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395 lines
12 KiB
ReStructuredText
395 lines
12 KiB
ReStructuredText
============
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Gridded Data
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============
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`Notebook <http://nbviewer.ipython.org/github/Unidata/python-awips/blob/master/examples/notebooks/Gridded_Data.ipynb>`_
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EDEX Grid Inventory
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-------------------
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.. code:: python
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from awips.dataaccess import DataAccessLayer
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# Set host
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DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu")
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# Init data request
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request = DataAccessLayer.newDataRequest()
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# Set datatype
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request.setDatatype("grid")
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# Get a list of all available models
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available_grids = DataAccessLayer.getAvailableLocationNames(request)
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# Sort
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available_grids.sort()
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for grid in available_grids:
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print grid
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.. parsed-literal::
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AVN211
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AVN225
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DGEX
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ECMF-Global
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ECMF1
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ECMF10
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ECMF11
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ECMF12
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ECMF2
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ECMF3
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ECMF4
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ECMF5
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ECMF6
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ECMF7
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ECMF8
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ECMF9
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ETA
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GFS
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GFS40
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GFSGuide
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GFSLAMP5
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HPCGuide
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HPCqpfNDFD
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HRRR
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LAMP2p5
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MRMS_1000
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NAM12
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NAM40
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NCWF
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NOHRSC-SNOW
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NamDNG
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NamDNG5
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QPE-MSR
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RAP13
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RAP40
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RTMA
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RTMA5
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URMA25
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estofsPR
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estofsUS
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**LocationNames** is different for different plugins - radar is icao -
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satellite is sector
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Requesting a Grid
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-----------------
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.. code:: python
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# Grid request
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request.setLocationNames('RAP40')
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request.setParameters("RH")
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request.setLevels("850MB")
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# Get available times
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t = DataAccessLayer.getAvailableTimes(request)
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# Select last available time [-1]
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response = DataAccessLayer.getGridData(request, [t[0]])
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data = response[0]
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lon,lat = data.getLatLonCoords()
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# Print info
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print 'Time :', t[-1]
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print 'Model:', data.getLocationName()
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print 'Unit :', data.getUnit()
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print 'Parm :', data.getParameter()
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# Print data array
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print data.getRawData().shape
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print data.getRawData()
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print "lat array =", lat
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print "lon array =", lon
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.. parsed-literal::
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Time : 2016-02-23 15:00:00 (12)
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Model: RAP40
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Unit : %
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Parm : RH
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(151, 113)
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[[ 93.05456543 93.05456543 87.05456543 ..., 73.05456543 72.05456543
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71.05456543]
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[ 70.05456543 70.05456543 67.05456543 ..., 69.05456543 46.05456924
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37.05456924]
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[ 40.05456924 56.05456924 68.05456543 ..., 51.05456924 73.05456543
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74.05456543]
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...,
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[ 65.05456543 62.05456924 63.05456924 ..., 67.05456543 65.05456543
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46.05456924]
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[ 48.05456924 59.05456924 62.05456924 ..., 4.05456877 5.05456877
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5.05456877]
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[ 7.05456877 8.05456829 10.05456829 ..., 91.05456543 95.05456543
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95.05456543]]
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lat array = [[ 54.24940109 54.35071945 54.45080566 ..., 57.9545517 57.91926193
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57.88272858]
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[ 57.84495163 57.80593109 57.76566696 ..., 58.07667542 58.08861542
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58.09931183]
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[ 58.10876846 58.11697769 58.12394714 ..., 56.40270996 56.46187973
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56.51980972]
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...,
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[ 19.93209648 19.89832115 19.86351395 ..., 20.054636 20.06362152
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20.07156372]
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[ 20.0784626 20.08431816 20.08912849 ..., 18.58354759 18.63155174
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18.67854691]
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[ 18.72453308 18.76950836 18.81346893 ..., 17.49624634 17.42861557
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17.36001205]]
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lon array = [[-139.83120728 -139.32348633 -138.81448364 ..., -79.26060486
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-78.70166016 -78.14326477]
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[ -77.58544922 -77.02822876 -76.47161865 ..., -100.70157623
