by the LDM is ready for processing. These Qpid messages include file +header information which allows EDEX to determine the appropriate data +decoder to use. The default ingest server (simply named ingest) handles +all data ingest other than grib messages, which are processed by a +separate ingestGrib server. After decoding, EDEX writes metadata to the +database via Postgres and saves the processed data in HDF5 via PyPIES. A +third EDEX server, request, feeds requested data to CAVE clients. EDEX +ingest and request servers are started and stopped with the commands +``edex start`` and ``edex stop``, which runs the system script +``/etc/rc.d/init.d/edex_camel`` + +CAVE +---- + +Common AWIPS Visualization Environment. The data rendering and +visualization tool for AWIPS. CAVE contains of a number of different +data display configurations called perspectives. Perspectives used in +operational forecasting environments include **D2D** (Display +Two-Dimensional), **GFE** (Graphical Forecast Editor), and **NCP** +(National Centers Perspective). CAVE is started with the command +``/awips2/cave/cave.sh`` or ``cave.sh`` + +.. figure:: http://www.unidata.ucar.edu/software/awips2/images/Unidata_AWIPS2_CAVE.png + :align: center + :alt: CAVE + + CAVE + +Alertviz +-------- + +**Alertviz** is a modernized version of an AWIPS I application, designed +to present various notifications, error messages, and alarms to the user +(forecaster). AlertViz can be executed either independently or from CAVE +itself. In the Unidata CAVE client, Alertviz is run within CAVE and is +not required to be run separately. The toolbar is also **hidden from +view** and is accessed by right-click on the desktop taskbar icon. + +LDM +--- + +`http://www.unidata.ucar.edu/software/ldm/ `_ + +The **LDM** (Local Data Manager), developed and supported by Unidata, is +a suite of client and server programs designed for data distribution, +and is the fundamental component comprising the Unidata Internet Data +Distribution (IDD) system. In AWIPS, the LDM provides data feeds for +grids, surface observations, upper-air profiles, satellite and radar +imagery and various other meteorological datasets. The LDM writes data +directly to file and alerts EDEX via Qpid when a file is available for +processing. The LDM is started and stopped with the commands +``edex start`` and ``edex stop``, which runs the commands +``service edex_ldm start`` and ``service edex_ldm stop`` + +edexBridge +---------- + +edexBridge, invoked in the LDM configuration file +``/awips2/ldm/etc/ldmd.conf``, is used by the LDM to post "data +available" messaged to Qpid, which alerts the EDEX Ingest server that a +file is ready for processing. + +Qpid +---- + +`http://qpid.apache.org `_ + +**Apache Qpid**, the Queue Processor Interface Daemon, is the messaging +system used by AWIPS to facilitate communication between services. +When the LDM receives a data file to be processed, it employs +**edexBridge** to send EDEX ingest servers a message via Qpid. When EDEX +has finished decoding the file, it sends CAVE a message via Qpid that +data are available for display or further processing. Qpid is started +and stopped by ``edex start`` and ``edex stop``, and is controlled by +the system script ``/etc/rc.d/init.d/qpidd`` + +PostgreSQL +---------- + +`http://www.postgresql.org `_ + +**PostgreSQL**, known simply as Postgres, is a relational database +management system (DBMS) which handles the storage and retrieval of +metadata, database tables and some decoded data. The storage and reading +of EDEX metadata is handled by the Postgres DBMS. Users may query the +metadata tables by using the termainal-based front-end for Postgres +called **psql**. Postgres is started and stopped by ``edex start`` and +``edex stop``, and is controlled by the system script +``/etc/rc.d/init.d/edex_postgres`` + +HDF5 +---- + +`http://www.hdfgroup.org/HDF5/ `_ + +**Hierarchical Data Format (v.5)** is +the primary data storage format used by AWIPS for processed grids, +satellite and radar imagery and other products. Similar to netCDF, +developed and supported by Unidata, HDF5 supports multiple types of data +within a single file. For example, a single HDF5 file of radar data may +contain multiple volume scans of base reflectivity and base velocity as +well as derived products such as composite reflectivity. The file may +also contain data from multiple radars. HDF5 is stored in +``/awips2/edex/data/hdf5/`` + +PyPIES (httpd-pypies) +--------------------- + +**PyPIES**, Python Process Isolated Enhanced Storage, was created for +AWIPS to isolate the management of HDF5 Processed Data Storage from +the EDEX processes. PyPIES manages access, i.e., reads and writes, of +data in the HDF5 files. In a sense, PyPIES provides functionality +similar to a DBMS (i.e PostgreSQL for metadata); all data being written +to an HDF5 file is sent to PyPIES, and requests for data stored in HDF5 +are processed by PyPIES. + +PyPIES is implemented in two parts: 1. The PyPIES manager is a Python +application that runs as part of an Apache HTTP server, and handles +requests to store and retrieve data. 2. The PyPIES logger is a Python +process that coordinates logging. PyPIES is started and stopped by +``edex start`` and ``edex stop``, and is controlled by the system script +``/etc/rc.d/init.d/https-pypies`` diff --git a/docs/source/api/DataAccessLayer.rst b/docs/source/api/DataAccessLayer.rst new file mode 100644 index 0000000..e000038 --- /dev/null +++ b/docs/source/api/DataAccessLayer.rst @@ -0,0 +1,7 @@ +=============== +DataAccessLayer +=============== + +.. automodule:: awips.dataaccess.DataAccessLayer + :members: + :undoc-members: diff --git a/docs/source/api/DateTimeConverter.rst b/docs/source/api/DateTimeConverter.rst new file mode 100644 index 0000000..75949fc --- /dev/null +++ b/docs/source/api/DateTimeConverter.rst @@ -0,0 +1,7 @@ +================= +DateTimeConverter +================= + +.. automodule:: awips.DateTimeConverter + :members: + :undoc-members: diff --git a/docs/source/api/IDataRequest.rst b/docs/source/api/IDataRequest.rst new file mode 100644 index 0000000..d818819 --- /dev/null +++ b/docs/source/api/IDataRequest.rst @@ -0,0 +1,7 @@ +=============================== +IDataRequest (newDataRequest()) +=============================== + +.. autoclass:: awips.dataaccess.IDataRequest + :members: + :special-members: diff --git a/docs/source/api/PyData.rst b/docs/source/api/PyData.rst new file mode 100644 index 0000000..4097955 --- /dev/null +++ b/docs/source/api/PyData.rst @@ -0,0 +1,7 @@ +====================== +PyData +====================== + +.. automodule:: awips.dataaccess.PyData + :members: + :undoc-members: diff --git a/docs/source/api/PyGeometryData.rst b/docs/source/api/PyGeometryData.rst new file mode 100644 index 0000000..24ac092 --- /dev/null +++ b/docs/source/api/PyGeometryData.rst @@ -0,0 +1,7 @@ +====================== +PyGeometryData +====================== + +.. automodule:: awips.dataaccess.PyGeometryData + :members: + :undoc-members: diff --git a/docs/source/api/PyGridData.rst b/docs/source/api/PyGridData.rst new file mode 100644 index 0000000..860e7b9 --- /dev/null +++ b/docs/source/api/PyGridData.rst @@ -0,0 +1,7 @@ +====================== +PyGridData +====================== + +.. automodule:: awips.dataaccess.PyGridData + :members: + :undoc-members: diff --git a/docs/source/api/RadarCommon.rst b/docs/source/api/RadarCommon.rst new file mode 100644 index 0000000..4d0509e --- /dev/null +++ b/docs/source/api/RadarCommon.rst @@ -0,0 +1,7 @@ +====================== +RadarCommon +====================== + +.. automodule:: awips.RadarCommon + :members: + :undoc-members: diff --git a/docs/source/api/SoundingsSupport.rst b/docs/source/api/SoundingsSupport.rst new file mode 100644 index 0000000..527a1da --- /dev/null +++ b/docs/source/api/SoundingsSupport.rst @@ -0,0 +1,7 @@ +====================== +SoundingsSupport +====================== + +.. automodule:: awips.dataaccess.SoundingsSupport + :members: + :undoc-members: diff --git a/docs/source/api/ThriftClientRouter.rst b/docs/source/api/ThriftClientRouter.rst new file mode 100644 index 0000000..d65f137 --- /dev/null +++ b/docs/source/api/ThriftClientRouter.rst @@ -0,0 +1,7 @@ +====================== +ThriftClientRouter +====================== + +.. automodule:: awips.dataaccess.ThriftClientRouter + :members: + :undoc-members: diff --git a/docs/source/api/index.rst b/docs/source/api/index.rst new file mode 100644 index 0000000..c18d4aa --- /dev/null +++ b/docs/source/api/index.rst @@ -0,0 +1,18 @@ +################# +API Documentation +################# + +.. toctree:: + :maxdepth: 2 + + DataAccessLayer + IDataRequest + PyData + PyGridData + PyGeometryData + SoundingsSupport + ThriftClientRouter + RadarCommon + DateTimeConverter + +* :ref:`genindex` diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 0000000..8d61aad --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,303 @@ +# -*- coding: utf-8 -*- +# +# python-awips documentation build configuration file, created by +# sphinx-quickstart on Tue Mar 15 15:59:23 2016. +# +# This file is execfile()d with the current directory set to its +# containing dir. +# +# Note that not all possible configuration values are present in this +# autogenerated file. +# +# All configuration values have a default; values that are commented out +# serve to show the default. + +import sys +import os + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +#sys.path.insert(0, os.path.abspath('.')) + +# -- General configuration ------------------------------------------------ + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +sys.path.insert(0, os.path.abspath('.')) +sys.path.insert(0, os.path.abspath('../..')) + +# If your documentation needs a minimal Sphinx version, state it here. +#needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.intersphinx', + 'sphinx.ext.viewcode', + 'notebook_gen_sphinxext' +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# source_suffix = ['.rst', '.md'] +source_suffix = '.rst' + +# The encoding of source files. +#source_encoding = 'utf-8-sig' + +# The master toctree document. +master_doc = 'index' + +# General information about the project. +project = 'python-awips' +copyright = '2016, Unidata' +author = 'Unidata' + +# The version info for the project you're documenting, acts as replacement for +# |version| and |release|, also used in various other places throughout the +# built documents. +# +# The short X.Y version. +version = '0.9.12' +# The full version, including alpha/beta/rc tags. + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# There are two options for replacing |today|: either, you set today to some +# non-false value, then it is used: +#today = '' +# Else, today_fmt is used as the format for a strftime call. +#today_fmt = '%B %d, %Y' + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +exclude_patterns = [] + +# The reST default role (used for this markup: `text`) to use for all +# documents. +#default_role = None + +# If true, '()' will be appended to :func: etc. cross-reference text. +#add_function_parentheses = True + +# If true, the current module name will be prepended to all description +# unit titles (such as .. function::). +#add_module_names = True + +# If true, sectionauthor and moduleauthor directives will be shown in the +# output. They are ignored by default. +#show_authors = False + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = 'sphinx' + +# A list of ignored prefixes for module index sorting. +#modindex_common_prefix = [] + +# If true, keep warnings as "system message" paragraphs in the built documents. +#keep_warnings = False + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = False + + +# -- Options for HTML output ---------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +#html_theme = 'alabaster' +html_theme = 'sphinx_rtd_theme' +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +#html_theme_options = {} + +# Add any paths that contain custom themes here, relative to this directory. +#html_theme_path = [] + +# The name for this set of Sphinx documents. If None, it defaults to +# " v documentation". +#html_title = None + +# A shorter title for the navigation bar. Default is the same as html_title. +#html_short_title = None + +# The name of an image file (relative to this directory) to place at the top +# of the sidebar. +#html_logo = None + +# The name of an image file (relative to this directory) to use as a favicon of +# the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 +# pixels large. +#html_favicon = None + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + +# Add any extra paths that contain custom files (such as robots.txt or +# .htaccess) here, relative to this directory. These files are copied +# directly to the root of the documentation. +#html_extra_path = [] + +# If not '', a 'Last updated on:' timestamp is inserted at every page bottom, +# using the given strftime format. +#html_last_updated_fmt = '%b %d, %Y' + +# If true, SmartyPants will be used to convert quotes and dashes to +# typographically correct entities. +#html_use_smartypants = True + +# Custom sidebar templates, maps document names to template names. +#html_sidebars = {} + +# Additional templates that should be rendered to pages, maps page names to +# template names. +#html_additional_pages = {} + +# If false, no module index is generated. +#html_domain_indices = True + +# If false, no index is generated. +#html_use_index = True + +# If true, the index is split into individual pages for each letter. +#html_split_index = False + +# If true, links to the reST sources are added to the pages. +#html_show_sourcelink = True + +# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. +#html_show_sphinx = True + +# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. +#html_show_copyright = True + +# If true, an OpenSearch description file will be output, and all pages will +# contain a tag referring to it. The value of this option must be the +# base URL from which the finished HTML is served. +#html_use_opensearch = '' + +# This is the file name suffix for HTML files (e.g. ".xhtml"). +#html_file_suffix = None + +# Language to be used for generating the HTML full-text search index. +# Sphinx supports the following languages: +# 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' +# 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr' +#html_search_language = 'en' + +# A dictionary with options for the search language support, empty by default. +# Now only 'ja' uses this config value +#html_search_options = {'type': 'default'} + +# The name of a javascript file (relative to the configuration directory) that +# implements a search results scorer. If empty, the default will be used. +#html_search_scorer = 'scorer.js' + +# Output file base name for HTML help builder. +htmlhelp_basename = 'python-awipsdoc' + +# -- Options for LaTeX output --------------------------------------------- + +latex_elements = { +# The paper size ('letterpaper' or 'a4paper'). +#'papersize': 'letterpaper', + +# The font size ('10pt', '11pt' or '12pt'). +#'pointsize': '10pt', + +# Additional stuff for the LaTeX preamble. +#'preamble': '', + +# Latex figure (float) alignment +#'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, 'python-awips.tex', 'python-awips Documentation', + 'Unidata', 'manual'), +] + +# The name of an image file (relative to this directory) to place at the top of +# the title page. +#latex_logo = None + +# For "manual" documents, if this is true, then toplevel headings are parts, +# not chapters. +#latex_use_parts = False + +# If true, show page references after internal links. +#latex_show_pagerefs = False + +# If true, show URL addresses after external links. +#latex_show_urls = False + +# Documents to append as an appendix to all manuals. +#latex_appendices = [] + +# If false, no module index is generated. +#latex_domain_indices = True + + +# -- Options for manual page output --------------------------------------- + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + (master_doc, 'python-awips', 'python-awips Documentation', + [author], 1) +] + +# If true, show URL addresses after external links. +#man_show_urls = False + + +# -- Options for Texinfo output ------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'python-awips', 'python-awips Documentation', + author, 'python-awips', 'One line description of project.', + 'Miscellaneous'), +] + +# Documents to append as an appendix to all manuals. +#texinfo_appendices = [] + +# If false, no module index is generated. +#texinfo_domain_indices = True + +# How to display URL addresses: 'footnote', 'no', or 'inline'. +#texinfo_show_urls = 'footnote' + +# If true, do not generate a @detailmenu in the "Top" node's menu. +#texinfo_no_detailmenu = False + +# Set up mapping for other projects' docs +intersphinx_mapping = { + 'matplotlib': ('http://matplotlib.org/', None), + 'metpy': ('http://docs.scipy.org/doc/metpy/', None), + 'numpy': ('http://docs.scipy.org/doc/numpy/', None), + 'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None), + 'pint': ('http://pint.readthedocs.org/en/stable/', None), + 'python': ('http://docs.python.org', None) + } diff --git a/docs/source/dev.rst b/docs/source/dev.rst new file mode 100644 index 0000000..e01cd5a --- /dev/null +++ b/docs/source/dev.rst @@ -0,0 +1,654 @@ + +Development Guide +================= + +The Data Access Framework allows developers to retrieve different types +of data without having dependencies on those types of data. It provides +a single, unified data type that can be customized by individual +implementing plug-ins to provide full functionality pertinent to each +data type. + +Writing a New Factory +--------------------- + +Factories will most often be written in a dataplugin, but should always +be written in a common plug-in. This will allow for clean dependencies +from both CAVE and EDEX. + +A new plug-in’s data access class must implement IDataFactory. For ease +of use, abstract classes have been created to combine similar methods. +Data factories do not have to implement both types of data (grid and +geometry). They can if they choose, but if they choose not to, they +should do the following: + +:: + + throw new UnsupportedOutputTypeException(request.getDatatype(), "grid"); + +This lets the code know that grid type is not supported for this data +factory. Depending on where the data is coming from, helpers have been +written to make writing a new data type factory easier. For example, +PluginDataObjects can use AbstractDataPluginFactory as a start and not +have to create everything from scratch. + +Each data type is allowed to implement retrieval in any manner that is +felt necessary. The power of the framework means that the code +retrieving data does not have to know anything of the underlying +retrieval methods, only that it is getting data in a certain manner. To +see some examples of ways to retrieve data, reference +**SatelliteGridFactory** and **RadarGridFactory**. + +Methods required for implementation: + +**public DataTime[] getAvailableTimes(IDataRequest request)** + +- This method returns an array of DataTime objects corresponding to + what times are available for the data being retrieved, based on the + parameters and identifiers being passed in. + +**public DataTime[] getAvailableTimes(IDataRequest request, BinOffset +binOffset)** + +- This method returns available times as above, only with a bin offset + applied. + +Note: Both of the preceding methods can throw TimeAgnosticDataException +exceptions if times do not apply to the data type. + +**public IGridData[] getGridData(IDataRequest request, +DataTime...times)** + +- This method returns IGridData objects (an array) based on the request + and times to request for. There can be multiple times or a single + time. + +**public IGridData[] getGridData(IDataRequest request, TimeRange +range)** + +- Similar to the preceding method, this returns IGridData objects based + on a range of times. + +**public IGeometryData[] getGeometryData(IDataRequest request, DataTime +times)** + +- This method returns IGeometryData objects based on a request and + times. + +**public IGeometryData[] getGeometryData(IDataRequest request, TimeRange +range)** + +- Like the preceding method, this method returns IGeometryData objects + based on a range of times. + +**public String[] getAvailableLocationNames(IDataRequest request)** + +- This method returns location names that match the request. If this + does not apply to the data type, an IncompatibleRequestException + should be thrown. + +Registering the Factory with the Framework +------------------------------------------ + +The following needs to be added in a spring file in the plug-in that +contains the new factory: + +:: + + + + + + + +This takes the RadarGridFactory and registers it with the registry and +allows it to be used any time the code makes a request for the data type +“radar.” + +Retrieving Data Using the Factory +--------------------------------- + +For ease of use and more diverse use, there are multiple interfaces into +the Data Access Layer. Currently, there is a Python implementation and a +Java implementation, which have very similar method calls and work in a +similar manner. Plug-ins that want to use the data access framework to +retrieve data should include **com.raytheon.uf.common.dataaccess** as a +Required Bundle in their MANIFEST.MF. + +To retrieve data using the Python interface : + +:: + + from awips.dataaccess import DataAccessLayer + req = DataAccessLayer.newDataRequest() + req.setDatatype("grid") + req.setParameters("T") + req.setLevels("2FHAG") + req.addIdentifier("info.datasetId", "GFS40") + times = DataAccessLayer.getAvailableTimes(req) + data = DataAccessLayer.getGridData(req, times) + +To retrieve data using the Java interface : + +:: + + IDataRequest req = DataAccessLayer.newDataRequest(); + req.setDatatype("grid"); + req.setParameters("T"); + req.setLevels("2FHAG"); + req.addIdentifier("info.datasetId", "GFS40"); + DataTime[] times = DataAccessLayer.getAvailableTimes(req) + IData data = DataAccessLayer.getGridData(req, times); + +**newDataRequest()** + +- This creates a new data request. Most often this is a + DefaultDataRequest, but saves for future implentations as well. + +**setDatatype(String)** + +- This is the data type being retrieved. This can be found as the value + that is registered when creating the new factory (See section above + **Registering the Factory with the Framework** [radar in that case]). + +**setParameters(String...)** + +- This can differ depending on data type. It is most often used as a + main difference between products. + +**setLevels(String...)** + +- This is often used to identify the same products on different + mathematical angles, heights, levels, etc. + +**addIdentifier(String, String)** + +- This differs based on data type, but is often used for more + fine-tuned querying. + +Both methods return a similar set of data and can be manipulated by +their respective languages. See DataAccessLayer.py and +DataAccessLayer.java for more methods that can be called to retrieve +data and different parts of the data. Because each data type has +different parameters, levels, and identifiers, it is best to see the +actual data type for the available options. If it is undocumented, then +the best way to identify what parameters are to be used is to reference +the code. + +Development Background +---------------------- + +In support of Hazard Services Raytheon Technical Services is building a +generic data access framework that can be called via JAVA or Python. The +data access framework code can be found within the AWIPS Baseline in + +:: + + com.raytheon.uf.common.dataaccess + +As of 2016, plugins have been written for grid, radar, satellite, Hydro +(SHEF), point data (METAR, SYNOP, Profiler, ACARS, AIREP, PIREP), maps +data, and other data types. The Factories for each can be found in the +following packages (you may need to look at the development baseline to +see these): + +:: + + com.raytheon.uf.common.dataplugin.grid.dataaccess + com.raytheon.uf.common.dataplugin.radar.dataaccess + com.raytheon.uf.common.dataplugin.satellite.dataaccess + com.raytheon.uf.common.dataplugin.binlightning.dataaccess + com.raytheon.uf.common.dataplugin.sfc.dataaccess + com.raytheon.uf.common.dataplugin.sfcobs.dataaccess + com.raytheon.uf.common.dataplugin.acars.dataaccess + com.raytheon.uf.common.dataplugin.ffmp.dataaccess + com.raytheon.uf.common.dataplugin.bufrua.dataaccess + com.raytheon.uf.common.dataplugin.profiler.dataaccess + com.raytheon.uf.common.dataplugin.moddelsounding.dataaccess + com.raytheon.uf.common.dataplugin.ldadmesonet.dataaccess + com.raytheon.uf.common.dataplugin.binlightning.dataaccess + com.raytheon.uf.common.dataplugin.gfe.dataaccess + com.raytheon.uf.common.hydro.dataaccess + com.raytheon.uf.common.pointdata.dataaccess + com.raytheon.uf.common.dataplugin.maps.dataaccess + +Additional data types may be added in the future. To determine what +datatypes are supported display the "type hierarchy" associated with the +classes + +**AbstractGridDataPluginFactory**, + +**AbstractGeometryDatabaseFactory**, and + +**AbstractGeometryTimeAgnosticDatabaseFactory**. + +The following content was taken from the design review document which is +attached and modified slightly. + +Design/Implementation +--------------------- + +The Data Access Framework is designed to provide a consistent interface +for requesting and using geospatial data within CAVE or EDEX. Examples +of geospatial data are grids, satellite, radar, metars, maps, river gage +heights, FFMP basin data, airmets, etc. To allow for convenient use of +geospatial data, the framework will support two types of requests: grids +and geometries (points, polygons, etc). The framework will also hide +implementation details of specific data types from users, making it +easier to use data without worrying about how the data objects are +structured or retrieved. + +A suggested mapping of some current data types to one of the two +supported data requests is listed below. This list is not definitive and +can be expanded. If a developer can dream up an interpretation of the +data in the other supported request type, that support can be added. + +Grids + +- Grib +- Satellite +- Radar +- GFE + +Geometries + +- Map (states, counties, zones, etc) +- Hydro DB (IHFS) +- Obs (metar) +- FFMP +- Hazard +- Warning +- CCFP +- Airmet + +The framework is designed around the concept of each data type plugin +contributing the necessary code for the framework to support its data. +For example, the satellite plugin provides a factory class for +interacting with the framework and registers itself as being compatible +with the Data Access Framework. This concept is similar to how EDEX in +AWIPS expects a plugin developer to provide a decoder class and +record class and register them, but then automatically manages the rest +of the ingest process including routing, storing, and alerting on new +data. This style of plugin architecture effectively enables the +framework to expand its capabilities to more data types without having +to alter the framework code itself. This will enable software developers +to incrementally add support for more data types as time allows, and +allow the framework to expand to new data types as they become +available. + +The Data Access Framework will not break any existing functionality or +APIs, and there are no plans to retrofit existing cosde to use the new +API at this time. Ideally code will be retrofitted in the future to +improve ease of maintainability. The plugin pecific code that hooks into +the framework will make use of existing APIs such as **IDataStore** and +**IServerRequest** to complete the requests. + +The Data Access Framework can be understood as three parts: + +- How users of the framework retrieve and use the data +- How plugin developers contribute support for new data types +- How the framework works when it receives a request + +How users of the framework retrieve and use the data +---------------------------------------------------- + +When a user of the framework wishes to request data, they must +instantiate a request object and set some of the values on that request. +Two request interfaces will be supported, for detailed methods see +section "Detailed Code" below. + +**IDataRequest** + +**IGridRequest** extends **IDataRequest** + +**IGeometryRequest** extends **IDataRequest** + +For the request interfaces, default implementations of +**DefaultGridRequest** and **DefaultGeometryRequest** will be provided +to handle most cases. However, the use of interfaces allows for custom +special cases in the future. If necessary, the developer of a plugin can +write their own custom request implementation to handle a special case. + +After the request object has been prepared, the user will pass it to the +Data Access Layer to receive a data object in return. See the "Detailed +Code" section below for detailed methods of the Data Access Layer. The +Data Access Layer will return one of two data interfaces. + +**IData** + +**IGridData** extends **IData** + +**IGeometryData** extends **IData** + +For the data interfaces, the use of interfaces effectively hides the +implementation details of specific data types from the user of the +framework. For example, the user receives an **IGridData** and knows the +data time, grid geometry, parameter, and level, but does not know that +the data is actually a **GFEGridData** vs **D2DGridData** vs +**SatelliteGridData**. This enables users of the framework to write +generic code that can support multiple data types. + +For python users of the framework, the interfaces will be very similar +with a few key distinctions. Geometries will be represented by python +geometries from the open source Shapely project. For grids, the python +**IGridData** will have a method for requesting the raw data as a numpy +array, and the Data Access Layer will have methods for requesting the +latitude coordinates and the longitude coordinates of grids as numpy +arrays. The python requests and data objects will be pure python and not +JEP PyJObjects that wrap Java objects. A future goal of the Data Access +Framework is to provide support to python local apps and therefore +enable requests of data outside of CAVE and EDEX to go through the same +familiar interfaces. This goal is out of scope for this project but by +making the request and returned data objects pure python it will not be +a huge undertaking to add this support in the future. + +How plugin developers contribute support for new datatypes +---------------------------------------------------------- + +When a developer wishes to add support for another data type to the +framework, they must implement one or both of the factory interfaces +within a common plugin. Two factory interfaces will be supported, for +detailed methods see below. + +**IDataFactory** + +**IGridFactory** extends **IDataFactory** + +**IGeometryFactory** extends **IDataFactory** + +For some data types, it may be desired to add support for both types of +requests. For example, the developer of grid data may want to provide +support for both grid requests and geometry requests. In this case the +developer would write two separate classes where one implements +**IGridFactory** and the other implements **IGeometryFactory**. +Furthermore, factories could be stacked on top of one another by having +factory implementations call into the Data Access Layer. + +For example, a custom factory keyed to "derived" could be written for +derived parameters, and the implementation of that factory may then call +into the Data Access Layer to retrieve “grid” data. In this example the +raw data would be retrieved through the **GridDataFactory** while the +derived factory then applies the calculations before returning the data. + +Implementations do not need to support all methods on the interfaces or +all values on the request objects. For example, a developer writing the +**MapGeometryFactory** does not need to support **getAvailableTimes()** +because map data such as US counties is time agnostic. In this case the +method should throw **UnsupportedOperationException** and the javadoc +will indicate this. + +Another example would be the developer writing **ObsGeometryFactory** +can ignore the Level field of the **IDataRequest** as there are not +different levels of metar data, it is all at the surface. It is up to +the factory writer to determine which methods and fields to support and +which to ignore, but the factory writer should always code the factory +with the user requesting data in mind. If a user of the framework could +reasonably expect certain behavior from the framework based on the +request, the factory writer should implement support for that behavior. + +Abstract factories will be provided and can be extended to reduce the +amount of code a factory developer has to write to complete some common +actions that will be used by multiple factories. The factory should be +capable of working within either CAVE or EDEX, therefore all of its +server specific actions (e.g. database queries) should go through the +Request/Handler API by using **IServerRequests**. CAVE can then send the +**IServerRequests** to EDEX with **ThriftClient** while EDEX can use the +**ServerRequestRouter** to process the **IServerRequests**, making the +code compatible regardless of which JVM it is running inside. + +Once the factory code is written, it must be registered with the +framework as an available factory. This will be done through spring xml +in a common plugin, with the xml file inside the res/spring folder of +the plugin. Registering the factory will identify the datatype name that +must match what users would use as the datatype on the **IDataRequest**, +e.g. the word "satellite". Registering the factory also indicates to the +framework what request types are supported, i.e. grid vs geometry or +both. + +An example of the spring xml for a satellite factory is provided below: + +:: + + + + + + + + + +How the framework works when it receives a request +-------------------------------------------------- + +**IDataRequest** requires a datatype to be set on every request. The +framework will have a registry of existing factories for each data type +(grid and geometry). When the Data Access Layer methods are called, it +will first lookup in the registry for the factory that corresponds to +the datatype on the **IDataRequest**. If no corresponding factory is +found, it will throw an exception with a useful error message that +indicates there is no current support for that datatype request. If a +factory is found, it will delegate the processing of the request to the +factory. The factory will receive the request and process it, returning +the result back to the Data Access Layer which then returns it to the +caller. + +By going through the Data Access Layer, the user is able to retrieve the +data and use it without understanding which factory was used, how the +factory retrieved the data, or what implementation of data was returned. +This effectively frees the framework and users of the framework from any +dependencies on any particular data types. Since these dependencies are +avoided, the specific **IDataFactory** and **IData** implementations can +be altered in the future if necessary and the code making use of the +framework will not need to be changed as long as the interfaces continue +to be met. + +Essentially, the Data Access Framework is a service that provides data +in a consistent way, with the service capabilities being expanded by +plugin developers who write support for more data types. Note that the +framework itself is useless without plugins contributing and registering +**IDataFactories**. Once the framework is coded, developers will need to +be tasked to add the factories necessary to support the needed data +types. + +Request interfaces +------------------ + +Requests and returned data interfaces will exist in both Java and +Python. The Java interfaces are listed below and the Python interfaces +will match the Java interfaces except where noted. Factories will only +be written in Java. + +**IDataRequest** + +- **void setDatatype(String datatype)** - the datatype name and + also the key to which factory will be used. Frequently pluginName + such as radar, satellite, gfe, ffmp, etc + +- **void addIdentifier(String key, Object value)** - an identifier the + factory can use to determine which data to return, e.g. for grib data + key "modelName" and value “GFS40” + +- **void setParameters(String... params)** + +- **void setLevels(Level... levels)** + +- **String getDatatype()** + +- **Map getIdentifiers()** + +- **String[] getParameters()** + +- **Level[] getLevels()** + +- Python Differences + +- **Levels** will be represented as **Strings** + +**IGridRequest extends IDataRequest** + +- **void setStorageRequest(Request request)** - a datastorage request + that allows for slab, line, and point requests for faster performance + and less data retrieval + +- **Request getStorageRequest()** + +- Python Differences + +- No support for storage requests + +**IGeometryRequest extends IDataRequest** + +- **void setEnvelope(Envelope env)** - a bounding box envelope to limit + the data that is searched through and returned. Not all factories may + support this. + +- **setLocationNames(String... locationNames)** - a convenience of + requesting data by names such as ICAOs, airports, stationIDs, etc + +- **Envelope getEnvelope()** + +- **String[] getLocationNames()** + +- Python Differences + +- Envelope methods will use a **shapely.geometry.Polygon** instead of + **Envelopes** (shapely has no concept of envelopes and considers them + as rectangular polygons) + +Data Interfaces +~~~~~~~~~~~~~~~ + +**IData** + +- **Object getAttribute(String key)** - **getAttribute** provides a way + to get at attributes of the data that the interface does not provide, + allowing the user to get more info about the data without adding + dependencies on the specific data type plugin + +- **DataTime getDataTime()** - some data may return null (e.g. maps) + +- **Level getLevel()** - some data may return null + +- Python Differences + +- **Levels** will be represented by **Strings** + +**IGridData extends IData** + +- **String getParameter()** + +- **GridGeometry2D getGridGeometry()** + +- **Unit getUnit()** - some data may return null + +- **DataDestination populateData(DataDestination destination)** - How + the user gets the raw data by passing in a **DataDestination** such + as **FloatArrayWrapper** or **ByteBufferWrapper**. This allows the + user to specify the way the raw data of the grid should be structured + in memory. + +- **DataDestination populateData(DataDestination destination, Unit + unit)** - Same as the above method but also attempts to convert the + raw data to the specified unit when populating the + **DataDestination**. + +- Python Differences + +- **Units** will be represented by **Strings** + +- **populateData()** methods will not exist, instead there will be + a **getRawData()** method that returns a numpy array in the native + type of the data + +**IGeometryData extends IData** + +- **Geometry getGeometry()** + +- **Set getParameters()** - Gets the list of parameters included in + this data + +- **String getString(String param)** - Gets the value of the parameter + as a String + +- **Number getNumber(String param)** - Gets the value of the parameter + as a Number + +- **Unit getUnit(String param)** - Gets the unit of the parameter, + may be null + +- **Type getType(String param)** - Returns an enum of the raw type of + the parameter, such as Float, Int, or String + +- **String getLocationName()** - Returns the location name of the piece + of data, typically to correlate if the request was made with + locationNames. May be null. + +- Python Differences + +- **Geometry** will be **shapely.geometry.Geometry** + +- **getNumber()** will return the python native number of the data + +- **Units** will be represented by **Strings** + +- **getType()** will return the python type object + +**DataAccessLayer** (in implementation, these methods delegate +processing to factories) + +- **DataTime[] getAvailableTimes(IDataRequest request)** + +- **DataTime[] getAvailableTimes(IDataRequest request, BinOffset + binOffset)** + +- **IData[] getData(IDataRequest request, DataTime... times)** + +- **IData[] getData(IDataRequest request, TimeRange timeRange)** + +- **GridGeometry2D getGridGeometry(IGridRequest request)** + +- **String[] getAvailableLocationNames(IGeometryRequest request)** + +- Python Differences + +- No support for **BinOffset** + +- **getGridGeometry(IGridRequest)** will be replaced by + **getLatCoords(IGridRequest)** and **getLonCoords(IGridRequest)** + that will return numpy arrays of the lat or lon of every grid + cell + +Factory Interfaces (Java only) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +- **IDataFactory** + +- **DataTime[] getAvailableTimes(R request)** - queries the + database and returns the times that match the request. Some factories + may not support this (e.g. maps). + +- **DataTime[] getAvailableTimes(R request, BinOffset binOffset)** - + queries the database with a bin offset and returns the times that + match the request. Some factories may not support this. + +- **D[] getData(R request, DataTime... times)** - Gets the data that + matches the request at the specified times. + +- **D[] getData(R request, TimeRange timeRange)** - Gets the data that + matches the request and is within the time range. + +**IGridDataFactory extends IDataFactory** + +- **GridGeometry2D** **getGeometry(IGridRequest request)** - Returns + the grid geometry of the data that matches the request BEFORE making + the request. Useful for then making slab or line requests for subsets + of the data. Does not support moving grids, but moving grids don’t + make subset requests either. + +**IGeometryDataFactory extends IDataFactory** + +- **getAvailableLocationNames(IGeometryRequest request)** - Convenience + method to retrieve available location names that match a request. Not + all factories may support this. + diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst new file mode 100644 index 0000000..6c9ba8f --- /dev/null +++ b/docs/source/examples/index.rst @@ -0,0 +1,11 @@ +.. _examples-index: + +###################### +Data Plotting Examples +###################### + +.. toctree:: + :maxdepth: 1 + :glob: + + generated/* diff --git a/docs/source/gridparms.rst b/docs/source/gridparms.rst new file mode 100644 index 0000000..ef48a3a --- /dev/null +++ b/docs/source/gridparms.rst @@ -0,0 +1,1456 @@ +Grid Parameters +=============== + +================================== =============================================================================================================================================================== ==================================== +Abbreviation Description Units +================================== =============================================================================================================================================================== ==================================== +WSPD 10 Metre neutral wind speed over waves m/s +WDRT 10 Metre Wind Direction Over Waves Degree +ARI12H1000YR 12H Average Recurrance Interval Accumulation 1000 Year in\*1000 +ARI12H100YR 12H Average Recurrance Interval Accumulation 100 Year in\*1000 +ARI12H10YR 12H Average Recurrance Interval Accumulation 10 Year in\*1000 +ARI12H1YR 12H Average Recurrance Interval Accumulation 1 Year in\*1000 +ARI12H200YR 12H Average Recurrance Interval Accumulation 200 Year in\*1000 +ARI12H25YR 12H Average Recurrance Interval Accumulation 25 Year in\*1000 +ARI12H2YR 12H Average Recurrance Interval Accumulation 2 Year in\*1000 +ARI12H500YR 12H Average Recurrance Interval Accumulation 500 Year in\*1000 +ARI12H50YR 12H Average Recurrance Interval Accumulation 50 Year in\*1000 +ARI12H5YR 12H Average Recurrance Interval Accumulation 5 Year in\*1000 +PRP12H 12 hour Precipitation Accumulation Return Period year +GaugeInfIndex12H 12 hour QPE Gauge Influence Index +FFG12 12-hr flash flood guidance mm +FFR12 12-hr flash flood runoff values mm +EchoTop18 18 dBZ Echo Top km +ARI1H1000YR 1H Average Recurrance Interval Accumulation 1000 Year in\*1000 +ARI1H100YR 1H Average Recurrance Interval Accumulation 100 Year in\*1000 +ARI1H10YR 1H Average Recurrance Interval Accumulation 10 Year in\*1000 +ARI1H1YR 1H Average Recurrance Interval Accumulation 1 Year in\*1000 +ARI1H200YR 1H Average Recurrance Interval Accumulation 200 Year in\*1000 +ARI1H25YR 1H Average Recurrance Interval Accumulation 25 Year in\*1000 +ARI1H2YR 1H Average Recurrance Interval Accumulation 2 Year in\*1000 +ARI1H500YR 1H Average Recurrance Interval Accumulation 500 Year in\*1000 +ARI1H50YR 1H Average Recurrance Interval Accumulation 50 Year in\*1000 +ARI1H5YR 1H Average Recurrance Interval Accumulation 5 Year in\*1000 +PRP01H 1 hour Precipitation Accumulation Return Period year +GaugeInfIndex01H 1 hour QPE Gauge Influence Index +QPEFFG01H 1 hour QPE-to-FFG Ratio % +FFG01 1-hr flash flood guidance mm +FFR01 1-hr flash flood runoff values mm +QPE01 1-hr Quantitative Precip Estimate mm +QPE01_ACR 1-hr Quantitative Precip Estimate mm +QPE01_ALR 1-hr Quantitative Precip Estimate mm +QPE01_FWR 1-hr Quantitative Precip Estimate mm +QPE01_KRF 1-hr Quantitative Precip Estimate mm +QPE01_MSR 1-hr Quantitative Precip Estimate mm +QPE01_ORN 1-hr Quantitative Precip Estimate mm +QPE01_PTR 1-hr Quantitative Precip Estimate mm +QPE01_RHA 1-hr Quantitative Precip Estimate mm +QPE01_RSA 1-hr Quantitative Precip Estimate mm +QPE01_STR 1-hr Quantitative Precip Estimate mm +QPE01_TAR 1-hr Quantitative Precip Estimate mm +QPE01_TIR 1-hr Quantitative Precip Estimate mm +QPE01_TUA 1-hr Quantitative Precip Estimate mm +EVEC1 1st Vector Component of Electric Field V\*m^1 +BVEC1 1st Vector Component of Magnetic Field T +VEL1 1st Vector Component of Velocity (Coordinate system dependent) m\*s^1 +TCSRG20 20% Tropical Cyclone Storm Surge Exceedance m +ARI24H1000YR 24H Average Recurrance Interval Accumulation 1000 Year in\*1000 +ARI24H100YR 24H Average Recurrance Interval Accumulation 100 Year in\*1000 +ARI24H10YR 24H Average Recurrance Interval Accumulation 10 Year in\*1000 +ARI24H1YR 24H Average Recurrance Interval Accumulation 1 Year in\*1000 +ARI24H200YR 24H Average Recurrance Interval Accumulation 200 Year in\*1000 +ARI24H25YR 24H Average Recurrance Interval Accumulation 25 Year in\*1000 +ARI24H2YR 24H Average Recurrance Interval Accumulation 2 Year in\*1000 +ARI24H500YR 24H Average Recurrance Interval Accumulation 500 Year in\*1000 +ARI24H50YR 24H Average Recurrance Interval Accumulation 50 Year in\*1000 +ARI24H5YR 24H Average Recurrance Interval Accumulation 5 Year in\*1000 +PRP24H 24 hour Precipitation Accumulation Return Period year +GaugeInfIndex24H 24 hour QPE Gauge Influence Index +FFG24 24-hr flash flood guidance mm +FFR24 24-hr flash flood runoff values mm +QPE24 24-hr Quantitative Precip Estimate mm +QPE24_ACR 24-hr Quantitative Precip Estimate mm +QPE24_ALR 24-hr Quantitative Precip Estimate mm +QPE24_FWR 24-hr Quantitative Precip Estimate mm +QPE24_KRF 24-hr Quantitative Precip Estimate mm +QPE24_MSR 24-hr Quantitative Precip Estimate mm +QPE24_ORN 24-hr Quantitative Precip Estimate mm +QPE24_PTR 24-hr Quantitative Precip Estimate mm +QPE24_RHA 24-hr Quantitative Precip Estimate mm +QPE24_RSA 24-hr Quantitative Precip Estimate mm +QPE24_STR 24-hr Quantitative Precip Estimate mm +QPE24_TAR 24-hr Quantitative Precip Estimate mm +QPE24_TIR 24-hr Quantitative Precip Estimate mm +QPE24_TUA 24-hr Quantitative Precip Estimate mm +QPF24 24-hr Quantitative Precip Forecast mm +QPF24_ACR 24-hr Quantitative Precip Forecast mm +QPF24_ALR 205 24-hr Quantitative Precip Forecast mm +QPF24_FWR 24-hr Quantitative Precip Forecast mm +QPF24_KRF 24-hr Quantitative Precip Forecast mm +QPF24_MSR 24-hr Quantitative Precip Forecast mm +QPF24_ORN 24-hr Quantitative Precip Forecast mm +QPF24_PTR 24-hr Quantitative Precip Forecast mm +QPF24_RHA 24-hr Quantitative Precip Forecast mm +QPF24_RSA 24-hr Quantitative Precip Forecast mm +QPF24_STR 24-hr Quantitative Precip Forecast mm +QPF24_TAR 24-hr Quantitative Precip Forecast mm +QPF24_TIR 24-hr Quantitative Precip Forecast mm +QPF24_TUA 24-hr Quantitative Precip Forecast mm +ARI2H1000YR 2H Average Recurrance Interval Accumulation 1000 Year in\*1000 +ARI2H100YR 2H Average Recurrance Interval Accumulation 100 Year in\*1000 +ARI2H10YR 2H Average Recurrance Interval Accumulation 10 Year in\*1000 +ARI2H1YR 2H Average Recurrance Interval Accumulation 1 Year in\*1000 +ARI2H200YR 2H Average Recurrance Interval Accumulation 200 Year in\*1000 +ARI2H25YR 2H Average Recurrance Interval Accumulation 25 Year in\*1000 +ARI2H2YR 2H Average Recurrance Interval Accumulation 2 Year in\*1000 +ARI2H500YR 2H Average Recurrance Interval Accumulation 500 Year in\*1000 +ARI2H50YR 2H Average Recurrance Interval Accumulation 50 Year in\*1000 +ARI2H5YR 2H Average Recurrance Interval Accumulation 5 Year in\*1000 +EVEC2 2nd Vector Component of Electric Field V\*m^1 +BVEC2 2nd Vector Component of Magnetic Field T +VEL2 2nd Vector Component of Velocity (Coordinate system dependent) m\*s^1 +EchoTop30 30 dBZ Echo Top km +ARI30M1000YR 30M Average Recurrance Interval Accumulation 1000 Year in\*1000 +ARI30M100YR 30M Average Recurrance Interval Accumulation 100 Year in\*1000 +ARI30M10YR 30M Average Recurrance Interval Accumulation 10 Year in\*1000 +ARI30M1YR 30M Average Recurrance Interval Accumulation 1 Year in\*1000 +ARI30M200YR 30M Average Recurrance Interval Accumulation 200 Year in\*1000 +ARI30M25YR 30M Average Recurrance Interval Accumulation 25 Year in\*1000 +ARI30M2YR 30M Average Recurrance Interval Accumulation 2 Year in\*1000 +ARI30M500YR 30M Average Recurrance Interval Accumulation 500 Year in\*1000 +ARI30M50YR 30M Average Recurrance Interval Accumulation 50 Year in\*1000 +ARI30M5YR 30M Average Recurrance Interval Accumulation 5 Year in\*1000 +PRP30min 30 min Precipitation Accumulation Return Period year +TCSRG30 30% Tropical Cyclone Storm Surge Exceedance m +SALIN 3-D Salinity +WTMPC 3-D Temperature ℃ +ARI3H1000YR 3H Average Recurrance Interval Accumulation 1000 Year in\*1000 +ARI3H100YR 3H Average Recurrance Interval Accumulation 100 Year in\*1000 +ARI3H10YR 3H Average Recurrance Interval Accumulation 10 Year in\*1000 +ARI3H1YR 3H Average Recurrance Interval Accumulation 1 Year in\*1000 +ARI3H200YR 3H Average Recurrance Interval Accumulation 200 Year in\*1000 +ARI3H25YR 3H Average Recurrance Interval Accumulation 25 Year in\*1000 +ARI3H2YR 3H Average Recurrance Interval Accumulation 2 Year in\*1000 +ARI3H500YR 3H Average Recurrance Interval Accumulation 500 Year in\*1000 +ARI3H50YR 3H Average Recurrance Interval Accumulation 50 Year in\*1000 +ARI3H5YR 3H Average Recurrance Interval Accumulation 5 Year in\*1000 +PRP03H 3 hour Precipitation Accumulation Return Period year +GaugeInfIndex03H 3 hour QPE Gauge Influence Index +QPEFFG03H 3 hour QPE-to-FFG Ratio % +FFG03 3-hr flash flood guidance mm +FFR03 3-hr flash flood runoff values mm +TSLSA 3-hr pressure tendency (Std. Atmos. Reduction) Pa/s +EVEC3 3rd Vector Component of Electric Field V\*m^1 +BVEC3 3rd Vector Component of Magnetic Field T +VEL3 3rd Vector Component of Velocity (Coordinate system dependent) m\*s^1 +TCSRG40 40% Tropical Cyclone Storm Surge Exceedance m +GaugeInfIndex48H 48 hour QPE Gauge Influence Index +EchoTop50 50 dBZ Echo Top km +TCSRG50 50% Tropical Cyclone Storm Surge Exceedance m +5WAVA 5-wave geopotential height anomaly gpm +5WAVA 5-Wave Geopotential Height Anomaly gpm +5WAVH 5-wave geopotential height gpm +5WAVH 5-Wave Geopotential Height gpm +EchoTop60 60 dBZ Echo Top km +TCSRG60 60% Tropical Cyclone Storm Surge Exceedance m +ARI6H1000YR 6H Average Recurrance Interval Accumulation 1000 Year in\*1000 +ARI6H100YR 6H Average Recurrance Interval Accumulation 100 Year in\*1000 +ARI6H10YR 6H Average Recurrance Interval Accumulation 10 Year in\*1000 +ARI6H1YR 6H Average Recurrance Interval Accumulation 1 Year in\*1000 +ARI6H200YR 6H Average Recurrance Interval Accumulation 200 Year in\*1000 +ARI6H25YR 6H Average Recurrance Interval Accumulation 25 Year in\*1000 +ARI6H2YR 6H Average Recurrance Interval Accumulation 2 Year in\*1000 +ARI6H500YR 6H Average Recurrance Interval Accumulation 500 Year in\*1000 +ARI6H50YR 6H Average Recurrance Interval Accumulation 50 Year in\*1000 +ARI6H5YR 6H Average Recurrance Interval Accumulation 5 Year in\*1000 +PRP06H 6 hour Precipitation Accumulation Return Period year +GaugeInfIndex06H 6 hour QPE Gauge Influence Index +QPEFFG06H 6 hour QPE-to-FFG Ratio % +FFG06 6-hr flash flood guidance mm +FFR06 6-hr flash flood runoff values mm +QPE06 6-hr Quantitative Precip Estimate mm +QPE06_ACR 6-hr Quantitative Precip Estimate mm +QPE06_ALR 6-hr Quantitative Precip Estimate mm +QPE06_FWR 6-hr Quantitative Precip Estimate mm +QPE06_KRF 6-hr Quantitative Precip Estimate mm +QPE06_MSR 6-hr Quantitative Precip Estimate mm +QPE06_ORN 6-hr Quantitative Precip Estimate mm +QPE06_PTR 6-hr Quantitative Precip Estimate mm +QPE06_RHA 6-hr Quantitative Precip Estimate mm +QPE06_RSA 6-hr Quantitative Precip Estimate mm +QPE06_STR 6-hr Quantitative Precip Estimate mm +QPE06_TAR 6-hr Quantitative Precip Estimate mm +QPE06_TIR 6-hr Quantitative Precip Estimate mm +QPE06_TUA 6-hr Quantitative Precip Estimate mm +QPF06 6-hr Quantitative Precip Forecast mm +QPF06_ACR 6-hr Quantitative Precip Forecast mm +QPF06_ALR 6-hr Quantitative Precip Forecast mm +QPF06_FWR 6-hr Quantitative Precip Forecast mm +QPF06_KRF 6-hr Quantitative Precip Forecast mm +QPF06_MSR 6-hr Quantitative Precip Forecast mm +QPF06_ORN 6-hr Quantitative Precip Forecast mm +QPF06_PTR 6-hr Quantitative Precip Forecast mm +QPF06_RHA 6-hr Quantitative Precip Forecast mm +QPF06_RSA 6-hr Quantitative Precip Forecast mm +QPF06_STR 6-hr Quantitative Precip Forecast mm +QPF06_TAR 6-hr Quantitative Precip Forecast mm +QPF06_TIR 6-hr Quantitative Precip Forecast mm +QPF06_TUA 6-hr Quantitative Precip Forecast mm +TCSRG70 70% Tropical Cyclone Storm Surge Exceedance m +GaugeInfIndex72H 72 hour QPE Gauge Influence Index +TCSRG80 80% Tropical Cyclone Storm Surge Exceedance m +TCSRG90 90% Tropical Cyclone Storm Surge Exceedance m +ABSD Absolute divergence s^-1 +ABSH Absolute Humidity kg m-3 +AH Absolute humidity kg/m^3 +ABSV Absolute vorticity s^-1 +ASD Accumulated Snow Depth m +ACOND Aerodynamic conductance m/s +AETYP Aerosol type (Code table 4.205) +AC137 Air concentration of Caesium 137 Bq/m^3 +AI131 Air concentration of Iodine 131 Bq/m^3 +ARADP Air concentration of radioactive pollutant Bq/m^3 +ALBDO Albedo % +ACWVH Altimeter corrected wave height m +ALRRC Altimeter Range Relative Correction +ASET Altimeter setting Pa +AWVH Altimeter wave height m +ALTMSL Altitude above mean sea level m +ANCConvectiveOutlook ANC Convective Outlook +ANCFinalForecast ANC Final Forecast dBZ +AOSGSO Angle Of Sub-Grid Scale Orography Rad +ASGSO Anisotropy Of Sub-Grid Scale Orography Numeric +APTMP Apparent Temperature K +ARBTXT Arbitrary text string CCITTIA5 +ASHFL Assimilative Heat Flux W/m^2 +AMIXL Asymptotic mixing length scale m +ATMDIV Atmospheric Divergence s^-1 +AVSFT Average surface skin temperature K +BARET Bare soil surface skin temperature K +BKENG Barotropic Kinectic Energy J/kg +UBARO Barotropic U velocity m/s +UBARO Barotropic U Velocity m/s +VBARO Barotropic V velocity m/s +VBARO Barotropic V Velocity m/s +BGRUN Baseflow-groundwater runoff mm +BASRV Base radial velocity m/s +BASR Base reflectivity dB +BASSW Base spectrum width m/s +4LFTX Best (4-layer) lifted index K +4LFTX Best (4 layer) Lifted Index K +BLI Best lifted index (to 500 mb) K +BMIXL Blackadars mixing length scale m +BLST Bottom layer soil temperature K +NONE Bottom of Ocean Isothermal Layer m +OBIL Bottom of Ocean Isothermal Layer m +NONE Bottom of Ocean Mixed Layer (m) m +OBML Bottom of Ocean Mixed Layer (m) m +BCBL Boundary layer cloud bottom level +BCBL Boundary Layer Cloud Bottom Level +BCLY Boundary Layer Cloud Layer +BCY Boundary layer cloud layer +BCY Boundary Layer Cloud Layer +BCTL Boundary layer cloud top level +BCTL Boundary Layer Cloud Top Level +BLYSP Boundary layer dissipation W/m^2 +BrightBandBottomHeight Bright Band Bottom Height m +BrightBandTopHeight Bright Band Top Height m +BRTMP Brightness temperature K +CAIIRAD CaII-K Radiance W\*s\*r^1\*m^2 +CCOND Canopy conductance m/s +EVCW Canopy water evaporation W/m^2 +CONVP Categorical Convection categorical +CFRZR Categorical Freezing Rain +CFRZR Categorical Freezing Rain Code table 4.222 +CFRZR Categorical Freezing Rain non-dim +CFRZR Categorical freezing rain (See Code table 4.222) +CFRZR Categorical Freezing Rain (yes=1; no=0) +CFRZR Categorical Freezing Rain (yes=1; no=0) non-dim +CICEP Categorical Ice Pellets +CICEP Categorical Ice Pellets Code table 4.222 +CICEP Categorical Ice Pellets non-dim +CICEP Categorical ice pellets (See Code table 4.222) +CICEP Categorical Ice Pellets (yes=1; no=0) +CICEP Categorical Ice Pellets (yes=1; no=0) non-dim +CLGTN Categorical Lightning categorical +OZCAT Categorical Ozone Concentration Non-Dim +OZCAT Categorical Ozone Concentration +CRAIN Categorical Rain Code table 4.222 +CRAIN Categorical Rain +CRAIN Categorical Rain non-dim +CRAIN Categorical rain (See Code table 4.222) +CRAIN Categorical Rain (yes=1; no=0) +CRAIN Categorical Rain (yes=1; no=0) non-dim +SVRTS Categorical Servre Thunderstorm +SVRTS Categorical Severe Thunderstorm +CSNOW Categorical Snow Code table 4.222 +CSNOW Categorical Snow +CSNOW Categorical Snow non-dim +CSNOW Categorical snow (See Code table 4.222) +CSNOW Categorical Snow (yes=1; no=0) +CSNOW Categorical Snow (yes=1; no=0) non-dim +CTSTM Categorical Thunderstorm (1-yes, 0-no) categorical +TSTMC Categorical Thunderstorm (1-yes, 0-no) categorical +CCEIL Ceiling m +LightningDensity15min CG Lightning Density (15 min.) Flashes/km^2/min +LightningDensity1min CG Lightning Density (1 min.) Flashes/km^2/min +LightningDensity30min CG Lightning Density (30 min.) Flashes/km^2/min +LightningDensity5min CG Lightning Density (5 min.) Flashes/km^2/min +LightningProbabilityNext30min CG Lightning Probability (0-30 min.) % +CAT Clear Air Turbulence (CAT) % +CAT Clear Air Turbulence(CAT) % +CSDLF Clear Sky Downward Long Wave Flux W/m^2 +CSDSF Clear sky downward solar flux W/m^2 +CSULF Clear Sky Upward Long Wave Flux W/m^2 +CSUSF Clear Sky Upward Solar Flux W/m^2 +CDUVB Clear sky UV-B downward solar flux W/m^2 +CAMT Cloud amount % +CBL Cloud Base Level +CBASE Cloud base m +CEIL Cloud Ceiling +CLG Cloud ceiling +CLG Cloud Ceiling +CloudCover Cloud Cover K +CFNLF Cloud Forcing Net Long Wave Flux W/m^2 +CFNSF Cloud Forcing Net Solar Flux W/m^2 +CDCIMR Cloud Ice Mixing Ratio kg/kg +CICE Cloud ice mm +CLOUDM Cloud mask (Code table 4.217) +CLWMR Cloud Mixing Ratio kg kg-1 +CLWMR Cloud mixing ratio kg/kg +CTOPHQI Cloud top height quality indicator (Code table 4.219) +CTOP Cloud top m +heightCTHGT Cloud top m +CTYP Cloud type (Code table 4.203) +CWAT Cloud water mm +CWORK Cloud work function J/kg +CWORK Cloud Work Function J/kg +CDWW Coefficient of Drag With Waves +CISOILW Column-Integrated Soil Water mm +REFC Composite reflectivity dB +MergedReflectivityQCComposite Composite Reflectivity dBZ +HeightCompositeReflectivity Composite Reflectivity Height m +MergedReflectivityQComposite Composite Reflectivity Mosaic dBZ +EF25M Cond 25% pcpn smy fractile past 24 hrs mm +EF50M Cond 50% pcpn smy fractile past 24 hrs mm +TCOND Condensate kg kg-1 +CONDE Condensate kg/kg +CONDP Condensation Pressure of Parcali Lifted From Indicate Surface Pa +CONDP Condensation Pressure of Parcal Lifted From Indicate Surface Pa +CPPAF Conditional percent precipitation amount fractile for an overall period (Encoded as an accumulation) mm +CICEL Confidence - Ceiling +CIFLT Confidence - Flight Category +CIVIS Confidence - Visibility +CONTB Contrail base m +CONTB Contrail Base m +CONTE Contrail engine type (Code table 4.211) +CONTET Contrail Engine Type See Table 4.211 +CONTI Contrail intensity (Code table 4.210) +CONTI Contrail Intensity See Table 4.210 +CONTT Contrail top m +CONTT Contrail Top m +CONUSMergedReflectivity CONUS Merged Reflectivity dBZ +CONUSPlusMergedReflectivity CONUS-Plus Merged Reflectivity dBZ +CONVP Convection Potential +CAPE Convective available potential energy J/kg +CCBL Convective cloud bottom level +CCBL Convective Cloud Bottom Level +CCLY Convective Cloud +CCY Convective cloud +CCY Convective Cloud +CDCON Convective cloud cover % +CUEFI Convective Cloud Efficiency +CUEFI Convective Cloud Efficiency non-dim +CUEFI Convective cloud efficiency Proportion +MFLUX Convective Cloud Mass Flux Pa/s +CCTL Convective cloud top level +CCTL Convective Cloud Top Level +CNVDEMF Convective detrainment mass flux mm/s +CNVDMF Convective downdraft mass flux mm/s +CNGWDV Convective Gravity wave drag meridional acceleration m/s^2 +CNGWDU Convective Gravity wave drag zonal acceleration m/s^2 +CIN Convective inhibition J/kg +CNVV Convective meridional momentum mixing acceleration m/s^2 +ACPCP Convective Precipitation kg m-2 +ACPCP Convective precipitation mm +ACPCPN Convective precipitation (nearest grid point) kg/m2 +ACPCPN Convective precipitation (nearest grid point) mm +CPRAT Convective Precipitation Rate kg m-2 s-1 +CPRAT Convective Precipitation Rate kg\*m^-2\*s^-1 +CPRAT Convective precipitation rate mm / s +CPRAT Convective Precipitation Rate mm / s +CSRATE Convective Snowfall Rate m s-1 +CSRATE Convective Snowfall Rate m/s +CSRWE Convective Snowfall Rate Water Equivalent kg m-2s-1 +CSRWE Convective Snowfall Rate Water Equivalent mm/s +SNOC Convective snow mm +CNVUMF Convective updraft mass flux mm/s +CWP Convective Water Precipitation kg m-2 +CWP Convective Water Precipitation mm +CWDI Convective Weather Detection Index +CNVU Convective zonal momentum mixing acceleration m/s^2 +SNO C Convect Snow kg m-2 +NTRNFLUX Cosmic Ray Neutron Flux h^1 +COVTZ Covariance between izonal component of the wind and temperature. Defined as [uT]-[u][T], where "[]" indicates the mean over the indicated time span. K\*m/s +COVMM Covariance between meridional and meridional components of the wind. Defined as [vv]-[v][v], where "[]" indicates the mean over the indicated time span. m^2/s^2 +COVMZ Covariance between Meridional and Zonal Components of the wind. m^2/s^2 +COVTM Covariance between meridional component of the wind and temperature. Defined as [vT]-[v][T], where "[]" indicates the mean over the indicated time span. K\*m/s +COVQM Covariance between specific humidity and meridional components of the wind. Defined as [vq]-[v][q], where "[]" indicates the mean over the indicated time span. kg/kg\*m/s +COVQQ Covariance between specific humidity and specific humidy. Defined as [qq]-[q][q], where "[]" indicates the mean over the indicated time span. kg/kg\*kg/kg +COVQVV Covariance between specific humidity and vertical components of the wind. Defined as [Ωq]-[Ω][q], where "[]" indicates the mean over the indicated time span. kg/kg\*Pa/s +COVQZ Covariance between specific humidity and zonal components of the wind. Defined as [uq]-[u][q], where "[]" indicates the mean over the indicated time span. kg/kg\*m/s +COVPSPS Covariance between surface pressure and surface pressure. Defined as [Psfc]-[Psfc][Psfc], where "[]" indicates the mean over the indicated time span. Pa\*Pa +COVTM Covariance between Temperature and Meridional Components of the wind. K\*m/s +COVTT Covariance between temperature and temperature. Defined as [TT]-[T][T], where "[]" indicates the mean over the indicated time span. K\*K +COVTW Covariance between temperature and vertical component of the wind. Defined as [wT]-[w][T], where "[]" indicates the mean over the indicated time span. K\*m/s +COVTVV Covariance between temperature and vertical components of the wind. Defined as [ΩT]-[Ω][T], where "[]" indicates the mean over the indicated time span. K\*Pa/s +COVTZ Covariance between Temperature and Zonal Components of the wind. K\*m/s +COVVVVV Covariance between vertical and vertical components of the wind. Defined as [ΩΩ]-[Ω][Ω], where "[]" indicates the mean over the indicated time span. Pa^2/s^2 +COVMZ Covariance between zonal and meridional components of the wind. Defined as [uv]-[u][v], where "[]" indicates the mean over the indicated time span. m^2/s^2 +COVZZ Covariance between zonal and zonal components of the wind. Defined as [uu]-[u][u], where "[]" indicates the mean over the indicated time span. m^2/s^2 +CrestMaxStreamflow CREST Maximum Streamflow (m^3)\*(s^-1) +CrestMaxUStreamflow CREST Maximum Unit Streamflow (m^3)\*(s^-1)\*(km^-2) +CrestSoilMoisture CREST Soil Moisture % +CRTFRQ Critical Frequency Hz +CB Cumulonimbus Base m +CBHE Cumulonimbus Horizontal Exten % +CT Cumulonimbus Top m +DIRC Current direction Degree true +SPC Current speed m/s +DCBL Deep convective cloud bottom level +DCBL Deep Convective Cloud Bottom Level +DCCBL Deep Convective Cloud Bottom Level +DCCTL Deep Convective Cloud Top Level +DCTL Deep convective cloud top level +DCTL Deep Convective Cloud Top Level +CNVHR Deep Convective Heating rate K/s +CNVMR Deep Convective Moistening Rate kg kg-1 s-1 +CNVMR Deep Convective Moistening Rate kg/kg\*s +DALT Density altitude m +DEN Density kg/m^3 +DBLL Depth Below Land surface m +DPBLW Depth below land surface m +DBSL Depth Below Sea Level m +DPMSL Depth below sea level m +REFZI Derived radar reflectivity backscatter from ice mm^6/m^3 +REFZI Derived radar reflectivity backscatter from ice mm^6\*m^-3 +REFZC Derived radar reflectivity backscatter from parameterized convection mm^6/m^3 +REFZC Derived radar reflectivity backscatter from parameterized convection mm^6\*m^-3 +REFZR Derived radar reflectivity backscatter from rain mm^6/m^3 +REFZR Derived radar reflectivity backscatter from rain mm^6\*m^-3 +REFD Derived radar reflectivity dB +DEVMSL Deviation of sea level from mean m +DEPR Dew point depression or deficit K +DPT Dew point temperature K +DIREC Direct Evaporation Cease(Soil Moisture) kg/m^3 +SMDRY Direct evaporation cease (soil moisture) Proportion +EVBS Direct evaporation from bare soil W/m^2 +DIRWWW Directional Width of The Wind Waves +Degree true Direction Degrees true DIRDEGTRU +WWSDIR Direction of combined wind waves and swell Degree +DICED Direction of ice drift Degree true +SWDIR Direction of swell waves Degree +WVDIR Direction of wind waves Degree +DSKDAY Disk Intensity Day J\*m^2\*s^1 +DSKINT Disk Intensity j\*m^2\*s^1 +DSKNGT Disk Intensity Night J\*m^2\*s^1 +DLWRF Downward Long-Wave Rad. Flux W/m^2 +DLWRF Downward long-wave radiation flux W/m^2 + Downward Long-W/m^2 DLWRF +DSWRF Downward Short-Wave Rad. Flux W/m^2 +DSWRF Downward short-wave radiation flux W/m^2 + Downward Short-W/m^2 DSWRF +DTRF Downward Total radiation Flux W/m^2 +DWUVR Downward UV Radiation W/m^2 +CD Drag Coefficient +CD Drag coefficient Numeric +ELON East Longitude (0 - 360) deg +ELON East Longitude (0 - 360) degrees +ELONN East Longitude (nearest neighbor) (0 - 360) degrees +RETOP Echo Top m +RADT Effective radiative skin temperature K +ETOT Electric Field Magnitude V\*m^1 +ELCDEN Electron Density m^3 +DIFEFLUX Electron Flux (Differential) (m^2\*s\*sr\*eV)^1 +INTEFLUX Electron Flux (Integral) (m^2\*s\*sr)^1 +ELECTMP Electron Temperature K +ELSCT Elevation of snow covered terrain (Code table 4.216) +EHELX Energy helicity index Numeric +EATM Entire atmosphere (considered as a single layer) +EA Entire Atmosphere +EATM Entire Atmosphere +NONE Entire Atmosphere +EOCN Entire ocean (considered as a single layer) +EOCN Entire Ocean +NONE Entire Ocean +EHLT Equilibrium level +EHLT Equilibrium Level +REFZC Equivalent radar reflectivity factor for parameterized convection m m6 m-3 +REFZR Equivalent radar reflectivity factor for rain m m6 m-3 +REFZI Equivalent radar reflectivity factor for snow m m6 m-3 +ESTPC Estimated precipitation mm +ESTUWIND Estimated u component of wind m/s +ESTVWIND Estimated v component of wind m/s +ELYR Eta Level Eta value +ETAL Eta Level Eta value +EUVRAD EUV Radiance W\*s\*r^1\*m^2 +EVP Evaporation kg m-2 +EVP Evaporation mm + Evaporation - Precipitation cm/day EMNP +EMNP Evaporation - Precipitation cm/day +EMNP Evaporation - Precipitation cm per day +EVAPT Evapotranspiration kg^-2\*s^-1 +SFEXC Exchange coefficient kg\*m^-2\*s^-1 +SFEXC Exchange coefficient mm \* s +ETCWL Extra Tropical Storm Surge Combined Surge and Tide m +ETSRG Extra Tropical Storm Surge m +F107 F10.7 W\*m^2\*H\*z^1 +FLDCP Field Capacity fraction +FIREDI Fire Detection Indicator (Code Table 4.223) +FIREODT Fire Outlook Due to Dry Thunderstorm index(see GRIB 2 code table 4.224) +FIREOLK Fire Outlook index (see GRIB 2 code table 4.224) +FFG Flash flood guidance (Encoded as an accumulation over a floating subinterval of time between the reference time and valid time) mm +FFRUN Flash flood runoff (Encoded as an accumulation over a floating subinterval of time) mm +FLGHT Flight Category +QREC Flood plain recharge mm +ModelHeight0C Freezing Level Height m +FRZR Freezing Rain kg/m2 +FRZR Freezing Rain mm +FPRATE Freezing Rain Precipitation Rate kg m-2s-1 +FPRATE Freezing Rain Precipitation Rate mm/s +FREQ Frequency s^-1 +FRICV Frictional velocity m/s +FRICV Frictional Velocity m/s +FRICV Friction Velocity m/s +FROZR Frozen Rain kg/m2 +FROZR Frozen Rain mm +GHT Geometrical height m +DBSS Geometric Depth Below Sea Surface m +DIST Geometric height m +GPA Geopotential height anomaly gpm +GH Geopotential height gpm +HGT Geopotential height gpm +HGTN Geopotential Height (nearest grid point) gpm +GP Geopotential m^2/s^2 +GRAD Global radiation flux W/m^2 +GRAUP Graupel (snow pellets) kg/kg +GRLE Grauple kg kg-1 +GSGSO Gravity Of Sub-Grid Scale Orography W/m^2 +GWDV Gravity wave drag meridional acceleration m/s^2 +GWDU Gravity wave drag zonal acceleration m/s^2 +GCBL Grid scale cloud bottom level +GCBL Grid Scale Cloud Bottom Level +GSCBL Grid Scale Cloud Bottom Level +GCTL Grid scale cloud top level +GCTL Grid Scale Cloud Top Level +GSCTL Grid Scale Cloud Top Level +GC137 Ground deposition of Caesium 137 Bq/m^2 +GI131 Ground deposition of Iodine 131 Bq/m^2 +GRADP Ground deposition of radioactive Bq/m^2 +GFLUX Ground heat flux W/m^2 +SFC Ground or Water Surface +GWREC Groundwater recharge mm +HAIL Hail m +HAILPROB Hail probability % +HINDEX Haines Index Numeric +SIGHAL Hall Conductivity S\*m^1 +HARAD H-Alpha Radiance W\*s\*r^1\*m^2 +HFLUX Heat Flux W/m^2 +HTX Heat index K +DIFIFLUX Heavy Ion Flux (Differential) ((m^2\*s\*sr\*eV)/nuc)^1 +INTIFLUX Heavy Ion Flux (iIntegral) (m^2\*s\*sr)^1 +HGTAG Height above ground (see Note 1) m +H50Above0C Height of 50 dBZ Echo Above 0C km +H50AboveM20C Height of 50 dBZ Echo Above -20C km +H60Above0C Height of 60 dBZ Echo Above 0C km +H60AboveM20C Height of 60 dBZ Echo Above -20C km +HELCOR Heliospheric Radiance W\*s\*r^1\*m^2 +ABSRB HF Absorption dB +ABSFRQ HF Absorption Frequency Hz +HPRIMF h'F m +HCBL High cloud bottom level +HCBL High Cloud Bottom Level +HCDC High cloud cover % +HCL High cloud layer +HCL High Cloud Layer +HCLY High Cloud Layer +HCTL High cloud top level +HCTL High Cloud Top Level +HSCLW Highest top level of supercooled liquid water layer +HSCLW Highest Top Level of Supercooled Liquid Water Layer +HTSLW Highest Top Level of Supercooled Liquid Water Layer +HTFL Highest tropospheric freezing level +HTFL Highest Tropospheric Freezing Level +HighLayerCompositeReflectivity High Layer Composite Reflectivity (24-60 kft) dBZ +HAVNI High-Level aviation interest +HRCONO High risk convective outlook categorical +MCONV Horizontal Moisture Convergence kg kg-1 s-1 +HMC Horizontal moisture convergence kg/kg\*s +MCONV Horizontal Moisture Divergence kg kg-1 s-1 +MCONV Horizontal Moisture Divergence kg\*kg^-1\*s^-1 +MCONV Horizontal moisture divergence kg/kg\*s +MCONV Horizontal Moisture Divergence kg/kg\*s +MFLX Horizontal momentum flux N/m^2 +MFLX Horizontal Momentum Flux N \* m-2 +MFLX Horizontal Momentum Flux N/m^2 +CompositeReflectivityMaxHourly Hourly Composite Reflectivity Maximum dBZ +MAXDVV Hourly Maximum of Downward Vertical Vorticity in the lowest 400hPa m/s +MAXREF Hourly Maximum of Simulated Reflectivity at 1 km AGL dB +MXUPHL Hourly Maximum of Updraft Helicity over Layer 2-5 km AGL m^2/s^2 +MXUPHL Hourly Maximum of Updraft Helicity over Layer 2km to 5 km AGL m^2/s^2 +MAXUVV Hourly Maximum of Upward Vertical Vorticity in the lowest 400hPa m/s +MIXR Humidity Mixing Ratio kg kg-1 +MIXR Humidity mixing ratio kg/kg +RCQ Humidity parameterin canopy conductance Fraction +RCQ Humidity parameter in canopy conductance Proportion +HYBL Hybrid Level +HCBB ICAO Height at Cumulonimbus Bas m +HCBT ICAO Height at Cumulonimbus To m +HECBB ICAO Height at Embedded Cumulonimbus Bas m +HECBT ICAO Height at Embedded Cumulonimbus To m +ICAHT ICAO Standard Atmosphere Reference Height m +ICEC Ice cover Proportion +ICED Ice divergence s^-1 +FICE Ice fraction of total condensate +FICE Ice fraction of total condensate non-dim +FICE Ice fraction of total condensate Proportion +surface Ice-free water ICWAT +ICWAT Ice-free water surface % +ICEG Ice growth rate m/s +IPRATE Ice Pellets Precipitation Rate kg m-2s-1 +IPRATE Ice Pellets Precipitation Rate mm/s +ICE T Ice Temperature K +ICETK Ice thickness m +ICMR Ice Water Mixing Ratio kg kg-1 +ICMR Ice water mixing ratio kg/kg +ICIB Icing base m +ICIB Icing Base m +ICIP Icing % +ICIP Icing Potential % +TIPD Icing Potential non-dim +ICPRB Icing probability non-dim +ICI Icing See Table 4.207 + Icing Severity ICI +ICSEV Icing severity non-dim +ICIT Icing top m +ICIT Icing Top m +CTP In-Cloud Turbulence % +IRBand4 Infrared Imagery K +INSTRR Instantaneous rain rate mm / s +LIPMF Integrated column particulate matter (fine) log10(mg \* m^-3) +LIPMF Integrated column particulate matter (fine) log10(mm\*g/m^3) +ILIQW Integrated Liquid Water kg m-2 +ILW Integrated liquid water mm +TSI Integrated Solar Irradiance W\*m^2 +INTFD Interface Depths m +IMFTSW Inverse Mean Frequency of The Total Swell s +IMFWW Inverse Mean Frequency of The Wind Waves s +IMWF Inverse Mean Wave Frequency s +IONDEN Ion Density m^3 +IDRL Ionospheric D-region level +IERL Ionospheric E-region level +IF1RL Ionospheric F1-region level +IF2RL Ionospheric F2-region level +IONTMP Ion Temperature K +THEL Isentropic (theta) level K +ISBL Isobaric Surface Pa +TMPL Isothermal Level K +KX K index K +KENG Kinetic Energy J/kg +MELBRNE KNES1 1 +KOX KO index K +BENINX Kurtosis of The Sea Surface Elevation Due to Waves +LAND Land cover (0=sea, 1=land) Proportion +LANDN Land-sea coverage (nearest neighbor) [land=1,sea=0] +LSPA Land Surface Precipitation Accumulation mm +LANDU Land use (Code table 4.212) +LAPR Lapse rate K/m +LRGHR Large Scale Condensate Heating rate K/s +LRGMR Large scale moistening rate kg/kg/s +NCPCP Large-Scale Precipitation (non-convective) kg m-2 +NCPCP Large scale precipitation (non-convective) mm +LSPRATE Large Scale Precipitation Rate kg m-2s-1 +LSPRATE Large Scale Precipitation Rate mm/s +LSSRATE Large Scale Snowfall Rate m s-1 +LSSRATE Large Scale Snowfall Rate m/s +LSSRWE Large Scale Snowfall Rate Water Equivalent kg m-2s-1 +LSSRWE Large Scale Snowfall Rate Water Equivalent mm/s +SNO L Large-Scale Snow kg m-2 +SNOL Large scale snow mm +LSWP Large Scale Water Precipitation (Non-Convective) kg m-2 +LSWP Large Scale Water Precipitation (Non-Convective) mm +LHTFL Latent heat net flux W/m^2 +NLAT Latitude (-90 to +90) deg +NLAT Latitude (-90 to +90) degrees +NLATN Latitude (nearest neighbor) (-90 to +90) degrees +LAPP Latitude of Presure Point degrees +LAUV Latitude of U Wind Component of Velocity degrees +LAVV Latitude of V Wind Component of Velocity degrees +NONE Layer Between Two Depths Below Ocean Surface +OLYR Layer between two depths below ocean surface +OLYR Layer Between Two Depths Below Ocean Surface +LBTHL Layer Between Two Hybrid Levels +NONE Layer Between Two Hybrid Levels +LMBSR Layer-maximum base reflectivity dB +LOS Layer Ocean Surface and 26C Ocean Isothermal Level +NONE Layer Ocean Surface and 26C Ocean Isothermal Level +LAYTH Layer Thickness m +LAI Leaf Area Index +PDLY Level at Specified Pressure Difference from Ground to Level Pa +SPDL Level at Specified Pressure Difference from Ground to Level Pa +0DEG level of 0C Isotherm +ADCL Level of Adiabatic Condensation Lifted from the Surface +CTL Level of Cloud Tops +LTNG Lightning +LTNG Lightning non-dim +LMBINT Limb Intensity J\*m^2\*s^1 +ARAIN Liquid precipitation (rainfall) kg/m2 +ARAIN Liquid precipitation (rainfall) mm +LSOIL Liquid soil moisture content (non-frozen) mm +LIQVSM Liquid Volumetric Soil Moisture (Non-Frozen) m^3/m^3 +SOILL Liquid volumetric soil moisture (non-frozen) Proportion +LOPP Longitude of Presure Point degrees +LOUV Longitude of U Wind Component of Velocity degrees +LOVV Longitude of V Wind Component of Velocity degrees +LWAVR Long wave radiation flux W/m^2 +LWHR Long-Wave Radiative Heating Rate K/s +LCBL Low cloud bottom level +LCBL Low Cloud Bottom Level +LCDC Low cloud cover % +LCLY Low Cloud Layer +LCY Low cloud layer +LCY Low Cloud Layer +LCTL Low cloud top level +LCTL Low Cloud Top Level +LLSM Lower layer soil moisture kg/m^3 +LBSLW Lowest Bottom Level of Supercooled Liquid Water Layer +LSCLW Lowest bottom level of supercooled liquid water layer +LSCLW Lowest Bottom Level of Supercooled Liquid Water Layer +LLTW Lowest level of the wet bulb zero +LLTW Lowest Level of the Wet Bulb Zero +LWBZ Lowest Level of the Wet Bulb Zero +WBZ Lowest Level of the Wet Bulb Zero +LowLayerCompositeReflectivity Low Layer Composite Reflectivity (0-24 kft) dBZ +LAVNI Low-Level aviation interest +MergedAzShear02kmAGL Low-Level Azimuthal Shear (0-2km AGL) 1/s +LLCompositeReflectivity Low-Level Composite Reflectivity dBZ +HeightLLCompositeReflectivity Low-Level Composite Reflectivity Height m +RotationTrackLL120min Low-Level Rotation Tracks 0-2km AGL (120 min. accum.) 1/s +RotationTrackLL1440min Low-Level Rotation Tracks 0-2km AGL (1440 min. accum.) 1/s +RotationTrackLL240min Low-Level Rotation Tracks 0-2km AGL (240 min. accum.) 1/s +RotationTrackLL30min Low-Level Rotation Tracks 0-2km AGL (30 min. accum.) 1/s +RotationTrackLL360min Low-Level Rotation Tracks 0-2km AGL (360 min. accum.) 1/s +RotationTrackLL60min Low-Level Rotation Tracks 0-2km AGL (60 min. accum.) 1/s +BTOT Magnetic Field Magnitude T +MTHA Main thermocline anomaly m +MTHD Main thermocline depth m +LMH Mass Point Model Surface +MAXAH Maximum absolute humidity kg/m^3 +MAXAH Maximum Absolute Humidity kg m-3 +MACAT Maximum Cloud Air Turbulence Potential mm +REFC Maximum/Composite radar reflectivity dB +MTHE Maximum equivalent potential temperature level +MTHE Maximum Equivalent Potential Temperature level +MESH Maximum Estimated Size of Hail (MESH) mm +MAIP Maximum Icing Potential mm +MAXWH Maximum Individual Wave Height m +PRPMax Maximum Precipitation Return Period year +QPEFFGMax Maximum QPE-to-FFG Ratio % +MAXRH Maximum relative humidity % +MAXRH Maximum Relative Humidity % +MXSALB Maximum snow albedo % +MXSALB Maximum Snow Albedo % +MXSALB Maximum Snow Albedo\* % +QMAX Maximum specific humidity at 2m kg/kg +TMAX Maximum temperature K +MWSL Maximum Wind Level +MAXWS Maximum wind speed m/s +MACTP Max in-Cloud Turbulence Potential mm +MECAT Mean Cloud Air Turbulence Potential mm +MEI Mean Icing Potential mm +MECTP Mean in-Cloud Turbulence Potential mm +MWSPER Mean period of combined wind waves and swell s +SWPER Mean period of swell waves s +WVPER Mean period of wind waves s +MSL Mean Sea Level Pa +M2SPW Mean square slope of waves +MZPTSW Mean Zero-Crossing Period of The Total Swell s +MZPWW Mean Zero-Crossing Period of The Wind Waves s +MZWPER Mean Zero-Crossing Wave Period s +MCDC Medium cloud cover % +MergedBaseReflectivityQC Merged Base Reflectivity dBZ +MergedReflectivityAtLowestAltitude Merged Reflectivity At Lowest Altitude (RALA) dBZ +VGWD Meridional flux of gravity wave stress N/m^2 +V-GWD Meridional Flux of Gravity Wave Stress N/m^2 +MESHTrack120min MESH Tracks (120 min. accum.) mm +MESHTrack1440min MESH Tracks (1440 min. accum.) mm +MESHTrack240min MESH Tracks (240 min. accum.) mm +MESHTrack30min MESH Tracks (30 min. accum.) mm +MESHTrack360min MESH Tracks (360 min. accum.) mm +MESHTrack60min MESH Tracks (60 min. accum.) mm +MCBL Middle cloud bottom level +MCBL Middle Cloud Bottom Level +MCLY Middle Cloud Layer +MCY Middle cloud layer +MCY Middle Cloud Layer +MCTL Middle cloud top level +MCTL Middle Cloud Top Level +MergedAzShear36kmAGL Mid-Level Azimuthal Shear (3-6km AGL) 1/s +RotationTrackML120min Mid-Level Rotation Tracks 3-6km AGL (120 min. accum.) 1/s +RotationTrackML1440min Mid-Level Rotation Tracks 3-6km AGL (1440 min. accum.) 1/s +RotationTrackML240min Mid-Level Rotation Tracks 3-6km AGL (240 min. accum.) 1/s +RotationTrackML30min Mid-Level Rotation Tracks 3-6km AGL (30 min. accum.) 1/s +RotationTrackML360min Mid-Level Rotation Tracks 3-6km AGL (360 min. accum.) 1/s +RotationTrackML60min Mid-Level Rotation Tracks 3-6km AGL (60 min. accum.) 1/s +RSMIN Minimal stomatal resistance s/m +DEPMN Minimum dew point depression K +MINRH Minimum Relative Humidity % +QMIN Minimum specific humidity at 2m kg/kg +TMIN Minimum temperature K +Entire Atmosphere Missing200 200 +MIXHT mixed layer depth m +MIXHT Mixed Layer Depth m +MIXL Mixed Layer Depth m +MLYNO Model Layer number (From bottom up) +MTHT Model terrain height m +MRCONO Moderate risk convective outlook categorical +MSTAV Moisture availability % +UFLX Momentum flux, u component N/m^2 +VFLX Momentum flux, v component N/m^2 +MNTSF Montgomery stream function m^2/s^2 +MSLET MSLP (Eta model reduction) Pa +MSLPM MSLP (MAPS System Reduction) Pa +NLGSP Natural Log of Surface Pressure ln(kPa) +NBDSF Near IR Beam Downward Solar Flux W/m^2 +NBSALB Near IR, Black Sky Albedo % +NDDSF Near IR Diffuse Downward Solar Flux W/m^2 +NWSALB Near IR, White Sky Albedo % +AOHFLX Net Air-Ocean Heat Flux W/m^2 +NLWRCS Net Long-Wave Radiation Flux, Clear Sky W/m^2 +NLWRS Net long wave radiation flux (surface) W/m^2 +NLWRT Net long wave radiation flux (top of atmosphere) W/m^2 +NLWRF Net Long-Wave Radiation Flux W/m^2 +NSWRFCS Net Short-Wave Radiation Flux, Clear Sky W/m^2 +NSWRS Net short-wave radiation flux (surface) W/m^2 +NSWRT Net short-wave radiation flux (top of atmosphere) W/m^2 +NSWRF Net Short Wave Radiation Flux W/m^2 +NTAT Nominal Top of the Atmosphere +CDLYR Non-convective cloud cover % +CDLYR Non-Convective Cloud Cover % + non-dim CD +NWSTR Normalised Waves Stress +NDVI Normalized Difference Vegetation Index +NCIP Number concentration for ice particles +NCIP Number concentration for ice particles non-dim +MIXLY Number of mixed layers next to surface integer +NPIXU Number Of Pixels Used Numeric +RLYRS Number of soil layers in root zone non-dim +RLYRS Number of soil layers in root zone Numeric +RLYRS Number of soil layers in root zone +OHC Ocean Heat Content J/m^2 +OITL Ocean Isotherm Level (1/10 deg C) C +NONE Ocean Isotherm Level 1/10 ℃ +OITL Ocean Isotherm Level 1/10 ℃ +NONE Ocean Mixed Layer +OML Ocean Mixed Layer +P2OMLT Ocean Mixed Layer Potential Density (Reference 2000m) kg/m^3 +OMLU Ocean Mixed Layer U Velocity m/s +OMLV Ocean Mixed Layer V Velocity m/s +ELEV Ocean Surface Elevation Relative to Geoid m +OMGALF Omega (Dp/Dt) divide by density K +EWATR Open water evaporation (standing water) W/m^2 +OSD Ordered Sequence of Data +OSEQ Ordered Sequence of Data +OZCON Ozone Concentration (PPB) PPB +OZMAX1 Ozone Daily Max from 1-hour Average ppbV +OZMAX8 Ozone Daily Max from 8-hour Average ppbV +O3MR Ozone Mixing Ratio kg \* kg^-1 +O3MR Ozone mixing ratio kg/kg +O3MR Ozone Mixing Ratio kg/kg +POZO Ozone production from col ozone term kg/kg/s +POZT Ozone production from temperature term kg/kg/s +POZ Ozone production kg/kg/s +TOZ Ozone tendency kg/kg/s +VDFOZ Ozone vertical diffusion kg/kg/s +SIGPAR Parallel Conductivity S\*m^1 +PRATMP Parallel Temperature K +PLI Parcel lifted index (to 500 mb) K +PLSMDEN Particle Number Density m^3 +PMTC Particulate matter (coarse) mg \* m^-3 +PMTC Particulate matter (coarse) mm\*g/m^3 +LPMTF Particulate matter (fine) log10(mg \* m^-3) +LPMTF Particulate matter (fine) log10(mm\*g/m^3) +PMTF Particulate matter (fine) mg \* m^-3 +PMTF Particulate matter (fine) mm\*g/m^3 +PPERTS Peak Period of The Total Swell s +PPERWW Peak Period of The Wind Waves s +PWPER Peak Wave Period s +SIGPED Pedersen Conductivity S\*m^1 +CPOFP Percent frozen precipitation % +PCTP1 Percent pcpn in 1st 6-h sub-period of 24 hr period % +PCTP2 Percent pcpn in 2nd 6-h sub-period of 24 hr period % +PCTP3 Percent pcpn in 3rd 6-h sub-period of 24 hr period % +PCTP4 Percent pcpn in 4th 6-h sub-period of 24 hr period % +PPSUB Percent precipitation in a sub-period of an overall period (Encoded as per cent accumulation over the sub-period) % +PMAXWH Period of Maximum Individual Wave Height s +PRPTMP Perpendicular Temperature K +PHOTAR Photosynthetically Active Radiation W/m^2 +PIXST Pixel scene type (Code table 4.218) +BLD Planetary Boundary Layer +HPBL Planetary boundary layer height m +HPBL Planetary Boundary Layer Height m +PLBL Planetary Boundary Layer +PBLR Planetary boundary layer regime (Code table 4.209) +PBLREG Planetary Boundary Layer Regime See Table 4.209 +CNWAT Plant canopy surface water mm +PDMAX1 PM 2.5 Daily Max from 1-hour Average ug/m^3 +PDMAX24 PM 2.5 Daily Max from 24-hour Average ug/m^3 +PEVAP Potential Evaporation kg m-2 +PEVAP Potential evaporation mm +PEVAP Potential Evaporation mm +PEVPR Potential Evaporation Rage W/m^2 +PEVPR Potential evaporation rate W/m^2 +PEVPR Potential Evaporation Rate W m-2 +THZ0 Potential temperature at top of viscous sublayer K +POT Potential temperature K +POT Potential temperature (theta) K +PV Potential vorticity K \*m^-2 \*kg^-1 \*s^-1 +PV Potential vorticity K \*m^-2\* kg^-1 \*s^-1 +PVL Potential Vorticity K \* m^2/kg^1\*s^1 +PVORT Potential vorticity K \* m^2 \* kg^-1\* s^-1 +PVMWW Potential Vorticity (Mass-Weighted) m/s +PWC Precipitable water category (Code table 4.202) +PWCAT Precipitable Water Category See Table 4.202 +P WAT Precipitable Water kg m-2 +PWAT Precipitable water mm +PRCP Precipitation mm +PRATE Precipitation Rate kg m-2 s-1 +PRATE Precipitation rate mm / s +PTYPE Precipitation type (Code table 4.201) +PTYPE Precipitation Type See Table 4.201 +PR Precip rate mm/hr +(See note 3) Predominant Weather Numeric +PWTHER Predominant Weather Numeric (See note 3) +PALT Pressure altitude m +PRESA Pressure anomaly Pa +PCBB Pressure at Cumulonimbus Bas Pa +PCBT Pressure at Cumulonimbus To Pa +PECBB Pressure at Embedded Cumulonimbus Bas Pa +PECBT Pressure at Embedded Cumulonimbus To Pa +PRESDEV Pressure deviation from ground to level Pa +PRESD Pressure deviation from mean sea level Pa +PRESN Pressure (nearest grid point) Pa +PLPL Pressure of level from which parcel was lifted Pa +PLPL Pressure of most parcel with highest theta-e in lowest 300 mb Pa +P Pressure Pa +PRES Pressure Pa +PRES Pressure Pa +PRMSL Pressure reduced to MSL Pa +PTEND Pressure tendency Pa/s +DIRPW Primary wave direction Degree +PERPW Primary wave mean period s +POP Probability of 0.01 inches of precipitation % +POP Probability of 0.01 inch of precipitation (POP) % +PROCON Probability of Convection % +PPFFG Probability of Excessive Rain % +CPOZP Probability of Freezing Precipitation % +PFREZPREC Probability of Freezing Precipitation % +CPOFP Probability of Frozen Precipitation % +PFROZPREC Probability of Frozen Precipitation % +PPFFG Probability of precipitation exceeding flash flood guidance values % +POSH Probability of Severe Hail (POSH) % +WarmRainProbability Probability of Warm Rain % +CWR Probability of Wetting Rain, exceeding in 0.10" in a given time period % +PTAN Prob of Temperature above normal % +PTBN Prob of Temperature below normal % +PTNN Prob of Temperature near normal % +PPAN Prob of Total Precipitation above normal % +PPBN Prob of Total Precipitation below normal % +PPNN Prob of Total Precipitation near normal % +PROTDEN Proton Density m^3 +DIFPFLUX Proton Flux (Differential) (m^2\*s\*sr\*eV)^1 +INTPFLUX Proton Flux (Integral) (m^2\*s\*sr)^1 +PROTTMP Proton Temperature K +EPOT Pseudo-adiabatic potential temperature or equivalent potential temperature K +MountainMapperQPE12H QPE - Mountain Mapper (12 hr. accum.) mm +MountainMapperQPE01H QPE - Mountain Mapper (1 hr. accum.) mm +MountainMapperQPE24H QPE - Mountain Mapper (24 hr. accum.) mm +MountainMapperQPE03H QPE - Mountain Mapper (3 hr. accum.) mm +MountainMapperQPE48H QPE - Mountain Mapper (48 hr. accum.) mm +MountainMapperQPE06H QPE - Mountain Mapper (6 hr. accum.) mm +MountainMapperQPE72H QPE - Mountain Mapper (72 hr. accum.) mm +GaugeOnlyQPE12H QPE - Radar Gauge Only (12 hr. accum.) mm +GaugeOnlyQPE01H QPE - Radar Gauge Only (1 hr. accum.) mm +GaugeOnlyQPE24H QPE - Radar Gauge Only (24 hr. accum.) mm +GaugeOnlyQPE03H QPE - Radar Gauge Only (3 hr. accum.) mm +GaugeOnlyQPE48H QPE - Radar Gauge Only (48 hr. accum.) mm +GaugeOnlyQPE06H QPE - Radar Gauge Only (6 hr. accum.) mm +GaugeOnlyQPE72H QPE - Radar Gauge Only (72 hr. accum.) mm +RadarOnlyQPE12H QPE - Radar Only (12 hr. accum.) mm +RadarOnlyQPE01H QPE - Radar Only (1 hr. accum.) mm +RadarOnlyQPE24H QPE - Radar Only (24 hr. accum.) mm +RadarOnlyQPE03H QPE - Radar Only (3 hr. accum.) mm +RadarOnlyQPE48H QPE - Radar Only (48 hr. accum.) mm +RadarOnlyQPE06H QPE - Radar Only (6 hr. accum.) mm +RadarOnlyQPE72H QPE - Radar Only (72 hr. accum.) mm +GaugeCorrQPE12H QPE - Radar with Gauge Bias Correction (12 hr. accum.) mm +GaugeCorrQPE01H QPE - Radar with Gauge Bias Correction (1 hr. accum.) mm +GaugeCorrQPE24H QPE - Radar with Gauge Bias Correction (24 hr. accum.) mm +GaugeCorrQPE03H QPE - Radar with Gauge Bias Correction (3 hr. accum.) mm +GaugeCorrQPE48H QPE - Radar with Gauge Bias Correction (48 hr. accum.) mm +GaugeCorrQPE06H QPE - Radar with Gauge Bias Correction (6 hr. accum.) mm +GaugeCorrQPE72H QPE - Radar with Gauge Bias Correction (72 hr. accum.) mm +PrecipRate Radar Precipitation Rate (SPR) mm/hr +RadarQualityIndex Radar Quality Index (RQI) +RDSP1 Radar spectra (1) +RDSP2 Radar spectra (2) +RDSP3 Radar spectra (3) +RDLNUM Radial number (2pi/lambda) m-1 +SWRAD Radiance (with respect to wave length) W \* m^-3 \*sr^-1 +LWRAD Radiance (with respect to wave number) W \* m^-1 \*sr^-1 +EPSR Radiative emissivity +EPSR Radiative emissivity mm +FRAIN Rain fraction of total cloud water Proportion +FRAIN Rain Fraction of Total Liquid Water +FRAIN Rain Fraction of Total Liquid Water non-dim +FRAIN Rain Fraction of Total Liquid Water Proportion +RWMR Rain Mixing Ratio kg kg-1 +RWMR Rain mixing ratio kg/kg +RPRATE Rain Precipitation Rate kg m-2s-1 +RPRATE Rain Precipitation Rate mm/s +RDRIP Rate of water dropping from canopy to ground unknown +ground Rate of water dropping from canopy to RDRIP +MergedReflectivityComposite Raw Composite Reflectivity Mosaic dBZ +MergedBaseReflectivity Raw Merged Base Reflectivity dBZ +RFL06 Reflectance in 0.6 Micron Channel % +RFL08 Reflectance in 0.8 Micron Channel % +RFL16 Reflectance in 1.6 Micron Channel % +RFL39 Reflectance in 3.9 Micron Channel % +Reflectivity0C Reflectivity at 0C dBZ +ReflectivityM10C Reflectivity at -10C dBZ +ReflectivityM15C Reflectivity at -15C dBZ +REFD Reflectivity at 1 km AGL dB +ReflectivityM20C Reflectivity at -20C dBZ +ReflectivityM5C Reflectivity at -5C dBZ +ReflectivityAtLowestAltitude Reflectivity At Lowest Altitude (RALA) dBZ +REFD Reflectivity dB +RAZA Relative Azimuth Angle Degree +RELD Relative divergence s^-1 +REV Relative Error Variance +RH Relative humidity % +R H Relative Humidity % +RHPW Relative Humidity with Respect to Precipitable Water % +RELV Relative vorticity s^-1 +RSSC Remotely sensed snow cover (Code table 4.215) +RI Richardson number Numeric +RI Richardson Number Numeric +FRIME Rime Factor +RIME Rime Factor non-dim +RIME Rime factor Numeric +RIME Rime Factor Numeric +RIME Rime Factor +SFCRH Roughness length for heat m +SALTY Salinity kg/kg +SLTFL Salt Flux mm\*s +SATD Saturation deficit Pa +SAT D Saturation Deficit Pa +SATOSM Saturation Of Soil Moisture kg/m^3 +SCALB Scaled albedo Numeric +SCBT Scaled brightness temperature Numeric +SCCTP Scaled cloud top pressure Numeric +SCLI Scaled lifted index Numeric +SCPW Scaled precipitable water Numeric +SCRAD Scaled radiance Numeric +SCST Scaled skin temperature Numeric +SCESTUWIND Scatterometer Estimated U Wind Component m/s +SCESTVWIND Scatterometer Estimated V Wind Component m/s +SCINT Scintillation Numeric +SEAB Sea Bottom +SeamlessHSR Seamless Hybrid Scan Reflectivity (SHSR) dBZ +SeamlessHSRHeight Seamless Hybrid Scan Reflectivity (SHSR) Height km +SSHG Sea Surface Height Relative to Geoid m +DIRSW Secondary wave direction Degree +PERSW Secondary wave mean periods s +s Seconds prior to initial reference time (defined in Section 1) TSEC +TSEC Seconds prior to initial reference time s +TSEC Seconds Prior To Initial Reference Time s +SHTFL Sensible heat net flux W/m^2 +SHI Severe Hail Index (SHI) +SCBL Shallow convective cloud bottom level +SCBL Shallow Convective Cloud Bottom Level +SCCBT Shallow Convective Cloud Bottom Level +SCCTL Shallow Convective Cloud Top Level +SCTL Shallow convective cloud top level +SCTL Shallow Convective Cloud Top Level +SHAHR Shallow Convective Heating rate K/s +SHAMR Shallow Convective Moistening Rate kg kg-1 s-1 +SHAMR Shallow Convective Moistening Rate kg/kg\*s +SWAVR Short wave radiation flux W/m^2 +SGCVV Sigma coordinate vertical velocity s^-1 +SIGL Sigma Level +SHAILPRO Significant Hail probability % +SIGHAILPROB Significant Hail probability % +HTSGW Significant height of combined wind waves and swell m +SWELL Significant height of swell waves m +WVHGT Significant height of wind waves (m) m +SIGTRNDPROB Significant Tornado probability % +STORPROB Significant Tornado probability % +SIGWINDPROB Significant Wind probability % +SWINDPRO Significant Wind probability % + Silt loam +SBC123 Simulated Brightness Counts for GOES 12, Channel 3 Byte +SBC124 Simulated Brightness Counts for GOES 12, Channel 4 Byte +SBTA1610 Simulated Brightness Temperature for ABI GOES-16, Band-10 K +SBTA1611 Simulated Brightness Temperature for ABI GOES-16, Band-11 K +SBTA1612 Simulated Brightness Temperature for ABI GOES-16, Band-12 K +SBTA1613 Simulated Brightness Temperature for ABI GOES-16, Band-13 K +SBTA1614 Simulated Brightness Temperature for ABI GOES-16, Band-14 K +SBTA1615 Simulated Brightness Temperature for ABI GOES-16, Band-15 K +SBTA1616 Simulated Brightness Temperature for ABI GOES-16, Band-16 K +SBTA167 Simulated Brightness Temperature for ABI GOES-16, Band-7 K +SBTA168 Simulated Brightness Temperature for ABI GOES-16, Band-8 K +SBTA169 Simulated Brightness Temperature for ABI GOES-16, Band-9 K +SBTA1710 Simulated Brightness Temperature for ABI GOES-17, Band-10 K +SBTA1711 Simulated Brightness Temperature for ABI GOES-17, Band-11 K +SBTA1712 Simulated Brightness Temperature for ABI GOES-17, Band-12 K +SBTA1713 Simulated Brightness Temperature for ABI GOES-17, Band-13 K +SBTA1714 Simulated Brightness Temperature for ABI GOES-17, Band-14 K +SBTA1715 Simulated Brightness Temperature for ABI GOES-17, Band-15 K +SBTA1716 Simulated Brightness Temperature for ABI GOES-17, Band-16 K +SBTA177 Simulated Brightness Temperature for ABI GOES-17, Band-7 K +SBTA178 Simulated Brightness Temperature for ABI GOES-17, Band-8 K +SBTA179 Simulated Brightness Temperature for ABI GOES-17, Band-9 K +AMSRE10 Simulated Brightness Temperature for AMSRE on Aqua, Channel 10 K +AMSRE11 Simulated Brightness Temperature for AMSRE on Aqua, Channel 11 K +AMSRE12 Simulated Brightness Temperature for AMSRE on Aqua, Channel 12 K +AMSRE9 Simulated Brightness Temperature for AMSRE on Aqua, Channel 9 K +SBT112 Simulated Brightness Temperature for GOES 11, Channel 2 K +SBT113 Simulated Brightness Temperature for GOES 11, Channel 3 K +SBT114 Simulated Brightness Temperature for GOES 11, Channel 4 K +SBT115 Simulated Brightness Temperature for GOES 11, Channel 5 K +SBT122 Simulated Brightness Temperature for GOES 12, Channel 2 K +SBT123 Simulated Brightness Temperature for GOES 12, Channel 3 K +SBT124 Simulated Brightness Temperature for GOES 12, Channel 4 K +SBT125 Simulated Brightness Temperature for GOES 12, Channel 5 K +SBT124 Simulated Brightness Temperature for GOES E Infrared K +SBT123 Simulated Brightness Temperature for GOES E Water Vapor K +SBT114 Simulated Brightness Temperature for GOES W Infrared K +SBT113 Simulated Brightness Temperature for GOES W Water Vapor K +SRFA161 Simulated Reflectance Factor for ABI GOES-16, Band-1 +SRFA162 Simulated Reflectance Factor for ABI GOES-16, Band-2 +SRFA163 Simulated Reflectance Factor for ABI GOES-16, Band-3 +SRFA164 Simulated Reflectance Factor for ABI GOES-16, Band-4 +SRFA165 Simulated Reflectance Factor for ABI GOES-16, Band-5 +SRFA166 Simulated Reflectance Factor for ABI GOES-16, Band-6 +SRFA171 Simulated Reflectance Factor for ABI GOES-17, Band-1 +SRFA172 Simulated Reflectance Factor for ABI GOES-17, Band-2 +SRFA173 Simulated Reflectance Factor for ABI GOES-17, Band-3 +SRFA174 Simulated Reflectance Factor for ABI GOES-17, Band-4 +SRFA175 Simulated Reflectance Factor for ABI GOES-17, Band-5 +SRFA176 Simulated Reflectance Factor for ABI GOES-17, Band-6 +SKTMP Skin Temperature K +SRCONO Slight risk convective outlook categorical +SSGSO Slope Of Sub-Grid Scale Orography Numeric +SNOAG Snow age day +SNOAG Snow Age day +SALBD Snow Albedo % +SCE Snow Cover by elevation (snow=0-252,neither=253,clouds=254) dm +SCP Snow Cover % +SC Snow Cover (snow=250,clouds=100,neither=50) +SNOWC Snow cover % +SNOWC Snow Cover % +SDEN Snow Density kg m-3 +SDEN Snow Density kg/m^3 +SNOD Snow depth m +SNO D Snow Depth m +SDWE Snow Depth Water Equivalent kg m-2 +SDWE Snow Depth Water Equivalent mm +SEVAP Snow Evaporation kg m-2 +SEVAP Snow Evaporation mm +SRWEQ Snowfall Rate Water Equivalent kg m-2 s-1 +SRWEQ Snowfall rate water equivalent mm / s +SNFALB Snow free albedo % +SNFALB Snow-Free Albedo % +SNO M Snow Melt kg m-2 +SNOM Snow melt mm +SNMR Snow Mixing Ratio kg kg-1 +SNMR Snow mixing ratio kg/kg +SNOHF Snow phase change heat flux W/m^2 +SNOHF Snow Phase Change Heat Flux W/m^2 +SPRATE Snow Precipitation Rate kg m-2s-1 +SPRATE Snow Precipitation Rate mm/s +SNOWT Snow temperature, depth-avg K +SNOT Snow temperature K +SNO T Snow temperature K +SNOT Snow Temperature K +SCE Snow water equivalent cm +SWEPN Snow water equivalent percent of normal % +SOILM Soil moisture content mm +SOILM Soil Moisture kg/m^3 +RCSOL Soil moisture parameter in canopy conductance Fraction +RCSOL Soil moisture parameter in canopy conductance Proportion +SOILP Soil Porosity m^3/m^3 +POROS Soil porosity Proportion +TSOIL Soil temperature K +SOTYP Soil type index +EUVIRR Solar EUV Irradiance W\*m^2 +RCS Solar parameter in canopy conductance Fraction +RCS Solar parameter in canopy conductance Proportion +SP Solar photosphere +SWHR Solar Radiative Heating Rate K/s +SOLRF Solar Radio Emissions W\*m^2\*Hz^1 +SPECIRR Solar Spectral Irradiance W\*m^2\*n\*m^1 +XLONG Solar X-ray Flux (XRS Long) W\*m^2 +XSHRT Solar X-ray Flux (XRS Short) W\*m^2 +SOLZA Solar Zenith Angle Degree +AMSL Specific Altitude Above Mean Sea Level m +QZ0 Specific humidity at top of viscous sublayer kg/kg +SPF H Specific Humidity kg kg-1 +SPFH Specific humidity kg/kg +HTGL Specified Height Level Above Ground m +SRCS Specified radius from the center of the Sun m +DWWW Spectal directional width of the wind waves +SPFTR Spectral Peakedness Factor s^-1 +SICED Speed of ice drift m/s +SPRDF Spread F m +HSTDV Standard deviation of height m +SDSGSO Standard Deviation Of Sub-Grid Scale Orography m +TSD1D Standard Dev. of IR Temp. over 1x1 deg. area K +HLCY Storm relative helicity J/kg +SSRUN Storm surface runoff mm +SURGE Storm Surge m +STPA Storm total precip accum mm +STRM Stream function m^2/s +SBSNO Sublimation (evaporation from snow) W m-2 +SBSNO Sublimation (evaporation from snow) W/m^2 +SUN Sunshine duration (ECMWF proposal, not WMO approved) s +SUNSD Sunshine Duration s +SUNS SunShine Numeric +SIPD Supercooled Large Droplet Icing mm +SIPD Supercooled Large Droplet (SLD) Icing See Table 4.207See Note (1) +SLDP Supercooled Large Droplet (SLD) Probabilitysee note 1 % +SLDP Supercooled Large Droplet (SLD) Probability % +SuperLayerCompositeReflectivity Super Layer Composite Reflectivity (33-60 kft) dBZ +AKHS Surface exchange coefficients for T and Q divided by delta z m/s +AKMS Surface exchange coefficients for U and V divided by delta z m/s +LFTX Surface Lifted Index K +SLI Surface lifted index K +PrecipType Surface Precipitation Type (SPT) +SFCR Surface roughness m +SSST Surface Salinity Trend psu per day +SLTYP Surface Slope Type Index +ModelSurfaceTemperature Surface Temperature C +SSTT Surface Temperature Trend degree per day +SSTOR Surface water storage mm +Surge Surge Height m +SX Sweat index Numeric +TMPA Temperature anomaly K +T Temperature K +TMP Temperature K +TMPSWP Temperature K +RCT Temperature parameter in canopy conductance Fraction +RCT Temperature parameter in canopy conductance Proportion +TTDIA Temperature Tendency By All Physics K/s +TTRAD Temperature tendency by all radiation K\*s^-1 +TTRAD Temperature tendency by all radiation K/s +TTPHY Temperature Tendency By Non-radiation Physics K/s +WTEND Tendency of vertical velocity m/s^2 + The Associated Legendre Functions of the first kind are defined by +MASK Thematic Mask Numeric +THICK Thickness m +TSC Thunderstorm coverage (Code table 4.204) +TSMT Thunderstorm maximum tops m +TSTM Thunderstorm probability % +TSTM Thunderstorm Probability % +TACONCP Time-integrated air concentration of caesium pollutant Bq\*s/m^3 +TACONIP Time-integrated air concentration of iodine pollutant Bq\*s/m^3 +TACONRDP Time-integrated air concentration of radioactive pollutant Bq\*s/m^3 +PTOR Tornado probability % +TORPROB Tornado probability % +TRNDPROB Tornado probability % +TCDC Total cloud cover % +TCOLI Total column-integrated cloud ice mm +TCOLI Total Column-Integrated Cloud Ice mm +TCOLW Total column-integrated cloud water mm +TCOLW Total Column-Integrated Cloud Water mm +TCOLC Total column-integrated condensate mm +TCOLC Total Column-Integrated Condensate mm +TCOLM Total column-integrated melting ice kg m-2 +TCOLM Total column-integrated melting ice mm +TCIOZ Total Column Integrated Ozone Dobson +TCOLR Total Column Integrated Rain kg m-2 +TCOLR Total column integrated rain mm +TCOLR Total Column Integrated Rain mm +TCOLS Total Column Integrated Snow kg m-2 +TCOLS Total column integrated snow mm +TCOLS Total Column Integrated Snow mm +TCLSW Total column-integrated supercooled liquid water kg m-2 +TCLSW Total column-integrated supercooled liquid water mm +TCIWV Total Column Integrated Water Vapour kg m-2 +TCIWV Total Column Integrated Water Vapour mm +TCOLG Total Column Integrate Graupel kg/m^2 +TCWAT Total Column Water (Vertically integrated total water (vapour+cloud water/ice) kg m-2 +TCWAT Total Column Water(Vertically integrated total water (vapour+cloud water/ice) mm +TCOND Total Condensate kg \* kg^-1 +TCOND Total condensate kg/kg +TCOND Total Condensate kg/kg +THFLX Total Downward Heat Flux at Surface W/m^2 +TIPD Total Icing Potential Diagnostic non-dim +TIPD Total Icing Potential Diagnostic +TOZNE Total ozone Dobson +A PCP Total Precipitation kg m-2 +APCP Total precipitation mm +APCPN Total precipitation (nearest grid point) kg/m2 +APCPN Total precipitation (nearest grid point) mm +TPRATE Total Precipitation Rate kg m-2s-1 +TPRATE Total Precipitation Rate mm/s +PRSIGSV Total Probability of Extreme Severe Thunderstorms (Days 2,3) % +PRSIGSVR Total Probability of Extreme Severe Thunderstorms (Days 2,3) % +PRSVR Total Probability of Severe Thunderstorms (Days 2,3) % +ASNOW Total Snowfall m +TOTSN Total snowfall m +TSRATE Total Snowfall Rate m s-1 +TSRATE Total Snowfall Rate m/s +TSRWE Total Snowfall Rate Water Equivalent kg m-2s-1 +TSRWE Total Snowfall Rate Water Equivalent mm/s +TSNOW Total Snow kg/m2 +TSNOW Total Snow mm +TSNOWP Total Snow Precipitation kg m-2 +TSNOWP Total Snow Precipitation mm +TTX Total totals index K +TWATP Total Water Precipitation kg m-2 +TWATP Total Water Precipitation mm +TTHDP Transient thermocline depth m +TRANSO Transpiration Stree-Onset(Soil Moisture) kg/m^3 +SMREF Transpiration stress-onset (soil moisture) Proportion +TRANS Transpiration W/m^2 +TCHP Tropical Cyclone Heat Potential J/m^2\*K +TRO Tropopause +TRBBS Turbulence base m +TURBB Turbulence Base m +TURB Turbulence (Code table 4.208) +TPFI Turbulence Potential Forecast Index +TURB Turbulence See Table 4.208 +TRBTP Turbulence top m +TURBT Turbulence Top m +TKE Turbulent Kinetic Energy J kg-1 +TKE Turbulent kinetic energy J/kg +UOGRD u-component of current cm/s +UOGRD u-component of current m/s +MAXUW U Component of Hourly Maximum 10m Wind Speed m/s +UICE u-component of ice drift m/s +UGUST u-component of wind gust m/s +UGRD u-component of wind m/s +USTM U-component storm motion m/s +USTM U-Component Storm Motion m/s +USSD U-component Surface Stokes Drift m/s +UVI Ultra Violet Index J/m^2 +UPHL Updraft Helicity in Layer 2-5 km AGL m^2/s^2 +UPHL Updraft Helicity m^2/s^2 +ULSM Upper layer soil moisture kg/m^3 +ULST Upper layer soil temperature K +ULWRF Upward Long-Wave Rad. Flux W/m^2 +ULWRF Upward long-wave radiation flux W/m^2 + Upward Long-W/m^2 ULWRF +USWRF Upward Short-Wave Rad. Flux W/m^2 +USWRF Upward short-wave radiation flux W/m^2 + Upward Short-W/m^2 USWRF +UTRF Upward Total radiation Flux W/m^2 +DUVB UV-B downward solar flux W/m^2 +UVI UV Index J/m^2 +UVIUCS UV Index (Under Clear Sky) Numeric +VAPP Vapor pressure Pa +VAPP Vapor Pressure Pa +VOGRD v-component of current cm/s +VOGRD v-component of current m/s +MAXVW V Component of Hourly Maximum 10m Wind Speed m/s +VICE v-component of ice drift m/s +UGUST v-component of wind gust m/s +VGRD v-component of wind m/s +VSTM V-component storm motion m/s +VSTM V-Component Storm Motion m/s +VSSD V-component Surface Stokes Drift m/s +VEGT Vegetation canopy temperature K +VGTYP Vegetation Type Integer 0-13 +VEG Vegetation % +SPEED Velocity Magnitude (Speed) m\*s^1 +LMV Velocity Point Model Surface +VPOT Velocity potential m^2/s +VRATE Ventilation Rate m^2/s +VDFHR Vertical Diffusion Heating rate K/s +VDFVA Vertical Diffusion Meridional Acceleration m/s^2 +VDFMR Vertical Diffusion Moistening Rate kg/kg\*s +VDFUA Vertical Diffusion Zonal Acceleration m/s^2 +VEDH Vertical Eddy Diffusivity Heat exchange m^2/s +VTEC Vertical Electron Content m^2 +VII Vertically Integrated Ice (VII) kg/m^2 +VILIQ Vertically-integrated liquid kg/m^2 +MRMSVILDensity Vertically Integrated Liquid (VIL) Density g/m^3 +MRMSVIL Vertically Integrated Liquid (VIL) kg/m^2 +VIL Vertically Integrated Liquid (VIL) kg/m^2 +VWSH Vertical speed shear s^-1 +VWSH Vertical speed sheer s^-1 +VUCSH Vertical u-component shear s^-1 +VVCSH Vertical v-component shear s^-1 +DZDT Vertical velocity geometric m/s +VVEL Vertical velocity pressure Pa/s +VPTMP Virtual potential temperature K +VTMP Virtual temperature K +VIS Visibility m +VBDSF Visible Beam Downward Solar Flux W/m^2 +SBSALB Visible, Black Sky Albedo % +VDDSF Visible Diffuse Downward Solar Flux W/m^2 +Visible Visible Imagery +SWSALB Visible, White Sky Albedo % +VASH Volcanic ash (Code table 4.206) +VAFTD Volcanic Ash Forecast Transport and Dispersion log10(kg/m^3) +VOLASH Volcanic Ash See Table 4.206 +VOLDEC Volumetric Direct Evaporation Cease(Soil Moisture) m^3/m^3 +VSOSM Volumetric Saturation Of Soil Moisture m^3/m^3 +SOILW Volumetric soil moisture content Proportion +VSOILM Volumetric Soil Moisture m^3/m^3 +VOLTSO Volumetric Transpiration Stree-Onset(Soil Moisture) m^3/m^3 +VWILTM Volumetric Wilting Moisture m^3/m^3 +WCINC Water condensate added by precip assimilation mm +WCCONV Water Condensate Flux Convergance (Vertical Int) mm +WCVFLX Water Condensate Meridional Flux (Vertical Int) mm +WCUFLX Water Condensate Zonal Flux (Vertical Int) mm +WEASD Water Equivalent of Accumulated Snow Depth kg m-2 +WEASD Water equivalent of accumulated snow depth mm +WATR Water runoff mm +TEMPWTR Water temperature K +WVINC Water vapor added by precip assimilation mm +WVCONV Water Vapor Flux Convergance (Vertical Int) mm +WaterVapor Water Vapor Imagery K +WVVFLX Water Vapor Meridional Flux (Vertical Int) mm +WVUFLX Water Vapor Zonal Flux (Vertical Int) mm +WDIRW Wave Directional Width +WESP Wave Engery Spectrum s/m^2 +WVSP1 Wave spectra (1) +WVSP2 Wave spectra (2) +WVSP3 Wave spectra (3) +WSTP Wave Steepness +WSTR Wave Stress N/m^2 +wxType Weather +ModelWetbulbTemperature Wet Bulb Temperature C +WHTCOR White Light Coronagraph Radiance W\*s\*r^1\*m^2 +WHTRAD White Light Radiance W\*s\*r^1\*m^2 +WILT Wilting Point kg/m^3 +WILT Wilting point Proportion +WCI Wind chill factor K +WDIR Wind direction (from which blowing) deg +WMIXE Wind mixing energy J +WINDPROB Wind probability % +WINDPROB Wind Probability % +WGS Wind speed gust m/s +PWS Wind speed m/s +WIND Wind speed m/s +HGT X X-gradient of Height m^-1 +LPS X X-gradient of Log Pressure m^-1 +XRAYRAD X-Ray Radiance W\*s\*r^1\*m^2 +HGT Y Y-gradient of Height m^-1 +LPS Y Y-gradient of Log Pressure m^-1 +UGWD Zonal flux of gravity wave stress N/m^2 +U-GWD Zonal Flux of Gravity Wave Stress N/m^2 +================================== =============================================================================================================================================================== ==================================== + diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 0000000..e4feb62 --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,106 @@ +================================== +Python AWIPS Data Access Framework +================================== + +`AWIPS `_ is a weather display and analysis package developed by the National Weather Service for operational forecasting. UCAR's `Unidata Program Center `_ supports a non-operational open-source release of the AWIPS software (`EDEX `_, `CAVE `_, and `python-awips `_). + +The python-awips package provides a data access framework for requesting grid and geometry datasets from an `EDEX `_ server. + +.. _Jupyter Notebook: http://nbviewer.jupyter.org/github/Unidata/python-awips/tree/master/examples/notebooks + +Install +------- + +- pip install python-awips + +Requirements +~~~~~~~~~~~~ + +- Python 2.7+ +- Shapely 1.4+ +- MetPy and enum34 to run the `Jupyter Notebook`_ examples + +Quick Example +~~~~~~~~~~~~~ + +:: + + from awips.dataaccess import DataAccessLayer + DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu") + dataTypes = DataAccessLayer.getSupportedDatatypes() + list(dataTypes) + + ['acars', + 'binlightning', + 'bufrmosavn', + 'bufrmoseta', + 'bufrmosgfs', + 'bufrmoshpc', + 'bufrmoslamp', + 'bufrmosmrf', + 'bufrua', + 'climate', + 'common_obs_spatial', + 'gfe', + 'grid', + 'hydro', + 'maps', + 'modelsounding', + 'obs', + 'practicewarning', + 'radar', + 'radar_spatial', + 'satellite', + 'sfcobs', + 'topo', + 'warning'] + + request = DataAccessLayer.newDataRequest() + request.setDatatype("satellite") + availableSectors = DataAccessLayer.getAvailableLocationNames(request) + availableSectors.sort() + for sector in availableSectors: + print sector + request.setLocationNames(sector) + availableProducts = DataAccessLayer.getAvailableParameters(request) + availableProducts.sort() + for product in availableProducts: + print " - " + product + + ECONUS + - ACTP + - ADP + - AOD + - CAPE + - CH-01-0.47um + - CH-02-0.64um + - CH-03-0.87um + - CH-04-1.38um + ... + EFD + - ACTP + - ADP + - AOD + - CAPE + - CH-01-0.47um + - CH-02-0.64um + - CH-03-0.87um + - CH-04-1.38um + ... + + +See the `API Documentation `_ for more information. + +------------- +Documentation +------------- + +.. toctree:: + :maxdepth: 2 + + install + api/index + examples/index + dev + gridparms + about diff --git a/docs/source/install.rst b/docs/source/install.rst new file mode 100644 index 0000000..3a36ebc --- /dev/null +++ b/docs/source/install.rst @@ -0,0 +1,37 @@ +.. _Jupyter Notebook: http://nbviewer.jupyter.org/github/Unidata/python-awips/tree/master/examples/notebooks + +Installation +------------------ + +- pip install python-awips + +Requirements +~~~~~~~~~~~~ + +- Python 2.7 or later +- pip install numpy shapely +- pip install metpy enum34 - to run `Jupyter Notebook`_ examples + +Install from Github +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +- git clone https://github.com/Unidata/python-awips.git && cd python-awips +- python setup.py install + + +Install for AWIPS +~~~~~~~~~~~~~~~~~ + +On standalone AWIPS systems, the full `AWIPS Python Stack `_ is installed to ``/awips2/python`` as RPM packages. + +Easy install on an AWIPS system + +* For Unidata AWIPS release **16.2.2+**: + + * /awips2/python/bin/easy_install pip + * /awips2/python/bin/pip install python-awips + +* For releases before and up to **16.1.5** you may need to run + + * yum install awips2-python-setuptools + diff --git a/docs/source/notebook_gen_sphinxext.py b/docs/source/notebook_gen_sphinxext.py new file mode 100644 index 0000000..59beae3 --- /dev/null +++ b/docs/source/notebook_gen_sphinxext.py @@ -0,0 +1,77 @@ +# +# Generation of RST from notebooks +# +import glob +import os +import os.path +import warnings + +warnings.simplefilter('ignore') + +from nbconvert.exporters import rst + +def setup(app): + setup.app = app + setup.config = app.config + setup.confdir = app.confdir + + app.connect('builder-inited', generate_rst) + + return dict( + version='0.1', + parallel_read_safe=True, + parallel_write_safe=True + ) + +notebook_source_dir = '../../examples/notebooks' +generated_source_dir = 'examples/generated' + + +def nb_to_rst(nb_path): + """convert notebook to restructured text""" + exporter = rst.RSTExporter() + out, resources = exporter.from_file(open(nb_path)) + basename = os.path.splitext(os.path.basename(nb_path))[0] + imgdir = basename + '_files' + img_prefix = os.path.join(imgdir, basename + '_') + resources['metadata']['basename'] = basename + resources['metadata']['name'] = basename.replace('_', ' ') + resources['metadata']['imgdir'] = imgdir + base_url = ('http://nbviewer.ipython.org/github/Unidata/python-awips/blob/master/' + 'examples/notebooks/') + out_lines = ['`Notebook <%s>`_' % (base_url + os.path.basename(nb_path))] + for line in out.split('\n'): + if line.startswith('.. image:: '): + line = line.replace('output_', img_prefix) + out_lines.append(line) + out = '\n'.join(out_lines) + + return out, resources + + +def write_nb(dest, output, resources): + if not os.path.exists(dest): + os.makedirs(dest) + rst_file = os.path.join(dest, + resources['metadata']['basename'] + resources['output_extension']) + name = resources['metadata']['name'] + with open(rst_file, 'w') as rst: + header = '=' * len(name) + rst.write(header + '\n') + rst.write(name + '\n') + rst.write(header + '\n') + rst.write(output) + + imgdir = os.path.join(dest, resources['metadata']['imgdir']) + if not os.path.exists(imgdir): + os.makedirs(imgdir) + basename = resources['metadata']['basename'] + for filename in resources['outputs']: + img_file = os.path.join(imgdir, filename.replace('output_', basename + '_')) + with open(img_file, 'wb') as img: + img.write(resources['outputs'][filename]) + + +def generate_rst(app): + for fname in glob.glob(os.path.join(app.srcdir, notebook_source_dir, '*.ipynb')): + write_nb(os.path.join(app.srcdir, generated_source_dir), *nb_to_rst(fname)) diff --git a/dynamicserialize/DynamicSerializationManager.py b/dynamicserialize/DynamicSerializationManager.py new file mode 100644 index 0000000..e0c8dcd --- /dev/null +++ b/dynamicserialize/DynamicSerializationManager.py @@ -0,0 +1,52 @@ +## +## + + +# +# A port of the Java DynamicSerializeManager. Should be used to read/write +# DynamicSerialize binary data. +# +# +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/09/10 njensen Initial Creation. +# +# +# + +from thrift.transport import TTransport +import SelfDescribingBinaryProtocol, ThriftSerializationContext + +class DynamicSerializationManager: + + def __init__(self): + self.transport = None + + def _deserialize(self, ctx): + return ctx.deserializeMessage() + + def deserializeBytes(self, bytes): + ctx = self._buildSerializationContext(bytes) + ctx.readMessageStart() + obj = self._deserialize(ctx) + ctx.readMessageEnd() + return obj + + def _buildSerializationContext(self, bytes=None): + self.transport = TTransport.TMemoryBuffer(bytes) + protocol = SelfDescribingBinaryProtocol.SelfDescribingBinaryProtocol(self.transport) + return ThriftSerializationContext.ThriftSerializationContext(self, protocol) + + def serializeObject(self, obj): + ctx = self._buildSerializationContext() + ctx.writeMessageStart("dynamicSerialize") + self._serialize(ctx, obj) + ctx.writeMessageEnd() + return self.transport.getvalue() + + def _serialize(self, ctx, obj): + ctx.serializeMessage(obj) \ No newline at end of file diff --git a/dynamicserialize/SelfDescribingBinaryProtocol.py b/dynamicserialize/SelfDescribingBinaryProtocol.py new file mode 100644 index 0000000..8fed3ba --- /dev/null +++ b/dynamicserialize/SelfDescribingBinaryProtocol.py @@ -0,0 +1,125 @@ +## +## + + +from thrift.protocol.TProtocol import * +from thrift.protocol.TBinaryProtocol import * +from struct import pack, unpack + + +# +# Partially compatible AWIPS-II Thrift Binary Protocol +# +# Missing functionality: +#
