diff --git a/awips/NotificationMessage.py b/awips/NotificationMessage.py index 47cd4c8..8b30cda 100644 --- a/awips/NotificationMessage.py +++ b/awips/NotificationMessage.py @@ -1,19 +1,19 @@ ## # This software was developed and / or modified by Raytheon Company, -# pursuant to Contract DG133W-05-CQ-1067 with the US Government. -# -# U.S. EXPORT CONTROLLED TECHNICAL DATA +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA # This software product contains export-restricted data whose # export/transfer/disclosure is restricted by U.S. law. Dissemination # to non-U.S. persons whether in the United States or abroad requires # an export license or other authorization. # -# Contractor Name: Raytheon Company -# Contractor Address: 6825 Pine Street, Suite 340 -# Mail Stop B8 -# Omaha, NE 68106 -# 402.291.0100 -# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# # See the AWIPS II Master Rights File ("Master Rights File.pdf") for # further licensing information. ## @@ -47,6 +47,8 @@ from dynamicserialize import DynamicSerializationManager # 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: @@ -58,13 +60,14 @@ class NotificationMessage: 4: 'EVENTB', 5: 'VERBOSE'} - def __init__(self, host='localhost', port=61999, message='', priority='PROBLEM', category="LOCAL", source="ANNOUNCER", audioFile="NONE"): + 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 @@ -113,9 +116,9 @@ class NotificationMessage: # of the message to AlertViz # 9581 is global distribution thru ThriftClient to Edex # 61999 is local distribution - if (self.port == 61999): + if (int(self.port) == 61999): # use stomp.py - conn = stomp.Connection(host_and_ports=[(self.host, self.port)]) + conn = stomp.Connection(host_and_ports=[(self.host, 61999)]) timeout = threading.Timer(5.0, self.connection_timeout, [conn]) try: @@ -132,6 +135,8 @@ class NotificationMessage: 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") @@ -144,7 +149,7 @@ class NotificationMessage: conn.stop() else: # use ThriftClient - alertVizRequest = createRequest(self.message, self.priority, self.source, self.category, self.audioFile) + alertVizRequest = createRequest(self.message, self.priority, self.source, self.category, self.audioFile, self.filters) thriftClient = ThriftClient.ThriftClient(self.host, self.port, "/services") serverResponse = None @@ -159,7 +164,7 @@ class NotificationMessage: else: print "Response: " + str(serverResponse) -def createRequest(message, priority, source, category, audioFile): +def createRequest(message, priority, source, category, audioFile, filters): obj = AlertVizRequest() obj.setMachine(socket.gethostname()) @@ -171,7 +176,7 @@ def createRequest(message, priority, source, category, audioFile): obj.setAudioFile(audioFile) else: obj.setAudioFile('\0') - + obj.setFilters(filters) return obj if __name__ == '__main__': diff --git a/awips/dataaccess/CombinedTimeQuery.py b/awips/dataaccess/CombinedTimeQuery.py new file mode 100644 index 0000000..8810e0e --- /dev/null +++ b/awips/dataaccess/CombinedTimeQuery.py @@ -0,0 +1,99 @@ +# # +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +# # + +# +# 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()) diff --git a/awips/dataaccess/DataAccessLayer.py b/awips/dataaccess/DataAccessLayer.py index 3516509..115305e 100644 --- a/awips/dataaccess/DataAccessLayer.py +++ b/awips/dataaccess/DataAccessLayer.py @@ -30,12 +30,16 @@ # 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 +# 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() # 10/07/16 ---- mjames@ucar Added getForecastRun # # @@ -44,6 +48,7 @@ import sys import subprocess +import warnings THRIFT_HOST = "edex" USING_NATIVE_THRIFT = False @@ -80,6 +85,7 @@ def getAvailableTimes(request, refTimeOnly=False): """ return router.getAvailableTimes(request, refTimeOnly) + def getGridData(request, times=[]): """ Gets the grid data that matches the request at the specified times. Each @@ -96,6 +102,7 @@ def getGridData(request, times=[]): """ return router.getGridData(request, times) + def getGeometryData(request, times=[]): """ Gets the geometry data that matches the request at the specified times. @@ -112,6 +119,7 @@ def getGeometryData(request, times=[]): """ return router.getGeometryData(request, times) + def getAvailableLocationNames(request): """ Gets the available location names that match the request without actually @@ -125,6 +133,7 @@ def getAvailableLocationNames(request): """ return router.getAvailableLocationNames(request) + def getAvailableParameters(request): """ Gets the available parameters names that match the request without actually @@ -138,6 +147,7 @@ def getAvailableParameters(request): """ return router.getAvailableParameters(request) + def getAvailableLevels(request): """ Gets the available levels that match the request without actually @@ -151,30 +161,52 @@ def getAvailableLevels(request): """ return router.getAvailableLevels(request) -def getRequiredIdentifiers(datatype): + +def getRequiredIdentifiers(request): """ - Gets the required identifiers for this datatype. These identifiers + Gets the required identifiers for this request. These identifiers must be set on a request for the request of this datatype to succeed. Args: - datatype: the datatype to find required identifiers for + request: the request to find required identifiers for Returns: a list of strings of required identifiers """ - return router.getRequiredIdentifiers(datatype) + if str(request) == request: + warnings.warn("Use getRequiredIdentifiers(IDataRequest) instead", + DeprecationWarning) + return router.getRequiredIdentifiers(request) -def getOptionalIdentifiers(datatype): + +def getOptionalIdentifiers(request): """ - Gets the optional identifiers for this datatype. + Gets the optional identifiers for this request. Args: - datatype: the datatype to find optional identifiers for + request: the request to find optional identifiers for Returns: a list of strings of optional identifiers """ - return router.getOptionalIdentifiers(datatype) + 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): """" diff --git a/awips/dataaccess/PyGeometryData.py b/awips/dataaccess/PyGeometryData.py index 1692867..2ad09bf 100644 --- a/awips/dataaccess/PyGeometryData.py +++ b/awips/dataaccess/PyGeometryData.py @@ -28,10 +28,11 @@ # 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() +# 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(). # # @@ -61,7 +62,7 @@ class PyGeometryData(IGeometryData, PyData.PyData): def getNumber(self, param): value = self.__dataMap[param][0] t = self.getType(param) - if t == 'INT': + if t == 'INT' or t == 'SHORT': return int(value) elif t == 'LONG': return long(value) diff --git a/awips/dataaccess/PyGridData.py b/awips/dataaccess/PyGridData.py index 2275710..87fffac 100644 --- a/awips/dataaccess/PyGridData.py +++ b/awips/dataaccess/PyGridData.py @@ -28,6 +28,7 @@ # Date Ticket# Engineer Description # ------------ ---------- ----------- -------------------------- # 06/03/13 #2023 dgilling Initial Creation. +# 11/10/16 #5900 bsteffen Correct grid shape # # @@ -51,7 +52,7 @@ class PyGridData(IGridData, PyData.PyData): ny = ny self.__parameter = gridDataRecord.getParameter() self.__unit = gridDataRecord.getUnit() - self.__gridData = numpy.reshape(numpy.array(gridDataRecord.getGridData()), (nx, ny)) + self.__gridData = numpy.reshape(numpy.array(gridDataRecord.getGridData()), (ny, nx)) self.__latLonGrid = latLonGrid def getParameter(self): diff --git a/awips/dataaccess/ThriftClientRouter.py b/awips/dataaccess/ThriftClientRouter.py index b568b56..2cf72ee 100644 --- a/awips/dataaccess/ThriftClientRouter.py +++ b/awips/dataaccess/ThriftClientRouter.py @@ -23,17 +23,22 @@ # # # -# SOFTWARE HISTORY +# 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. +# 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() +# 11/10/16 5900 bsteffen Correct grid shape # @@ -49,6 +54,7 @@ from dynamicserialize.dstypes.com.raytheon.uf.common.dataaccess.request import G 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 awips import ThriftClient @@ -87,8 +93,8 @@ class ThriftClientRouter(object): for location in locNames: nx = response.getSiteNxValues()[location] ny = response.getSiteNyValues()[location] - latData = numpy.reshape(numpy.array(response.getSiteLatGrids()[location]), (nx, ny)) - lonData = numpy.reshape(numpy.array(response.getSiteLonGrids()[location]), (nx, ny)) + 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 = [] @@ -142,18 +148,31 @@ class ThriftClientRouter(object): response = self._client.sendRequest(levelReq) return response - def getRequiredIdentifiers(self, datatype): + def getRequiredIdentifiers(self, request): + if str(request) == request: + # Handle old version getRequiredIdentifiers(str) + request = self.newDataRequest(request) idReq = GetRequiredIdentifiersRequest() - idReq.setDatatype(datatype) + idReq.setRequest(request) response = self._client.sendRequest(idReq) return response - def getOptionalIdentifiers(self, datatype): + def getOptionalIdentifiers(self, request): + if str(request) == request: + # Handle old version getOptionalIdentifiers(str) + request = self.newDataRequest(request) idReq = GetOptionalIdentifiersRequest() - idReq.setDatatype(datatype) + 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: diff --git a/awips/test/dafTests/__init__.py b/awips/test/dafTests/__init__.py new file mode 100644 index 0000000..6041dc1 --- /dev/null +++ b/awips/test/dafTests/__init__.py @@ -0,0 +1,36 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + + +# +# __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..652d838 --- /dev/null +++ b/awips/test/dafTests/baseBufrMosTestCase.py @@ -0,0 +1,59 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +from awips.dataaccess import DataAccessLayer as DAL + +import baseDafTestCase + +# +# Base TestCase for BufrMos* tests. +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# 01/19/16 4795 mapeters Initial Creation. +# 04/11/16 5548 tgurney Cleanup +# +# +# + + +class BufrMosTestCase(baseDafTestCase.DafTestCase): + """Base class for testing DAF support of bufrmos data""" + + 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("KOMA") + self.runTimesTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.setLocationNames("KOMA") + req.setParameters("temperature", "dewpoint") + self.runGeometryDataTest(req) diff --git a/awips/test/dafTests/baseDafTestCase.py b/awips/test/dafTests/baseDafTestCase.py new file mode 100644 index 0000000..f857ac1 --- /dev/null +++ b/awips/test/dafTests/baseDafTestCase.py @@ -0,0 +1,218 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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 +# 10/05/16 5926 dgilling Better checks in runGeometryDataTest. +# 11/08/16 5985 tgurney Do not check data times on +# time-agnostic data +# +# + + +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 setUp(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: + 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) + print("Number of geometry records: " + str(len(geomData))) + print("Sample geometry data:") + for record in geomData[:self.sampleDataLimit]: + if checkDataTimes and times: + 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) + 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) + 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/testAcars.py b/awips/test/dafTests/testAcars.py new file mode 100644 index 0000000..9ab9167 --- /dev/null +++ b/awips/test/dafTests/testAcars.py @@ -0,0 +1,61 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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..32cd903 --- /dev/null +++ b/awips/test/dafTests/testAirep.py @@ -0,0 +1,165 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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 +# +# + + +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 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..134d059 --- /dev/null +++ b/awips/test/dafTests/testBinLightning.py @@ -0,0 +1,192 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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 +# 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 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..38409bd --- /dev/null +++ b/awips/test/dafTests/testBufrMosAvn.py @@ -0,0 +1,45 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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..9c7c8d3 --- /dev/null +++ b/awips/test/dafTests/testBufrMosEta.py @@ -0,0 +1,45 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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..1b5819c --- /dev/null +++ b/awips/test/dafTests/testBufrMosGfs.py @@ -0,0 +1,45 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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..e1ab295 --- /dev/null +++ b/awips/test/dafTests/testBufrMosHpc.py @@ -0,0 +1,52 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL + +import baseBufrMosTestCase +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 +# +# + + +class BufrMosHpcTestCase(baseBufrMosTestCase.BufrMosTestCase): + """Test DAF support for bufrmosHPC data""" + + datatype = "bufrmosHPC" + + # Most tests inherited from superclass + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.setLocationNames("KOMA") + req.setParameters("forecastHr", "maxTemp24Hour") + self.runGeometryDataTest(req) diff --git a/awips/test/dafTests/testBufrMosLamp.py b/awips/test/dafTests/testBufrMosLamp.py new file mode 100644 index 0000000..a3dc723 --- /dev/null +++ b/awips/test/dafTests/testBufrMosLamp.py @@ -0,0 +1,45 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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..27d1b0f --- /dev/null +++ b/awips/test/dafTests/testBufrMosMrf.py @@ -0,0 +1,52 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL + +import baseBufrMosTestCase +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 +# +# + + +class BufrMosMrfTestCase(baseBufrMosTestCase.BufrMosTestCase): + """Test DAF support for bufrmosMRF data""" + + datatype = "bufrmosMRF" + + # Most tests inherited from superclass + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.setLocationNames("KOMA") + req.setParameters("forecastHr", "maxTempDay") + self.runGeometryDataTest(req) diff --git a/awips/test/dafTests/testBufrUa.py b/awips/test/dafTests/testBufrUa.py new file mode 100644 index 0000000..46c7c4f --- /dev/null +++ b/awips/test/dafTests/testBufrUa.py @@ -0,0 +1,205 @@ +# # +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +# # + +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 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 +# +# + + +class BufrUaTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for bufrua data""" + + datatype = "bufrua" + + location = "72558" + """stationid corresponding to KOAX""" + + 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 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 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..61dc4a7 --- /dev/null +++ b/awips/test/dafTests/testClimate.py @@ -0,0 +1,413 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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 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 +# 10/06/16 5926 dgilling Add additional time and location tests. +# +# + + +class ClimateTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for climate data""" + + datatype = 'climate' + + 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('KOMA', '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('KOMA', '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('KOMA', '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('KOMA', '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 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', '=', 'KOMA') + for record in geometryData: + self.assertEqual(record.getString('station_code'), 'KOMA') + + def testGetDataWithEqualsUnicode(self): + geometryData = self._runConstraintTest('station_code', '=', u'KOMA') + for record in geometryData: + self.assertEqual(record.getString('station_code'), 'KOMA') + + 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', '!=', 'KOMA') + for record in geometryData: + self.assertNotEqual(record.getString('station_code'), 'KOMA') + + 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 = ('KOMA', 'KABR') + geometryData = self._runConstraintTest('station_code', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('station_code'), collection) + + def testGetDataWithInList(self): + collection = ['KOMA', 'KABR'] + geometryData = self._runConstraintTest('station_code', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('station_code'), collection) + + def testGetDataWithInGenerator(self): + collection = ('KOMA', '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 testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('station_code', 'junk', 'KOMA') + + 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) + diff --git a/awips/test/dafTests/testCombinedTimeQuery.py b/awips/test/dafTests/testCombinedTimeQuery.py new file mode 100644 index 0000000..b3527db --- /dev/null +++ b/awips/test/dafTests/testCombinedTimeQuery.py @@ -0,0 +1,67 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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..822783c --- /dev/null +++ b/awips/test/dafTests/testCommonObsSpatial.py @@ -0,0 +1,179 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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 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 +# + + +class CommonObsSpatialTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for common_obs_spatial data""" + + datatype = "common_obs_spatial" + + envelope = box(-97.0, 41.0, -96.0, 42.0) + """Default request area (box around KOAX)""" + + 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']) + + @unittest.skip('avoid EDEX error') + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + @unittest.skip('avoid EDEX error') + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.setEnvelope(self.envelope) + req.setParameters("name", "stationid") + self.runGeometryDataTest(req) + + 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) + + 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 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/testFfmp.py b/awips/test/dafTests/testFfmp.py new file mode 100644 index 0000000..600c76d --- /dev/null +++ b/awips/test/dafTests/testFfmp.py @@ -0,0 +1,98 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL + +import baseDafTestCase +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 +# 11/08/16 5985 tgurney Do not check data times +# +# + + +class FfmpTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for ffmp data""" + + datatype = "ffmp" + + @staticmethod + def addIdentifiers(req): + req.addIdentifier("wfo", "OAX") + 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) + self.runTimesTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + self.addIdentifiers(req) + req.setParameters("PRTM") + 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 + # These two not yet supported + ids.remove('huc') + ids.remove('accumHrs') + self.runGetIdValuesTest(ids) + + def testGetInvalidIdentifierValuesThrowsException(self): + self.runInvalidIdValuesTest() + + def testGetNonexistentIdentifierValuesThrowsException(self): + self.runNonexistentIdValuesTest() diff --git a/awips/test/dafTests/testGfe.py b/awips/test/dafTests/testGfe.py new file mode 100644 index 0000000..f5d5a7d --- /dev/null +++ b/awips/test/dafTests/testGfe.py @@ -0,0 +1,196 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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 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 +# 11/07/16 5991 bsteffen Improve vector tests +# +# + + +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('modelName', 'Fcst') + self.runLocationsTest(req) + + def testGetAvailableTimes(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('modelName', 'Fcst') + req.addIdentifier('siteId', 'OAX') + self.runTimesTest(req) + + def testGetGridData(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('modelName', 'Fcst') + req.addIdentifier('siteId', 'OAX') + req.setParameters('T') + self.runGridDataTest(req) + + def testGetVectorGridData(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('modelName', 'Fcst') + req.addIdentifier('siteId', 'OAX') + 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('OAX') + req.setParameters('T') + return self.runGridDataTest(req) + + def testGetDataWithEqualsString(self): + geometryData = self._runConstraintTest('modelName', '=', 'Fcst') + for record in geometryData: + self.assertEqual(record.getAttribute('modelName'), 'Fcst') + + def testGetDataWithEqualsUnicode(self): + geometryData = self._runConstraintTest('modelName', '=', u'Fcst') + for record in geometryData: + self.assertEqual(record.getAttribute('modelName'), 'Fcst') + + # No numeric tests since no numeric identifiers are available. + + def testGetDataWithEqualsNone(self): + geometryData = self._runConstraintTest('modelName', '=', None) + for record in geometryData: + self.assertIsNone(record.getAttribute('modelName')) + + def testGetDataWithNotEquals(self): + geometryData = self._runConstraintTest('modelName', '!=', 'Fcst') + for record in geometryData: + self.assertNotEqual(record.getAttribute('modelName'), 'Fcst') + + def testGetDataWithNotEqualsNone(self): + geometryData = self._runConstraintTest('modelName', '!=', None) + for record in geometryData: + self.assertIsNotNone(record.getAttribute('modelName')) + + def testGetDataWithGreaterThan(self): + geometryData = self._runConstraintTest('modelName', '>', 'Fcst') + for record in geometryData: + self.assertGreater(record.getAttribute('modelName'), 'Fcst') + + def testGetDataWithLessThan(self): + geometryData = self._runConstraintTest('modelName', '<', 'Fcst') + for record in geometryData: + self.assertLess(record.getAttribute('modelName'), 'Fcst') + + def testGetDataWithGreaterThanEquals(self): + geometryData = self._runConstraintTest('modelName', '>=', 'Fcst') + for record in geometryData: + self.assertGreaterEqual(record.getAttribute('modelName'), 'Fcst') + + def testGetDataWithLessThanEquals(self): + geometryData = self._runConstraintTest('modelName', '<=', 'Fcst') + for record in geometryData: + self.assertLessEqual(record.getAttribute('modelName'), 'Fcst') + + def testGetDataWithInTuple(self): + collection = ('Fcst', 'SAT') + geometryData = self._runConstraintTest('modelName', 'in', collection) + for record in geometryData: + self.assertIn(record.getAttribute('modelName'), collection) + + def testGetDataWithInList(self): + collection = ['Fcst', 'SAT'] + geometryData = self._runConstraintTest('modelName', 'in', collection) + for record in geometryData: + self.assertIn(record.getAttribute('modelName'), collection) + + def testGetDataWithInGenerator(self): + collection = ('Fcst', 'SAT') + generator = (item for item in collection) + geometryData = self._runConstraintTest('modelName', 'in', generator) + for record in geometryData: + self.assertIn(record.getAttribute('modelName'), collection) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('modelName', 'junk', 'Fcst') + + def testGetDataWithInvalidConstraintValueThrowsException(self): + with self.assertRaises(TypeError): + self._runConstraintTest('modelName', '=', {}) + + def testGetDataWithEmptyInConstraintThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('modelName', 'in', []) diff --git a/awips/test/dafTests/testGrid.py b/awips/test/dafTests/testGrid.py new file mode 100644 index 0000000..a216420 --- /dev/null +++ b/awips/test/dafTests/testGrid.py @@ -0,0 +1,123 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +from __future__ import print_function +from shapely.geometry import box, Point +from awips.dataaccess import DataAccessLayer as DAL + +import baseDafTestCase +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 +# 10/13/16 5942 bsteffen Test envelopes +# 11/08/16 5985 tgurney Skip certain tests when no +# data is available +# + + +class GridTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for grid data""" + + datatype = "grid" + + model = "GFS160" + + envelope = box(-97.0, 41.0, -96.0, 42.0) + + 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(self.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(self.envelope.bounds[0] - 1, self.envelope.bounds[1] - 1, + self.envelope.bounds[2] + 1, self.envelope.bounds[3] + 1 ) + + for i in range(len(lons)): + self.assertTrue(testEnv.contains(Point(lons[i], lats[i]))) + diff --git a/awips/test/dafTests/testHydro.py b/awips/test/dafTests/testHydro.py new file mode 100644 index 0000000..0d8bef4 --- /dev/null +++ b/awips/test/dafTests/testHydro.py @@ -0,0 +1,261 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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 +# 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 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..3e0c3d0 --- /dev/null +++ b/awips/test/dafTests/testLdadMesonet.py @@ -0,0 +1,77 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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 +# +# + + +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) diff --git a/awips/test/dafTests/testMaps.py b/awips/test/dafTests/testMaps.py new file mode 100644 index 0000000..6b08442 --- /dev/null +++ b/awips/test/dafTests/testMaps.py @@ -0,0 +1,227 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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 +# +# + + +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) + + 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') + + @unittest.skip('avoid EDEX error') + def testGetColumnIdValuesWithNonexistentTableThrowsException(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'mapdata.nonexistentjunk') + req.addIdentifier('geomField', 'the_geom') + with self.assertRaises(ThriftRequestException): + idValues = DAL.getIdentifierValues(req, 'state') + + @unittest.skip('avoid EDEX error') + def testGetNonexistentColumnIdValuesThrowsException(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier('table', 'mapdata.county') + req.addIdentifier('geomField', 'the_geom') + 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', '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) + + 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 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..152abe7 --- /dev/null +++ b/awips/test/dafTests/testModelSounding.py @@ -0,0 +1,209 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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 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 +# 11/10/16 5985 tgurney Mark expected failures prior +# to 17.3.1 +# +# + + +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("KOMA") + + self.runTimesTest(req) + + @unittest.expectedFailure + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.addIdentifier("reportType", "ETA") + req.setLocationNames("KOMA") + 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 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('KOMA', '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. + + @unittest.expectedFailure + def testGetDataWithEqualsString(self): + geometryData = self._runConstraintTest('reportType', '=', 'ETA') + for record in geometryData: + self.assertIn('/ETA/', record.getString('dataURI')) + + @unittest.expectedFailure + 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. + + @unittest.expectedFailure + def testGetDataWithEqualsNone(self): + geometryData = self._runConstraintTest('reportType', '=', None) + + @unittest.expectedFailure + def testGetDataWithNotEquals(self): + geometryData = self._runConstraintTest('reportType', '!