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-100.13801575 -99.57427216]
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[ -99.01037598 -98.44634247 -97.88218689 ..., -121.69165039
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-121.15060425 -120.60871887]
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...,
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[ -82.65139008 -82.26644897 -81.88170624 ..., -98.52494049
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-98.13802338 -97.75105286]
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[ -97.36403656 -96.97698212 -96.58989716 ..., -113.07767487
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-112.69831085 -112.31866455]
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[-111.93874359 -111.5585556 -111.17810822 ..., -69.85433197
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-69.48160553 -69.10926819]]
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Plotting a Grid with Basemap
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----------------------------
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Using **matplotlib**, **numpy**, and **basemap**:
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.. code:: python
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import matplotlib.tri as mtri
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import matplotlib.pyplot as plt
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from matplotlib.transforms import offset_copy
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from mpl_toolkits.basemap import Basemap, cm
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import numpy as np
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from numpy import linspace, transpose
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from numpy import meshgrid
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plt.figure(figsize=(12, 12), dpi=100)
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lons,lats = data.getLatLonCoords()
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map = Basemap(projection='cyl',
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resolution = 'c',
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llcrnrlon = lons.min(), llcrnrlat = lats.min(),
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urcrnrlon =lons.max(), urcrnrlat = lats.max()
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)
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map.drawcoastlines()
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map.drawstates()
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map.drawcountries()
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#
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# We have to reproject our grid, see https://stackoverflow.com/questions/31822553/m
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#
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x = linspace(0, map.urcrnrx, data.getRawData().shape[1])
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y = linspace(0, map.urcrnry, data.getRawData().shape[0])
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xx, yy = meshgrid(x, y)
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ngrid = len(x)
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rlons = np.repeat(np.linspace(np.min(lons), np.max(lons), ngrid),
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ngrid).reshape(ngrid, ngrid)
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rlats = np.repeat(np.linspace(np.min(lats), np.max(lats), ngrid),
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ngrid).reshape(ngrid, ngrid).T
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tli = mtri.LinearTriInterpolator(mtri.Triangulation(lons.flatten(),
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lats.flatten()), data.getRawData().flatten())
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rdata = tli(rlons, rlats)
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cs = map.contourf(rlons, rlats, rdata, latlon=True, vmin=0, vmax=100, cmap='YlGn')
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# add colorbar.
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cbar = map.colorbar(cs,location='bottom',pad="5%")
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cbar.set_label(data.getParameter() + data.getUnit() )
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# Show plot
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plt.show()
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.. image:: Gridded_Data_files/Gridded_Data_5_0.png
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or use **pcolormesh** rather than **contourf**
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.. code:: python
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plt.figure(figsize=(12, 12), dpi=100)
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map = Basemap(projection='cyl',
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resolution = 'c',
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llcrnrlon = lons.min(), llcrnrlat = lats.min(),
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urcrnrlon =lons.max(), urcrnrlat = lats.max()
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)
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map.drawcoastlines()
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map.drawstates()
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map.drawcountries()
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cs = map.pcolormesh(rlons, rlats, rdata, latlon=True, vmin=0, vmax=100, cmap='YlGn')
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.. image:: Gridded_Data_files/Gridded_Data_7_0.png
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Plotting a Grid with Cartopy
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----------------------------
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.. code:: python
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import os
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import matplotlib.pyplot as plt
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import numpy as np
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import iris
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import cartopy.crs as ccrs
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from cartopy import config
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lon,lat = data.getLatLonCoords()
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plt.figure(figsize=(12, 12), dpi=100)
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ax = plt.axes(projection=ccrs.PlateCarree())
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cs = plt.contourf(rlons, rlats, rdata, 60, transform=ccrs.PlateCarree(), vmin=0, vmax=100, cmap='YlGn')
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ax.coastlines()
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ax.gridlines()
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# add colorbar
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cbar = plt.colorbar(orientation='horizontal')
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cbar.set_label(data.getParameter() + data.getUnit() )
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plt.show()
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.. image:: Gridded_Data_files/Gridded_Data_9_0.png
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.. code:: python
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import matplotlib.pyplot as plt
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import numpy as np
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from metpy.calc import get_wind_components
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from metpy.cbook import get_test_data
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from metpy.plots import StationPlot, StationPlotLayout, simple_layout
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from metpy.units import units
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# Initialize
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data,latitude,longitude,stationName,temperature,dewpoint,seaLevelPress,windDir,windSpeed = [],[],[],[],[],[],[],[],[]
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request = DataAccessLayer.newDataRequest()
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request.setDatatype("obs")
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#
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# we need to set one station to query latest time. this is hack-y and should be fixed
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# because when you DON'T set a location name, you tend to get a single observation
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# that came in a second ago, so your "latest data for the last time for all stations"
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# data array consists of one village in Peru and time-matching is suspect right now.
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#
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# So here take a known US station (OKC) and hope/assume that a lot of other stations
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# are also reporting (and that this is a 00/20/40 ob).