    +#
  • Custom Serializers +#
  • Inheritance +#
+# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 11/11/09 chammack Initial Creation. +# 06/09/10 njensen Added float, list methods +# Apr 24, 2015 4425 nabowle Add F64List support. +# +# +# + +import struct, numpy + +FLOAT = 64 + +intList = numpy.dtype(numpy.int32).newbyteorder('>') +floatList = numpy.dtype(numpy.float32).newbyteorder('>') +longList = numpy.dtype(numpy.int64).newbyteorder('>') +shortList = numpy.dtype(numpy.int16).newbyteorder('>') +byteList = numpy.dtype(numpy.int8).newbyteorder('>') +doubleList = numpy.dtype(numpy.float64).newbyteorder('>') + +class SelfDescribingBinaryProtocol(TBinaryProtocol): + + def readFieldBegin(self): + type = self.readByte() + if type == TType.STOP: + return (None, type, 0) + name = self.readString() + id = self.readI16() + return (name, type, id) + + def readStructBegin(self): + return self.readString() + + def writeStructBegin(self, name): + self.writeString(name) + + def writeFieldBegin(self, name, type, id): + self.writeByte(type) + self.writeString(name) + self.writeI16(id) + + def readFloat(self): + d = self.readI32() + dAsBytes = struct.pack('i', d) + f = struct.unpack('f', dAsBytes) + return f[0] + + def writeFloat(self, f): + dAsBytes = struct.pack('f', f) + i = struct.unpack('i', dAsBytes) + self.writeI32(i[0]) + + def readI32List(self, sz): + buff = self.trans.readAll(4*sz) + val = numpy.frombuffer(buff, dtype=intList, count=sz) + return val + + def readF32List(self, sz): + buff = self.trans.readAll(4*sz) + val = numpy.frombuffer(buff, dtype=floatList, count=sz) + return val + + def readF64List(self, sz): + buff = self.trans.readAll(8*sz) + val = numpy.frombuffer(buff, dtype=doubleList, count=sz) + return val + + def readI64List(self, sz): + buff = self.trans.readAll(8*sz) + val = numpy.frombuffer(buff, dtype=longList, count=sz) + return val + + def readI16List(self, sz): + buff = self.trans.readAll(2*sz) + val = numpy.frombuffer(buff, dtype=shortList, count=sz) + return val + + def readI8List(self, sz): + buff = self.trans.readAll(sz) + val = numpy.frombuffer(buff, dtype=byteList, count=sz) + return val + + def writeI32List(self, buff): + b = numpy.asarray(buff, intList) + self.trans.write(numpy.getbuffer(b)) + + def writeF32List(self, buff): + b = numpy.asarray(buff, floatList) + self.trans.write(numpy.getbuffer(b)) + + def writeF64List(self, buff): + b = numpy.asarray(buff, doubleList) + self.trans.write(numpy.getbuffer(b)) + + def writeI64List(self, buff): + b = numpy.asarray(buff, longList) + self.trans.write(numpy.getbuffer(b)) + + def writeI16List(self, buff): + b = numpy.asarray(buff, shortList) + self.trans.write(numpy.getbuffer(b)) + + def writeI8List(self, buff): + b = numpy.asarray(buff, byteList) + self.trans.write(numpy.getbuffer(b)) diff --git a/dynamicserialize/ThriftSerializationContext.py b/dynamicserialize/ThriftSerializationContext.py new file mode 100644 index 0000000..75324a6 --- /dev/null +++ b/dynamicserialize/ThriftSerializationContext.py @@ -0,0 +1,407 @@ +## +## + + +# +# A port of the Java ThriftSerializationContext, used for reading/writing +# DynamicSerialize objects to/from thrift. +# +# For serialization, it has no knowledge of the expected types in other +# languages, it is instead all based on inspecting the types of the objects +# passed to it. Therefore, ensure the types of python objects and primitives +# match what they should be in the destination language. +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/09/10 njensen Initial Creation. +# 06/12/13 #2099 dgilling Implement readObject() and +# writeObject(). +# Apr 24, 2015 4425 nabowle Add Double support +# Oct 17, 2016 5919 njensen Optimized for speed +# +# + +from thrift.Thrift import TType +import inspect +import sys +import types +import time +import dynamicserialize +from dynamicserialize import dstypes, adapters +import SelfDescribingBinaryProtocol +import numpy + +DS_LEN = len('dynamicserialize.dstypes.') + +dsObjTypes = {} + + +def buildObjMap(module): + if '__all__' in module.__dict__: + for i in module.__all__: + name = module.__name__ + '.' + i + __import__(name) + buildObjMap(sys.modules[name]) + else: + clzName = module.__name__[module.__name__.rfind('.') + 1:] + clz = module.__dict__[clzName] + tname = module.__name__ + tname = tname[DS_LEN:] + dsObjTypes[tname] = clz + +buildObjMap(dstypes) + +pythonToThriftMap = { + types.StringType: TType.STRING, + types.IntType: TType.I32, + types.LongType: TType.I64, + types.ListType: TType.LIST, + types.DictionaryType: TType.MAP, + type(set([])): TType.SET, + types.FloatType: SelfDescribingBinaryProtocol.FLOAT, + # types.FloatType: TType.DOUBLE, + types.BooleanType: TType.BOOL, + types.InstanceType: TType.STRUCT, + types.NoneType: TType.VOID, + numpy.float32: SelfDescribingBinaryProtocol.FLOAT, + numpy.int32: TType.I32, + numpy.ndarray: TType.LIST, + numpy.object_: TType.STRING, # making an assumption here + numpy.string_: TType.STRING, + numpy.float64: TType.DOUBLE, + numpy.int16: TType.I16, + numpy.int8: TType.BYTE, + numpy.int64: TType.I64 +} + +primitiveSupport = (TType.BYTE, TType.I16, TType.I32, TType.I64, + SelfDescribingBinaryProtocol.FLOAT, TType.DOUBLE) + + +class ThriftSerializationContext(object): + + def __init__(self, serializationManager, selfDescribingBinaryProtocol): + self.serializationManager = serializationManager + self.protocol = selfDescribingBinaryProtocol + self.typeDeserializationMethod = { + TType.STRING: self.protocol.readString, + TType.I16: self.protocol.readI16, + TType.I32: self.protocol.readI32, + TType.LIST: self._deserializeArray, + TType.MAP: self._deserializeMap, + TType.SET: self._deserializeSet, + SelfDescribingBinaryProtocol.FLOAT: self.protocol.readFloat, + TType.BYTE: self.protocol.readByte, + TType.I64: self.protocol.readI64, + TType.DOUBLE: self.protocol.readDouble, + TType.BOOL: self.protocol.readBool, + TType.STRUCT: self.deserializeMessage, + TType.VOID: lambda: None + } + self.typeSerializationMethod = { + TType.STRING: self.protocol.writeString, + TType.I16: self.protocol.writeI16, + TType.I32: self.protocol.writeI32, + TType.LIST: self._serializeArray, + TType.MAP: self._serializeMap, + TType.SET: self._serializeSet, + SelfDescribingBinaryProtocol.FLOAT: self.protocol.writeFloat, + TType.BYTE: self.protocol.writeByte, + TType.I64: self.protocol.writeI64, + TType.DOUBLE: self.protocol.writeDouble, + TType.BOOL: self.protocol.writeBool, + TType.STRUCT: self.serializeMessage, + TType.VOID: lambda x: None + } + self.listDeserializationMethod = { + TType.BYTE: self.protocol.readI8List, + TType.I16: self.protocol.readI16List, + TType.I32: self.protocol.readI32List, + TType.I64: self.protocol.readI64List, + SelfDescribingBinaryProtocol.FLOAT: self.protocol.readF32List, + TType.DOUBLE: self.protocol.readF64List + } + self.listSerializationMethod = { + TType.BYTE: self.protocol.writeI8List, + TType.I16: self.protocol.writeI16List, + TType.I32: self.protocol.writeI32List, + TType.I64: self.protocol.writeI64List, + SelfDescribingBinaryProtocol.FLOAT: self.protocol.writeF32List, + TType.DOUBLE: self.protocol.writeF64List + } + + def readMessageStart(self): + msg = self.protocol.readMessageBegin() + return msg[0] + + def readMessageEnd(self): + self.protocol.readMessageEnd() + + def deserializeMessage(self): + name = self.protocol.readStructBegin() + if name.isdigit(): + obj = self._deserializeType(int(name)) + return obj + name = name.replace('_', '.') + if name in adapters.classAdapterRegistry: + return adapters.classAdapterRegistry[name].deserialize(self) + elif '$' in name: + # it's an inner class, we're going to hope it's an enum, treat it + # special + fieldName, fieldType, fieldId = self.protocol.readFieldBegin() + if fieldName != '__enumValue__': + raise dynamiceserialize.SerializationException( + "Expected to find enum payload. Found: " + fieldName) + obj = self.protocol.readString() + self.protocol.readFieldEnd() + return obj + else: + clz = dsObjTypes[name] + obj = clz() + + while self._deserializeField(name, obj): + pass + + self.protocol.readStructEnd() + return obj + + def _deserializeType(self, b): + try: + return self.typeDeserializationMethod[b]() + except KeyError: + raise dynamicserialize.SerializationException( + "Unsupported type value " + str(b)) + + def _deserializeField(self, structname, obj): + fieldName, fieldType, fieldId = self.protocol.readFieldBegin() + if fieldType == TType.STOP: + return False + elif fieldType != TType.VOID: + result = self._deserializeType(fieldType) + lookingFor = "set" + fieldName[0].upper() + fieldName[1:] + + try: + setMethod = getattr(obj, lookingFor) + setMethod(result) + except: + raise dynamicserialize.SerializationException( + "Couldn't find setter method " + lookingFor) + + self.protocol.readFieldEnd() + return True + + def _deserializeArray(self): + listType, size = self.protocol.readListBegin() + result = [] + if size: + if listType not in primitiveSupport: + m = self.typeDeserializationMethod[listType] + result = [m() for n in xrange(size)] + else: + result = self.listDeserializationMethod[listType](size) + self.protocol.readListEnd() + return result + + def _deserializeMap(self): + keyType, valueType, size = self.protocol.readMapBegin() + result = {} + for n in xrange(size): + # can't go off the type, due to java generics limitations dynamic serialize is + # serializing keys and values as void + key = self.typeDeserializationMethod[TType.STRUCT]() + value = self.typeDeserializationMethod[TType.STRUCT]() + result[key] = value + self.protocol.readMapEnd() + return result + + def _deserializeSet(self): + setType, setSize = self.protocol.readSetBegin() + result = set([]) + for n in xrange(setSize): + result.add(self.typeDeserializationMethod[TType.STRUCT]()) + self.protocol.readSetEnd() + return result + + def _lookupType(self, obj): + pyt = type(obj) + if pyt in pythonToThriftMap: + return pythonToThriftMap[pyt] + elif pyt.__module__[:DS_LEN - 1] == ('dynamicserialize.dstypes'): + return pythonToThriftMap[types.InstanceType] + else: + raise dynamicserialize.SerializationException( + "Don't know how to serialize object of type: " + str(pyt)) + + def serializeMessage(self, obj): + tt = self._lookupType(obj) + + if tt == TType.STRUCT: + fqn = obj.__module__[DS_LEN:] + if fqn in adapters.classAdapterRegistry: + # get proper class name when writing class name to serialization stream + # in case we have a special inner-class case + m = sys.modules[adapters.classAdapterRegistry[fqn].__name__] + if isinstance(m.ClassAdapter, list): + fqn = m.ClassAdapter[0] + self.protocol.writeStructBegin(fqn) + adapters.classAdapterRegistry[fqn].serialize(self, obj) + return + else: + self.protocol.writeStructBegin(fqn) + methods = inspect.getmembers(obj, inspect.ismethod) + fid = 1 + for m in methods: + methodName = m[0] + if methodName.startswith('get'): + fieldname = methodName[3].lower() + methodName[4:] + val = m[1]() + ft = self._lookupType(val) + if ft == TType.STRUCT: + fc = val.__module__[DS_LEN:] + self._serializeField(fieldname, ft, fid, val) + else: + self._serializeField(fieldname, ft, fid, val) + fid += 1 + self.protocol.writeFieldStop() + + self.protocol.writeStructEnd() + else: + # basic types + self.protocol.writeStructBegin(str(tt)) + self._serializeType(obj, tt) + self.protocol.writeStructEnd() + + def _serializeField(self, fieldName, fieldType, fieldId, fieldValue): + self.protocol.writeFieldBegin(fieldName, fieldType, fieldId) + self._serializeType(fieldValue, fieldType) + self.protocol.writeFieldEnd() + + def _serializeType(self, fieldValue, fieldType): + if fieldType in self.typeSerializationMethod: + return self.typeSerializationMethod[fieldType](fieldValue) + else: + raise dynamicserialize.SerializationException( + "Unsupported type value " + str(fieldType)) + + def _serializeArray(self, obj): + size = len(obj) + if size: + if type(obj) is numpy.ndarray: + t = pythonToThriftMap[obj.dtype.type] + size = obj.size + else: + t = self._lookupType(obj[0]) + else: + t = TType.STRUCT + self.protocol.writeListBegin(t, size) + if t == TType.STRING: + if type(obj) is numpy.ndarray: + if len(obj.shape) == 1: + for x in obj: + s = str(x).strip() + self.typeSerializationMethod[t](s) + else: + for x in obj: + for y in x: + s = str(y).strip() + self.typeSerializationMethod[t](s) + else: + for x in obj: + s = str(x) + self.typeSerializationMethod[t](s) + elif t not in primitiveSupport: + for x in obj: + self.typeSerializationMethod[t](x) + else: + self.listSerializationMethod[t](obj) + self.protocol.writeListEnd() + + def _serializeMap(self, obj): + size = len(obj) + self.protocol.writeMapBegin(TType.VOID, TType.VOID, size) + for k in obj.keys(): + self.typeSerializationMethod[TType.STRUCT](k) + self.typeSerializationMethod[TType.STRUCT](obj[k]) + self.protocol.writeMapEnd() + + def _serializeSet(self, obj): + size = len(obj) + self.protocol.writeSetBegin(TType.VOID, size) + for x in obj: + self.typeSerializationMethod[TType.STRUCT](x) + self.protocol.writeSetEnd() + + def writeMessageStart(self, name): + self.protocol.writeMessageBegin(name, TType.VOID, 0) + + def writeMessageEnd(self): + self.protocol.writeMessageEnd() + + def readBool(self): + return self.protocol.readBool() + + def writeBool(self, b): + self.protocol.writeBool(b) + + def readByte(self): + return self.protocol.readByte() + + def writeByte(self, b): + self.protocol.writeByte(b) + + def readDouble(self): + return self.protocol.readDouble() + + def writeDouble(self, d): + self.protocol.writeDouble(d) + + def readFloat(self): + return self.protocol.readFloat() + + def writeFloat(self, f): + self.protocol.writeFloat(f) + + def readI16(self): + return self.protocol.readI16() + + def writeI16(self, i): + self.protocol.writeI16(i) + + def readI32(self): + return self.protocol.readI32() + + def writeI32(self, i): + self.protocol.writeI32(i) + + def readI64(self): + return self.protocol.readI64() + + def writeI64(self, i): + self.protocol.writeI64(i) + + def readString(self): + return self.protocol.readString() + + def writeString(self, s): + self.protocol.writeString(s) + + def readBinary(self): + numBytes = self.protocol.readI32() + return self.protocol.readI8List(numBytes) + + def readFloatArray(self): + size = self.protocol.readI32() + return self.protocol.readF32List(size) + + def writeFloatArray(self, floats): + self.protocol.writeI32(len(floats)) + self.protocol.writeF32List(floats) + + def readObject(self): + return self.deserializeMessage() + + def writeObject(self, obj): + self.serializeMessage(obj) diff --git a/dynamicserialize/__init__.py b/dynamicserialize/__init__.py new file mode 100644 index 0000000..2129c0d --- /dev/null +++ b/dynamicserialize/__init__.py @@ -0,0 +1,41 @@ +## +## + + +# +# TODO +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 08/20/10 njensen Initial Creation. +# +# +# + +__all__ = [ + ] + +import dstypes, adapters +import DynamicSerializationManager + +class SerializationException(Exception): + + def __init__(self, message=None): + self.message = message + + def __str__(self): + if self.message: + return self.message + else: + return "" + +def serialize(obj): + dsm = DynamicSerializationManager.DynamicSerializationManager() + return dsm.serializeObject(obj) + +def deserialize(bytes): + dsm = DynamicSerializationManager.DynamicSerializationManager() + return dsm.deserializeBytes(bytes) \ No newline at end of file diff --git a/dynamicserialize/adapters/ActiveTableModeAdapter.py b/dynamicserialize/adapters/ActiveTableModeAdapter.py new file mode 100644 index 0000000..7520755 --- /dev/null +++ b/dynamicserialize/adapters/ActiveTableModeAdapter.py @@ -0,0 +1,34 @@ +## +## + + +# +# Adapter for com.raytheon.uf.common.activetable.ActiveTableMode +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/29/10 wldougher Initial Creation. +# +# +# + +from thrift.Thrift import TType +from dynamicserialize.dstypes.com.raytheon.uf.common.activetable import ActiveTableMode + +ClassAdapter = 'com.raytheon.uf.common.activetable.ActiveTableMode' + +def serialize(context, mode): + context.protocol.writeFieldBegin('__enumValue__', TType.STRING, 0) + context.writeString(mode.value) + +def deserialize(context): + result = ActiveTableMode() + # Read the TType.STRING, "__enumValue__", and id. + # We're not interested in any of those, so just discard them. + context.protocol.readFieldBegin() + # now get the actual enum value + result.value = context.readString() + return result diff --git a/dynamicserialize/adapters/ByteBufferAdapter.py b/dynamicserialize/adapters/ByteBufferAdapter.py new file mode 100644 index 0000000..ee7ae3b --- /dev/null +++ b/dynamicserialize/adapters/ByteBufferAdapter.py @@ -0,0 +1,29 @@ +## +## + + +# +# Adapter for java.nio.ByteBuffer +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 08/03/11 dgilling Initial Creation. +# +# +# + +ClassAdapter = ['java.nio.ByteBuffer', 'java.nio.HeapByteBuffer'] + + +def serialize(context, set): + raise NotImplementedError("Serialization of ByteBuffers is not supported.") + +def deserialize(context): + byteBuf = context.readBinary() + return byteBuf + + + diff --git a/dynamicserialize/adapters/CalendarAdapter.py b/dynamicserialize/adapters/CalendarAdapter.py new file mode 100644 index 0000000..2d7a613 --- /dev/null +++ b/dynamicserialize/adapters/CalendarAdapter.py @@ -0,0 +1,29 @@ +## +## + + +# +# Adapter for java.util.Calendar +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/29/10 wldougher Initial Creation. +# +# +# + +from dynamicserialize.dstypes.java.util import Calendar + +ClassAdapter = 'java.util.Calendar' + +def serialize(context, calendar): + calTiM = calendar.getTimeInMillis() + context.writeI64(calTiM) + +def deserialize(context): + result = Calendar() + result.setTimeInMillis(context.readI64()) + return result diff --git a/dynamicserialize/adapters/CommutativeTimestampAdapter.py b/dynamicserialize/adapters/CommutativeTimestampAdapter.py new file mode 100644 index 0000000..4a72214 --- /dev/null +++ b/dynamicserialize/adapters/CommutativeTimestampAdapter.py @@ -0,0 +1,29 @@ +## +## + + +# +# Adapter for CommutativeTimestamp +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 9/21/2015 4486 rjpeter Initial creation. +# Jun 23, 2016 5696 rjpeter Handle CommutativeTimestamp. +# +# + +from dynamicserialize.dstypes.com.raytheon.uf.common.time import CommutativeTimestamp + + +ClassAdapter = 'com.raytheon.uf.common.time.CommutativeTimestamp' + +def serialize(context, date): + context.writeI64(date.getTime()) + +def deserialize(context): + result = CommutativeTimestamp() + result.setTime(context.readI64()) + return result \ No newline at end of file diff --git a/dynamicserialize/adapters/CoordAdapter.py b/dynamicserialize/adapters/CoordAdapter.py new file mode 100644 index 0000000..1a47532 --- /dev/null +++ b/dynamicserialize/adapters/CoordAdapter.py @@ -0,0 +1,33 @@ +## +## + + +# +# Adapter for com.vividsolutions.jts.geom.Coordinate +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/20/11 dgilling Initial Creation. +# +# +# + +from dynamicserialize.dstypes.com.vividsolutions.jts.geom import Coordinate + +ClassAdapter = 'com.vividsolutions.jts.geom.Coordinate' + +def serialize(context, coordinate): + context.writeDouble(coordinate.getX()) + context.writeDouble(coordinate.getY()) + +def deserialize(context): + x = context.readDouble() + y = context.readDouble() + coord = Coordinate() + coord.setX(x) + coord.setY(y) + return coord + diff --git a/dynamicserialize/adapters/DatabaseIDAdapter.py b/dynamicserialize/adapters/DatabaseIDAdapter.py new file mode 100644 index 0000000..9380ec8 --- /dev/null +++ b/dynamicserialize/adapters/DatabaseIDAdapter.py @@ -0,0 +1,27 @@ +## +## + + +# +# Adapter for com.raytheon.uf.common.dataplugin.gfe.db.objects.DatabaseID +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 03/29/11 dgilling Initial Creation. +# +# +# + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.db.objects import DatabaseID + +ClassAdapter = 'com.raytheon.uf.common.dataplugin.gfe.db.objects.DatabaseID' + +def serialize(context, dbId): + context.writeString(str(dbId)) + +def deserialize(context): + result = DatabaseID(context.readString()) + return result \ No newline at end of file diff --git a/dynamicserialize/adapters/DateAdapter.py b/dynamicserialize/adapters/DateAdapter.py new file mode 100644 index 0000000..6a15a02 --- /dev/null +++ b/dynamicserialize/adapters/DateAdapter.py @@ -0,0 +1,28 @@ +## +## + + +# +# Adapter for java.util.Date +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 12/06/10 dgilling Initial Creation. +# +# +# + +from dynamicserialize.dstypes.java.util import Date + +ClassAdapter = 'java.util.Date' + +def serialize(context, date): + context.writeI64(date.getTime()) + +def deserialize(context): + result = Date() + result.setTime(context.readI64()) + return result \ No newline at end of file diff --git a/dynamicserialize/adapters/EnumSetAdapter.py b/dynamicserialize/adapters/EnumSetAdapter.py new file mode 100644 index 0000000..9291b64 --- /dev/null +++ b/dynamicserialize/adapters/EnumSetAdapter.py @@ -0,0 +1,40 @@ +## +## + + +# +# Adapter for java.util.EnumSet +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 07/28/11 dgilling Initial Creation. +# 12/02/13 2537 bsteffen Serialize empty enum sets. +# +# +# + + + +from dynamicserialize.dstypes.java.util import EnumSet + +ClassAdapter = ['java.util.EnumSet', 'java.util.RegularEnumSet'] + + +def serialize(context, set): + setSize = len(set) + context.writeI32(setSize) + context.writeString(set.getEnumClass()) + for val in set: + context.writeString(val) + + +def deserialize(context): + setSize = context.readI32() + enumClassName = context.readString() + valList = [] + for i in xrange(setSize): + valList.append(context.readString()) + return EnumSet(enumClassName, valList) diff --git a/dynamicserialize/adapters/FloatBufferAdapter.py b/dynamicserialize/adapters/FloatBufferAdapter.py new file mode 100644 index 0000000..d7f9ca2 --- /dev/null +++ b/dynamicserialize/adapters/FloatBufferAdapter.py @@ -0,0 +1,29 @@ +## +## + + +# +# Adapter for java.nio.FloatBuffer +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 08/01/11 dgilling Initial Creation. +# +# +# + +ClassAdapter = ['java.nio.FloatBuffer', 'java.nio.HeapFloatBuffer'] + + +def serialize(context, set): + raise NotImplementedError("Serialization of FloatBuffers is not supported.") + +def deserialize(context): + floatBuf = context.readFloatArray() + return floatBuf + + + diff --git a/dynamicserialize/adapters/FormattedDateAdapter.py b/dynamicserialize/adapters/FormattedDateAdapter.py new file mode 100644 index 0000000..ff1af26 --- /dev/null +++ b/dynamicserialize/adapters/FormattedDateAdapter.py @@ -0,0 +1,29 @@ +## +## + + +# +# Adapter for FormattedDate +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 9/21/2015 4486 rjpeter Initial creation. +# +# +# + +from dynamicserialize.dstypes.com.raytheon.uf.common.time import FormattedDate + + +ClassAdapter = 'com.raytheon.uf.common.time.FormattedDate' + +def serialize(context, date): + context.writeI64(date.getTime()) + +def deserialize(context): + result = FormattedDate() + result.setTime(context.readI64()) + return result \ No newline at end of file diff --git a/dynamicserialize/adapters/GeomDataRespAdapter.py b/dynamicserialize/adapters/GeomDataRespAdapter.py new file mode 100644 index 0000000..6ce1f23 --- /dev/null +++ b/dynamicserialize/adapters/GeomDataRespAdapter.py @@ -0,0 +1,100 @@ +## +## + + +# +# Efficient adapter for GetGeometryDataResponse +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Oct 17, 2016 5919 njensen Initial creation +# +# +# + +from thrift.Thrift import TType +from dynamicserialize import SelfDescribingBinaryProtocol +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.response import GeometryResponseData +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.response import GetGeometryDataResponse + + +ClassAdapter = 'com.raytheon.uf.common.dataaccess.response.GetGeometryDataResponse' + + +def serialize(context, resp): + wkbs = resp.getGeometryWKBs() + # write list size + context.writeI32(len(wkbs)) + # write byte arrays + for b in wkbs: + context.writeBinary(b) + + geoData = resp.getGeoData() + # write list size + context.writeI32(len(geoData)) + # write objects + for geo in geoData: + context.writeI32(geo.getGeometryWKBindex()) + context.writeObject(geo.getTime()) + context.writeObject(geo.getLevel()) + context.writeObject(geo.getLocationName()) + context.writeObject(geo.getAttributes()) + + # write data map + params = geo.getDataMap() + context.writeI32(len(params)) + for p in params: + context.writeString(p) + value = params[p] + # actual value + context.writeObject(value[0]) + # value type as string + context.writeString(str(value[1])) + # unit + context.writeObject(value[2]) + +def deserialize(context): + size = context.readI32() + wkbs = [] + for i in xrange(size): + wkb = context.readBinary() + wkbs.append(wkb) + + geoData = [] + size = context.readI32() + for i in xrange(size): + data = GeometryResponseData() + # wkb index + wkbIndex = context.readI32() + data.setGeometryWKBindex(wkbIndex) + + time = context.readObject() + data.setTime(time) + level = context.readObject() + data.setLevel(level) + locName = context.readObject() + data.setLocationName(locName) + attrs = context.readObject() + data.setAttributes(attrs) + + # parameters + paramSize = context.readI32() + paramMap = {} + for k in xrange(paramSize): + paramName = context.readString() + value = context.readObject() + tName = context.readString() + unit = context.readObject() + paramMap[paramName] = [value, tName, unit] + data.setDataMap(paramMap) + geoData.append(data) + + # make the response object + resp = GetGeometryDataResponse() + resp.setGeometryWKBs(wkbs) + resp.setGeoData(geoData) + + return resp diff --git a/dynamicserialize/adapters/GeometryTypeAdapter.py b/dynamicserialize/adapters/GeometryTypeAdapter.py new file mode 100644 index 0000000..249bad0 --- /dev/null +++ b/dynamicserialize/adapters/GeometryTypeAdapter.py @@ -0,0 +1,39 @@ +## +## + + +# +# Adapter for com.vividsolutions.jts.geom.Polygon +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/20/11 dgilling Initial Creation. +# +# +# + +# TODO: Implement serialization/make deserialization useful. +# Deserialization was simply implemented to allow GridLocation objects to be +# passed through thrift, but the resulting Geometry object will not be transformed into +# useful data; the base byte array is passed to a worthless Geometry class. + +from dynamicserialize.dstypes.com.vividsolutions.jts.geom import Geometry + +# NOTE: At the moment, EDEX serializes Polygon, MultiPolygons, Points, and +# Geometrys with the tag of the base class Geometry. Java's serialization +# adapter is smarter and can determine the exact object by reading the binary +# data. This adapter doesn't need this _yet_, so it has not been implemented. +ClassAdapter = 'com.vividsolutions.jts.geom.Geometry' + +def serialize(context, coordinate): + raise dynamicserialize.SerializationException('Not implemented yet') + +def deserialize(context): + data = context.readBinary() + geom = Geometry() + geom.setBinaryData(data) + return geom + diff --git a/dynamicserialize/adapters/GregorianCalendarAdapter.py b/dynamicserialize/adapters/GregorianCalendarAdapter.py new file mode 100644 index 0000000..ec1470c --- /dev/null +++ b/dynamicserialize/adapters/GregorianCalendarAdapter.py @@ -0,0 +1,29 @@ +## +## + + +# +# Adapter for java.util.Calendar +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/29/10 wldougher Initial Creation. +# +# +# + +from dynamicserialize.dstypes.java.util import GregorianCalendar + +ClassAdapter = 'java.util.GregorianCalendar' + +def serialize(context, calendar): + calTiM = calendar.getTimeInMillis() + context.writeI64(calTiM) + +def deserialize(context): + result = GregorianCalendar() + result.setTimeInMillis(context.readI64()) + return result diff --git a/dynamicserialize/adapters/GridDataHistoryAdapter.py b/dynamicserialize/adapters/GridDataHistoryAdapter.py new file mode 100644 index 0000000..14e59ca --- /dev/null +++ b/dynamicserialize/adapters/GridDataHistoryAdapter.py @@ -0,0 +1,30 @@ +## +## + + +# +# Adapter for com.raytheon.uf.common.dataplugin.gfe.GridDataHistory +# +# TODO: REWRITE THIS ADAPTER when serialization/deserialization of this +# class has been finalized. +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 03/29/11 dgilling Initial Creation. +# +# +# + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe import GridDataHistory + +ClassAdapter = 'com.raytheon.uf.common.dataplugin.gfe.GridDataHistory' + +def serialize(context, history): + context.writeString(history.getCodedString()) + +def deserialize(context): + result = GridDataHistory(context.readString()) + return result \ No newline at end of file diff --git a/dynamicserialize/adapters/JTSEnvelopeAdapter.py b/dynamicserialize/adapters/JTSEnvelopeAdapter.py new file mode 100644 index 0000000..06f66e1 --- /dev/null +++ b/dynamicserialize/adapters/JTSEnvelopeAdapter.py @@ -0,0 +1,34 @@ +## +## + + +# +# Adapter for com.vividsolutions.jts.geom.Envelope +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 05/29/13 2023 dgilling Initial Creation. +# +# + +from dynamicserialize.dstypes.com.vividsolutions.jts.geom import Envelope + +ClassAdapter = 'com.vividsolutions.jts.geom.Envelope' + +def serialize(context, envelope): + context.writeDouble(envelope.getMinX()) + context.writeDouble(envelope.getMaxX()) + context.writeDouble(envelope.getMinY()) + context.writeDouble(envelope.getMaxY()) + +def deserialize(context): + env = Envelope() + env.setMinX(context.readDouble()) + env.setMaxX(context.readDouble()) + env.setMinY(context.readDouble()) + env.setMaxY(context.readDouble()) + return env + diff --git a/dynamicserialize/adapters/LocalizationLevelSerializationAdapter.py b/dynamicserialize/adapters/LocalizationLevelSerializationAdapter.py new file mode 100644 index 0000000..7dc8bdf --- /dev/null +++ b/dynamicserialize/adapters/LocalizationLevelSerializationAdapter.py @@ -0,0 +1,39 @@ +## +## + + +# +# Adapter for com.raytheon.uf.common.localization.LocalizationContext$LocalizationLevel +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/11/11 dgilling Initial Creation. +# +# +# + + + +from dynamicserialize.dstypes.com.raytheon.uf.common.localization import LocalizationLevel + +ClassAdapter = [ + 'com.raytheon.uf.common.localization.LocalizationContext$LocalizationLevel', + 'com.raytheon.uf.common.localization.LocalizationLevel' + ] + +def serialize(context, level): + context.writeString(level.getText()) + context.writeI32(level.getOrder()) + context.writeBool(level.isSystemLevel()); + +def deserialize(context): + text = context.readString() + order = context.readI32() + systemLevel = context.readBool() + level = LocalizationLevel(text, order, systemLevel=systemLevel) + return level + + diff --git a/dynamicserialize/adapters/LocalizationTypeSerializationAdapter.py b/dynamicserialize/adapters/LocalizationTypeSerializationAdapter.py new file mode 100644 index 0000000..aaae690 --- /dev/null +++ b/dynamicserialize/adapters/LocalizationTypeSerializationAdapter.py @@ -0,0 +1,33 @@ +## +## + + +# +# Adapter for com.raytheon.uf.common.localization.LocalizationContext$LocalizationType +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/11/11 dgilling Initial Creation. +# +# +# + + + +from dynamicserialize.dstypes.com.raytheon.uf.common.localization import LocalizationType + +ClassAdapter = [ + 'com.raytheon.uf.common.localization.LocalizationContext$LocalizationType', + 'com.raytheon.uf.common.localization.LocalizationType' + ] + +def serialize(context, type): + context.writeString(type.getText()) + +def deserialize(context): + typeString = context.readString() + return LocalizationType(typeString) + diff --git a/dynamicserialize/adapters/LockTableAdapter.py b/dynamicserialize/adapters/LockTableAdapter.py new file mode 100644 index 0000000..64b899e --- /dev/null +++ b/dynamicserialize/adapters/LockTableAdapter.py @@ -0,0 +1,72 @@ +## +## + +# +# Adapter for com.raytheon.uf.common.dataplugin.gfe.server.lock.LockTable +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------- -------- --------- --------------------------------------------- +# Apr 22, 2013 rjpeter Initial Creation. +# Jun 12, 2013 2099 dgilling Use new Lock constructor. +# Feb 06, 2017 5959 randerso Removed Java .toString() calls +# +## + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.server.lock import LockTable +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.server.lock import Lock + +ClassAdapter = 'com.raytheon.uf.common.dataplugin.gfe.server.lock.LockTable' + +def serialize(context, lockTable): + index=0 + wsIds = {str(lockTable.getWsId()) : index} + index += 1 + locks = lockTable.getLocks() + lockWsIdIndex = [] + for lock in locks: + wsIdString = str(lock.getWsId()) + + if wsIds.has_key(wsIdString): + lockWsIdIndex.append(wsIds[wsIdString]) + else: + lockWsIdIndex.append(index) + wsIds[wsIdString] = index + index += 1 + + context.writeObject(lockTable.getParmId()) + + context.writeI32(index) + for wsId in sorted(wsIds, key=wsIds.get): + context.writeObject(wsId) + + context.writeI32(len(locks)) + for lock, wsIndex in zip(locks, lockWsIdIndex): + serializer.writeI64(lock.getStartTime()) + serializer.writeI64(lock.getEndTime()) + serializer.writeI32(wsIndex) + +def deserialize(context): + parmId = context.readObject() + numWsIds = context.readI32() + wsIds = [] + for x in xrange(numWsIds): + wsIds.append(context.readObject()) + + numLocks = context.readI32() + locks = [] + for x in xrange(numLocks): + startTime = context.readI64() + endTime = context.readI64() + wsId = wsIds[context.readI32()] + lock = Lock(parmId, wsId, startTime, endTime) + locks.append(lock) + + lockTable = LockTable() + lockTable.setParmId(parmId) + lockTable.setWsId(wsIds[0]) + lockTable.setLocks(locks) + + return lockTable \ No newline at end of file diff --git a/dynamicserialize/adapters/ParmIDAdapter.py b/dynamicserialize/adapters/ParmIDAdapter.py new file mode 100644 index 0000000..75c4936 --- /dev/null +++ b/dynamicserialize/adapters/ParmIDAdapter.py @@ -0,0 +1,27 @@ +## +## + + +# +# Adapter for com.raytheon.uf.common.dataplugin.gfe.db.objects.ParmID +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 03/29/11 dgilling Initial Creation. +# +# +# + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.db.objects import ParmID + +ClassAdapter = 'com.raytheon.uf.common.dataplugin.gfe.db.objects.ParmID' + +def serialize(context, parmId): + context.writeString(str(parmId)) + +def deserialize(context): + result = ParmID(context.readString()) + return result \ No newline at end of file diff --git a/dynamicserialize/adapters/PointAdapter.py b/dynamicserialize/adapters/PointAdapter.py new file mode 100644 index 0000000..e317b43 --- /dev/null +++ b/dynamicserialize/adapters/PointAdapter.py @@ -0,0 +1,33 @@ +## +## + + +# +# Adapter for java.awt.Point +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 08/31/10 njensen Initial Creation. +# +# +# + +from dynamicserialize.dstypes.java.awt import Point + +ClassAdapter = 'java.awt.Point' + +def serialize(context, point): + context.writeI32(point.getX()) + context.writeI32(point.getY()) + +def deserialize(context): + x = context.readI32() + y = context.readI32() + point = Point() + point.setX(x) + point.setY(y) + return point + diff --git a/dynamicserialize/adapters/StackTraceElementAdapter.py b/dynamicserialize/adapters/StackTraceElementAdapter.py new file mode 100644 index 0000000..a5b93bb --- /dev/null +++ b/dynamicserialize/adapters/StackTraceElementAdapter.py @@ -0,0 +1,35 @@ +## +## + + +# +# Adapter for java.lang.StackTraceElement[] +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/21/10 njensen Initial Creation. +# +# +# + +import dynamicserialize +from dynamicserialize.dstypes.java.lang import StackTraceElement + +ClassAdapter = 'java.lang.StackTraceElement' + + +def serialize(context, obj): + raise dynamicserialize.SerializationException('Not implemented yet') + +def deserialize(context): + result = StackTraceElement() + result.setDeclaringClass(context.readString()) + result.setMethodName(context.readString()) + result.setFileName(context.readString()) + result.setLineNumber(context.readI32()) + return result + + diff --git a/dynamicserialize/adapters/TimeConstraintsAdapter.py b/dynamicserialize/adapters/TimeConstraintsAdapter.py new file mode 100644 index 0000000..6f84694 --- /dev/null +++ b/dynamicserialize/adapters/TimeConstraintsAdapter.py @@ -0,0 +1,29 @@ +## +## + + +# +# Adapter for com.raytheon.uf.common.dataplugin.gfe.db.objects.ParmID +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 03/20/13 #1774 randerso Initial Creation. +# +# +# + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.db.objects import TimeConstraints + +ClassAdapter = 'com.raytheon.uf.common.dataplugin.gfe.db.objects.TimeConstraints' + +def serialize(context, timeConstraints): + context.writeI32(timeConstraints.getDuration()); + context.writeI32(timeConstraints.getRepeatInterval()); + context.writeI32(timeConstraints.getStartTime()); + +def deserialize(context): + result = TimeConstraints(context.readI32(), context.readI32(), context.readI32()) + return result \ No newline at end of file diff --git a/dynamicserialize/adapters/TimeRangeTypeAdapter.py b/dynamicserialize/adapters/TimeRangeTypeAdapter.py new file mode 100644 index 0000000..7f90b72 --- /dev/null +++ b/dynamicserialize/adapters/TimeRangeTypeAdapter.py @@ -0,0 +1,46 @@ +## +## + + +# +# Adapter for com.raytheon.uf.common.message.WsId +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/16/10 dgilling Initial Creation. +# 01/22/14 2667 bclement use method to get millis from time range +# 02/28/14 2667 bclement deserialize now converts millis to micros +# +# +# + + +from dynamicserialize.dstypes.com.raytheon.uf.common.time import TimeRange + + +ClassAdapter = 'com.raytheon.uf.common.time.TimeRange' + +MICROS_IN_MILLISECOND = 1000 +MILLIS_IN_SECOND = 1000 + +def serialize(context, timeRange): + context.writeI64(timeRange.getStartInMillis()) + context.writeI64(timeRange.getEndInMillis()) + +def deserialize(context): + startTime = context.readI64() + endTime = context.readI64() + + timeRange = TimeRange() + # java uses milliseconds, python uses microseconds + startSeconds = startTime // MILLIS_IN_SECOND + endSeconds = endTime // MILLIS_IN_SECOND + startExtraMicros = (startTime % MILLIS_IN_SECOND) * MICROS_IN_MILLISECOND + endExtraMicros = (endTime % MILLIS_IN_SECOND) * MICROS_IN_MILLISECOND + timeRange.setStart(startSeconds, startExtraMicros) + timeRange.setEnd(endSeconds, endExtraMicros) + + return timeRange diff --git a/dynamicserialize/adapters/TimestampAdapter.py b/dynamicserialize/adapters/TimestampAdapter.py new file mode 100644 index 0000000..997ab86 --- /dev/null +++ b/dynamicserialize/adapters/TimestampAdapter.py @@ -0,0 +1,27 @@ +## +## + + +# +# Adapter for java.sql.Timestamp +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/30/11 dgilling Initial Creation. +# +# +# + +from dynamicserialize.dstypes.java.sql import Timestamp + +ClassAdapter = 'java.sql.Timestamp' + +def serialize(context, timestamp): + context.writeI64(timestamp.getTime()) + +def deserialize(context): + result = Timestamp(context.readI64()) + return result \ No newline at end of file diff --git a/dynamicserialize/adapters/WsIdAdapter.py b/dynamicserialize/adapters/WsIdAdapter.py new file mode 100644 index 0000000..72f5da4 --- /dev/null +++ b/dynamicserialize/adapters/WsIdAdapter.py @@ -0,0 +1,41 @@ +## +## + + +# +# Adapter for com.raytheon.uf.common.message.WsId +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------- -------- --------- --------------------------------------------- +# Sep 16, 2010 dgilling Initial Creation. +# Apr 25, 2012 545 randerso Repurposed the lockKey field as threadId +# Feb 06, 2017 5959 randerso Removed Java .toString() calls +# +## + + + +from dynamicserialize.dstypes.com.raytheon.uf.common.message import WsId + +ClassAdapter = 'com.raytheon.uf.common.message.WsId' + + +def serialize(context, wsId): + context.writeString(str(wsId)) + +def deserialize(context): + wsIdString = context.readString() + wsIdParts = wsIdString.split(":", 5) + + wsId = WsId() + wsId.setNetworkId(wsIdParts[0]) + wsId.setUserName(wsIdParts[1]) + wsId.setProgName(wsIdParts[2]) + wsId.setPid(wsIdParts[3]) + wsId.setThreadId(long(wsIdParts[4])) + + return wsId + diff --git a/dynamicserialize/adapters/__init__.py b/dynamicserialize/adapters/__init__.py new file mode 100644 index 0000000..92907cf --- /dev/null +++ b/dynamicserialize/adapters/__init__.py @@ -0,0 +1,94 @@ +## +## + + +# +# __init__.py for Dynamic Serialize adapters. +# +# Plugins can contribute to dynamicserialize.adapters by either including their +# classes directly in pythonPackages/dynamicserialize/adapters/ within their +# plugin. The plugin's adapter will automatically be added to __all__ at runtime +# and registered. +# Plugins should not include a custom __init__.py in +# pythonPackages/dynamicserialize/adapters/ because it will overwrite this file. +# If custom package initialization is needed, a subpackage should be created +# with an __init__.py that includes the following: +# +# __all__ = ['CustomAdapter1', 'CustomAdapter2'] +# from dynamicserialize.adapters import registerAdapters +# registerAdapters(__name__, __all__) +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 08/31/10 njensen Initial Creation. +# 03/20/13 #1774 randerso Added TimeConstraintsAdapter +# 04/22/13 #1949 rjpeter Added LockTableAdapter +# 02/06/14 #2672 bsteffen Added JTSEnvelopeAdapter +# 06/22/2015 #4573 randerso Added JobProgressAdapter +# 09/21/2015 #4486 rjpeter Added FormattedDateAdapter +# 06/23/2016 #5696 rjpeter Added CommutativeTimestampAdapter +# 10/17/2016 #5919 njensen Added GeomDataRespAdapter +# 01/09/2017 #5997 nabowle Allow contribution from plugins. +# + +__all__ = [ + 'PointAdapter', + 'StackTraceElementAdapter', + 'CalendarAdapter', + 'GregorianCalendarAdapter', + 'DateAdapter', + 'GeometryTypeAdapter', + 'CoordAdapter', + 'TimestampAdapter', + 'EnumSetAdapter', + 'FloatBufferAdapter', + 'ByteBufferAdapter', + 'JTSEnvelopeAdapter' +] + +classAdapterRegistry = {} + + +def getAdapterRegistry(): + import pkgutil + + discoveredPackages = [] + # allow other plugins to contribute to adapters by dropping their adapter or + # package into the dynamicserialize.adapters package + for _, modname, ispkg in pkgutil.iter_modules(__path__): + if ispkg: + discoveredPackages.append(modname) + else: + if modname not in __all__: + __all__.append(modname) + + registerAdapters(__name__, __all__) + + for pkg in discoveredPackages: + __import__(__name__ + '.' + pkg) + + +def registerAdapters(package, modules): + import sys + if not package.endswith('.'): + package += '.' + for x in modules: + exec 'import ' + package + x + m = sys.modules[package + x] + d = m.__dict__ + if d.has_key('ClassAdapter'): + if isinstance(m.ClassAdapter, list): + for clz in m.ClassAdapter: + classAdapterRegistry[clz] = m + else: + clzName = m.ClassAdapter + classAdapterRegistry[clzName] = m + else: + raise LookupError('Adapter class ' + x + ' has no ClassAdapter field ' + + 'and cannot be registered.') + + +getAdapterRegistry() diff --git a/dynamicserialize/dstypes/__init__.py b/dynamicserialize/dstypes/__init__.py new file mode 100644 index 0000000..6484535 --- /dev/null +++ b/dynamicserialize/dstypes/__init__.py @@ -0,0 +1,12 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'com', + 'gov', + 'java' + ] + + diff --git a/dynamicserialize/dstypes/com/__init__.py b/dynamicserialize/dstypes/com/__init__.py new file mode 100644 index 0000000..9471c71 --- /dev/null +++ b/dynamicserialize/dstypes/com/__init__.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'raytheon', + 'vividsolutions' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/__init__.py b/dynamicserialize/dstypes/com/raytheon/__init__.py new file mode 100644 index 0000000..7c6025d --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/__init__.py @@ -0,0 +1,10 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'uf' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/__init__.py new file mode 100644 index 0000000..46f5b98 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/__init__.py @@ -0,0 +1,10 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'common' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/__init__.py new file mode 100644 index 0000000..735339c --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/__init__.py @@ -0,0 +1,24 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'activetable', + 'alertviz', + 'auth', + 'dataaccess', + 'dataplugin', + 'dataquery', + 'datastorage', + 'localization', + 'management', + 'message', + 'pointdata', + 'pypies', + 'serialization', + 'site', + 'time' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableKey.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableKey.py new file mode 100644 index 0000000..3cde08a --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableKey.py @@ -0,0 +1,64 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 05/22/2015 4522 randerso Initial creation +# 03/17/2016 5426 randerso Add issueYear to primary key +# 08/03/2016 19213 ryu Add pil to primary key +# +## +class ActiveTableKey(object): + + def __init__(self): + self.officeid = None + self.phen = None + self.sig = None + self.etn = None + self.ugcZone = None + self.issueYear = None + self.pil = None + + def getOfficeid(self): + return self.officeid + + def setOfficeid(self, officeid): + self.officeid = officeid + + def getPhen(self): + return self.phen + + def setPhen(self, phen): + self.phen = phen + + def getSig(self): + return self.sig + + def setSig(self, sig): + self.sig = sig + + def getEtn(self): + return self.etn + + def setEtn(self, etn): + self.etn = etn + + def getUgcZone(self): + return self.ugcZone + + def setUgcZone(self, ugcZone): + self.ugcZone = ugcZone + + def getIssueYear(self): + return self.issueYear + + def setIssueYear(self, issueYear): + self.issueYear = issueYear + + def getPil(self): + return self.pil + + def setPil(self, pil): + self.pil = pil diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableMode.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableMode.py new file mode 100644 index 0000000..22eceff --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableMode.py @@ -0,0 +1,12 @@ +## +## + +# File generated against equivalent DynamicSerialize Java class +# Jul 27, 2016 #5769 randerso Fixed __str__ method + +class ActiveTableMode(object): + def __init__(self): + self.value = None + + def __str__(self): + return str(self.value) \ No newline at end of file diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableRecord.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableRecord.py new file mode 100644 index 0000000..22a3c96 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableRecord.py @@ -0,0 +1,272 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 05/22/2015 4522 randerso Initial creation (hand generated) +# 03/17/2016 5426 randerso Add issueYear to primary key +# 06/27/2016 5707 nabowle Remove geometry +# +## + +import ActiveTableKey +import abc + +class ActiveTableRecord(object): + __metaclass__ = abc.ABCMeta + + @abc.abstractmethod + def __init__(self): + self.key = ActiveTableKey.ActiveTableKey() + self.wmoid = None + self.pil = None + self.xxxid = None + self.countyheader = None + self.vtecstr = None + self.productClass = None + self.act = None + self.startTime = None + self.endTime = None + self.issueTime = None + self.purgeTime = None + self.ufn = None + self.forecaster = None + self.motdir = None + self.motspd = None + self.loc = None + self.rawmessage = None + self.seg = None + self.phensig = None + self.region = None + self.overviewText = None + self.segText = None + self.locationID = None + self.floodSeverity = None + self.immediateCause = None + self.floodRecordStatus = None + self.floodBegin = None + self.floodCrest = None + self.floodEnd = None + self.identifier = None + + def getKey(self): + return self.key + + def setKey(self, key): + self.key = key + + def getWmoid(self): + return self.wmoid + + def setWmoid(self, wmoid): + self.wmoid = wmoid + + def getPil(self): + return self.pil + + def setPil(self, pil): + self.pil = pil + + def getXxxid(self): + return self.xxxid + + def setXxxid(self, xxxid): + self.xxxid = xxxid + + def getCountyheader(self): + return self.countyheader + + def setCountyheader(self, countyheader): + self.countyheader = countyheader + + def getUgcZone(self): + return self.key.getUgcZone() + + def setUgcZone(self, ugcZone): + self.key.setUgcZone(ugcZone) + + def getVtecstr(self): + return self.vtecstr + + def setVtecstr(self, vtecstr): + self.vtecstr = vtecstr + + def getProductClass(self): + return self.productClass + + def setProductClass(self, productClass): + self.productClass = productClass + + def getAct(self): + return self.act + + def setAct(self, act): + self.act = act + + def getOfficeid(self): + return self.key.getOfficeid() + + def setOfficeid(self, officeid): + self.key.setOfficeid(officeid) + + def getPhen(self): + return self.key.getPhen() + + def setPhen(self, phen): + self.key.setPhen(phen) + + def getSig(self): + return self.key.getSig() + + def setSig(self, sig): + self.key.setSig(sig) + + def getEtn(self): + return self.key.getEtn() + + def setEtn(self, etn): + self.key.setEtn(etn) + + def getStartTime(self): + return self.startTime + + def setStartTime(self, startTime): + self.startTime = startTime + + def getEndTime(self): + return self.endTime + + def setEndTime(self, endTime): + self.endTime = endTime + + def getIssueTime(self): + return self.issueTime + + def setIssueTime(self, issueTime): + from datetime import datetime + date = datetime.utcfromtimestamp(issueTime.getTime()/1000) + self.key.setIssueYear(date.year) + self.issueTime = issueTime + + def getPurgeTime(self): + return self.purgeTime + + def setPurgeTime(self, purgeTime): + self.purgeTime = purgeTime + + def isUfn(self): + return self.ufn + + def setUfn(self, ufn): + self.ufn = ufn + + def getForecaster(self): + return self.forecaster + + def setForecaster(self, forecaster): + self.forecaster = forecaster + + def getMotdir(self): + return self.motdir + + def setMotdir(self, motdir): + self.motdir = motdir + + def getMotspd(self): + return self.motspd + + def setMotspd(self, motspd): + self.motspd = motspd + + def getLoc(self): + return self.loc + + def setLoc(self, loc): + self.loc = loc + + def getRawmessage(self): + return self.rawmessage + + def setRawmessage(self, rawmessage): + self.rawmessage = rawmessage + + def getSeg(self): + return self.seg + + def setSeg(self, seg): + self.seg = seg + + def getPhensig(self): + return self.phensig + + def setPhensig(self, phensig): + self.phensig = phensig + + def getRegion(self): + return self.region + + def setRegion(self, region): + self.region = region + + def getOverviewText(self): + return self.overviewText + + def setOverviewText(self, overviewText): + self.overviewText = overviewText + + def getSegText(self): + return self.segText + + def setSegText(self, segText): + self.segText = segText + + def getLocationID(self): + return self.locationID + + def setLocationID(self, locationID): + self.locationID = locationID + + def getFloodSeverity(self): + return self.floodSeverity + + def setFloodSeverity(self, floodSeverity): + self.floodSeverity = floodSeverity + + def getImmediateCause(self): + return self.immediateCause + + def setImmediateCause(self, immediateCause): + self.immediateCause = immediateCause + + def getFloodRecordStatus(self): + return self.floodRecordStatus + + def setFloodRecordStatus(self, floodRecordStatus): + self.floodRecordStatus = floodRecordStatus + + def getFloodBegin(self): + return self.floodBegin + + def setFloodBegin(self, floodBegin): + self.floodBegin = floodBegin + + def getFloodCrest(self): + return self.floodCrest + + def setFloodCrest(self, floodCrest): + self.floodCrest = floodCrest + + def getFloodEnd(self): + return self.floodEnd + + def setFloodEnd(self, floodEnd): + self.floodEnd = floodEnd + + def getIdentifier(self): + return self.identifier + + def setIdentifier(self, identifier): + self.identifier = identifier + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/DumpActiveTableRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/DumpActiveTableRequest.py new file mode 100644 index 0000000..db26e1c --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/DumpActiveTableRequest.py @@ -0,0 +1,86 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class DumpActiveTableRequest(object): + + def __init__(self): + self.actions = None + self.etns = None + self.fileContent = None + self.fileName = None + self.fromSite = None + self.ids = None + self.mode = None + self.phens = None + self.pils = None + self.sigs = None + self.sites = None + + def getActions(self): + return self.actions + + def setActions(self, actions): + self.actions = actions + + def getEtns(self): + return self.etns + + def setEtns(self, etns): + self.etns = etns + + def getFileContent(self): + return self.fileContent + + def setFileContent(self, fileContent): + self.fileContent = fileContent + + def getFileName(self): + return self.fileName + + def setFileName(self, fileName): + self.fileName = fileName + + def getFromSite(self): + return self.fromSite + + def setFromSite(self, fromSite): + self.fromSite = fromSite + + def getIds(self): + return self.ids + + def setIds(self, ids): + self.ids = ids + + def getMode(self): + return self.mode + + def setMode(self, mode): + self.mode = mode + + def getPhens(self): + return self.phens + + def setPhens(self, phens): + self.phens = phens + + def getPils(self): + return self.pils + + def setPils(self, pils): + self.pils = pils + + def getSigs(self): + return self.sigs + + def setSigs(self, sigs): + self.sigs = sigs + + def getSites(self): + return self.sites + + def setSites(self, sites): + self.sites = sites + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/DumpActiveTableResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/DumpActiveTableResponse.py new file mode 100644 index 0000000..0adf2a5 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/DumpActiveTableResponse.py @@ -0,0 +1,33 @@ +## +## + +class DumpActiveTableResponse(object): + def __init__(self): + self.dump = None + self.unfilteredCount = 0 + self.filteredCount = 0 + self.message = None + + def getUnfilteredCount(self): + return self.unfilteredCount + + def getFilteredCount(self): + return self.filteredCount + + def getDump(self): + return self.dump + + def getMessage(self): + return self.message + + def setUnfilteredCount(self, unfilteredCount): + self.unfilteredCount = unfilteredCount + + def setFilteredCount(self, filteredCount): + self.filteredCount = filteredCount + + def setDump(self, dump): + self.dump = dump + + def setMessage(self, message): + self.message = message diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/GetActiveTableDictRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/GetActiveTableDictRequest.py new file mode 100644 index 0000000..35a174e --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/GetActiveTableDictRequest.py @@ -0,0 +1,27 @@ +## +## + +class GetActiveTableDictRequest(object): + + def __init__(self): + self.requestedSiteId = None + self.mode = None + self.wfos = None + + def getRequestedSiteId(self): + return self.requestedSiteId + + def setRequestedSiteId(self, requestedSiteId): + self.requestedSiteId = requestedSiteId + + def getMode(self): + return self.mode + + def setMode(self, mode): + self.mode = mode + + def getWfos(self): + return self.wfos + + def setWfos(self, wfos): + self.wfos = wfos; diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/GetActiveTableDictResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/GetActiveTableDictResponse.py new file mode 100644 index 0000000..bb072d9 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/GetActiveTableDictResponse.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GetActiveTableDictResponse(object): + + def __init__(self): + self.activeTable = None + self.mode = None + + def getActiveTable(self): + return self.activeTable + + def setActiveTable(self, activeTable): + self.activeTable = activeTable + + def getMode(self): + return self.mode + + def setMode(self, mode): + self.mode = mode + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/GetFourCharSitesRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/GetFourCharSitesRequest.py new file mode 100644 index 0000000..556cbe3 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/GetFourCharSitesRequest.py @@ -0,0 +1,15 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GetFourCharSitesRequest(object): + + def __init__(self): + self.sites = None + + def getSites(self): + return self.sites + + def setSites(self, sites): + self.sites = sites diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/GetFourCharSitesResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/GetFourCharSitesResponse.py new file mode 100644 index 0000000..5488019 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/GetFourCharSitesResponse.py @@ -0,0 +1,15 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GetFourCharSitesResponse(object): + + def __init__(self): + self.sites = None + + def getSites(self): + return self.sites + + def setSites(self, sites): + self.sites = sites diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/GetVtecAttributeRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/GetVtecAttributeRequest.py new file mode 100644 index 0000000..01bf558 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/GetVtecAttributeRequest.py @@ -0,0 +1,27 @@ +## +## + +class GetVtecAttributeRequest(object): + + def __init__(self): + self.siteId = None + self.attribute = None + self.defaultValue = None + + def getSiteId(self): + return self.siteId + + def setSiteId(self, site): + self.siteId = site + + def getAttribute(self): + return self.attribute + + def setAttribute(self, attribute): + self.attribute = attribute + + def getDefaultValue(self): + return self.defaultValue + + def setDefaultValue(self, default): + self.defaultValue = default diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/GetVtecAttributeResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/GetVtecAttributeResponse.py new file mode 100644 index 0000000..a33ed83 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/GetVtecAttributeResponse.py @@ -0,0 +1,13 @@ +## +## + +class GetVtecAttributeResponse(object): + + def __init__(self): + self.value = None + + def getValue(self): + return self.value + + def setValue(self, value): + self.value = value diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/OperationalActiveTableRecord.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/OperationalActiveTableRecord.py new file mode 100644 index 0000000..f2fe558 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/OperationalActiveTableRecord.py @@ -0,0 +1,17 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 05/22/2015 4522 randerso Changed to inherit from ActiveTableRecord +# +## + +import ActiveTableRecord + +class OperationalActiveTableRecord(ActiveTableRecord.ActiveTableRecord): + + def __init__(self): + super(OperationalActiveTableRecord, self).__init__() diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/PracticeActiveTableRecord.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/PracticeActiveTableRecord.py new file mode 100644 index 0000000..2dd43d6 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/PracticeActiveTableRecord.py @@ -0,0 +1,17 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 05/22/2015 4522 randerso Changed to inherit from ActiveTableRecord +# +## + +import ActiveTableRecord + +class PracticeActiveTableRecord(ActiveTableRecord.ActiveTableRecord): + + def __init__(self): + super(PracticeActiveTableRecord, self).__init__() diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/SendPracticeProductRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/SendPracticeProductRequest.py new file mode 100644 index 0000000..34db30f --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/SendPracticeProductRequest.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class SendPracticeProductRequest(object): + + def __init__(self): + self.drtString = None + self.notifyGFE = None + self.productText = None + + def getDrtString(self): + return self.drtString + + def setDrtString(self, drtString): + self.drtString = drtString + + def getNotifyGFE(self): + return self.notifyGFE + + def setNotifyGFE(self, notifyGFE): + self.notifyGFE = notifyGFE + + def getProductText(self): + return self.productText + + def setProductText(self, productText): + self.productText = productText + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/VTECChange.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/VTECChange.py new file mode 100644 index 0000000..9efd803 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/VTECChange.py @@ -0,0 +1,37 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# 03/25/14 #2884 randerso Added xxxid to VTECChange + +class VTECChange(object): + + def __init__(self): + self.site = None + self.pil = None + self.phensig = None + self.xxxid = None + + def getSite(self): + return self.site + + def setSite(self, site): + self.site = site + + def getPil(self): + return self.pil + + def setPil(self, pil): + self.pil = pil + + def getPhensig(self): + return self.phensig + + def setPhensig(self, phensig): + self.phensig = phensig + + def getXxxid(self): + return self.xxxid + + def setXxxid(self, xxxid): + self.xxxid = xxxid diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/VTECTableChangeNotification.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/VTECTableChangeNotification.py new file mode 100644 index 0000000..4404cb0 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/VTECTableChangeNotification.py @@ -0,0 +1,42 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class VTECTableChangeNotification(object): + + def __init__(self): + self.mode = None + self.modTime = None + self.modSource = None + self.changes = None + + def getMode(self): + return self.mode + + def setMode(self, mode): + self.mode = mode + + def getModTime(self): + return self.modTime + + def setModTime(self, modTime): + self.modTime = modTime + + def getModSource(self): + return self.modSource + + def setModSource(self, modSource): + self.modSource = modSource + + def getChanges(self): + return self.changes + + def setChanges(self, changes): + self.changes = changes + + def __repr__(self): + msg = 'Table Name: ' + str(self.mode) + '\n' + msg += 'ModTime: ' + str(self.modTime) + '\n' + msg += 'ModSource: ' + str(self.modSource) + return msg diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/__init__.py new file mode 100644 index 0000000..13f654b --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/__init__.py @@ -0,0 +1,43 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'request', + 'response', + 'ActiveTableKey', + 'ActiveTableMode', + 'ActiveTableRecord', + 'DumpActiveTableRequest', + 'DumpActiveTableResponse', + 'GetActiveTableDictRequest', + 'GetActiveTableDictResponse', + 'GetFourCharSitesRequest', + 'GetFourCharSitesResponse', + 'GetVtecAttributeRequest', + 'GetVtecAttributeResponse', + 'OperationalActiveTableRecord', + 'PracticeActiveTableRecord', + 'SendPracticeProductRequest', + 'VTECChange', + 'VTECTableChangeNotification' + ] + +from ActiveTableKey import ActiveTableKey +from ActiveTableRecord import ActiveTableRecord +from ActiveTableMode import ActiveTableMode +from DumpActiveTableRequest import DumpActiveTableRequest +from DumpActiveTableResponse import DumpActiveTableResponse +from GetActiveTableDictRequest import GetActiveTableDictRequest +from GetActiveTableDictResponse import GetActiveTableDictResponse +from GetFourCharSitesRequest import GetFourCharSitesRequest +from GetFourCharSitesResponse import GetFourCharSitesResponse +from GetVtecAttributeRequest import GetVtecAttributeRequest +from GetVtecAttributeResponse import GetVtecAttributeResponse +from OperationalActiveTableRecord import OperationalActiveTableRecord +from PracticeActiveTableRecord import PracticeActiveTableRecord +from SendPracticeProductRequest import SendPracticeProductRequest +from VTECChange import VTECChange +from VTECTableChangeNotification import VTECTableChangeNotification + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/request/ClearPracticeVTECTableRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/request/ClearPracticeVTECTableRequest.py new file mode 100644 index 0000000..27d9244 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/request/ClearPracticeVTECTableRequest.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + + +class ClearPracticeVTECTableRequest(object): + + def __init__(self): + self.siteID = None + self.workstationID = None + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/request/MergeActiveTableRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/request/MergeActiveTableRequest.py new file mode 100644 index 0000000..b3377e8 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/request/MergeActiveTableRequest.py @@ -0,0 +1,77 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class MergeActiveTableRequest(object): + + def __init__(self, incomingRecords=[], tableName='PRACTICE', site=None, + timeOffset=0.0, xmlSource=None, fromIngestAT=False, + makeBackups=True): + self.incomingRecords = incomingRecords + self.site = site + self.tableName = tableName.upper() if tableName.upper() in ['OPERATIONAL', 'PRACTICE'] else 'PRACTICE' + self.timeOffset = float(timeOffset) + self.xmlSource = xmlSource + self.fromIngestAT = bool(fromIngestAT) + self.makeBackups = bool(makeBackups) + + def __repr__(self): + retVal = "MergeActiveTableRequest(" + retVal += repr(self.incomingRecords) + ", " + retVal += repr(self.tableName) + ", " + retVal += repr(self.site) + ", " + retVal += repr(self.timeOffset) + ", " + retVal += repr(self.xmlSource) + ", " + retVal += repr(self.fromIngestAT) + ", " + retVal += repr(self.makeBackups) + ")" + return retVal + + def __str__(self): + return self.__repr__() + + def getIncomingRecords(self): + return self.incomingRecords + + def setIncomingRecords(self, incomingRecords): + self.incomingRecords = incomingRecords + + def getTableName(self): + return self.tableName + + def setTableName(self, tableName): + value = tableName.upper() + if value not in ['OPERATIONAL', 'PRACTICE']: + raise ValueError("Invalid value " + tableName + " specified for ActiveTableMode.") + self.tableName = value + + def getSite(self): + return self.site + + def setSite(self, site): + self.site = site + + def getTimeOffset(self): + return self.timeOffset + + def setTimeOffset(self, timeOffset): + self.timeOffset = float(timeOffset) + + def getXmlSource(self): + return self.xmlSource + + def setXmlSource(self, xmlSource): + self.xmlSource = xmlSource + + def getFromIngestAT(self): + return self.fromIngestAT + + def setFromIngestAT(self, fromIngestAT): + self.fromIngestAT = bool(fromIngestAT) + + def getMakeBackups(self): + return self.makeBackups + + def setMakeBackups(self, makeBackups): + self.makeBackups = bool(makeBackups) + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/request/RetrieveRemoteActiveTableRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/request/RetrieveRemoteActiveTableRequest.py new file mode 100644 index 0000000..7c1a91f --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/request/RetrieveRemoteActiveTableRequest.py @@ -0,0 +1,82 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class RetrieveRemoteActiveTableRequest(object): + + def __init__(self, serverHost=None, serverPort=0, serverProtocol=None, + mhsId=None, siteId=None, ancfAddress=None, bncfAddress=None, + transmitScript=None): + self.serverHost = serverHost + self.serverPort = int(serverPort) + self.serverProtocol = serverProtocol + self.mhsId = mhsId + self.siteId = siteId + self.ancfAddress = ancfAddress + self.bncfAddress = bncfAddress + self.transmitScript = transmitScript + + def __repr__(self): + retVal = "RetrieveRemoteActiveTableRequest(" + retVal += repr(self.serverHost) + ", " + retVal += repr(self.serverPort) + ", " + retVal += repr(self.serverProtocol) + ", " + retVal += repr(self.mhsId) + ", " + retVal += repr(self.siteId) + ", " + retVal += repr(self.ancfAddress) + ", " + retVal += repr(self.bncfAddress) + ", " + retVal += repr(self.transmitScript) + ")" + return retVal + + def __str__(self): + return self.__repr__() + + def getServerHost(self): + return self.serverHost + + def setServerHost(self, serverHost): + self.serverHost = serverHost + + def getServerPort(self): + return self.serverPort + + def setServerPort(self, serverPort): + self.serverPort = int(serverPort) + + def getServerProtocol(self): + return self.serverProtocol + + def setServerProtocol(self, serverProtocol): + self.serverProtocol = serverProtocol + + def getMhsId(self): + return self.mhsId + + def setMhsId(self, mhsId): + self.mhsId = mhsId + + def getSiteId(self): + return self.siteId + + def setSiteId(self, siteId): + self.siteId = siteId + + def getAncfAddress(self): + return self.ancfAddress + + def setAncfAddress(self, ancfAddress): + self.ancfAddress = ancfAddress + + def getBncfAddress(self): + return self.bncfAddress + + def setBncfAddress(self, bncfAddress): + self.bncfAddress = bncfAddress + + def getTransmitScript(self): + return self.transmitScript + + def setTransmitScript(self, transmitScript): + self.transmitScript = transmitScript + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/request/SendActiveTableRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/request/SendActiveTableRequest.py new file mode 100644 index 0000000..658f296 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/request/SendActiveTableRequest.py @@ -0,0 +1,123 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class SendActiveTableRequest(object): + + def __init__(self, serverHost=None, serverPort=None, serverProtocol=None, + serverSite=None, mhsId=None, sites=None, filterSites=None, + mhsSites=None, issueTime=None, countDict=None, fileName=None, + xmlIncoming=None, transmitScript=None): + self.serverHost = serverHost + self.serverPort = None if serverPort is None else int(serverPort) + self.serverProtocol = serverProtocol + self.serverSite = serverSite + self.mhsId = mhsId + self.sites = sites if sites is not None else [] + self.filterSites = filterSites if filterSites is not None else [] + self.mhsSites = mhsSites if mhsSites is not None else [] + self.issueTime = None if issueTime is None else float(issueTime) + self.countDict = countDict if countDict is not None else {} + self.fileName = fileName + self.xmlIncoming = xmlIncoming + self.transmitScript = transmitScript + + def __repr__(self): + retVal = "SendActiveTableRequest(" + retVal += repr(self.serverHost) + ", " + retVal += repr(self.serverPort) + ", " + retVal += repr(self.serverProtocol) + ", " + retVal += repr(self.serverSite) + ", " + retVal += repr(self.mhsId) + ", " + retVal += repr(self.sites) + ", " + retVal += repr(self.filterSites) + ", " + retVal += repr(self.mhsSites) + ", " + retVal += repr(self.issueTime) + ", " + retVal += repr(self.countDict) + ", " + retVal += repr(self.fileName) + ", " + retVal += repr(self.xmlIncoming) + ", " + retVal += repr(self.transmitScript) + ")" + return retVal + + def __str__(self): + return self.__repr__() + + def getServerHost(self): + return self.serverHost + + def setServerHost(self, serverHost): + self.serverHost = serverHost + + def getServerPort(self): + return self.serverPort + + def setServerPort(self, serverPort): + self.serverPort = serverPort + + def getServerProtocol(self): + return self.serverProtocol + + def setServerProtocol(self, serverProtocol): + self.serverProtocol = serverProtocol + + def getServerSite(self): + return self.serverSite + + def setServerSite(self, serverSite): + self.serverSite = serverSite + + def getMhsId(self): + return self.mhsId + + def setMhsId(self, mhsId): + self.mhsId = mhsId + + def getSites(self): + return self.sites + + def setSites(self, sites): + self.sites = sites + + def getFilterSites(self): + return self.filterSites + + def setFilterSites(self, filterSites): + self.filterSites = filterSites + + def getMhsSites(self): + return self.mhsSites + + def setMhsSites(self, mhsSites): + self.mhsSites = mhsSites + + def getIssueTime(self): + return self.issueTime + + def setIssueTime(self, issueTime): + self.issueTime = issueTime + + def getCountDict(self): + return self.countDict + + def setCountDict(self, countDict): + self.countDict = countDict + + def getFileName(self): + return self.fileName + + def setFileName(self, fileName): + self.fileName = fileName + + def getXmlIncoming(self): + return self.xmlIncoming + + def setXmlIncoming(self, xmlIncoming): + self.xmlIncoming = xmlIncoming + + def getTransmitScript(self): + return self.transmitScript + + def setTransmitScript(self, transmitScript): + self.transmitScript = transmitScript + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/request/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/request/__init__.py new file mode 100644 index 0000000..a153aed --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/request/__init__.py @@ -0,0 +1,17 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'ClearPracticeVTECTableRequest', + 'MergeActiveTableRequest', + 'RetrieveRemoteActiveTableRequest', + 'SendActiveTableRequest' + ] + +from ClearPracticeVTECTableRequest import ClearPracticeVTECTableRequest +from MergeActiveTableRequest import MergeActiveTableRequest +from RetrieveRemoteActiveTableRequest import RetrieveRemoteActiveTableRequest +from SendActiveTableRequest import SendActiveTableRequest + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/response/ActiveTableSharingResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/response/ActiveTableSharingResponse.py new file mode 100644 index 0000000..8ec0cf8 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/response/ActiveTableSharingResponse.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class ActiveTableSharingResponse(object): + + def __init__(self): + self.taskSuccess = None + self.errorMessage = None + + def getTaskSuccess(self): + return self.taskSuccess + + def setTaskSuccess(self, taskSuccess): + self.taskSuccess = bool(taskSuccess) + + def getErrorMessage(self): + return self.errorMessage + + def setErrorMessage(self, errorMessage): + self.errorMessage = errorMessage + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/response/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/response/__init__.py new file mode 100644 index 0000000..8e282df --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/response/__init__.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'ActiveTableSharingResponse' + ] + +from ActiveTableSharingResponse import ActiveTableSharingResponse + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/alertviz/AlertVizRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/alertviz/AlertVizRequest.py new file mode 100755 index 0000000..2f34ed1 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/alertviz/AlertVizRequest.py @@ -0,0 +1,63 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# Jul 27, 2015 4654 skorolev Added filters + +class AlertVizRequest(object): + + def __init__(self): + self.message = None + self.machine = None + self.priority = None + self.sourceKey = None + self.category = None + self.audioFile = None + self.filters = None + + def getMessage(self): + return self.message + + def setMessage(self, message): + self.message = message + + def getMachine(self): + return self.machine + + def setMachine(self, machine): + self.machine = machine + + def getPriority(self): + return self.priority + + def setPriority(self, priority): + self.priority = priority + + def getSourceKey(self): + return self.sourceKey + + def setSourceKey(self, sourceKey): + self.sourceKey = sourceKey + + def getCategory(self): + return self.category + + def setCategory(self, category): + self.category = category + + def getAudioFile(self): + return self.audioFile + + def setAudioFile(self, audioFile): + self.audioFile = audioFile + + def getFilters(self): + return self.filters + + def setFilters(self, filters): + if filters is None: + self.filters = {} + elif not(filters.has_key(None) or filters.values().count(None)>0 or filters.has_key('') or filters.values().count('')>0): + self.filters = filters + else: + raise ValueError('Filters must not contain None or empty keys or values: %s' % filters) diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/alertviz/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/alertviz/__init__.py new file mode 100644 index 0000000..623f23d --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/alertviz/__init__.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'AlertVizRequest' + ] + +from AlertVizRequest import AlertVizRequest + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/auth/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/auth/__init__.py new file mode 100644 index 0000000..0331888 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/auth/__init__.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'resp', + 'user' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/auth/resp/AbstractFailedResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/auth/resp/AbstractFailedResponse.py new file mode 100644 index 0000000..f908104 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/auth/resp/AbstractFailedResponse.py @@ -0,0 +1,21 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +import abc + + +class AbstractFailedResponse(object): + __metaclass__ = abc.ABCMeta + + @abc.abstractmethod + def __init__(self): + self.request = None + + def getRequest(self): + return self.request + + def setRequest(self, request): + self.request = request + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/auth/resp/AuthServerErrorResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/auth/resp/AuthServerErrorResponse.py new file mode 100644 index 0000000..bf71c56 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/auth/resp/AuthServerErrorResponse.py @@ -0,0 +1,16 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + + +from dynamicserialize.dstypes.com.raytheon.uf.common.serialization.comm.response import ServerErrorResponse + +class AuthServerErrorResponse(ServerErrorResponse): + + def __init__(self): + super(AuthServerErrorResponse, self).__init__() + + ## nothing to implement here that isn't already covered by ServerErrorResponse ## + ## Just need the separate class for de-serialization. ## + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/auth/resp/SuccessfulExecution.py b/dynamicserialize/dstypes/com/raytheon/uf/common/auth/resp/SuccessfulExecution.py new file mode 100644 index 0000000..0935b3a --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/auth/resp/SuccessfulExecution.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class SuccessfulExecution(object): + + def __init__(self): + self.response = None + self.updatedData = None + + def getResponse(self): + return self.response + + def setResponse(self, response): + self.response = response + + def getUpdatedData(self): + return self.updatedData + + def setUpdatedData(self, updatedData): + self.updatedData = updatedData + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/auth/resp/UserNotAuthorized.py b/dynamicserialize/dstypes/com/raytheon/uf/common/auth/resp/UserNotAuthorized.py new file mode 100644 index 0000000..ff35dff --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/auth/resp/UserNotAuthorized.py @@ -0,0 +1,19 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +from dynamicserialize.dstypes.com.raytheon.uf.common.auth.resp import AbstractFailedResponse + + +class UserNotAuthorized(AbstractFailedResponse): + + def __init__(self): + super(UserNotAuthorized, self).__init__() + self.message = None + + def getMessage(self): + return self.message + + def setMessage(self, message): + self.message = message diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/auth/resp/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/auth/resp/__init__.py new file mode 100644 index 0000000..d50c784 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/auth/resp/__init__.py @@ -0,0 +1,17 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'AbstractFailedResponse', + 'AuthServerErrorResponse', + 'SuccessfulExecution', + 'UserNotAuthorized' + ] + +from AbstractFailedResponse import AbstractFailedResponse +from AuthServerErrorResponse import AuthServerErrorResponse +from SuccessfulExecution import SuccessfulExecution +from UserNotAuthorized import UserNotAuthorized + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/auth/user/User.py b/dynamicserialize/dstypes/com/raytheon/uf/common/auth/user/User.py new file mode 100644 index 0000000..1f770c9 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/auth/user/User.py @@ -0,0 +1,28 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +from dynamicserialize.dstypes.com.raytheon.uf.common.auth.user import UserId + +class User(object): + + def __init__(self, userId=None): + if userId is None: + self.userId = UserId.UserId() + else: + self.userId = userId + self.authenticationData = None + + def getUserId(self): + return self.userId + + def setUserId(self, userId): + self.userId = userId + + def getAuthenticationData(self): + return self.authenticationData + + def setAuthenticationData(self, authenticationData): + self.authenticationData = authenticationData + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/auth/user/UserId.py b/dynamicserialize/dstypes/com/raytheon/uf/common/auth/user/UserId.py new file mode 100644 index 0000000..a28e35f --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/auth/user/UserId.py @@ -0,0 +1,21 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +import os, pwd + +class UserId(object): + + def __init__(self, id = None): + if id is None: + self.id = pwd.getpwuid(os.getuid()).pw_name + else: + self.id = id + + def getId(self): + return self.id + + def setId(self, id): + self.id = id + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/auth/user/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/auth/user/__init__.py new file mode 100644 index 0000000..cd7b045 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/auth/user/__init__.py @@ -0,0 +1,12 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'User', + 'UserId' + ] + +from User import User +from UserId import UserId diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/__init__.py new file mode 100644 index 0000000..4c00135 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/__init__.py @@ -0,0 +1,12 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'impl', + 'request', + 'response' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/impl/DefaultDataRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/impl/DefaultDataRequest.py new file mode 100644 index 0000000..0bf4346 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/impl/DefaultDataRequest.py @@ -0,0 +1,84 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to sub-class IDataRequest. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 05/28/13 2023 dgilling Initial Creation. +# 12/15/16 6040 tgurney Override __str__ +# +# + + +from awips.dataaccess import IDataRequest + +from dynamicserialize.dstypes.com.vividsolutions.jts.geom import Envelope +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.level import Level + + +class DefaultDataRequest(IDataRequest): + + def __init__(self): + self.datatype = None + self.identifiers = {} + self.parameters = [] + self.levels = [] + self.locationNames = [] + self.envelope = None + + def setDatatype(self, datatype): + self.datatype = str(datatype) + + def addIdentifier(self, key, value): + self.identifiers[key] = value + + def removeIdentifier(self, key): + del self.identifiers[key] + + def setParameters(self, *params): + self.parameters = map(str, params) + + def setLevels(self, *levels): + self.levels = map(self.__makeLevel, levels) + + def __makeLevel(self, level): + if type(level) is Level: + return level + elif type(level) is str: + return Level(level) + else: + raise TypeError("Invalid object type specified for level.") + + def setEnvelope(self, env): + self.envelope = Envelope(env.envelope) + + def setLocationNames(self, *locationNames): + self.locationNames = map(str, locationNames) + + def getDatatype(self): + return self.datatype + + def getIdentifiers(self): + return self.identifiers + + def getParameters(self): + return self.parameters + + def getLevels(self): + return self.levels + + def getEnvelope(self): + return self.envelope + + def getLocationNames(self): + return self.locationNames + + def __str__(self): + fmt = ('DefaultDataRequest(datatype={}, identifiers={}, parameters={}, ' + + 'levels={}, locationNames={}, envelope={})') + return fmt.format(self.datatype, self.identifiers, self.parameters, self.levels, + self.locationNames, self.envelope) diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/impl/DefaultNotificationFilter.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/impl/DefaultNotificationFilter.py new file mode 100644 index 0000000..20aa2a8 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/impl/DefaultNotificationFilter.py @@ -0,0 +1,43 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to sub-class IDataRequest. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/03/16 2416 rjpeter Initial Creation. +# 08/01/16 2416 tgurney Implement accept() +# +# + + +from awips.dataaccess import INotificationFilter +import sys + +if sys.version_info.major == 2: + from itertools import izip + # shadowing built-in zip + zip = izip + +class DefaultNotificationFilter(INotificationFilter): + + def __init__(self): + self.constraints = None + + def getConstraints(self): + return self.constraints + + def setConstraints(self, constraints): + self.constraints = constraints + + def accept(self, dataUri): + tokens = dataUri.split('/')[1:] + if len(self.constraints) != len(tokens): + return False + for constraint, token in zip(self.constraints, tokens): + if not constraint.evaluate(token): + return False + return True diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/impl/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/impl/__init__.py new file mode 100644 index 0000000..dad522e --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/impl/__init__.py @@ -0,0 +1,12 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'DefaultDataRequest', + 'DefaultNotificationFilter' + ] + +from DefaultDataRequest import DefaultDataRequest +from DefaultNotificationFilter import DefaultNotificationFilter \ No newline at end of file diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/AbstractDataAccessRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/AbstractDataAccessRequest.py new file mode 100644 index 0000000..12fa14a --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/AbstractDataAccessRequest.py @@ -0,0 +1,29 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to make it a abstract base class. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 05/28/13 #2023 dgilling Initial Creation. +# +# + +import abc + + +class AbstractDataAccessRequest(object): + __metaclass__ = abc.ABCMeta + + def __init__(self): + self.requestParameters = None + + def getRequestParameters(self): + return self.requestParameters + + def setRequestParameters(self, requestParameters): + self.requestParameters = requestParameters + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/AbstractIdentifierRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/AbstractIdentifierRequest.py new file mode 100644 index 0000000..12c0eef --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/AbstractIdentifierRequest.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to make it a abstract base class. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 07/23/14 #3185 njensen Initial Creation. +# Jun 01, 2016 5587 tgurney Change self.datatype to +# self.request +# +# + +import abc + +class AbstractIdentifierRequest(object): + __metaclass__ = abc.ABCMeta + + def __init__(self): + self.request = None + + def getRequest(self): + return self.request + + def setRequest(self, request): + self.request = request + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetAvailableLevelsRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetAvailableLevelsRequest.py new file mode 100644 index 0000000..8caa860 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetAvailableLevelsRequest.py @@ -0,0 +1,21 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to make it sub class +# AbstractDataAccessRequest. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 07/23/14 #3185 njensen Initial Creation. +# +# + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import AbstractDataAccessRequest + +class GetAvailableLevelsRequest(AbstractDataAccessRequest): + + def __init__(self): + super(GetAvailableLevelsRequest, self).__init__() diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetAvailableLocationNamesRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetAvailableLocationNamesRequest.py new file mode 100644 index 0000000..8d5b81d --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetAvailableLocationNamesRequest.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to make it sub class +# AbstractDataAccessRequest. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 05/28/13 #2023 dgilling Initial Creation. +# +# + + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import AbstractDataAccessRequest + +class GetAvailableLocationNamesRequest(AbstractDataAccessRequest): + + def __init__(self): + super(GetAvailableLocationNamesRequest, self).__init__() + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetAvailableParametersRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetAvailableParametersRequest.py new file mode 100644 index 0000000..1561dc1 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetAvailableParametersRequest.py @@ -0,0 +1,21 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to make it sub class +# AbstractDataAccessRequest. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 07/23/14 #3185 njensen Initial Creation. +# +# + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import AbstractDataAccessRequest + +class GetAvailableParametersRequest(AbstractDataAccessRequest): + + def __init__(self): + super(GetAvailableParametersRequest, self).__init__() diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetAvailableTimesRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetAvailableTimesRequest.py new file mode 100644 index 0000000..cb537fd --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetAvailableTimesRequest.py @@ -0,0 +1,31 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to make it sub class +# AbstractDataAccessRequest. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 05/28/13 #2023 dgilling Initial Creation. +# 03/03/14 #2673 bsteffen Add ability to query only ref times. +# +# + + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import AbstractDataAccessRequest + + +class GetAvailableTimesRequest(AbstractDataAccessRequest): + + def __init__(self): + super(GetAvailableTimesRequest, self).__init__() + self.refTimeOnly = False + + def getRefTimeOnly(self): + return self.refTimeOnly + + def setRefTimeOnly(self, refTimeOnly): + self.refTimeOnly = refTimeOnly diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetGeometryDataRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetGeometryDataRequest.py new file mode 100644 index 0000000..e54629d --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetGeometryDataRequest.py @@ -0,0 +1,37 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to make it sub class +# AbstractDataAccessRequest. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 05/28/13 #2023 dgilling Initial Creation. +# +# + + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import AbstractDataAccessRequest + +class GetGeometryDataRequest(AbstractDataAccessRequest): + + def __init__(self): + super(GetGeometryDataRequest, self).__init__() + self.requestedTimes = None + self.requestedPeriod = None + + def getRequestedTimes(self): + return self.requestedTimes + + def setRequestedTimes(self, requestedTimes): + self.requestedTimes = requestedTimes + + def getRequestedPeriod(self): + return self.requestedPeriod + + def setRequestedPeriod(self, requestedPeriod): + self.requestedPeriod = requestedPeriod + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetGridDataRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetGridDataRequest.py new file mode 100644 index 0000000..4d01940 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetGridDataRequest.py @@ -0,0 +1,44 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to make it sub class +# AbstractDataAccessRequest. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 05/28/13 #2023 dgilling Initial Creation. +# 05/28/13 #5916 bsteffen Add includeLatLonData +# +# + + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import AbstractDataAccessRequest + +class GetGridDataRequest(AbstractDataAccessRequest): + + def __init__(self): + super(GetGridDataRequest, self).__init__() + self.requestedTimes = None + self.requestedPeriod = None + self.includeLatLonData = True + + def getRequestedTimes(self): + return self.requestedTimes + + def setRequestedTimes(self, requestedTimes): + self.requestedTimes = requestedTimes + + def getRequestedPeriod(self): + return self.requestedPeriod + + def setRequestedPeriod(self, requestedPeriod): + self.requestedPeriod = requestedPeriod + + def getIncludeLatLonData(self): + return self.includeLatLonData + + def setIncludeLatLonData(self, includeLatLonData): + self.includeLatLonData = includeLatLonData; \ No newline at end of file diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetGridLatLonRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetGridLatLonRequest.py new file mode 100644 index 0000000..341b034 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetGridLatLonRequest.py @@ -0,0 +1,43 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Oct 10, 2016 5916 bsteffen Generated + +class GetGridLatLonRequest(object): + + def __init__(self): + self.envelope = None + self.crsWkt = None + self.nx = None + self.ny = None + + def getEnvelope(self): + return self.envelope + + def setEnvelope(self, envelope): + self.envelope = envelope + + def getCrsWkt(self): + return self.crsWkt + + def setCrsWkt(self, crsWkt): + self.crsWkt = crsWkt + + def getNx(self): + return self.nx + + def setNx(self, nx): + self.nx = nx + + def getNy(self): + return self.ny + + def setNy(self, ny): + self.ny = ny + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetIdentifierValuesRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetIdentifierValuesRequest.py new file mode 100644 index 0000000..d1fe916 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetIdentifierValuesRequest.py @@ -0,0 +1,28 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to make it subclass +# AbstractDataAccessRequest. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 04/15/2016 5379 tgurney Initial creation +# +# + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import AbstractDataAccessRequest + +class GetIdentifierValuesRequest(AbstractDataAccessRequest): + + def __init__(self): + super(GetIdentifierValuesRequest, self).__init__() + self.identifierKey = None + + def getIdentifierKey(self): + return self.identifierKey + + def setIdentifierKey(self, identifierKey): + self.identifierKey = identifierKey diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetNotificationFilterRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetNotificationFilterRequest.py new file mode 100644 index 0000000..a9e7d08 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetNotificationFilterRequest.py @@ -0,0 +1,21 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to make it sub class +# AbstractDataAccessRequest. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 05/26/16 2416 rjpeter Initial Creation. +# +# + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import AbstractDataAccessRequest + +class GetNotificationFilterRequest(AbstractDataAccessRequest): + + def __init__(self): + super(GetNotificationFilterRequest, self).__init__() diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetOptionalIdentifiersRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetOptionalIdentifiersRequest.py new file mode 100644 index 0000000..5028c0a --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetOptionalIdentifiersRequest.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to make it sub class +# AbstractIdentifierRequest. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 07/23/14 #3185 njensen Initial Creation. +# 07/30/14 #3185 njensen Renamed valid to optional +# +# + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import AbstractIdentifierRequest + +class GetOptionalIdentifiersRequest(AbstractIdentifierRequest): + + def __init__(self): + super(GetOptionalIdentifiersRequest, self).__init__() + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetRequiredIdentifiersRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetRequiredIdentifiersRequest.py new file mode 100644 index 0000000..7ac4f76 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetRequiredIdentifiersRequest.py @@ -0,0 +1,21 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to make it sub class +# AbstractIdentifierRequest. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 07/23/14 #3185 njensen Initial Creation. +# +# + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import AbstractIdentifierRequest + +class GetRequiredIdentifiersRequest(AbstractIdentifierRequest): + + def __init__(self): + super(GetRequiredIdentifiersRequest, self).__init__() diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetSupportedDatatypesRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetSupportedDatatypesRequest.py new file mode 100644 index 0000000..86f95ef --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetSupportedDatatypesRequest.py @@ -0,0 +1,19 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified to do nothing on __init__. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 07/23/14 #3185 njensen Initial Creation. +# +# + +class GetSupportedDatatypesRequest(object): + + def __init__(self): + pass + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/__init__.py new file mode 100644 index 0000000..9fe5d6c --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/__init__.py @@ -0,0 +1,37 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'AbstractDataAccessRequest', + 'AbstractIdentifierRequest', + 'GetAvailableLevelsRequest', + 'GetAvailableLocationNamesRequest', + 'GetAvailableParametersRequest', + 'GetAvailableTimesRequest', + 'GetGeometryDataRequest', + 'GetGridDataRequest', + 'GetGridLatLonRequest', + 'GetIdentifierValuesRequest', + 'GetNotificationFilterRequest', + 'GetOptionalIdentifiersRequest', + 'GetRequiredIdentifiersRequest', + 'GetSupportedDatatypesRequest' + ] + +from AbstractDataAccessRequest import AbstractDataAccessRequest +from AbstractIdentifierRequest import AbstractIdentifierRequest +from GetAvailableLevelsRequest import GetAvailableLevelsRequest +from GetAvailableLocationNamesRequest import GetAvailableLocationNamesRequest +from GetAvailableParametersRequest import GetAvailableParametersRequest +from GetAvailableTimesRequest import GetAvailableTimesRequest +from GetGeometryDataRequest import GetGeometryDataRequest +from GetGridDataRequest import GetGridDataRequest +from GetGridLatLonRequest import GetGridLatLonRequest +from GetIdentifierValuesRequest import GetIdentifierValuesRequest +from GetNotificationFilterRequest import GetNotificationFilterRequest +from GetOptionalIdentifiersRequest import GetOptionalIdentifiersRequest +from GetRequiredIdentifiersRequest import GetRequiredIdentifiersRequest +from GetSupportedDatatypesRequest import GetSupportedDatatypesRequest + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/AbstractResponseData.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/AbstractResponseData.py new file mode 100644 index 0000000..84eaeca --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/AbstractResponseData.py @@ -0,0 +1,42 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +import abc + + +class AbstractResponseData(object): + __metaclass__ = abc.ABCMeta + + @abc.abstractmethod + def __init__(self): + self.time = None + self.level = None + self.locationName = None + self.attributes = None + + def getTime(self): + return self.time + + def setTime(self, time): + self.time = time + + def getLevel(self): + return self.level + + def setLevel(self, level): + self.level = level + + def getLocationName(self): + return self.locationName + + def setLocationName(self, locationName): + self.locationName = locationName + + def getAttributes(self): + return self.attributes + + def setAttributes(self, attributes): + self.attributes = attributes + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/GeometryResponseData.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/GeometryResponseData.py new file mode 100644 index 0000000..c6408c4 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/GeometryResponseData.py @@ -0,0 +1,37 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to use AbstractResponseData. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/04/13 #2023 dgilling Initial Creation. +# 01/06/14 #2537 bsteffen Store geometry index instead of WKT. +# 06/30/15 #4569 nabowle Rename *WKT* to *WKB*. +# +# + + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.response import AbstractResponseData + +class GeometryResponseData(AbstractResponseData): + + def __init__(self): + super(GeometryResponseData, self).__init__() + self.dataMap = None + self.geometryWKBindex = None + + def getDataMap(self): + return self.dataMap + + def setDataMap(self, dataMap): + self.dataMap = dataMap + + def getGeometryWKBindex(self): + return self.geometryWKBindex + + def setGeometryWKBindex(self, geometryWKBindex): + self.geometryWKBindex = geometryWKBindex diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/GetGeometryDataResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/GetGeometryDataResponse.py new file mode 100644 index 0000000..8824d6f --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/GetGeometryDataResponse.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GetGeometryDataResponse(object): + + def __init__(self): + self.geometryWKBs = None + self.geoData = None + + def getGeometryWKBs(self): + return self.geometryWKBs + + def setGeometryWKBs(self, geometryWKBs): + self.geometryWKBs = geometryWKBs + + def getGeoData(self): + return self.geoData + + def setGeoData(self, geoData): + self.geoData = geoData + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/GetGridDataResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/GetGridDataResponse.py new file mode 100644 index 0000000..54cbe13 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/GetGridDataResponse.py @@ -0,0 +1,58 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GetGridDataResponse(object): + + def __init__(self): + self.gridData = None + self.siteNxValues = None + self.siteNyValues = None + self.siteLatGrids = None + self.siteLonGrids = None + self.siteEnvelopes = None + self.siteCrsWkt = None + + def getGridData(self): + return self.gridData + + def setGridData(self, gridData): + self.gridData = gridData + + def getSiteNxValues(self): + return self.siteNxValues + + def setSiteNxValues(self, siteNxValues): + self.siteNxValues = siteNxValues + + def getSiteNyValues(self): + return self.siteNyValues + + def setSiteNyValues(self, siteNyValues): + self.siteNyValues = siteNyValues + + def getSiteLatGrids(self): + return self.siteLatGrids + + def setSiteLatGrids(self, siteLatGrids): + self.siteLatGrids = siteLatGrids + + def getSiteLonGrids(self): + return self.siteLonGrids + + def setSiteLonGrids(self, siteLonGrids): + self.siteLonGrids = siteLonGrids + + def getSiteEnvelopes(self): + return self.siteEnvelopes + + def setSiteEnvelopes(self, siteEnvelopes): + self.siteEnvelopes = siteEnvelopes + + def getSiteCrsWkt(self): + return self.siteCrsWkt + + def setSiteCrsWkt(self, siteCrsWkt): + self.siteCrsWkt = siteCrsWkt + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/GetGridLatLonResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/GetGridLatLonResponse.py new file mode 100644 index 0000000..4ae7131 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/GetGridLatLonResponse.py @@ -0,0 +1,43 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Oct 10, 2016 5916 bsteffen Generated + +class GetGridLatLonResponse(object): + + def __init__(self): + self.lats = None + self.lons = None + self.nx = None + self.ny = None + + def getLats(self): + return self.lats + + def setLats(self, lats): + self.lats = lats + + def getLons(self): + return self.lons + + def setLons(self, lons): + self.lons = lons + + def getNx(self): + return self.nx + + def setNx(self, nx): + self.nx = nx + + def getNy(self): + return self.ny + + def setNy(self, ny): + self.ny = ny + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/GetNotificationFilterResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/GetNotificationFilterResponse.py new file mode 100644 index 0000000..81e2e8b --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/GetNotificationFilterResponse.py @@ -0,0 +1,22 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GetNotificationFilterResponse(object): + + def __init__(self): + self.notificationFilter = None + self.jmsConnectionInfo = None + + def getNotificationFilter(self): + return self.notificationFilter + + def setNotificationFilter(self, notificationFilter): + self.notificationFilter = notificationFilter + + def getJmsConnectionInfo(self): + return self.jmsConnectionInfo + + def setJmsConnectionInfo(self, jmsConnectionInfo): + self.jmsConnectionInfo = jmsConnectionInfo diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/GridResponseData.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/GridResponseData.py new file mode 100644 index 0000000..9d2125a --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/GridResponseData.py @@ -0,0 +1,42 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to use AbstractResponseData. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/04/13 #2023 dgilling Initial Creation. +# +# + + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.response import AbstractResponseData + +class GridResponseData(AbstractResponseData): + + def __init__(self): + super(GridResponseData, self).__init__() + self.parameter = None + self.unit = None + self.gridData = None + + def getParameter(self): + return self.parameter + + def setParameter(self, parameter): + self.parameter = parameter + + def getUnit(self): + return self.unit + + def setUnit(self, unit): + self.unit = unit + + def getGridData(self): + return self.gridData + + def setGridData(self, gridData): + self.gridData = gridData diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/__init__.py new file mode 100644 index 0000000..1aa438f --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/response/__init__.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'AbstractResponseData', + 'GeometryResponseData', + 'GetGeometryDataResponse', + 'GetGridDataResponse', + 'GetGridLatLonResponse', + 'GetNotificationFilterResponse', + 'GridResponseData' + ] + +from AbstractResponseData import AbstractResponseData +from GeometryResponseData import GeometryResponseData +from GetGeometryDataResponse import GetGeometryDataResponse +from GetGridDataResponse import GetGridDataResponse +from GetGridLatLonResponse import GetGridLatLonResponse +from GetNotificationFilterResponse import GetNotificationFilterResponse +from GridResponseData import GridResponseData + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/__init__.py new file mode 100644 index 0000000..f2ff21e --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/__init__.py @@ -0,0 +1,16 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'events', + 'gfe', + 'grid', + 'level', + 'message', + 'radar', + 'text' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/events/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/events/__init__.py new file mode 100644 index 0000000..1be0370 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/events/__init__.py @@ -0,0 +1,10 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'hazards' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/events/hazards/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/events/hazards/__init__.py new file mode 100644 index 0000000..d01496e --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/events/hazards/__init__.py @@ -0,0 +1,10 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'requests' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/events/hazards/requests/RegionLookupRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/events/hazards/requests/RegionLookupRequest.py new file mode 100644 index 0000000..0190e12 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/events/hazards/requests/RegionLookupRequest.py @@ -0,0 +1,29 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Oct 08, 2014 reblum Generated + +class RegionLookupRequest(object): + + def __init__(self): + self.region = None + self.site = None + + def getRegion(self): + return self.region + + def setRegion(self, region): + self.region = region + + def getSite(self): + return self.site + + def setSite(self, site): + self.site = site + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/events/hazards/requests/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/events/hazards/requests/__init__.py new file mode 100644 index 0000000..9d591bf --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/events/hazards/requests/__init__.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'RegionLookupRequest' + ] + +from RegionLookupRequest import RegionLookupRequest + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/GridDataHistory.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/GridDataHistory.py new file mode 100644 index 0000000..d3ffca7 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/GridDataHistory.py @@ -0,0 +1,80 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + + +class GridDataHistory(object): + + def __init__(self): + self.origin = None + self.originParm = None + self.originTimeRange = None + self.timeModified = None + self.whoModified = None + self.updateTime = None + self.publishTime = None + self.lastSentTime = None + + def __str__(self): + return self.__repr__() + + def __repr__(self): + retVal = "Origin: " + self.origin + '\n' + retVal += "Origin Parm: " + str(self.originParm) + '\n' + retVal += "Origin Time Range: " + str(self.originTimeRange) +\ + " Time Modified: " + str(self.timeModified) +\ + " Who Modified: " + str(self.whoModified) + '\n' + retVal += "Update Time: " + str(self.updateTime) + '\n' + retVal += "Publish Time: " + str(self.publishTime) + '\n' + retVal += "Last Sent Time: " + str(self.lastSentTime) + '\n' + return retVal + + def getOrigin(self): + return self.origin + + def setOrigin(self, origin): + self.origin = origin + + def getOriginParm(self): + return self.originParm + + def setOriginParm(self, originParm): + self.originParm = originParm + + def getOriginTimeRange(self): + return self.originTimeRange + + def setOriginTimeRange(self, originTimeRange): + self.originTimeRange = originTimeRange + + def getTimeModified(self): + return self.timeModified + + def setTimeModified(self, timeModified): + self.timeModified = timeModified + + def getWhoModified(self): + return self.whoModified + + def setWhoModified(self, whoModified): + self.whoModified = whoModified + + def getUpdateTime(self): + return self.updateTime + + def setUpdateTime(self, updateTime): + self.updateTime = updateTime + + def getPublishTime(self): + return self.publishTime + + def setPublishTime(self, publishTime): + self.publishTime = publishTime + + def getLastSentTime(self): + return self.lastSentTime + + def setLastSentTime(self, lastSentTime): + self.lastSentTime = lastSentTime + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/__init__.py new file mode 100644 index 0000000..0827d48 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/__init__.py @@ -0,0 +1,26 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/22/2015 4573 randerso Added svcbu package +# +## + +__all__ = [ + 'config', + 'db', + 'discrete', + 'grid', + 'request', + 'server', + 'slice', + 'svcbu', + 'weather', + 'GridDataHistory' + ] + +from GridDataHistory import GridDataHistory + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/config/ProjectionData.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/config/ProjectionData.py new file mode 100644 index 0000000..443a902 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/config/ProjectionData.py @@ -0,0 +1,99 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class ProjectionData(object): + + def __init__(self): + self.projectionID = None + self.projectionType = None + self.latLonLL = None + self.latLonUR = None + self.latLonOrigin = None + self.stdParallelOne = None + self.stdParallelTwo = None + self.gridPointLL = None + self.gridPointUR = None + self.latIntersect = None + self.lonCenter = None + self.lonOrigin = None + + def getProjectionID(self): + return self.projectionID + + def setProjectionID(self, projectionID): + self.projectionID = projectionID + + def getProjectionType(self): + return self.projectionType + + def setProjectionType(self, projectionType): + self.projectionType = projectionType + + def getLatLonLL(self): + return self.latLonLL + + def setLatLonLL(self, latLonLL): + self.latLonLL = latLonLL + + def getLatLonUR(self): + return self.latLonUR + + def setLatLonUR(self, latLonUR): + self.latLonUR = latLonUR + + def getLatLonOrigin(self): + return self.latLonOrigin + + def setLatLonOrigin(self, latLonOrigin): + self.latLonOrigin = latLonOrigin + + def getStdParallelOne(self): + return self.stdParallelOne + + def setStdParallelOne(self, stdParallelOne): + self.stdParallelOne = stdParallelOne + + def getStdParallelTwo(self): + return self.stdParallelTwo + + def setStdParallelTwo(self, stdParallelTwo): + self.stdParallelTwo = stdParallelTwo + + def getGridPointLL(self): + return self.gridPointLL + + def setGridPointLL(self, gridPointLL): + self.gridPointLL = gridPointLL + + def getGridPointUR(self): + return self.gridPointUR + + def setGridPointUR(self, gridPointUR): + self.gridPointUR = gridPointUR + + def getLatIntersect(self): + return self.latIntersect + + def setLatIntersect(self, latIntersect): + self.latIntersect = latIntersect + + def getLonCenter(self): + return self.lonCenter + + def setLonCenter(self, lonCenter): + self.lonCenter = lonCenter + + def getLonOrigin(self): + return self.lonOrigin + + def setLonOrigin(self, lonOrigin): + self.lonOrigin = lonOrigin + + def keys(self): + return ['projectionID', 'projectionType', 'latLonLL', 'latLonUR', + 'latLonOrigin', 'stdParallelOne', 'stdParallelTwo', + 'gridPointLL', 'gridPointUR', 'latIntersect', 'lonCenter', + 'lonOrigin'] + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/config/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/config/__init__.py new file mode 100644 index 0000000..60ccea2 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/config/__init__.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'ProjectionData' + ] + +from ProjectionData import ProjectionData + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/__init__.py new file mode 100644 index 0000000..8c14751 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/__init__.py @@ -0,0 +1,10 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'objects' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/DatabaseID.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/DatabaseID.py new file mode 100644 index 0000000..bd98001 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/DatabaseID.py @@ -0,0 +1,195 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# Modified by njensen to add __repr__ + +import time + +class DatabaseID(object): + + def __init__(self, dbIdentifier=None): + self.siteId = None + self.format = "NONE" + self.dbType = None + self.modelName = None + self.modelTime = None + self.modelId = None + self.shortModelId = None + if dbIdentifier is not None: + if (self.__decodeIdentifier(dbIdentifier)): + self.__encodeIdentifier() + else: + self.format = "NONE" + self.dbType = "" + self.siteId = "" + self.modelName = "" + self.modelTime = "00000000_0000" + self.modelId = "" + self.shortModelId = "" + + def isValid(self) : + return self.format != "NONE"; + + def getSiteId(self): + return self.siteId + + def setSiteId(self, siteId): + self.siteId = siteId + + def getFormat(self): + return self.format + + def setFormat(self, format): + self.format = format + + def getDbType(self): + return self.dbType + + def setDbType(self, dbType): + self.dbType = dbType + + def getModelName(self): + return self.modelName + + def setModelName(self, modelName): + self.modelName = modelName + + def getModelTime(self): + return self.modelTime + + def setModelTime(self, modelTime): + self.modelTime = modelTime + + def getModelId(self): + return self.modelId + + def setModelId(self, modelId): + self.modelId = modelId + + def getShortModelId(self): + return self.shortModelId + + def setShortModelId(self, shortModelId): + self.shortModelId = shortModelId + + def __encodeIdentifier(self): + if self.dbType is not None: + self.modelId = self.siteId + "_" + self.format + "_" + self.dbType + "_" + self.modelName + else: + self.modelId = self.siteId + "_" + self.format + "__" + self.modelName + + self.shortModelId = self.modelName + if self.dbType is not None and self.dbType != "": + self.shortModelId += "_" + self.dbType + + if self.modelTime != "00000000_0000": + self.modelId += "_" + self.modelTime; + self.shortModelId += "_" + self.modelTime[6:8] + self.modelTime[9:11] + else: + self.modelId += "_" + "00000000_0000" + + self.shortModelId += " (" + self.siteId + ")" + + def __decodeIdentifier(self, dbIdentifier): + self.format = "NONE" + self.dbType = "" + self.siteId = "" + self.modelName = "" + self.modelTime = "00000000_0000" + + # parse into '_' separated strings + strings = dbIdentifier.split("_"); + if len(strings) != 6: + return False + + # store the data + if strings[1] == "GRID": + self.format = "GRID" + else: + return False + + self.siteId = strings[0] + self.dbType = strings[2] + self.modelName = strings[3] + + # date-time group + if (len(strings[4]) != 8 or len(strings[5]) != 4): + return False + + # make sure the digits are there + dtg = strings[4] + '_' + strings[5]; # back together + if dtg != "00000000_0000": + if not self.__decodeDtg(dtg): + return False + + return True + + @staticmethod + def decodeDtg(dtgString): + dateStruct = time.gmtime(0) + try: + dateStruct = time.strptime(dtgString, "%Y%m%d_%H%M") + except: + return (False, dateStruct) + return (True, dateStruct) + + def __decodeDtg(self, dtgString): + try: + time.strptime(dtgString, "%Y%m%d_%H%M") + self.modelTime = dtgString + except: + return False + return True + + def getModelTimeAsDate(self): + if self.modelTime == "00000000_0000": + return time.gmtime(0) + else: + return time.strptime(self.modelTime, "%Y%m%d_%H%M") + + def __str__(self): + return self.__repr__() + + def __repr__(self): + return self.modelId + + def __hash__(self): + prime = 31; + result = 1; + result = prime * result + (0 if self.dbType is None else hash(self.dbType)) + result = prime * result + (0 if self.format is None else hash(self.format)) + result = prime * result + (0 if self.modelId is None else hash(self.modelId)) + result = prime * result + (0 if self.modelTime is None else hash(self.modelTime)) + result = prime * result + (0 if self.siteId is None else hash(self.siteId)) + return result; + + def __cmp__(self, other): + if not isinstance(other, DatabaseID): + siteComp = cmp(self.siteId, other.siteId) + if siteComp != 0: + return siteComp + + formatComp = cmp(self.format, other.format) + if formatComp != 0: + return formatComp + + typeComp = cmp(self.dbType, other.dbType) + if typeComp != 0: + return typeComp + + nameComp = cmp(self.modelName, other.modelName) + if nameComp != 0: + return nameComp + + return -cmp(self.getModelTimeAsDate(), other.getModelTimeAsDate()) + else: + return NotImplemented + + def __eq__(self, other): + if not isinstance(other, DatabaseID): + return False + return (str(self) == str(other)) + + def __ne__(self, other): + return (not self.__eq__(other)) diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/GFERecord.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/GFERecord.py new file mode 100644 index 0000000..4583efc --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/GFERecord.py @@ -0,0 +1,98 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.db.objects import ParmID +from dynamicserialize.dstypes.com.raytheon.uf.common.time import DataTime +from dynamicserialize.dstypes.com.raytheon.uf.common.time import TimeRange + + +class GFERecord(object): + + def __init__(self, parmId=None, timeRange=None): + self.gridHistory = [] + self.dataURI = None + self.pluginName = "gfe" + self.insertTime = None + self.messageData = None + self.identifier = None + self.dataTime = None + self.parmId = None + if timeRange is not None: + if type(timeRange) is TimeRange: + self.dataTime = DataTime(refTime=timeRange.getStart(), validPeriod=timeRange) + else: + raise TypeError("Invalid TimeRange object specified.") + if parmId is not None: + if type(parmId) is ParmID.ParmID: + self.parmId = parmId + self.parmName = parmId.getParmName() + self.parmLevel = parmId.getParmLevel() + self.dbId = parmId.getDbId() + else: + raise TypeError("Invalid ParmID object specified. Type:" + str(type(parmId))) + + def getParmName(self): + return self.parmName + + def setParmName(self, parmName): + self.parmName = parmName + + def getParmLevel(self): + return self.parmLevel + + def setParmLevel(self, parmLevel): + self.parmLevel = parmLevel + + def getParmId(self): + return self.parmId + + def setParmId(self, parmId): + self.parmId = parmId + + def getDbId(self): + return self.dbId + + def setDbId(self, dbId): + self.dbId = dbId + + def getGridHistory(self): + return self.gridHistory + + def setGridHistory(self, gridHistory): + self.gridHistory = gridHistory + + def getDataURI(self): + return self.dataURI + + def setDataURI(self, dataURI): + self.dataURI = dataURI + + def getPluginName(self): + return "gfe" + + def getDataTime(self): + return self.dataTime + + def setDataTime(self, dataTime): + self.dataTime = dataTime + + def getInsertTime(self): + return self.insertTime + + def setInsertTime(self, insertTime): + self.insertTime = insertTime + + def getMessageData(self): + return self.messageData + + def setMessageData(self, messageData): + self.messageData = messageData + + def getIdentifier(self): + return self.identifier + + def setIdentifier(self, identifier): + self.identifier = identifier + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/GridLocation.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/GridLocation.py new file mode 100644 index 0000000..cd405f2 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/GridLocation.py @@ -0,0 +1,119 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GridLocation(object): + + def __init__(self): + self.siteId = None + self.nx = None + self.ny = None + self.timeZone = None + self.projection = None + self.origin = None + self.extent = None + self.geometry = None + self.crsWKT = None + self.identifier = None + + def __str__(self): + return self.__repr__() + + def __repr__(self): + s = "[SiteID =" + self.siteId + ",ProjID=" + self.projection.getProjectionID() +\ + ",gridSize=(" + str(self.nx) + ',' + str(self.ny) + ")" + # TODO: Handle geometry in dynamicserialize + # ,loc=" + this.geometry.getGeometryType(); + s += ']' + return s + + def __eq__(self, other): + if not isinstance(other, GridLocation): + return False + if self.siteId != other.siteId: + return False + if self.crsWKT != other.crsWKT: + return False + # FIXME: Geometry/Polygon objects don't really work in dynamicserialize + # commenting out this check unless it causes problems +# if self.geometry != other.geometry: +# return False + if self.nx != other.nx: + return False + if self.ny != other.ny: + return False + return True + + def __ne__(self, other): + return (not self.__eq__(other)) + + def getSiteId(self): + return self.siteId + + def setSiteId(self, siteId): + self.siteId = siteId + + def getNx(self): + return self.nx + + def setNx(self, nx): + self.nx = nx + + def getNy(self): + return self.ny + + def setNy(self, ny): + self.ny = ny + + def getTimeZone(self): + return self.timeZone + + def setTimeZone(self, timeZone): + self.timeZone = timeZone + + def getProjection(self): + return self.projection + + def setProjection(self, projection): + self.projection = projection + + def getOrigin(self): + return self.origin + + def setOrigin(self, origin): + self.origin = origin + + def getExtent(self): + return self.extent + + def setExtent(self, extent): + self.extent = extent + + def getGeometry(self): + return self.geometry + + def setGeometry(self, geometry): + self.geometry = geometry + + def getCrsWKT(self): + return self.crsWKT + + def setCrsWKT(self, crsWKT): + self.crsWKT = crsWKT + + def getIdentifier(self): + return self.identifier + + def setIdentifier(self, identifier): + self.identifier = identifier + + def isValid(self): + if self.projection is None: + return False + if self.nx < 2 or self.ny < 2: + return False + if self.origin is None or self.extent is None: + return False + return True + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/GridParmInfo.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/GridParmInfo.py new file mode 100644 index 0000000..ec2298f --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/GridParmInfo.py @@ -0,0 +1,193 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +import warnings + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.db.objects import GridLocation +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.db.objects import ParmID +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.db.objects import TimeConstraints + + +class GridParmInfo(object): + + def __init__(self, id=None, gridLoc=None, gridType="NONE", unit=None, + descriptiveName="", minValue=0.0, maxValue=0.0, precision=0, + timeIndependentParm=False, timeConstraints=None, rateParm=False): + self.parmID = id + self.gridLoc = gridLoc + self.gridType = gridType + self.descriptiveName = descriptiveName + self.unitString = unit + self.minValue = float(minValue) + self.maxValue = float(maxValue) + self.precision = int(precision) + self.rateParm = rateParm + self.timeConstraints = timeConstraints + self.timeIndependentParm = timeIndependentParm + +# (valid, errors) = self.__validCheck() +# if not valid: +# errorMessage = "GridParmInfo is invalid: " + str(errors) +# warnings.warn(errorMessage) +# self.__setDefaultValues() + + def __str__(self): + return self.__repr__() + + def __repr__(self): + out = "" + if self.isValid(): + out = "ParmID: " + str(self.parmID) + \ + " TimeConstraints: " + str(self.timeConstraints) + \ + " GridLoc: " + str(self.gridLoc) + \ + " Units: " + self.unitString + \ + " Name: " + self.descriptiveName + \ + " Min/Max AllowedValues: " + str(self.minValue) + "," + \ + str(self.maxValue) + " Precision: " + str(self.precision) + \ + " TimeIndependent: " + str(self.timeIndependentParm) + \ + " RateParm: " + str(self.rateParm) + \ + " GridType: " + self.gridType + else: + out = "" + return out + + def __eq__(self, other): + if not isinstance(other, GridParmInfo): + return False + if self.descriptiveName != other.descriptiveName: + return False + if self.gridLoc != other.gridLoc: + return False + if self.gridType != other.gridType: + return False + if self.minValue != other.minValue: + return False + if self.maxValue != other.maxValue: + return False + if self.parmID != other.parmID: + return False + if self.precision != other.precision: + return False + if self.rateParm != other.rateParm: + return False + if self.timeConstraints != other.timeConstraints: + return False + if self.timeIndependentParm != other.timeIndependentParm: + return False + if self.unitString != other.unitString: + return False + return True + + def __ne__(self, other): + return (not self.__eq__(other)) + + def __validCheck(self): + status = [] + + if not self.parmID.isValid(): + status.append("GridParmInfo.ParmID is not valid [" + str(self.parmID) + "]") + if not self.timeConstraints.isValid(): + status.append("GridParmInfo.TimeConstraints are not valid [" + + str(self.timeConstraints) + "]") + if not self.gridLoc.isValid(): + status.append("GridParmInfo.GridLocation is not valid") + if self.timeIndependentParm and self.timeConstraints.anyConstraints(): + status.append("GridParmInfo is invalid. There are time constraints" + + " for a time independent parm. Constraints: " + + str(self.timeConstraints)) + if len(self.unitString) == 0: + status.append("GridParmInfo.Units are not defined.") + if self.precision < -2 or self.precision > 5: + status.append("GridParmInfo is invalid. Precision out of limits." + + " Precision is: " + str(precision) + ". Must be between -2 and 5.") + + retVal = True + if len(status) > 0: + retVal = False + return (retVal, status) + + def isValid(self): + (valid, errors) = self.__validCheck() + return valid + + def __setDefaultValues(self): + self.parmID = ParmID() + self.gridLoc = GridLocation() + self.gridType = "NONE" + self.descriptiveName = "" + self.unitString = "" + self.minValue = 0.0 + self.maxValue = 0.0 + self.precision = 0 + self.rateParm = False + self.timeConstraints = TimeConstraints() + self.timeIndependentParm = False + + def getParmID(self): + return self.parmID + + def setParmID(self, parmID): + self.parmID = parmID + + def getGridLoc(self): + return self.gridLoc + + def setGridLoc(self, gridLoc): + self.gridLoc = gridLoc + + def getGridType(self): + return self.gridType + + def setGridType(self, gridType): + self.gridType = gridType + + def getDescriptiveName(self): + return self.descriptiveName + + def setDescriptiveName(self, descriptiveName): + self.descriptiveName = descriptiveName + + def getUnitString(self): + return self.unitString + + def setUnitString(self, unitString): + self.unitString = unitString + + def getMinValue(self): + return self.minValue + + def setMinValue(self, minValue): + self.minValue = minValue + + def getMaxValue(self): + return self.maxValue + + def setMaxValue(self, maxValue): + self.maxValue = maxValue + + def getPrecision(self): + return self.precision + + def setPrecision(self, precision): + self.precision = precision + + def getRateParm(self): + return self.rateParm + + def setRateParm(self, rateParm): + self.rateParm = rateParm + + def getTimeConstraints(self): + return self.timeConstraints + + def setTimeConstraints(self, timeConstraints): + self.timeConstraints = timeConstraints + + def getTimeIndependentParm(self): + return self.timeIndependentParm + + def setTimeIndependentParm(self, timeIndependentParm): + self.timeIndependentParm = timeIndependentParm + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/ParmID.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/ParmID.py new file mode 100644 index 0000000..9367d95 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/ParmID.py @@ -0,0 +1,134 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# Modified by njensen to add __repr__ + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.db.objects import DatabaseID + +class ParmID(object): + + def __init__(self, parmIdentifier=None, dbId=None, level=None): + self.parmName = None + self.parmLevel = None + self.dbId = None + self.compositeName = None + self.shortParmId = None + self.parmId = None + + if (parmIdentifier is not None) and (dbId is not None): + self.parmName = parmIdentifier + + if type(dbId) is DatabaseID: + self.dbId = dbId + elif type(dbId) is str: + self.dbId = DatabaseID(dbId) + else: + raise TypeError("Invalid database ID specified.") + + if level is None: + self.parmLevel = self.defaultLevel() + else: + self.parmLevel = level + + self.__encodeIdentifier() + + elif parmIdentifier is not None: + self.__decodeIdentifier(parmIdentifier) + self.__encodeIdentifier() + + def getParmName(self): + return self.parmName + + def getParmLevel(self): + return self.parmLevel + + def getDbId(self): + return self.dbId + + def getCompositeName(self): + return self.compositeName + + def getShortParmId(self): + return self.shortParmId + + def getParmId(self): + return self.parmId + + def __decodeIdentifier(self, parmIdentifier): + parts = parmIdentifier.split(":") + nameLevel = parts[0].split("_") + self.dbId = DatabaseID(parts[1]) + if (len(nameLevel) == 2): + self.parmName = nameLevel[0] + self.parmLevel = nameLevel[1] + else: + self.parmName = nameLevel[0] + self.parmLevel = self.defaultLevel() + + def __encodeIdentifier(self): + self.compositeName = self.parmName + "_" + self.parmLevel + self.shortParmId = self.compositeName + ":" + self.dbId.getShortModelId() + self.parmId = self.compositeName + ":" + self.dbId.getModelId() + + def isValid(self): + if len(self.parmName) is None or len(self.parmLevel) is None or self.dbId is None: + return False + if len(self.parmName) < 1 or len(self.parmLevel) < 1 or not self.dbId.isValid(): + return False + + if not self.parmName.isalnum(): + return False + if not self.parmLevel.isalnum(): + return False + + return True + + @staticmethod + def defaultLevel(): + return "SFC" + + @staticmethod + def parmNameAndLevel(composite): + pos = composite.find('_') + if pos != -1: + return (composite[:pos], composite[pos+1:]) + else: + return (composite, "SFC") + + def __str__(self): + return self.__repr__() + + def __repr__(self): + return self.parmName + '_' + self.parmLevel + ":" + str(self.dbId) + + def __hash__(self): + return hash(self.parmId) + + def __cmp__(self, other): + if isinstance(other, ParmID): + nameComp = cmp(self.parmName, other.parmName) + if nameComp != 0: + return nameComp + + levelComp = cmp(self.parmLevel, other.parmLevel) + if levelComp != 0: + return levelComp + + return cmp(self.dbId, other.dbId) + else: + return NotImplemented + + def __eq__(self, other): + if not isinstance(other, ParmID): + return False + if self.dbId != other.dbId: + return False + if self.parmLevel != other.parmLevel: + return False + if self.parmName != other.parmName: + return False + return True + + def __ne__(self, other): + return (not self.__eq__(other)) diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/TimeConstraints.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/TimeConstraints.py new file mode 100644 index 0000000..b433035 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/TimeConstraints.py @@ -0,0 +1,83 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# +# 03/20/2013 #1774 randerso Removed setters, added isValid. + + +import logging + +HOUR = 3600; +DAY = 24 * HOUR; + +class TimeConstraints(object): + + def __init__(self, duration=0, repeatInterval=0, startTime=0): + duration = int(duration) + repeatInterval = int(repeatInterval) + startTime = int(startTime) + + self.valid = False; + if duration == 0 and repeatInterval == 0 and startTime == 0: + self.valid = True; + else: + if repeatInterval <= 0 or repeatInterval > DAY \ + or DAY % repeatInterval != 0 \ + or repeatInterval < duration \ + or startTime < 0 or startTime > DAY \ + or duration < 0 or duration > DAY: + + logging.warning("Bad init values for TimeConstraints: ", self); + self.valid = False; + duration = 0; + repeatInterval = 0; + startTime = 0; + else: + self.valid = True; + + self.duration = duration + self.repeatInterval = repeatInterval + self.startTime = startTime + + def __str__(self): + return self.__repr__() + + def __repr__(self): + if not self.isValid(): + return "" + elif not self.anyConstraints(): + return "" + else: + return "[s=" + str(self.startTime / HOUR) + "h, i=" + \ + str(self.repeatInterval / HOUR) + "h, d=" + \ + str(self.duration / HOUR) + "h]" + + def __eq__(self, other): + if not isinstance(other, TimeConstraints): + return False + if self.isValid() != other.isValid(): + return False + if self.duration != other.duration: + return False + if self.repeatInterval != other.repeatInterval: + return False + return (self.startTime == other.startTime) + + def __ne__(self, other): + return (not self.__eq__(other)) + + def anyConstraints(self): + return (self.duration != 0) + + def isValid(self): + return self.valid + + def getDuration(self): + return self.duration + + def getRepeatInterval(self): + return self.repeatInterval + + def getStartTime(self): + return self.startTime diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/__init__.py new file mode 100644 index 0000000..873105b --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/db/objects/__init__.py @@ -0,0 +1,21 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'DatabaseID', + 'GFERecord', + 'GridLocation', + 'GridParmInfo', + 'ParmID', + 'TimeConstraints' + ] + +from DatabaseID import DatabaseID +from GFERecord import GFERecord +from GridLocation import GridLocation +from GridParmInfo import GridParmInfo +from ParmID import ParmID +from TimeConstraints import TimeConstraints + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/discrete/DiscreteKey.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/discrete/DiscreteKey.py new file mode 100644 index 0000000..e571ee9 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/discrete/DiscreteKey.py @@ -0,0 +1,89 @@ +## +## + +## NOTE: Because the pure python dynamicserialize code does not +# have a means of accessing the DiscreteDefinition, this class +# is only really useful as a container for deserialized data +# from EDEX. I would not recommend trying to use it for anything +# else. + + +SUBKEY_SEPARATOR = '^' +AUXDATA_SEPARATOR = ':' + +class DiscreteKey(object): + + def __init__(self): + self.siteId = None + self.subKeys = None + self.parmID = None + + def __str__(self): + return self.__repr__() + + def __repr__(self): + return SUBKEY_SEPARATOR.join(self.subKeys) + + def __getitem__(self, key): + try: + index = int(key) + except: + raise TypeError("list indices must be integers, not " + str(type(key))) + if index < 0 or index > len(self.subKeys): + raise IndexError("index out of range") + return self.subKeys[index] + + def __hash__(self): + prime = 31 + result = 1 + result = prime * result + (0 if self.parmID is None else hash(self.parmID)) + result = prime * result + (0 if self.siteId is None else hash(self.siteId)) + result = prime * result + (0 if self.subKeys is None else hash(self.subKeys)) + return result + + def __eq__(self, other): + if not isinstance(other, DiscreteKey): + return False + if self.parmID != other.parmID: + return False + if self.siteId != other.siteId: + return False + return self.subKeys == other.subKeys + + def __ne__(self, other): + return (not self.__eq__(other)) + + @staticmethod + def auxData(subkey): + pos = subkey.find(AUXDATA_SEPARATOR) + if pos != -1: + return subkey[pos + 1:] + else: + return "" + + @staticmethod + def baseData(subkey): + pos = subkey.find(AUXDATA_SEPARATOR) + if pos != -1: + return subkey[:pos] + else: + return subkey + + def getSiteId(self): + return self.siteId + + def setSiteId(self, siteId): + self.siteId = siteId + + def getSubKeys(self): + return self.subKeys + + def setSubKeys(self, subKeys): + self.subKeys = subKeys + + def getParmID(self): + return self.parmID + + def setParmID(self, parmID): + self.parmID = parmID + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/discrete/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/discrete/__init__.py new file mode 100644 index 0000000..55c7a4b --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/discrete/__init__.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'DiscreteKey' + ] + +from DiscreteKey import DiscreteKey + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/grid/Grid2DByte.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/grid/Grid2DByte.py new file mode 100644 index 0000000..3b5911c --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/grid/Grid2DByte.py @@ -0,0 +1,36 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +import numpy + + +class Grid2DByte(object): + + def __init__(self): + self.buffer = None + self.xdim = None + self.ydim = None + + def getBuffer(self): + return self.buffer + + def setBuffer(self, buffer): + self.buffer = buffer + + def getXdim(self): + return self.xdim + + def setXdim(self, xdim): + self.xdim = xdim + + def getYdim(self): + return self.ydim + + def setYdim(self, ydim): + self.ydim = ydim + + def getNumPyGrid(self): + return numpy.resize(self.buffer, (self.xdim, self.ydim)) + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/grid/Grid2DFloat.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/grid/Grid2DFloat.py new file mode 100644 index 0000000..77728a2 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/grid/Grid2DFloat.py @@ -0,0 +1,35 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +import numpy + + +class Grid2DFloat(object): + + def __init__(self): + self.buffer = None + self.xdim = None + self.ydim = None + + def getBuffer(self): + return self.buffer + + def setBuffer(self, buffer): + self.buffer = buffer + + def getXdim(self): + return self.xdim + + def setXdim(self, xdim): + self.xdim = xdim + + def getYdim(self): + return self.ydim + + def setYdim(self, ydim): + self.ydim = ydim + + def getNumPyGrid(self): + return numpy.resize(self.buffer, (self.xdim, self.ydim)) diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/grid/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/grid/__init__.py new file mode 100644 index 0000000..0efdd01 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/grid/__init__.py @@ -0,0 +1,13 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'Grid2DByte', + 'Grid2DFloat' + ] + +from Grid2DByte import Grid2DByte +from Grid2DFloat import Grid2DFloat + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/AbstractGfeRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/AbstractGfeRequest.py new file mode 100644 index 0000000..0d57eb9 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/AbstractGfeRequest.py @@ -0,0 +1,27 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +import abc + + +class AbstractGfeRequest(object): + __metaclass__ = abc.ABCMeta + + @abc.abstractmethod + def __init__(self): + self.siteID = None + self.workstationID = None + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/CommitGridsRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/CommitGridsRequest.py new file mode 100644 index 0000000..c4204b3 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/CommitGridsRequest.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class CommitGridsRequest(object): + + def __init__(self): + self.commits = None + self.workstationID = None + self.siteID = None + + def getCommits(self): + return self.commits + + def setCommits(self, commits): + self.commits = commits + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/ConfigureTextProductsRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/ConfigureTextProductsRequest.py new file mode 100644 index 0000000..2398174 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/ConfigureTextProductsRequest.py @@ -0,0 +1,37 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class ConfigureTextProductsRequest(object): + + def __init__(self): + self.mode = None + self.template = None + self.site = None + self.destinationDir = None + + def getMode(self): + return self.mode + + def setMode(self, mode): + self.mode = mode + + def getTemplate(self): + return self.template + + def setTemplate(self, template): + self.template = template + + def getSite(self): + return self.site + + def setSite(self, site): + self.site = site + + def getDestinationDir(self): + return self.destinationDir + + def setDestinationDir(self, destinationDir): + self.destinationDir = destinationDir + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/ExecuteIfpNetCDFGridRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/ExecuteIfpNetCDFGridRequest.py new file mode 100644 index 0000000..3675107 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/ExecuteIfpNetCDFGridRequest.py @@ -0,0 +1,180 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to use AbstractGfeRequest and +# implement str(), repr() +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# xx/xx/?? dgilling Initial Creation. +# 03/13/13 1759 dgilling Add software history header. +# 05/13/15 4427 dgilling Add siteIdOverride field. +# +# + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.request import AbstractGfeRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.message import WsId + +class ExecuteIfpNetCDFGridRequest(AbstractGfeRequest): + + def __init__(self, outputFilename=None, parmList=[], databaseID=None, + startTime=None, endTime=None, mask=None, geoInfo=False, + compressFile=False, configFileName=None, compressFileFactor=0, + trim=False, krunch=False, userID=None, logFileName=None, siteIdOverride=None): + super(ExecuteIfpNetCDFGridRequest, self).__init__() + self.outputFilename = outputFilename + self.parmList = parmList + self.databaseID = databaseID + self.startTime = startTime + self.endTime = endTime + self.mask = mask + self.geoInfo = geoInfo + self.compressFile = compressFile + self.configFileName = configFileName + self.compressFileFactor = compressFileFactor + self.trim = trim + self.krunch = krunch + self.userID = userID + self.logFileName = logFileName + self.siteIdOverride = siteIdOverride + if self.userID is not None: + self.workstationID = WsId(progName='ifpnetCDF', userName=self.userID) + if self.databaseID is not None: + self.siteID = self.databaseID.getSiteId() + + def __str__(self): + retVal = "ExecuteIfpNetCDFGridRequest[" + retVal += "wokstationID: " + str(self.workstationID) + ", " + retVal += "siteID: " + str(self.siteID) + ", " + retVal += "outputFilename: " + str(self.outputFilename) + ", " + retVal += "parmList: " + str(self.parmList) + ", " + retVal += "databaseID: " + str(self.databaseID) + ", " + retVal += "startTime: " + str(self.startTime) + ", " + retVal += "endTime: " + str(self.endTime) + ", " + retVal += "mask: " + str(self.mask) + ", " + retVal += "geoInfo: " + str(self.geoInfo) + ", " + retVal += "compressFile: " + str(self.compressFile) + ", " + retVal += "configFileName: " + str(self.configFileName) + ", " + retVal += "compressFileFactor: " + str(self.compressFileFactor) + ", " + retVal += "trim: " + str(self.trim) + ", " + retVal += "krunch: " + str(self.krunch) + ", " + retVal += "userID: " + str(self.userID) + ", " + retVal += "logFileName: " + str(self.logFileName) + ", " + retVal += "siteIdOverride: " + str(self.siteIdOverride) + retVal += "]" + return retVal + + def __repr__(self): + retVal = "ExecuteIfpNetCDFGridRequest(" + retVal += "wokstationID=" + repr(self.workstationID) + ", " + retVal += "siteID=" + repr(self.siteID) + ", " + retVal += "outputFilename=" + repr(self.outputFilename) + ", " + retVal += "parmList=" + repr(self.parmList) + ", " + retVal += "databaseID=" + repr(self.databaseID) + ", " + retVal += "startTime=" + repr(self.startTime) + ", " + retVal += "endTime=" + repr(self.endTime) + ", " + retVal += "mask=" + repr(self.mask) + ", " + retVal += "geoInfo=" + repr(self.geoInfo) + ", " + retVal += "compressFile=" + repr(self.compressFile) + ", " + retVal += "configFileName=" + repr(self.configFileName) + ", " + retVal += "compressFileFactor=" + repr(self.compressFileFactor) + ", " + retVal += "trim=" + repr(self.trim) + ", " + retVal += "krunch=" + repr(self.krunch) + ", " + retVal += "userID=" + repr(self.userID) + ", " + retVal += "logFileName=" + repr(self.logFileName) + ", " + retVal += "siteIdOverride: " + str(self.siteIdOverride) + retVal += ")" + return retVal + + def getOutputFilename(self): + return self.outputFilename + + def setOutputFilename(self, outputFilename): + self.outputFilename = outputFilename + + def getParmList(self): + return self.parmList + + def setParmList(self, parmList): + self.parmList = parmList + + def getDatabaseID(self): + return self.databaseID + + def setDatabaseID(self, databaseID): + self.databaseID = databaseID + + def getStartTime(self): + return self.startTime + + def setStartTime(self, startTime): + self.startTime = startTime + + def getEndTime(self): + return self.endTime + + def setEndTime(self, endTime): + self.endTime = endTime + + def getMask(self): + return self.mask + + def setMask(self, mask): + self.mask = mask + + def getGeoInfo(self): + return self.geoInfo + + def setGeoInfo(self, geoInfo): + self.geoInfo = geoInfo + + def getCompressFile(self): + return self.compressFile + + def setCompressFile(self, compressFile): + self.compressFile = compressFile + + def getConfigFileName(self): + return self.configFileName + + def setConfigFileName(self, configFileName): + self.configFileName = configFileName + + def getCompressFileFactor(self): + return self.compressFileFactor + + def setCompressFileFactor(self, compressFileFactor): + self.compressFileFactor = compressFileFactor + + def getTrim(self): + return self.trim + + def setTrim(self, trim): + self.trim = trim + + def getKrunch(self): + return self.krunch + + def setKrunch(self, krunch): + self.krunch = krunch + + def getUserID(self): + return self.userID + + def setUserID(self, userID): + self.userID = userID + + def getLogFileName(self): + return self.logFileName + + def setLogFileName(self, logFileName): + self.logFileName = logFileName + + def getSiteIdOverride(self): + return self.siteIdOverride + + def setSiteIdOverride(self, siteIdOverride): + self.siteIdOverride = siteIdOverride diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/ExecuteIscMosaicRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/ExecuteIscMosaicRequest.py new file mode 100644 index 0000000..ad27b27 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/ExecuteIscMosaicRequest.py @@ -0,0 +1,217 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to use AbstractGfeRequest and +# implement str(), repr() +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# xx/xx/?? dgilling Initial Creation. +# 03/13/13 1759 dgilling Add software history header. +# +# +# + + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.request import AbstractGfeRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.message import WsId + +class ExecuteIscMosaicRequest(AbstractGfeRequest): + + def __init__(self, userID=None, databaseID=None, parmsToProcess=[], + blankOtherPeriods=False, startTime=None, endTime=None, + altMask=None, replaceOnly=False, eraseFirst=False, announce="", + renameWE=False, iscSends=False, inFiles=[], ignoreMask=False, + adjustTranslate=False, deleteInput=False, parmsToIgnore=[], + gridDelay=0.0, logFileName=None): + super(ExecuteIscMosaicRequest, self).__init__() + self.userID = userID + self.databaseID = databaseID + self.parmsToProcess = parmsToProcess + self.blankOtherPeriods = blankOtherPeriods + self.startTime = startTime + self.endTime = endTime + self.altMask = altMask + self.replaceOnly = replaceOnly + self.eraseFirst = eraseFirst + self.announce = announce + self.renameWE = renameWE + self.iscSends = iscSends + self.inFiles = inFiles + self.ignoreMask = ignoreMask + self.adjustTranslate = adjustTranslate + self.deleteInput = deleteInput + self.parmsToIgnore = parmsToIgnore + self.gridDelay = gridDelay + self.logFileName = logFileName + if self.userID is not None: + self.workstationID = WsId(progName='iscMosaic', userName=self.userID) + if self.databaseID is not None: + self.siteID = self.databaseID.getSiteId() + + def __str__(self): + retVal = "ExecuteIscMosaicRequest[" + retVal += "wokstationID: " + str(self.workstationID) + ", " + retVal += "siteID: " + str(self.siteID) + ", " + retVal += "userID: " + str(self.userID) + ", " + retVal += "databaseID: " + str(self.databaseID) + ", " + retVal += "parmsToProcess: " + str(self.parmsToProcess) + ", " + retVal += "blankOtherPeriods: " + str(self.blankOtherPeriods) + ", " + retVal += "startTime: " + str(self.startTime) + ", " + retVal += "endTime: " + str(self.endTime) + ", " + retVal += "altMask: " + str(self.altMask) + ", " + retVal += "replaceOnly: " + str(self.replaceOnly) + ", " + retVal += "eraseFirst: " + str(self.eraseFirst) + ", " + retVal += "announce: " + str(self.announce) + ", " + retVal += "renameWE: " + str(self.renameWE) + ", " + retVal += "iscSends: " + str(self.iscSends) + ", " + retVal += "inFiles: " + str(self.inFiles) + ", " + retVal += "ignoreMask: " + str(self.ignoreMask) + ", " + retVal += "adjustTranslate: " + str(self.adjustTranslate) + ", " + retVal += "deleteInput: " + str(self.deleteInput) + ", " + retVal += "parmsToIgnore: " + str(self.parmsToIgnore) + ", " + retVal += "gridDelay: " + str(self.gridDelay) + ", " + retVal += "logFileName: " + str(self.logFileName) + "]" + return retVal + + def __repr__(self): + retVal = "ExecuteIscMosaicRequest(" + retVal += "wokstationID= " + str(self.workstationID) + ", " + retVal += "siteID= " + str(self.siteID) + ", " + retVal += "userID= " + str(self.userID) + ", " + retVal += "databaseID= " + str(self.databaseID) + ", " + retVal += "parmsToProcess= " + str(self.parmsToProcess) + ", " + retVal += "blankOtherPeriods= " + str(self.blankOtherPeriods) + ", " + retVal += "startTime= " + str(self.startTime) + ", " + retVal += "endTime= " + str(self.endTime) + ", " + retVal += "altMask= " + str(self.altMask) + ", " + retVal += "replaceOnly= " + str(self.replaceOnly) + ", " + retVal += "eraseFirst= " + str(self.eraseFirst) + ", " + retVal += "announce= " + str(self.announce) + ", " + retVal += "renameWE= " + str(self.renameWE) + ", " + retVal += "iscSends= " + str(self.iscSends) + ", " + retVal += "inFiles= " + str(self.inFiles) + ", " + retVal += "ignoreMask= " + str(self.ignoreMask) + ", " + retVal += "adjustTranslate= " + str(self.adjustTranslate) + ", " + retVal += "deleteInput= " + str(self.deleteInput) + ", " + retVal += "parmsToIgnore= " + str(self.parmsToIgnore) + ", " + retVal += "gridDelay= " + str(self.gridDelay) + ", " + retVal += "logFileName= " + str(self.logFileName) + ")" + return retVal + + def getUserID(self): + return self.userID + + def setUserID(self, userID): + self.userID = userID + + def getDatabaseID(self): + return self.databaseID + + def setDatabaseID(self, databaseID): + self.databaseID = databaseID + + def getParmsToProcess(self): + return self.parmsToProcess + + def setParmsToProcess(self, parmsToProcess): + self.parmsToProcess = parmsToProcess + + def getBlankOtherPeriods(self): + return self.blankOtherPeriods + + def setBlankOtherPeriods(self, blankOtherPeriods): + self.blankOtherPeriods = blankOtherPeriods + + def getStartTime(self): + return self.startTime + + def setStartTime(self, startTime): + self.startTime = startTime + + def getEndTime(self): + return self.endTime + + def setEndTime(self, endTime): + self.endTime = endTime + + def getAltMask(self): + return self.altMask + + def setAltMask(self, altMask): + self.altMask = altMask + + def getReplaceOnly(self): + return self.replaceOnly + + def setReplaceOnly(self, replaceOnly): + self.replaceOnly = replaceOnly + + def getEraseFirst(self): + return self.eraseFirst + + def setEraseFirst(self, eraseFirst): + self.eraseFirst = eraseFirst + + def getAnnounce(self): + return self.announce + + def setAnnounce(self, announce): + self.announce = announce + + def getRenameWE(self): + return self.renameWE + + def setRenameWE(self, renameWE): + self.renameWE = renameWE + + def getIscSends(self): + return self.iscSends + + def setIscSends(self, iscSends): + self.iscSends = iscSends + + def getInFiles(self): + return self.inFiles + + def setInFiles(self, inFiles): + self.inFiles = inFiles + + def getIgnoreMask(self): + return self.ignoreMask + + def setIgnoreMask(self, ignoreMask): + self.ignoreMask = ignoreMask + + def getAdjustTranslate(self): + return self.adjustTranslate + + def setAdjustTranslate(self, adjustTranslate): + self.adjustTranslate = adjustTranslate + + def getDeleteInput(self): + return self.deleteInput + + def setDeleteInput(self, deleteInput): + self.deleteInput = deleteInput + + def getParmsToIgnore(self): + return self.parmsToIgnore + + def setParmsToIgnore(self, parmsToIgnore): + self.parmsToIgnore = parmsToIgnore + + def getGridDelay(self): + return self.gridDelay + + def setGridDelay(self, gridDelay): + self.gridDelay = gridDelay + + def getLogFileName(self): + return self.logFileName + + def setLogFileName(self, logFileName): + self.logFileName = logFileName diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/ExportGridsRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/ExportGridsRequest.py new file mode 100644 index 0000000..f84c43c --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/ExportGridsRequest.py @@ -0,0 +1,62 @@ +## +## + +# +# A pure python implementation of com.raytheon.uf.common.dataplugin.gfe.request.ExportGridsRequest +# for use by the python implementation of DynamicSerialize. +# +# File auto-generated against equivalent DynamicSerialize Java class, but additional +# useful methods have been added. +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 04/05/13 dgilling Initial Creation. +# +# +# + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.request import AbstractGfeRequest + + +class ExportGridsRequest(AbstractGfeRequest): + + def __init__(self): + super(ExportGridsRequest, self).__init__() + self.site = None + self.mode = None + + def getSite(self): + return self.site + + def setSite(self, site): + self.site = site + + def getMode(self): + return self.mode + + def setMode(self, mode): + validValues = ['CRON', 'MANUAL', 'GRIB2'] + inputVal = str(mode).upper() + if inputVal in validValues: + self.mode = mode + else: + raise ValueError(inputVal + " not a valid ExportGridsMode value. Must be one of " + str(validValues)) + + def __str__(self): + retVal = "ExportGridsRequest[" + retVal += "wokstationID: " + str(self.workstationID) + ", " + retVal += "siteID: " + str(self.siteID) + ", " + retVal += "site: " + str(self.site) + ", " + retVal += "mode: " + str(self.mode) + "]" + return retVal + + def __repr__(self): + retVal = "ExportGridsRequest(" + retVal += "wokstationID=" + repr(self.workstationID) + ", " + retVal += "siteID=" + repr(self.siteID) + ", " + retVal += "site=" + repr(self.site) + ", " + retVal += "mode=" + repr(self.mode) + ")" + return retVal diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetASCIIGridsRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetASCIIGridsRequest.py new file mode 100644 index 0000000..34553ec --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetASCIIGridsRequest.py @@ -0,0 +1,51 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GetASCIIGridsRequest(object): + + def __init__(self): + self.databaseIds = None + self.parmIds = None + self.timeRange = None + self.coordConversionString = None + self.workstationID = None + self.siteID = None + + def getDatabaseIds(self): + return self.databaseIds + + def setDatabaseIds(self, databaseIds): + self.databaseIds = databaseIds + + def getParmIds(self): + return self.parmIds + + def setParmIds(self, parmIds): + self.parmIds = parmIds + + def getTimeRange(self): + return self.timeRange + + def setTimeRange(self, timeRange): + self.timeRange = timeRange + + def getCoordConversionString(self): + return self.coordConversionString + + def setCoordConversionString(self, coordConversionString): + self.coordConversionString = coordConversionString + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetGridDataRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetGridDataRequest.py new file mode 100644 index 0000000..0e2c847 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetGridDataRequest.py @@ -0,0 +1,46 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +import abc + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.server.request import GetGridRequest + + +class GetGridDataRequest(object): + __metaclass__ = abc.ABCMeta + + @abc.abstractmethod + def __init__(self): + self.requests = [] + self.workstationID = None + self.siteID = None + + def addRequest(self, gridDataReq): + if not isinstance(gridDataReq, GetGridRequest): + raise TypeError("Invalid request specified: " + str(type(gridDataReq)) + \ + ". Only GetGridRequests are supported.") + else: + self.requests.append(gridDataReq) + + def getRequests(self): + return self.requests + + def setRequests(self, requests): + del self.requests[:] + for req in requests: + self.addRequest(req) + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetGridInventoryRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetGridInventoryRequest.py new file mode 100644 index 0000000..1d84c9d --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetGridInventoryRequest.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GetGridInventoryRequest(object): + + def __init__(self): + self.parmIds = None + self.workstationID = None + self.siteID = None + + def getParmIds(self): + return self.parmIds + + def setParmIds(self, parmIds): + self.parmIds = parmIds + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetLatestDbTimeRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetLatestDbTimeRequest.py new file mode 100644 index 0000000..3260f71 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetLatestDbTimeRequest.py @@ -0,0 +1,53 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to use AbstractGfeRequest and +# implement str(), repr() +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 05/22/13 2025 dgilling Initial Creation. +# +# + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.request import AbstractGfeRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.db.objects import DatabaseID + + +class GetLatestDbTimeRequest(AbstractGfeRequest): + + def __init__(self, dbId=None): + super(GetLatestDbTimeRequest, self).__init__() + if dbId is not None and isinstance(dbId, DatabaseID): + self.dbId = dbId + self.siteID = dbId.getSiteId() + elif dbId is not None and not isinstance(dbId, DatabaseID): + raise TypeError( + "Attempt to construct GetLatestDbTimeRequest without providing a valid DatabaseID.") + + def __str__(self): + retVal = "GetLatestDbTimeRequest[" + retVal += "wokstationID: " + str(self.workstationID) + ", " + retVal += "siteID: " + str(self.siteID) + ", " + retVal += "dbId: " + str(self.dbId) + "]" + return retVal + + def __repr__(self): + retVal = "ExecuteIfpNetCDFGridRequest(" + retVal += "wokstationID=" + repr(self.workstationID) + ", " + retVal += "siteID=" + repr(self.siteID) + ", " + retVal += "dbId=" + repr(self.dbId) + ")" + return retVal + + def getDbId(self): + return self.dbId + + def setDbId(self, dbId): + if isinstance(dbId, DatabaseID): + self.dbId = dbId + else: + raise TypeError( + "Attempt to call GetLatestDbTimeRequest.setDbId() without providing a valid DatabaseID.") diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetLatestModelDbIdRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetLatestModelDbIdRequest.py new file mode 100644 index 0000000..2f9a222 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetLatestModelDbIdRequest.py @@ -0,0 +1,46 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to use AbstractGfeRequest and +# implement str(), repr() +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 05/22/13 2025 dgilling Initial Creation. +# +# + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.request import AbstractGfeRequest + + +class GetLatestModelDbIdRequest(AbstractGfeRequest): + + def __init__(self, siteId=None, modelName=None): + super(GetLatestModelDbIdRequest, self).__init__() + if siteId is not None: + self.siteID = str(siteId) + if modelName is not None: + self.modelName = str(modelName) + + def __str__(self): + retVal = "GetLatestModelDbIdRequest[" + retVal += "wokstationID: " + str(self.workstationID) + ", " + retVal += "siteID: " + str(self.siteID) + ", " + retVal += "modelName: " + str(self.modelName) + "]" + return retVal + + def __repr__(self): + retVal = "ExecuteIfpNetCDFGridRequest(" + retVal += "wokstationID=" + repr(self.workstationID) + ", " + retVal += "siteID=" + repr(self.siteID) + ", " + retVal += "modelName=" + repr(self.modelName) + ")" + return retVal + + def getModelName(self): + return self.modelName + + def setModelName(self, modelName): + self.modelName = str(modelName) diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetLockTablesRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetLockTablesRequest.py new file mode 100644 index 0000000..63cd9b3 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetLockTablesRequest.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GetLockTablesRequest(object): + + def __init__(self): + self.requests = None + self.workstationID = None + self.siteID = None + + def getRequests(self): + return self.requests + + def setRequests(self, requests): + self.requests = requests + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetOfficialDbNameRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetOfficialDbNameRequest.py new file mode 100644 index 0000000..94b882e --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetOfficialDbNameRequest.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GetOfficialDbNameRequest(object): + + def __init__(self): + self.workstationID = None + self.siteID = None + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetParmListRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetParmListRequest.py new file mode 100644 index 0000000..4419eb7 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetParmListRequest.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GetParmListRequest(object): + + def __init__(self): + self.dbIds = None + self.workstationID = None + self.siteID = None + + def getDbIds(self): + return self.dbIds + + def setDbIds(self, dbIds): + self.dbIds = dbIds + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetSelectTimeRangeRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetSelectTimeRangeRequest.py new file mode 100644 index 0000000..45249f4 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetSelectTimeRangeRequest.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GetSelectTimeRangeRequest(object): + + def __init__(self): + self.name = None + self.workstationID = None + self.siteID = None + + def getName(self): + return self.name + + def setName(self, name): + self.name = name + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetSingletonDbIdsRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetSingletonDbIdsRequest.py new file mode 100644 index 0000000..fe6ee5d --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetSingletonDbIdsRequest.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GetSingletonDbIdsRequest(object): + + def __init__(self): + self.workstationID = None + self.siteID = None + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetSiteTimeZoneInfoRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetSiteTimeZoneInfoRequest.py new file mode 100644 index 0000000..17ec9dc --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GetSiteTimeZoneInfoRequest.py @@ -0,0 +1,27 @@ +## +## + +## +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# xx/xx/?? dgilling Initial Creation. +# 12/02/15 5129 dgilling Refactor based on AbstractGfeRequest. +# +## + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.request import AbstractGfeRequest + +class GetSiteTimeZoneInfoRequest(AbstractGfeRequest): + + def __init__(self): + super(GetSiteTimeZoneInfoRequest, self).__init__() + self.requestedSiteIDs = None + + def getRequestedSiteIDs(self): + return self.requestedSiteIDs + + def setRequestedSiteIDs(self, requestedSiteIDs): + self.requestedSiteIDs = requestedSiteIDs + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GfeClientRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GfeClientRequest.py new file mode 100644 index 0000000..6d5e4d4 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GfeClientRequest.py @@ -0,0 +1,63 @@ +## +## +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.request.AbstractGfeRequest import AbstractGfeRequest + +# Manually updated +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------- -------- --------- --------------------------------------------- +# Dec 06, 2016 6092 randerso Initial Creation + +class GfeClientRequest(AbstractGfeRequest): + + def __init__(self, script, siteID, configFile, user, args=[]): + super(GfeClientRequest, self).__init__() + self.script = script + self.siteID = siteID + self.configFile = configFile + self.user = user + self.args = args + self.time = None + + def getConfigFile(self): + return self.configFile + + def setConfigFile(self, configFile): + self.configFile = configFile + + def getUser(self): + return self.user + + def setUser(self, user): + self.user = user + + def getArgs(self): + return self.args + + def setArgs(self, args): + self.args = args + + def getTime(self): + return self.time + + def setTime(self, time): + self.time = time + + def getScript(self): + return self.script + + def setScript(self, script): + self.script = script + + def __str__(self): + retval = "GfeClientRequest(" + retval += "siteID:" + self.siteID + ", " + retval += "script:" + self.script + ", " + retval += "configFile:" + self.configFile + ", " + retval += "user:" + self.user + ", " + if self.time: + retval += "time:" + str(self.time) + ", " + retval += "args:" + str(self.args) + ")" + return retval diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GridLocRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GridLocRequest.py new file mode 100644 index 0000000..17237bb --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/GridLocRequest.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GridLocRequest(object): + + def __init__(self): + self.workstationID = None + self.siteID = None + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/IscDataRecRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/IscDataRecRequest.py new file mode 100644 index 0000000..866a918 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/IscDataRecRequest.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class IscDataRecRequest(object): + + def __init__(self): + self.argString = None + self.workstationID = None + self.siteID = None + + def getArgString(self): + return self.argString + + def setArgString(self, argString): + self.argString = argString + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/LockChangeRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/LockChangeRequest.py new file mode 100644 index 0000000..f70a994 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/LockChangeRequest.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class LockChangeRequest(object): + + def __init__(self): + self.requests = None + self.workstationID = None + self.siteID = None + + def getRequests(self): + return self.requests + + def setRequests(self, requests): + self.requests = requests + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/ProcessReceivedConfRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/ProcessReceivedConfRequest.py new file mode 100644 index 0000000..ce5568a --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/ProcessReceivedConfRequest.py @@ -0,0 +1,29 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class ProcessReceivedConfRequest(object): + + def __init__(self): + self.receivedConfFile = None + self.workstationID = None + self.siteID = None + + def getReceivedConfFile(self): + return self.receivedConfFile + + def setReceivedConfFile(self, receivedConfFile): + self.receivedConfFile = receivedConfFile + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID \ No newline at end of file diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/ProcessReceivedDigitalDataRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/ProcessReceivedDigitalDataRequest.py new file mode 100644 index 0000000..30c8819 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/ProcessReceivedDigitalDataRequest.py @@ -0,0 +1,29 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class ProcessReceivedDigitalDataRequest(object): + + def __init__(self): + self.receivedConfFile = None + self.workstationID = None + self.siteID = None + + def getReceivedDataFile(self): + return self.receivedConfFile + + def setReceivedDataFile(self, receivedConfFile): + self.receivedConfFile = receivedConfFile + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID \ No newline at end of file diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/PurgeGfeGridsRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/PurgeGfeGridsRequest.py new file mode 100644 index 0000000..1eaaba7 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/PurgeGfeGridsRequest.py @@ -0,0 +1,25 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.request import AbstractGfeRequest + +class PurgeGfeGridsRequest(AbstractGfeRequest): + + def __init__(self): + super(PurgeGfeGridsRequest, self).__init__() + self.databaseID = None + + def __str__(self): + retVal = "PurgeGfeGridsRequest[" + retVal += "wokstationID: " + str(self.workstationID) + ", " + retVal += "siteID: " + str(self.siteID) + ", " + retVal += "databaseID: " + str(self.databaseID) + "]" + return retVal + + def getDatabaseID(self): + return self.databaseID + + def setDatabaseID(self, databaseID): + self.databaseID = databaseID diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/RsyncGridsToCWFRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/RsyncGridsToCWFRequest.py new file mode 100644 index 0000000..82b6dbf --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/RsyncGridsToCWFRequest.py @@ -0,0 +1,20 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Jul 15, 2015 #4013 randerso Initial creation (hand generated) +# +# + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.request import AbstractGfeRequest + + +class RsyncGridsToCWFRequest(AbstractGfeRequest): + + def __init__(self, siteId=None): + super(RsyncGridsToCWFRequest, self).__init__() + if siteId is not None: + self.siteID = str(siteId) diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/SaveASCIIGridsRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/SaveASCIIGridsRequest.py new file mode 100644 index 0000000..1efaa14 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/SaveASCIIGridsRequest.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class SaveASCIIGridsRequest(object): + + def __init__(self): + self.asciiGridData = None + self.workstationID = None + self.siteID = None + + def getAsciiGridData(self): + return self.asciiGridData + + def setAsciiGridData(self, asciiGridData): + self.asciiGridData = asciiGridData + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/SmartInitRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/SmartInitRequest.py new file mode 100644 index 0000000..a9dcb24 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/SmartInitRequest.py @@ -0,0 +1,44 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class SmartInitRequest(object): + + def __init__(self): + self.moduleName = None + self.modelTime = None + self.calculateAll = None + self.workstationID = None + self.siteID = None + + def getModuleName(self): + return self.moduleName + + def setModuleName(self, moduleName): + self.moduleName = moduleName + + def getModelTime(self): + return self.modelTime + + def setModelTime(self, modelTime): + self.modelTime = modelTime + + def getCalculateAll(self): + return self.calculateAll + + def setCalculateAll(self, calculateAll): + self.calculateAll = calculateAll + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/__init__.py new file mode 100644 index 0000000..f4dc124 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/request/__init__.py @@ -0,0 +1,68 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Jul 15, 2015 #4013 randerso Added RsyncGridsToCWFRequest +# + +__all__ = [ + 'AbstractGfeRequest', + 'CommitGridsRequest', + 'ConfigureTextProductsRequest', + 'ExecuteIfpNetCDFGridRequest', + 'ExecuteIscMosaicRequest', + 'ExportGridsRequest', + 'GetASCIIGridsRequest', + 'GetGridDataRequest', + 'GetGridInventoryRequest', + 'GetLatestDbTimeRequest', + 'GetLatestModelDbIdRequest', + 'GetLockTablesRequest', + 'GetOfficialDbNameRequest', + 'GetParmListRequest', + 'GetSelectTimeRangeRequest', + 'GetSingletonDbIdsRequest', + 'GetSiteTimeZoneInfoRequest', + 'GfeClientRequest', + 'GridLocRequest', + 'IscDataRecRequest', + 'LockChangeRequest', + 'ProcessReceivedConfRequest', + 'ProcessReceivedDigitalDataRequest', + 'PurgeGfeGridsRequest', + 'RsyncGridsToCWFRequest', + 'SaveASCIIGridsRequest', + 'SmartInitRequest' + ] + +from AbstractGfeRequest import AbstractGfeRequest +from CommitGridsRequest import CommitGridsRequest +from ConfigureTextProductsRequest import ConfigureTextProductsRequest +from ExecuteIfpNetCDFGridRequest import ExecuteIfpNetCDFGridRequest +from ExecuteIscMosaicRequest import ExecuteIscMosaicRequest +from ExportGridsRequest import ExportGridsRequest +from GetASCIIGridsRequest import GetASCIIGridsRequest +from GetGridDataRequest import GetGridDataRequest +from GetGridInventoryRequest import GetGridInventoryRequest +from GetLatestDbTimeRequest import GetLatestDbTimeRequest +from GetLatestModelDbIdRequest import GetLatestModelDbIdRequest +from GetLockTablesRequest import GetLockTablesRequest +from GetOfficialDbNameRequest import GetOfficialDbNameRequest +from GetParmListRequest import GetParmListRequest +from GetSelectTimeRangeRequest import GetSelectTimeRangeRequest +from GetSingletonDbIdsRequest import GetSingletonDbIdsRequest +from GetSiteTimeZoneInfoRequest import GetSiteTimeZoneInfoRequest +from GfeClientRequest import GfeClientRequest +from GridLocRequest import GridLocRequest +from IscDataRecRequest import IscDataRecRequest +from LockChangeRequest import LockChangeRequest +from ProcessReceivedConfRequest import ProcessReceivedConfRequest +from ProcessReceivedDigitalDataRequest import ProcessReceivedDigitalDataRequest +from PurgeGfeGridsRequest import PurgeGfeGridsRequest +from SaveASCIIGridsRequest import SaveASCIIGridsRequest +from SmartInitRequest import SmartInitRequest +from RsyncGridsToCWFRequest import RsyncGridsToCWFRequest + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/__init__.py new file mode 100644 index 0000000..581359e --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/__init__.py @@ -0,0 +1,13 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'lock', + 'message', + 'notify', + 'request' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/lock/Lock.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/lock/Lock.py new file mode 100644 index 0000000..fbcbe2d --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/lock/Lock.py @@ -0,0 +1,59 @@ +## +## + +# +# File auto-generated against equivalent DynamicSerialize Java class +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# xx/xx/xxxx xxxxxxx Initial Creation. +# xx/xx/xxxx xxxx njensen Implemented __repr__. +# 06/12/2013 2099 dgilling Make class immutable, +# add getTimeRange(). +# +# + +import time + +from dynamicserialize.dstypes.com.raytheon.uf.common.time import TimeRange + + +class Lock(object): + + def __init__(self, parmId, wsId, startTime, endTime): + self.parmId = parmId + self.wsId = wsId + self.startTime = startTime + self.endTime = endTime + self.timeRange = None + + def getParmId(self): + return self.parmId + + def getWsId(self): + return self.wsId + + def getStartTime(self): + return self.startTime + + def getEndTime(self): + return self.endTime + + def getTimeRange(self): + if not self.timeRange: + start = self.startTime / 1000.0 + end = self.endTime / 1000.0 + self.timeRange = TimeRange(start, end) + return self.timeRange + + def __repr__(self): + t0 = time.gmtime(self.getStartTime() / 1000.0) + t1 = time.gmtime(self.getEndTime() / 1000.0) + format = '%b %d %y %H:%M:%S %Z' + msg = 'TR: (' + time.strftime(format, t0) + ', ' + time.strftime(format, t1) + msg += " WsId: " + str(self.wsId) + return msg + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/lock/LockTable.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/lock/LockTable.py new file mode 100644 index 0000000..9a1dab7 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/lock/LockTable.py @@ -0,0 +1,46 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------- -------- --------- --------------------------------------------- +# Feb 06, 2017 5959 randerso Removed Java .toString() calls +# +## + +# File auto-generated against equivalent DynamicSerialize Java class +# Modified by njensen to add __repr__ + +class LockTable(object): + + def __init__(self): + self.locks = None + self.wsId = None + self.parmId = None + + def getLocks(self): + return self.locks + + def setLocks(self, locks): + self.locks = locks + + def getWsId(self): + return self.wsId + + def setWsId(self, wsId): + self.wsId = wsId + + def getParmId(self): + return self.parmId + + def setParmId(self, parmId): + self.parmId = parmId + + def __repr__(self): + msg = "ParmID: " + str(self.parmId) + msg += " LockTable WsId: " + str(self.wsId) + for i in self.locks: + msg += "\n Lock: " + str(i) + return msg + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/lock/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/lock/__init__.py new file mode 100644 index 0000000..17334d4 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/lock/__init__.py @@ -0,0 +1,13 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'Lock', + 'LockTable' + ] + +from Lock import Lock +from LockTable import LockTable + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/message/ServerMsg.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/message/ServerMsg.py new file mode 100644 index 0000000..3892460 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/message/ServerMsg.py @@ -0,0 +1,16 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class ServerMsg(object): + + def __init__(self): + self.message = None + + def getMessage(self): + return self.message + + def setMessage(self, message): + self.message = message + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/message/ServerResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/message/ServerResponse.py new file mode 100644 index 0000000..1630c3f --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/message/ServerResponse.py @@ -0,0 +1,48 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class ServerResponse(object): + + def __init__(self): + self.messages = None + self.payload = None + self.notifications = None + + def getMessages(self): + return self.messages + + def setMessages(self, messages): + self.messages = messages + + def getPayload(self): + return self.payload + + def setPayload(self, payload): + self.payload = payload + + def getNotifications(self): + return self.notifications + + def setNotifications(self, notifications): + self.notifications = notifications + + def isOkay(self): + return (self.messages is None or len(self.messages) == 0) + + def message(self): + if (self.isOkay()): + return "" + else: + compMessage = "" + for serverMsg in self.messages: + compMessage += serverMsg.getMessage() + "\n" + + return compMessage + + def __str__(self): + return self.message() + + def __nonzero__(self): + return self.isOkay() \ No newline at end of file diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/message/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/message/__init__.py new file mode 100644 index 0000000..f0637b7 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/message/__init__.py @@ -0,0 +1,13 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'ServerMsg', + 'ServerResponse' + ] + +from ServerMsg import ServerMsg +from ServerResponse import ServerResponse + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/CombinationsFileChangedNotification.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/CombinationsFileChangedNotification.py new file mode 100644 index 0000000..d96dae3 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/CombinationsFileChangedNotification.py @@ -0,0 +1,36 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/22/2015 4573 randerso Initial creation (hand generated) +# +## + +import GfeNotification + +class CombinationsFileChangedNotification(GfeNotification.GfeNotification): + + def __init__(self): + super(CombinationsFileChangedNotification, self).__init__() + self.combinationsFileName = None + self.whoChanged = None + + def __str__(self): + msg = "fileName: " + str(self.combinationsFileName) + msg += '\n' + "whoChanged: " + str(self.whoChanged) + return msg + + def getCombinationsFileName(self): + return self.combinationsFileName + + def setCombinationsFileName(self, combinationsFileName): + self.combinationsFileName = combinationsFileName + + def getWhoChanged(self): + return self.whoChanged + + def setWhoChanged(self, whoChanged): + self.whoChanged = whoChanged diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/DBInvChangeNotification.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/DBInvChangeNotification.py new file mode 100644 index 0000000..b84b58a --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/DBInvChangeNotification.py @@ -0,0 +1,39 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# ??/??/???? ???? njensen Modified to add __repr__ +# 06/22/2015 4573 randerso Change to extend GfeNotification +# removed inventory methods +# +## + +import GfeNotification + +class DBInvChangeNotification(GfeNotification.GfeNotification): + + def __init__(self): + super(DBInvChangeNotification, self).__init__() + self.additions = None + self.deletions = None + + def getAdditions(self): + return self.additions + + def setAdditions(self, additions): + self.additions = additions + + def getDeletions(self): + return self.deletions + + def setDeletions(self, deletions): + self.deletions = deletions + + def __str__(self): + msg = 'Additions' + str(self.additions) + '\n' + msg += 'Deletions' + str(self.deletions) + return msg + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/GfeNotification.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/GfeNotification.py new file mode 100644 index 0000000..4fa69a5 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/GfeNotification.py @@ -0,0 +1,31 @@ +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 10/07/2014 3684 randerso Manually updated to add sourceID +# +## +import abc + +class GfeNotification(object): + __metaclass__ = abc.ABCMeta + + @abc.abstractmethod + def __init__(self): + self.siteID = None + self.sourceID = None + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + + + def getSourceID(self): + return self.sourceID + + def setSourceID(self, sourceID): + self.sourceID = sourceID diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/GridHistoryUpdateNotification.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/GridHistoryUpdateNotification.py new file mode 100644 index 0000000..b6de799 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/GridHistoryUpdateNotification.py @@ -0,0 +1,44 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/22/2015 4573 randerso Initial creation (hand generated) +# +## + +import GfeNotification + +class GridHistoryUpdateNotification(GfeNotification.GfeNotification): + + def __init__(self): + super(GridHistoryUpdateNotification, self).__init__() + self.parmId = None + self.workstationID = None + self.histories = None + + def getParmId(self): + return self.parmId + + def setParmId(self, parmId): + self.parmId = parmId + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID + + def getHistories(self): + return self.histories + + def setHistories(self, histories): + self.histories = histories + + def __str__(self): + msg = "ParmID: " + str(self.parmId) + msg += '\n' + "Histories: " + str(self.histories) + return msg + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/GridUpdateNotification.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/GridUpdateNotification.py new file mode 100644 index 0000000..be3efa0 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/GridUpdateNotification.py @@ -0,0 +1,53 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# ??/??/???? ???? njensen Modified to add __repr__ +# 06/22/2015 4573 randerso Change to extend GfeNotification +# +## + +import GfeNotification + +class GridUpdateNotification(GfeNotification.GfeNotification): + + def __init__(self): + super(GridUpdateNotification, self).__init__() + self.parmId = None + self.replacementTimeRange = None + self.workstationID = None + self.histories = None + + def getParmId(self): + return self.parmId + + def setParmId(self, parmId): + self.parmId = parmId + + def getReplacementTimeRange(self): + return self.replacementTimeRange + + def setReplacementTimeRange(self, replacementTimeRange): + self.replacementTimeRange = replacementTimeRange + + def getWorkstationID(self): + return self.workstationID + + def setWorkstationID(self, workstationID): + self.workstationID = workstationID + + def getHistories(self): + return self.histories + + def setHistories(self, histories): + self.histories = histories + + def __str__(self): + msg = "ParmID: " + str(self.parmId) + msg += '\n' + "Replacement TimeRange: " + str(self.replacementTimeRange) + msg += '\n' + "Histories: " + str(self.histories) + return msg + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/LockNotification.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/LockNotification.py new file mode 100644 index 0000000..b6b9f7e --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/LockNotification.py @@ -0,0 +1,29 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# ??/??/???? ???? njensen Modified to add __repr__ +# 06/22/2015 4573 randerso Change to extend GfeNotification +# +## + +import GfeNotification + +class LockNotification(GfeNotification.GfeNotification): + + def __init__(self): + super(LockNotification, self).__init__() + self.lockTable = None + + def getLockTable(self): + return self.lockTable + + def setLockTable(self, lockTable): + self.lockTable = lockTable + + def __str__(self): + return str(self.lockTable) + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/ServiceBackupJobStatusNotification.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/ServiceBackupJobStatusNotification.py new file mode 100644 index 0000000..656f882 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/ServiceBackupJobStatusNotification.py @@ -0,0 +1,36 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/22/2015 4573 randerso Initial creation (hand generated) +# +## + +import GfeNotification + +class ServiceBackupJobStatusNotification(GfeNotification.GfeNotification): + + def __init__(self): + super(ServiceBackupJobStatusNotification, self).__init__() + self.name = None + self.state = "UNKNOWN" + + def __str__(self): + msg = "name: " + str(self.name) + msg += '\n' + "state: " + str(self.state) + return msg + + def getName(self): + return self.name + + def setName(self, name): + self.name = name + + def getState(self): + return self.state + + def setState(self, state): + self.state = state diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/UserMessageNotification.