=', 'ETA') + for record in geometryData: + self.assertNotIn('/ETA/', record.getString('dataURI')) + + @unittest.expectedFailure + def testGetDataWithNotEqualsNone(self): + geometryData = self._runConstraintTest('reportType', '!=', None) + + @unittest.expectedFailure + def testGetDataWithGreaterThan(self): + geometryData = self._runConstraintTest('reportType', '>', 'ETA') + + @unittest.expectedFailure + def testGetDataWithLessThan(self): + geometryData = self._runConstraintTest('reportType', '<', 'ETA') + + @unittest.expectedFailure + def testGetDataWithGreaterThanEquals(self): + geometryData = self._runConstraintTest('reportType', '>=', 'ETA') + + @unittest.expectedFailure + def testGetDataWithLessThanEquals(self): + geometryData = self._runConstraintTest('reportType', '<=', 'ETA') + + @unittest.expectedFailure + 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) + + @unittest.expectedFailure + 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) + + @unittest.expectedFailure + 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 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..3bc593b --- /dev/null +++ b/awips/test/dafTests/testObs.py @@ -0,0 +1,168 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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 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 +# +# + + +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("KOMA") + self.runTimesTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.setLocationNames("KOMA") + req.setParameters("temperature", "seaLevelPress", "dewpoint") + 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("temperature", "reportType") + req.setLocationNames("KOMA") + 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 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..876a378 --- /dev/null +++ b/awips/test/dafTests/testPirep.py @@ -0,0 +1,83 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +from __future__ import print_function +from awips.dataaccess import DataAccessLayer as DAL + +import baseDafTestCase +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 +# +# + + +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('OMA') + self.runTimesTest(req) + + def testGetGeometryData(self): + req = DAL.newDataRequest(self.datatype) + req.setLocationNames('OMA') + 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") + diff --git a/awips/test/dafTests/testPracticeWarning.py b/awips/test/dafTests/testPracticeWarning.py new file mode 100644 index 0000000..90dee74 --- /dev/null +++ b/awips/test/dafTests/testPracticeWarning.py @@ -0,0 +1,49 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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..a05f6d9 --- /dev/null +++ b/awips/test/dafTests/testProfiler.py @@ -0,0 +1,80 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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/testRadar.py b/awips/test/dafTests/testRadar.py new file mode 100644 index 0000000..8adaf5f --- /dev/null +++ b/awips/test/dafTests/testRadar.py @@ -0,0 +1,197 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +from __future__ import print_function +from dynamicserialize.dstypes.com.raytheon.uf.common.dataquery.requests import RequestConstraint +from shapely.geometry import box +from awips.dataaccess import DataAccessLayer as DAL +from awips.ThriftClient import ThriftRequestException + +import baseDafTestCase +import unittest + +# +# Test DAF support for radar 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 +# 06/08/16 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) +# +# + + +class RadarTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for radar data""" + + datatype = 'radar' + + envelope = box(-97.0, 41.0, -96.0, 42.0) + """Request area (box around KOAX)""" + + 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(self.envelope) + self.runTimesTest(req) + + def testGetGridData(self): + req = DAL.newDataRequest(self.datatype) + req.setEnvelope(self.envelope) + req.setLocationNames('koax') + req.setParameters('94') + # Don't test shapes since they may differ. + self.runGridDataTest(req, testSameShape=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('94') + # Don't test shapes since they may differ. + return self.runGridDataTest(req, testSameShape=False) + + def testGetDataWithEqualsString(self): + gridData = self._runConstraintTest('icao', '=', 'koax') + for record in gridData: + self.assertEqual(record.getAttribute('icao'), 'koax') + + def testGetDataWithEqualsUnicode(self): + gridData = self._runConstraintTest('icao', '=', u'koax') + for record in gridData: + self.assertEqual(record.getAttribute('icao'), 'koax') + + 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', '!=', 'koax') + for record in gridData: + self.assertNotEqual(record.getAttribute('icao'), 'koax') + + def testGetDataWithNotEqualsNone(self): + gridData = self._runConstraintTest('icao', '!=', None) + for record in gridData: + self.assertIsNotNone(record.getAttribute('icao')) + + def testGetDataWithGreaterThan(self): + gridData = self._runConstraintTest('icao', '>', 'koax') + for record in gridData: + self.assertGreater(record.getAttribute('icao'), 'koax') + + def testGetDataWithLessThan(self): + gridData = self._runConstraintTest('icao', '<', 'koax') + for record in gridData: + self.assertLess(record.getAttribute('icao'), 'koax') + + def testGetDataWithGreaterThanEquals(self): + gridData = self._runConstraintTest('icao', '>=', 'koax') + for record in gridData: + self.assertGreaterEqual(record.getAttribute('icao'), 'koax') + + def testGetDataWithLessThanEquals(self): + gridData = self._runConstraintTest('icao', '<=', 'koax') + for record in gridData: + self.assertLessEqual(record.getAttribute('icao'), 'koax') + + def testGetDataWithInTuple(self): + gridData = self._runConstraintTest('icao', 'in', ('koax', 'tpbi')) + for record in gridData: + self.assertIn(record.getAttribute('icao'), ('koax', 'tpbi')) + + def testGetDataWithInList(self): + gridData = self._runConstraintTest('icao', 'in', ['koax', 'tpbi']) + for record in gridData: + self.assertIn(record.getAttribute('icao'), ('koax', 'tpbi')) + + def testGetDataWithInGenerator(self): + generator = (item for item in ('koax', 'tpbi')) + gridData = self._runConstraintTest('icao', 'in', generator) + for record in gridData: + self.assertIn(record.getAttribute('icao'), ('koax', 'tpbi')) + + def testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('icao', 'junk', 'koax') + + 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/testRadarSpatial.py b/awips/test/dafTests/testRadarSpatial.py new file mode 100644 index 0000000..9c0d13d --- /dev/null +++ b/awips/test/dafTests/testRadarSpatial.py @@ -0,0 +1,175 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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 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 +# +# + + +class RadarSpatialTestCase(baseDafTestCase.DafTestCase): + """Test DAF support for radar_spatial data""" + + datatype = "radar_spatial" + + envelope = box(-97.0, 41.0, -96.0, 42.0) + """ + Default request area (box around KOAX) + """ + + def testGetAvailableLocations(self): + req = DAL.newDataRequest(self.datatype) + req.setEnvelope(self.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) + + 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) + + def testGetDataWithEqualsString(self): + geometryData = self._runConstraintTest('wfo_id', '=', 'OAX') + for record in geometryData: + self.assertEqual(record.getString('wfo_id'), 'OAX') + + def testGetDataWithEqualsUnicode(self): + geometryData = self._runConstraintTest('wfo_id', '=', u'OAX') + for record in geometryData: + self.assertEqual(record.getString('wfo_id'), 'OAX') + + 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', '!=', 'OAX') + for record in geometryData: + self.assertNotEquals(record.getString('wfo_id'), 'OAX') + + 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 = ('OAX', 'GID') + geometryData = self._runConstraintTest('wfo_id', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('wfo_id'), collection) + + def testGetDataWithInList(self): + collection = ['OAX', 'GID'] + geometryData = self._runConstraintTest('wfo_id', 'in', collection) + for record in geometryData: + self.assertIn(record.getString('wfo_id'), collection) + + def testGetDataWithInGenerator(self): + collection = ('OAX', '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 testGetDataWithInvalidConstraintTypeThrowsException(self): + with self.assertRaises(ValueError): + self._runConstraintTest('wfo_id', 'junk', 'OAX') + + 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/testSatellite.py b/awips/test/dafTests/testSatellite.py new file mode 100644 index 0000000..0bee61b --- /dev/null +++ b/awips/test/dafTests/testSatellite.py @@ -0,0 +1,186 @@ +#!/usr/bin/env python +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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 +# +# + + +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 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..8967b35 --- /dev/null +++ b/awips/test/dafTests/testSfcObs.py @@ -0,0 +1,177 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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 +# +# + + +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 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 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..1ed4131 --- /dev/null +++ b/awips/test/dafTests/testTopo.py @@ -0,0 +1,96 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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 +# +# + + +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 ["gmted2010", "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..0cdba36 --- /dev/null +++ b/awips/test/dafTests/testWarning.py @@ -0,0 +1,228 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +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 +# +# + + +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): + allRecordsCount = len(self._getAllRecords()) + allLocationNames = self._getLocationNames() + if allRecordsCount == 0: + errmsg = "No {0} data exists on {1}. Try again with {0} data." + raise unittest.SkipTest(errmsg.format(self.datatype, DAL.THRIFT_HOST)) + if len(allLocationNames) != 1: + 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) + self.assertLess(len(geomData), allRecordsCount) + 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 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/docs/source/about.rst b/docs/source/about.rst index b3fb40b..20ae52b 100644 --- a/docs/source/about.rst +++ b/docs/source/about.rst @@ -51,7 +51,7 @@ installations. Package Version RPM Name ====================== ============== ============================== Python 2.7.10 awips2-python -**awips** **0.9.4** **awips2-python-awips** +**awips** **0.9.8** **awips2-python-awips** basemap 1.0.7 awips2-python-basemap cartopy 0.13.0 awips2-python-cartopy cherrypy 3.1.2 awips2-python-cherrypy diff --git a/docs/source/conf.py b/docs/source/conf.py index 03d0c69..142f2e8 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -65,7 +65,7 @@ author = u'Unidata' # built documents. # # The short X.Y version. -version = u'0.9.4' +version = u'0.9.8' # The full version, including alpha/beta/rc tags. # The language for content autogenerated by Sphinx. Refer to documentation diff --git a/dynamicserialize/DynamicSerializationManager.py b/dynamicserialize/DynamicSerializationManager.py index 60d8512..2615149 100644 --- a/dynamicserialize/DynamicSerializationManager.py +++ b/dynamicserialize/DynamicSerializationManager.py @@ -66,4 +66,4 @@ class DynamicSerializationManager: return self.transport.getvalue() def _serialize(self, ctx, obj): - ctx.serializeMessage(obj) \ No newline at end of file + ctx.serializeMessage(obj) diff --git a/dynamicserialize/adapters/CommutativeTimestampAdapter.py b/dynamicserialize/adapters/CommutativeTimestampAdapter.py new file mode 100644 index 0000000..9b52c15 --- /dev/null +++ b/dynamicserialize/adapters/CommutativeTimestampAdapter.py @@ -0,0 +1,46 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + + +# +# 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 diff --git a/dynamicserialize/adapters/__init__.py b/dynamicserialize/adapters/__init__.py index f8509da..d7e643f 100644 --- a/dynamicserialize/adapters/__init__.py +++ b/dynamicserialize/adapters/__init__.py @@ -1,19 +1,19 @@ ## # This software was developed and / or modified by Raytheon Company, # pursuant to Contract DG133W-05-CQ-1067 with the US Government. -# +# # U.S. EXPORT CONTROLLED TECHNICAL DATA # This software product contains export-restricted data whose # export/transfer/disclosure is restricted by U.S. law. Dissemination # to non-U.S. persons whether in the United States or abroad requires # an export license or other authorization. -# +# # Contractor Name: Raytheon Company # Contractor Address: 6825 Pine Street, Suite 340 # Mail Stop B8 # Omaha, NE 68106 # 402.291.0100 -# +# # See the AWIPS II Master Rights File ("Master Rights File.pdf") for # further licensing information. ## @@ -21,10 +21,10 @@ # # __init__.py for Dynamic Serialize adapters. -# -# +# +# # SOFTWARE HISTORY -# +# # Date Ticket# Engineer Description # ------------ ---------- ----------- -------------------------- # 08/31/10 njensen Initial Creation. @@ -33,6 +33,7 @@ # 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 # __all__ = [ @@ -52,6 +53,7 @@ __all__ = [ 'ParmIDAdapter', 'DatabaseIDAdapter', 'TimestampAdapter', + 'CommutativeTimestampAdapter', 'EnumSetAdapter', 'FloatBufferAdapter', 'ByteBufferAdapter', @@ -60,12 +62,12 @@ __all__ = [ 'JTSEnvelopeAdapter', 'JobProgressAdapter', ] - + classAdapterRegistry = {} - + def getAdapterRegistry(): - import sys + import sys for x in __all__: exec 'import ' + x m = sys.modules['dynamicserialize.adapters.' + x] @@ -80,7 +82,7 @@ def getAdapterRegistry(): else: raise LookupError('Adapter class ' + x + ' has no ClassAdapter field ' + \ 'and cannot be registered.') - + getAdapterRegistry() diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/__init__.py index f155bf4..dfd5b4a 100644 --- a/dynamicserialize/dstypes/com/raytheon/uf/common/__init__.py +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/__init__.py @@ -26,6 +26,7 @@ __all__ = [ 'auth', 'dataaccess', 'dataplugin', + 'dataquery', 'datastorage', 'localization', 'management', diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableKey.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableKey.py index 0828fe8..2d81715 100644 --- a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableKey.py +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableKey.py @@ -24,6 +24,7 @@ # ------------ ---------- ----------- -------------------------- # 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): @@ -35,6 +36,7 @@ class ActiveTableKey(object): self.etn = None self.ugcZone = None self.issueYear = None + self.pil = None def getOfficeid(self): return self.officeid @@ -71,3 +73,9 @@ class ActiveTableKey(object): 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 index 8fbaddb..188405d 100644 --- a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableMode.py +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableMode.py @@ -19,10 +19,11 @@ ## # 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 repr(self.value) \ No newline at end of file + return str(self.value) diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableRecord.py b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableRecord.py index a2b4799..6ac4ec2 100644 --- a/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableRecord.py +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/activetable/ActiveTableRecord.py @@ -24,6 +24,7 @@ # ------------ ---------- ----------- -------------------------- # 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 # ## @@ -48,7 +49,6 @@ class ActiveTableRecord(object): self.issueTime = None self.purgeTime = None self.ufn = None - self.geometry = None self.forecaster = None self.motdir = None self.motspd = None @@ -162,9 +162,9 @@ class ActiveTableRecord(object): return self.issueTime def setIssueTime(self, issueTime): - from datetime import datetime - date = datetime.utcfromtimestamp(issueTime.getTime()/1000) - self.key.setIssueYear(date.year) + from datetime import datetime + date = datetime.utcfromtimestamp(issueTime.getTime()/1000) + self.key.setIssueYear(date.year) self.issueTime = issueTime def getPurgeTime(self): @@ -179,12 +179,6 @@ class ActiveTableRecord(object): def setUfn(self, ufn): self.ufn = ufn - def getGeometry(self): - return self.geometry - - def setGeometry(self, geometry): - self.geometry = geometry - def getForecaster(self): return self.forecaster diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/AbstractIdentifierRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/AbstractIdentifierRequest.py index 0288580..3919193 100644 --- a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/AbstractIdentifierRequest.py +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/AbstractIdentifierRequest.py @@ -26,6 +26,8 @@ # Date Ticket# Engineer Description # ------------ ---------- ----------- -------------------------- # 07/23/14 #3185 njensen Initial Creation. +# Jun 01, 2016 5587 tgurney Change self.datatype to +# self.request # # @@ -35,11 +37,11 @@ class AbstractIdentifierRequest(object): __metaclass__ = abc.ABCMeta def __init__(self): - self.datatype = None + self.request = None - def getDatatype(self): - return self.datatype + def getRequest(self): + return self.request - def setDatatype(self, datatype): - self.datatype = datatype + def setRequest(self, request): + self.request = request 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..0669307 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/GetIdentifierValuesRequest.py @@ -0,0 +1,45 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +# 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/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/__init__.py index 75a770d..c8248eb 100644 --- a/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/__init__.py +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataaccess/request/__init__.py @@ -31,7 +31,8 @@ __all__ = [ 'GetGridDataRequest', 'GetRequiredIdentifiersRequest', 'GetSupportedDatatypesRequest', - 'GetOptionalIdentifiersRequest' + 'GetOptionalIdentifiersRequest', + 'GetIdentifierValuesRequest' ] from AbstractDataAccessRequest import AbstractDataAccessRequest @@ -45,4 +46,4 @@ from GetGridDataRequest import GetGridDataRequest from GetRequiredIdentifiersRequest import GetRequiredIdentifiersRequest from GetSupportedDatatypesRequest import GetSupportedDatatypesRequest from GetOptionalIdentifiersRequest import GetOptionalIdentifiersRequest - +from GetIdentifierValuesRequest import GetIdentifierValuesRequest 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..10183a3 --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataquery/__init__.py @@ -0,0 +1,27 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +# 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..d2e282f --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataquery/requests/RequestConstraint.py @@ -0,0 +1,124 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +# +# SOFTWARE HISTORY +# +# Date Ticket# Engineer Description +# ------------ ---------- ----------- -------------------------- +# Jun 01, 2016 5574 tgurney Initial creation +# +# + +class RequestConstraint(object): + + def __init__(self): + self.constraintValue = None + self.constraintType = None + + def getConstraintValue(self): + return self.constraintValue + + def setConstraintValue(self, constraintValue): + self.constraintValue = constraintValue + + def getConstraintType(self): + return self.constraintType + + def setConstraintType(self, constraintType): + self.constraintType = constraintType + + # 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 _construct_in(cls, constraintType, constraintValue): + """Build a new "IN" constraint from an iterable.""" + try: + iterator = iter(constraintValue) + except TypeError: + raise TypeError("value for IN constraint must be an iterable") + stringValue = ', '.join(cls._stringify(item) for item in iterator) + if len(stringValue) == 0: + raise ValueError('cannot use IN with empty collection') + obj = cls() + obj.setConstraintType(constraintType) + obj.setConstraintValue(stringValue) + return obj + + @classmethod + def _construct_eq_not_eq(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': + return cls._construct_in(constraintType, constraintValue) + elif constraintType in {'EQUALS', 'NOT_EQUALS'}: + return cls._construct_eq_not_eq(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..b66872c --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/dataquery/requests/__init__.py @@ -0,0 +1,28 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## + +# File auto-generated by PythonFileGenerator + +__all__ = [ + 'RequestConstraint' + ] + +from RequestConstraint import RequestConstraint + diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/CopyRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/CopyRequest.py index f89c669..ecb92ab 100644 --- a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/CopyRequest.py +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/CopyRequest.py @@ -26,7 +26,6 @@ class CopyRequest(object): self.repack = None self.repackCompression = None self.outputDir = None - self.timestampCheck = None self.minMillisSinceLastChange = None self.maxMillisSinceLastChange = None self.filename = None @@ -49,12 +48,6 @@ class CopyRequest(object): def setOutputDir(self, outputDir): self.outputDir = outputDir - def getTimestampCheck(self): - return self.timestampCheck - - def setTimestampCheck(self, timestampCheck): - self.timestampCheck = timestampCheck - def getMinMillisSinceLastChange(self): return self.minMillisSinceLastChange diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DeleteOrphansRequest.py b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DeleteOrphansRequest.py index 13f2e5e..bad350c 100644 --- a/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DeleteOrphansRequest.py +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DeleteOrphansRequest.py @@ -19,21 +19,29 @@ ## # 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 - self.oldestDate = None - def getOldestDate(self): - return self.oldestDate + def getOldestDateMap(self): + return self.oldestDateMap - def setOldestDate(self, oldestDate): - self.oldestDate = oldestDate + 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/time/CommutativeTimestamp.py b/dynamicserialize/dstypes/com/raytheon/uf/common/time/CommutativeTimestamp.py new file mode 100644 index 0000000..c11e3ac --- /dev/null +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/time/CommutativeTimestamp.py @@ -0,0 +1,37 @@ +## +# This software was developed and / or modified by Raytheon Company, +# pursuant to Contract DG133W-05-CQ-1067 with the US Government. +# +# U.S. EXPORT CONTROLLED TECHNICAL DATA +# This software product contains export-restricted data whose +# export/transfer/disclosure is restricted by U.S. law. Dissemination +# to non-U.S. persons whether in the United States or abroad requires +# an export license or other authorization. +# +# Contractor Name: Raytheon Company +# Contractor Address: 6825 Pine Street, Suite 340 +# Mail Stop B8 +# Omaha, NE 68106 +# 402.291.0100 +# +# See the AWIPS II Master Rights File ("Master Rights File.pdf") for +# further licensing information. +## +# ---------------------------------------------------------------------------- +# +# 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/FormattedDate.py b/dynamicserialize/dstypes/com/raytheon/uf/common/time/FormattedDate.py index aa082e2..25ba789 100644 --- a/dynamicserialize/dstypes/com/raytheon/uf/common/time/FormattedDate.py +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/time/FormattedDate.py @@ -24,16 +24,13 @@ # Date Ticket# Engineer Description # ------------ ---------- ----------- -------------------------- # 09/21/2015 4486 rjpeter Initial creation. -# +# 06/23/2016 #5696 rjpeter Extend CommutativeTimestamp ## -from time import gmtime, strftime -from dynamicserialize.dstypes.java.util import Date +from CommutativeTimestamp import CommutativeTimestamp -class FormattedDate(Date): +# TODO: Remove after 16.4.1 no longer in field +class FormattedDate(CommutativeTimestamp): def __init__(self, timeInMillis=None): super(FormattedDate, self).