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python-awips/docs/source/examples/generated/Gridded_Data.rst
mjames-upc 4491987304 gridded data example cleanup
2016-03-22 11:15:54 -05:00

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============
Gridded Data
============
`Notebook <http://nbviewer.ipython.org/github/Unidata/python-awips/blob/master/examples/notebooks/Gridded_Data.ipynb>`_
EDEX Grid Inventory
-------------------
.. code:: python
from awips.dataaccess import DataAccessLayer
# Set host
DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu")
# Init data request
request = DataAccessLayer.newDataRequest()
# Set datatype
request.setDatatype("grid")
# Get a list of all available models
available_grids = DataAccessLayer.getAvailableLocationNames(request)
# Sort
available_grids.sort()
for grid in available_grids:
print grid
.. parsed-literal::
AUTOSPE
AVN211
AVN225
DGEX
ECMF-Global
ECMF1
ECMF10
ECMF11
ECMF12
ECMF2
ECMF3
ECMF4
ECMF5
ECMF6
ECMF7
ECMF8
ECMF9
ESTOFS
ETA
FFG-ALR
FFG-FWR
FFG-KRF
FFG-MSR
FFG-ORN
FFG-RHA
FFG-RSA
FFG-TAR
FFG-TIR
FFG-TUA
GFS
GFS40
GFSGuide
GFSLAMP5
GribModel:9:151:172
HFR-EAST_6KM
HFR-EAST_PR_6KM
HFR-US_EAST_DELAWARE_1KM
HFR-US_EAST_FLORIDA_2KM
HFR-US_EAST_NORTH_2KM
HFR-US_EAST_SOUTH_2KM
HFR-US_EAST_VIRGINIA_1KM
HFR-US_HAWAII_1KM
HFR-US_HAWAII_2KM
HFR-US_HAWAII_6KM
HFR-US_WEST_500M
HFR-US_WEST_CENCAL_2KM
HFR-US_WEST_LOSANGELES_1KM
HFR-US_WEST_LOSOSOS_1KM
HFR-US_WEST_NORTH_2KM
HFR-US_WEST_SANFRAN_1KM
HFR-US_WEST_SOCAL_2KM
HFR-US_WEST_WASHINGTON_1KM
HFR-WEST_6KM
HPCGuide
HPCqpf
HPCqpfNDFD
HRRR
LAMP2p5
MPE-Local-ORN
MPE-Local-RHA
MPE-Local-RSA
MPE-Local-TAR
MPE-Local-TIR
MPE-Mosaic-MSR
MPE-Mosaic-ORN
MPE-Mosaic-RHA
MPE-Mosaic-TAR
MPE-Mosaic-TIR
MRMS_1000
NAM12
NAM40
NCWF
NOHRSC-SNOW
NamDNG
NamDNG5
QPE-ALR
QPE-Auto-TUA
QPE-FWR
QPE-KRF
QPE-MSR
QPE-RFC-RSA
QPE-RFC-STR
QPE-TIR
QPE-TUA
QPE-XNAV-ALR
QPE-XNAV-FWR
QPE-XNAV-KRF
QPE-XNAV-MSR
QPE-XNAV-RHA
QPE-XNAV-SJU
QPE-XNAV-TAR
QPE-XNAV-TIR
QPE-XNAV-TUA
RAP13
RAP40
RCM
RFCqpf
RTMA
RTMA5
UKMET-Global
UKMET37
UKMET38
UKMET39
UKMET40
UKMET41
UKMET42
UKMET43
UKMET44
URMA25
estofsPR
fnmocWave
**LocationNames** is different for different plugins - radar is icao -
satellite is sector
Requesting a Grid
-----------------
.. code:: python
# Grid request
request.setLocationNames('RAP40')
request.setParameters("RH")
request.setLevels("850MB")
# Get available times
t = DataAccessLayer.getAvailableTimes(request)
# Select last available time [-1]
response = DataAccessLayer.getGridData(request, [t[0]])
data = response[0]
lon,lat = data.getLatLonCoords()
# Print info
print 'Time :', t[-1]
print 'Model:', data.getLocationName()
print 'Unit :', data.getUnit()
print 'Parm :', data.getParameter()
# Print data array
print data.getRawData().shape
print data.getRawData()
print "lat array =", lat
print "lon array =", lon
.. parsed-literal::
Time : 2016-02-23 15:00:00 (12)
Model: RAP40
Unit : %
Parm : RH
(151, 113)
[[ 93.05456543 93.05456543 87.05456543 ..., 73.05456543 72.05456543
71.05456543]
[ 70.05456543 70.05456543 67.05456543 ..., 69.05456543 46.05456924
37.05456924]
[ 40.05456924 56.05456924 68.05456543 ..., 51.05456924 73.05456543
74.05456543]
...,
[ 65.05456543 62.05456924 63.05456924 ..., 67.05456543 65.05456543
46.05456924]
[ 48.05456924 59.05456924 62.05456924 ..., 4.05456877 5.05456877
5.05456877]
[ 7.05456877 8.05456829 10.05456829 ..., 91.05456543 95.05456543
95.05456543]]
lat array = [[ 54.24940109 54.35071945 54.45080566 ..., 57.9545517 57.91926193
57.88272858]