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#
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request.setLocationNames("KOKC")
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datatimes = DataAccessLayer.getAvailableTimes(request)
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# Get most recent time for location
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time = datatimes[-1].validPeriod
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# "presWeather","skyCover","skyLayerBase"
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# are multi-dimensional(??) and returned seperately (not sure why yet)... deal with those later
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request.setParameters("presWeather","skyCover", "skyLayerBase","stationName","temperature","dewpoint","windDir","windSpeed",
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"seaLevelPress","longitude","latitude")
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request.setLocationNames()
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response = DataAccessLayer.getGeometryData(request,times=time)
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print time
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PRES_PARAMS = set(["presWeather"])
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SKY_PARAMS = set(["skyCover", "skyLayerBase"])
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# Build ordered arrays
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wx,cvr,bas=[],[],[]
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for ob in response:
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#print ob.getParameters()
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if set(ob.getParameters()) & PRES_PARAMS :
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wx.append(ob.getString("presWeather"))
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continue
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if set(ob.getParameters()) & SKY_PARAMS :
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cvr.append(ob.getString("skyCover"))
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bas.append(ob.getNumber("skyLayerBase"))
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continue
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latitude.append(float(ob.getString("latitude")))
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longitude.append(float(ob.getString("longitude")))
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#stationName.append(ob.getString("stationName"))
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temperature.append(float(ob.getString("temperature")))
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dewpoint.append(float(ob.getString("dewpoint")))
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seaLevelPress.append(float(ob.getString("seaLevelPress")))
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windDir.append(float(ob.getString("windDir")))
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windSpeed.append(float(ob.getString("windSpeed")))
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print len(wx)
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print len(temperature)
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# Convert
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data = dict()
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data['latitude'] = np.array(latitude)
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data['longitude'] = np.array(longitude)
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data['air_temperature'] = np.array(temperature)* units.degC
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data['dew_point_temperature'] = np.array(dewpoint)* units.degC
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#data['air_pressure_at_sea_level'] = np.array(seaLevelPress)* units('mbar')
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u, v = get_wind_components(np.array(windSpeed) * units('knots'),
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np.array(windDir) * units.degree)
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data['eastward_wind'], data['northward_wind'] = u, v
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# Convert the fraction value into a code of 0-8, which can be used to pull out
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# the appropriate symbol
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#data['cloud_coverage'] = (8 * data_arr['cloud_fraction']).astype(int)
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# Map weather strings to WMO codes, which we can use to convert to symbols
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# Only use the first symbol if there are multiple
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#wx_text = make_string_list(data_arr['weather'])
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#wx_codes = {'':0, 'HZ':5, 'BR':10, '-DZ':51, 'DZ':53, '+DZ':55,
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# '-RA':61, 'RA':63, '+RA':65, '-SN':71, 'SN':73, '+SN':75}
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#data['present_weather'] = [wx_codes[s.split()[0] if ' ' in s else s] for s in wx]
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# Set up the map projection
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import cartopy.crs as ccrs
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import cartopy.feature as feat
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from matplotlib import rcParams
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rcParams['savefig.dpi'] = 255
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proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=35,
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standard_parallels=[35])
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state_boundaries = feat.NaturalEarthFeature(category='cultural',
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name='admin_1_states_provinces_lines',
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scale='110m', facecolor='none')
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# Create the figure
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fig = plt.figure(figsize=(20, 10))
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ax = fig.add_subplot(1, 1, 1, projection=proj)
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# Add map elements
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ax.add_feature(feat.LAND, zorder=-1)
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ax.add_feature(feat.OCEAN, zorder=-1)
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ax.add_feature(feat.LAKES, zorder=-1)
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ax.coastlines(resolution='110m', zorder=2, color='black')
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ax.add_feature(state_boundaries)
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ax.add_feature(feat.BORDERS, linewidth='2', edgecolor='black')
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ax.set_extent((-118, -73, 23, 50))
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# Start the station plot by specifying the axes to draw on, as well as the
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# lon/lat of the stations (with transform). We also the fontsize to 12 pt.
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stationplot = StationPlot(ax, data['longitude'], data['latitude'],
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transform=ccrs.PlateCarree(), fontsize=12)
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# The layout knows where everything should go, and things are standardized using
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# the names of variables. So the layout pulls arrays out of `data` and plots them
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# using `stationplot`.
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simple_layout.plot(stationplot, data)
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.. parsed-literal::
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(Mar 15 16 22:52:00 , Mar 15 16 22:52:00 )
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430
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.. image:: Gridded_Data_files/Gridded_Data_10_1.png
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