__init__(timeInMillis) - - def __repr__(self): - return strftime("%Y-%m-%d %H:%M:%S.", gmtime(self.time/1000.0)) + '{:03d}'.format(self.time%1000) diff --git a/dynamicserialize/dstypes/com/raytheon/uf/common/time/__init__.py b/dynamicserialize/dstypes/com/raytheon/uf/common/time/__init__.py index bcbcb2c..95104e8 100644 --- a/dynamicserialize/dstypes/com/raytheon/uf/common/time/__init__.py +++ b/dynamicserialize/dstypes/com/raytheon/uf/common/time/__init__.py @@ -23,10 +23,12 @@ __all__ = [ 'DataTime', 'TimeRange', - 'FormattedDate' + 'FormattedDate', + 'CommutativeTimestamp' ] from DataTime import DataTime from TimeRange import TimeRange from FormattedDate import FormattedDate +from CommutativeTimestamp import CommutativeTimestamp 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 index 0c34127..1139684 100644 --- 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 @@ -1,8 +1,8 @@ # File auto-generated against equivalent DynamicSerialize Java class -# +# # SOFTWARE HISTORY -# + # Date Ticket# Engineer Description # ------------ ---------- ----------- -------------------------- # May 05, 2016 root Generated 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 index 97b9a3c..1fd4902 100644 --- 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 @@ -1,8 +1,8 @@ # File auto-generated against equivalent DynamicSerialize Java class -# +# # SOFTWARE HISTORY -# +# # Date Ticket# Engineer Description # ------------ ---------- ----------- -------------------------- # May 06, 2016 root Generated 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 index f9111ee..bb72063 100644 --- 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 @@ -1,8 +1,8 @@ # File auto-generated against equivalent DynamicSerialize Java class -# +# # SOFTWARE HISTORY -# +# # Date Ticket# Engineer Description # ------------ ---------- ----------- -------------------------- # May 06, 2016 root Generated diff --git a/dynamicserialize/dstypes/java/sql/Timestamp.py b/dynamicserialize/dstypes/java/sql/Timestamp.py index 0bf23bf..2426b8f 100644 --- a/dynamicserialize/dstypes/java/sql/Timestamp.py +++ b/dynamicserialize/dstypes/java/sql/Timestamp.py @@ -20,20 +20,24 @@ # File auto-generated against equivalent DynamicSerialize Java class # and then modified post-generation to add additional features to better -# match Java implementation. +# 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. +# 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) diff --git a/examples/notebooks/Grid_Levels_and_Parameters.ipynb b/examples/notebooks/Grid_Levels_and_Parameters.ipynb index f0a7231..946c49a 100644 --- a/examples/notebooks/Grid_Levels_and_Parameters.ipynb +++ b/examples/notebooks/Grid_Levels_and_Parameters.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This examples 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 Cartopy and Basemap." + "This examples 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." ] }, { @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 380, + "execution_count": 37, "metadata": { "collapsed": true }, @@ -40,51 +40,54 @@ }, { "cell_type": "code", - "execution_count": 381, + "execution_count": 38, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "acars\n", - "airep\n", - "binlightning\n", - "bufrmosavn\n", - "bufrmoseta\n", - "bufrmosgfs\n", - "bufrmoshpc\n", - "bufrmoslamp\n", - "bufrmosmrf\n", - "bufrmosngm\n", - "bufrua\n", - "climate\n", - "common_obs_spatial\n", - "ffmp\n", - "gfe\n", - "grid\n", - "hydro\n", - "ldadmesonet\n", - "maps\n", - "modelsounding\n", - "obs\n", - "pirep\n", - "practicewarning\n", - "profiler\n", - "radar\n", - "radar_spatial\n", - "satellite\n", - "sfcobs\n", - "warning\n" - ] + "data": { + "text/plain": [ + "['acars',\n", + " 'airep',\n", + " 'binlightning',\n", + " 'bufrmosavn',\n", + " 'bufrmoseta',\n", + " 'bufrmosgfs',\n", + " 'bufrmoshpc',\n", + " 'bufrmoslamp',\n", + " 'bufrmosmrf',\n", + " 'bufrmosngm',\n", + " 'bufrua',\n", + " 'climate',\n", + " 'common_obs_spatial',\n", + " 'ffmp',\n", + " 'gfe',\n", + " 'grid',\n", + " 'hydro',\n", + " 'ldadmesonet',\n", + " 'maps',\n", + " 'modelsounding',\n", + " 'obs',\n", + " 'pirep',\n", + " 'practicewarning',\n", + " 'profiler',\n", + " 'radar',\n", + " 'radar_spatial',\n", + " 'satellite',\n", + " 'sfcobs',\n", + " 'warning']" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "dataTypes = DataAccessLayer.getSupportedDatatypes()\n", - "for type in dataTypes: print type" + "list(dataTypes)" ] }, { @@ -98,14 +101,177 @@ }, { "cell_type": "code", - "execution_count": 382, + "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "request = DataAccessLayer.newDataRequest()\n", - "request.setDatatype(\"grid\")\n", + "request.setDatatype(\"grid\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DataAccessLayer.getAvailableLocationNames()\n", + "\n", + "With datatype set to \"grid\", we can query all available grid names with **getAvailableLocationNames()**" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['CMC',\n", + " 'DGEX',\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", + " 'GFS',\n", + " 'GFS20',\n", + " 'GFSGuide',\n", + " 'GFSLAMP5',\n", + " 'GLERL',\n", + " 'GribModel:58:0:135',\n", + " 'GribModel:58:0:18',\n", + " 'GribModel:58:0:78',\n", + " 'GribModel:9:151:172',\n", + " 'HFR-EAST_6KM',\n", + " 'HFR-EAST_PR_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", + " 'MOSGuideExtended',\n", + " 'MPE-Local-ALR',\n", + " 'MPE-Local-MSR',\n", + " 'MPE-Local-ORN',\n", + " 'MPE-Local-RHA',\n", + " 'MPE-Local-RSA',\n", + " 'MPE-Local-SJU',\n", + " 'MPE-Local-STR',\n", + " 'MPE-Local-TAR',\n", + " 'MPE-Local-TIR',\n", + " 'MPE-Mosaic-ALR',\n", + " 'MPE-Mosaic-FWR',\n", + " 'MPE-Mosaic-MSR',\n", + " 'MPE-Mosaic-ORN',\n", + " 'MPE-Mosaic-RHA',\n", + " 'MPE-Mosaic-SJU',\n", + " 'MPE-Mosaic-TAR',\n", + " 'MPE-Mosaic-TIR',\n", + " 'NAM12',\n", + " 'NAM40',\n", + " 'NAVGEM',\n", + " 'NCWF',\n", + " 'NOHRSC-SNOW',\n", + " 'NamDNG',\n", + " 'QPE-ALR',\n", + " 'QPE-Auto-TUA',\n", + " 'QPE-FWR',\n", + " 'QPE-KRF',\n", + " 'QPE-MSR',\n", + " 'QPE-ORN',\n", + " 'QPE-RFC-PTR',\n", + " 'QPE-RFC-RSA',\n", + " 'QPE-RFC-STR',\n", + " 'QPE-TIR',\n", + " 'QPE-TUA',\n", + " 'QPE-XNAV-ALR',\n", + " 'QPE-XNAV-FWR',\n", + " 'QPE-XNAV-KRF',\n", + " 'QPE-XNAV-MSR',\n", + " 'QPE-XNAV-ORN',\n", + " 'QPE-XNAV-RHA',\n", + " 'QPE-XNAV-SJU',\n", + " 'QPE-XNAV-TAR',\n", + " 'QPE-XNAV-TIR',\n", + " 'QPE-XNAV-TUA',\n", + " 'RAP13',\n", + " 'RAP20',\n", + " 'RAP40',\n", + " 'RFCqpf',\n", + " 'RTMA',\n", + " 'UKMET-Global',\n", + " 'UKMET-MODEL1',\n", + " 'UKMET37',\n", + " 'UKMET38',\n", + " 'UKMET39',\n", + " 'UKMET40',\n", + " 'UKMET41',\n", + " 'UKMET42',\n", + " 'UKMET43',\n", + " 'UKMET44',\n", + " 'URMA25',\n", + " 'WaveWatch',\n", + " 'fnmocWave']" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "available_grids = DataAccessLayer.getAvailableLocationNames(request)\n", + "available_grids.sort()\n", + "list(available_grids)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set grid name with `setLocationNames()`" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ "request.setLocationNames(\"RAP40\")" ] }, @@ -113,6 +279,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "# List Available Parameters for a Grid\n", + "\n", "### DataAccessLayer.getAvailableParameters()\n", "\n", "After datatype and model name (locationName) are set, you can query all available parameters with **getAvailableParameters()**" @@ -120,109 +288,151 @@ }, { "cell_type": "code", - "execution_count": 383, + "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "AV\n", - "BGRUN\n", - "BGRUN1hr\n", - "BLI\n", - "CAPE\n", - "CFRZR\n", - "CICEP\n", - "CIn\n", - "CP\n", - "CP10hr\n", - "CP11hr\n", - "CP12hr\n", - "CP13hr\n", - "CP14hr\n", - "CP15hr\n", - "CP16hr\n", - "CP17hr\n", - "CP18hr\n", - "CP1hr\n", - "CP2hr\n", - "CP3hr\n", - "CP4hr\n", - "CP5hr\n", - "CP6hr\n", - "CP9hr\n", - "CRAIN\n", - "CSNOW\n", - "DpD\n", - "DpT\n", - "EPT\n", - "GH\n", - "HCDC\n", - "HINDEX\n", - "HPBL\n", - "Heli\n", - "LCDC\n", - "LTNG\n", - "LgSP\n", - "LgSP10hr\n", - "LgSP11hr\n", - "LgSP12hr\n", - "LgSP13hr\n", - "LgSP14hr\n", - "LgSP15hr\n", - "LgSP16hr\n", - "LgSP17hr\n", - "LgSP18hr\n", - "LgSP1hr\n", - "LgSP2hr\n", - "LgSP3hr\n", - "LgSP4hr\n", - "LgSP5hr\n", - "LgSP6hr\n", - "LgSP9hr\n", - "MCDC\n", - "MMSP\n", - "MSTAV\n", - "P\n", - "PLPL\n", - "PR\n", - "PT\n", - "PVV\n", - "PW\n", - "PoT\n", - "REFC\n", - "REFD\n", - "RETOP\n", - "RH\n", - "SH\n", - "SLI\n", - "SSRUN\n", - "SSRUN1hr\n", - "SnD\n", - "T\n", - "TCC\n", - "USTM\n", - "VSTM\n", - "VUCSH\n", - "VVCSH\n", - "Vis\n", - "WEASD\n", - "WEASD1hr\n", - "WEASD2hr\n", - "WEASD3hr\n", - "WGS\n", - "uW\n", - "vW\n" - ] + "data": { + "text/plain": [ + "['AV',\n", + " 'BLI',\n", + " 'CAPE',\n", + " 'CFRZR',\n", + " 'CICEP',\n", + " 'CIn',\n", + " 'CP',\n", + " 'CP10hr',\n", + " 'CP11hr',\n", + " 'CP12hr',\n", + " 'CP13hr',\n", + " 'CP14hr',\n", + " 'CP15hr',\n", + " 'CP16hr',\n", + " 'CP17hr',\n", + " 'CP18hr',\n", + " 'CP1hr',\n", + " 'CP2hr',\n", + " 'CP3hr',\n", + " 'CP4hr',\n", + " 'CP5hr',\n", + " 'CP6hr',\n", + " 'CP9hr',\n", + " 'CRAIN',\n", + " 'CSNOW',\n", + " 'DpD',\n", + " 'DpT',\n", + " 'EPT',\n", + " 'GH',\n", + " 'HCDC',\n", + " 'HINDEX',\n", + " 'HPBL',\n", + " 'Heli',\n", + " 'LCDC',\n", + " 'LTNG',\n", + " 'LgSP',\n", + " 'LgSP10hr',\n", + " 'LgSP11hr',\n", + " 'LgSP12hr',\n", + " 'LgSP13hr',\n", + " 'LgSP14hr',\n", + " 'LgSP15hr',\n", + " 'LgSP16hr',\n", + " 'LgSP17hr',\n", + " 'LgSP18hr',\n", + " 'LgSP1hr',\n", + " 'LgSP2hr',\n", + " 'LgSP3hr',\n", + " 'LgSP4hr',\n", + " 'LgSP5hr',\n", + " 'LgSP6hr',\n", + " 'LgSP9hr',\n", + " 'MCDC',\n", + " 'MMSP',\n", + " 'MSTAV',\n", + " 'P',\n", + " 'PLPL',\n", + " 'PR',\n", + " 'PVV',\n", + " 'PW',\n", + " 'PoT',\n", + " 'REFD',\n", + " 'RH',\n", + " 'SH',\n", + " 'SLI',\n", + " 'SnD',\n", + " 'T',\n", + " 'TOTSN',\n", + " 'TOTSN10hr',\n", + " 'TOTSN11hr',\n", + " 'TOTSN12hr',\n", + " 'TOTSN13hr',\n", + " 'TOTSN14hr',\n", + " 'TOTSN15hr',\n", + " 'TOTSN16hr',\n", + " 'TOTSN17hr',\n", + " 'TOTSN18hr',\n", + " 'TOTSN1hr',\n", + " 'TOTSN2hr',\n", + " 'TOTSN3hr',\n", + " 'TOTSN4hr',\n", + " 'TOTSN5hr',\n", + " 'TOTSN6hr',\n", + " 'TOTSN9hr',\n", + " 'TP',\n", + " 'TP10hr',\n", + " 'TP11hr',\n", + " 'TP12hr',\n", + " 'TP13hr',\n", + " 'TP14hr',\n", + " 'TP15hr',\n", + " 'TP16hr',\n", + " 'TP17hr',\n", + " 'TP18hr',\n", + " 'TP1hr',\n", + " 'TP2hr',\n", + " 'TP3hr',\n", + " 'TP4hr',\n", + " 'TP5hr',\n", + " 'TP6hr',\n", + " 'TP9hr',\n", + " 'USTM',\n", + " 'VSTM',\n", + " 'VUCSH',\n", + " 'VVCSH',\n", + " 'Vis',\n", + " 'WEASD',\n", + " 'WEASD10hr',\n", + " 'WEASD11hr',\n", + " 'WEASD12hr',\n", + " 'WEASD13hr',\n", + " 'WEASD14hr',\n", + " 'WEASD15hr',\n", + " 'WEASD16hr',\n", + " 'WEASD17hr',\n", + " 'WEASD18hr',\n", + " 'WEASD1hr',\n", + " 'WEASD2hr',\n", + " 'WEASD3hr',\n", + " 'WEASD4hr',\n", + " 'WEASD5hr',\n", + " 'WEASD6hr',\n", + " 'WEASD9hr',\n", + " 'WGS',\n", + " 'uW',\n", + " 'vW']" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "availableParms = DataAccessLayer.getAvailableParameters(request)\n", "availableParms.sort()\n", - "for parm in availableParms: print parm" + "list(availableParms)" ] }, { @@ -237,7 +447,7 @@ }, { "cell_type": "code", - "execution_count": 384, + "execution_count": 43, "metadata": { "collapsed": false }, @@ -250,12 +460,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### DataAccessLayer.getAvailableLevels()" + "## List Available Levels for Parameter\n", + "\n", + "Using **DataAccessLayer.getAvailableLevels()**" ] }, { "cell_type": "code", - "execution_count": 385, + "execution_count": 44, "metadata": { "collapsed": false, "scrolled": true @@ -265,60 +477,60 @@ "name": "stdout", "output_type": "stream", "text": [ - "60.0_90.0BL\n", + "875.0MB\n", + "575.0MB\n", + "650.0MB\n", + "675.0MB\n", "0.0TROP\n", - "1000.0MB\n", + "700.0MB\n", + "250.0MB\n", + "350.0MB\n", + "150.0MB\n", + "550.0MB\n", + "375.0MB\n", "0.0SFC\n", + "150.0_180.0BL\n", + "120.0_150.0BL\n", + "1000.0MB\n", + "725.0MB\n", + "125.0MB\n", + "850.0MB\n", + "0.0_30.0BL\n", + "325.0MB\n", + "225.0MB\n", + "400.0MB\n", + "450.0MB\n", + "600.0MB\n", + "2.0FHAG\n", "975.0MB\n", "950.0MB\n", - "925.0MB\n", - "900.0MB\n", - "875.0MB\n", - "850.0MB\n", - "825.0MB\n", - "775.0MB\n", - "650.0MB\n", - "2.0FHAG\n", - "80.0FHAG\n", - "600.0MB\n", "30.0_60.0BL\n", - "90.0_120.0BL\n", - "120.0_150.0BL\n", - "150.0_180.0BL\n", - "800.0MB\n", - "750.0MB\n", - "725.0MB\n", - "700.0MB\n", - "0.0_30.0BL\n", - "675.0MB\n", - "625.0MB\n", - "575.0MB\n", - "550.0MB\n", - "525.0MB\n", - "500.0MB\n", "475.0MB\n", - "450.0MB\n", + "825.0MB\n", "425.0MB\n", - "400.0MB\n", - "375.0MB\n", - "350.0MB\n", - "325.0MB\n", - "300.0MB\n", - "275.0MB\n", - "250.0MB\n", - "225.0MB\n", + "525.0MB\n", "200.0MB\n", + "775.0MB\n", + "60.0_90.0BL\n", + "300.0MB\n", "175.0MB\n", - "150.0MB\n", - "125.0MB\n", - "100.0MB\n" + "275.0MB\n", + "100.0MB\n", + "90.0_120.0BL\n", + "800.0MB\n", + "80.0FHAG\n", + "500.0MB\n", + "625.0MB\n", + "925.0MB\n", + "750.0MB\n", + "900.0MB\n" ] } ], "source": [ "availableLevels = DataAccessLayer.getAvailableLevels(request)\n", - "availableLevels.sort()\n", - "for level in availableLevels: print level" + "for level in availableLevels:\n", + " print(level)" ] }, { @@ -338,7 +550,7 @@ }, { "cell_type": "code", - "execution_count": 386, + "execution_count": 45, "metadata": { "collapsed": true }, @@ -354,246 +566,55 @@ "### 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)`" + "* **getAvailableTimes(request)** will return an object of all times - formatted as `YYYY-MM-DD HH:MM:SS (F:ff)`\n", + "* **getForecastCycle(cycle, times)** will return a DataTime array for a single forecast cycle." ] }, { "cell_type": "code", - "execution_count": 387, + "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "2016-05-24 19:00:00\n" - ] + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "cycles = DataAccessLayer.getAvailableTimes(request, True)\n", - "print cycles[-1] # 0 for FIRST time, -1 for LAST" - ] - }, - { - "cell_type": "code", - "execution_count": 388, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2016-05-24 12:00:00 (0)\n", - "2016-05-24 13:00:00 (0)\n", - "2016-05-24 12:00:00 (1)\n", - "2016-05-24 14:00:00 (0)\n", - "2016-05-24 13:00:00 (1)\n", - "2016-05-24 12:00:00 (2)\n", - "2016-05-24 15:00:00 (0)\n", - "2016-05-24 14:00:00 (1)\n", - "2016-05-24 13:00:00 (2)\n", - "2016-05-24 12:00:00 (3)\n", - "2016-05-24 16:00:00 (0)\n", - "2016-05-24 15:00:00 (1)\n", - "2016-05-24 14:00:00 (2)\n", - "2016-05-24 13:00:00 (3)\n", - "2016-05-24 12:00:00 (4)\n", - "2016-05-24 17:00:00 (0)\n", - "2016-05-24 16:00:00 (1)\n", - "2016-05-24 15:00:00 (2)\n", - "2016-05-24 14:00:00 (3)\n", - "2016-05-24 13:00:00 (4)\n", - "2016-05-24 12:00:00 (5)\n", - "2016-05-24 18:00:00 (0)\n", - "2016-05-24 17:00:00 (1)\n", - "2016-05-24 16:00:00 (2)\n", - "2016-05-24 15:00:00 (3)\n", - "2016-05-24 14:00:00 (4)\n", - "2016-05-24 13:00:00 (5)\n", - "2016-05-24 12:00:00 (6)\n", - "2016-05-24 19:00:00 (0)\n", - "2016-05-24 18:00:00 (1)\n", - "2016-05-24 17:00:00 (2)\n", - "2016-05-24 16:00:00 (3)\n", - "2016-05-24 15:00:00 (4)\n", - "2016-05-24 14:00:00 (5)\n", - "2016-05-24 13:00:00 (6)\n", - "2016-05-24 19:00:00 (1)\n", - "2016-05-24 18:00:00 (2)\n", - "2016-05-24 17:00:00 (3)\n", - "2016-05-24 16:00:00 (4)\n", - "2016-05-24 15:00:00 (5)\n", - "2016-05-24 14:00:00 (6)\n", - "2016-05-24 19:00:00 (2)\n", - "2016-05-24 18:00:00 (3)\n", - "2016-05-24 17:00:00 (4)\n", - "2016-05-24 16:00:00 (5)\n", - "2016-05-24 15:00:00 (6)\n", - "2016-05-24 12:00:00 (9)\n", - "2016-05-24 19:00:00 (3)\n", - "2016-05-24 18:00:00 (4)\n", - "2016-05-24 17:00:00 (5)\n", - "2016-05-24 16:00:00 (6)\n", - "2016-05-24 13:00:00 (9)\n", - "2016-05-24 12:00:00 (10)\n", - "2016-05-24 19:00:00 (4)\n", - "2016-05-24 18:00:00 (5)\n", - "2016-05-24 17:00:00 (6)\n", - "2016-05-24 14:00:00 (9)\n", - "2016-05-24 13:00:00 (10)\n", - "2016-05-24 12:00:00 (11)\n", - "2016-05-24 19:00:00 (5)\n", - "2016-05-24 18:00:00 (6)\n", - "2016-05-24 15:00:00 (9)\n", - "2016-05-24 14:00:00 (10)\n", - "2016-05-24 13:00:00 (11)\n", - "2016-05-24 12:00:00 (12)\n", - "2016-05-24 19:00:00 (6)\n", - "2016-05-24 16:00:00 (9)\n", - "2016-05-24 15:00:00 (10)\n", - "2016-05-24 14:00:00 (11)\n", - "2016-05-24 13:00:00 (12)\n", - "2016-05-24 12:00:00 (13)\n", - "2016-05-24 17:00:00 (9)\n", - "2016-05-24 16:00:00 (10)\n", - "2016-05-24 15:00:00 (11)\n", - "2016-05-24 14:00:00 (12)\n", - "2016-05-24 13:00:00 (13)\n", - "2016-05-24 12:00:00 (14)\n", - "2016-05-24 18:00:00 (9)\n", - "2016-05-24 17:00:00 (10)\n", - "2016-05-24 16:00:00 (11)\n", - "2016-05-24 15:00:00 (12)\n", - "2016-05-24 14:00:00 (13)\n", - "2016-05-24 13:00:00 (14)\n", - "2016-05-24 12:00:00 (15)\n", - "2016-05-24 19:00:00 (9)\n", - "2016-05-24 18:00:00 (10)\n", - "2016-05-24 17:00:00 (11)\n", - "2016-05-24 16:00:00 (12)\n", - "2016-05-24 15:00:00 (13)\n", - "2016-05-24 14:00:00 (14)\n", - "2016-05-24 13:00:00 (15)\n", - "2016-05-24 12:00:00 (16)\n", - "2016-05-24 19:00:00 (10)\n", - "2016-05-24 18:00:00 (11)\n", - "2016-05-24 17:00:00 (12)\n", - "2016-05-24 16:00:00 (13)\n", - "2016-05-24 15:00:00 (14)\n", - "2016-05-24 14:00:00 (15)\n", - "2016-05-24 13:00:00 (16)\n", - "2016-05-24 12:00:00 (17)\n", - "2016-05-24 19:00:00 (11)\n", - "2016-05-24 18:00:00 (12)\n", - "2016-05-24 17:00:00 (13)\n", - "2016-05-24 16:00:00 (14)\n", - "2016-05-24 15:00:00 (15)\n", - "2016-05-24 14:00:00 (16)\n", - "2016-05-24 13:00:00 (17)\n", - "2016-05-24 12:00:00 (18)\n", - "2016-05-24 19:00:00 (12)\n", - "2016-05-24 18:00:00 (13)\n", - "2016-05-24 17:00:00 (14)\n", - "2016-05-24 16:00:00 (15)\n", - "2016-05-24 15:00:00 (16)\n", - "2016-05-24 14:00:00 (17)\n", - "2016-05-24 13:00:00 (18)\n", - "2016-05-24 19:00:00 (13)\n", - "2016-05-24 18:00:00 (14)\n", - "2016-05-24 17:00:00 (15)\n", - "2016-05-24 16:00:00 (16)\n", - "2016-05-24 15:00:00 (17)\n", - "2016-05-24 14:00:00 (18)\n", - "2016-05-24 19:00:00 (14)\n", - "2016-05-24 18:00:00 (15)\n", - "2016-05-24 17:00:00 (16)\n", - "2016-05-24 16:00:00 (17)\n", - "2016-05-24 15:00:00 (18)\n", - "2016-05-24 19:00:00 (15)\n", - "2016-05-24 18:00:00 (16)\n", - "2016-05-24 17:00:00 (17)\n", - "2016-05-24 16:00:00 (18)\n", - "2016-05-24 19:00:00 (16)\n", - "2016-05-24 18:00:00 (17)\n", - "2016-05-24 17:00:00 (18)\n", - "2016-05-24 19:00:00 (17)\n", - "2016-05-24 18:00:00 (18)\n", - "2016-05-24 19:00:00 (18)\n" - ] - } - ], - "source": [ - "t = DataAccessLayer.getAvailableTimes(request)\n", - "for time in t: \n", - " print str(time)" + "times = DataAccessLayer.getAvailableTimes(request)\n", + "fcstRun = DataAccessLayer.getForecastCycle(cycles[-1], times)\n", + "list(fcstRun)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "* note the difference above where the forecast hour () is included in the DataTime object\n", - "* also note that the array is sorted by *Validation Time* (2016-05-24 10:00:00 (0), 2016-05-24 11:00:00 (0), 2016-05-24 10:00:00 (1), 2016-05-24 12:00:00 (0), etc.).\n", + "# Request a Grid\n", "\n", - "### Querying a complete model run\n", - "\n", - "If you want to get all available times (grids) for a particular run, you must supply a DataTime array to the **getGridData()** method which includes all forecast hours. The DAF does not handle a direct request for a single cycle run (yet), so you need to filter the DataTime array.\n", - "\n", - "In this case we take the `t` object from above (all times) and compare a substring to the last available cycle `cycles[-1]` and append to a new array `fcstRun`." - ] - }, - { - "cell_type": "code", - "execution_count": 389, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "looking for 2016-05-24 19:00:00\n", - "2016-05-24 19:00:00 (0)\n", - "2016-05-24 19:00:00 (1)\n", - "2016-05-24 19:00:00 (2)\n", - "2016-05-24 19:00:00 (3)\n", - "2016-05-24 19:00:00 (4)\n", - "2016-05-24 19:00:00 (5)\n", - "2016-05-24 19:00:00 (6)\n", - "2016-05-24 19:00:00 (9)\n", - "2016-05-24 19:00:00 (10)\n", - "2016-05-24 19:00:00 (11)\n", - "2016-05-24 19:00:00 (12)\n", - "2016-05-24 19:00:00 (13)\n", - "2016-05-24 19:00:00 (14)\n", - "2016-05-24 19:00:00 (15)\n", - "2016-05-24 19:00:00 (16)\n", - "2016-05-24 19:00:00 (17)\n", - "2016-05-24 19:00:00 (18)\n" - ] - } - ], - "source": [ - "print \"looking for \", str(cycles[-1])\n", - "\n", - "fcstRun = []\n", - "for time in t:\n", - " if str(time)[:19] == str(cycles[-1]):\n", - " fcstRun.append(time)\n", - "\n", - "for time in fcstRun: print time" - ] - }, - { - "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." @@ -601,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 390, + "execution_count": 47, "metadata": { "collapsed": false }, @@ -610,16 +631,9 @@ "response = DataAccessLayer.getGridData(request, fcstRun)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Let's look at what we have**" - ] - }, { "cell_type": "code", - "execution_count": 391, + "execution_count": 48, "metadata": { "collapsed": false }, @@ -628,98 +642,60 @@ "name": "stdout", "output_type": "stream", "text": [ - "(151, 113)\n", - "Time : 2016-05-24 19:00:00\n", - "Model: RAP40\n", - "Parm : T\n", - "Unit : K\n" + "('Time :', '2016-10-20 20:00:00 (6)')\n", + "('Time :', '2016-10-20 20:00:00 (10)')\n", + "('Time :', '2016-10-20 20:00:00 (11)')\n", + "('Time :', '2016-10-20 20:00:00 (9)')\n", + "('Time :', '2016-10-20 20:00:00 (13)')\n", + "('Time :', '2016-10-20 20:00:00 (14)')\n", + "('Time :', '2016-10-20 20:00:00 (12)')\n", + "('Time :', '2016-10-20 20:00:00 (16)')\n", + "('Time :', '2016-10-20 20:00:00 (17)')\n", + "('Time :', '2016-10-20 20:00:00 (15)')\n", + "('Time :', '2016-10-20 20:00:00 (18)')\n", + "('Time :', '2016-10-20 20:00:00 (1)')\n", + "('Time :', '2016-10-20 20:00:00 (2)')\n", + "('Time :', '2016-10-20 20:00:00 (3)')\n", + "('Time :', '2016-10-20 20:00:00 (0)')\n", + "('Model:', 'RAP40')\n", + "('Parm :', 'T')\n", + "('Unit :', 'K')\n", + "(151, 113)\n" ] } ], "source": [ - "grid = response[0]\n", + "for grid in response:\n", + " data = grid.getRawData()\n", + " lons, lats = grid.getLatLonCoords()\n", + " print('Time :', str(grid.getDataTime()))\n", "\n", - "data = grid.getRawData()\n", - "\n", - "print data.shape\n", - "print 'Time :', cycles[-1]\n", - "print 'Model:', grid.getLocationName()\n", - "print 'Parm :', grid.getParameter()\n", - "print 'Unit :', grid.getUnit()" + "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": [ - "### getLatLonCoords()" + "# Plot a Grid with Matplotlib and Cartopy\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 392, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 54.24940109 54.35071945 54.45080566 ..., 57.9545517 57.91926193\n", - " 57.88272858]\n", - " [ 57.84495163 57.80593109 57.76566696 ..., 58.07667542 58.08861542\n", - " 58.09931183]\n", - " [ 58.10876846 58.11697769 58.12394714 ..., 56.40270996 56.46187973\n", - " 56.51980972]\n", - " ..., \n", - " [ 19.93209648 19.89832115 19.86351395 ..., 20.054636 20.06362152\n", - " 20.07156372]\n", - " [ 20.0784626 20.08431816 20.08912849 ..., 18.58354759 18.63155174\n", - " 18.67854691]\n", - " [ 18.72453308 18.76950836 18.81346893 ..., 17.49624634 17.42861557\n", - " 17.36001205]] [[-139.83120728 -139.32348633 -138.81448364 ..., -79.26060486\n", - " -78.70166016 -78.14326477]\n", - " [ -77.58544922 -77.02822876 -76.47161865 ..., -100.70157623\n", - " -100.13801575 -99.57427216]\n", - " [ -99.01037598 -98.44634247 -97.88218689 ..., -121.69165039\n", - " -121.15060425 -120.60871887]\n", - " ..., \n", - " [ -82.65139008 -82.26644897 -81.88170624 ..., -98.52494049\n", - " -98.13802338 -97.75105286]\n", - " [ -97.36403656 -96.97698212 -96.58989716 ..., -113.07767487\n", - " -112.69831085 -112.31866455]\n", - " [-111.93874359 -111.5585556 -111.17810822 ..., -69.85433197\n", - " -69.48160553 -69.10926819]]\n" - ] - } - ], - "source": [ - "lons,lats = grid.getLatLonCoords()\n", - "print lats, lons" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting Gridded Data\n", - "\n", - "What happens if we try to plot our data array with Basemap?" - ] - }, - { - "cell_type": "code", - "execution_count": 393, + "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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o/TC8O93TP7Rsmzcu1f+jjgDvafE13Q8aA/XuhocfNceRCiCV3VFS8P/YRVNO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Aw19h9HtVNVrDtIKLQDgs69K+rQFvniGaMAwdsnohMe+lecEdXyv/xrNrnbD1OhbhX7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\nYu8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ThkwrwBRwKR/vV/jge9h+GF64FiNMyhAZbtgyuTdmcGiV9NJeKtejWuf394Q89Ncn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UsFmC6iDw+e+C2nXtyEy863oE2euleBtMN1hWx5MYs5DHJU3h00c0qfZrFTC6UCdAWU+gGp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1pAvRJiiPwkHN8Hln6DccWASr/gAnPNuVkt61PPPbungg1WH0vPi/VFunWQsLsd0dhiMF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\npkyZwsyZM3nnnXfIyPDI9B4qVKidRiGs7mr6mbav/QKpaH3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xzkfGWm1FmmItQqN3ndrT2kfDqFpGpGY1vJUH5KUPuRKA3zqMLjy1oBUPEN++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\n9Pwit7lSSLYOdMyvBg6PW15zkhoiAy2opZ8bzc0O5XKm35fMOVEln8deZ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N4TRb3dqp2o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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -730,210 +706,95 @@ "%matplotlib inline\n", "import matplotlib.tri as mtri\n", "import matplotlib.pyplot as plt\n", - "from matplotlib.transforms import offset_copy\n", - "from mpl_toolkits.basemap import Basemap, cm\n", + "import matplotlib\n", + "import cartopy.crs as ccrs\n", "import numpy as np\n", - "from numpy import linspace, transpose\n", - "from numpy import meshgrid\n", - "\n", - "fig = plt.figure(figsize=(12, 12), dpi=100)\n", - "m = Basemap(projection='cyl',\n", - " resolution = 'c',\n", - " llcrnrlon = lons.min(), llcrnrlat = lats.min(),\n", - " urcrnrlon =lons.max(), urcrnrlat = lats.max())\n", - "m.drawcoastlines()\n", - "m.drawstates()\n", - "m.drawcountries()\n", - "\n", - "cs = m.pcolormesh( lons, lats, data, shading='flat', latlon=True, \n", - " vmin=data.min(), vmax=data.max())\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That looks awful. The data points are irregularly spaced and must be interpolated onto a regular in order to be displayed. \n", - "\n", - "**And how about with contourf?