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/UserMessageNotification.py new file mode 100644 index 0000000..eb33bc7 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/UserMessageNotification.py @@ -0,0 +1,45 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/22/2015 4573 randerso Change to extend GfeNotification +# +## + +import GfeNotification + +class UserMessageNotification(GfeNotification.GfeNotification): + + def __init__(self): + super(UserMessageNotification, self).__init__() + self.category = None + self.priority = None + self.message = None + + def getCategory(self): + return self.category + + def setCategory(self, category): + self.category = category + + def getPriority(self): + return self.priority + + def setPriority(self, priority): + self.priority = priority + + def getMessage(self): + return self.message + + def setMessage(self, message): + self.message = message + + def __str__(self): + msg = 'Message: ' + str(self.message) + '\n' + msg += 'Priority: ' + str(self.priority) + '\n' + msg += 'Category: ' + str(self.category) + '\n' + return msg + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/__init__.py new file mode 100644 index 0000000..80b6054 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/notify/__init__.py @@ -0,0 +1,25 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'CombinationsFileChangedNotification', + 'DBInvChangeNotification', + 'GfeNotification', + 'GridHistoryUpdateNotification', + 'GridUpdateNotification', + 'LockNotification', + 'ServiceBackupJobStatusNotification', + 'UserMessageNotification' + ] + +from .CombinationsFileChangedNotification import CombinationsFileChangedNotification +from .DBInvChangeNotification import DBInvChangeNotification +from .GfeNotification import GfeNotification +from .GridHistoryUpdateNotification import GridHistoryUpdateNotification +from .GridUpdateNotification import GridUpdateNotification +from .LockNotification import LockNotification +from .ServiceBackupJobStatusNotification import ServiceBackupJobStatusNotification +from .UserMessageNotification import UserMessageNotification + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/request/CommitGridRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/request/CommitGridRequest.py new file mode 100644 index 0000000..e96d20f --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/request/CommitGridRequest.py @@ -0,0 +1,37 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class CommitGridRequest(object): + + def __init__(self): + self.parmId = None + self.dbId = None + self.timeRange = None + self.clientSendStatus = False + + def getParmId(self): + return self.parmId + + def setParmId(self, parmId): + self.parmId = parmId + + def getDbId(self): + return self.dbId + + def setDbId(self, dbId): + self.dbId = dbId + + def getTimeRange(self): + return self.timeRange + + def setTimeRange(self, timeRange): + self.timeRange = timeRange + + def getClientSendStatus(self): + return self.clientSendStatus + + def setClientSendStatus(self, clientSendStatus): + self.clientSendStatus = bool(clientSendStatus) + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/request/GetGridRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/request/GetGridRequest.py new file mode 100644 index 0000000..77b9e9a --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/request/GetGridRequest.py @@ -0,0 +1,37 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.db.objects import GFERecord +from dynamicserialize.dstypes.com.raytheon.uf.common.message import WsId + + +class GetGridRequest(object): + + def __init__(self, parmId=None, trs=[]): + self.convertUnit = False + self.records = [] + self.parmId = parmId + if self.parmId is not None: + for tr in trs: + self.records.append(GFERecord(parmId, tr)) + + def getRecords(self): + return self.records + + def setRecords(self, records): + self.records = records + + def getParmId(self): + return self.parmId + + def setParmId(self, parmId): + self.parmId = parmId + + def getConvertUnit(self): + return self.convertUnit + + def setConvertUnit(self, convertUnit): + self.convertUnit = convertUnit + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/request/LockRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/request/LockRequest.py new file mode 100644 index 0000000..ee5612b --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/request/LockRequest.py @@ -0,0 +1,37 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class LockRequest(object): + + def __init__(self): + self.timeRange = None + self.parmId = None + self.dbId = None + self.mode = None + + def getTimeRange(self): + return self.timeRange + + def setTimeRange(self, timeRange): + self.timeRange = timeRange + + def getParmId(self): + return self.parmId + + def setParmId(self, parmId): + self.parmId = parmId + + def getDbId(self): + return self.dbId + + def setDbId(self, dbId): + self.dbId = dbId + + def getMode(self): + return self.mode + + def setMode(self, mode): + self.mode = mode + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/request/LockTableRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/request/LockTableRequest.py new file mode 100644 index 0000000..48f8dff --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/request/LockTableRequest.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class LockTableRequest(object): + + def __init__(self): + self.parmId = None + self.dbId = None + + def getParmId(self): + return self.parmId + + def setParmId(self, parmId): + self.parmId = parmId + + def getDbId(self): + return self.dbId + + def setDbId(self, dbId): + self.dbId = dbId + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/request/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/request/__init__.py new file mode 100644 index 0000000..c2b1580 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/server/request/__init__.py @@ -0,0 +1,17 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'CommitGridRequest', + 'GetGridRequest', + 'LockRequest', + 'LockTableRequest' + ] + +from CommitGridRequest import CommitGridRequest +from GetGridRequest import GetGridRequest +from LockRequest import LockRequest +from LockTableRequest import LockTableRequest + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/slice/AbstractGridSlice.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/slice/AbstractGridSlice.py new file mode 100644 index 0000000..fda00c2 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/slice/AbstractGridSlice.py @@ -0,0 +1,36 @@ +## +## + +import abc + + +class AbstractGridSlice(object): + __metaclass__ = abc.ABCMeta + + @abc.abstractmethod + def __init__(self): + self.validTime = None + self.gridParmInfo = None + self.gridDataHistory = None + + @abc.abstractmethod + def getNumPyGrid(self): + raise NotImplementedError + + def getValidTime(self): + return self.validTime + + def setValidTime(self, validTime): + self.validTime = validTime + + def getGridParmInfo(self): + return self.gridParmInfo + + def setGridParmInfo(self, gridParmInfo): + self.gridParmInfo = gridParmInfo + + def getGridDataHistory(self): + return self.gridDataHistory + + def setGridDataHistory(self, gridDataHistory): + self.gridDataHistory = gridDataHistory \ No newline at end of file diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/slice/DiscreteGridSlice.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/slice/DiscreteGridSlice.py new file mode 100644 index 0000000..792ac9d --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/slice/DiscreteGridSlice.py @@ -0,0 +1,29 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.slice import AbstractGridSlice + + +class DiscreteGridSlice(AbstractGridSlice): + + def __init__(self): + super(DiscreteGridSlice, self).__init__() + self.discreteGrid = None + self.key = [] + + def getDiscreteGrid(self): + return self.discreteGrid + + def setDiscreteGrid(self, discreteGrid): + self.discreteGrid = discreteGrid + + def getNumPyGrid(self): + return (self.discreteGrid.getNumPyGrid(), self.key) + + def getKey(self): + return self.key + + def setKey(self, key): + self.key = key diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/slice/ScalarGridSlice.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/slice/ScalarGridSlice.py new file mode 100644 index 0000000..4b42c41 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/slice/ScalarGridSlice.py @@ -0,0 +1,21 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.slice import AbstractGridSlice + +class ScalarGridSlice(AbstractGridSlice): + + def __init__(self): + super(ScalarGridSlice, self).__init__() + self.scalarGrid = None + + def getNumPyGrid(self): + return self.scalarGrid.getNumPyGrid() + + def getScalarGrid(self): + return self.scalarGrid + + def setScalarGrid(self, scalarGrid): + self.scalarGrid = scalarGrid diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/slice/VectorGridSlice.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/slice/VectorGridSlice.py new file mode 100644 index 0000000..81e80cc --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/slice/VectorGridSlice.py @@ -0,0 +1,28 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.slice import ScalarGridSlice + + +class VectorGridSlice(ScalarGridSlice): + + def __init__(self): + super(VectorGridSlice, self).__init__() + self.dirGrid = None + + def getNumPyGrid(self): + return (self.scalarGrid.getNumPyGrid(), self.dirGrid.getNumPyGrid()) + + def getDirGrid(self): + return self.dirGrid + + def setDirGrid(self, dirGrid): + self.dirGrid = dirGrid + + def getMagGrid(self): + return self.scalarGrid + + def setMagGrid(self, magGrid): + self.scalarGrid = magGrid diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/slice/WeatherGridSlice.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/slice/WeatherGridSlice.py new file mode 100644 index 0000000..c4e3bce --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/slice/WeatherGridSlice.py @@ -0,0 +1,29 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.gfe.slice import AbstractGridSlice + + +class WeatherGridSlice(AbstractGridSlice): + + def __init__(self): + super(WeatherGridSlice, self).__init__() + self.weatherGrid = None + self.keys = [] + + def getNumPyGrid(self): + pass + + def getWeatherGrid(self): + return self.weatherGrid + + def setWeatherGrid(self, weatherGrid): + self.weatherGrid = weatherGrid + + def getKeys(self): + return self.keys + + def setKeys(self, keys): + self.keys = keys diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/slice/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/slice/__init__.py new file mode 100644 index 0000000..2d9f313 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/slice/__init__.py @@ -0,0 +1,19 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'AbstractGridSlice', + 'DiscreteGridSlice', + 'ScalarGridSlice', + 'VectorGridSlice', + 'WeatherGridSlice' + ] + +from AbstractGridSlice import AbstractGridSlice +from DiscreteGridSlice import DiscreteGridSlice +from ScalarGridSlice import ScalarGridSlice +from VectorGridSlice import VectorGridSlice +from WeatherGridSlice import WeatherGridSlice + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/svcbu/JobProgress.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/svcbu/JobProgress.py new file mode 100644 index 0000000..3f0a247 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/svcbu/JobProgress.py @@ -0,0 +1,20 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/22/2015 4573 randerso Initial creation (hand generated) +# 08/27/2015 4812 randerso Change __str__ to return the self.value +# instead of __repr__(self.value) to eliminate +# ''s around string. +# +## + +class JobProgress(object): + def __init__(self): + self.value = None + + def __str__(self): + return self.value diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/svcbu/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/svcbu/__init__.py new file mode 100644 index 0000000..31bc152 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/svcbu/__init__.py @@ -0,0 +1,16 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/22/2015 4573 randerso Initial creation (hand generated) +# +## + +__all__ = [ + 'JobProgress' + ] + +from JobProgress import JobProgress diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/weather/WeatherKey.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/weather/WeatherKey.py new file mode 100644 index 0000000..be9e444 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/weather/WeatherKey.py @@ -0,0 +1,52 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + + +SUBKEY_SEPARATOR = '^' + +class WeatherKey(object): +## FIXME: Implement WeatherSubKey and use it in this class when needed. ## + + def __init__(self, siteId="", subKeys=[]): + self.siteId = siteId + if type(subKeys) is str: + self.__parseString(str(subKeys)) + else: + self.subKeys = subKeys + + def __str__(self): + return self.__repr__() + + def __repr__(self): + return SUBKEY_SEPARATOR.join(self.subKeys) + + def __eq__(self, other): + if not isinstance(other, WeatherKey): + return False + return self.subKeys == self.subKeys + + def __ne__(self, other): + return (not self.__eq__(other)) + + def __hash__(self): + prime = 31 + result = 1 + result = prime * result + (0 if self.subKeys is None else hash(self.subKeys)) + return result + + def getSiteId(self): + return self.siteId + + def setSiteId(self, siteId): + self.siteId = siteId + + def getSubKeys(self): + return self.subKeys + + def setSubKeys(self, subKeys): + self.subKeys = subKeys + + def __parseString(self, subKeys): + self.subKeys = subKeys.split(SUBKEY_SEPARATOR) diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/weather/WeatherSubKey.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/weather/WeatherSubKey.py new file mode 100644 index 0000000..bc17e16 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/weather/WeatherSubKey.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +## TODO: Implement WeatherSubKey when it is explicitly needed. For now +# WeatherSubKeys will be list of str within the WeatherKey class. + +class WeatherSubKey(object): + + def __init__(self): + raise NotImplementedError("WeatherSubKey is not currently supported.") diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/weather/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/weather/__init__.py new file mode 100644 index 0000000..0ff209a --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/gfe/weather/__init__.py @@ -0,0 +1,13 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'WeatherKey', + 'WeatherSubKey' + ] + +from WeatherKey import WeatherKey +from WeatherSubKey import WeatherSubKey + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/grid/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/grid/__init__.py new file mode 100644 index 0000000..58300a3 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/grid/__init__.py @@ -0,0 +1,10 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'request' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/grid/request/DeleteAllGridDataRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/grid/request/DeleteAllGridDataRequest.py new file mode 100644 index 0000000..6afa963 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/grid/request/DeleteAllGridDataRequest.py @@ -0,0 +1,16 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class DeleteAllGridDataRequest(object): + + def __init__(self, modelName=None): + self.modelName = modelName + + def getModelName(self): + return self.modelName + + def setModelName(self, modelName): + self.modelName = modelName + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/grid/request/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/grid/request/__init__.py new file mode 100644 index 0000000..9fa2071 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/grid/request/__init__.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'DeleteAllGridDataRequest' + ] + +from DeleteAllGridDataRequest import DeleteAllGridDataRequest + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/level/Level.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/level/Level.py new file mode 100644 index 0000000..5067604 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/level/Level.py @@ -0,0 +1,188 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to add additional features to better +# match Java implementation. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 05/29/13 2023 dgilling Initial Creation. +# 02/12/14 2672 bsteffen Allow String constructor to parse floats. +# 06/29/15 4480 dgilling Implement __hash__, __eq__, +# __str__ and rich comparison operators. +# + + +import numpy +import re + +from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.level import MasterLevel + + +LEVEL_NAMING_REGEX = re.compile("^(\d*(?:\.\d*)?)(?:_(\d*(?:\.\d*)?))?([a-zA-Z]+)$") +INVALID_VALUE = numpy.float64(-999999) + +class Level(object): + + def __init__(self, levelString=None): + self.id = 0L + self.identifier = None + self.masterLevel = None + self.levelonevalue = INVALID_VALUE + self.leveltwovalue = INVALID_VALUE + + if levelString is not None: + matcher = LEVEL_NAMING_REGEX.match(str(levelString)) + if matcher is not None: + self.levelonevalue = numpy.float64(matcher.group(1)) + self.masterLevel = MasterLevel.MasterLevel(matcher.group(3)) + levelTwo = matcher.group(2) + if levelTwo: + self.leveltwovalue = numpy.float64(levelTwo) + + def __hash__(self): + # XOR-ing the 3 items in a tuple ensures that order of the + # values matters + hashCode = hash(self.masterLevel) ^ hash(self.levelonevalue) ^ hash(self.leveltwovalue) + hashCode ^= hash((self.masterLevel, self.levelonevalue, self.leveltwovalue)) + return hashCode + + def __eq__(self, other): + if type(self) != type(other): + return False + else: + return (self.masterLevel, self.levelonevalue, self.leveltwovalue) == \ + (other.masterLevel, other.levelonevalue, other.leveltwovalue) + + def __ne__(self, other): + return not self.__eq__(other) + + def __lt__(self, other): + if type(self) != type(other): + return NotImplemented + elif self.masterLevel.getName() != other.masterLevel.getName(): + return NotImplemented + + myLevel1 = self.levelonevalue + myLevel2 = self.leveltwovalue + otherLevel1 = other.levelonevalue + otherLevel2 = other.leveltwovalue + if myLevel1 == INVALID_VALUE and myLevel2 != INVALID_VALUE: + myLevel1 = myLevel2 + myLevel2 = INVALID_VALUE + if otherLevel1 == INVALID_VALUE and otherLevel2 != INVALID_VALUE: + otherLevel1 = otherLevel2 + otherLevel2 = INVALID_VALUE + + # We default to descending order to make sorting levels from the DAF easier + compareType = self.masterLevel.getType() if self.masterLevel.getType() else "DEC" + if myLevel1 != INVALID_VALUE and otherLevel1 != INVALID_VALUE: + level1Cmp = self.__compareLevelValues(compareType, myLevel1, otherLevel1) + if level1Cmp == -1: + if myLevel2 != INVALID_VALUE and otherLevel2 != INVALID_VALUE: + level2Cmp = self.__compareLevelValues(compareType, myLevel2, otherLevel2) + return level2Cmp == -1 + elif myLevel2 != INVALID_VALUE: + level2Cmp = self.__compareLevelValues(compareType, myLevel2, otherLevel1) + return level2Cmp == -1 + else: + return True + return False + + def __le__(self, other): + if type(self) != type(other): + return NotImplemented + elif self.masterLevel.getName() != other.masterLevel.getName(): + return NotImplemented + + return self.__lt__(other) or self.__eq__(other) + + def __gt__(self, other): + if type(self) != type(other): + return NotImplemented + elif self.masterLevel.getName() != other.masterLevel.getName(): + return NotImplemented + + myLevel1 = self.levelonevalue + myLevel2 = self.leveltwovalue + otherLevel1 = other.levelonevalue + otherLevel2 = other.leveltwovalue + if myLevel1 == INVALID_VALUE and myLevel2 != INVALID_VALUE: + myLevel1 = myLevel2 + myLevel2 = INVALID_VALUE + if otherLevel1 == INVALID_VALUE and otherLevel2 != INVALID_VALUE: + otherLevel1 = otherLevel2 + otherLevel2 = INVALID_VALUE + + # We default to descending order to make sorting levels from the DAF easier + compareType = self.masterLevel.getType() if self.masterLevel.getType() else "DEC" + if myLevel1 != INVALID_VALUE and otherLevel1 != INVALID_VALUE: + level1Cmp = self.__compareLevelValues(compareType, myLevel1, otherLevel1) + if level1Cmp == 1: + if myLevel2 != INVALID_VALUE and otherLevel2 != INVALID_VALUE: + level2Cmp = self.__compareLevelValues(compareType, myLevel2, otherLevel2) + return level2Cmp == 1 + elif otherLevel2 != INVALID_VALUE: + level2Cmp = self.__compareLevelValues(compareType, myLevel1, otherLevel2) + return level2Cmp == 1 + else: + return True + return False + + def __ge__(self, other): + if type(self) != type(other): + return NotImplemented + elif self.masterLevel.getName() != other.masterLevel.getName(): + return NotImplemented + + return self.__gt__(other) or self.__eq__(other) + + def __compareLevelValues(self, compareType, val1, val2): + returnVal = 0 + if val1 < val2: + returnVal = -1 if compareType == 'INC' else 1 + elif val2 < val1: + returnVal = 1 if compareType == 'INC' else -1 + return returnVal + + def __str__(self): + retVal = "" + if INVALID_VALUE != self.levelonevalue: + retVal += str(self.levelonevalue) + if INVALID_VALUE != self.leveltwovalue: + retVal += "_" + str(self.leveltwovalue) + retVal += str(self.masterLevel.getName()) + return retVal + + def getId(self): + return self.id + + def setId(self, id): + self.id = id + + def getMasterLevel(self): + return self.masterLevel + + def setMasterLevel(self, masterLevel): + self.masterLevel = masterLevel + + def getLevelonevalue(self): + return self.levelonevalue + + def setLevelonevalue(self, levelonevalue): + self.levelonevalue = numpy.float64(levelonevalue) + + def getLeveltwovalue(self): + return self.leveltwovalue + + def setLeveltwovalue(self, leveltwovalue): + self.leveltwovalue = numpy.float64(leveltwovalue) + + def getIdentifier(self): + return self.identifier + + def setIdentifier(self, identifier): + self.identifier = identifier diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/level/MasterLevel.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/level/MasterLevel.py new file mode 100644 index 0000000..1237619 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/level/MasterLevel.py @@ -0,0 +1,77 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to add additional features to better +# match Java implementation. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 05/29/13 2023 dgilling Initial Creation. +# 06/29/15 4480 dgilling Implement __hash__, __eq__ +# and __str__. +# +# + +class MasterLevel(object): + + def __init__(self, name=None): + self.name = name + self.description = None + self.unitString = None + self.type = None + self.identifier = None + + def __hash__(self): + return hash(self.name) + + def __eq__(self, other): + if type(self) != type(other): + return False + else: + return self.name == other.name + + def __ne__(self, other): + return not self.__eq__(other) + + def __str__(self): + retVal = "MasterLevel[" + retVal += "name=" + str(self.name) + "," + retVal += "type=" + str(self.type) + "," + retVal += "unit=" + str(self.unitString) + "," + retVal += "description=" + str(self.description) + retVal += "]" + return retVal + + def getName(self): + return self.name + + def setName(self, name): + self.name = name + + def getDescription(self): + return self.description + + def setDescription(self, description): + self.description = description + + def getUnitString(self): + return self.unitString + + def setUnitString(self, unitString): + self.unitString = unitString + + def getType(self): + return self.type + + def setType(self, type): + self.type = type + + def getIdentifier(self): + return self.identifier + + def setIdentifier(self, identifier): + self.identifier = identifier + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/level/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/level/__init__.py new file mode 100644 index 0000000..eeb61b8 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/level/__init__.py @@ -0,0 +1,13 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'Level', + 'MasterLevel' + ] + +from Level import Level +from MasterLevel import MasterLevel + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/message/DataURINotificationMessage.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/message/DataURINotificationMessage.py new file mode 100644 index 0000000..4be67f8 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/message/DataURINotificationMessage.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class DataURINotificationMessage(object): + + def __init__(self): + self.dataURIs = None + self.ids = None + + def getDataURIs(self): + return self.dataURIs + + def setDataURIs(self, dataURIs): + self.dataURIs = dataURIs + + def getIds(self): + return self.ids + + def setIds(self, ids): + self.ids = ids + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/message/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/message/__init__.py new file mode 100644 index 0000000..8bd0a20 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/message/__init__.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'DataURINotificationMessage' + ] + +from DataURINotificationMessage import DataURINotificationMessage + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/radar/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/radar/__init__.py new file mode 100644 index 0000000..b8353d7 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/radar/__init__.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'request', + 'response' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/radar/request/GetRadarDataRecordRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/radar/request/GetRadarDataRecordRequest.py new file mode 100644 index 0000000..417ba79 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/radar/request/GetRadarDataRecordRequest.py @@ -0,0 +1,45 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Aug 19, 2014 nabowle Generated + +import numpy + +class GetRadarDataRecordRequest(object): + + def __init__(self): + self.timeRange = None + self.productCode = None + self.radarId = None + self.primaryElevationAngle = None + + def getTimeRange(self): + return self.timeRange + + def setTimeRange(self, timeRange): + self.timeRange = timeRange + + def getProductCode(self): + return self.productCode + + def setProductCode(self, productCode): + self.productCode = productCode + + def getRadarId(self): + return self.radarId + + def setRadarId(self, radarId): + self.radarId = radarId + + def getPrimaryElevationAngle(self): + return self.primaryElevationAngle + + def setPrimaryElevationAngle(self, primaryElevationAngle): + self.primaryElevationAngle = numpy.float64(primaryElevationAngle) + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/radar/request/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/radar/request/__init__.py new file mode 100644 index 0000000..c7c7066 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/radar/request/__init__.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'GetRadarDataRecordRequest' + ] + +from GetRadarDataRecordRequest import GetRadarDataRecordRequest + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/radar/response/GetRadarDataRecordResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/radar/response/GetRadarDataRecordResponse.py new file mode 100644 index 0000000..c8c7eb7 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/radar/response/GetRadarDataRecordResponse.py @@ -0,0 +1,22 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Aug 19, 2014 nabowle Generated + +class GetRadarDataRecordResponse(object): + + def __init__(self): + self.data = None + + def getData(self): + return self.data + + def setData(self, data): + self.data = data + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/radar/response/RadarDataRecord.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/radar/response/RadarDataRecord.py new file mode 100644 index 0000000..3cf22cd --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/radar/response/RadarDataRecord.py @@ -0,0 +1,71 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Aug 19, 2014 nabowle Generated + +class RadarDataRecord(object): + + def __init__(self): + self.hdf5Data = None + self.trueElevationAngle = None + self.elevationNumber = None + self.elevation = None + self.longitude = None + self.latitude = None + self.dataTime = None + self.volumeCoveragePattern = None + + def getHdf5Data(self): + return self.hdf5Data + + def setHdf5Data(self, hdf5Data): + self.hdf5Data = hdf5Data + + def getTrueElevationAngle(self): + return self.trueElevationAngle + + def setTrueElevationAngle(self, trueElevationAngle): + self.trueElevationAngle = trueElevationAngle + + def getElevationNumber(self): + return self.elevationNumber + + def setElevationNumber(self, elevationNumber): + self.elevationNumber = elevationNumber + + def getElevation(self): + return self.elevation + + def setElevation(self, elevation): + self.elevation = elevation + + def getLongitude(self): + return self.longitude + + def setLongitude(self, longitude): + self.longitude = longitude + + def getLatitude(self): + return self.latitude + + def setLatitude(self, latitude): + self.latitude = latitude + + def getDataTime(self): + return self.dataTime + + def setDataTime(self, dataTime): + self.dataTime = dataTime + + def getVolumeCoveragePattern(self): + return self.volumeCoveragePattern + + def setVolumeCoveragePattern(self, volumeCoveragePattern): + self.volumeCoveragePattern = volumeCoveragePattern + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/radar/response/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/radar/response/__init__.py new file mode 100644 index 0000000..02f70a5 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/radar/response/__init__.py @@ -0,0 +1,13 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'GetRadarDataRecordResponse', + 'RadarDataRecord' + ] + +from GetRadarDataRecordResponse import GetRadarDataRecordResponse +from RadarDataRecord import RadarDataRecord + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/__init__.py new file mode 100644 index 0000000..f0e85b9 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/__init__.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'dbsrv', + 'subscription' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/dbsrv/TextDBRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/dbsrv/TextDBRequest.py new file mode 100644 index 0000000..d1dba1b --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/dbsrv/TextDBRequest.py @@ -0,0 +1,16 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class TextDBRequest(object): + + def __init__(self): + self.message = None + + def getMessage(self): + return self.message + + def setMessage(self, message): + self.message = message + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/dbsrv/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/dbsrv/__init__.py new file mode 100644 index 0000000..c211070 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/dbsrv/__init__.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'TextDBRequest' + ] + +from TextDBRequest import TextDBRequest + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/subscription/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/subscription/__init__.py new file mode 100644 index 0000000..58300a3 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/subscription/__init__.py @@ -0,0 +1,10 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'request' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/subscription/request/SubscriptionRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/subscription/request/SubscriptionRequest.py new file mode 100644 index 0000000..88b9a6d --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/subscription/request/SubscriptionRequest.py @@ -0,0 +1,22 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Sep 05, 2014 bclement Generated + +class SubscriptionRequest(object): + + def __init__(self): + self.message = None + + def getMessage(self): + return self.message + + def setMessage(self, message): + self.message = message + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/subscription/request/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/subscription/request/__init__.py new file mode 100644 index 0000000..20b9b71 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataplugin/text/subscription/request/__init__.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'SubscriptionRequest' + ] + +from SubscriptionRequest import SubscriptionRequest + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataquery/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataquery/__init__.py new file mode 100644 index 0000000..d01496e --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataquery/__init__.py @@ -0,0 +1,10 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'requests' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataquery/requests/RequestConstraint.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataquery/requests/RequestConstraint.py new file mode 100644 index 0000000..a8d17df --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataquery/requests/RequestConstraint.py @@ -0,0 +1,276 @@ +## +## + +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Jun 01, 2016 5574 tgurney Initial creation +# Jun 27, 2016 5725 tgurney Add NOT IN +# Jul 22, 2016 2416 tgurney Add evaluate() +# +# + +import re +from ...time import DataTime + + +class RequestConstraint(object): + + TOLERANCE = 0.0001 + + IN_PATTERN = re.compile(',\s?') + + def __init__(self): + self.constraintValue = None + self.constraintType = None + + def getConstraintValue(self): + return self.constraintValue + + def setConstraintValue(self, constraintValue): + if hasattr(self, '_evalValue'): + del self._evalValue + self.constraintValue = constraintValue + + def getConstraintType(self): + return self.constraintType + + def setConstraintType(self, constraintType): + if hasattr(self, '_evalValue'): + del self._evalValue + self.constraintType = constraintType + + def evaluate(self, value): + if not hasattr(self, '_evalValue'): + self._setupEvalValue() + + if self.constraintType == 'EQUALS': + return self._evalEquals(value) + elif self.constraintType == 'NOT_EQUALS': + return not self._evalEquals(value) + elif self.constraintType == 'GREATER_THAN': + return self._evalGreaterThan(value) + elif self.constraintType == 'GREATER_THAN_EQUALS': + return self._evalGreaterThanEquals(value) + elif self.constraintType == 'LESS_THAN': + return self._evalLessThan(value) + elif self.constraintType == 'LESS_THAN_EQUALS': + return self._evalLessThanEquals(value) + elif self.constraintType == 'BETWEEN': + return self._evalBetween(value) + elif self.constraintType == 'IN': + return self._evalIn(value) + elif self.constraintType == 'NOT_IN': + return not self._evalIn(value) + elif self.constraintType == 'LIKE': + return self._evalLike(value) + # setupConstraintType already adds correct flags for ilike + # on regex pattern + elif self.constraintType == 'ILIKE': + return self._evalLike(value) + elif self.constraintType == 'ISNULL': + return self._evalIsNull(value) + elif self.constraintType == 'ISNOTNULL': + return not self._evalIsNull(value) + else: + errmsg = '{} is not a valid constraint type.' + raise ValueError(errmsg.format(self.constraintType)) + + def _makeRegex(self, pattern, flags): + """Make a pattern using % wildcard into a regex""" + pattern = re.escape(pattern) + pattern = pattern.replace('\\%', '.*') + pattern = pattern.replace('\\_', '.') + pattern = pattern + '$' + return re.compile(pattern, flags) + + def _setupEvalValue(self): + if self.constraintType == 'BETWEEN': + self._evalValue = self.constraintValue.split('--') + self._evalValue[0] = self._adjustValueType(self._evalValue[0]) + self._evalValue[1] = self._adjustValueType(self._evalValue[1]) + elif self.constraintType in ('IN', 'NOT_IN'): + splitValue = self.IN_PATTERN.split(self.constraintValue) + self._evalValue = { + self._adjustValueType(value) + for value in splitValue + } + # if collection now contains multiple types we have to force + # everything to string instead + initialType = next(iter(self._evalValue)).__class__ + for item in self._evalValue: + if item.__class__ is not initialType: + self._evalValue = {str(value) for value in splitValue} + break + elif self.constraintType == 'LIKE': + self._evalValue = self._makeRegex(self.constraintValue, re.DOTALL) + elif self.constraintType == 'ILIKE': + self._evalValue = self._makeRegex(self.constraintValue, re.IGNORECASE | re.DOTALL) + elif self.constraintValue is None: + self._evalValue = None + else: + self._evalValue = self._adjustValueType(self.constraintValue) + + def _adjustValueType(self, value): + ''' + Try to take part of a constraint value, encoded as a string, and + return it as its 'true type'. + + _adjustValueType('3.0') -> 3.0 + _adjustValueType('3') -> 3.0 + _adjustValueType('a string') -> 'a string' + ''' + try: + return float(value) + except Exception: + pass + try: + return DataTime(value) + except Exception: + pass + return value + + def _matchType(self, value, otherValue): + ''' + Return value coerced to be the same type as otherValue. If this is + not possible, just return value unmodified. + ''' + # cannot use type() because otherValue might be an instance of an + # old-style class (then it would just be of type "instance") + if not isinstance(value, otherValue.__class__): + try: + return otherValue.__class__(value) + except Exception: + pass + return value + + def _evalEquals(self, value): + value = self._matchType(value, self._evalValue) + if isinstance(value, float): + return abs(float(self._evalValue) - value) < self.TOLERANCE + else: + return value == self._evalValue + + def _evalGreaterThan(self, value): + value = self._matchType(value, self._evalValue) + return value > self._evalValue + + def _evalGreaterThanEquals(self, value): + value = self._matchType(value, self._evalValue) + return value >= self._evalValue + + def _evalLessThan(self, value): + value = self._matchType(value, self._evalValue) + return value < self._evalValue + + def _evalLessThanEquals(self, value): + value = self._matchType(value, self._evalValue) + return value <= self._evalValue + + def _evalBetween(self, value): + value = self._matchType(value, self._evalValue[0]) + return value >= self._evalValue[0] and value <= self._evalValue[1] + + def _evalIn(self, value): + anEvalValue = next(iter(self._evalValue)) + if isinstance(anEvalValue, float): + for otherValue in self._evalValue: + try: + if abs(otherValue - float(value)) < self.TOLERANCE: + return True + except Exception: + pass + return False + else: + value = self._matchType(value, anEvalValue) + return value in self._evalValue + + def _evalLike(self, value): + value = self._matchType(value, self._evalValue) + if self.constraintValue == '%': + return True + return self._evalValue.match(value) is not None + + def _evalIsNull(self, value): + return value is None or 'null' == value + + # DAF-specific stuff begins here ########################################## + + CONSTRAINT_MAP = {'=': 'EQUALS', + '!=': 'NOT_EQUALS', + '>': 'GREATER_THAN', + '>=': 'GREATER_THAN_EQUALS', + '<': 'LESS_THAN', + '<=': 'LESS_THAN_EQUALS', + 'IN': 'IN', + 'NOT IN': 'NOT_IN' + } + + @staticmethod + def _stringify(value): + if type(value) in {str, int, long, bool, float, unicode}: + return str(value) + else: + # Collections are not allowed; they are handled separately. + # Arbitrary objects are not allowed because the string + # representation may not be sufficient to reconstruct the object. + raise TypeError('Constraint values of type ' + repr(type(value)) + + 'are not allowed') + + @classmethod + def _constructIn(cls, constraintType, constraintValue): + """Build a new "IN" or "NOT IN" constraint from an iterable.""" + try: + iterator = iter(constraintValue) + except TypeError: + raise TypeError("value for IN / NOT IN constraint must be an iterable") + stringValue = ', '.join(cls._stringify(item) for item in iterator) + if len(stringValue) == 0: + raise ValueError('cannot use IN / NOT IN with empty collection') + obj = cls() + obj.setConstraintType(constraintType) + obj.setConstraintValue(stringValue) + return obj + + @classmethod + def _constructEq(cls, constraintType, constraintValue): + """Build a new = or != constraint. Handle None specially by making an + "is null" or "is not null" instead. + """ + obj = cls() + if constraintValue is None: + if constraintType == 'EQUALS': + obj.setConstraintType('ISNULL') + elif constraintType == 'NOT_EQUALS': + obj.setConstraintType('ISNOTNULL') + else: + obj = cls._construct(constraintType, constraintValue) + return obj + + @classmethod + def _construct(cls, constraintType, constraintValue): + """Build a new constraint.""" + stringValue = cls._stringify(constraintValue) + obj = cls() + obj.setConstraintType(constraintType) + obj.setConstraintValue(stringValue) + return obj + + @classmethod + def new(cls, operator, constraintValue): + """Build a new RequestConstraint.""" + try: + constraintType = cls.CONSTRAINT_MAP[operator.upper()] + except KeyError, AttributeError: + errmsg = '{} is not a valid operator. Valid operators are: {}' + validOperators = list(sorted(cls.CONSTRAINT_MAP.keys())) + raise ValueError(errmsg.format(operator, validOperators)) + if constraintType in ('IN', 'NOT_IN'): + return cls._constructIn(constraintType, constraintValue) + elif constraintType in {'EQUALS', 'NOT_EQUALS'}: + return cls._constructEq(constraintType, constraintValue) + else: + return cls._construct(constraintType, constraintValue) + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataquery/requests/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataquery/requests/__init__.py new file mode 100644 index 0000000..fdd0a6d --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataquery/requests/__init__.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'RequestConstraint' + ] + +from RequestConstraint import RequestConstraint + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/Request.py b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/Request.py new file mode 100644 index 0000000..f53f439 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/Request.py @@ -0,0 +1,42 @@ + +# File auto-generated against equivalent DynamicSerialize Java class + +class Request(object): + + def __init__(self): + self.points = None + self.indices = None + self.minIndexForSlab = None + self.maxIndexForSlab = None + self.type = None + + def getPoints(self): + return self.points + + def setPoints(self, points): + self.points = points + + def getIndices(self): + return self.indices + + def setIndices(self, indices): + self.indices = indices + + def getMinIndexForSlab(self): + return self.minIndexForSlab + + def setMinIndexForSlab(self, minIndexForSlab): + self.minIndexForSlab = minIndexForSlab + + def getMaxIndexForSlab(self): + return self.maxIndexForSlab + + def setMaxIndexForSlab(self, maxIndexForSlab): + self.maxIndexForSlab = maxIndexForSlab + + def getType(self): + return self.type + + def setType(self, type): + self.type = type + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/StorageProperties.py b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/StorageProperties.py new file mode 100644 index 0000000..bacf6db --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/StorageProperties.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class StorageProperties(object): + + def __init__(self): + self.compression = None + self.chunked = None + + def getCompression(self): + return self.compression + + def setCompression(self, compression): + self.compression = compression + + def getChunked(self): + return self.chunked + + def setChunked(self, chunked): + self.chunked = chunked + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/StorageStatus.py b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/StorageStatus.py new file mode 100644 index 0000000..98782b0 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/StorageStatus.py @@ -0,0 +1,21 @@ + +# File auto-generated against equivalent DynamicSerialize Java class + +class StorageStatus(object): + + def __init__(self): + self.operationPerformed = None + self.indexOfAppend = None + + def getOperationPerformed(self): + return self.operationPerformed + + def setOperationPerformed(self, operationPerformed): + self.operationPerformed = operationPerformed + + def getIndexOfAppend(self): + return self.indexOfAppend + + def setIndexOfAppend(self, indexOfAppend): + self.indexOfAppend = indexOfAppend + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/__init__.py new file mode 100644 index 0000000..1a02d76 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/__init__.py @@ -0,0 +1,28 @@ +## +## + + +# +# Package definition for com.raytheon.uf.common.datastorage +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 08/31/10 njensen Initial Creation. +# +# +# + + +__all__ = [ + 'records', + 'Request', + 'StorageProperties', + 'StorageStatus' + ] + +from Request import Request +from StorageProperties import StorageProperties +from StorageStatus import StorageStatus diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/ByteDataRecord.py b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/ByteDataRecord.py new file mode 100644 index 0000000..7bd3df6 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/ByteDataRecord.py @@ -0,0 +1,91 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified by njensen + +class ByteDataRecord(object): + + def __init__(self): + self.byteData = None + self.name = None + self.dimension = None + self.sizes = None + self.maxSizes = None + self.props = None + self.minIndex = None + self.group = None + self.dataAttributes = None + self.fillValue = None + self.maxChunkSize = None + + def getByteData(self): + return self.byteData + + def setByteData(self, byteData): + self.byteData = byteData + + def getName(self): + return self.name + + def setName(self, name): + self.name = name + + def getDimension(self): + return self.dimension + + def setDimension(self, dimension): + self.dimension = dimension + + def getSizes(self): + return self.sizes + + def setSizes(self, sizes): + self.sizes = sizes + + def getMaxSizes(self): + return self.maxSizes + + def setMaxSizes(self, maxSizes): + self.maxSizes = maxSizes + + def getProps(self): + return self.props + + def setProps(self, props): + self.props = props + + def getMinIndex(self): + return self.minIndex + + def setMinIndex(self, minIndex): + self.minIndex = minIndex + + def getGroup(self): + return self.group + + def setGroup(self, group): + self.group = group + + def getDataAttributes(self): + return self.dataAttributes + + def setDataAttributes(self, dataAttributes): + self.dataAttributes = dataAttributes + + def getFillValue(self): + return self.fillValue + + def setFillValue(self, fillValue): + self.fillValue = fillValue + + def getMaxChunkSize(self): + return self.maxChunkSize + + def setMaxChunkSize(self, maxChunkSize): + self.maxChunkSize = maxChunkSize + + def retrieveDataObject(self): + return self.getByteData() + + def putDataObject(self, obj): + self.setByteData(obj) + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/DoubleDataRecord.py b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/DoubleDataRecord.py new file mode 100644 index 0000000..432fe03 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/DoubleDataRecord.py @@ -0,0 +1,99 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class and +# modified. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Sep 8, 2014 kustert Initial Creation +# Apr 24, 2015 4425 nabowle Bring in. + +class DoubleDataRecord(object): + + def __init__(self): + self.sizes = None + self.dimension = None + self.maxChunkSize = None + self.name = None + self.fillValue = None + self.dataAttributes = None + self.group = None + self.minIndex = None + self.props = None + self.doubleData = None + self.maxSizes = None + + def getSizes(self): + return self.sizes + + def setSizes(self, sizes): + self.sizes = sizes + + def getDimension(self): + return self.dimension + + def setDimension(self, dimension): + self.dimension = dimension + + def getMaxChunkSize(self): + return self.maxChunkSize + + def setMaxChunkSize(self, maxChunkSize): + self.maxChunkSize = maxChunkSize + + def getName(self): + return self.name + + def setName(self, name): + self.name = name + + def getFillValue(self): + return self.fillValue + + def setFillValue(self, fillValue): + self.fillValue = fillValue + + def getDataAttributes(self): + return self.dataAttributes + + def setDataAttributes(self, dataAttributes): + self.dataAttributes = dataAttributes + + def getGroup(self): + return self.group + + def setGroup(self, group): + self.group = group + + def getMinIndex(self): + return self.minIndex + + def setMinIndex(self, minIndex): + self.minIndex = minIndex + + def getProps(self): + return self.props + + def setProps(self, props): + self.props = props + + def getDoubleData(self): + return self.doubleData + + def setDoubleData(self, doubleData): + self.doubleData = doubleData + + def getMaxSizes(self): + return self.maxSizes + + def setMaxSizes(self, maxSizes): + self.maxSizes = maxSizes + + def retrieveDataObject(self): + return self.getDoubleData() + + def putDataObject(self, obj): + self.setDoubleData(obj) diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/FloatDataRecord.py b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/FloatDataRecord.py new file mode 100644 index 0000000..3cba74e --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/FloatDataRecord.py @@ -0,0 +1,91 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified by njensen + +class FloatDataRecord(object): + + def __init__(self): + self.floatData = None + self.name = None + self.dimension = None + self.sizes = None + self.maxSizes = None + self.props = None + self.minIndex = None + self.group = None + self.dataAttributes = None + self.fillValue = None + self.maxChunkSize = None + + def getFloatData(self): + return self.floatData + + def setFloatData(self, floatData): + self.floatData = floatData + + def getName(self): + return self.name + + def setName(self, name): + self.name = name + + def getDimension(self): + return self.dimension + + def setDimension(self, dimension): + self.dimension = dimension + + def getSizes(self): + return self.sizes + + def setSizes(self, sizes): + self.sizes = sizes + + def getMaxSizes(self): + return self.maxSizes + + def setMaxSizes(self, maxSizes): + self.maxSizes = maxSizes + + def getProps(self): + return self.props + + def setProps(self, props): + self.props = props + + def getMinIndex(self): + return self.minIndex + + def setMinIndex(self, minIndex): + self.minIndex = minIndex + + def getGroup(self): + return self.group + + def setGroup(self, group): + self.group = group + + def getDataAttributes(self): + return self.dataAttributes + + def setDataAttributes(self, dataAttributes): + self.dataAttributes = dataAttributes + + def getFillValue(self): + return self.fillValue + + def setFillValue(self, fillValue): + self.fillValue = fillValue + + def getMaxChunkSize(self): + return self.maxChunkSize + + def setMaxChunkSize(self, maxChunkSize): + self.maxChunkSize = maxChunkSize + + def retrieveDataObject(self): + return self.getFloatData() + + def putDataObject(self, obj): + self.setFloatData(obj) + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/IntegerDataRecord.py b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/IntegerDataRecord.py new file mode 100644 index 0000000..a430c10 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/IntegerDataRecord.py @@ -0,0 +1,90 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified by njensen + +class IntegerDataRecord(object): + + def __init__(self): + self.intData = None + self.name = None + self.dimension = None + self.sizes = None + self.maxSizes = None + self.props = None + self.minIndex = None + self.group = None + self.dataAttributes = None + self.fillValue = None + self.maxChunkSize = None + + def getIntData(self): + return self.intData + + def setIntData(self, intData): + self.intData = intData + + def getName(self): + return self.name + + def setName(self, name): + self.name = name + + def getDimension(self): + return self.dimension + + def setDimension(self, dimension): + self.dimension = dimension + + def getSizes(self): + return self.sizes + + def setSizes(self, sizes): + self.sizes = sizes + + def getMaxSizes(self): + return self.maxSizes + + def setMaxSizes(self, maxSizes): + self.maxSizes = maxSizes + + def getProps(self): + return self.props + + def setProps(self, props): + self.props = props + + def getMinIndex(self): + return self.minIndex + + def setMinIndex(self, minIndex): + self.minIndex = minIndex + + def getGroup(self): + return self.group + + def setGroup(self, group): + self.group = group + + def getDataAttributes(self): + return self.dataAttributes + + def setDataAttributes(self, dataAttributes): + self.dataAttributes = dataAttributes + + def getFillValue(self): + return self.fillValue + + def setFillValue(self, fillValue): + self.fillValue = fillValue + + def getMaxChunkSize(self): + return self.maxChunkSize + + def setMaxChunkSize(self, maxChunkSize): + self.maxChunkSize = maxChunkSize + + def retrieveDataObject(self): + return self.getIntData() + + def putDataObject(self, obj): + self.setIntData(obj) diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/LongDataRecord.py b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/LongDataRecord.py new file mode 100644 index 0000000..a0af75c --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/LongDataRecord.py @@ -0,0 +1,90 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified by njensen + +class LongDataRecord(object): + + def __init__(self): + self.longData = None + self.name = None + self.dimension = None + self.sizes = None + self.maxSizes = None + self.props = None + self.minIndex = None + self.group = None + self.dataAttributes = None + self.fillValue = None + self.maxChunkSize = None + + def getLongData(self): + return self.longData + + def setLongData(self, longData): + self.longData = longData + + def getName(self): + return self.name + + def setName(self, name): + self.name = name + + def getDimension(self): + return self.dimension + + def setDimension(self, dimension): + self.dimension = dimension + + def getSizes(self): + return self.sizes + + def setSizes(self, sizes): + self.sizes = sizes + + def getMaxSizes(self): + return self.maxSizes + + def setMaxSizes(self, maxSizes): + self.maxSizes = maxSizes + + def getProps(self): + return self.props + + def setProps(self, props): + self.props = props + + def getMinIndex(self): + return self.minIndex + + def setMinIndex(self, minIndex): + self.minIndex = minIndex + + def getGroup(self): + return self.group + + def setGroup(self, group): + self.group = group + + def getDataAttributes(self): + return self.dataAttributes + + def setDataAttributes(self, dataAttributes): + self.dataAttributes = dataAttributes + + def getFillValue(self): + return self.fillValue + + def setFillValue(self, fillValue): + self.fillValue = fillValue + + def getMaxChunkSize(self): + return self.maxChunkSize + + def setMaxChunkSize(self, maxChunkSize): + self.maxChunkSize = maxChunkSize + + def retrieveDataObject(self): + return self.getLongData() + + def putDataObject(self, obj): + self.setLongData(obj) diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/ShortDataRecord.py b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/ShortDataRecord.py new file mode 100644 index 0000000..df122dc --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/ShortDataRecord.py @@ -0,0 +1,90 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified by njensen + +class ShortDataRecord(object): + + def __init__(self): + self.shortData = None + self.name = None + self.dimension = None + self.sizes = None + self.maxSizes = None + self.props = None + self.minIndex = None + self.group = None + self.dataAttributes = None + self.fillValue = None + self.maxChunkSize = None + + def getShortData(self): + return self.shortData + + def setShortData(self, shortData): + self.shortData = shortData + + def getName(self): + return self.name + + def setName(self, name): + self.name = name + + def getDimension(self): + return self.dimension + + def setDimension(self, dimension): + self.dimension = dimension + + def getSizes(self): + return self.sizes + + def setSizes(self, sizes): + self.sizes = sizes + + def getMaxSizes(self): + return self.maxSizes + + def setMaxSizes(self, maxSizes): + self.maxSizes = maxSizes + + def getProps(self): + return self.props + + def setProps(self, props): + self.props = props + + def getMinIndex(self): + return self.minIndex + + def setMinIndex(self, minIndex): + self.minIndex = minIndex + + def getGroup(self): + return self.group + + def setGroup(self, group): + self.group = group + + def getDataAttributes(self): + return self.dataAttributes + + def setDataAttributes(self, dataAttributes): + self.dataAttributes = dataAttributes + + def getFillValue(self): + return self.fillValue + + def setFillValue(self, fillValue): + self.fillValue = fillValue + + def getMaxChunkSize(self): + return self.maxChunkSize + + def setMaxChunkSize(self, maxChunkSize): + self.maxChunkSize = maxChunkSize + + def retrieveDataObject(self): + return self.getShortData() + + def putDataObject(self, obj): + self.setShortData(obj) \ No newline at end of file diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/StringDataRecord.py b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/StringDataRecord.py new file mode 100644 index 0000000..35b8924 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/StringDataRecord.py @@ -0,0 +1,107 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified by njensen + +class StringDataRecord(object): + + def __init__(self): + self.stringData = None + self.maxLength = None + self.name = None + self.dimension = None + self.sizes = None + self.maxSizes = None + self.props = None + self.minIndex = None + self.group = None + self.dataAttributes = None + self.fillValue = None + self.maxChunkSize = None + self.numpyData = None + + def getStringData(self): + return self.stringData + + def setStringData(self, stringData): + self.stringData = stringData + + def getMaxLength(self): + return self.maxLength + + def setMaxLength(self, maxLength): + self.maxLength = maxLength + + def getName(self): + return self.name + + def setName(self, name): + self.name = name + + def getDimension(self): + return self.dimension + + def setDimension(self, dimension): + self.dimension = dimension + + def getSizes(self): + return self.sizes + + def setSizes(self, sizes): + self.sizes = sizes + + def getMaxSizes(self): + return self.maxSizes + + def setMaxSizes(self, maxSizes): + self.maxSizes = maxSizes + + def getProps(self): + return self.props + + def setProps(self, props): + self.props = props + + def getMinIndex(self): + return self.minIndex + + def setMinIndex(self, minIndex): + self.minIndex = minIndex + + def getGroup(self): + return self.group + + def setGroup(self, group): + self.group = group + + def getDataAttributes(self): + return self.dataAttributes + + def setDataAttributes(self, dataAttributes): + self.dataAttributes = dataAttributes + + def getFillValue(self): + return self.fillValue + + def setFillValue(self, fillValue): + self.fillValue = fillValue + + def getMaxChunkSize(self): + return self.maxChunkSize + + def setMaxChunkSize(self, maxChunkSize): + self.maxChunkSize = maxChunkSize + + def retrieveDataObject(self): + if not self.numpyData: + import numpy + from h5py import h5t + if self.maxLength: + dtype = h5t.py_create('S' + str(self.maxLength)) + else: + from pypies.impl.H5pyDataStore import vlen_str_type as dtype + #dtype.set_strpad(h5t.STR_NULLTERM) + numpyData = numpy.asarray(self.getStringData(), dtype) + return numpyData + + def putDataObject(self, obj): + self.setStringData(obj) diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/__init__.py new file mode 100644 index 0000000..cbfc808 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/datastorage/records/__init__.py @@ -0,0 +1,36 @@ +## +## + +# +# Package definition for com.raytheon.uf.common.datastorage.records +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 08/31/10 njensen Initial Creation. +# Apr 24, 2015 4425 nabowle Add DoubleDataRecord +# +# +# + + +__all__ = [ + 'ByteDataRecord', + 'DoubleDataRecord', + 'FloatDataRecord', + 'IntegerDataRecord', + 'LongDataRecord', + 'ShortDataRecord', + 'StringDataRecord' + ] + +from ByteDataRecord import ByteDataRecord +from DoubleDataRecord import DoubleDataRecord +from FloatDataRecord import FloatDataRecord +from IntegerDataRecord import IntegerDataRecord +from LongDataRecord import LongDataRecord +from ShortDataRecord import ShortDataRecord +from StringDataRecord import StringDataRecord + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/localization/LocalizationContext.py b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/LocalizationContext.py new file mode 100644 index 0000000..0cb2943 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/LocalizationContext.py @@ -0,0 +1,39 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class LocalizationContext(object): + + def __init__(self): + self.localizationType = None + self.localizationLevel = None + self.contextName = None + + def getLocalizationType(self): + return self.localizationType + + def setLocalizationType(self, localizationType): + self.localizationType = localizationType + + def getLocalizationLevel(self): + return self.localizationLevel + + def setLocalizationLevel(self, localizationLevel): + self.localizationLevel = localizationLevel + + def getContextName(self): + return self.contextName + + def setContextName(self, contextName): + self.contextName = contextName + + def __str__(self): + return self.__repr__() + + def __repr__(self): + delimitedString = str(self.localizationType).lower() + "." + str(self.localizationLevel).lower() + if self.contextName is not None and self.contextName != "": + delimitedString += "." + self.contextName + return delimitedString + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/localization/LocalizationLevel.py b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/LocalizationLevel.py new file mode 100644 index 0000000..0c43763 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/LocalizationLevel.py @@ -0,0 +1,58 @@ +## +## + +knownLevels = {"BASE": {"text" : "BASE", + "order" : 0, + "systemLevel" : True, + }, + "CONFIGURED": {"text" : "CONFIGURED", + "order" : 250, + "systemLevel" : True, + }, + "SITE": {"text" : "SITE", + "order" : 500, + "systemLevel" : False, + }, + "USER": {"text" : "USER", + "order" : 1000, + "systemLevel" : False, + }, + "UNKNOWN": {"text" : "UNKNOWN", + "order" : -1, + } + } + + +class LocalizationLevel(object): + + def __init__(self, level, order=750, systemLevel=False): + if knownLevels.has_key(level.upper()): + self.text = level.upper() + self.order = knownLevels[self.text]["order"] + self.systemLevel = knownLevels[self.text]["systemLevel"] + else: + self.text = level.upper() + self.order = int(order) + self.systemLevel = systemLevel + + def getText(self): + return self.text + + def setText(self, text): + self.text = text + + def getOrder(self): + return self.order + + def setOrder(self, order): + self.order = int(order) + + def isSystemLevel(self): + return self.systemLevel + + def __str__(self): + return self.__repr__() + + def __repr__(self): + return str(self.text) + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/localization/LocalizationType.py b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/LocalizationType.py new file mode 100644 index 0000000..169d420 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/LocalizationType.py @@ -0,0 +1,20 @@ +## +## + +class LocalizationType(object): + + def __init__(self, text=None): + self.text = text + + def __str__(self): + return self.__repr__() + + def __repr__(self): + return str(self.text) + + def getText(self): + return self.text + + def setText(self, text): + self.text = text + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/localization/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/__init__.py new file mode 100644 index 0000000..2065c7a --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/__init__.py @@ -0,0 +1,17 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'msgs', + 'stream', + 'LocalizationContext', + 'LocalizationLevel', + 'LocalizationType' + ] + +from LocalizationContext import LocalizationContext +from LocalizationLevel import LocalizationLevel +from LocalizationType import LocalizationType + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/DeleteUtilityCommand.py b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/DeleteUtilityCommand.py new file mode 100644 index 0000000..4281a82 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/DeleteUtilityCommand.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class DeleteUtilityCommand(object): + + def __init__(self): + self.filename = None + self.context = None + self.myContextName = None + + def getFilename(self): + return self.filename + + def setFilename(self, filename): + self.filename = filename + + def getContext(self): + return self.context + + def setContext(self, context): + self.context = context + + def getMyContextName(self): + return self.myContextName + + def setMyContextName(self, contextName): + self.myContextName = str(contextName) + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/DeleteUtilityResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/DeleteUtilityResponse.py new file mode 100644 index 0000000..7a53aa8 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/DeleteUtilityResponse.py @@ -0,0 +1,42 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class DeleteUtilityResponse(object): + + def __init__(self): + self.context = None + self.pathName = None + self.errorText = None + self.timeStamp = None + + def getContext(self): + return self.context + + def setContext(self, context): + self.context = context + + def getPathName(self): + return self.pathName + + def setPathName(self, pathName): + self.pathName = pathName + + def getErrorText(self): + return self.errorText + + def setErrorText(self, errorText): + self.errorText = errorText + + def getTimeStamp(self): + return self.timeStamp + + def setTimeStamp(self, timeStamp): + self.timeStamp = timeStamp + + def getFormattedErrorMessage(self): + return "Error deleting " + self.getContextRelativePath() + ": " + self.getErrorText() + + def getContextRelativePath(self): + return str(self.getContext()) + "/" + self.getPathName() diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/ListResponseEntry.py b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/ListResponseEntry.py new file mode 100644 index 0000000..e69b048 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/ListResponseEntry.py @@ -0,0 +1,64 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class ListResponseEntry(object): + + def __init__(self): + self.fileName = None + self.context = None + self.date = None + self.checksum = None + self.directory = None + self.protectedLevel = None + self.existsOnServer = None + + def getFileName(self): + return self.fileName + + def setFileName(self, fileName): + self.fileName = fileName + + def getContext(self): + return self.context + + def setContext(self, context): + self.context = context + + def getDate(self): + return self.date + + def setDate(self, date): + self.date = date + + def getChecksum(self): + return self.checksum + + def setChecksum(self, checksum): + self.checksum = checksum + + def getDirectory(self): + return self.directory + + def setDirectory(self, directory): + self.directory = directory + + def getProtectedFile(self): + return self.protectedLevel is not None + + def getProtectedLevel(self): + return self.protectedLevel + + def setProtectedLevel(self, protectedLevel): + self.protectedLevel = protectedLevel + + def getExistsOnServer(self): + return self.existsOnServer + + def setExistsOnServer(self, existsOnServer): + self.existsOnServer = existsOnServer + + def __str__(self): + return self.fileName + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/ListUtilityCommand.py b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/ListUtilityCommand.py new file mode 100644 index 0000000..d306036 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/ListUtilityCommand.py @@ -0,0 +1,44 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class ListUtilityCommand(object): + + def __init__(self): + self.subDirectory = None + self.recursive = None + self.filesOnly = None + self.localizedSite = None + self.context = None + + def getSubDirectory(self): + return self.subDirectory + + def setSubDirectory(self, subDirectory): + self.subDirectory = subDirectory + + def getRecursive(self): + return self.recursive + + def setRecursive(self, recursive): + self.recursive = recursive + + def getFilesOnly(self): + return self.filesOnly + + def setFilesOnly(self, filesOnly): + self.filesOnly = filesOnly + + def getLocalizedSite(self): + return self.localizedSite + + def setLocalizedSite(self, localizedSite): + self.localizedSite = localizedSite + + def getContext(self): + return self.context + + def setContext(self, context): + self.context = context + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/ListUtilityResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/ListUtilityResponse.py new file mode 100644 index 0000000..a1abc36 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/ListUtilityResponse.py @@ -0,0 +1,43 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class ListUtilityResponse(object): + + def __init__(self): + self.entries = None + self.context = None + self.pathName = None + self.errorText = None + + def getEntries(self): + return self.entries + + def setEntries(self, entries): + self.entries = entries + + def getContext(self): + return self.context + + def setContext(self, context): + self.context = context + + def getPathName(self): + return self.pathName + + def setPathName(self, pathName): + self.pathName = pathName + + def getErrorText(self): + return self.errorText + + def setErrorText(self, errorText): + self.errorText = errorText + + def __str__(self): + if self.errorText is None: + return str(self.entries) + else: + return "Error retrieving file listing for " + self.pathName + ": " + \ + self.errorText diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/PrivilegedUtilityRequestMessage.py b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/PrivilegedUtilityRequestMessage.py new file mode 100644 index 0000000..04cf4b7 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/PrivilegedUtilityRequestMessage.py @@ -0,0 +1,25 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +from dynamicserialize.dstypes.com.raytheon.uf.common.auth.user import User + +class PrivilegedUtilityRequestMessage(object): + + def __init__(self): + self.commands = None + self.user = User() + + def getCommands(self): + return self.commands + + def setCommands(self, commands): + self.commands = commands + + def getUser(self): + return self.user + + def setUser(self, user): + self.user = user + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/UtilityRequestMessage.py b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/UtilityRequestMessage.py new file mode 100644 index 0000000..88d0a67 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/UtilityRequestMessage.py @@ -0,0 +1,16 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class UtilityRequestMessage(object): + + def __init__(self): + self.commands = None + + def getCommands(self): + return self.commands + + def setCommands(self, commands): + self.commands = commands + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/UtilityResponseMessage.py b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/UtilityResponseMessage.py new file mode 100644 index 0000000..454436c --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/UtilityResponseMessage.py @@ -0,0 +1,16 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class UtilityResponseMessage(object): + + def __init__(self): + self.responses = None + + def getResponses(self): + return self.responses + + def setResponses(self, responses): + self.responses = responses + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/__init__.py new file mode 100644 index 0000000..ff6ec26 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/msgs/__init__.py @@ -0,0 +1,25 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'DeleteUtilityCommand', + 'DeleteUtilityResponse', + 'ListResponseEntry', + 'ListUtilityCommand', + 'ListUtilityResponse', + 'PrivilegedUtilityRequestMessage', + 'UtilityRequestMessage', + 'UtilityResponseMessage' + ] + +from DeleteUtilityCommand import DeleteUtilityCommand +from DeleteUtilityResponse import DeleteUtilityResponse +from ListResponseEntry import ListResponseEntry +from ListUtilityCommand import ListUtilityCommand +from ListUtilityResponse import ListUtilityResponse +from PrivilegedUtilityRequestMessage import PrivilegedUtilityRequestMessage +from UtilityRequestMessage import UtilityRequestMessage +from UtilityResponseMessage import UtilityResponseMessage + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/localization/stream/AbstractLocalizationStreamRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/stream/AbstractLocalizationStreamRequest.py new file mode 100644 index 0000000..83322a2 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/stream/AbstractLocalizationStreamRequest.py @@ -0,0 +1,45 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +import abc +import os +from dynamicserialize.dstypes.com.raytheon.uf.common.auth.user import User + +class AbstractLocalizationStreamRequest(object): + __metaclass__ = abc.ABCMeta + + @abc.abstractmethod + def __init__(self): + self.context = None + self.fileName = None + self.myContextName = None + self.user = User() + + def getContext(self): + return self.context + + def setContext(self, context): + self.context = context + + def getFileName(self): + return self.fileName + + def setFileName(self, fileName): + if fileName[0] == os.sep: + fileName = fileName[1:] + self.fileName = fileName + + def getMyContextName(self): + return self.myContextName + + def setMyContextName(self, contextName): + self.myContextName = str(contextName) + + def getUser(self): + return self.user + + def setUser(self, user): + self.user = user + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/localization/stream/LocalizationStreamGetRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/stream/LocalizationStreamGetRequest.py new file mode 100644 index 0000000..afe39d9 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/stream/LocalizationStreamGetRequest.py @@ -0,0 +1,28 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +import os +from dynamicserialize.dstypes.com.raytheon.uf.common.localization.stream import AbstractLocalizationStreamRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.auth.user import User + +class LocalizationStreamGetRequest(AbstractLocalizationStreamRequest): + + def __init__(self): + super(LocalizationStreamGetRequest, self).__init__() + self.offset = None + self.numBytes = None + + def getOffset(self): + return self.offset + + def setOffset(self, offset): + self.offset = offset + + def getNumBytes(self): + return self.numBytes + + def setNumBytes(self, numBytes): + self.numBytes = numBytes + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/localization/stream/LocalizationStreamPutRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/stream/LocalizationStreamPutRequest.py new file mode 100644 index 0000000..4e59945 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/stream/LocalizationStreamPutRequest.py @@ -0,0 +1,51 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +import os +import uuid +from dynamicserialize.dstypes.com.raytheon.uf.common.localization.stream import AbstractLocalizationStreamRequest +from dynamicserialize.dstypes.com.raytheon.uf.common.auth.user import User + + +class LocalizationStreamPutRequest(AbstractLocalizationStreamRequest): + + def __init__(self): + super(LocalizationStreamPutRequest, self).__init__() + self.id = str(uuid.uuid4()) + self.bytes = None + self.end = None + self.offset = None + self.localizedSite = None + + def getId(self): + return self.id + + def setId(self, id): + self.id = id + + def getBytes(self): + return self.bytes + + def setBytes(self, bytes): + self.bytes = bytes + + def getEnd(self): + return self.end + + def setEnd(self, end): + self.end = end + + def getOffset(self): + return self.offset + + def setOffset(self, offset): + self.offset = offset + + def getLocalizedSite(self): + return self.localizedSite + + def setLocalizedSite(self, localizedSite): + self.localizedSite = localizedSite + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/localization/stream/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/stream/__init__.py new file mode 100644 index 0000000..78f0207 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/localization/stream/__init__.py @@ -0,0 +1,15 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'AbstractLocalizationStreamRequest', + 'LocalizationStreamGetRequest', + 'LocalizationStreamPutRequest' + ] + +from AbstractLocalizationStreamRequest import AbstractLocalizationStreamRequest +from LocalizationStreamGetRequest import LocalizationStreamGetRequest +from LocalizationStreamPutRequest import LocalizationStreamPutRequest + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/management/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/management/__init__.py new file mode 100644 index 0000000..b8353d7 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/management/__init__.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'request', + 'response' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/ChangeContextRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/ChangeContextRequest.py new file mode 100644 index 0000000..5ace07b --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/ChangeContextRequest.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class ChangeContextRequest(object): + + def __init__(self): + self.action = None + self.contextName = None + + def getAction(self): + return self.action + + def setAction(self, action): + self.action = action + + def getContextName(self): + return self.contextName + + def setContextName(self, contextName): + self.contextName = contextName + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/PassThroughRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/PassThroughRequest.py new file mode 100644 index 0000000..b049e3e --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/PassThroughRequest.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class PassThroughRequest(object): + + def __init__(self): + self.request = None + self.hostname = None + self.jvmName = None + + def getRequest(self): + return self.request + + def setRequest(self, request): + self.request = request + + def getHostname(self): + return self.hostname + + def setHostname(self, hostname): + self.hostname = hostname + + def getJvmName(self): + return self.jvmName + + def setJvmName(self, jvmName): + self.jvmName = jvmName + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/__init__.py new file mode 100644 index 0000000..077a22f --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/__init__.py @@ -0,0 +1,14 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'diagnostic', + 'ChangeContextRequest', + 'PassThroughRequest' + ] + +from ChangeContextRequest import ChangeContextRequest +from PassThroughRequest import PassThroughRequest + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/diagnostic/GetClusterMembersRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/diagnostic/GetClusterMembersRequest.py new file mode 100644 index 0000000..99486cd --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/diagnostic/GetClusterMembersRequest.py @@ -0,0 +1,9 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GetClusterMembersRequest(object): + + def __init__(self): + pass diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/diagnostic/GetContextsRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/diagnostic/GetContextsRequest.py new file mode 100644 index 0000000..d6b3396 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/diagnostic/GetContextsRequest.py @@ -0,0 +1,16 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GetContextsRequest(object): + + def __init__(self): + self.contextState = None + + def getContextState(self): + return self.contextState + + def setContextState(self, contextState): + self.contextState = contextState + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/diagnostic/StatusRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/diagnostic/StatusRequest.py new file mode 100644 index 0000000..612a38d --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/diagnostic/StatusRequest.py @@ -0,0 +1,9 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class StatusRequest(object): + + def __init__(self): + pass diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/diagnostic/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/diagnostic/__init__.py new file mode 100644 index 0000000..d7540a6 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/management/request/diagnostic/__init__.py @@ -0,0 +1,15 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'GetClusterMembersRequest', + 'GetContextsRequest', + 'StatusRequest' + ] + +from GetClusterMembersRequest import GetClusterMembersRequest +from GetContextsRequest import GetContextsRequest +from StatusRequest import StatusRequest + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/management/response/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/management/response/__init__.py new file mode 100644 index 0000000..e1e5fcf --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/management/response/__init__.py @@ -0,0 +1,10 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'diagnostic' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/management/response/diagnostic/ClusterMembersResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/management/response/diagnostic/ClusterMembersResponse.py new file mode 100644 index 0000000..18def48 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/management/response/diagnostic/ClusterMembersResponse.py @@ -0,0 +1,22 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class ClusterMembersResponse(object): + + def __init__(self): + self.status = None + + def getStatus(self): + return self.status + + def setStatus(self, status): + self.status = status + + def __repr__(self): + msg = '' + for x in self.status: + msg += str(x) + '\n' + return msg + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/management/response/diagnostic/ContextsResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/management/response/diagnostic/ContextsResponse.py new file mode 100644 index 0000000..406addd --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/management/response/diagnostic/ContextsResponse.py @@ -0,0 +1,26 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class ContextsResponse(object): + + def __init__(self): + self.contextState = None + self.contexts = None + + def getContextState(self): + return self.contextState + + def setContextState(self, contextState): + self.contextState = contextState + + def getContexts(self): + return self.contexts + + def setContexts(self, contexts): + self.contexts = contexts + + def __repr__(self): + return str(self.contexts) + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/management/response/diagnostic/StatusResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/management/response/diagnostic/StatusResponse.py new file mode 100644 index 0000000..031d883 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/management/response/diagnostic/StatusResponse.py @@ -0,0 +1,33 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class StatusResponse(object): + + def __init__(self): + self.hostname = None + self.jvmName = None + self.statistics = None + + def getHostname(self): + return self.hostname + + def setHostname(self, hostname): + self.hostname = hostname + + def getJvmName(self): + return self.jvmName + + def setJvmName(self, jvmName): + self.jvmName = jvmName + + def getStatistics(self): + return self.statistics + + def setStatistics(self, statistics): + self.statistics = statistics + + def __repr__(self): + return self.hostname + ':' + self.jvmName + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/management/response/diagnostic/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/management/response/diagnostic/__init__.py new file mode 100644 index 0000000..68aa8b0 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/management/response/diagnostic/__init__.py @@ -0,0 +1,15 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'ClusterMembersResponse', + 'ContextsResponse', + 'StatusResponse' + ] + +from ClusterMembersResponse import ClusterMembersResponse +from ContextsResponse import ContextsResponse +from StatusResponse import StatusResponse + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/message/Body.py b/dynamicserialize/dstypes/com/raytheon/uf/common/message/Body.py new file mode 100644 index 0000000..b58d33f --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/message/Body.py @@ -0,0 +1,16 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class Body(object): + + def __init__(self): + self.responses = None + + def getResponses(self): + return self.responses + + def setResponses(self, responses): + self.responses = responses + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/message/Header.py b/dynamicserialize/dstypes/com/raytheon/uf/common/message/Header.py new file mode 100644 index 0000000..89205ea --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/message/Header.py @@ -0,0 +1,26 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +from Property import Property + +class Header(object): + + def __init__(self, properties=None, multimap=None): + if properties is None: + self.properties = [] + else: + self.properties = properties + + if multimap is not None: + for k, l in multimap.iteritems(): + for v in l: + self.properties.append(Property(k, v)) + + def getProperties(self): + return self.properties + + def setProperties(self, properties): + self.properties = properties + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/message/Message.py b/dynamicserialize/dstypes/com/raytheon/uf/common/message/Message.py new file mode 100644 index 0000000..cf71f50 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/message/Message.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class Message(object): + + def __init__(self, header=None, body=None): + self.header = header + self.body = body + + def getHeader(self): + return self.header + + def setHeader(self, header): + self.header = header + + def getBody(self): + return self.body + + def setBody(self, body): + self.body = body + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/message/Property.py b/dynamicserialize/dstypes/com/raytheon/uf/common/message/Property.py new file mode 100644 index 0000000..886e11b --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/message/Property.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class Property(object): + + def __init__(self, name=None, value=None): + self.name = name + self.value = value + + def getName(self): + return self.name + + def setName(self, name): + self.name = name + + def getValue(self): + return self.value + + def setValue(self, value): + self.value = value + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/message/WsId.py b/dynamicserialize/dstypes/com/raytheon/uf/common/message/WsId.py new file mode 100644 index 0000000..7516009 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/message/WsId.py @@ -0,0 +1,78 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# Modified by njensen to add __repr__ +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------- -------- --------- --------------------------------------------- +# Apr 25, 2012 545 randerso Repurposed the lockKey field as threadId +# Jun 12, 2013 2099 dgilling Implemented toPrettyString(). +# Feb 06, 2017 5959 randerso Removed Java .toString() calls +# +## + +import struct +import socket +import os +import pwd +import thread + +class WsId(object): + + def __init__(self, networkId=None, userName=None, progName=None): + self.networkId = networkId + if networkId is None: + self.networkId = str(struct.unpack('') + compressedBuffer = numpy.getbuffer(self.compressedData) + self.compressedData = None + uncompressedSize = datatype.itemsize + for s in self.sizes: + uncompressedSize *= s + + # zlib.MAX_WBITS | 16, add 16 to window bits to support gzip header/trailer + # http://www.zlib.net/manual.html#Advanced + decompressedBuffer = zlib.decompress(compressedBuffer, zlib.MAX_WBITS | 16, uncompressedSize) + self.uncompressedData = numpy.frombuffer(decompressedBuffer, datatype) + + def retrieveDataObject(self): + if self.uncompressedData is None: + self.decompress() + return self.uncompressedData + + def putDataObject(self, obj): + self.compressedData = None + self.uncompressedData = obj + + prepareStore = decompress diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/records/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/records/__init__.py new file mode 100644 index 0000000..c0a2cd5 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/records/__init__.py @@ -0,0 +1,23 @@ +## +## + +# +# Package definition for com.raytheon.uf.common.datastorage.records +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 08/31/10 njensen Initial Creation. +# Apr 24, 2015 4425 nabowle Add DoubleDataRecord +# +# +# + + +__all__ = [ + 'CompressedDataRecord' + ] +from CompressedDataRecord import CompressedDataRecord + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/CopyRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/CopyRequest.py new file mode 100644 index 0000000..f139f05 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/CopyRequest.py @@ -0,0 +1,50 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class CopyRequest(object): + + def __init__(self): + self.repack = None + self.repackCompression = None + self.outputDir = None + self.minMillisSinceLastChange = None + self.maxMillisSinceLastChange = None + self.filename = None + + def getRepack(self): + return self.repack + + def setRepack(self, repack): + self.repack = repack + + def getRepackCompression(self): + return self.repackCompression + + def setRepackCompression(self, repackCompression): + self.repackCompression = repackCompression + + def getOutputDir(self): + return self.outputDir + + def setOutputDir(self, outputDir): + self.outputDir = outputDir + + def getMinMillisSinceLastChange(self): + return self.minMillisSinceLastChange + + def setMinMillisSinceLastChange(self, minMillisSinceLastChange): + self.minMillisSinceLastChange = minMillisSinceLastChange + + def getMaxMillisSinceLastChange(self): + return self.maxMillisSinceLastChange + + def setMaxMillisSinceLastChange(self, maxMillisSinceLastChange): + self.maxMillisSinceLastChange = maxMillisSinceLastChange + + def getFilename(self): + return self.filename + + def setFilename(self, filename): + self.filename = filename diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/CreateDatasetRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/CreateDatasetRequest.py new file mode 100644 index 0000000..e3a160e --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/CreateDatasetRequest.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class CreateDatasetRequest(object): + + def __init__(self): + self.record = None + self.filename = None + + def getRecord(self): + return self.record + + def setRecord(self, record): + self.record = record + + def getFilename(self): + return self.filename + + def setFilename(self, filename): + self.filename = filename + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DatasetDataRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DatasetDataRequest.py new file mode 100644 index 0000000..36eefa1 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DatasetDataRequest.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class DatasetDataRequest(object): + + def __init__(self): + self.datasetGroupPath = None + self.request = None + self.filename = None + + def getDatasetGroupPath(self): + return self.datasetGroupPath + + def setDatasetGroupPath(self, datasetGroupPath): + self.datasetGroupPath = datasetGroupPath + + def getRequest(self): + return self.request + + def setRequest(self, request): + self.request = request + + def getFilename(self): + return self.filename + + def setFilename(self, filename): + self.filename = filename + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DatasetNamesRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DatasetNamesRequest.py new file mode 100644 index 0000000..8d10e90 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DatasetNamesRequest.py @@ -0,0 +1,23 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class DatasetNamesRequest(object): + + def __init__(self): + self.group = None + self.filename = None + + def getGroup(self): + return self.group + + def setGroup(self, group): + self.group = group + + def getFilename(self): + return self.filename + + def setFilename(self, filename): + self.filename = filename + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DeleteFilesRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DeleteFilesRequest.py new file mode 100644 index 0000000..898994a --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DeleteFilesRequest.py @@ -0,0 +1,22 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class DeleteFilesRequest(object): + + def __init__(self): + self.datesToDelete = None + + def getDatesToDelete(self): + return self.datesToDelete + + def setDatesToDelete(self, datesToDelete): + self.datesToDelete = datesToDelete + + def getFilename(self): + return self.filename + + def setFilename(self, filename): + self.filename = filename + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DeleteOrphansRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DeleteOrphansRequest.py new file mode 100644 index 0000000..0c2b8ff --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DeleteOrphansRequest.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Jul 27, 2015 1574 nabowle Generated +# Feb 23, 2016 5389 nabowle Regenerated + +class DeleteOrphansRequest(object): + + def __init__(self): + self.oldestDateMap = None + self.filename = None + + def getOldestDateMap(self): + return self.oldestDateMap + + def setOldestDateMap(self, oldestDateMap): + self.oldestDateMap = oldestDateMap + + def getFilename(self): + return self.filename + + def setFilename(self, filename): + self.filename = filename + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DeleteRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DeleteRequest.py new file mode 100644 index 0000000..791941e --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DeleteRequest.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class DeleteRequest(object): + + def __init__(self): + self.datasets = None + self.groups = None + self.filename = None + + def getDatasets(self): + return self.datasets + + def setDatasets(self, datasets): + self.datasets = datasets + + def getGroups(self): + return self.groups + + def setGroups(self, groups): + self.groups = groups + + def getFilename(self): + return self.filename + + def setFilename(self, filename): + self.filename = filename + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/GroupsRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/GroupsRequest.py new file mode 100644 index 0000000..f57e491 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/GroupsRequest.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GroupsRequest(object): + + def __init__(self): + self.groups = None + self.request = None + self.filename = None + + def getGroups(self): + return self.groups + + def setGroups(self, groups): + self.groups = groups + + def getRequest(self): + return self.request + + def setRequest(self, request): + self.request = request + + def getFilename(self): + return self.filename + + def setFilename(self, filename): + self.filename = filename + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/RepackRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/RepackRequest.py new file mode 100644 index 0000000..8ba50d4 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/RepackRequest.py @@ -0,0 +1,22 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class RepackRequest(object): + + def __init__(self): + self.compression = None + self.filename = None + + def getCompression(self): + return self.compression + + def setCompression(self, compression): + self.compression = compression + + def getFilename(self): + return self.filename + + def setFilename(self, filename): + self.filename = filename diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/RetrieveRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/RetrieveRequest.py new file mode 100644 index 0000000..3507dff --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/RetrieveRequest.py @@ -0,0 +1,37 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class RetrieveRequest(object): + + def __init__(self): + self.group = None + self.dataset = None + self.request = None + self.filename = None + + def getGroup(self): + return self.group + + def setGroup(self, group): + self.group = group + + def getDataset(self): + return self.dataset + + def setDataset(self, dataset): + self.dataset = dataset + + def getRequest(self): + return self.request + + def setRequest(self, request): + self.request = request + + def getFilename(self): + return self.filename + + def setFilename(self, filename): + self.filename = filename + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/StoreRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/StoreRequest.py new file mode 100644 index 0000000..19b4ef8 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/StoreRequest.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class StoreRequest(object): + + def __init__(self): + self.op = None + self.records = None + self.filename = None + + def getOp(self): + return self.op + + def setOp(self, op): + self.op = op + + def getRecords(self): + return self.records + + def setRecords(self, records): + self.records = records + + def getFilename(self): + return self.filename + + def setFilename(self, filename): + self.filename = filename + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/__init__.py new file mode 100644 index 0000000..762c469 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/__init__.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'CopyRequest', + 'CreateDatasetRequest', + 'DatasetDataRequest', + 'DatasetNamesRequest', + 'DeleteFilesRequest', + 'DeleteOrphansRequest', + 'DeleteRequest', + 'GroupsRequest', + 'RepackRequest', + 'RetrieveRequest', + 'StoreRequest' + ] + +from CopyRequest import CopyRequest +from CreateDatasetRequest import CreateDatasetRequest +from DatasetDataRequest import DatasetDataRequest +from DatasetNamesRequest import DatasetNamesRequest +from DeleteFilesRequest import DeleteFilesRequest +from DeleteOrphansRequest import DeleteOrphansRequest +from DeleteRequest import DeleteRequest +from GroupsRequest import GroupsRequest +from RepackRequest import RepackRequest +from RetrieveRequest import RetrieveRequest +from StoreRequest import StoreRequest diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/response/DeleteResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/response/DeleteResponse.py new file mode 100644 index 0000000..57107b7 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/response/DeleteResponse.py @@ -0,0 +1,16 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class DeleteResponse(object): + + def __init__(self): + self.success = None + + def getSuccess(self): + return self.success + + def setSuccess(self, success): + self.success = success + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/response/ErrorResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/response/ErrorResponse.py new file mode 100644 index 0000000..91a723b --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/response/ErrorResponse.py @@ -0,0 +1,16 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class ErrorResponse(object): + + def __init__(self): + self.error = None + + def getError(self): + return self.error + + def setError(self, error): + self.error = error + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/response/FileActionResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/response/FileActionResponse.py new file mode 100644 index 0000000..993a94d --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/response/FileActionResponse.py @@ -0,0 +1,22 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class FileActionResponse(object): + + def __init__(self): + self.successfulFiles = None + self.failedFiles = None + + def getSuccessfulFiles(self): + return self.successfulFiles + + def setSuccessfulFiles(self, successfulFiles): + self.successfulFiles = successfulFiles + + def getFailedFiles(self): + return self.failedFiles + + def setFailedFiles(self, failedFiles): + self.failedFiles = failedFiles diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/response/RetrieveResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/response/RetrieveResponse.py new file mode 100644 index 0000000..8b86674 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/response/RetrieveResponse.py @@ -0,0 +1,16 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class RetrieveResponse(object): + + def __init__(self): + self.records = None + + def getRecords(self): + return self.records + + def setRecords(self, records): + self.records = records + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/response/StoreResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/response/StoreResponse.py new file mode 100644 index 0000000..6405391 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/response/StoreResponse.py @@ -0,0 +1,30 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class StoreResponse(object): + + def __init__(self): + self.status = None + self.exceptions = None + self.failedRecords = None + + def getStatus(self): + return self.status + + def setStatus(self, status): + self.status = status + + def getExceptions(self): + return self.exceptions + + def setExceptions(self, exceptions): + self.exceptions = exceptions + + def getFailedRecords(self): + return self.failedRecords + + def setFailedRecords(self, failedRecords): + self.failedRecords = failedRecords + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/response/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/response/__init__.py new file mode 100644 index 0000000..5226fb5 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/response/__init__.py @@ -0,0 +1,18 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'DeleteResponse', + 'ErrorResponse', + 'FileActionResponse', + 'RetrieveResponse', + 'StoreResponse' + ] + +from DeleteResponse import DeleteResponse +from ErrorResponse import ErrorResponse +from FileActionResponse import FileActionResponse +from RetrieveResponse import RetrieveResponse +from StoreResponse import StoreResponse diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/serialization/SerializableExceptionWrapper.py b/dynamicserialize/dstypes/com/raytheon/uf/common/serialization/SerializableExceptionWrapper.py new file mode 100644 index 0000000..0c71adb --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/serialization/SerializableExceptionWrapper.py @@ -0,0 +1,57 @@ + +# File auto-generated against equivalent DynamicSerialize Java class and +# modified. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 2015-02-27 4174 nabowle Output full stacktrace. +# + +class SerializableExceptionWrapper(object): + + def __init__(self): + self.stackTrace = None + self.message = None + self.exceptionClass = None + self.wrapper = None + + def __str__(self): + return self.__repr__() + + def __repr__(self): + if not self.message: + self.message = '' + retVal = "" + self.exceptionClass + " exception thrown: " + self.message + "\n" + for element in self.stackTrace: + retVal += "\tat " + str(element) + "\n" + + if self.wrapper: + retVal += "Caused by: " + self.wrapper.__repr__() + return retVal + + def getStackTrace(self): + return self.stackTrace + + def setStackTrace(self, stackTrace): + self.stackTrace = stackTrace + + def getMessage(self): + return self.message + + def setMessage(self, message): + self.message = message + + def getExceptionClass(self): + return self.exceptionClass + + def setExceptionClass(self, exceptionClass): + self.exceptionClass = exceptionClass + + def getWrapper(self): + return self.wrapper + + def setWrapper(self, wrapper): + self.wrapper = wrapper + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/serialization/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/serialization/__init__.py new file mode 100644 index 0000000..5e3214d --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/serialization/__init__.py @@ -0,0 +1,24 @@ +## +## + + +# +# Package definition for com.raytheon.uf.common.serialization +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/21/10 njensen Initial Creation. +# +# +# + + +__all__ = [ + 'comm', + 'SerializableExceptionWrapper' + ] + +from SerializableExceptionWrapper import SerializableExceptionWrapper diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/serialization/comm/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/serialization/comm/__init__.py new file mode 100644 index 0000000..4c582dd --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/serialization/comm/__init__.py @@ -0,0 +1,22 @@ +## +## + + +# +# Package definition for com.raytheon.uf.common.serialization.comm +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/21/10 njensen Initial Creation. +# +# +# + + +__all__ = [ + 'response' + ] + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/serialization/comm/response/ServerErrorResponse.py b/dynamicserialize/dstypes/com/raytheon/uf/common/serialization/comm/response/ServerErrorResponse.py new file mode 100644 index 0000000..6b5a892 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/serialization/comm/response/ServerErrorResponse.py @@ -0,0 +1,14 @@ + +# File auto-generated against equivalent DynamicSerialize Java class + +class ServerErrorResponse(object): + + def __init__(self): + self.exception = None + + def getException(self): + return self.exception + + def setException(self, exception): + self.exception = exception + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/serialization/comm/response/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/serialization/comm/response/__init__.py new file mode 100644 index 0000000..4481204 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/serialization/comm/response/__init__.py @@ -0,0 +1,23 @@ +## +## + + +# +# Package definition for com.raytheon.uf.common.serialization.comm.response +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/21/10 njensen Initial Creation. +# +# +# + + +__all__ = [ + 'ServerErrorResponse' + ] + +from ServerErrorResponse import ServerErrorResponse diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/site/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/site/__init__.py new file mode 100644 index 0000000..a71d5f5 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/site/__init__.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'notify', + 'requests' + ] + + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/site/notify/ClusterActivationNotification.py b/dynamicserialize/dstypes/com/raytheon/uf/common/site/notify/ClusterActivationNotification.py new file mode 100644 index 0000000..a88de39 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/site/notify/ClusterActivationNotification.py @@ -0,0 +1,41 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/10/14 #3623 randerso Manually created, do not regenerate +# +## + +# File auto-generated against equivalent DynamicSerialize Java class +from SiteActivationNotification import SiteActivationNotification +class ClusterActivationNotification(SiteActivationNotification): + + def __init__(self): + self.clusterActive = False + SiteActivationNotification.__init__(self) + + def isClusterActive(self): + return self.clusterActive + + def setClusterActive(self, clusterActive): + self.clusterActive = clusterActive + + def __str__(self): + s = self.modifiedSite + + if self.type == 'ACTIVATE': + if self.status == 'FAILURE': + s += " has failed to activate on some or all cluster members. See logs for details" + else: + s += " has been successfully activated on all cluster members" + + else: + if self.status == 'FAILURE': + s += " has failed to deactivate on some or all cluster members. See logs for details" + else: + s += " has been successfully deactivated on all cluster members" + + return s \ No newline at end of file diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/site/notify/SiteActivationNotification.py b/dynamicserialize/dstypes/com/raytheon/uf/common/site/notify/SiteActivationNotification.py new file mode 100644 index 0000000..066c4b2 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/site/notify/SiteActivationNotification.py @@ -0,0 +1,71 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/10/14 #3623 randerso Manually created, do not regenerate +# +## + +class SiteActivationNotification(object): + + def __init__(self): + self.type = None + self.status = None + self.primarySite = None + self.modifiedSite = None + self.runMode = None + self.serverName = None + self.pluginName = None + + def getType(self): + return self.type + + def setType(self, type): + self.type = type + + def getStatus(self): + return self.status + + def setStatus(self, status): + self.status = status + + def getPrimarySite(self): + return self.primarysite + + def setPrimarySite(self, primarysite): + self.primarysite = primarysite + + def getModifiedSite(self): + return self.modifiedSite + + def setModifiedSite(self, modifiedSite): + self.modifiedSite = modifiedSite + + def getRunMode(self): + return self.runMode + + def setRunMode(self, runMode): + self.runMode = runMode + + def getServerName(self): + return self.serverName + + def setServerName(self, serverName): + self.serverName = serverName + + def getPluginName(self): + return self.pluginName + + def setPluginName(self, pluginName): + self.pluginName = pluginName + + def __str__(self): + return self.pluginName.upper() + ":" \ + + self.status + ":" \ + + self.type + " " \ + + self.modifiedSite.upper() + " on " \ + + self.serverName + ":" \ + + self.runMode diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/site/notify/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/site/notify/__init__.py new file mode 100644 index 0000000..dc246a2 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/site/notify/__init__.py @@ -0,0 +1,13 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'ClusterActivationNotification', + 'SiteActivationNotification' + ] + +from ClusterActivationNotification import ClusterActivationNotification +from SiteActivationNotification import SiteActivationNotification + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/site/requests/ActivateSiteRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/site/requests/ActivateSiteRequest.py new file mode 100644 index 0000000..fb9fefc --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/site/requests/ActivateSiteRequest.py @@ -0,0 +1,28 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/10/14 #3623 randerso Manually created, do not regenerate +# +## + +class ActivateSiteRequest(object): + + def __init__(self, siteID=None, plugin=None): + self.siteID = siteID + self.plugin = plugin + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + + def getPlugin(self): + return self.plugin + + def setPlugin(self, plugin): + self.plugin = plugin diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/site/requests/DeactivateSiteRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/site/requests/DeactivateSiteRequest.py new file mode 100644 index 0000000..f7da706 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/site/requests/DeactivateSiteRequest.py @@ -0,0 +1,28 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/10/14 #3623 randerso Manually created, do not regenerate +# +## + +class DeactivateSiteRequest(object): + + def __init__(self, siteID=None, plugin=None): + self.siteID = siteID + self.plugin = plugin + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + + def getPlugin(self): + return self.plugin + + def setPlugin(self, plugin): + self.plugin = plugin diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/site/requests/GetActiveSitesRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/site/requests/GetActiveSitesRequest.py new file mode 100644 index 0000000..4b38059 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/site/requests/GetActiveSitesRequest.py @@ -0,0 +1,10 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GetActiveSitesRequest(object): + + def __init__(self): + super(GetActiveSitesRequest, self).__init__() + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/site/requests/GetPrimarySiteRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/site/requests/GetPrimarySiteRequest.py new file mode 100644 index 0000000..413b560 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/site/requests/GetPrimarySiteRequest.py @@ -0,0 +1,10 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class GetPrimarySiteRequest(object): + + def __init__(self): + super(GetPrimarySiteRequest, self).__init__() + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/site/requests/ValidateConfigRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/site/requests/ValidateConfigRequest.py new file mode 100644 index 0000000..b6def62 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/site/requests/ValidateConfigRequest.py @@ -0,0 +1,30 @@ +## +## +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/12/16 #5888 dgilling Initial creation. +# +## + +### This class was manually created. DO NOT AUTOGENERATE ### + +class ValidateConfigRequest(object): + + def __init__(self, siteID=None, plugin=None): + self.siteID = siteID + self.plugin = plugin + + def getSiteID(self): + return self.siteID + + def setSiteID(self, siteID): + self.siteID = siteID + + def getPlugin(self): + return self.plugin + + def setPlugin(self, plugin): + self.plugin = plugin diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/site/requests/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/site/requests/__init__.py new file mode 100644 index 0000000..61c8725 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/site/requests/__init__.py @@ -0,0 +1,19 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'ActivateSiteRequest', + 'DeactivateSiteRequest', + 'GetActiveSitesRequest', + 'GetPrimarySiteRequest', + 'ValidateConfigRequest' + ] + +from ActivateSiteRequest import ActivateSiteRequest +from DeactivateSiteRequest import DeactivateSiteRequest +from GetActiveSitesRequest import GetActiveSitesRequest +from GetPrimarySiteRequest import GetPrimarySiteRequest +from ValidateConfigRequest import ValidateConfigRequest + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/time/CommutativeTimestamp.py b/dynamicserialize/dstypes/com/raytheon/uf/common/time/CommutativeTimestamp.py new file mode 100644 index 0000000..bc5f830 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/time/CommutativeTimestamp.py @@ -0,0 +1,20 @@ +## +## +# ---------------------------------------------------------------------------- +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 06/23/2016 #5696 rjpeter Initial creation. +# +## + +from time import gmtime, strftime +from dynamicserialize.dstypes.java.sql import Timestamp + +class CommutativeTimestamp(Timestamp): + + def __init__(self, timeInMillis=None): + super(CommutativeTimestamp, self).__init__(timeInMillis) + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/time/DataTime.py b/dynamicserialize/dstypes/com/raytheon/uf/common/time/DataTime.py new file mode 100644 index 0000000..1ffde0f --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/time/DataTime.py @@ -0,0 +1,272 @@ +# # +# # + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to add additional features to better +# match Java implementation. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# ??/??/?? xxxxxxxx Initial Creation. +# 05/28/13 2023 dgilling Implement __str__(). +# 01/22/14 2667 bclement preserved milliseconds in string representation +# 03/03/14 2673 bsteffen allow construction using a Date for refTime +# 06/24/14 3096 mnash implement __cmp__ +# 06/24/15 4480 dgilling implement __hash__ and __eq__, +# replace __cmp__ with rich comparison +# operators. +# 05/26/16 2416 rjpeter Added str based constructor. +# 08/02/16 2416 tgurney Forecast time regex bug fix, +# plus misc cleanup + + +import calendar +import datetime +import numpy +import re +import StringIO +import time + +from dynamicserialize.dstypes.java.util import Date +from dynamicserialize.dstypes.java.util import EnumSet + +from TimeRange import TimeRange + +_DATE=r'(\d{4}-\d{2}-\d{2})' +_TIME=r'(\d{2}:\d{2}:\d{2})' +_MILLIS='(?:\.(\d{1,3})(?:\d{1,4})?)?' # might have microsecond but that is thrown out +REFTIME_PATTERN_STR=_DATE + '[ _]' + _TIME + _MILLIS +FORECAST_PATTERN_STR=r'(?:[ _]\((\d+)(?::(\d{1,2}))?\))?' +VALID_PERIOD_PATTERN_STR=r'(?:\['+ REFTIME_PATTERN_STR + '--' + REFTIME_PATTERN_STR + r'\])?' +STR_PATTERN=re.compile(REFTIME_PATTERN_STR + FORECAST_PATTERN_STR + VALID_PERIOD_PATTERN_STR) + +class DataTime(object): + + def __init__(self, refTime=None, fcstTime=None, validPeriod=None): + """ + Construct a new DataTime. + May also be called as DataTime(str) to parse a string and create a + DataTime from it. Some examples of valid DataTime strings: + + '2016-08-02 01:23:45.0' + '2016-08-02 01:23:45.123' + '2016-08-02 01:23:45.0 (17)', + '2016-08-02 01:23:45.0 (17:34)' + '2016-08-02 01:23:45.0[2016-08-02_02:34:45.0--2016-08-02_03:45:56.0]' + '2016-08-02 01:23:45.456_(17:34)[2016-08-02_02:34:45.0--2016-08-02_03:45:56.0]' + """ + if fcstTime is not None: + self.fcstTime = int(fcstTime) + else: + self.fcstTime = 0 + self.refTime = refTime + if validPeriod is not None and type(validPeriod) is not TimeRange: + raise ValueError("Invalid validPeriod object specified for DataTime.") + self.validPeriod = validPeriod + self.utilityFlags = EnumSet('com.raytheon.uf.common.time.DataTime$FLAG') + self.levelValue = numpy.float64(-1.0) + + if self.refTime is not None: + if isinstance(self.refTime, datetime.datetime): + self.refTime = long(calendar.timegm(self.refTime.utctimetuple()) * 1000) + elif isinstance(self.refTime, time.struct_time): + self.refTime = long(calendar.timegm(self.refTime) * 1000) + elif hasattr(self.refTime, 'getTime'): + # getTime should be returning ms, there is no way to check this + # This is expected for java Date + self.refTime = long(self.refTime.getTime()) + else: + try: + self.refTime = long(self.refTime) + except ValueError: + # Assume first arg is a string. Attempt to parse. + match = STR_PATTERN.match(self.refTime) + if match is None: + raise ValueError('Could not parse DataTime info from ' + + str(refTime)) + + groups = match.groups() + rDate = groups[0] + rTime = groups[1] + rMillis = groups[2] or 0 + fcstTimeHr = groups[3] + fcstTimeMin = groups[4] + periodStart = groups[5], groups[6], (groups[7] or 0) + periodEnd = groups[8], groups[9], (groups[10] or 0) + self.refTime = self._getTimeAsEpochMillis(rDate, rTime, rMillis) + + if fcstTimeHr is not None: + self.fcstTime = long(fcstTimeHr) * 3600 + if fcstTimeMin is not None: + self.fcstTime += long(fcstTimeMin) * 60 + + if periodStart[0] is not None: + self.validPeriod = TimeRange() + periodStartTime = self._getTimeAsEpochMillis(*periodStart) + self.validPeriod.setStart(periodStartTime / 1000) + periodEndTime = self._getTimeAsEpochMillis(*periodEnd) + self.validPeriod.setEnd(periodEndTime / 1000) + + self.refTime = Date(self.refTime) + + if self.validPeriod is None: + validTimeMillis = self.refTime.getTime() + long(self.fcstTime * 1000) + self.validPeriod = TimeRange() + self.validPeriod.setStart(validTimeMillis / 1000) + self.validPeriod.setEnd(validTimeMillis / 1000) + + # figure out utility flags + if self.fcstTime: + self.utilityFlags.add("FCST_USED") + if self.validPeriod and self.validPeriod.isValid(): + self.utilityFlags.add("PERIOD_USED") + + def getRefTime(self): + return self.refTime + + def setRefTime(self, refTime): + self.refTime = refTime + + def getFcstTime(self): + return self.fcstTime + + def setFcstTime(self, fcstTime): + self.fcstTime = fcstTime + + def getValidPeriod(self): + return self.validPeriod + + def setValidPeriod(self, validPeriod): + self.validPeriod = validPeriod + + def getUtilityFlags(self): + return self.utilityFlags + + def setUtilityFlags(self, utilityFlags): + self.utilityFlags = utilityFlags + + def getLevelValue(self): + return self.levelValue + + def setLevelValue(self, levelValue): + self.levelValue = numpy.float64(levelValue) + + def __str__(self): + buffer = StringIO.StringIO() + + if self.refTime is not None: + refTimeInSecs = self.refTime.getTime() / 1000 + micros = (self.refTime.getTime() % 1000) * 1000 + dtObj = datetime.datetime.utcfromtimestamp(refTimeInSecs) + dtObj = dtObj.replace(microsecond=micros) + # This won't be compatible with java or string from java since its to microsecond + buffer.write(dtObj.isoformat(' ')) + + if "FCST_USED" in self.utilityFlags: + hrs = int(self.fcstTime / 3600) + mins = int((self.fcstTime - (hrs * 3600)) / 60) + buffer.write(" (" + str(hrs)) + if mins != 0: + buffer.write(":" + str(mins)) + buffer.write(")") + + if "PERIOD_USED" in self.utilityFlags: + buffer.write("[") + buffer.write(self.validPeriod.start.isoformat(' ')) + buffer.write("--") + buffer.write(self.validPeriod.end.isoformat(' ')) + buffer.write("]") + + strVal = buffer.getvalue() + buffer.close() + return strVal + + def __repr__(self): + return "" + + def __hash__(self): + hashCode = hash(self.refTime) ^ hash(self.fcstTime) + if self.validPeriod is not None and self.validPeriod.isValid(): + hashCode ^= hash(self.validPeriod.getStart()) + hashCode ^= hash(self.validPeriod.getEnd()) + hashCode ^= hash(self.levelValue) + return hashCode + + def __eq__(self, other): + if type(self) != type(other): + return False + + if other.getRefTime() is None: + return self.fcstTime == other.fcstTime + + dataTime1 = (self.refTime, self.fcstTime, self.validPeriod, self.levelValue) + dataTime2 = (other.refTime, other.fcstTime, other.validPeriod, other.levelValue) + return dataTime1 == dataTime2 + + def __ne__(self, other): + return not self.__eq__(other) + + def __lt__(self, other): + if type(self) != type(other): + return NotImplemented + + myValidTime = self.getRefTime().getTime() + self.getFcstTime() + otherValidTime = other.getRefTime().getTime() + other.getFcstTime() + if myValidTime < otherValidTime: + return True + + if self.fcstTime < other.fcstTime: + return True + + if self.levelValue < other.levelValue: + return True + + myValidPeriod = self.validPeriod + otherValidPeriod = other.validPeriod + if myValidPeriod != otherValidPeriod: + if myValidPeriod.duration() < otherValidPeriod.duration(): + return True + return myValidPeriod.getStartInMillis() < otherValidPeriod.getStartInMillis() + return False + + def __le__(self, other): + if type(self) != type(other): + return NotImplemented + + return self.__lt__(other) or self.__eq__(other) + + def __gt__(self, other): + if type(self) != type(other): + return NotImplemented + + myValidTime = self.getRefTime().getTime() + self.getFcstTime() + otherValidTime = other.getRefTime().getTime() + other.getFcstTime() + if myValidTime > otherValidTime: + return True + + if self.fcstTime > other.fcstTime: + return True + + if self.levelValue > other.levelValue: + return True + + myValidPeriod = self.validPeriod + otherValidPeriod = other.validPeriod + if myValidPeriod != otherValidPeriod: + if myValidPeriod.duration() > otherValidPeriod.duration(): + return True + return myValidPeriod.getStartInMillis() > otherValidPeriod.getStartInMillis() + return False + + def __ge__(self, other): + if type(self) != type(other): + return NotImplemented + + return self.__gt__(other) or self.__eq__(other) + + def _getTimeAsEpochMillis(self, dateStr, timeStr, millis): + t = time.strptime(dateStr + ' ' + timeStr, '%Y-%m-%d %H:%M:%S') + epochSeconds = calendar.timegm(t) + return long(epochSeconds * 1000) + long(millis) diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/time/FormattedDate.py b/dynamicserialize/dstypes/com/raytheon/uf/common/time/FormattedDate.py new file mode 100644 index 0000000..8070905 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/time/FormattedDate.py @@ -0,0 +1,19 @@ +## +## +# ---------------------------------------------------------------------------- +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/21/2015 4486 rjpeter Initial creation. +# 06/23/2016 #5696 rjpeter Extend CommutativeTimestamp +## + +from CommutativeTimestamp import CommutativeTimestamp + +# TODO: Remove after 16.4.1 no longer in field +class FormattedDate(CommutativeTimestamp): + + def __init__(self, timeInMillis=None): + super(FormattedDate, self).__init__(timeInMillis) diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/time/TimeRange.py b/dynamicserialize/dstypes/com/raytheon/uf/common/time/TimeRange.py new file mode 100644 index 0000000..8aab7cb --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/time/TimeRange.py @@ -0,0 +1,144 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class. Then modified to add functionality +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# ??/??/?? xxxxxxxx Initial Creation. +# 01/22/14 2667 bclement fixed millisecond support +# 02/28/14 2667 bclement constructor can take extra micros for start and end +# 06/24/15 4480 dgilling fix __eq__. +# +# +# + +import calendar +import datetime +import time + +MAX_TIME = 2147483647 +MICROS_IN_SECOND = 1000000 + +class TimeRange(object): + def __init__(self, start=None, end=None, startExtraMicros=None, endExtraMicros=None): + self.start = self.__convertToDateTimeWithExtra(start, startExtraMicros) + self.end = self.__convertToDateTimeWithExtra(end, endExtraMicros) + + def __str__(self): + return self.__repr__() + + def __repr__(self): + return "(" + self.start.strftime("%b %d %y %H:%M:%S %Z") + ", " + self.end.strftime("%b %d %y %H:%M:%S %Z") + ")" + + def __eq__(self, other): + if type(self) != type(other): + return False + + if self.isValid() and other.isValid(): + return self.getStart() == other.getStart() and self.getEnd() == other.getEnd() + elif not self.isValid() and not other.isValid(): + return True + else: + return False + + def __ne__(self, other): + return (not self.__eq__(other)) + + def __convertToDateTimeWithExtra(self, timeArg, extraMicros): + rval = self.__convertToDateTime(timeArg) + if rval is not None and extraMicros is not None: + rval = rval + datetime.timedelta(microseconds=extraMicros) + return rval + + def __convertToDateTime(self, timeArg): + if timeArg is None: + return None + if isinstance(timeArg, datetime.datetime): + return timeArg + elif isinstance(timeArg, time.struct_time): + return datetime.datetime(*timeArg[:6]) + elif isinstance(timeArg, float): + # seconds as float, should be avoided due to floating point errors + totalSecs = long(timeArg) + micros = int((timeArg - totalSecs) * MICROS_IN_SECOND) + return self.__convertSecsAndMicros(totalSecs, micros) + elif isinstance(timeArg, (int, long)): + # seconds as integer + totalSecs = timeArg + return self.__convertSecsAndMicros(totalSecs, 0) + else: + return None + + def __convertSecsAndMicros(self, seconds, micros): + if seconds < MAX_TIME: + rval = datetime.datetime.utcfromtimestamp(seconds) + else: + extraTime = datetime.timedelta(seconds=(seconds - MAX_TIME)) + rval = datetime.datetime.utcfromtimestamp(MAX_TIME) + extraTime + return rval.replace(microsecond=micros) + + def getStart(self): + return self.start.utctimetuple() + + def getStartInMillis(self): + return self._getInMillis(self.start) + + def setStart(self, start, extraMicros=None): + self.start = self.__convertToDateTimeWithExtra(start, extraMicros) + + def getEnd(self): + return self.end.utctimetuple() + + def getEndInMillis(self): + return self._getInMillis(self.end) + + def _getInMillis(self, time): + rval = long(calendar.timegm(time.utctimetuple()) * 1000) + rval += time.microsecond // 1000 + return rval + + def setEnd(self, end, extraMicros=None): + self.end = self.__convertToDateTimeWithExtra(end, extraMicros) + + def duration(self): + delta = self.end - self.start + return long(delta.total_seconds()) + + def contains(self, timeArg): + if isinstance(timeArg, TimeRange): + if self.duration() == 0: + return self.__eq__(timeArg) + elif timeArg.duration() == 0: + return self.contains(timeArg.start) + return (timeArg.start >= self.start and timeArg.end <= self.end) + else: + convTime = self.__convertToDateTime(timeArg) + if type(convTime) is not datetime.datetime: + raise TypeError("Invalid type for argument time specified to TimeRange.contains().") + if self.duration() != 0: + return (convTime >= self.start and convTime < self.end) + return convTime == self.start + + def isValid(self): + return bool(self.start != self.end) + + def overlaps(self, timeRange): + return (timeRange.contains(self.start) or self.contains(timeRange.start)) + + def combineWith(self, timeRange): + if self.isValid() and timeRange.isValid(): + newStart = min(self.start, timeRange.start) + newEnd = max(self.end, timeRange.end) + return TimeRange(newStart, newEnd) + elif self.isValid(): + return self + + return timeRange + + @staticmethod + def allTimes(): + return TimeRange(0, MAX_TIME) diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/time/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/time/__init__.py new file mode 100644 index 0000000..5862608 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/time/__init__.py @@ -0,0 +1,17 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'CommutativeTimestamp', + 'DataTime', + 'FormattedDate', + 'TimeRange' + ] + +from DataTime import DataTime +from TimeRange import TimeRange +from FormattedDate import FormattedDate +from CommutativeTimestamp import CommutativeTimestamp + diff --git a/dynamicserialize/dstypes/com/vividsolutions/__init__.py b/dynamicserialize/dstypes/com/vividsolutions/__init__.py new file mode 100644 index 0000000..66a9259 --- /dev/null +++ b/dynamicserialize/dstypes/com/vividsolutions/__init__.py @@ -0,0 +1,10 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'jts' + ] + + diff --git a/dynamicserialize/dstypes/com/vividsolutions/jts/__init__.py b/dynamicserialize/dstypes/com/vividsolutions/jts/__init__.py new file mode 100644 index 0000000..215b186 --- /dev/null +++ b/dynamicserialize/dstypes/com/vividsolutions/jts/__init__.py @@ -0,0 +1,10 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'geom' + ] + + diff --git a/dynamicserialize/dstypes/com/vividsolutions/jts/geom/Coordinate.py b/dynamicserialize/dstypes/com/vividsolutions/jts/geom/Coordinate.py new file mode 100644 index 0000000..d1210db --- /dev/null +++ b/dynamicserialize/dstypes/com/vividsolutions/jts/geom/Coordinate.py @@ -0,0 +1,29 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class Coordinate(object): + + def __init__(self, x=None, y=None): + self.x = x + self.y = y + + def getX(self): + return self.x + + def getY(self): + return self.y + + def setX(self, x): + self.x = x + + def setY(self, y): + self.y = y + + def __str__(self): + return str((self.x, self.y)) + + def __repr__(self): + return self.__str__() + diff --git a/dynamicserialize/dstypes/com/vividsolutions/jts/geom/Envelope.py b/dynamicserialize/dstypes/com/vividsolutions/jts/geom/Envelope.py new file mode 100644 index 0000000..473bccf --- /dev/null +++ b/dynamicserialize/dstypes/com/vividsolutions/jts/geom/Envelope.py @@ -0,0 +1,51 @@ +## +## + +# This class is a dummy implementation of the +# com.vividsolutions.jts.geom.Envelope class. It was simply created to allow +# serialization/deserialization of IDataRequest objects from the Data Access +# Framework. This should be re-implemented if useful work needs to be +# performed against serialized Envelope objects. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 05/29/13 2023 dgilling Initial Creation. +# +# + +class Envelope(object): + + def __init__(self, env=None): + self.maxx = -1.0 + self.maxy = -1.0 + self.minx = 0.0 + self.miny = 0.0 + if env is not None: + (self.minx, self.miny, self.maxx, self.maxy) = env.bounds + + def getMaxX(self): + return self.maxx + + def getMaxY(self): + return self.maxy + + def getMinX(self): + return self.minx + + def getMinY(self): + return self.miny + + def setMaxX(self, value): + self.maxx = value + + def setMaxY(self, value): + self.maxy = value + + def setMinX(self, value): + self.minx = value + + def setMinY(self, value): + self.miny = value + diff --git a/dynamicserialize/dstypes/com/vividsolutions/jts/geom/Geometry.py b/dynamicserialize/dstypes/com/vividsolutions/jts/geom/Geometry.py new file mode 100644 index 0000000..fd49365 --- /dev/null +++ b/dynamicserialize/dstypes/com/vividsolutions/jts/geom/Geometry.py @@ -0,0 +1,20 @@ +## +## + +# This class is a dummy implementation of the +# com.vividsolutions.jts.geom.Geometry class. It was simply created to allow +# serialization/deserialization of GridLocation objects. This should be +# reimplemented if useful work needs to be performed against serialized +# Geometry objects. + +class Geometry(object): + + def __init__(self): + self.binaryData = None + + def getBinaryData(self): + return self.binaryData + + def setBinaryData(self, data): + self.binaryData = data + diff --git a/dynamicserialize/dstypes/com/vividsolutions/jts/geom/__init__.py b/dynamicserialize/dstypes/com/vividsolutions/jts/geom/__init__.py new file mode 100644 index 0000000..46e0e5d --- /dev/null +++ b/dynamicserialize/dstypes/com/vividsolutions/jts/geom/__init__.py @@ -0,0 +1,15 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'Coordinate', + 'Envelope', + 'Geometry' + ] + +from Coordinate import Coordinate +from Envelope import Envelope +from Geometry import Geometry + diff --git a/dynamicserialize/dstypes/gov/__init__.py b/dynamicserialize/dstypes/gov/__init__.py new file mode 100644 index 0000000..c19855a --- /dev/null +++ b/dynamicserialize/dstypes/gov/__init__.py @@ -0,0 +1,8 @@ + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'noaa' + ] + + diff --git a/dynamicserialize/dstypes/gov/noaa/__init__.py b/dynamicserialize/dstypes/gov/noaa/__init__.py new file mode 100644 index 0000000..47f9c4d --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/__init__.py @@ -0,0 +1,8 @@ + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'nws' + ] + + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/__init__.py b/dynamicserialize/dstypes/gov/noaa/nws/__init__.py new file mode 100644 index 0000000..925c05f --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/__init__.py @@ -0,0 +1,8 @@ + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'ncep' + ] + + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/__init__.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/__init__.py new file mode 100644 index 0000000..3a99cc7 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/__init__.py @@ -0,0 +1,8 @@ + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'common' + ] + + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/__init__.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/__init__.py new file mode 100644 index 0000000..c28322f --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/__init__.py @@ -0,0 +1,8 @@ + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'dataplugin' + ] + + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/__init__.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/__init__.py new file mode 100644 index 0000000..adba431 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/__init__.py @@ -0,0 +1,11 @@ + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'atcf', + 'gempak', + 'gpd', + 'pgen' + ] + + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/atcf/__init__.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/atcf/__init__.py new file mode 100644 index 0000000..a944c4e --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/atcf/__init__.py @@ -0,0 +1,8 @@ + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'request' + ] + + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/atcf/request/RetrieveAtcfDeckRequest.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/atcf/request/RetrieveAtcfDeckRequest.py new file mode 100644 index 0000000..9fdc815 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/atcf/request/RetrieveAtcfDeckRequest.py @@ -0,0 +1,14 @@ + +# File auto-generated against equivalent DynamicSerialize Java class + +class RetrieveAtcfDeckRequest(object): + + def __init__(self): + self.deckID = None + + def getDeckID(self): + return self.deckID + + def setDeckID(self, deckID): + self.deckID = deckID + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/atcf/request/__init__.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/atcf/request/__init__.py new file mode 100644 index 0000000..1d83f34 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/atcf/request/__init__.py @@ -0,0 +1,9 @@ + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'RetrieveAtcfDeckRequest' + ] + +from RetrieveAtcfDeckRequest import RetrieveAtcfDeckRequest + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/__init__.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/__init__.py new file mode 100644 index 0000000..a944c4e --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/__init__.py @@ -0,0 +1,8 @@ + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'request' + ] + + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/GetGridDataRequest.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/GetGridDataRequest.py new file mode 100644 index 0000000..9d72ee0 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/GetGridDataRequest.py @@ -0,0 +1,69 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Sep 16, 2016 pmoyer Generated + +class GetGridDataRequest(object): + + def __init__(self): + self.vcoord = None + self.level2 = None + self.modelId = None + self.parm = None + self.level1 = None + self.reftime = None + self.pluginName = None + self.fcstsec = None + + def getVcoord(self): + return self.vcoord + + def setVcoord(self, vcoord): + self.vcoord = vcoord + + def getLevel2(self): + return self.level2 + + def setLevel2(self, level2): + self.level2 = level2 + + def getModelId(self): + return self.modelId + + def setModelId(self, modelId): + self.modelId = modelId + + def getParm(self): + return self.parm + + def setParm(self, parm): + self.parm = parm + + def getLevel1(self): + return self.level1 + + def setLevel1(self, level1): + self.level1 = level1 + + def getReftime(self): + return self.reftime + + def setReftime(self, reftime): + self.reftime = reftime + + def getPluginName(self): + return self.pluginName + + def setPluginName(self, pluginName): + self.pluginName = pluginName + + def getFcstsec(self): + return self.fcstsec + + def setFcstsec(self, fcstsec): + self.fcstsec = fcstsec + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/GetGridInfoRequest.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/GetGridInfoRequest.py new file mode 100644 index 0000000..4d8a426 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/GetGridInfoRequest.py @@ -0,0 +1,41 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Sep 16, 2016 pmoyer Generated + +class GetGridInfoRequest(object): + + def __init__(self): + self.modelId = None + self.reftime = None + self.pluginName = None + self.fcstsec = None + + def getModelId(self): + return self.modelId + + def setModelId(self, modelId): + self.modelId = modelId + + def getReftime(self): + return self.reftime + + def setReftime(self, reftime): + self.reftime = reftime + + def getPluginName(self): + return self.pluginName + + def setPluginName(self, pluginName): + self.pluginName = pluginName + + def getFcstsec(self): + return self.fcstsec + + def setFcstsec(self, fcstsec): + self.fcstsec = fcstsec + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/GetGridNavRequest.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/GetGridNavRequest.py new file mode 100644 index 0000000..c64776d --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/GetGridNavRequest.py @@ -0,0 +1,27 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Sep 16, 2016 pmoyer Generated + +class GetGridNavRequest(object): + + def __init__(self): + self.modelId = None + self.pluginName = None + + def getModelId(self): + return self.modelId + + def setModelId(self, modelId): + self.modelId = modelId + + def getPluginName(self): + return self.pluginName + + def setPluginName(self, pluginName): + self.pluginName = pluginName + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/GetStationsRequest.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/GetStationsRequest.py new file mode 100644 index 0000000..cc5cd51 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/GetStationsRequest.py @@ -0,0 +1,20 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Sep 16, 2016 pmoyer Generated + +class GetStationsRequest(object): + + def __init__(self): + self.pluginName = None + + def getPluginName(self): + return self.pluginName + + def setPluginName(self, pluginName): + self.pluginName = pluginName + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/GetTimesRequest.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/GetTimesRequest.py new file mode 100644 index 0000000..11d9cab --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/GetTimesRequest.py @@ -0,0 +1,27 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Sep 16, 2016 pmoyer Generated + +class GetTimesRequest(object): + + def __init__(self): + self.pluginName = None + self.timeField = None + + def getPluginName(self): + return self.pluginName + + def setPluginName(self, pluginName): + self.pluginName = pluginName + + def getTimeField(self): + return self.timeField + + def setTimeField(self, timeField): + self.timeField = timeField + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/GetTimesResponse.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/GetTimesResponse.py new file mode 100644 index 0000000..d22ca67 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/GetTimesResponse.py @@ -0,0 +1,20 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Sep 16, 2016 pmoyer Generated + +class GetTimesResponse(object): + + def __init__(self): + self.times = None + + def getTimes(self): + return self.times + + def setTimes(self, times): + self.times = times + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/Station.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/Station.py new file mode 100644 index 0000000..8959f2b --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/Station.py @@ -0,0 +1,64 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Sep 16, 2016 pmoyer Generated + +import numpy + +class Station(object): + + def __init__(self): + self.elevation = None + self.state = None + self.stationId = None + self.longitude = None + self.latitude = None + self.wmoIndex = None + self.country = None + + def getElevation(self): + return self.elevation + + def setElevation(self, elevation): + self.elevation = elevation + + def getState(self): + return self.state + + def setState(self, state): + self.state = state + + def getStationId(self): + return self.stationId + + def setStationId(self, stationId): + self.stationId = stationId + + def getLongitude(self): + return self.longitude + + def setLongitude(self, longitude): + self.longitude = numpy.float64(longitude) + + def getLatitude(self): + return self.latitude + + def setLatitude(self, latitude): + self.latitude = numpy.float64(latitude) + + def getWmoIndex(self): + return self.wmoIndex + + def setWmoIndex(self, wmoIndex): + self.wmoIndex = wmoIndex + + def getCountry(self): + return self.country + + def setCountry(self, country): + self.country = country + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/StationDataRequest.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/StationDataRequest.py new file mode 100644 index 0000000..5244b95 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/StationDataRequest.py @@ -0,0 +1,48 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Sep 16, 2016 pmoyer Generated + +class StationDataRequest(object): + + def __init__(self): + self.refTime = None + self.pluginName = None + self.parmList = None + self.stationId = None + self.partNumber = None + + def getRefTime(self): + return self.refTime + + def setRefTime(self, refTime): + self.refTime = refTime + + def getPluginName(self): + return self.pluginName + + def setPluginName(self, pluginName): + self.pluginName = pluginName + + def getParmList(self): + return self.parmList + + def setParmList(self, parmList): + self.parmList = parmList + + def getStationId(self): + return self.stationId + + def setStationId(self, stationId): + self.stationId = stationId + + def getPartNumber(self): + return self.partNumber + + def setPartNumber(self, partNumber): + self.partNumber = partNumber + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/SurfaceDataRequest.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/SurfaceDataRequest.py new file mode 100644 index 0000000..a13efdb --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/SurfaceDataRequest.py @@ -0,0 +1,48 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Sep 16, 2016 pmoyer Generated + +class SurfaceDataRequest(object): + + def __init__(self): + self.refTime = None + self.pluginName = None + self.parmList = None + self.stationId = None + self.partNumber = None + + def getRefTime(self): + return self.refTime + + def setRefTime(self, refTime): + self.refTime = refTime + + def getPluginName(self): + return self.pluginName + + def setPluginName(self, pluginName): + self.pluginName = pluginName + + def getParmList(self): + return self.parmList + + def setParmList(self, parmList): + self.parmList = parmList + + def getStationId(self): + return self.stationId + + def setStationId(self, stationId): + self.stationId = stationId + + def getPartNumber(self): + return self.partNumber + + def setPartNumber(self, partNumber): + self.partNumber = partNumber + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/UpperAirDataRequest.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/UpperAirDataRequest.py new file mode 100644 index 0000000..bf247ca --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/UpperAirDataRequest.py @@ -0,0 +1,48 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Sep 16, 2016 pmoyer Generated + +class UpperAirDataRequest(object): + + def __init__(self): + self.refTime = None + self.pluginName = None + self.parmList = None + self.stationId = None + self.partNumber = None + + def getRefTime(self): + return self.refTime + + def setRefTime(self, refTime): + self.refTime = refTime + + def getPluginName(self): + return self.pluginName + + def setPluginName(self, pluginName): + self.pluginName = pluginName + + def getParmList(self): + return self.parmList + + def setParmList(self, parmList): + self.parmList = parmList + + def getStationId(self): + return self.stationId + + def setStationId(self, stationId): + self.stationId = stationId + + def getPartNumber(self): + return self.partNumber + + def setPartNumber(self, partNumber): + self.partNumber = partNumber + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/__init__.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/__init__.py new file mode 100644 index 0000000..9ef0c44 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gempak/request/__init__.py @@ -0,0 +1,27 @@ + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'GetGridDataRequest', + 'GetGridInfoRequest', + 'GetGridNavRequest', + 'GetStationsRequest', + 'GetTimesRequest', + 'GetTimesResponse', + 'Station', + 'StationDataRequest', + 'SurfaceDataRequest', + 'UpperAirDataRequest' + ] + +from GetGridDataRequest import GetGridDataRequest +from GetGridInfoRequest import GetGridInfoRequest +from GetGridNavRequest import GetGridNavRequest +from GetStationsRequest import GetStationsRequest +from GetTimesRequest import GetTimesRequest +from GetTimesResponse import GetTimesResponse +from Station import Station +from StationDataRequest import StationDataRequest +from SurfaceDataRequest import SurfaceDataRequest +from UpperAirDataRequest import UpperAirDataRequest + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gpd/__init__.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gpd/__init__.py new file mode 100644 index 0000000..2e70919 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gpd/__init__.py @@ -0,0 +1,6 @@ + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'query' + ] diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gpd/query/GenericPointDataReqMsg.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gpd/query/GenericPointDataReqMsg.py new file mode 100644 index 0000000..59e9729 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gpd/query/GenericPointDataReqMsg.py @@ -0,0 +1,83 @@ + +# File auto-generated against equivalent DynamicSerialize Java class + +class GenericPointDataReqMsg(object): + + def __init__(self): + self.reqType = None + self.refTime = None + self.productName = None + self.stnId = None + self.slat = None + self.slon = None + self.productVersion = None + self.querySpecifiedProductVersion = False + self.queryKey = None + self.gpdDataString = None + self.maxNumLevel = 1 + + def getReqType(self): + return self.reqType + + def setReqType(self, reqType): + self.reqType = reqType + + def getRefTime(self): + return self.refTime + + def setRefTime(self, refTime): + self.refTime = refTime + + def getProductName(self): + return self.productName + + def setProductName(self, productName): + self.productName = productName + + def getStnId(self): + return self.stnId + + def setStnId(self, stnId): + self.stnId = stnId + + def getSlat(self): + return self.slat + + def setSlat(self, slat): + self.slat = slat + + def getSlon(self): + return self.slon + + def setSlon(self, slon): + self.slon = slon + + def getMaxNumLevel(self): + return self.maxNumLevel + + def setMaxNumLevel(self, maxNumLevel): + self.maxNumLevel = maxNumLevel + + def getProductVersion(self): + return self.productVersion + + def setProductVersion(self, productVersion): + self.productVersion = productVersion + + def getQuerySpecifiedProductVersion(self): + return self.querySpecifiedProductVersion + + def setQuerySpecifiedProductVersion(self, querySpecifiedProductVersion): + self.querySpecifiedProductVersion = querySpecifiedProductVersion + + def getQueryKey(self): + return self.queryKey + + def setQueryKey(self, queryKey): + self.queryKey = queryKey + + def getGpdDataString(self): + return self.gpdDataString + + def setGpdDataString(self, gpdDataString): + self.gpdDataString = gpdDataString diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gpd/query/__init__.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gpd/query/__init__.py new file mode 100644 index 0000000..bfec6fc --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/gpd/query/__init__.py @@ -0,0 +1,8 @@ + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'GenericPointDataReqMsg' + ] + +from GenericPointDataReqMsg import GenericPointDataReqMsg diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/ActivityInfo.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/ActivityInfo.py new file mode 100644 index 0000000..8ce7a83 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/ActivityInfo.py @@ -0,0 +1,77 @@ + +# File auto-generated against equivalent DynamicSerialize Java class + +class ActivityInfo(object): + + def __init__(self): + self.activityName = None + self.activityType = None + self.activitySubtype = None + self.activityLabel = None + self.site = None + self.desk = None + self.forecaster = None + self.refTime = None + self.mode = None + self.status = None + + def getActivityName(self): + return self.activityName + + def setActivityName(self, activityName): + self.activityName = activityName + + def getActivityType(self): + return self.activityType + + def setActivityType(self, activityType): + self.activityType = activityType + + def getActivitySubtype(self): + return self.activitySubtype + + def setActivitySubtype(self, activitySubtype): + self.activitySubtype = activitySubtype + + def getActivityLabel(self): + return self.activityLabel + + def setActivityLabel(self, activityLabel): + self.activityLabel = activityLabel + + def getSite(self): + return self.site + + def setSite(self, site): + self.site = site + + def getDesk(self): + return self.desk + + def setDesk(self, desk): + self.desk = desk + + def getForecaster(self): + return self.forecaster + + def setForecaster(self, forecaster): + self.forecaster = forecaster + + def getRefTime(self): + return self.refTime + + def setRefTime(self, refTime): + self.refTime = refTime + + def getMode(self): + return self.mode + + def setMode(self, mode): + self.mode = mode + + def getStatus(self): + return self.status + + def setStatus(self, status): + self.status = status + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/DerivedProduct.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/DerivedProduct.py new file mode 100644 index 0000000..888e270 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/DerivedProduct.py @@ -0,0 +1,28 @@ + +# File auto-generated against equivalent DynamicSerialize Java class + +class DerivedProduct(object): + + def __init__(self): + self.name = None + self.productType = None + self.product = None + + def getName(self): + return self.name + + def setName(self, name): + self.name = name + + def getProductType(self): + return self.productType + + def setProductType(self, productType): + self.productType = productType + + def getProduct(self): + return self.product + + def setProduct(self, product): + self.product = product + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/ResponseMessageValidate.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/ResponseMessageValidate.py new file mode 100644 index 0000000..2d7dc5c --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/ResponseMessageValidate.py @@ -0,0 +1,42 @@ + +# File auto-generated against equivalent DynamicSerialize Java class + +class ResponseMessageValidate(object): + + def __init__(self): + self.result = None + self.message = None + self.fileType = None + self.dataURI = None + self.validTime = None + + def getResult(self): + return self.result + + def setResult(self, result): + self.result = result + + def getMessage(self): + return self.message + + def setMessage(self, message): + self.message = message + + def getFileType(self): + return self.fileType + + def setFileType(self, fileType): + self.fileType = fileType + + def getDataURI(self): + return self.dataURI + + def setDataURI(self, dataURI): + self.dataURI = dataURI + + def getValidTime(self): + return self.validTime + + def setValidTime(self, validTime): + self.validTime = validTime + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/__init__.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/__init__.py new file mode 100644 index 0000000..9e3cae9 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/__init__.py @@ -0,0 +1,15 @@ + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'request', + 'response', + 'ActivityInfo', + 'DerivedProduct', + 'ResponseMessageValidate' + ] + +from ActivityInfo import ActivityInfo +from DerivedProduct import DerivedProduct +from ResponseMessageValidate import ResponseMessageValidate + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/request/RetrieveActivityMapRequest.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/request/RetrieveActivityMapRequest.py new file mode 100644 index 0000000..f5279ab --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/request/RetrieveActivityMapRequest.py @@ -0,0 +1,13 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# May 05, 2016 root Generated + +class RetrieveActivityMapRequest(object): + + def __init__(self): + return diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/request/RetrieveAllProductsRequest.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/request/RetrieveAllProductsRequest.py new file mode 100644 index 0000000..e3a1124 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/request/RetrieveAllProductsRequest.py @@ -0,0 +1,14 @@ + +# File auto-generated against equivalent DynamicSerialize Java class + +class RetrieveAllProductsRequest(object): + + def __init__(self): + self.dataURI = None + + def getDataURI(self): + return self.dataURI + + def setDataURI(self, dataURI): + self.dataURI = dataURI + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/request/StoreActivityRequest.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/request/StoreActivityRequest.py new file mode 100644 index 0000000..3f36740 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/request/StoreActivityRequest.py @@ -0,0 +1,21 @@ + +# File auto-generated against equivalent DynamicSerialize Java class + +class StoreActivityRequest(object): + + def __init__(self): + self.activityInfo = None + self.activityXML = None + + def getActivityInfo(self): + return self.activityInfo + + def setActivityInfo(self, activityInfo): + self.activityInfo = activityInfo + + def getActivityXML(self): + return self.activityXML + + def setActivityXML(self, activityXML): + self.activityXML = activityXML + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/request/StoreDerivedProductRequest.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/request/StoreDerivedProductRequest.py new file mode 100644 index 0000000..92d64a9 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/request/StoreDerivedProductRequest.py @@ -0,0 +1,21 @@ + +# File auto-generated against equivalent DynamicSerialize Java class + +class StoreDerivedProductRequest(object): + + def __init__(self): + self.dataURI = None + self.productList = None + + def getDataURI(self): + return self.dataURI + + def setDataURI(self, dataURI): + self.dataURI = dataURI + + def getProductList(self): + return self.productList + + def setProductList(self, productList): + self.productList = productList + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/request/__init__.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/request/__init__.py new file mode 100644 index 0000000..7e4413c --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/request/__init__.py @@ -0,0 +1,15 @@ + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'RetrieveActivityMapRequest', + 'RetrieveAllProductsRequest', + 'StoreActivityRequest', + 'StoreDerivedProductRequest' + ] + +from RetrieveAllProductsRequest import RetrieveAllProductsRequest +from StoreActivityRequest import StoreActivityRequest +from StoreDerivedProductRequest import StoreDerivedProductRequest +from RetrieveActivityMapRequest import RetrieveActivityMapRequest + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/response/ActivityMapData.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/response/ActivityMapData.py new file mode 100644 index 0000000..1fd4902 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/response/ActivityMapData.py @@ -0,0 +1,55 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# May 06, 2016 root Generated + +class ActivityMapData(object): + + def __init__(self): + self.refTime = None + self.activityLabel = None + self.activitySubtype = None + self.dataURI = None + self.activityType = None + self.activityName = None + + def getRefTime(self): + return self.refTime + + def setRefTime(self, refTime): + self.refTime = refTime + + def getActivityLabel(self): + return self.activityLabel + + def setActivityLabel(self, activityLabel): + self.activityLabel = activityLabel + + def getActivitySubtype(self): + return self.activitySubtype + + def setActivitySubtype(self, activitySubtype): + self.activitySubtype = activitySubtype + + def getDataURI(self): + return self.dataURI + + def setDataURI(self, dataURI): + self.dataURI = dataURI + + def getActivityType(self): + return self.activityType + + def setActivityType(self, activityType): + self.activityType = activityType + + def getActivityName(self): + return self.activityName + + def setActivityName(self, activityName): + self.activityName = activityName + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/response/RetrieveActivityMapResponse.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/response/RetrieveActivityMapResponse.py new file mode 100644 index 0000000..bb72063 --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/response/RetrieveActivityMapResponse.py @@ -0,0 +1,20 @@ + +# File auto-generated against equivalent DynamicSerialize Java class +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# May 06, 2016 root Generated + +class RetrieveActivityMapResponse(object): + + def __init__(self): + self.data = None + + def getData(self): + return self.data + + def setData(self, data): + self.data = data + diff --git a/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/response/__init__.py b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/response/__init__.py new file mode 100644 index 0000000..99b5b4f --- /dev/null +++ b/dynamicserialize/dstypes/gov/noaa/nws/ncep/common/dataplugin/pgen/response/__init__.py @@ -0,0 +1,11 @@ + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'ActivityMapData', + 'RetrieveActivityMapResponse' + ] + +from ActivityMapData import ActivityMapData +from RetrieveActivityMapResponse import RetrieveActivityMapResponse + diff --git a/dynamicserialize/dstypes/java/__init__.py b/dynamicserialize/dstypes/java/__init__.py new file mode 100644 index 0000000..8c810e9 --- /dev/null +++ b/dynamicserialize/dstypes/java/__init__.py @@ -0,0 +1,13 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'awt', + 'lang', + 'sql', + 'util' + ] + + diff --git a/dynamicserialize/dstypes/java/awt/Point.py b/dynamicserialize/dstypes/java/awt/Point.py new file mode 100644 index 0000000..66a7e7e --- /dev/null +++ b/dynamicserialize/dstypes/java/awt/Point.py @@ -0,0 +1,39 @@ + +# +# Custom python class representing a java.awt.Point. +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 08/31/10 njensen Initial Creation. +# +# +# + + +class Point(object): + + def __init__(self): + self.x = None + self.y = None + + def __str__(self): + return str((self.x, self.y)) + + def __repr__(self): + return self.__str__() + + def getX(self): + return self.x + + def getY(self): + return self.y + + def setX(self, x): + self.x = x + + def setY(self, y): + self.y = y + diff --git a/dynamicserialize/dstypes/java/awt/__init__.py b/dynamicserialize/dstypes/java/awt/__init__.py new file mode 100644 index 0000000..041ce68 --- /dev/null +++ b/dynamicserialize/dstypes/java/awt/__init__.py @@ -0,0 +1,23 @@ +## +## + + +# +# Package definition for java.awt +# +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 08/31/10 njensen Initial Creation. +# +# +# + + +__all__ = [ + 'Point' + ] + +from Point import Point diff --git a/dynamicserialize/dstypes/java/lang/StackTraceElement.py b/dynamicserialize/dstypes/java/lang/StackTraceElement.py new file mode 100644 index 0000000..3cf1976 --- /dev/null +++ b/dynamicserialize/dstypes/java/lang/StackTraceElement.py @@ -0,0 +1,56 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class + +class StackTraceElement(object): + + def __init__(self): + self.declaringClass = None + self.methodName = None + self.fileName = None + self.lineNumber = 0 + + def getDeclaringClass(self): + return self.declaringClass + + def setDeclaringClass(self, clz): + self.declaringClass = clz + + def getMethodName(self): + return self.methodName + + def setMethodName(self, methodName): + self.methodName = methodName + + def getFileName(self): + return self.fileName + + def setFileName(self, filename): + self.fileName = filename + + def getLineNumber(self): + return self.lineNumber + + def setLineNumber(self, lineNumber): + self.lineNumber = int(lineNumber) + + def isNativeMethod(self): + return (self.lineNumber == -2) + + def __str__(self): + return self.__repr__() + + def __repr__(self): + msg = self.declaringClass + "." + self.methodName + if self.isNativeMethod(): + msg += "(Native Method)" + elif self.fileName is not None and self.lineNumber >= 0: + msg += "(" + self.fileName + ":" + str(self.lineNumber) + ")" + elif self.fileName is not None: + msg += "(" + self.fileName + ")" + else: + msg += "(Unknown Source)" + return msg + + diff --git a/dynamicserialize/dstypes/java/lang/__init__.py b/dynamicserialize/dstypes/java/lang/__init__.py new file mode 100644 index 0000000..639d0c0 --- /dev/null +++ b/dynamicserialize/dstypes/java/lang/__init__.py @@ -0,0 +1,11 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'StackTraceElement' + ] + +from StackTraceElement import StackTraceElement + diff --git a/dynamicserialize/dstypes/java/sql/Timestamp.py b/dynamicserialize/dstypes/java/sql/Timestamp.py new file mode 100644 index 0000000..0b46249 --- /dev/null +++ b/dynamicserialize/dstypes/java/sql/Timestamp.py @@ -0,0 +1,26 @@ +## +## + +# File auto-generated against equivalent DynamicSerialize Java class +# and then modified post-generation to add additional features to better +# match Java implementation. Unlike real timestamp, does not support nanos precision. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# ??/??/?? xxxxxxxx Initial Creation. +# 06/24/15 4480 dgilling implement based on Date class. +# Jun 23, 2016 5696 rjpeter Make String version match java. +# + +from dynamicserialize.dstypes.java.util import Date +from time import gmtime, strftime + +class Timestamp(Date): + + def __init__(self, time=None): + super(Timestamp, self).__init__(time) + + def __repr__(self): + return strftime("%Y-%m-%d %H:%M:%S.", gmtime(self.time/1000.0)) + '{:03d}'.format(self.time%1000) \ No newline at end of file diff --git a/dynamicserialize/dstypes/java/sql/__init__.py b/dynamicserialize/dstypes/java/sql/__init__.py new file mode 100644 index 0000000..a7fe633 --- /dev/null +++ b/dynamicserialize/dstypes/java/sql/__init__.py @@ -0,0 +1,10 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'Timestamp' + ] + +from Timestamp import Timestamp diff --git a/dynamicserialize/dstypes/java/util/Calendar.py b/dynamicserialize/dstypes/java/util/Calendar.py new file mode 100644 index 0000000..b084ab5 --- /dev/null +++ b/dynamicserialize/dstypes/java/util/Calendar.py @@ -0,0 +1,33 @@ +## +## + +## +# Custom python class representing a java.util.GregorianCalendar. +# +# This is a stripped-down version of the class that only supports +# minimal methods for serialization. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/29/10 wldougher Initial Creation. +# +# +## +class Calendar(object): + """ +""" + def __init__(self): + self.time = None + + # Methods from the real class that we typically use + @staticmethod + def getInstance(): + return GregorianCalendar() + + def getTimeInMillis(self): + return self.time + + def setTimeInMillis(self, timeInMillis): + self.time = timeInMillis diff --git a/dynamicserialize/dstypes/java/util/Date.py b/dynamicserialize/dstypes/java/util/Date.py new file mode 100644 index 0000000..2cc69ed --- /dev/null +++ b/dynamicserialize/dstypes/java/util/Date.py @@ -0,0 +1,41 @@ +## +## +# ---------------------------------------------------------------------------- +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 04/28/2015 4027 randerso Added optional construction parameter to set the time +# 06/26/2015 4480 dgilling Implement __eq__ and __hash__. +# +## + +from time import gmtime, strftime + + +class Date(object): + + def __init__(self, timeInMillis=None): + self.time = timeInMillis + + def getTime(self): + return self.time + + def setTime(self, timeInMillis): + self.time = timeInMillis + + def __str__(self): + return self.__repr__() + + def __repr__(self): + return strftime("%b %d %y %H:%M:%S GMT", gmtime(self.time/1000.0)) + + def __eq__(self, other): + return self.time == other.time + + def __ne__(self, other): + return not self.__eq__(other) + + def __hash__(self): + return hash(self.time) diff --git a/dynamicserialize/dstypes/java/util/EnumSet.py b/dynamicserialize/dstypes/java/util/EnumSet.py new file mode 100644 index 0000000..664c690 --- /dev/null +++ b/dynamicserialize/dstypes/java/util/EnumSet.py @@ -0,0 +1,51 @@ +## +## + +## +# NOTE: Please do not ever use this class unless you really must. It is not +# designed to be directly accessed from client code. Hide its use from end- +# users as best as you can. +## + +## +# IMPLEMENTATION DETAILS: +# This class is an attempt to simulate Java's EnumSet class. When creating +# a new instance of this class, you must specify the name of the Java enum +# contained within as this is needed for serialization. Do not append the +# "dynamicserialize.dstypes" portion of the Python package to the supplied +# class name as Java won't know what class that is when deserializing. +# +# Since Python has no concept of enums, this class cannot provide the value- +# checking that Java class does. Be very sure that you add only valid enum +# values to your EnumSet. +## + +import collections + + +class EnumSet(collections.MutableSet): + + def __init__(self, enumClassName, iterable=[]): + self.__enumClassName = enumClassName + self.__set = set(iterable) + + def __repr__(self): + return "EnumSet({0})".format(list(self.__set)) + + def __len__(self): + return len(self.__set) + + def __contains__(self, key): + return key in self.__set + + def __iter__(self): + return iter(self.__set) + + def add(self, value): + self.__set.add(value) + + def discard(self, value): + self.__set.discard(value) + + def getEnumClass(self): + return self.__enumClassName diff --git a/dynamicserialize/dstypes/java/util/GregorianCalendar.py b/dynamicserialize/dstypes/java/util/GregorianCalendar.py new file mode 100644 index 0000000..bfd259a --- /dev/null +++ b/dynamicserialize/dstypes/java/util/GregorianCalendar.py @@ -0,0 +1,33 @@ +## +## + +## +# Custom python class representing a java.util.GregorianCalendar. +# +# This is a stripped-down version of the class that only supports +# minimal methods for serialization. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 09/29/10 wldougher Initial Creation. +# +# +## +class GregorianCalendar(object): + """ +""" + def __init__(self): + self.time = None + + # Methods from the real class that we typically use + @staticmethod + def getInstance(): + return GregorianCalendar() + + def getTimeInMillis(self): + return self.time + + def setTimeInMillis(self, timeInMillis): + self.time = timeInMillis diff --git a/dynamicserialize/dstypes/java/util/__init__.py b/dynamicserialize/dstypes/java/util/__init__.py new file mode 100644 index 0000000..5c13725 --- /dev/null +++ b/dynamicserialize/dstypes/java/util/__init__.py @@ -0,0 +1,17 @@ +## +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'Calendar', + 'Date', + 'EnumSet', + 'GregorianCalendar' + ] + +from Calendar import Calendar +from Date import Date +from EnumSet import EnumSet +from GregorianCalendar import GregorianCalendar + diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..2b17e98 --- /dev/null +++ b/environment.yml @@ -0,0 +1,21 @@ + name: python-awips + channels: + - https://conda.anaconda.org/conda-forge + dependencies: + - python=2.7 + - numpy + - matplotlib + - cartopy + - jupyter + - pandas + - metpy + - pint + - h5py + - shapely + - sphinx>=1.3 + - sphinx_rtd_theme + - nbconvert>=4.1 + - enum34 + - pip + - pip: + - python-awips diff --git a/examples/md/GISOperations.md b/examples/md/GISOperations.md new file mode 100644 index 0000000..ff770ed --- /dev/null +++ b/examples/md/GISOperations.md @@ -0,0 +1,82 @@ +```python +#!python +from awips.dataaccess import DataAccessLayer +from shapely.geometry import Polygon,Point +from datetime import datetime + +#Initiate a new DataRequest +#Get all of the states from the database +print "Requesting all states from DAF" +t1=datetime.utcnow() +request = DataAccessLayer.newDataRequest() +request.setDatatype("maps") +request.addIdentifier("geomField","the_geom") +request.addIdentifier("table","mapdata.states") +request.setParameters("state","fips","name") +states = DataAccessLayer.getGeometryData(request, None) +t2=datetime.utcnow() +tdelta = t2-t1 +print 'DAF query to get all states took %i.%i seconds' %(tdelta.seconds,tdelta.microseconds) + +#Create a polygon object...for example this is a polygon around Oklahoma City +polygon = Polygon([(-97.82,35.63),(-97.21,35.63),(-97.21,35.15),(-97.82,35.15),(-97.82,35.63)]) +#Now lets filter the states down to the one that contains the above polygon +#We will use the python built in filter method to accomplish this +#the expression below goes through each state object, gets it's geometry, and calls +#it's contains method. It in essence creates a new list, state_contain_polygon, with +#only the states where the contains method evaluates to true. +t1=datetime.utcnow() +state_contain_polygon = filter(lambda state: state.getGeometry().contains(polygon),states) +t2= datetime.utcnow() +tdelta = t2-t1 +print '\nFilter state objects to one that contains polygon took %i.%i seconds' %(tdelta.seconds,tdelta.microseconds) +print state_contain_polygon +print "Polygon is in the state of",state_contain_polygon[0].getString('name') + +#Lets also create a point object...this one is located in the state of Iowa +point = Point(-93.62,41.60) +#Now lets see what state our point is in +t1=datetime.utcnow() +state_contain_point = filter(lambda state: state.getGeometry().contains(point),states) +t2= datetime.utcnow() +tdelta = t2-t1 +print '\nFilter state objects to one that contains point took %i.%i seconds ' %(tdelta.seconds,tdelta.microseconds) +print state_contain_point +print "Point is in the state of",state_contain_point[0].getString('name') + +#One last example...this time for an intersection. Lets find all of the states this polygon intersects +#This polygon is the same as above just extended it further south to 33.15 degrees +polygon2 = Polygon([(-97.82,35.63),(-97.21,35.63),(-97.21,33.15),(-97.82,33.15),(-97.82,35.63)]) +t1=datetime.utcnow() +state_intersect_polygon = filter(lambda state: state.getGeometry().intersects(polygon2),states) +t2= datetime.utcnow() +tdelta = t2-t1 +print '\nFilter state objects to the ones that intersect polygon took %i.%i seconds ' %(tdelta.seconds,tdelta.microseconds) +print state_intersect_polygon +for state in state_intersect_polygon: + print "Polygon intersects the state of",state.getString('name') +``` + +```python +Requesting all states from DAF +DAF query to get all states took 21.915029 seconds +``` + +```python +Filter state objects to one that contains polygon took 0.382097 seconds +[] +Polygon is in the state of Oklahoma +``` + +```python +Filter state objects to one that contains point took 0.2028 seconds +[] +Point is in the state of Iowa +``` + +```python +Filter state objects to the ones that intersect polygon took 0.4032 seconds +[, ] +Polygon intersects the state of Texas +Polygon intersects the state of Oklahoma +``` diff --git a/examples/md/GetSatelliteIR.md b/examples/md/GetSatelliteIR.md new file mode 100644 index 0000000..69c9d2e --- /dev/null +++ b/examples/md/GetSatelliteIR.md @@ -0,0 +1,49 @@ +```python +#!/awips2/python/bin/python +from awips.dataaccess import DataAccessLayer +import numpy as np + +request = DataAccessLayer.newDataRequest() +request.setDatatype("satellite") +request.setLocationNames("East CONUS") +request.setParameters("Imager 6.7-6.5 micron IR (WV)") + +t = DataAccessLayer.getAvailableTimes(request) +print t[-1].getRefTime() + +response = DataAccessLayer.getGridData(request, times=[t[-1]]) +print response +data = response[0] + +print 'Units are in', data.getUnit() +lon,lat = data.getLatLonCoords() + +print 'Parameter we requested is',data.getParameter() +print data.getRawData() +``` + +```python +May 04 15 18:45:19 GMT +``` + +```python +[] +``` + +```python +Units are in None +``` + +```python +Parameter we requested is Imager 6.7-6.5 micron IR (WV) +``` + +```python +[[ 186. 185. 186. ..., 180. 181. 181.] + [ 186. 185. 186. ..., 180. 181. 181.] + [ 186. 186. 185. ..., 180. 181. 181.] + ..., + [ 0. 0. 0. ..., 145. 145. 145.] + [ 0. 0. 0. ..., 145. 145. 145.] + [ 0. 0. 0. ..., 145. 145. 144.]] +``` diff --git a/examples/md/GetStates.md b/examples/md/GetStates.md new file mode 100644 index 0000000..1e196c3 --- /dev/null +++ b/examples/md/GetStates.md @@ -0,0 +1,69 @@ +```python +#!python +#!/awips2/python/bin/python +from awips.dataaccess import DataAccessLayer + +#Initiate a new DataRequest +b = DataAccessLayer.newDataRequest() +#Set the datatype to maps so it knows what plugin to route the request too +b.setDatatype("maps") +#setParameters indicates the columns from table that we want returned along with our geometry +b.setParameters("state","fips") +#Add a couple of identifiers to indicate the geomField and table we are querying +b.addIdentifier("geomField","the_geom") +b.addIdentifier("table","mapdata.states") + +#getAvailableLocationNames method will return a list of all available locations +#based off the table we have specified previously. LocationNames mean different +#things to different plugins beware...radar is icao, satellite is sector, etc +a = DataAccessLayer.getAvailableLocationNames(b) +print a + +#Use setLocationNames to set the states we want data from +b.setLocationNames("Oklahoma","Texas","Kansas") + +#Finally lets request some data. There are two types of data (Grid, Geometry) here we are +#requesting geometry data and therefore use the getGeometryData method. We pass it our DataRequest object +#that has all of our parameters and None for the DataTime object argument since maps are time agnostic +#This returns a list of awips.dataaccess.PyGeometryData.PyGeometryData objects. +c = DataAccessLayer.getGeometryData(b, None) +print c + +#Now lets loop through our list of PyGeometryData objects of states and look at some data +for shape in c: + #Lets print the locationname for this object + print 'Location name is',shape.getLocationName() + #getGeometry returns a shapely geometry object for this state. Using shapely allows + #us to perform postgis type operations outside the database (contains,within,etc). If + #not familiar with shapely recommend you look at the documentation available online. + #This is a 3rd party python module so just Google search python shapely to find the docs + mpoly = shape.getGeometry() + #These next few items allow us to access the column data we requested when we set the + #parameters + print 'Parameters requested are',shape.getParameters() + print 'state column is',shape.getString('state') + print 'fips column is',shape.getString('fips') +``` + +```python +['Alabama', 'Alaska', 'American Samoa', 'Arizona', 'Arkansas', 'California', 'Colorado', 'Connecticut', 'Delaware', 'District of Columbia', 'Florida', 'Georgia', 'Guam', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada', 'New Hampshire', 'New Jersey', 'New Mexico', 'New York', 'North Carolina', 'North Dakota', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'Puerto Rico', 'Rhode Island', 'South Carolina', 'South Dakota', 'Tennessee', 'Texas', 'Utah', 'Vermont', 'Virgin Islands', 'Virginia', 'Washington', 'West Virginia', 'Wisconsin', 'Wyoming'] +``` + +```python +[, , ] +``` + +```python +Location name is Texas +Parameters requested are ['state', 'fips'] +state column is TX +fips column is 48 +Location name is Kansas +Parameters requested are ['state', 'fips'] +state column is KS +fips column is 20 +Location name is Oklahoma +Parameters requested are ['state', 'fips'] +state column is OK +fips column is 40 +``` diff --git a/examples/notebooks/AWIPS_Grids_and_Cartopy.ipynb b/examples/notebooks/AWIPS_Grids_and_Cartopy.ipynb new file mode 100644 index 0000000..e80df03 --- /dev/null +++ b/examples/notebooks/AWIPS_Grids_and_Cartopy.ipynb @@ -0,0 +1,147 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The simplest example of requesting and plotting AWIPS gridded data with Matplotlib and Cartopy." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from awips.dataaccess import DataAccessLayer\n", + "import cartopy.crs as ccrs\n", + "import matplotlib.pyplot as plt\n", + "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n", + "%matplotlib inline\n", + "\n", + "DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n", + "request = DataAccessLayer.newDataRequest()\n", + "request.setDatatype(\"grid\")\n", + "request.setLocationNames(\"RAP13\")\n", + "request.setParameters(\"T\")\n", + "request.setLevels(\"2.0FHAG\")\n", + "cycles = DataAccessLayer.getAvailableTimes(request, True)\n", + "times = DataAccessLayer.getAvailableTimes(request)\n", + "fcstRun = DataAccessLayer.getForecastRun(cycles[-1], times)\n", + "response = DataAccessLayer.getGridData(request, [fcstRun[0]])\n", + "grid = response[0]\n", + "data = grid.getRawData()\n", + "lons, lats = grid.getLatLonCoords()\n", + "bbox = [lons.min(), lons.max(), lats.min(), lats.max()]\n", + "\n", + "def make_map(bbox, projection=ccrs.PlateCarree()):\n", + " fig, ax = plt.subplots(figsize=(16, 9),\n", + " subplot_kw=dict(projection=projection))\n", + " ax.set_extent(bbox)\n", + " ax.coastlines(resolution='50m')\n", + " gl = ax.gridlines(draw_labels=True)\n", + " gl.xlabels_top = gl.ylabels_right = False\n", + " gl.xformatter = LONGITUDE_FORMATTER\n", + " gl.yformatter = LATITUDE_FORMATTER\n", + " return fig, ax" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### with pcolormesh" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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BoAsRrQYAIrIQUSqAAQBE7+BaAIOsry0AXAHoHJpKgCAsRxaB3QoKCgrPPWPG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\nr43sxkqV7uO6Sakr7SONOzkq4g3TrGQaLfCrgBY5XK6nei5eH6URsI9j5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SRS68rn+u/W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HDl7phx5MaL/4wx0dR1lPPy7TrA18A86DBWMWfQ8Hbl/eGHjdV3f1/al7OC22civWYIyd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\nsr5YcpmbFBtCmW9GieEil7nb3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dS8baTP12emQXWA1kiS+7ui/O/qVv5fYwX3b+1lrE/m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\naIGkHSXdKek1SacUyu9QaJeZkpZKOqlBnQ/K/bhA0mmF9PMlP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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cmap = plt.get_cmap('rainbow')\n", + "fig, ax = make_map(bbox=bbox)\n", + "cs = ax.pcolormesh(lons, lats, data, cmap=cmap)\n", + "cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal')\n", + "cbar.set_label(str(grid.getLocationName()) +\" \" \\\n", + " + str(grid.getLevel()) + \" \" \\\n", + " + str(grid.getParameter()) \\\n", + " + \" (\" + str(grid.getUnit()) + \") \" \\\n", + " + \"valid \" + str(grid.getDataTime().getRefTime()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### with contourf" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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lue3U1FQiIiI4duwY8fHxNrRaGnd3d2bOnMnNmzdp2bIlixYtYu3atdSrV49Lly4V+y3r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2AAAg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B5tk5OPoMYWOCZ6rMNaF2OJvss4mXYZWq+M92etZviCjYy5/HKuvfZa9ttvv+TurxE74YQT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\nRK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig2, ax2 = make_map(bbox=bbox)\n", + "cs2 = ax2.contourf(lons, lats, data, 80, cmap=cmap,\n", + " vmin=data.min(), vmax=data.max())\n", + "cbar2 = fig2.colorbar(cs2, shrink=0.7, orientation='horizontal')\n", + "cbar2.set_label(str(grid.getLocationName()) +\" \" \\\n", + " + str(grid.getLevel()) + \" \" \\\n", + " + str(grid.getParameter()) \\\n", + " + \" (\" + str(grid.getUnit()) + \") \" \\\n", + " + \"valid \" + str(grid.getDataTime().getRefTime()))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/notebooks/Grid_Levels_and_Parameters.ipynb b/examples/notebooks/Grid_Levels_and_Parameters.ipynb new file mode 100644 index 0000000..7104843 --- /dev/null +++ b/examples/notebooks/Grid_Levels_and_Parameters.ipynb @@ -0,0 +1,855 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example covers the callable methods of the Python AWIPS DAF when working with gridded data. We start with a connection to an EDEX server, then query data types, then grid names, parameters, levels, and other information. Finally the gridded data is plotted for its domain using Matplotlib and Cartopy." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DataAccessLayer.getSupportedDatatypes()\n", + "\n", + "getSupportedDatatypes() returns a list of available data types offered by the EDEX server defined above. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['acars',\n", + " 'binlightning',\n", + " 'bufrmosavn',\n", + " 'bufrmoseta',\n", + " 'bufrmosgfs',\n", + " 'bufrmoshpc',\n", + " 'bufrmoslamp',\n", + " 'bufrmosmrf',\n", + " 'bufrua',\n", + " 'climate',\n", + " 'common_obs_spatial',\n", + " 'gfe',\n", + " 'grid',\n", + " 'hydro',\n", + " 'maps',\n", + " 'modelsounding',\n", + " 'obs',\n", + " 'practicewarning',\n", + " 'radar',\n", + " 'radar_spatial',\n", + " 'satellite',\n", + " 'sfcobs',\n", + " 'topo',\n", + " 'warning']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from awips.dataaccess import DataAccessLayer\n", + "DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n", + "dataTypes = DataAccessLayer.getSupportedDatatypes()\n", + "list(dataTypes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DataAccessLayer.getAvailableLocationNames()\n", + "\n", + "Now create a new data request, and set the data type to **grid** to request all available grids with **getAvailableLocationNames()**" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['CMC',\n", + " 'ESTOFS',\n", + " 'ETSS',\n", + " 'FFG-ALR',\n", + " 'FFG-FWR',\n", + " 'FFG-KRF',\n", + " 'FFG-MSR',\n", + " 'FFG-ORN',\n", + " 'FFG-PTR',\n", + " 'FFG-RHA',\n", + " 'FFG-RSA',\n", + " 'FFG-STR',\n", + " 'FFG-TAR',\n", + " 'FFG-TIR',\n", + " 'FFG-TUA',\n", + " 'FNMOC-FAROP',\n", + " 'FNMOC-NCODA',\n", + " 'FNMOC-WW3',\n", + " 'GFS',\n", + " 'GFS20',\n", + " 'GribModel:7:14:16',\n", + " 'HFR-EAST_6KM',\n", + " 'HFR-US_EAST_DELAWARE_1KM',\n", + " 'HFR-US_EAST_FLORIDA_2KM',\n", + " 'HFR-US_EAST_NORTH_2KM',\n", + " 'HFR-US_EAST_SOUTH_2KM',\n", + " 'HFR-US_EAST_VIRGINIA_1KM',\n", + " 'HFR-US_HAWAII_1KM',\n", + " 'HFR-US_HAWAII_2KM',\n", + " 'HFR-US_HAWAII_6KM',\n", + " 'HFR-US_WEST_500M',\n", + " 'HFR-US_WEST_CENCAL_2KM',\n", + " 'HFR-US_WEST_LOSANGELES_1KM',\n", + " 'HFR-US_WEST_LOSOSOS_1KM',\n", + " 'HFR-US_WEST_NORTH_2KM',\n", + " 'HFR-US_WEST_SANFRAN_1KM',\n", + " 'HFR-US_WEST_SOCAL_2KM',\n", + " 'HFR-US_WEST_WASHINGTON_1KM',\n", + " 'HFR-WEST_6KM',\n", + " 'HPCGuide',\n", + " 'HPCqpf',\n", + " 'HPCqpfNDFD',\n", + " 'HRRR',\n", + " 'LAMP2p5',\n", + " 'MOSGuide',\n", + " 'NAM12',\n", + " 'NAM40',\n", + " 'NAVGEM',\n", + " 'NCWF',\n", + " 'NDFD',\n", + " 'NOHRSC-SNOW',\n", + " 'PROB3HR',\n", + " 'QPE-RFC-STR',\n", + " 'RAP13',\n", + " 'RFCqpf',\n", + " 'RTMA',\n", + " 'SeaIce',\n", + " 'TPCWindProb',\n", + " 'UKMET-MODEL1',\n", + " 'URMA25']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "request = DataAccessLayer.newDataRequest()\n", + "request.setDatatype(\"grid\")\n", + "available_grids = DataAccessLayer.getAvailableLocationNames(request)\n", + "available_grids.sort()\n", + "list(available_grids)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DataAccessLayer.getAvailableParameters()\n", + "\n", + "After datatype and model name (locationName) are set, you can query all available parameters with **getAvailableParameters()**" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['AV',\n", + " 'Along',\n", + " 'AppT',\n", + " 'BLI',\n", + " 'BlkMag',\n", + " 'BlkShr',\n", + " 'CAPE',\n", + " 'CFRZR',\n", + " 'CICEP',\n", + " 'CIn',\n", + " 'CP',\n", + " 'CP1hr',\n", + " 'CPr',\n", + " 'CPrD',\n", + " 'CRAIN',\n", + " 'CSNOW',\n", + " 'CURU',\n", + " 'CXR',\n", + " 'CapeStk',\n", + " 'Corf',\n", + " 'CorfF',\n", + " 'CorfFM',\n", + " 'CorfM',\n", + " 'CritT1',\n", + " 'DivF',\n", + " 'DivFn',\n", + " 'DivFs',\n", + " 'DpD',\n", + " 'DpT',\n", + " 'EHI',\n", + " 'EHI01',\n", + " 'EHIi',\n", + " 'EPT',\n", + " 'EPTA',\n", + " 'EPTC',\n", + " 'EPTGrd',\n", + " 'EPTGrdM',\n", + " 'EPTs',\n", + " 'EPVg',\n", + " 'EPVs',\n", + " 'EPVt1',\n", + " 'EPVt2',\n", + " 'FVecs',\n", + " 'FeatMot',\n", + " 'FnVecs',\n", + " 'FsVecs',\n", + " 'Fzra1',\n", + " 'Fzra2',\n", + " 'GH',\n", + " 'GHxSM',\n", + " 'GHxSM2',\n", + " 'Gust',\n", + " 'HI',\n", + " 'HI1',\n", + " 'HI3',\n", + " 'HI4',\n", + " 'HIdx',\n", + " 'HPBL',\n", + " 'Heli',\n", + " 'Into',\n", + " 'KI',\n", + " 'L-I',\n", + " 'LIsfc2x',\n", + " 'LgSP1hr',\n", + " 'MAdv',\n", + " 'MCon',\n", + " 'MCon2',\n", + " 'MMSP',\n", + " 'MSFDi',\n", + " 'MSFi',\n", + " 'MSFmi',\n", + " 'MSG',\n", + " 'MTV',\n", + " 'Mix1',\n", + " 'Mix2',\n", + " 'Mmag',\n", + " 'MpV',\n", + " 'NBE',\n", + " 'OmDiff',\n", + " 'P',\n", + " 'PAdv',\n", + " 'PBE',\n", + " 'PFrnt',\n", + " 'PGrd',\n", + " 'PGrd1',\n", + " 'PGrdM',\n", + " 'PIVA',\n", + " 'PR',\n", + " 'PTvA',\n", + " 'PTyp',\n", + " 'PVV',\n", + " 'PW',\n", + " 'PW2',\n", + " 'PoT',\n", + " 'PoTA',\n", + " 'QPV1',\n", + " 'QPV2',\n", + " 'QPV3',\n", + " 'QPV4',\n", + " 'REFC',\n", + " 'RH',\n", + " 'RH_001_bin',\n", + " 'RH_002_bin',\n", + " 'RM5',\n", + " 'RRtype',\n", + " 'RV',\n", + " 'Rain1',\n", + " 'Rain2',\n", + " 'Rain3',\n", + " 'Ro',\n", + " 'SH',\n", + " 'SHx',\n", + " 'SLI',\n", + " 'SNW',\n", + " 'SNWA',\n", + " 'SRMm',\n", + " 'SRMmM',\n", + " 'SSi',\n", + " 'Shear',\n", + " 'ShrMag',\n", + " 'SnD',\n", + " 'Snow1',\n", + " 'Snow2',\n", + " 'Snow3',\n", + " 'SnowT',\n", + " 'St-Pr',\n", + " 'St-Pr1hr',\n", + " 'StrTP',\n", + " 'StrmMot',\n", + " 'T',\n", + " 'TAdv',\n", + " 'TGrd',\n", + " 'TGrdM',\n", + " 'TP',\n", + " 'TP1hr',\n", + " 'TQIND',\n", + " 'TV',\n", + " 'TW',\n", + " 'T_001_bin',\n", + " 'Tdef',\n", + " 'Tdend',\n", + " 'ThGrd',\n", + " 'TmDpD',\n", + " 'Tmax',\n", + " 'Tmin',\n", + " 'TotQi',\n", + " 'Tstk',\n", + " 'TwMax',\n", + " 'TwMin',\n", + " 'Twstk',\n", + " 'TxSM',\n", + " 'USTM',\n", + " 'VAdv',\n", + " 'VAdvAdvection',\n", + " 'VSTM',\n", + " 'Vis',\n", + " 'WD',\n", + " 'WEASD',\n", + " 'WEASD1hr',\n", + " 'WGS',\n", + " 'Wind',\n", + " 'WndChl',\n", + " 'ageoVC',\n", + " 'ageoW',\n", + " 'ageoWM',\n", + " 'cCape',\n", + " 'cCin',\n", + " 'cTOT',\n", + " 'capeToLvl',\n", + " 'dCape',\n", + " 'dP',\n", + " 'dT',\n", + " 'dVAdv',\n", + " 'dZ',\n", + " 'defV',\n", + " 'del2gH',\n", + " 'df',\n", + " 'fGen',\n", + " 'fnD',\n", + " 'fsD',\n", + " 'gamma',\n", + " 'gammaE',\n", + " 'geoVort',\n", + " 'geoW',\n", + " 'geoWM',\n", + " 'mixRat',\n", + " 'msl-P',\n", + " 'muCape',\n", + " 'pV',\n", + " 'pVeq',\n", + " 'qDiv',\n", + " 'qVec',\n", + " 'qnVec',\n", + " 'qsVec',\n", + " 'shWlt',\n", + " 'snoRatCrocus',\n", + " 'snoRatEMCSREF',\n", + " 'snoRatSPC',\n", + " 'snoRatSPCdeep',\n", + " 'snoRatSPCsurface',\n", + " 'swtIdx',\n", + " 'tTOT',\n", + " 'tWind',\n", + " 'tWindU',\n", + " 'tWindV',\n", + " 'uFX',\n", + " 'uW',\n", + " 'vSmthW',\n", + " 'vTOT',\n", + " 'vW',\n", + " 'vertCirc',\n", + " 'wDiv',\n", + " 'wSp',\n", + " 'wSp_001_bin',\n", + " 'wSp_002_bin',\n", + " 'wSp_003_bin',\n", + " 'wSp_004_bin',\n", + " 'zAGL']" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "request.setLocationNames(\"RAP13\")\n", + "availableParms = DataAccessLayer.getAvailableParameters(request)\n", + "availableParms.sort()\n", + "list(availableParms)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DataAccessLayer.getAvailableLevels()\n", + "\n", + "Selecting **\"T\"** for temperature." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0SFC\n", + "350.0MB\n", + "475.0MB\n", + "225.0MB\n", + "120.0_150.0BL\n", + "900.0MB\n", + "125.0MB\n", + "450.0MB\n", + "575.0MB\n", + "325.0MB\n", + "100.0MB\n", + "1000.0MB\n", + "60.0_90.0BL\n", + "275.0MB\n", + "1.0PV\n", + "950.0MB\n", + "150.0MB\n", + "1.5PV\n", + "700.0MB\n", + "825.0MB\n", + "150.0_180.0BL\n", + "250.0MB\n", + "375.0MB\n", + "1000.0_500.0MB\n", + "800.0MB\n", + "925.0MB\n", + "2.0PV\n", + "0.5PV\n", + "0.0TROP\n", + "750.0MB\n", + "500.0MB\n", + "625.0MB\n", + "400.0MB\n", + "0.0FHAG\n", + "2.0FHAG\n", + "875.0MB\n", + "175.0MB\n", + "850.0MB\n", + "600.0MB\n", + "725.0MB\n", + "975.0MB\n", + "550.0MB\n", + "675.0MB\n", + "425.0MB\n", + "200.0MB\n", + "0.0_30.0BL\n", + "30.0_60.0BL\n", + "650.0MB\n", + "525.0MB\n", + "300.0MB\n", + "90.0_120.0BL\n", + "775.0MB\n", + "340.0_350.0K\n", + "290.0_300.0K\n", + "700.0_600.0MB\n", + "700.0_300.0MB\n", + "320.0Ke\n", + "800.0_750.0MB\n", + "60.0TILT\n", + "5.3TILT\n", + "1000.0_900.0MB\n", + "340.0K\n", + "255.0K\n", + "255.0_265.0K\n", + "25.0TILT\n", + "1000.0_850.0MB\n", + "850.0_250.0MB\n", + "280.0_290.0Ke\n", + "320.0_330.0K\n", + "0.0TILT\n", + "310.0_320.0Ke\n", + "310.0Ke\n", + "330.0K\n", + "900.0_800.0MB\n", + "550.0_500.0MB\n", + "2.4TILT\n", + "50.0TILT\n", + "35.0TILT\n", + "12.0TILT\n", + "300.0_310.0K\n", + "0.9TILT\n", + "320.0K\n", + "400.0_350.0MB\n", + "750.0_700.0MB\n", + "345.0K\n", + "250.0_260.0K\n", + "300.0Ke\n", + "290.0Ke\n", + "950.0_900.0MB\n", + "275.0_285.0Ke\n", + "335.0Ke\n", + "295.0_305.0Ke\n", + "275.0_285.0K\n", + "600.0_550.0MB\n", + "310.0K\n", + "335.0K\n", + "700.0_500.0MB\n", + "325.0_335.0K\n", + "300.0K\n", + "0.0MAXOMEGA\n", + "315.0_325.0K\n", + "325.0K\n", + "340.0Ke\n", + "300.0_250.0MB\n", + "1.5TILT\n", + "335.0_345.0K\n", + "315.0K\n", + "3.4TILT\n", + "330.0Ke\n", + "500.0_400.0MB\n", + "305.0K\n", + "285.0_295.0Ke\n", + "14.0TILT\n", + "325.0_335.0Ke\n", + "850.0_800.0MB\n", + "295.0Ke\n", + "305.0Ke\n", + "265.0_275.0K\n", + "700.0_650.0MB\n", + "0.5TILT\n", + "450.0_400.0MB\n", + "1.8TILT\n", + "330.0_340.0K\n", + "800.0_700.0MB\n", + "850.0_300.0MB\n", + "6.0TILT\n", + "900.0_850.0MB\n", + "320.0_330.0Ke\n", + "8.7TILT\n", + "650.0_600.0MB\n", + "600.0_400.0MB\n", + "55.0TILT\n", + "270.0_280.0Ke\n", + "30.0TILT\n", + "310.0_320.0K\n", + "1000.0_950.0MB\n", + "250.0_200.0MB\n", + "400.0_300.0MB\n", + "500.0_100.0MB\n", + "285.0Ke\n", + "290.0K\n", + "305.0_315.0K\n", + "285.0_295.0K\n", + "925.0_850.0MB\n", + "275.0Ke\n", + "300.0_200.0MB\n", + "260.0_270.0K\n", + "315.0_325.0Ke\n", + "600.0_500.0MB\n", + "16.7TILT\n", + "280.0K\n", + "500.0_250.0MB\n", + "40.0TILT\n", + "400.0_200.0MB\n", + "300.0_310.0Ke\n", + "270.0_280.0K\n", + "1000.0_700.0MB\n", + "45.0TILT\n", + "850.0_500.0MB\n", + "295.0K\n", + "4.3TILT\n", + "295.0_305.0K\n", + "330.0_340.0Ke\n", + "270.0K\n", + "280.0_290.0K\n", + "925.0_700.0MB\n", + "260.0K\n", + "10.0TILT\n", + "325.0Ke\n", + "285.0K\n", + "290.0_300.0Ke\n", + "7.5TILT\n", + "280.0Ke\n", + "500.0_450.0MB\n", + "305.0_315.0Ke\n", + "250.0K\n", + "250.0_350.0K\n", + "270.0Ke\n", + "275.0K\n", + "315.0Ke\n", + "500.0_300.0MB\n", + "350.0_300.0MB\n", + "19.5TILT\n", + "850.0_700.0MB\n", + "350.0K\n", + "265.0K\n", + "0.0_0.0SFC\n" + ] + } + ], + "source": [ + "request.setParameters(\"T\")\n", + "availableLevels = DataAccessLayer.getAvailableLevels(request)\n", + "for level in availableLevels:\n", + " print(level)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* **0.0SFC** is the Surface level\n", + "* **FHAG** stands for Fixed Height Above Ground (in meters)\n", + "* **NTAT** stands for Nominal Top of the ATmosphere\n", + "* **BL** stands for Boundary Layer, where **0.0_30.0BL** reads as *0-30 mb above ground level* \n", + "* **TROP** is the Tropopause level\n", + "\n", + "**request.setLevels()**\n", + "\n", + "For this example we will use Surface Temperature" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "request.setLevels(\"2.0FHAG\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DataAccessLayer.getAvailableTimes()\n", + "\n", + "* **getAvailableTimes(request, True)** will return an object of *run times* - formatted as `YYYY-MM-DD HH:MM:SS`\n", + "* **getAvailableTimes(request)** will return an object of all times - formatted as `YYYY-MM-DD HH:MM:SS (F:ff)`\n", + "* **getForecastRun(cycle, times)** will return a DataTime array for a single forecast cycle." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cycles = DataAccessLayer.getAvailableTimes(request, True)\n", + "times = DataAccessLayer.getAvailableTimes(request)\n", + "fcstRun = DataAccessLayer.getForecastRun(cycles[-1], times)\n", + "list(fcstRun)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DataAccessLayer.getGridData()\n", + "\n", + "Now that we have our `request` and DataTime `fcstRun` arrays ready, it's time to request the data array from EDEX." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Time :', '2018-02-11 19:00:00 (21)')\n", + "('Model:', 'RAP13')\n", + "('Parm :', 'T')\n", + "('Unit :', 'K')\n", + "(337, 451)\n" + ] + } + ], + "source": [ + "response = DataAccessLayer.getGridData(request, [fcstRun[-1]])\n", + "for grid in response:\n", + " data = grid.getRawData()\n", + " lons, lats = grid.getLatLonCoords()\n", + " print('Time :', str(grid.getDataTime()))\n", + "\n", + "print('Model:', str(grid.getLocationName()))\n", + "print('Parm :', str(grid.getParameter()))\n", + "print('Unit :', str(grid.getUnit()))\n", + "print(data.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting with Matplotlib and Cartopy\n", + "\n", + "**1. pcolormesh**" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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4sM3x8PLLL/Pqq6/SvXt3wsPDqVmzJp999hmtWrXi6NGjnsK3RRmr1cqcOXNYsWIF69at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ZBpAG5l1c89lVXr1KfD8lnL2rFv4N5TOxHog7UwNgUQpwbj4FFWI9/33w/t/9/09hCQ+yi2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K1ipZ59drf++ckfAiBmrBvq2H25zNhzcv8u0+lqVQe0BcYGoPgVbXBr99llCddb0f5wNn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\nij9w5Q2PYUu2qmBx8cphvnznZmx8Tvmve3jndY8ytqfKw3um2SNVosn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pusGU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\nTf7D7n4D8ExdvazcPK+/U8Nk4OeRnlv1a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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n", + "import numpy as np\n", + "import numpy.ma as ma\n", + "from scipy.io import loadmat\n", + "def make_map(bbox, projection=ccrs.PlateCarree()):\n", + " fig, ax = plt.subplots(figsize=(16, 9),\n", + " subplot_kw=dict(projection=projection))\n", + " ax.set_extent(bbox)\n", + " ax.coastlines(resolution='50m')\n", + " gl = ax.gridlines(draw_labels=True)\n", + " gl.xlabels_top = gl.ylabels_right = False\n", + " gl.xformatter = LONGITUDE_FORMATTER\n", + " gl.yformatter = LATITUDE_FORMATTER\n", + " return fig, ax\n", + "\n", + "cmap = plt.get_cmap('rainbow')\n", + "bbox = [lons.min(), lons.max(), lats.min(), lats.max()]\n", + "fig, ax = make_map(bbox=bbox)\n", + "cs = ax.pcolormesh(lons, lats, data, cmap=cmap)\n", + "cbar = fig.colorbar(cs, extend='both', shrink=0.5, orientation='horizontal')\n", + "cbar.set_label(str(grid.getLocationName()) +\" \" \\\n", + " + str(grid.getLevel()) + \" \" \\\n", + " + str(grid.getParameter()) \\\n", + " + \" (\" + str(grid.getUnit()) + \") \" \\\n", + " + \"valid \" + str(grid.getDataTime().getRefTime()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2. contourf**" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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mT5/O5s2b2bp1qyWSUObJQZIk/P39sywAr1Ao2LlzJwA9e/YsDNMKnA0bNjB+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8pX3uaRq/5J95wplEdKrH5tOatfW4ZXceldZyptvV34nsfg0lUMrxykc3I33TMnMfj2ala+/Da+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d3y6JWRxsMbS+mxuJicgPDjdiHu7ZZHGKgxxrscqs1pIKdax558STYWfDVlkERbjjxtLNDy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\nfrJFRHCuB1sMvI/zQwtNr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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig2, ax2 = make_map(bbox=bbox)\n", + "cs2 = ax2.contourf(lons, lats, data, 80, cmap=cmap,\n", + " vmin=data.min(), vmax=data.max())\n", + "cbar2 = fig2.colorbar(cs2, extend='both', shrink=0.5, orientation='horizontal')\n", + "cbar2.set_label(str(grid.getLocationName()) +\" \" \\\n", + " + str(grid.getLevel()) + \" \" \\\n", + " + str(grid.getParameter()) \\\n", + " + \" (\" + str(grid.getUnit()) + \") \" \\\n", + " + \"valid \" + str(grid.getDataTime().getRefTime()))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.14" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/notebooks/Map_Resources_and_Topography.ipynb b/examples/notebooks/Map_Resources_and_Topography.ipynb new file mode 100644 index 0000000..34fc1a2 --- /dev/null +++ b/examples/notebooks/Map_Resources_and_Topography.ipynb @@ -0,0 +1,556 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The python-awips package provides access to the entire AWIPS Maps Database for use in Python GIS applications. Map objects are returned as Shapely geometries (*Polygon*, *Point*, *MultiLineString*, etc.) and can be easily plotted by Matplotlib, Cartopy, MetPy, and other packages. \n", + "\n", + "Each map database table has a geometry field called `the_geom`, which can be used to spatially select map resources for any column of type geometry,\n", + "\n", + "## Notes\n", + "\n", + "\n", + "* This notebook requires: **python-awips, numpy, matplotplib, cartopy, shapely**\n", + "* Use datatype **maps** and **addIdentifier('table', <postgres maps schema>)** to define the map table:\n", + " DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n", + " request = DataAccessLayer.newDataRequest('maps')\n", + " request.addIdentifier('table', 'mapdata.county')\n", + "* Use **request.setLocationNames()** and **request.addIdentifier()** to spatially filter a map resource. In the example below, WFO ID **BOU** (Boulder, Colorado) is used to query counties within the BOU county watch area (CWA) \n", + " \n", + " request.addIdentifier('geomField', 'the_geom')\n", + " request.addIdentifier('inLocation', 'true')\n", + " request.addIdentifier('locationField', 'cwa')\n", + " request.setLocationNames('BOU')\n", + " request.addIdentifier('cwa', 'BOU')\n", + " \n", + "See the Maps Database Reference Page for available database tables, column names, and types. \n", + " \n", + " > Note the geometry definition of `the_geom` for each data type, which can be **Point**, **MultiPolygon**, or **MultiLineString**.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "from awips.dataaccess import DataAccessLayer\n", + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "import numpy as np\n", + "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n", + "from cartopy.feature import ShapelyFeature,NaturalEarthFeature\n", + "from shapely.geometry import Polygon\n", + "from shapely.ops import cascaded_union\n", + "\n", + "# Standard map plot\n", + "def make_map(bbox, projection=ccrs.PlateCarree()):\n", + " fig, ax = plt.subplots(figsize=(12,12),\n", + " subplot_kw=dict(projection=projection))\n", + " ax.set_extent(bbox)\n", + " ax.coastlines(resolution='50m')\n", + " gl = ax.gridlines(draw_labels=True)\n", + " gl.xlabels_top = gl.ylabels_right = False\n", + " gl.xformatter = LONGITUDE_FORMATTER\n", + " gl.yformatter = LATITUDE_FORMATTER\n", + " return fig, ax\n", + "\n", + "# Server, Data Request Type, and Database Table\n", + "DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n", + "request = DataAccessLayer.newDataRequest('maps')\n", + "request.addIdentifier('table', 'mapdata.county')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Request County Boundaries for a WFO\n", + "\n", + "* Use **request.setParameters()** to define fields to be returned by the request." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using 25 county MultiPolygons\n" + ] + }, + { + "data": { + "image/png": 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QOOtNw+6LyY+/9ZKlTtfVoYaTBHRbUb9+AIBhgwcmtN9mupMSyM3meLzdleXz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define a WFO ID for location\n", + "# tie this ID to the mapdata.county column \"cwa\" for filtering\n", + "request.setLocationNames('BOU')\n", + "request.addIdentifier('cwa', 'BOU')\n", + "\n", + "# enable location filtering (inLocation)\n", + "# locationField is tied to the above cwa definition (BOU)\n", + "request.addIdentifier('geomField', 'the_geom')\n", + "request.addIdentifier('inLocation', 'true')\n", + "request.addIdentifier('locationField', 'cwa')\n", + "\n", + "# This is essentially the same as \"'\"select count(*) from mapdata.cwa where cwa='BOU';\" (=1)\n", + "\n", + "# Get response and create dict of county geometries\n", + "response = DataAccessLayer.getGeometryData(request, [])\n", + "counties = np.array([])\n", + "for ob in response:\n", + " counties = np.append(counties,ob.getGeometry())\n", + "print(\"Using \" + str(len(counties)) + \" county MultiPolygons\")\n", + "\n", + "\n", + "%matplotlib inline\n", + "# All WFO counties merged to a single Polygon\n", + "merged_counties = cascaded_union(counties)\n", + "envelope = merged_counties.buffer(2)\n", + "boundaries=[merged_counties]\n", + "\n", + "# Get bounds of this merged Polygon to use as buffered map extent\n", + "bounds = merged_counties.bounds\n", + "bbox=[bounds[0]-1,bounds[2]+1,bounds[1]-1.5,bounds[3]+1.5]\n", + "\n", + "\n", + "fig, ax = make_map(bbox=bbox)\n", + "# Plot political/state boundaries handled by Cartopy\n", + "political_boundaries = NaturalEarthFeature(category='cultural',\n", + " name='admin_0_boundary_lines_land',\n", + " scale='50m', facecolor='none')\n", + "states = NaturalEarthFeature(category='cultural',\n", + " name='admin_1_states_provinces_lines',\n", + " scale='50m', facecolor='none')\n", + "ax.add_feature(political_boundaries, linestyle='-', edgecolor='black')\n", + "ax.add_feature(states, linestyle='-', edgecolor='black',linewidth=2)\n", + "\n", + "# Plot CWA counties\n", + "for i, geom in enumerate(counties):\n", + " cbounds = Polygon(geom)\n", + " intersection = cbounds.intersection\n", + " geoms = (intersection(geom)\n", + " for geom in counties\n", + " if cbounds.intersects(geom))\n", + " shape_feature = ShapelyFeature(geoms,ccrs.PlateCarree(), \n", + " facecolor='none', linestyle=\"-\",edgecolor='#86989B')\n", + " ax.add_feature(shape_feature)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a merged CWA with cascaded_union" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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3H2H3528wNB/H+aNG4vxRI+tdN6xfP7y94BPM/nghbrp8gk2VZb9gOAyXptpd\nhuNIabbpIAC3X3dN/cvXXt3EkvW53YmuH16vt9X7HH/O2c0uc+f119a7fPcNxx41e3zadLj5w/aE\nKELg202bc2cEDSkTQ/akLnIkjUzHdzZZShzegai/2O4yHKdbp04AgH49ukLXdbY2p4mUQFEBx+Nt\nrXy/H6FIxO4yMoIiRL0gRC1nSplbJwIqKmAYHBveQfjNS5bZuXwBfFXfo+iip+wuxRE2bd+Bxd+s\nRGVNDeLJvs0fL1mGj5csg9/rReeS9hg7aiS6d+lic6XZw+txozoQsLsMx1FUFQC/+IHEo2CaHGnj\nRJ0xdKjdJVhHUSD5WnEUnlVEltn99uMAgE7Dmj8MmusOHzmCOR9/irKKCiiKgn49e+DeSdfjoSm3\noG+P7ojpOnbs3YcZ78y3u9Ss4tI0xDiVdqvpus6xxZMUVeF8FSdh7ZYcGg/bNCEUdgdzErY0k2XO\n+NXLWH9/X5Rt+hqFHbvbXU5GW/jVcgDAL++585iuGJOvvBwA8NHni7Fy0yarS8tqgWAILpVfYq0l\npYRpmnaXkRGEEDAN3e4yHEkIgSNV1XaXYS12zXAUtjSTZfJLuiDaaxT2L3jB7lIyXiSaGCv4eH2X\n2Y2g7fm8XqgMza3mUlV++ScpigKDTc0nREqJwrw8u8sgahJbmslSxRfeiSNv/AbvflaKy8eezxPa\nmhAIh457coiu69i6Zw+8bneTy1DrKYpAJBazuwzHEYrCM/+T3JqGgGni7zNnoX1xMfzJkTyisRiO\nVFfB7/WiMM+PqpoApBCQpkS+3wddT0wMY0oTkAKGaQJIPKZSAhCAKhRAAJqiIhSJwJQSHYqLAQGU\nHamAhETXDh1QVVMDVdVQkJ+HWDyOyuoadGxXjGA4DF03ICERCifWrx35pGO7dsgTJp6YNh1CCEgp\nE59BUkICME0z0YouJfxeLzxuNyqrE63CRQUF0PU4AqFwal2XywW/CgxY/yo8MBL3pamJS5JdFIZX\nVUFVBBate/HobUJJTgAiEtuQZuJhkWbygZGJ/6UJKU3ANJO3mYBp1Pk78b+AhETyZE0hUK8vfsPL\nTV0vjcT26t6n5GPVLClTNbkqd0OcxvG8nYSJhSyV374zyjQv1m3ZinVbtqJ31y645eqJdpeVcUKh\nCLTjtHhu2rkTAPDTKbdYVFFu4DTaJ4Zn/x8V1xN9u2uCQdQEgzAbBKlKpQZ7GnRl0VQ10cUluWwy\nHtYjhIB+xFKTAAAgAElEQVRAoiVbURQYhgEpJb4Ph5MBO7Gdnfv2J6b8FgIHDh9ObS8UDqdOKK69\nzuVyJbOjQFlFBdyFedANA16PO5kHBZRkwJSQUBUVkBLRWAyBUAiqoqAgLw+RaBShSASqoqAoPx8S\nieEbo2W74d+5FP7r/hMCIhFqG5IydTKcEolgX9lhdO/T5+jtplnvZDkhlERAVRL/CwgIRUmEV6FA\nUVUIRQMUBULVoCgqROqfcjTcSgkpj25XmrKRRz05DJyUR/8GUvtI/EAw6qwrIJTm3gsKRLI2oSjo\nMfIiYOMTzaxDmYKhmSxlRENwef345T134vFp0xlQmtC+uBgHy8ubvL32w/vdzz7HdZdcZFVZOSHf\n77e7BMcxTZNjZyRpqgpVUfCrH53YRC92ajjJzMnau3YJdqz5J06f/Ms22yaRnZhYyFLS1AFFgaYl\nZtratf9Ai9fVdR1/mTETjz03Fa9k8agRsWQrzvGMGDgQvbp2xabtOzBr/vsWVZb9pJRQ+UOu1Yxk\nyyYlRmBhR5UERdUgGmtdJnIofjuQpaRppvqA3Xb1VTBNE7v27mt2vVAohCdfnIFgOAy/z4vdB1oe\ntp3kH6++nrqfHtfxDwTdevWVGNCrJ3bs3YcN27ZZVGF20zQVwfDxf7DQsXS2NKdIKdm/O0koamM9\nHogci6GZLFU3NHfu2AEAMO/Tz6DrOo5UVELXjx2qadpbs/HMy6/CNE3cfu1V6FxSAgBYvTF7hlv7\nePES/OlfL6CypgYjhw3GD847Gw/efluz6914+aUQQuDr1WstqDL7xXUDpslv+dYyDIPZKMnl0ni0\nohZbmSnLsE8zWco0jdSZ0gDQv1dPbPt+Nx6fNj11nRACqqJgyjUTUZiXh0PlR6AIgaLCQnTv3BnX\nXnQhnn7pFbz/xWL07t4NxYWZP+1xIBDAuq3bMHJY47NdfbN+AwDghksvwaA+vVu17Q7FxdhXdhj7\nDx5E186dT7rWXKYIge5dOtldhiPxZMCE4oICHDjc9PkIucQ0dEj+gKAswlczWUrq8XrDDl1/yUUY\n3Lc3OrVvj4em3IKzRgxH3+7doBsGXpz9Dp55+VUAQNeOHfB/Jt8IAPB6vbh70nUAgBfenmv9nWiF\n9Vu24E//egHPzpyFz5Z9jadfegWV1TWY+/FCVNfUAABC4Uhq+dYGZgC496YboAiB6XPfRUVVVZvV\nnouklDhYxsDTWoZhtmy4rRxwqOIIJ3pJSoziwZjRUuzWk/nY0kzWkrLeCUOapuH6CZekLl983rkA\nEif9HayowMIvl6Jbp0646Lxz6m2mU0kJzhg6FKs2bsSGrdswqE9vbN+9G726doU3OS7q4m9W4Ot1\n6xGLxyEgcNWF4zB0wIDUNgKhEP7x2huQpgkzOVbpmJGnY9xZo1p9twLhCL5YthzjR4+C2+3GzHnv\nYf/hw4nxSjUNY0aORFVNNVZv3oJILIod+w9i4/btmPiD8Xj/s88BAL27dWn1fmv9+Nab8Y+Zr+Gf\ns97EJeefi1HDh5/wtnKZYZrQ+MXVal06lnCynSRpSo4KhMSIKrFgFRod95jq4VEa52BoJksJVYUI\nHkHFnq3wt+sMT15Bo8tpmobuHTtiyrVXN7mtCeefi807duCdTz+rvw8hoIjE5ACaqqIoPx/VgSDm\nflqKWCyG04YNAwDMnPcedF2H2+2CW3ED0sSXK7/FeWec3uikK5U1Nfho0ZcY2LsXRg5PbEPXdbw4\n+x0crqgAAKzevCW1fGGeH+POGoURgwelrrti/DiUlpZi0rXX4pkZMzF/YSkA4K7rrkGXTh1b8Ag2\nrjDPj5/ffQf++spr+HjxUny8eCl6d+uKW6668oS3mS2mvvk2yo5UHHN935Ji/PH5aTCTkzjUfm0N\n6dfP2gKzwJadu1JjBee68soq9mkGsOJfjwAfPQmz+xl2l0LUZhiayVL+Tr0QPLwVmx8+DbHup2Pc\nU1+e8LYURcFDd9yG0q+Wo6ioECMGDsC23bvx8eKlMKSJUcOHYcyZI1PLP/PSy3h/0RK8v2gJBvft\njXAk0S3i53fdASARgB+fNh1/nj4Dv7q3/hirsVgM/3ztDUgpsX33Hiz6ZiXOGDYUX65cBQDo3b0b\nbrpsAtZv/g5VoWCzrdWapuFnd07B1+vWoaSo+KQCc91t/tudU7Bm02YsXbUau/btxwtvzcbdk64/\n6W07WVXN0RbQovx8qIoCwzSgKSpMKVFcWACXpqWCdXllpV2lOpZhmMedjCfX3Ho1f6ya0RCMs2/D\nuF9Pt7sUojbD0EyW6jN6AvrM1rHpo5dxaP4zbbLN8eeMTv09uG9fDO7bt9HlHrpjClas34hv1q7D\n5h27AAA+jyd1u6ZpuPGyCXjzwwX4479eSA0dpShKqo/ij2/5IbZs34lPv1qGL1eugtftxo9umoT8\nvMSEGKcNb/xEv8YoioKzTz211fe3OacOGYxThwzG2x99jC07d+EPz01F186dcOvEK3Jy2nKXlghz\nP7/7jnrXl5aWYtL119lRUtbRVBVd2+CHX7ZoV9D4EbScwh4HlIVy7xuUMoI0TdjxqXrm8KE4c/hQ\nVIdC0ONxtC8qqnf7gN69cPn4sVi4ZBk8bhc6t2+PYCQKCYmrL7wAxQUFGH3aCIw+bQSmz54LTdNS\ngTnT3HDpJThYXo43P1iAfQcPYfaCj3HTFZfbXZblXC4X+A2eXi5Nw+FGusDkqsaGzsxNPD+AsgtD\nM+WkwuNMlXz64ME4ffDgZrdx5/XXtmVJadG5pAQP3HYzHntuKrbt3oul367GuaefZndZllKFgupo\n1O4yspoQAmE+xilut9vuEmwnFBUwDLvLcBSOnpH5eLYC2UOabPyz0CP33wsAWLJipc2VWM+QHA4t\n3TgLXkJtC3MudoM6hlAhOblJi3D0DOdgaCZbJL5e+UFhpTy/DzHdQCQSaX7hLOLzeBwxAY7T8d2c\nOGEYYGgGkHhB8IcUZRmGZrJHnem0yRq3X30VAGDVps02V2KtQCiMUI79ULCabprJvuO5zeRnWorg\nY0FZiD+HySZsgbBacVEhhBAoXfY1Fn+zEiOHD0W/nj1xsLwchm7A7/PicGUldN1AXI/DMEy0KyhA\nMBJGcX4BKgMBeD0eDOnbG9WBEIoKC1BZU4P2hQXIzy9Avs/b6H6rAgF8vWYNqmqCUFUVHrcLhiFR\nUVMNaRowDAnd0CGlhM/rgVtzQ1EEovEYNFWFx+1BcUEhdENH++IiBENh1ASCyPN7URUIwjQMKJoK\nPa7D7XIjL8+HIxVVME0DhmEiGA4jz+ez+NHOLbquo5gjRsAweAIgUTZjaCZbyAYzA5I1HrjtZixb\n9S2Wr9uA5WvWYfmada3exvI1a094/7UTz0gkR1Bp8BpQaxToyZOHBI7+tBJCHLfPbFO3126DLc3p\nlfhxw5Pf4snuGUSUnRiayR6NBCZKv3y/HxecPRrL120AALQvKsL9k29s9XZisRhqgkGUtGsHINHS\nGIpEEAgGEYnGEAxHENPjUCDQs1sXdEguZ4cX3pqTmrGR0kMIAVXl+zkQ5I8zOnE8mTbzMTSTPaQ9\n4zQT8MYHCwAAv7znzhM+YcntdqOkTsuipmkozM9HYX5+W5TYpuLxOEx+GaWVbhiIxtg1IRRlaKbW\n4+gZzsGe+mQLCXbPsMvu/fvh0tScOcM/HItxiuc0c7tcCASDdpdhuwi7ZxBlNYZmsoWp64DCIGMH\nU0oU5efOSVuKorAlJ808LhcDIwDDiNtdQsZo7JwFIqdjaCZ7mAzNdvh6beLEvysuON/mSqzTrWMH\n9hUkS0Si/OFQSxo6hJobR7ModzA0ky2klBzH0waLvlkBVVHQvUsXu0uxVFxnf9t0isQ4hTYAxGJs\naa4lTR1C5djdlF2YWsgWeqCCU6zawO1ywTBNVAUCdpdimSNV1VB4mDit4nEdqsKvk7hu2F1C5jDi\nPJrYSjwilvn4KUe2CH/5KhS33+4ycs59N00CAJR+tdzmSqxTlJ8PhYEurYQQKMjLs7sM22kaX2e1\nTD0OobGluSV4zoVz8B1OtjC9Bej8g9vtLiPnuJPDxIUiuXM4PRKNpiZMofRITFZkdxX2i+TQ+6pZ\nehwKQzNlGYZmsgcPQ9nG63Fj5969ePaVV7Fp63a7y0k7r9djdwlZTwgBlYfiIXhE4yjBj3nKPnyH\nkz2kCfALxhY/u/N2DOzdG8FQGHM+XYi/vfyq3SWlVdmRI3aXkPUUVYHHxVZFqkOCJ3tT1uF4MEQ5\naNJllwAAPl26FMvXrMeGrdswbEB/m6tKD5/Hg2CYM7WlU57Ph6pAjd1l2M6VIxMGEeUq/gwkewgF\nMDl6ht0uOvdcAMCK9RtsriR9ItEYjxOnWSwe5w8TAG4XQ3OKAEdIaiWOnpH5GJrJHjxbOCMsW70W\nANC5fXubK0kfVeWMgOkWicagcqpyeD1eu0vIHOya0WL8fHIO/iwmymGrNmwEAEwYO8bmStInGo/D\n5FGNtPK63TDZSga3h/26ibIZQzNRDsuFw6cel5uTm6SZS9NyasKcpnhVt90lEFEaMTQT5TBFUbI+\nUEZjMRgcp5mswB4JRwkFMPm+o+zC0EyUo3Rdx5GqamhZ3hfV5XKxC32axfS43SVkhGx/L7WG0NyQ\nOid7oezC38VEOerpl14BAFz9gwtsriS9fG43IjGGunSKRGMMjAAM9p1PEYoCyZZmyjIMzUQ5Kq7r\ncGkaBvfrZ3cpaRUIh2Gye0ZaCSHQsV07u8uwnaEzNKcoKiR/RFCWaXFoFkKoQohVQoj5yct9hRDL\nhBDfCSFeF0K4k9fnCyHmCSEWCiG6Ja+7UwhhCiFOrbO9dUKIPm17d8gxeKa97S47/zzEdR0Ll35l\ndylppSoK8vL8jd5WUV2Nx56biseem4rHp75gcWXZI9/vR3UwaHcZtlM1tkPVEorGPs2UdVrzDn8I\nwMY6l/8I4Ckp5UAAFQDuSV5/G4Dnkss/WGf5PQAePfFSKatwGm3bfbtpMwCgZ9cuNleSXoZpQpqN\n/0j7ePFSAImWUt1gq9jJCIbDdpdgO3adJ8puLUotQogeAK4EMDV5WQD4AYC3kou8BODa5N8qADP5\nr+5nyHwAw4UQg0++bHI8zghouarqapRXVODTr77C32e+hgOHy3HG8CEY2KeP3aWllSnNRsdpPnD4\nMLbt3g2AkwtQ21DYr/sovqcoC7V09IynAfwKQEHycgmASimlnry8B0D35N8zAbwGwAtgSp1tmAD+\nBOA/ANxxEjVTNuAHquX+/tob9S4P7tMbl51/vk3VWKdDu3Y4UHa43nVrNm9J/f3I/fdi9oJPsGXn\nLqtLoyxjsDtCihA8EbC1Dh48iDVr1iAcDmPNmjV2l0ONaDY0CyEmAjgkpVwhhBhfe3Uji0oAkFJW\nAri8ic29CuBRIUTfE6iVsolQcGDWb1DYfQA6Dx5pdzU5Y2ifPrj20ovtLsNSsVgcsXgcf3x+GoDE\nB5WUEn1LiiGEwN9ffR1VNTUAgH+98XZqvepgAIV5eQASYz1HY3GoigLdMFKjJLiTw9mZhgmXywVd\n1yEB5Pm8qAmGIKUJRVFhmGZyPGwJs0FXkXy/H9F4DLFYHD6fF25NQyyuw+XSUBMIQtNU6LqRmnFP\nVRT4fT5Eo8nhvIRALB6H2+VCLB6H1+2GpmkIhELwuFyIxo+OHCKEgJQy9b/H7YJLcyEYDkNKCZ/X\ng3Akinx/nT7gUiKQ7Hrh93mhKse2plYnJzb56yuvIRQOQwKJfcdiMKVEQZ4fgVAYPq8XAkAsHofH\nfXT2vM55Pjz78sxGn79AKAxFCPh9iSmqdcNENBaDlBL5fh8i0cTfEICAgGEaEELAl5zSWkqJcDQK\nt8sFwzAggeSRB4nkalAUBT6vB0jeFo7GAAA+T+K6SCyG1MIQME0TLk2DbhjwexP7CUcjxzy3uWr7\nkvegfvIUjPPutLsUR/B6vWjfvj2mTp2KqVOn4oknnsAVV1xhd1nUCCGbOSFLCPEYEi3GOhKtx4UA\n5gC4FEAXKaUuhDgXwH9LKS9tYht3AhglpXxACHEfgJEAzgcwUUq5s5Hl5WeffXbCd4oaFwgEkJ+f\nb3cZAAAjFkF4/zZoxZ3hLepgdzmWsut5OHA40drapUNuPd7llVWI6/F6YU9VFbhVFcFoFFJKaKoK\nvcEIGwICQgiYyVkTRSKVATKxPpDsL50MoQKAmfo7EUoVRYEQybyVDFyJ5Y/uxzQlTGkes79j95mo\nXwgBwzCaXK52G7Xbk1Im1637WS+S+zZTy6qqCrP2/iiiXpeWurebTcwiKSAglMT+XJoGXTcSj62m\npvqV125XEQogkNqHR1URbWKEEwEBlysRUGuXFxDQNC3RsitlcizuxH0yDBOKIqDreurHTe1zknrM\npQmXdjS0K0Igbuj1tu9yaTCSP5AUocClaXXWl8kALqEIAcM0IYSAS9PgcbuR5/M1el8yXVt9NoXK\n9sCMBuDvOpBdVlrgwIED2Lt3LzRNg8vlQseOHVFWVmZ3WTnlJz/5CaSUzR4CbzY011s40dL8Cynl\nRCHEmwDellLOEkL8E8AaKeXfm1jvThwNzW4AG5Do6nF2U6G5NXVRy5SWlmL8+PF2l5HyxUNno8Ml\n92HYxHuaXziL2PU8PP3SK4hGo/j1fbn1eL80dx72HTyER+6/FwDw1kcL8N3O79G3pBg7yisxpF9f\nXHfJRTZXmdusfE9EIhE89dIrqdcDHdVWz8PXz/0asUM7MOb/f6P5hQm///3v8eijj+Lf//3f8dhj\nj2Xcd3UuSDYwNBuaT2b4gl8DeFgIsRWJPs7TWrKSlDIG4C8AOp3EvilbcAQNy0QiEfiSh5JzyeEj\nFfUu79yzr96Jf2cMG2p1SWSjYCRidwlZT3G5IfWY3WU4Ru3nERsLM1+rptGWUpYCKE3+vR3A6Bau\nNx3A9DqX/4JEcKZcJk0O0WQhCUBVsvMRD4UjCEciKGlXnLpO13W8NHceYvFjZwP0etzo0qEDJk+a\nZGWZlAFCYYbmdBOKiycBtgJDs3O0KjQTtSnThFD5ErTCofJyAEBRYaHNlaTHi7PnoDoQhABwz6Tr\n0K6oCLMXfIJD5UeOWdalaQhFIiivrEIsrsPt4mswl3jcbrtLyHqK5gIMTl3fUgzNzsFvC7IPh52z\nTCAYAgC0K0pPaK6qrsbcT0uhGzpunXgFDNNELB5HTTCIXt26pWWftd75ZCGqA4nZ6CSAOZ98hvLK\nytTtA3r1xNbvd6cu134xxfU4pr31Nn588w/TWh9lFq+HoTntVJXj8LcCQ7NzMDSTfYRA9dZV2OYv\nhFA1KKoKoaiJiU+AxOE9acLUdQhVTY0EUG8KbiEgFBUh4cNBw4NOHdqjqjqArh07oG/PHjbdsczT\nr1dPdCppjzWbtmDNpi3Nr3ASnnrplXqX03nC1eqNm7Bh2/ajrw0A5ZWVUBUFD991OzRNwxsffFRv\nnZiuI9/vS3TP4Mk2OSfaSHcdaluK5mZLcyswNDsHQzPZxjtkHKKrP8C+1R8kptWWEpDG0VAslERr\ntFATt9dKtVAnlvMe2QHF1LH8kr/CVD2pxS4bOwZuzYWYoSPP54MiFPi8bhypqoEejyMQDuHrdeth\nGib8Xi/y8/wY0Ls3Ro8YDk3LvrfGpWPOxcvz3sPIIUOwv/wwyo5UoCg/D+NGn4Wlq77FgcOJLhwd\n27XDxWPOxWfLluNA2WF0bFcMoSipodb2lx1OLHPeOQCAhV8tw8HyI+jZtQsmX3EZvly5CoZpwjAN\nfLN2Aw5XVCIe12GYOoLhMGKxOEratUMsFsORykoYpoSqCgjFBUOPw+NxId+fhyOVlQgGw4lhvVQF\nqqJAAtA0FYpQkO/3YcGXiWmwRw4biny/F+FoHBecdWa95y+UHGP4X2+8hcMViRboYhu7qZSVlyMQ\njqB3t65QeCKs5eIMzWknlAaf2XRcDM3OkX3JgBxj9P/3JIAnT3o7e1Yvxp7fXYbLSiI47Yc/ga7r\neHzadHy46MsWrS+EQDAcRnUggL0HD+GL5V/j+gkXYVDf7JqD580PP4YiBC694NhZAIf0O/a+3nX9\ntS3a7t2Trq93+YLRZwEAVqxdDyARVtNtxfoNqb+/XrsWD9+ZaGX+x6uvozqY6LpxuKISLk3DkH59\nMWHMuViyZEna66rLNE3Meu8D7Nq3P3VdcUEBhBDwetwY1r8/Rp82wtKachFzSfopmhuSLc0txtDs\nHAzN5Hg9Tjsf+y64FzUbFwP4OTRNO+EuAaFQCP+c9SbeXvApbp54Ofp07978Sg6we/9+RGIx9O7W\n1bJ91k4UkY7uGTNmv4O9ZWXweTwIR6NQFQUTxpyHIf364KmXXsGTL844Zh27xuV9Zd587DlwMPWF\nqKoK3FpitrzqYDBxfQ2wv+wwPlu+HFICHrcLky+/DF07c2TOtub1eppfiE6KcLkBQ7e7DMdgaHYO\nhmbKCr6uAxDZtuKkt+P3+/Hw3Xfg8akv4rX5H+D+yTeifVFRG1Ror1nvfQghBG66vNFJO9Mi1sQM\nb22hc8eO2FtWhnByKmnDNPH519/g9GFDcPMVl2Pp6tUwDBOVgQC6tm+PLd9/n7ZamrN7/wFoqop2\nRYXo37MnLjzn2JE6TdPEP157A9FYDF06dMCuffvw9oJP8MCUW2yoOLsFkyfFUvooqgZwyLkWY2h2\nDoZmygpVa0shvHlttr0f33oznp3xCp6b9SbOO+N0XDB6VJtt2w6GYaCkuNjSvtoFaZxK+NKx52H4\nwP5YuX4DKqpr0L9nd5w/6kwAQJ+e3dGn59EjBB98vihtdbRUz66dMfnKK5q8XVEU/OTWydi1dx8+\n+GIxgMR9pLZnsAU07VSXBzD5OLcUQ7NzMDRTdti1Et3vebrNNpfv8+LeSdfjpbnzsGTVtzj39FPh\ndvD4rkIIhCyeCS2c5hnBenTpjB5dOje7nKapaa2jOW5NQ3lFVYuWffOjBYjHdXQuaY+Bffqkt7Ac\nxRMB009xuSHYp7nFGJqdg6duU1ZQBpyHvXOfatNtdixpjweTh8effHEGPlq0uE23b6WSdsWWh2a3\n5rJ0f00Jhu09HK8oSupkxOaoydE0Gp5cSW1HCLYVpZtQXeye0QoMzc7BTw/KCqP+7Z9YdUdHlG1f\nh479Tmmz7brdbvzsjtsw7e25WLlhE9Zs/g4D+/TChPPOxXc7v8eegwdw0bnnwOv1ttk+02HsqJGY\nveBTPPbcVAgkBusTQqAgz49xo87EiMGD2nyfPk9mnHClWBSSTNNEMDm83epNm7Fx2w4IAURiMXhb\neJQiGo1x8o20YzBJN83tZUtzKzA0OwdDM2UFtz8fsaIe2P7ev9Dxp8+06ba9Xi9+cutkrPtuKz78\nfBE2btuBjdt2pG5fu2UrHpxyC/x+f5vuty2sXL8BC79ajrie6F/Yo1MnxE0Tndq3w96DB1FZXYP5\npV+gW8cOKGnfvk33nSmf/3E9nmrBTac3PvgIO/bsrXedS1OhqipuuOSiFm1DAujZpUsaqqNaqsoD\nrOmmur2c3KQVGJqdg6GZssaQR2Zj63+Mwa5zJqL3WZe0+fZPGTgApwwcAABYs3EzOnfqgAKfD8+8\n/CqeeflV3DrxCvTqnt4po1tD13V8tHgJFCFQXFCAK8ePbXRK6z/96wU8/+ZsdCppj0mXTcDWXd9D\nFQKnDBp4UicOFuSl70TA1ghFopZ8GdW2Mj9y/704WF6O/YfKEAiHYRoGVm/5Dt9u/g6qqkBRFAgh\noCoKhvbvB103sHjlShw4VAYAyMvAH1/ZRIrml6GTo6ic3KQ1GJqdg6GZskbH/iOw9awbsWfBi2kJ\nzXWdOnRw6u+HptyCv7/2OmbOfx9nnTIcF485N637bqkX354DALh/8o3HnQHv4btux7Q3Z+NQ+RH8\nfeas1PXfbtqCO6+/5oT3XztOs90K8/w4cJItzbv27cPqjZvRt2d3jBjUeFeWiqrq1Jffi2/PbdEX\n4Dfr1te7XFJc3OiQdNR2TCMzXpfZzDTNxIyu1CIMzc7B0ExZZcB1/4Zt/zEGe9d8ie6njrFkn36/\nH7+45y48/8Zb+HrdesTjOi4fP9aSfVfV1GDJym/RqX17nDliOACgsrISM9/7ANWBIIb279vslNGa\npuH+m2+CaZpYvnoNThk4AM/OnIVg6OROoDtSXX1S67eVmpoAjJMI8Lqu47X5H0BKifVbt2HFug24\ns5HZEgvy8lInHSpCwO/344Hbbm5yu5u378CcTxYCAP79vntOuD5qHQaT9DONOMAp4luModk5GJop\nq3TsPwJbTrkc21//Pbqf+p6l+77vpkl4dsZMfLt5c5uE5rIjFXjhrdkwpYSmqvB7vbhn0nWpkw7n\nffoZ1m/dllr+4yVLoShKKiAO6tMb117csr60QGKUh3POOB0A0KmkBIfKy/GH56fhhgkXY2Cf3q2u\nv0NxcavXSQev1wMpJXbvP4ADZWWoCYUwqHcvxGJx7Nq7D1+tXoPtu/egd/duUBUFHrcL551xBjqV\ntMe0txIt8ABwysD+WPfdNhwsP4I/PD8t0WlbCAgAZvLLrm53lppgEH94fhpURcHP7pxyTFeXwf36\nMizbgd0z0k7qOiDsHerRSRianYOhmbLOoFv+E9//4lSsnP5bjLzzvyzd98VjzsXcTxaisqoKxSc5\nk+A7nyyEKSUG9OqBYDiC/WWH8dRLr9RbpmNJO9w76QbsO3AQ732xGJFoFGcOH4rzRp5xUvu+Z9J1\n2L1vP1577wO89dHHuGHCxRjUt0+rtrFr34GTqqEtvPPJQnz3/W4Aiemsay1bvRZ9S4rx6vz3U9ft\n2rsPQghIKbFx2w707NIJh8qPQBECZ55yCsaPPhP9e/fCoq9XQtd19O/dG16PC+u3bIUhTQzr3w/j\nkhOsTLn2KsyYMw9+nw+BUAivzJvfaOs0Wa+ooMDuErKeBMcoaQ2GZudgaKas07H/CGwdczfMPRss\n35Rh0fQAACAASURBVPfA3r0AAC/NfRcP3XHbSW2rKlADTVVx4+WXAQBCoTBWbFiPI5U1CIaDGD96\nNLp17gQA6NalM3500w0nV3wDPbt1xa9+dDcee24q5n/2OR7o3q1VE7yUFB+/W4gVBvftiw3btkMR\nAr9u0KpbWlqKyZMmNbre86+/hT0HDgEA2hUV4uLzzgYADOvfH8P69z+6jWVfY/igAYhGY4hEY/jb\nq7MQi+sYOXxYan+PPTcV1/xgfNvfOaJMJdmnuTUYmp2DoZmyUodRl6Ps+fuwbdFc9B9rXQufpmk4\nfehgfLtxMw4eLkfnDiUnvK0BvXpjw7ZtqAkEUJCfD7/fh7GjrJ/O++xTR2DZmrV48sUZUBQFHk1D\nn549MHH8uOOOrlH7RWCnIf37YuS+oVi5YWOr1rvvh42HaQD4w/PT6n25KYqAadb/svtm7Tp8s3Zd\n6nJBfn6r9k/pc6Sy0u4Ssh7DX+swNDsHQzNlpYHjJyF0YCfKnr0V5Wvuh159CMaub3Ha/3yEwo7d\n07rvS847F99u3Ix3Pl2I+3544wlv58rxY7Fh2za8+eECW2eI+8G5Z+MH556NFWvWYfm69QiEQti4\nbTs2btsOIDmLnQDcqgbdNCEAXHnhOOw9UGZbzXWVV7ZsCuvjOVh2GAu+XAqIo19s/bp3x8gRwzCw\n97H9vXVdx8tz34Vu6Ljh0gknNXQftS3BiXDTT5pABvxodgqGZufgJzllrdMm/wL7R5yP76b9AsKT\nDxR3w5pfnI3R/9gEtz99LX+apkFTVVRV15z0dgAgP0NaKc889RSceWpitsUNW7dh36GD8Hm8+GrN\nOujxOOLCgNvlQigcwZyPF9pc7VF+vxfKSZ7J/9LcefVG4CjMy8NZp49AnseH7/ftR54vMSZ1VTCI\neCwKIVRcePZouN0aQpEIZIVEMBKBS1WR5/dBAohEIlCEgFAUmLqB4qJCuN3uxPVKYjxnJ4RtXdeh\n6zpM04QpJeK6Dj2uw+tLnLAaCkfrzXIYi0WhKArCsRiEUOB1uRDXdaz/7jsoior2hUfPBQiEw/B5\nPQiFI4hEIlA1FR6XC16vF7F4HEX5ecfUE9N1eN1HZ6MMRSLwedyIxeNQNQ1VgZN7X1LzpMnQ3BoM\nzc6R+Z/IRCeh6/Bz0PXPi1OXv/jZeVj+2C04+9HX4fKmb/KNwX16Y32yJfZErfvuOwCAloFDNw0b\n0B/DBiT69o45c2S923bvP4Btu3fDNCWWrV5jR3n1BIOhkx4zuuGQddXBIF5/78OT2mZbavJHgfx/\n7d13nGRVnf//9+dW6Dg5MYEMAwgzpCE4pAEkSJIkoiKgsOp3FXV1/bqyrrvq7hfF7G91F0UZMKCI\nJJEcmjQMaYjDgMAMYXIOHSvc8/ujqnt6YndPd9WpU/16Ph4DXbeq7v1Una6qd5869xwnJ7fJ6ozd\nh81Uyof07qOG69m33vZdBgZInMtIEfGitwjN4eC3GoPKQVf+WS9+81TNvuJgDTnxMu1/9udLEp6P\nOXya5r41X/c+9phOOaZv08/Ne3O+bnuw0FObTqV0xoxjB7y+Utp5/E7aefxOml0BgVlS1xLi/ZFO\npZTJZnXFRR9TY0N5VuzL5XJq78hILlZHNqv2TEa5fF4PPPGksvm83n/wgWpra1cqldKcV17Vym2M\n1Y3M1FBfp2kH7K+XXn9d65ublSsu8DF86BAdf8Thqk2n1dbRodp0jZqeflrOOZ1+3DG6+7EntGL1\nGuXyeY0dOUJnHD9DtbU1uu3+ByVJH/ngKYVvVvrZI769kzIH2pIVKzTzltvLcqzBKs5m5AjNvUZo\nDge/1RhUho6ZqOk/m6NX7/hfrb33F5r94oM65qqB7zEcMXSo6mtrNefV1zX3zQX68icv7tX9mpub\ndftDDyuKTP90ySf6NFtFpclmMr5LkCTV1tb2e2rek6e/X3c+8qj+58Y/6auXf3JA6upJMplUY+cQ\nnW7bL/vwlrOkHLr/+3q1z/cffGCPt9l953O6ft7WNHmXnLPjK0X6ls1kfZdQ/YpzmKN3CM3hqLzv\nfYESS6RSmnLeFTrku01Kv/mY5v71V5oz8z+06KUnBvQ4X7zkIl142qnqyGT07Euv9HwHSdfc9BfJ\nOX38rDOCDsySVFtT0/ONyqAmler3nLFT9p2s3SZOUC6f12+Ky5MjTOtbmn2XUPUsmZLl+/8Nz2BB\naA4HPc0YtBpGjlP9R7+n1Xf+RJZp1nt3fV8Tb2oZ0GPsvvMkJZNJPfTU05q67+SuIJzL5TTr+ee1\nfNVaLVq2VHW1hUUwMtmsRgwbqknjxg1oHT4kEpWxIpj1IzLfdPe9equ4OEoXPtiCVldTunMZUODi\nPMto9wGhORyEZgxqU879vHTu57Vk7my9+x/H6905DynO5bTLtA/0e8aFTqcfd4xuf/Bh/fC6GyQV\nTnr6/q9ndl1vZmpt71BkpkRkWr+hOnrCXIWsCbZ4+cpNLsdxrEwvxzlP3m3XrtB8xNQDVFNTq6MO\nOWjAa0T5NDaWZ0z6YBZnM3KJlO8ygkFoDgehGVBhlo0F0y7Qez/+mGqbl6n9M9dr31N7Nw65J+/b\na09N3m1XvfrGW3rsuedkMh11yEGafvBBW5xAddU112rX8eH3MktSJuv369mXXv+71m/YoHXNm/4R\nMvOW27Rs1WoduOukHvfxwKzZkgon082bv0BmRmgGepBn9ow+ITSHg99qoGj6166XJD16xTS1PvYn\nNS98TVM/duWAzOmcTCY1db99NHW/fdTU1KRjD9v6yn416bTeWbJUV//qN0omE9pvzz314rzX5CQd\nf/g0HXlwOIFt3Yb13o7d2tqqvzU92nU5WRwqkslkuhY7Wd/crKuuubbrNtMPPkjHHb5pu4wYOlTL\nV69W7JzWNxeG7lx/62069rDDtPuk0i6SAwSLeZr7hNAcDgYdAZvZ+7P/Lckpf+9P9fTnp+rxb39Y\nz/zq61rw5F0lP/aXP3mxJu+2q2rSKXVksnph3muqr6tTOpnUw08/q9wATJ9WLkMbhng7dn19vdLF\nXvwJY8foK5+6RE+99JJ+eN0NyuXzkqRUMqXxY8YoKn62z3r+BX33l7/Wf//uxq75pT953tkaNXyY\n9tptFyUTCSUSCS1evlJ//NvdXh4XEALnYsmIF71FaA4HPc3AZsbvf6TG/9ddWjH/FS178RG1vPea\n2l6fpaUP/a/GH7BAtUOGl/T4551yUtfPmUxG6XRamUxGP7zuBn3/1zOVTqVUX1urM084TpN22qmk\ntfTHmBEjvB170dLl2mX8TnrzvYVavHyFoijSC3NfkyR99bJLlUwm1dTUpPNmzOi6z09v+J3iOFZr\ne7semv20Hn/ueV12/jn69Ec+rN/cfEtX2E4mEjrxiMM9PCogDC6flaLKOBE4BITmcBCagW0Ys8cB\nGrPHAV2XH7t8b/393hs09fwvlK2Gztk20um0PnX+ObrnsSe0bsMGrd2wQb+9/c6u240dOVKXffjc\nstXVG0tWruz5RgPsRzNvUEfHpvNDjx01Ujfcdrvai/NGL1q2XLtOnLDFfb948UWSCmOxf/H7G9XW\n0aE7H35EF33oTC1btVqJKNIFHzxFuzEsA9gul8/LCM29RmgOB6EZ6CXbeao6Vi70dvxxo0bpkrPP\nkiRtaGnR35oeU31NWi3tbXp70RK99uZ87bvXHt7q21xDffmn9to8MEtSS2ubWtraui6/s2jxVkNz\np3QqqS9d+gld88c/672ly/T9X18nSTps6v4E5iqQ2crvCAaWy2cZntEHhOZwEJqBXrJkjfLtlTEd\n3JCGBl14+qldl797zbV65NnnKio0L1+1uuzHvPD0U/XHv226wmNnYP76Zy7v074uOut0/fneB5SK\nIq1cu1YLlywfsDrhTz6OfZdQ9VwcMzyjDwjN4SA0A9vxRtPNWv6rf1RcN1zpdQs19ss3+y5pCwuX\nLFEURVq9bp3vUjaRj/NlP+bukybpkrPP1GPPzdHKNWuVy+WVTCa1obnvf+w01Nfr0nPOKkGV8Gnt\nhg2+S6h+ZiwC1AeE5nAQmoHtWHbdlzTsvG+qZuho1Y3cSTsfMsN3SZu49qabtWLNWpmkIfWVtWhD\n7KlHb8K4cfrIaR/sunzr/Q/otR0IzahOA7VoEbbN5bISi5v0GqE5HIRmYBtaVi9TunmF9jn10gGZ\nq3mgvLNwke574kmtXr9ecRxryuQ9dcbxx/suawvpZGV8aOZjPoiwUUeGMc0lZyY5hsH0FqE5HIRm\nYBtWzn9FziJFFdRjMu/N+brtwYdkkmpranTUoYfosCn793k/uVxOmUxG7R0dam1vV1tHh1ra2tXS\n1qb29g5Fkam2pkY16RrV1taovqZGdbW1qkunVF9fv8VKhluTz5d/eMbWdLR3+C4B/ZTL5Qq/s8V5\nyjtnlelJHMfK5nJKSF2/s+lU5byepcJj6+z97v7tzObbevOa21w+m1Uu0658tkMuziufz8nlMspl\ns4pzGbk4L7NIcS6rOJ9Ttr1Fi16eJRfHcvmc4jgvF+clF3cFOjOTJVKyyBRnC3+ARImUomRKlkjK\nzNT67tx+PSeDDaE5HIRmYBt2PuR4vdc4Rq/e/nO970OfU7KmxndJsuJKHE5SW0eHHpj1pB6Y9WTh\nOjOZCm+8vPVutGh54QS+7qv/SdLuo4ZvsS1EZrbJh61tvEKRmZwKwatze/ffjWQiodi5rYY159zG\noLTZ/TY/Vn9/33y0RTW0/eYOe+p7GrHmTcVRUlY8pyCOEnKWlMzkLCr8i5JSlJCTyeTkooScJdRx\nwte04MbvF3qKi7eVRcXLVmxoJ8X5wv2scLKfuVhyeal4zETHBtUcc5mnZyE8hOZwEJqBzbRtWCOX\nz6t++GjVHn2Rsjd+RS82r9Ghn/qO79K07x6765D99tGcea+roa5Wxx95uHLZvHJxXs+/+ppWrV0r\nSRo+ZIimH3qwkhYplUoqEUVKJBNqqK1TXX29brnnXkWJhC4664w+19De3q5cPlZkhYCVyWR020NN\nWrRs09klatMptWeyA/K4d9Q1f/yz8nGsxvo6Td1nshYsWqwly1dowpgxShWHtiYTCY0bPUoXnnaq\n2nM5KY4VWaQoinTzvfd1Pa5kIqFcPq8JY8bIItvk8Y4bOVIXnHaKbrn/QS1atlxjR45QMpkshFWL\n5OSUiKJN7tO5P5N0+QXnF9opkVRtqvC2nMvl1JHLKZvNKZvLdv2/tb1d6ze0qLmtVa1t7Xpn8RJF\nZpowbmyhJ1FSnI/l4lhDGhsVRZGa21qViBKqTaeUSqSkSGqoq9Orb83X6rXr1Fhfp/333kuvzV+g\nlta2roVchg8ZoigyrdvQrCGNDUpYpHXNG5TLxxo5fJhSiYSWFWdJGTV8mI49bJqefP5FORfrtOOO\n0T2PPaElK1Zq7MiRssi0bOWqrsc/fsxonXvSiYrM9PTTTyuTSGnR8hUb22TUKEWJSO8tWSpJ2nn8\nTjv0+7q5q665VkPq6zW0sUEXfPCUrh7c9o6Mbrn/Abk41jmnnKSkmW6+7wEtW7mq6/kYP2a0zjrh\neN3xUJPyzilh0pIVG+cjHztyhE6bcazue/xJxS7epM3HjxmtC049WTff94AWLVuuCWPH6ONnnr5F\nD3Imk1EURZtsz+Vy+t0dd3aFq+WrVnfV1GncqFFqaF2h8d+ZpdF7TFEiVaNEH3vVm5qadPT17/Xp\nPug/QnM4CM1AN7N/+jmlmn6h1pF76Ij/70W1z7pR8a5H6n3Hf8x3aV1OOfYYvfLmfLW0tWvK5Mld\n2w+bcsB27rWpi8/50A4fv7a2dovLF5+95SwTdz78iF7++xs7fJz+uumue7V63Tode+jBOmraoZKk\n47pd39TUpK+fvenzsPnX/lt7XNvT19tfdc21qqlJa/SILVeZTCaTqt3KfQbSMcXnpdMJRx7R733u\nu8fuXT9feu7ZvbpPFEX9+p3sqymT99Jxm63q2JhMbtF+22rPS8/dfq2XbGfWlZ5+R7Y29CSZTPbq\nuZx1y2WqGTKios7BQM8IzeEgNANFS159Wnrieo35twf19g3/qqe+fKTq17ytva96TEPHTfJaW9NT\nT+vNdxfqmEMO0v2zZiuTzaq2AoaLbJ/1fJMSenvRIjXU1XYF5ko1ebddfZcw6CQq5CTVgWbOsRJf\ngAjN4SA0A0UbFr+pXP1ITTpohkbv8Ve9fucvFWfa1Thm26vHlUNzS4uefOElSdItDzykyExDGxt0\n7gdO8FpXT2pr/AaTfBxr+NChXmvojfkLF/kuYdBJRH7/oCulzgCGcBCaw0FoBorG7nuEVuSzmvOb\nb2ja5f9PB190pe+SJElr1xfmGJ4wZrTOPfkDGtIYxlev2ay/2TOaW1slSSOGVXZoNjO1tLT6LmPQ\nqdZo4mTe5kfHjiM0h4NZ3oGiTHuzXKpWqcaRvkvZxE5jRkmSFq9YqYYKW8Bke9Jpfz3Nv7/jTknS\niUf0f4xuKTnn6BkssyiKNPeNt3yXURIW55SssCn10DNCczjoaQaK5t95jerWvK3akTv5LqXLwsVL\n9du/FgLgJeecFdRqZjW9nEt3oORyOcVxrLfeW6jV69aroa5W9fV1Za1hRzBBYHnFcaxs1u+sLqVi\nLpYly/u6Q/8RmsNBaAaKDv3sD/XUi3cpnv+ipIt8l6MfX3eD2jMZRZHpio9/VPUB9TJLUjnz/ePP\nzdFjz87pupxMJPo8k4UvkYXzh1C1SCaq9GQ5s665khEOQnM4CM1AUaq2TpZr105H9n8u2P66+Z77\n1J7J6LApB+jYw6YpnQrvpTpiSHnGE7+7eHFXYD7sgAM044hpO7R6mi9Dhw7xXcKgk6/acb8M9QkR\noTkc4XyyAGUQ1w3XO3/5oSZOOVpRGXujVq5Zo1vue1DtHR1KRJHWt7RoSEODPjD9yLLVMNA2tLWV\n5TitxeOcNP39mrYDS4r7dty0Q3yXMOgce3hlT0OIwYXQHA6+FwS6OfA7D0jvztErt/68rMf9w1/v\n0qq1a5XJZtXS1qYh9fX69AXnlbWGgdbRUZ5xo/vuuadqa9K6v7iceGga6ip/3HU1MTM9VZzCEagE\nnAwcDnqagW6GjpukkR/+ltb+4f/q7V321W6Hn1zS491w+1+1bOVK5XJ5TRw7VhdvZyWx0AwpYxgc\nP2asFixcWLbjDaRUlS60Uamcc1ssQV096KkMGT3NlY+eZmAz7zvjMuUnHaR3Z3615MdatHSZcrm8\nRg0fpo+deVrJj1dOjUMaynKcVWvWaMHChcH21tz96GO+Sxh0Tjn6KN8llIZzEisCBofhGeGgpxnY\nTBzHqn3zEdVe9JOSH+sD04/UA7Nma6dRo4I6ea03UsnSfnivb27WHQ816b0lSxVFkS7+kP8TOHdE\nHPNBWW6B/n3VK1E1P7gq1TmVKKG58lXXpzQwAKIoUuaQ8xXf+DW93jhC+5z88QE/xrIVK/XHu+/t\nOomtub19wI9RzWbecpuWrFgpSRo3aqQuPvusYP/oiKp4SedK9eCTT+mT553ju4wBZ85JTGEYnM6e\nZlZzrHxhfsoAJXbUN/6oNx+5RSt+cameeftlHfbp7w7YvleuWaPf3HKbzEy7Thyv6Qcfot0mjh+w\n/VeKJctWlG7fK1YqnUzqK5ddWrJjlAudS+W3cs1a3yWUSCxjeEZwGJ4RDkIzsA17HXeu2tcsVetv\n/0nx5f+v36vxPffKXD345GztMmKYdp80URd88JSgVvjrq3SJVwSMq+QDhp7m8qvWEwHNxYoC/cZl\nMCM0h4NXF7Adq+/7pRrijB7/yjFbXNe2Ya3+vt9H5MbsKZMUy2na/u/TUYdufd7d+554Uoko0shh\nw3TySSeVuHL/NrQ2l2S/uVxOkrTXrruUZP/lRmQuv7EjR/guoTRcrCjiYz00hOZw8OoCtmPqlX/R\n4heaVJvPbbJ93YO/0uhVr+qdZKR1mUwxNEuPPjtHjz33vMaMGKFLztlynO25J52ohW8vKN8D8CiX\nLU1vXudzumjp0pLsv9wuPP2DvksYdE54/xG+SygJZwnlcxnfZaCPCM3hIDQD2zF80p4aPmnPTbat\nX/au2md+VrWfvEafPuvTm1w3Z+48PfPyy1q+erW+/+uZSiWTMjNlsoWFPnabNHHQhOZ0TenmH47M\nlMtXx0kzf7rrHl1y7tm+yxhUstnyLLxTfnxvESJCczgIzUAfDR23i1p2nqbE+lVbXHfI/vvpkP33\nUyaT0S33P6RlK1cqnUppt4kTdMoxRwU7w8MOKVGmvfvRJxQ7pxOPPLw0ByiTTKbQI5gYTL8TFcDM\n9Phzz2vy7rv7LgWQRGgOCe/WwA4YefylWv/nb0oXfX2r16fTaV14+qllrqqyZDcb0jIQ1m7YoBfm\nzVNjfb2m7LvPgO+/nDpPAo2rpMc8FNW8IqApllXxycXVitAcDl5dQB90zqO5bu7jyu98sOdqKlui\nBLNC3PlQkyTpkrPDX268a2z28uWeKxl8GMSASkJoDgc9zUAvLXn1ab3zrRNl+axSzmnfX7zlu6SK\nFtnAzxe7aPkKmaShQxoHfN8YTIjNqByE5nAQmoFeWLlgrhb+21GKTrhC+3+sMCSjYcQYz1VVtrq6\nugHd35/+drfiONYpxxw1oPvF4HPiUUf6LqE0XGn+WEVpEZrDQWgGeiHX3qo4SuuIz/3IdynB6JxP\neSA8/fIrmr9wkdKplA55334Dtl8MTvW1A/sHXeUgdIWI0BwOxjQDvdDRsl75VK3vMoIytHHghlA8\n8tTTkqQvXvzxAdsnBq8NzaVZeMc/JxlDT0JDaA4HoRnohaVP3qZ48nG+ywjK0MaGAdnP3xe8rVw+\n1vQDpw6uKftQMo8++5zvEkrGCM3BITSHg9AM9ELt2N0Ur3rXdxlBGT5kyIDs5+7HnpCZ6bjA52VG\n5Vi+arXvEoAuhOZwEJqBXtjvzM+ofuEcZVqr9Wvdgdfe0S5p4yIefRXHsX71p5vV2tamRIKTmzBw\nklX7+2RyMfN+h4bQHA6+6wR6oX3DWrkooXQ9U5311triuNF0Ot3n+z4/b55mzXlB65tbNHLoUNXV\nMp4cA6daFzdBmAjN4SA0A72wYfm7iuKcHv/WeXr/lTcqkep7EBxs6mpqduh+s198WQ/PfkpSYYjH\nZz56wUCWVXFSjNMuu1HDhvkuAehCaA4H79ZAL0ycMl0NP39Lr31pqhbPfVI7H8RJgT3JZLI7dL+H\nZz+ldCqlr3zqkgGuqDKNHjHcdwmDzglVOk+zs0j5/I697uAPoTkcjGkGemnuzH9TrmGUdtqXE9J6\nY9GyZZI2Lj3eF7uM32mgy6lY5578Ad8lDDqpah3TbIxpDhGhORyEZqAX3nn2QdU+8wdN/tc7lKra\nhREGVj7u2wfAI88+p6uuuVaSFCUGz1vTbQ885LuEQefh2U/7LqE0nGTR4HntVAtCczgYngH0woQD\npmtxzVDfZQRl+LDClHNRLz7Ec7mcZj33vNKppM6Ycaz22WOPUpdXMRYtW+67hEEnn6/W3lhCV4gI\nzeEgNAO9sPD5h2VxXo2jJ/ouJRgNfZjxYvaLL0mSvnjxRSxggpI79rBpvksoCZNTFFXp0JMqRmgO\nB9/jAL3QMHqCZKZM8zrfpVSd+e++q8efnSMzIzCjLOK4Sqecc5IZH+uhITSHg1cX0Atj9z5ImRG7\natmrT/ouJRgjhxVmhcjlctu8zeNzntef7r5PZqaLzjytXKVVlPFjRvsuYdDZkbnDgVIhNIeD0Az0\nUjR2T619/SnfZQRj0bKlkrTN3uNcLqfHnnlOqWRSV1z0UU0aP76c5VUOPifLrr6OxXJQOQjN4SA0\nA7009IDj1LHgBd9lBCOWbff66/5ymyTpC5/4mOrr68tRUkWK+aAsu3sefcJ3CUAXQnM4GEAI9EKc\nz2vts3dKuQ7fpQSjc77Yx555TnGcVyqdloud4jhWe0eHWhfO04h0WmsWvCIVPzQsigpjMs3ktjHu\ntPP6zg+aruM5V7hP8YOn+weQy+c2uZ1crGx7i5bMnd1tx1Fx/9sP+533l0Ubb2tR11RfW/sA3Hwa\nMCvevq51hVLpnFYumCsXx4XHXzyRy8VOUucsD93vv3HmB+ec4ly267lOpGsUJZIbazJTZJEUmSxK\nyCyhKFE8hkWFxyEVnjeLFCWSXfc3i+RcrDjOF2orPq4okey6Lkql5fJ5uTjf1S75XEaJREqJVM0m\nj9uKcyP3ZjaVUlu8vFpnLCF0hYjQHA5CM9CDOI416z8/IluzWAf+532+ywnGuFGFsbqPz3l+i+sm\nLHxC0+f+Vh0NY/Xm0z8ubHROck5W/OB3sq4w3aXb9VuzxX26ft50u5Op44Sv6a0bP7Px2NJ29735\ncUyukFGs8/5uk311HW8rH4TmYsk5TSte//f7i/W5zqC8MYxvsu+u7d0ei0XFwG+yOCu5fFddVnxO\nJVf8OS4ev/txTM4kc5JcLOsM5c4Vr+v23Lli7YUzzmRxvnC9oq4anUUyF8tcvniczsoLP0+5sUXp\nWj/fLNx8T/W+fmd992LVxFklUju2fD38ITSHg9AMbMf6FYv0wr+fpqh9vfa58jYNHbeL75KCMX7s\naH39M5dv9bonf/CUckdfrqO//L9lrmqjpqYmHX39Qm/HH4yePSdSIuHvY2fx8hWqq62tyqXL8+88\nr7rLfqVkDaE5NITmcPj/ngyoYC/+4BI1LHlJmjhFb/7lR1q3ZIHvkqqCRdbV64nBIY5jmZwSKX8z\nV0SRKZVM6KKzzvBWQymlGob5LgE7oHPIEqG58hGage3Y6ZRPKz7tX1QzaT/l16/Qy1cerzhfpXO8\nlpFTtNVhC6heLp8vDJ/xWYOTzHMNpeKc23I4E4LQ2dMcx3QkVDqGZwDbsfcJF0gnXCBJWjD7bq28\n+ky1rlmmxtETPFcWNksk5PKcVDmY5PNZOc+r1XVkMqqt1jmao4TEH/RBYnhGOOhpBnrhlVt/lmKQ\nUQAAGvtJREFUruU/PF/1l/ycwDwALIqKs0NgsMh1tCk2v/00zjkpqtLe2DgvJVhCO0SE5nDQ0wz0\nQsvvv6Ix/3ST9jjqLN+lVAdLMKZ5kImzHXKep5szM+Vz9MaishCaw0FPM9AbZor5sB0wZpHE+L1B\nJZ/tkDO/PaGpZFKJRHV+7FnXlIUIDaE5HNX57gEMMHf0p7Tkrl/4LqN6RNtevATVKdPWIpf0Ox1a\nMpEoLPZShZwl+EM0UITmcPT47mFmtWb2tJm9aGZzzexbxe0nmNkcM3vFzK43KwxWM7PIzG4ws1lm\ntn9x2wwzc2Z2Zrf93mlmM0r0uIABlVu1SK5tne8yqoZFycIiHBg0XD5XWIjFo2FDh6ito0pPQDWT\n4zUVJEJzOHrzDtYh6QTn3IGSDpJ0qplNl3S9pAudcwdIekfSJcXbnyzpKUnnSPpKt/0slPSvA1U4\nUC5xHKvu5du1z+f8LcRRdaKoa+lnoJwy2azvEkrEiS+Pw0RoDkePrzBX0Fy8mCr+y0vqcM79vbj9\nfknnFX9OqLA+a7e1YCVJL0paZ2YnDUThQLl0tBR6mF//9T+rbcMaz9VUB7ONyy4DwGBGaA5Hr/4s\nNbOEmb0gabkKAflpSSkzm1a8yfmSdi7+fK+k4yTdIelHm+3qPyV9o79FA+VUN2SE9rl2iWzdUr36\n5x8rjmOteuc1AnQ/FE5aoqcZ5bV81SpF1boAiHNd4QthITSHw/rSSGY2XNKtkq6QNETS1ZJqJN0n\n6XTn3MHbuN8MSf/snDvDzB5RITh/TdIPnHNNW7m9e/jhh/v2SNCj5uZmNTY2+i4jWB3Na5Vf9lZh\ngQbnZM4Vfm4YqZrhY5RM1/ZqP7SD1LpqseKONjVO2NNbDbRDeeXaW9Wx5E017D51i+vK1RYrVq9R\nHMcaN3pUyY9Vbs3vvqrUiPGqGTJix/fBa8KLXC6nF198UalUSlOnTqUdPDj++OPlnOvxr84+zdPs\nnFtrZk2STnXO/UDSMZJkZidLmtzL3fyXCmObc9u70YwZM/pSGnqhqamJ57Wf3njoJtUOH6sJU45S\n2/pVWvPe63r7j/+l+tfv1z7XLlHjqJ163AftIM254dtqeeMZHfOxv3qrgXYor6WvPae3fn+Zjvrt\nki2uK1db/PSG36m1rV0fOf+8nm8cmEf/zxc0/oJ/0979eB55TfixdOlSnXTSSRo7dqyWLVtGO1Sw\n3syeMabYwywzq5P0AUmvmdnY4rYaFXqNe3WWlHPuPkkjJB24o0UDvux9wgXa+ZAZSqRSahy1k3Y+\n6Dgd89371LLbdM27+ce+ywuGWaKwghkGjSiZ9N7muXy+aocwGOcIBIvhGeHozZjm8ZIeNrOXJD0j\n6X7n3J2Svmpm8yS9JOmvzrmH+nDc/5I0qc/VAhWobcMaJVa8qaG7T/FdSjDMIsY0DzJWCfMjOymd\nSvmuojSck0Usox0iQnM4ehye4Zx7SdIWY5Wdc1+V9NXeHKQ4brmp2+U7tOnMGkCw5j90k+L6kdrn\n5It8lxKOBAsxDDpm3ntDTVX8weOcqvjRVTVCczj6NKYZwJaSdY1Krl+iOTP/QzXDx2nIpMmaeOAx\nmv3dixS/87zcyJ2lbLv2/dz/aOxejEqSVOgRo6d5UIlzWe+Lm8hMcdUGEyeLCM0hIjSHg9AM9NMe\nx56r5vfmqeW1WWpeu1jr1y7Uio51yo+fqvEXXaWWJfPVtmS+5l95lJZ95CppFMM4LGJM82CTa2/1\nvoy2mRTHVRpM6GkOFqE5HIRmoJ9StXU69LL/7Locx7GWvPKkRu85VTUNQ7q2v/vsh7Xoh+er9bz/\n1uv3/V47H36K6oeP9lGyd5ZIyhGaB5V8pl0u8vuRk4ii6g3NYkxzqAjN4aiAMzOA6hJFkSZOPWqT\nwCxJu0w7UXt86yHFzau14g9X6uV/2FVz7/ilpyr9ihIpKd7urJOoMrmONinh/yS8mLH0qDCE5nAQ\nmoEyGjf5YDXuNkVHz3xHuQPPUvt1n1GcH3w9rsaJgINOvqNVSqa91pBIJBnBgIpDaA4HoRnwYNnr\nc5Sec5N05jfUvHKJ2jesVaa91XdZ5cOJgINOPtPufUxzZCamM0alITSHg9AMeDB84l7K1o5Q9p4f\nad7n9tLLl4zSM5fvrmWvz/FdWlkU5mnmA2IwyWc7vA/PMLPqHZ7B6ylYhOZwcCIg4EFN41Adft0i\nSVKUSinOZvXMzz6rhf9yqMbdWv1vnC6fLUxlgEGjva1Vze0duvpXvynO2VwIC2amSUMb9aPrbpBz\nTlFxWrhEIiHJycwUWSQnp0QUKYoiRWYaPnSo1jU3S5J2GjVK6XRa7y1dqmQUaWhjozLZrDoyWSUT\npjiWYue0vqXF63NQUi4urLqI4BCaw8ErDPAkWbPxq+pMpk3Z+c8pt+exHisqn1zrellNg+8yUEaJ\nYtitra2RXCEgOBX+djIzRVFU6AU2ycVOcRwrjuPCbZxT7Nwm4WLN+g1yxW1r1q7rmn85MtOylau6\nRmGYmVQ8liTtPmliuR96ecR5WQWcaIm+IzSHg9AMVIAXfvV/FXU067AfPOG7lPKIEnydPMikUmnV\n16T1hU98fIvrmpqadMF553qoqnpU9WqHVY7QHA7GNAMVID1ygpykVN1g6X3l432wsQRzCJeWk1gR\nMEiE5nAQmoEKsNfp/yAlkpr172cPminoHNMYDD6EgpIyPtKDRGgOB68woAIMHTNRh/7gSbkV8/X4\n109SR8sG3yWVlHES4KBjiRRLpwNbQWgOB6EZqBD1w0bp8B/Mkq1bpmeu/oTvckrKxfnCtHMYNKJE\nklUgga0gNIeDTy2ggtQ0DlW08xS5TLvvUkrK5XOFkwExaBRCMz3NwOYIzeFg9gyggjx33b8r/fxf\nNOZrf/NdSkm5OC/R0zyoROlaWT7ruwyg4hCaw0FoBirE/CfulO74tkZ++VbtdvjJvssBBpRFJtaw\nBrZEaA4HoRmoAHNmflvu9n9X7sQvas9jzvZdTulxIuDgY8zNDWwNoTkcfD8KVIDa0RPUOnJPHfH5\nn/gupTyKq7Rh8Eik0pwICGwFoTkchGagAjS/84pc3TDfZZSNia/qB5tEqkaWJzSXDIErWEzBGQ5C\nM+BZtr1NiQd+qpEnfsp3KWVjybQcJ4UNKsmaOk4ELKU4L0sw4jJE3UMzvc2VjdAMeJaqrVPbiN0V\npWp8l1JGrtjbjMEiUVMniwnNpeOUSBKaQ0dormyEZsCz5W+8oLo1CzR00mTfpZRPnJeLePsZTNJ1\nDbJ8xncZ1cs5pnEMGOOaw8CfpYBH781p0sKrz5aO+YwmTD3adzll4xw9zYNNqm6IohyhGdgaM5Nz\njtBc4QjNgCctq5dp+XeOV3b3ozRm3yP02l3XyeVzivNZuTivXOs65VrWKVk3TJZIFMYrOle4Ple4\njXPxpicAOVfscbKNM1R0Xe+Kty8uLuLycnFc6PXN5yWXk8vnC9fFeakr1Bbu7+K8XEerlM9KuYxc\nPiPFceH6OJZc8T7bPKnFuuqyDSuksXuV5HlFZbJEQpz8WTrGcxs0eprDQGgGPMll2tWy0xQpn9WK\ne35R+Go1SkpRQmaRVFOvqHaIMpk3CkE2n5UsKoTnKNH1b8szrzebmWKTr2yLoTWKCve3qPhzSpZI\nK4qSsighSyTkXFyoo/N+UaREbYOiREpRTX1hNoREQpLJEklFicT23/CdKwTvYtAevc+0gXgaEYhs\ne6scS6eXxJPf/6TSzctVM2SE71KwgwjNYSA0A54M22lXHfs/L/kuAyiLTPNaxck632VUpdyyBWr4\n6A80cue9fZeCHURoDgNnDQAASi6fzdDTXCLOxUw3FzhCcxgIzQCAkotzWbmIYFcqLJARNkJzGAjN\nAICSy2faC2P2MfAIWsEjNIeB0AwAKLl8tkOOIQSlQ09z0AjNYSA0AwBKzsV5Ft8oFRf7rgD9RGgO\nA+9gAICSK4QBekNLIs4rStb4rgL9QGgOA6EZAFByLk9Pc8m4fGG+dQSL0BwGXmUAgPKgo7k0nGPK\nucARmsNAaAYAlJxFEWNvS8XFTDkXOEJzGAjNAICSK4RmAgGwNYTmMBCaAQAlFzOmuXQskovpxQ8Z\noTkMvIMBAMqEIQSlwfMaOkJzGAjNAIDSIwyUFGErbITmMBCaAQAImZkcJ1kGjRM5w8AcNQCAkrMo\nUv3K1zXre5dKcnL5nBTnJefUstcpevxb/y3ls1KclzOTdfa4WSRFkRQlZVFCkhTVD5MsKqwcXdwm\nSWZRcaRCJCkujPON84VZO6KkLJGQLCE5JxfnpHxWLp+Vi+NCPd16+SyK9L5Lvq1UbYMskVQyVaMo\nkVKUSinqNifyygXzNO/n/0cuLsyVvEk/YZQo1hRtfCxmheO4WC7ObXycklwxONnWhlvEOblsR/E5\nysnyWSmfk8VZ1axfqogp56oCPc2VjVcZAKDkxh8wXStP+WclMu2SSRalCiFWJkvVqGbC3oqS6cK2\nWFJUDI5xXi7OK87npHxeklOuZV1hu2Ip1yGpmHcL/+k6pkXFwGoJKduhuCMvxblCmI2Shf+n6xV1\nBvLOYzpJD/5Mf3/2JrkoWZjSzeUVxXmZnJxMziI5MyXinBK1IzT0gu8Uwnf30OMKYdy5uBiUnZyL\nC0E6igpBtzP0d95vGz3GlkgpUVOnRLq2EN7TNYpSNUqka5RM12ncPtMGqKXgA8MzwkBoBgCUXO2Q\n4Trss9/f6nVNTU067PyPl7miHvzjD7e6OY5juXwhxMf5nOI4p1S6XskalrHGjiM0h4HQDABAL0VR\noZc4kUr5LgVVhNAcBk4EBAAA8IjQHAZCMwAAgEeE5jAQmgEAADwiNIeB0AwAAOARoTkMhGYAAACP\nCM1hIDQDAAB4RGgOA6EZAADAI0JzGAjNAAAAHhGaw0BoBgAA8IjQHAZCMwAAgEeE5jAQmgEAADwi\nNIeB0AwAAOARoTkMhGYAAACPCM1hIDQDAAB4RGgOA6EZAADAI0JzGAjNAAAAHhGaw0BoBgAA8IjQ\nHAZCMwAAgEeE5jAQmgEAADwiNIeB0AwAAOARoTkMhGYAAACPCM1hIDQDAAB4RGgOA6EZAADAI0Jz\nGAjNAAAAHhGaw0BoBgAA8IjQHAZCMwAAgEeE5jAQmgEAADwiNIeB0AwAAOARoTkMhGYAAACPCM1h\nIDQDAAB4RGgOA6EZAADAI0JzGAjNAAAAHhGaw0BoBgAA8CiKCnGM0FzZCM0AAAAedfY0x3HsuRJs\nD6EZAADAI4ZnhIHQDAAA4BGhOQyEZgAAAI8IzWEgNAMAAHhEaA4DoRkAAMAjQnMYCM0AAAAeEZrD\nQGgGAADwiNAcBkIzAACAR4TmMBCaAQAAPCI0h4HQDAAA4BGhOQw9hmYzqzWzp83sRTOba2bfKm4/\n0czmmNkLZva4me1V3N5oZneY2UNmNqG47VIzi81sarf9vmJmu5XmYQEAAISB0ByG3vQ0d0g6wTl3\noKSDJJ1qZkdK+h9JH3fOHSTpD5K+Ubz9RZKukfRFSV/otp+Fkv51oAoHAACoBoTmMPQYml1Bc/Fi\nqvjPFf8NLW4fJmlx8eeEpLj4z7rt6k5J+5vZPgNQNwAAQFUgNIch2ZsbmVlC0nOS9pL0c+fcU2Z2\nuaS7zKxN0npJRxZv/ntJN0qqlfSJbruJJV0t6UpJlwxM+QAAAGEjNIfB+tJAZjZc0q2SrpD0bUnf\nKwbor0raxzl3+Tbud6mkaZK+JGmupFMl/VXSGc65t7dye/fwww/37ZGgR83NzWpsbPRdxqBHO1QG\n2qFy0BaVgXbw54033tD69eu19957K4oi2qHMjj/+eDnnrKfb9aqnuZNzbq2ZNUn6oKQDnXNPFa/6\nk6R7enH/nJn9UNLXerrtjBkz+lIaeqGpqYnntQLQDpWBdqgctEVloB38ufrqq3X33XfrzjvvVEND\nA+1QoXoze8aYYg+zzKxO0gckzZM0zMwmF292UnFbb8ws7mNMn6sFAACoMgzPCENveprHS7q+OK45\nknSTc+5OM/sHSX8xs1jSGkmf6s0BnXMZM/uZpJ/uaNEAAADVgtAchh5Ds3PuJUkHb2X7rSqMb+6R\nc26mCj3MnZd/JulnvS0SAACgWhGaw8CKgAAAAB4RmsNAaAYAAPCI0BwGQjMAAIBHhOYwEJoBAAA8\nIjSHgdAMAADgEaE5DIRmAAAAjwjNYSA0AwAAeERoDgOhGQAAwCNCcxgIzQAAAB4RmsNAaAYAAPCI\n0BwGQjMAAIBHhOYwEJoBAAA8IjSHgdAMAADgEaE5DIRmAAAAjwjNYSA0AwAAeERoDgOhGQAAwCNC\ncxgIzQAAAB4RmsNAaAYAAPCI0BwGQjMAAIBHhOYwEJoBAAA8IjSHgdAMAADgEaE5DIRmAAAAjwjN\nYSA0AwAAeERoDgOhGQAAwCNCcxgIzQAAAB4RmsNAaAYAAPCI0BwGQjMAAIBHhOYwEJoBAAA8IjSH\ngdAMAADgEaE5DIRmAAAAjwjNYSA0AwAAeERoDgOhGQAAwCNCcxgIzQAAAB4RmsNAaAYAAPCoMzSj\nshGaAQAAKgA9zZWN0AwAAOARwzPCQGgGAADwiNAcBkIzAACAR4TmMBCaAQAAPCI0h4HQDAAA4BGh\nOQyEZgAAAI8IzWEgNAMAAHgURYU4RmiubIRmAAAAjzp7muM49lwJtofQDAAA4BHDM8JAaAYAAPCI\n0BwGQjMAAIBHhOYwEJoBAAA8IjSHgdAMAADgEaE5DIRmAAAAjwjNYSA0AwAAeERoDgOhGQAAwCNC\ncxgIzQAAAB4RmsNAaAYAAPCI0BwGQjMAAIBHhOYwEJoBAAA8IjSHgdAMAADgEaE5DIRmAAAAjwjN\nYSA0AwAAeERoDgOhGQAAwCNCcxgIzQAAAB4RmsNAaAYAAPCI0BwGQjMAAIBHhOYwEJoBAAA8IjSH\ngdAMAADgEaE5DIRmAAAAjwjNYSA0AwAAeERoDgOhGQAAwCNCcxgIzQAAAB4RmsNAaAYAAPCI0BwG\nQjMAAIBHhOYwEJoBAAA8IjSHgdAMAADgEaE5DIRmAAAAjwjNYSA0AwAAeERoDgOhGQAAwCNCcxgI\nzQAAAB4RmsNAaAYAAPCI0BwGQjMAAIBHhOYwEJoBAAA8IjSHgdAMAADgEaE5DIRmAAAAjwjNYSA0\nAwAAeERoDgOhGQAAwCNCcxgIzQAAAB4RmsNAaAYAAPCI0BwGQjMAAIBHhOYwEJoBAAA8IjSHgdAM\nAADgEaE5DIRmAAAAjwjNYSA0AwAAeERoDgOhGQAAwCNCcxgIzQAAAB4RmsNgldhAZlZ5RQEAAKAa\nveOc262nG1VkaAYAAAAqCcMzAAAAgB4QmgEAAIAeEJoBAACAHhCaA2JmvzGz5Wb2SrdtI83sfjN7\no/j/EcXtXzWzF4r/XjGzvJmNLF53oZnNMbMvFS9/0cx+0m2f15jZA90uX2FmPyvfI61sfWmH4nUz\niu0w18we6badduinPr4mZpjZum6vi292uw9t0Q99fU0Urz+s+L50frdt/1Rsh48UL/+4s02Kl+81\ns2u7Xf6hmX25tI8uHH18PXzIzF4qvhaeNbOju92HduiHPrbDx4vt8JKZzTKzA7vdh/elCkNoDstM\nSadutu1fJD3onNtb0oPFy3LOfd85d5Bz7iBJX5f0iHNudfE+F0o6TNKRZtYoaZak6d32eZCkYWaW\nKF6eLumJEjyeUM1UL9vBzIZL+oWks5xz+0v6cLf70A79N1O9bIuixzpfF865b3fbTlv0z0z1oR2K\nz+P3JN3bbVujCm1wuKSPFTd3tYOZRZJGS9q/2zFoh03NVO/b4UFJBxY/Iz4l6VqJdhggM9X7dlgg\n6Tjn3FRJ35H0y2734X2pwhCaA+Kce1TS6s02f0jS9cWfr5d09lbu+lFJN3a7bJ27LP78vKTJZlZn\nZsMktUp6QdKU4u2mq/BihfrcDh+TdItz7t3ifZd3uw/t0E/9eE1sjrbohx1ohysk/UXStl4PnZ7Q\nxpCwv6RXJG0wsxFmViNpPxXaCupbOzjnmt3G6bMatPF5px36qY/tMMs5t6a4fbakSd3uw/tShUn6\nLgD9Ns45t0SSnHNLzGxs9yvNrF6Fv3g/323zLZKelfQ759yG4u1eUOEv2jpJT0l6Q9J0M1uuwtSE\n75X8kYRtW+0wWVLKzJokDZH0U+fcDcXraIfS2N5r4v1m9qKkxZL+2Tk3t7idthh4W20HM5so6RxJ\nJ6jw/Kp4mw1m9rIK7fD94rbFZpYzs11UCARPSpoo6f2S1kl6yTmXKeNjCtE2Xw9mdo6kqySNlXR6\n8Ta0Q2ls97O66DJJd3e7zPtShSE0V78zJT3RbWiGnHPXa+NfvJ06exLqVHhDfEPSlZJWiL9c+yMp\n6VBJJ6r43JrZbOfc32mHspsjaVfnXLOZnSbpNkl7S7wmyuwnkr7mnMub2SZXOOeuUiHEddfZDtMl\n/UiFsDZdhbBGO/SDc+5WSbea2bEqDA34QHE77VBmZna8CqG5a2w570uVh+EZ4VtmZuMlqfj/5Ztd\nf6E2HZqxLZ1jpd6vwgtxnqT3iTFSvbWtdlgo6R7nXItzbqWkRyUduI19SLTDQNhqWzjn1jvnmos/\n36XCNwCjt7Mf2qJ/tvWamCbpj2b2tqTzJf3CzLY3hKazHaaoMCxgtgptQjv0Tk+fEZ3DCfbs5euB\ndtgx22wHM5uqwpjyDznnVvWwH96XPCI0h+8OSZcUf75E0u2dVxTHPB3Xfdt2zJJ0pKQxzrnlxbFu\nK1QYh8Vfrz3bVjvcLukYM0sWh8ococKb3LbQDv231bYws52s2LVpZoer8P63vQ8o2qJ/ttoOzrnd\nnXO7FZesvVnSPzrnbtvOfp6QdIak1c65fPFbs+HaGBqwfdt6PezV7fVwiKS0tv96oB36Z1vtsIsK\nwzA+4Zz7ey/2w/uSR4TmgJjZjSq8Oe1jZgvN7DJJ35V0kpm9Iemk4uVO50i6zznX0tO+iycirJA0\nt9vmJ1UY6/biAD2EqtCXdnDOzZN0j6SXJD0t6Vrn3Ctb3zPt0Fd9fE2cL+mV4pjmn0m6sNuJUFug\nLXpvB96b+uJlFWZrmL3ZtnXFb29Q1Md2OE+F18MLkn4u6SPbez2Idui1PrbDNyWNUuEblxfM7Nnt\n7Zv3Jb9s+68RAAAAAPQ0AwAAAD0gNAMAAAA9IDQDAAAAPSA0AwAAAD0gNAMAAAA9IDQDAAAAPSA0\nAwAAAD0gNAMAAAA9+P8BtWSz3psMQgQAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Plot CWA envelope\n", + "for i, geom in enumerate(boundaries):\n", + " gbounds = Polygon(geom)\n", + " intersection = gbounds.intersection\n", + " geoms = (intersection(geom)\n", + " for geom in boundaries\n", + " if gbounds.intersects(geom))\n", + " shape_feature = ShapelyFeature(geoms,ccrs.PlateCarree(), \n", + " facecolor='none', linestyle=\"-\",linewidth=3.,edgecolor='#cc5000')\n", + " ax.add_feature(shape_feature)\n", + "\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WFO boundary spatial filter for interstates\n", + "\n", + "Using the previously-defined **envelope=merged_counties.buffer(2)** in **newDataRequest()** to request geometries which fall inside the buffered boundary. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using 223 interstate MultiLineStrings\n" + ] + }, + { + "data": { + "image/png": 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zBPa+Z6bbLL7CHO82mLUCvaea3RmdiPmkLFLVeuObzKFm0W3mYEgfZJ7XQzlx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\n6ZffCkv/DWZerAIsI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+ "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "request = DataAccessLayer.newDataRequest('maps', envelope=envelope)\n", + "request.addIdentifier('table', 'mapdata.interstate')\n", + "request.addIdentifier('geomField', 'the_geom')\n", + "request.addIdentifier('locationField', 'hwy_type')\n", + "request.addIdentifier('hwy_type', 'I') # I (interstate), U (US highway), or S (state highway)\n", + "request.setParameters('name')\n", + "interstates = DataAccessLayer.getGeometryData(request, [])\n", + "print(\"Using \" + str(len(interstates)) + \" interstate MultiLineStrings\")\n", + "\n", + "# Plot interstates\n", + "for ob in interstates:\n", + " shape_feature = ShapelyFeature(ob.getGeometry(),ccrs.PlateCarree(), \n", + " facecolor='none', linestyle=\"-\",edgecolor='orange')\n", + " ax.add_feature(shape_feature)\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Road type from `select distinct(hwy_type) from mapdata.interstate;`\n", + ">\n", + "> I - Interstates\n", + "> U - US Highways\n", + "> S - State Highways\n", + " \n", + " \n", + "## Nearby cities\n", + "\n", + "Request the city table and filter by population and progressive disclosure level:\n", + "\n", + "**Warning**: the `prog_disc` field is not entirely understood and values appear to change significantly depending on WFO site. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1201 city Points\n" + ] + } + ], + "source": [ + "request = DataAccessLayer.newDataRequest('maps', envelope=envelope)\n", + "request.addIdentifier('table', 'mapdata.city')\n", + "request.addIdentifier('geomField', 'the_geom')\n", + "request.setParameters('name','population','prog_disc')\n", + "cities = DataAccessLayer.getGeometryData(request, [])\n", + "print(\"Found \" + str(len(cities)) + \" city Points\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using 57 city Points\n" + ] + }, + { + "data": { + "image/png": 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hvRWsrAb21VQwqdIaqnUCjxZgZYa/NgYDpJ2E85tUAPQZAtU6FwpuQpjMtiFq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\n7yIAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "citylist = []\n", + "cityname = []\n", + "# For BOU, progressive disclosure values above 50 and pop above 5000 looks good\n", + "for ob in cities:\n", + " if ((ob.getNumber(\"prog_disc\")>50) and int(ob.getString(\"population\")) > 5000):\n", + " citylist.append(ob.getGeometry())\n", + " cityname.append(ob.getString(\"name\"))\n", + "print(\"Using \" + str(len(cityname)) + \" city Points\")\n", + "\n", + "# Plot city markers\n", + "ax.scatter([point.x for point in citylist],\n", + " [point.y for point in citylist],\n", + " transform=ccrs.Geodetic(),marker=\"+\",facecolor='black')\n", + "# Plot city names\n", + "for i, txt in enumerate(cityname):\n", + " ax.annotate(txt, (citylist[i].x,citylist[i].y),\n", + " xytext=(3,3), textcoords=\"offset points\")\n", + "\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lakes" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using 208 lake MultiPolygons\n" + ] + }, + { + "data": { + "image/png": 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ryUSI2ZMGeOBrlkVV9SBjQGoJVVUTgcTff5bXpY4Z0SQMIwae3ZHC4Zx81p49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//vEPXn/9dY4ePYqnpydr165l2LBheHt74+Pjw/Dhw1mzZg0A4eHhxMfHl3ssV9K3b1+OHz9O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\nYOjQoTUHkgPs3LmT3r17U1FRQVFREVdeeSX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+ "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "request = DataAccessLayer.newDataRequest('maps', envelope=envelope)\n", + "request.addIdentifier('table', 'mapdata.lake')\n", + "request.addIdentifier('geomField', 'the_geom')\n", + "request.setParameters('name')\n", + "\n", + "# Get lake geometries\n", + "response = DataAccessLayer.getGeometryData(request, [])\n", + "lakes = np.array([])\n", + "for ob in response:\n", + " lakes = np.append(lakes,ob.getGeometry())\n", + "print(\"Using \" + str(len(lakes)) + \" lake MultiPolygons\")\n", + "\n", + "# Plot lakes\n", + "for i, geom in enumerate(lakes):\n", + " cbounds = Polygon(geom)\n", + " intersection = cbounds.intersection\n", + " geoms = (intersection(geom)\n", + " for geom in lakes\n", + " if cbounds.intersects(geom))\n", + " shape_feature = ShapelyFeature(geoms,ccrs.PlateCarree(), \n", + " facecolor='blue', linestyle=\"-\",edgecolor='#20B2AA')\n", + " ax.add_feature(shape_feature)\n", + "fig\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Major Rivers" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using 758 river MultiLineStrings\n" + ] + }, + { + "data": { + "image/png": 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001x77bWMGDECvV5/1mV//vlnZs6cyZNPPkl+fj7jxo3j3XffJTk5mQ8//JCt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irvrnG3PcjOL96qcbiyALXKfpQ2IZDLGxVUb7827GVEredUgsqIYHLlL1B1/0X5JMn+3f63sMfP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tXMutSIQGBTH1yjEARIWHo9Nq2ZCYyGsLFnLvDTMJ8Gu+B1NT+HDxEgD+etcddYqJmIhwpk8Y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LV2+PlasNjtIu6bz8UVi1eIDRVlIu6bz0WTzdbu55FlWWypKBe9v/lCxH3zuRBCCWxzF07+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+ "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "request = DataAccessLayer.newDataRequest('maps', envelope=envelope)\n", + "request.addIdentifier('table', 'mapdata.majorrivers')\n", + "request.addIdentifier('geomField', 'the_geom')\n", + "request.setParameters('pname')\n", + "rivers = DataAccessLayer.getGeometryData(request, [])\n", + "print(\"Using \" + str(len(rivers)) + \" river MultiLineStrings\")\n", + "\n", + "# Plot rivers\n", + "for ob in rivers:\n", + " shape_feature = ShapelyFeature(ob.getGeometry(),ccrs.PlateCarree(), \n", + " facecolor='none', linestyle=\":\",edgecolor='#20B2AA')\n", + " ax.add_feature(shape_feature)\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Topography\n", + "\n", + "Spatial envelopes are required for topo requests, which can become slow to download and render for large (CONUS) maps." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[]\n", + "Number of grid records: 1\n", + "Sample grid data shape:\n", + "(778, 1058)\n", + "\n", + "Sample grid data:\n", + "[[ 1694. 1693. 1688. ..., 757. 761. 762.]\n", + " [ 1701. 1701. 1701. ..., 758. 760. 762.]\n", + " [ 1703. 1703. 1703. ..., 760. 761. 762.]\n", + " ..., \n", + " [ 1767. 1741. 1706. ..., 769. 762. 768.]\n", + " [ 1767. 1746. 1716. ..., 775. 765. 761.]\n", + " [ 1781. 1753. 1730. ..., 766. 762. 759.]]\n", + "\n" + ] + } + ], + "source": [ + "import numpy.ma as ma\n", + "request = DataAccessLayer.newDataRequest()\n", + "request.setDatatype(\"topo\")\n", + "request.addIdentifier(\"group\", \"/\")\n", + "request.addIdentifier(\"dataset\", \"full\")\n", + "request.setEnvelope(envelope)\n", + "gridData = DataAccessLayer.getGridData(request)\n", + "print(gridData)\n", + "print(\"Number of grid records: \" + str(len(gridData)))\n", + "print(\"Sample grid data shape:\\n\" + str(gridData[0].getRawData().shape) + \"\\n\")\n", + "print(\"Sample grid data:\\n\" + str(gridData[0].getRawData()) + \"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "623.0\n", + "4328.0\n" + ] + }, + { + "data": { + "image/png": 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MJclXWCzaoyaQ97fdesfY9ucC5+otYLDmh4SpShzNURif74XagS4EF7vKPsl3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zX+WfqQGXaRfgXUO+E/rPNQz7bg5u3i4A3EoCbsVNF+nYkKsCzo4IUGSAxaWNLrfwlKCh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0nbVNvKCJVALXn/rF9w8wuiJLJqdWn0A03sdx659tp0G+QA4cr0k0rt8X2w7J0wknvZvoMmN9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0NRZBFJHKKIcccBf1n1j9ANlyjYIktDdMPB9Vto+t6gaZoiJcQuvU43GhSZGtNyIgyrzrB3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\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uZUd3JfZpOwfpPFcd45jqf6U1P/KRj/PYu1+CUZLHHr5M8yueYikIeHj+5JH7HHX+q893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\nXp6FqIaBq84SyjI50IeuwqoQrCeraMJkyVwBoK6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uG8sp5wNUq0Yc+WRJhu3MUmQ6ul7H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gWzZW\nJUMzTE72H5HGY4o8olqv0e+UdKLrz7+IW6sxOD7CsJvUW2sgSRi2gyiKkp+9/xjDNFB0HQmBqqgo\nqkJjY4skiQkDD4QgyzNUrdQUfEXAn0pyhAzI8Hm9zmccid2s/EKxJvzk1tYWnf3D2e+fxjF5lqMb\npfVmnqa8+HXfwN5779A9uoda1bnz/Eco8hiKjDg+QtVkDh88orGxBqTolsrunV1O9vY5uP82pu2w\nd/fLFLmPEBrXX/gYSRSV1p69DqIoGA163H3zSwDU1tooikql3iSOI/zxkDgMMCsugTemubGJU63S\nP9nHsBzG/Q43PvI8SRSgT1bD3LqOyBOSOCOJh7i1Fm51g8Poy+iyiWJUEUUyoWlZqFpZxOpmiyzr\no+oqAqOkAa2AJjmzwLCLsIwWdNXJgCJJfGN7je/+1V/jxz71dVc6ZxlS4aNJE66+AEVpYs/Virp2\njaLIMN0mX/3CG+S5jFvfQZIEWVp+ZmRJoFsalXoTkQlyRSDnBSxhJOQCZK0B8oAiTcmz8vcXxMiy\nhKpPpyvWzLe/EOX/chGgyhZgokhlM2HaNogB1ZZCkRlkeczazjqRlxCHMZrR4OYrt7HdJpCgSSam\nrBEVp4XBuaAwSUZMqC/zk575LIB+NqZYUVssWoVO8U60Tz/zGecBFcVGQaKXjWcBeHA+gbitVull\n49lxOWLWCPQyj6bqzjIEADYnRexRWmYIbGhVNnVn1hwsw2X2oecSfucL8gvoQCuvd4VJwcxmdC5z\nYNGCdLrtMszyDy4o0oPc55/s77Fh2mxbp+/FGUrPChrQKgrQuedYKNQXJwHTa001Eaf3XaGdWCj2\nz72+SEtxRfHxmetd4Ei0uO8yW9H5qUCQZfzs0QP+7Asfv7DYPQrEmUJ/KiS+LCzsIm3ARVh0F7rI\nTWgqIp6fElwV7arG66yRxvAxa5ON9fIaP/npzz/Vc3+t4FkT8AwfKvijEZIiaG60icMUTTMY98aM\nhn0evfNVBp0j0iRCUQTIGXmW4Y2GFGQUIintFDUDBfCGQ/I0pnd0yLh3wJ2PfoRRr49VaaOnGWma\noKgqaqFRX99ANy1UXccfj3CqNdKk/AKt1Jv4vkeRpWR5gawqGG6ldBwKfGRZRbct8iTBditU6i7/\nqN9DyPBKZvCn12vcrtZJYg9Jkgi9EZIskSYe3aNHpEmGaTvIsgyTECfHrdI92CfwRnB8yMGDd2ht\n1hgJmSKPaaytURQCWbEwLQdNfRffD5BlnSItMJ0m7e06j+/t8ei9L5AkKdVGHa8fYVcqrO9ewxsO\ncKpVAm80S1KWJIlh54Tmxha6YWBXXB699zbj3jFpEuFUGwTeiN3bL9Bob3P/K79OUaQ0N9fRdYck\n8cmzkDyD1vom49EBhqmg6wpJ1KG1vg5Amg4BlSTuoxuNkpNehJAFaHqLQvRQlG3AvjQb4LJpwBRP\nQwsC+OzaGt/QavGP9va47bq8Uqs90fnz4uBSGxCcaQQU1cR0PY4ePODh2++SJiqKphEFHtdfeAXD\nNAj9Mf5wQGPjBkkcgSSjoiErDpIcUxQJslQGqhWT4l6WWqBAkR+RF8fIUvncuRghCYtMiFmTNS20\ndblFJgIKAbJUBnsJDFTNxKpZaGaf40cPCEYjkljFsCpce+F5HncjssQjyxRUu45fBHh5jpBM4iVc\nrppSgbwsMudX+6euQNMCf54KtCwgbL74P72GT0N1UJFoqOeL7kKcNhrLrtdNx2cmAuuay3F6qmmY\nFv3LkoVBOeMoNK8VmAqFn0QkfJlr0Hzw2CIWG4DFVf7514uZA6uK/mVJx8sajWWFvBCCL3T7nMQh\nf+zaC79tYt1lVB5YThOCFb7/U+3CEiEyLOQCPGWa8YXc/2V2oZPX865B00bgMBQcRRESEqko0KWr\nv/ezhmGhCZjSehaxyinoIjxJ5sDTNABTzITBI/BDcOb+BBRC/K6iAU3xrAl4hg8VNq49T+/gMWko\nARZhkGK6Va7deQFvNCoTdhFolkLkDUmTnDSKUDWF0Osy7sdISIz7HSRJJs1i8jyktVXlvd94G6vS\nwrBGVOtN1ra38cdjqo0mYeCVrkBZSpGlxGGA7bhkcYxiGKhpQhpmKIpCHIUoikIvDIiCEIEABLXm\nGpbrkMY+10Kfl1OVH9xtI8KA48f3ECKhudkmnTiGZFlEe2eTR++8zfHje6zv3mJ9Zxd/PMIfDiiK\nHH/UIc8S3JrBzs2beA+HuNUmmmHPVs8B2rsv0EgD0iTk6MH7FLmKW2/y+md2SOJyavLwnbcZjzyC\ncVlM+aMRkiSRxB5O1SHPBHEU4VRr5Hn5B/bk/iOyOKTSqJAkKrqu4g37M7tI03GIwi6qrjLyToi8\nFKfapN/ZI00OaG2uEwXHmEmA7Zbpv0ncx6m0UNSyAC2bhhhBjKKWwlZ54gY0Xzwvw5NOA56mEVAk\nCSSJpq6f46HD5dSiRUwbAbBRFNCMCDUK0A0X0y5ovmphWGs8/MpDHr//TvkMigJ45MkIGRlhuJjW\nBmk6QFFsZF05TVWeCIPL98UEbR2VGFFEFHmMotXIMZEkzq2yx5P3MhchOuX7VE4HBIpkoBoN1q+B\n5RwT+RlePyINh4jCJA513FqFXMQkhYIpN9Am6cG54IwbzzAfU6MsNPvZeNYIrMoHuBcfTPafFtCr\njm2oDv3Mp6265wr93iSHYNoIAEsbjF62vBFoqi5CnBb9G3pZ5E8nBJI0ETJOKEPz9KAnaQSuGh62\nuFo/o+VMfl5sEhanA1dZ4Z/da47SNNUorKIBLQp7f/rxPf77d9/iR7/uG3HUp3PrWXXO4r2eiKIz\nn4D8lJSjs89y3qb0g8ZVJgvzTcK0KWjpvTO4PgAAIABJREFUFm+MkkvTgp/UInS+YD/yizOc/yfF\nvGbgtxLtqnZGJLwf+vzAl3+Fv/fJb/otv/eHDc+agGf4UGHQPWbj5h1GvQ7NtTaKZuBWyy/R5voG\nQgg6B3vkaUptZ41+910OH76FW2uTphGykjDoPcSu6Kzv3CAOU4adI0IvQTVcDNNiffc6Ii/wRmM2\nd69RiIKiyEnjGCXPCD2PLMtRFIUkiogCHyEKdNMGIUiikCIri+R8RgHySRyTOPRpbmzxEd/jP7cS\nRnv3icIxpmOytrVGnoV4w2NqrS0U1aHa2GDU63Kyf0Dvi49xqv9KGQE7gaZbJFFEnkfEUQCiwK64\nqJqNaW+QTVYoJVmiKzRSNNav76JqFrpReiuHXgckiede/gh5/jZ7d79Mc+Mailq6FZXpvw9Z275O\nkeWkWUqapSiyQq29jqppeMMOmpFTqdeIo5ju0V0ADEujUrXRNKg22+jGiDgcoBsyUTBi1MmoNFvE\ncU7oPyQJhuQiRdXL37HaaGDadSxnDVlpl8LWWTpw+f8fVDjYfCPwNPhcu80/eLzH+57HH97ZPnft\nyzBPCSoxaXAUE9ttsn5NZ+vmS8Shz8GDR8h6hiQCas11Rv0BmiqhGQqSpJPGOZJsgwiIowCBgqxY\nZW6AVIp9hRCocou4CMkxUJU6yCGJAERYTg4oV/vzSeNQnivIRIEiTf4DSCa5CEmKCE2SUI06zc06\ncTDAriQMu2PEOMW0VZB9MiGRCANNgjj3sebem6kzUD8fMczHNK9g+dTSKshSWawPJ9kAuWBplgCU\n2oB+trwxnBb886FjM03BkmTiVY0AlNahcNoAzOMoHbGpV8/Rgxb1AfOZAuV+98yxF2FVYNgiFlf5\n57ddFbMJwMIEYflznbUeBTgIQ77vIx+jopl8XWtzad7A6fnnpwgXNQCXHZsW4kzY2WWYFxWvaiiW\n0YGWNQ7L6EbLjlvMNljctgwXpRLPv542Aw2joJesft+nOEcHWtEELCvY512D5vddpSF40uJ/MV/g\nqqnAcNYa1M9SHFXjb330c090/68VPGsCnuFDBcNQiXyP3TsvAiDyjIMH9xieHCGKnDRPUVW49vx1\nIt/j9c9+hjwJOXi4R5EV3P70x/EGfQ4f7XP86DG6UUGSayRJF8s2EEKgKDJ5IdB1jSiOZiPAIs+J\n44jQ89hqrmE4DkePHmI5bkmT6XYIfQ9FVRFGQVEU6KaBYVsoSoHtVnAmDYvluDQ3tgi8DrK8OQtt\nmlJmRv1jNiZJmbdf/RS7tz1++f/6p/zSz/4k11/8JHa1hqLrOPU1ZEVn0Hl85n0y7Y0zr/Ms4O3e\nMb/Y6fJ61eFxIfiu51zisIOiKuhmgzjw2bi2w6P3HtM58Km3t3CqLbI0x62XrjyW62K7FUK/TC02\nrJLmJITAGz5E1mR2n3+eaOwzHvRoX7uGZbukcYAqG7iVKoatEQcabrWFYbsksU/38BjbbSCpMram\nUhRjZE1GknTUyURAiHAmar1IDPybwW/GNhTgmzfW+ULvNGjrqg3FPCVoilyEqFILiRBVNXFrm8Rh\nB8NyWN9dx63pKLJOmhVUqqDqJpKkI6kGpWd/MHNVkjAQhaCYOCopkkUmgkli8TzlxyIjKIv9uQLc\nlNdIhT8L+JqflpTbTDS5bC4QMapsoegjdEtFVSQkSSBEQKGaRNgoUlnATF2BFtFQqvSLzoxiMD8N\nuAg1pcKj5JDqJAthGi42xTRQ7I6xeaEDyvR+8+nE/TkK0nwjMNUITEPIphkCx+mIda06mwbM4ygZ\nTaYFK4rXM4Fiy61EF6k3l2UGnDk3SjiJl6/4XyjwvUDAfKYBKC4vHU7ijLah8hMP3+U7dm5xy7Em\nz7a6yJ0PMHsSXFbkn+P/r2wqzjoVzVuWrrr+KuHw4rZV91/cN33flj5fcBp8dhneHyU4qsYv9fb4\nVLPOmyOPUXaxRmRqJTqPyyYBy+g7880AcK4hWFXwX0QDWnr8EgehqzQD85OAnzt+jJen/IlrLzzR\nvb9W8KwJeIYPFaIwIE3LPzyVRpNxv8ewe4xmyUgoiCBFUXXuf/URAP4wJY584nhEo1XDG/R56wtv\n4TprVJrtMkm31aa5vsH+3XfZuL6OYdnoTRvTMBn2Omi6SRT4aEb5B36cJuzde5+dW7exq1W8Qelv\nb1g2o0EPSZawHINhp4s3GODW4jLoy7tHc30Np1olz2K8QUxRhOS5QNUkkvjsH+A0LVcJ0yRA023k\nT38j/9OX3uZb3/oCL994ifWdawTDIQcP3qaxXi2LZlnBtNsc7/0aaRKRxhGKrGNXGzwf9tkm5CuH\nx3x+Z4uf2Hf4fEMnD/aJQp8CHd002b21g1XZLMOhdIs4HFJfK7n6dqVCMB5TFDlOpQp5mS7c3Njk\nq792gDfos3XDRUbDrd/AtB003SZNBggSFMWgWt0gMsaITOCNTjAMBaeikWUjNF1GUVNsu4U9SYZE\nYmUoWCGu1ghcVRfwm2kA3hwO+XO/8QZ/7zOffuprTKcBJSUoRBAgyVDkEbJsohs2kmxjuevU1wKi\noEvvaA8Unbd+9SsossP6bot6uyxIrUkasyTLFHmBjExBiCBElZulwHWO9pMjZqv988jFaVFqyA5x\n4S85ppwQFAgoQgythpL0sWs6ipqguxqRJMiEToagOqECwSkVqDpx8OnnI6QiB+nJvvTLa5xOU6aT\ngVVYxv2fFx3D2YnC9PW0EYDTxmLqLNTJSorPlPs/byE6Hyw2xbJQscswT+NZpOFc5Tqbpn5lqs9V\nnIumzzK972GUXOIIVBb4tiJhyApHUYqlqGVS7yWhZJdRcRZX/+dX2C/PFzhbzK8q7ud1ABdNLWbP\nMKcRuGj/ZfsOw5y2oV46BbjMQvQwEPzQ27/Ij3z0s6QiRJEafH1zk0/U1y+87jJc1ARcavO5sG1x\nQjDfdDxpA3DmPk+pE3in5/NHtm6y3PD0dweeNQHP8KGCEKcfyf177zE4OSaJI2QMNMul1qyTJDHX\nn7/Jo/e/SppCc/0mYeCRZn36x11qtQ3ufPRTFAgsxyUOAnqHB7S2dwForG0QBh6B75Wr/6FPHIZk\nacp40KPWWpsV/qbtoGoa4WjI8d7j0gUoibAcl87+Ie2da2xev4E/GtE5eMzJ3kOQNtEMiTwXxEEE\nQmbY6dDevs6g+xjdNHFq2+RZiGY4yJMly4fjPl+0Nb5o3+Yv3v8q/niELMvYFZ1rL9xE03NkRWE8\neMj+3XexqxtoeilgFhio5gZp74DdOCE53uM3NJlx5PJHbB1ZqSLlgjg8JI084rDHxvVP449GKKpE\n6HtEgc+wWzoVFUX5TJbjEvpj4shDVk+XVmuta0AZdCVJEs31cnKTpT5ZGlBkEXlhoCkWmq5iu2uM\nh3touoVhOZOgKkqPe8VBiBAhwskUoMSy1fN5LNMCLCvy56k605/TIkA84Z9+VZL4sU99koauz227\nuj3o9Pc5pQVZE3tMC1kBSSo9/yW5LHJlGdJkMnXIVRx3jWpzCxjSaO0AIIppkV2gqC6lfEGevJ99\nFKlxrhGA0wmGo5Q2emFx+l5Of7Zk59y2TIAq6aRijISEauaYAmRVAafFIEtBEuhLGo1FNPx98sqd\nK713cFrQ15TKueJ/fpIwLfIbauVcgT/FMlHw/H0WJxNTWlA/K61Hp2Fi8w2AEJxJFgYudAtahsPE\nm4WFwfIC+zI6zqVF/RLL0otsRFe5FK06Z1rEP/TH/I/vv8VffO3rUSSJkzi7vLidnDtfeC+lB61o\nFk7ibOG4abG/3O//zLFXWF2fNQ3Wag3C4rMtayiehJY0vSZwpYTgnz884R2vy/fd+RTjVOEbW3dm\nQYvdWAJWZwksw2WTgCfh8V+lUXgSzDsFPQmmU4B/cPw+n8xbvGZuLD1u/Smu/TsNX/u/4TP8jsKd\nVz9KUQjiIMDr92m01ynynEq9ydatWww7XXTTII0TnEoNSZKxXJfxsA8oOFWLYbdH52CvdNyRZGRZ\nJpuIfWutNmHgEXoe/nhEGsdlkyHL5FlWZgokMYZpE8cRSp6xd/c9ht1jsjTGrpg4NZssG+CP+6XP\nvR9w/Pgh4biP23CwHIco8HGqLqZd/mw5NQJ/iFuv4ThNkiik2tigyAJMqyy2vq0moxZ1/tLJgF/b\n3uIPjYa41RZuvYVpKrj1DXi0T2f/PuNhRp7HaEZOe+c6SZQx6p3QP+rgVOvEYca/4w6wGm16GdjB\nEfF4hG4UjLsRur2BbjjobYfu4R5hNMa0HYqipJekcQbSNPRMQpKgUmsSjDsEoz6G5WDaZQGZJj0U\n1UJRbTSjXN33Rg+x3C0E5ZTE1CrU166Xwt8iQNXbZMkJeRYgyfIs5VZw2ghcpQFQpFN6T1R0Jtvm\nGgmk2f5lzcCT4GcODvlce41ta7Vt6WVYbASKqfBZtmbmG6LwZ42AYTapNgOQYkzHRIgMWSkLiCIv\n0PQKReajTrzqs6yPqtkUeYSknn1OWQJE+YVuyA65EMSFjzG5lzlX9EeFf6YxmN+fFAFJkdNUcrK8\nILPXKHhMJ0vJ0NEx0aZ6gwv6gLT3K+T111Y6/8xjWtAvNgLzeQLTFftlIl84LfynDYSX+7hztKIp\n5p9nek04zSeYphWr0ik1CJhZiF7UABwuBI8tmxAc8hThYouUIik7c/4ZJ6An4PfPr/7Pti1xA1os\nrH/24D6OqvGDr39mOe1n4b6bhvnUE4D515umxhvDcCmdZtHFZ/568xOEVYX6qsZjcQqwSCm67H6L\n11rmXLQK8xOBOM/5kXf+JX9w/QU+Vtvklcbpv+fSUvS8a9AiloaATZuAJ41s/y3GdNV/2gg86RRg\nvaLyZ158lawQmIrC8ZLz57d9rTYEX5u/1TP8jkXoBwhR0NouVzrjKKS2tk6RZRzevw/AqF86PzfW\nNpAUhUHnmDT0cJsmummiGyZ5npXhSKLAdqqs7+wCEnF0+uWj6jqNjQ36R4cYplXqBVTYu3uf9naL\n48cP6R0fEIc+1WaFO6+/hlN1kWSFIglQNMHgZB9Jlgi9IWs721Sb9fLamoSiOOSZR55OgpnSiERk\n6GYp2A2GJzMRrOW2sRybP6TmuJbBTV0QfvlNNEPBMAI0w6LIBXmaMOx56HqTJMrRDJVR74SjB4ck\ncYxpVVE1E8PW0BGsawrf+ZUe/8ZWjWa9zmebKv+39z6vDx9y7ytjtm58HMut0NzYJhiP6B4O0E2H\nPJMJRl0qjTV6xx0s20RWdJJYZu/eHlE4ZuN6jOW20PSyAYBSm5DE/fK/XdhD03Pc+u4s1TbPgkkj\n4M/OAZAkC1m2KUSXeVvQi6hA8w2AKtnkiDPC21T4M27704qB5/G9L77AXhDya70+n2w2zuy7SmLw\n/HPPNzi5CFEmjY+slMnUWXKCrMgIYoa9I9LYoFJrkkQpuiGj6M7Sa8uyhCSDpmzPCuQpp30KVbLJ\n56YoceGTTN7HaaG/bBXfz73J81skoscwH2OrFbw8p0ABqYqOeU4HsEgFmkIbv0tWe3n2enHVflEj\nsLiyP9UDrNISTAv4lnZ+IvD/sffe4ZLldbnvZ+VQuWrXTp13d8/0JJjAEEQBhYNyDQcBEQVERFRE\nr+FBPIJeVFQUOQbUo14T3kc5oGLAox6OIjnOMMMEhpnpMB12rl155Xj/WFW1a9cOvbsZdMR+n6ef\nmb1qpdqh1vv9fb/v+0oIlKT81tyCy6AR7W+/oXPQprPihG2oujVcbByzShFlMCI1KRrOjs2I/fgq\n/E5EvhFOCJK1/duSbr22vMUFaPN8Y9cfEFZTEri3vUFFNXjG1Py2lePRSvyegWTagCDvPhZ0uW7C\ncJxmXJw8ecz49i2r9hNEfb/X3EkHsNt5rtR5aNf8ggnhryZJfNXUHEfzKrXJ7+uEaHhYFEwWAsPx\nnHFx8Obv8d5FwOWsPR8PTBL9mbzEmhWPtu9HDzDsAnysucJpq8v3HDkFZCR/p0LgK5X8D/GV/e6u\n4T8cfNchSWIi30OSZYqVKla3g5LPEwU+SRwjqwqCIBAnEZ31FURRpDY3S6GqEYU2rt1HM0vohokx\n8PPXcnk0Xcf3PMxcgX67japo9NttCuUqGyuLJEmEYZpEvsPF0w/j2l3yJZP6gcN0N9qcuf8hcoU8\noiygagl/XT3K/b7H7ygiB44fp1AuEUWZ1aOq5Ql8izj2EaTsg6lYq+HbDknoEsc2VpiturRWzzO/\ncBNmforQv8TX1sqoWo7lQxs4fYd8sUR15ibWLt5NHCW4QcLMoQP02xtU6nN0NhoYuSLV6SoAnY1l\nBMmgXMvmxt/7tNt5YP0c6xfP8MHHXNbkPAs338jnzl1EvPRxHCslV5zOXGqKOXLFPKqaki8W8Ryb\nqdk6vucQBR6V+jxR4NFcXUXRlqgCZj6bSxel3CiUysyZRLFFoTyXzbkLHmma2YFmIy9bHxabBcDY\ntn2KgsdFrPuxC4XdtQHDYmE3G1Enjvh0s8ntlTKv/sxd/PYdt5FXspGgnToOe97DqBuQzfCPP2Ml\nZfAwi0HVCviWR6EyQ3PtEpqmkiQOcSSSRCKymiNJXCTRJI49ZCX7PZBGLksusH10QxrzxNZFk4Ss\nAzDeEQBIADu2yY3N4itiBScJiGMJO00AAXFQAOSkPL3I2mIJOl4ADEm3HgdEUva92snKc1yoO/na\nTuM8VTlPM+wjicKoM7BTjsDk/Uzicl2JRtQFtnYBhpi0DIWtpB82i4TZCUHxeFEwq5rbtARDe9HN\n8+a3vDaOurL1Zzg8brK7MKvprAbWltczkr4/arDqhVy0+3TDgH9evcTNpTKLrstzZ+cG51d2LFJ2\nCjLrBj7dwKekaoN9Jol8ViBsW9EfJC1vnnsraVb2UojvcJ3xbXvtO5lqDNszBXYrQnbDaGV/v/tP\njAQ9u35gX8ftN8QMskBOgDTZfXzyaqw9r2Tlfnylf5LoXwnxH2K6IFNJZpjWjG3b/7PhP987voYn\nNNob66iqTKfRIFcqEYUhR2+4kcbiEv1WkzhyKNfrKKpBHKVMHzhIoVwk8B3CsEupkidfyRF4Dr1W\nkySJB6m/S4S+T2V6BsPMMTV/gE5jHVmS6Xfa+K6LY1m4loORz6PnTTqNkF6rj2dlWQUnnnw7i488\niCCGgMxNNYMPNUPe2OzxK3JEoVwazGxD4PXRzTxW10GRBURFRNUNktin313HdyMCv49hZmNC8AXm\nF26iVDuEY20QRyLzx24h9FuY+anR9ydJYlStmBUaus36pRVKtTrR4CHu2D08t0lZnMm6B4mPIYnc\nkNMwBAGjeoKTvTXufeQC70oL/MHtC/zF2Qs8Rwtx+22cXh7H7lCqTqFqOp4X4NhdVE0lVyrRWFpk\n/ujNtNYFEDTiKBkR+yTOSI5u1tB0k5QyqpYRUkEUSOJMA5Gm3mgsBSAO2yCCrFQQhNplw8F2gybk\nRj73mrDzSvkQVzMOBHCyUOBkISOQ339iAX1Q4I13HPZTDGztBhgDVx9IklY2kiVlAWq+t0Q4IFGu\n3UQzFIr1KXSzgCRm31dBEBAEgzRJEQQtKwikrDBLcbZcM0kzi1QJgWQwghWRCX5lYbMQkAQBJ7bR\nRBMRRgWAKW6SyCAtYqceaaohII46AL1ok6juRbajqachXvhLugsv27K9JBVGJL8V9flsa4Vfu+cS\nX32oyEuPzeGkDt14/Bp53nXmMf76wiJVWeOdX30j7UEewLCQ6MTWlsKht4uguLVL4QGbugCAjdAa\nBYhVx7ILdrILHWJWze0SMMaWxOEwjUfFwGTOAOw88z8S706Q/Z26CbsVBLDz+A9M2IsO0oTP9S0W\nHYuconK63+GVC6eoqzqNYHsIWSPwqavaNrHykMB/odvj++76MGIkUVE13v91zxtcd/sc/07agr26\nC5PYiWDvtW3V91j14/2de58FwI4uQ1eQbwBX5hR0pdjJIvTLhUkSv2bFW7Zd6Ur/bhgn+G4c8YYH\nP80v3njnVZ/vKwXXioBreEIhjXwkU6LXadFpNhAlCSOXx+q2M3tOKULVJZxem6n5TJDrWH2Wzj6K\n57TRzBQJ6Hc7WO0W6uBDW8sVKFarrJw/RxSGlGpTGPk8gefSazXRDINStY7VaeH7HvZqEzNXpnzi\nEKqm0202OPv5u0CMkRSdtYvrnFRa3KZPcW/e4Ds6Lt/jP8oL6lUqcvZBpUgCqiLT7q0ThwmubWH1\nbXSjgqaVqM3UMXIFWM3GnpYvnKY+d2AL6VeUKfRcJloq1Q4hKRah36XbXKVUm2X14nn6HQvdzJEr\nFGksn6NYLVOdnSeOUgTRJY4cVK1CdXoOux9QqBxF7Df5MWeNC+fOcZcr85LjJ/hiqwkXH8MQdESh\nQOD7mGaCIqWopo4sRxg5mdb6RezuCoVyHUkuI6s14shBlERESUSSddJURBDzo9AvYDMVmOEMvEuS\ndBCV7GckjM3yX4k16HgA2OXI/+OJZ0xN8TP3P8i3HjzA7YPxoMliYD/FRuYUBAkuklRlUFcQBS2i\n0EM3ijhWE003KFXnkDUJScxE2cPOC0AcZdqMMGgj6gabuWYGkA7sQrMtaeoi0SUOe8iyRkJhIPod\nfB8nxoJyYh47sXCSjECaYh5TzHPJP09ZrrHE6mgEqCBl5LIb97eR7aJUGOUENOb/C4fv+xlqToOk\ncBxglB8whCSAFbucDTucPdfhG+YOcLKYFdpDwu7FMb935gwAt1VLWwqI6phYePI+enF/NA7Ujfuj\n4mOyEzB5bF0ujexCG1F3W0dgRtluGTqOSUvRoZ3ozBZ9wPaRoa36gbE8gcFq/tbOwNavx4PKdtqn\nEfij7sFkAbDqBdssRj+4tko/iEkFgTuqVe6oVmkEDqBT1wakVxys9A7sRBuBA2K0Y8jYB1eW8T+0\nQOM912P8jw9tXntslX8v55+dOgs7rebvlpp8ufGqyX2G97P1etq27dvHkHYeBZosAHYSBW953Rmf\n7x/LLLjCgmBXbcAOXYLLjQNdKXab47+alf7dMNkBAPiH1Qtcly/zumM3Ykp7U+DheNBXcofgK/ed\nXcN/SJhFE6NgkkQplelDLJ09S2ttBVGWUDWRfDlPdWYWVe2zfO4McSIiywqyksMsGtid89z+nKdx\n8fQFAj9F1RRaax2sXgtBEJBVFcfqU6hkpC3w/CwHQJYwzByB72EUCuiGiaJpdDY2WDz3CIahU5ub\nxXc9rL5FqXYESVF4VXONQ2LM+808f+zG/PHFBgB3SAZvyvkgimwsdSiW60RBiqYVUTWD+oHDmIMV\n5W5TJwpB8FKaaw1c20GWBWpzp0bfl2EomCTJmHmZxvJpfNfFyBlZiBkpaZoSBgH9TsjGcoN8wUQ1\nfXSzShRkDwpFztKUa7OHUXsmvtvlzTNFZNHgo7bEbQu3cTRtEgYiubyMomYahzhKUFQDz+3Ra3fQ\nTajOHMDM1xAED0ESESVzIPD1EQQBUTQQJsi8KEIct7LRFwFEqYwg1MiCs658dX4yAGyvNOD9kPIr\nTRP+qRtPseJukoYLto0oCBwy9+catNUylIFbUPY9cu1VfCdiY7VDZ8NC1TxEEaYOHCVOQBzrzgui\nMEpgTuKEKGwhKeMC6c3/H+oD0sDFt0IUzUfNaUToSIKwRQ+Qk/LYsYWbWIiAMVYMmGKeslwb7VuU\nszGg/mCVfjJduT+2ei8hUFSnCY+9Eu3sH+He+ktAlh8AbJnTf259gY8//wi9MESWwy0kfbEf8yv3\n3AvAKb3C6248MnptPFxsJ8SktOPeqCgBdswc2A3DsaAhSR92AdYmR4SGoWLhzl2CoaB4LewhCIw0\nAVuuFbqQbn1cT67yX+7rbeR+PL04lXck/w0/oK5tEuyGH2KFIf+wdIkXHTrOiUKeRuBQ11VAohF4\n1DVlUBCQbRcj6rrEZGbCJrkP+eRSg+6nT1H5xrMoorhdOLzDyM4khoXAeOEwqys099hv9F53I/67\nFBeb36Nw13Ginc+3u7h4OEo0LvYdioSvJIH4cvaho2vuMhI0mQB8OXegfw/EaUpKirwPi+Ehgbej\nkFXfpT4YAbq5WN339db70VdsIfCV+a6u4T8sNMPg4InjnH3gPpbOnkU1cuSKJTobK8iKRGlqGt+1\nWL14ntBPyBWrqLpBvljm/KNfIF8u8PDd99Bc88lXZrEJEESTgwtzlOvTuLaNosiQpNmKf7+PLMsU\nckV81yNfKhOGIa3GOkkUIcoSU7Oz2L0eVs/ByOWZmi1TmZ6mvb5OpTbNd6giLwlD7k0C/sURWRFl\nTrJBIOcYjLeSpgKHr7uZfq9LGsV4Tja2IooiruMRhxkJTpOAfGkWx17HtBoUKsdGBYAoD1bqji4g\nqw3SJAYseq0uoqjgOQ6lqTlCz6HfckmTmKpqksQJYeAgpCq25SIIAp6TnTMK+kShgCge4ceuuxk/\ntvnlh5v8wEyC52T76maVOLIRJZHpgyeozrikqY+RzyNKIoIojFb8g0RDk0QEwSTF2TKOAmRFgdDK\nQsEEYEROrz4UbNz2cy/iPV4AfCl5AeO4q9Xi3Rcu8vt3PgU/jnn+hz8KwGef/zxyl/l03ckCVRJM\n0rSJ73RoLK3QXHW4eHqNQmGKwqE6omKQximylr2XNEmJQhtFy5MkDDotGkmSkvgOoiggKcZI3JeJ\nkAHBJASSOCCNdcTERxL9wWq+PhILB4mDKpqj0SA3sbZ0BQpSASexSEiwY4uivHXEZCgmzkmb24da\ngV7cRzz+Ggr//Czcm98Mg9/vISEfd+xxUgd58P0cH9V5zd2f4lLUp5BqvOkpRzEHXbhx8l+RiqOO\nxHhOwRCTwuBxp6HxLsBwFGhI/iGzTB2iGfWpje0/JPfjmFGLl80VgE39QCN0qcsF6nJhy4jPbgT/\nSgLFLofh6v84Ee4EPr/44F18/dxhSprIw3aTU4XS6Ji6pjCr6WOr7NtXerNxps0/jooiciltM/1T\nnwLgrU955i73s300aHuxsJ20b7sSulZUAAAgAElEQVT+RGdjfDswVtTAahAxq5vbiovJexon91dC\n2LecayKg7GpwJSNFl9MEDJ2CrqQIuBpx8F4hX9mYokg39CkpGr9z9kFuKFa4rTTFGx/8FL9y89Op\nqjuPaA27AO+6+Ai3lmpUFI0/u3Sat5y644ruD651Aq7hGv5N0V2/RJr6RIGE7/s43RayKlObm6a7\nsU5pEGyVIqKaJmYuR3N1ldB3MWc0ls63KNROcPTUTciyQhJHpAiYZg5RkoijCKvbptduk6QJM4dO\nAuD0LfqdFnEUkS+XB6NEKY899AXK9WkKlRppmlKZnsbudJAkCUWREEQB3/G4s1ThaYZEu7GKMWCA\nVicgiQNa64vY3Q7Hb70Tz3HQNR3PcRBkCUmUiVFprrUgDSDV0PSYMHDw7DXCwEEcOOlEYcAX774f\nTc+hGQZJlCIrGrNHFnCtPpceXUbRdJLERhATFK3Kxso5ZKWA1enTXusgiiqlWh1RCjAHK9/ZvLlH\nGne4tTpNuWrSXH0UMBGlAFEKkaRstVnRJCSpPiL/d7U63NVq89rjC7zuc5/juoLOT96wwIrnM6dX\nxmZKHZK0iShWEIQc7aCLJIQUr+6ZuQ1XO+f/peDZ09N8Tb3OH509hxVFvOWm61hzAnQpAK5mltYh\nCtusXDhNYEtoxgyVmkyulHWuhuNtgW8hSiKKmiMKmzBahNRIEg9ShSTxUDQJQhdwESWDzCU80x9o\n+hyiUGJj+WFkp0W+VicZpOj6iYMsbD5cTWlzPGjYFXDGCimJ7MFvx9tn0IfbJSHrdIgIFKQ83bhP\nR69gTD0D9fx7WDv2baP9S3toCYZoR33++zOvZ9F2eXp1DlUS6cZ9unEfK7ZH7j+TI0njRcb4yv+k\nLehOBcDQHnQnTCkFpncYBRrvAKyPjfashr0tBcCwY7AyyAmYVYpbVv93ShmGrBOxuovYeHyf0TkZ\nswndxYZ0dJ0xUv1Iv0VB1vjFW5/CvJ6lqNd1dUT46+qYd/4EOW/4HnVN3zFpWJMkfvXWp1FUNW4o\nlJAEYU/r0v2Q7EnCvmUMaETwJxYCBtx1WABsGRfaoXuw5Z40fX/3tUMOQnb+7WFlm69d/rz7yQ/Y\ncv6RnmDvz6gZU9h3EXC54LAdz79DyFeapsRpSpgmvO7zH+UPb3sOb3jgU7zlhqfw6qOncOOIsqLy\nq7c8gzDJ9vkfT/4avthvczRXRACWXJtialAvyDydGYqywryR21cBcOnCeV75shfy4U99frTtHW/7\neVLZ5MwjD/Gs572Al7/s2/Y4w38sXCsCruEJBd918b2EUq1AqZajsbQOQsz88cNYbY92cxHSkMby\nGqVaNhssijIrF04TBusYhQXmjsywdK7NpUcfJkkSNN2gXJ/GcxwUTcPtd2mtryGKImEQDOxDIYpC\nJEnORm6KJeIgoNPcoFyfpjY9Q23+IL1Wk16zSRQGOHaffDGP77rkShUOLJzEzBf4zAcuoNULWC2b\nyswBEFRaa0toesrZ+z/PdbfegWNbyKqKJMnMHjlKr9XCc51M0NxepTKdra51m5eQtTJKEqMOHrz5\nQo0UjTQRiaKAwPdZOnuW6sw0R244xdK5BzHyItPz86wvXiCKJCrTJTbWugS+jySmXHjki5RqNaqz\nhwi9DnbPAjRSfJ6jebz73DqpUuabChFnrITrDZ2PdCxuqaicsUJOFfPc2+mx7G7w9bN11CkRRfB4\n7fFj3NPORqJ+7sHTvPb4SdY9n79eXCRJA15/4gD3d5s0/IQDhsGGb/Ga4zl+9J57eeGBeb7pQOZu\nsd+k4CcCREHglUePIIkiKS7/9yfO8MoPf4G3P+0mjl2ZMyNp6hL6HoKg0++FtNcWESUd17YJ/ZT6\nwRniBAYL3rTXHib0PTSzjFnMchskUSNNUnzXxnN8JDl7IMuKjiwbpPhZARm7xJGL7wp4bkSumGAo\nAjECqpTpUpzEIhloLhI2HYWGDkLDiSRR2FzFKwwI/FA/MD5e5CQ2ppgbrdQXpQL+da/DvOcn4OhL\nKMmbJHZ8NX98zn88yGvBrLJgbt2/IhWRBgXYXgXAEEPr0Z06AOO2oLXBvdXl0qgQGObnRbvwo+H8\n//jq/3ja8Gi/ieJhUgw8xG4r/UNR8eR5J7Ea9kaFxeUKgCEagUNdNVlxAh4KunzrkUM0PHdztXwX\nUW5GtsdW3AcFwCjnYOy4hUE3Ydyxaj+r+rOazgP9zaJsmJS8E2EfX/1/wGpSV42RngK2fz/2On7L\nfhPX2ks8vON8/z6Sjredx/zyinWHWHNSesH+OwFXUwiMrmXF5PSEi47F20/fy5/c/rX8wo1PRRZF\n/uD254z2G87xlxWNNE158/W3IwgC/3PxDK85egMi8OeXTvPauVv4wc9/glfMXU81d4UfxJfBV9J4\n0FfGu7iGrxj4Xky/G6AZCjMHNVS9Rr5YwbFsHKsBcUxjdQ1R0qlOz7GxvMiSexqvv8qTnnWKNM0B\nIgcXSvTafTTdwCwUEUQJRZaQZRnDzGHk8nQ21hBJWblwPiPmfQu9mj1gfWsQRiXK5At5gjCitbqM\n57g4Vo8w8DPHnEKe1toaheo0URDSbqwhqRpJlBKFIcXqFMVqRqjSOMR1HNYvXaQ6kxUwYeDjuy6B\n5yIKKbXZEtOHZrA6HS48chozV6Q0XUIUweqskcQJvu+i57Lk3WK5hmNbRKFPa22d6sw0ei7H7OEZ\nOq0W64stVL1GoZKiaQUK5QMYZp5Oc51+u4/V7aMaMpqhEPgu1Zk6ttXmhdUagS4i63P88t2f55dv\nnOahnssdNYH7WhtMKwk3qDalRGBKtJkr1UgSl2fUTJ5RO4IgmPz+nbcAcN5OuKF4Aw3f50mVCk+u\nQD/qU1EVoE6YeLz8yGFS4Jxl8btnHuVXnnzbl/X37PHuGqiSxPsuLRKmPp/ormH3BV7zkXv512/8\n6n0dPwwNi8I2GysXWDm7RkKJQmWaQqnK6qXzSLKAoubwPZskEZAkkcVzj6GoJUi7zB/VEGQBVcsN\n0qireE4fe2MVPZ8jiWNa7QtEcZfqzCF0s4IoltGNDp3GKoEXo+WGIm2IEhdTzBOThYoN9RJDByFg\n1BEI0wA7aVEcuBINC4AhhqJhU9wM5xqS9qj+NQDojU/SnX7mlteG8/qTwt2h68+kXehQUzDeARgS\n/m7cH2UmDM8HXLYAqMnbV9eHeoeaXOQL7tLIIWh9DxI+O0H0h12DHceBdisaBHYl+rO7ZEdsOe8e\nCcY7uQ41vICyrLPuuVR1GX9A5OtjhLgR2iNR8SRx3uw4bIqBt62mT4iFt51jXBy8w0r8LYMCYtX3\nRqM+4wVEuIO15WQBMIndXJJ2wvBak8XHronKO7w+XgCsujENL+GWirJjcNi+OgP71APsJgyGTW3A\nlWoCrsQu9IFuE1UUOWQUaFgRP/K5j/GmE0/lrSe/GkEQOGjsTd7H93nrjU8dbf/BA08G4M0Ld6KJ\n0lUT9vHj8vpmUfOZj3+I//mu3+Pd730fAB/50L/wp3/0+/zBn76HH/+h7+O+z38OQRB42cu/m+9/\n/Y/wh7/328P7vR94KE3TlwmCkAN+C7iFjIv/bJqmfycIwk3An5BV0CLw4jRNT1/VG9gHrhUB1/CE\nwuzhbFXec5s4vQ4HT94MgCwLVGoHsO0WD911L/niLJIs4/S7xFGDueNVIj/gobvOIooqeq6PouUp\nmDmMfIHa9Axmqcjq+cdorq1i93sEnkd9fobOxjrt9VVyxQJGvsDG6grFapXm6gpT8/MEno8ggDiw\nbdF0I0vBNQ06zQYIEtXpGVy7j5ErUCjXcPotwjDg0plHOHX7nUzNHmD5/Fmm5g4SBSFGIU+v2WRj\nZYl+a52UhDhxqc7MEngunQ2L2UPXo6gyURCRJj0C30MQJUq1WTSjQBSEaKaBrKp0NtZprS3jBy2K\nJZPF02eR1DKiXCGOElqrDQQhe3B3muv4rku+VMPuNhEFFSNXRJRi0gRmDl5HHLmD96rx/91SIIlF\nfuBwEUkIefVckTgKicKY6ySNwLNJogRRFpGkdDAmlP08BcHk6ICbHMsbgEiaNsnTJgp0ZLWKIop8\n7UxGvNw45sWHZgkSmzfd/wj//bY7uKvZoqwqI2vOxwOPlyZgHM+ertMKeogClE2JbzlS35cYeZQa\njIvnd4lCEdmoETgRcegTBj6u3WXm8HGs7jqyYgIyqRiTpgZGbpb1pbMsX3gMPZenPn+UJEkwZJM0\n6qGbM9j9FhCwfmmNJI5QdWsg+nbQDJPpwwfRjTySoA/orUcqZBoCARAJAHGLlehQNGyKJhIihmDQ\nCjdgjKOYYp7+2Gr8sBMwFAkPiXqy8Cpm7n0zl57z13QHfGhI6GHnRN+dsgL2wrC4GBcCTxYVsHsO\nwTiaUZ+NyKYkbR43SfI3oh7tyKIyoZMYJ/HjYuIZpZiJgweaieHq/uj8Y1/vRPgnLUdhb9I/jm3O\nNwOiWldNnDDgD89+kZceWSBOEx7u2Zwq5kYkuq5qTIbJzurqtqJinJg3oj4kEvWhluEyK/7b7nfY\nhditsBjbpymKO44A7VYAjI7fowDYOTV5c9vlrEQ3j9lZJLybPuByM//79f7fvNbeHYUvpzD47vY6\nd7cbfMPMYX7pkc/xovkF/vApz8F2H58uR0bgH1+KqysCJUPkzmc+h1/66R9jY6PB1FSd9/75n/Ky\nl7+KBx+4j9WV5dEoUbfTAeC3f+NXAUjT9EmCIJQHp3sz8K9pmn7PYNtnBUH4F+AHgN9M0/TPheyh\n/fh7wI7hWhFwDU8oJEkfzTBQtBIILoHfIVeooegJpAGqlv09SAOVoO85TM3p3PqsZ/LQZ+6lVJtH\nVkwK5Sq+59LeWMd3HVQ9s/kMwohee4Mo6DJ/dJ7GYhPZyOFaNuZcEc920HSdKIoQZRlVNwg8H89x\ns6LD6uHaFiQh8lQJq+ti5POZg5EkU52do1Ct4VrdLOis3+fsg/dz6OR1lGt1jFweJ7XoNZvY/ayj\nUJ2dJl/RUWSB9aULLF9YpVieIUlS4jjB6llIakS/1UYQivTbbSRJR9E01i5epLlyCUVXMIsGiiLS\n77poRh7dKFGqziMrEnavh6xkHZBuc5Vc0UCWJURJRc/pxEEPI1cY5C+4yIqBKJlEgYUg5BFQ8N0+\nspIQeDGhHyPKeSRJJPB8Aiw0I4egJYiIpEI22rIlE0wAcEgSF1Eok6YeAiZRvEgagygZ6CI8vVYm\nTVOeVjNJ05THbJuTYp5k8BASv8y+1VeLKU2jrOa46wWZsFEWzH1bhYq4pAmIQgGBNpom0NuwyJey\nVU5N1xBFjThU6TXXKday5GrfDVm/dIkUnZVz6yhGkzSJqU3PZrkBIoQReHaAJBv0OwFpEuC7EaKY\nERUjryMIPqIsko2ju4iigSwIyAJEqYshCESpT0JKlGa2pl5iE6cuRblOOmCBU8pYpgWbHYFhcNms\nOjPKERhfobeOvpTqA2/lwIdfyNqz/opc/sSW78+ky48kMFrN38kBaLILsBeGhcAwWGzYERjmAjSj\n3pbgM4CKnB+R+yl5O/kHaEfWSCg8efywC7BTuBiAIMQDUr89bXgt7G0pGoZoRH3qg+uthT0aocOU\nXNgaOBb2Rnah49g2+qLpPNDtUVdlVEnmO48d52g+x4linsaA7GYjNdrmeNHE+M5O1xmJjakNXtsq\nHB6u+u+tCdieNbDT9iHCNNlyH3t1AMbPB3sXJ/sl+tl5QuqazJLrc9jUByNAIWESc9AwLn+CK8R+\nHYL2gy9XEdDwPW4pVjmaK/ALNz1ttN0m+pJsQYe46pGdXZ4vwpjGa6ao8O3f8XLe995387KXv4q7\nP/sZ3vl7f4Jl9blw/jHe/BM/ynO//gU85+v+CwA33HQLa6srCILwCuBvB6d5PvAtgiC8YfC1DhwG\nPgW8WRCEg8Bffzm7AHCtCLiGJximZmYQVZPQc0iSkDQNkFWJXLFKFMg4vTaakWdj9RLrS+eJvCbl\nqcOZy40f43sR9QPHQBDIFYuomo6ia4iiQBAnmUg4iajM1PE9j3x1CjNXoNfcQDMMRElG1TRUXUeS\nZJy+RRRHlGem8foW1elZHKtHHDlUp2dZv3QJr91j/thJcoUivuugaTqlqVlaayvMHT7Mxuo6rmUR\nhSH9dhtl8PCQ1eyhpRoynhXiSxHtDZtK/RjlWp3WeoPq9BSrF89RKJu01jqkJY28ZlCsT9Fvt+m0\n1ilN13DtHr1mH8PMoRjgOV2stke5fgBRTLNk5OrmB6tn2xw8cRLPVciXaiShiyALRKFLkohIskEc\nOdj9ZayuRRImSFoRWcoeilanT+CtU5s9hCArJJGHoiY4VpM09dGNCik+qlZFlMzsczXN3GvSGEgT\nRNkgjptEg3AhSa6SJC3ARZIMXnLoCCkuLz18CIAXfewT/O6dd9DwfYpKxEHziacZmLQYHRYCuyFN\nXVJ8osglTTTsXotes83KeQtZKSCK0mDcK2Lp7FmmDy4Q+iEbKw1810VAQcsVSQHXthEQ6XdsCkWH\nMLRRjTypbSHJBmZ+Ck1bxXMzkpsVCQKBZxPHHqIUAk00w0RWPURJH/1sUjzUgcuQPRivSNKsQIBM\nE2COJQoP3YQSMo3AeDdgJyvRRNJoHfxmtNCi/slX4zz3gyBlfx/DlfvxzgBsdfWZfG0S4xqE8eN2\nSwcedgOGhcBQB9COLK7TN1NZG1GXjajHlFwckf/hexoWAM2xawwLhuHY0LiQeCcL0eHq/2TQ2Pj+\nQxeim43N+1oN7C0FwCYJlncNGxsnvw90e5y3+qwJLu9bfIxnzcxyNJ/9fOu7zcaPuf4Mr9EIxzsZ\nu/+9jhcQWwoAMRppCVZ9j4YfjMaHrgY7FQBb8hV2C0u7AsK/E6Y1med+8B+JkpQbtDqGIvFwr40l\nu3zH4et4yeHjW693FWFi412A/SYCr7p7C4RncuKoCHB3E75cBZI0xYlDvn7m0ON2znHUi8qOGQH7\nQbVaG63gD9Fptzl85OiWbS97+av4rpd9K7qu880vfBGyLFMuV/jgx+/mQx/8P7zrD36Xv/+bv+LX\nf+cP+LO/+DsO1gyAO4CfGYz8CGSjPo9M3MIXBUH4DPCNwAcEQfjeNE3/9arezD5wrQi4hicULMtG\n1SIC30cUfArlJxFFPfqdRTrrLnEiUaunVOsSqpZHz09RqlXxnYjqzBTrS+dxLAtZUZg9soCWy5PE\nMdWZWXrtNq3VZTSjQL/TIl/MEwUBas1Ay2UCX1XXSRSJ9aVLQEoSxSAKBI6OrKpohsHS2S9SnZ1i\nffE8impQmTnE8mOnkRUFWdGQZYW5owv0Oy167S6iKNJrtShWa7Qaa3RXFmk3VimUM8cXUdRIiEkT\nH0WRcftdDp24ns5GgzgCzdBZX2yQK07jDlYjuusNXKePADh9DzNfg9TCc1tIqkkYiBTKU+QKJRy7\nT6FapzIzS2tlGUGAUq3O+tJjzB1ZoN/ZQJZDhDBELdeQBmrHbusc/bYFQo71Sw0E0UIzFGr1KQRJ\nRVIVmuur6LkCqi6yvnSRwPfQDZlSLUUzKiRxAjhZonDi4rtNBDRU3UQUc6Spi6oN/e0dhotNUdpB\nlg3idFMk/P8+9SmUFIV7Wm36kcNzZ/O0goDj+cdX9PVvhaE9qJCmJIlGv91g7cIy6ytdklCmNDNF\neXoG17Zxuk3CKMbu94nCgCQKyJcKxImIbuTwXBszpyOIoGoiYewhSiJJ5NBuLpEkUmaxWy9SiFVE\nSUOUc4SBTZwAaPTbFmnso5oOuVIecOisr+JaIUZeoVyvohYr5MQcqaATpykyWUcgSRP8xEHboeMx\ntBLtx33s2CIn5UdWouNdAffIt1O6/62kYR/hwbeSPvmt2T6DWf5huNc49uoCTO4zXgBMCoPHUVMy\nfcDkWJCIMNICTDoEDQuAulyiGfWoycVRB6Ei57d0C/YSEw8XIQUB0pRRdsA44V8Pe1uLh2B7cSAI\n0Aidwb31SVN5i83oJMaJtxNFfLbR4IZShSXH5rUnTzFvbpLgIbEfdwMa1wVko0CDFX4h2nLMEDuK\nm3cpLsbvr66pjHcPhsVDw9/Z9nPL+fdB5Eei5YEAe7K7sVeHYq/riYLAnflZPtFb5gvhGvZH5wnP\nH2P2pWd43szstuyBq8GVioX3WyiUBrPwj1cnwI0jdFFi2XUIkwRN+vJNu1xNNyCXzzM9M8vHPvKv\nfM2zv452u8WHPvgBvvd1P8QnPvaR0X6zc/PMzs7xG+94G+/5m38EoNncQFVUvum/voijxxb40R/8\nXpIkYXnx0vCwNwLfCeSBDwA/LAjCD6dpmgqCcFuapvcKgrAAnEvT9J2D/38ScK0IuIb/HJg5eAMA\na4tfJA5jwsCm3z6DpIqISmZNOHP4AK21Jt2NLgglTn/+PnxfJJfTUU2ZtQunyVemOX3v3QCUp2cJ\n/AC3t/XBXT8wz7n7H8PqdZg7cgzIfPs1M4fveiw9dgZNU0hTkekDh0iSGMceJKYWc3i2g9XboL2x\nThjYKGoOIyfh2X0Wz8UcXDjJpTOPUKlPkyYpupnDzBewOk0UTWB98TGm5g+zfmkZo1Civb6Kqqu4\ndp9Lpx/ByBdora+iKDqqNhxxCtE0nSRNUDWdfLnKzMEjtNfX6LXWOHTd9Vx4+Ayl2hxmrkiv3URW\nFFzHpvfFL4wKjzjy0U2T5fOPUKlpKJpMrpBHN2uD111c28GzoN9r02s5FCt53H7AsruCqhqIkkrg\nhUhyhGe7SLJJFEAgJoCK1d1AM2QK5YOk6WDlmU3ikMQZMei1zxCFHkaujKpVEcQASSqTpC6SYIxE\npVNaduwL5ucIU5vHrIA/OfcYr7/uBL/16Gl+9uab9v1A+fewE90JsmBCukksRLGIqiSIRZk0TalO\nT7N47hyyZiLKMdMHD+PaNr5tEUUBRs7E6ncQhRRFk5hfOEoc++RLGdlTVJNybQrHtnGdJoVKEUUq\nISoCipJHUfIEcp9e6wJCqrKyuEZttkiukB0f+CopMmHoI8k6kqATpR5Z53oToiCiiSa6mMOOLTTR\nxBCzc9iDkaDxQiAe4xPDhOGg/lRa138fxuqHKZz7U1aPvhSzlInLh2M/44XAuP//JPEfHzUaYjdr\n0HEMtQGikHU6hmgOSP4JfX5UAIwT+42xDAERgbP+MuWxbITxLsG4TmBI6CfzA8Yx7CRMKYXRPpOF\nwKS70HgBUFdMGqGTzeEzkQ480QFYd32cMEEQoKDKnFI3MwB2sgIFQIgmCgBnrFDY7A7Nqpvi3lE3\nQogglbcIiIFtX09u2/K6GA2Kg637jWMkLB45AW1eZzJdeRy7aQ62jSQNE5DHtAqTNqVvf/odnLVO\n8oVOm/cKl/CfvsSP33obp0rlsfNcXQFwtdhPITCyeP4SioD/vXqRm3Nz5LWED28s0wsDfvjELVd9\nvv1gv92AF7/4JQC8731/Ndr2zt/7Y970hh/h5978RgB+/Cd/mqPHjm879kUv/Q6azQ2uP3UjAKvL\nS/zo619LOuiW/tRbfoE4jnn993/38JB7gV9P07QjCMJbgd8A7heyb/J54JuAbwdeIQhCCKwCP3+l\n7/1KcK0IuIYnHNqNc6RRi+rcPEnURRAMcsU8uWKN7sYKS2dWaK3GOJaCa20gSDmq9Xkcu0cSORgF\nGUVVcWwLM5cnjSI2lpeQJZHG8hKhayMoIVFo47kW84XsAaroOktnHsXudanPHyJXKOL22zh9i5Xz\njyFKEs3VRaLQxem3UDWDuaNzBH6A7xboNRzKU9P4noOq6vTabUq1Or7j0ms3WblwGt+1EYSYUmUO\nWZEp17LMgyRJWLjxDhpLjzF9oMbapZWsCwE4fRszX6TbbCAWKgwH7aMwpFyfQZQkgsBDVmSSKEU3\ny1SnM/ch3cgRRQGabjA1W2f1wgUUTUeUBBQi0tgjSiIqpWMEroNjXURWRMLAQ9WKwDJpEqMbGakS\nRQVREAGFjZUGUegPVvshSSyMgoqq5/EdG0UzgBhJrhH6mW2oMAgVCwcPakk2EQQNQUhQtSry4GGc\nJi6XC4M8Ucjz80+6mTBJeNHBgwRJws888AV+6sZTVNS9VwW/HMLgq4UoGsSDUDWr38TquuRLUyBK\nhJ5PEgUYuTySJDM1N8f6xQv4NuhmDklOEfAxCjl0s4SsyohRQBJ5JJGErOcwC3kkWSBFy8a2NJNe\na5lW4wyqlss6amaO1UsPEAUpTt/Bd20UTWZqboYodBHlCEXTiUkR0EcT7iKbNqHDAgA2k4WH6O+w\nYr/52uZYkHrkO4mPfCe9L/4q1Xt+kvWvec9oeXy3+f+95v7Hi4Fxp6FxtCcKgqFGoDu4r/qA7A/1\nAeMWobBJ8EUEOoNjalKBmHRbavJ+oAzsVofca0rZfH+ykBUfkynFkxCFeHTcRtgHJAQhGq3MDzEk\ntw0vIE1T7mk1eajb4ZULJ3Yc+9lpnOZhu80ps75t31GxIERZ4vEQI31I/rLi3J2wVdzr8LDd5jmV\nA9v2GxLw5sSM96RWYV/jQJfrMoy9vpeO4Hi+yPF8kW85eGTXffayCr3aILLLYS+XoCvRBOzkCvRz\nD32WTuBzamGWv1g6Q03VeeH8sS/thq8AV9MNuP7Ujbzvf/3ztu2/+bt/tOXrz376k7z8u14z+vqm\nW57MP3/0s9uOe////jBzZZU0TW8ebkvT1AW+f3LfNE3fBrztim74S8C1IuAanlBwrR5RmH3YlKp1\nemnCK+5p847bNQ7463TXEzobGrlilfqBKWRNQ1U00jQhCkPa62sUqzVc28LM51G0zMnH6rRpt9tE\nYcD0kRnSxEbVdVI8zj34OU7e9gzUMCTwPERJottskCuVaa0touVUojCgt94iiR1mD89CqtFa65Ik\nNvW5OkZOorm2wsVH74FUpjx9AEEQMAb+xJXpGTobiyzceD2+59JrW9QPHKPVWMUYtNq7rQaF6jSu\n7TA1N4/d6yHJMoqqAwK5UlVBhfUAACAASURBVIl2EGGYOVzbGmQquLTimMh3qM3OYnX7pGmM3e8x\nd/QooR/gOjah5+LaFoWqSuBHuPY6YZAyfaiMbppZAdAPEVIVq9vF6nao1KoUalUCfwVNlxFlhcCL\nmZo7QppA4GWEQhAFFFUiCHqoho5vO6h6DVES0Y0inn2RKHIzQXHgohsVFNUcuAilaHoVfdDKTmJn\ndM5x7JYbIAkCkiRxZ61KNwh5zcIxFFHkuz71WX7jjlvpBAELu4wLCVyZj/Uk/u5v3s9LX/Jy7vvC\n3Zw6dT3nz1/gW7/l27j3/u0Pgd0QpQ5y6uF7baIwwbUDFM2gPD1Lvlik3+siiTJJklCfn6WxvIxq\n5gjX1/DdPoomMn1oGj1XRJZ1JFkiSQR0s4Cm5xAEAVnJkSQ+oijju0KWDxAnWF2LwFukPn8YUQpI\nIhe728NzTDaWlzh56w1MzR4iX6qTpB0SSQB0RMEgJkUSBOI0xRC3OtUMOwCw6Q7Uj+2RPegQzsBm\ntCDlyEmbOoGClEe4/scQL72f3IW/RD7+GvaL/kRY2TCUbBLS2K/XeBHRivo7dgkuFxY2sgwdO9d4\n8QDZiv5QUDwk8tPK1g6ALEBEvMUWdDg+1IosqnJ+S1dhiEm9wLgeYUopDAoBOO2tUJbKIGSC3qE1\n52fWl+iEHs+fP8g3HJgfiX93E+mOr6SfylVojFyLzLHRoCgTKysGdcUYOQgNBcyjAmDQDRiebxzb\nhMU7jvRUdlz93w8uVwBcjvzvee4rdAh6PAj+lboD7Qf7KQLGyf/QWvS+zgYrnkM7CLguV+doUeW7\n86dQhE2dwb8nhh2AT3/6U1u+Hu8I7IXnP/tpmGaOt/zC2788N/hvhC/tKXgN1/A4Q5REipUZAFJ8\n/n4t4LHAZdnPPjQC30dRcgiihiAIaEZG8qMwRFYUjtxwI5Isky+VESWZOApRVBWzUCQlRZRE1i+s\nsXKhw+r5izzj/3oeYezSa20gSRJh4GN1GvSa69n1ggBBhNVL57C7DarTeURJABRyxSkiX6bX6ZMv\nV3nWN38jd37d7VSnZezuMp7TIQx8OuvLeHYPCFBNGde2KE8dxnNsrM4aa4vnB9oBiUK5SmVmjjRJ\nURQNWVHQcyXKU3NIkowgiBQqFURJQhu4SkRhiKyZSLKJouVRdZOZg4fZWFri0plH2FhezN6L56Ll\nchSrRfScwMzhadIkwncjoiAm8F2CMCJNTCr1k4hiAUXVKdYqSIqEbproponT72MWChi5PLKi0G6s\nsnLhNKom4lsdFF0iXyggySJxnGL3m/RbPdYWL3H+i49x8fQjeK5NmqR0m+dxrI3Rz1+Sa4hS9hAW\nyN6fNPSnT52RneZOKKkK1xcL5GWZn7jheqqqys89+BAt3+fd5y/w8UaDKEkI4s355DBJRq5DV4r3\nvueveOZXP4O/HHhFXw2GQmIBFatrQSpTqtXJF4vkikUkWebg9aeYO3acbqvJxvIiK+fP4buDzkGv\nQ+D7yCJIsoTnbpBEGZmJApsotEkSl1zxEKKUfdwrWhVRylYbVb2M3Wviuw4LN9/K1LyJ727gORaB\n1ybFRxAFZKUCgkaKQTD4GfgDEu8O/jueFmxP5ARMFgCQkf/CQEw8TBSGjMj3U4/27W+n+uDbELyN\nbeR+LxTl/Ohc43qA3TBZJFTkAkkKJSm/RROwU17AOCGvy0WqcmH0bxw1uTgi/+NjROMJwuMuPhtR\nD1nYLAAAqjscD4zGfCaRptm/jTArBKaUAjXFQBAiGoFPI7Sz0Z3AoRX63FadQhCzv4XxILBZTd82\nTpON0Wz+Ld6Sr4y211VtW8cByDoCw3vdoSsBA+2AEG255jhWA2vbvyGGxzR2mdvfjyPQboLjB6zm\nrsde7pw7vjZwBxof/3k8R4HGbUb3t//upHxI2DVx989JKwpoBR4zOZFF18KPY+7vNYlimZ+76U5e\nNRilUUXpCVEAPB74Px/5DH/7T/+Kpu0/5O2JiGudgGt4QkEcfG4lSfaAeMdDFyARycsxkiCSr9Tx\n/R6h38MdzIuYpVLmuqNqpEmC73uZu48o4TqDUCNR5MDCCZIkRpJlzj30IJ3WKp7dIZeXca0+URBw\nYOEkD937af6pUOXk+hq35ssEbpt8AfLlKgjg2TGzR2ZZu3QR1SyQJjFWp42ixlTnphEkj9a6Q68V\noygQmNDvrWKYoOkSgedjdc9Tmz1KHPfxXQ9F02g3Vmk3VtHNPPlimWKtRhQEhIGPls9RmZmhc+Ys\nG8vLVGdn6LXb6EZGohy7h93vUa7PkCuUWblwjjDwMXMacRLgu20EMUCVDVRDplSZJ4592o3MJz6N\nVijUqtm4kgi6OUO7cZaiksdzI1wb3H4HVTcBG6ffZ+bwYRbPnsbMG9TmZjNbSnx0M4coCYRBgtNb\nJ185hNtbhdTAyOsomkwcJ9hWH0k2kUSIQgdJEpBkG1GaJIzOqBCIx4qAMHFGhBRAHesUnCoqhInD\nO2+/AVOOubFUZFY3eP/SMmetDkXF4ESS8MbP3883HZjjWfU6iihmK/P7SCq2LItPffLTfOCD/8CL\nX/jt/Mxb3rTl9fPnL/A9r3ottu2QkvDO3/o1nvFVT+cjH/4YP/+zv8j0zDT3ff4+Xvit38jNNx3m\nnb/5J1g9i5/6odcwOyWy0erwy7/1O2xsNCkW8vz6299O6tp88KMf5c/+6m9I04RiIc+f/dE76Nh9\nvvt1b8R1fURR4lfe9pM852uPEkfOoAuQjWKoWg0BjTiB6vRhAt8m8B18OyYK+8iKyKmnPIvjN7dR\ndBNREpHVTGycjfxkpEwSsuJsmBcAm12V8TEgc6wjIO3y3C+MkfN+3B85BzmJDaUFzMMvRr73DfDU\n396SLbAXqe9F1rbrDTUD0th4zrhQuBv3t+gUhhh2BTpjRchwHGh8DGg3NHZYtR9iONozxPB8MtK2\nkSPIhMQ7BYWNuwLthvUBYReFoSYBmqEHIVgh2GHIx9ZXefHCwey6qbwZALbNTWfr3P4Qo7yAVB4R\n/LpcyLYNv1a22mEOuwIjwj/YbzS7r+UzMj0UGA86C8POQbbP1mLhlmKZvbCpJ8h+P3cqALa/t8vb\neF5J1sGsrmwh/ZdLC76cM9DoPKMQsPiKioC9xoF6g7eVsnMRECUJoejxrguP8uPqU/jlR+/jJXPX\nsWR7PP/wSUrKv99a81AXMBwJerDX4phZ4L899Fle/lu/yidaq9h/8XeE01X+8vU/QZhuD5b7Sse1\nIuAanlAYCmxlTUSU9Gz1PhERkwhZVwmcDcpTswhohEFKmqY0l5cQJQlZVeh12rj9HpIogqoSBT5p\nFOO5NlEYUp2eodtqEQYesgJpnH3CSZKAIAiYxQJ6vsyHigqf9lSepkY0e6tcf+spVhdXWVta5/CJ\np9BaWxvdsyhmrkCdxhLTB49Qnp7FtS7R3XDx3T6HT9QJfR9ZE2ksnafTcjh5y1fRXLmI57cpVafx\n3S5x6KDpeaLAw7MdHGuYmaDhWzaiIJDEMeuLF1hbvECxUkU3TQQhIfQtAs/D7qrkCmV8zyX0faIw\nJI5sZg4fQNVLqIaMIAQgiPS7Np6dEnoeruNh9ZeYOTCHbTt4zoDIFWp0Gqv4roemlZk7ehJBEGiu\nrhB4HqHvMDU3S6GaJ4xdBALCQCD0XaIoxbdtnP45NN0kV8w85EWCUSKzoAgUKzMIQBz7BF4TSXZG\n2gBSSHEH89GbHYEhhoQUtttzAhSVbOTmppKGIup884F52kGBT260EYD/5+Ybed+lRc5aFq9dWADY\nVyHw/r/9Xzz/65/HddedpFqtcO89n6dSrYxen56u848feD+6rvPwow/y6lf8IJ++66MA3H/fg9z/\n0N2UKho3nngq3/3q/8pHPvTH/No7/ph/+tCHecULX8DP/9q7eOYdt/O8Z7+af/n4p/lvb3oTb/3J\nN/Cn7/lLfuMXf5oTpw6DmqNYkNEDg7//u3dDEnDusYv84A//NB/78FehaPksk0GENHFIEpDVHAQ2\nkiiiGbP4TgNVM0njFNduZAUh2v/P3nuHyZbXdf6v78nnVK6u6u7bN8fJkRlyRoL+VAwkAfeHigqy\nIOqKuisuoq6K8KhgQN3dx1VXREyIiIorICCSJM3MnTt35s6NnSp1pZPP+f7+OFXV1fHeOwM/x/W+\nn6efrnCq6tTp6jqfz/fzDiNr3jIoNik2sXQRwkZKMEWOQG4fQuVtmgC46QBViA0Nwfj2zbfB+kRg\nPCFYvv61HPmHb6K6+knaU2nCm4XAYxTUPP0p4fFOeoHNicTTYuFO3GeajZbKdY7/GHUtE8y2Jq5A\nO1uUTlOIxgFiO0FBELNeiEw3CcuXcQWaxpge1IhcbnbmWQ3D0f5mr92KfDpRQCwD/nm5xU2lMpaq\nQaKDCq3I3SD4hW148hvEte5IcLxDSTFqDIRImNOLkxCzulbYfpIxrSMY/S0mDcDo/nm9OBH2btyv\n3d3CdqIA7WadejlL0qsNCtu86r/sB5dtBL7aeoDLUYdWhustPmxPB/p48xK/d+4UP3XD3fzCrZnX\n/7vuyFLSnzE/y+owYWWYTihC/5rwkpjfOnuSX73lyfzCjY/HVjWeN7uP//fedyBPKrzj+V/menOG\nY0qVmYLObOlro794rOFf/y9zDdcwhTDo4g7a42u89vh+UiMmZwh8d0C+Mkeahgx7bQZrqwzWVrNi\nPk1J0gS318UddOl1WqxeOEeaJERRQHNlGTtfQFE11horhF6f2T1FAm+AopnYhRL5cpkoCBkE2clH\nKpIoCCiWC4SBTxymVOvHyBVK2Lk8xcoMTr5AZTajL+lmlkegqgpOsUSpZjOzx2Z2/16O3vI4nLxD\ne7VHoVCnv9am320zv38BVY3xhz2iKGX24DEKlRqDbgPDVIjjiCgI0E2TS2ceIk1T9h07QaVWxht0\n0TQDz81OZLP79zPs9ciXytk0oVQmVyxi2QXSxEeIECtXIfAT3EFEMIxQ1QIHTtxKobSAlFnBlBX3\nKWmU4rtDDKdIba5Kb63Bufu/hJSSwPcIfZ/Qz1478F1C38NyqgRBgtuPiGKNxbOXWDy3TBjFjIY7\nuG6E70e0Gk101SZNUpLEwzAzZyJVy0642TnHm9CCYJ0aNEYy8qk3xVa6yRjjgj4L7VKoGDov3HsI\nQ1GoGAYvPbCfTzfbvP/SIppwrmgS8N4/eh8vfmnGIX3xS1/Ee//ofRvuj6KI137f67nztifwype9\nmpP33T+5766772TPnnkcq8Lho4d43vOfi27b3P3Eu+j5AcdvOcjpc+d58Uuexsy8yVMedx1fOXkS\n8LnlphP8/LvezV/9zUdR1BTbyWM5ZV73uh/hGc95Ed/7mjdx6oEHiaI10jRzbFI1hyT2SGIPKX1U\nXZ2kOiuag2HmyJeyz3AUdjEsFSEM0sQnljITAwubVEIo3Q0NwG56AHeqIXDTAW46oB2v4qYDvHQ4\nuW38owpBQS1QUAuZ1kMISladtce9A/Ozb0AJsu+FklrY8LMZBTU/+dmMbtLf1jEI2EDlGdN5xgX7\nuAHYjhK0ExobqEJZ0zDOD7je2suMVthgOzreZjOWo962EwDYmgy8OSG4rjsbCu6yWqYVJlSVKke1\nA+zTSxzNl3jq7ALfvPcgdcOhrpYyoe/0arvhbLsfk8JZarRGFCPIHIiyY9DPivzt6EGsF/bb0YTq\nur3lcfNGjnkjRyPqT4LPxvu3vk+Dbak/80Z+2wZg+n1MF/CPJBvg0eYJbIcrnQI8Uszb4oroQOMm\noBX4vPnefwbg9KDHG4/fxn5n+2Z7Nvc1DbvdFY1eNHEHmsmrrEUhbz78BJqDhJymowhBXtP50z/9\nE/7sfX/Mi/ce4Wl76iyKkQNXN5r8/N+Ma5OAa3hMwbDsbMUxzRPHXV5z4iDfvL/EgiMZNFroho9u\nSMjrOMVclm6r5emvDWgtXiIKXJIwIBAqcbhepNbmF4gCn5Vuh37nEjMLFWYWSrSWXUqVfZRn6iRp\nwtK5M2hun/9mFuldXKGX+MzvL1KoVli91KZQrlCZn8fr9bByOVrLiwS+Owo0q5Ek2etV60cpVRcQ\nSoiw90C0BkIjlSbQpddqY9sSVQ8YrgYUK/uYmXM4f+pe/GGX6vwsncYqdr5CpTY3coLJyAytlYeI\ngoB+Lws00QyLwGth2Nrouo7l2LjDAVEwwLCTUf7AkJZczRxhdI2BGpH6KYPuOu0gitNRUJqCVajS\nXl4lCGKcQpXZfRpub0hreQnbydNYuohhWVhODikCdNXC7WYncytXobfW5NCNd9JZbXDm3gcolus4\nxQL+cEhxpshMvYaZy2OYeeIoKxqEkoWU6WYdKT3SlKnUYZfxNAAyaoPBlRXtsLMtaEHX+e+Pv4v/\ns7LKe8+f56UHDuz6PK1Wi49+5B+5796TCCFIkgQhBN//2u+dbPPOX/l1ZufqfO4LnyJMBlRz689p\nTtkZKoqCZc2iqGVUQwEhOHTjraiayuEbjyNQqMwVUDWVfcdrvONtP8LJ05f4+D99iee+4OV88uN/\nyW/9zu9TKef4wJ/8JuWZGnsOPB5VE+ijwiiJXVTNnmTPC8UBXKKwDTIlCobIRKLrReK4i2E7o32z\nCaWHKmYm1KtYpjC1Ij7WA6RyvVDJjRqBzVMBAGf0N6hpc7TiTHczo81u0BCMmwcvHWIrOcLZJ+Pv\n/yYqX/gJWk94NwVtZyoQrK/yb24QNusDxg1AItmQCTAtDD5i7aEV9SehYbC++j/G5aYA48esF/vF\nDb8bo/umn3d6cjAWCe82BZgfWXGOf0upTuxBhUhoxwNIDR7oBZSNKn99fpkXLOzj7ae+xHcduX5L\nCvd6UR1Pivjm1HGZ32JJOuT6fJFGNFxvBKZX7lm3Kx0nIcNGetD09uNtpVQ3NAWThkZfD0LbvhHY\nXSg83QBsLtynr19NLsB4+yvKI5iiA+00AZgu/q92CnAlVKB16tBWKtD6FADOFQXaXJ3QsWkEHkc0\njZfvPwHAdx+6YdfX2M4taDes9LdvFh8pxq5An26v8qGli/zAgVt33PaQU6AdBnx2uMxz9s7zF0tn\nuVGvUdbNSSPwf+N04FoTcA2PKaRxgFOokcQKUkYgYMHJig6nMMMwCajUa3jugH5nQBRK+u0VdDOf\nUWLCPpoZ4+RNei0Xw85x4MT19Fpt4jjE7TeozhU5ctNhNF2hs5r9cwuhkEYx1dl50qTDoX0zfOn8\nWYy8wcEbjrP08DnW2i5hsIimGZntpm3j9ltouortGGj6+hepUAS6qtJJCjz1w58kj8ZHHn+Exz3j\nAFG4hqrF2Ll5Lj10L2uNRfJlm72H9xD6fY7cepzmpQa6madYrRGFIZaTY3b/QfoXFllrtDh++41E\nQZnz9z+I0Gxm6mVWzl+kUNnDWquJ7WQhaUY5R6exSLvRzYTEgwhFMcgXHALPw3clQggUVWDaehZs\n1uoTWKuEoUfgeZDq+MMG+XKV6myZOIYozo6baduE4ZA46CHzJXprHUzTpFI/hJADWssrLJ09j27k\n0c08iw+fJ0kSPLdLsVpE6ALDHGIYKlEwQCgCmQZAA1VzstUnhRGvGBhZaaoIEiSp9GCXJmC3tN5p\nCCGomQafbDa54Lrs3yWN+M/+5P284ju/g9949zsnt33ds17ApYuXJte73R579y2gKAp/+L/eR5Ls\nfDKMpYciZlCUGYSio+qCJz3pTj704X/iFa/4dv7yQx/lKU+5i4VD1/PQQ2d59nOezbOf82z+9sP/\nwMVL5+n3ByzsncNxdN7z3j8nSRI0zd6irUhiN5sIjByYNN1BMW3SROK7bQQm/sAjqWb/b6nSAVVM\ndBiqsIlHUwBTyU0aAEvJraeKThXzQepuSBEGSEariWvx6qQpmX5Me9QYOIqDreQmlKHezT9G/e+/\ngeJ9b6N/3euRmrPtSv80tnMF2pwYPK0DmC7+xxkBrWjjc1xNAzDdOMBGPcJ2z9HYlDgMkO7Aw4at\n2QDTDkHN2EVKhUbk0op8qloeUvjr85f4tgOH8dKQh+LzfN3hPCuyRSGcKgWUmOUwzop5JUSMaDyI\nBGT2V1uOehsagbEd6C25+ihoa2z/GU8K+rq+TUDYKBG5rts04/5k2zGEyP5vpFTX05P1rcf8at2B\nvhYr9mNcTSPwle7IVWmHRuBKi/9H6gp0uZyAOUegCIGwLJQowlE1eoHKDcXqZZ973ACMqUBSym1F\nwXGastiPMEeCwLn8zg1MKiWKEHxo+Tw3FMr8Q2ORW0tV7qrMbtiu0YtIpSSVkp9/4As8u3yQ1+6/\n5bLZAVXD5MdP3I6UkrUowDQ37sv0VGC2pG+5/m8R15qAa3hMQdUiVAWk9FCMyugkbU2EfopSwHMH\nhH5K6Mf4gxBVtxmsNTFzGuWaTRIZNBZXqO89SqFcZfnsWQCWzj6AZsUcOjFPrmQw6KyRK83iDdpI\nmSKEoNtuMljrsXTe5YlffxdJmPKlT3yZJDUwzDK1hX3ZCripkC8dxLA1ZuZnCYMuQuiE/hqGVSZN\neoDJl/uj+HEJUdTAcQ5hWDGmVSWJMyqLbjooqsq9n/kYuhHQb6+h6yaKotBeWeLYrbfTXlkh8IbI\nNKFSn+fhe06j2yq3Pf1O3F6XB798EcuZoTo7j9fvoRQzakHop+h6AYlCMPSQSUwYNlGVCNO2aS1d\nZPnCWXKFbAUul58DVuh3+mRVt8HMnv3E0RDTctDNHHF/QOi6GR3IUICIMAxRVEnoxYRDSOJF/OGQ\n0kydmfnM7rW5dIlcsUIchgy6PfrtM8g0IfD6HLn5NpJkiKoKnMJsJmaNXWA8IRAoig0iowQl0kUX\nOWK8yxb60xOA3fIBbq9UODt00YXgpZ/8FG+/4zb2Ow4velFG+/mTP8ms4/74ve/jP73phzc89lu+\n7YX84s+/Y3L9+1/7al724lfyZ3/y5zztGU8il9uerjQW1EZymOUloINi8853/RLf8z2v51ff+b+Z\nmSnx7t/4GRRN4c3/9W089NBZpIRnPvPJPO5xd1AuV3jZd/wAH/q7T/HUp9xOLudg5Q5MwtiSOHvP\nYZD5/wfukCjwKdX2ohsJQsk+g0mSopNHYOIOWhiWiqLHSL1DKi0EoIlMPD1FW8cfNQPq1Ak+TF3A\nJ5Vig4B4vN24ORg3BTklj5cOJpOCLVBtWk/7A0pfegt7PvbtLD7rA/QZTBqBzQX/TsLh7QLDKhPq\nz1gTMNjiDAS7F/w7YfyY6QZjcxLxtIUoQCyTbYv/LUnBUW8DHWh8+OeMUVMRrX/W2/EAN1C5vVrl\ncTPZTyPpTO57KDoPHKBuONvy41txHwW2HJPxKv2Yn/8Vd10r1Qg9UOJJenFdd5gziqyEvUlxvxL1\naI4+n9PF//TlzZSnzdipAdhMCVoPCstve//0fZPrO1ikbnncppyAK2kElv2IumnsqgW4UvvQcTF/\nNWLg3TCXU7jYj/nI6hIqgujcBZRbA0zlygrdcQPwwKDNZzoBec3gY+2zvPmGu/hka4mn1xZ474UH\nOZovYioqv3/+FG88eDcKsDJItjQCX1hrUtENfv6BL/Brtz2NQRJRM22eN7cPVSg0A58Zw6QR+qS+\ngioU/qZ5jgfOr/HN9cPMGjazpSu3exVC8KoD1/E7Z09yW2mGvRSpFzWMUaOy2ovXpwNFjdXeV3eC\n8f8nxFcrCvoaruHRQgghzz/4ARRyINpUDxwnIDsBaPRQQo/Ic1hrNhh0+hj2HIEXE/pD+t1FilUD\nw7LotTxMq46dK6MZBq2lRQbdFoHfY/9xh7mDC+QKFVpLi0RhhcFaiD8cEng+IGgtP8ieQ3lq+2o0\nLzbp90yO3vhkOqsrtFaWUBVJbV8WjpMmPnbeoDq3D1VVEGp7VCxnJ4BYGvzqqQ7fdjDHHumSRCma\nbuHk99HrPEhntckDn7+PMIhwSha1+VnOnVqlOn90kllQnZ0n8H0Cb0g3EZheRhUw7IT9x+uE/oCT\nn32YXGk/pWqNQW8NZCZU0w1rtAKjoJk2pWoNt98kV9Ip1eZZvfAwg7WQ+t4DeMNlFg4epr/W5Cuf\n/SLFwixHb7+b9tIiTiE7KZt2HpnCYG2NTnMFy9aRUqCbDpqeWbxGgWTYa4IQ1Bf2c+nMWcq1Onau\nQBJFtFaWcIc98oU8lbk6cdSlUCpg2Bq5Qg7LmUESYJgOQoCiOghFQYixSDhLEVaEQySHW3QCuyFK\n10W/n/zHz/CUpz9+2+3WwpBOGPHBxUU++p9/AlhvAq4WsXQvm1A8bmpgXfg8bnzTxCOOXATmRC8x\nhpQ+QlgbriuqQxS2SOOUNM1W4MaBbm5/gGlVGXQbpEmfUm0v+dJh3OHK6LXW0HQb3+uQy1fRDBU0\ngRAWQSonGQHJpvPG5z9xD7c95cjovWRFk6k4o8RniZ+6GJuOwXS42GZsdhkaU4QG8YCDH30pwS3/\nhXb9SRuagN0cg6ZX/8c0oXbcp6IVJsV/RStMVv7HgWHjy48W7bjPzCi8qxVlTcD0yv900X/xn+9n\n3xOvB9aFwWMu/rgJ2EwHakQutdF+bl5NH08DZGzQDSQlO8sRGK/eN5MOD7pdyukeZgydW4rlSepv\nO22jiJjrnNlJ1kAqVWpagWbcpxW5VLU8dT1HI/Iyfn/kbRD8jik+QiSTBmU3UfMY0wLiMabzE6Zv\nm8bylO3qcujS+ty9zNx107avsTWheGMTcDnr0J2w7IdX1ARM9mO3RsBLLtsEjFfzr8oRaGoCsJ0m\n4CvtAb925vMsXGrw2698Cc/6+hfyoz/8RuZveRpzzu42nyuDGDeJyVuSDy8t8aTKAroeUdIN3n76\ni7z1xsfz0KCLqarss/O4ccyDwy7/4+xJ/vORJ3LO6/G4Wpk33/cZ3njsVj7SuMTdlVlimXIkV0Sb\nSpJshwE/dfIz/MwNj+eX7v8Sz6ru45ZCpi97/+rDfMv+A8xZU/8PveiKw8NWfBcJ/OHFB8lLk7tL\ns+w18wyTiMOVkUV3SaMRHwAAIABJREFUmtIeZN+vu00DRmFhjzl/1GuTgGt4TKFQMRGyRhS7JDIg\nkC6qsBgmTcrSIEl6hIEHik6apKSpTxj2KJYFc/tm6TR7yNTAsLKC6tKZ0wy6DZABcwfq2PmUcm1P\nxt0XGr3WMk5xDl1XCIKIOGwzf8jixJ038sC/3Ed7VUNVNM6fvh9FpNg5gz2HDhOGbSzHAEwsS8Uw\ns9dTlBrIEH/YxcqVMDSFH71hL4omiCNQRBlFdYjDAZZdoTwb8vivfyIASZTyqQ9+knzxAE6uwMzc\nAoZl4blD0iQiiUMkBqXaHP12RptIo5S5A4dYvdSgtdxBygQhXIrVPFaujBA6aSxRVIMoSvHcPoZl\nU6nvwx2sUqzMYNkSTVdRNYvli0sAXH/HbTx872nOn/wKhp0njiKG/R6ze/cReB52rkB9Yd/oPauk\naZIFmPWHKIpCrlhDSkkSg2FaaLqZvZc4JkliTEMnCn3iqEupNksaeVi5ClHogxhi5/JZAZt6EHkj\n69F9SAli03kuke5VNQJXgrJh8N3f+Z309+3nvi9+Ef3JT+HbX/xihJSPuBm4UijCIZUuicwaAaGA\nogiEUBDKRi8HgbPluhAgZYBmzE+mAOgQ+gNUTRDFLoruEHgug34fw+qjaQ5x7KIoJoE7QAhjlBPg\nYKgOEh+JzGgywkIVYpITYCo5BKDQH1230JUKqcwEtZnb0kYXipRsgpCbovRMpw1Pw00H+KmbTQmE\nYLj/G7Ev/AXUnzQJF4N1G9DNzcBODcAYlW2K/M0r92M8moZgmlo0bgCmi/9JwY+6xRUI2DAFGK+o\nj39nRf865WzcFJzyVqhqRSpU+MDiIk/cW6BmaoBLM3GRUqOuVZCmA4lO3TImBXHdMFkLYrqJyyl3\nlWeUjrEc9Tjlrk5e53qnlvH2YZ02xFZ+/83OPCtRb7LP0+9h/Zj0N9idTjcA4+J/UuAbuck2G4r+\nbQTUY9vHrQX/ZRx/HkUDcCUYF/ZXkg+w7EeXnwZcpgHYTPuRUpLXYwr6+gr5JW/AgpXjXafv487S\nHL9259P4+8X389uj7TWFyzYAH21cIpWST7SWeM2BO/m6+sERJShrdN56Y7bwcjS/LoR3NI1bilXe\ndvOTEAJ+9OQX+Z+1Z/DK/SfIqRov3Xdsx9erGibvuvWpCCH4oUN38KHOGd57+jSvP34TiRpjq1vL\n3CtNER43Dz909BZS4Ds+9/f8/uOeza/cey+392rcWZjlJ898ij+481mTRuDfGq65A13DYwt6Spw0\n0Y0qMg3pJWv4qSTBQegmceQTjr6cFVXBzuWxc1CYyVOoLGCYFk4xR7/TxHIcTMtACMnMnlnqe2pU\n5mooegWhKDj5MqV6LaPG5DykXMTKh5y44zDtlSUUrYhp5bDyJRQhWThymAPXHUUoPghBmgSUKgXs\ngoE3PAdAFGZiVsPcT7qJBq7pFRRNkMo2qq5i2HkKpQUKpQVMyyGOUhTDxveHGS2p2+HCg6eIAp8o\nDHH7LdIkGzvqpolQVAa9Lr32Rco1C9MaIOMVyjM6tT11TNums9IkTQ3iCJxcEbeb0ZOGvR691oDF\ns8sMutmJM3Bj0ljLKD1+THVPFaFIojCgXJvFyRVor64gU0mcxLiDAYZlkyuVUEYhMJbtkCtV0K0s\nIVRVNZx8gTiO8Nwhg+4aiqqSK8+g6g5xqBF4mbPQoNvCHQ4Z9lqsNc7T76ww6PZYOneabnOJYe88\naeKRpa17pFOr54l0N2QI7IYr1QkoSULp3FmIItB1kJLIvrxf+CNFNOW6o0xyEUAIG0W1UTWBomRF\n/vTP5DGj+wBU1SJNOwglGFmEZtSzJJa0Vy7iD3voVpleo0lr8Qy6kUfTHBA2CIFp5zCdhclza0oF\nS7GR+AjpoyLQhIImlAnX3RCCsraQbSc9UukhpI8lVEwlhyrE5NgrZJShMZVojM0NgJ+6RKmLALzU\nJa/m6Cw8F/3iBxDhuqB92imoN7IQnbYRHd8/nQi8XfEPbHAHmtELkxX83dCIe1t+Nj/n5su7OQ2N\nC97tGoAxpovp6Z+bc/PUdYdm3CdMFC4NPd5+371UHfBTfys/X2qQqqBGG4W9YUBZLXPQyD4H97iZ\n5uU6ZxZFJBPh7kQwLJINFCTIVv/XxcDb7/sYm/MOxk5A4+Mxffvm7TZvsxkbUoBHlKed7t+MzcFo\nV4KrTQt+NLhaPUAzbPOV3gV6SZefuCdLy/2Dc/fTjQLefeYe7u93eMbMfvbmDXRlPd13N9ZIPwr5\n9Ye+Qpym/N3KeZ5V38ubr7/rqvZLCIGpqhiKyi/d8HQ0oXBjsUJOuzwFSQgx4fo/r3yIVy5cxwG7\nwA8cuZmivpEGVC9ePXdfjNzK/vju52IqKj93812YpuADzYf52RvuQhstzowpQv+WXIWuTQKu4TGF\n2DAw0hSRGsRJi1BKEEVMIE0yfni+WMkK1aANAjRDoTo7TxSuYZg2zcEipl1n+fxDFCoZbScMOii6\ngqZrME6i1RTsnESmfVTDZOHILEIYrDUi1hoKpZkDGKZLFMQYtkaSeOjksHJFAreLNwjRtTbFWhFV\nT4ijFQxrD0m8hm72QAkZBywBCDGywFSzE4qUHgo2gdciClRkolFf2MfayoCFQ0dZay4RBT06qwky\nTdHMhFCm9DsreEMXXTcwTB0hDI7fcQfFmQfwBj4z8/N0VobI2MQuOMThECkFw26bKAhoXlokTSSh\n72FY2epRc3EZ07YxrKwIS2MwjSK5vEV7pY3vuhSr2f6X63XWGg3sWn39vWkquXIFGSestVYzQTEQ\nBB6aplMoVHH7PdxBD11XsXMmUTAgSfrk8jPEiUO3uZplQ8hMryDjHr21DkIxKVQMEG1UTckKYtWe\niDrHjUAkh5edCuiKs6suYBrjFf8XvehF0F3j5b/8K1z0vIk47WqQ2ZPuvF9jnUMkh5P3sz4R8FDV\nrLCWsoXARoixjao7KfzlqMAWIqNmSQlp4k7oQkniomoC29ZQFI0oCajMHaezcpr82hKGlUdVbNwg\nQdXGAWNleq3TFMrZBMZSbGIpEQI0KdCUEdWDkd/LSMetEoDI/kCxnFzMmobRDluKM5kIjDEWCdtK\nfvQ8TPQDbjIkSl26poW39wUUH/hN0tt+dsNxHDcCmzUC3aS/wS1opwZgM6ZX77ebAmwu9qfdgDbf\nV9eKnA4Wt80b2BwMBtsX/leClbDHea+Pn6ScbHn4ScKbbjuyZbtW5CNTnboNKMlEBNyKfFpen2eW\nDwEZd769Q7bBuMgfTyGkVLYEgo0xXfBvuU8vbmkUxpg3MlpQIxpuyC3I7sttqxWYdg5qTVFHpqcG\n09gpI2Byf+CPBM9b/4evJiRsO1yOCpRts33hOt0A7BYQNr2dIXKs+F2+brbMr93xDABKukkqJW+9\n8QlbxLvbNQF+EuMlMY6q80cXT/Pivcc4liuhKQo/e9MTJ4+ZzalX7RD0aKEKhWcuzF92u92mAScv\nuswU1o/5ZpqPEIJvmj/Iqh1x2m/xp4tn+bHjt23QCgD/JlyFrjUB1/CYgic1VN2DaJnzoce3fFzy\nt885QiB6xPEQ07YQoy91RbdRVZMobAAgCQndLsVqlf5aD6HC4tl7MUxlImBTtewLV1FNpOygaENK\ntQO0Fs8w7CjkS7PEaUihAuVqiST2KFX3kEoXmYRE/hrDNY98pcBas4VqFul3Vpndv4Db74L0UBQT\niYuqV0hjD02rksgOgvGJxkMBAn8JUhOBCTLG9xMM0yCKQtZaqwx7HUq1At1mi3x5P+2lS1jzBntm\nDS6eHmLaJXrtVfJFleqegL3Hn4BMfeKwhxAOrWWX+QMHGfaaBJ5Hv9cBAYYpgAhV93EKDmfuewBF\n2Fx/59PwhgPSVGA5OXx3iNvtEwU+ndVlZvdlNpfDXo8oCPCGWYEkFIUoCCjVZzNLVplg2Tph4KPr\n0Gut4g2H2Lkcdj5PrmCTKxYpVsqkeAwHQ6qz++k2V/EGHeb2nSBJUrxQxc7ZhL43zXRA4gFbCw1d\n5Daspn+18c17F3jb/af43YfP8rR6DVNROLCD4HcamnCuaPowbgSmkTUCYxcbe6ITkLgZFWhTLyJw\nkNIlTTMdQRqnxHFz5BYkRlkZCmmSEnUHDLsXKZRn6LYbzOzJE3gD3OGQwI3IlfP4bps4Eqw1L1Ko\n1FBUH01fIEGiClDkSPhOiioswqCV2fYaCppqoSg2msj2PZYuUgYIKZDCmoSyjZ2GxtSgYTLASwcT\nd6HNWQSe9Ojf8MPU/v75DK77QaRV39EJCNYDwWCd698Z6QG2wzT9Z/yY3bDdiv7m28ZNQVnNb2gU\npoPExojkzkXTNH1mp6K6Ebl8penhy4BnLVQRQkxW/ltxHyGyBOFOFHDUrHK/t8yMYdIKfVTVp27Y\nNMJoIpw95S+hiJiavu4IUxtNR6YXh+tagUbkTiYDtanjNt7X1alCf7PIeXq76fe5bgua2zIFgI3U\nINha4MOmScIOzdVuIWNXIhD+WjgOzdvqrjkB81PUnCudCBR1k6+bPcyKD/Ojr9FvWji84/bTTUAv\nCumEPmfdPh9YOssPHbstawY0jefPZ+eHq10g2YxHahN6NSv8O7kEjYv/mYJOLNOJ9mDzqv5sKcsZ\nUITgmDbDdQeyZn62uA31aFNj8FjDtSbgGh5TkOQY0iLUQZXz/NNzb6Kd3kdJNtC0GsPegDS2CQMP\nRdWJIxdFNYmiPg+mOqYuqZmCfGkGdzBkZn4vhmmjaCaWPfY49+gmOf68EfINFZHZeZolqvN1ZBIw\nUzvAyoVzLJ59AEUrEIUNdC0mV8zGilFsEUd9KrNFZCyJIwW338UuOHj9JTRtDzCuz6yMviLWV3YB\nkshDJgaaViJNwffO015eQlUrFCtV/GEb3YLSTJ4kGjJYu4CRs1E0g3I1z/nkIQbdBroBlp0njSVC\nWAjVIokb9NoNhMjjDVfIl3IYlmRu3/X01xqEwQBBh4KdnfT2HT2O1w+5dOY+NF0lChIsJ0euUGIt\nDMiXK5i2w6DbpVibIQ5DNEPHHXQo1+sE3hB/2KXfaRKHAXE6ZOHgPHZBQ1Oh0wgpFxcwLJu15ipr\nzTW8gYuUIQtHDiFsBVKfhYOHGQ7beP0lUHTcwRDDsEllSBiF6EYVVbs8HefRaAS2Swue1gC84cRx\ncqrKX1y6hKmomKqKrihUjSt3nrhajKlB6zqBzOdfsn1jkaYeMpWkcUoUJqSJjkwTotAjDLoYZgnd\ncCiUMnqQaVXptFYJvAGmncdwS/iDDosPL5OmIcXqHLoRU6oWgR7goUgfmfoIYZLVrBIhJYHfYdgd\ngIzIlSuoqoJuWiiqh6lW0aQgSCUqAlPJZbqCNKMGhSPx8HhSoAmxga/qJkNMxcEQNkmuirv3G7C+\n9FN4T/hNYHctAGTTgOQyddJm6k8r6vOue0/x3rOX2GPk+OnHX8+NpfHq/eXFrZPnnWoKpov+8eXa\npqZhcybAdME/XjUfF8rThXMjcvnbCw2evqeKqVrMmTlOuavUdSfj6U8V5secEq2ozYyRZy1ZQyhZ\nOVDTC7SiVU4HF0inAsOaUZ95o0BNy7j3mTB4yHX2/EQ0XB8dv0aUCYuFSCcNyOoO3P3N2oDp27YT\nB2+H6UnBZvvSMcZTg53u3+weNLn9csW/ZVyxI9DkOb8KNKCrwfw2PP6dMgK2YKoJSJG0w4A7ynXu\nKGeT4Nccufmrvr+72YQ+WuxkEXreHXCJPp9s9Pnuo8d599n7+Ia5A1R0k8AdTTZGTkDTRf07Ln6e\nNx65BS+JMRV1SxO0XWPwWMJje++u4d8dBEMS9RbSJOU3Ti7zxLl/4enVNUxXEMgIfxgh0jxRqGGY\nCTIJsR0dRZj8yFdarCQJv3trkeNpRH3PLIoWI4RJEqdAmK2Mhiu877zC2x/2SHB49XwRyx7i5GoM\nuk3OnfwXDLOCU8zSiQ3TRFM9cuU8UeiTSIGmpViOhtvto+kF4jhEKCaK1gdlQBJHSK2BIo1s9VNs\nPDlIJUAzFAQhaeyiGzbV+T302gN8r4VhChYOLFCs13HyOTqNHlHQp5uqlOsHOHbzGpfOrVDfW8fM\nO6iaRRyuEAU9+q01ZGyimyVUMaBYLZJKATIiX5nDtG8dJcimdBqL0GvRba2i6CbhWoptz9JtNynP\n1Nl75DidlRWSNEZRBPlCiXYjc5Ix7Dxu3yUKhhQqBbqtFkkSsPdQnWK1hmHZtJcXcYdd1OYyimZg\nWjbhYIiiJORLBYb9JsVKHcPKIVSBXSzi5hoITMLgJHHoU6g45It7EMIkjVMURUUqGV0lletFMlz5\nNCCWLpKrF3Lltewr81v3ZaLodz5wmv2OPbn+tcQ6PchdnwjILFFZMv49pgRZCCUlTfpEUYoMJWEQ\n4Q9DRDEhJqa1vEIYSQwzs4rsNlep781W81qrXRqLF6gv7GGw1qG+kCOVvWx1n8yvO40NPD8rnNIk\nxu23ERjEUUp/zaOz0qUyX0PTBziFPIYJaAJVyEzrACiEpLKPIUwUUQbWNRuayDQBKVkD4EuPJM1o\nRImUoNkYD7+HaP8LYe4p9KYcgqatQKfR3jQB6MRbJwhjVLRMD7A0DIj7BufKXX7v/gv8whO2T/ad\nxnb0nmnUtCLNUROxuQGAjXagm1f8x6vmY9vNe4bL9MKYd97zMK+/+RD9JMJUFTQl+3zP6PZo28KE\nOjY7orW0ovUCd9bUmdGy1f5pEfBxaw+fGZxhZkTzecBfpKjmqWkFaloW2jUu7JtRn5SsGRjrA+aM\n4o4NwBi7UYUm+QCjFf9p2tDYPlSIhFtymVf8tFgYdp+srL/GVlvU7bATJWjcCOz62E2F/5WEhF0p\nHmlOwG6QUiKBngD90EEk0I1CDm8jtP1q4asdFrYZ4wZgmgbkJwmfXVvl1uIMn/dWKGsGkUx5UmWO\nGcPk5059ge/cf4Khm/I7ly7wvXvXnaZSKXntoRvZZ+f44Xs+hRfHnCiUOewUcFSN581+7c8LjxbX\nmoBreEwhkDZpmvLbDzR59xOeyUduOsjPxxJNVfmOl34T/+HlL8AbdAGNMAio1uoEwRrJ0OWnj9u8\n5v6I131hlQ8/8TCKEqHpFoaVG3ny60RRE0WtcFulDJzhLy8KvuuEh2GViUMIhi5mbg9OvkLkr2Hn\ndCBFJmDYGooq0Q0LRU0x7Txx1EPVKiRRk2F3lXxljjDsocp54nAZjGxkrqaQihYZjWUs9IQkaWdp\nrlpCqVpHVy3yeYlTLJMv1+i2FzHNOoWKzmAtAjcTRJfnaph5h9LMUdJkDYmLomZFlBA2VrFE6DUx\ncg66mWCYC6SpJIk9hv0lgmFWRLv9USGysAdV1el1BvhehGnaeIMBhUqVYr2GncvjDQe0GyvkCyUu\nnnmA0O3j+0NyhTyGnefoTUdZvnCGS2caNC4NkXjM7q2x78g8w/6QMBgSdZsAFKr7cHIGvu8Sh8Hk\n75/GLpaTw3NXmNs/i25YaLqNptvIkfWkEBZiRI3ZaWV3PeBq+4lALL1H8zGd4AeOHaUXf3VPXCu+\nj6kolLeZLqxPkxykbCFTQGR/81RKVCU3cgfKCkDf6+AOIsDEMC0UtYhmVOl1VhGahakaGKaNlDDs\nrnDpzFkOXX8TTr5CseIxMzuDmZOYOQPdqBCFHVQVotAliVOGvQHB0CUOI1bPLqPoykjAbHD/F88g\nv/wwpZLJwrF5Fo4cwXaqCNFHVQIQJgXFAWboJ4sEaRtdKGjCQhU2QTrEUBwsJUciZeYWRWYZ2o+6\naIOHad39Tqqfez3ps/+Ojl0Ddm4AdsJ2dJ8xZQjgZ+6+mW843OLebounz9Um22wOA9uMzcX95qJ/\nu+IfQN9sfzWFzXSZRjQKBRumHCvmKBk6rz1+jEbkMjN6XzW9wClvebIiP14Fny6UU6mxGkRI2Z9Q\nfTQx3u8+x+x1KlBV27hS3ox7xJIJDWg8bVBEwoxW2LEBWN1F9LwZm2lAm6cH4+nIdIDa+DEtoWxL\nI9r6Gs5IBDzYMA24Um3AZZ//CtyANjcAV5sUvJsuYMv+XCYo7C33fYZXHbqBpiKxb70ZOUioGw4L\n9uWP5WMR2zUA/+Pc/Vz0hswYJk+b2cOPHb99ct8d5ex//S3X30WKpG+EvLJ4lIql8o5TX+G7F27k\n95buZ950OHq4yPcfuoFl3+WgWiGSKe0o4Cfu+Rzfc/g4x3KXXzj418K1JuAaHlMIZEBesTlRSrFs\ng49/+t3k5BwPnT7Ja1/3NpqNDj/yg99HKiWaqhBGDazcLGnS4y5LpZqEtIXPQ37ILQUbSR8pUsYZ\nJ45zkG6U59fvOYVM4VQ05B1fbvLD1+2hv3YJoZrk8wZR0kM3EnLFMlHoIaUGMsQu5gg9FykT5CjI\nKk165ArzoAxIkWhGgThcRjFMBAbEaXa7CikeqrCBEaUjIdMEMAACnJJBoXo0WxkXAZajEUct7Bw4\n+VlWT/VI0zUKlTqKtkTgnUM3si8YRS2jmwF2XhBHHrqZkiua6KZFf+0cnUaPOEjpNjsomkN9Tw3D\nzOP1PWRqs7K4jG4YIFOcQhHdNHGHA0zDxBsO6KwsM+iuEQU+breNUAMO33wM07Tor63x5U9/jjgI\nsXI1StV5+u1VLp1pcPjG/Ry87jhR4OH2Mg65mbeJAg8tHLnWpKCNzl264WDljmWJwam7wRtfKEom\nkB2tfENGk9k8DQB2nAjoigPpelDXNDZTgS6HDy+v8MHFJX7trjt33Ga8sn25xiSRLqf7Q178kS8R\nqBF/9KQnctfMzsmcQtigeAjgJR/5Mp9329ydq/OeZ2QrVYoCUoY4uTyDXpswCDAsG99r0m1cwnLm\niCKXYqWONxyg6Rbd1hq+O8AddDBtHcPWsHM6uUKdKOyQJin9zkXiKEXTCiyfv8jZ+xehfIAvfuIs\ndl4nX3Eo1+ZJQkl1/gj9zgq9dsD8AZ00lahagVQGpAQEMiCUHTL7wJRIpqMGZrhBOJw5C2X/O/2k\nTyNp07rzpwk0g/j61zHzsW+h+8w/g9yj9/SH9cagHffpp0NurlrcXN07SRKexpjbP41VL+BN//I5\nzg6H7Ldy/M4z1z8fp/1FAI5bC2yHK1m5HjsBrYQ9fuW+03zjwVleP3tscl+2X31udtYdd8b8/TEd\n5pS7yoyen9yuiBjQmdezKcVqlE15OvEAVUknxX9dK00mHePLmoB5vTB5jBBZ8zEWFM/q2WN38vTf\njO1yBLYTDm9325UU/I8UY23AjiLhKerQTtSgeUtn2Y9Y9oPR9fWJwHQBv+wlVxUWBmOKz5U1Ajs1\nABe9ATXD4rsO3cBeO89xNHp/+UF41vNxtEdG01kZppPk4N0wV9BY6cfbBoY9GowbgFPDDu0oWzBZ\niwPm1ALnZJ//uAulyVKz/XBGE5AgTbiuVGS2pPPGwk2sdCP+dvUC5/seL6xnAvzZko6U2eewEDv8\nrwsPfNXey1cb15qAa3hM4Ystlc83z/Pq64YIwJYO7qDNTHkP73jbW3nBN76SH3nDf+DP3v9x7rn3\nXn7urd9PHDV51ff+FD/4hlexzzrIF771xfzG97yc+z/xz/zCL/4gH//Ul/jrD34c3wu54/F3cfbb\nXs5DfzdH+69+iOqdh/mDUyf5n4MhP/uTr+Hpz/p6XLfDm/7LL3H6wYe54fqjnDt/kV/+5Z/g7rtv\n4j1//AHe/ou/SypTnvucx/GTP/4GpAxR9A5SRqSiiKoU0S2TKGwhpERT9pLK7GSliZkRZcPN3IFU\nmxQPO5+dJKMgQdWqREETzYgp17JxYjqqPtSHBuiGg1ChWF0gTXxkaqIohfWVcrWDaShoWhlVK4EE\nIXLIaEiSOPi+D0gaS01O3PEk/MG9DLou1bn9BJ6HQeYA1FldRTV02qvLE1tWXVdZfvg0UqQcvuEY\nvWaTXHEegcPM7CHKtT1ImVmIarpOcykkDlO6jSVQDZxiGd20SUZOT4VKhX6nRa/dolCeQzeczIXJ\nzPim0w2AouayYzZKDoZ1Me3mRmCMK9UHrFNQrq4J2OvYvOmG63ioP+BoYWdhoa5sFf1uh/c8vESg\nRqixyoWhx10zu23tEEeLyNTlcVWDgraHFx1dbxpUzaFQXiCOPJxCRvNJE0m7sYqVczAdDUet015d\nxh8O8QYeSZQFve07dhQpPTQtxnKquP0W/c4qYuxwlOQY+j167RBVOKimRXnhEEkS0202cfI+9X11\nGhfPo+gx+cIsipIVOzIFRS8jsVAk6EISpMMJxSmWYCgOgxEFqKTOkFPzJDRRyRKHy1oOy6jTjVtc\nPPACFL/J3CdeifusD1ExynSS3hZHoDE2i4J3CwWrbrPdNMbTgM2NwAPdASe9DijwQBjyuc4yJ0p5\nFAQzWoFW3J9MBsbveRqXWx1fCXvc1+uQ0zVunSlwwCpsK76dxjRVeWz32RkV6TN6HkUkKELQjHsb\n3IA6scsJp8asnp+4GY1/jy9P059acZ9e4nLCnps8phF3aca9HalPO733iWB4F4vU8fGYxk6Pm9YF\nwM4i4c3TgA3PsYtIeJwcfDmNwJVMBcaF/NXQgy63uj/Gsis5PWjz1FqVS96AWdNBH9lcvv/SGe4o\n13lybaRtG2sCtkmxvhJcrUPQdCMwue1RNATjBkBKyVf6LZ46N0sr9BkSc0t+lhNO+aqez1RUvnUk\npDYUFVNJ2a+UOViubHABEkJw2M4+X8+v7+PHH/E7+NriWhNwDY8pPK6W43DBJSdSBAJ/EBG4MUIp\ncORwRhXqe0MUESBJKFSykZ2m66h6wsuOmPylH/DMWw/xrje/DCvvcNOdt/NT//VtxMkZvvVlP8rp\nT/8jRu3/QclFPLeg8rPv++/8w8c+xbve/QfcfttdvPfPPkQhZ/P3f/07nDx5L8/7xtcDIUuLXd78\nX36dT376PRQqdV74/FfyN//wN3zjNz6N0EjRUw2vtUgSX0TXc1hOHX3ksa0SZtSNqe8ygQ3CQyhg\n6DPEURuAOFqTXuhcAAAgAElEQVQEJURRHdQRD1clS2Idi/fSxEfRK6QEIPtESYAQJkIvYjoQui5e\nkJJETUxbQzctzLyDu9pD1y0sp0Rv7WEWHz5Jb62D74XEseDA8etYazY49S+fwykU0HSd0B9ijvzx\nNUOlMnc9a80mwSAmV5wnTVL84ZBUZicRt9/DHfaRSUh9YYH6whHCYEAcufi+ip2r4A8aBL6g3+qA\nGmPZGkKJ0AwVTXcQSnbiiUKXOHJRVYFhMQnLEjgT/vtOjcDl9AFjTcCV5gZsh1vLZX7rwYdY9Dx+\n+padV5MuZxEK2ft41dG9vGBhL/O2xdF8fiIkh43C8sltqkWc+PzQ9XVMaz9p6hKPBI5CcdD07EdR\nbHy3ge8O8ftDuu2AKHqYNJaomkG+VCVfdhistei121i5Aoal4OSzpiLwYuK4AJikkYdQY6JQx3aq\nBHmNUEoGvQ5R6GMYKTN7aqgi4uDxzKovX5lBUUeuXqqNhFGWAKjCxlHy+GkbRVgk0sUfNb0ldQZv\nZOlqCHtyLNXR74o2Qywl54+/Ai3o4HziJXhPfz8VrThpBMYoqYUNScCwbhXamcoPgN0dgdpx/7KO\nQU+dn+Fb9h0mkRIFaCV96lqJ1qjwP2HtnVxOkZOiu6rl0TbRgXZK1z078DjX93jtdUczx5+pYnq8\nEj/G5mK3GfWpaHk68WBDGvB4xf46ay+NuMusnud+bxmA1WiwofgfY9wArEaDybTAGP3/TjcN2zUC\n21GTprGF9rODdmC83U42o9PYyVY0u29joNh2uFwDML58pWLhZT/Y1Sb0arBbA7DkDdlj5/jEagtT\n5nn/0kluLt3Fb525h5cfuI5m4HFu2Od1x27d8Li1IHtOQzw63cGVTgMgawQmj3sUOoFpEfBbHvoM\nb7v1CeQ35Q7sJBS+UsyWdGbZOqmZ2IMWNR7LpfZjd8+u4d8l7l9L+HSzzZuOZifCNNYwzKwQUbUI\nKSVWLoduSVRVQ+KjmRWE0FF0i5cc28t3qiovf8XzSIWHNIr8n7/6EO96x6txh0Pa7R4vOHSIJ78o\n4jc/qPGSFz4NzfK4/fYjLK00qNSKfObzX+L7Xv0qNH2eo4cTrjtxiH57yMce+ChPefLt1Eo2qezw\n0le8kE9+9iQveMnXI2RE6PaJfAfDnCWJPLxBRKhdwMnPAAYpHoq6fnIZe70jMvcgiY9mAGgYVn20\nTeaHn0ovSxeVCUKAolSydRlhkujmqLewSMILJF5C6CusNQeEgY+hKeTKJlImaKqBU9TotdoYRnZc\ni+UK0Ke91GHlwnkK5SpOIfvidfJFhKLgDQcoQjLoeliOQzCMyRfL9NqLOLM1Ph8q1JqrdFYvMjO3\nF2RIrpQnV6zS76zRbTWwcjniqIVME1AUcqV95EqgqNBaPI3vrWLZbYrVKvnSPnTDIU49pPQJ/OwE\npJs5hCJQ1Y0F9W4Tge2mAeOCfNwAXE1+wGa8+ugRvDjmS501biwVJytqY4wtQrdrBDZPBw7kbI7m\nszFyKrc2OePrk/etVlFHNUY6tf9pCqG7ShR6U9sqDHptUPRRY1VAkJCmKe3VJoqqZs20ahMFMZCg\nqnMkiUfoJ5h2FUVY9Dor+IMh/iAkX67jDVyCNMEwY/YdO4TlqGiaQrFaR9UEmmYTxx66qWbJx0rG\n7c+OSxuwSZDoSoVESiQpljIOS5PYioOt5HFTd0IJmkZdr9GgSeO2n2Tf53+M3D9/D8On/MEkxAyg\nm2zvcz/GZrHwdqv+kLkHTWcHXA7qJqeQsSXoZuHwuPienghs5rwDfGGwiK4ofGypRULMi47s2SK8\nhd2Ds8ZQRMKMnlm4zhtbm5pJwW9nBf5Nm8K8YKsAuh1njcBm3cD4+caNwBjjhmC7/d6w0r/N+5kW\nT2/ZfofGaRo7TwEu/z3wSDQB2z7PiBq0E65mCjBuAKapQFGashq4FDWDH/rix3nPE5/PH124jzcc\nu5v/dOIpVAzBW2/KEuvvSVNu31vf8rxXEha2E6YnAFfaAGyHR0IPmqYA3V6v8B+P3TSh9nytsbEB\neGzjsb+H1/DvCidKKt3IABKQEsMskyQ9NN3i4bNnUVWF+b0z6LaNFMmoAajg+UPSyCeOPCzLAHMP\nCuB7F/jRN7yNT3z8D9m//yBvecsvAQGvOJznf5s6M7N7KVaLxDhIBLqpZlZocbbaU66fQFUtBCWS\nuA+pQuQ5RPF50nSIikBPI/REJ1ZKRAJUzaZYOcywv4zvLmFaDoadnxqmTlk8iqyQAwfdWBil4TJa\no85Sd7PrkkRmFJokkajK/8fem0dZkqblfb/viz3ulnlzr716mellhp6lhxnGDCCEWGSJERoLjBBo\nASwd5DECbRwLjCSEkGUM4khIlo+NbAk8ssTx8UGSWSSxaAM8wwzM0kxPb9VdVVm53cy8S+wR3+c/\nIuJudXOp6m4oTL3n5KmsuHHjfhF57433ed/ned6IRCVoHAQuQnpotcOot48hVzk+GJCGOUJ22X7t\nBvZuxLUnn6bRDrnx/G0GvYTOWgvL3eTlFz6LygSm42MYJo7nlt78QJanJShod9B5gVIFXrPBrRef\n4+X+AZ9E8GPbPbSAb+mu8VV2gePZZKmF6/uVGLmkpqSxIhymtJZcGs02y+sXiYIBcTBkeeNxpAFp\neIhWJnHYm3lfaJ0g5CJuTFhdz8U++6d1AxZpAu4nDCEIi4J/sb2NLSVPdu5OLk4DAqdpGOqEfzGl\naTIcrB4UptXE8UgVmqKwSEYBWqR4jckxmp0uSZzit9okUTkILgkikiSit7NDe6U8hzgKGB1tI4yy\nUpkmI9K4QOUmpm0RhyGt5RZxBI9cfwvNpRaGWU4clobE9ZtIw8eu3tdCTt7biQrJdDlJLKkGhpnC\nx66ujysbBFXyHqkRvvRJqtOLdUTLWKk0AyGO9LCFz8GzP8zmL32I4MW/D4/88fH5dqoZBP1iRK4X\nDwurk5xFj53mIgSnT/9dFPO0mBMr4XVyXFWuf/LFPZ7s+jy51GTD9lm1ZivINd//pAS33qeXRbzF\nW686COU1XpTkA0jEiY9Nx7rVZC8bjQXFi7oGdUxTiBbRhGYsURdYiJ5EeTpPnGY5WgOA02YGzO63\nGAzUTkFndwHeGKvQGgAM8gG/dPOA9yyv87/d+E0+/Pgz/O3PfpwffOYL+Sfv/XKEEPzdd30hQtxN\nG3pbZzH38H7pQDUAeD3JP0zoQfcS+4OyYCiE4OXsCHcIzy6vv6513Eusd6wHejbAdLwxd8GH8TDe\noPiXt/Y5TCWBGoAQGFaI2zQZREO+8y/+IH/6276WXAquPfJOPvnJV8iKkNdee46PffTTGJZLIcov\nX01MjmYUp2igu+EziHb5F//yl7CcFk5zCyEtDKuD4y9hWBkIjTAC3vfet/Mv/p+fRamQX/+1X+E3\nP/s5krjPe979Nv7jL3+M3uHL2F6Tn/zn/54v/sCzuHqLIvXIEg/LcpGGJAr3ynUUijg6QhXRONkv\nwwd8ivEgqHIyrBBeKfishisV6PJHA3TQQqCMIYkeoHEwxDJSeGTqCPIEITziSIKyEKbL4HiHaJSS\nJh6j/oggCDneP2TjynVGxwN+/Rf/AxKbrWtv4fJjb6W13OX44IBgOGBwfMjezde4/fIL7N18lTSO\nuXPjRQaHu7zsmHx7mvG/FqAF/AlL8KUqw7Q6ZKnGsgWmKTjaK7nH3a0LSMPAsidV3H7vNnXRPE1G\nSENguz6Ov4plLSGlh5Aejtul0bow3leOp9TW1/Lsyt15+PivJ9Zdlyu+j2Oc/yt1EWCpAcHdEbLo\nPCfXYGqbLOk/lu0TB0dkhSYYZkRBiN/wScIB+9svkEYjDu/cROicKBjhNHy6m1v4zQ6HO/v09w8I\n+0MKbeE2lnHcRlXZN0jjci1Bv1cvhOZSC9dv4DVb2G4T26mnGpeUN2nU7+3aojWu6D8lQDCFPzZt\nnQYAdTSMJo70cSqQEFedj1xrEhWxm91CSofDZ76Pjef+DiIb0DZak88QmqbRoGU0Fib1f+znP8bn\n/8y/5hv/zUf5j/v7M48tm61zTxk+KRZ1AOZjXguwm5UUFyEKhCh4erlF27B5W2eZNc9hfi7Tack/\nMLbxXDab9PLhDPd/fm29SutQ/z6/vZcPWDM7Mz91SMTMfnXM71d3Baa7A3vZ4NQkf2+Bw9BJcRo9\naBHwmtCBTu8cnSfOOzPg9VKBdkLNR159gZ/deYVNt0HXcthwfb7p2hOsOR4/8s4vwpJy3KFcBABO\ni27VWUjmhSu/RXE/dKCDYcGnRj3+j73f5JuuvPVMALD3JlmT7g1y9gZvru3p642HnYCH8UDFW9sF\nz3SeJNIjoijh/R/4RrK8wDBMvvYbPsi3f8efBDK+4P2Pc/XaGu95x4d429ue4p3vejtCNBDGOiDQ\nOiElorl0jW/4U/8F73n3N3L16iXe9Z53ApQiWp2TZ/uoYh3TWkIIA6/p821/9uv5lm/9Hr70y7+O\np598jKeefIzNi5dZ6Ur+6vd+M1/9NX+BQhd86Vd+gN//n7+PNA7QygYUltuAKqmRhoftdTGsEGEw\nTnbqqm6dBErhU+geSsfMT8Kt3UjkmAJhgXF1jN6VjjBEaecnRclJNWSf5nKDcCTRah/TtBkehWyz\nR7u7jON1qor/MrbTIUsj+kcHWJZDFI4wTZMsTfAaTYoiR+UZlmVzfLCNNDUfl4r/IZBgK/6IkHyx\nbbBCRmupAaTYpk8U9OmsbpJlr9E/PKCxVIqvLMcnCkY02m1cv0yq/FYLaYDjtSiqJFpIgVLV9TL9\nsRbgfuKkbsD9zAmYj6M05Rf39vjqixf5uTs7PNlu8UjzdIFwHdOdi3p9J4OAOurOxwQQ1BOCk6hH\nkSuSKEQIB8NwaXcvcXD7BrbrIbRk9+YtHL/No2/fJIlCssQkGpW2rGZlSZqlKcsbF2i22xzs3iSJ\nIm5+7gaXHnuMdneDxAtwmzZpFLG8sYbfMjm4PcJyfLzmGlrH2O4aqughpJgRche6TNqF8BC65PkX\naPLq/L1qenBcTRAOitGYUhOrYDxF2BUeTkUTqiOrKvlHncdpbX0pG5/8fsL3/D1g1i70JKrP9ZbL\n5w7gs9kRf+5jR/zMl33BeHjYvVCAFiX6J9mBwiQZXbfaM0ntPP/9RnhEyzJ4dLnkH9eWm2clwgcL\nbEylKFi1WuOqfc37h3nRb7s6p/lkvn3XNii7CRPnoNnnzndLpl+npgmZgvEwskXX4qx5A3A2FWh6\nwvCiwWHnnRlwFiXoLD1A3QV4PZqAf/raCzSNBt9w/RJBZhLmBr+vmtz7WPN0weuZQ8LquE860L0K\nghdFDQDuhQq0P8j4dLDPl1xd5Wp2Nm3rpOnBrzdqkfCD3hF4CAIexgMVv7AjsHTK1U6XV4KPsmyV\nbehEBRQ6xpAZUiUICT/+E/8zWsfjQVx1crHXf45Cx3hAqGK+6699O9/z178DW3iYsuTz52h++ud/\nHEt45DqivQKfe+nfAtBsj/hHP/b92FaLV17Z4Q989Tdy7epFTHvAH/kvv4yv+xN/mp3sBrZwQOdk\naQ9VeAjRJgkPcBsWWo8ochPDdNB6RI6JocVU5a78cqr561KsUCd29bZChci572mBpEATFj0asoEh\nwCApedKWx/KqSxQecnTnGGgipEGjA0pbWFaD3vY+0hAMjg8p8pw0HuE0JHs3X0LrDK/VwHZ80ljj\neg1WNy/y2gufJegfIU3Ji5sb/NDhEGy4EBr8fisnD2MSy8C2FWka0e4arF28wq0XnycOYi49+hhh\nv48qCrobG5jWJKEXUpBEwzEgmA7D9Cuby7gUAledkjomE3PPx899PZOE5yNVin/44kv84UuXeHFY\nJqrvW13hz37s43zk/V9wl1PQaeLj08TLp615uguglUbgIIVNnqbkRcZg/w4AbqtLEoXcvr2LyjVe\nnCC6ZZdlcLhDGiXEUYLnN2ktdykKTZ4W3HzhJbI0xrJdBA1G/YDOiqTZaqOLFK0z/JaJLtJKpyHI\n0kNMy0dX9J4aABRTcxlqapQmJtUaQ3iYwseRDY7ysoO2ZJaVuxoA1ODANyYgKZnSQJhCYAnBsOgR\nqogXnvwwT370z9P41W/l6N3/PX1mnYIWVfW/453X+LbsMX5xZ493LJV6menkf15UfJ5YlIAvAgTT\nHYB8yiJ0OqH9j3s9fmX/gD+zdGmcuJ6WDE/HmI+/AMysmZ2Z2sN+3r+LyrO2YM3TSf4iUFBvn56n\nMP34NCioX29aMzCtEzgJ6JzmolRTqQ5O3OP+YtomFO4GA2cNDRsfZ8ohqAYCO3HIqu3yb3Z2UWh2\n4pBvuPIoUA6mei0cctVv8fde/BQfWL3As8vrLNsOK44L7pszNOz1aALg3gTB0/HcnYSVKvmfdgqq\n4zRgsC2P0KzyWPO315//QQcA8BAEPIwHLL7uyjVazghIAYOgOMAUHoWOy0FCcpOc2lteYwhnbIBd\nJ4iWXAEVkuoQQ6jK9rHk+OcqItUTd4eY2vnAowAMAVE64su/8ptJ0xy04Ef+zvfS7Ei09hEiQ+s9\ndOUGYNvrGJ0+aRgS9G9juxeJwwNay7UGoEAJD6VSyCMgQrqzdn11EljrAcQU97K2rAzVAYbwUExa\ni1J45PoIU3oUWTSZSqxT8qIPIuDyo5tkSczqhWVcf539269x58YdtA7J02O66y3Wr1zgcHcX129x\nfDDiYPuQi9efIE1ijvZ3aS0t02xfZ//2KzwZRrw9BlnAX768RrfR5ODObfI0Is8KVF7qFny/QRLF\nrGxew2+2ONzbwbTAtCSj4wP8dgPTFjhei//9zpDtqMdfffra+NzyLMRyykRaGv7EGvSuOF9Sf1I3\noE5I7yW01vTSAaYo2PA0f/HJJwD4r9/yON/y6CP8q+077CYx719dnXmeJWc7QGfFrBvQNPVptgsA\n5TXK8wOKLEeaLq7tkzQytHIZHvXo93qgLNrdVUbHPfK84ODOLVa3LhH0RwTD8tq4jQZeo0lnZRPH\nKxPlOAwYHR+gipgiDXAaTZxGk2bQQ5ouKo+RB8djZyerSopqh6fSBShG4YzPoZwY7FKLXhJ1SK5D\nlI4xhccgn9BxTAFBEc7Yt9ZgYFQE1QAxH1/Wn5WQYxGw94U/zub/++1c/uX/ilvv/VEwZsW/i4DA\nlufx9devzmw7j2vQdKV7XgC7Znb4XHybFbOFQi8UxtYi13Wrzf6UO1Bv6rWlkfOhRzZ4e2N2Cuki\nCtAi8AGlY9BBPuRt3sUZ8FCvWVYtm/kK/2mxNj7vMtmvjzEPHHoVKIDZzsIiPcWic1q0bV4cDLNO\nQYusRWGiCziLPnVSnGYTWj9+nqi7AZ867vEJlfO5YZ93LK+gyHmsucqnjg84zhKOg4S3tJb4X17+\nTb75+pN89YXr+KbJmrPoO/F8sRPpc3UDahDAfYCAuhuwG5Rd13sBAytNY8YlaDpOogj9wu0dfiPa\n48/NORydJ/aG+cwgsdcbv1PEwQ/26h7G77r4B//jX6HTtPjuP//f0jJWGRYHVaLWBzzCorQRNIRH\noSMkPkpHmLIeHDVxVcm1GicOuVYUpGhsoDW1PcSRDWzRKDn4OsJvrfCffuUjCOWM/fmFTCiEJK1S\ndEFWVuWzPkWqSKIc0+qSRAFFkeN4MXZ7mVSnmEJi0yYnRjN748h0QK4ipPBQGhxjZZwkOsYKQXFA\nXk1/NapJsIXW+MZKyaHWUemYYgp01RFwG0tcuN4hz8rqiVarHNx5mf1bL+G3N5DygKIoKArB0kaX\n1c3LjPrHvPzcDVQu6axssrS6zvHBHmEwxJCa44MIaUF0eMx/d+VRDNPCsW0M02BpdZ3ezjY7N26i\nRUaWZXRX13A8lzwNcBsNllbXS96/FPjtBkurWwhZgoCPbO9wJBI+POqyZE8SoCIPyVWEYXpIQ6LF\nBDzpc+gAzhPzAGD6//MzA6KKo/7SKORHX3iZH33288hVBFP7uYbBqmOz5Xp3Ha8OraPxsaXwT+0E\n3B0lFajsjJQ0ICE8VFG+ThwdkcYFeT4qh8vdfgXLFrSWGoyOU0bHPWyvQdAfIaTD8DggjRMs28bx\nfAzLwmu2OD7YI40DlBYsrawRDA5wG02EKSjyCMuW+ONuRxMh+hiGKB2d8iMADMOr/malA42i7NbV\nIYWHUocoHePJBqbwcGRjSiQ8mzAYQlBMPT8sAiQTQFBoPX4NV3qMtOLOe/8+K5/4y2z9u6/jzhf9\nM7Tpjyv6i4DAIvvPzx0nvDIKeGs7Q2nuGuC2aFbAdCW93F5N0Z1yLKoBwTytJ9fF+PdVq8VBNuRn\nb+8SZYLfu3lhTGE5KdGfrGuWblNe03INz0XbM/tKxEzyftok5PPEPABY9P/p7sAiADXvHHQvsUhM\nPB/3CwBgYhN6ojC4fvwEMFAn/58bHJHpgqfaS+zEIV914crMfn/8+tMcpjH/+MbzfM9Tz/J9b/t8\nYCoxX3TscwwLO+88genXut9OwHqjXMu9UoNOAgCnxedvrHAlufdu75tFCQIeeE3AQ2Hww3igIh9m\nNKSJIbKqku9WAsAOChuFDbgUFY2gQJcVcRWRFkeoqppeVBVGBWPmd64VuYaGUdqguRWfuNBRVYWs\nKvHCQxpLKDFEWBnCLNu7ttygKS+PHYEznYwde4SwkYaHaXtYVps8S9AqoaCJxEHphNwYoWyPVEck\n6nB8zrKiQ9S8f0P4GGJiWWkKH1P4DPMDQM4kllJ4CLGMEksU6DIpxEUaLrZbWU0WmjSRpKlBOEhZ\nvXCB7rrH5mUfKWN2b36Wfm9Is7lKs7PKU+95Hwd3SkFve6mL7XpsXr1CEia0Vta58MijLG+sk+cF\neVFguy6rWxe59tQztJa3iIOA4fEBrbZPkgwRQtBoT264rt8cA4DtOKKvcuyDFq8l5bXMkgDD8FCF\nwjC71fV1q3+nv+Dv/ct+URW+rtBb0h//QJnAT//89J19vv8zr/DW9ip//e2T8fLTf49Ca36ld4hn\nDElVD6UjpAApyteeTA0u91c6nLGyPDtm1y+Eh1YKIVxc/xLt5Us02itAQjjaw3INdFHe3Io8xXZd\nlje2sN2S9iWEQIrSera3u83g4AApJcFwgNf0sSxIolkKSUn7KT8rXnMd220ihMQwq06cvYLtrICM\nQUymO0smwKqk5UXYDHHFEFGB40JHmELiyya28Gd+piNVIaYQmEIQFkH5M0cPMoQgJuX5t38Xx/4W\n3U99/8LhYXXU/P/n+mXC/Kd/9aM81+9zwfN4/+oqbcviKE35+OEhu9EsmK+T3PkKem/cDWgvSIQn\nSXrt6GNWQHfTanOQDbkV9/np2zt8YGOVr79+dbzPdJdh+niLRLolDehuoeu61Rw7+syvcdF6p+Pw\nBBvVs553WpxHOP16YycNTnUHKvc5X4Hh9diE1lQg2zAYZDkX/Qbv7t5tz7npGSzbLj/wee/DN02E\nEKcCgHuNnejsxP71goA3MnaH+YldgF4a812f+VUebdw/uHsjY71jjX8e5HjYCXgYD1T80A/+IIK4\nEhCW6bs9Z6moKNFroTW5DjGFrHjFXjlwCD3uAtQAoOwmlNW3SJUVxFiNxlQhQS2wFZTUIQ9ttMg0\nGDIBESCEhyFcUA6ZPsISDqpIcZ3HKYqXybNDbLMFjoU0HAqdkqEAm1RHCLGMQ7UOHQM9bOGRV84l\n08lgDQDKjkdFWZIehjAxhV9qJESEXQGGXPUwEKVdqFF67+dZiBAuWTpEZQopbIb9XVa3VkEZLG9c\npt3d4JXPPMfqxhbbr+6SxwW3X34J1/cZHB8ipUTrgsPdPSzb49oTT+N4Hgc7d4iCIYd7d1i7cInj\ng32W1zZotNokpmY0DFm9sMbgeFhy/htlghUFI6xcIqsseMNt0pQmg9Uhvnsd02kS9A+r4WANIMIw\nfZSKMGQDpcIpKtXEHnTm/XHC9OB5SpBAnjjAa3p7VBT81K1tvvbKNb5iq8CWkq4doDUYIqbQfXI2\nMYVPqhTPLPnYcogtGtXfde415m7ipTD8/N2AxY5ADVABSsVYtsR1DY4Pe0hpYzU7WJmNKkasbGyB\nIQkHfaLhIc1OKR7USLoba0TBiPbyMnEYMDjssX55k92bN+l0N9EqJw6OaDSvIAuBaVXdDMlY+Fvr\nN2BifVvoHjDRBIgx2AVTXiPXIVbVCbOr7pYUZVfPFD7J1LWpNQKNqvofFAGZjmgaKxwXPVDlZ9it\nJg4nKmLVbvHyU3+W9/7C13Pz6b9Ey5ul00yH1pq/8elP84PPPs33vuNxHClYsSY6hMdaLf7xy6/Q\nNC1+Y7DHbpzwlRc3gLs7AtMAYDrm6S+LLDJfCnt86nCIZxhs2R2uex1ENc23POZs8l/HxM1nWL1W\ni6N8xJNeOZxMVcWRp6v/9/IhVvW325+i6yyKRUn/adOWzxMnWaueJqSuY7qDcper0pvYBThzXdWg\nsPPEtUaba29w0npWFwDO1g7U9B2A43pGy29jyXg68V+kBUhVQddy+EuPv+Ouxx7G6fEQBDyMByqG\nKqQlfSIVYgk9bvW7sr7plyJMRVkNrIcKTSgE7hg8hEUPKTxs6TMqepjCGx9HUYoNg2K/oh3EmBpU\nHiNMj0RHaBJybROrAYaIscUAVFkB1FSdAAl5cQPHXcb2ekhhoXRKoQ0ioTGEW1EUCkzhEo993G1y\nHZGJAFN448rwtI98qEYkKqJh3O3fXOgYVzQQRJV9KCVAqV1YsogijwC7pGgUKc3OKqYVYRgj3I5B\ne2UFyFm5sEkwSEmjEe2Vq6RxxPWn3kY32GJ/++Y4UaTiKkejgGg4IM8zHNdm9/ar2K5P/3Af23EJ\nhyl5mqOyNu1uF79V3uT8Zos0CXH9JnbF9zeE4A9ubfETOzf42f0hTzSbtJauUKioAjHlaxqmX1a9\na4FpycliHgicNjRscu3ujUp0mKSkSpEphWekgDleS0nBcsYqjm/72K/x3U9dpGN3MYTAANQ4AfbG\n/xbV0EzqmQ0AACAASURBVKuioq+dNd34pNA6Kit0qkzGHW8VrTRZGrHhdwkGPbIUgsEhQX/I8d42\nURiQpRErW+uM+sdsXXsreZqRpcn4uEkc4TfK7ku720apIUmY0ey4FEWENCRCJCiVYRhVUl+5OAnB\nuPo/Pt/qCmkdVmDaJ1ERQpT2t9P5RU2NA0h0QKLKZN6R3gxFyBDeGAwkKsQTHr7RmIBmIWhbqzSM\nJvtej3Dl3Xh7/w6u/tG7RL5Ka/7mp5/jj12/xj95/xdgVm5UvWx4F0Xomx65DkBfBXQdm+2h4t8f\n3OHrrt/tmnNWVXxeP7CXjUhUwQ/82m/yTLfNn3r8yngt9b7zot1F/P0VszXz2tP7WGJeoNtauM7p\n866v1Yo1m+zfi2vSoph3DTpP8g8TR6XThNGL9ACnTQue3e/0OQFvRCwSBs/H/QwLu6c1nEMT0PXK\n99/r6QS8HpegeYegfpbwM7s3+SMXHyUqckwp+ac3X2TJtvng1vX7fp03On4niILhIQh4GA9Y2MLD\nEB7NKrEotKZRDfqpfcMnlXF/nMzHeZ3YTToAtbNOnfjXv8dqchMoq+qHmDJHTn3HlQm7RgoXxQUy\nvU9WFATFHgU+GgtPrjHiCMuQCJESFClaDFHCQtMgVBFLpk8/P8Cqzgsm1UwbH8npzjENYwWnckYp\nz0tVlVNZiijl8phjrigtQ2tqhTQkWo9QhNhuShof4LiaVtehu3ENx71AEm/T6a6SZy/S6i6ji5Tj\n3h4H3RJ4RMEIwzRptDrYrjemCUXBiDwNaLSXMQwIjneBlDSNaLZ8ltauoVXC6Ogmnr9Ku7tBo92h\nOCzXJqQgjQOEEPw31zf4iZ0b/NjtV/i2qxs4hkQVCrRGawelgDykyENMu4nWZTFd4FfagNnrdxoQ\nOG0w10mxn8R87ZVVpDhGa8infO1zDaZcIVMhaVHwPU8/ykXfBWKKKecqqr+zEN4c/Sdi3hZ2Pk5y\nCBKick8SoIoQaayACojDfbI0QhoWhrVEUYQ4boGz5TMaZnS3Ntm/3WPQG9LdvERv9w7NzhLCkHie\nX9KLECRRwuHOPkKmQM7S2gqmWXYATEtimH5ptUv59yhtd8W40l87AmkdwZjO5Y11O3WyX9Pg5qv+\ndZjCxzRmJzxP6zXMqoM33fmrqUGhCglViC89dja/iCvP/wNGV//oDAD4uTs7fNH6Gt/0yDV80xwn\n3XD3hODpyvd7ljbZzwfsBZrft3EJ0Hdx6fcXgIE68Z1YaU6ccX55f593CoMPv/0CXceaWcv0vvVx\nJtsnx5+v6E+LcY/y0cKk/yTx8/T2eQAw//x76