[ 57.84495163 57.80593109 57.76566696 ..., 58.07667542 58.08861542
58.09931183]
[ 58.10876846 58.11697769 58.12394714 ..., 56.40270996 56.46187973
56.51980972]
...,
[ 19.93209648 19.89832115 19.86351395 ..., 20.054636 20.06362152
20.07156372]
[ 20.0784626 20.08431816 20.08912849 ..., 18.58354759 18.63155174
18.67854691]
[ 18.72453308 18.76950836 18.81346893 ..., 17.49624634 17.42861557
17.36001205]]
lon array = [[-139.83120728 -139.32348633 -138.81448364 ..., -79.26060486
-78.70166016 -78.14326477]
[ -77.58544922 -77.02822876 -76.47161865 ..., -100.70157623
-100.13801575 -99.57427216]
[ -99.01037598 -98.44634247 -97.88218689 ..., -121.69165039
-121.15060425 -120.60871887]
...,
[ -82.65139008 -82.26644897 -81.88170624 ..., -98.52494049
-98.13802338 -97.75105286]
[ -97.36403656 -96.97698212 -96.58989716 ..., -113.07767487
-112.69831085 -112.31866455]
[-111.93874359 -111.5585556 -111.17810822 ..., -69.85433197
-69.48160553 -69.10926819]]
Plotting a Grid with Basemap
----------------------------
Using **matplotlib**, **numpy**, and **basemap**:
.. code:: python
import matplotlib.tri as mtri
import matplotlib.pyplot as plt
from matplotlib.transforms import offset_copy
from mpl_toolkits.basemap import Basemap, cm
import numpy as np
from numpy import linspace, transpose
from numpy import meshgrid
plt.figure(figsize=(12, 12), dpi=100)
lons,lats = data.getLatLonCoords()
map = Basemap(projection='cyl',
resolution = 'c',
llcrnrlon = lons.min(), llcrnrlat = lats.min(),
urcrnrlon =lons.max(), urcrnrlat = lats.max()
)
map.drawcoastlines()
map.drawstates()
map.drawcountries()
#
# We have to reproject our grid, see https://stackoverflow.com/questions/31822553/m
#
x = linspace(0, map.urcrnrx, data.getRawData().shape[1])
y = linspace(0, map.urcrnry, data.getRawData().shape[0])
xx, yy = meshgrid(x, y)
ngrid = len(x)
rlons = np.repeat(np.linspace(np.min(lons), np.max(lons), ngrid),
ngrid).reshape(ngrid, ngrid)
rlats = np.repeat(np.linspace(np.min(lats), np.max(lats), ngrid),
ngrid).reshape(ngrid, ngrid).T
tli = mtri.LinearTriInterpolator(mtri.Triangulation(lons.flatten(),
lats.flatten()), data.getRawData().flatten())
rdata = tli(rlons, rlats)
cs = map.contourf(rlons, rlats, rdata, latlon=True, vmin=0, vmax=100, cmap='YlGn')
# add colorbar.
cbar = map.colorbar(cs,location='bottom',pad="5%")
cbar.set_label(data.getParameter() + data.getUnit() )
# Show plot
plt.show()
.. image:: Gridded_Data_files/Gridded_Data_5_0.png
or use **pcolormesh** rather than **contourf**
.. code:: python
plt.figure(figsize=(12, 12), dpi=100)
map = Basemap(projection='cyl',
resolution = 'c',
llcrnrlon = lons.min(), llcrnrlat = lats.min(),
urcrnrlon =lons.max(), urcrnrlat = lats.max()
)
map.drawcoastlines()
map.drawstates()
map.drawcountries()
cs = map.pcolormesh(rlons, rlats, rdata, latlon=True, vmin=0, vmax=100, cmap='YlGn')
.. image:: Gridded_Data_files/Gridded_Data_7_0.png
Plotting a Grid with Cartopy
----------------------------
.. code:: python
import os
import matplotlib.pyplot as plt
import numpy as np
import iris
import cartopy.crs as ccrs
from cartopy import config
lon,lat = data.getLatLonCoords()
plt.figure(figsize=(12, 12), dpi=100)
ax = plt.axes(projection=ccrs.PlateCarree())
cs = plt.contourf(rlons, rlats, rdata, 60, transform=ccrs.PlateCarree(), vmin=0, vmax=100, cmap='YlGn')
ax.coastlines()
ax.gridlines()
# add colorbar
cbar = plt.colorbar(orientation='horizontal')
cbar.set_label(data.getParameter() + data.getUnit() )
plt.show()
.. image:: Gridded_Data_files/Gridded_Data_9_0.png
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