**" - ] - }, - { - "cell_type": "code", - "execution_count": 394, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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jWFV9RW3VqlWpWrXgdvIF4eDgwPTp0wF48uQJBw4c4ODBg/z5558FjtFarJpK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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(12, 12), dpi=100)\n", - "m = Basemap(projection='cyl',\n", - " resolution = 'c',\n", - " llcrnrlon = lons.min(), llcrnrlat = lats.min(),\n", - " urcrnrlon =lons.max(), urcrnrlat = lats.max())\n", - "m.drawcoastlines()\n", - "m.drawstates()\n", - "m.drawcountries()\n", - "\n", - "cs = m.contourf( lons, lats, data, shading='flat', latlon=True, \n", - " vmin=data.min(), vmax=data.max())\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Doesn't look any better here.\n", - "\n", - "So now let's interpolate.\n", - "\n", - "### Plotting an interpolated grid with Basemap\n", - "\n", - "Because the grid is irregularly spaced (grid x/y does not correspond to lat/lon since the grid has a native projection - notice the boundary curvature in the following Mercator-projected plots), the data and lat/lon must be interpolated onto a regular grid using **`mtri.LinearTriInterpolator()`**. " - ] - }, - { - "cell_type": "code", - "execution_count": 395, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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fMrUV5sHlEzBjMLSUCZ+evsK1WFD0AnQfiru7O1OmTKFs2bK88cYbrFmjf4yy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LojoJ0dzh6WtOxkzyOv+zdoWzYticHGQcao541PsVaZ+nIbODjwA/pFvF9lelnHp10UV0fldh8Z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\nWc5hVppFrypI0XsLs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4ezmNhrF9OTmxMUYv7QddTya2mcjlpofH2Fu8PHC0W6n0XToHpF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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 12), dpi=100)\n", - "map = Basemap(projection='cyl',\n", - " resolution = 'c',\n", - " llcrnrlon = lons.min(), llcrnrlat = lats.min(),\n", - " urcrnrlon =lons.max(), urcrnrlat = lats.max()\n", - ")\n", - "map.drawcoastlines()\n", - "map.drawstates()\n", - "map.drawcountries()\n", - "\n", - "x = linspace(0, map.urcrnrx, data.shape[1])\n", - "y = linspace(0, map.urcrnry, data.shape[0])\n", - "xx, yy = meshgrid(x, y)\n", - "ngrid = len(x)\n", + "ngrid = data.shape[1]\n", "rlons = np.repeat(np.linspace(np.min(lons), np.max(lons), ngrid),\n", - " ngrid).reshape(ngrid, ngrid)\n", + " ngrid).reshape(ngrid, ngrid)\n", "rlats = np.repeat(np.linspace(np.min(lats), np.max(lats), ngrid),\n", - " ngrid).reshape(ngrid, ngrid).T\n", + " ngrid).reshape(ngrid, ngrid).T\n", "tli = mtri.LinearTriInterpolator(mtri.Triangulation(lons.flatten(),\n", - " lats.flatten()), data.flatten())\n", + " lats.flatten()), data.flatten())\n", "rdata = tli(rlons, rlats)\n", "\n", - "cs = map.contourf(rlons, rlats, rdata, latlon=True, vmin=data.min(), vmax=data.max())\n", - "cbar = map.colorbar(cs,location='bottom',pad=\"5%\")\n", - "cbar.set_label(grid.getParameter() + \" (\" + grid.getUnit() + \")\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**and with pcolormesh**" - ] - }, - { - "cell_type": "code", - "execution_count": 396, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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QlFcFOp2O1q1b07p1aw4cOMDcuXMJDAxk4MCBvPjii4SGhvL+++8TFxeHl5cXLVq0oHLl\nyvnaV+uSEGZ/QzDBSsnnJSQTpmR2d45SR+VbdGA2ZP6MvYu6PVHCgKTqLpLJSiDcXm2mlxLHS4nm\npchwCSlSXeqnLMHpfjdixoMIMVWZFPFeCLXpztYE+tJ8kNwJpPaq8QSxXtlWNcdNJIoZDqRj2upO\nIGV0kK6VrYUaHFG73tg6Tn8uKeXSNZTkTsJ4HuQmEbNoBVGzv8Rj5ss4Pd3U2n5IB1IuXyX+hz+I\nH/EqluRkHPv3xL5vNwzF02eCCY8uInQsHFAy8e4R5E0FeXZvDNv6lJZM2pL5XiJEJQwEvTtsPgA+\nNdL/6UPJrC9YN7oLmXyWrBb6EdIHlBfcAxoK3Uhu7pIRxpaKyFJbyTtOKvggzSnJJWTZe9BvpTgs\nEQc3KNMC/rqdv1mvh7L1oVAJKNfA9v5yEE15fQDVqlXjhx9+4OLFi8yfP59atWrRrl07xowZQ7Vq\n1QgPD2f9+vX88ssvAFSoUIEWLVrg46NWLjQeDYMBzOZUjEbtrV9D43HAfDOC8F6voPf2xHffUvQZ\n0vIYivjiMv55nMcNJ/nfQ8R88weRtdtiqFoBh349MHVrh845n5iO8jvxt6xRs1HnMiuvGrmHR3HY\n+QE0f/M/fNYfBi6FY3/C6RUQchy8i0FoMBSrkPVjzSI0DeAhKVasGO+++y5nz56lRIkSVK9enS1b\ntuDl5UWvXr2YPn0606ZNo2rVqvz4449MnDiRadOmsXnzZpKSbCupqSFjMICWWUdD4/Hg1oc/EtZ8\nEO5TRuL949xMimtadDodpjpVcfloBgUu7cFheD8Sf13BrWK1iRn8CuzbYjVna2QPcTfh6xZQvB2U\n7Jrbo9FIS68f4cpB2Py27Z/V6aB4PTAnwflDEH0Ttv4Eb3eB4SVhVFVIttEkmQNoyquNJCcns3r1\nal544QUaN25MbGwsCxYswK9pKTr/M5oPKq7n+GgdN2b4cH6sI32TT+L39mh8v5xCWQ7d/SnEReXP\nS8xR/kgmzDDBfKyKwgao5STZxNRMQ/0mV0rIFC5Frx9D/QYXbF9WKT/qXVI9IJMR1bvAOW+h5IlU\nJM3GTC9SkQLJfH9OMLtL5njpeklR9pJptphg+o0XcqxIkfnS9ZIT5av7OUxlpfyGjYn1JdO4FCGv\nQi6AYNvLpWRe9yJc6F+98EsZOKRsAJK8oBBOX5lDSrnkKiJlLZDcGKR+JNKeh+SQa1xsNICkY2fx\n3bcUh4YPv4vnqI9HZ2+PffencVvxLR5HN2GoVBbDJ8OhW0n4YgpcTlOdpI1gg5WyH0qm2SWC3NYI\nc6HwiCiX+pHkUhYFyap/XJCnTXEeFw5fB0Fgc4ieq85ZaFRnIgHBPUDK6PSM8IchAWq5VPDL1vyM\nQvdKpAB86ZpIKa6k9uUEeYAgLwj0WQohu2DrO/fkkjeaao63mgxfH4LPz8K0jRBYHdBB/1lQ1B7d\nLjL/bBV+1go/v8k/tqKz2BABo9PpLLa0f9y4dOkSrVq1omvXrowYMYKPP/6YL774goYNG3I9yMC+\naWtp99dwCta451u3mWYA3HjzY1KjYvF+fyw6nU5USiRf0u2o/U+kfgoLDxaj8CCSHrxnUCuR8xml\nlG8VVoxa7FXKJR9KSaEY/fQF5nzuiK9f+vcuyVeyxKUrSjlSVVChqE9UabXyetJQRimXros0Tsk/\nUfKXlBQKSQHJyOimR5i3uZL4913Ck72m4BAo+TNKyrqPcAGk7yvNT9V5jhOeFGGKNF/vNV1Nn80D\nle2lsUtKp3TNpfFIqa9E/3Bh7kjXPKvSlEnHla6VlE7tznm4NuVzYlb+Q5GFk3CoVEp8YZHmlDT3\n41IcSNl/hMRvfyHx52UYKpTGvn9PYusPAhfFMXYImsMWtVjKyEQbQW6rMprdSL6/6nexe8puwg1Y\n3wL8WkP1WbBTiO04L/Qj+W9+JcjVLuvi2iz6sHYQ5CsEeYAgry/IVRwR5Lb6RT9KuraPO0GpRtB6\nrHzc+ynNx3fDqKZgZw99JkDxClC8BPiVAMcMF0F63EgvdNIcBCzCZr5Op8NisWSadNrO60MSHBxM\n48aNqVmzJpcuXaJSpUpER0ezY8cO/vjjDyr97ykaf9qD1e2/IOJEZoUsKfgCEfMXaeHyj4jRDhLz\nngVDQ0PjASQcP8eZ2gOwJCVT8t9vcahUKkv71+l0GGtUxnn+NKtbwUtDSFq+FuqXgJcHwt8bNbcC\nW0m4AeuDwK+tVXHNx0HJTwwvLoPgzbDuvf/2+cAq8MYi6D8JwkJg+afweg9oUxDa+cLQBvBmH1g0\n19o+LCRX7qsnPmBromAWT7vbEHYglD9aLyTueixRW36n2f/KMXl+K5wLRLKJqVhTk9ekRNeqRJ+/\nyY5X/qDtquHp+ov905rqSqelF3kkDAZITkpFe+/SkIjGVWliN5Ci3NV0SI0hQW9rZneNhyU1NZWL\noz4gZt8Z/JfMwlRccPHJQnQmE/Zd2mLfpS3hB3Sw/GeYMQ4ib0GXPtC9H2L0uoaVhOuwLgiKdoBq\n0zXF9WEwkv3ZJx6G/62ED9oCBmimtpKK2DvAU10yyLAqqOFXrS45ezdalddbt+Cnd+CnM1Co2H8f\nrxF0y23+iMb9iL4UwZImn+NRuiBPzWtPx+5GjHaZFac7FYZuOYdjKZx69/c7voPBsfH49G+Bz21b\nTGUOK4+3TTC7S2Z0qR/J1NeA7Uq55LNYSzATS/37oTbTSyZVyWdU8t3ELpSIRGdiMqTpkc6PRTA1\n6SSfV6G9faK6/6VOikozQEnOKOVSeiGpvYR0vSSTasaqR4mc4iL+SlM6yO4Bkq+t1I/kqyr5wkrz\n8yRq9wzJD/Rh01CZHA0kRCTh5JnZBF6N/co+bK10JY1F8h++Ivg/B3BOOK5tqb7ihGtia7o2ydUl\nrW9r1K7jnHn+A7z7BeH/4au3penHmyKMR3JxkkjSq+8tx1Ku8MogeGUQqYePYP7pJ1J6NIGAktaS\ntB27onO7tyCkivZpASkNkvQ+JD11JaXnuiCX/Cil40rmddVpjguDTUFQrjM0nJpeca0m9NNSkEv+\nm4IrLC0E+XlBLlniJPcD6bxJ6RNtRXV9pWsimdGl9ra8Y09ZCdPagosB2oxM/zdbfXBdAfTg7geB\nfnBmN9y8hm7ZB9i1bYJHDyBNbvmws+rUmxzJuhcgTXl9AE6FXOi+eRje1ayVKIxCGcw7XDsWgW+F\nAulksYetDx3PTnk7b1p+wGgHyVryBo0swsnDjsiQaJw8JQdFjf9CqtnMqYFzSbpyk4rrZmEq6C7q\neDmJvnIlTJVnYHlrComrtmBZ/ANMfQNLUCt0PfvAU81ye4i5T+w1WNIcynaHBlO0Hdf8il4PE1bD\n262tJsuWL2Rd32Uqw5yf0H0yDufXRmRdvzaQb5XXRah3vKQdOCloAtTR7ncwmIz4VFfvhKi4euwW\n5drce6W0WCycHvo+AOaI+3grazwUBoOOpETNb1hDJgZXXB4yI7uzpz2Rl2MpXEUqbK5hK7fW7OHc\n2C8oMqY7hfpL23G5i87ODl2rduhatcNyMxx+/xXLrKnwygio0x+eGgBFpC3Dx5iYy7C0JZTpBQ0n\n5/Zo8i4nkHeU78fJdVCoPHhI285ZjF4Pr62B6UHg6g311HqTzTRoCX8uwuDvh12DWnfFqTcjSPhp\nGegrQOvsTaeWb5XXrMJHiHaXKk5JkcZ3CDt2kxIVHHEinsTYZK4Of4PoXScoWKc4AQVjKH670pFk\nRpdMcZL5NUCwpUj9S6bEahxQyg2CLUvqp4Rg2pyDuuZyMzYr5YWEaHS9nYHYRGOm8+STom6vu6kU\ni+4BFwvblnrM1vMvRWhL7gSSG4Ct8oymZT0W7EkSxym5AUhmeim1ltReMslLEf62nucYhQlc9WLr\n4m1P8tWbuCrcMKQ5LpnpbR3jJtS7fJKrjnRtpbkppUeT1hjJ7UE6DxnXBnNcAgefnUdKio4a2+di\ndHEirU1aGr+cUUO99jyoUlemfozq4xrtbssLecDzQ+H5oaSeOI75h19gdjNwdQM3d7C3B5M92DlZ\n/7V3AJPp3v9POYDJ4d7vdrf/vW5/7/8m+3tt4hzA3gkKl0i/qykVlrosyKWnt5Q6SopgLxkM21fA\nP8vh1H7o8xr0fQ3BA0yuIiWZ3SU9TYpIl7w2pOMGCHJpr0j9qJPPjzTOEwqZZMC5Y+6/vA0WPQPl\nmkPfL6Ggm7q9NMX/iyuKORWSoqF0KbhjFLYxVSQeGdaG1FRY+DbJr80m7Fwh2LYefv0W1i6zBqV/\nuhrCY+DGbf2qXWn4biuUrAouiu+srOZ2f5545TUriYtMIj4ymQL+zlw5Hsmn3bfgXLsszv4eOPm5\n417ah9SUVPSK8qYaD4fBqMOcrO28amQNrj6ORF/Xql48Kpd/2sa5t3+nzMxncX36qdwezn9GX648\nvDETJkyF4OMQHwdJidYUJzEpkJhw+/c0/8YkWf+Nj7knT8rwb2KCNdF7YgLEJ8KVszBnPVSSaphm\nIylmOLMdDq2AQ8vBHA0NOkDvsVAzCOwlK6WGTURdhaRYKJgm3WS7N+HoX1B/IMwLguJ1oNt8uE9x\njkfmzfbQcxyUkpyV/wPrV0P4Ddj9N0x4HrwLQY8BsHWt9Z55sYN1rqfNrjSmtzXd1rd/Z8kQclx5\nncNLSrn09i25AeTFYoBXj0fiW96dPb+c5+f//UuXt6tz5LInjr1diT4bztoOnxIXGoln1SLU+X0M\nDr5SEWoNCaOdjiQtVZbGfXAkXtx1zIibjwPXz6lzyGo8mKSb0Rzo9R52BV1psGc2epNdnvBtfWSM\nRqiQIagwQW0dIcZGn9AbwCvNctZ5PyES9qyxKqxHVoOnP1TpCIMXQasaVvOyRtYSewPerwVFqkKx\nKtB+EngWA68AsHeB1/+FLV/CnNpQoye0HJ/112H+81CuPjTpmbX9/rTQuvuamADfroCyt3OGNwqy\n3jtrN8C0NL6wNRpB31HwxiCrQpvW4pCaCpHhEB4KN69AsfLgU/yBQ8i3O69SpK6wvIimJilyuIRg\nApRMWQZSuHrsFlePR7L8jQOMXtWU3YsvUKFfHdwDPTn+/T6iT1zBWMyDCs9UYpt7Z3RJmb9DA5M6\n2vo8JZRyqcKW5PMnmRilqkGS2fdB7hMZeZFPbBrPZpoq5ammY0QmO2UyxaYY1POhrF+wUp5or54p\nkvne1spY0suYlKXhlo2VtCSk65UxO4EFHSkYxCwHUjaA8kLA4g0h+4H08im55UjuItJ5lr6vys1G\nea8UTiTixnVlpL3smqFeA6SxS9dccieQov4lFycJ6R6VClCUFKrmSRXCTn60npAvN1Dx4yEUaHgv\n7ZStLi2yC5V0j9pWvMBgVF8vk40xeqKqKfUjKbtHdJBkgZukr4YlLalStgGJO9H618/B/hVwYAWc\n2QnVGkHjjjB+JhRKc/9JWQ6k5BMBNo5H0jIkE7h0PiW3dKm9VExSvVTdv3pVRiSXhLTf1aMSDF8E\nGz+GOh3gx6EQHwmFSsHyCTBlO3QbAl0Gw69T4b1a8PRoaNrf9nOj4pd5kBQJIz7N9KfCddXufdFx\n6oseczrDSXjta3B1B7vbc/zO3C1QHnpVh5MHoXojmPcHXDwNletYFda3X4JpQyDqFoSFWn9uXAMn\nF4iLgdQUmPETVHmMlde8SKrZQpX2fvT9pDbL3zpMlaf92PD5bk7+dBD/5iVp+kEHijYLRKfTcUCh\nuGo8GINRT3KS5jagkTW4F3Um7pbmNmALcSE32dF7AU6VAmiwbw56bdfuv5HVUfyWVIjaDUtWwP7l\nEHkNqraH5i/A/36DyralQtPIAmp2g7AzsHMxTFhj3WXcvQQOpnFw1uuh1xTo+jp8ORJWfwR12kAB\nX/AsBJ5+ULCI9d+HVdl2r4GtS+ETqVzcI+IpvElMfs6quL73GwTdzhXrkeZtYeJ8uHEVfPzAu/Dt\nf31h+w54vjlUrgexUXBiP5SseE85VpAlyussXlbKpTfhx5Umw0rRZJi1akyzEaV5o/xKAIaETMCl\niK0e0hoqrKmytCo5GlmDa2FnEqK03GsPy5Epv3Plz4PU/uY5DJXUQXoaD0F2VFo82AnCV0Obl2Hg\nZ1CyLuhR89wQAAAgAElEQVS1TZJc4fJROPUPhJ2G8PNwdA3sWgJ1u0O9ntafjNiZ4IXPISYCDv4O\nt67B1TMQFQ7RtyAuCnQPmDcOjuBSAM4fhQW7ct4dZMIH8MYCa2CiirY91PLqjeHLrXB8H+zbCj/O\ng0unoeMg8VB5ZufVVjcAWxNYS+ZFKYWWZO6MoIBSnjEi99rZeHQ66xr1ZdFZvHJrLA4e9/b8C5nU\nJkZpnMe3VlfKwxqp3Qaa6jcr5ZJ7gFQswI9QpVwybUrtJdOjZI4/J8ivGctwPdkFQ4ZM2VL0usHJ\nNtOv5EYimTBtdZ+Q5psqOh5kE6l0XCliPGMkvAMJBHA+yyK9q9hYLCNYSFEnzSvp/Hjx8P6qyr5N\n1g0rVUS9dC9KWQUk1waVu4LFYqF9/C+YnOwy/U26tpJ5vZDgTiBlCZAKQUhzPAZXIo9fYceArygU\nVI62eyYBECeskdIckeam5A4h9SPNEcnlJ8WkPg+y67y6H5OD+hMmByE9Y7T6Xk91cQYD4KRLbx4+\nLwxHetRl/Fpl5sOJBNi/Hor1BrsMDQKEfiS3AUmufgTK45e8XdTeK4i3tKStSPq5FJ8kmfylEJQA\nheyW0BbAbIYF3aBxXyhXC4oOgDI/qoOyVO4BBT2glKy0KTFg3dWNjrCWay0cAC6yj8GVY2o3RFyE\ni/6wmqLb7S/kICjZ1wVrQ6wRijeEVHdIMkBsAty8DmGyz0yeUV4fN8LPx1Aw0JXCjQM59M1B3isw\nhx7Le1G6g7pSkMbDYTDpSUl6snb0NR4/Lm29wOoBSxmxqzeuhWys6JSDpKamsmf0Im7uvUCjX5/H\npbiUr0jDJrJj59UpEKqvBbfv4Yd2UKUvNJ8Kprw7vx5LNnwO9XpA1zfuyXJC09Lrwd3T+pMfMCfD\niX1wYCvs2QpH/wG3glDlKajTDobMAt8AaKHePdaU12yi9jMBOBUwceKknvI9K3D8l2P82nExLoVd\n8CzrRXSN8xScNQqdXeadFw0Zg8lAarKmvGrkbzxKeBBxIZrvO61g6Kbu2DnmvaU47N8LbBv2C/59\nG1Lrgz65PZzHj+yoXKXTQbX+ULot/PUKfFwJOnwKpVpn/bE01Gz/CV6TkvY+wcTHwd7dsGcjrPzG\nukN8h1LVoHw9aNEXarUC9we/JOe9FTMDkunLViT/23gbzZpSdHZGc7lrAfB9piAut20jrRdD5Lmb\nbBq5nJDNZ/Gtm4SPMRwd6RewgoLNxFQpSik3m4UE4ibJ7Ks2iRUQTJWyuV9tdpBNmOqpJplgJabb\nVcOSkEQondPJM0bT30EyPUqJ4KUE9NJ1kfqXi0TYdstJJlKpzr1UzCLjdbdgvbeuCcUIbB2/ZGKX\n3IEkuZSFQMq6oDJ1S9fQUXCRMJKsdD+QzsF6gpRyV8EeqYrut/hbsHP/DLO7J18N2knjn4aiu63M\nlOGksh/JdUIqOiDdE9J5uHPeUs1m1g9eQuzlSDqve46kgkVR2a7l4gXqtVZyIZFcVySksCNpbZPO\nT4pRPf6kBMFfT1hrzclqeWqskNAxBEi0QBjpk8VL2d0k8720lJwA8IaK34PnGlj8LLTeA40EU7HU\n/05BbmvRgSKCXNqDkMz3khU8WZBLif5rCXKhSIGpYOZnb1JtYRYejwM7C/hn8AfI7s1vW/fApGuV\nYKNKKLlgmHUQfgX+/AxWfm79//2oURdKVYIdi+GDF6BEOWjcFpp3Ej+ihYnmAAk341g3ZCk/1fqI\nglV8GXx+HAGznrv7sEqLOT6ZbS/9TsJN2xb0JwW9yQ5LkrRaaWjkD3Q6HQUqF6H8qCBu7b/ElfXH\nc3tIAFxYc5Kfa3yAf1ApumwYjlNBKWePxiOTHTuvGSncGvy7wqXfsv9YGhB2EWIiIUoq7fgE8Vwl\n+O4tq+JatSl0HQWmNG9oegM89x18EguTP4U+I+GjZbDtOox+G+JjYdMKsfs8v/P6OBATGsXp345S\naUhtGs5ohU6IAEyJT2RVp68JWRdM7cmtcniU+QSjAcya24BG3mfHvH85v/kS8ZUu4FypOM6VAnAq\nUwS9ybpN4lGlKFHB16jyVgcOTlpG4RbllS+0OYE5IYl1vb8hNTmV7ttHYLpPsIdGFpAdPq8SRbvB\n0WnAqzl3zCeV4uVh/LfwchMYtxDKStu8TwB/hENiPKz4FBa8Agc3W+VNhkHbCeAtWAJMJqjX3PoD\n8PFkZbNsVV5VZqXsTp9laxS5FNkrmWuliFkvbijlfoTiVwmKHezFT33WsrLVWXp915IKfultROa4\nJNZ0+hJLfCIuRd1x87IjbVrsMp5qU+KR0CpKeaifOvJZSqQuXZdLFFPKKwjJ6iUTnZRV4IAQElqH\nXUp5efs4biWFUY9t6eRbaaxsvyjpWaV8k6m5Ui6ZVCXTpiSXTM7S/JTmmzQeCaloRcZ+UtGTiEk0\ntUr3RZjgZiCZqKV+JDcDyY1Huu/CFRnEywpm93BhDibgoJyf0pyV5nipNEn+b54KZ+v0HQTNb8s/\npwoQvmQfSVN+wXwhFGNJf+wrlcLhRjjxCTqqvtSVxBlrCF51jkLtayj7BvketfUcZ7zmxxYfZueM\nbTSe3pzSHcsBqZBmvkhZCKQ5K81xKROJlIFDGr+ENB7JXUGqwuZeUB1KnmJWP0OkrAIIWQhwsVdn\nG7DVPUAyr2fcLHdtDjufhcOhUEDxXJBMv4IZXS7KIMgLq8Uu1dTPzJirQhUBs/Bi5yLkjZCKRAgp\nSk0e6vnm45n5mRkiuZY420P1GvDZFhjdGrqOgA6DbDbrmwLUboJJ591s60giqzQ/6Zr/fQK+7A5X\njlp/t3eB536F3i1BVVBIyk5wH7Sd1xzCw9+V4Zu6sGHGHubXWEyjr4wUa18RuK24dvwSO3cHKgyt\nybFv9mKxWHJtFyYvo7czYDFreV418jZbxq+jzpgGVOxTheN0vytPTUgk+cQ5ko6cwu3ITpzL+qHT\n6yn7Vk9OTFqMT7vqkEO3fUJEPMt6/oqDpyP99wzDaNIeBzlGTu68GkxQ/GnY+zu0eDHnjvsk4+4J\nX+2C8V0heL81/+njTooZfpsHX469JwsaA21eB6fb+dWyMO2wtlrlIHqDnpZv1qFU86J832cJAV1O\nUnNyG9Z1X0hKgpnLG4I5/9shAObrX0/32QLPdaTIl2+oun2icC7izv53NlEzKgGTm2ba1Hh09EY9\nSXFJmJzkai62cHHLea7uu0KHH7tlPpaDPfbVymFfrRzlKXlX7tulDsHTf+Pq77uha5YM477s+2Q3\nBz/fS4uP2uHf6MGlGDWygxzcnAjsCns+0JTXnESvhzl/wILX4cXWMH+lOtdrfufyKZjYBq6ctf7u\n4QPD/oTitbP1sNl6JrPTRUCKVpbcA2xNxi6ZoG4oCx3LZnRVNH1AIyi6vwOLh27lp6JvUqVbCZ5d\nGMS57df4uv92vGoWp8KrLYkLjSAqOIzo4GsYO9RWRjN7+apNL1IUvJTUXTKtSSZJydxchmClXIpM\nPilkdVhFe/V4Ghmo8Y6BpU0WUKJnTaq81gaQi1b4mdRRjnvEkFM1UnS/VMxCytIguRNI2Go6lRLl\nZ7zuZoxEUEC8LtJ9JN3TUpEL6fxI10tyk5CyKKjms+yCoe7D6OHK1cupFCid/nPnhIwaHkKGckfi\nSU21sPnVvwia3hhXBzNgxkdw1Ul3rXTgM+0Fjkz4lPGdy6DXZ1ZspAIpoYJdVmUWjwqJ5I9nfqRA\neR967HsZvV5/94zLri7qayWvqbat+5Irk4StBU8kN5JLenVmC6kyzi2T+vzERQv9XBJMyyFAkgWu\nQ7plt6+6uRj1L5n7Vfi3hi0DIPE6uGWwmwfY2L9g1ReLFzion7FeTupnl1Ogel7FxQnZKpzU7ROT\n1Bcy8oZ6oIU91W4tqkw8SX7qvg1+irlfdyhXPqgBA2vDZyuhUJr7XnAVyTL3ACm2+bwglzTCjBkm\nUlPhqxnw6Zv3ZM9NhgGvWatrqZMyIST5gRa2v8hp2QZyCWdPBwYtacnwv9ry7MIm6A16SjYuzNMH\n38ToYs8//RfiVLQAlV9rS4OFA/Hs1DC3h5xn8AsqT4e9r5EUEc+KWm9z/d/zuT0kjXyMfQFHYi+r\nfcxsJTU5BQd3e/4avYE/R6zl0o7LWB7SROzWrj4GZwe2//KAtDL/kb/f2sRvnX+mycddCPq8O/qc\nLh2pkYEc3Hk1OkLV1rBnec4dU+MebbrDgJfh46m5PZJH5/AuqG2wKq6+xeDn47DTAkOnyGVhswFt\n9cpFdDodJRsXRm+4dxlMbo40/GYQ1Wd1YXPHjzk4ZTmpyZIn/5OLXq+n1uwutFj1IvsnLuNc1wmY\nY7T0Yhq241DQibgrWaO8Gu2NDNjQm2F7BuBWxIU/Bq1if+l+XJryDQlnLt/3szqdDr9pQ1k85TQp\nWejXHX7yOt/W+RxzgpmBe4bjXUWIoNHIOXLS5/UOdbrB7qU5f1wNK1tXQzcby77+F9YshiQhWDAr\nKFICJi+EPanwxwUIKJd9x7oPWeI2IJn6VJHDuZVtQEJKfu7PJaV8DepKJVLSe8ncLJnd70Tk+nUr\nTKX6Q1k9eBkbG+6jzg8v4FYmc6R3hF5typKOu4IOSnlNYfy2RgJLZmgpG0NlDinlZ9L4A6YlU8Sy\nDwSs7c2q1QbONh6Kb78g/F+55zQomdGHHftOPc5yagXjbf1rSrmtRRmkyG3JjC65x0imcclMn/m6\n6EjBIGYJkIoFSO4l0n1tqylaGr9kqledNykbQEFhDhbwtiMlLDzTnK7OfmV7aU6lNaM7BdhRZGI1\nWr9elaWrXdjdfhYhb31HB8svd9uoXJC8WlTB6GNi66JQmg1Ib6uTzN/SuTmVGsjy0dsI2RPGgMWt\n8CrhDoSL59jWtVlaG6Q5K2WSkFyTpP6le6Upm5VyWzN5SO4ZYgYaR/X5jLcTMtNXwuouUFIHaZ/7\n6ukJktFtvSCXEuI3aQdfDgXHCHBJc18WtVGRPq3eMfape1EtF9ZI6TpKz5ZwJ7XLnrQGS24egucf\nJQRbuup+kVyQpOefR5kIzkccI6CHCdKsK+ejApTt42PU94qji6BrXS0AVy7C6N7gUxgWvw8//i1+\nV2IF1S9ELeZq2l98wG8g7EXOSKF+fEM9ea5ZAtXzSrJPaDuveRhXPzd6rO5Lxf5V+avBLII/3fLQ\nJsgnDa+2tai590MSQ26wt84oog9ITjcaGulxLuRMXFhslvcbdvIWi3r9xb5nP6DogCY8tW/2Az+j\n0+l4ZnoZfp16GnPyf999Pf3vLT6ouRgPf1dGbu9xW3HVyDP4lYEfJsINSVvIBpxcrcnid/yZc8fU\nACA1Lj5TsJb5WjjJH31M8tcLH/0AC2bAyK4w8yv4cCkMHQ9zxz96v3mYxzD07fFCp9NRc2RdnILq\n8XffrwhZeZgmS5/HoKW1yYRer6fUe8NICA3nRP+52Hm64v7dXPQOWlYCDRlnX2cublPvGj0KERei\nObrsLPW3TKVAvTIP/bmKT3lSKNCJTd9cpuVQIRjoAXz83BGGrO+Fi49QolQjd/nfV7B0FrxSE4Z/\nBA175MxxG3eDbUuhpRQdppEdRHyzAjs/b66MmEni4dNgsaB3doSKdUk9dBgG/3d3gtQb4bD5T1ia\nxnoadgUGjMqCkeddclwDym63Abl2vNq0IyU/l0xZTdmklEumRKkfSS5F6ycX9Oawpx5LbCQBXMBw\n2zxhIomkyHgOzVlP1KnrNF88GICTqB+WNwQzcSkhPFAy+TxsAvQ7+Ceq3TBS7NXXqzO/K+XfoL7J\n05kM/aDN+tacWBbM+nqdqTisLlVGNEjXPthDneUgfL26EPfJVurzmVXzWTK1SkUBJOyFeZ4x8jwZ\nO8LxEt0AbgnmfimLhZSAXopIl7JeSJHk0n0dnSkju+w6JJ1jPz84Eh6Z6Z6UzOuS+bhxhsIZjVtB\n4S+K8lH3GUzc/BS+pdKPVXJB2k4DKk7zZ1HPxdj374bx9j3Shx+V7VX3orunHh8fwIYiF9K1ks6D\nZNb3Qx1wJq21Uv/SWiJdR2luSv1I45H6Ed0tjMIaIFXWdQEwwuA3oEErmNkX9v8J/T8EJ0WU+Qmh\nH6E2gigPAYp3gA/+B2diwd7qX1C4+Xllc8kEHnZAXbimjODWIq1J0r0uuRNIrnwS0n1a1qR+xkrP\nOhW2ZoExuN3gqkMM/u3K4PdBS/S3d2F3frSPFO/i+Luld9tLclPPwd0XG2QWzpkJfZ6zZnU4cxJe\nfQ4qVYfiQyBC0IfOCwPNmFXgDlcFuTDH3aurP/CyaZ7QEcCs+/wtM5rbQD7gxOZrvOczn0v/hNBn\n7TMYTNZFxZxo5sj7G/ml5BSOf7KNyq+qq0Y9qZTrVIZe+0Zx6/g1fqn3IeFHsieKWyN/4+7vQkJk\n9gQ4tOvvSedJ5ZjVYhs3Lj68IulXz5+ClQtx+Aspt4zGY0G5OvDZPmuU9qvV4MQ/2Xs8F08oWQ8O\nrc7e42iko0Tf+tRfOJiiT1e7q7gCRGzYj1f3px6t891/Q/f+MG4YjB8OH34P0z+05pl9jNFsz3kY\ni8XCvLabObLGqnQZHYwkxSZhcjFx5MejbJ60lahLUTh4u9Bm3Ug8K6t3DZ9k9Ho9TT7sQvTFW6wb\nsBinwm60+KZnbg9LIw/h4GIi5RH8Sx9E0PBAkuNTmNl8K5O2NqGAn7Q1lp6GU5vzR8dFVHquJnaO\nNtaX1Mg/OLrAK5/D2mUwpxu0GAo93gRjNl3z2t3g36VQu/uD22pkK+YbUTiVfoTndnSUdZ6YzVYl\nduORx15pvUOWKK+j+Ewpn8XLmWSSKcLWLAGye4AaW827kgmtoGDulBJkS99XMoGkNUd8PSWEI2uu\nMPrj4lQY8RRfPLONf0Yv4+K+m5icDATWcOO4xY7a6yaTUEYy3KYnIkltDr5mkpLMS2Zc9fc6RGWl\n3MVebWaRzC9S0YfXmWnTeLZz28xSDCpsas/BX4L5vda7lHvpCkWGZDbbblivLo7w7j51dbN+Nb5Q\nyqtxQCmXTGLS/JQiouVsBmoymkJT0BOHk3jexKIPgr1JMsEWFmalKvE3yN83XAibVa0btq8NEdhZ\nEjOZDaVMJFJmEWkuV+YwlUeDZ7wj7wWt56stRfHyMYpZEe7MWfuaBfCqG8jOBYep/EqQaLZWjdNA\nqrJACsjmVAlpjkhI30tC5foBYq0AHIVnRZJ4ftRz6pyNa7b0jLJ3ENwPCgr16ROEBPTNO0G1uvDu\nIHizEYz/AYqUlktqSqdZyjZQ7fa//p2h4zgonwD2DuI8kc5Dha7qtVl6Atma3UK6j6RnrNRPZQ4r\n5dL3kp7tqmeRVKxIclGRsq44E0N1xbNCyvZiypBtwPzRHHRdO1K+1FFuvt6D2Dc74//dW3f/fmS9\nUOWqhXoujy82QymfHTxZKS9TRp0tKPhYFaV8qtck9XiAybZ5yGk7r3mZfhP96P+GH0Y7PSeBVmMq\nsGjELjpMqULIoVvsWnSOetvm4FjM+4F9aVip2rMMFbuW4qMRwYR+vY4KC0fjXFZy9NHQyDqee82T\nhPhUnm8Zwpeb/MHTKk8xp3Ji0zVCDkXQ+tXy6T5T4612/NXyI8oNayT7UGo8Pnj6wvRVsPxjeLkB\nDJwJvkNAl0UFDaJuwfQXwL8U6LOw0LyGzZiTzERfjGBpkwX3hLcvcyK3g4xvZxdKTTJTaW5fKN8u\nXR+pWzZhXPYncA7PIZ2JWrKR6A27cQ2qkwPfIHfRlNc8jJ0p/fZ/8ZpevLazLUvG7uPY2lDGbm3N\nRl9NcbUVo1FP+c//R9y5qxwb8D6OJQpR/ouR6E1ZU9teQ0NixFteJMRZeKF1CO3eCWDvb5fY8+tF\nChR1IvxCLJXb+kGFe+09KxehcLMyHPtwM6hTDWs8buh00GkkVGsOs/uA/Uro+gW4POJav/8feL0P\nNOsMs38CO80VJTcxmowMuvC68m8Zd16TouLY1mgKqYt6oi9idTNIjYsDnS7dc8t/ySzONniOhMEd\nrYKdgg/1efVu9b+eO9Ttw9SBVjd90udFd2pRD4dKpdV9ZDGPXbYBCVtNX7aaNCTTi2QGlfqXshC4\nEk1qioWvRxzk/IFIpmxugIunWTSRnhfqskeGqPfm3/1IbRaf9N5EpfwfFFGPyKZWyQ1Aai8lCpdM\ngJJJ6TOGK+VD+BJKQMetLdn/w3G21h5G41drQFm124CQ/5z1NVoo5ZLJSkKaD1KkrhTpLdWJzzhP\ndFjuey9K81kqsiAhJaa/ImTnkNxppLGq5o+UtF8yl6dgRE9qpr7kc6yOSpZMfemKC+hg8hx7Zrya\nxLKXttCuhwMTt7lSorSRqaMSuLl0C4MqpI/UDZqSwIjGqzk6oiHO7pnvd9W9ZSKRkpxWjkdaMyST\np3TepEwVkonU1gIdklxKEB9vY6EPae2R1mypvYdevWYbPNXjCRMS0BOf4bpUqQALd8KCN+HTajDu\nK6jb5t7fpSIF5TP8npICv82G2fNh/BfQuKM1+c7tpUWaz5LLmLQmSc8u+d5Vn2fpGSu1l+an1F6a\nD9Lao/peUqYQad2U3Om68IdSnunecoNWvw5kVc8OVNy1AL2DPaFf/gjdquHneYDdi9MEffX9jWv7\nNlr/L7jTunneVMol96/aPmoluESGYkiR36/k7K5wigk+LdeRX8Ca2ujOpO285iP2r7rK7t9CmXem\nJU5u2ltzVlG9b3kq9y7LH0PXw7uNYNK34C+VCNHQeDR0Oh1vvJfZ77FNd0fe+l8k3TK4hRUr60C9\ntm4sm3eRZycH5tAoNfIEJnt4fjbUaQNvD4CGneCFd8D+4YL+sFhgRkeIj4av94KP5iKVX/EoW4ii\nbw/lROuxVNjyARErtlNu3buZGxavYv0BEDZB3WqoX2wbCMrrGUoJ7bcr5dKL/BLkIMHNVFfKdYLL\nzJMRlvaYULV1Idx87Dm81rq7FnMzic2F+rPOrusDPqnxIIxGPd0XtoJJC2Fqf5gxxBrBqaGRQ9Rs\nYMf1q6mEnE64KzMnWzi6K5aBkwrz54chRN9MzsURauQaNZrB1wfh/FH45b0Htw/eDZ8MB3MSnN0P\nIz7TFNfHgALt6uPRvh6nn51qLXTgYFtw5ONEtu68qhKR2xrpaiuSe4Akl0wIBlGuNoFISdcl05d0\nHqSI1rKcBBO89aknrz+znzMrg1nxTSQAbf4cSrEM2/fS9r9roNokEzqrsFI+N2qMUu7hpjYRSWaT\nxmxVyqXzIyGZsqTzP4QvlfJFPKuU9+y+HboP4+xXWzk1IpDyr7enWK96bK7bTNle+r7XUGdvkJDm\nm9SPdN4kk17G+W9BTxIm0UQqJWqX3BKk+SyZiqXjSvejdH1Vx5XMf8fSOpOmwYD5thtF+ntPyogg\nRU9LBUC+o79S/nLK+5lkHTvDgR9PQk09y5em8NfyFCIjYMt+ewK7VuLDuSk0ndkk3WdUmS30WEQz\nruQ28AqKXRxgoo0ZPqR7VHLtke4haa2V3BskwoXrIpmnPQR3EVtdWqSsC44e6uPGS89G853z7AFl\nSkKxAlDUAvWEIK4CZ2FYFyhcBL7tD23bw+VlFH/RtqAvyU3CVjcAWwv1SGtzbxYr5ZIbiTRPJHeX\nKoKrlyqzwIrQjsq28/xeUsqlOS67Wqj1gADOETCuPrsHH8O9e2UCbj/3d9durGxfM1C9Myq5FEnX\nvC67lHLpXi8rFKzISv1P23nNZ1Rv5Eh8bCorvomkUXsXep58HZfinoTtvoA5PnsSrT+JBD73FC3+\nnUzoioNsbPI25ouXH/whjXyLTqfDbM6+XK8PS9ceemZPMTNvlplKVfVs3m9Pzbp6oiItNHyjMfs/\n20fs9djcHqZGbnHhDBRXm3ABiIuGDvVgyGhYvBGuX4Xd/8Bfat9KjfxJna+fo+xotd9tfiY5OZnL\nly+zd+9eVq5cyd9//y221Xxe8xlblscQHWF9yP69MgZWWndFnAq70WLJIHwbqAO1NGzHYDJS74fh\nRB69zIZeL2KsVgGXj6ejf0KSQD9JOLoZibyaiFfRh/QlzCYaN9VzPtIBV9d7u2QurhAdDe7F3Knw\nTEV2zt5O0NyWuThKjVzj4hnYfduKZSkPBYveS6OVkgJz+liT1A8dbZV/+Tv0bgH7d2EODcPoZ5tF\nSEMjO0j8Zy/xf6wj9eoNUq5eJ+XqDbyv3iQiIgJvb298fX3x9fWlTZs2Yh/ZqrzmVmYBFZI5UjKh\npdg4dum7SsfNWGv+DpJJ4872vMXTSLHy4Vw8fm97385Bj5tzCv/0/ozkhFSSE1Op8UJNWs5qmqkf\nKWl8sKmsUr75tHryxLuot//9i6lNa3/QRSl/mcymU4CTqMdTIUWdtaCgQW1WliKfS3FGKVfhXrEI\ns3fUZ/uCI+yoUYuWk2tRpYs1oGshA5Wf2UstpVwyv9hezCJGKZfMTRmzT6SiJxF7cX7aWo/cXzB/\nSY77komxmPB9r6HOkqEaj5ThQDKJJWHCrYCBiMtxFCr6YJN0mODKURZ1zXQps0KcQb0G7HFNn8kj\n3nU/u6J98Saamj0DWPrcejrPrXf376prrsOi7Btkc+p8Rgnt1eZvyaVFOs9SBg5pLkjcEu5p2QVM\nvTZL95w0/hRhjktZKaS1x+CmvkdjXIRo+oh7a23K5Dex7NyB5YvpWE6egrgYKFEWSpWH+DjQRaOr\nUwc719vfwcWIZcmvJLdvTeq6zTgN6PDQ45RM2gW5oZTbEq0PsuuQNB5pDWghpF2Qi3qojys9e1Um\n8B1+9RQtIRS1+51UZEUq0LEKddYbKZPKlEB1Hr3KqIsISC5UUuYJ6d6S1gZJf0rCRNyC74mdMg+n\nlwZiqFwRO19vDL7eHPWtjpeXFwZD+mONGqVel7Sd13xGsfJOPDc7gIiwZJZ9EMqF4/EkJ6Ry9XQs\nBYs5UqN9ISo190JXvypxN+OxdzVhsMs88aLOhRN/NYpC9bWd2oelwQuVqDWoHD8P3MjfHx6mzw8t\nEJ59HpUAACAASURBVPQSjXyGi6cdN0Ol+mS5i6Orgfhoq8Kz85ND1B5SMZdHpJFbGLr3gO49AEiK\ncIWoCDhzwvpz5RL0HYHlpc7pPqPz9sZu7QbcCmmuTxq5hzk5leiXJpK0ZScFtv+GsWTxdH/3sTFO\nRFNe8xnuBe2o38GLK+cSuBwcT6mG3sRHm0mISSEh2kzIsWhO775F2KVjJEXfe/Ps9XsXyncuQ+iW\n0/zZ9CMAvGsXo8vuV9L1f/GdX0mJS6TElL45+r3yCyYHI/1/bsXlA9f5putfxNZNpND7L2uuBPkc\nN28TkWF502fcwcVIQrSZa8fCObPxEl2/VOcW1ngCcfOA6vWsP/dB51EAvbM6t6eGRnYTfSOBBT3+\nJsW5EgV2/oHe7dEDt3JcebW1bnRW9W9rYm4JKYo/O4ogbFwczplDcfiXccC+bHF8y7ji4mkdr6EE\ndJxdnP13C1anZ9Pkv9k3dQ2ugV40mN+V0NBIFutm3/374J0D8K3hi95yDZ1ORyHCSI5LZvP4r6k1\nogaN2QZAgwrqaEXJFHRAGI8UdS6NX4pWPGBQt5ej/tWmptasUcoXMkgpz1Q0oRqU29mXi/P/4FKN\n3pSZ/gw+T99zFZCj79VmFmleSf3IWQXUJsyM5iYjyXhwS+xfisSWzESSqV4yAUp10EsK7hzSfFNd\nX9l8rF5jPIjDz8fM9cvx6Y4jzVmpgIZkPpbGI7lU1GV3ut83u8bhEAP7p1+j78sFaOGS3vXEKSWz\nmVtvSVHKAYwG26Lj32C6Uj6IhUq55NIimUglNwBb5740N6U5KMltTZJv67NLmj9hevV1MXmqv2+0\ng1qeYGfGwzPz/SKN30cwFcvmfqm4g1ounU/pvujP90q5lFPUS3BjKChkKLG1iIbKPUaa41KGEilP\nquSq0IuflXJp7kvrqaT3SOdGGo+tetLHvHj3/wlHThPS8WXcerYjcMaLHDdkjeVI23nNwzi6GPjl\n3at4+Zlw8Irgyslo7Oz1FC7rim8ZF9x9HUiprMenqi/uJT0x2t+7nLXeakuxpyuyb9oa1nT4AgAH\nbxeG7u6PR4AHSTFJzDTNpvefPSnd3npj/fbsMgBazdOCQR6WEqOexn9oCw71/5Dz81dSddEo7H3U\ni6NG3sWzsB2nD9rme5lTOLvq2L8zmT3boxn3WfEHf0BDQ0MjDxC9bDNXhkyl0LwxuPdpl6V9a8pr\nHqZ+ew/mrivH5O6n6DytFLW6+PHv0stsWHCWrQsv3G51kgJlvIi6EIlLEVcKlCmIZ9mCOJUtgnsZ\nH6q82pSo0zdIDI+l5LM1ibsRj3txdz6vas1/GtjK6vN66+wtgpcFU/P56nd9ZM2JZkL+vYxHYAFc\n/bI3P29+xujkQI0lY4ncc5o97WZQYmwnXHu1ze1hadiAl6+JqPC8E2CaFhdXHWuWJTN0uh9OrrZZ\neDQ0NDRyGovFQvjMr7i1YAn+qz7EsXbW++lnq/I6mEWZZO+l2U5+FLKq6IBkKpOQTERS/3I/6gdl\nRnntxvZ8tLEko9seYuHwvZSs5kK56s5cPKCn48jCNJvdAp1OR0pyKtfPRhMWHMW1k5Fc3H+SkJ93\ncuPkTZJiknEr5sq1tUf4/pvd6HSQeCsenVHPpzW/w+hsIu54CABPN4viwpRvOLAlmmM7Y0hKsPDs\nH53w6ZTe7HFYqNV8DnUA2I/7BivltWrsVcrDBHO/lAheSmIvm/Rsq+P+MFGY7rVKUX/n22yvOY5m\nXWqgN2W+vSTzizROyRQaKkSKSePMWNRAd1smmaGk8yBFqUqmQVtNsFIieylyWGUClM6NdI6LcQnP\nog5E3UpJt65IBRkkufRd27NKKfdJUfeTkYLOKbh7wPTBobjHqc2DGdGngn2i+py5xarlt7zVa9II\nPlHKJVcX6fxLSKZNKWPHdhoo5f34zqbj2pqdQMqWILlKSa40EraeTycn9T10jURl5L+tmUskAjIU\nxLmDZDKX7gsXQV4BdUYZWzOsOCYK2Rvs1WublBFAui62YGtyfluviYTkmiS5OEnPAymziOreTY5L\nImTwRJLPhlBk988Y/HyEXh8Nbec1HxBY0ZEvj9XAYrGQlGBhfNBhuoz2Y9CM4py9nePPYKfHt6w7\nvmXdoYN/OiUvITKRG8G3CA++xZkTZsJ2XSAuNJKos+HYudpT483W6H5ZyuqFN/j1/atUa+JG20He\nXD6VQI1R9Snf6T5JsTXSoTcaKTG2E/uHfk7Nb0fk9nA0HhL3gkYS4vLmzmvjpnq++l6Hu7tWrlhD\n43Hh0vYQrh1QbDborBXyMsoyNdPdE7rffnHQZWiXqFMoqTpIwk7ZlzHNi0Da/iNvv/joMhwgFX2m\ngx78aAeUq4bflm/QOzpkPr4NrF+vToNmHatGvsDRxcCta0mMCzqCt7891Zp7sPPPm1yMO0dSnJlU\ns4VavUvg4GqX6bMO7vYUre1L0dq+eKZ5E0s1p7B91O/sHLOcBSv8GPVhcRycDERHmHm5+QnaDPSm\nzNja6fo6vzUEi8UCTdR5WDWgSN8mhHy2huiTobiW1XJp5Qf0en2mhTmvUNRfR1F/HVmwAaShoZEH\nOLXyNCsG/0m5rmXTK38Wq9KaNk+zRZWyOYMwkjhlwyRLen3gTpO0Fqa0HzOQeluWvq94HJX9p1oy\nr5kVB9XkwogZj7yeRkZG0rlzZ/HvOa68SmZNyRwsZyeQEk/bVnRA6l8yFUhmXAmpBr2tBRyMpHD8\n7wiMRoiLTOaXWZewd9JjcrqOg5OeiOvJ/D3/AFP+KE+RUo5i5GS682M0Uu+jXhx5fyPPN/yLF5c3\no3AFd95vv57ijfxpNK02Fdl8t/m/66NY+sw5dHoYsc8Z9yKZo4eDhITRY2rMUcolM7R0HaUsBFJy\naOl6SdGZ3VmilO+hplIumZDrfjOY3f0XEPTPG+nkcl1w2+pzP6zbyR0yFn2Ix5HTlBL7PyeY76Uo\nVQlpPFKCcqk4heRmYIvbzwPvRUtquvFKBRmkcyCNRTJrugmpuRLdlWJMCWq5TvG19CnwsZN653+4\n4VOl3OOmUFzAWZ3oPNxebWaVotR3UVcpl9ZgW5PbS/cQQjS6rRlipCh1yaVFmvtyphD1mieZ+6X+\nIwS3ASmrgGTul9wqpPMsFcCR1khbi1OYUoSiA4IbTJyzem2T3DCktVA1TqlYgDRH0vZxat0FVgz6\ni/4rOlKqrm16j5QNwFa3rTOUVMolpLVNcocozYb79PZw/q87duygVq1abNmyRfl3bec1H/FUNy+e\n6pZ+st9ZYCwWCys/u8rLDQ8x7rsyOD1k2WOdTkflV4KoWeIm89tsoPvcGkRfT+Di/lsc33CVCkEW\ndDodezdG8daz55jxe0n2b4pm8YB1DFnbGb0+b+5W5TauJQvhUaMYZxduI3BQ49weTibsCzyaOedx\nxNHl8QmGqlQ+t0egkVsYnW3bYNHIOc5uCWFxn7/o+9vTFKtbGLLFG/TxYNu2bTRu3FhUXrXM6o8J\nOp2Op58vzKQl5Zg76BQb5xzMtPV/P6p3Kcb/Vjbn99cO0HREWZ4aXpqfXtz9f/bOOzyqamvjv+nJ\nZJJMegLpEHpC70UEpAkIgiIoXEAQsCEq+iEqcG1YERVFFEQBr4qiFBUQEKVJCV1aEkISCKT3Mply\nvj8mkHY2MBBI0LzPMw9kz5l96t57nbXe9S4e7XaK7z9MZfYD8byyKpyW3Qw8NNMfc5GV7fMP3sQz\nuv3Rev5oYt7fhLWk9nEVTVkCN96/GEX5tZPzej04dqKmj6AONQVLQR2/pDYiYVcyX9/3M6O+GUho\nt/o1fTi1Hjt27KBbt27C72uN8SoKmYi3/+d4SaoTkd3d+WBPSw5+G8fyB7dSUnjthlNYB2+e39mP\n7YtjSNifweyjdzPsMV/+/DGbud+E0foOe4jAZpVo3D+Yn5/dycGvT7Hxpb9YOWoDVvM/Z/GvDijV\napo8N4D9E5fW9KH8qyCihIgKWVxSqqitnNc61KEOtQsiJYkMAf0xeX8yy4eu476v+tGgl7wCQB3K\nYDKZiI6OpnPnzsJtahVtQM6AFfHU7NtXNWBFPCARf0fUv6OVkMRcWPntRcfpKMdRDvWC1Ly5vQ2f\nTj7GJ11X8dxPbfENsWcdivhWDYm1/xsGzXc2YvbwU6y8P4XnVjah22j7W2JqtoVHIg+Qfq6sj/gl\nf9Kqi46da3K5M78Ii4e8xJVoUIvkSUQvJyKZnNF8Ldsu4ouJpD9EHJ7KVY8uQXS//sROFdA8OIKs\nRbuIPWlB3yRYKIEikvoSHY+IDyhCZd6lAgkVFkyC/kU8OBFvztEKdtvpIdsu4sGJ+hFdt+sxYNWS\nuUJ/Im6fmG/soIdd4PzWiWZlQfeSzNQjKSBV8Ix/q3tAtn1CrvwYUlttsu0iTrBoYRddexFfvRXy\nkR3xXCLfLoLoWRYdv4iPLZIdEs1housm5sPLjwkR1/YoJtnnX8R/FM0lorlTxPcUccH9CuU5ryoH\nh4u6QPCFYBwVusnfF9FzIppL5K6bFdU1G7AJh7P5YtB6Rnzei2b9g6DcfRbNU6J2EW/c0Wpxlypo\nVoao4mW6YJ0W4axAMvNaER0dTaNGjXBzcxNuU2s8r3WoXuicVTzxZRQ9xwUys9Nujv5+7Yk2rh5q\n3tzQFINRzYyeR8m8aJ8kE44Xkn6uhCGPBfDDgQCOmoP5Yosf014x4qRXYK190fFagUZfzSBmwrs1\nfRh1qEMd6lCHW4jzx3N5q/9Ohn50B82HhNf04dRaHDlyhE8//ZRZs2YxZswYJk2aRM+ePa/4m1tu\nvIreFutQ/VAoFAyaFsq0FVG8P+oQP39w9qo82MI8K6YiKxqtkue+aECnIZ5M63SYs38X0LyLG5uk\nbjz+UQOatdaiVpeFWdVqsFiunWP7b4JTWAD6ZiFc+PzXmj6UfwUc9oCWg80m72W83ZCfD4VpIldV\nHepQhxvBZvpc8fviAgt/LD3LvLu2M/qdSKJG1GmlXwlvv/02s2fPxsnJiT59+vDRRx/x2muvXfE3\nNUIbcMSAdbQKiqNcWBFtwPEKXo5Jd4ko9aLtHV2Qy4cLuvbWEb47irlDj3P2YA5TP2mK1qnsPNIS\ni/huXQZ71qZzdFsWD84N577/CwUF3PFSN7Rh8Txz5wEe+borzfr4l/ZfMfytUCeTbXUnUhDuEIW+\nRCFDUUjGVyC7IgpliUKnjsq3RCIvFyQK6++vVB0octEk/mrzNNqxk1HLVN46jnx6eJgg1Cp63kTS\nY5Wr1jhTREPihJXSvAXyQqIQo1hKpaqcmh0iGoD8fkXVeERhMbnxIrpml/bp6mxBnZ2Oh6f9nV5E\nsdEXytMJTHr5cxL1g0jwQZRLJ5itrZXa09Jh6BA4MOdLJs8LqlJSVlQpqsBN3pchog3orQJ5N5X8\ns7CHDrLtogpkWYJQqKMya6Jnqp6DjhTR2BKF10XHKVr/RJQ00ZgTzT1azLLzpKO0ARGtoh4XHOrH\nUXqAEKJ3