QZMX6d7ESHDrL//eZyRTjrGom7AeahANd9/Jw3f\nsMnBrzcWTQs+Lc7SBNQC3t1AcVTVBu4XBNzv0LD56v/ffelTPNpo85+tbGIKiRSCv/W5T/DBret8\n7aVHydTrt3yu5wW8UQLhB10UDA9BwMN4wCJVilwHbFhliz1Ss1zW2hc8UqNxQh+pYIYyVH8VtM0J\nx3IaCNQRqQBPloLgVKcYUpS+2SoGEWMKh7p+MSwMDOGSYGAKF6lLLUKiYg4LSaJHgIMp2ihiLDQm\nLv38gKaxQqLCsaWhKcQ4UTfErHDMkhMtgCPLqmakAo7zWzSkj6AUTNqi5MorzUxKWSdU2owxTA/b\nXcZ2IgQ2Ra5QOsBv+djuMkpFpElIODjGsl1MsyBLA/IiZvvlz+H5Hkk0JE2OabSeQQjB4LCH1kXJ\ncSckTQ1anS62pxgcRjz1nncyOj5i1D/m4vVH6a7H9I9ewG00GRztAtD1t8rz88q/Zae5xM88+25y\nrccDbRqtTbJ0hFYhaTbC9deAqExQZV2ZCtHUjkpeOTm3AgXTQKC8Lvd/s/7nr73KN16/yBOtJpII\npSLyfIAyHJDOuLPw46++hAb+5COXKqCyjBQ+qSpNCsfJMRqtNbk+BFyE0EgWzwiYdQhaHFpHFEVE\nnpWOOlka4fpbpElAEgU4XoP2cgvD9HjlMx8H7dBc2qC1vEIahtjLPrbrorKcNE2JkhjDNGl2miyt\nbZJEI472bqMKgzAIaS6VHRrTbjLlZok068mi0cz6CjSC2bkIdcJf6GjM968T/JrmV0ed8Kvq91iV\nGg1X+mNhP5TUIFv6JFWHcFQEYyFypjXxpT+I/cm/CapAa80v7x/x3lXNS6NjNnzJlYYHpBzlEyrH\nvHC47iBMJ71PdjrsRjH/8LMv8bVXr/BYa65afkqSq7Tmh5//DF94ocVvHo341H7M25XgA0vX7gIJ\ni445n8yfJOjdzwe8nOywbDQXAoTDfDgGPOdN/Kcfn3/edMyDg3qN81SgeT3AaQl+Tf9ZRAWq46RB\nYXU34CQgcFoX4CxB8PgYFSXorFkBk/0XzwnY9Ax2ooKdOBt3Dk6L84iC7zXeCE3AIgBwHuvQjabB\nv927xc3M4VuvPVkWBU2LD118BIC/9uR7kLWN8Os87TdSHPw7pQsAD0HAw3jAYt3eYCfZJVAjGnK2\nAzAf9fZ6gBDM0oZOeh5MqvFBsU+u+zSlAZWFYZHbSJlgmiD0AFN0WLVWybWiVXH6h0yoSBrQ2mbZ\nXKkSFMY2pK70GBU9cj3Ak+XNXDDCUSGm0QRRVTu0QUGMKiJyStFzUlX/AXzZrc5xe9xRyHWEOeWb\nX4xJKS4aXYIN4SENaHZWq7XGmFYJAMLBDvu37zA8LkjjhM1r10iigjR5lUZ7haPdl7jwyFW2XykH\nCXl+E7/dob+/x/DogCwC29S4TQsRNci9gOc++glsu0MY9Dnc3uGJZ99FmsSYtqC7MbGdhPKmksYB\nSTTiot8cU0DSeISQgjwPMWTZ0VCq1AZMnsvs3AAxscusaULzQ9nuJ1Kl+OClDbambtJpFFLkEmlE\nKL2P1QQplvnQ5U0skaJUiCUnHYfp2Qbjiv5UQlzvB+cZFjbrjFQKpcukO89CBOWQsJIKBrYrKbJj\nVJW0OK7HcBAhhUXQPyIYDKAHraVlvEYLz/fYu90jzzOkIRj191la3cC0DNLkELcxO4BITA2yENXk\n6tq9Cx2VAKA6/0xNxPe1o1It7p8HA9OV/jrJj1Qw4zBUg/1EBeQaIhWOH4unRMKu9Cm0xo1eI2tc\nBWmQZopf2u3xlZuX+bbHnxjvO13prx2E6u11Mts1W+zng5mEe911+OZHH2XDc/lnN17la65cHgNa\nmE3O6+RXIngl2WHTdbAF+HbBV15ZxX/+mJOiTv6PixGPOxfuevw0+tGyMUluFyXsvWx4ZtI/P2m5\nBkkn0YRO6w5MaxQWRU31mR+iNh0nAYB6uNgibcD92oOeZgn6ZsdZAGDTLz+H90oJOssmdKMh7xsE\nTFOA5hP+WnOwCAjUHYBCa24OUjw8HAmucXe6Kt8AkfR84v+7qQsAD0HAw3jA4iOvvsQro4DvfLK8\nYU1PBJ2O+e01RWg68V/03OnOQqpCBDGO8DBIEWmfXDtoZSE0mDJBApnokygTjUumIyzh4VWJjSt9\nBkWPgnhcgfTkOpEaVZX/GE8KEuVgCYEjfQqd41hL2MJDKyj0MVBWwQJiFC1AjwcolVSgMtlRWpGo\nAEc2cOVK5VwTk+sYgVvaiBZ7CAVK2GBEKFKkWc1T0KLUCxQxRW6SxBZJZJBEBf2DPu3uCu2lNW6+\n/BxrF5bZv30H12tz3NtjeHzIxsVrXHzkcfZuOwwP98mSmNFRTDg6IE2g3bnEW9/1Pg7ubHP75U/w\n2Y99nCeefRe9nRex7BaONxp3AByvhWX7jAb7GBKKKqszJKTJiCKPUIZAGoI46+H6IG1/rAvQSoMo\nn6R0jDT8OZrQWXGe9nHET9y4zQ8880T1PosRRoqBDdhImhiUGpUlu803/fIv88GLXf7Q5ap6rUv7\n10V0nvlt5wMrc9OCCZHSwzA0GSOyNERW18xC4nqTRCdLFZ31yxh2n1sv3ACg2Vli1B+ilWY0OMYw\nV5FS4jXKxLvZbjM8vkmapEiZkWeCLLWqzgxT6ygTeYVGElNoj5LmVCXsc5OvU9UD4c5U/XM9wpGN\ncQcsViG29Imm6HthEeBU1f6wCCohfxlWNesj1RFb1lUCNWJUBARFQMNo4Aa3oPMk/+r2Nm9tt/ju\nz3ucQAdQTCYKT1f666hFynUsqmQLIdjwXLTWPNcfsLa3h20V7McpT3YtPOHTMM2xYPgoH/ErOwFh\nofkzjz7Gfj7gkrtUDojiuPLUX8z9NxCsGHcPLpvWOcwn34voQvPPPy8A6Jotbqc79IuQYRHQMhoz\nHZPTAMBhPhzT4XrFcPy9vWw2Z/QA01X6aQCwvmBbnZvOT1g+LU6jAu2kozM1AeelAp3VBaitQk98\n/B40Afca57UJ7VYl9vuZGFxHPStgoyH59H7Omr+4A7A7zNFas+wLfur2bV4O+/zlJ5850RHo9UYN\nAN7I+QB17A3y3xFA4MFf4cP4XRW/Z6vJl+nVEx8P1N3V/cYJQOE8YQmJJcr2v5QdlNJoFNKYpIiZ\nTjBEEyl88kKPq40/desm715ps2yXGoJIR1j4BGo0FjqalUe6aczzvkvBsBYxBktjCoVNQqDiMb85\nmtMviClhZK7DsvovlkprRSDXMbZwEMJBE6N0gjSWQEUUmUblJnk+QgiXJMqR0iaJjljZeAQpM0zL\nR6k9Ni8vYbs2o+OCK0+/lb1br9BoT6rAfruD5zeJwhG24+C3VvAaLRrtNsFgUA6c6l4kCrbprFxm\nb/tXMc01hocjMl/jt5tEo/JGrlVBGgcYdgPbbSBEaR+axUOEFAgpUHl5HfL8qJpIqxHCLV1x4Hz5\n/Mzf/eyK+z999TWapsmPvPvZ0t4TjSGWMawYaTvoonTVmeb9//C7niZTapw0lvz3CMO4PzrShApU\n/jseKCc8pgGB1jGGIVCGwHYr0XXVOdFKE4clVSiJDgkHB3Q326SJYnTYZ2XrKoZtkYSzNJ40jkhT\ni7WL1xFCkieHCFOg9URALKWHUtPPc8l1qdepuxtGRWmqJ123jPLzXVrzKqTw8ar5ANNzANxK8zOd\n+PtGg7AIsGRJC1o0wKhtrLCbldQzT/ok1XeGGdziH/N+PnHQ40JDskaDZaPNUTGgX5SJ60lThad1\nAaf56Ash+PDT5bCx3SimF2k6RpNv/U+/zre+5SrvXbY51AP+9e1DvubyJfxK1F4f8ygfkVdv5mnR\n7DT9Z5FId16gO8/R31+gH6hBQ93Mma/yL4rpY3YMn6bRGBsTzMf8Gg7zIf1ixLJZvj9NBCtmi14x\n5Cgv/0b51LHm6UDzVf+6U9DLh6yYrTMdgc6KOqk/SRcwndCf1hU4rzPQudd1DhrQzOu/wZSguhOQ\nFvfPuZ8fGlbHWHswleR/ZPt51hyP37tyha+6eHG8fXdUnDol+L7X9iYAgPWOxV4/+x0BBB7s1T2M\n33XRMhq0jBahGs0k/A3ZnPm/XyX+YbVtHhyMqURzx/Bkk0iNxslGqiNsYWDLJXJ9jGE4SGmQi4BC\ntBEkJMocJyQABSUl4U6UMsxDWraNLTxsYFD0SFUfRzi40iuFo1PCx1iFpKog04flc0SKg0bphFQ4\ngIMjHWJ1h1zbOLJbCSz7FHofdIZkCDoj1rqamwCSFC0ErhQYYotMHaFwARcJ6AJUrghHI5IwAAaE\nQUi/NyKLC26+8BnaXQPLWsFrhHjNFbIkwbQyXviNX6W13CFPIHPKwV2GNOhsbdC7s43r+3jNCRDz\nWy2iYIjfbmFYbQ73XwKVcXDnRS5c+zz6h3u4jSZ+xZ2WhkAISZEGHPVv0V4uLRzr5CRLA6RMKPJ4\nrAcQOAgJqAB5Iq10sYXodEx8++/e7+1LHVqmOd5nDByMLQwBhSwdcHKtx66fzw+G/MBzL/F/feDZ\nUjh86qufHmq8tsoFaPr+KaPxawrhI40IaYaIPEKrCNtdQcgGRQWeTMvHMDTWVoNmZ4VkFDAcDEij\nnDAYYOcezXabLMuxfZ9oOEDYEtdvkCYRli0QpkAQYHtLaBKgiSYaA9hSJKwRYnkMAEowFJKrI5Rw\nMIRLqsPS2hZNrku73mmwC4wF8/VMjxoIhEW5XzzlDDYdFmVX0JM++9nBmGLUCPb5tVc+xlc99Qd4\nx8YFuo49fs6yUSaXNRiYpwKpE5Lc07j+a2YbPHiqtQ7AT33xF/OR117i+eCA600fg2E58dW8u1Js\nIu9yGRofE+5yNrqXmNYAANxO9yiYUBc7hk+/CGlIn0riQdNo3DVfwZP1gLa7X2NeTF1HDQDq9S+b\nzTHQKtAz1KBpCtAi+8/6vT9d0R93BOb2P1hwHaYBxuKuwMndgPMAgPPoAOouwEl6gPuJexkCdt6Y\nnhic3wcOmHcGWvPlwuT//955kS23wQc3HuX6kvuGzkP47YjfKUDgwV3Zw3gYlMn+NCDwF1T95x+b\nBxDTx6jBQVING8q1TapzUMcgwNIJhrGEWU0nHamCXNuMioCoqsLGheIfvXDIl24ts+pK2kapBUh1\nhCc8PENgCYEvDXI9wBIFOeWNxpY+tvRJVUioIjIRgUiQGjJqa8OEVCWkuiBWt7FFTtNYwpNdpBgC\nKaGKgRaZjvClgSUEruwCEckUN9qkj8o1cRiicoujnds43hq269Pb2UHlBuuXL5NnfRwnZfXSZTbM\nK5imx2uf+zRC9MnViDSRuN7kZuU1msRhwMrWBYL+MQd3tonDMkFzfR+/1aLTXWf7xg53Xr7B1iPX\n+Mx/+jjD/pCrjz9DHIyQVVFHGgKNxmuvkxURwWgbT3fHk4JVHqJlWvrymwrTdAAPrcrhaNL2kUZj\nYh/K3c5Bi5yCBHJszbmIivN0p6RjFNVArzop1/oITX2T9xBCj0HEM0sdPvyWa8BE6HrSLILzRA0A\nBB5U03fL/0/OE8qKvGV7mOYyadwjHN0adyikdBkc3ULlijzXpFGOMGySaHK9gv4xhmHSaLfYu/Uq\neZZA0yPPQhrtLRyvSRTsIWWGFFXHSB+C8NBCIBBlZwZ3JmlOdYipReWypavrojCEHtv65tXfpgbm\nhhA4soFSJTioE38oAbgr/XGyD6UWoDllDTosAnzZwKpe09bLeJ/8C/yzxh/iwxe+hDVz8j4eFJNk\n1UDQr+iEtWVvoblLMNs9wVITJhX36Wm4dXzFxRIQrJltPvx49+4nU9p1bnNnvN8id6F5AHCWTee8\nW9AryR32sx5t0yfSfTbNLk3DZ1SELBstOkaTO9keqYa2bDAqAkZFQLPSXdWAYF40fd4oAdaIjtG8\na+2mKLsBJwl+62r/wYJzrqcEjzn/9uJu3/T2k+xCz6IDvRGuQJuudSod6M2kAs2+zum6gDoZN+9x\nTsAiW9A6+d+LQz52cMS7Oxv83P4N3t3Z4Jn2GpfaDpe8093S3ujYG+ZvSjcAZoHAgxoPQcDDeCBj\nOtlflPiftX84BQzq3+dpQ7nWmMJjVBzTNGqhYookIlM9cixSZSCFS0FJM/Blk4EY8MUbJteaBoJ0\nDABWzFXCojfm/kNCQ3YodEyqYzIdonGxpU/bXCNWAZkS5MKvNAWlLiAVNolWmMLFIKVplDScQkOh\nE0ZFQY6J5hgLl1QLLOETqTsUWmOJpUofEGPpJXIVkkYD8hRyZWJhg3RJojKZO9rdx2mYtNouhinw\nm5cYHL2I47ssrfk89swT3HrxJqblURQ5qlAc7u9imRZBv8/h/g6GVCyvb5DEIUrFxEFGHAYYRouj\nXo9m75B3fMn72X75BY57n2Vt621YdrNyAII0GZLGISrXuI0lkvAQv7UCuhwaZlrr5Fk5DEtVnvjT\noYoAIUsBsZRldbwGAsacCHc+FlGDpi1EtY5Qqo8UDlJ65FmfMNkBAU5jE03pIqU0vBZGBHnxuhL/\n6Sh0VHZydGWvWQTlQC4pkNKbtYaqQkiPIsspihzDEFi2Bm2jhUOWDghGGWkUYbstlDrGdzxM08b1\nXFRRsLSyRjDo0V5po4sUQ0KeVJQay0MaBkKCFN2qEhyjKch1REGEgUuuYwocDOERq4BCT9ywcg15\nMZt82aIE5tPV/vpfR/pjfY+CGcoQlJ/jeluhwZcNhsWIJLf4dC/gF1/7KD+U3OEvvO/zUUISq5BR\nUb4fmoaPP+Uc1jGaUwL7Mo6mnHPg7AFZK3N2oOedoFvvt71gWx0n+fVPdwfmK/HzAGJUhDQNSVsa\nrJuXuGBvAjAshtXMBsFFa4O22WSQj8bgrW20ZqhT8zHfOZm/Pjma/XqNU52FWmh9lJedgrOAQF3p\nX7faMxSgeTrQ2ZOB7378vNOC//8S5+kczAuDd0PNhn96lX4vKCi0plccc81v8X/eepFvuf4U/9PL\nn+Gi1+CdnVUOihHrzQv4A8FWy+Yp+2RQudEyy2nBowmweCOoQW+kI9BJUQOBBzUegoCH8cDF8ISb\nTB2tqRvISQDhtI4BlBVFX5YzAhxZfkBthkjtEOfbhIZPpn1SHWGJSaKxne7w13/jFb5sc4P3WEsE\n6hCD8oOUqhBLCBqygWbSMpYaLCGqSrQ7Q1/XuCQqpCEb+KZHrmOO8z1MsVwmTsLlMK/XPSLXIEUL\nG2jLFoE+BmwyrQG7ygn76MrpSBURKlcI4ZAXGZbdwjR9gsEQgUU0GpElI1a3Nsmnrk+exTQ7y2XC\nv3OH4VGI69tcuPYo0pDkacrwqIcUGtfzGB0fcri7h+N5SEOwv79NZ2WFIg1Y27qEtGyyNObqE5/H\nwfYNjvY/Sf/oBWxLYjsuuTJZXnu0vCaFxmtcACFQRYw2Sk67YXioQlFQIGyNEKKaJkyV+M9WkObF\nwacNEFsUM8BBafIiJhy+CsLCNNogU/LkCOFsjnfr2i0Sdf/c5EwHY1Aiq+m5SkWgNKISQQvholVM\nXhxiWrVDULXmal6GZZfVcigF13mhONh+DcfpgLYQ0uDo4BiUpigKmsvL2J7P8OiIJI7I0pil1Udx\n3fIWkeUhhpGhdcY0xUpl22iVIDAwENWwvloMXM5KKLSaJP96MgF8usKfF8HMoK+a/gOl178CRtX+\nXrW9rvj7ssFx9RmJi4JXhopnV5b425/+LF+wssx3bR6R3Wgg7BaOECQqom0ahEVEoSWpFuM1z1OM\nDAHHFaWqpqctm62FQ8Tq6J1ggfl64izbzpPAwfzj/WJEQUxHNti0K6rV1OfeWJDbSQQKzaAYMigC\n2kaD4dTfbr4j0C9GXHe2Zrbt5wOWKneiXj5kLx8gESybTT4b366O02QvK9fSNSff33VyP3b8qag8\n9XatDYSovOit9oz7z0k2o/MOQdPJ/2/FoDA4WxT8oMQ0CDitGzBd+f8PvVu0HcHP7d7k+57+fFYd\nD6U133D5cSwpcQ2Tb7r6VgA+dPHRc61jY6paXwOCN0Mj8EbHgwwA4CEIeBgPYNQ391CVzhPzw8Lu\nN+aBQaJCtD6kYXQwSNEKhHYwjCaSBEuMiIkJFdhiaVxlvNZocrkpiSobwlKsGFdDi3wMAaYoqTkS\nUEIjSMi1Vbn5lIDCEKIceKYZU4kyHeMbLkd5TE5Z6O2YF8frNcTBuC1vCp82oLVb2oqSYAoXU0gM\nsQxEKANghGG46CwliUKkCEniYyynoN/ro3XBcW+ft7zzrThuFyEl7e5Fbr30aY52I/Zu9TEtn62r\nj2A5DjuvvYphmKBBIQDJ+qVHUGpyEzCtDtEwIxgkDPWItYvrNDsNRkc9tq49iWn55FmIafkMjm7R\nP9hl2HseabcwG1NUCeGS5xohPYT0scxJ1TbPAkyrMbapVEUAIikpM2MAUHd4zp4kvCgs0SjP0YjL\nGQ12C4E95udrnSEoq58/efMO1xtLfOjy1szk5/NGvcZpIABUVCBnLIIuef4uE6puWM5I0GEJitIQ\nIQWm1SDPAlAa2/FxPA+tIlShsOwSMDp+g7VLl1FFgSENVJ5RFDntbpc8C8FtY7tNVBRjGC6GZWA7\n3ep1I3QBhrkBHEMRgQEGJaXHFKWo3hEdUh1hCntG+AmME30oK/zhFNe/Bsu1xacn/fF3wSAfURQO\nP/T8S3znk4/ykZcPWLItPrDR5Od3dnl7p833PfMkCo1/o0/UvEqsjvEND1skuMLDM31iFaEQ1efR\nHTsJAWNq0NLUe67Qpwtopwdz3esQrLPipAT/LFefw3w4w/23hWDLXpuh+CyKcjo74+5A6fxUgjFT\nTABS7RB0lN/9/VyDj+NiNAYBK2Zr3F1Q6LEz0HPRNl2zeaLt53RobYxpPTtpgNbG+PdFU4GnKUIn\nAYDzJP9vhiD4pGnB9zojYOaYZ4iDx8PFamvRUyhB0yDgJE3AXlDwatjnSA35io0rfOLGHb7v8ufz\npeuXMITgD10oJ/m2LHvxAe4xakCwO8zfECDwZlGCagDwUBPwMB7GOaPQjFv1LWPi+V9HLRqepvnA\n3Qn+dDehfs70dkMIsspRJ1ARtpD4wiGnvHG3RRtVfSce5AkjFfL8UczzwwO+5rqPpE+sDnCEZJSP\n8I11HNkgU0eEOsEWMaYoJ8caQpCphLAIkWINR/qMih4ds3RJyTW40kXjkekATQtDhPz07X3e011m\no12KmX2jgUCSa4UpJIU+whCgiLGrwVSWaExx18tOAEBRxBhWhu1qkmQXx/NYWuuQJlnFHQ+wnVkP\neIBgGOC4bbqblxBCsHf7NY4P9jBNG60LijxESotrT7wDv9Oh0SxvrCsXSuASDQYc7W+j1Qidg+G0\nGR0fIE2JZXu89vyvEwYZzZZHZ2uDcHhMrroYClQeYNtGRYUR5DmYpl+6BamJ+02Rh2D6JwiEJ+Lg\ns2hB03EXRUh6GBIsR6KLEGGU5y+ddsXThydaXZZsu5okfH+0gkVr1EQInLHIdzqUijCMlbu2S2Ny\nMVQeYjsNNi9d43D/Dkk4Ih4FOK5NHAYc7myzcfURkjCoKFwmweCYreulD/3oeBfHN4EUafgURYSQ\nPqqIKApFURyitSpBYR4hhYs0vVIQr8IyiRQuWa1VUZOpy4UW4wFfIxWO9TUwW5U3hCh1OdVjveyQ\nZiGlPAAAIABJREFUlrHEW9o2QTHiD1/tYgqBbUj+yttKO9e6Wm0ITTPcZsXs4kpBrjOaojxOIBSZ\nFgzVMYaIceUSsuqnmdW/BmJMhanjJPFrHfMC3DoWCXpPohUt2rcGIOfl49eOPJYUuNrAkS4jFfJC\n/CoWkqZXC3wXU0LqggyUWoCO0WJYjBgWIwwE150tjooBHaM1BgEdo7mwc1Gf57yz0upY8DwLAObp\nPfX/97OQVbM1k+hPV/3nAUA2pag/LwAYDwVz3LsS/9/KKcE1ELifOI9L0HlExDUIyCoV+DwV6M4o\nZzcJyGXCSiV0/xtPvxcoCwJvdrzejkBNCXqzgMCDDADgIQh4GA9YDIsRphBjlyCYbVfXv0/z/AM1\nGgODxccczvwL5U1PCo9YDYiLgrYBhe4RaZMlIVECDvOCoYo4zgx++DO3+OpL6zy1rFkxfQwUbSlw\nZItCJ2iRIXSMprzp5LoAgspFBQSKVCd0jBIAWKIUmhY6RukhSrvEOq5oPZogt3jX8jpbnjMz26C2\nCLWFj9ADDFyEcDDlpIpsICh06dYiDJCmxG82sV0Dv92iGJdz1lCZYu/2MYZRTpp1fAutDaQUdLob\n3DH2GQZD0iiiyHOGvT0uPnKBfq9HHIRsXV1l+9Ud9m69zMa1xwE42t3hwiOP0Wx36G3fxnYbDA77\nHB70sWxBmqTYlsBtZWSpxcXr72RwfJskCEuQEt5EcLmcHWBoDEMgpSSJD1F5iGX7yKo6q7VGFQoI\n0fVgMSKknJ4gPBv32g0oaTllZdr1HiHL7yCkS4FT8vSr++jbl5b46e07XDqDL3uemHQDPJiawGsY\nDYoiQKuQv/apHT5xNOInf8/n4Uwl/XHYQ0oH01JopSkKjRSa3s6r3H7lNVxvBcttkmcZhllg2i5J\nFHJ8sIdhmCTREGkKhDTIFag0RGPgt5oYhkeRH2NaPlkaogq76sRIhFhCQAXSImqfXCk80CGGcNFA\nqo5pm93xQLx65Z7wZgaKZVNVR0f6mELgSh8J4wr2By81y0TVNGgZDSIVkukIt9rfkz6d3q8RLD1R\nzvjQkqbIyQYHOF4X38gpxDGm3aCXl59HowKAS2aTQjPjWjPvzHOaPqB+bJ42dBaAKFCnAoD697OA\nwOEYMDTZySIyFMH/196dR0eT3/W9f3+rulvqbkmPnv2ZDc+Md+OAMcYHAnGMsYEYjoFAgjkkAQK5\nCUm4QC4J+PgkJDe5uRjnkBByE5JrQhbMbht8SeIFvLAkHhsbL+OV8Trbsy9aWq1WV/3uH1UttTTS\ns82zzdT7dU6fR1VdXf1VPS2pPlW/pVjlYKsKjQ+OPgtrcLB9gPk9hvrsZX3m8zkuFMubfQEygvl8\njuWpizMXimWeOnvbtrp31ven64+wmM9xph4ZaNrxHU18do75f2q8zOF6f4d2+b6PbyxxamONw+0u\npOq05vhosNlnZvOkf49Zgjf3s+OEf3qG4Ev1Fzg2W13lvtw7ApfqGAyX3zl458n8bif/j93m8n5P\n7dYc6ORqwfvOH2e9GPOsuQP8whc+wo8/7YV0svwxw4BOu9QMwVdqZ1+Bqw0D1yMI3OrNgCYMAbql\n9PM+GdUfiOVieXNm3349tOdKcQaoJgaC6dOj7ebzeR4ZHX9Ms6Kl8QoLrTlWi5X6SuNRcmCpOAHM\nMBsFpBHDcsQoZQSHGIxHfPc9C9wxt858a55eltGOLjPZLJ3oVq9NS6ynAZ3o0op9mzOYstkyu02K\nPueLM7SoJp0abv6unCOLPp3ocWb8AJ1slvtOrXFyfZ2//tSq6UW++Yt4TMYyZVohK5eJbAaimo0V\nqjHpU/27vRX7yWIN8kRk52l15ijLRDkuKYpEWc4wv3iQ0yfOkuUtBivnmO0n8nZQFNUfv0O338l4\ndJbR+pBysEqnO8fnPv5pymLE7ffewd3P+jJmZj/B5z75IK1Hehw4coyyKDjzSNXOtzc3z+ryEvuP\n3MH62iqt1hz7DiwwGg4gweKhBWZ784zH+9kYnidrd+nOZaTiAr19zyALKMo1sjyY6VZDXZZFSZZB\nWVbDh+YZlJRARt6a3TaXwrWSTca6T5Dlt9XLq5tj4Leix8cuXOD4sDpuj29EoK2mSymdIxVDKAfE\n1D6HzPBrjzxKkRc8MkzcO1d1Gi7GA2Z7B8myLikl1oerrA1WII1YvrDMeJQT/RmGgwF5q83i4aNV\nP44sY7w+IlEy051h/sAC5WjAzFwfOn1Wzh8nbwVZntGZWSSiW89ODGQbTCYFS2kICbK8yyitEbGf\nopwetWnIXN6jRWxecV8tzkyNtjRkvu6kPxlStzomQU6VK9brdZOheifW6hG/cmBQnKEENlJQLjyd\n+Ud/l+nrw8W45MLps/XEauv09x9ikAVr5RIbqUci0Ykeg6m5C1Z2dGaeXOGeHjkIdu+kOx0Epmfm\n3atvwW5NfKYn65oewhQee8I9ed9zxTL783mOtQ/vuu1qOeBcUQ2ZPJ/P1W3/q3b/UF39n5z8L+Tz\nLBXLLBcrzNdNey4UK49pUnSh2H3kn+nmQBNn6u+hFXByY2WzL8DJjWoY0OnOv5MgMGn/P2kCtE3a\nfkpzrL3Amci3LV9O59/d5gy41B2AK20OdKkhQicB4EqaAl3q5H+3E/+LnbQDnB1ORu2q/n1oecSF\njXWe0l3g5Poqzzkwz88d+JpL1jY9Ydi1NN00aLrj8ObzlxkMrmUQeCI0A5q49StUoywVq3TqOwET\nG+WA4+OHSYzoZvs2x9/v1COJFMCB1pHN7ffqWLxU365eGldtXSd3DvrZHIsc4fjok3SygpxZZgM6\nKed8sca7Hh1yZLbDM/f1aUWwVJznYKtPK7qcHX+BYZmIOFCd0gSM0wU62W3kUx1Vtw3xPvX1ZKjS\nlWKV2azkxHCNzy6f46W372dpo1u3wT1DN+ttNo9oxywzWZcyjdgol4i8OoGajEufqDoxjtOATgRl\nXCDqycryVFWQUpvxaJXevnluv+cIo8ES7c4sxbg6iS3GQ9ZWB7TyGbJWMFofsr66RN5qMbfvIKSC\nzkyH1dXTLBw+RPvzj3D+5CNsjNZZ2L+PpbMnGQ5WueNpz+DsyeP05nt0+3N05+bpzi3QnVvg7IlH\nWDx0jOGg+v/qLhwi0gXmF49w9uRnWD77KdozXdqdHrT6tNo92p0eo+EZRuvnSWlffRcgo5VVnYOr\nk9B4TCfhiekT7CtRbb91MjseP0rKqlGBAB4erNHNW3zvvceu2chAsEYar5HKGbKsRz71M1GMBxR5\nwe35HPf0+5sBILIeo8FJymKZdqfHYOkUo2HJxvoKK+dHdOcPMNvtU3QKWp02B48cZWXpPA898ClW\nly4QaczCwX30F+5gZeUc0Q72HzxKWSwR2QYpDcnyOVJao9Wp/riWxUY1U3NWd+QmqO5gwLgOAJOR\nfzI26GaLm1edizSkFd3NY1amoFWHuPXybN3HpQoD62kNSjYnDAM40j66+fO+kdbIqe4SjFMwrN87\ntXpAoh1d8ghS5Mx010hF0Grto0zHyaNDP0patBmV50gxy0aqmvQNyzWGZdCi+jntZv1tJ+BZbJ1Y\nTweCyYn8ziFGd3OpEYegeo9z48eGhp2BYLLtdJDYbeKzftZnvu7ke6ZYZTx1x+MLo5MA7Mur35lz\neX+zOVQ/71OSGJMeM4fAbvVP1k2+x+nRl6b7Bxxpz20GgUnznkkYeG6val54YrS0ORTodJ+AvRwf\nDbY1B5rYrf3/bu39L7fpz5XMD7Bt/1cRAI4P0mXPB7DZ9v8SJ/87m/icqF93aDYxX39L46fdQ5kS\nv3f68xQp8c1Hn8pzD1z+MLHTE4Zd6yAA2zsOT1zpTMPXYrSgJ1IAAEOAbjGTzD4Z+/vs+GHawGLr\nADNTQ/m1JuPgR7BcnIXxVgdC2BoqENg2BOBuJqMGrad1OmVOylp0yFguV0ipxfc/7V5Ojk4zLC8w\nnyU6OQQrrBQj1soW68wxKs7TiaAVOcH2WU4nY5vv1M3mGJYDNlIimOXU+Cwlbd535iRfeShxx+xd\n9Xa9uiNeNalWMMt6mcizw1XzBabCRj2xWNSd+UZ0INtXXbktjtfXXjtErJO3NyASh247SkqLzHb3\nQ75cDT3JeTbWhyxfGNPt7SNijUO3fxEA505U0+/MdLu08oy8FRy98xAPPXCC+cX9dOd69BfmefBT\nn2K4Vp1s7z9yhFY7I8uC9bUVyqJkpm6PPBoOaM8GKa1RjBMrF07Tm6v6J7TaLVrtnIh12p2qGcNs\n/4tYufBJNtbPAjOQgjLLSeRE1ttsCrTnZ+wK+gZsP/nvbo7bX5aJPAuKdI4yrfOp5TafXhnwvffe\ndVn7vRwpDQlmiWyRPO8zrkdOKcs1+q2cP3jJi+lkGakcUIwH1ShJJDZGaxRFm9Fomaw1C6xw/vRx\nRsMhrTLn5NIXOHD0NnoL+1hbG1AWBZ3ODKnfpSzXiWxEUaxz4NBhNoo1NkYD5vYdojPTI29VISyx\nzkz3IFnerfoEACmDMqoJ6kblGsHs5ihZkxGzZuoAkEWX0aTvCvWV+7TOTDbLbN0cp5MfYpQGm52M\n9+U9WtFjpVhluRjQy3ucHZ/cPF6zWY+NcsCwHNDP+/VoRInxoRfS+9jP1HN/DIkYk3e6lOkMsI+I\nPoNzJ+nM5Mz05lkpc4ZpSGJEYkgng2G5zmpRMtdaJI+tuwJzeX9z9CC48s67O5sGnd3R5GfaXs1/\ndp54T4eCS3Uanuxz0rF3vW6K1c96jFPi9MYqi60+p+vPXitis6P0ZLbg3cLNZASlST0HWvObQ4FO\nmgNNRgI6O15hMe9zqD1PmRKnN5Y5vbG8eeI/CQOTmz47+wpMOvsebnc5tbHVbOjUaJ297JwMbLrZ\nz56vmbrSP2n6M718NR2Dd+sUfDl3ALZO7rf6DOy8C7BbAJg+8U/1EJ7/68xxfuCe5/CjH/oDfvLZ\nL+QD50/x6HCV77jjaXz7e95CXhTEzAzlPU9hnEq++ylPpbX3DI0Xdb2DwM32RAsAYAjQLWg+75NH\nVLOCxizzeY+ZrL9r57XJSB7jlFgqzpAxuzmE4OSW9XKxstkUaGK52BrVYiJY4FwxpFUOmIkZ1svE\n/edK3nfq03zDnV2esTALVJ2IocPp8QZjOrQC5vJjlGmNPHqbVyChass8KAfbThoG5YBe1uNMfQJz\nejjizt4ir/vEA3zd7Rl/85n7KBhxfnyaxdahegShQT1mfKtqYw20sh6prJqiVMdgQKLq3Ei5xka5\nRJF1yKNbjeGT30ZK58jbibyd0yrmKIohETNk2TzVXfPFqj03MFhaYrQ+SzEGUmLx0GFanRad2RYz\n3R7zi4e4cOYzHDp2FweO3c5geZ31tRUiSvoL8zzl2c/i3MkTABTjNYara8z2+vQW5hiuDpjfv3X3\nZun8GWZmZhmtbzAzs0gxgmgFxTgjy0pSGsGwagrWmYWZ2QOMNx4hsUrWniWyjCxbrAPM1og5ld1n\nDk5c/Db41oy92+8qRHTJ2kNgljwNGRZLDIr9fP9Tn0F2GbNcTppuXeyOQUprFBsXiDRPlrEZAPJW\nn2J4lrxzkNvzrSZAZQmj4QplsVzfHYEs63Hh3AlOP/ww5XidQ3ccIctmePCBz7K6dIGyLOnNzTNY\nWYYsI5HR6S6ycuEMkR1nudfj2J23URQJNup2+suPEDFD3gqGg0fpzt0GWRcoKYvztGIGGFZ3xMpT\nJDq0s2rStVYcqK4Cp7XNdv/VeggCYpZ9rUOUaY2s7mvRid7mleNJAADo5T1ytkYXWi8Hm8d1er+d\nrM/853+L8bGXTI3etc54dJ6gTdbq0WodqoJUOkVro6DfySmLjLm8T5ZWGKcR58lYKzNWxufZKLeu\nGJ8rh2xQslaybeSjycn15ZzI7zxRf3CqOdv0VfTJvvJgs4nOzpl8d75mN9NNkSYn6gfb83Xflu1X\nyPflc5wpllnM53iwvjswaYI16QOw2/d0ZmN583XTIyZVx2CurrP6d3/dT+D0xjIHW/Oc2hhwbrzC\nuy88wDN71e+ISbOg6SFAt3UMru8KnNrY3kD0zGiduVQ+plnP8dHgoh1+N6/s7zzZ36Wj8LTj68Mr\nvhuw7fWX2QRo+oT/Yp1/dwsAR3vBP/3Yn/Cc+UN86cJhntU/CsCPPP15tLOM5y0e4lnFfmbynF98\nwUt59FOf5GXr67Te8nt0vulbrzoATNyoIHCldwGuhVt9ToDdGAJ0S5nZ8QtmTPXHtaz/+Ld2njil\nxGzWq07niqqd8Fo5oLPjSvAkAEyCwUSRYFCu1h3gDjIoVxmWa4wT9PN9jMtzvOWRk/yPR+A/v+go\na3nGUjmiE8GpjTU6WYt2rDGKC8xnXcZpg+WyoJfNMigH1XCJwPliwPJGwbGZanz7QTng+HCZVHb4\nqY9+ntd++dO4dy7jKd02nQhmoyCxwqisfqEEI2ajS2KDIp2rxmMv15g0u1hPq0SajEhUdeYqGdVD\nhVbNMsZAHjN1Z7khrXaXTrtbH4dqToKUhozXz1EWLeYXD7G6cp615RVIwSOf+Rx3PeMe5ub6bBRD\nls8/TJaNKYohaSPRndvP2vJGNaRkmYCS2++p5hXIcli5MGRuf3XCsHT+1GYI6Mz2mO3PURYl441T\nbIwLZrpzRGQUxQBG1X9u6lR/IPPWgPbMYfbVV/IiqqvjZbG2OVpOSoOtoTN36RycX+ROQZkGvPPE\nGf7O+z7K999zF9/3tIMcmGlPvbYLzJLFQUbpNGNu590nH+Ll9Z2SayWLDhH7q7sA5UrVxn/tJBGQ\nTYfXrEe5sUKed1k5/yBL51fozC7SmRnTymfpzR3m5BceYrB8kkO330F/fh9lUTDT7XHq4QeZ6XVp\ntWeYme1QJpiZ7XLw6F2sLp9hXEJaH9DuzLFy4TSkDrO9DrDBeGPI+vBRWjP7gYwoZyjLDpGNaOWQ\nZ9XJX9VpvQqpreiR6pmPKdcgq+bOaEW2ORpPFt3687o1w/A4wbDcmjxs0oxuffPEv2pWtJ4GlCmY\nTRnDco1e3mPUO8rM8bdTrj5C2T8E0d02etJEKz9CKk7THq9zAMjSDHmao0wjyFZIrTYbqfrc9PMe\nq/UoZqlcZ6Mc0s5mt80rMDE5Kf/8+vHNdbudOE9O8otUPiY8TF/tv1Assz9f2Jy4a7cgsHOfe70n\nwJlie/+FyfL58ermVf9Prx9nrRgyl3dZTyWPjs7U+6zH/q/DxJliefN1kz4A06MBHWwtcGp8YbPZ\nz+TEv6zb939icJpndo+SUouz4xVObyzznPqOAFRzBExy9tH2Ah9Zre5K7jz533q/HkVkHOvMcXy0\n9xDT1cn9YNc7Abtd4b+Sq/47O/9OdwievgtwNX0AHvNel2gi9MfnH2V8tuQv3/Esnr6vSxbB/OoM\nJwaJWebot4I+W++/vzPD8R2ThV0L00Fg4loFgumT/5s1j8DJpfET5m7Ak+9+jJ7Q5upRgVaKVcYp\n0Ylu9W/Wo5P1GE1d7avaGfc3r+ceaB+pJwBbY1Cubo5eMZ/PbbsrMOnYNhnhokiJkqpj3GzW40Dr\nEIutgxxtH+XP7DvIvnrirfc80iYxz/mNLqc2cmCuGroxrQMj1tOQ5WKNUVm1Px6nxFpa46HRI6xs\nFLz+M6d558lTzESX02vB//mhR7itN8PPfsUzWGzN8S133st8a4GDrT6LrSPsb+3nYOtO+lmPhXyR\nXn4blGMYnGC8+nmK0YOU6TiD8jTr5Sor5dnNYzMqLpBSdeWzFT2y6JJFl1Z2kFZ2kIj9jFNilKrr\n4VGHhUiJLPaRyg5z+2/n4JGDtDoFGxvrrC6vcOqhE6ysrNKZ7XLg8BEO3XYvg5VzrA8HLCweJqIa\n3SjLgv5C1QF4OFhhPCo3R+RZX1thttfj/OnjrK9t/WEeDQfMLRwmz/t0ZuZIqSTLemQxS95aJK/7\nNawuP1LNEBxdyqJqChPRJZWdzb4AZVkFgrTZ7Gf35j979Q24qzfLOEr+/ec+z8t/92MUTCbB6lJM\nRq9JVQ1ZdHnRkQOsjVcfczX6akyaKpVpRFmeoyhWKcs1IqqOuXlr9/4ORQnDYUG7M8/6YMyZ4yfZ\nWC+Zme3RXdhPq9Pn+Bceot2ZYf7AAU4/+hCrF84wWlshAvr75lhbPkurnbO6fIZ2J1hfq05o19dW\nyfIFZmYPUBSpOtYcIBUd0niNIFgbnGWwcoa1wVnGoyFZmq0nvauu/E/uWLWiR75tquMhZdp+dTWL\nLuvlgHEq67H8t05AJgGgurIf9ahBXVoBM9GjE1VwKEiMygHn7v5mVu78Bg6+4zsolz9PkSDaM8z0\nepTjAePxYOp952lzB23uoBNfRDBLluaZLdv0Ykg7lunEOqPyBHPZKnPZKv08I1EFgWEx2BxDP4/q\n5H0yss5iq/+YybjO1Vf4pzv9tiLbbD4zMdnH9Gy9+/OFxzw3/fxkHwf2uCsxfQdiOggcbs3TIrh7\nprpCvFSsspj3eU7vKaynxOnxKu1sllPjlc1gMb2vhbzPiY0VzoyX+eTwOJ8cVuHnzHi6tur38aH2\nPGeLZbIoONzuEVFyerwMZYtUtjg12thzxKATOyYCO9yah7JTNfNJLQ63+xAJiOoKfX3RYHNY0Kkr\n9he7uj8x2f7YzOy2106Wd66H3ScEu1iH4MsNAMfXis3HrrV2Y9tdgJXxBnlrnV7e5ksO7OOZi73N\nu5ZH+9nmCfiJQdp8TOycMfhaOdLPNx9Q3am4VCflS5kEgKNz+S0xkdjJpTEnl278HYkr8cSIKmqM\nlWJ1c2KgtXpc8TyCErYFAKiG+Vyrrw4Oiq1RPLau0vU3T/iXi5XNUYKWi9XNUT/6eX9z1IvJH9DJ\nCceFYpnberO8+LbD/NajD/HvHniIX3qgwzIjWiljITr8wLOP8mdvW6hHA6pHhomZ+spnYliWlKnD\nP/7gQ/z4F9/NsV7in3zko3z5gUVe8/yncn58lnbMsloMWMh7zGSztCOvZ14dMizPbnZQHI/OEmRk\n3EFZDsnTeLNjait69eys1XFJ2QLBLOM02GxeUR2zASntdtWsntisXirLdaDNwqHDjIt1LpxZoTPT\nAdbpzR1huHyemZmcfQfvZrBytmrnnwb0FuZpdzKK8TrDtdNEltOfP8hwsFKfcM+zfP4sC4t3kOUZ\nw9VqfWQ5KRVMmu2UJZRFIstLijKjVf8+r+4qLFKMB4zq5kF5q0tEWU2UVQwoiwFZ3tu8Yhjsfjcg\n6msguw0Z+rT5Pr/+1V/F3/yjD/D8fYtVPwKot6vnJ0h1E6/WHO8982leduwwZ9ZHHKz/tu/V3Odi\nzYAmAaCTHaJor8M4VR2es/r/KNuaH6HcMVrNYOkU/f4BVpaX6jH7Wzz8mS/QnqkKesqzv5gzDz9M\np9cjjQu6vVkO3fZ0Vs6f5fSjn+LInbdx59PvphgtMb+4j2jNsHTuC3RmD5C1Fmm3psbtrwPZaHie\n8cYSZX3HqjOznyzPiFgnlYmUVZ+1aj63NfI4SJEGm/MoTCa4i2xqgjjY1lxoNuvSzw9zZuMkw/Ic\ni60DDMu1ah4CqjkH+vkh1stVxmmt7hhc1jMYQyvvMX7232G9HDN3/79h44u+nuK255PlBcEGEeuU\ndQflyDfq0Y4gpeoOVknQ4TD7Y4l+uQJZIk+QR86oXCGiBXmXTvSrPggpkRObk41N25c/dmSficnJ\nepHKbSfzE5OT/mrm3rppUD2HwcRedwemRxQq9ziXm+6bUJI2J/ia/F6dngzsnpmjfGTwOe5b+VOe\nNjVj9sF8noLEqXrbp88e5RNrxzle73tyF6CcOqEsU86ZjQFlytmfL5ISHO50Odzp8onho3xicJZT\nGwOe2z+2NWtwHQyOdDqbowSd2ljmcGee46MVTm2s82f6hznW6fHRunlVtX6Vw+3+tv4AuzUDmrZX\ne/8rafazVwdguPxhQDf3dZmdgifWi4J3n3qYs6Mhf/Upz9pzu+kr8SdWy80gkNV35zeKvScLe7wm\nQWB61uGr9XhO/h9vp+Bp0yf/t3IQiGud7qSrFRF+GCVJ0pPN51NKd9/sInYyBEiSJEkNY58ASZIk\nqWEMAZIkSVLDGAIkSZKkhjEESLegiPhLEfHRiCgj4gU7nntVRDwQEZ+MiG+o1z0zIj449ViKiB+5\nOdXfWq70WNbrFyPiNyPiExHx8Yj4qhtf+a3pKo/n5yLiI/Vn849vfNW3pqs5lvVzeUT8SUT8zo2t\n+NZ1Fb8zZyPivRHxofp1/+TmVH7ruYpjeVdEvLP+XfnRiPjhm1O5rpRDhEq3pvuBvwj8++mVEfEc\n4JXAFwO3A78bEc9IKX0SeF69TQ48DLzphlZ867rSY1kAPwu8JaX0HRHRYbcph5vrao4nwNemlE7f\n0EpvfVd7LH8Y+DiwffD8ZruiYwmsAy9JKa1ERBv4w4j4Hyml99zgum9FV3osx8D/kVL6QETMA++P\niLenlD52g+vWFfJOgHQLSil9vD6x3+lbgF9NKa2nlD4LPAC8cMc2Xwd8OqX0+etd5xPBlR7LiFgA\nXgT8Qv36UUrp/I2r+Nb2OD+bmnI1xzIi7gS+CXjdjav01nelxzJVJpMftOuHwyVyVcfy0ZTSB+rX\nLlMF1Dt2eb1uMYYA6YnlDuDBqeWHeOwv21cCv3LDKnri2utY3gucAn6xbnLxuojo34wCn2Au9tlM\nwNsi4v0R8b/d8MqeeC52LP8V8A/YmttPF7fnsaybVX0QOAm8PaV0302o74nkkn9/IuJu4MsAj+UT\ngM2BpJskIn4XOLbLU69OKf32Xi/bZd3m1au66corgFc9/gqfOK7xsWwBzwd+KKV0X0T8LPATwD+8\nJsU+AVyHz+ZXp5QeiYgjwNsj4hMppd+/FrXe6q7lsYyIbwZOppTeHxEvvlY1PlFc689l3bzqeRGx\nCLwpIp6bUrr/2lR7a7tOf3/mgDcAP5JSWnr8Vep6MwRIN0lK6aVX8bKHgLumlu8EHpla/guTKxEp\nAAAZyUlEQVTAB1JKJx5PbU801/hYPgQ8NHVV8DepQkBjXOvPZkpp8u/JiHgTVdOWRoSAa3wsXwG8\nIiJeDswCCxHxSymlv/L4K731XaffmaSUzkfEu4BvpGoP/6R3rY9l3a/iDcDrU0pvfPwV6kawOZD0\nxPJm4JURMRMR9wBPB9479fx3YVOgy7XrsUwpHQcejIhn1tt9HWAHt0vb9XhGRL/uLEjdrOrraciJ\n1uOw12fzVSmlO1NKd1M1+3tHUwLA47DX5/JwfQeAiOgCLwU+cRPrfCLY61gGVR+qj6eUfuamVqgr\nYgiQbkER8W0R8RDwVcB/i4i3AqSUPgr8OtVJ6VuAvzMZMSQiesDLAK/CTLmaYwn8EPD6iPgw1ahL\n//zGV35ruorjeZRq5JUPUQXW/5ZSesvNqf7WcpWfTe3iKo7lbcA765/x91H1CXDIVa7qWH418FeB\nl8TWMNUvv0nl6wpESnaGlyRJkprEOwGSJElSwxgCJEmSpIYxBEiSJEkNYwiQJEmSGsYQIEmSJDWM\nIUCSJElqGEOAJEmS1DCGAEmSJKlhDAGSJElSwxgCJEmSpIYxBEiSJEkNYwiQJEmSGsYQIEmSJDWM\nIUCSJElqGEOAJEmS1DCGAEmSJKlhDAGSJElSwxgCJEmSpIYxBEiSJEkNYwiQJEmSGsYQIEmSJDWM\nIUCSJElqGEOAJEmS1DCGAEmSJKlhDAGSJElSwxgCJEmSpIYxBEiSJEkNYwiQJEmSGsYQIEmSJDWM\nIUCSJElqGEOAJEmS1DCGAEmSJKlhDAGSJElSwxgCJEmSpIYxBEiSJEkNYwiQJEmSGsYQIEmSJDWM\nIUCSJElqGEOAJEmS1DCGAEmSJKlhDAGSJElSwxgCJEmSpIYxBEiSJEkNYwiQJEmSGsYQIEmSJDWM\nIUCSJElqGEOAJEmS1DCGAEmSJKlhDAGSJElSwxgCJEmSpIYxBEiSJEkNYwiQJEmSGsYQIEmSJDWM\nIUCSJElqGEOAJEmS1DCGAEmSJKlhDAGSJElSwxgCJEmSpIYxBEiSJEkNYwiQJEmSGsYQIEmSJDWM\nIUCSJElqGEOAJEmS1DCGAEmSJKlhDAGSJElSwxgCJEmSpIYxBEiSJEkNYwiQJEmSGsYQIEmSJDWM\nIUCSJElqGEOAJEmS1DCGAEmSJKlhDAGSJElSwxgCJEmSpIYxBEiSJEkNYwiQJEmSGsYQIEmSJDWM\nIUCSJElqGEOAJEmS1DCGAEmSJKlhDAGSJElSwxgCJEmSpIYxBEiSJEkNYwiQJEmSGqZ1swuQ9OT1\nkpe8LJ09e2ZzeTwuabW2rj2Mi5JWvrW8UaRty5vbbHtNopXHjm22r9ttm40S2lPrNoq0bblaB+18\nx2t2XCrZKBPtLHYs79gmJdoxtU0qt72mel1JO5v63lNBO/Idy9t3vPs2+bZtxqmgNbWu2LG86zYU\n5GzfpqCgxfQ2JfmO60YFJflUjWUqyXe8V5GKbevKVJBdZBkg7ViXKIgd9SVKYls9O5d33+ax1752\nrnvsNikVxHQ9aZd6UgHT38fO5d32UxbE1GcgleW2ZYCyLMmmj3FZkGX5jm0KYmrdrtsUBVmeb1/e\nZT/T64piTJ5vP02o1k1vU2xb3lrX2nObnbVsfp9TP/tlUT52mx2vS2VJttvxyrI9lwFSSpc87ikl\nYupneOdyvRJ2rNu5zWNet8trdnr/+9//1pTSN150I+kaMARIum7Onj3D29/+B5vLp88OOXiwu7V8\nbsihA1vLp86vc2j/7LZ9nLow4vDizNTyBocWO9u2Obk05si+9tby8pjDC+1t25xYKTg6v/Ur78Rq\nyZH+9pOME4PE0f7WycDxtcSx3vY/2MfXim3rjg83ONbdvp/jw3WOzW69//H1Icdmt9d8fDTg2Mzs\n1PIKxzq9reWNJY51+tvr21jiaGdh6/vcWOJYe2HbNqfHSxxu7dtcPjNe4nBr+zZnx8scbM9vLp8b\nL3OgNb9tmwvFMvvzrdctFcvsy7dvs1yssNCa21xeLVaY37HNoFyhn21ts1au0M+3loflKt1s+/e5\nXq4yO/WajbTKTGzfZpwGtLOt41WkAe0d25RpQB69qTUDgt62bVIaEFPblOWAiO62bcpilZiqsRiv\nkuc76tlYIW/1dyzPbdtmY7RCu721brS+TLsztTxcpj27/fitry3TmZ3btjyzY5vhYImZ7tb/1drq\nErO97dusrSzR7W9tM1i+QHdu++eiWrf12VldOk9/fnHbNisXztNf2LdteW7f9m1Wly7Qn1q3urzE\n3NRrBsvL9Be2v/faygq9+a2a11ZX6PZ3fp+rdPtTn521Ad3ejs/O2hozva3/z9FwyEx3+//naH3I\nzOzWuo3ROu2Z7b93xhsj2p3O1PIG7c7Mtm2K8Zi8vfU7pSwK8tb23ztlWZLvDBzT4WZHIAHIsuwQ\n0g1gcyBJkiSpYQwBkiRJUsMYAiRJkqSGMQRIkiRJDWMIkCRJkhrGECBJkiQ1jCFAkiRJahhDgCRJ\nktQwhgBJkiSpYQwBkiRJUsMYAiRJkqSGMQRIkiRJDWMIkCRJkhrGECBJkiQ1jCFAkiRJaphIKd3s\nGiQ9SUXE/cDwZtdROwScvtlFTLGevd1KtYD1XMqtVM+tVAtcXT2nU0rfeD2Kkaa1bnYBkp7Uhiml\nF9zsIgAi4o9vlVrAei7mVqoFrOdSbqV6bqVa4NarR5pmcyBJkiSpYQwBkiRJUsMYAiRdT//hZhcw\n5VaqBaznYm6lWsB6LuVWqudWqgVuvXqkTXYMliRJkhrGOwGSJElSwxgCJF21iFiMiN+MiE9ExMcj\n4qsi4kBEvD0i/rT+d3+9bUTEv46IByLiwxHx/Gvw/v8xIk7WQ5FO1l3x+0fE99Tb/2lEfM+1rGfq\nuR+LiBQRh25EPXscm+dFxHsi4oMR8ccR8cIbUUu9n7si4p315+SjEfHD9frX1p+fD0fEmyJiceo1\nr6pr+mREfMPU+m+s1z0QET9xrWqpn/uhet8fjYifvt611PuYjYj3RsSH6vf9J/X6eyLivvrY/1pE\ndOr1M/XyA/Xzd1+qzmtRz9TzPxcRK1PLN6WeiPi6iPhA/Xn+w4h42o2op95PHhF/EhG/Uy+/vt7n\n/fXPXrtef91/tqSrllLy4cOHj6t6AP8Z+IH66w6wCPw08BP1up8AXlN//XLgfwABfCVw3zV4/xcB\nzwfun1p3Re8PHAA+U/+7v/56/7Wqp15/F/BW4PPAoRtRzx7H5m3AX5h6/3fdwGNzG/D8+ut54FPA\nc4CvB1r1+tdM/X89B/gQMAPcA3wayOvHp4F768/ch4DnXKNavhb4XWCmfu7I9a6l3n8Ac/XXbeC+\n+v/h14FX1ut/HvjB+uu/Dfx8/fUrgV+7WJ3Xqp56+QXAfwVWpra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+ "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid=gridData[0]\n", + "topo=ma.masked_invalid(grid.getRawData()) \n", + "lons, lats = grid.getLatLonCoords()\n", + "print(topo.min())\n", + "print(topo.max())\n", + "\n", + "# Plot topography\n", + "cs = ax.contourf(lons, lats, topo, 80, cmap=plt.get_cmap('terrain'),alpha=0.1)\n", + "cbar = fig.colorbar(cs, extend='both', shrink=0.5, orientation='horizontal')\n", + "cbar.set_label(\"topography height in meters\")\n", + "\n", + "fig" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/notebooks/Model_Sounding_Data.ipynb b/examples/notebooks/Model_Sounding_Data.ipynb new file mode 100644 index 0000000..16ea41e --- /dev/null +++ b/examples/notebooks/Model_Sounding_Data.ipynb @@ -0,0 +1,311 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The EDEX modelsounding plugin creates 64-level vertical profiles from GFS and ETA (NAM) BUFR products distirubted over NOAAport. Paramters which are requestable are **pressure**, **temperature**, **specHum**, **uComp**, **vComp**, **omega**, **cldCvr**." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": true, + "scrolled": true + }, + "outputs": [], + "source": [ + "from awips.dataaccess import DataAccessLayer\n", + "import matplotlib.tri as mtri\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n", + "from math import exp, log\n", + "import numpy as np\n", + "from metpy.calc import get_wind_components, lcl, dry_lapse, parcel_profile, dewpoint\n", + "from metpy.calc import get_wind_speed,get_wind_dir, thermo, vapor_pressure\n", + "from metpy.plots import SkewT, Hodograph\n", + "from metpy.units import units, concatenate\n", + "\n", + "DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n", + "request = DataAccessLayer.newDataRequest()\n", + "request.setDatatype(\"modelsounding\")\n", + "forecastModel = \"GFS\"\n", + "request.addIdentifier(\"reportType\", forecastModel)\n", + "request.setParameters(\"pressure\",\"temperature\",\"specHum\",\"uComp\",\"vComp\",\"omega\",\"cldCvr\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Available Locations" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['KFRM', 'KFSD', 'KMHE', 'KYKN', 'OAX']" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "locations = DataAccessLayer.getAvailableLocationNames(request)\n", + "locations.sort()\n", + "list(locations)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "request.setLocationNames(\"KFRM\")\n", + "cycles = DataAccessLayer.getAvailableTimes(request, True)\n", + "times = DataAccessLayer.getAvailableTimes(request)\n", + "\n", + "try:\n", + " fcstRun = DataAccessLayer.getForecastRun(cycles[-1], times)\n", + " list(fcstRun)\n", + " response = DataAccessLayer.getGeometryData(request,[fcstRun[0]])\n", + "except:\n", + " print('No times available')\n", + " exit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Sounding Parameters\n", + "\n", + "Construct arrays for each parameter to plot (temperature, pressure, moisutre (spec. humidity), wind components, and cloud cover)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "parms = ['uComp', 'cldCvr', 'temperature', 'vComp', 'pressure', 'omega', 'specHum']\n", + "site = KFRM\n", + "geom = POINT (-94.41999816894531 43.65000152587891)\n", + "datetime = 1970-01-18 13:45:50.400000 (0)\n", + "reftime = Jan 18 70 13:45:50 GMT\n", + "fcstHour = 15\n", + "period = (Jan 18 70 13:46:05 , Jan 18 70 13:46:05 )\n" + ] + } + ], + "source": [ + "tmp,prs,sh = np.array([]),np.array([]),np.array([])\n", + "uc,vc,om,cld = np.array([]),np.array([]),np.array([]),np.array([])\n", + "\n", + "for ob in response:\n", + " tmp = np.append(tmp,ob.getNumber(\"temperature\"))\n", + " prs = np.append(prs,ob.getNumber(\"pressure\"))\n", + " sh = np.append(sh,ob.getNumber(\"specHum\"))\n", + " uc = np.append(uc,ob.getNumber(\"uComp\"))\n", + " vc = np.append(vc,ob.getNumber(\"vComp\"))\n", + " om = np.append(om,ob.getNumber(\"omega\"))\n", + " cld = np.append(cld,ob.getNumber(\"cldCvr\"))\n", + "\n", + "print(\"parms = \" + str(ob.getParameters()))\n", + "print(\"site = \" + str(ob.getLocationName()))\n", + "print(\"geom = \" + str(ob.getGeometry()))\n", + "print(\"datetime = \" + str(ob.getDataTime()))\n", + "print(\"reftime = \" + str(ob.getDataTime().getRefTime()))\n", + "print(\"fcstHour = \" + str(ob.getDataTime().getFcstTime()))\n", + "print(\"period = \" + str(ob.getDataTime().getValidPeriod()))\n", + "sounding_title = forecastModel + \" \" + str(ob.getLocationName()) + \"(\"+ str(ob.getGeometry())+\")\" + str(ob.getDataTime())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculating Dewpoint from Specific Humidity\n", + "\n", + "Because the modelsounding plugin does not return dewpoint values, we must calculate the profile ourselves. Here are three examples of dewpoint calculated from specific humidity, including a manual calculation following NCEP AWIPS/NSHARP. \n", + "\n", + "**1) MetPy calculated mixing ratio and vapor pressure**" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "t = (tmp-273.15) * units.degC\n", + "p = prs/100 * units.mbar\n", + "\n", + "u,v = uc*1.94384,vc*1.94384 # m/s to knots\n", + "spd = get_wind_speed(u, v) * units.knots\n", + "dir = get_wind_dir(u, v) * units.deg" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "rmix = (sh/(1-sh)) *1000 * units('g/kg')\n", + "e = vapor_pressure(p, rmix)\n", + "td = dewpoint(e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2) metpy calculated assuming spec. humidity = mixing ratio**" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "td2 = dewpoint(vapor_pressure(p, sh))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3) NCEP AWIPS soundingrequest plugin**\n", + "\n", + "based on GEMPAK/NSHARP, from https://github.com/Unidata/awips2-ncep/blob/unidata_16.2.2/edex/gov.noaa.nws.ncep.edex.plugin.soundingrequest/src/gov/noaa/nws/ncep/edex/plugin/soundingrequest/handler/MergeSounding.java#L1783" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mjames/miniconda2/envs/python-awips/lib/python2.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: divide by zero encountered in log\n", + " \n", + "/Users/mjames/miniconda2/envs/python-awips/lib/python2.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in divide\n", + " \n" + ] + } + ], + "source": [ + "# new arrays\n", + "ntmp = tmp\n", + "\n", + "# where p=pressure(pa), T=temp(C), T0=reference temp(273.16)\n", + "rh = 0.263*prs*sh / (np.exp(17.67*ntmp/(ntmp+273.15-29.65)))\n", + "vaps = 6.112 * np.exp((17.67 * ntmp) / (ntmp + 243.5))\n", + "vapr = rh * vaps / 100\n", + "dwpc = np.array(243.5 * (np.log(6.112) - np.log(vapr)) / (np.log(vapr) - np.log(6.112) - 17.67)) * units.degC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MetPy SkewT and Hodograph" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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6Wmu9QmudorUeprVu+X4m9UCs4AaxgntodVbwLVugoACGDYNrtOC6XK7qG8n1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\nSM3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5c+aQk5PDq6++itPp9G6U8me+//57vv/+e73LOCcNDQ1s3bqV1157jTVr1lBVVUXP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YLMU089lXXr1hEbGxuU2WQRHiZIC6KBv1oQn7/2flJJotZWx40vPOOn6YJLXl4e4yZORLng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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "plt.rcParams['figure.figsize'] = (12, 14)\n", + "\n", + "# Create a skewT plot\n", + "skew = SkewT()\n", + "\n", + "# Plot the data\n", + "skew.plot(p, t, 'r', linewidth=2)\n", + "skew.plot(p, td, 'b', linewidth=2)\n", + "skew.plot(p, td2, 'y')\n", + "skew.plot(p, dwpc, 'g', linewidth=2)\n", + "\n", + "skew.plot_barbs(p, u, v)\n", + "skew.ax.set_ylim(1000, 100)\n", + "skew.ax.set_xlim(-40, 60)\n", + "\n", + "plt.title(sounding_title)\n", + "\n", + "# An example of a slanted line at constant T -- in this case the 0 isotherm\n", + "l = skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)\n", + "\n", + "# Draw hodograph\n", + "ax_hod = inset_axes(skew.ax, '40%', '40%', loc=2)\n", + "h = Hodograph(ax_hod, component_range=get_wind_speed(u, v).max())\n", + "h.add_grid(increment=20)\n", + "h.plot_colormapped(u, v, spd)\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.14" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/notebooks/NEXRAD_Level_3_Plot_with_Matplotlib.ipynb b/examples/notebooks/NEXRAD_Level_3_Plot_with_Matplotlib.ipynb new file mode 100644 index 0000000..0243a05 --- /dev/null +++ b/examples/notebooks/NEXRAD_Level_3_Plot_with_Matplotlib.ipynb @@ -0,0 +1,361 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Shown here are plots for Base Reflectivity (N0Q, 94) and Base Velocity (N0U, 99) using AWIPS data rendered with Matplotlib, Cartopy, and MetPy. This example improves upon existing Level 3 Python rendering by doing the following:\n", + "\n", + "* Display scaled and labeled colorbar below each figure.\n", + "* Plot radar radial images as coordinate maps in Cartopy and label with lat/lon.\n", + "* 8 bit Z and V colormap and data scaling added to MetPy from operational AWIPS. \n", + "* Level 3 data are retrieved from the [Unidata EDEX Cloud server](http://unidata.github.io/awips2/docs/install/install-cave.html#how-to-run-cave) (`edex-cloud.unidata.ucar.edu`)\n", + "* Raw HDF5 byte data are converted to product values and scaled according to (page 3-34 https://www.roc.noaa.gov/wsr88d/PublicDocs/ICDS/2620001U.pdf)\n", + "\n", + " The threshold level fields are used to describe (up to) 256 levels as follows:\n", + " halfword 31 contains the minimum data value in m/s*10 (or dBZ*10)\n", + " halfword 32 contains the increment in m/s*10 (or dBZ*10)\n", + " halfword 33 contains the number of levels (0 - 255) \n", + "\n", + "According to the [ICD for the Product Specification](https://www.roc.noaa.gov/WSR88D/PublicDocs/NewTechnology/B17_2620003W_draft.pdf), *\"the 256 data levels of the digital product cover a range of reflectivity between -32.0 to +94.5 dBZ, in increments of 0.5 dBZ. Level codes 0 and 1 correspond to 'Below Threshold' and 'Range Folded', respectively, while level codes 2 through 255 correspond to the reflectivity data itself\"*.\n", + "\n", + "So it's really 254 color values between -32 and +94.5 dBZ.\n", + "\n", + "The ICD lists 16 specific color levels and directs 256-level reflectivity products to use corresponding colors, leaving it the rendering application to scale and blend between the 16 color values, and to make decisions about discrete color changes, apparently.\n", + "![](http://i.imgur.com/cqphoe3.png)\n", + "\n", + "For AWIPS, the National Weather Service uses a mostly-blended color scale with a discrete jump to red at reflectivity values of 50 dBZ:\n", + " \n", + "![](http://i.imgur.com/o18gmio.png)\n", + "\n", + "50 dBZ corresponds to the 16-level color *light red* (**FF6060**). Note that `FF6060` is not used in the NWS AWIPS color scale, instead RGB value is given as `255,0,0` (hex code **FF0000**). 60 dBZ is not quite exactly where white starts, but it makes sense that it would. Obviously the AWIPS D2D authors took some liberties with their 256-level rendering, not adhering strictly to \"dark red\" for dBZ values between 60-65 (white was for 70 dBZ and above on the 16-level colormap). For this exercise we will assume 50 dBZ should be red and 60 dBZ white, and 75 dBZ cyan.\n", + "\n", + "**Setup**\n", + "\n", + "> pip install python-awips matplotlib cartopy metpy\n", + "\n", + "**Python Script**\n", + "\n", + "Download this script as a [Jupyter Notebook](http://nbviewer.jupyter.org/github/Unidata/python-awips/blob/master/examples/notebooks/NEXRAD_Level_3_Plot_with_Matplotlib.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processed 2 grids.\n" + ] + } + ], + "source": [ + "from awips.dataaccess import DataAccessLayer\n", + "from awips import ThriftClient, RadarCommon\n", + "from dynamicserialize.dstypes.com.raytheon.uf.common.time import TimeRange\n", + "from dynamicserialize.dstypes.com.raytheon.uf.common.dataplugin.radar.request import GetRadarDataRecordRequest\n", + "from datetime import datetime\n", + "from datetime import timedelta\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from numpy import ma\n", + "from metpy.plots import ctables\n", + "import cartopy.crs as ccrs\n", + "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n", + "\n", + "# set EDEX server and radar site definitions\n", + "site = 'kmux'\n", + "DataAccessLayer.changeEDEXHost('edex-cloud.unidata.ucar.edu')\n", + "request = DataAccessLayer.newDataRequest()\n", + "request.setDatatype('radar')\n", + "request.setLocationNames(site)\n", + "\n", + "# Get latest time for site\n", + "datatimes = DataAccessLayer.getAvailableTimes(request)\n", + "dateTimeStr = str(datatimes[-1])\n", + "dateTimeStr = \"2017-02-02 03:53:03\"\n", + "buffer = 60 # seconds\n", + "dateTime = datetime.strptime(dateTimeStr, '%Y-%m-%d %H:%M:%S')\n", + "# Build timerange +/- buffer\n", + "beginRange = dateTime - timedelta(0, buffer)\n", + "endRange = dateTime + timedelta(0, buffer)\n", + "timerange = TimeRange(beginRange, endRange)\n", + "\n", + "# GetRadarDataRecordRequest to query site with timerange\n", + "client = ThriftClient.ThriftClient('edex-cloud.unidata.ucar.edu')\n", + "request = GetRadarDataRecordRequest()\n", + "request.setTimeRange(timerange)\n", + "request.setRadarId(site)\n", + "\n", + "# Map config\n", + "def make_map(bbox, projection=ccrs.PlateCarree()):\n", + " fig, ax = plt.subplots(figsize=(12, 12),\n", + " subplot_kw=dict(projection=projection))\n", + " ax.set_extent(bbox)\n", + " ax.coastlines(resolution='50m')\n", + " gl = ax.gridlines(draw_labels=True)\n", + " gl.xlabels_top = gl.ylabels_right = False\n", + " gl.xformatter = LONGITUDE_FORMATTER\n", + " gl.yformatter = LATITUDE_FORMATTER\n", + " return fig, ax\n", + "\n", + "# ctable defines the colortable, beginning value, data increment\n", + "# * For N0Q the scale is -20 to +75 dBZ in increments of 0.5 dBZ\n", + "# * For N0U the scale is -100 to +100 kts in increments of 1 kt\n", + "nexrad = {}\n", + "nexrad[\"N0Q\"] = {\n", + " 'id': 94, \n", + " 'unit':'dBZ', \n", + " 'name':'0.5 deg Base Reflectivity', \n", + " 'ctable': ['NWSStormClearReflectivity',-20., 0.5], \n", + " 'res': 1000.,\n", + " 'elev': '0.5'\n", + "}\n", + "nexrad[\"N0U\"] = {\n", + " 'id': 99, \n", + " 'unit':'kts', \n", + " 'name':'0.5 deg Base Velocity', \n", + " 'ctable': ['NWS8bitVel',-100.,1.], \n", + " 'res': 250.,\n", + " 'elev': '0.5'\n", + "}\n", + "grids = []\n", + "for code in nexrad:\n", + " request.setProductCode(nexrad[code]['id'])\n", + " request.setPrimaryElevationAngle(nexrad[code]['elev'])\n", + " response = client.sendRequest(request)\n", + " \n", + " if response.getData():\n", + " for record in response.getData():\n", + " # Get record hdf5 data\n", + " idra = record.getHdf5Data()\n", + " rdat,azdat,depVals,threshVals = RadarCommon.get_hdf5_data(idra)\n", + " dim = rdat.getDimension()\n", + " lat,lon = float(record.getLatitude()),float(record.getLongitude())\n", + " radials,rangeGates = rdat.getSizes()\n", + " \n", + " # Convert raw byte to pixel value\n", + " rawValue=np.array(rdat.getByteData())\n", + " array = []\n", + " for rec in rawValue:\n", + " if rec<0:\n", + " rec+=256\n", + " array.append(rec)\n", + " \n", + " if azdat:\n", + " azVals = azdat.getFloatData()\n", + " az = np.array(RadarCommon.encode_radial(azVals))\n", + " dattyp = RadarCommon.get_data_type(azdat)\n", + " az = np.append(az,az[-1])\n", + "\n", + " header = RadarCommon.get_header(record, format, rangeGates, radials, azdat, 'description')\n", + " rng = np.linspace(0, rangeGates, rangeGates + 1)\n", + "\n", + " # Convert az/range to a lat/lon\n", + " from pyproj import Geod\n", + " g = Geod(ellps='clrk66')\n", + " center_lat = np.ones([len(az),len(rng)])*lat \n", + " center_lon = np.ones([len(az),len(rng)])*lon\n", + " az2D = np.ones_like(center_lat)*az[:,None]\n", + " rng2D = np.ones_like(center_lat)*np.transpose(rng[:,None])*nexrad[code]['res']\n", + " lons,lats,back=g.fwd(center_lon,center_lat,az2D,rng2D)\n", + " bbox = [lons.min(), lons.max(), lats.min(), lats.max()]\n", + " \n", + " # Create 2d array\n", + " multiArray = np.reshape(array, (-1, rangeGates))\n", + " data = ma.array(multiArray)\n", + " \n", + " # threshVals[0:2] contains halfwords 31,32,33 (min value, increment, num levels)\n", + " data = ma.array(threshVals[0]/10. + (multiArray)*threshVals[1]/10.)\n", + " \n", + " if nexrad[code]['unit'] == 'kts':\n", + " data[data<-63] = ma.masked\n", + " data *= 1.94384 # Convert to knots\n", + " else:\n", + " data[data<=((threshVals[0]/10.)+threshVals[1]/10.)] = ma.masked\n", + " \n", + " # Save our requested grids so we can render them multiple times\n", + " product = {\n", + " \"code\": code,\n", + " \"bbox\": bbox,\n", + " \"lats\": lats,\n", + " \"lons\": lons,\n", + " \"data\": data\n", + " }\n", + " grids.append(product)\n", + " \n", + "print(\"Processed \"+str(len(grids))+\" grids.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot N0Q and N0U with Cartopy" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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rb9OnDJD1dk95Y6+MfdOP7rceMz3KAPbFKZ7zHH4DdnWGq/Jl3B2QEJtPxmRp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\n8OVnA0aVOqmfak6UDYnYolVVWc/PRktKwbdvq9g/LkmQaW8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J6iekNpda0b+JQEuMFJ7S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1B/87VWvvMW8wU0f0sbr+TzrprcYYk0QG0ySUZrXsnnqVSKwxMJKhOF1ZZVZUsYl5vIkNwo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XWZx64zOyY6EqqN3V19i7mwlNHk9rwzuMq/sc4BTU95481XERlU1l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\nv4nZug6D2n0Mz79MZVMntyYwDhDMWqaAnMDPWctfAWEjGRl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9t8r+vuK8e17n48umkmrM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\nPOW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rm5j5/PvHXZU+Xjhtmg3/HZE58k+P02VdLCYJi0nCFwgrHtz2MGPey8qgIIC7NdxG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iImQh1T39Klz1Yr0Ol2mT01fvSyRHF+Ks3tYSYUWNlW5YuT8wjpFhHQIrV6wWXDEJ+qjQYj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kMK/RQ55NeYlJvHMyL5s67f5dxgPipmjZzvv08abvtWm7V5RZygLwtYK71PI+NqnlXlum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\nZieHDiBPsQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for rec in grids:\n", + " code = rec[\"code\"]\n", + " bbox = rec[\"bbox\"]\n", + " lats = rec[\"lats\"]\n", + " lons = rec[\"lons\"]\n", + " data = rec[\"data\"]\n", + " # Create figure\n", + " %matplotlib inline\n", + " fig, ax = make_map(bbox=bbox)\n", + " # Colortable filename, beginning value, increment\n", + " ctable = nexrad[code]['ctable'][0]\n", + " beg = nexrad[code]['ctable'][1]\n", + " inc = nexrad[code]['ctable'][2]\n", + "\n", + " norm, cmap = ctables.registry.get_with_steps(ctable, beg, inc)\n", + " cs = ax.pcolormesh(lons, lats, data, norm=norm, cmap=cmap)\n", + " ax.set_aspect('equal', 'datalim')\n", + "\n", + " cbar = plt.colorbar(cs, extend='both', shrink=0.75, orientation='horizontal')\n", + " cbar.set_label(site.upper()+\" \"+ str(nexrad[code]['res']/1000.) +\"km \" \\\n", + " +nexrad[code]['name']+\" (\"+code+\") \" \\\n", + " +nexrad[code]['unit']+\" \" \\\n", + " +str(record.getDataTime()))\n", + "\n", + " # Zoom to within +-2 deg of center\n", + " ax.set_xlim(lon-2., lon+2.)\n", + " ax.set_ylim(lat-2., lat+2.)\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "compare with the same product scan rendered in AWIPS CAVE (slightly different projections and still some color mapping differences, most noticeable in ground clutter).\n", + "\n", + "![](http://i.imgur.com/q7zPRod.gif)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Two-panel plot, zoomed in" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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8xRPv7Mfs+fLznxXZcgndYCcIAaBqqvY32uFvHqGf8BcqxEqlfjuEjPeFUwgAbZsexcct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UAAAg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mkN+p8JzlNSUXXwvP1SCTeiuaVIaes8ohU75Peb6qzJGjnD1qlC7hxC/NwGsyD2YqE2cywylJ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\n3t9tmWzw85YXFHpBXxD9874F1t+FAj7SKden0AsKubx6YUklgcI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H1wc+7m98/HOB9oPc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g0D8xfwn1jWRXy7bYxbduQcPtppYESXOomycFKZozjj/U3oPJCEQqd7ncVcWFCsPcJZZV8E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\n+PTgMwBI+GBST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JjqS+Lzt7+CiuYE+t7YhBdvVv+9doWuupF8blsaSBhnV3+aA0OqTV3j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Dvn5ot+LXSDn82uilDcPg0re8HXCrgtrGOZ5SLqSOYBEMNgqE2f0aNU4ngPEl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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(ncols=2,figsize=(12,9),\n", + " subplot_kw=dict(projection=ccrs.PlateCarree()))\n", + "i=0\n", + "for rec,ax in zip(grids, axes):\n", + " \n", + " code = rec[\"code\"]\n", + " bbox = rec[\"bbox\"]\n", + " lats = rec[\"lats\"]\n", + " lons = rec[\"lons\"]\n", + " data = rec[\"data\"]\n", + " \n", + " # Create figure\n", + " ax.set_extent(bbox)\n", + " ax.coastlines(resolution='50m')\n", + " gl = ax.gridlines(draw_labels=True)\n", + " gl.xlabels_top = gl.ylabels_right = False\n", + " if i>0: gl.ylabels_left = False # hide right-pane left axis label\n", + " gl.xformatter = LONGITUDE_FORMATTER\n", + " gl.yformatter = LATITUDE_FORMATTER\n", + "\n", + " # Colortable filename, beginning value, increment\n", + " colorvals=nexrad[code]['ctable']\n", + " ctable = nexrad[code]['ctable'][0]\n", + " beg = nexrad[code]['ctable'][1]\n", + " inc = nexrad[code]['ctable'][2]\n", + "\n", + " norm, cmap = ctables.registry.get_with_steps(ctable, beg, inc)\n", + " cs = ax.pcolormesh(lons, lats, data, norm=norm, cmap=cmap)\n", + " ax.set_aspect('equal', 'datalim')\n", + " cbar = fig.colorbar(cs, orientation='horizontal', ax=ax)\n", + " cbar.set_label(site.upper()+\" \"+code+\" \"+nexrad[code]['unit']+\" \"+str(record.getDataTime()))\n", + " plt.tight_layout()\n", + " \n", + " # Zoom\n", + " ax.set_xlim(lon-.1, lon+.1)\n", + " ax.set_ylim(lat-.1, lat+.1)\n", + " i+=1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and again compared to CAVE\n", + "\n", + "![](http://i.imgur.com/YSr7sKB.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.14" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/notebooks/Plotting_a_Sounding_with_MetPy.ipynb b/examples/notebooks/Plotting_a_Sounding_with_MetPy.ipynb new file mode 100644 index 0000000..165fa72 --- /dev/null +++ b/examples/notebooks/Plotting_a_Sounding_with_MetPy.ipynb @@ -0,0 +1,175 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "83900.0 1.5\n", + "100000.0 -9999998.0\n", + "92500.0 -9999998.0\n", + "85000.0 -9999998.0\n", + "70000.0 0.5\n", + "50000.0 6.09999990463\n", + "40000.0 3.0\n", + "30000.0 7.69999980927\n", + "25000.0 16.8999996185\n", + "20000.0 7.19999980927\n", + "15000.0 10.1999998093\n", + "10000.0 13.8000001907\n", + "7000.0 9.19999980927\n", + "5000.0 7.69999980927\n", + "3000.0 5.59999990463\n", + "2000.0 6.59999990463\n", + "1000.0 10.8000001907\n", + "700.0 5.09999990463\n", + "500.0 -9999.0\n", + "300.0 -9999.0\n", + "200.0 -9999.0\n", + "100.0 -9999.0\n" + ] + }, + { + "data": { + "image/png": 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HnSMdhyhsaW47fv94bxhrViJ1xAjpOERE1Aac6UFERGGJTWy/Y9Vis+tLdI8d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\nQGpqqkXN5fIwoooeAPDYb8/Cr6USPrt2CDk/IYSQ+rO0ogcNb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4uOoxf5+fnYsmULNm3a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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "from awips.dataaccess import DataAccessLayer\n", + "\n", + "import matplotlib.tri as mtri\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n", + "import numpy as np\n", + "\n", + "from metpy.calc import get_wind_components, lcl, dry_lapse, parcel_profile\n", + "from metpy.plots import SkewT, Hodograph\n", + "from metpy.units import units, concatenate\n", + "\n", + "plt.rcParams['figure.figsize'] = (12, 14)\n", + "\n", + "# Set EDEX host\n", + "DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n", + "request = DataAccessLayer.newDataRequest()\n", + "\n", + "# Data type bufrua\n", + "request.setDatatype(\"bufrua\")\n", + "# Parameters\n", + "request.setParameters(\"tpMan\",\"tdMan\",\"prMan\",\"htMan\",\"wdMan\",\"wsMan\")\n", + "# Station ID (name doesn't work yet)\n", + "request.setLocationNames(\"72469\")\n", + "datatimes = DataAccessLayer.getAvailableTimes(request)\n", + "\n", + "# Get most recent record\n", + "response = DataAccessLayer.getGeometryData(request,times=datatimes[-1].validPeriod)\n", + "\n", + "# Initialize data arrays\n", + "tpMan,tdMan,prMan,htMan,wdMan,wsMan = [],[],[],[],[],[]\n", + "\n", + "# Build ordered arrays\n", + "for ob in response:\n", + " print float(ob.getString(\"prMan\")), float(ob.getString(\"wsMan\"))\n", + " tpMan.append(float(ob.getString(\"tpMan\")))\n", + " tdMan.append(float(ob.getString(\"tdMan\")))\n", + " prMan.append(float(ob.getString(\"prMan\")))\n", + " htMan.append(float(ob.getString(\"htMan\")))\n", + " wdMan.append(float(ob.getString(\"wdMan\")))\n", + " wsMan.append(float(ob.getString(\"wsMan\")))\n", + "\n", + "# we can use units.* here...\n", + "T = np.array(tpMan)-273.15\n", + "Td = np.array(tdMan)-273.15\n", + "p = np.array(prMan)/100\n", + "height = np.array(htMan)\n", + "direc = np.array(wdMan)\n", + "spd = np.array(wsMan)\n", + "u, v = get_wind_components(spd, np.deg2rad(direc))\n", + "\n", + "p = p * units.mbar\n", + "T = T * units.degC\n", + "Td = Td * units.degC\n", + "spd = spd * units.knot\n", + "direc = direc * units.deg\n", + "\n", + "# Create a skewT plot\n", + "skew = SkewT()\n", + "\n", + "# Plot the data using normal plotting functions, in this case using\n", + "# log scaling in Y, as dictated by the typical meteorological plot\n", + "skew.plot(p, T, 'r')\n", + "skew.plot(p, Td, 'g')\n", + "skew.plot_barbs(p, u, v)\n", + "skew.ax.set_ylim(1000, 100)\n", + "skew.ax.set_xlim(-40, 60)\n", + "\n", + "# Calculate LCL height and plot as black dot\n", + "l = lcl(p[0], T[0], Td[0])\n", + "lcl_temp = dry_lapse(concatenate((p[0], l)), T[0])[-1].to('degC')\n", + "skew.plot(l, lcl_temp, 'ko', markerfacecolor='black')\n", + "\n", + "# Calculate full parcel profile and add to plot as black line\n", + "prof = parcel_profile(p, T[0], Td[0]).to('degC')\n", + "skew.plot(p, prof, 'k', linewidth=2)\n", + "\n", + "# Example of coloring area between profiles\n", + "skew.ax.fill_betweenx(p, T, prof, where=T>=prof, facecolor='blue', alpha=0.4)\n", + "skew.ax.fill_betweenx(p, T, prof, where=T" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from awips.dataaccess import DataAccessLayer\n", + "from awips.tables import profiler\n", + "import matplotlib.tri as mtri\n", + "from datetime import datetime, timedelta\n", + "from matplotlib.dates import date2num\n", + "from metpy.units import units\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Query ESRL/PSD profiler data from Unidata AWIPS\n", + "DataAccessLayer.changeEDEXHost(\"js-157-49.jetstream-cloud.org\")\n", + "request = DataAccessLayer.newDataRequest()\n", + "request.setDatatype(\"profiler\")\n", + "profilerSites = DataAccessLayer.getAvailableLocationNames(request)\n", + "\n", + "%matplotlib inline\n", + "fig = plt.figure(figsize=(16,7*len(profilerSites)))\n", + " \n", + "for i, site in enumerate(profilerSites):\n", + " request = DataAccessLayer.newDataRequest()\n", + " request.setDatatype(\"profiler\")\n", + " request.setLocationNames(site)\n", + " request.setParameters(\"uComponent\",\"vComponent\")\n", + "\n", + " # Request the last twelve hourly obs\n", + " hrs=12\n", + " requestTimes = DataAccessLayer.getAvailableTimes(request)[-1*hrs:]\n", + " response = DataAccessLayer.getGeometryData(request,requestTimes)\n", + "\n", + " # Build arrays \n", + " u,v,times=[],[],[]\n", + " for time in requestTimes:\n", + " uu,vv,heights=[],[],[]\n", + " for ob in response:\n", + " if str(ob.getDataTime().getValidPeriod().start) == str(time):\n", + " uu.append(float(ob.getString(\"uComponent\")))\n", + " vv.append(float(ob.getString(\"vComponent\")))\n", + " heights.append(float(ob.getLevel().translate(None, 'FHAG')))\n", + " u.append(uu)\n", + " v.append(vv)\n", + " times.append(time.validPeriod.start)\n", + "\n", + " # Convert u,v components to knots and transpose arrays to match t,h\n", + " u = (np.asarray(u, dtype=np.float32) * units('m/s')).to('knots').T\n", + " v = (np.asarray(v, dtype=np.float32) * units('m/s')).to('knots').T\n", + " t, h = np.meshgrid(times, heights)\n", + " C = np.sqrt(u**2 + v**2)\n", + " cmap=plt.cm.RdYlGn_r\n", + " \n", + " profilerName=str(profiler[site]['profilerName'])\n", + " profilerId=str(profiler[site]['profilerId'])\n", + " profilerLat=str(profiler[site]['latitude'])\n", + " profilerLng=str(profiler[site]['longitude'])\n", + " imgTitle=site +\" \"+ profilerId +\", \"+ profilerName +\" (\"+ profilerLat +\",\"+ profilerLng +\")\"\n", + " \n", + " ax = fig.add_subplot(len(profilerSites),1,i+1)\n", + " ax.title.set_text(imgTitle)\n", + " ax.barbs(date2num(t), h, u, v, C, cmap=cmap)\n", + " ax.xaxis_date()\n", + " ax.set_xlim(times[0]-timedelta(hours=1), times[-1]+timedelta(hours=1))\n", + " ax.set_ylim(0,10000)\n", + " ax.grid(axis='x', which='major', alpha=0.5)\n", + " ax.grid(axis='y', which='major', linestyle=':')\n", + " plt.gca().invert_xaxis()\n", + "\n", + "fig.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.14" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/notebooks/Satellite_Imagery.ipynb b/examples/notebooks/Satellite_Imagery.ipynb new file mode 100644 index 0000000..ba2bc88 --- /dev/null +++ b/examples/notebooks/Satellite_Imagery.ipynb @@ -0,0 +1,390 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Satellite images are returned by Python AWIPS as grids, and can be rendered with Cartopy pcolormesh the same as gridded forecast models in other python-awips examples. \n", + "\n", + "### Available Sectors and Products" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AREA7201\n", + " - Unknown-1\n", + "Arctic\n", + " - Unknown-2\n", + " - Unknown-4\n", + " - Unknown-5\n", + "ECONUS\n", + " - ACTP\n", + " - ADP\n", + " - AOD\n", + " - CAPE\n", + " - CH-01-0.47um\n", + " - CH-02-0.64um\n", + " - CH-03-0.87um\n", + " - CH-04-1.38um\n", + " - CH-05-1.61um\n", + " - CH-06-2.25um\n", + " - CH-07-3.90um\n", + " - CH-08-6.19um\n", + " - CH-09-6.95um\n", + " - CH-10-7.34um\n", + " - CH-11-8.50um\n", + " - CH-12-9.61um\n", + " - CH-13-10.35um\n", + " - CH-14-11.20um\n", + " - CH-15-12.30um\n", + " - CH-16-13.30um\n", + " - CSM\n", + " - CTH\n", + " - FDC Area\n", + " - FDC Power\n", + " - FDC Temp\n", + " - KI\n", + " - LI\n", + " - LST\n", + " - SI\n", + " - TPW\n", + " - TT\n", + "EFD\n", + " - ACTP\n", + " - ADP\n", + " - AOD\n", + " - CAPE\n", + " - CH-01-0.47um\n", + " - CH-02-0.64um\n", + " - CH-03-0.87um\n", + " - CH-04-1.38um\n", + " - CH-05-1.61um\n", + " - CH-06-2.25um\n", + " - CH-07-3.90um\n", + " - CH-08-6.19um\n", + " - CH-09-6.95um\n", + " - CH-10-7.34um\n", + " - CH-11-8.50um\n", + " - CH-12-9.61um\n", + " - CH-13-10.35um\n", + " - CH-14-11.20um\n", + " - CH-15-12.30um\n", + " - CH-16-13.30um\n", + " - CSM\n", + " - CTH\n", + " - CTT\n", + " - FDC Area\n", + " - FDC Power\n", + " - FDC Temp\n", + " - KI\n", + " - LI\n", + " - LST\n", + " - RRQPE\n", + " - SI\n", + " - SST\n", + " - TPW\n", + " - TT\n", + " - VAH\n", + " - VAML\n", + "EMESO-1\n", + " - ACTP\n", + " - ADP\n", + " - CAPE\n", + " - CH-01-0.47um\n", + " - CH-02-0.64um\n", + " - CH-03-0.87um\n", + " - CH-04-1.38um\n", + " - CH-05-1.61um\n", + " - CH-06-2.25um\n", + " - CH-07-3.90um\n", + " - CH-08-6.19um\n", + " - CH-09-6.95um\n", + " - CH-10-7.34um\n", + " - CH-11-8.50um\n", + " - CH-12-9.61um\n", + " - CH-13-10.35um\n", + " - CH-14-11.20um\n", + " - CH-15-12.30um\n", + " - CH-16-13.30um\n", + " - CSM\n", + " - CTH\n", + " - CTT\n", + " - KI\n", + " - LI\n", + " - LST\n", + " - SI\n", + " - TPW\n", + " - TT\n", + "EMESO-2\n", + " - ACTP\n", + " - ADP\n", + " - CAPE\n", + " - CH-01-0.47um\n", + " - CH-02-0.64um\n", + " - CH-03-0.87um\n", + " - CH-04-1.38um\n", + " - CH-05-1.61um\n", + " - CH-06-2.25um\n", + " - CH-07-3.90um\n", + " - CH-08-6.19um\n", + " - CH-09-6.95um\n", + " - CH-10-7.34um\n", + " - CH-11-8.50um\n", + " - CH-12-9.61um\n", + " - CH-13-10.35um\n", + " - CH-14-11.20um\n", + " - CH-15-12.30um\n", + " - CH-16-13.30um\n", + " - CSM\n", + " - CTH\n", + " - CTT\n", + " - KI\n", + " - LI\n", + " - LST\n", + " - SI\n", + " - TPW\n", + " - TT\n", + "East CONUS\n", + " - Low cloud base imagery\n", + "GOES-East\n", + " - Imager 11 micron IR\n", + " - Imager 13 micron IR\n", + " - Imager 3.5-4.0 micron IR (Fog)\n", + " - Imager 6.7-6.5 micron IR (WV)\n", + " - Imager Visible\n", + "GOES-West\n", + " - Imager 11 micron IR\n", + " - Imager 13 micron IR\n", + " - Imager 3.5-4.0 micron IR (Fog)\n", + " - Imager 6.7-6.5 micron IR (WV)\n", + " - Imager Visible\n", + "NEXRCOMP\n", + " - DHR\n", + " - DVL\n", + " - EET\n", + " - HHC\n", + " - N0R\n", + " - N1P\n", + " - NTP\n", + "NH Composite - Meteosat-GOES E-GOES W-GMS\n", + " - Imager 11 micron IR\n", + " - Imager 6.7-6.5 micron IR (WV)\n", + " - Imager Visible\n", + "Northern Hemisphere Composite\n", + " - Imager 11 micron IR\n", + " - Imager 6.7-6.5 micron IR (WV)\n", + " - Imager Visible\n", + "PRREGI\n", + " - CH-01-0.47um\n", + " - CH-02-0.64um\n", + " - CH-03-0.87um\n", + " - CH-04-1.38um\n", + " - CH-05-1.61um\n", + " - CH-06-2.25um\n", + " - CH-07-3.90um\n", + " - CH-08-6.19um\n", + " - CH-09-6.95um\n", + " - CH-10-7.34um\n", + " - CH-11-8.50um\n", + " - CH-12-9.61um\n", + " - CH-13-10.35um\n", + " - CH-14-11.20um\n", + " - CH-15-12.30um\n", + " - CH-16-13.30um\n", + "Supernational\n", + " - Gridded Cloud Amount\n", + " - Gridded Cloud Top Pressure or Height\n", + " - Imager 11 micron IR\n", + " - Imager 6.7-6.5 micron IR (WV)\n", + " - Imager Visible\n", + " - Percent of Normal TPW\n", + " - Rain fall rate\n", + " - Sounder Based Derived Lifted Index (LI)\n", + " - Sounder Based Derived Precipitable Water (PW)\n", + " - Sounder Based Derived Surface Skin Temp (SFC Skin)\n", + "West CONUS\n", + " - Imager 11 micron IR\n", + " - Imager 13 micron (IR)\n", + " - Imager 13 micron IR\n", + " - Imager 3.9 micron IR\n", + " - Imager 6.7-6.5 micron IR (WV)\n", + " - Imager Visible\n", + " - Low cloud base imagery\n", + " - Sounder 11.03 micron imagery\n", + " - Sounder 14.06 micron imagery\n", + " - Sounder 3.98 micron imagery\n", + " - Sounder 4.45 micron imagery\n", + " - Sounder 6.51 micron imagery\n", + " - Sounder 7.02 micron imagery\n", + " - Sounder 7.43 micron imagery\n", + " - Sounder Visible imagery\n" + ] + } + ], + "source": [ + "from awips.dataaccess import DataAccessLayer\n", + "\n", + "DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n", + "request = DataAccessLayer.newDataRequest()\n", + "request.setDatatype(\"satellite\")\n", + "\n", + "availableSectors = DataAccessLayer.getAvailableLocationNames(request)\n", + "availableSectors.sort()\n", + "\n", + "for sector in availableSectors:\n", + " print sector\n", + " request.setLocationNames(sector)\n", + " availableProducts = DataAccessLayer.getAvailableParameters(request)\n", + " availableProducts.sort()\n", + " for product in availableProducts:\n", + " print \" - \" + product" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### GOES 16 Mesoscale Sectors Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Latest image available: 2018-02-11 20:04:28 (44m ago)\n", + "Image grid size: (727, 513)\n", + "Image grid extent: [-81.83214, -68.16786, 32.143394, 45.88625]\n", + "Latest image available: 2018-02-11 20:04:58 (44m ago)\n", + "Image grid size: (703, 606)\n", + "Image grid extent: [-95.21105, -78.94757, 30.756178, 44.556156]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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58jcAuvGDMYgGZTQapW2JxPEBBRsbG8mzJ9+GPeURePJS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jXFDVz87fLuBjqCCn9Xzig6+xKNr9fHAYOv8We8/Fl89ygetLczUupezxhCcPo5skMA8wgKcl55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\nPaR+AATYL38fIB49O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re2tnT79m29/fbbeuqpp1JOgnQmj74SCNllXmJEvY65kJj3bQSR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gZh1GNoJ3Rbcha9RZCGQKgW0evByj9wFAAHMHnw4eiZshbsHryRr2NRbb6v2fY5Z8/jjQdXBI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xXwpvw5nUJ09sSNn7M5aeKXzzsKbn19XYeHhxqNRlpcXFS9Xtfi4mLkH7iSSnNVnPZ3wJll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\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CnmlIJBKq1WpmNZLJkEjs1MEfHBzU+fPndeTIEWMffPaFLz4DkEJxbG9v60//9E/V09Oj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\nQurc11bns/yM9e6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I9BYLmRWePfHKExamVCpJkj73uc/prrvuamMR4jjWI488oj/5kz/R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\nG1XyQTQ0ej3wzqhyhNGVjwPIx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5sEwCcNZffa13211cXDQOo+Q7xqrqalcby5VrRn2xvb2dtpWgT7FEPKOJ6fS7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yt+7ud+LrrdbrRareIuOjw8jEajEYeHh8U1RB8DIg4ODuLg4KCAHactWF1djW63W9rghIye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\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vf12vfe1rx3aDSQdAhaNjiCPD6MzPz6fnUT73EQy7vr6e2Ib9/X3V63WVy2Vtb2/rhRde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\nIYQQDfj/p7HNWolzFjUAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeat\n", + "import matplotlib.pyplot as plt\n", + "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n", + "import numpy as np\n", + "import datetime\n", + "\n", + "def make_map(bbox, projection=ccrs.PlateCarree()):\n", + " fig, ax = plt.subplots(figsize=(10,12),\n", + " subplot_kw=dict(projection=projection))\n", + " ax.set_extent(bbox)\n", + " ax.coastlines(resolution='50m')\n", + " gl = ax.gridlines(draw_labels=True)\n", + " gl.xlabels_top = gl.ylabels_right = False\n", + " gl.xformatter = LONGITUDE_FORMATTER\n", + " gl.yformatter = LATITUDE_FORMATTER\n", + " return fig, ax\n", + "\n", + "sectors = [\"EMESO-1\",\"EMESO-2\"]\n", + "fig = plt.figure(figsize=(16,7*len(sectors)))\n", + "\n", + "for i, sector in enumerate(sectors):\n", + "\n", + " request = DataAccessLayer.newDataRequest()\n", + " request.setDatatype(\"satellite\")\n", + " request.setLocationNames(sector)\n", + " request.setParameters(\"CH-13-10.35um\")\n", + "\n", + " utc = datetime.datetime.utcnow()\n", + " times = DataAccessLayer.getAvailableTimes(request)\n", + " hourdiff = utc - datetime.datetime.strptime(str(times[-1]),'%Y-%m-%d %H:%M:%S')\n", + " hours,days = hourdiff.seconds/3600,hourdiff.days\n", + " minute = str((hourdiff.seconds - (3600 * hours)) / 60)\n", + " offsetStr = ''\n", + " if hours > 0:\n", + " offsetStr += str(hours) + \"hr \"\n", + " offsetStr += str(minute) + \"m ago\"\n", + " if days > 1:\n", + " offsetStr = str(days) + \" days ago\"\n", + "\n", + " response = DataAccessLayer.getGridData(request, [times[-1]])\n", + " grid = response[0]\n", + " data = grid.getRawData()\n", + " lons,lats = grid.getLatLonCoords()\n", + " bbox = [lons.min(), lons.max(), lats.min(), lats.max()]\n", + "\n", + " print(\"Latest image available: \"+str(times[-1]) + \" (\"+offsetStr+\")\")\n", + " print(\"Image grid size: \" + str(data.shape))\n", + " print(\"Image grid extent: \" + str(list(bbox)))\n", + " \n", + " fig, ax = make_map(bbox=bbox)\n", + " states = cfeat.NaturalEarthFeature(category='cultural',\n", + " name='admin_1_states_provinces_lines',\n", + " scale='50m', facecolor='none')\n", + " ax.add_feature(states, linestyle=':')\n", + " cs = ax.pcolormesh(lons, lats, data, cmap='Greys_r')\n", + " cbar = fig.colorbar(cs, shrink=0.6, orientation='horizontal')\n", + " cbar.set_label(str(grid.getLocationName())+\" \" \\\n", + " +str(grid.getParameter())+\" \" \\\n", + " +str(grid.getDataTime().getRefTime()))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.14" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/notebooks/Surface_Obs_Plot_with_MetPy.ipynb b/examples/notebooks/Surface_Obs_Plot_with_MetPy.ipynb new file mode 100644 index 0000000..f3129c0 --- /dev/null +++ b/examples/notebooks/Surface_Obs_Plot_with_MetPy.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the MetPy example [\"Station Plot with Layout\"](http://metpy.readthedocs.org/en/latest/examples/generated/Station_Plot_with_Layout.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [ + "import datetime\n", + "import pandas\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pprint\n", + "from awips.dataaccess import DataAccessLayer\n", + "from metpy.calc import get_wind_components\n", + "from metpy.cbook import get_test_data\n", + "from metpy.plots.wx_symbols import sky_cover, current_weather\n", + "from metpy.plots import StationPlot, StationPlotLayout, simple_layout\n", + "from metpy.units import units\n", + "\n", + "def get_cloud_cover(code):\n", + " if 'OVC' in code:\n", + " return 1.0\n", + " elif 'BKN' in code:\n", + " return 6.0/8.0\n", + " elif 'SCT' in code:\n", + " return 4.0/8.0\n", + " elif 'FEW' in code:\n", + " return 2.0/8.0\n", + " else:\n", + " return 0\n", + "\n", + "state_capital_wx_stations = {'Washington':'KOLM', 'Oregon':'KSLE', 'California':'KSAC',\n", + " 'Nevada':'KCXP', 'Idaho':'KBOI', 'Montana':'KHLN',\n", + " 'Utah':'KSLC', 'Arizona':'KDVT', 'New Mexico':'KSAF',\n", + " 'Colorado':'KBKF', 'Wyoming':'KCYS', 'North Dakota':'KBIS',\n", + " 'South Dakota':'KPIR', 'Nebraska':'KLNK', 'Kansas':'KTOP',\n", + " 'Oklahoma':'KPWA', 'Texas':'KATT', 'Louisiana':'KBTR',\n", + " 'Arkansas':'KLIT', 'Missouri':'KJEF', 'Iowa':'KDSM',\n", + " 'Minnesota':'KSTP', 'Wisconsin':'KMSN', 'Illinois':'KSPI',\n", + " 'Mississippi':'KHKS', 'Alabama':'KMGM', 'Nashville':'KBNA',\n", + " 'Kentucky':'KFFT', 'Indiana':'KIND', 'Michigan':'KLAN',\n", + " 'Ohio':'KCMH', 'Georgia':'KFTY', 'Florida':'KTLH',\n", + " 'South Carolina':'KCUB', 'North Carolina':'KRDU',\n", + " 'Virginia':'KRIC', 'West Virginia':'KCRW',\n", + " 'Pennsylvania':'KCXY', 'New York':'KALB', 'Vermont':'KMPV',\n", + " 'New Hampshire':'KCON', 'Maine':'KAUG', 'Massachusetts':'KBOS',\n", + " 'Rhode Island':'KPVD', 'Connecticut':'KHFD', 'New Jersey':'KTTN',\n", + " 'Delaware':'KDOV', 'Maryland':'KNAK'}\n", + "single_value_params = [\"timeObs\", \"stationName\", \"longitude\", \"latitude\", \n", + " \"temperature\", \"dewpoint\", \"windDir\",\n", + " \"windSpeed\", \"seaLevelPress\"]\n", + "multi_value_params = [\"presWeather\", \"skyCover\", \"skyLayerBase\"]\n", + "all_params = single_value_params + multi_value_params\n", + "obs_dict = dict({all_params: [] for all_params in all_params})\n", + "pres_weather = []\n", + "sky_cov = []\n", + "sky_layer_base = []" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from dynamicserialize.dstypes.com.raytheon.uf.common.time import TimeRange\n", + "from datetime import datetime, timedelta\n", + "lastHourDateTime = datetime.utcnow() - timedelta(hours = 1)\n", + "start = lastHourDateTime.strftime('%Y-%m-%d %H')\n", + "beginRange = datetime.strptime( start + \":00:00\", \"%Y-%m-%d %H:%M:%S\")\n", + "endRange = datetime.strptime( start + \":59:59\", \"%Y-%m-%d %H:%M:%S\")\n", + "timerange = TimeRange(beginRange, endRange)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n", + "request = DataAccessLayer.newDataRequest()\n", + "request.setDatatype(\"obs\")\n", + "request.setParameters(*(all_params))\n", + "request.setLocationNames(*(state_capital_wx_stations.values()))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "response = DataAccessLayer.getGeometryData(request,timerange)\n", + "for ob in response:\n", + " avail_params = ob.getParameters()\n", + " if \"presWeather\" in avail_params:\n", + " pres_weather.append(ob.getString(\"presWeather\"))\n", + " elif \"skyCover\" in avail_params and \"skyLayerBase\" in avail_params:\n", + " sky_cov.append(ob.getString(\"skyCover\"))\n", + " sky_layer_base.append(ob.getNumber(\"skyLayerBase\"))\n", + " else:\n", + " for param in single_value_params:\n", + " if param in avail_params:\n", + " if param == 'timeObs':\n", + " obs_dict[param].append(datetime.fromtimestamp(ob.getNumber(param)/1000.0))\n", + " else:\n", + " try:\n", + " obs_dict[param].append(ob.getNumber(param))\n", + " except TypeError:\n", + " obs_dict[param].append(ob.getString(param))\n", + " else:\n", + " obs_dict[param].append(None)\n", + "\n", + " obs_dict['presWeather'].append(pres_weather);\n", + " obs_dict['skyCover'].append(sky_cov);\n", + " obs_dict['skyLayerBase'].append(sky_layer_base);\n", + " pres_weather = []\n", + " sky_cov = []\n", + " sky_layer_base = []" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now use pandas to retrieve desired subsets of our observations.\n", + "\n", + "In this case, return the most recent observation for each station." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "df = pandas.DataFrame(data=obs_dict, columns=all_params)\n", + "#sort rows with the newest first\n", + "df = df.sort_values(by='timeObs', ascending=False)\n", + "#group rows by station\n", + "groups = df.groupby('stationName')\n", + "#create a new DataFrame for the most recent values\n", + "df_recent = pandas.DataFrame(columns=all_params)\n", + "#retrieve the first entry for each group, which will\n", + "#be the most recent observation\n", + "for rid, station in groups:\n", + " row = station.head(1)\n", + " df_recent = pandas.concat([df_recent, row])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert DataFrame to something metpy-readable by \n", + "attaching units and calculating derived values" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "data = dict()\n", + "data['stid'] = np.array(df_recent[\"stationName\"])\n", + "data['latitude'] = np.array(df_recent['latitude'])\n", + "data['longitude'] = np.array(df_recent['longitude'])\n", + "data['air_temperature'] = np.array(df_recent['temperature'], dtype=float)* units.degC\n", + "data['dew_point'] = np.array(df_recent['dewpoint'], dtype=float)* units.degC\n", + "data['slp'] = np.array(df_recent['seaLevelPress'])* units('mbar')\n", + "u, v = get_wind_components(np.array(df_recent['windSpeed']) * units('knots'),\n", + " np.array(df_recent['windDir']) * units.degree)\n", + "data['eastward_wind'], data['northward_wind'] = u, v\n", + "data['cloud_frac'] = [int(get_cloud_cover(x)*8) for x in df_recent['skyCover']]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AezbLSezBkxnRyjQHJluW1StyhzEmFTfPKEnI/fMPUMYYY5z7MtJN921CWJa1H/gY+NhJ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\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Tk9ixY1WmiWPG44xJ5etERcVgZWWhKThmb2+LUqnk6tUALCzMNVHmcrmc+PjnODjY5cs+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Pj5fVJ1KMJPEhsby8CBo4mJkbN2rUuqjzt9+hI1alTO9aXdvnX37kOCg0PSdezbt+9/uNpo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NDZUdSrYTHR2Dvr6oIlFTU6NzpTYsOLmcyQfn4FO+JR/jIpnw90wSkhKQIycmIbm1XN1i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Vs3z3JcRMQHUlNTSU2VEh7+ikWLPFFXV8PBwQ6AwYN7s27dNqT/N9W7ZElTTp++yNmzlzJta5Nf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\n3YGEpFSh4xCRCqSlv2x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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as feat\n", + "from matplotlib import rcParams\n", + "rcParams['savefig.dpi'] = 100\n", + "proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=35,\n", + " standard_parallels=[35])\n", + "state_boundaries = feat.NaturalEarthFeature(category='cultural',\n", + " name='admin_1_states_provinces_lines',\n", + " scale='110m', facecolor='none')\n", + "# Create the figure\n", + "fig = plt.figure(figsize=(20, 15))\n", + "ax = fig.add_subplot(1, 1, 1, projection=proj)\n", + "\n", + "# Add map elements \n", + "ax.add_feature(feat.LAND, zorder=-1)\n", + "ax.add_feature(feat.OCEAN, zorder=-1)\n", + "ax.add_feature(feat.LAKES, zorder=-1)\n", + "ax.coastlines(resolution='110m', zorder=2, color='black')\n", + "ax.add_feature(state_boundaries)\n", + "ax.add_feature(feat.BORDERS, linewidth='2', edgecolor='black')\n", + "ax.set_extent((-120, -70, 20, 50))\n", + "\n", + "# Start the station plot by specifying the axes to draw on, as well as the\n", + "# lon/lat of the stations (with transform). We also set the fontsize to 12 pt.\n", + "stationplot = StationPlot(ax, data['longitude'], data['latitude'],\n", + " transform=ccrs.PlateCarree(), fontsize=12)\n", + "\n", + "# The layout knows where everything should go, and things are standardized using\n", + "# the names of variables. So the layout pulls arrays out of `data` and plots them\n", + "# using `stationplot`.\n", + "simple_layout.plot(stationplot, data)\n", + "\n", + "# Plot the temperature and dew point to the upper and lower left, respectively, of\n", + "# the center point. Each one uses a different color.\n", + "stationplot.plot_parameter('NW', np.array(data['air_temperature']), color='red')\n", + "stationplot.plot_parameter('SW', np.array(data['dew_point']), color='darkgreen')\n", + "\n", + "# A more complex example uses a custom formatter to control how the sea-level pressure\n", + "# values are plotted. This uses the standard trailing 3-digits of the pressure value\n", + "# in tenths of millibars.\n", + "stationplot.plot_parameter('NE', np.array(data['slp']),\n", + " formatter=lambda v: format(10 * v, '.0f')[-3:])\n", + "\n", + "# Plot the cloud cover symbols in the center location. This uses the codes made above and\n", + "# uses the `sky_cover` mapper to convert these values to font codes for the\n", + "# weather symbol font.\n", + "stationplot.plot_symbol('C', data['cloud_frac'], sky_cover)\n", + "\n", + "# Also plot the actual text of the station id. Instead of cardinal directions,\n", + "# plot further out by specifying a location of 2 increments in x and 0 in y.\n", + "stationplot.plot_text((2, 0), np.array(obs_dict[\"stationName\"]))\n", + "\n", + "plt.title(\"Most Recent Observations for State Capitals\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/notebooks/Upper_Air_BUFR_Soundings.ipynb b/examples/notebooks/Upper_Air_BUFR_Soundings.ipynb new file mode 100644 index 0000000..8727fe4 --- /dev/null +++ b/examples/notebooks/Upper_Air_BUFR_Soundings.ipynb @@ -0,0 +1,179 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following script takes you through the steps of retrieving an Upper Air vertical profile from an AWIPS EDEX server and plotting a Skew-T/Log-P chart with Matplotlib and MetPy.\n", + "\n", + "The **bufrua** plugin returns separate objects for parameters at **mandatory levels** and at **significant temperature levels**. For the Skew-T/Log-P plot, significant temperature levels are used to plot the pressure, temperature, and dewpoint lines, while mandatory levels are used to plot the wind profile." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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4EDjQObcm7RjHA3cA7wT3nwPOiyD2yN1+++1xhxCL5uS9vnI9AIZRYiW5CikS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\nEeXL26VdIcRdjt4x9zA6ACGEcDRKqalAW6BZ6k55shAgAXgaSBmE2Aegd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G9wRDbi7g4/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87SnvnTt3Ur26eXyWMWPGcOLEiWwHT7UWUjEXQgg7U5iBpOxF5j7mqU3Z4+ISmT9/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\nVatWUVBQwJQpU7zLjx49yqWXXkrv3r3ZsWMHL7zwAn/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "from awips.dataaccess import DataAccessLayer\n", + "import matplotlib.tri as mtri\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n", + "import numpy as np\n", + "import math\n", + "from metpy.calc import get_wind_speed, get_wind_components, lcl, dry_lapse, parcel_profile\n", + "from metpy.plots import SkewT, Hodograph\n", + "from metpy.units import units, concatenate\n", + "\n", + "# Set host\n", + "DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n", + "request = DataAccessLayer.newDataRequest()\n", + "\n", + "# Set data type\n", + "request.setDatatype(\"bufrua\")\n", + "availableLocs = DataAccessLayer.getAvailableLocationNames(request)\n", + "availableLocs.sort()\n", + "\n", + "# Set Mandatory and Significant Temperature level parameters\n", + "MAN_PARAMS = set(['prMan', 'htMan', 'tpMan', 'tdMan', 'wdMan', 'wsMan'])\n", + "SIGT_PARAMS = set(['prSigT', 'tpSigT', 'tdSigT'])\n", + "request.setParameters(\"wmoStaNum\", \"validTime\", \"rptType\", \"staElev\", \"numMand\",\n", + " \"numSigT\", \"numSigW\", \"numTrop\", \"numMwnd\", \"staName\")\n", + "request.getParameters().extend(MAN_PARAMS)\n", + "request.getParameters().extend(SIGT_PARAMS)\n", + "\n", + "# Set station ID (not name)\n", + "request.setLocationNames(\"72562\") #KLBF\n", + "\n", + "# Get all times\n", + "datatimes = DataAccessLayer.getAvailableTimes(request)\n", + "\n", + "# Get most recent record\n", + "response = DataAccessLayer.getGeometryData(request,times=datatimes[-1].validPeriod)\n", + "\n", + "# Initialize data arrays\n", + "tdMan,tpMan,prMan,wdMan,wsMan = np.array([]),np.array([]),np.array([]),np.array([]),np.array([])\n", + "prSig,tpSig,tdSig = np.array([]),np.array([]),np.array([])\n", + "manGeos = []\n", + "sigtGeos = []\n", + "\n", + "# Build arrays\n", + "for ob in response:\n", + " if set(ob.getParameters()) & MAN_PARAMS:\n", + " manGeos.append(ob)\n", + " prMan = np.append(prMan,ob.getNumber(\"prMan\"))\n", + " tpMan = np.append(tpMan,ob.getNumber(\"tpMan\"))\n", + " tdMan = np.append(tdMan,ob.getNumber(\"tdMan\"))\n", + " wdMan = np.append(wdMan,ob.getNumber(\"wdMan\"))\n", + " wsMan = np.append(wsMan,ob.getNumber(\"wsMan\"))\n", + " continue\n", + " if set(ob.getParameters()) & SIGT_PARAMS:\n", + " sigtGeos.append(ob)\n", + " prSig = np.append(prSig,ob.getNumber(\"prSigT\"))\n", + " tpSig = np.append(tpSig,ob.getNumber(\"tpSigT\"))\n", + " tdSig = np.append(tdSig,ob.getNumber(\"tdSigT\"))\n", + " continue\n", + "\n", + "# Sort mandatory levels (but not sigT levels) because of the 1000.MB interpolation inclusion\n", + "ps = prMan.argsort()[::-1]\n", + "wpres = prMan[ps]\n", + "direc = wdMan[ps]\n", + "spd = wsMan[ps]\n", + "tman = tpMan[ps]\n", + "dman = tdMan[ps]\n", + "\n", + "# Flag missing data\n", + "prSig[prSig <= -9999] = np.nan\n", + "tpSig[tpSig <= -9999] = np.nan\n", + "tdSig[tdSig <= -9999] = np.nan\n", + "wpres[wpres <= -9999] = np.nan\n", + "tman[tman <= -9999] = np.nan\n", + "dman[dman <= -9999] = np.nan\n", + "direc[direc <= -9999] = np.nan\n", + "spd[spd <= -9999] = np.nan\n", + "\n", + "# assign units\n", + "p = (prSig/100) * units.mbar\n", + "T = (tpSig-273.15) * units.degC\n", + "Td = (tdSig-273.15) * units.degC\n", + "wpres = (wpres/100) * units.mbar\n", + "tman = tman * units.degC\n", + "dman = dman * units.degC\n", + "u,v = get_wind_components(spd, np.deg2rad(direc))\n", + "\n", + "# Create SkewT/LogP\n", + "plt.rcParams['figure.figsize'] = (8, 10)\n", + "skew = SkewT()\n", + "skew.plot(p, T, 'r', linewidth=2)\n", + "skew.plot(p, Td, 'g', linewidth=2)\n", + "skew.plot_barbs(wpres, u, v)\n", + "skew.ax.set_ylim(1000, 100)\n", + "skew.ax.set_xlim(-30, 30)\n", + "\n", + "title_string = \" T(F) Td \" \n", + "title_string += \" \" + str(ob.getString(\"staName\"))\n", + "title_string += \" \" + str(ob.getDataTime().getRefTime())\n", + "title_string += \" (\" + str(ob.getNumber(\"staElev\")) + \"m elev)\"\n", + "title_string += \"\\n\" + str(round(T[0].to('degF').item(),1))\n", + "title_string += \" \" + str(round(Td[0].to('degF').item(),1))\n", + "plt.title(title_string, loc='left')\n", + "\n", + "# Calculate LCL height and plot as black dot\n", + "l = lcl(p[0], T[0], Td[0])\n", + "lcl_temp = dry_lapse(concatenate((p[0], l)), T[0])[-1].to('degC')\n", + "skew.plot(l, lcl_temp, 'ko', markerfacecolor='black')\n", + "\n", + "# Calculate full parcel profile and add to plot as black line\n", + "prof = parcel_profile(p, T[0], Td[0]).to('degC')\n", + "skew.plot(p, prof, 'k', linewidth=2)\n", + "\n", + "# An example of a slanted line at constant T -- in this case the 0 isotherm\n", + "l = skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)\n", + "\n", + "# Draw hodograph\n", + "ax_hod = inset_axes(skew.ax, '30%', '30%', loc=3)\n", + "h = Hodograph(ax_hod, component_range=max(wsMan))\n", + "h.add_grid(increment=20)\n", + "h.plot_colormapped(u, v, spd)\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/notebooks/Watch_and_Warning_Polygons.ipynb b/examples/notebooks/Watch_and_Warning_Polygons.ipynb new file mode 100644 index 0000000..d79d93e --- /dev/null +++ b/examples/notebooks/Watch_and_Warning_Polygons.ipynb @@ -0,0 +1,250 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example uses matplotlib, cartopy, shapely, and python-awips to plot watch and warning polygons requested from a real-time AWIPS EDEX server.\n", + "\n", + "First, set up our imports and define functions to be used later:" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "from awips.dataaccess import DataAccessLayer\n", + "from awips.tables import vtec\n", + "from datetime import datetime\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n", + "from cartopy.feature import ShapelyFeature,NaturalEarthFeature\n", + "from shapely.geometry import MultiPolygon,Polygon\n", + "\n", + "def warning_color(phensig):\n", + " return vtec[phensig]['color']\n", + "\n", + "def make_map(bbox, projection=ccrs.PlateCarree()):\n", + " fig, ax = plt.subplots(figsize=(20,12),\n", + " subplot_kw=dict(projection=projection))\n", + " ax.set_extent(bbox)\n", + " gl = ax.gridlines(draw_labels=True)\n", + " gl.xlabels_top = gl.ylabels_right = False\n", + " gl.xformatter = LONGITUDE_FORMATTER\n", + " gl.yformatter = LATITUDE_FORMATTER\n", + " return fig, ax" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we create a request for the \"warning\" data type:" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using 54 records\n" + ] + } + ], + "source": [ + "DataAccessLayer.changeEDEXHost('edex-cloud.unidata.ucar.edu')\n", + "request = DataAccessLayer.newDataRequest()\n", + "request.setDatatype(\"warning\")\n", + "request.setParameters('phensig')\n", + "times = DataAccessLayer.getAvailableTimes(request)\n", + "\n", + "# Get records for last 30 available times\n", + "response = DataAccessLayer.getGeometryData(request, times[-30:-1])\n", + "print(\"Using \" + str(len(response)) + \" records\")\n", + "\n", + "# Each record will have a numpy array the length of the number of \"parameters\"\n", + "# Default is 1 (request.setParameters('phensig'))\n", + "parameters = {}\n", + "for x in request.getParameters():\n", + " parameters[x] = np.array([])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now loop through each record and plot it as either Polygon or MultiPolygon, with appropriate colors" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Severe Thunderstorm Warning (SV.W) issued at 2018-06-18 21:31:00.000 (Polygon)\n", + "Severe Thunderstorm Warning (SV.W) issued at 2018-06-18 21:31:00.000 (Polygon)\n", + "Severe Thunderstorm Warning (SV.W) issued at 2018-06-18 21:32:00.000 (Polygon)\n", + "Severe Thunderstorm Watch (SV.A) issued at 2018-06-18 21:33:00.000 (Polygon)\n", + "Severe Thunderstorm Watch (SV.A) issued at 2018-06-18 21:33:00.000 (Polygon)\n", + "Severe Thunderstorm Watch (SV.A) issued at 2018-06-18 21:33:00.000 (Polygon)\n", + "Tornado Warning (TO.W) issued at 2018-06-18 21:34:00.000 (Polygon)\n", + "Severe Thunderstorm Warning (SV.W) issued at 2018-06-18 21:34:00.000 (Polygon)\n", + "Small Craft Advisory (SC.Y) issued at 2018-06-18 23:00:00.000 (Polygon)\n", + "Small Craft Advisory (SC.Y) issued at 2018-06-18 23:00:00.000 (Polygon)\n", + "Small Craft Advisory (SC.Y) issued at 2018-06-18 23:00:00.000 (Polygon)\n", + "Flash Flood Watch (FF.A) issued at 2018-06-19 00:00:00.000 (Polygon)\n", + "Flash Flood Watch (FF.A) issued at 2018-06-19 00:00:00.000 (Polygon)\n", + "Small Craft Advisory (SC.Y) issued at 2018-06-19 00:00:00.000 (MultiPolygon, 6 geometries)\n", + "Small Craft Advisory (SC.Y) issued at 2018-06-19 00:00:00.000 (Polygon)\n", + "Small Craft Advisory (SC.Y) issued at 2018-06-19 00:00:00.000 (MultiPolygon, 6 geometries)\n", + "Small Craft Advisory for hazardous seas (SW.Y) issued at 2018-06-19 00:00:00.000 (Polygon)\n", + "Small Craft Advisory (SC.Y) issued at 2018-06-19 00:00:00.000 (Polygon)\n", + "Small Craft Advisory (SC.Y) issued at 2018-06-19 00:00:00.000 (MultiPolygon, 6 geometries)\n", + "Flood Watch (FL.A) issued at 2018-06-19 00:00:00.000 (Polygon)\n", + "Small Craft Advisory (SC.Y) issued at 2018-06-19 01:00:00.000 (Polygon)\n", + "Gale Watch (GL.A) issued at 2018-06-19 01:00:00.000 (MultiPolygon, 2 geometries)\n", + "Gale Watch (GL.A) issued at 2018-06-19 01:00:00.000 (MultiPolygon, 2 geometries)\n", + "Gale Watch (GL.A) issued at 2018-06-19 01:00:00.000 (MultiPolygon, 2 geometries)\n", + "Gale Watch (GL.A) issued at 2018-06-19 01:00:00.000 (MultiPolygon, 2 geometries)\n", + "Flood Warning (FL.W) issued at 2018-06-19 04:40:00.000 (Polygon)\n", + "Flood Warning (FL.W) issued at 2018-06-19 06:00:00.000 (Polygon)\n", + "Flood Warning (FL.W) issued at 2018-06-19 07:30:00.000 (Polygon)\n", + "Flash Flood Watch (FF.A) issued at 2018-06-19 12:00:00.000 (Polygon)\n", + "Flood Warning (FL.W) issued at 2018-06-19 12:00:00.000 (Polygon)\n", + "Heat Advisory (HT.Y) issued at 2018-06-19 15:00:00.000 (Polygon)\n", + "Heat Advisory (HT.Y) issued at 2018-06-19 15:00:00.000 (MultiPolygon, 609 geometries)\n", + "Heat Advisory (HT.Y) issued at 2018-06-19 15:00:00.000 (MultiPolygon, 33 geometries)\n", + "Flood Watch (FL.A) issued at 2018-06-19 16:00:00.000 (Polygon)\n", + "Flood Warning (FL.W) issued at 2018-06-19 16:00:00.000 (Polygon)\n", + "Flood Warning (FL.W) issued at 2018-06-19 16:00:00.000 (Polygon)\n", + "Flood Warning (FL.W) issued at 2018-06-19 16:15:00.000 (Polygon)\n", + "Flash Flood Watch (FF.A) issued at 2018-06-19 18:00:00.000 (Polygon)\n", + "Flash Flood Watch (FF.A) issued at 2018-06-19 18:00:00.000 (Polygon)\n", + "Flash Flood Watch (FF.A) issued at 2018-06-19 18:00:00.000 (Polygon)\n", + "Flash Flood Watch (FF.A) issued at 2018-06-19 18:00:00.000 (Polygon)\n", + "Flash Flood Watch (FF.A) issued at 2018-06-19 18:00:00.000 (Polygon)\n", + "Small Craft Advisory (SC.Y) issued at 2018-06-19 21:00:00.000 (Polygon)\n", + "Small Craft Advisory (SC.Y) issued at 2018-06-19 21:00:00.000 (Polygon)\n", + "Small Craft Advisory (SC.Y) issued at 2018-06-19 21:00:00.000 (Polygon)\n", + "Small Craft Advisory (SC.Y) issued at 2018-06-19 21:00:00.000 (Polygon)\n", + "Small Craft Advisory (SC.Y) issued at 2018-06-19 21:00:00.000 (Polygon)\n", + "Small Craft Advisory (SC.Y) issued at 2018-06-19 21:00:00.000 (Polygon)\n", + "Flash Flood Watch (FF.A) issued at 2018-06-20 00:00:00.000 (Polygon)\n", + "Flood Warning (FL.W) issued at 2018-06-20 02:40:00.000 (Polygon)\n", + "Flood Watch (FL.A) issued at 2018-06-20 18:00:00.000 (Polygon)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "bbox=[-127,-64,24,49]\n", + "fig, ax = make_map(bbox=bbox)\n", + "\n", + "siteids=np.array([])\n", + "periods=np.array([])\n", + "reftimes=np.array([])\n", + "\n", + "for ob in response:\n", + " \n", + " poly = ob.getGeometry()\n", + " site = ob.getLocationName()\n", + " pd = ob.getDataTime().getValidPeriod()\n", + " ref = ob.getDataTime().getRefTime()\n", + " \n", + " # do not plot if phensig is blank (SPS)\n", + " if ob.getString('phensig'):\n", + " siteids = np.append(siteids,site)\n", + " periods = np.append(periods,pd)\n", + " reftimes = np.append(reftimes,ref)\n", + "\n", + " for parm in parameters:\n", + " parameters[parm] = np.append(parameters[parm],ob.getString(parm))\n", + "\n", + " if poly.geom_type == 'MultiPolygon':\n", + " geometries = np.array([])\n", + " geometries = np.append(geometries,MultiPolygon(poly))\n", + " geom_count = \", \" + str(len(geometries)) +\" geometries\"\n", + " else:\n", + " geometries = np.array([])\n", + " geometries = np.append(geometries,Polygon(poly))\n", + " geom_count=\"\"\n", + "\n", + " # Is this needed?\n", + " for geom in geometries:\n", + " bounds = Polygon(geom)\n", + " intersection = bounds.intersection\n", + " geoms = (intersection(geom)\n", + " for geom in geometries\n", + " if bounds.intersects(geom))\n", + " \n", + " print(vtec[str(ob.getString('phensig'))]['hdln'] \n", + " + \" (\" + str(ob.getString('phensig')) + \") issued at \" + str(ref)\n", + " + \" (\"+str(poly.geom_type) + geom_count + \")\")\n", + " \n", + " color = warning_color(ob.getString('phensig'))\n", + " shape_feature = ShapelyFeature(geoms,ccrs.PlateCarree(), \n", + " facecolor=color, edgecolor=color)\n", + " ax.add_feature(shape_feature)\n", + " \n", + "states_provinces = cfeature.NaturalEarthFeature(\n", + " category='cultural',\n", + " name='admin_1_states_provinces_lines',\n", + " scale='50m',\n", + " facecolor='none')\n", + "political_boundaries = cfeature.NaturalEarthFeature(category='cultural',\n", + " name='admin_0_boundary_lines_land',\n", + " scale='50m', facecolor='none')\n", + "ax.add_feature(cfeature.LAND)\n", + "ax.add_feature(cfeature.COASTLINE)\n", + "ax.add_feature(states_provinces, edgecolor='black')\n", + "ax.add_feature(political_boundaries, edgecolor='black')\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/prep.sh b/prep.sh new file mode 100755 index 0000000..cd842b7 --- /dev/null +++ b/prep.sh @@ -0,0 +1,57 @@ +#!/bin/bash +# +# python-awips prep script +# author: mjames@ucar.edu +# +# This script is designed to +# + + +# should be /awips2/repo/python-awips or ~/python-awips +dir="$( cd "$(dirname "$0")" ; pwd -P )" + +# Find plugin-contributed files and add them to the site packages. +find /awips2/repo/awips2-core/common/ -path '*/pythonPackages/dynamicserialize' \ + -exec cp {} -rv ${dir} \; +find /awips2/repo/awips2-builds/edexOsgi/ -path '*/pythonPackages/dynamicserialize' \ + -exec cp {} -rv ${dir} \; + +#bash %{_baseline_workspace}/build.edex/opt/tools/update_dstypes.sh %{_build_root}/awips2/python/lib/python2.7/site-packages/dynamicserialize + +# Update __init__.py files under dynamicserialize/dstypes/ to include +# all contributed python packages and modules within __all__ in the packages' +# __init__.py + +echo "Updating dynamicserialize/dstypes" +# Update __all__ for every package under dstypes +for package in `find dynamicserialize/dstypes -name __init__.py -printf '%h '` +do + pushd $package > /dev/null + # find non-hidden packages + subpackages=(`find . -maxdepth 1 -type d ! -name ".*" -printf '%f\n' | sort`) + + # find non-hidden python modules + modules=(`find . -maxdepth 1 -type f \( -name "*.py" ! -name "__init__.py" ! -name ".*" \) -printf '%f\n' | sed 's/\.py//' | sort`) + + # join subpackages and modules into a single list, modules first + all=("${subpackages[@]}" "${modules[@]}") + joined=$(printf ",\n \'%s\'" "${all[@]}") + + #replace the current __all__ definition with the rebuilt __all__, which now includes all contributed packages and modules. + #-0777 allows us to match the multi-line __all__ definition + perl -0777 -p -i -e "s/__all__ = \[[^\]]*\]/__all__ = \[`echo \"${joined:1}\"`\n \]/g" __init__.py + + popd > /dev/null +done +echo "Done" + +#find ${dir} -type f | xargs sed -i 's/[ \t]*$//' +git grep -l 'ufpy' | xargs sed -i 's/ufpy/awips/g' + +#find ${dir} -type f | xargs sed -i '/# This software was developed and \/ or modified by Raytheon Company,/,/# further licensing information./d' + + + +# update import strings for python3 compliance + + diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 0000000..eea5a4d --- /dev/null +++ b/setup.cfg @@ -0,0 +1,8 @@ +[metadata] +description-file = README.rst +source-dir = docs/source +build-dir = docs/build + +[versioneer] +VCS = git +style = pep440 diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..be56f8a --- /dev/null +++ b/setup.py @@ -0,0 +1,33 @@ +# Copyright (c) 2017 UCAR Unidata Program Center. +# Distributed under the terms of the BSD 3-Clause License. +# SPDX-License-Identifier: BSD-3-Clause +from __future__ import print_function +import sys +from distutils.core import setup +from setuptools import find_packages + +dependencies = ['numpy','six'] +if sys.version_info < (3, 4): + dependencies.append('enum34') + +ver="0.9.12" + +setup( + name='python-awips', + version=ver, + description='A framework for requesting AWIPS meteorological datasets from an EDEX server', + packages=find_packages(exclude='data'), + license='BSD', + url='http://python-awips.readthedocs.io', + download_url='https://github.com/Unidata/python-awips/archive/{}.tar.gz'.format(ver), + author='Unidata', + author_email='mjames@ucar.edu', + install_requires=dependencies, + extras_require={ + 'cdm': ['pyproj>=1.9.4'], + 'dev': ['ipython[all]>=3.1'], + 'doc': ['sphinx>=1.4', 'sphinx-gallery', 'doc8'], + 'examples': ['cartopy>=0.13.1','metpy>=0.4.0'] + } +) + diff --git a/thrift/TSCons.py b/thrift/TSCons.py new file mode 100644 index 0000000..d3176ed --- /dev/null +++ b/thrift/TSCons.py @@ -0,0 +1,35 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +from os import path +from SCons.Builder import Builder + + +def scons_env(env, add=''): + opath = path.dirname(path.abspath('$TARGET')) + lstr = 'thrift --gen cpp -o ' + opath + ' ' + add + ' $SOURCE' + cppbuild = Builder(action=lstr) + env.Append(BUILDERS={'ThriftCpp': cppbuild}) + + +def gen_cpp(env, dir, file): + scons_env(env) + suffixes = ['_types.h', '_types.cpp'] + targets = ['gen-cpp/' + file + s for s in suffixes] + return env.ThriftCpp(targets, dir + file + '.thrift') diff --git a/thrift/TSerialization.py b/thrift/TSerialization.py new file mode 100644 index 0000000..fbbe768 --- /dev/null +++ b/thrift/TSerialization.py @@ -0,0 +1,38 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +from .protocol import TBinaryProtocol +from .transport import TTransport + + +def serialize(thrift_object, + protocol_factory=TBinaryProtocol.TBinaryProtocolFactory()): + transport = TTransport.TMemoryBuffer() + protocol = protocol_factory.getProtocol(transport) + thrift_object.write(protocol) + return transport.getvalue() + + +def deserialize(base, + buf, + protocol_factory=TBinaryProtocol.TBinaryProtocolFactory()): + transport = TTransport.TMemoryBuffer(buf) + protocol = protocol_factory.getProtocol(transport) + base.read(protocol) + return base diff --git a/thrift/Thrift.py b/thrift/Thrift.py new file mode 100644 index 0000000..707a8cc --- /dev/null +++ b/thrift/Thrift.py @@ -0,0 +1,157 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +import sys + + +class TType: + STOP = 0 + VOID = 1 + BOOL = 2 + BYTE = 3 + I08 = 3 + DOUBLE = 4 + I16 = 6 + I32 = 8 + I64 = 10 + STRING = 11 + UTF7 = 11 + STRUCT = 12 + MAP = 13 + SET = 14 + LIST = 15 + UTF8 = 16 + UTF16 = 17 + + _VALUES_TO_NAMES = ('STOP', + 'VOID', + 'BOOL', + 'BYTE', + 'DOUBLE', + None, + 'I16', + None, + 'I32', + None, + 'I64', + 'STRING', + 'STRUCT', + 'MAP', + 'SET', + 'LIST', + 'UTF8', + 'UTF16') + + +class TMessageType: + CALL = 1 + REPLY = 2 + EXCEPTION = 3 + ONEWAY = 4 + + +class TProcessor: + """Base class for procsessor, which works on two streams.""" + + def process(iprot, oprot): + pass + + +class TException(Exception): + """Base class for all thrift exceptions.""" + + # BaseException.message is deprecated in Python v[2.6,3.0) + if (2, 6, 0) <= sys.version_info < (3, 0): + def _get_message(self): + return self._message + + def _set_message(self, message): + self._message = message + message = property(_get_message, _set_message) + + def __init__(self, message=None): + Exception.__init__(self, message) + self.message = message + + +class TApplicationException(TException): + """Application level thrift exceptions.""" + + UNKNOWN = 0 + UNKNOWN_METHOD = 1 + INVALID_MESSAGE_TYPE = 2 + WRONG_METHOD_NAME = 3 + BAD_SEQUENCE_ID = 4 + MISSING_RESULT = 5 + INTERNAL_ERROR = 6 + PROTOCOL_ERROR = 7 + + def __init__(self, type=UNKNOWN, message=None): + TException.__init__(self, message) + self.type = type + + def __str__(self): + if self.message: + return self.message + elif self.type == self.UNKNOWN_METHOD: + return 'Unknown method' + elif self.type == self.INVALID_MESSAGE_TYPE: + return 'Invalid message type' + elif self.type == self.WRONG_METHOD_NAME: + return 'Wrong method name' + elif self.type == self.BAD_SEQUENCE_ID: + return 'Bad sequence ID' + elif self.type == self.MISSING_RESULT: + return 'Missing result' + else: + return 'Default (unknown) TApplicationException' + + def read(self, iprot): + iprot.readStructBegin() + while True: + (fname, ftype, fid) = iprot.readFieldBegin() + if ftype == TType.STOP: + break + if fid == 1: + if ftype == TType.STRING: + self.message = iprot.readString() + else: + iprot.skip(ftype) + elif fid == 2: + if ftype == TType.I32: + self.type = iprot.readI32() + else: + iprot.skip(ftype) + else: + iprot.skip(ftype) + iprot.readFieldEnd() + iprot.readStructEnd() + + def write(self, oprot): + oprot.writeStructBegin('TApplicationException') + if self.message is not None: + oprot.writeFieldBegin('message', TType.STRING, 1) + oprot.writeString(self.message) + oprot.writeFieldEnd() + if self.type is not None: + oprot.writeFieldBegin('type', TType.I32, 2) + oprot.writeI32(self.type) + oprot.writeFieldEnd() + oprot.writeFieldStop() + oprot.writeStructEnd() diff --git a/thrift/__init__.py b/thrift/__init__.py new file mode 100644 index 0000000..48d659c --- /dev/null +++ b/thrift/__init__.py @@ -0,0 +1,20 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +__all__ = ['Thrift', 'TSCons'] diff --git a/thrift/protocol/TBase.py b/thrift/protocol/TBase.py new file mode 100644 index 0000000..6cd6c28 --- /dev/null +++ b/thrift/protocol/TBase.py @@ -0,0 +1,81 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +from thrift.Thrift import * +from thrift.protocol import TBinaryProtocol +from thrift.transport import TTransport + +try: + from thrift.protocol import fastbinary +except: + fastbinary = None + + +class TBase(object): + __slots__ = [] + + def __repr__(self): + L = ['%s=%r' % (key, getattr(self, key)) + for key in self.__slots__] + return '%s(%s)' % (self.__class__.__name__, ', '.join(L)) + + def __eq__(self, other): + if not isinstance(other, self.__class__): + return False + for attr in self.__slots__: + my_val = getattr(self, attr) + other_val = getattr(other, attr) + if my_val != other_val: + return False + return True + + def __ne__(self, other): + return not (self == other) + + def read(self, iprot): + if (iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and + isinstance(iprot.trans, TTransport.CReadableTransport) and + self.thrift_spec is not None and + fastbinary is not None): + fastbinary.decode_binary(self, + iprot.trans, + (self.__class__, self.thrift_spec)) + return + iprot.readStruct(self, self.thrift_spec) + + def write(self, oprot): + if (oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and + self.thrift_spec is not None and + fastbinary is not None): + oprot.trans.write( + fastbinary.encode_binary(self, (self.__class__, self.thrift_spec))) + return + oprot.writeStruct(self, self.thrift_spec) + + +class TExceptionBase(Exception): + # old style class so python2.4 can raise exceptions derived from this + # This can't inherit from TBase because of that limitation. + __slots__ = [] + + __repr__ = TBase.__repr__.__func__ + __eq__ = TBase.__eq__.__func__ + __ne__ = TBase.__ne__.__func__ + read = TBase.read.__func__ + write = TBase.write.__func__ diff --git a/thrift/protocol/TBinaryProtocol.py b/thrift/protocol/TBinaryProtocol.py new file mode 100644 index 0000000..dbcb1e9 --- /dev/null +++ b/thrift/protocol/TBinaryProtocol.py @@ -0,0 +1,264 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +from .TProtocol import * +from struct import pack, unpack + + +class TBinaryProtocol(TProtocolBase): + """Binary implementation of the Thrift protocol driver.""" + + # NastyHaxx. Python 2.4+ on 32-bit machines forces hex constants to be + # positive, converting this into a long. If we hardcode the int value + # instead it'll stay in 32 bit-land. + + # VERSION_MASK = 0xffff0000 + VERSION_MASK = -65536 + + # VERSION_1 = 0x80010000 + VERSION_1 = -2147418112 + + TYPE_MASK = 0x000000ff + + def __init__(self, trans, strictRead=False, strictWrite=True): + TProtocolBase.