McGSny+4PgsFVT5nIW8siWQn5QxY57+j2fDpOf5ceYEmXY08vbw5Ub08MLKnyra5mRb2\n/5bDnl9zOB9bjIu7CoNRjYu7inBjJu5GcHNX4GYEd6MCN3cFbY2xuBkVuBmVODmVOY++YozsMYok\nsUT0A/F8LQ/R3CaieVWGJElkZmayYMEC2rdvT0REBA88IE9rqoxaxXmtw81DQJgT83e15K0JZ3ih\nxz7umxVO7P5c9q1LI+N8Me3v9qbdQC9O7sqhz/iKBmXnh8LwDNKz6P6dDJ/Xkm7jqw4IlRpKTHWe\nVxGUajVhL4xg64RV9F0xqqYPpw4ycPeAC+dsl43X2xV79kGvO6FlVH0Wv5DEo+8Eo9Xd3udUGeYi\nM2ondV2SXR1qHOZiC0d/iGPPp8fIjs3gron1mX+wEz7BFTm3NpvE6QMF7PnVbrDGHyukZQ9XOg4w\nMmC8N4V5VvKzrRTkWNFnQ0Y6nIm1kZMNuTkSOdkSOTkWcrMlsjNtzPnAlYemyPN6bwfk5OQwbtw4\nfv75Z9q0aUO3bt14+OGHad68OZGR8k6V8qgzXv9FcHZRMeObKLZ8kcz7Y4/Rf0ogkxc2oVEnI1qV\njY+mnGTgo4F4+FX1nDS+w4/n/+zD+wO3kXYmn6b/LVs4PpxxHlcPFWOiTtK0pZoWbTR4eFdcLIsx\nyx7TMX6UbbdQtVIYQFawloLEql7WjwRv/RmliWiVIfLC/bk1jpBeVcnmsaUe5aNb0onsXUZeF72V\nx/I/2XZzWj575myq0p6Pi+z250XeDMHxFwvceWdIq/C32knF+jkHSRF4ykVeICeBW9DiYMKWEnlv\nnsjDWn6/ZpOVmB3pNOvtJ5tUpcGMTaaeoshLYCztW6mEb5eZcXWzu4pE11hT+ignJkJwcFl7sUb+\n2ggLNcifKr/vgDvl8hIF9qet0iUuKIBXXob93hpGzQhg8cwkpr4djEr1zzH0Pg99F1OOCbdgd1xD\njLiFGNGE1MMlxBNDiCcuIV64BBpRqv9dqjT/FNrL7YC0mGz2LD5G9JcnqdfKm25PtWToYDNqTdlA\nzU0v4eCmDI7+msy+jTm4eanpOMCd8XPrE9XdFZ2T/KCOFEQGL3nbo3ebeXpsLqMfcbottdYPHz7M\niBEj6Nu3L1lZWezYsYPvvvsOrVbLuHHjiI6OvmofCke0QBUKheTI9nJYxRDZdlH4+EpqA3JwlFMr\nyvwULXTiqimOaes5WiFJ3I98TMbRyl4x8Wqmt9vLp6e74OZVZjhWDk3lppl4Z8hf+IQbmLS0LRqd\nih/mHGf4nGZcjMnj7yX7WLssl9DGWu6d5M5dIwzonJTC8PTv3CnbLgr39yxX8as8RCGrMOJl20VV\nVp7oeYJO216p0t6ntGLIwp4/MmnDYLROdmPtrKASlShbNAV5NYZDgixPUVaoyCgU0VpE1+eC4DqI\n0EhQ4UykoiDK7BUZ/aKM5fIhzPzsEuY/cJiXNrSXNV5FY1GUTRxqPSvbLqrcIwqDlghoAIV6ee+I\niH4wZgS8MAVaVY5SCwRNJPn3nsuVt46fgq9Xwyv/Z69EaRH0I6pItF0lX2Sjo0woFMThbNGYE4Wz\n5zO9wt+SzcbmQR+jXfQu5/tNxW/Zf1G6GbAkXMCckIwm4QymhJTLH3NKFhp/T5xCfNGF+KIL8cMp\nxBdDu0YY2kTQj42y+xUpx4hUBUQUJ9F5OQtULESUJVE/cmPIUmLhkwGbmLxlWJXvRNUJRf2L1Qbk\n5xJjrvwc8Jrb87Lts3LflG0XQS1YYlf7DJBtv1OwVnzLSNn2ytSqS7ikHANgNVs5uTaOmEV/EHe4\nkP7jvBn8iC+BDe2DP7LkIAf3S/y2wcZvGyRiTkl0u0NB94F6evbXEBRacX4Wrd+iZ+rSGJIkiQlt\nTjD1zUA69HXjd3rKbi96lkXzsqiCl2i9GSig/IioGQCPLEtgxowZLFiwgNGjR1fcT0kJiYmJNGxY\nxhFWKBRIUtU6tHWe1zoA8Oui86g0Sr6aFUe9CGfqN9JTL0KPIdwFtbbs7dDNR8eLW7uxcOwB5t21\ng6d+7HT5O/8IV+6a58Nj//Xmj3X5LJ+fxaGdRbz4ibzBdjuifmsfTqw/S8s6An6NQalUINn+uRSV\nAT3gh40yxut1olljGHQXPDsbenaFsCYQHgraa6Ol1RqcWbmP87/+TUMPNzQNArGmZOLcMQpdU3sW\nd+WXE5vZQsm5NMwJyaUGbSp5e09xdtYyWmx4DUHF59salhIbitvQE1fbkZ2Yy/7PjnJgyVG8IjwY\nM8WHHvd6oNUpyckws3F5Ont+zebgJjN+/gr69FMw+zUVnboq0OkU5Kmqt6KhQqFg2KM+/PhxKh36\niuWkahOsRSbOPvkh87bHsW3bNpo3r1omVqvVVjBcr4RbbrzqMDlUj7wOtwb3zQyhZW8PkmMKSY4p\n4vCWLJJjCklLNOEZ6IR/hAH/CBcCGhnwjzBw/+vN2bIonrldthHZr6JxqtEq6DPcleAILc/eJ++B\nvF0ROSycvUtP1BmvNQilWsGVAkBeZDis+VmbMKwvjHi8evvs1M5uxJ6Og4OHYfUaKCnnWPbxgeDm\nNho2UhAYRK2jGFiKzRyYtQbXhj4UHzxJSWwStmwB76IUSo0ap7AAXMJ8KrQb79rGyVFvYDpwLzrD\nbWbBXwU2iw1lLbt3tyusVom9G3JYsehHEncl0/KhpozbfB++zbzowXYsZhvfvnuBlW9cIKqHKx36\nu7PgtTyCgm/N9b9rtCeL/u88FxNLIPjq29ckis8kc3rEHJwiAtm3bx+urq5YrVYKCgrIz89Hp9Ph\n5eXYnF0jnle5sJ4oROFoWFOkEuBoOF4U1hTRCUTh2uqCo8fvaD+BxgIC+6qgrytxx1Rs+cZMVAcj\nuvqeOLmokSSJ7BQTF0/kcHBtMsmnC8hJKcFSYiPrXCH3Tq+Pb5hLhYxcW3OJtJTz7E5tQCNfeSNW\nFLJax2DZ9unMl20XCaCLRJdFoS9vMhjCuirtl56Hht282TAr+3K4RyQOLXoORaFrUbgmCflqLCJa\ngigkKVa9kH+RFIXQRCoNouspCuGL9nstAuglahtqWwneZAj3K3edRdu65siPaYXovUswa+rcBdsL\n7okuR9CPBKYioPL3gqmw0EWeN2dVVzpQT2gUApGmiucrSZCWBmePW9j3C3x/Dmw2O8VAoQDvBtto\nGKGgQYQCP/+ySmR5Knl6gIg2IAq7V6ZuFRfaWP52Ot98Ow+3O1piHNKVgujTFKbkI1ltmCa8jO+s\n8RhH9UJR7rciqlHlrH/NyKE4bTrKiicO0uGLiVW2b0icbD+i50eUfS969kVj1FG6i9z1zC4x4a7M\nky3wIBq7ov6NJvmxqDPJ82pFYX0RPUAteP4LfOWf50IX+YHXzySgf+jk55ihgjyLE6VrV8aFEn5d\nmsr6xSl4BWhpNPkOhn07HI3eTqcrAcy/7WXaU+DvDzu2QEREFpBlpxTJqCKodPLXUnSMonB/BUUN\nF2hzfz5fLLbR7VX5dT1fYMeI6JWieVlk3/zCQNn2MOJZyQQAMjIyaNj2XgpzcvDJstCgQQPy8/Mx\nmUyXOdoDBgzgl1/kKQgi1NEG6lAFv63M4uu3LuJVT42bfwHObmpUagUqjRIXdzUBEXoadzHi10CP\n1lnFkd/SZQsUKFUKGnXx4vTODBoN+2ckTiiV/6ys7dsRarXduPqnw2azJ5HdbCgU4OsLgW7QrVPF\n72w2OJmnJC5GYv0aG6kXpcteb6u2kKAwFeGNlIQ3UmH0uLGDlSSJzd/l8uFzF2nRyZmwxS+Rv+sY\nya+voDD6NA2/n4slpwBls8Y4RTZEobn+5aveB8+S1GYkif/7i+BRna7+g9sENouE6p8x1d5SSJJE\n9JZs1i1K4cCWHO4c6c0rPzUhorULv9Py8nb5Kflsee53lm6Bt+bBvcPs4+dW48yedDbNP8nfmy4y\n9L9Xz8yvSXh6erJlyxbUajUGgwGDwUBqaiqvvvoqf/75J7Nnz+bhhx92uN9bbrwOYSNr6Xerd1sH\nB9D+Lld2rM3mrfUNOZmoJy2hmPTEItIS7J+46FzSEgrR6FQYPDXo9Cp+eucM9Rsb8AhxxTtYT2hr\nd1RqJY27eXJqZyaDhvlcfce3CdQ6FYXZxeiN1ctjqsO1QalUXrXYxu2O8Pqw+yh0bXn1bW8mlEoI\nClYQFKygZ++K32XanEk6ayP+tJVVu0xkZ9nvSRESTs5KghpqCGmkJaihBr3AO3wJJ6KLeO+pCxQX\n2Ji7IpDW3V14nChcu0Xh/+wD5O08Su7maCSzBZVCR86qLaiMBoxj70YT4FjpSgClizOdvpnKn/3e\nwbNjOIZwea/t7QaL2YZSXUcbcBTrv7cwb1osY14OZMbSBri4VTSNbFYb0YsO8Oec7bQcF8WWaHCV\nDzDcNFgsEnt/SOC390+Sm1JMnycb85/FHXF20wh86rUDCoWCNm3aAJCWlsarr77KypUrmTZtGkuW\nLMHFRZBxehXUiOd1iEyWp6MGbXWF0UXhVEfVAxwtLuBosQNH4aiaQfkwd+uerpgKbeRlWWlyhx9N\nZLb/TepFcVo++YlZ5CVkkZ+YRXRCJprtZzjyczJTvu1Km2FBhHStx/fPH8IokL4S0UW6sEu2XRRy\nE4VBGnNatl0k3qzAJttX+Uxp385h7PohndYPtxIev+g6i0Knezf1kG336ysvFC4SXhcJo4vQWlA/\nvhWHZNtFNIlrFaW+BBFdRBSCrZyJrcaCkWzZfkThXY8L8s+OcOaXT54WVxgR0QBE7ziiObsYBnaA\n1Zuga3ndfYFIu9pd3g29XDdatn1KwTLZdpEKgYgeUKAy4hkBnhHQ9u6y9myMFBfaOB9nIiammK0b\niigusJGH3bh189HiYtTgFeSMxlnFF4sMnP81kTavDqPl+C7sVinZJUmo9u4i7deD2EosGDs3IviF\nAah0Gs4ShitgScsi66v1WNOyMAzqjqFrC1ndV1EI1ty6A4EvjGL76CW03v4OylJPrkg5QzRWRIVT\nRBA9+36CogCiuU2OmqQpKcFNmS8774nUDEQqAZWLX1yCiB4ggmh7kUrGBZ08P0Yko1ekkqdciYpW\nyM3BafWzcA2KpfGUOzhX6bsuf83nuUdNGFxh4zYdTZr/jQkv2WnA1Sp/jDqToDBFbtU5SZIgw+CO\nxQJWi0R+rsSPy4v46qMCPMKOM+K5UNoN8SvlpdvvnYgyI7IzRPO1qEiBaAwNFFAw7Jhw+X8FBQXM\nnz+f999/n9GjR3P8+HF8fW/shbGONlCHKlAqFQwc78X6JemMaiOfmKRQKHD2dcXZ1xWfdmVsca/d\nP5NwIBP/Jm7E7EgltL0X545mU1TojbP+nxFyb3ZfE7a/upPWD8vzaetQhxvF4K6w6KeaPorrh5Ne\nSYNIZxpElhkKl14AS4qspJwp5NcP49mx4hxurUJp+J9OZP99gc13L8S7XTCSVULTsSXhzw9FDCM3\npwAAIABJREFU5Sy/cKp9PPB6ZgySxUL++u2kPPch2sbBGEf1Q+lybeLtgdOGkvXbQfY2mojaw4DS\nWccpJwsqJw1qZw0qJw0qJzV+3RrQelztTzS21tEGrgtBzQxc+Dsbm026rJuan1HMmpkHiFlfzOy3\ntIx4sGpRjIP7zKQm28hMt5GdCYXpFnKyJXJzIS/XrrksSdJl2To/X/jhSvZeKZTKi6jVoFIrUGug\n92AnFv3oiaVt++o+9ZsKi8XC0qVLmTt3Lt27d2fPnj00aCDvOHIUdcZrHWQx4D9eTGhzguHvWNE6\nX/tsuGb2UTqPCeO9vr/j5ufEy/v7Exhl5O+9RbTreX3hgdoGvyg/8i+K3HJ1qMONQ+8E6n/Gu14F\nSJLEkc1pLH/mOAGNXJgX3YNDje8H4OCcdbR9axheUYEApAi8SZWhUKtxHXonxqHdMZ08S9pby1Eo\nFbg/2B9dQ/mEx8u/VSho8eNLFMUkYysuwVZcgn9xApZiM9ZiM9YiM0WpeUS/uJ6R44bf2MnfAljM\nUp3awHXAxajB2aglMyEfzxADu5bGsHbWAdqODGPncT3uxorX1GazMfbufHROEBSqxN1DidFTQViU\nAm8vJV4+4OcPXt6gVisve16tVnjoIcjIgJwcyMmE7BzIyrb/HRsHHdrBy8ur6t6XlEj8+FcWap2S\n8NbC7NBahe3btzN16lS2bdtG9+7yetHx8fHk5eXRpEkTtA7o99Ua49XRMLfjYv6ifqonrC9WOaie\nws7V1Y+4/4rnVT9ETZO2eo7+FE/PUVV1WuXCUme2J3NmZypZMZmMetKHZa9cICDrbzp2U3N6Zwb9\nelatQCSSNBKJH4tCX/WT5DNm84LkwyCi0LINFUUyYaVTNKrwdxF6TtFIGOYWhfXl6mEDOHeSz0YV\nZW6L2sVZ//JhK1HIU9S/qN55BvK8QxGtQqTmca1C7SqsuJInG4J1lQnFARQIyr66ZAiyv0ThflGN\ndVE41dGhWzorl9ioOEPLP+JClYOJlmXyXwjeu6wOvluK7pWoIMnbx+/mzPRFmBJTCf/gBfT92/M9\nZSFJ3ZxmpIDgCStDqKDwSBF6aOJK/bnDseQVkvr1NnI+TUbq2QND/y4oKrkk0y/NPVqguf/l9guV\n5kJJkih5cxskJeEVVDVELToeUaEYEbVHRFdwREHHYLHgggm/wmunMpgEpYNdUuXHxeSg92XbF2U+\nJduuEIwXq+C5FdEDRBDNVaICMm3ZX6Vt98Y8rIUmzLui+Xi0vRrhgg1hNGqtxwV1hSFcUmJjeK88\nRoxzZtTEimuFK1W5+FbKCpuogEHlloDKBU+ysqFxB5i55QL+PrD7IOzYb//sPQyFRRf5zwt+dGtd\n0bg9LaBIiCBSFZAr+gLiZ/kQrYX7+A/Qs2dP7r33Xr777rvLxmtaWhpbt25ly5YtbN68maKiIoxG\nI2fPniUiIoKoqCiioqKIjIykVStxdLPWGK91qH0YNMGTH5dclDVe5bBx9j6KC20MGOfF6Bn+7N+S\nx6E/8onqZmDDInnj7HaF2uBEYXKWUL6oDnWoDtwqxYGbifysEn6Yc5IjX/9O0IujCXh08GV+6c2C\n2lVPvckDkSSJ+D/OkTZrIWp/L9zG3I3aS563LYJCoUDfOZLTuzLpPFKeX1lbYDFLqG7z5+VWwmKW\n+PTlFFZ9lIl/qJYPnz7H5NfrM3C8l2zZ1fx8G8N7FvDESwb63lP9CbseRpg2GbrcB8UmaNsCureH\n5yfDDxvgYJqBiXP8r95RLYFCoeCzzz6jTZs25ObmcvjwYc6ePUuPHj3o06cPTz31FE2bNkWhUFBU\nVMSJEyc4cuQIR44cYdOmTTRt2lTYd53xWgchegx15+3HzpNytgi/0CtzyGK3nSf29/OMnO7L+JcD\nkCQJnZOC+L/tb3JJZ6onEa22wLdnY5K+20vDp6Jq+lDq8A+FpyvEn4cGV45811pYLTa2LD7LD3NP\n0eHeANoe/wyNj2OG441CoVDg0rMdLj3bYT6XQvbi1djyC3Ed3htDm4hr7selSySnd/9K55GBN/Fo\nbxwWC3Wc12tESlIJsx44x5FdhahU0KKLgcmv18fdq8wsMpfYOHDQQpuOalIv2nigbwFvfOxM6243\nT2lmxhPQrz1ENYFLMrBLvoVtf8FHB0JRa24vWojRaGTt2rWsX7+eKVOm0L59e9SVNagBZ2dn2rRp\nc1mZ4BI++ugj2X5rjfF682kDjsXuRG5yUQhHBEdVCByF6DhFGaqi/cr1o3OCvqOM7PgygYdnV3zb\nqxwqW/n2fnpNCOTxd31RKBQsnZvM/s15nI8z4eapZs76SOJkwvEpyHt1Ha1DL0r4NQbJh9FF2ZMi\nVKYHeE5swprBX7J2z2m6L3kQtb5iGDyZqpwlEF//MW5fybbvootsu1Fw3UTnJaorP0yQLeqo2kZD\nYmXbRcUXRONXVHxBa6p4PFqbDV+TfIBZLcrKjxHQA0ThcoF6gJA2IFrPRBHcTEF76ZTRxA/27oMG\nl47vCuoEst0IaAaifrSCfgS3UBiW1WLi6NYMlk47jqu3ltmb2hHa0o2PbE5gKURZbuFK/WEnOh9X\nPHtULRUpeqZEShhfMF62/dKYUwV64TRzLLYSM7k/bCX5fxtwatUY1xF9UOrKxq/cXOjSJZL4p5fJ\nKgI0tsormjgKlUUwN1vln9vKIWeAbkEw9yBo8qp67Avc5F2yIqF8vYt8CHlhroAeIJgyLAJ6QJ6b\nvEPE0TVTRD0TrSFW1Gxfl8Or4xLJzbTSuI0zMz4JpFeHfCoP1vdfz8PgpuTX9VY2rC7iw289adxC\ng0Yq4NTfNrb+aqHHXWpatLLbBSqr/LwmomboLVXvrVYL7cuVLv5zD7zwLvy5CuI9vWWJYQGCeTNd\ncG1ElB9Hizxdq8pMixYtaNGixTVte62oCzDU4YoYPMGT9V9kYLtKLfnx85syZbFdqmbd5+ksnXMB\nFDDsUV8++L0RgRHXlv17u8DJU8/InVMJf6At63vMZ8+M1ZerhdShDtWBVuFw6ExNH4VjSD5TzFv3\nHuDjh49y/5wI5m7tQGhLN94cFs2BnjPJO1T20pu87DdOTFmIsdOVPaD557I5MG8LO59eUy3HqNRq\nMI7qh9/b09E1b0D6fxeT/trnmJMuCn+jb9uYhL+LMBXV7giS0QiPjIHJz9T0kdROWEpsLHj6PM8N\niUeywbMLA1mytxHNO1R9o9uwuojmrTVMfNqAh7eCDj20pJy3Mn92Hm/MMnH8iJWR4zUc3m/ljVnF\npKVU//x/JgHufxRWvA+NqydJ/x+DWuN5rUPtROM2elw91ET/nk/73mJV5nqN7IN/9y85vDMlgU4D\n3Hh2UQj+wf+s2uGVEXJPFCH3RHH0vS2safMmTaZ2p+nkbjV9WHX4B6BDI/jfHzV9FNeGwjwrX79x\nnvWfpjDomXCe+rolWqcyL87zP7blYx6r8BvjHZE0/3I6SpkMY0tBMad+jObUl/tIiz5H+PAo4n86\nRovHu0F49R23U6vGOLVqjDUrl5yv1mM+n4pH//YY7mxbQRZJ6exEaAs9p/YXENXdrfoO4CZg6njo\nNQz2REPHtlff/t+ClDMFvD8ymrj9OQz8jwePvVUPT195/fGEOAunj1l48mX7mjd+moGUZCtnYyw8\nNsuAu7bMK/3gRC25ORKff2DC08XKpEeVaKohtF9YBIMnQMMQOBkHJ2Lhb/ckAho402mw44U5bgas\nJaVRDe2tNyVrvfEqylYWhQpEqC7xfxEcpROIwv2i43R0e0f3K6IZAAyb4MY371wkPaGQCwlm3DxU\ndHlKPiz+118Kpn7Riu4PBWJTKC4HM0RFAUSiyO1kMkJBHFoTCrgLzleUNa9GI5uxL3oOL2VWN3u6\nL02e7MXe6d/zU7u38HntMTz6VdXkE4lJi+qOi4osiLCHjrLtonC8KPQrUgNoxCnZdlHxCFHoTgSP\nk4JCApVCjyozuGTakGQinnJtAAqR8pIofC9qFw05ET1ARD8QhelLp7YQJ3v28eXfi+gKovMSMT/k\nHwUUgtwIUbjWmSJsNolfl2ex6IULtOttYMrrfqxdkU6HEfVJPZPDuRN5GDy09BwXRL/KxWnC7B/X\nUuqKzSYRuz2Fv76M5dCPiXRvX8LrD8Hgb8DZeQ/TlFBv+ev0m13xhG02CYUCUgQ3eDvyBUCMlEsi\n9QCmtUCy2fDfuIJTz3+Cd5iBTg+F4+RqN3CWdGnK2l0GMrp3rtDPnYm75S+c6DkRqRyJngfBlGcR\nLIEFbkq+XGXj7v6w86+yktZFOnn+h4jaYxRlngue5wJf+UBuhk7e0BKtmaK5R0QnEM095deQX74v\nYubDOdQPUbFhm5rO3QqAmAr3KFVlf35MxRKfv1fIC/ONWFFczsx3rQeR9ey3w4q5wr5c3GHaSxp+\n+SafA0eVtG5f8dxELhyrWn7dVQMlJdC7K1iscOoMKBWQps5gzbv53DO4YrSisWBeviCgi4mKzYhU\nluSQl5DJDwOW0GhiZyKf7n31H1Qzar3xWoeax4AH3dm1sYADfxRyLq4Eo7eKLvK0J0b+V64e178D\nSrWaTh8+QEluIRsm/MT511YQ/sl09M1Da/rQ6nAborarDBz7q4D3njzPyf2FdB7ohsVsY/FLKRTk\nWflvr93Ub2LAUmJDq1fRc5w46yw1Npc9y+PY81UcOoOaTv9pyD2vteEp9+8qbDfyfnjsCeg/0cqh\nvRaO7DNzeJ+ZI/ss1AtWMnBtDh6hN6Z/qVAqiRxQn8gB9UmNy+O3d49jKbHRaUw49TvX59jKv2+o\n/1sFT08lk6fYmDoZPv2spo+m5mC1Ssx5Ipc1K4qYNtfA2Mdd8FeK3krt+Pi1XB553hWt1nHvqYtB\ngUyRt+uC0R0++G/Ftr3+/nR1OYnFIqGuwTLAaQfO8cvgJajdnLEWma/+g5uAOuO1DleFu6eKD9bb\nF58H28UzfLJHDR9R7YbWTU/j7+dSHH+BuInvoHTW0WDZc2i9b22m9T8Z1bVA1OH6cOKolak9YnH1\nUNJ9iBshTZ0IberEA9N9UTdpgN7N7q1cNv0Y7r5VXeFFOSaOfBdD9JcnyYjJot2oMCavvpPAVp5l\n4fpKOSWdOoLJBANbZxLVXkOrDhoenq6nZXsNa74u5r0u/+OB7wcT3KX+NZ2DJEmY0vMoSsqkMCmT\nwsQMCpMyiU86QWZiAZlJBeQkFxHY0gOfhq6c3VvM2W0JWM1WVJran9I/boKSkydt3NnDxvMvwB33\n1PQR3TrkZFqY2CcNJ2cFUR20/HbSB796pffsCsHKX74rpFUnLfWCr880kqSbOzdpNAo8fNSkJVsI\nCJanPNxsJPxygq3jvuGOT0dwYZ8gjHMLUGuMV0ezmx3NpncUohBFTUFED3D0OjharEFVLoxwPLqY\nnHQLXfs6kSTop0gQghKFIxxVdXDJFZDiBQ4XUd3uL9zGyLY78SOhnJXvTAYiGkA2RghTErLlOdJ3\nnOTIXdPpdXAekRyR3X4bd8q2N+O4bLuIBiC6bqLsUtG5ijK6RfdL1H9llYBLEAmgi6YBS6V8CpvS\n3iaXsSwMg7rIaw27yGT8AuLwrmi+djDsK0R5eoCt3N8igQwRLUGkfiA4L5OgXW+tGpZt2wxGjVPi\nZlQy5y0bdkvT/gxklwvjvrIjBWuImj1vJGPw2ImTXsHGVQVEbzfRuY8zM55zYWRfKxpNHBBnP99S\n6GQYJyc2gc1fQqEogXJzX7NJUK+RLzOHfsf/zfdg8IMGCvJsXEiyoEzcQFqSibQkE6mJpsv/zzhn\nQuesICBIhX+QmsZBKuoFqwluaaNekJKAIGe2/qzkyL5CXpkQh2W8kg4/mol/4gNCG6oZNtYZb18V\nJ8NCZK+baEyLCrAEZcrzTkoE9zFZL0/dKi/aP2kBjMy28Ppjabz6WgGTn9ExdGTFILZcoQ+APHf5\nYLerYM0R0RJEIeok5L3xojlJlNleXvkm+Zxdh/VsnP1BeuczZx6cqKb8xHKJHlAZ0acMHI+xMG6W\nf4USM3K2ieiczLYirApllfXOWUB3s4rMDMFYbH3yBA18wH1HLK3LKUqtajJYdvvBrJNtF61boiI6\nYaX3ZMfik2yffYBH1/YhrJOKj/eqQaGVLexzs1G7LLQ61Gp8/2kO905yR1VXftAhGNuEojXWbnHz\nOtROGJ0hIR1Cakd+xmXMfdGCzaZkzltXlpx7cq4bp46ayc2SSDpbQm6mhfNnLYQ21lCYbyMny4ZC\nIQHXNqdotWASbNpjgDPLfvfj0cFpvPJYJhYz+Aep8AguwidIh0+Qlhbd3fAN1uETpKN1UBp6l6rc\njPJVho4fsnDqmIVXn83D219NRqrE+m+KGTDCiXdeyKN5azXDp3jU6jnRzahm3soADIUXePHJYj55\n28TYKVoenOiYXGBtRsxJK/d0KyArw66K8/pHTox7VFsh6e5KKC6SWPVhOk+9f21eexFutucVICgA\nEi9A15u7mwqQJIl1L0Zz8Lt4pm8fhE/Dmk9arDNe63BNyM+1smlVHj8eD63pQ7mpMJuuLAl2Pbiw\n7gDu7aoxRboO/xo09oc98bXLeP3gXQuxpySWrL66UHvPgc70HGj3ylT2Rl08Z2HlwjwG9bZgtUKL\nKAVTnlDRuOn1k30jmmtZ93cAxUUS7h5KFAqF0MOnvwalyKnP6zn4l5nUizZSL9ho20VD9C4z3y2x\nG7guBnj92XwCw9SERKgJbqgmJEJDSISajIb5eAS5yFZqqgno9Ure+1xPSYmNuc8W07dtHiPGaHns\nSelyUtfthmN7i+jQNYdLjs35S50ZOU5zzUbrJSx8JZexM4NumEd6K4zX4ACIP3dz91Ee1hIrX034\ng7S4XJ7eNQhXn4pe1oz9Z0nfewbvDrd2jas1xuud7JJt/10g0i4KUzoKR1UCRHCUruBo+N7R/Ypq\n2WsFcVlROPhSuOXQtkLysm30qXdJeNKeBe/mo+WJLyMJbGbAO8gJi1L+ejYkTrZdFBYX1be2CJwF\nak/59lNu8ioHKYKwiX9zDz59O5+hMyoORBE9QxSiLt+e/MsxAh/pRyF64XUW9SMKT8nV5wbwFaS8\ni4omiOqCi0J0oprYwuPXyR9/mPqCbHtuC/n9lqgqtlvUhWS76WXpKKIxLRJjd1ELFA5EjikRPUDE\nfBINaVHRgXIhw6gmcCgJ7ve8Qv+OMa6E0CUK2i1lz/6Xa2DbOtj8OZAmT8nJ85F/pirfK/9ANc+8\n4YErGmw2G5vXm3npJRMZqTac9fDA3TDuIbu3tcLxCAQs/PSlz75z6acUZwmT3V5k1MbRsOyPCHCK\ngGAgDOtlLY+89GLmddpAWEcvol7oBzaJzJhMEmIyORSdReY3mWTEbKI4sxD3cE+MDb0wRnhjjPBC\n01BFSIQGv0B1BcP2uKf8dRONXVEdehEuz/1amPEBWCw2PpydQ++2uQwaDM/PhISzCsLC7cckpAF4\nOlqoR357Ubuc2guUrXWSJLFtk4XR/ctsgM++UjHiASVKpRVs9jk2QyUvx5BOxTfBjSszadLTB1X9\nAFltFLk1U6QOIzJedaZrLzQBV6ATmKBfBxg1E/YegidGQa8O0JqDACQnWlizspDJzxtQKpXsp51s\nN4doJdvelugKfxdkm3nn3n3UMxawZIsnzvo/L3+XEGvh5PwMbCYzKTviCJ85nOCnhjj84nC9qDXG\nax1qN3qUek8eesIVi8VGYoaB/CwLbt5aotensenTJFLiCrn/vTa0uOv2qb1cGY9+HsnMLrtp0dOD\nhu2rJzGt6MxF3Ds3rpa+6vDvQsdG8OOemj4KO9b/AYu+g53Lq79vpVJJ3yE6+g6xvzGkp9pY9W4W\n/YbYpYKaNoZHH4FWN7Eac26Gmc3LU7loKsLgrcPg44SrjxMGHyf73+6qywuzq7cTT/7Size7bcDk\ntIe7lwzGN7Ji1btC9JgLSsiOyyA7JoPsmHRS9p1j0ddZJMSYyc2yERhu99QGR2gIbqgp/b8Wv/qq\nm+6xVauVTH/Ng/ov5vLe29C9Mxw+LAESHy9ScN/km7p7h2GxSKz/3szUUWVG+6JvnBl5r+W6r9WZ\nv4tIPW+m34OeDor6yeNWeF7v6gwJv8KKn2HaW2CTYOQz+Qwdq8c/0O5BX/JuPpNm3FhoPz2xkNcH\n7CGyjw/z33OqQI3ZsraIpx7IxGaSaPLhI/gMasfhEW+SveskzZc+gdr15tPk6ozXOlwTkhMsBASr\nmPWB3cV5nGZVtikutPDq4EMc25TCA2+3vNWH6DBsNhsFqYVknM4mMy6LnMQ8tp1LoklXdxZOOMbr\nuzvjbKieIXK7huVqK27V231No4EfpOfW9FHA7sMweyHsXnFrJLy8fZXMmWX/v80GW/6A19+BixfB\nWQVD+8HDI0F/lTXSZpOQFNIVn5cLZ4pZPf88W1em0XGQBwp/F1JO5pCXVkx+ejH5aSby04oxF1sx\neOvsBq23DoOPDt+GbhxbeRSNi4Yus7ph8KuoW61x0eITFYBPVJn3dGKpvmxhgY3EWDOJMWYSY80c\n3Wvi55X5JMaaycu2G7bBEVo8GxZTP8KZ1r3dqdeg+hNjlEolzz4Pzz4PHy6wMXc2PDpF4tEp+aze\n4kT3XjVrJhQVSaxaZuKFR8syGRd/p+fu4XbPtVJQkvVqMBXb+HFRxg3zXCvgFhivAC7OMHkEPDIc\ntu2H138u4p2Z2Qwfb+Chx1wIjbhxJYLPHztGi17ejF/QAlWpZ9dqlXj/pVx+Wl6I0UuJqu+dBD02\nEIVCQetfXmZnxFT+qD+BXlkrqaagthAKSbp2jp9CoZAc2b468KdAdN3RcL8o212kKuBoqMNRiMLQ\nItUF0fbCLG8H+xfRGC4JRv++vpCvP8rjsw1278IWQVZiM47z2cwEju/O57X1jdGXGn+OhsW7CGgk\noZny4eZTniGUlNhIirPYP/EWLiRaOJgcQH5qEdaS0vMrnViUpWnNOoMWV39njIEueIa4Yg5rhEcT\nb7JOpbP7pd+470+7+0F03URh9EthGUtuAceGvUKrLfMAcahSFCqL5KhsuwiizGHR/RWpFgiLRAj6\nERVxEN1fEb1BhMrZrD2CkwkKV6Pg2ksyqgTbakoEfYjqdpQ6fga3h3X7yrU7ShsQKZdXGioHY6F1\nQxjaAZ4aJvhNdUBAk/g7GUbNhh0fg1t52yxYfntHa9mL6sGLCpKY4mx88hWs+AGWzS+rIhUbEFhx\nO5PEw/3TKXb3ZcLXd6DVV5zrd+1Rs/edXST+Hk/LR9rS5vEOuNZzEz6b9xR/Q1aGjcw0iYw0G5np\n9n93pjehxZBgTv12Hskq0WVyUww+zhXpB+UgmrMblStIUpxvJjU2n9TYPA7HuJB9Op34n08yeM0Y\n6nW2qxt050/ZfjKQJ0iXz8ovD7k5ZvGCYmY/VfZAH98NTRrZ/5/tKX8fRWujaM4TzZ3NrHYqWXa2\nxOef2HjlpbLn48sVCobdSwVPq4jekIKfbPsp7Cfy3dwY7hwfiE+w/Xw8BHOwXKEVEf1uz7fnadwE\nmrWo+IanM4mLAMkhVi9Pdwuyls2zJ49LLP/CircPfPc/ifFTVJxPghVLLLRsq+SRJ9T07K+R9Uob\nCvLJyICzCXD2LCQk2P9/KtmZbv2cGPWoK1vXFvHJf7P54UAAfym7kZdWzKejdgJQr5k78fsymLSi\nC64b+rFmzRr++usvunfvzqBBg5g6dapD53slKBQKJEmqchJ1ntc6XBPOnDAT3vTa3uYmvRHCga05\nPN75GM9+3oBmHcVlZa+E3GwbcacsJJ2xcu6slQtJVpQZkHDOHp4pjyLNRZRKMHop8amnJiBIRcuO\nOlzDW+PdyIiToaKlIJp8LvHjXPzdCOzVgK1PrKHXh9cvkJj2027cOv57CzfcLASFq1m5zfeKleEq\nQ2Q0eAu4m0JJrDNl/32m/KMhb7+LpaxElbEqVbq640nY9gHiCls3EefSYOTLsPn9SoZrDcLTA2ZN\ng8kPQb/REL2x6jaSJPHylCzcjApw0zK/1wYeXdcHFy8dR9cl8ds7x0hLMtF+emcGLL0Hnau8IRW9\n8jR7PjtO00EhtL/HRniEioBKjjr3Ul5hcDtf8tOL2b34BJIkUW9KAM5eImLzleFk0BDcyoPgVh7o\nSqv4xf96inVDljPwu1EE3XlzC90/Ms2Jl8aYeG8hPPsyNOsMemeIPwQaQX5BdeFCssTC9618+F7Z\nS+XSr1UMv8dWbaoOhzalUa+xy2XDtbogSVf28t8o8vIkflxl46slNvbtkQgNhxfmqFi7TYOnp91g\nnvGymtXfWHntRTPPP2lm7CMaNBpIiJdIOmsjMV4i7jRYrdC8GYSFQWgINGsKzfu58M2ifFZ9ls+s\nDzzQ6hT8+m0h+c2yWDDoDxRKu+G65cPTuAc482qHjdx7t5HJkyezevVqDIZbN0nUGa91uCacOWGm\nZadrl1Zp08udD7a3YObdJ+gwwMiwFyJISzJx/nQhKfHFpCUUkXG+hLQUGyWFVT0vruTg5KTA20+F\nX30l9YJVNG+lo6tPIaHBoK705J7ylOfZrhNaCFdH5zl9+HHAUuLWHqfZkOtbLDI3RBM4fSjm7HxM\n5zMoTEnGmpKBNTsXt0kjUFY+kTrUoRbhzW9gzgTwd6wa9y2BtxcMGwAzX4M3ZlX87rO38zlxyMz/\ntvuwy6Uba188yNudf0ahUuDkquGuGS3wHN4DpVocSTMVmFn/3G56v9CGC0cyGNEjB3cPJX3v0dB3\niJbWHdVVvFoGbyfueqE1+WlFrP/kLxQqBS0md8LJ88Y5gGEDGjNw1Wh+uf9r+i4bQfeBN9zlVfH0\nY/bPq+/Ay2+AX2OIbF3Ehp1OODtXr5GWeLqYFW+lsm6JvWKTXg8fLlYx7D4lKpUClaV6or656SUc\n2ZzB2Leq36nQsrWSFV9YUattdOqqpNddihs2ZiVJ4tBuE6uX5LJ1tZku3RSkp0tEtlSG+aEmAAAg\nAElEQVTww89q/PwVWFVlnl5nZwUPjlczepyKv3Yr+GaZGZ2TguBQBd3u1BAcpqCZbxFGmaBYvN6F\nvsP1bPqhkJnjMggKV/Pe/2XRYkQ8WefsL/+ZiYU06eXH4JdaENHNh0nqr27o/K4XtX7lFIUpqwui\n7G9RSONmQ1yMoHpSiq/3esadMDNsfNlblaiedPmQm68Rvt3px1tPpzP9zhM4G3W4BjhjDDTg2cSf\n5gPdCGqsx+BdVXJHFIY+i0lWwjpJEMMUZVWKwvSV6SVKg55is1pIO7la/56NvUh8dhEqJw1qNz1q\nD1ecPAzYSszk3/ML7X62r7rZyCeHiWgAcpxjEKsHFApEpOshT8NoQKxsu6hWtihk6Kj6gUglo+p1\nkFBhld2vs8DDKnr2JYH9ohA4ZIVDUeRkE3lwRZ7UStFygxMkX4R6IoaEiIFxDWoGFSCTseKtgLxc\n5M9ZUGVTLfA0e7jLzxkimoEoE7v8dXtxEnS4F5JHgSHA/oz8+pOZFR8Usf4vA36GAlLxp9NrAzB2\niEHnriP0jiAUCgXeghtzSelk3YLjNO7qwT2PBQABjJl4nH3Hraz5w8oLDxWTngODu8OQ/ovp0xWc\nK01jhhcfIS+lkJ0frUOtVdFlSlP0Rh17BVQ40ZxUfkwbe7akz1o3Nt6zGMvCwUSMiKyyvYgeICog\nI6LOlS+O8NyL9s+zz8FHC6G+vphmTcHHF0zFYCoBk9n+r/nSx2yPkNkUJyv0q8D+G7/6Klq0tUfE\nks5YOLjbPvY9veCNBRrufUB12dNqBawq+YEqmvPkhPglSeKn108zenZ4lbkmQPA8eMsMDNE+jY31\nzJgHZrPEzt8tfLDQxqRpOuIENIBYAbXEm3SyUs38tjyNX5ekYrVKDHzYl/mHo1j6bAyGoBJe/Kkl\np93VnAa6W7fL9tOii5FXZQSbkgmVPdskgkABYSNgwUAr38+L58DOePZ8m8iA2a1pNTyUei08Lhvk\njpEhqhe13nitQ81DkiTijptpcI20gcp47j1vNtNb9juRsVIbkPjLcaxFZsKGtwSBEXk1NJ09AmaP\nuPy3qRzR8diji4l99XsavjhC7qd1uAL+LQlbAI2DYM9JGNbo1u87wAjn0279fh3B56/D6Kfhmz1w\n7JCVZycV8fLbOsYOLkDvoiBFsQK/KF8iH2pOcOdrS87JzzCx8b3TvLi7bN5SKqFjC/vn9ccg7hys\n2QbvLIEHn4HeXWBILxh0J/iUeqpd/fT0f7ktORcK+XPBMTTOKpwfb4Vaf/3OEd9OYfTf+BibBizE\nXGim2dg2V/9RNeGdt+CtefDQf2D1auDEtfxK3mN65qSFMyfL3lD86imZ8aY7Yx8oumG9VRE2LEuj\n8zAfXNxvrumj0Sjo2VfD3h3FZGdJCHwTVWCzShzelMqOz2M5sCWHbkM9mf5pOJHdXCkusPHC0ONc\njCti/NsNWfdhEp71dGi0Cs4WWzGbwWK2vzRc+hTYCrh/ghP1ghzP1dHpVTz434b0HlePhU8nsm9F\nLKEdfagfeZN5I9eIOuO1DldF2gUrWp0CD+/aX8+7umApLmHP/61j2F/Tb9o+Wnz8CH91n4VHj6ao\nenS7afv5J+JWJ47WJFqGw8GYmjFeAz3hqLxEc61BVBMIqW9PMvr03RJatlfy7TIzq7a44OGp5GPL\naGJ+iWPfwgP8NuN3AELvCGLka80r9GOzSZw7mEGztjrWvX6C9vcF4R8h5us3CISnH7KH1TOy4Odt\nsGYzPPUavPw4qN8o29Y9QE//2W05dzCdnT9EEzamqjvsyLen7Ocz8uqyel6tAhmxdSKr+y7FUmgm\naoq8N/dmQKmEr5cDlSTTTDr5DMRkVcUoy/aNRXz5Xi4Hd5ooLLCP4zeXeTDkQT1qtQK1UEjZMdhs\nEkX5VrJSzaxdlEKbXu5kXTRzx/jqkUC8Fox/TMu7c4qJ6pdPVGdn3Dzk19DU+EK2Lk3kj2VJGAOc\nGPKwJzOWNsBQzshOTbKHP4oLrMy779jl9hZ3GIkKkVBrQFPqX1r/k42Ui9ClVwkjxl29mMiVoNIo\nCevsS8wfF/n55QM06x9YK5wHt63xKhb5l28Xia6Lwp0i8X9Ruyj0IoKjWf8iVFexAxGsqIk5UUJ4\nU22FcxRdN9F5iTJgvSpUkS5DOvIkO1H2uiicnSUU+Y+Wbb/0nCwftZZBr7QjQm8ProjCO6KM1mvN\nyh/w8yR+6foOA7a7oJMpIXtQQEsQwVF6wLbMnrLtAz1/lm1vJ7hu85E38kezUrZdJPJdIqDriOgT\nchQBj0JRhpQ8FKLNRbkHIqeZo8UIRLSBSsUC2rnB+q1ATwf7Eb1rimgMMg6VECdISxfsQzTlia6P\ngB6gFtAwhAVJZK7nR/PAM8qEm5eGsB71eOD/gjgHnAO0ahvNh4TRfEhZsYL81EJCy2X3Z14w8d7Y\n4xzekslnsV3YtewMC491ooL6p4g+HwNewNi29s+3v8P3f8LD5fq/hEatJDauNsnOD9n1GrK+50fE\nxato+XzvywbCCL6X3e3+pm2Z9McIvui9Cm1BFl2fsSeOiegHIiqcaC7XCmzIEoEtlKeSN/TLF1NI\nTrLy+D0ZlJTe86c/DWfABF9UasXlGeoSJc1ikcjLkcjJlsjNlsjOUZCbbSM3296el2NDkqBEUXw5\ngVehKEvmzVBm4mRQ42LUsOuXPJoOCKHXsw1wxbFQgmgtkkMVZQU/OH0uhs/vTkJn0OAR4kpoV3/C\nuvoT0smfhOgM9n3+NxeOpNP6wcaM/WUYAZHedGc7YC792KGTTIS4Z5OilnhxgYr/TFTyyQc2Fi/M\n5oH3vIloruaHZYV88kYBEZFaPljliku3VmRBqThbGZJl1ktJkvjjfEPSD5wjPTrJ/jlwDmuJlVbD\ngnn457sJ7exPSS0wXOE2Nl7rcOtw5oSZsKYiXZ9/Ho79aOd7Nrvn5mb0Amjd9HT9/CE2D/yIu3c9\nd8P92YqLSZ37OZK1omcyD3kCp6Vws2z7GufdVdokG+wmRXb7w3JGqiRh4mTVdqpm/l/Nk2ooZz3Z\nbDayMm4uF742oWkApNaQ1muYV+3Qmb0SJAkmzYDWvd2Z/G4YwU3k3zritiQQ1DkArV6LwbfsRXH/\nr+ksmHCC/pPrE7Mvl8VPnWbgo4F4BlxfaN/dBXIELxSlsj+y3wV0b0DzJ7qzd+Z68hOz6PLBvVdM\nKAPwDDcy8c+RfNHne0z5Jdz5cufLcoC1Cft2lPB/j+QRe8I+bnsP0hLQ0of08yUsf+Wc/ZhLL4uX\nohBJgrxsGyENVbi5K3EzKjAYVYQ0UOFmLP3bVYFSqZDlnx7cWYSqfQRqrT2RaduXSUT29rlVp1sB\nzy4OZd/vhTywrBfu9VyI33mB4+sT+PXFvfg08aDDI81pMbQBap38vT4XZ2LJnIvs2ZjH9GeUfLpM\niV5vv8lPzVAR1kDBuH6Z6JygUaSGBf8z0qazfb2Wr19pn28zEwtIiM4k8UBG6b+ZmCU1Pm0D8WoT\nROMJHem6cASGYA+aKGJuxqW5IdQZr3W4Ks6cKLlmmazbHSWFJWyevZtH9466Zfv06RhO8LBW7Ji4\nnG6fj7mhvooOxVC4+xhej99Xod29yru3Hefym4Oiqup8qEHe6GxAgaxIfQLyMe1mSnlj1yioK68U\nvNV7lvNApF6wEvP3teu73u6oyfoWei2Ya/l7wtx3IeE8zN7RHK2T/MXKu5jPxhl/MmX/g5fbzCU2\nvpwZx85VKTz3TQsi7/Bg61cXOLkrh2dXNJft51pgNIiNVwCdhx5TZgE6z6ou5A6vDyLx5+Oc+Gw3\n+eey6f2/sWLPfSncg9wuG7BKtZIxs6pRdP8GkZNtY3CnbM6csj9E/3nCmYnTnfDxU3PeOUg2/Nyo\n1PP6QJ8cZrymx2Cw31NHNNbnPZXOS+Uq09Uky8jdS8Md01vyw5Q/mHV2DEHtfOkxzV7E50rnlJJU\nwhevprDth2zue8KHZxcG0t7lVJXt7rlXiV9zI8VFENm26jotSRIp8cXERucSeyCPuOg8Th/YgUqj\nJKStJyFtvbhjSiNC2noSW797raAEXAtqvfEqCr86WiygplQLRHCU9uBoP6LjcfR6lqAl9oSF7ve4\nV6BeiMK4coLOV+pfhF3IpEgiDn+LQmVRApH/OOS9qgdHvsq0eX50cqoYHheFjkTaoSmCGKOw6MCM\nZ0gbNpNtK87j9VD/y82irNbyRRyyT1wgad0Rmj/dm1xsFIR5ETKibYXtRfcrE/l6myEck20fTIJs\nezTtZdv9yhmd5SEqgiCCsdzzcz6+hC0b0sjDVZa2odfJ+xv0BYLkQBElTDQURcaEIPteGEZ3dPYV\nDSFRu8iAEu1X4Fyx2AS/EexXipBvVwjoAbmeAkqXILvco6AsDP3tGnjlfXhmMqx8MYaMCxYyLpi5\ne7wnA8bYeRBW1Kwev4mhXw1CUmqxAhmxWbz4wE78A1WsOeiDh1cGkIG3p42HJ5oZYfmj6v0UqUxU\n0uV1V0N2iXhuaN7FleLdewi6u+KFasZx0EPgsubMH7obLymDP3vNY8Y6Ez6+VY3y8hSt83lFkJNL\n+6AUggXFOESKKaI5JttTfs4QFZwpvz5YrRJPj8lm3f/s3INxT7sy/Q0PtFq7YSQBqcjLG15SKNG6\nFhGfZiCoVKNbRImqTEnb+0s6/s08KFaWDVRnTycS4q34hBmusNbJD1S5ELvo3p6iKl85IyGfrNyL\n5KUUcfxACfU7lRXTaMWhKttnp5j47I1T/PBVMaMmOfPHKQ88vGxAilBl5nCz7qX7t+Pkr2eJ+/08\nCdGxXDyQgsZFQ0BbfwLaBtPoSX9at22MS0DF8rHngKkslu1/I/1k22sStd54rUPN4+wJE2FNa0Y6\n7FYi9rtDaJwUdBp46wj95RH+w2ucbPcwLh2b4xQhX5UGwHIxnUOfrufC76eRzFZ0vq54tKjHz53e\nxu/VWlaQ/GZAWbOelH8bhneFtk/AuxOhZy2r+nw2Ce4bBGYLePqqiWjpzJI5F3F2qWjsjVo3ApW6\nrG395I0MuseZqS+6V/A0vfM/b3q7i0jB1wZ3V8i5gjiJfxt/dr+5i4Z3y1v5jbp6021sCLmpJkJa\nGxnY5RQLljrRoqUSN/eqXrHzccU80/s4D70USN+xNRMavwRJklgwN58P5tppSkMfcmLuEt/LRqsj\ncHFVkp1uIyjs6tuWx6o3k5i1qnmFd43QNh78vSWVnhOvX0TfVGTltRF/8+LqFlwSi7BYJHLSzHgF\nVHwBkySJ2J1pbFlwglNbL9J8XBumxj6GMUy8tuRnlbD27Tg2f5rAiAfVbD7uia//1Z0+kiRVoYp8\n89BvdJzcnM7PdsK/jV+VssXZVDRcb0fUGa91uCJys6wUFUj4Bf6zH5WS/GKiX93Myv0Ct9EtgFKp\npOFP84gd/DxN9i1GqbVPiLb8QnKWrSV/7e9IBUUoDXp8h4Zy1y+Po9aXTZohw1uzafhHuLR0cLa/\nzaBU/PuMV73Wznv1rYE15+l7YWxvmPoRPP+F3Zh9eljtWDyef7zs/38F+HH+jImCXCtdB1W8UOUN\nVwCr2Uq7Hk5VQqShERrUIg/6NcLd7crGq1qnLitXLUCDDh78sfQsU5e3p2doPLOmmTgTY8PFRUF4\nIwUNGilxaXQe32Adn89MZPTM+gyaJJ88erORnycRvdvMhrVFLF9o90i2aKNm1S5vdDoFpusk4dqN\nV8cikRnJxSiVCoy+2grs/Mbdvdn1dSI9J4Zf17HA/7N33uFRVVsb/82ZyaT3AoQuSBMB6U1AqoIi\nWEBUlOJVL14LgnLt7VPsDcHescAFVFAEpIMgCgpK7zUQ0ntmMuX7YwKZTPYKOZAK532ePEnW7Nln\nn7bPOnu9612wYPox/liYii3Xic3tYtFnyXzz8nGy0x189Fdrajf0x2F3snH2IZa9uYO8jAL63t+C\nMZ9050Ro6c+VxTMOMvvJnXQaXoeX/+rFkAZlKwv+zRsnee/R48S0OEa9jnHU61iLep3isASY6XpX\naywNqw+FpLxRHeafUqE3618vZLWB8qEZ6KcBqPervPRQpf21CWoMO3aYaNAigDxT8VippBIgqQFI\nx2Ezlynt7fhLaZdUC/6hpFg3FAmO+6ILvxf7f82svXQeEkFD6wll+16oRaCl8zLdPkFpX2nto7Q3\noVCPqAEEPzmE3VfchX9sKOakRDQ/jVb9G9N+ZmdC4jxv0J6wlY8YeTvImXED+2duoLFPKYeNhSUs\nfZGSKag6hKnPY4pAn7gNdZWVAzRS2qXrTTqe3tdVtmangHTSiVCGPKVws4gyiOEXg94ysBItQc2w\nUdISLo6B347BULWohhrS+CV7S8HugJhg+N8UcDjgpXnQYxJ07ghv/bckJ9ckFE1wC9n6Ut13f+H4\nHK4vV81b+HkqA0ZF4mctGpQqxGv1g/ikRFqoEs+l8yI8Ld0+QalAq0djM99uKjaOU4gmhTB/O+G2\nk8WSdLypQB9+lsq42wPpzjoCxwUwclyAh7t43M3+3S727Xaxe3cKv/3q4IEnAhg5Ph8K73mJBnBQ\nuBc30kFpv0zxrHC73azeXZtd69PZvT6d3b+lc+jvojm2dn0znyyNo3Ezz/1tQy7AcqbiAlp4HodT\ngjiJZzX5AOqXcu/zO33SHwx94pISz6GLe8bw/bPbALnAjjSeFKLJzSzgfy8fQzOb+PqNFJZ/dJj6\nHWIYMXMQB9YlMmXUQVoNrs+aGT8Q1yqK3k/3pPngxmiaiRzkwikRhbkI857dydTFl9CsQwhgEykS\np2gDbrebH5/fxvovsnj67yFsTavLiT+Oseu3Y6yato3sk3kkE0OwsE8SLtuqFu79rvVwXf1UBqq9\n82qganFoRz4NW56bTlxNQHSjEPb/cY5LLuWEutd3wuRvJqRJLbq21Cf7ZA0POmOG8nkB14W19Nqm\nHmw+BEPV1OJKg8UCj43w/Hz0J1x2I7w1Bfp0rtpxgUfXc+Hnqbz4XZGTs3F5FmlNs4hsUPwhrvlp\nOCooDWLuAoiOKj06UK9DLEc2naRx95KV5k4cc7J5g50Zc4s7fSaTidrxJmrHa3TvI1fMKk9kZzr5\nZ0M+f/+Wz9/r8/hnQz7W0KM06xZBs64RuN2QdDCPjkPjaN8yh/EPh5dbgYHQSAtZaWVfpHK5XCTu\nzeGSviWpExarGfc55Hj+/Poe2lxViz3rUjixJ5u7fr6Kum09L/INu9bi2OYUUg9mMW7xcGpfql5g\nkcftJjPFwUVtynY+3W438x7dwub5x3h4VX8i6gSSQj3qdC7i0rqcLjSzVsGZPlULw3k1UCoO7sin\nYUv1W+D5hLimIWSeyKO63BLxV5+qmqPPeb0QYLoAOa+dG8HU5VU9iuK443oYMQhGToYZs2DmVLBW\noaLeX6uyCQk306xd0Xz1ybMnGD6/JF9fs2gUqHMJzwmzvoOJj8HiOZDmL8tENOxWi98/2al0Xr+f\nmcdVNwQQGFQ1Wd8bZh1m29JEEn47xr5tdtxuCIvUGDomjKc/rk1GnebY8528euNfJB/Oo/uI2nS9\noTa3DCrfUmwhkRZSEsoecfzx1b10uFadBFZWZKY5CAzWiq2YZybZ+GXaPp7d2Je4xp6wiHcCr6aZ\nGD2zL3B2LxTZaQ6CQs1Y/M4sK+Jyufl24ib2rEni4VX9CFWUVgfQzFUoUVJJqB5P6lLQScjO/luR\n1QdyWFwqIlBe2f3lpX6gV7WgvCAdn8M78rj2zugyHydVZibI2Zm+ov2nIGV+Ssf5dNjdB1J2v+94\nYuu7cKTlECGUMpRUFCSaRH2rer+kfqTj0JyS0iggZ/xm0Rw71hLjkvrPDSs9POWLy1mttEt0DglS\ne0kVId5LWd+mOTC7HWJbq01KCxcgLe7ozdvRqyogqRYo3ldaBcLxk6CU2ZVuTWm/pECK3uILORBm\ngp9fg9lLoeMIeO4uuLaXurlJKFKg199tcETNS5j/qZNBY+IoMBXtyPB76xAW5sZXKsDPDwpOou8c\nC8fHVCjM8fUvMGkGLHkVD4lp53rybfDbFlj1BwzsDt0ug9wWQRAJW9MSaO4VHo4gHbfbzXef2Zj2\nid9pBRWJYiPNAdJcmyzcc75zxd9rd5Bv8yO8/UV06GgiNzWf7fMPcMRyEX/W6UEE6aSczOHvZal0\nu+0iBjzRhsi6QaQIc6f0TJDu39OfR5o5tKPo4j4TVXDDnASeWVd08cX4zLX+Zgdh+Yk4A9TPkBSi\neWz4H+zdmEnrPpG0GxhNuwHR/PjhTrreVO+04wrFCy94Q3puSdhOK06cTCMgdmexOVdFtXA53Uy/\neydJ23IZufwOTkYECKVeiiAVDbIK9KxtrdUqPHr9m8pAtXdeDVQtDu7Ip9EFoDSgVaWYpgFd0C7A\nlVeLRaoQXz0woj8M7Qk3PwU/r4f3plTu9jOzYN38dCa82qCYvff1UUr2vNlPw1E+aRMAzFwCD78H\nP70EqVnwzGewYits3Aatm0LdWrDuL1j8kdeXTJ4QsHfS2Kbf3bhd0Klr+cxHbrcbu82TFZ/rsON0\nuHA53LicbpwONy6Hi3SHu9Dm+azj6GY4HS4cDhMFeQ4WTFxDn4fbc9WLRfKFe1YnEhjuR+dRjYis\nWzH0hbBoP3IyynaSdq5NJrZhEBaLfNzqNg9i65oM+g4o3RG777NLcLtg8y8p/PDaITJTCnh1T8VJ\nRWWfzCM0rvToptPhYvrtm0lNsDBqyS1YQy6cokESDOfVgIi8XBfJxwuIv+j8d14N1BxciLSBmoCA\nAJj3Etz4KPyyAQZ0qbxtz1kI7fqEEhlXtmIqmsVEQTk5r18sgjEvQr1Y6HUftGoIfdrBlDugR3sI\nC4G8fKjXB46eAFp4vlevRTBHd+VSv0XRit43nzkYNcZcLkLxGWkuJo9OZe3ifPysJrD8gGYxYbZo\nmMwmzBYTmsUEFstpm2bRCu0aJouGZtboNuFSevzHo5Fmzylg3nN/0qJ/HXrd2YwtC47RrPe5heol\nRMT6kZtZNqLqnKd2ctcn6uTfU2jeJYxtq9PpO6Ao5G/Ld5FQqFBxItNNTrqDwBAL7a+MoeeI2rjd\nbpKyAwkMrbgiPfYcB5nHczm8MYkGHUvydR12F2+O2oQ918nIhaPxC7wwCgadCReM8+qPTaztbECN\ng7sKqNfUv9wI+AYMlAfOtPKa5R9KqK1kSDIrLJDQzJLhPnc4mFTU4otRC/dL9gbAYXlcFwpGDYAV\nf1au8/rpbLjy0bJrnJr9tHJzXn/bDg+OgCsug56XQvgpSU0vUYTAALjxSvhyPrTp47G16hHO5qWp\np53XvDw33892snrLuSfI7vqngAnDk7ni6kBmfFcXPz8Tv+R05/BfqbjdcNo3NpnIJuS0TqjJZDr9\nmctUuEJpgiN/JJKfaWfb9/sZ9mgLIuoEERrrz7vDV3HDK+0rpCpTeKyFvOwz09WyU+047C5iG5Ze\niqxdv2imz9kJhXxVt9vNpEF7SDpWQFi0GWtYALWbBhLfrGgl2WQyKR1XJ2ZdlEMb/soQ/hHq03yg\ni94TL+XjYUuo2y6aK59qT1anZjRnN/Y8J69dvxG/AI2Hvu/ENn/DcT2FC8Z5BTX/Q6o6InFAJcgV\ns8oxNlUOkDnBJe17dzho0DKoWOWUU5BkRSTOz16a6hilzPWUxi9xOiXOjwS90l0Sp1a6HiQ+miTp\nJXGTJGmwYHLxo6AEn0x6cTt0WH1ebmvwpdIucVUljmwjXzmvQuwVKpw1Z7fS7s3vy9ccON0auQSJ\nvLks/5LXpz92shQc34jUPNwKPqYpFY+j6osEwZ6Bx4H1hTQFSFNMfknT4VTIy4Off4cwX99GijhK\nz1av27lYBnaU0P6YYFcXtcNshd+2UZJbK+QemtTqa7Kkl8+lvPcg7NoPE4Y0I1FRdlg5J/n5URAO\nbkUtEJO0XfWlzIxHhPY+fNoxPeH2l2DuawcwmUzExbjYdCydRoVV6Fa/n0+nVtA6Px/2Fn3vcFP1\nXCvNhctnp/DIPfk880YAw27SWL00jfWrnDgDl3FplwBOKcmdegE86Y4t+t996jM3FgrAXdTObDFx\n3ZsRNDJv5OiBAtZ9kUX6kWxaJ68kKtYszg3SHCzNSae4uY4oF1m5B07/HylccK9O3s2gyS1LcIOb\n+BDE4xqAIyuPI3hO+tqvj5CRbeblXf3QzKZic7b3iKU5WC8PVNrfBEtDGt3TkDHj+7H1kz/54Lrl\nxLfZQs+HO7HimfWE1I7i2s+vZL+fudxya6TnkCSFOIjF9GKD8rOqwgXlvBrQh/077BeETJaBmoUL\nkTZw0ydQPwJm/VNSV1WcxaWIq8/zTzu1aCY5wdKiWikSkomVqDr3+Ty4ZSglsrWf6beOp5apy0xX\nlNpAaejS0rPiuXm9jcu6B/C/DzIZNqboIH42D8Zcd/b9Oxxupj2azPI5+cz6JZjW7czce1suo8ZZ\neeR5f5I1tWNyRHBYrD5Op9vtZvPyDF55+yR/rbNx3bgQFu2tS1RsxSTzWCzaGYneLpeLI39nMP4T\nfVptuZkFfP3wNh6Y0xnNXD0ii5YAP9pN6ELr8R3Y9/Fa5t2+iKYDG3LNu/0vCPUAvTCcVwMiDuyw\n0/2GqimVWhUwaWC3u7AqhMUNVB+ciTaQQoyyiEYuQeKKQ3VH+/owqBFcoyokIEVLpRVfiT0lFWiS\nfJNSihX1Gid/Vp5wOuHzubDgI0qs2bsc8kVS3glbZYHJBGMGwQ+fZXJxaytZ6S7iG/qxbZON959L\nZf8BGDbg7Pt/5ObjLPlfNv0GW/joLRtmM+z4x8kPs+wsnGfCZj6JZjahmcFc+NtiMZFldhTavT6z\nmLCanadtORlOFn+cCCYYd18gr34TQ2BQ1c+TSz48Rsu+ctEKCd89t4s2A+No1k0KN1QdLP4Wukxo\nR5cJ7ap6KNUaF4zzqlcSS5KskmgG5SVxJdMPyoeWINMkStr377AzsmWw8jMpXOoC5XgAACAASURB\nVHtQqIJyEvUEEyeIfUgyMNJxOBUK8oUUnh7JrBK2mGgXmw9EUKd5yRiyRBvoIoRSpIoqkrSWpA8o\ntZdkbzLJxIaVNJ/vSTSPlkIZws2oJ85aSq0mueKaFEr0XdU5BSks5i23k685yHcfI4F45XiiSVFu\nN4J05XUVFKCWvfEXpJ3EW05y8qRVSKl/RT9Xtoef/4ZreivaK2gGpUIK00tOrTROCf6eF0GH2aOS\ncBpSOF7S+ymD852aBilpcEkwLPOZA5oObS7OC5pFI9NqIT2qJIcw8oD6ehCPgw65rdG94OX/ZNHO\nmsWQZjClVxqbd8GUsXDHKx5urC+kOcA3TH7df2LpMTQCp9ONy+mRVsoz5+EM1oip74e/Mw9nocqA\n0wkuJzidbmrn7sfl8lRPczqLfmy2wr8dYPGDj5+HKy6HQ9HhqE6O3mes9CLpvV8m3Kefcapn19JP\nE3h85eXK0L7qmRwRrbF5USKrPjvMy1v7+oxffcFVRjGIc92mRNeLEKgWkl167lZHXDDOqwF9KChw\nc3RfAfWaXTi0gdh4C0n7spTOq4HqA02DApuTlKN5aIqHlkN8CKkfrjbBV7FKi7Seio7ExylC+KUg\n3w4BZ6lwM7A1vPrz2X23KnBFF3j9U3j4XxW7ndhIaNUIVm8BfBz7PpPk7HOzn4brHCounS3qxkC7\n5vDW1xDgD/8dC3Ne9fwt6u+WEe17lVyCb3d5PtMfSSQzzckTLwcRGV3SAaxjU79VBOVUwQHSgQOb\nMwiLsWINKLsbc9XNYXzw1A6GP96ciFoXzrPtfIThvBpQ4ug+O3H1LPgHVj9x4opCXD0/UvZnV/Uw\nzh3nOR9U0zSSDuby8T3/KFdwTbjxoyShUWUDsAorfGZpIanQX+7VCRas8LJLHMpCu6PQFzBr0L0F\njOsNF5cssKQeowUCalCi8ZMToN9YqFsbbrmmYrd1XW+Ytwouuq/s3zm4NoEmU6om7N26CQzpCf8Z\nBf7lLNeZm+1k08ocNizJZsOSbDJSnJhMYA3QCAouPRu/puGrR3Zz++stytzebnfhcLix57oYeI86\nQlhW6FUbMFD+OO+cVykzUILeilnn6wXrG5I5keAiItYi7q9EP5DC6FKVlSxCdNmPC/10YJPSLtEb\n/BXXSf2GJg79mab8znM8qeyns7C/UhhHtV2Qw0T7BNqDFHLLNoVhx79EhRapnw5sVNolmoF0HiW7\nRDOQ1AlqCTFk7wpbDs1F3aaBPP1Da+VxkOaARhxU2iNsaUp7sKQx6VW4Z9JYL7tUydfHnm+HWSth\n8leQkuXJh+rUDMYNhNaNEOkH+U7UoX2JNiC9d0rjlGgDUj+l0Cc0YNmH0GM0RAbD4F7I9AB10TkZ\nPk+t65pB39nwlms/mlY8+ebnGYfoMKF4Mo8918HJHan0DbATqZ421JCOj+QTKvYrIweiImGSghN8\nor464lNa1UKn082ev3L4Y0kmG5dksGtTDq07B9BtYDAvfB1PcKjGmO6HuO2hKNID1Hqsaf5qWoK0\nvxKNQaLOSVn2Zam6aHHacO3aQ0qik5QTBaQnO8lIdpKSWMCBjZnMe2QLKVn+uJyFb+wm02lC/KkX\nW5PJhNvtBjcc2JbHf+e0JcJScrVZqmSmh2qnF/IxU49FquwlQVLhuf34bKV9fp2BuvqvSpx3zquB\n8sGlXYM4tNNGynE70XUujGoe9Zv4kfaTztKiBiodNb3CVoAVbh8It/fz/G+3w3fr4amZkJgOuKBN\nQxjbFzqpJLlqCDQNVn0K3W6FsGDoqT+vpkxoHg8RQbD393SadS3+0rZu1nE6TCjefsv8wzTuEkOd\nGL31f88eGTkwfjo0iIXJD5xbXzmZDtb+L4ktS5L4c1kmkbX86DQwjFFT6tC2Vyi2E+ms+C6Lpf/L\nIjfbSXiUxogJkTpFA6sHstOdzHjsJGGRGmGRZsKiLMTEWwiP1qj/WDRXDI8gvVYzJXUg3oeM/PET\nR4mOt9Kmz4WThHw+w3BeDSgREKTRe1gYS79JYeSDZYxt1nA0bGYl40RmVQ/DwBlwplK+ZpzVsha3\nBKsVRvb2/AC4cuHHTfD6AjiS7HHUW9aD9BxwufTxbKsaVius+syzAjv1ZmhZ3zN+sxksGljMoGUX\n/q15Erw0PG307Od1nWH93MRizmuB3YVJKymD9PvM/XS+tQm6Mq3OAW43jJsOJ9Jg8Wa45RYPX7pk\nOzfpaW5ycyA3x01eruf31pwU8nNc2HKd5Oc4WfddMn8uKYoWvPd7K9xuWPNdGn+vzqJRYzcDRoTy\n+7Jc3n86iYffro25mshB6UFmuoPo2hZemqMSTy5CbhncmKN78/nh3ZN8tLl1eQ3PQBWjxjqvUshB\nWuKXoLcYgZwlXT6HUsp41Nt/eRRfuGp0BG9MOql0XqXw9zrUuooSGguhXKmogSTmnyuIVDb1Vvv2\nwkEalTSGQbp9lzLTXqJPSEUNJOdJEoGWwjtSCFw6DkHUUhYp8F2FOIWm7FPapfFLdAJp/ImCyoR0\nnUv9+953Jlzivejpv+T5ksTSLU6BHlBe9GfplhOGr2XB0GaeH/A4rMu2Q/IJ6P4E/HZ/GfsP0zke\nSRVBCouXcSkvBFg2FZqNhk5NINDqceicbs++ueye/11eP6WurCs+G98NPpp3nLtfboDJZOL4vlym\njtqO2+mmFdtPt0tPKuDg2uO88m0tWFGyHwDhkpUhPXIKWQBvzoF9SdCtFdw1DPrerfHh4jjadPaE\n09NTnSyYmcPXNxeQeDCPoDAL/kEaAcFmAoItRATvJSBIIzDYRFCwRutLTHTsFMGRA072/GNn6afH\nqF3fjxtuDyU708Vn7xYw7bHDNOscxt3vXUKzq2JIQM4wlxQZ4jmutEuUKIvwTFYVuQGIQE3XORVK\nX/ZNGh37BJ7+X6L97BOeFd5z54yJB7n14Vjq1YM8YZx6Mu310g2luUc6NhKk4j3SeJoIzz8Wqs2O\n8TXnpb/GOq8GKh7teweTkVTA/q25XNS65khoGDBwPkHTYEBrGBAPfaZDZr6iylY1R0wEbH4JBr8E\nSx6BeG95Tb2cV8Vz2u2Gh9bCgb9zuKhtCHWaBDF1aTuSDhcnBC+flUq3qyMICqmch/SqLfDgu1Av\nFtZtg8+XQP1mZg7vdZCX42bOR1ms+imP3kMCufOt5rTuHVmCt+vttB3eW8DS77LJSHHRoU8wk9+q\nTVikmV9/zuaJ2xLYvjGfvmPr8vqGTtS+SKo6UTPw68IcHnpbEh8uO2z5Lv5Zl0Pv4YaKzPkEw3k1\nIMJsNtH/lhh+mZnCXS8azqsBA1WNoa1h+q/wSL+qHol+NIiBuRPhyhdh5ZMQpc7JPCuYTNDjuhh+\nnZfERW09HQeHWQhuHQJeK22/zExmzNN1y2/DpeBkGgx82PP3A9dDz9YQGw7vHAlm2pPpBASZuPFf\nITw2LYqIKDP7hPq8h/cWsOz7HNKTndRv4sfwsWFExphJTNL44eN05ryXTmSsGUyw8HBTUgPrVcr+\nVTQy053UbXzu+Rb+ARrvrbmYiVfuIzvDybCJDcthdAaqGjXWeW0qhCn3CiEQvdAr/i+FBfTSGCTo\npRPoHb/Uz1WjI5l81R7ufqFOsRUBSfxfCvvGCCL2EqTwSLIQdpdoDBKkUJmTPcrwkRSi1ksbiBaW\nmaTjNozvlHYpVFZAPVxoJcJRTQR6gBRK1EsbkGgbUnasFHqU4Ht+NVz4Y1fSKqRwZKBNHS40S7eK\nNDtKYvuSXQqv623vD3f3gSunwyODvex6xylBElKR6ATSdvfL/TcHPrkG+j4B6+6GICtyFr/O/bpv\n0FHumQifTzpUzJ4c5llx27/HSdLBXEb1P4FFUMEodbuSXXHcnE4Y9ga0vBTy8yGnoZlnF7tYt9bN\nVTfaeeubcNp09MNkMhV24Cw25x3cU8Av3+eTnuIivmkAw8aEEhljxu128/cGGy8/mMzy+fl0GR7H\ng7Pbc3GncB7t8ztJgQ1FdQIJsgKK+p6W5gwpBC7NeZKyiBknWzbkcnSvjdcnJuDnB35+JsKsLvys\n4Gc14Wf1aOP6+cNe6xEsVg0/fzN1LwmlVmMPz8V7DmvQKpjpa1swadAekk7CHS80KDz24HK5Wf5N\nMu1uCi8zN1iic0nqNtK8KYX7mwnPv8tZo7RHONXnJChHeC4KogLSOKsjaqzzaqBycFHrQMKiLbxx\n72EObCsKweVxSNlemsDMYqF1NUyCWGnykTzC65ck85mE/uUqLn8r7XnJLub2ebeEXRPGky1IermF\ngvD2AwkE1o1Esxa/9fyEyXCN4PTns0dpT8hcg8lSgzJ6DOhCkBUKnDUvccsbbWp7NGun/wYP9Sq/\nfrt1hpQU2L0Xmvm822Vlupnxio1ho/ywWCo+eenZzz2/h1yr8etqN9/PcTL+bjMffqlhC1eHr70d\n1oZNLVw/NoioGDM2/MnLdTH340xmzcjg2EEHdzwSyYg3OhIWXTTfNm4bxqN9fi9Fpk+f3oAmOFbS\nXOjymfOyUh2Mfrwe3UboX+lOSnBy8aVWulwRiN3uosAG/vZ87HaPOoc9H7Iz3TgK4Ig9E0eBC6fd\nzbtj/+S6x5tz1f0lF1dqN7AyfU1zJg/Zx6t37OPB95tgtpj45PEjfD31GC+3iKNJB4NaUFNgOK8G\nzohBo6M5uD2Pd1Y2P22TVl5PCgXS9daUl95IX+qzlPErR5awS6sH0gqutPK6i+ZKe3mtvO4a9hjt\n3r6NoAbFy5dKeqh6V14/2NCWhHd/Un5m4PzAJXVg8Q646pKqHol+/LAdnvgFnuoH15dz4remwdVX\nwXc/QPt2EBoKCcdh5oJcVi4uoFtvC/83reLJwos2wEc/wcrNftSqbSIl2c0nH7g4kQD5eWDy8o/2\n73Z4HNZUFw0vtnLDuOJVsA7uKeDLd7OY/0UWbbsFcO//RfHRi2mMnRzBPp+Fgn+95RHsl1ZepaRK\nCdKcLa06+i5cOBwuJvbZTn6BhStu0cddXf9LPqMfiKDX4KJswWhhDl6J50ZwuVzs/zONQ1syeGnI\netq0h5serE1YZJGbExFj4bVlrXjq+l08df0uul0TyYpvk7nipmj+WZ5iOK81COed81peRQSkcL/k\nJEnbNesUONZLM9BLJ9ArIZRLID1H1eGL1n9x9zvW0xW3pHCw5OSt5nKlvT/LlHZpBdeNSblv6UKm\nqJRBGiiMX8poXSOMXy89IIB8apFIqM911J11yvbdbWr7P/5tlPZsQrBjLSEkLu2XBEmIXH5wqVd7\npOOzkQ5Ku/SA9b2/TMiSWKGCTICkKmDRK4ApzZoS/UBSZJLC+mdg/DzQA55YBFeden+RpjypGIGk\nHiBtV7JLiVaKaHC2HW5aBP5m+H08BFgoKq4gTUnSeRFUICz/g73rYfVCeCsHUnOhQyMYN6CA/EZg\n2+fgocFeYV1p5Vpxfrfuh0svLqmC4AZwUmzNcfshj5bvE2MLaFwXGsdDr3gID4WZTzsxhR3H3wpp\nGdC0ETw0BKKj4FidAMCO0+lm2U8FfD49n61/Obh1nJkVf/jRsLEbyODLl+zEk0ACagnDOOFFWAoJ\nRwvRHUnRRCqo4nuPmi3wzurmTLxiBxZ7NteMLU75kpxjM072b83jubcDiykYnGmOyUguIDDUyr8+\n6cS6bw4z4+Y/SNhv5+mvLirWPjDEzPMLWvDSmL28++Ah3lh1CX+vzmT9omRueKgkH1a1Xb1+hvRc\nl469VOynwVah0oc0xwjvasm91BHD8lJNqgzUnJEaqDJEx/tzcccQ1s9Ppc/I2KoejgEDFzRa14GT\nNaiK8czt8MpGeHUgDFAHbMoNThe0rgedG8OoztAgGgiHuwYoGktOvEJirNcE+GW6R4e2BHxeQlIy\nYPcR2J8KB47Bhq3w7RI4kAAJSRAbAw3rQrOLIDAAFi6HxvXB1dLJz/PsfPlePnF1NG6/J4CPfwgl\nKkBfVaXqBE3TmL6iCff234/D7mb4XTFn/hKelwSLTvpT2tE8giI9ix61moQQ18DCitlpRNU6wn2v\n1ycz1cGhnfns3+ng8M48cjOdmDT4T7d/qNXQn8sGV1AVDQMVAsN5NVAm9L0llpXfJhnOqwED1QCh\n/nA0HeoJlT2rA9Lz4cYFUCsYNt0KlkrIBdFMMHfCmdvp7lcTHFcFosOhWzh0UzjHBQ44Chw4DPsP\nwYEj8PNyz+/DJzO5fIAfH84LpU2H8+fRrGka05ZexANXHqDA7mbEvaU/Q5JOOAgK1s9LTk3II7SQ\nA3x0WybtLg+jWfsgPnk6gSUzUymwuWjQIoD6LYJp0CKQq8bHcdcrDYlvEoCfVatRyUoGDOdVXP6X\nwqPlRUvQi/JSLdCLUwLRGSdsRMVZTv8vZVVuR63hcw8zlPZEgSO7i2ZKu4ZTGW6SMswlNOaA0r6R\njkq7tL+SCPRxgXfmRwFhZBLuk9wg9e9vU4e6nf7q66ExB3GQXkyYHeTiEVKIUSpeUEeIT0k0g90C\nh1jqXxY0L55Na8FBBOnKB45EVbDmK81yMQIpy17qR5oapPb6BEGKtb+1Hby1Cl4ZQpmLBZwRaiEP\n/f0XLmCN+hieuAp6XVR683JTS5Cgt1iDBGk8UnK/IsrtBzS2QOMG0Ldn8c8cwS48B9sGXoX+bP7F\nVyA1lxurzUZj/4PKzUpOmFT4RQrHS3OSZJfmPPA4sG8uasw1dbez75887n89HmuI+hn789dZ9Bmg\nEegzz6voCgUFbvIPHuHY7jw2z07k+N48Xum7jP1bshn3XH2uvqsWLXtEENfAn6jaHnUH1bPUgUwx\nU81JNp1qKRLNrg3/KO0tDqgTokV6gM4CI3r2tbrigndeDZQNy79J5p43G1X1MAwYMADc1Abe+62q\nR1E60vPL4LgauGDw2Qsn6T8igku6BnFvv/3Ur+vgmbdDqFOvuBuy6hcHb35aJNPldrtJSnSzfZeN\nA7udHNjtYP8uBwd2Ozh60ElUfDp1mwVSr3kQLW4JP/13vfpgMplo2cVYUT0fYTivBs6IQztySU8q\n4NLLpSUMAwYMVCZOhbAdDqgE5SfdmL8dLq1d1aMwUF2wb2sevy7I5OMNnojawFFRHF17kPtvzcRs\nNvHYKyG0bu8HQOJxF1++b2ffbhf7d7vYv9uJn9VE42YWGjWzcFFzC9ePCeSi5hYaNrFwMKCFcpum\ncgtJGKiOuGCcV73hfilr3iL0U15hfb1qAHqzA2VVBHUM04Y/S75JoNfIWjjMAadbSaGpsXymtHc9\nvllpf6zOE6WO1xduNGyK0IaUpS4dz8OCVJYUEpOy9VfQR2mXwkFRpNKBTcT5pIFK4ZoVYT2Vdkmq\nzF4ngKQ/j/Jzn9eK2dOS1Tyz1JgTJWx5R9M4VE+dprqaAvV2hTDaUbu62k+IVR2r3yco3C/3uR+T\njzsY2+eoUk9Xunf9vMP3Jlg5u/BvvUUHhIRfMbtfmnqkZ6uUxe9zq3evBV//Cre1K1v7M9qlBCYp\nQqpm3oAFZvwG79/gsy29T5uKfjrpKU7hBgQVJYeQ5+Mb7j8TgveoKUK2i9XtpWIB0rNIkg2UQ8j6\nFETaoZ7jj7pq89ytx3h3YZ1i82vnnlZmr4zi0D4HD9+RRb1GZuLraxw95CLfptFroIUx95q5qJmZ\nyChNSU1yIssMSlQmSS1B9VwB9fGUnpfxHFfafWlcp3BJkpo+JaqaZQp2nfeK9Lzpz1KxAFR1wwXj\nvBo4O7jdblZ9c5KHv25Z1UMxUAYENojl8r/fKGH/ef51yvZ9h35ewra8z1RGrxyhbC9JgEmyPW8k\nTlTam9dSP+im8l+lXX45KTmFxQvEsMgjRbyzPjcqm9QoPNARxi4sxXmtQmTkQ0N1tVMDFxim3HKS\nW+8PJy5e7W40bGIhN9vN7m0FJBzWWLUzkthaNbQCh4FKg3GFGCgVe//Mxu1206yjwRsycP7AV6+z\nJiI+FNLywaGveF2FY/5WuFT9LmPgAsOcb+zk5jgZPlamnE0Ykc6+XQ56D/Jn5pIIw3E1UCacdyuv\nUthXCqVIYXeJHqCXfiD1ozdbsaqw8ptE+oyqdboO9ClIYRMpVCOFQo/UUYfvpTC9qbCmvS+kkI8U\n+pLCJmnS+HVCyuzNJ4BEauHwidE2FVQLttNKaZeKGszlBqX9naF3lLmfvRwWVzr11k3vXks9TqkS\nm3y+1HZV6DQoV9DF9KYBuLz+l8L3Em1AogdI0Cv+L9kV251yCbSeAXVDINQCYVYIt0KkP0SFQ1QA\nxARAXDDEBXl+AvQWKZCmKiGMPuMPeP92wHflVTrO0vGUnk5S+ww8++D7uXqKkekcEoTjINEDsvzV\nL/yhNvUzShKUDz5Z9HbicsGB3R5bsEVNvUmuo5aNkObmLrm/K+25QYFKeyJqnoSKSpaelwsuh/J5\nYXHl898JeSz+voDP5wfR90o/wC7ODRJdIVmQydBLqZN8B5VSgDQ/dklVR5RMy4WNSkok0j0qXfvS\nvbtRbbZdUzP8j9Jw3jmvBsoPbreb1bOS+L/F6mpOBgzUVJiqYZLT2eD6xrDqOIT5wZ0t4WQeJOVD\nig2Sgb3psNHmCeNn2CHLDk6LZ/+9V59NJnB7vc+ZALMGoYEQFgLhwRAVApEhEB3q+allgrhQiAsD\nq9eTJCMXGpZNi96ATsxaUFjVq4Zg5Lggdu/J5+l/p/D0u8WdzBWLHMz6rIDml2hMm2rDbIbeA/yq\naKQGahoM59VAqfDzN5GTrleQ0oABA5WFt7tDnx+hb13o670wLpVplwoJ+Syq5+ZDUhacLIDEDEjJ\nhJRs2HUMUrMhIw2y8iHL5lkRBDiWDv/qfY47dA6oSY7d2eCDr6CROgey2mLS1EgevjWJD6amc+cj\nRau/TZprRESa+OXPkBKRPQMGzoTzznnVm/Uvhb/1Qi+dQNqu1E9FFylQ0htMMOKBWvzw2kE6di+e\n8pqLOqT0D5cq7VdmrFTapRrOUrg8jyB2KYTvJfWDy1mttEuhqWTUS0ZSuFwKNTVnt9K+nxAO0JgU\nnxDeFaxQtlftK8BBGivtTQTxf6nohiqU6MAihuik4/zmB+pEqxF3lkwIk7YLcuaztF3V8c8KUtft\n9nd4hVndFIXaJHqAFNKTUF7veEL4uLQw+o8DofsCWD7EQxMAZOdV6t9n/EEWaBgJDdWHE9Ul0udx\nmHw16mwK6TiXlwqBg+Ln9RQkuoLUjyqiqiEWccjzV1+zQU41fcXurw7ZWuLV7f0LQ8UFBWArAKsf\nnrEL16dEsfEV/T8TzE71syjCrFY5kLL4m6ce4oe3of9w2BybwcjC3NHDF8Vi9c9l2w4LzVoVnQxp\nbpDmAOmZKakxSIghWWlXJalGHhCoSUuEziVlDimpUXrBlJROpOIFPdRmvapG1REGM9pAqbhqbAxb\nVmVxbJ/eJ7kBA9UX50PCljdCrPBFHxiyuGgVtCoQHQZ71MpFlYLzef1u+mcwWF3AsEZgyn3w0ltF\n/5tMJrr3s7JuufrF2oCB0nDerbwaKF8EBpu5+l+xzHkrkfvfbljVwzFgoNqg76s+jqLkEEtBGcku\nOZ9S/14rjan5cOUiWDJYaFvBaBgLW49Cc0NtoNwx50dY8g0sVQeTqjXe+RC+nQdrfy5u79HPysL/\n5TPmP+pVWwMGJJx3zmttIVSQLIQcpKx/VUZ7aZBCLE6zenleUiGoKkhhZSt2Rt4bxS2td3LXM7GE\nRXouGSlc3kTImpdChn1YqbT/xWWljtcX0nikENpGOirtJ4V4jRTOvkwQ5paLQTjxx0agTwbrCq5Q\ntpcEuKWQ2PcMU9olgfKl9C9hS+MYiQKtQtJQnXDn60q71M8RuzoFPMGq9nqkMJfKLilV4J2dbYI3\nv4LvlyGHsyUULu8N6wEPXOtlPyy0l+qOS5nDUpi7jKHEUYvh/b1wl1SaVUo01vs0UNAPmtaH3Wqd\ndv39SzQMKdQagCeW6DsuqeiD+tKUoU7uxxqlnjuluV+Cw6wOhPpbXGRmgkmDoDA8158FbML4pXul\nqUQpEoJqNuE6iclUX7g2/6I5ODnZRUyMZ3/umQTJqbBmPmgap1/asgilbd9AnrjnCOnOEMxmz40l\nKcdIc6o0R0qa1FI/dQ8IF4rq/pXuXWku0Rull+YAKaqxQ7DfpTZLz7OahPPOeTVQ/oiN96PnNWH8\n8EEKo6fonfENGKh+MJnggds9P+LEL71fVnOVma8GQJc50O1SaFPJK6CtGsB0QZ7HwNnjpWlwa6EK\n3okkGHOvRzUCoEULmPxgUdvkEw4CgiAkrHIf79u3u3joQThxAgIDYOVaFzcMh66tYcbL6u8U2N0U\n2N2kJDrFIgYGDKhgXC0GyoRRD8Yxach+bpoYi5/VoEobMFBdoWnw09XQfzb89m8IUi9kVQh6tYZP\nFsCQV2HufRBQids+X7F5K8z9Cbav9fz/01eelUx7Yc5sRDi8/Q4sWOD5f9fBAwSHmYiMLf54D/DO\n8HJDfj5kZ7nJz4K8PAgNhXpeealOi3pF2ax4qXO54cgxuKQ1PD/VxI3XuenQFh5/EsZcpd6v1CQn\ndw48zn3PRxmOqwHduOCvmCCdGZh6IdEJpDCCFNKQaAZ6hZglSOoH/oXxi9ZtNRq38GPlrGSuHh0u\nhqakML2U+XzJEXUoq359tQh0LoHsolkJ+2X8pWwvCVhLkMIp0v5KdIUvuE1pz2YhB2hMsM+4pCIF\nEj1AKoJwOWuUdincr0IANgaxWPnZEUHxfToTlPZIYfz1rerzK13nesJc0TZ11rB3KM7t9PpfysqX\nQoNSGFqqOy6FEvcIdgmScLni0owLhdeuhSFfwgrfU1NetAHFfmnAF3fAj5uh81Pw9s3Q51Rlab3F\nHSRI48zHwxcua26ppKKgKgblRj6/AqRiGWaBDuH02a+DB2HCf8DihsWzCkPuQJPGnp+02CLFlzva\nwR0Pe/6+/d5IOg+OpuNVRXOMy+Ui9cvVfPQJBAdDejoEWKBnP2jVBtq2/N8wVQAAIABJREFUg2lv\ngb8/TJvh2ZC/TU2+thSeR7cbtmyHz+fA1z9Am5bQrgm0jXWjaXA8ATo1QEn/yMyCu0clMOjGIMY8\nEFKskUTZCxTmEokmJB1/f+n+VU9JavqK3sIa0rUm0QMkCpI0jetU8pCK9NQkXPDOq4GyY/SkaN55\n9CRDbg07v9N6DRg4DzCgOazeDxO/hzfUVOgKw9XtoE8LGD4Nvt4A791WOdI2fjVfAYjkZLh7gsfB\nfON16KhWxRNhz3fjF+g52ltWpjHn5SNkpzno2w6+/BxiY2HwNRAeBpv+gj/+BD8/6NARnA4YeYOL\nb2bL/R9P9Dirn8+BzGxoEO+h4dx5KyxbAw3qwbhRsOJXuPo2+O1HqBVb9P28PBg6Btp2tXLfs9Kb\nowEDpcOI/xooM7pfGUyB3c0fKyp2tdqAAQPlg+eugh0n4bu/K3/bIQHwy0PQKh46Pwf7pBWvcsLW\n45VLkShv5ObCmPFw3Y1w/32wdAlc2lp/Pw67i6+ePsjEbn+y+MPj3P1WU95Y357XX4O4OLjpZrhy\nEHz7NSxbAr8s15g91xPyf/QJ6D8QBvUHu2IBdOFiaH4FbN0Fbz8DG36AfYfgyzegVTNIzQCrFe6+\nFRwOuPV6uHasx2EFj1btyLuhbm14fFqUUZzAwFnjgll5lUT+9RYX0AuzQx0jkjJRrUIcQQpbl1eR\nBYl+UOz4mGD0xEi+ei2Fl/uqU37jpHRItca/GH4ZVF8dtp6GgxhF3HYd3ZXtu7NOaZfCJlKGqgSp\nKIBEA9hFDg05RLhPCEwKfUlFChJQZ+JIQuESnaAV20vYPiWNFNqWuT3AbXyptH/EHUq7dF7SBIFy\nafyq8xW8R6fQqRS60xNGBDEbXQxjS/1IRQSkVUVpFi/cr/m3QJd34arGZ+CglleRBZ/xPDAYru0I\nI16DEa3hoZ6ltz9bvLAaJvej5PGTQqr7BbviPOamQ9ebYUBHuPcGiPOa/sJSBWUaKbTsQ/9wOGDi\nU7B+Ezx2Pww/xREt/L5DoItIc0P/e0NwFrhp1q1okFlAYlAcD43LpF5rjZumhJyeqUPJwuoP143x\n/H/7vVCrkYOeVxSw7GuI9Lola/l7Knx9+ppHJu7qsTD6OhjQCzIzPavFOOCylhAcCAN6wM4TGjdP\ngU++MjNhvBObxc2nX1s4pqkvdL1qCZGbhIIB0vGXCgZI96mKeSY9JiQ1AGmOkYoOSP1LtAFpzhAW\ntiUVmJoEY+XVgC4MuTWU7ZvyObijaooWuBxOHPmGqLWBc8OFtOBjtUDtEMitwirPjePg97vgUDr0\n+xQyK2D6OJACl6up4OeEZds8CUlLXof6teGWZ6HH3XDjE7B807n1vXYDdLgS2raCjYu8HNdzQJOO\nkcUc11N4/qEs/Kzw8PMSAbMIV15j4YMXodeNsP9Qkb19azh41JMw9ubHkJoOz032fBYWVrRaazLB\nbdfDl3Nh+kdmTiS4ubyjg0MH3Hw+y4Kf3wV0AxqoEBjOqwFd8A/QuOX+SN584BguV+WXKWr+yLWs\n6vw4aX+o38INGCgLzrcKWzUBJhO8czU8cwX0/AgWqqWHzwrbEiH2zD6ZbizbBlP+B2sfhbAQuHMo\n/PIm/PoePDIavlwE3QdD76Hw8jTP6qMeTHkeVvwP7ril/Mfuje9f2kPCYScvvKfKRlOjYxuYPQM6\nXVNk8/ODHh1hxhfwwjvwzTSPTYVbhsPcnz332ldzLXTraeLbHywEBRmOq4FzxwVDG4jMVccQJGFo\nqV61RDOQaAASbUCClG2tV4VAoknohWq74x4KY82iE3zx/HHGP1FcOV3KFD1RXx2/qBWsPi/+QpzF\nMmQALbt14c9rniRiUEcaPnkrACdppWwfJ8RlpOMjhawkOscmOijtw/heaX8ZNxquEsf1Hy5VtpdC\ngyvpo7SPFsL3Er1EVaTDhSaGlQ7QSGlPEVQdJJqBdJxVlBCANjt3K+3KkLB0y3nTAPIpyvaXQn3S\n6qDENJJCgxKk2VcS4ZfaS3bvQ2wq/N9cSnvpuLUU7FJGtNR/Yfi75yXwmgU++wMGtxfaltaPYsqY\nuhQm90R9zqTwsTRFFrZfdgCmrIC1t0HAIfC99NvXhU8fAGpBdi58MBeuvQlsdk+xhnvHQicV+6aw\n/+W/QXw0RGmljBFElQlJKceXsvT9x+kcWJHBvEWBqC7SyANC2N0GAU6IjaDYfXZFD1i3ESwW2H0Y\nGp8qhuFz/dSt7XGAFyzUGDbCj6kzPPZTTwhpDpCoW5E7hXGq68TI+s3SdSsJ06geXdKcoVdxRBqL\nXmUO4R51C8oiN6QuwBRVs9/gjZVXA7phsZh4/tv6zH03ld+XSkS/ioM1Koy2v76JIzmDf/o/jCO3\naigMBmoujLWfqsUrK+GFcixhuz8VekkVxc4Cr/0Gb/xe6LiWYYknJAgeHA0rPoJ1X8Ad18Frn0D3\nEdBvNMyY6dFV9cbjb8G7T5bfmAGOHbTj9gorrJyfxQ+fpjNtoVrirjTMWgjtroN/jShu79Pdk6Q1\n5wMYfS9sF94rwcOHnf1FFfJVDJy3uGBWXg2UL2Lq+PHsV/V44uajfL6xCXF1hdhRBaLJ2/eQtmQT\nf3e5j+gPniWwmzrJyIABXxi0gaqDrQCybdBQWmHWiR1JECNp4J4Fxv0INgf8OPLs++jVAXr18vyd\nmg7TvoQBY8DpgjYXQ6dLIT4OYsrpGAD8vSGPR285QXC4Rq9rQunUN4gPn03m03UN0TT961Rf/Qj+\nVnjlE0hJh6fu8fzf/lI4fAyaN4VXn/TIYW34CWLDocABe7wSotq2hAlPODlx3EHtOoa7YaD8cMFc\nTRI9wOJUZybrVSEQixEIsNrUMUa7v77ak3L4W1IP0PcWLIV2LDjpekUgN90byeMjD/P+iob4+ZlI\nF7LFswhVbyBKnZUvZd+XyDgdGEGbNffy/TWvE3RFR+KeLa7IripoANBLyF6XIO1XOzFmpYYLjQL8\nSqgdSMUOcglU2qXjKZ1f6XrYqKA9ZJEh0iT20URpX83lSruEdkJRCatAOxEF+lVC/1KIzjvLOM/r\nu1LfqprmIBc1kCD1L/Uj2fWqEHhv1wIEFtqkKUBvOVy9NIbC/Zr6A4zsVvR/mYsKCON5YS1M6oM8\nfun4+4Rm7Q64ch70qwePdUa+jsqKwvMSFQ1PPeD5efkDiAqGRWvhvad82gvHOT1MTeb1fkY5nW6m\nTkjkq/V1aRKdwnuvZ/PR42nMXxlEiPUEALW3CLFoRfAsOxcWr4Eel8I3T8Jdr0Hn6+DLx6BNN+h5\nGaxaBrcNgp3bYPjtsOwLuOYKeOQFTx9ut6e2g9nPxJo1GkNHlO3Z1sSmfiaIU+1vgl2iDei9blUs\nBkklQLpmJLvQT4Fwj/o1EPoR1Hzs0pxBta9yfUYYtAED54Sx/40mJFzjnUeku7ni4R8RRKM1n+DO\nyedQ3ztxZRs6tAYMVFcs2gr39i2//valQO9zpAycyIbOX8P9lxU6rhWEYf3hw9kw+43yXXV99aEU\n+g0PISrWjKZpTJjsz/erQwgJObtHvMnkkQVb8ze0HQftL4b7r4d+E2HqB3B5R1j5u6ft/90PtaLh\nzidgyp0w5x3Pz9zpMHwA1K6rMXBoKV6UAQNnAcN5NXBO0DQTz34Rz9I5WSz/riRb3eFwk5pYUCnK\nBLVee5CYx+7gYLfbyVmrXtkzYMBA1aJ2GExbXj597UqC6LJXDgbg8o+g54eQXPiOuyEBBsyBrwbD\ntReXz7gkNLsIBveGh14uvz53bs5n20Yb/3pUHR06GwQHwo8vwaHZMH4wvPIt3D8N4mNg3i/w1hdF\nzqumwRcvwtbd8OL7RX0cOgYPvgDTZoYQEGCwzA2ULy4Y2kCwvzqmZMtV31RSXWSbv1rlW1IVkGgJ\nEo1BLyS1gcpERLSFl2bX5f6rj9BxVjapJx2kJjpIO+kgK82JNUBj8PhY7nurYYnvHkGdSHBZ5lal\n/Zqw+Ur7AlehnssVVxO0phcJQ8di6d6R/i+W3CbApfxThj0rQoIQl6kvKNlLYf0A8qlFIlE+caRk\nIdV1t6A2MJJZSnsbYb8k9QZVUQMXZpEmsZGOSns6kUp7SqZ6v2qFqVURJFqCpFZRO0ERCpWKC3iV\n2TQFev0vXQpSXE1vGF1vOFuiDUjhfok24L3YpRX+H8AZixqUgPpWlFURpH4Kj9u8B2Hwq2C2wn8G\nIO9XGegNzy+FiT0KbVJ7r/P1r7kwrDX0bgJ9Z0P3hrDpGKz5N0ScyQmWQr8685GeehB63gh/7YXL\nLimyu4XzLs0l9Z1HsNncTH68gJemmmnoPgpOCM0QqDeS8H0p9Ih4P3h+JMSHwJFk+OUv2LITnE44\nkQz2JE+xgh0H4IZxGi887eLH302YNdjyl5tuPU20aqu+4CRVgeCdQpER6T6V6ARSxr56Cis2PxSD\nKqgoFQvQm5smnHM/aY6Riv30EIZTip9R02kDF4zzaqBi0bpzIK/Oq0fCESeRcRai4ixExJqJiLGQ\ndtLBTa33Mv65egSHVXzxcS0sjPCVc8n57/PM6/MeVy8YgzXUCFsZMFAdoGmwcDIMfAn8NLir69n3\nteUEPPgThAeAySu4E2yFyECIDYa4MIgLgZRcOJ4JH17vafPnPfDWepgx1DOmysTsd+Dq8fD79x7J\nqbPFK887ia8LnbtW7Lz67RpYux16t4a/Z8L0OfDRfGh/O7zxAHy2EOp2gI6doUkTqN8IHA4T33xn\nEQS9DBg4NxjOq4Fyw2U9g7jUaxlo89ocbmu/j2dm1qND/3CWfJnM8Hsqryxd8IuP0Xmtm/91n07v\nacOo10e9qmfgwoOhNlC10DRYMgX6vwgWJ4wXVo5Kg8MBqbnwxz1QO5TTq14ul4cScDTT83MiH45l\nQpYN5o0u+r7FApP05RmWG+JrwZjr4V+PwKevnF0f69a42LHNzRszKl7pZc2L8PkyGPMWtLwJNnzs\nUY2oGwsDunh+UtuZadnQxfi7zTzwbwfL1/thNht0AQMVA4PzqhN66QFmIYwgtRe3W8HFCCoCQSEa\n+bkunrr1KOlJBXw3PbGYBmFloF6vi7hx/T38/swvrH3op0rdtoEqghT+05sgI4mWS2FxKWRYxhr3\nZ+xHr0qA3ix+aX8rKL6oabD0v/DFb54fvZi0CO7rXui4+vQbFwLt42FoC7izCzzZH14Z4imVW11w\n31hIOAmLVun/bnaWi88/dtHrChNxtSrHQby9H2TP9iRzdRkPH/4A/76u6POVy9zUqm3i5f9z8sxU\nM40an8eOq5T1L63NSHNPHZ39S/0I965UbOl8QDW6lWsOysOBdVrU7QW2kqefcnBgJY6sZFdVYCor\n6jYwYzLB13825r+3JXFoRxZ/rMqjbZ8iPqXE57KkqIl2V4StVNpbaupKTgDWkACuW3E36x/7mbmX\nz2DIgjGsi+iubDuaL5R2iZMqSUo1F0hR0WTSmQ3U8ZHKkri//ViqtDcWpMQkLm+ugtsKMIzvSti+\nIFvkzm4SCGNSJa1ZYfrEMkuTZlNCdVoyUDuwKRRN/n6UzZlV9Z9D+TiwwZSPA2vhzA6sq/BvC6U7\no3ocWL1SUoqxa8CKh6D3K2C2wC1dS29/aizbjsPGBHjr+jK01yt51kKwS3xD6RyWoVztM4/ANbfC\n36sgSnB8An0C7263m8cm5uHGzbi7Sj7CTdL+6p3Kfa7Zjg/Brb3A9Rd8OBfufA4ScyCu8LjM+dLB\n4YPQ63IYP8qJyea5KJ1BauJA5HGhYpbEbd0k2KX9le5vyTGU7CrG2Q6hfSpqBza7lPFUkQNbjtLI\nVQLDeTVQYQiL1CiwuwkIMvHCokuY92YCtjx9K87liW7PX0XCrweZ2+s9Br3ej8b9y7Ekj4EahfN4\nTajGQdNgxSTo9Qr4mWFEpzN/5/ZvYd6YCh9ahSElFcbdD5lZ8NgDcOeD8OZ0qFdX3T45ycXP3xXw\n83cFZGZA555mHn/Mgp9f5V3J3ZvDxE/BHAfJaXBtH2jd1PNZTh7MXwDBQTB9mmd11oCBioThvBqo\nMJhMJmo38CPxiIPwS0zc8KAwM1ci4ns04sbf/sOioR+x98c9DHhzUFUPyUBlIQD9oXQDlQKLBVY/\nBJe/7HFgh7eX2z68wKMY0EAtcFGtYbfDPVNgy3Z45UnoXcj1TU6FUTdD584ePnZiIiQleSpyObRs\nrP7QoauZVz8IIr6+h+0X5BRWLssZKVlw/8dw10DIscF9L3nsx5YUOak/rICcHPjiM4iLq5RhVS3i\nUSsORCGvBBsoV1zwzqskJRGUI6wQBujjtuqFWKlLYAZI4VSJSiBRD/RWFCsrVaFFGwtLZiZz41R1\nzE0Ku6c1VleWityinrDfa/tvpb2HfW1JowXmL53PZ88cZX6PN5i6oBlhUZ5bob5NrbMU46+WdQm0\nqUNi/jb1dRKeAy0P59DA53BHNE5Xto9AbZeOm0TzkCSomlGyMLkbk9IOMq3ipED2SkT9JJPoARLW\noaZ59Gm8soQtbHMp5JtTIUDN62+9QhSKikSATCWQoHfOkNpLDnmYYJdmfemUSO2lMLTeeiX5RZtZ\ncx/0eMMznw5tVLLpnmRYcxDWT1T0I4VTpXC/dNyk60EvVcTL7nbDC2/DnB/hjof8eHmm56CmFX4+\n6Q242R7Fp29kEVPLzKB2fjRt5YfVavKRlCrqNCxBuM4PC+PRW7mt8Pw6/eCr1Z4fb4Q4OE0t+Go+\njB0DQwYLfakgSVwtE+z7Bbt03iXuu16qjqp9BurrKkEYj17KhjRGnfZzof1Vd1zwzquBisXj06IY\n1e0E4c0TGTim8pQGyoIxT9Wjy+Bs7u+zg7tfqk+Xq8pP5FtCng0+/BYifR4kadFqAmE6iUq7mQKl\nPQy1c58scHBjFO0Tjjh5/3V1P4kcVdqzhaykXSwS2h9T2vOEcrgnBY7vVlfJJ06ApMHo5XwcS4bX\nT0nl7lO2RhgKpyr5mvAJj0oLYRJTRkoSl5wJqR8p7daLc3lQ0visZrBYYM1E6PE6HGkLOXaPHNbR\nwssrtwBmj6/aMerFt9/DS9Nh6EDYtBgy6qofu1aribumSG8cVYe4SHCvhXwbTJ8Hk6d77HWvh8dv\ng+t6ecrE9lOvHxgwUCEwnFcDFYroODPv/hjHrX0OUquRf7FkreqAlp1CeO/3S3js2t2s+ymdma9V\n/DbNZs9PcZuaJCb5JWaBtSktnsn9lITJZCoxvjNtVxPsJmHLmk67uL8Kcp1Zg2mz4F7fXDGvffqP\nV5a0eHAke+Ema5LclmaCSihyVy6wWuDnCdDuBZjSy1OEoF1tLz3UGpJpsn4jTHwaWjaF9QsgoIZL\nTQf4w6RRMGkEFDhg1WaYtxp63QtXXAbDHq7qEVYi/NEfaTFQrrjgndfgTH0JRFYh1GQSou5uvUpW\nwiq/WYhpWAV9ArPgxkjtJejtXxUObt5S441vIpk8cjtfrKrNRS2Klpz20VTZj1ThKTJRvbx1yfvq\n5bO8G9WZlllRXioHAfDU4k7Mev4AA1qcZP7dEOOTKRzcTh0rzumo9nAsQugrMBjGjYUGPvTfFbGX\nKtsfFlQIIgU6QeipZcEyQpXo+tN3Jxh6v7oymUQXOU5tpf0y4Tw2JVlpTyRWaW8uEMniqFfC1mLT\nIb5bAfeP8vlACgsuFOzSw0m9GC5nl0vhackJ0+vkSJFBr9X9xdtBc+DZJ728X+k46LVLx1/R/rbP\n4IvB0PfUZegdmJDGrzcsLtklFQXpfPmM/9BxGP8sWKLhm+8hLk7D6dWtDXWVxkSBehMoLOlH5ghL\n/ZJihN5nkeQd5HsqQPXv4fl5/UEI7A3PbYUmylC9ME4FowsAqXSwdJ1LJX0luoh0fqXrSg9tpryi\n9HppA8J27cK1dj7A0Hk1UCno2jeQiVMj+ffVJ0lNqvqStiqMfKwxb4+Avm/DAn3VYw0YMFBOmPu3\np0JWX/X7U7VFZjbcOAVufQLenARzv9eIi9P3iHW5qk6N5WwRUOg49RleteMwcGHhgl95NVB5uH5c\nCIf3FvCfYUl8uqwW/gHVT0+lYwP4fTIM+xB+2gbv3VTVIzKgG/mQkgGzf/GxS+9M2wS7tFJYuOh9\ndX0IqiEzqMMJ87ZAaADyCnFZCzWdum0lv0yqB+rjlw1oAVFeY8nOhx2J8OAPMHUwzNru+YrLDW48\nlbPcgMvi9bfbQ99wucEd4Gl/+n934SYDfGy+/RXaT3/mX/SZ9/fcgUV/4/2ZBdwuSMqA3Hxo3QTe\nmQ32Ja6iNq6ibdi1XI/d5dFsdbshOdHF5r820+yyQMKjLeA1pkB3zum/3V59+Wd5focEQWaO1+dO\nz9hPjfH0T+H/ZZaxkmgmgn99NMGjpmA9fxf7DFQj1JCptwIhPaCEIyPRA8oL/jZ1mN4ZpC/m4xR2\nwF8nUUcKE9uFOIVcSMGT/XL//0Uw6aZkHh2bzCtfxXBEU4fFpfB344Dj6oEKu9UuSp3SKqornPRE\nExfdCC//Cl2nwvyRECcoas3xv0Fpv90yW/2FLGAPkFncHBGrpgFItIEEISbWTkjhlWgYquvEjUkM\nYUrnRS8dJU5ISZeyY5uxS2mvfUSdKJaWCZt8V8+le13KYpYSsCywKQWO5MCk1kIbn/ZKSLei3jlG\nmhq8DqVm9oj6RwYjS/mU4WlQjOcrnXJpv7ycpm83Q71boEdjTodx7bnwwHfQoTZsPuSZazXApHm+\nqpkKfxd2ZcKjEXv6s4LC3yavtqaiOfu0TStsc+o3Rf9rgCmy+GentxFe9H2tcF9MJtAivdoUfqZp\nYAr0+b9wXIcvikLTTJhMkJ/r4q0nMnC5zcz4MZqQMK2wvQmT5vle88Ppnn68+tA00DKKxmExe9ld\nRfvCqTFpoIV5fp8zFOH1176CydNg2gswyYdr7i9JZ21Qmw8KagmN1MwqmSYhQap4J0EPx1qvR3UG\npYcytxe2K1FUzgcYzquBSoWmmZj6eTRj+iYy7akM2j5X1SOS8XAPGNQE+s+EZ5rA8IFVPSIDZUXT\nuvDSBB+jxHVbINilDH1/mLYd7DUowls7DMZ3h06NKD+ureQESPbCp43DAcv3FjquXvjjGEQGwryb\nCw0SB1Fy7qX90isNJnElJSdJcs6E8US28Bzoj17OYOG3OTz8aiRd+waKCw5NpJVSab+kl7RyJgmu\n2QwvfQm3D4YhPTzO6+QZJZ1XAwYqAobzaqDSERCoMf2HOG7qeuL/2Tvv8KiKtYH/tqT3Qgq9CtIE\nQ1GpgqAUQRQFqaJgFxXUa68fdsVy7VwFLCiI/YIoCopSFEQpAtJ7D0lISNvsfn+c5ZJszptkwtk0\n5vc8+wRmZ+fMzs455z1v5UTTLZw/1jxoqypwThKsHA+Xz4FvFsE7U9S0F1v3w3erYcU/sP0gHMiA\ncCkFk0bjJyKD4bCUn7aCmfIjDG9ftM3lgnu+g2UTKmdOFcnff+Ty6I2pdOwZzKcrk7Bbog6tOPLy\n4ZF3YPp/4V+j4akZEFjI5eTPzdBOCqLSaCxCC6/JgmPPfkV/TEU3A9XAz8BcwRYnmBcKLCpGIGM+\nH9ldoaiNsXYCvPvfaK7ssYIWDU7QtmfRcjm+9bxP4hHMtDZhQS9ioWl7bSHvKVcWbwoEvukBU+fC\neYPgy0cg2auFqd/UvKgBtaHBQEg523hNGAmdWxkRyGbsFtwDVN0zJLO+5C5idtxcjohuBlLWi1Dh\n92rJ36btUpGCeCELQdJ+QZ1n4iXhzsYwaft6mEjCm2r0vZNTWqyyXEFVr7KS5lLSLEqndKGfPDoQ\njqR72yTNojRPSWMtuQdI/b18vQYWjoZ9uyErH7Jy4NGf4e5OEOrm1O8hfS9JsyitWxmyMRRB0rBK\n+0S4lBy5tKhzcU6Om9uuyePgkWO89VUstZKcGI6lxhdtsXGn+UDS95U05dI+lywJChrr9Vth1KOQ\nEAtfv2m4kbRoCXsPwtYX4EgadLkVsgpnEtgsjC+0x0rfS9q3qsU7FF0FTfe5akEPCVX3AOm4wm/Y\nYtVOSKkmOfIU0cKrptJoenYAd89qzbPD1vL0zynUbV61EzjeeQX0SYFLHoAHr4Yre5Tcv34SfPZc\nxcxNcwpXwSlfQI1BTCikliJUVhRxITDsUwh0QJAT0rNh73EYJfk01gCmv5XHe6+7uOfRALoMMU8H\nV9XZvAtSRkNunqFpHXKbIcRu3wt3jIacPHj5Lrj9eaOgQXDNLe6kqQJo4VVTqbTrHcuYp5ry2IC/\neH55B6Liq7aDeeuGsOrfMPT/4L+/w+iL3NXO7FfTcRVYFJhSg4gLga1VpOb6glGn/u12Q8o78Mvo\nypuPP9myyc1t43Jo2dbOotXB2O12IUNz1adJXVg/G+KSISriVNaCobfDlLcgJhKivMrmB9+A5++o\nvLlqaj5aeFVFtR65Re4EQWJtSHPbXV6Qfx97nYruB1JRg5b8TctrIW+znRcvW84HC+MICraxhSam\n/ZfEdjJt7x7+m2l7gpBRvmHBDtP2Xa3Noy/qJ56KjncCX3wJr06Duzuu4OM5UKeOj7TkgqRYWLcR\nWhdy6c0VTF9StH6g8PtKZv3jRJi2S24DZkms3djE4+4QCobXw9x9QnIbkJJn1zthXn6W5ebNZiZb\n1y6wZwK+2QZUzeWSWVn1GqCKdOpK7WUwYcZFwh/7vW2SECuNr+pOoNB+809wXXOIdVEsA4eI9H2l\nssCSQad+GY93EuGSlzGo+F52udxcN87O7i0upnxYnzqNAjngfa/t9n/MB5LcMCTztxQoJrkNSOOX\n0VxuB5rEUOy8is2B9vXgjkEw/XP441lvVovt3g5Szmzhe0W2EPqbVVQBpeIXgLx/pHHM2lULGihm\nCdCUjtZPaKoEd02JILG2g3uuTcNTTepu3jYe3nkXhg6BOZ8Uf7jo2hbmLauEiZ3huPG/20A1SjQA\nQK1wwzxflVhzFP5Og1vbVvZMrGXuJwX06OSiQ88Qpi8xBNfqjMuKRQMWAAAgAElEQVQFOw/I5ZBv\n6wVPDIZX5kFoELRvBA0lwVqjsQgtvGqqBHa7jednRLN7WwEvPVJFwqLLQIsWdpYshc8/g+uucRep\nkDOwCyxdU4mTO0NxuSvGbaDMyd6rAAkRkKEamOZnrl0EH19U2bOwjr173PTvlcf8b9wsWu5k8FhJ\ndV+9WLkJGg4De08IvwQmfwIfLIN1eyHfBb/vgBHvwLAL4NPJlTtXzZmDVlpLqJrKVMcRkNwJnEJ7\ngVPQAQnZCQqcahNSzk7gMNcySNkPCpuPA0Lgza/iGXreIWo33cBlY4qXAvqT9sXaALJamEsracSY\ntu9zmNugIiSbm2DpW9ulLQTBfV/AZ28epmPXVJ7+rCGXbdhAE8CdAWw41d9xtvk4UrS+nD3A3M3g\nqBAqLZnvzcfOFceXiBY8+ervNy9GsD3ZfP2DJNOvdN6ZDO/yJmsvdu5tL94XKHPN+iL9gzECxQvL\nJ6XkNy0zqv3LUHekVhgcP9lP9RomuRmommsLHffulXBFXa8nh4q5FuT1kS5VUkUx862J4Bkjmult\neXncNQn+WAWvvQGtWtsAN213CxcN6bhC4hIihXYJaR0kt5BSHmrOawJH5sBTH8MLc+HF742iCE0S\nYcdhaF4bFj0GrdMxr1ZnXjdF/l0kdw6pv6TrUHXvUXUnUOmrek5L46hmzqjBaOFVU6WIS3Dw9jfx\njOp5mDoNnXTsrpo5vfK4/MZadOwdwT2XbSc9BcZ2rXqm2jMBlxsc1UgrWhGEB0O+n6sDlpWtGfDL\nYVjWr7JncvrMWwb33gajxsCLL5Vf3e92w+5DsHY3/LPPEArPrmNoM2NVhVc/EBcJT4yFxBh4aAZc\nfxG8cq2R5iw4ABwOZN9WjcYPaOFVU+Vo1jKAmx+OYu5/MquV8ApQr1kw01edxUud1zBfX8wrhQJ3\n9TLpn2mM+hU+6lpyH7cb2bHYDTkumL8bQpwQ5IAgOwRlnPp3sN34d7Adgl0QaLfWleRIGox4wig4\n8sNiCA01HzwvDzZugfWbYfN22L4b9u6E3PxTPqQn92q0A+rGGRrNi9rAmp0wbCpku43Ss60bwtCu\ncGG7is2m4fHA61/Dkx9D5xbw+1PQpoHxXlj1ujxrahBaeJVQ9Q9TNX1ZRKAwT4fgTpCrOCHJzaDA\noVZmQUpuL7klxEa6sLtdhPoUmJfM66OCPjRtn8grpu1bMa/qJY1fp7F55NV5P5rbxC64EqavhLcO\nUMRM6PzOtDtNhm41bZdqUxdgrtLdJ4bUmxNnkrncg13MNiC5B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CCkpkLbbGBboQ9IidSl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\n0+HForIa1TLtR/V/GDAyD05IhaURM5GnuaM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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 12), dpi=100)\n", - "map = Basemap(projection='cyl',\n", - " resolution = 'c',\n", - " llcrnrlon = lons.min(), llcrnrlat = lats.min(),\n", - " urcrnrlon =lons.max(), urcrnrlat = lats.max()\n", - ")\n", - "map.drawcoastlines()\n", - "map.drawstates()\n", - "map.drawcountries()\n", - "\n", - "cs = map.pcolormesh(rlons, rlats, rdata, latlon=True, vmin=data.min(), vmax=data.max())\n", - "cbar = map.colorbar(cs,location='bottom',pad=\"5%\")\n", - "cbar.set_label(grid.getParameter() + \" (\" + grid.getUnit() + \")\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting an interpolated grid with Cartopy" - ] - }, - { - "cell_type": "code", - "execution_count": 397, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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duxPWvArrUuS2qAMccQqMPQVGRLLJrQpduS5k3pXg+inXFTZCni8vtKu22g/2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\nzfem7eMtf5F3zszDdDKz/eDner9bA30Ya4xf5pn13tc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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 12), dpi=100)\n", + "# Create Map\n", + "cmap = plt.get_cmap('rainbow')\n", + "matplotlib.rcParams.update({'font.size': 8})\n", + "plt.figure(figsize=(9, 6), dpi=100)\n", "ax = plt.axes(projection=ccrs.PlateCarree())\n", - "cs = plt.contourf(rlons, rlats, rdata, 60, \n", - " transform=ccrs.PlateCarree(), \n", + "\n", + "cs = plt.contourf(rlons, rlats, rdata, 60, cmap=cmap,\n", + " transform=ccrs.PlateCarree(),\n", " vmin=rdata.min(), vmax=rdata.max())\n", + "\n", "ax.gridlines()\n", "ax.coastlines()\n", + "ax.set_aspect('auto', adjustable=None)\n", "\n", "cbar = plt.colorbar(orientation='horizontal')\n", - "cbar.set_label(grid.getParameter() + \" (\" + grid.getUnit() + \")\")\n", - "plt.show()" + "cbar.set_label(str(grid.getLocationName()) +\" \"+ str(grid.getLevel()) + \" \" + str(grid.getParameter()) + \" \" \\\n", + " \"(\" + str(grid.getUnit()) + \") \" + \" valid \" + str(grid.getDataTime().getRefTime()) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Downsampling Large Grids" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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l14aVbcXySynZ+3VwuqXzWf7HuWFhIz2PqtpCFq8aT6Mz+DrWbZvMxh3BA6C/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\nWMZNkJW2a9M6Km69EsFspv20j8jq1Rf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AyWERNbHINgNQ/IE+mXMU4inW1yiIDCHxviwk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GyDJq0WN1lJgTZfagAuZf0GyicrUuu3VvhWKzFvpkh/9BrtZqWnIp9R+YtZWb9Jy2TbIbOAEWBu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XB53Rl4+em8ThVx9NfILztRL4mg21XcWvxXC/ChS/3kKFg8DXTFm9toGC5yueHtgbXDws4/kJ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\n1kxVErqhJHg3qZKViYi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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import scipy.ndimage\n", + "\n", + "SCALING = 0.3\n", + "data = scipy.ndimage.zoom(data, SCALING, order=0)\n", + "lons = scipy.ndimage.zoom(lons, SCALING, order=0)\n", + "lats = scipy.ndimage.zoom(lats, SCALING, order=0)\n", + " \n", + "rlons = np.repeat(np.linspace(np.min(lons), np.max(lons), ngrid),\n", + " ngrid).reshape(ngrid, ngrid)\n", + "rlats = np.repeat(np.linspace(np.min(lats), np.max(lats), ngrid),\n", + " ngrid).reshape(ngrid, ngrid).T\n", + "tli = mtri.LinearTriInterpolator(mtri.Triangulation(lons.flatten(),\n", + " lats.flatten()), data.flatten())\n", + "rdata = tli(rlons, rlats)\n", + "\n", + "# Create Map\n", + "cmap = plt.get_cmap('rainbow')\n", + "matplotlib.rcParams.update({'font.size': 8})\n", + "plt.figure(figsize=(9, 6), dpi=100)\n", + "ax = plt.axes(projection=ccrs.PlateCarree())\n", + "\n", + "cs = plt.contourf(rlons, rlats, rdata, 60, cmap=cmap,\n", + " transform=ccrs.PlateCarree(),\n", + " vmin=rdata.min(), vmax=rdata.max())\n", + "\n", + "ax.gridlines()\n", + "ax.coastlines()\n", + "ax.set_aspect('auto', adjustable=None)\n", + "\n", + "cbar = plt.colorbar(orientation='horizontal')\n", + "cbar.set_label(str(grid.getLocationName()) +\" \"+ str(grid.getLevel()) + \" \" + str(grid.getParameter()) + \" \" \\\n", + " \"(\" + str(grid.getUnit()) + \") \" + \" valid \" + str(grid.getDataTime().getRefTime()) )" ] } ], diff --git a/examples/notebooks/Model_Sounding_Data.ipynb b/examples/notebooks/Model_Sounding_Data.ipynb index 4b9f081..3bcaa1a 100644 --- a/examples/notebooks/Model_Sounding_Data.ipynb +++ b/examples/notebooks/Model_Sounding_Data.ipynb @@ -4,12 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The EDEX modelsounding plugin creates 64-level vertical profiles from GFS and ETA (NAM) model runs. As of AWIPS release 16.1.5, the available locations are limited to stations around OAX (Omaha) due to localization (16.2.2 will allow requests for all stations)." + "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": 37, + "execution_count": 92, "metadata": { "collapsed": false, "scrolled": true @@ -20,133 +20,24 @@ "DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n", "request = DataAccessLayer.newDataRequest()\n", "request.setDatatype(\"modelsounding\")\n", - "request.addIdentifier(\"reportType\", \"ETA\")\n", - "request.setParameters(\"pressure\",\"temperature\",\"specHum\",\"uComp\",\"vComp\")" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CHE\n", - "CRL\n", - "EAX\n", - "HSI\n", - "KDSM\n", - "KFOE\n", - "KFRM\n", - "KFSD\n", - "KGRI\n", - "KLNK\n", - "KMCI\n", - "KMCW\n", - "KMHE\n", - "KMHK\n", - "KMKC\n", - "KOFK\n", - "KOMA\n", - "KRSL\n", - "KSLN\n", - "KSTJ\n", - "KSUX\n", - "KTOP\n", - "KYKN\n", - "OAX\n", - "P#8\n", - "P#9\n", - "P#A\n", - "P#G\n", - "P#I\n", - "RDD\n", - "WSC\n" - ] - } - ], - "source": [ - "availableLocs = DataAccessLayer.getAvailableLocationNames(request)\n", - "availableLocs.sort()\n", - "for loc in availableLocs: print loc" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "request.setLocationNames(\"WSC\")" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "using 2016-07-15 00:00:00\n" - ] - } - ], - "source": [ + "forecastModel = \"ETA\"\n", + "request.addIdentifier(\"reportType\", forecastModel)\n", + "request.setParameters(\"pressure\",\"temperature\",\"specHum\",\"uComp\",\"vComp\",\"omega\",\"cldCvr\")\n", + "\n", + "request.setLocationNames(\"KDSM\")\n", "cycles = DataAccessLayer.getAvailableTimes(request, True)\n", - "print \"using \", str(cycles[-1]) # 0 for FIRST time, -1 for LAST" + "times = DataAccessLayer.getAvailableTimes(request)\n", + "try:\n", + " fcstRun = DataAccessLayer.getForecastCycle(cycles[-1], times)\n", + " list(fcstRun)\n", + " response = DataAccessLayer.getGeometryData(request,[fcstRun[0]])\n", + "except:\n", + " print('No times available')" ] }, { "cell_type": "code", - "execution_count": 41, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "allTimes = DataAccessLayer.getAvailableTimes(request)\n", - "\n", - "# Build one complete model run\n", - "fcstRun = []\n", - "for time in allTimes:\n", - " if str(time)[:19] == str(cycles[-1]):\n", - " fcstRun.append(time)\n", - "\n", - "#for time in fcstRun: print time" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Request data for a single time" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "response = DataAccessLayer.getGeometryData(request,times=[fcstRun[0]])" - ] - }, - { - "cell_type": "code", - "execution_count": 43, + "execution_count": 93, "metadata": { "collapsed": false }, @@ -155,31 +46,38 @@ "name": "stdout", "output_type": "stream", "text": [ - "parms = ['pressure', 'uComp', 'temperature', 'specHum', 'vComp']\n", - "site = WSC\n", - "datetime = 1970-01-17 23:55:40.800000 (0)\n", - "geom = POINT (-93.55999755859375 44.13999938964844)\n", - "\n", - "2016-07-15 00:00:00 (0)\n" + "parms = ['uComp', 'cldCvr', 'temperature', 'vComp', 'pressure', 'omega', 'specHum']\n", + "site = KDSM\n", + "geom = POINT (-93.68000030517578 41.52000045776367)\n", + "datetime = 1970-01-18 02:22:12 (0)\n", + "reftime = Jan 18 70 02:22:12 GMT\n", + "fcstHour = 0\n", + "period = (Jan 18 70 02:22:12 , Jan 18 70 02:22:12 )\n" ] } ], "source": [ - "print \"parms = \" + str(response[0].getParameters())\n", - "print \"site = \" + response[0].getLocationName()\n", - "print \"datetime = \" + str(response[0].getDataTime())\n", - "print \"geom = \" + str(response[0].getGeometry())\n", - "print \"\"\n", + "# initialize arrays\n", + "tmp,prs,sh = np.array([]),np.array([]),np.array([])\n", + "uc,vc,om,cld = np.array([]),np.array([]),np.array([]),np.array([])\n", "\n", - "print fcstRun[0]\n", - "\n", - "tmp,prs,sh,uc,vc = [],[],[],[],[]\n", "for ob in response:\n", - " tmp.append(float(ob.getString(\"temperature\")))\n", - " prs.append(float(ob.getString(\"pressure\")))\n", - " sh.append(float(ob.getString(\"specHum\")))\n", - " uc.append(float(ob.getString(\"uComp\")))\n", - " vc.append(float(ob.getString(\"vComp\")))" + " 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())" ] }, { @@ -191,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 94, "metadata": { "collapsed": false, "scrolled": false @@ -209,15 +107,13 @@ "from metpy.plots import SkewT, Hodograph\n", "from metpy.units import units, concatenate\n", "\n", - "pres = np.array(prs)\n", "# we can use units.* here...\n", - "t = (np.array(tmp)-273.16) * units.degC\n", - "p = np.array(pres)/100 * units.mbar\n", - "s = np.array(sh)\n", + "t = (tmp-273.15) * units.degC\n", + "p = prs/100 * units.mbar\n", "\n", - "u,v = np.array(uc)*1.94384,np.array(vc)*1.94384 # m/s to knots\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\n" + "dir = get_wind_dir(u, v) * units.deg" ] }, { @@ -233,13 +129,13 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "rmix = (s/(1-s)) *1000 * units('g/kg')\n", + "rmix = (sh/(1-sh)) *1000 * units('g/kg')\n", "e = vapor_pressure(p, rmix)\n", "td = dewpoint(e)" ] @@ -253,13 +149,13 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "td2 = dewpoint(vapor_pressure(p, s))" + "td2 = dewpoint(vapor_pressure(p, sh))" ] }, { @@ -272,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 97, "metadata": { "collapsed": false }, @@ -288,38 +184,15 @@ ], "source": [ "# new arrays\n", - "ntmp,nprs,nsh = np.array(tmp)-273.16,np.array(prs),np.array(sh)\n", + "ntmp = tmp\n", "\n", "# where p=pressure(pa), T=temp(C), T0=reference temp(273.16)\n", - "rh = 0.263*nprs*nsh / (np.exp(17.67*ntmp/(ntmp+273.16-29.65)))\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": "code", - "execution_count": 48, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-92.478017542 degC 13.9116676363 degC\n", - "-92.4780705498 degC 13.7561341768 degC\n", - "-92.4797373828 degC 14.0048053654 degC\n" - ] - } - ], - "source": [ - "print min(td), max(td)\n", - "print min(td2), max(td2)\n", - "print min(dwpc), max(dwpc)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -329,16 +202,16 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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AiiVLluiaa67RmDFj9KMf/Ujdu3dXVVWVwuGw6urq1LNnT914442aO3eufvnL\nX2rUqFHas2ePzjvvPElSXl6e7rjjDm3btq3pMW+//XY9+eST6tmz5weez7UzJRhKAIAjGEoASDtv\nvCE980zj3TcAII3dcsstmjFjhkpKStS9e3dlZ2erY8eOamho0IQJE/T9739f8+fP1/XXX990LsTH\nPvYx7dmzp+kxfvjDH+qxxx5Tt27dPvK5XBtKsL4BAClqwoQJqq6uth3DCWPHjlUgELAdwwnPP/+8\nwuGw7RhOeOaZZxSLxWzHcMI//vEPJRIJ2zE+aOHCVl/deOqpp1rkNPtU9I9//MN2BGfQhe+pp56y\nHcEJxphW7eK9tYo+ffooKytLq1evVjAY1FNPPaXvfe97GjhwoG644Qbl5uY2/Zo9e/bo7rvvVk1N\njYwxGjdu3HEHEi7iSgkAcMTJXilRXl7+oZfkpSO68NGFjy58znZx0UXShAnSpz7Vak/pbBcW0IWP\nLnx00cgYo8rKSvXo0aPVnrOoqEi5ubn6zne+o1deeUV33HGHJk6cqE6dOqmhoaHp8372s59pxIgR\nOuOMM07peVy7UoKhBAA4gvUNAGnl8GHp4osbz5PIzLSdBgCc8NRTT+kXv/iFOnbsqGAw2PTxX//6\n1/rrX/+qTp06nfZzuDaUYH0DAFJMJBJRWVmZ7RhOCAaDqqystB3DCYFAQDU1NbZjOKG2tlZ1dXW2\nYzihurr6fT9dc8o770jXXttqA4nKysoWPUE/lZSXl7PmlVRaWqpoNGo7hhNKSkoUj8dtx3BCcXGx\ntZW3n//85/rMZz6jYDCo++67T8FgUMYYjRw5slkGEi5iKAEAKWbdunVN97FOdytXrmQokbRkyRJu\n35a0cOFCd78Rb2Xz5s1z95vPVj5PYvbs2ZwxkjRr1izO1UiaOXOm7QjOeOutt2xHcMabb77ZdJik\nDcuXL5cxRg899JA6pMGdiVjfAABHsL4BIG0YIw0YIC1YIF1wge00AJBWWN8AAABAetu9u3Ewcf75\ntpMAACxjKAEAKSIWi2n69Om2YzghHA7rzTfftB3DCYFAQLNmzbIdwwnV1dWaN2+e7RhOKCsr0+LF\ni23HOLb3Vjda4fLow4cPa+XKlS3+PKlg7969Wr9+ve0YTtixY4e2bt1qO4YTNm7cqPz8fNsxnLBm\nzRoVFhbajpF2GEoAQIoIhUIaNmyY7RhOoAtfOBymi6RIJKKhQ4fajuGEaDSqSy65xHaMY1uwoNXO\nk4jH4xoKlkXEAAAgAElEQVQyZEirPJfrPM/ThRdeaDuGE7KysjR48GDbMZzQoUMHDRo0yHYMJ3Tu\n3Fn9+/e3HSPtcKYEADiCMyUApIV4XOrdW9q8WcrNtZ0GANIOZ0oAAE4atyvz0YWPLnx04XO+i40b\nG4cSrTCQcL6LVkQXPrrw0YWPLuxhKAEAKeCRRx6xHcEJxhi6SEokEnr00Udtx3BCNBrVY489ZjuG\nE0KhkEaNGmU7xkdrpVuB1tfXa/To0S3+PKmgsrJSY8aMsR3DCcXFxRo/frztGE7Yv3+/Jk+ebDuG\nE3bu3KkZM2bYjpG2WN8AAEewvgEgLXzxi9L/+3/S175mOwkApCXX1jcYSgCAIxhKAGjzwmGpZ0/p\nwAGpWzfbaQAgLbk2lGB9AwActnr1anYck1asWKF4PG47hhOWL18uBliNli1bRhdJy5Ytsx3h+Fat\nki68sMUHEinRRSuhCx9d+OjCRxf2MZQAAIfV19crOzvbdgwnBINBZWZm2o7hhFAoJM9z5gccVoXD\nYbpICofDtiMcXyudJ5ESXbQCYwxdJNGFLx6P8wOPpEgkokQiYTtG2mN9AwAcwfoGgDbvqqukv/5V\nuv5620kAIG25tr7BUAIAHMFQAkCbVlsr9esnlZVJHTvaTgMAacu1oQTrGwDgoBkzZmjnzp22Yzhh\n0qRJ2r9/v+0YTnjppZdUXFxsO4YTxowZo8rKStsxnPD000+rvr7edozjW7pUuuKKFh1IPPnkkwqF\nQi32+Knkscce4xL9pEcffZRL9JMeeeQRzuFJ4hbj7uBKCQBwxNFXSjQ0NKhTp06WE7mBLnx04aML\nX8p08atfSb16Sb//fYs9Rcp00QrowkcXPrrwpXMXrl0pwVACABzB+gaANu3ii6V//lP69KdtJwGA\ntObaUIL1DQBwiDFGe/bssR3DCfF4XHv37rUdwwnRaJQVlqRQKKSDBw/ajuGE+vp6FRUV2Y5xYkpK\npIMHpcsua5GHr6mpUWlpaYs8dqqprKxURUWF7RhOKC0tVXV1te0YTiguLlZdXZ3tGE44fPiwGhoa\nbMfAURhKAIBDDhw4oCNHjtiO4YQ9e/bwhXXSjh07VFNTYzuGEzZv3swXk0l5eXmpc2bAO+9In/+8\nlJXVIg+/bt06xePxFnnsVLN69WrbEZyxatUqZWTw7Y4krVixQlkt9Ocv1SxfvpwuHMP6BgA4gvUN\nAG3WXXdJl1wi/fzntpMAQNpjfQMAAADpwxhpwQLp+uttJwEAOIihBADAKYlEQhMmTLAdwwmxWEwv\nv/yy7RhOCIfDevXVV23HcEJDQ4MmT55sO8aJKyiQIhHpwgub/aFramo0bdq0Zn/cVFReXq633nrL\ndgwnFBUVae7cubZjOGHfvn1avHix7RhO2Llzp1atWmU7hnVBB1fdWN8AAEewvtEoGo2quLhY/fv3\ntx3FukgkorKyMuXm5tqOYl0wGFR1dbX69u1rO4p1gUBA9fX16tOnj+0oJ2bMGGnpUmnixGZ/6Nra\nWkUiEfXs2bPZHzvVVFdXyxijbt262Y5iXUVFhbKzs9WlSxfbUawrKSnRGWecoZycHNtRrCsqKlL3\n7t3VoUMH21GO67777tOwYcN06623tsjju7a+wVACABzBUAJAm3TrrdKXviT98Ie2kwCAs+699161\na9dOjz/+uG677TZNnjxZlZWVLTJodG0owfoGAMAZ9fX1tiM4gy58dOFLuS4SicY7b1x3XbM/dMp1\n0YLowkcXPrrwudrFT37yE1100UWSpCFDhuiJJ57Qxo0b9dprr0mSunfv3uzP6WIXDCUAAM549tln\nbUdwBl346KKRMSb1utiyRerWTRowoFkfNpFI6Pnnn2/Wx0xVsVhML7zwgu0YTgiHwxo3bpztGE4I\nBAKcz5RUVVXV9E2+a773ve9px44dGj9+vO655x5J0rBhwyRJ27dvlyQ98sgjTZ+/e/dunc5VtcXF\nxfrd7353GolbBusbAOAI1jcAtDlPPSVt3dp4rgQA4AOuu+46vfPOO6qrq5Mkde7cWddff73mz5+v\nH/zgBxo/frxKS0vVq1cveZ6nwYMHKz8//4QfPxAIaMSIERoxYsT7Ps76BgAAANq+lSulz3zGdgoA\ncNaCBQskNQ4jzjjjDL344otasGCB1qxZo5deekmS1Lt3b0nSzJkztXv3bs2ePfsjHzM/P19f+tKX\n5HmezjjjDI0YNUoZf/yj/vLKK4o7ePcNhhIAAOtWrVqlmpoa2zGcsHTpUjU0NNiO4YR33nlHkUjE\ndgwnzJ8/38kvJI9r5Urpqqua9SHnzp17WpcvtyV04ZszZ47tCM6gC18qdOF5nvLy8iRJzzzzjH7w\ngx8oOztbV155pRKJhPbu3StJ+tOf/qSbb75ZgwYN0k033aRoNNr0GMYYTZ8+velqigsuuEBz587V\nVVddpby8PP1t61YlOnfWoosuUkaGeyMA9xIBANKOMYZbtyVlZ2erU6dOtmM4oUOHDmrXrp3tGE7I\nyclRZmam7Rgn5+BBKRiUPvaxZn3Yzp07y/OcuerYqi5dutBFEv+G+OjClypdDB06VLfccot++tOf\nqrq6uukHNVdeeaUGDRqke++9Vw888IAOHz6sXbt2SZJ69uypP/zhD/I8TxkZGfr617+u8vJy3Xvv\nvaqsrJQxRitWrNCgT3xCjx08KOXk6M8DB9r8bR4TZ0oAgCM4UwJAmzJlivTKK9KMGbaTAIDzjDFN\nVzEYYzRlyhTdeuutWrRoka655pqmAeSXvvSl910B0q5dO40ZM0Z33HHHh14FMaKwUH/Yt0+fO/NM\nLRk6VJ7ncUtQAADeY4zh0uMkevDRhS+lu2jm8yRSuotmRhc+uvDRhS8Vu/A8r+mOG3//+9/1rW99\nS927d9e1116rHj16NH3enDlzdNVVV2nDhg0yxigcDuv73//+hw4k6mIxjTxwQJJ0/znnOHtlFUMJ\nAIA1S5cu1bJly2zHcMLs2bP17rvv2o7hhGnTpjV9YZbuXnnlFe3bt892jFPTzOdJjB07VkVFRc32\neKnsmWeeUUVFhe0YThg1alTTXQvS3ciRIxUMBm3HcMIjjzzyvjMXUsWFF16oO+64Q7/97W/leZ4q\nKyslSZWVlR9Yy3jv1qEfZX1dneonTNBVOTn6QteuLR3/lLG+AQCOSMf1jXg8royMDGcn960pHo+n\n3pkBLYQufCnbRUOD1KuXVF4udezYLA+Zsl20ALrw0YWPLnyp3kXv3r1VU1PzkWsZJ6okGFRVIqGP\n5+Q0fcy19Y0s2wEAAOkrlb9gaG504aMLX8p2sW6ddPHFzTaQkFK4ixZAFz668NGFL9W7KC0tbbbH\n6tOxo/o026O1DNY3AABWbNq0yXYEJxhj6CIpkUhoy5YttmM4IRaLadu2bbZjnLpmXN0Ih8PasWNH\nszxWqgsEAtq9e7ftGE6oqalJ3dWmZlZeXq5Dhw7ZjuGE4uJiFRcX247hhMOHD6usrMx2jBPCUAIA\n0Oqqqqqa9iTTXWlpqerr623HcMLhw4cVCoVsx3BCYWGhYrGY7RinrhmHEnv37mXFKyk/Pz/lfwLc\nXHbu3Mktg5O2b9+uDh062I7hhG3btqljM16hlcq2bNmSMrcY50wJAHBEOp4pAaANMkbq2VPaskXq\n1892GgBIK3FjlCF95DDXtTMluFICAAAAzSc/X+rcmYEEAFjw9OHD+lxenlbV1NiOcsIYSgAAWtXo\n0aNtR3CCMUbPPPOM7RhOoAtfPB7Xc889ZzvG6Wmm1Y1IJKIxY8Y0Q6DUFwwGNW7cONsxnFBXV6cJ\nEybYjuGEyspKvfbaa7ZjOKG4uFhTp061HcO6UDyuEWvXasWcOSpLoVuisr4BAI5Il/WNkpIS9enj\n+jnQLc8Yo7KyMvXu3dt2FOsSiYQqKirUq1cv21Gsi8fjqqqqUs+ePW1HOXU/+pH0yU9K9957Wg8T\ni8VUU1OjHj16NFOw1BWNRlVXV6fu3bvbjmJdOBxWMBhU165dbUexLhQKKRKJqEuXLrajWNfQ0KB4\nPK7OnTvbjmLVPw4d0s+3bNElnTtr42c+c8wVDtfWNxhKAIAj0mUoAaCNGzJEevll6dJLbScBgLQR\nisd13po1KopENG3IEH3tIwb9rg0lWN8AALSKsrIyxeNx2zGcUFpaqkQiYTuGE0pKSsQwrlFJSYnt\nCKevqko6cEC65JLTepg20UUzoQsfXfjowkcXjcYVF6uouFiX5OToqyl2tR1DCQBAq2DX0zd16lRu\ncZjE68L3+uuv245w+lavlq64QsrKOq2H4XXho4tGxhi6SKILXzwe1xtvvGE7hhOyYzF1XrNGfz7n\nHGWk2NcYrG8AgCNY3wCQ8v70p8b//dvf7OYAgDTUEI+rQ0bGcYcSrG8AAACgbVqxolnuvAEAOHmd\nMjNT7ioJiaEEAKCFbdy4UXv37rUdwwlr167VwYMHbcdwwvLly9kDTnrnnXdUXV1tO8bpi8Wkdeuk\nK6885YeYO3euAoFAM4ZKXTNnzlQ4HLYdwwkzZsxQLBazHcMJ06dP50yipGnTpnEmUdK0adNsRzgt\nDCUAAC0qOztbAwcOtB3DCR07dlRubq7tGE7o0qULt0NN6tGjR9u4veHmzdKAAVK3bqf8EGeddZZy\ncnKaMVTqGjBggNq3b287hhPOOeccZZ3mOSVtxaBBg5SRwbdwknTeeedxPlPSxz72MdsRTgtnSgCA\nIzhTAkBKe/ppadMm6YUXbCcBgLSwLxjUgA4dlHmSwxnOlAAApA0ut/XRRSNjDF0kGWPa1m1yV648\n5fMkEokEl6QnxeNxLklPogsfXfj4N6RRNJHQdevXa8jatdofDNqOc1oYSgAAWkRBQYEmTZpkO4YT\ntm3bphkzZtiO4YR3331X8+fPtx3DCUuXLtXy5cttx2g+pzGUmDt3rtavX9/MgVLTjBkztG3bNtsx\nnDBp0iQVFBTYjuGE8ePHq6ioyHYMJ7zwwgsqKyuzHcO6V0pKtG/SJMXq69W/QwfbcU4L6xsA4Ii2\nuL5hjGHfM4kufHTR6L0/722ii8OHpUsukcrLpVP4/fCa8NGFjy58dOGjCymWSOjCdeu0p6FBEy68\nUHecddZJ/XrWNwAAaSPdv2g4Gl346KKR53ltp4tVqxqvkjjF30+b6aEZ0IWPLnx04WvLXVRUVEiS\n6uvr5XneMdfaXist1Z5gUB/r1EnfbgOHRjOUAAA0u5UrV9qO4Ay68NFFI2NM2+viFFc3EomEVq1a\n1QKBUk8sFtOaNWtsx3BCJBLRu+++azuGExoaGpSXl2c7hhNqa2u1ZcsW2zGaVU1NjYYMGSKpcSDR\ns2dP7d69Wx07dpQk3XXXXR/4NXFj9JfNm6UDB/SHAQOU1QbuxpL6vwMAgFPi8bgaGhpsx3BCOBxW\nOBy2HcMJgUCgbR3qeBpqa2vb3i39TnEoUVlZqXbt2rVAoNRTWlra9I1Iujty5IjOOOMM2zGccPjw\nYXXp0sV2DCccPHhQ3U7jlsMuys7O1vbt2/XQQw+pR48ekqTzzz9fmZmZuvvuu/Xiiy8qGo1+4Nf9\nwPN0bf/+ur1Pn9aO3CI4UwIAHNEWz5QAkAaCQalnT6msTOrUyXYaAEgp1157rRYvXqxEIqE1a9Zo\n+PDhWr9+vYYNG6aMjAzddNNNmjVrVrM+J2dKAAAAoO14911pyBAGEgBwCubMmSNJ+p//+R9deeWV\nkqTLLrtMnufpz3/+s95+++2mK1A/+9nPtsm7eTGUAAA0m0cffZT7qCc9+uijtiM4gy58bbKLU1jd\nMMa0zS5OQSKR0GOPPWY7hhNisZieeOIJ2zGcEA6H9dRTT9mO4YRAIKDRo0fbjtFi2rdvr1tvvVVP\nPvmkEomEtm/fLklatGiR/vKXv0iSrr76aklSUVGRbrnlFmtZWwrrGwDgiLawvlFfX88ecBJd+OjC\n1ya7+NrXpNtuk2699aR+WZvs4hTRRSNjjAKBAF2ILo6WSCQUDAaVk5NjO0qLicfjysrK0re//W29\n+uqrTXcYMcbo6aef1s9+9jNVVlbK8zx169ZNI0eO1K9//esTeuzq6mq9/vrrevnll7V06dKmj7u0\nvsFQAgAc0RaGEgDS0Mc/Lk2d2rjCAQA4Jb/85S81atQohcNhlZSUaMCAAXr99df1jW98Q15Ghrre\nequKJk7U9VdfrZUrVyqRSLzv9qjGGK1atUoTJ07UxIkTVVdX94Hn6Nmzp+644w498cQTDCUAAB+U\nykOJkpISnXHGGW36pxgn6siRI+ratSun6Es6dOiQevXqpfbt29uOYt2BAwfUt29fZWdn247SvKJR\nqXNnqbpa6tDhhH7J/v371b9/f2VmZrZwOPft27dP55xzzvu+sUhXBQUFOvfcc+lCjV0MGjTIdgwn\npFMXxhhlZGTo2muv1TvvvKNevXqpvLxcxhj9+o039HhDg4aee65WDh2qTp06aeDAgerbt69Wr179\noY93ww036Lvf/a6+9rWvqXPnzu/7bxx0CQBoc5YsWdL2bnF4ihYvXsw3W0mLFy9WVlaW7RhOaLOv\ni/37pX79TnggIfH3xdGWLl3KN+FJy5Yto4ukZcuW2Y7gjHTqwvM8jRgxQosWLVJ9fb127NghSbrz\nv/9bL8bj0ubN2vj3v6tT8lDhwsJCrV69Wn379tVvfvMbbd68WcaYprd58+bpe9/73gcGEi7iSgkA\ncEQqXykBIE3NnCk9/bSUPD0eAHB6PjCc+8xnpAcekMrKdPZ99+nRBx/UV77ylabhxKk+h0tXSvDj\nCwAAAJya/HzpggtspwCANmPZsmW688479a1vfUu33367bg8ElFdfr6eGD9fP9u61Ha9FcO0cAOCU\n7d+/P60urfwoO3fu1Nq1a23HcMKmTZu0efNm2zGcsGbNGu3atct2jJaza5d0/vkn9KlLly5VYWFh\nCwdKDfPnz9eRI0dsx3DCrFmzVFlZaTuGE6ZPn/6hhxOmo3/9618KhUK2Y1jx2c9+Vvn5+XrggQdU\nfNZZypsxQ30yMnRX3762o7UYrpQAAJyy9u3b69JLL7Udwwk5OTk655xzbMdwwplnnqnc3FzbMZzQ\nq1evtv26yM+X/uu/TuhTc3NzNWDAgBYOlBrOPfdcnXXWWbZjOOGCCy5Q9+7dbcdwwsUXX5wS+/+t\nYdiwYepwEmfVtFXXdO2qcV/9qjoPGKCObfFcoiTOlAAAR3CmBICU06+ftHq1xLABAFKGa2dKsL4B\nADglDQ0NtiM4gy58dOFr813U1TXeCvTss4/7qW2+ixNkjKGLpEQioWAwaDuGE2KxmMLhsO0YTohG\no4pGo7ZjOCESiSgWi9mO0SoYSgAATlp9fb1efPFF2zGcUFlZqYkTJ9qO4YTi4mL961//sh3DCfv3\n79dbb71lO0bLys+XBg+WjnN7z507d2r+/PmtFMpteXl5Wr58ue0YTli1apXWr19vO4YTFi1apG3b\nttmO4YTZs2dr9+7dtmM4YcaMGTpw4IDtGK2C9Q0AcATrGwBSymuvSdOnS1Om2E4CADgJrG8AAAAg\n9eXnn/CdNwAAJ2ZRVZX+UFCgijRaY2EoAQA4KVyG7aML34IFC2xHcEbavC527ZIuuOAjPyVtujgO\nYwxdJBljtHDhQtsxnBCPx/XOO+/YjuGEaDSqxYsX245hnTFGf9i5UyNmztTYNLptMEMJAMBJ6dix\no+0ITjDG0EUSXfji8bhycnJsx2gdx7lSIhKJqEuXLq0YyF3BYJDbXibV1dWpZ8+etmM4oaamRn36\n9LEdwwlVVVXq16+f7RjWvVNdrVWHDunMs87SPWnUB2dKAIAjOFMCQMowRjrzTKmwUOrWzXYaAGgT\nPp+Xp6U1NXrw3HP1+4EDW+x5OFMCAJCSGJj46MJHF7606qK4WOrQ4ZgDibTq4jjowkcXPrrw0UWj\nxVVVWlpdrW5ZWbo3N9d2nFbFUAIAcEJGjhypUChkO4YTHn74Ye6jnvTQQw8pkUjYjuGEBx98MH2+\nuN616yNXNx588MFWDOO2Bx54wHYEZ/C6aGSMoYukRCKhESNG2I7hhAXl5dKrr+pXZ5+tLllZtuO0\nKtY3AMARrq9vRKNRZWdn247hBLrw0YUvrboYM0Zas0YaO/ZD/3NadXEcdOGjCx9d+OjCt6ayUh/v\n0kVntvBQwrX1jfQawQAAThlfMPjowkcXvrTq4jhXSqRVF8dBFz668NGFjy58n07TA3FZ3wAAfKSK\nigoVFRXZjuGEkpISlZaW2o7hhKKiIlVUVNiO4YQDBw6opqbGdozWlZ//obcD3bdvn+rr6y0Ecs+e\nPXsUDAZtx3DCrl27FIlEbMdwwo4dOxSLxWzHcMLWrVtZ/0vaunVr+qz/fQiGEgCAj7R582Z+ipG0\nadMmtW/f3nYMJ2zcuFEdOnSwHcMJadnFMa6USMsujoG/L3xbtmzh35Gkbdu2KSvNzgs4lh07digj\ng29HpcYuPM+ZbYpWx5kSAOAI18+UAABJUjQqde4s1dRIfNMNAKcsYYwyLAwjXDtTgtEUAAAATty+\nfVJuLgMJADhNX9u6Vd9+913tKC6WJE2bNk1TpkyR1Hi3mptvvtlmvFbDUAIA8KEqKyub/mFMd8XF\nxZo+fbrtGE44cOCA3n77bdsxnJCfn6+FCxfajtH6du36wHkSmzdv1sqVKy0FcsvatWu1YcMG2zGc\nsHTpUm3fvt12DCfMnTtXBQUFtmM44c033+SsKklra2v15tSpmlRQoIuSf6e+8soruvXWWyVJffv2\n1dtvv50W57GwvgEAjnBtfaOhoUGRSERdu3a1HcW6+vp6JRIJdenSxXYU62pqapSVlaWcnBzbUayr\nrKxUp06d0u8Mhccekw4elJ58sulD5eXl6tKli9q1a2cxmBtKSkrUs2dPZWZm2o5iXXFxsfr06ZPW\nu/LvOXLkiM466yy6UGMXffv2tR3Dui9v3qxZu3fru716aeLnP6+SkhJ16NBBZ555pmbNmqWbbrpJ\nnufpnnvu0TPPPNOsz+3a+gZDCQBwhGtDCQD4UD/+sTRsmHTPPbaTAEBKere2Vpdv2KBOGRnaf+WV\n6t2+vYYPH66VK1c2Da6MMbr66qu1bNmyZr8zh2tDCdY3AAAfUF5ebjuCM+jCRxe+tO4iP/99d95I\n6y7+DV346KKRMYYukowx3Eo66a8FBVJdnX6am6te7drpZz/7mVatWiWpcdVHkqqrqzVt2jRJ0rJl\nyyQ1XmVSWFhoJ3QLYigBAHifRCKhyZMn247hhFgspn/961+2YzghHA43fXGU7gKBgN58803bMew5\n6kyJqqoqzZ4923IgN5SUlOidd96xHcMJBw4c0IoVK2zHcMLu3bu1fv162zGcsHnzZm3bts12DOvi\nxsjLz1dOUZF+3b+/JOmxxx6TJD333HO68cYbJUm33XabevbsKUm6+uqrJUl33nmnzjnnnNYP3cJY\n3wAAR7C+AcB5tbVSv36N/5vBz7YA4FTVxmLqkpXV9H936dJFdXV1Msbo+9//viZMmCBjjKZOnapv\nfvObCgQCqqqq0tlnn60NGzZo2LBhp/zcrG8AAAAgNeXnS4MHM5AAgNN09EBCkpYvXy5JKiws1PPP\nPy9Jeu211/SNb3xDUuNVErm5uZKkq666qhWTtjz+RQEANJkxY0azH6aUqujC98Ybb9iO4IwZM2bY\njmDXrl1N50nwuvCl/eviKLwuGhljeF0k0YUvkUgcc/3vkksukSTdeOONTXd1+s53viNJ+spXvtK0\nWjt27FiFQqFj3iq0tLRUixYt0tNPP6177rlHV199tXr06CHP85reXJN1/E8BAKSL/v37O/mPlQ10\n4RswYIDtCM7on9z/TVubN0vJL5x5XTQyxtBFUiKRaJP77qciFotp0KBBtmM4IRwOa/DgwbZjOCEY\nDOqC5Jk8H+b+++/XX/7yFxljtGrVKg0fPlw7duzQj3/8Y7355pu67LLLmm5P3r59+5N67l69emnI\nkCEaMmSIRo8efVq/j+bGmRIA4AjOlADgvBtvlH7xC+nmm20nAYCUciAU0oDkFRDHkkgklJmZecKP\n+dnPfrZp0PDeW+/evY/7QxXXzpTgSgkAgBKJhDLYEZdEF0ejCx9dSDJGystT4pOfZP83ideFjy58\ndOGji0bbAgFdvGaNvtWnj1676KJjDg0yMjK0bt06rVixQhdddJEuuugi9evXr81fuckrBACgcePG\nqaioyHYMJzz77LPcRz1p1KhRqqursx3DCSNHjlQwGLQdw66iIsnz9MiECYpGo7bTOGHEiBGKx+O2\nYzjhwQcf5ByepAceeMB2BGc8+OCDtiM44YHCQpmXX1aP7OzjDhg+9alP6Re/+IVuuOEG5ebmtvmB\nhMT6BgA4w+b6hjEmLf7ROxF04aMLH11ImjlT+sc/ZObMoYskXhc+uvDRhY8upB2BgIasW6dMSQVX\nXqn+x1nhaA2urW9wpQQAIO2/YDgaXfjowkcXkjZulIYOpYuj0IWPLnx04aOL5FUSku7q18+JgYSL\nGEoAQBoLBoPavHmz7RhOqK2t1fbt223HcEJFRYV2795tO4YTiouLtX//ftsxnHBoxQod4i4TkqT9\n+/erpKTEdgwn7N69m5W3pB07dqimpsZ2DCds2bJFgUDAdgzrdjU06LWVK5UVjep3/P15TAwlACCN\nFRYWKicnx3YMJ+zfv1+dO3e2HcMJ+/bt05lnnmk7hhPowlewaZO6Dh9uO4YTCgoKeF0k7d+/v+kW\nhemusLBQZ5xxhu0YTjh06JA6depkO4Z1PbOz9U3P08/OOee4d95IZ5wpAQCO4JagAJxVUyOdfbZU\nXS2dxO3qAADu4UwJAAAApJaNG6WLL2YgAQBodgwlACANhUIhjR492nYMJwQCAT3//PO2Yzihurpa\n48aNsx3DCWVlZXr55Zdtx3DCoUOHNHncOGnoUNtRrNu7d69mzJhhO4YTtm3bpjlz5tiO4YT169dr\nyQuCGI4AACAASURBVJIltmM4YdmyZVq7dq3tGE6YN2+etmzZYjtGSmB9AwAc0ZrrG4lEQvX19ewB\nq7GLQCDAeRKS4vG4gsEgO9GSotGootEoO9GSIpGI4nfeqY6f/7z0ox/ZjmNVKBSSJHVgN1zBYFCZ\nmZlq166d7SjWBQIBtW/fXllZWbajWFdXV6ecnBxlZPCz79raWnXu3NnJO5C4tr7BUAIAHMGZEgCc\nNXSo9MIL0uWX204CAE47EAppV0ODru/WzcmBhOTeUIIRFgCkmcLCQtsRnEEXPrrw0YWvsLBQCoel\n/PzGMyXSGK8LH1346MJHF43+tn+/bpw3T3+jjxPGUAIA0syCBQtsR3AGXTQyxmjhwoW2YziBLnzx\neFyLFy+Wtm2TzjtPSuOVhUgkoqVLl9qO4YSGhgatXLnSdgwnVFdXa926dbZjOKGsrEybNm2yHcO6\nwlBIL27ZIu/AAd3au7ftOCmD9Q0AcATrGwCcNG6ctGiRxMGfAPCR7snP13NFRfpO79565aKLbMc5\nJtY3AAAAkDry8qRhw2ynAACnHQyFNPbIEXmS/jhwoO04KYWhBACkiRkzZigQCNiO4YSpU6cqHA7b\njuGEyZMnKxaL2Y7hhEmTJimRSNiO4YRXX31VTVdubdyY1rcDffXVV21HcAZd+OjCRxeNnjp8WNH5\n8/WtXr10YU6O7TgphfvWAECa+PjHP64c/pGUJH3iE59Q+/btbcdwwtChQ7mNXdKwYcO4jV3SZZdd\n1nhqfCIhbdqU1kOJyy67zHYEZ9CFjy4aGWP0qU99ynYMJ9w/YICyb7xR3z3nHNtRUg5nSgCAIzhT\nAoBzdu+Wrr9e4hR5AGgzOFMCANCq4vG4otGo7RhOiEajrCokRSIRxeNx2zGcEA6HWdtICofDet9w\ndOPGtD1PIhQKiUFxI7rwhUIh2xGcQRe+luoiEok0vV9QUNAiz+EChhIA0MbNnz9fW7ZssR3DCTNn\nztTu3bttx3DCtGnTdODAAdsxnDBp0iQVFxfbjuGE8ePHq6Kiwv9AGh9yOXbsWNXV1dmO4YTnnntO\nwWDQdgwnjB49+n3fKKazUaNGMdxOevLJJ095cFdQUNC4Liepvr5enuc1DYjbt2+vw4cPS5LOO+88\nLVu2rNkyu4T1DQBwBOsbAJxz003S3XdLX/2q7SQA0CbFYjFlZ2fr1Vdf1be//W15nqff/va3evjh\nh+V5nq677jotWLBAnuepW7duqqysPO3ndG19g6EEADiCoQQA5/TtK61ZIw0YYDsJADjniYMH1add\nO93au7cyvVP/Hv+9KyWMMRowYIAOHjwoY4xuueUWzZgxQ8YY/e1vf9Of//znZlmlcm0owfoGALRR\n8XhcS5YssR3DCdFotM1e8niyQqGQVq5caTuGE+rr67V27VrbMZxQVVWlvLy893+wuFgKh6X+/e2E\nsqSkpERbt261HcMJhw4dUn5+vu0YTigoKND+/fttx3DCrl27mlYK0llJJKLfLVyo21eu1LbTvOX6\niy++KElKJBKaNm2aJKm6+v+zd9/hUVbp+8Dvk0oCofcOogIqIiC6WBbr2svadVV015/r6tpdsXwt\nK4gggsiiCIgF6QKCdAiGEggQSEgIJIGQSkJ6MunTzu+PGXhBWsrMnDMz9+e6uDaZzMy5ffZlmDxz\nShlmzpwJAEhMTMQ777wDwHGUt69hU4KIyEeVlZWhXbt2qmNoobi4GB06dFAdQwuFhYXo1KmT6hha\nKCgoQOfOnVXH0EJ+fj66dOly6o3HN7lswqd/3qigoOD0Wvgp1sJQVFTE104n/pvqMDE7G3UmE+7u\n0weDWrRo0nM9/fTTABx7Uxw/YvWll146UeeHHnroxPHdjz76aJPG0hGXbxARaYLLN4hIK+PGAUVF\nwBdfqE5CRKSVQrMZvWNiUG23I3boUAyNiGjyczZr1uzEBpcjRozAjh07IKXESy+9hGnTpkFKie+/\n/x7PPvssbDYbAgIaP7+AyzeIiIiISH9+fBwoEdG5fJGdjWq7HXe2beuShgQArFixAoBjmeXcuXMB\nAFlZWZgwYQIAYNOmTRg1ahQAx4wKX8KmBBGRj5FSYuzYsapjaMFut2PcuHGqY2jBarVi/PjxqmNo\noba2Fl/w038Ajn01pkyZcuYf+tlxoCUlJfj6669Vx9DCsWPHMGvWLNUxtJCVlYU5c+aojqGF1NRU\nLF68WHUM5exSYvnOncCOHfigd2+XPe+tt94KAHj33XfRp08fAMCTTz6J8PBwAMC9994LIQRCQkLw\nxhtvuGxcHXD5BhGRJly5fKOurg6hoaEueS5vx1oYWAuD2WxGSEiI6hjKSSlhsVhOr0VFBdC5M1Be\nDjjXMfu6s9bCD7EWBrvdDpvNhuDgYNVRlLPb7bDb7Sf2NvBnZqsVv5eW4i8u3lujX79+SEtLg5QS\nDz/8MBYvXgwpJT777DO88847kFJi3bp1uO2221BTU4NmzZo1eIyamhqEh4drtXyDVxQRkQ/iL54G\n1sLAWhj4y5bD8U/dTpOYCAwc6DcNCeActfBDrIUhICCgSWv3fQlrYQgJCnJ5QwIAlixZgsGDB+OL\nL744scnsddddhwjnEhEhxInNL8PCwlw+viqcKUFEpAlXzJRISUlBv379EBgY6KJU3uvgwYPo37//\nibO//dmBAwcwYMAA1gKOWgwcOFB1DC0cPHgQAwYMOPMPZ8wAYmKA2bM9G0qRc9bCz/DviIG1MLAW\nBne/XjTk3+qePXuibdu25/3Tpk0btGnTBm3btkV4eDgCAgI4U4KIiNwjPj4eF198seoYWkhISOAv\nGU6JiYl8M+mUkJDAWjid8+/I/v3ApZd6NpAiUkq+XjhJKfl64WSz2ZCUlMRawLHcLTk5mbWAY+nD\noUOH3Pp64Y+TBjhTgohIEzwSlIi0ceONwOjRgHPjNSIifyel9JkZhzwSlIiIiIj05kczJYiIzqfM\nYsElu3fjy+xs2PkBksuxKUFE5AM2bNiAjIwM1TG08Ntvv+HYsWOqY2hhyZIlKCkpUR1DC/Pnz0dF\nRYXqGFqYM2cOamtrz36HggLAagWcm6z5su+//x4Wi0V1DC189913sNvtqmNoYebMmX45hf5MZs6c\nqTqCFqYePYqDixZheXExAnxktoROuHyDiEgTTVm+kZOTg27duvnMtMKmyMnJQffu3VXH0AJrYWAt\nDOetxaZNwEcfAVu2eCyTKrwuDKyFgbUwsBaAyWpF75gYlObl4febb8bINm1UR2oy3ZZvsClBRKQJ\n7ilBRFr46isgORn4+mvVSYiIlPs0MxPvpafj+latsPmKK1THcQndmhJcvkFE5MWklCgtLVUdQwtS\nSpSVlamOoQWbzQaTyaQ6hhYsFgsqKytVx9BCbW0tqqurz39HP9hPoqam5txLWPxIVVUV6urqVMfQ\nQmVlJZfzOJlMJlitVtUxlKuwWvH5wYOAzYYPevdWHcdnsSlBROTFUlJSsHPnTtUxtBAfH499+/ap\njqGFnTt3Ijk5WXUMLWzZsgXp6emqY2ghMjISR48ePf8d/aApsWbNGhQUFKiOoYUVK1awoeu0ZMkS\nNjGdFi1axMYdgHyzGW22bMGfwsNxY+vWquP4LC7fICLSBJdvEJFyUgKtWgHp6UC7dqrTEBEpJ6VE\nudWK1sHBqqO4DJdvEBEREZGesrOBFi3YkCAichJC+FRDQkdsShAReamVK1eqjqAFKSVr4WS327F6\n9WrVMbRgtVqxdu1a1TG0UFdXh/Xr19fvzj6+dKOqqgqbNm1SHUMLZWVl2Lp1q+oYWigsLERMTIzq\nGFo4evQo9u7dqzqGFo4cOYKkpCTVMfwCmxJERF7IbDajR48eqmNooba2Fr25+RQAxy9cffv2VR1D\nC5WVlbjgggtUx9BCRUUFLrzwwvrd2cebEg2qhY9jLQyVlZXo16+f6hhaqKqq4munU01NDfr06aM6\nhl/gnhJERJrgnhJEpNxTTwEjRwLPPqs6CRGREjU2G2rsdrT14SUb3FOCiIiaxG63q46gDdbCwFoY\nWAtDg2vhwzMleF0YWAsDa2FgLRy+zc1Fz+3bMSM3V3UUv8GmBBGRl/nkk09UR9AGa2EYM2aM6gha\nkFKyFk52ux1jx46t/wNsNiA5GRg40H2hFLFarRg3bpzqGFqoq6vDhAkTVMfQQlVVFSZNmqQ6hhbK\nysowdepU1TGUq7HZ8Om+faj69Vd0CQlRHcdvcPkGEZEm6rt8w263IyCAPWWAtTgZa2FgLQwNqkVq\nKnDbbcCRI+4NpQivCwNrYWAtDKwF8FVODl45fBiDw8Ox98orIYQ2Kxxciss3iIioSfz9DcPJWAsD\na2FgLQwNqoUPL90AeF2cjLUwsBYGf69Frc2G8VlZAIAP+/Tx2YaEjvz7yiMi8iIpKSkwmUyqY2gh\nKSkJ1dXVqmNoISEhAWazWXUMLcTFxcFqtaqOoYU9e/ZwPwmn2NhYcGawQ2xsrOoI2mAtDKyFw3fH\njiE3MRGXN2+Oe9u3Vx3Hr7ApQUTkJQ4fPozmzZurjqGF9PR0hIWFqY6hhczMTAT78A7hDZGTk4Og\noCDVMbSQm5vb8E89fbQpkZeXx088nfLy8lRH0AZrYWAtHEZEROAqiwUf9O7N1wwP454SRESa4JGg\nRKTUwIHAggXAoEGqkxAR+RUhBPbt24dBgwahXbt2WLJkCUaOHImLL74YY8eOxYMPPujy8XTaU4JN\nCSIiTbApQUTK1NUBrVsDZWVAaKjqNEREfkUIgQEDBuDAgQMQQiAsLAzV1dUnZmy4+v2hbk0JLt8g\nItJcbGwstm3bpjqGFrZu3Yo9e/aojqGF9evXIykpSXUMLaxYsQJHfPTEiIZatGgRcnNzG/7AlBSg\nTx+fakjMmTMHxcXFqmNoYfbs2dyTyOnbb79FTU2N6hhamDZtGiwWi+oYWpg6dSpsNpuy8Z944gkc\nPHgQAPDee++duEZnzpwJwPVNCd1wpgQRkSbONlPCZDIhPDyca+XhOEe9ZcuWfr9DOACUlpaidevW\nXPcKoKSkBG3btlUdQwuNrsW8ecCvvwKLFrk+lCK8LgylpaVo06aN6hha4HVhYC0MqmtRUFCATp06\n4ciRI+jcuTPCw8Oxfft2XHXVVQgMDMTChQvx8MMPu2w83WZKsClBRKQJLt8gImXefRdo1gz44APV\nSYiIPGZbWRm6h4aitwabZwshcPvtt2P16tVnXc7hyrF0akrwoyYiIo3l5OSojqAN1sJBSslaOEkp\ncfToUdUxtGCz2Rq3bOM4Hzp5w2KxID8/X3UMLdTW1qKwsFB1DC1UVVWhpKREdQwtVFRUoLy8XHUM\n5Sx2O57YvRv9oqIQo0E9rrnmGqxZswbA2ZdzlJSUHG8oKMvpDmxKEBFpqrS0FDt27FAdQwv5+fnY\nu3ev6hhayM7O5l4STocOHcLhw4dVx9BCUlISsrKyGv8EiYk+05SIi4vDsWPHVMfQwq5du/iLuFN0\ndDT31XCKiopy6afu3mpufj6yduxA78BAXNmypeo4mDFjBgDHUtVJkyYBcByB/t577wEAduzYgdat\nWwMAFi9erCakm3D5BhGRJrh8g4iUqKgAOnVy/G9goOo0RERuZ7Xb0X/XLqTV1uKn/v3xZOfOqiMB\ncCyreOGFF/D111+7dTkHl28QEXk5IUR3IcRmIUSiECJZCPEf5+1thBDrhRD7hBBrhRCtTnrMFCFE\nkhBijxDiCnXpiYj+4MABoH9/NiSIyG/MLyhAWm0t+oWF4bGOHVXHOaFbt2745ptvANRvOYevYFOC\niKjhLABelFJeBmAYgL8LIQYB+BjAainl5QDWAvgvAAgh/gqgp5TyEgD/APD9+QZYuHChu7J7HdbC\nsGDBAtURtCCl5HXhZLfbsaipJ2b4yH4SVqsVv/zyi+oYWjCbzVi6dKnqGFqorq7GihUrVMfQgslk\nwurVq1XHUE5KibGJiUBsLN7v1QtBGp3o9f33jreIFoulXss5fAXPlyMiaiApZT6AfOfXlUKIRADd\nAdwJYLjzbj8D2AHgFeftPzvvHyeECBRCdJNSnnWHvssuu8yN/wXeQ0qJQYMGqY6hBbvdjsGDB6uO\noQWr1YorruCEI8DxxnXo0KGNfwIpgRUrgJEjXZZJFbPZ3LRa+JC6ujoMGzZMdQwtmM1m1sKpya8X\nPkIIgZ8uvBA/t2mDJzSaJQEAt9xyCwAgJCTkxG0nH+fbt2/fE1+PGDHCc8HcjHtKEBE1gRCiN4Ao\nAJcBOCqlbHnSz8qllK2EEOsA/J+Ucpfz9rUAPpRS7vzDc3FPCSLyrC++AObNA7ZtAzQ4Eo+IyN/V\n1NSgtLQUgOMDiQDnTI4/fi2EgBCN2xaiW7duWu0pwZkSRESNJIRoAWAxgFeklBVCiCZ3FMxm8ynd\ncX/GWhhYCwNrYWhyLTZtAj7/HNi50+sbErwuDKyFgbUwsBYG3WsRFhaGMC9/TW4ofRbQEBF5ESFE\nEIBfAMyVUi533lwohGjn/Hl7AAXO23MA9Djp4d2dt52mV69eePLJJ/HRRx/hyy+/RFRU1ImfRUVF\n+dX3L774IiIjI7XJo/L7iRMn4vfff9cmj8rvP//8c63yqPz+hRdeaPzjFy5E1IMPOmZJ9OqlxX9P\nU75/4YUXtMqj6nspJT7//HNt8qj8/vfff8fEiRO1yaPy+8jISLz00kva5FH5vdlsxssvv6xNHk99\n/+WXX+Kjjz7CqFGjtFwKyuUbRESNIIT4CUCRlPL1k277CsARKeWXQojXAPSRUr4shHgAwBNSyr8K\nIYYA+N65GeYfn5PLN4jI/WpqgOuuAx59FHjzTdVpiIjIw3Q7EpRNCSKiBhJCXANgC4BEANL5510A\nuwAsBNAJwDEAD0spy5yP+R+AGwDUAfi7lDLuDM/LpgQRuZeUwLPPOhoT8+cDjVyPTETkTexS4rXD\nh/F0584YEhGhOo5yujUluHyDiKiBpJTRUspAKeVgKeUVUsohUsq1UsoSKeUtUspBUspbjzcknI95\nSUp5ifO+pzUkyJCamoq8vDzVMbSQlJSEoqIi1TG0EB8fj7KysvPf0Q/ExsaisrKycQ/+5hsgNhb4\n7jufaEjExMSgrq5OdQwtREdHw2q1qo6hhS1btsBut6uOoYXNmzeDH3gASwsL8dWqVbhv/35YeW1o\nh00JIiLSSn5+Ptq1a6c6hhaKiopOOQrMn5WWlqJVq1aqY2jBZDKhefPmDX9gdDTw0UfAsmVAYx6v\noerqaoSGhqqOoYW6ujoEBXEPe8BxbPDxUwr8nc1ma/QJDb7CLiU+ycwE7HaM7tkTQbw2tMPlG0RE\nmuDyDSJym9xc4MorgZkzgTvuUJ2GiMhjlhUW4q9JSegWEoK0q69GKJsSXL5BRERERB5kNgMPPQT8\n859sSBCRX5FS4r+ZmQCAt3v2ZENCU/x/hYiItJCSkoKlS5eqjqGFffv2Yc2aNapjaCEmJuaUo838\nWWRkJHbu3NnwB776KtC+PfDee64PpcjKlSuRmJioOoYWFi9ejMOHD6uOoYU5c+YgJ+eMJ277nVmz\nZqGwsFB1DOVSqqtxcN48dDKb8VyXLqrjKDdlyhRUV1erjnEaLt8gItKEvy/fsFqtkFIiODhYdRTl\nLBYLhBBcHw7HmfJBQUFcHw6gtrYWoaGhDVsf/v33wGefAbt2AT60J0ejauGjamtr0axZM9UxtMBa\nGFgLw9GKCmTa7RjhQ6+BjXX8utBt+QabEkREmvD3pgQRuVhsLHD77cCWLcCAAarTEBGRJnRrSvBj\nByIiUi4lJUV1BG2wFgbWwtDgWhQUAA88AHz7rU81JKSUSE1NVR1DC3a7HYcOHVIdQwtWqxVpaWmq\nY2ihrq4OGRkZqmNooaqqCtnZ2apjaMFkMml93DqbEkREpJTZbEZCQoLqGFqorq7GgQMHVMfQQllZ\nGX/hciosLESmc6O2erFagUceAZ54AvjrX90XTIGjR49q/cbak9LT01FcXKw6hhZSU1NhMplUx9DC\ngQMHtNwzQIWEhARYLBbVMbSwd+9e2O121THOiss3iIg0weUbROQSb7wBJCUBq1YBgYGq0xARedTx\n91Lcc+bsuHyDiIiIiNxj/nxg2TJg3jw2JIjIL20oLcW1cXH4vbRUdRSqJzYliIhImdmzZ6uOoA3W\nwvD999+rjqCNBl0XCQnAyy87mhJt27ovlAJSSv4dcZJS8u+Ik81mw48//qg6hhYsFgvmzJmjOoZy\nUkp8kJyM7UuXYieX9KCiogKLFi1SHeO8uHyDiEgT/rh8IzMzE7169VIdQwushYG1cJBSIisrq361\nKCkBrrwS+OQT4PHH3R/Ow+x2O3JyctCzZ0/VUZSz2WzIy8tD9+7dVUdRzmKxoLCwEF27dlUdRbm6\nujqUlpaic+fOqqMotbGkBLfs3o3WZjOybr8dEX5+tHZlZSVqa2vRvn37U27XbfkGmxJERJrwx6YE\nEbmAzQbcdRfQvz8webLqNERESkgpcX18PLaVl2Nsnz5418ua20uWLMEDDzwAwLEJeEhIiNvG0q0p\nweUbRETkcZWVlbDZbKpjaKGiokLrHbE9yWQygY05hwbV4sMPgZoaYMIE94ZShNeFgSdMGFgLA2vh\nEFVWhm25uWgTFISXunVTHadB7HY7HnzwwRPHuYaGhp44dUkI0aijTb3pumBTgoiIPG7u3Lk8sszp\np59+gtlsVh1DCz/88AOsVqvqGFqYPXt2/ZpVv/4K/PQTsGgREBzs/mAKzJo1S3UEbcycOZMNGqeZ\nM2eqjqAN1sKhxGpFxNq1eL17d7T0smUbAQGOX8snnzTb7eSvJ06c2KDnk1J61XXB5RtERJrg8g0i\napDkZOC66xxHfw4frjoNEZFyNTYb7ACae+HpQ8ePMJVSnvVrV47F5RtERERE1HgmE3D//cBnn7Eh\nQUTkFBYY6JUNCQB45ZVXTnz96quvnvj6tddeUxHHo9iUICIijzl69Cj27dunOoYW0tPTcfDgQdUx\ntJCSkoK0tDTVMbSQmJh4/rXDdjswahTw5z8Df/+7R3KpsGfPHhw7dkx1DC3ExMSgpKREdQwtbN26\nFRUVFapjaGHTpk2ora1VHUMLGzZsgMViUR2jSV5//XUAQFVV1SlfH29KVFVV1et51q5d63V7VbEp\nQUREHmMymdC7d2/VMbRQWVnJYy+dqqurebyhU11dHbp06XLuO332GZCXB0yZ4plQithsNnTs2FF1\nDC0IIdCmTRvVMbQQEhKCiIgI1TG00Lx5czRr1kx1DC1EREQg2Mv31Tl+5PHs2bPRo0eP077+7rvv\n6vU8bdq0ObFHhbfgnhJERJrgnhJEdF5r1zpmR+zeDXTtqjoNEZFShWYz2gYHI1Bosz1Ckwgh0K1b\nN+Tk5Jz2dZcuXZCbm+uycbinBBER+R02XAyshYG1MJy3FkeOAE8/DSxc6PMNCV4XDlJK1sKJtTCw\nFoa/HTiAS3btQpyPLOm56667cPToUQDA3XfffcrXeXl553ysN18TbEoQEZHblZSU4H//+5/qGFo4\nduwYZsyYoTqGFjIzM/HTTz+pjqGF5ORkLFq06Ox3qKpybGz5f/8HXHut54IpEBcXh5UrV6qOoYXt\n27cjMjJSdQwtbNy4ETExMapjaGHFihVISEhQHUO5nSYT1i9diuy0NPT0kWUsb7/9NgBHg+Hkr0eP\nHn3i67P54YcfkJWV5f6QbsDlG0REmvD15Rs2mw2BXrojtquxFg5SStjtdtYCxiefZ1wHLCXwxBNA\ncDDwww+Aj0xTPptz1sLP2O12CCFOHAnoz2w2GwICAlgL8N+Q4+5MSMDqwkKM7tMH4/r2VR3HZYQQ\nuO+++3DPPffg2Wefxc8//4zHH38cAQEBWLlyJe68884zPq4h14VuyzfYlCAi0oSvNyWIqJEmTwZ+\n/hnYtg0IC1OdhohIud0mE4bv3YvmAQHIuPpqtA8JUR3JZYYMGYK4uLh6379r16646KKLTvvTt2/f\ns27+yaYEERGdka82JeLi4nDFFVeojqEF1sLAWhjOWYvNm4FHHgF27gT84LQWXhcG1sJBSon4+HjW\nAo6ZMwkJCRg8eLDqKMrdHR+PlXv24K2RIzHhggtUx3G74uJiJCYmIj09HWlpaUhNTUVKSgpSU1Mb\nfSysTk2JINUBiIjId0kpkZmZyTeTcNQiKyuLtYBjiml2djZrAcBsNiM3N/fMtaipcZy08d13ftGQ\nqKmpQUFBgeoYWjCZTCgtLVUdQwvFxcWorKxUHUMLx44dQ11dneoYWrjJYsHhZs3wpvO4TF/Xrl07\njBw5EiNHjjztZ6mpqbBarRg4cOApt5vNZqSnpyM1NfW0P646xcNVOFOCiEgTvjpTgoga6f33gdRU\n4FwbYBIRETUQl28QEdEZsSlBRCccPAhcfz2wb5/PH/9JRESepVtTgtsaExGRW0yfPh0Wi0V1DC1M\nmzYNdrtddQwt/O9///Pqs9Rd6azH5EoJ/POfwIcf+k1DYurUqaojaIPHJztIKXldOEkpeV042e12\nTJs2TXUMLVgsFkyfPl11DJfgTAkiIk342kyJoqIitG/fXnUMLbAWBtbCcNZafP898PXXQEwM4CfH\n/vG6MLAWDlJKFBcXsxZgLU5mt9tRWlqKdu3aqY6inM1mQ3l5Odq2bdvgx+o2U4JNCSIiTfhaU4KI\nGqGoCLjkEmDNGmDIENVpiIi0sbKoCDe1aYMwP2nWupNuTQku3yAiIpeqqKhARUWF6hhaKC8vR1VV\nleoYWigtLW30sWW+pri4+Ow76L/1FvD4437TkCgqKuIyL6eCggJYrVbVMbSQn5/PJW9Ox44d45I3\nAElVVbh782b037kTdbw2kJeXpzqCS7EpQURELrVp0yYe3+a0fv16/iLutGbNGpjNZtUxtLBy5UrY\nbLbTfxAVBURGAv/9r8czqbJixQr+wuW0fPly1RG08euvv6qOoA3WwuGTjAwgOhp3t2+P0AD+x3U+\n8QAAIABJREFUCutrrxdcvkFEpAku3yDyY3V1wOWXA+PGAfffrzoNEZE2DlRV4dLduxEsBNKuugrd\nmzVTHcnrcfkGEREREZ1qwgTgoouA++5TnYSISCtjMjMhATzbpQsbEj6KTQkiInIJk8mEdevWqY6h\nheLiYvz++++qY2ghLy8P27ZtUx1DCxkZGdi9e/fpPzh0CJgyBZg6FRDafHDlVikpKUhISFAdQwsJ\nCQlISUlRHUMLsbGxyMjIUB1DC9HR0cjNzVUdQ7kjNTWYv24dgsrL8U7PnqrjKLdu3TqYTCbVMVwu\nSHUAIiLyDXV1dbj88stVx9CC2WzGZZddpjqGFqxWKy699FLVMbQgpcSAAQP+eCPwr38Bo0cDvXqp\nCaaAEAIXXXSR6hhaCA4ORt++fVXH0EJYWBh69OihOoYWWrVqhS5duqiOoVyfZs0w/corUdWzJ3py\nlgS6du2Kli1bqo7hctxTgohIE9xTgsgPzZsHjB8PxMYCwcGq0xARkR/gnhJERORzeKSfgbUwsBaG\nM9aitBR44w3g22/9qiHB68LAWhhYCwNr4SCl5DG5Tna7/cynNvkINiWIiKhJrFYrJk6cqDqGFurq\n6jB58mTVMbRQVVWFqVOnqo6hhdLSUkyfPv30H7zzjmNjy6uv9nwoRfLz8zF79mzVMbSQlZWFefPm\nqY6hhdTUVCxdulR1DC3s27cPa9euVR1DCzt27EBUVJTqGB5XWlqKVq1anXLbpk2bsGvXLkWJ3I/L\nN4iINOHNyzeklBB+skHf+bAWBtbCcFotduwAHngAOHAAaN1aXTAFeF0YWAsDa2FgLQz+WIvY2Fhc\neeWVOPk9oavrwOUbRETkc/ztDcO5sBYG1sJwSi0sFuD554EvvvC7hgTA6+JkrIWBtTD4ey3Sa2rw\n3pEjKLZY/LIWERERp93m63VgU4KIiBpt27Zt8NbZHa7GYy8NrIXhjLX48kugc2fg0Uc9H0ghXhcG\n1sLAWhhYC4exmZn4dNUqvJ2WpjqKEiefriGlRHR0tMI0nsEjQYmIqNFqamp8vntfXzU1NaojaIO1\nMJxWi8xMx2kbMTGAH/3dkVKitrZWdQwt2O121NXVqY6hBYvFwk0dnWpra9nkB5BZW4sf0tMhAgLw\nn549VcdR4uSmRGlpKUJDQxWm8QzuKUFEpAlv3lOCiOpBSuCee4CrrgLef191GiIi7fwzJQXf5uXh\niY4d8fPAgarjKCGlREBAACwWC4KC3DOHQLc9JThTgoiIiMgTli0D0tKAX35RnYSISDtZtbWYfewY\nBID3e/VSHUeZ4zNQKysr0aJFCwQHB/v8LBruKUFERA02a9YsFBcXq46hhW+++QYmk0l1DC189dVX\nXLrhNHny5FOnpVdUAK+8AkyfDvjBVNyTTZw4ETabTXUMLUyYMMHnf7mor/Hjx7MWThMmTFAdQQur\ni4thmTcPj3TsiP7Nm6uOo9yECRP8ZnkTl28QEWnCm5ZvVFdXIzw8XHUMLbAWBtbCcFotXn0VMJmA\n2bPVhVKE14WBtTCwFgbWwrA9Px+dWrbEBWFhqqMoJYTA7t27MXToUAQEBMBsNiM4ONilz8/lG0RE\n5NX45snAWhhYC8Mptdi7F1iwANi/X10ghXhdGFgLA2thYC0MIzp1Uh1BG5aTjkStqKhA27ZtFSdy\nHy7fICKiequrq0N2drbqGFqorq5Gbm6u6hhaMJlMKCgoUB1DC6WlpacubbLZgOefBz77DGjfXl0w\nBYqKilBWVqY6hhby8/NRUVGhOoYWcnNzUVVVpTqGFnJycngqjVNWVhbMZrPqGFrIyMgAAMyfP//E\n0jdff/1gU4KIiOotMTHR5/9hrK+9e/fyzaTT7t27/Wbd6/nExMTAbrcbN3z9NdC8OfD00+pCKbJ9\n+3YeGey0bds2BATwbTcAbN261W0nCngb1sLAWhi2bduGvn37YurUqSdqcs899+DQoUOKk7kP95Qg\nItKEN+0pQUT1cPQoMHgwsHUr0L+/6jRERORlNm7ciFtuueWU2wIDAzFlyhQ8//zzjW7k6LanBFu2\nRERERO7wyivAP//JhgQR0Vm8c+QInktJQTZnHp7RzTffDCklpJQoLy/H6NGjYbPZ8NJLLyE4OBhC\nCNx+++04cOCA6qhNwqYEERGdl8Viwbx581TH0EJNTQ0WLlyoOoYWKioqsGTJEtUxtFBSUoIVK1YY\nN6xaBcTHA+++qy6UIvn5+Vi7dq3qGFrIysrCpk2bVMfQwqFDh7B9+3bVMbSQmJiIPXv2qI6hXL7Z\njMkbNmDW9u0o5hJAbNu2DYcPHz7rz1u2bIlx48adaFJs3boVQ4cOxdq1a3HJJZdACAEhBL744guv\n25+DyzeIiDSh8/KN2tpalJSUoGvXrqqjKFdVVYWKigp07txZdRTlKioqUFtbiw4dOqiOolxZWRls\nNhvatWsHVFUBl14KzJgB/GHarT8oLi5GYGAgWrdurTqKcgUFBQgPD0eLFi1UR1EuNzcXbdq0QZif\nH/UIANnZ2ejUqRNCQkJUR1HqrbQ0TNy9G3f3748VgwerjqNceno6evfu3ai9eKqqqvD555/j448/\nPuX2kSNHYvLkyRj8h/rqtnyDTQkiIk3o3JQgogZ4+20gOxvg7CIiojMqMJvRJyYG1XY7YocOxdCI\nCNWRfMquXbvw2muvnTY7acyYMXj99dcRHh6uVVOCyzeIiOicKisrVUfQhjfUYuueOFwxYi1+WP6b\nW8fxhlp4yim1SE8HZs8GJk1SF0ghXhcG1sLAWhhYC4dJ2dmorqrCXe3asSEB118Xw4cPR3R0NKSU\nqK6uxrhx4wAA77//PsLDw106liuwKUFEROf09ddfq46gBSklvvnmG9UxzuvSCy/A4Kvz8Mbfh+CK\nP63FnN9WuXwMb6mFJ9hsNsyYMcO44cABYNgwwA+X95jNZsyaNUt1DC1UV1fjxx9/VB1DC+Xl5Zg7\nd67qGFooLCzE4sWLVcfQQmFuLgK2b8cHvXqpjqLc4cOHsX79erc9f1hYGEaPHn1iL4r4+Hi3jdVY\nXL5BRKQJLt8gVyo1lePVD5bitzl/Qe+L9+HN94HH77hddSzf99NPwIYNwJw5qpMQEWkt32xGJz/f\nV0MV3faU4EwJIiIiH9SmZSv8+OUzOHQkHAOHHcOLTw7G0GtXYuHadaqj+baiIqB9e9UpiIi0x4YE\nHcemBBERnVFUVBTq6upUx9BCZGQkrFar6hiN0q5Va/z81TNIPRSK/oML8c/HL8ew63/DLxsaN1V0\nw4YNsNvtLk7pndatW4fTZjcVFQHt2qkJpJA7px57m3Xr2Pg7jteFgdeFgdeFgbVwYFOCiIjOKCQk\nBKGhoapjaCEsLAxBQUGqYzRJh7ZtMfd/z+DgoSD0u6QY/3j4cgwfuRzLIiMb9DzNmzdHQADfPgBA\nRETE6Ue3FRf75UwJHnlpiOCmfSfwunCQUvK6cLLb7ayFk9VqRatWrVTH0AL3lCAi0gT3lCBPyi0s\nxKvvr8K6hXeg/5DteO/DVrjnzzeojuX9HngAeOwx4MEHVSchItJKkdmM9lyyoQXuKUFERFpjY8Tg\ny7Xo2qEDFn07CkmpEj0vKMNT916CP920FKu3bj7j/X25Fg11zlr42fINXhcG1sLAWhhYC4cyiwX9\ndu7EvYmJqLbZVMdRjtfFqdiUICKiUyxfvhyJiYmqY2hhwYIFOHz4sOoYbtW9YycsnjkKCck2dO1t\nwmN3D8SIW5ZgTfTWU+73/fffIycnR1FKvUyfPh2FhYVn/qGfLd+YOnUqysvLVcfQwqRJk1BdXa06\nhhYmTJgAs9msOoYWxo0bBxt/CcdXR4+i/IcfYLJaER4YqDqOcmPGjGFj4iRcvkFEpAldlm9YrVav\n3z/BVfyxFpm5R/HqexsRufROXHb17/jo46645epr/LIWZ3POWnTuDMTFAV26eDaUIrwuDKyFgbUw\nsBaAyWpFr5gYlNXVIWroUPy5dWvVkZRTfV1w+QYREWnN3988ncwfa9Grazcs+/5p7EuqQfvO1Xjg\nLxfjutsXY0tcrOpo2jjrdSElUFLiV8s3/PHvyNmwFgbWwsBaAFOPHkWZ1Yrr27ZlQ8KJ18Wp2JQg\nIiIAjh2xuWzDwWazISkpSXUMpfp074HlPz6NHXvKERiUhvtuuhDX37EQUbG7VUdTpqamBocOHTr7\nHUwmoFkzwA82cjOZTEhPT1cdQwslJSXIzs5WHUMLBQUFyMvLUx1DC3l5eWdf5uVHKqxWfB4bC1RU\n4MPevVXHUS49PR0mk0l1DO2wKUFERAAcb6CqqqpUx9BCZmYm10M7BVotmPbZ3didUIGWbcy458a+\nGHnXAmz1w5kTqamppx8BerKiIr/ZTyI5OZmf9DkdOHAAIX7QiKqP/fv3o1mzZqpjaCEhIQFhYWGq\nYygnAdxaVoZbO3fGDZwlgcTERF4XZ8A9JYiINKHLnhJE53LgSBrefG87tq26A0NHrseYTy7GNZcP\nUR1LD7t2AS++COz239kkRERnIqU8d1OXPIp7ShAREZHXGtj3Aqye/yS2x5WiWZgNt13XGzfeNw87\n98erjqaeH82UICJqCDYk6FzYlCAi8nNSSnzzzTeqY2jBbrdj+vTpqmNowWKxYMaMGWf9+aUX9MOa\nhX9DdGwRgoIlbh7REzffPxe7kxI8mNIzamtr8d13353/jn7QlKiqqsKPP/6oOoYWysrKMG/ePNUx\ntFBYWIjFixerjqGFnJwcLF++XHUMLaSlpWHdunWqY2ghKSkJUVFRqmO4jBBitBBCOv8Mb/Lzcaow\nEZEeVC3fkFKisLAQHTt29PjYurHb7SguLkaHDh1UR1HOarWirKwM7ev5S3Z8SjLeencvYjbchqtv\nXY3xYwZjSP9L3ZzSMywWC0wmE9qd71SNyZOBzEzgyy89E0yBuro6VFdXo02bNqqjKFdbW4va2lq0\n5jp5VFdXw2q1omXLlqqjKFdRUQEhBFq0aKE6inLl5eUICQnhHgpwbIjbokULbfafacryDSHEaADj\nACwE8MhJP+ovpUxpzHNypgQRkZ8TQrAh4RQQEMCGhFNQUFC9GxIAMPji/tiw5HFE7TwGaQvAn4d3\nxV8emoN9qQfdmNIzgoODz9+QAPxipkRoaCgbEk7NmjVjQ8IpPDycDQmniIgIv29IVNts2FBSgpYt\nW7Ih4dS2bVttGhJNcVJDYoKU8lFnY2OY88fJQgi7EKJbQ5+XTQkiIj9WUFAAzphzYC0MBQUFjX7s\n0AEDsXHZ49i0PQ8WczCuHdYJtz3yExIPJ7swoec0qBY+3pRoynXha1gLA2thYC0cvs3Nxa2bN+OF\n1FTVUbTgK9fFHxoSbx+/XUq5x9mcuBmAAJAjhMgVQtS7g82mBBGRH+MaYMPixYu5EZeTK66LKy+9\nBJuWP4r1W7NRVx2Ka4Z0wB2P/YikI971JrVBtSguBuozo8ILSSn5euHEWhhsNhuWLFmiOoYWLBYL\nfv31V9UxlKux2TD+8GFgxw7c5aOvhw1hMpmwdu1a1TGa7GwNiZNJKSOdzYmHAXQBUCKE2Cnq8eaK\ne0oQEWmCR4KSL9sWH4/33k9F3JabcP3dv+HzsddiQO9+qmO51siRwIcfAjfcoDoJEZESX+Xk4JXD\nhzGkRQvEDh3KZr+mGrKnRH0aEmd53AsAvgbQXkpZfK77cqYEERERud21gwdj88qHsSoqA6bScFw1\nqA3ufvIHpGalq47mOj6+fIOI6FxqbTaMz8oCAHzQuzcbEj6gsQ0JAJBSfiOlFOdrSABsShAR+aXt\n27cjLy9PdQwtREVFoaSkRHUMLWzYsAEVFRVuHeO6IUOxZfXDWL7xCEoLW2DYZRG49+nZOJyd6dZx\nG2r16tWora1t2IN8dPnGb7/9BovFojqGFpYvXw673a46hhaWLVvGfXicli1bpjqCFn44dgy5Gzdi\ncIsWuMcHXwsbytuvi6Y0JBqKTQkiIj/UvHlzdO7cWXUMLbRu3ZqnCTh16NABERERHhnrhuFXYtva\nB7F03REU5bXC0MvCcf8zs3HkaJZHxj+frl27olmzZvV/gJQ+25To0aMHgoODVcfQQq9evRAQwLfP\nANCnTx9+Eu7Up08f1RG08FTnznjzqqswjtcGAO++LjzZkAC4pwQRkTa4pwT5s/Xbd+CjD/OQtPs6\n3PTAb5j0yS3o3bWH6lj1ZzIB3boBbp5pQkRE1FTn2lPC0w0JgDMliIj8js1mUx1BC1JK1sLJbrcr\nn5J+64g/YfuGv2L+b4eQm9EWgweG4sH/9x2yjh31aA673d64Kek+uJ+EzWbj9Hwn1sLA100Da2Fg\nLQzeXAsVDQmATQkiIr+yd+9erFmzRnUMLURHR2Pz5s2qY2hh48aN2LVrl+oYAIA7rhuBmMj78POK\nFGQfao9BA4Lw8PPfIafAM3ugrFixAomJiQ1/oA82JRYtWoTDhw+rjqGFn376CdnZ2apjaGHWrFko\nKChQHUML06ZNQ1lZmeoYWvjyyy9RVVWlOoYWJk6ciLq6OtUxGkxVQwLg8g0iIm14avmGlJJrPYET\nn3qyFnpfE8t/34IxH5lwKOFK3PbYb5j037vRtX0nt43X6FqsWQNMmQL4wHn0x+l8XXgaa2FgLQys\nhYG1MHhDLf64fENlQwLgTAkiIr+j+z+UniKEYC2cdK7DvTdcj92b78J3iw/gcGIXXHqxHY+/OAvH\nit3zSW2ja+GDMyV0vi48jbUwsBYG1gJYVliIufn5sPGD7hO87bpQ3ZAA2JQgIvIbO3bsUB1BC1JK\n1sLJm2rxwM03IHbrnZg+7wBS9nXFwIus+Nu/Z6GgtMglz2+z2bBz587GP4EPNSUsFgt2796tOoYW\namtrERcXpzqGFiorK5GQkKA6hhZKS0tx8OBB1TGUs9jteHX3bvxt40YsLy5WHUe5o0ePIjNTr+Ot\nz0eHhgTApgQRkV+oqamB2WxWHUMLFRUV3LDOqaSkBEFBQapjNMjDf7kJe7bdgWlzkpC0pxsGXFiH\np16dheKykiY9b0FBAUJDQxv/BD50HGheXh7Cw8NVx9BCTk4OWrRooTqGFrKystCqVSvVMbSQmZmJ\n1q1bq46h3M/5+cjKzMQFHTrgPh9pyjZFRkYG2rZtqzpGvenSkAC4pwQRkTZ4JChRw/28cj0mjpHI\nTrsEdz+5FpM/fhBtIhT8svDPfwKXXw688ILnxyYi8jCr3Y7+u3YhrbYWP/Xvjyc7d1YdiRrgpCUm\nyhsSAGdKEBERkRf72123Ij7mL5g0KxFx0b1wYd8KfLd0ueeDlJQAzZt7flwiIgXmFRQgrbYW/cLC\n8FjHjqrjUANs3br1+JdaNCQANiWIiHzexIkTVUfQBmvhIKX0uVo8fe/t2LfzFox6PQrvvXQJsgty\n6/U4u92OL774oukB7rwT+OYbwItnO9lsNkyePFl1DC2YzWZ89dVXqmNoobq6GtOmTVMdQwsmkwkz\nZsxQHUMLcw8dAtaswfu9eiEowL9/pczLy8PcuXNVx6i3YcOGAQB0aUgAXL5BRKQNdy3fqKioQERE\nhMuf1xuxFgZfrsU1ty5FYJAFW1Y/ct77SilRWVnZ9FrY7cDw4cAbbwCPPda051LEZbXwAVJKVFVV\ncT8JOBp31dXVrAUctaipqUFzzoqC2WrF4pwcPNKzp983JaxWKywWC8LCwlRHqbc/HgmqGpsSRESa\n4J4SRK6RnZ+PYYOr8Nx/NmPMa894buCtW4G//Q1ITga86M0pERH5F92aEv7d1iIi8mG5ubmoq6tT\nHUMLOTk5sFgsqmNoISsrCzabTXUMt+rRqRM+mrQfX4+5DXuSE896v4yMDNjtdtcNfN11wJVXAl64\nBCI9PZ2n0jilp6erjqCNjIwM1RG0wevCwFoYWAvXYFOCiMhHbdq0CYGBgapjaCEyMpK1cNq0aRMC\n/GCq7QuP3YNrbt+IZ5/Kg/UsTZioqCjX12L8eGDSJODYMdc+r5tt3rz55N3Y/drmzZtVR9BGVFSU\n6gja4HXhIKVkLZzsdju2bNmiOoZP4PINIiJNcPkGkWtVVFVj0OAU3HBfLGZ//pznBn7rLaC0FJg1\ny3NjEhER1ROXbxARERF5QETzcHw1qxq/zLgfK7b87rmB338fWLkSiI/33JhERG5klxK37NuHydnZ\nqHPlsjcisClBRORzEhMTEc9fhgAAsbGxSE5OVh1DC9HR0X659vXuP1+DB59bideei0BFdSUAxxKW\n3Nz6HRnaKK1aAR9+6DiJQ/PZT2vWrEFRUZHqGFpYsWIFTCaT6hhaWLJkCWpqalTH0MLChQu5JxGA\npYWF2Lh4MSZlZkKbj9cVmjt3LvfhcSE2JYiIfExERAQGDhyoOoYW2rZtiwsvvFB1DC106tQJvXv3\nVh1DiZnjn0J4y3I88fxiAECPHj3QpUsX9w763HOOfSV++8294zRRv3790L59e9UxtDBgwAC0bNlS\ndQwtDBo0yKuON3SnIUOGIDg4WHUMpexS4r+ZmUD//ni3Tx+E+MG+ROczfPhw7sPjQtxTgohIE9xT\ngsh99h48iJuuaYOxX0fjX48+4JlB164FXn4Z2L8fCAnxzJhERC62rLAQf01KQreQEKRdfTVC2ZTw\netxTgoiI3Ka6ulp1BG2wFgbWAhgyYAD+9d5GfPzaxcg6dtQzg952G3DBBcA333hmvAaQUnJ6vpPN\nZuPxyU4Wi4VLFZzMZjOsVqvqGMpJKfHRoUOAzYbRPXv6fUOitrbWtUdJEwA2JYiIfEZ+fj4WLFig\nOoYWsrKysGzZMtUxtJCamoq1a9eqjqGFh266FO27zMcTo6I9N+gXXwBjxwIlJZ4bsx5iYmKwc+dO\n1TG0EBUVhX379qmOoYV169YhJSVFdQwtrFixAhkZGapjKFdssaB60yZ0NJnwD3cve/MC8+bN4z48\nbsDlG0REmuDyDSL3yynIx7DBlXj2ja349I1Rnhn0X/8CgoOBKVM8Mx4RkQtJKZFnNqNraKjqKOQi\nui3fYFOCiEgTbEoQeca3C1dg9AvDsSG6EMMGXOb+AQsLgYEDgW3bgIsvdv94RERE56BbU4LLN4iI\nfEBkZKTqCNrYuHGj6gjaYC0MJ9fi+UfuwXV3rsczTx2D1WZz/+AdOgBvvw289Zb7xzoPKSVfL5zs\ndjs2bdqkOoYWrFYroqKiVMfQQl1dHbZu3ao6hhaqqqqwY8cO1TG0UFZWht27d6uO4bPYlCAi8nJS\nSoRwZ38Ajl8yeIydg8ViQfPmzVXH0EJtbS1atWp1ym3zZzyEqvK2eO7t2Z4J8e9/AwcOAIobAlVV\nVWjbtq3SDLooLy9Hhw4dVMfQQmlpKTp37qw6hhaKi4vdf2SwlygqKkLXrl1Vx9BCUVERunXrpjqG\nz+LyDSIiTXD5BpFnrdqyDY/fczHmLE/EPX++0f0DLl0KfPwxsHcvEBjo/vGIiBpBSokyqxVtgoNV\nRyE34fINIiIiIg3cef21ePAfq/Dqc61QUV3p/gHvvx9o3RqY7aHZGUREjbC+tBQ9Y2IwPitLdRTy\nE2xKEBF5sU8//RQ2T6yJ9wJjx44FZ5o4jBkzhrVwGjt27Dl/PmP8U2jRqgyPP7/Y/WGEACZNAj74\nAKiocP94fzBmzBiPj6kjKSVr4SSlPO/fEX9hs9kwbtw41TGUk1Liw0OHUPnzz9DmY3SFqqurMWnS\nJNUxtCaE6C2EkEKIeY1+Dr5pISLSQ2OWb1gsFgRzeiUA1uJkrIWhPrWIT0nGDX9qjTHTovHiYw+4\nP9SoUUDXrsCnn7p/rJPwujCwFgbWwsBaABtLSnBLQgLaAsi89lq0CApSHUk5X7wuXLV8QwgxF8Dj\nJ91kBRAqpbQ36HnYlCAi0gP3lCBS5/++nIeZE67Czj2h6NWlu3sHO3oUGDQI2LMH6N3bvWMREdWT\nlBLXx8djW3k5Pu3TB+/06qU6ErlJU5sSQohhAI4fR5IvpewshLgcQLzztrZSytL6Ph+XbxAReaG8\nvDwUFxerjqGFnJwclJWVqY6hhczMTFRWemBvBC9w5MgR1NTU1Pv+n7z6OC68PA5PjPLA8XfdugEv\nvwyMHu3+sQAcOnQIdXV1HhlLd8nJybBarapjaOHAgQOw2xv0YabPSkpK4pI3AFFlZdgWH482QUF4\nkSdNICkpSXUE7QghAoUQu2E0JH4C0EkIsUZKuQ/A8eOdSoQQg+v7vGxKEBF5obi4OISGhqqOoYW9\ne/eiWbNmqmNoYe/evbwunOLi4hp8VO6CH6/H4cQr8M7EH9wT6mRvvglERwM73N8EiY+P57HBTgkJ\nCQjkyScAgP379yMggL8KAI5fPoXgDgqdQkJwZUkJ3ujRAy25bAMHDhxQHUErQog74VieMQzAE1JK\nIaV8GsCjAG4TQkgAFQACAZgBxAkhnq7Xc7MrSESkBy7fIFJvxuLf8PbzV2L91gJceckg9w42Zw4w\nbRqwfTvAXw6JSBNSSjZpfFxDlm8IIcIAHAPQEkAGgIullOY/3GcggCQAJ55XCLEIwEMAfpBSPnOu\nMfgvIBEREZHT/3voblx/1zo8OyofVnefbPPEE4DNBixY4N5xiIgagA0JOk4I8TyAajgaEjdIKfv8\nsSEBAFLKAwBaAOh/0m0PA3gBwCghRIk4x4XFpgQRkRfJycnBypUrVcfQQlpaGjZs2KA6hhaSkpKw\nbds21TG0sGfPHsTGxjbpOeZ9+zCqTW3wj//MdlGqswgIcBwROno0UF3t8qePjo7G/v37Xf683igy\nMhKHDx9WHUMLq1atQk5OjuoYWli6dCkKCwtVx9DCggULUF5erjqGFubMmYNqN7wmexMhRHvncozp\nANYDCJBSRp3rMVLKKillyh9umw7gKgBtAAw463icKkxEpIf6LN8oLy9HYGAgWrRo4aFU+iotLUVo\naCjCw8NVR1GuqKgIERER3E8CQH5+Ptq1a4egJq6HXrMtGo/edRF+XJaI+2640UXpzuIn7Do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LzUokhFrUVYaE3eHnGAyR/fQWT0yRLdp9S1eOMN2LEDVq0qY0pzyfOFS2JiInXr1tUdwwjJycmE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\n+D828vmw1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k65lv2buzH8y/mXvK1HHxf9+kGzZjB6tOfGcBOllPyMFHE6nYwfP153DCN8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TZfJ5kvyJqmw2kLSe/LEuddybL2mw/Am8BcrtJ/YNIhS9L5R0kXPu2gxNavPa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Pm//7uJKVP68P/+33Igu9b+oqIKnn76a+bN+4W33x7D4MEdWjzT+1/BtEVQEATYqBzxXWPaeE8L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\ngwdZv3491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Ta3BQ5ccGguQE5Ojk7J6hcYGMjJkyfZt28fffr0MTSLXuLi4oiLi2Pt2rV0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fYD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\n73Gk9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k9rI6fxHcmZO5eQAOPGlY1HjPDP010BL2/cCK+9Bj//zN9SUqiup72d0H3unjJ3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\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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -354,121 +227,35 @@ "skew = SkewT()\n", "\n", "# Plot the data\n", - "skew.plot(p, t, 'r')\n", - "skew.plot(p, td, 'g')\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, 'b')\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", "# 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", + "\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=3)\n", - "h = Hodograph(ax_hod, component_range=80.)\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()" ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "972.6 millibar 13.9116676363 degC 13.7561341768 degC 14.0048053654 degC\n", - "968.1 millibar 13.795309995 degC 13.6403678572 degC 13.8880708933 degC\n", - "963.5 millibar 13.7070447902 degC 13.5523590688 degC 13.7996354037 degC\n", - "958.9 millibar 13.6034524052 degC 13.4491913275 degC 13.6957654641 degC\n", - "954.2 millibar 13.5430001542 degC 13.3886616121 degC 13.6353487333 degC\n", - "949.4 millibar 13.4955511833 degC 13.3409697537 degC 13.5880407718 degC\n", - "944.6 millibar 13.4327262238 degC 13.2780704887 degC 13.5252528231 degC\n", - "939.6 millibar 13.3214175505 degC 13.1671951789 degC 13.4136681067 degC\n", - "934.5 millibar 13.1780826854 degC 13.0246305994 degC 13.2698459052 degC\n", - "929.1 millibar 12.9840646965 degC 12.8318898986 degC 13.0750280636 degC\n", - "923.7 millibar 12.7587117079 degC 12.6081461774 degC 12.8486706315 degC\n", - "917.9 millibar 12.5100387601 degC 12.3612538202 degC 12.5988843283 degC\n", - "911.9 millibar 12.2408903895 degC 12.094052447 degC 12.3285202779 degC\n", - "905.6 millibar 11.8707829848 degC 11.7268890509 degC 11.956583349 degC\n", - "898.9 millibar 11.5848832854 degC 11.4429383778 degC 11.6694675004 degC\n", - "891.7 millibar 10.7024681841 degC 10.5684121493 degC 10.782184737 degC\n", - "883.7 millibar 10.3675094536 degC 10.2355668766 degC 10.4459068236 degC\n", - "874.4 millibar 9.57881581015 degC 9.4530891449 degC 9.65338390277 degC\n", - "863.7 millibar 9.06297448338 degC 8.9405219329 degC 9.13551253922 degC\n", - "851.4 millibar 8.52959620858 degC 8.41026712977 degC 8.6001943448 degC\n", - "837.3 millibar 7.8463517454 degC 7.73112740211 degC 7.91440526624 degC\n", - "821.3 millibar 7.05865450398 degC 6.94802011914 degC 7.12386015443 degC\n", - "803.3 millibar 6.13786844357 degC 6.0324581565 degC 6.19983982692 degC\n", - "783.1 millibar 4.96638946899 degC 4.86761101024 degC 5.02427139898 degC\n", - "760.7 millibar 3.49028027301 degC 3.39965174803 degC 3.54314509801 degC\n", - "736.3 millibar 2.01375469655 degC 1.93042025112 degC 2.06212831003 degC\n", - "710.0 millibar -0.0814385343568 degC -0.154522524712 degC -0.039357011505 degC\n", - "682.1 millibar -3.3723881986 degC -3.43052326592 degC -3.33943614929 degC\n", - "653.2 millibar -7.36829567182 degC -7.41166867572 degC -7.34434733517 degC\n", - "623.5 millibar -13.9853134577 degC -14.0107174126 degC -13.9722607858 degC\n", - "593.2 millibar -27.435498651 degC -27.4428708731 degC -27.4332329337 degC\n", - "562.4 millibar -28.0124591985 degC -28.0197921015 degC -28.0102992759 degC\n", - "531.4 millibar -21.9801025193 degC -21.9942242484 degC -21.9740756264 degC\n", - "500.3 millibar -20.9592100388 degC -20.9757571349 degC -20.951921361 degC\n", - "469.1 millibar -24.7329831608 degC -24.7452030637 degC -24.7284099272 degC\n", - "438.1 millibar -26.2142515313 degC -26.2255402965 degC -26.2103242606 degC\n", - "407.3 millibar -28.0020354968 degC -28.0121700893 degC -27.9988837034 degC\n", - "376.9 millibar -32.7137422477 degC -32.7204493669 degC -32.712754352 degC\n", - "346.8 millibar -37.2686443182 degC -37.2730902274 degC -37.2691563363 degC\n", - "317.4 millibar -40.851081496 degC -40.8543253515 degC -40.8524421238 degC\n", - "289.3 millibar -46.189552697 degC -46.1914523695 degC -46.1918308 degC\n", - "263.1 millibar -51.9389914221 degC -51.9400147149 degC -51.9418882721 degC\n", - "239.4 millibar -63.7858391889 degC -63.7860643717 degC -63.7889037616 degC\n", - "218.0 millibar nan degC nan degC nan degC\n", - "198.7 millibar nan degC nan degC nan degC\n", - "181.3 millibar nan degC nan degC nan degC\n", - "165.6 millibar nan degC nan degC nan degC\n", - "151.1 millibar nan degC nan degC nan degC\n", - "137.3 millibar nan degC nan degC nan degC\n", - "124.0 millibar nan degC nan degC nan degC\n", - "111.3 millibar nan degC nan degC nan degC\n", - "98.9 millibar nan degC nan degC nan degC\n", - "86.9 millibar nan degC nan degC nan degC\n", - "75.1 millibar -79.2738862478 degC -79.27394893 degC -79.2769313644 degC\n", - "63.6 millibar -80.3091524605 degC -80.3092143549 degC -80.3120880214 degC\n", - "52.3 millibar -81.5110089092 degC -81.5110698952 degC -81.5138426851 degC\n", - "41.0 millibar -82.9820927066 degC -82.98215259 degC -82.9847684189 degC\n", - "29.8 millibar -84.8703007167 degC -84.8703591996 degC -84.872780293 degC\n", - "18.7 millibar -87.5495154933 degC -87.5495720173 degC -87.5517264671 degC\n", - "7.6 millibar -92.478017542 degC -92.4780705498 degC -92.4797373828 degC\n" - ] - } - ], - "source": [ - "# manually inspect the three calculated dewpoint profiles\n", - "for i in range(0,len(p)):\n", - " print p[i], td[i], td2[i], dwpc[i]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { 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/setup.py b/setup.py index 62da9a3..7cbcb90 100644 --- a/setup.py +++ b/setup.py @@ -3,12 +3,12 @@ from setuptools import find_packages setup( name='python-awips', - version='0.9.4', + version='0.9.8', description='A framework for requesting AWIPS meteorological datasets from an EDEX server', packages=find_packages(exclude='data'), license='Apache 2.0 / Various + US Export Controlled Technical Data', url='http://www.unidata.ucar.edu/software/awips2', - download_url='https://github.com/Unidata/python-awips/tarball/0.9.4', + download_url='https://github.com/Unidata/python-awips/tarball/0.9.8', author='Unidata', author_email='mjames@ucar.edu', requires=['argparse','shapely','numpy']