__init__(self, trans) + self.strictRead = strictRead + self.strictWrite = strictWrite + + def writeMessageBegin(self, name, type, seqid): + if self.strictWrite: + self.writeI32(TBinaryProtocol.VERSION_1 | type) + self.writeString(name) + self.writeI32(seqid) + else: + self.writeString(name) + self.writeByte(type) + self.writeI32(seqid) + + def writeMessageEnd(self): + pass + + def writeStructBegin(self, name): + pass + + def writeStructEnd(self): + pass + + def writeFieldBegin(self, name, type, id): + self.writeByte(type) + self.writeI16(id) + + def writeFieldEnd(self): + pass + + def writeFieldStop(self): + self.writeByte(TType.STOP) + + def writeMapBegin(self, ktype, vtype, size): + self.writeByte(ktype) + self.writeByte(vtype) + self.writeI32(size) + + def writeMapEnd(self): + pass + + def writeListBegin(self, etype, size): + self.writeByte(etype) + self.writeI32(size) + + def writeListEnd(self): + pass + + def writeSetBegin(self, etype, size): + self.writeByte(etype) + self.writeI32(size) + + def writeSetEnd(self): + pass + + def writeBool(self, bool): + if bool: + self.writeByte(1) + else: + self.writeByte(0) + + def writeByte(self, byte): + buff = pack("!b", byte) + self.trans.write(buff) + + def writeI16(self, i16): + buff = pack("!h", i16) + self.trans.write(buff) + + def writeI32(self, i32): + buff = pack("!i", i32) + self.trans.write(buff) + + def writeI64(self, i64): + buff = pack("!q", i64) + self.trans.write(buff) + + def writeDouble(self, dub): + buff = pack("!d", dub) + self.trans.write(buff) + + def writeString(self, str): + self.writeI32(len(str)) + self.trans.write(str) + + def readMessageBegin(self): + sz = self.readI32() + if sz < 0: + version = sz & TBinaryProtocol.VERSION_MASK + if version != TBinaryProtocol.VERSION_1: + raise TProtocolException( + type=TProtocolException.BAD_VERSION, + message='Bad version in readMessageBegin: %d' % (sz)) + type = sz & TBinaryProtocol.TYPE_MASK + name = self.readString() + seqid = self.readI32() + else: + if self.strictRead: + raise TProtocolException(type=TProtocolException.BAD_VERSION, + message='No protocol version header') + name = self.trans.readAll(sz) + type = self.readByte() + seqid = self.readI32() + return (name, type, seqid) + + def readMessageEnd(self): + pass + + def readStructBegin(self): + pass + + def readStructEnd(self): + pass + + def readFieldBegin(self): + type = self.readByte() + if type == TType.STOP: + return (None, type, 0) + id = self.readI16() + return (None, type, id) + + def readFieldEnd(self): + pass + + def readMapBegin(self): + ktype = self.readByte() + vtype = self.readByte() + size = self.readI32() + return (ktype, vtype, size) + + def readMapEnd(self): + pass + + def readListBegin(self): + etype = self.readByte() + size = self.readI32() + return (etype, size) + + def readListEnd(self): + pass + + def readSetBegin(self): + etype = self.readByte() + size = self.readI32() + return (etype, size) + + def readSetEnd(self): + pass + + def readBool(self): + byte = self.readByte() + if byte == 0: + return False + return True + + def readByte(self): + buff = self.trans.readAll(1) + val, = unpack('!b', buff) + return val + + def readI16(self): + buff = self.trans.readAll(2) + val, = unpack('!h', buff) + return val + + def readI32(self): + buff = self.trans.readAll(4) + try: + val, = unpack('!i', buff) + except TypeError: + #str does not support the buffer interface + val, = unpack('!i', buff) + return val + + def readI64(self): + buff = self.trans.readAll(8) + val, = unpack('!q', buff) + return val + + def readDouble(self): + buff = self.trans.readAll(8) + val, = unpack('!d', buff) + return val + + def readString(self): + len = self.readI32() + str = self.trans.readAll(len) + return str + + +class TBinaryProtocolFactory: + def __init__(self, strictRead=False, strictWrite=True): + self.strictRead = strictRead + self.strictWrite = strictWrite + + def getProtocol(self, trans): + prot = TBinaryProtocol(trans, self.strictRead, self.strictWrite) + return prot + + +class TBinaryProtocolAccelerated(TBinaryProtocol): + """C-Accelerated version of TBinaryProtocol. + + This class does not override any of TBinaryProtocol's methods, + but the generated code recognizes it directly and will call into + our C module to do the encoding, bypassing this object entirely. + We inherit from TBinaryProtocol so that the normal TBinaryProtocol + encoding can happen if the fastbinary module doesn't work for some + reason. (TODO(dreiss): Make this happen sanely in more cases.) + + In order to take advantage of the C module, just use + TBinaryProtocolAccelerated instead of TBinaryProtocol. + + NOTE: This code was contributed by an external developer. + The internal Thrift team has reviewed and tested it, + but we cannot guarantee that it is production-ready. + Please feel free to report bugs and/or success stories + to the public mailing list. + """ + pass + + +class TBinaryProtocolAcceleratedFactory: + def getProtocol(self, trans): + return TBinaryProtocolAccelerated(trans) diff --git a/thrift/protocol/TCompactProtocol.py b/thrift/protocol/TCompactProtocol.py new file mode 100644 index 0000000..a3385e1 --- /dev/null +++ b/thrift/protocol/TCompactProtocol.py @@ -0,0 +1,403 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +from .TProtocol import * +from struct import pack, unpack + +__all__ = ['TCompactProtocol', 'TCompactProtocolFactory'] + +CLEAR = 0 +FIELD_WRITE = 1 +VALUE_WRITE = 2 +CONTAINER_WRITE = 3 +BOOL_WRITE = 4 +FIELD_READ = 5 +CONTAINER_READ = 6 +VALUE_READ = 7 +BOOL_READ = 8 + + +def make_helper(v_from, container): + def helper(func): + def nested(self, *args, **kwargs): + assert self.state in (v_from, container), (self.state, v_from, container) + return func(self, *args, **kwargs) + return nested + return helper +writer = make_helper(VALUE_WRITE, CONTAINER_WRITE) +reader = make_helper(VALUE_READ, CONTAINER_READ) + + +def makeZigZag(n, bits): + return (n << 1) ^ (n >> (bits - 1)) + + +def fromZigZag(n): + return (n >> 1) ^ -(n & 1) + + +def writeVarint(trans, n): + out = [] + while True: + if n & ~0x7f == 0: + out.append(n) + break + else: + out.append((n & 0xff) | 0x80) + n = n >> 7 + trans.write(''.join(map(chr, out))) + + +def readVarint(trans): + result = 0 + shift = 0 + while True: + x = trans.readAll(1) + byte = ord(x) + result |= (byte & 0x7f) << shift + if byte >> 7 == 0: + return result + shift += 7 + + +class CompactType: + STOP = 0x00 + TRUE = 0x01 + FALSE = 0x02 + BYTE = 0x03 + I16 = 0x04 + I32 = 0x05 + I64 = 0x06 + DOUBLE = 0x07 + BINARY = 0x08 + LIST = 0x09 + SET = 0x0A + MAP = 0x0B + STRUCT = 0x0C + +CTYPES = {TType.STOP: CompactType.STOP, + TType.BOOL: CompactType.TRUE, # used for collection + TType.BYTE: CompactType.BYTE, + TType.I16: CompactType.I16, + TType.I32: CompactType.I32, + TType.I64: CompactType.I64, + TType.DOUBLE: CompactType.DOUBLE, + TType.STRING: CompactType.BINARY, + TType.STRUCT: CompactType.STRUCT, + TType.LIST: CompactType.LIST, + TType.SET: CompactType.SET, + TType.MAP: CompactType.MAP + } + +TTYPES = {} +for k, v in list(CTYPES.items()): + TTYPES[v] = k +TTYPES[CompactType.FALSE] = TType.BOOL +del k +del v + + +class TCompactProtocol(TProtocolBase): + """Compact implementation of the Thrift protocol driver.""" + + PROTOCOL_ID = 0x82 + VERSION = 1 + VERSION_MASK = 0x1f + TYPE_MASK = 0xe0 + TYPE_SHIFT_AMOUNT = 5 + + def __init__(self, trans): + TProtocolBase.__init__(self, trans) + self.state = CLEAR + self.__last_fid = 0 + self.__bool_fid = None + self.__bool_value = None + self.__structs = [] + self.__containers = [] + + def __writeVarint(self, n): + writeVarint(self.trans, n) + + def writeMessageBegin(self, name, type, seqid): + assert self.state == CLEAR + self.__writeUByte(self.PROTOCOL_ID) + self.__writeUByte(self.VERSION | (type << self.TYPE_SHIFT_AMOUNT)) + self.__writeVarint(seqid) + self.__writeString(name) + self.state = VALUE_WRITE + + def writeMessageEnd(self): + assert self.state == VALUE_WRITE + self.state = CLEAR + + def writeStructBegin(self, name): + assert self.state in (CLEAR, CONTAINER_WRITE, VALUE_WRITE), self.state + self.__structs.append((self.state, self.__last_fid)) + self.state = FIELD_WRITE + self.__last_fid = 0 + + def writeStructEnd(self): + assert self.state == FIELD_WRITE + self.state, self.__last_fid = self.__structs.pop() + + def writeFieldStop(self): + self.__writeByte(0) + + def __writeFieldHeader(self, type, fid): + delta = fid - self.__last_fid + if 0 < delta <= 15: + self.__writeUByte(delta << 4 | type) + else: + self.__writeByte(type) + self.__writeI16(fid) + self.__last_fid = fid + + def writeFieldBegin(self, name, type, fid): + assert self.state == FIELD_WRITE, self.state + if type == TType.BOOL: + self.state = BOOL_WRITE + self.__bool_fid = fid + else: + self.state = VALUE_WRITE + self.__writeFieldHeader(CTYPES[type], fid) + + def writeFieldEnd(self): + assert self.state in (VALUE_WRITE, BOOL_WRITE), self.state + self.state = FIELD_WRITE + + def __writeUByte(self, byte): + self.trans.write(pack('!B', byte)) + + def __writeByte(self, byte): + self.trans.write(pack('!b', byte)) + + def __writeI16(self, i16): + self.__writeVarint(makeZigZag(i16, 16)) + + def __writeSize(self, i32): + self.__writeVarint(i32) + + def writeCollectionBegin(self, etype, size): + assert self.state in (VALUE_WRITE, CONTAINER_WRITE), self.state + if size <= 14: + self.__writeUByte(size << 4 | CTYPES[etype]) + else: + self.__writeUByte(0xf0 | CTYPES[etype]) + self.__writeSize(size) + self.__containers.append(self.state) + self.state = CONTAINER_WRITE + writeSetBegin = writeCollectionBegin + writeListBegin = writeCollectionBegin + + def writeMapBegin(self, ktype, vtype, size): + assert self.state in (VALUE_WRITE, CONTAINER_WRITE), self.state + if size == 0: + self.__writeByte(0) + else: + self.__writeSize(size) + self.__writeUByte(CTYPES[ktype] << 4 | CTYPES[vtype]) + self.__containers.append(self.state) + self.state = CONTAINER_WRITE + + def writeCollectionEnd(self): + assert self.state == CONTAINER_WRITE, self.state + self.state = self.__containers.pop() + writeMapEnd = writeCollectionEnd + writeSetEnd = writeCollectionEnd + writeListEnd = writeCollectionEnd + + def writeBool(self, bool): + if self.state == BOOL_WRITE: + if bool: + ctype = CompactType.TRUE + else: + ctype = CompactType.FALSE + self.__writeFieldHeader(ctype, self.__bool_fid) + elif self.state == CONTAINER_WRITE: + if bool: + self.__writeByte(CompactType.TRUE) + else: + self.__writeByte(CompactType.FALSE) + else: + raise AssertionError("Invalid state in compact protocol") + + writeByte = writer(__writeByte) + writeI16 = writer(__writeI16) + + @writer + def writeI32(self, i32): + self.__writeVarint(makeZigZag(i32, 32)) + + @writer + def writeI64(self, i64): + self.__writeVarint(makeZigZag(i64, 64)) + + @writer + def writeDouble(self, dub): + self.trans.write(pack('!d', dub)) + + def __writeString(self, s): + self.__writeSize(len(s)) + self.trans.write(s) + writeString = writer(__writeString) + + def readFieldBegin(self): + assert self.state == FIELD_READ, self.state + type = self.__readUByte() + if type & 0x0f == TType.STOP: + return (None, 0, 0) + delta = type >> 4 + if delta == 0: + fid = self.__readI16() + else: + fid = self.__last_fid + delta + self.__last_fid = fid + type = type & 0x0f + if type == CompactType.TRUE: + self.state = BOOL_READ + self.__bool_value = True + elif type == CompactType.FALSE: + self.state = BOOL_READ + self.__bool_value = False + else: + self.state = VALUE_READ + return (None, self.__getTType(type), fid) + + def readFieldEnd(self): + assert self.state in (VALUE_READ, BOOL_READ), self.state + self.state = FIELD_READ + + def __readUByte(self): + result, = unpack('!B', self.trans.readAll(1)) + return result + + def __readByte(self): + result, = unpack('!b', self.trans.readAll(1)) + return result + + def __readVarint(self): + return readVarint(self.trans) + + def __readZigZag(self): + return fromZigZag(self.__readVarint()) + + def __readSize(self): + result = self.__readVarint() + if result < 0: + raise TException("Length < 0") + return result + + def readMessageBegin(self): + assert self.state == CLEAR + proto_id = self.__readUByte() + if proto_id != self.PROTOCOL_ID: + raise TProtocolException(TProtocolException.BAD_VERSION, + 'Bad protocol id in the message: %d' % proto_id) + ver_type = self.__readUByte() + type = (ver_type & self.TYPE_MASK) >> self.TYPE_SHIFT_AMOUNT + version = ver_type & self.VERSION_MASK + if version != self.VERSION: + raise TProtocolException(TProtocolException.BAD_VERSION, + 'Bad version: %d (expect %d)' % (version, self.VERSION)) + seqid = self.__readVarint() + name = self.__readString() + return (name, type, seqid) + + def readMessageEnd(self): + assert self.state == CLEAR + assert len(self.__structs) == 0 + + def readStructBegin(self): + assert self.state in (CLEAR, CONTAINER_READ, VALUE_READ), self.state + self.__structs.append((self.state, self.__last_fid)) + self.state = FIELD_READ + self.__last_fid = 0 + + def readStructEnd(self): + assert self.state == FIELD_READ + self.state, self.__last_fid = self.__structs.pop() + + def readCollectionBegin(self): + assert self.state in (VALUE_READ, CONTAINER_READ), self.state + size_type = self.__readUByte() + size = size_type >> 4 + type = self.__getTType(size_type) + if size == 15: + size = self.__readSize() + self.__containers.append(self.state) + self.state = CONTAINER_READ + return type, size + readSetBegin = readCollectionBegin + readListBegin = readCollectionBegin + + def readMapBegin(self): + assert self.state in (VALUE_READ, CONTAINER_READ), self.state + size = self.__readSize() + types = 0 + if size > 0: + types = self.__readUByte() + vtype = self.__getTType(types) + ktype = self.__getTType(types >> 4) + self.__containers.append(self.state) + self.state = CONTAINER_READ + return (ktype, vtype, size) + + def readCollectionEnd(self): + assert self.state == CONTAINER_READ, self.state + self.state = self.__containers.pop() + readSetEnd = readCollectionEnd + readListEnd = readCollectionEnd + readMapEnd = readCollectionEnd + + def readBool(self): + if self.state == BOOL_READ: + return self.__bool_value == CompactType.TRUE + elif self.state == CONTAINER_READ: + return self.__readByte() == CompactType.TRUE + else: + raise AssertionError("Invalid state in compact protocol: %d" % + self.state) + + readByte = reader(__readByte) + __readI16 = __readZigZag + readI16 = reader(__readZigZag) + readI32 = reader(__readZigZag) + readI64 = reader(__readZigZag) + + @reader + def readDouble(self): + buff = self.trans.readAll(8) + val, = unpack('!d', buff) + return val + + def __readString(self): + len = self.__readSize() + return self.trans.readAll(len) + readString = reader(__readString) + + def __getTType(self, byte): + return TTYPES[byte & 0x0f] + + +class TCompactProtocolFactory: + def __init__(self): + pass + + def getProtocol(self, trans): + return TCompactProtocol(trans) diff --git a/thrift/protocol/TProtocol.py b/thrift/protocol/TProtocol.py new file mode 100644 index 0000000..56d323a --- /dev/null +++ b/thrift/protocol/TProtocol.py @@ -0,0 +1,406 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +from thrift.Thrift import * + + +class TProtocolException(TException): + """Custom Protocol Exception class""" + + UNKNOWN = 0 + INVALID_DATA = 1 + NEGATIVE_SIZE = 2 + SIZE_LIMIT = 3 + BAD_VERSION = 4 + + def __init__(self, type=UNKNOWN, message=None): + TException.__init__(self, message) + self.type = type + + +class TProtocolBase: + """Base class for Thrift protocol driver.""" + + def __init__(self, trans): + self.trans = trans + + def writeMessageBegin(self, name, type, seqid): + pass + + def writeMessageEnd(self): + pass + + def writeStructBegin(self, name): + pass + + def writeStructEnd(self): + pass + + def writeFieldBegin(self, name, type, id): + pass + + def writeFieldEnd(self): + pass + + def writeFieldStop(self): + pass + + def writeMapBegin(self, ktype, vtype, size): + pass + + def writeMapEnd(self): + pass + + def writeListBegin(self, etype, size): + pass + + def writeListEnd(self): + pass + + def writeSetBegin(self, etype, size): + pass + + def writeSetEnd(self): + pass + + def writeBool(self, bool): + pass + + def writeByte(self, byte): + pass + + def writeI16(self, i16): + pass + + def writeI32(self, i32): + pass + + def writeI64(self, i64): + pass + + def writeDouble(self, dub): + pass + + def writeString(self, str): + pass + + def readMessageBegin(self): + pass + + def readMessageEnd(self): + pass + + def readStructBegin(self): + pass + + def readStructEnd(self): + pass + + def readFieldBegin(self): + pass + + def readFieldEnd(self): + pass + + def readMapBegin(self): + pass + + def readMapEnd(self): + pass + + def readListBegin(self): + pass + + def readListEnd(self): + pass + + def readSetBegin(self): + pass + + def readSetEnd(self): + pass + + def readBool(self): + pass + + def readByte(self): + pass + + def readI16(self): + pass + + def readI32(self): + pass + + def readI64(self): + pass + + def readDouble(self): + pass + + def readString(self): + pass + + def skip(self, type): + if type == TType.STOP: + return + elif type == TType.BOOL: + self.readBool() + elif type == TType.BYTE: + self.readByte() + elif type == TType.I16: + self.readI16() + elif type == TType.I32: + self.readI32() + elif type == TType.I64: + self.readI64() + elif type == TType.DOUBLE: + self.readDouble() + elif type == TType.STRING: + self.readString() + elif type == TType.STRUCT: + name = self.readStructBegin() + while True: + (name, type, id) = self.readFieldBegin() + if type == TType.STOP: + break + self.skip(type) + self.readFieldEnd() + self.readStructEnd() + elif type == TType.MAP: + (ktype, vtype, size) = self.readMapBegin() + for i in range(size): + self.skip(ktype) + self.skip(vtype) + self.readMapEnd() + elif type == TType.SET: + (etype, size) = self.readSetBegin() + for i in range(size): + self.skip(etype) + self.readSetEnd() + elif type == TType.LIST: + (etype, size) = self.readListBegin() + for i in range(size): + self.skip(etype) + self.readListEnd() + + # tuple of: ( 'reader method' name, is_container bool, 'writer_method' name ) + _TTYPE_HANDLERS = ( + (None, None, False), # 0 TType.STOP + (None, None, False), # 1 TType.VOID # TODO: handle void? + ('readBool', 'writeBool', False), # 2 TType.BOOL + ('readByte', 'writeByte', False), # 3 TType.BYTE and I08 + ('readDouble', 'writeDouble', False), # 4 TType.DOUBLE + (None, None, False), # 5 undefined + ('readI16', 'writeI16', False), # 6 TType.I16 + (None, None, False), # 7 undefined + ('readI32', 'writeI32', False), # 8 TType.I32 + (None, None, False), # 9 undefined + ('readI64', 'writeI64', False), # 10 TType.I64 + ('readString', 'writeString', False), # 11 TType.STRING and UTF7 + ('readContainerStruct', 'writeContainerStruct', True), # 12 *.STRUCT + ('readContainerMap', 'writeContainerMap', True), # 13 TType.MAP + ('readContainerSet', 'writeContainerSet', True), # 14 TType.SET + ('readContainerList', 'writeContainerList', True), # 15 TType.LIST + (None, None, False), # 16 TType.UTF8 # TODO: handle utf8 types? + (None, None, False) # 17 TType.UTF16 # TODO: handle utf16 types? + ) + + def readFieldByTType(self, ttype, spec): + try: + (r_handler, w_handler, is_container) = self._TTYPE_HANDLERS[ttype] + except IndexError: + raise TProtocolException(type=TProtocolException.INVALID_DATA, + message='Invalid field type %d' % (ttype)) + if r_handler is None: + raise TProtocolException(type=TProtocolException.INVALID_DATA, + message='Invalid field type %d' % (ttype)) + reader = getattr(self, r_handler) + if not is_container: + return reader() + return reader(spec) + + def readContainerList(self, spec): + results = [] + ttype, tspec = spec[0], spec[1] + r_handler = self._TTYPE_HANDLERS[ttype][0] + reader = getattr(self, r_handler) + (list_type, list_len) = self.readListBegin() + if tspec is None: + # list values are simple types + for idx in range(list_len): + results.append(reader()) + else: + # this is like an inlined readFieldByTType + container_reader = self._TTYPE_HANDLERS[list_type][0] + val_reader = getattr(self, container_reader) + for idx in range(list_len): + val = val_reader(tspec) + results.append(val) + self.readListEnd() + return results + + def readContainerSet(self, spec): + results = set() + ttype, tspec = spec[0], spec[1] + r_handler = self._TTYPE_HANDLERS[ttype][0] + reader = getattr(self, r_handler) + (set_type, set_len) = self.readSetBegin() + if tspec is None: + # set members are simple types + for idx in range(set_len): + results.add(reader()) + else: + container_reader = self._TTYPE_HANDLERS[set_type][0] + val_reader = getattr(self, container_reader) + for idx in range(set_len): + results.add(val_reader(tspec)) + self.readSetEnd() + return results + + def readContainerStruct(self, spec): + (obj_class, obj_spec) = spec + obj = obj_class() + obj.read(self) + return obj + + def readContainerMap(self, spec): + results = dict() + key_ttype, key_spec = spec[0], spec[1] + val_ttype, val_spec = spec[2], spec[3] + (map_ktype, map_vtype, map_len) = self.readMapBegin() + # TODO: compare types we just decoded with thrift_spec and + # abort/skip if types disagree + key_reader = getattr(self, self._TTYPE_HANDLERS[key_ttype][0]) + val_reader = getattr(self, self._TTYPE_HANDLERS[val_ttype][0]) + # list values are simple types + for idx in range(map_len): + if key_spec is None: + k_val = key_reader() + else: + k_val = self.readFieldByTType(key_ttype, key_spec) + if val_spec is None: + v_val = val_reader() + else: + v_val = self.readFieldByTType(val_ttype, val_spec) + # this raises a TypeError with unhashable keys types + # i.e. this fails: d=dict(); d[[0,1]] = 2 + results[k_val] = v_val + self.readMapEnd() + return results + + def readStruct(self, obj, thrift_spec): + self.readStructBegin() + while True: + (fname, ftype, fid) = self.readFieldBegin() + if ftype == TType.STOP: + break + try: + field = thrift_spec[fid] + except IndexError: + self.skip(ftype) + else: + if field is not None and ftype == field[1]: + fname = field[2] + fspec = field[3] + val = self.readFieldByTType(ftype, fspec) + setattr(obj, fname, val) + else: + self.skip(ftype) + self.readFieldEnd() + self.readStructEnd() + + def writeContainerStruct(self, val, spec): + val.write(self) + + def writeContainerList(self, val, spec): + self.writeListBegin(spec[0], len(val)) + r_handler, w_handler, is_container = self._TTYPE_HANDLERS[spec[0]] + e_writer = getattr(self, w_handler) + if not is_container: + for elem in val: + e_writer(elem) + else: + for elem in val: + e_writer(elem, spec[1]) + self.writeListEnd() + + def writeContainerSet(self, val, spec): + self.writeSetBegin(spec[0], len(val)) + r_handler, w_handler, is_container = self._TTYPE_HANDLERS[spec[0]] + e_writer = getattr(self, w_handler) + if not is_container: + for elem in val: + e_writer(elem) + else: + for elem in val: + e_writer(elem, spec[1]) + self.writeSetEnd() + + def writeContainerMap(self, val, spec): + k_type = spec[0] + v_type = spec[2] + ignore, ktype_name, k_is_container = self._TTYPE_HANDLERS[k_type] + ignore, vtype_name, v_is_container = self._TTYPE_HANDLERS[v_type] + k_writer = getattr(self, ktype_name) + v_writer = getattr(self, vtype_name) + self.writeMapBegin(k_type, v_type, len(val)) + for m_key, m_val in val.items(): + if not k_is_container: + k_writer(m_key) + else: + k_writer(m_key, spec[1]) + if not v_is_container: + v_writer(m_val) + else: + v_writer(m_val, spec[3]) + self.writeMapEnd() + + def writeStruct(self, obj, thrift_spec): + self.writeStructBegin(obj.__class__.__name__) + for field in thrift_spec: + if field is None: + continue + fname = field[2] + val = getattr(obj, fname) + if val is None: + # skip writing out unset fields + continue + fid = field[0] + ftype = field[1] + fspec = field[3] + # get the writer method for this value + self.writeFieldBegin(fname, ftype, fid) + self.writeFieldByTType(ftype, val, fspec) + self.writeFieldEnd() + self.writeFieldStop() + self.writeStructEnd() + + def writeFieldByTType(self, ttype, val, spec): + r_handler, w_handler, is_container = self._TTYPE_HANDLERS[ttype] + writer = getattr(self, w_handler) + if is_container: + writer(val, spec) + else: + writer(val) + + +class TProtocolFactory: + def getProtocol(self, trans): + pass diff --git a/thrift/protocol/__init__.py b/thrift/protocol/__init__.py new file mode 100644 index 0000000..d53359b --- /dev/null +++ b/thrift/protocol/__init__.py @@ -0,0 +1,20 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +__all__ = ['TProtocol', 'TBinaryProtocol', 'fastbinary', 'TBase'] diff --git a/thrift/protocol/fastbinary.c b/thrift/protocol/fastbinary.c new file mode 100644 index 0000000..d5947a5 --- /dev/null +++ b/thrift/protocol/fastbinary.c @@ -0,0 +1,1219 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +#include +#include "cStringIO.h" +#include +#ifndef _WIN32 +# include +# include +#else +# include +# pragma comment (lib, "ws2_32.lib") +# define BIG_ENDIAN (4321) +# define LITTLE_ENDIAN (1234) +# define BYTE_ORDER LITTLE_ENDIAN +# if defined(_MSC_VER) && _MSC_VER < 1600 + typedef int _Bool; +# define bool _Bool +# define false 0 +# define true 1 +# endif +# define inline __inline +#endif + +/* Fix endianness issues on Solaris */ +#if defined (__SVR4) && defined (__sun) + #if defined(__i386) && !defined(__i386__) + #define __i386__ + #endif + + #ifndef BIG_ENDIAN + #define BIG_ENDIAN (4321) + #endif + #ifndef LITTLE_ENDIAN + #define LITTLE_ENDIAN (1234) + #endif + + /* I386 is LE, even on Solaris */ + #if !defined(BYTE_ORDER) && defined(__i386__) + #define BYTE_ORDER LITTLE_ENDIAN + #endif +#endif + +// TODO(dreiss): defval appears to be unused. Look into removing it. +// TODO(dreiss): Make parse_spec_args recursive, and cache the output +// permanently in the object. (Malloc and orphan.) +// TODO(dreiss): Why do we need cStringIO for reading, why not just char*? +// Can cStringIO let us work with a BufferedTransport? +// TODO(dreiss): Don't ignore the rv from cwrite (maybe). + +/* ====== BEGIN UTILITIES ====== */ + +#define INIT_OUTBUF_SIZE 128 + +// Stolen out of TProtocol.h. +// It would be a huge pain to have both get this from one place. +typedef enum TType { + T_STOP = 0, + T_VOID = 1, + T_BOOL = 2, + T_BYTE = 3, + T_I08 = 3, + T_I16 = 6, + T_I32 = 8, + T_U64 = 9, + T_I64 = 10, + T_DOUBLE = 4, + T_STRING = 11, + T_UTF7 = 11, + T_STRUCT = 12, + T_MAP = 13, + T_SET = 14, + T_LIST = 15, + T_UTF8 = 16, + T_UTF16 = 17 +} TType; + +#ifndef __BYTE_ORDER +# if defined(BYTE_ORDER) && defined(LITTLE_ENDIAN) && defined(BIG_ENDIAN) +# define __BYTE_ORDER BYTE_ORDER +# define __LITTLE_ENDIAN LITTLE_ENDIAN +# define __BIG_ENDIAN BIG_ENDIAN +# else +# error "Cannot determine endianness" +# endif +#endif + +// Same comment as the enum. Sorry. +#if __BYTE_ORDER == __BIG_ENDIAN +# define ntohll(n) (n) +# define htonll(n) (n) +#elif __BYTE_ORDER == __LITTLE_ENDIAN +# if defined(__GNUC__) && defined(__GLIBC__) +# include +# define ntohll(n) bswap_64(n) +# define htonll(n) bswap_64(n) +# else /* GNUC & GLIBC */ +# define ntohll(n) ( (((unsigned long long)ntohl(n)) << 32) + ntohl(n >> 32) ) +# define htonll(n) ( (((unsigned long long)htonl(n)) << 32) + htonl(n >> 32) ) +# endif /* GNUC & GLIBC */ +#else /* __BYTE_ORDER */ +# error "Can't define htonll or ntohll!" +#endif + +// Doing a benchmark shows that interning actually makes a difference, amazingly. +#define INTERN_STRING(value) _intern_ ## value + +#define INT_CONV_ERROR_OCCURRED(v) ( ((v) == -1) && PyErr_Occurred() ) +#define CHECK_RANGE(v, min, max) ( ((v) <= (max)) && ((v) >= (min)) ) + +// Py_ssize_t was not defined before Python 2.5 +#if (PY_VERSION_HEX < 0x02050000) +typedef int Py_ssize_t; +#endif + +/** + * A cache of the spec_args for a set or list, + * so we don't have to keep calling PyTuple_GET_ITEM. + */ +typedef struct { + TType element_type; + PyObject* typeargs; +} SetListTypeArgs; + +/** + * A cache of the spec_args for a map, + * so we don't have to keep calling PyTuple_GET_ITEM. + */ +typedef struct { + TType ktag; + TType vtag; + PyObject* ktypeargs; + PyObject* vtypeargs; +} MapTypeArgs; + +/** + * A cache of the spec_args for a struct, + * so we don't have to keep calling PyTuple_GET_ITEM. + */ +typedef struct { + PyObject* klass; + PyObject* spec; +} StructTypeArgs; + +/** + * A cache of the item spec from a struct specification, + * so we don't have to keep calling PyTuple_GET_ITEM. + */ +typedef struct { + int tag; + TType type; + PyObject* attrname; + PyObject* typeargs; + PyObject* defval; +} StructItemSpec; + +/** + * A cache of the two key attributes of a CReadableTransport, + * so we don't have to keep calling PyObject_GetAttr. + */ +typedef struct { + PyObject* stringiobuf; + PyObject* refill_callable; +} DecodeBuffer; + +/** Pointer to interned string to speed up attribute lookup. */ +static PyObject* INTERN_STRING(cstringio_buf); +/** Pointer to interned string to speed up attribute lookup. */ +static PyObject* INTERN_STRING(cstringio_refill); + +static inline bool +check_ssize_t_32(Py_ssize_t len) { + // error from getting the int + if (INT_CONV_ERROR_OCCURRED(len)) { + return false; + } + if (!CHECK_RANGE(len, 0, INT32_MAX)) { + PyErr_SetString(PyExc_OverflowError, "string size out of range"); + return false; + } + return true; +} + +static inline bool +parse_pyint(PyObject* o, int32_t* ret, int32_t min, int32_t max) { + long val = PyInt_AsLong(o); + + if (INT_CONV_ERROR_OCCURRED(val)) { + return false; + } + if (!CHECK_RANGE(val, min, max)) { + PyErr_SetString(PyExc_OverflowError, "int out of range"); + return false; + } + + *ret = (int32_t) val; + return true; +} + + +/* --- FUNCTIONS TO PARSE STRUCT SPECIFICATOINS --- */ + +static bool +parse_set_list_args(SetListTypeArgs* dest, PyObject* typeargs) { + if (PyTuple_Size(typeargs) != 2) { + PyErr_SetString(PyExc_TypeError, "expecting tuple of size 2 for list/set type args"); + return false; + } + + dest->element_type = PyInt_AsLong(PyTuple_GET_ITEM(typeargs, 0)); + if (INT_CONV_ERROR_OCCURRED(dest->element_type)) { + return false; + } + + dest->typeargs = PyTuple_GET_ITEM(typeargs, 1); + + return true; +} + +static bool +parse_map_args(MapTypeArgs* dest, PyObject* typeargs) { + if (PyTuple_Size(typeargs) != 4) { + PyErr_SetString(PyExc_TypeError, "expecting 4 arguments for typeargs to map"); + return false; + } + + dest->ktag = PyInt_AsLong(PyTuple_GET_ITEM(typeargs, 0)); + if (INT_CONV_ERROR_OCCURRED(dest->ktag)) { + return false; + } + + dest->vtag = PyInt_AsLong(PyTuple_GET_ITEM(typeargs, 2)); + if (INT_CONV_ERROR_OCCURRED(dest->vtag)) { + return false; + } + + dest->ktypeargs = PyTuple_GET_ITEM(typeargs, 1); + dest->vtypeargs = PyTuple_GET_ITEM(typeargs, 3); + + return true; +} + +static bool +parse_struct_args(StructTypeArgs* dest, PyObject* typeargs) { + if (PyTuple_Size(typeargs) != 2) { + PyErr_SetString(PyExc_TypeError, "expecting tuple of size 2 for struct args"); + return false; + } + + dest->klass = PyTuple_GET_ITEM(typeargs, 0); + dest->spec = PyTuple_GET_ITEM(typeargs, 1); + + return true; +} + +static int +parse_struct_item_spec(StructItemSpec* dest, PyObject* spec_tuple) { + + // i'd like to use ParseArgs here, but it seems to be a bottleneck. + if (PyTuple_Size(spec_tuple) != 5) { + PyErr_SetString(PyExc_TypeError, "expecting 5 arguments for spec tuple"); + return false; + } + + dest->tag = PyInt_AsLong(PyTuple_GET_ITEM(spec_tuple, 0)); + if (INT_CONV_ERROR_OCCURRED(dest->tag)) { + return false; + } + + dest->type = PyInt_AsLong(PyTuple_GET_ITEM(spec_tuple, 1)); + if (INT_CONV_ERROR_OCCURRED(dest->type)) { + return false; + } + + dest->attrname = PyTuple_GET_ITEM(spec_tuple, 2); + dest->typeargs = PyTuple_GET_ITEM(spec_tuple, 3); + dest->defval = PyTuple_GET_ITEM(spec_tuple, 4); + return true; +} + +/* ====== END UTILITIES ====== */ + + +/* ====== BEGIN WRITING FUNCTIONS ====== */ + +/* --- LOW-LEVEL WRITING FUNCTIONS --- */ + +static void writeByte(PyObject* outbuf, int8_t val) { + int8_t net = val; + PycStringIO->cwrite(outbuf, (char*)&net, sizeof(int8_t)); +} + +static void writeI16(PyObject* outbuf, int16_t val) { + int16_t net = (int16_t)htons(val); + PycStringIO->cwrite(outbuf, (char*)&net, sizeof(int16_t)); +} + +static void writeI32(PyObject* outbuf, int32_t val) { + int32_t net = (int32_t)htonl(val); + PycStringIO->cwrite(outbuf, (char*)&net, sizeof(int32_t)); +} + +static void writeI64(PyObject* outbuf, int64_t val) { + int64_t net = (int64_t)htonll(val); + PycStringIO->cwrite(outbuf, (char*)&net, sizeof(int64_t)); +} + +static void writeDouble(PyObject* outbuf, double dub) { + // Unfortunately, bitwise_cast doesn't work in C. Bad C! + union { + double f; + int64_t t; + } transfer; + transfer.f = dub; + writeI64(outbuf, transfer.t); +} + + +/* --- MAIN RECURSIVE OUTPUT FUCNTION -- */ + +static int +output_val(PyObject* output, PyObject* value, TType type, PyObject* typeargs) { + /* + * Refcounting Strategy: + * + * We assume that elements of the thrift_spec tuple are not going to be + * mutated, so we don't ref count those at all. Other than that, we try to + * keep a reference to all the user-created objects while we work with them. + * output_val assumes that a reference is already held. The *caller* is + * responsible for handling references + */ + + switch (type) { + + case T_BOOL: { + int v = PyObject_IsTrue(value); + if (v == -1) { + return false; + } + + writeByte(output, (int8_t) v); + break; + } + case T_I08: { + int32_t val; + + if (!parse_pyint(value, &val, INT8_MIN, INT8_MAX)) { + return false; + } + + writeByte(output, (int8_t) val); + break; + } + case T_I16: { + int32_t val; + + if (!parse_pyint(value, &val, INT16_MIN, INT16_MAX)) { + return false; + } + + writeI16(output, (int16_t) val); + break; + } + case T_I32: { + int32_t val; + + if (!parse_pyint(value, &val, INT32_MIN, INT32_MAX)) { + return false; + } + + writeI32(output, val); + break; + } + case T_I64: { + int64_t nval = PyLong_AsLongLong(value); + + if (INT_CONV_ERROR_OCCURRED(nval)) { + return false; + } + + if (!CHECK_RANGE(nval, INT64_MIN, INT64_MAX)) { + PyErr_SetString(PyExc_OverflowError, "int out of range"); + return false; + } + + writeI64(output, nval); + break; + } + + case T_DOUBLE: { + double nval = PyFloat_AsDouble(value); + if (nval == -1.0 && PyErr_Occurred()) { + return false; + } + + writeDouble(output, nval); + break; + } + + case T_STRING: { + Py_ssize_t len = PyString_Size(value); + + if (!check_ssize_t_32(len)) { + return false; + } + + writeI32(output, (int32_t) len); + PycStringIO->cwrite(output, PyString_AsString(value), (int32_t) len); + break; + } + + case T_LIST: + case T_SET: { + Py_ssize_t len; + SetListTypeArgs parsedargs; + PyObject *item; + PyObject *iterator; + + if (!parse_set_list_args(&parsedargs, typeargs)) { + return false; + } + + len = PyObject_Length(value); + + if (!check_ssize_t_32(len)) { + return false; + } + + writeByte(output, parsedargs.element_type); + writeI32(output, (int32_t) len); + + iterator = PyObject_GetIter(value); + if (iterator == NULL) { + return false; + } + + while ((item = PyIter_Next(iterator))) { + if (!output_val(output, item, parsedargs.element_type, parsedargs.typeargs)) { + Py_DECREF(item); + Py_DECREF(iterator); + return false; + } + Py_DECREF(item); + } + + Py_DECREF(iterator); + + if (PyErr_Occurred()) { + return false; + } + + break; + } + + case T_MAP: { + PyObject *k, *v; + Py_ssize_t pos = 0; + Py_ssize_t len; + + MapTypeArgs parsedargs; + + len = PyDict_Size(value); + if (!check_ssize_t_32(len)) { + return false; + } + + if (!parse_map_args(&parsedargs, typeargs)) { + return false; + } + + writeByte(output, parsedargs.ktag); + writeByte(output, parsedargs.vtag); + writeI32(output, len); + + // TODO(bmaurer): should support any mapping, not just dicts + while (PyDict_Next(value, &pos, &k, &v)) { + // TODO(dreiss): Think hard about whether these INCREFs actually + // turn any unsafe scenarios into safe scenarios. + Py_INCREF(k); + Py_INCREF(v); + + if (!output_val(output, k, parsedargs.ktag, parsedargs.ktypeargs) + || !output_val(output, v, parsedargs.vtag, parsedargs.vtypeargs)) { + Py_DECREF(k); + Py_DECREF(v); + return false; + } + Py_DECREF(k); + Py_DECREF(v); + } + break; + } + + // TODO(dreiss): Consider breaking this out as a function + // the way we did for decode_struct. + case T_STRUCT: { + StructTypeArgs parsedargs; + Py_ssize_t nspec; + Py_ssize_t i; + + if (!parse_struct_args(&parsedargs, typeargs)) { + return false; + } + + nspec = PyTuple_Size(parsedargs.spec); + + if (nspec == -1) { + return false; + } + + for (i = 0; i < nspec; i++) { + StructItemSpec parsedspec; + PyObject* spec_tuple; + PyObject* instval = NULL; + + spec_tuple = PyTuple_GET_ITEM(parsedargs.spec, i); + if (spec_tuple == Py_None) { + continue; + } + + if (!parse_struct_item_spec (&parsedspec, spec_tuple)) { + return false; + } + + instval = PyObject_GetAttr(value, parsedspec.attrname); + + if (!instval) { + return false; + } + + if (instval == Py_None) { + Py_DECREF(instval); + continue; + } + + writeByte(output, (int8_t) parsedspec.type); + writeI16(output, parsedspec.tag); + + if (!output_val(output, instval, parsedspec.type, parsedspec.typeargs)) { + Py_DECREF(instval); + return false; + } + + Py_DECREF(instval); + } + + writeByte(output, (int8_t)T_STOP); + break; + } + + case T_STOP: + case T_VOID: + case T_UTF16: + case T_UTF8: + case T_U64: + default: + PyErr_SetString(PyExc_TypeError, "Unexpected TType"); + return false; + + } + + return true; +} + + +/* --- TOP-LEVEL WRAPPER FOR OUTPUT -- */ + +static PyObject * +encode_binary(PyObject *self, PyObject *args) { + PyObject* enc_obj; + PyObject* type_args; + PyObject* buf; + PyObject* ret = NULL; + + if (!PyArg_ParseTuple(args, "OO", &enc_obj, &type_args)) { + return NULL; + } + + buf = PycStringIO->NewOutput(INIT_OUTBUF_SIZE); + if (output_val(buf, enc_obj, T_STRUCT, type_args)) { + ret = PycStringIO->cgetvalue(buf); + } + + Py_DECREF(buf); + return ret; +} + +/* ====== END WRITING FUNCTIONS ====== */ + + +/* ====== BEGIN READING FUNCTIONS ====== */ + +/* --- LOW-LEVEL READING FUNCTIONS --- */ + +static void +free_decodebuf(DecodeBuffer* d) { + Py_XDECREF(d->stringiobuf); + Py_XDECREF(d->refill_callable); +} + +static bool +decode_buffer_from_obj(DecodeBuffer* dest, PyObject* obj) { + dest->stringiobuf = PyObject_GetAttr(obj, INTERN_STRING(cstringio_buf)); + if (!dest->stringiobuf) { + return false; + } + + if (!PycStringIO_InputCheck(dest->stringiobuf)) { + free_decodebuf(dest); + PyErr_SetString(PyExc_TypeError, "expecting stringio input"); + return false; + } + + dest->refill_callable = PyObject_GetAttr(obj, INTERN_STRING(cstringio_refill)); + + if(!dest->refill_callable) { + free_decodebuf(dest); + return false; + } + + if (!PyCallable_Check(dest->refill_callable)) { + free_decodebuf(dest); + PyErr_SetString(PyExc_TypeError, "expecting callable"); + return false; + } + + return true; +} + +static bool readBytes(DecodeBuffer* input, char** output, int len) { + int read; + + // TODO(dreiss): Don't fear the malloc. Think about taking a copy of + // the partial read instead of forcing the transport + // to prepend it to its buffer. + + read = PycStringIO->cread(input->stringiobuf, output, len); + + if (read == len) { + return true; + } else if (read == -1) { + return false; + } else { + PyObject* newiobuf; + + // using building functions as this is a rare codepath + newiobuf = PyObject_CallFunction( + input->refill_callable, "s#i", *output, read, len, NULL); + if (newiobuf == NULL) { + return false; + } + + // must do this *AFTER* the call so that we don't deref the io buffer + Py_CLEAR(input->stringiobuf); + input->stringiobuf = newiobuf; + + read = PycStringIO->cread(input->stringiobuf, output, len); + + if (read == len) { + return true; + } else if (read == -1) { + return false; + } else { + // TODO(dreiss): This could be a valid code path for big binary blobs. + PyErr_SetString(PyExc_TypeError, + "refill claimed to have refilled the buffer, but didn't!!"); + return false; + } + } +} + +static int8_t readByte(DecodeBuffer* input) { + char* buf; + if (!readBytes(input, &buf, sizeof(int8_t))) { + return -1; + } + + return *(int8_t*) buf; +} + +static int16_t readI16(DecodeBuffer* input) { + char* buf; + if (!readBytes(input, &buf, sizeof(int16_t))) { + return -1; + } + + return (int16_t) ntohs(*(int16_t*) buf); +} + +static int32_t readI32(DecodeBuffer* input) { + char* buf; + if (!readBytes(input, &buf, sizeof(int32_t))) { + return -1; + } + return (int32_t) ntohl(*(int32_t*) buf); +} + + +static int64_t readI64(DecodeBuffer* input) { + char* buf; + if (!readBytes(input, &buf, sizeof(int64_t))) { + return -1; + } + + return (int64_t) ntohll(*(int64_t*) buf); +} + +static double readDouble(DecodeBuffer* input) { + union { + int64_t f; + double t; + } transfer; + + transfer.f = readI64(input); + if (transfer.f == -1) { + return -1; + } + return transfer.t; +} + +static bool +checkTypeByte(DecodeBuffer* input, TType expected) { + TType got = readByte(input); + if (INT_CONV_ERROR_OCCURRED(got)) { + return false; + } + + if (expected != got) { + PyErr_SetString(PyExc_TypeError, "got wrong ttype while reading field"); + return false; + } + return true; +} + +static bool +skip(DecodeBuffer* input, TType type) { +#define SKIPBYTES(n) \ + do { \ + if (!readBytes(input, &dummy_buf, (n))) { \ + return false; \ + } \ + } while(0) + + char* dummy_buf; + + switch (type) { + + case T_BOOL: + case T_I08: SKIPBYTES(1); break; + case T_I16: SKIPBYTES(2); break; + case T_I32: SKIPBYTES(4); break; + case T_I64: + case T_DOUBLE: SKIPBYTES(8); break; + + case T_STRING: { + // TODO(dreiss): Find out if these check_ssize_t32s are really necessary. + int len = readI32(input); + if (!check_ssize_t_32(len)) { + return false; + } + SKIPBYTES(len); + break; + } + + case T_LIST: + case T_SET: { + TType etype; + int len, i; + + etype = readByte(input); + if (etype == -1) { + return false; + } + + len = readI32(input); + if (!check_ssize_t_32(len)) { + return false; + } + + for (i = 0; i < len; i++) { + if (!skip(input, etype)) { + return false; + } + } + break; + } + + case T_MAP: { + TType ktype, vtype; + int len, i; + + ktype = readByte(input); + if (ktype == -1) { + return false; + } + + vtype = readByte(input); + if (vtype == -1) { + return false; + } + + len = readI32(input); + if (!check_ssize_t_32(len)) { + return false; + } + + for (i = 0; i < len; i++) { + if (!(skip(input, ktype) && skip(input, vtype))) { + return false; + } + } + break; + } + + case T_STRUCT: { + while (true) { + TType type; + + type = readByte(input); + if (type == -1) { + return false; + } + + if (type == T_STOP) + break; + + SKIPBYTES(2); // tag + if (!skip(input, type)) { + return false; + } + } + break; + } + + case T_STOP: + case T_VOID: + case T_UTF16: + case T_UTF8: + case T_U64: + default: + PyErr_SetString(PyExc_TypeError, "Unexpected TType"); + return false; + + } + + return true; + +#undef SKIPBYTES +} + + +/* --- HELPER FUNCTION FOR DECODE_VAL --- */ + +static PyObject* +decode_val(DecodeBuffer* input, TType type, PyObject* typeargs); + +static bool +decode_struct(DecodeBuffer* input, PyObject* output, PyObject* spec_seq) { + int spec_seq_len = PyTuple_Size(spec_seq); + if (spec_seq_len == -1) { + return false; + } + + while (true) { + TType type; + int16_t tag; + PyObject* item_spec; + PyObject* fieldval = NULL; + StructItemSpec parsedspec; + + type = readByte(input); + if (type == -1) { + return false; + } + if (type == T_STOP) { + break; + } + tag = readI16(input); + if (INT_CONV_ERROR_OCCURRED(tag)) { + return false; + } + if (tag >= 0 && tag < spec_seq_len) { + item_spec = PyTuple_GET_ITEM(spec_seq, tag); + } else { + item_spec = Py_None; + } + + if (item_spec == Py_None) { + if (!skip(input, type)) { + return false; + } else { + continue; + } + } + + if (!parse_struct_item_spec(&parsedspec, item_spec)) { + return false; + } + if (parsedspec.type != type) { + if (!skip(input, type)) { + PyErr_SetString(PyExc_TypeError, "struct field had wrong type while reading and can't be skipped"); + return false; + } else { + continue; + } + } + + fieldval = decode_val(input, parsedspec.type, parsedspec.typeargs); + if (fieldval == NULL) { + return false; + } + + if (PyObject_SetAttr(output, parsedspec.attrname, fieldval) == -1) { + Py_DECREF(fieldval); + return false; + } + Py_DECREF(fieldval); + } + return true; +} + + +/* --- MAIN RECURSIVE INPUT FUCNTION --- */ + +// Returns a new reference. +static PyObject* +decode_val(DecodeBuffer* input, TType type, PyObject* typeargs) { + switch (type) { + + case T_BOOL: { + int8_t v = readByte(input); + if (INT_CONV_ERROR_OCCURRED(v)) { + return NULL; + } + + switch (v) { + case 0: Py_RETURN_FALSE; + case 1: Py_RETURN_TRUE; + // Don't laugh. This is a potentially serious issue. + default: PyErr_SetString(PyExc_TypeError, "boolean out of range"); return NULL; + } + break; + } + case T_I08: { + int8_t v = readByte(input); + if (INT_CONV_ERROR_OCCURRED(v)) { + return NULL; + } + + return PyInt_FromLong(v); + } + case T_I16: { + int16_t v = readI16(input); + if (INT_CONV_ERROR_OCCURRED(v)) { + return NULL; + } + return PyInt_FromLong(v); + } + case T_I32: { + int32_t v = readI32(input); + if (INT_CONV_ERROR_OCCURRED(v)) { + return NULL; + } + return PyInt_FromLong(v); + } + + case T_I64: { + int64_t v = readI64(input); + if (INT_CONV_ERROR_OCCURRED(v)) { + return NULL; + } + // TODO(dreiss): Find out if we can take this fastpath always when + // sizeof(long) == sizeof(long long). + if (CHECK_RANGE(v, LONG_MIN, LONG_MAX)) { + return PyInt_FromLong((long) v); + } + + return PyLong_FromLongLong(v); + } + + case T_DOUBLE: { + double v = readDouble(input); + if (v == -1.0 && PyErr_Occurred()) { + return false; + } + return PyFloat_FromDouble(v); + } + + case T_STRING: { + Py_ssize_t len = readI32(input); + char* buf; + if (!readBytes(input, &buf, len)) { + return NULL; + } + + return PyString_FromStringAndSize(buf, len); + } + + case T_LIST: + case T_SET: { + SetListTypeArgs parsedargs; + int32_t len; + PyObject* ret = NULL; + int i; + + if (!parse_set_list_args(&parsedargs, typeargs)) { + return NULL; + } + + if (!checkTypeByte(input, parsedargs.element_type)) { + return NULL; + } + + len = readI32(input); + if (!check_ssize_t_32(len)) { + return NULL; + } + + ret = PyList_New(len); + if (!ret) { + return NULL; + } + + for (i = 0; i < len; i++) { + PyObject* item = decode_val(input, parsedargs.element_type, parsedargs.typeargs); + if (!item) { + Py_DECREF(ret); + return NULL; + } + PyList_SET_ITEM(ret, i, item); + } + + // TODO(dreiss): Consider biting the bullet and making two separate cases + // for list and set, avoiding this post facto conversion. + if (type == T_SET) { + PyObject* setret; +#if (PY_VERSION_HEX < 0x02050000) + // hack needed for older versions + setret = PyObject_CallFunctionObjArgs((PyObject*)&PySet_Type, ret, NULL); +#else + // official version + setret = PySet_New(ret); +#endif + Py_DECREF(ret); + return setret; + } + return ret; + } + + case T_MAP: { + int32_t len; + int i; + MapTypeArgs parsedargs; + PyObject* ret = NULL; + + if (!parse_map_args(&parsedargs, typeargs)) { + return NULL; + } + + if (!checkTypeByte(input, parsedargs.ktag)) { + return NULL; + } + if (!checkTypeByte(input, parsedargs.vtag)) { + return NULL; + } + + len = readI32(input); + if (!check_ssize_t_32(len)) { + return false; + } + + ret = PyDict_New(); + if (!ret) { + goto error; + } + + for (i = 0; i < len; i++) { + PyObject* k = NULL; + PyObject* v = NULL; + k = decode_val(input, parsedargs.ktag, parsedargs.ktypeargs); + if (k == NULL) { + goto loop_error; + } + v = decode_val(input, parsedargs.vtag, parsedargs.vtypeargs); + if (v == NULL) { + goto loop_error; + } + if (PyDict_SetItem(ret, k, v) == -1) { + goto loop_error; + } + + Py_DECREF(k); + Py_DECREF(v); + continue; + + // Yuck! Destructors, anyone? + loop_error: + Py_XDECREF(k); + Py_XDECREF(v); + goto error; + } + + return ret; + + error: + Py_XDECREF(ret); + return NULL; + } + + case T_STRUCT: { + StructTypeArgs parsedargs; + PyObject* ret; + if (!parse_struct_args(&parsedargs, typeargs)) { + return NULL; + } + + ret = PyObject_CallObject(parsedargs.klass, NULL); + if (!ret) { + return NULL; + } + + if (!decode_struct(input, ret, parsedargs.spec)) { + Py_DECREF(ret); + return NULL; + } + + return ret; + } + + case T_STOP: + case T_VOID: + case T_UTF16: + case T_UTF8: + case T_U64: + default: + PyErr_SetString(PyExc_TypeError, "Unexpected TType"); + return NULL; + } +} + + +/* --- TOP-LEVEL WRAPPER FOR INPUT -- */ + +static PyObject* +decode_binary(PyObject *self, PyObject *args) { + PyObject* output_obj = NULL; + PyObject* transport = NULL; + PyObject* typeargs = NULL; + StructTypeArgs parsedargs; + DecodeBuffer input = {0, 0}; + + if (!PyArg_ParseTuple(args, "OOO", &output_obj, &transport, &typeargs)) { + return NULL; + } + + if (!parse_struct_args(&parsedargs, typeargs)) { + return NULL; + } + + if (!decode_buffer_from_obj(&input, transport)) { + return NULL; + } + + if (!decode_struct(&input, output_obj, parsedargs.spec)) { + free_decodebuf(&input); + return NULL; + } + + free_decodebuf(&input); + + Py_RETURN_NONE; +} + +/* ====== END READING FUNCTIONS ====== */ + + +/* -- PYTHON MODULE SETUP STUFF --- */ + +static PyMethodDef ThriftFastBinaryMethods[] = { + + {"encode_binary", encode_binary, METH_VARARGS, ""}, + {"decode_binary", decode_binary, METH_VARARGS, ""}, + + {NULL, NULL, 0, NULL} /* Sentinel */ +}; + +PyMODINIT_FUNC +initfastbinary(void) { +#define INIT_INTERN_STRING(value) \ + do { \ + INTERN_STRING(value) = PyString_InternFromString(#value); \ + if(!INTERN_STRING(value)) return; \ + } while(0) + + INIT_INTERN_STRING(cstringio_buf); + INIT_INTERN_STRING(cstringio_refill); +#undef INIT_INTERN_STRING + + PycString_IMPORT; + if (PycStringIO == NULL) return; + + (void) Py_InitModule("thrift.protocol.fastbinary", ThriftFastBinaryMethods); +} diff --git a/thrift/server/THttpServer.py b/thrift/server/THttpServer.py new file mode 100644 index 0000000..f6d1ff5 --- /dev/null +++ b/thrift/server/THttpServer.py @@ -0,0 +1,87 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +import http.server + +from thrift.server import TServer +from thrift.transport import TTransport + + +class ResponseException(Exception): + """Allows handlers to override the HTTP response + + Normally, THttpServer always sends a 200 response. If a handler wants + to override this behavior (e.g., to simulate a misconfigured or + overloaded web server during testing), it can raise a ResponseException. + The function passed to the constructor will be called with the + RequestHandler as its only argument. + """ + def __init__(self, handler): + self.handler = handler + + +class THttpServer(TServer.TServer): + """A simple HTTP-based Thrift server + + This class is not very performant, but it is useful (for example) for + acting as a mock version of an Apache-based PHP Thrift endpoint. + """ + def __init__(self, + processor, + server_address, + inputProtocolFactory, + outputProtocolFactory=None, + server_class=http.server.HTTPServer): + """Set up protocol factories and HTTP server. + + See BaseHTTPServer for server_address. + See TServer for protocol factories. + """ + if outputProtocolFactory is None: + outputProtocolFactory = inputProtocolFactory + + TServer.TServer.__init__(self, processor, None, None, None, + inputProtocolFactory, outputProtocolFactory) + + thttpserver = self + + class RequestHander(http.server.BaseHTTPRequestHandler): + def do_POST(self): + # Don't care about the request path. + itrans = TTransport.TFileObjectTransport(self.rfile) + otrans = TTransport.TFileObjectTransport(self.wfile) + itrans = TTransport.TBufferedTransport( + itrans, int(self.headers['Content-Length'])) + otrans = TTransport.TMemoryBuffer() + iprot = thttpserver.inputProtocolFactory.getProtocol(itrans) + oprot = thttpserver.outputProtocolFactory.getProtocol(otrans) + try: + thttpserver.processor.process(iprot, oprot) + except ResponseException as exn: + exn.handler(self) + else: + self.send_response(200) + self.send_header("content-type", "application/x-thrift") + self.end_headers() + self.wfile.write(otrans.getvalue()) + + self.httpd = server_class(server_address, RequestHander) + + def serve(self): + self.httpd.serve_forever() diff --git a/thrift/server/TNonblockingServer.py b/thrift/server/TNonblockingServer.py new file mode 100644 index 0000000..764c9ae --- /dev/null +++ b/thrift/server/TNonblockingServer.py @@ -0,0 +1,346 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# +"""Implementation of non-blocking server. + +The main idea of the server is to receive and send requests +only from the main thread. + +The thread poool should be sized for concurrent tasks, not +maximum connections +""" +import threading +import socket +import queue +import select +import struct +import logging + +from thrift.transport import TTransport +from thrift.protocol.TBinaryProtocol import TBinaryProtocolFactory + +__all__ = ['TNonblockingServer'] + + +class Worker(threading.Thread): + """Worker is a small helper to process incoming connection.""" + + def __init__(self, queue): + threading.Thread.__init__(self) + self.queue = queue + + def run(self): + """Process queries from task queue, stop if processor is None.""" + while True: + try: + processor, iprot, oprot, otrans, callback = self.queue.get() + if processor is None: + break + processor.process(iprot, oprot) + callback(True, otrans.getvalue()) + except Exception: + logging.exception("Exception while processing request") + callback(False, '') + +WAIT_LEN = 0 +WAIT_MESSAGE = 1 +WAIT_PROCESS = 2 +SEND_ANSWER = 3 +CLOSED = 4 + + +def locked(func): + """Decorator which locks self.lock.""" + def nested(self, *args, **kwargs): + self.lock.acquire() + try: + return func(self, *args, **kwargs) + finally: + self.lock.release() + return nested + + +def socket_exception(func): + """Decorator close object on socket.error.""" + def read(self, *args, **kwargs): + try: + return func(self, *args, **kwargs) + except socket.error: + self.close() + return read + + +class Connection: + """Basic class is represented connection. + + It can be in state: + WAIT_LEN --- connection is reading request len. + WAIT_MESSAGE --- connection is reading request. + WAIT_PROCESS --- connection has just read whole request and + waits for call ready routine. + SEND_ANSWER --- connection is sending answer string (including length + of answer). + CLOSED --- socket was closed and connection should be deleted. + """ + def __init__(self, new_socket, wake_up): + self.socket = new_socket + self.socket.setblocking(False) + self.status = WAIT_LEN + self.len = 0 + self.message = '' + self.lock = threading.Lock() + self.wake_up = wake_up + + def _read_len(self): + """Reads length of request. + + It's a safer alternative to self.socket.recv(4) + """ + read = self.socket.recv(4 - len(self.message)) + if len(read) == 0: + # if we read 0 bytes and self.message is empty, then + # the client closed the connection + if len(self.message) != 0: + logging.error("can't read frame size from socket") + self.close() + return + self.message += read + if len(self.message) == 4: + self.len, = struct.unpack('!i', self.message) + if self.len < 0: + logging.error("negative frame size, it seems client " + "doesn't use FramedTransport") + self.close() + elif self.len == 0: + logging.error("empty frame, it's really strange") + self.close() + else: + self.message = '' + self.status = WAIT_MESSAGE + + @socket_exception + def read(self): + """Reads data from stream and switch state.""" + assert self.status in (WAIT_LEN, WAIT_MESSAGE) + if self.status == WAIT_LEN: + self._read_len() + # go back to the main loop here for simplicity instead of + # falling through, even though there is a good chance that + # the message is already available + elif self.status == WAIT_MESSAGE: + read = self.socket.recv(self.len - len(self.message)) + if len(read) == 0: + logging.error("can't read frame from socket (get %d of " + "%d bytes)" % (len(self.message), self.len)) + self.close() + return + self.message += read + if len(self.message) == self.len: + self.status = WAIT_PROCESS + + @socket_exception + def write(self): + """Writes data from socket and switch state.""" + assert self.status == SEND_ANSWER + sent = self.socket.send(self.message) + if sent == len(self.message): + self.status = WAIT_LEN + self.message = '' + self.len = 0 + else: + self.message = self.message[sent:] + + @locked + def ready(self, all_ok, message): + """Callback function for switching state and waking up main thread. + + This function is the only function witch can be called asynchronous. + + The ready can switch Connection to three states: + WAIT_LEN if request was oneway. + SEND_ANSWER if request was processed in normal way. + CLOSED if request throws unexpected exception. + + The one wakes up main thread. + """ + assert self.status == WAIT_PROCESS + if not all_ok: + self.close() + self.wake_up() + return + self.len = '' + if len(message) == 0: + # it was a oneway request, do not write answer + self.message = '' + self.status = WAIT_LEN + else: + self.message = struct.pack('!i', len(message)) + message + self.status = SEND_ANSWER + self.wake_up() + + @locked + def is_writeable(self): + """Return True if connection should be added to write list of select""" + return self.status == SEND_ANSWER + + # it's not necessary, but... + @locked + def is_readable(self): + """Return True if connection should be added to read list of select""" + return self.status in (WAIT_LEN, WAIT_MESSAGE) + + @locked + def is_closed(self): + """Returns True if connection is closed.""" + return self.status == CLOSED + + def fileno(self): + """Returns the file descriptor of the associated socket.""" + return self.socket.fileno() + + def close(self): + """Closes connection""" + self.status = CLOSED + self.socket.close() + + +class TNonblockingServer: + """Non-blocking server.""" + + def __init__(self, + processor, + lsocket, + inputProtocolFactory=None, + outputProtocolFactory=None, + threads=10): + self.processor = processor + self.socket = lsocket + self.in_protocol = inputProtocolFactory or TBinaryProtocolFactory() + self.out_protocol = outputProtocolFactory or self.in_protocol + self.threads = int(threads) + self.clients = {} + self.tasks = queue.Queue() + self._read, self._write = socket.socketpair() + self.prepared = False + self._stop = False + + def setNumThreads(self, num): + """Set the number of worker threads that should be created.""" + # implement ThreadPool interface + assert not self.prepared, "Can't change number of threads after start" + self.threads = num + + def prepare(self): + """Prepares server for serve requests.""" + if self.prepared: + return + self.socket.listen() + for _ in range(self.threads): + thread = Worker(self.tasks) + thread.setDaemon(True) + thread.start() + self.prepared = True + + def wake_up(self): + """Wake up main thread. + + The server usualy waits in select call in we should terminate one. + The simplest way is using socketpair. + + Select always wait to read from the first socket of socketpair. + + In this case, we can just write anything to the second socket from + socketpair. + """ + self._write.send('1') + + def stop(self): + """Stop the server. + + This method causes the serve() method to return. stop() may be invoked + from within your handler, or from another thread. + + After stop() is called, serve() will return but the server will still + be listening on the socket. serve() may then be called again to resume + processing requests. Alternatively, close() may be called after + serve() returns to close the server socket and shutdown all worker + threads. + """ + self._stop = True + self.wake_up() + + def _select(self): + """Does select on open connections.""" + readable = [self.socket.handle.fileno(), self._read.fileno()] + writable = [] + for i, connection in list(self.clients.items()): + if connection.is_readable(): + readable.append(connection.fileno()) + if connection.is_writeable(): + writable.append(connection.fileno()) + if connection.is_closed(): + del self.clients[i] + return select.select(readable, writable, readable) + + def handle(self): + """Handle requests. + + WARNING! You must call prepare() BEFORE calling handle() + """ + assert self.prepared, "You have to call prepare before handle" + rset, wset, xset = self._select() + for readable in rset: + if readable == self._read.fileno(): + # don't care i just need to clean readable flag + self._read.recv(1024) + elif readable == self.socket.handle.fileno(): + client = self.socket.accept().handle + self.clients[client.fileno()] = Connection(client, + self.wake_up) + else: + connection = self.clients[readable] + connection.read() + if connection.status == WAIT_PROCESS: + itransport = TTransport.TMemoryBuffer(connection.message) + otransport = TTransport.TMemoryBuffer() + iprot = self.in_protocol.getProtocol(itransport) + oprot = self.out_protocol.getProtocol(otransport) + self.tasks.put([self.processor, iprot, oprot, + otransport, connection.ready]) + for writeable in wset: + self.clients[writeable].write() + for oob in xset: + self.clients[oob].close() + del self.clients[oob] + + def close(self): + """Closes the server.""" + for _ in range(self.threads): + self.tasks.put([None, None, None, None, None]) + self.socket.close() + self.prepared = False + + def serve(self): + """Serve requests. + + Serve requests forever, or until stop() is called. + """ + self._stop = False + self.prepare() + while not self._stop: + self.handle() diff --git a/thrift/server/TProcessPoolServer.py b/thrift/server/TProcessPoolServer.py new file mode 100644 index 0000000..3714ead --- /dev/null +++ b/thrift/server/TProcessPoolServer.py @@ -0,0 +1,119 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + + +import logging +from multiprocessing import Process, Value, Condition, reduction + +from .TServer import TServer +from thrift.transport.TTransport import TTransportException +import collections + + +class TProcessPoolServer(TServer): + """Server with a fixed size pool of worker subprocesses to service requests + + Note that if you need shared state between the handlers - it's up to you! + Written by Dvir Volk, doat.com + """ + def __init__(self, *args): + TServer.__init__(self, *args) + self.numWorkers = 10 + self.workers = [] + self.isRunning = Value('b', False) + self.stopCondition = Condition() + self.postForkCallback = None + + def setPostForkCallback(self, callback): + if not isinstance(callback, collections.Callable): + raise TypeError("This is not a callback!") + self.postForkCallback = callback + + def setNumWorkers(self, num): + """Set the number of worker threads that should be created""" + self.numWorkers = num + + def workerProcess(self): + """Loop getting clients from the shared queue and process them""" + if self.postForkCallback: + self.postForkCallback() + + while self.isRunning.value: + try: + client = self.serverTransport.accept() + self.serveClient(client) + except (KeyboardInterrupt, SystemExit): + return 0 + except Exception as x: + logging.exception(x) + + def serveClient(self, client): + """Process input/output from a client for as long as possible""" + itrans = self.inputTransportFactory.getTransport(client) + otrans = self.outputTransportFactory.getTransport(client) + iprot = self.inputProtocolFactory.getProtocol(itrans) + oprot = self.outputProtocolFactory.getProtocol(otrans) + + try: + while True: + self.processor.process(iprot, oprot) + except TTransportException as tx: + pass + except Exception as x: + logging.exception(x) + + itrans.close() + otrans.close() + + def serve(self): + """Start workers and put into queue""" + # this is a shared state that can tell the workers to exit when False + self.isRunning.value = True + + # first bind and listen to the port + self.serverTransport.listen() + + # fork the children + for i in range(self.numWorkers): + try: + w = Process(target=self.workerProcess) + w.daemon = True + w.start() + self.workers.append(w) + except Exception as x: + logging.exception(x) + + # wait until the condition is set by stop() + while True: + self.stopCondition.acquire() + try: + self.stopCondition.wait() + break + except (SystemExit, KeyboardInterrupt): + break + except Exception as x: + logging.exception(x) + + self.isRunning.value = False + + def stop(self): + self.isRunning.value = False + self.stopCondition.acquire() + self.stopCondition.notify() + self.stopCondition.release() diff --git a/thrift/server/TServer.py b/thrift/server/TServer.py new file mode 100644 index 0000000..9e340f4 --- /dev/null +++ b/thrift/server/TServer.py @@ -0,0 +1,269 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +import queue +import logging +import os +import sys +import threading +import traceback + +from thrift.Thrift import TProcessor +from thrift.protocol import TBinaryProtocol +from thrift.transport import TTransport + + +class TServer: + """Base interface for a server, which must have a serve() method. + + Three constructors for all servers: + 1) (processor, serverTransport) + 2) (processor, serverTransport, transportFactory, protocolFactory) + 3) (processor, serverTransport, + inputTransportFactory, outputTransportFactory, + inputProtocolFactory, outputProtocolFactory) + """ + def __init__(self, *args): + if (len(args) == 2): + self.__initArgs__(args[0], args[1], + TTransport.TTransportFactoryBase(), + TTransport.TTransportFactoryBase(), + TBinaryProtocol.TBinaryProtocolFactory(), + TBinaryProtocol.TBinaryProtocolFactory()) + elif (len(args) == 4): + self.__initArgs__(args[0], args[1], args[2], args[2], args[3], args[3]) + elif (len(args) == 6): + self.__initArgs__(args[0], args[1], args[2], args[3], args[4], args[5]) + + def __initArgs__(self, processor, serverTransport, + inputTransportFactory, outputTransportFactory, + inputProtocolFactory, outputProtocolFactory): + self.processor = processor + self.serverTransport = serverTransport + self.inputTransportFactory = inputTransportFactory + self.outputTransportFactory = outputTransportFactory + self.inputProtocolFactory = inputProtocolFactory + self.outputProtocolFactory = outputProtocolFactory + + def serve(self): + pass + + +class TSimpleServer(TServer): + """Simple single-threaded server that just pumps around one transport.""" + + def __init__(self, *args): + TServer.__init__(self, *args) + + def serve(self): + self.serverTransport.listen() + while True: + client = self.serverTransport.accept() + itrans = self.inputTransportFactory.getTransport(client) + otrans = self.outputTransportFactory.getTransport(client) + iprot = self.inputProtocolFactory.getProtocol(itrans) + oprot = self.outputProtocolFactory.getProtocol(otrans) + try: + while True: + self.processor.process(iprot, oprot) + except TTransport.TTransportException as tx: + pass + except Exception as x: + logging.exception(x) + + itrans.close() + otrans.close() + + +class TThreadedServer(TServer): + """Threaded server that spawns a new thread per each connection.""" + + def __init__(self, *args, **kwargs): + TServer.__init__(self, *args) + self.daemon = kwargs.get("daemon", False) + + def serve(self): + self.serverTransport.listen() + while True: + try: + client = self.serverTransport.accept() + t = threading.Thread(target=self.handle, args=(client,)) + t.setDaemon(self.daemon) + t.start() + except KeyboardInterrupt: + raise + except Exception as x: + logging.exception(x) + + def handle(self, client): + itrans = self.inputTransportFactory.getTransport(client) + otrans = self.outputTransportFactory.getTransport(client) + iprot = self.inputProtocolFactory.getProtocol(itrans) + oprot = self.outputProtocolFactory.getProtocol(otrans) + try: + while True: + self.processor.process(iprot, oprot) + except TTransport.TTransportException as tx: + pass + except Exception as x: + logging.exception(x) + + itrans.close() + otrans.close() + + +class TThreadPoolServer(TServer): + """Server with a fixed size pool of threads which service requests.""" + + def __init__(self, *args, **kwargs): + TServer.__init__(self, *args) + self.clients = queue.Queue() + self.threads = 10 + self.daemon = kwargs.get("daemon", False) + + def setNumThreads(self, num): + """Set the number of worker threads that should be created""" + self.threads = num + + def serveThread(self): + """Loop around getting clients from the shared queue and process them.""" + while True: + try: + client = self.clients.get() + self.serveClient(client) + except Exception as x: + logging.exception(x) + + def serveClient(self, client): + """Process input/output from a client for as long as possible""" + itrans = self.inputTransportFactory.getTransport(client) + otrans = self.outputTransportFactory.getTransport(client) + iprot = self.inputProtocolFactory.getProtocol(itrans) + oprot = self.outputProtocolFactory.getProtocol(otrans) + try: + while True: + self.processor.process(iprot, oprot) + except TTransport.TTransportException as tx: + pass + except Exception as x: + logging.exception(x) + + itrans.close() + otrans.close() + + def serve(self): + """Start a fixed number of worker threads and put client into a queue""" + for i in range(self.threads): + try: + t = threading.Thread(target=self.serveThread) + t.setDaemon(self.daemon) + t.start() + except Exception as x: + logging.exception(x) + + # Pump the socket for clients + self.serverTransport.listen() + while True: + try: + client = self.serverTransport.accept() + self.clients.put(client) + except Exception as x: + logging.exception(x) + + +class TForkingServer(TServer): + """A Thrift server that forks a new process for each request + + This is more scalable than the threaded server as it does not cause + GIL contention. + + Note that this has different semantics from the threading server. + Specifically, updates to shared variables will no longer be shared. + It will also not work on windows. + + This code is heavily inspired by SocketServer.ForkingMixIn in the + Python stdlib. + """ + def __init__(self, *args): + TServer.__init__(self, *args) + self.children = [] + + def serve(self): + def try_close(file): + try: + file.close() + except IOError as e: + logging.warning(e, exc_info=True) + + self.serverTransport.listen() + while True: + client = self.serverTransport.accept() + try: + pid = os.fork() + + if pid: # parent + # add before collect, otherwise you race w/ waitpid + self.children.append(pid) + self.collect_children() + + # Parent must close socket or the connection may not get + # closed promptly + itrans = self.inputTransportFactory.getTransport(client) + otrans = self.outputTransportFactory.getTransport(client) + try_close(itrans) + try_close(otrans) + else: + itrans = self.inputTransportFactory.getTransport(client) + otrans = self.outputTransportFactory.getTransport(client) + + iprot = self.inputProtocolFactory.getProtocol(itrans) + oprot = self.outputProtocolFactory.getProtocol(otrans) + + ecode = 0 + try: + try: + while True: + self.processor.process(iprot, oprot) + except TTransport.TTransportException as tx: + pass + except Exception as e: + logging.exception(e) + ecode = 1 + finally: + try_close(itrans) + try_close(otrans) + + os._exit(ecode) + + except TTransport.TTransportException as tx: + pass + except Exception as x: + logging.exception(x) + + def collect_children(self): + while self.children: + try: + pid, status = os.waitpid(0, os.WNOHANG) + except os.error: + pid = None + + if pid: + self.children.remove(pid) + else: + break diff --git a/thrift/server/__init__.py b/thrift/server/__init__.py new file mode 100644 index 0000000..1bf6e25 --- /dev/null +++ b/thrift/server/__init__.py @@ -0,0 +1,20 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +__all__ = ['TServer', 'TNonblockingServer'] diff --git a/thrift/transport/THttpClient.py b/thrift/transport/THttpClient.py new file mode 100644 index 0000000..20be338 --- /dev/null +++ b/thrift/transport/THttpClient.py @@ -0,0 +1,149 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +import http.client +import os +import socket +import sys +import urllib.request, urllib.parse, urllib.error +import urllib.parse +import warnings + +from io import StringIO + +from .TTransport import * + + +class THttpClient(TTransportBase): + """Http implementation of TTransport base.""" + + def __init__(self, uri_or_host, port=None, path=None): + """THttpClient supports two different types constructor parameters. + + THttpClient(host, port, path) - deprecated + THttpClient(uri) + + Only the second supports https. + """ + if port is not None: + warnings.warn( + "Please use the THttpClient('http://host:port/path') syntax", + DeprecationWarning, + stacklevel=2) + self.host = uri_or_host + self.port = port + assert path + self.path = path + self.scheme = 'http' + else: + parsed = urllib.parse.urlparse(uri_or_host) + self.scheme = parsed.scheme + assert self.scheme in ('http', 'https') + if self.scheme == 'http': + self.port = parsed.port or http.client.HTTP_PORT + elif self.scheme == 'https': + self.port = parsed.port or http.client.HTTPS_PORT + self.host = parsed.hostname + self.path = parsed.path + if parsed.query: + self.path += '?%s' % parsed.query + self.__wbuf = StringIO() + self.__http = None + self.__timeout = None + self.__custom_headers = None + + def open(self): + if self.scheme == 'http': + self.__http = http.client.HTTP(self.host, self.port) + else: + self.__http = http.client.HTTPS(self.host, self.port) + + def close(self): + self.__http.close() + self.__http = None + + def isOpen(self): + return self.__http is not None + + def setTimeout(self, ms): + if not hasattr(socket, 'getdefaulttimeout'): + raise NotImplementedError + + if ms is None: + self.__timeout = None + else: + self.__timeout = ms / 1000.0 + + def setCustomHeaders(self, headers): + self.__custom_headers = headers + + def read(self, sz): + return self.__http.file.read(sz) + + def write(self, buf): + self.__wbuf.write(buf) + + def __withTimeout(f): + def _f(*args, **kwargs): + orig_timeout = socket.getdefaulttimeout() + socket.setdefaulttimeout(args[0].__timeout) + result = f(*args, **kwargs) + socket.setdefaulttimeout(orig_timeout) + return result + return _f + + def flush(self): + if self.isOpen(): + self.close() + self.open() + + # Pull data out of buffer + data = self.__wbuf.getvalue() + self.__wbuf = StringIO() + + # HTTP request + self.__http.putrequest('POST', self.path) + + # Write headers + self.__http.putheader('Host', self.host) + self.__http.putheader('Content-Type', 'application/x-thrift') + self.__http.putheader('Content-Length', str(len(data))) + + if not self.__custom_headers or 'User-Agent' not in self.__custom_headers: + user_agent = 'Python/THttpClient' + script = os.path.basename(sys.argv[0]) + if script: + user_agent = '%s (%s)' % (user_agent, urllib.parse.quote(script)) + self.__http.putheader('User-Agent', user_agent) + + if self.__custom_headers: + for key, val in self.__custom_headers.items(): + self.__http.putheader(key, val) + + self.__http.endheaders() + + # Write payload + self.__http.send(data) + + # Get reply to flush the request + self.code, self.message, self.headers = self.__http.getreply() + + # Decorate if we know how to timeout + if hasattr(socket, 'getdefaulttimeout'): + flush = __withTimeout(flush) diff --git a/thrift/transport/TSSLSocket.py b/thrift/transport/TSSLSocket.py new file mode 100644 index 0000000..e0ff4f9 --- /dev/null +++ b/thrift/transport/TSSLSocket.py @@ -0,0 +1,202 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +import os +import socket +import ssl + +from thrift.transport import TSocket +from thrift.transport.TTransport import TTransportException + + +class TSSLSocket(TSocket.TSocket): + """ + SSL implementation of client-side TSocket + + This class creates outbound sockets wrapped using the + python standard ssl module for encrypted connections. + + The protocol used is set using the class variable + SSL_VERSION, which must be one of ssl.PROTOCOL_* and + defaults to ssl.PROTOCOL_TLSv1 for greatest security. + """ + SSL_VERSION = ssl.PROTOCOL_TLSv1 + + def __init__(self, + host='localhost', + port=9090, + validate=True, + ca_certs=None, + unix_socket=None): + """Create SSL TSocket + + @param validate: Set to False to disable SSL certificate validation + @type validate: bool + @param ca_certs: Filename to the Certificate Authority pem file, possibly a + file downloaded from: http://curl.haxx.se/ca/cacert.pem This is passed to + the ssl_wrap function as the 'ca_certs' parameter. + @type ca_certs: str + + Raises an IOError exception if validate is True and the ca_certs file is + None, not present or unreadable. + """ + self.validate = validate + self.is_valid = False + self.peercert = None + if not validate: + self.cert_reqs = ssl.CERT_NONE + else: + self.cert_reqs = ssl.CERT_REQUIRED + self.ca_certs = ca_certs + if validate: + if ca_certs is None or not os.access(ca_certs, os.R_OK): + raise IOError('Certificate Authority ca_certs file "%s" ' + 'is not readable, cannot validate SSL ' + 'certificates.' % (ca_certs)) + TSocket.TSocket.__init__(self, host, port, unix_socket) + + def open(self): + try: + res0 = self._resolveAddr() + for res in res0: + sock_family, sock_type = res[0:2] + ip_port = res[4] + plain_sock = socket.socket(sock_family, sock_type) + self.handle = ssl.wrap_socket(plain_sock, + ssl_version=self.SSL_VERSION, + do_handshake_on_connect=True, + ca_certs=self.ca_certs, + cert_reqs=self.cert_reqs) + self.handle.settimeout(self._timeout) + try: + self.handle.connect(ip_port) + except socket.error as e: + if res is not res0[-1]: + continue + else: + raise e + break + except socket.error as e: + if self._unix_socket: + message = 'Could not connect to secure socket %s' % self._unix_socket + else: + message = 'Could not connect to %s:%d' % (self.host, self.port) + raise TTransportException(type=TTransportException.NOT_OPEN, + message=message) + if self.validate: + self._validate_cert() + + def _validate_cert(self): + """internal method to validate the peer's SSL certificate, and to check the + commonName of the certificate to ensure it matches the hostname we + used to make this connection. Does not support subjectAltName records + in certificates. + + raises TTransportException if the certificate fails validation. + """ + cert = self.handle.getpeercert() + self.peercert = cert + if 'subject' not in cert: + raise TTransportException( + type=TTransportException.NOT_OPEN, + message='No SSL certificate found from %s:%s' % (self.host, self.port)) + fields = cert['subject'] + for field in fields: + # ensure structure we get back is what we expect + if not isinstance(field, tuple): + continue + cert_pair = field[0] + if len(cert_pair) < 2: + continue + cert_key, cert_value = cert_pair[0:2] + if cert_key != 'commonName': + continue + certhost = cert_value + if certhost == self.host: + # success, cert commonName matches desired hostname + self.is_valid = True + return + else: + raise TTransportException( + type=TTransportException.UNKNOWN, + message='Hostname we connected to "%s" doesn\'t match certificate ' + 'provided commonName "%s"' % (self.host, certhost)) + raise TTransportException( + type=TTransportException.UNKNOWN, + message='Could not validate SSL certificate from ' + 'host "%s". Cert=%s' % (self.host, cert)) + + +class TSSLServerSocket(TSocket.TServerSocket): + """SSL implementation of TServerSocket + + This uses the ssl module's wrap_socket() method to provide SSL + negotiated encryption. + """ + SSL_VERSION = ssl.PROTOCOL_TLSv1 + + def __init__(self, + host=None, + port=9090, + certfile='cert.pem', + unix_socket=None): + """Initialize a TSSLServerSocket + + @param certfile: filename of the server certificate, defaults to cert.pem + @type certfile: str + @param host: The hostname or IP to bind the listen socket to, + i.e. 'localhost' for only allowing local network connections. + Pass None to bind to all interfaces. + @type host: str + @param port: The port to listen on for inbound connections. + @type port: int + """ + self.setCertfile(certfile) + TSocket.TServerSocket.__init__(self, host, port) + + def setCertfile(self, certfile): + """Set or change the server certificate file used to wrap new connections. + + @param certfile: The filename of the server certificate, + i.e. '/etc/certs/server.pem' + @type certfile: str + + Raises an IOError exception if the certfile is not present or unreadable. + """ + if not os.access(certfile, os.R_OK): + raise IOError('No such certfile found: %s' % (certfile)) + self.certfile = certfile + + def accept(self): + plain_client, addr = self.handle.accept() + try: + client = ssl.wrap_socket(plain_client, certfile=self.certfile, + server_side=True, ssl_version=self.SSL_VERSION) + except ssl.SSLError as ssl_exc: + # failed handshake/ssl wrap, close socket to client + plain_client.close() + # raise ssl_exc + # We can't raise the exception, because it kills most TServer derived + # serve() methods. + # Instead, return None, and let the TServer instance deal with it in + # other exception handling. (but TSimpleServer dies anyway) + return None + result = TSocket.TSocket() + result.setHandle(client) + return result diff --git a/thrift/transport/TSocket.py b/thrift/transport/TSocket.py new file mode 100644 index 0000000..82ce568 --- /dev/null +++ b/thrift/transport/TSocket.py @@ -0,0 +1,176 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +import errno +import os +import socket +import sys + +from .TTransport import * + + +class TSocketBase(TTransportBase): + def _resolveAddr(self): + if self._unix_socket is not None: + return [(socket.AF_UNIX, socket.SOCK_STREAM, None, None, + self._unix_socket)] + else: + return socket.getaddrinfo(self.host, + self.port, + socket.AF_UNSPEC, + socket.SOCK_STREAM, + 0, + socket.AI_PASSIVE | socket.AI_ADDRCONFIG) + + def close(self): + if self.handle: + self.handle.close() + self.handle = None + + +class TSocket(TSocketBase): + """Socket implementation of TTransport base.""" + + def __init__(self, host='localhost', port=9090, unix_socket=None): + """Initialize a TSocket + + @param host(str) The host to connect to. + @param port(int) The (TCP) port to connect to. + @param unix_socket(str) The filename of a unix socket to connect to. + (host and port will be ignored.) + """ + self.host = host + self.port = port + self.handle = None + self._unix_socket = unix_socket + self._timeout = None + + def setHandle(self, h): + self.handle = h + + def isOpen(self): + return self.handle is not None + + def setTimeout(self, ms): + if ms is None: + self._timeout = None + else: + self._timeout = ms / 1000.0 + + if self.handle is not None: + self.handle.settimeout(self._timeout) + + def open(self): + try: + res0 = self._resolveAddr() + for res in res0: + self.handle = socket.socket(res[0], res[1]) + self.handle.settimeout(self._timeout) + try: + self.handle.connect(res[4]) + except socket.error as e: + if res is not res0[-1]: + continue + else: + raise e + break + except socket.error as e: + if self._unix_socket: + message = 'Could not connect to socket %s' % self._unix_socket + else: + message = 'Could not connect to %s:%d' % (self.host, self.port) + raise TTransportException(type=TTransportException.NOT_OPEN, + message=message) + + def read(self, sz): + try: + buff = self.handle.recv(sz) + except socket.error as e: + if (e.args[0] == errno.ECONNRESET and + (sys.platform == 'darwin' or sys.platform.startswith('freebsd'))): + # freebsd and Mach don't follow POSIX semantic of recv + # and fail with ECONNRESET if peer performed shutdown. + # See corresponding comment and code in TSocket::read() + # in lib/cpp/src/transport/TSocket.cpp. + self.close() + # Trigger the check to raise the END_OF_FILE exception below. + buff = '' + else: + raise + if len(buff) == 0: + raise TTransportException(type=TTransportException.END_OF_FILE, + message='TSocket read 0 bytes') + return buff + + def write(self, buff): + if not self.handle: + raise TTransportException(type=TTransportException.NOT_OPEN, + message='Transport not open') + sent = 0 + have = len(buff) + while sent < have: + plus = self.handle.send(buff) + if plus == 0: + raise TTransportException(type=TTransportException.END_OF_FILE, + message='TSocket sent 0 bytes') + sent += plus + buff = buff[plus:] + + def flush(self): + pass + + +class TServerSocket(TSocketBase, TServerTransportBase): + """Socket implementation of TServerTransport base.""" + + def __init__(self, host=None, port=9090, unix_socket=None): + self.host = host + self.port = port + self._unix_socket = unix_socket + self.handle = None + + def listen(self): + res0 = self._resolveAddr() + for res in res0: + if res[0] is socket.AF_INET6 or res is res0[-1]: + break + + # We need remove the old unix socket if the file exists and + # nobody is listening on it. + if self._unix_socket: + tmp = socket.socket(res[0], res[1]) + try: + tmp.connect(res[4]) + except socket.error as err: + eno, message = err.args + if eno == errno.ECONNREFUSED: + os.unlink(res[4]) + + self.handle = socket.socket(res[0], res[1]) + self.handle.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + if hasattr(self.handle, 'settimeout'): + self.handle.settimeout(None) + self.handle.bind(res[4]) + self.handle.listen(128) + + def accept(self): + client, addr = self.handle.accept() + result = TSocket() + result.setHandle(client) + return result diff --git a/thrift/transport/TTransport.py b/thrift/transport/TTransport.py new file mode 100644 index 0000000..dcedd3d --- /dev/null +++ b/thrift/transport/TTransport.py @@ -0,0 +1,333 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +from six import BytesIO +from struct import pack, unpack +from thrift.Thrift import TException + + +class TTransportException(TException): + """Custom Transport Exception class""" + + UNKNOWN = 0 + NOT_OPEN = 1 + ALREADY_OPEN = 2 + TIMED_OUT = 3 + END_OF_FILE = 4 + + def __init__(self, type=UNKNOWN, message=None): + TException.__init__(self, message) + self.type = type + + +class TTransportBase: + """Base class for Thrift transport layer.""" + + def isOpen(self): + pass + + def open(self): + pass + + def close(self): + pass + + def read(self, sz): + pass + + def readAll(self, sz): + buff = b'' + have = 0 + while (have < sz): + chunk = self.read(sz - have) + have += len(chunk) + buff += chunk + + if len(chunk) == 0: + raise EOFError() + + return buff + + def write(self, buf): + pass + + def flush(self): + pass + + +# This class should be thought of as an interface. +class CReadableTransport: + """base class for transports that are readable from C""" + + # TODO(dreiss): Think about changing this interface to allow us to use + # a (Python, not c) StringIO instead, because it allows + # you to write after reading. + + # NOTE: This is a classic class, so properties will NOT work + # correctly for setting. + @property + def cstringio_buf(self): + """A cStringIO buffer that contains the current chunk we are reading.""" + pass + + def cstringio_refill(self, partialread, reqlen): + """Refills cstringio_buf. + + Returns the currently used buffer (which can but need not be the same as + the old cstringio_buf). partialread is what the C code has read from the + buffer, and should be inserted into the buffer before any more reads. The + return value must be a new, not borrowed reference. Something along the + lines of self._buf should be fine. + + If reqlen bytes can't be read, throw EOFError. + """ + pass + + +class TServerTransportBase: + """Base class for Thrift server transports.""" + + def listen(self): + pass + + def accept(self): + pass + + def close(self): + pass + + +class TTransportFactoryBase: + """Base class for a Transport Factory""" + + def getTransport(self, trans): + return trans + + +class TBufferedTransportFactory: + """Factory transport that builds buffered transports""" + + def getTransport(self, trans): + buffered = TBufferedTransport(trans) + return buffered + + +class TBufferedTransport(TTransportBase, CReadableTransport): + """Class that wraps another transport and buffers its I/O. + + The implementation uses a (configurable) fixed-size read buffer + but buffers all writes until a flush is performed. + """ + DEFAULT_BUFFER = 4096 + + def __init__(self, trans, rbuf_size=DEFAULT_BUFFER): + self.__trans = trans + self.__wbuf = BytesIO() + self.__rbuf = BytesIO("") + self.__rbuf_size = rbuf_size + + def isOpen(self): + return self.__trans.isOpen() + + def open(self): + return self.__trans.open() + + def close(self): + return self.__trans.close() + + def read(self, sz): + ret = self.__rbuf.read(sz) + if len(ret) != 0: + return ret + + self.__rbuf = BytesIO(self.__trans.read(max(sz, self.__rbuf_size))) + return self.__rbuf.read(sz) + + def write(self, buf): + self.__wbuf.write(buf) + + def flush(self): + out = self.__wbuf.getvalue() + # reset wbuf before write/flush to preserve state on underlying failure + self.__wbuf = BytesIO() + self.__trans.write(out) + self.__trans.flush() + + # Implement the CReadableTransport interface. + @property + def cstringio_buf(self): + return self.__rbuf + + def cstringio_refill(self, partialread, reqlen): + retstring = partialread + if reqlen < self.__rbuf_size: + # try to make a read of as much as we can. + retstring += self.__trans.read(self.__rbuf_size) + + # but make sure we do read reqlen bytes. + if len(retstring) < reqlen: + retstring += self.__trans.readAll(reqlen - len(retstring)) + + self.__rbuf = BytesIO(retstring) + return self.__rbuf + + +class TMemoryBuffer(TTransportBase, CReadableTransport): + """Wraps a cStringIO object as a TTransport. + + NOTE: Unlike the C++ version of this class, you cannot write to it + then immediately read from it. If you want to read from a + TMemoryBuffer, you must either pass a string to the constructor. + TODO(dreiss): Make this work like the C++ version. + """ + + def __init__(self, value=None): + """value -- a value to read from for stringio + + If value is set, this will be a transport for reading, + otherwise, it is for writing""" + if value is not None: + self._buffer = BytesIO(value) + else: + self._buffer = BytesIO() + + def isOpen(self): + return not self._buffer.closed + + def open(self): + pass + + def close(self): + self._buffer.close() + + def read(self, sz): + return self._buffer.read(sz) + + def write(self, buf): + try: + self._buffer.write(buf) + except TypeError: + self._buffer.write(buf.encode('cp437')) + + def flush(self): + pass + + def getvalue(self): + return self._buffer.getvalue() + + # Implement the CReadableTransport interface. + @property + def cstringio_buf(self): + return self._buffer + + def cstringio_refill(self, partialread, reqlen): + # only one shot at reading... + raise EOFError() + + +class TFramedTransportFactory: + """Factory transport that builds framed transports""" + + def getTransport(self, trans): + framed = TFramedTransport(trans) + return framed + + +class TFramedTransport(TTransportBase, CReadableTransport): + """Class that wraps another transport and frames its I/O when writing.""" + + def __init__(self, trans,): + self.__trans = trans + self.__rbuf = BytesIO() + self.__wbuf = BytesIO() + + def isOpen(self): + return self.__trans.isOpen() + + def open(self): + return self.__trans.open() + + def close(self): + return self.__trans.close() + + def read(self, sz): + ret = self.__rbuf.read(sz) + if len(ret) != 0: + return ret + + self.readFrame() + return self.__rbuf.read(sz) + + def readFrame(self): + buff = self.__trans.readAll(4) + sz, = unpack('!i', buff) + self.__rbuf = BytesIO(self.__trans.readAll(sz)) + + def write(self, buf): + self.__wbuf.write(buf) + + def flush(self): + wout = self.__wbuf.getvalue() + wsz = len(wout) + # reset wbuf before write/flush to preserve state on underlying failure + self.__wbuf = BytesIO() + # N.B.: Doing this string concatenation is WAY cheaper than making + # two separate calls to the underlying socket object. Socket writes in + # Python turn out to be REALLY expensive, but it seems to do a pretty + # good job of managing string buffer operations without excessive copies + buf = pack("!i", wsz) + wout + self.__trans.write(buf) + self.__trans.flush() + + # Implement the CReadableTransport interface. + @property + def cstringio_buf(self): + return self.__rbuf + + def cstringio_refill(self, prefix, reqlen): + # self.__rbuf will already be empty here because fastbinary doesn't + # ask for a refill until the previous buffer is empty. Therefore, + # we can start reading new frames immediately. + while len(prefix) < reqlen: + self.readFrame() + prefix += self.__rbuf.getvalue() + self.__rbuf = BytesIO(prefix) + return self.__rbuf + + +class TFileObjectTransport(TTransportBase): + """Wraps a file-like object to make it work as a Thrift transport.""" + + def __init__(self, fileobj): + self.fileobj = fileobj + + def isOpen(self): + return True + + def close(self): + self.fileobj.close() + + def read(self, sz): + return self.fileobj.read(sz) + + def write(self, buf): + self.fileobj.write(buf) + + def flush(self): + self.fileobj.flush() diff --git a/thrift/transport/TTwisted.py b/thrift/transport/TTwisted.py new file mode 100644 index 0000000..ffe5494 --- /dev/null +++ b/thrift/transport/TTwisted.py @@ -0,0 +1,221 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +from io import StringIO + +from zope.interface import implements, Interface, Attribute +from twisted.internet.protocol import Protocol, ServerFactory, ClientFactory, \ + connectionDone +from twisted.internet import defer +from twisted.protocols import basic +from twisted.python import log +from twisted.web import server, resource, http + +from thrift.transport import TTransport + + +class TMessageSenderTransport(TTransport.TTransportBase): + + def __init__(self): + self.__wbuf = StringIO() + + def write(self, buf): + self.__wbuf.write(buf) + + def flush(self): + msg = self.__wbuf.getvalue() + self.__wbuf = StringIO() + self.sendMessage(msg) + + def sendMessage(self, message): + raise NotImplementedError + + +class TCallbackTransport(TMessageSenderTransport): + + def __init__(self, func): + TMessageSenderTransport.__init__(self) + self.func = func + + def sendMessage(self, message): + self.func(message) + + +class ThriftClientProtocol(basic.Int32StringReceiver): + + MAX_LENGTH = 2 ** 31 - 1 + + def __init__(self, client_class, iprot_factory, oprot_factory=None): + self._client_class = client_class + self._iprot_factory = iprot_factory + if oprot_factory is None: + self._oprot_factory = iprot_factory + else: + self._oprot_factory = oprot_factory + + self.recv_map = {} + self.started = defer.Deferred() + + def dispatch(self, msg): + self.sendString(msg) + + def connectionMade(self): + tmo = TCallbackTransport(self.dispatch) + self.client = self._client_class(tmo, self._oprot_factory) + self.started.callback(self.client) + + def connectionLost(self, reason=connectionDone): + for k, v in self.client._reqs.items(): + tex = TTransport.TTransportException( + type=TTransport.TTransportException.END_OF_FILE, + message='Connection closed') + v.errback(tex) + + def stringReceived(self, frame): + tr = TTransport.TMemoryBuffer(frame) + iprot = self._iprot_factory.getProtocol(tr) + (fname, mtype, rseqid) = iprot.readMessageBegin() + + try: + method = self.recv_map[fname] + except KeyError: + method = getattr(self.client, 'recv_' + fname) + self.recv_map[fname] = method + + method(iprot, mtype, rseqid) + + +class ThriftServerProtocol(basic.Int32StringReceiver): + + MAX_LENGTH = 2 ** 31 - 1 + + def dispatch(self, msg): + self.sendString(msg) + + def processError(self, error): + self.transport.loseConnection() + + def processOk(self, _, tmo): + msg = tmo.getvalue() + + if len(msg) > 0: + self.dispatch(msg) + + def stringReceived(self, frame): + tmi = TTransport.TMemoryBuffer(frame) + tmo = TTransport.TMemoryBuffer() + + iprot = self.factory.iprot_factory.getProtocol(tmi) + oprot = self.factory.oprot_factory.getProtocol(tmo) + + d = self.factory.processor.process(iprot, oprot) + d.addCallbacks(self.processOk, self.processError, + callbackArgs=(tmo,)) + + +class IThriftServerFactory(Interface): + + processor = Attribute("Thrift processor") + + iprot_factory = Attribute("Input protocol factory") + + oprot_factory = Attribute("Output protocol factory") + + +class IThriftClientFactory(Interface): + + client_class = Attribute("Thrift client class") + + iprot_factory = Attribute("Input protocol factory") + + oprot_factory = Attribute("Output protocol factory") + + +class ThriftServerFactory(ServerFactory): + + implements(IThriftServerFactory) + + protocol = ThriftServerProtocol + + def __init__(self, processor, iprot_factory, oprot_factory=None): + self.processor = processor + self.iprot_factory = iprot_factory + if oprot_factory is None: + self.oprot_factory = iprot_factory + else: + self.oprot_factory = oprot_factory + + +class ThriftClientFactory(ClientFactory): + + implements(IThriftClientFactory) + + protocol = ThriftClientProtocol + + def __init__(self, client_class, iprot_factory, oprot_factory=None): + self.client_class = client_class + self.iprot_factory = iprot_factory + if oprot_factory is None: + self.oprot_factory = iprot_factory + else: + self.oprot_factory = oprot_factory + + def buildProtocol(self, addr): + p = self.protocol(self.client_class, self.iprot_factory, + self.oprot_factory) + p.factory = self + return p + + +class ThriftResource(resource.Resource): + + allowedMethods = ('POST',) + + def __init__(self, processor, inputProtocolFactory, + outputProtocolFactory=None): + resource.Resource.__init__(self) + self.inputProtocolFactory = inputProtocolFactory + if outputProtocolFactory is None: + self.outputProtocolFactory = inputProtocolFactory + else: + self.outputProtocolFactory = outputProtocolFactory + self.processor = processor + + def getChild(self, path, request): + return self + + def _cbProcess(self, _, request, tmo): + msg = tmo.getvalue() + request.setResponseCode(http.OK) + request.setHeader("content-type", "application/x-thrift") + request.write(msg) + request.finish() + + def render_POST(self, request): + request.content.seek(0, 0) + data = request.content.read() + tmi = TTransport.TMemoryBuffer(data) + tmo = TTransport.TMemoryBuffer() + + iprot = self.inputProtocolFactory.getProtocol(tmi) + oprot = self.outputProtocolFactory.getProtocol(tmo) + + d = self.processor.process(iprot, oprot) + d.addCallback(self._cbProcess, request, tmo) + return server.NOT_DONE_YET diff --git a/thrift/transport/TZlibTransport.py b/thrift/transport/TZlibTransport.py new file mode 100644 index 0000000..a21dc80 --- /dev/null +++ b/thrift/transport/TZlibTransport.py @@ -0,0 +1,248 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +"""TZlibTransport provides a compressed transport and transport factory +class, using the python standard library zlib module to implement +data compression. +""" + + +import zlib +from io import StringIO +from .TTransport import TTransportBase, CReadableTransport + + +class TZlibTransportFactory(object): + """Factory transport that builds zlib compressed transports. + + This factory caches the last single client/transport that it was passed + and returns the same TZlibTransport object that was created. + + This caching means the TServer class will get the _same_ transport + object for both input and output transports from this factory. + (For non-threaded scenarios only, since the cache only holds one object) + + The purpose of this caching is to allocate only one TZlibTransport where + only one is really needed (since it must have separate read/write buffers), + and makes the statistics from getCompSavings() and getCompRatio() + easier to understand. + """ + # class scoped cache of last transport given and zlibtransport returned + _last_trans = None + _last_z = None + + def getTransport(self, trans, compresslevel=9): + """Wrap a transport, trans, with the TZlibTransport + compressed transport class, returning a new + transport to the caller. + + @param compresslevel: The zlib compression level, ranging + from 0 (no compression) to 9 (best compression). Defaults to 9. + @type compresslevel: int + + This method returns a TZlibTransport which wraps the + passed C{trans} TTransport derived instance. + """ + if trans == self._last_trans: + return self._last_z + ztrans = TZlibTransport(trans, compresslevel) + self._last_trans = trans + self._last_z = ztrans + return ztrans + + +class TZlibTransport(TTransportBase, CReadableTransport): + """Class that wraps a transport with zlib, compressing writes + and decompresses reads, using the python standard + library zlib module. + """ + # Read buffer size for the python fastbinary C extension, + # the TBinaryProtocolAccelerated class. + DEFAULT_BUFFSIZE = 4096 + + def __init__(self, trans, compresslevel=9): + """Create a new TZlibTransport, wrapping C{trans}, another + TTransport derived object. + + @param trans: A thrift transport object, i.e. a TSocket() object. + @type trans: TTransport + @param compresslevel: The zlib compression level, ranging + from 0 (no compression) to 9 (best compression). Default is 9. + @type compresslevel: int + """ + self.__trans = trans + self.compresslevel = compresslevel + self.__rbuf = StringIO() + self.__wbuf = StringIO() + self._init_zlib() + self._init_stats() + + def _reinit_buffers(self): + """Internal method to initialize/reset the internal StringIO objects + for read and write buffers. + """ + self.__rbuf = StringIO() + self.__wbuf = StringIO() + + def _init_stats(self): + """Internal method to reset the internal statistics counters + for compression ratios and bandwidth savings. + """ + self.bytes_in = 0 + self.bytes_out = 0 + self.bytes_in_comp = 0 + self.bytes_out_comp = 0 + + def _init_zlib(self): + """Internal method for setting up the zlib compression and + decompression objects. + """ + self._zcomp_read = zlib.decompressobj() + self._zcomp_write = zlib.compressobj(self.compresslevel) + + def getCompRatio(self): + """Get the current measured compression ratios (in,out) from + this transport. + + Returns a tuple of: + (inbound_compression_ratio, outbound_compression_ratio) + + The compression ratios are computed as: + compressed / uncompressed + + E.g., data that compresses by 10x will have a ratio of: 0.10 + and data that compresses to half of ts original size will + have a ratio of 0.5 + + None is returned if no bytes have yet been processed in + a particular direction. + """ + r_percent, w_percent = (None, None) + if self.bytes_in > 0: + r_percent = self.bytes_in_comp / self.bytes_in + if self.bytes_out > 0: + w_percent = self.bytes_out_comp / self.bytes_out + return (r_percent, w_percent) + + def getCompSavings(self): + """Get the current count of saved bytes due to data + compression. + + Returns a tuple of: + (inbound_saved_bytes, outbound_saved_bytes) + + Note: if compression is actually expanding your + data (only likely with very tiny thrift objects), then + the values returned will be negative. + """ + r_saved = self.bytes_in - self.bytes_in_comp + w_saved = self.bytes_out - self.bytes_out_comp + return (r_saved, w_saved) + + def isOpen(self): + """Return the underlying transport's open status""" + return self.__trans.isOpen() + + def open(self): + """Open the underlying transport""" + self._init_stats() + return self.__trans.open() + + def listen(self): + """Invoke the underlying transport's listen() method""" + self.__trans.listen() + + def accept(self): + """Accept connections on the underlying transport""" + return self.__trans.accept() + + def close(self): + """Close the underlying transport,""" + self._reinit_buffers() + self._init_zlib() + return self.__trans.close() + + def read(self, sz): + """Read up to sz bytes from the decompressed bytes buffer, and + read from the underlying transport if the decompression + buffer is empty. + """ + ret = self.__rbuf.read(sz) + if len(ret) > 0: + return ret + # keep reading from transport until something comes back + while True: + if self.readComp(sz): + break + ret = self.__rbuf.read(sz) + return ret + + def readComp(self, sz): + """Read compressed data from the underlying transport, then + decompress it and append it to the internal StringIO read buffer + """ + zbuf = self.__trans.read(sz) + zbuf = self._zcomp_read.unconsumed_tail + zbuf + buf = self._zcomp_read.decompress(zbuf) + self.bytes_in += len(zbuf) + self.bytes_in_comp += len(buf) + old = self.__rbuf.read() + self.__rbuf = StringIO(old + buf) + if len(old) + len(buf) == 0: + return False + return True + + def write(self, buf): + """Write some bytes, putting them into the internal write + buffer for eventual compression. + """ + self.__wbuf.write(buf) + + def flush(self): + """Flush any queued up data in the write buffer and ensure the + compression buffer is flushed out to the underlying transport + """ + wout = self.__wbuf.getvalue() + if len(wout) > 0: + zbuf = self._zcomp_write.compress(wout) + self.bytes_out += len(wout) + self.bytes_out_comp += len(zbuf) + else: + zbuf = '' + ztail = self._zcomp_write.flush(zlib.Z_SYNC_FLUSH) + self.bytes_out_comp += len(ztail) + if (len(zbuf) + len(ztail)) > 0: + self.__wbuf = StringIO() + self.__trans.write(zbuf + ztail) + self.__trans.flush() + + @property + def cstringio_buf(self): + """Implement the CReadableTransport interface""" + return self.__rbuf + + def cstringio_refill(self, partialread, reqlen): + """Implement the CReadableTransport interface for refill""" + retstring = partialread + if reqlen < self.DEFAULT_BUFFSIZE: + retstring += self.read(self.DEFAULT_BUFFSIZE) + while len(retstring) < reqlen: + retstring += self.read(reqlen - len(retstring)) + self.__rbuf = StringIO(retstring) + return self.__rbuf diff --git a/thrift/transport/__init__.py b/thrift/transport/__init__.py new file mode 100644 index 0000000..c9596d9 --- /dev/null +++ b/thrift/transport/__init__.py @@ -0,0 +1,20 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +__all__ = ['TTransport', 'TSocket', 'THttpClient', 'TZlibTransport']