From a1a83b8d39380c4a2bd0274a26d362a3b52c24c3 Mon Sep 17 00:00:00 2001 From: srcarter3 Date: Mon, 30 Oct 2023 23:10:38 +0000 Subject: [PATCH] deploy: fb385bde734da890d18191ab62613ed4d7fb5acc --- ...ip_Accumulation-Region_Of_Interest_7_1.png | Bin 129625 -> 0 bytes ...ip_Accumulation-Region_Of_Interest_9_1.png | Bin 94985 -> 0 bytes ...p_Accumulation_Region_of_Interest_27_1.png | Bin 0 -> 67361 bytes ...p_Accumulation_Region_of_Interest_29_1.png | Bin 0 -> 17021 bytes ...p_Accumulation_Region_of_Interest_37_2.png | Bin 0 -> 52528 bytes ...ip_Accumulation-Region_Of_Interest.rst.txt | 215 ------- ...ip_Accumulation_Region_of_Interest.rst.txt | 479 +++++++++++++++ .../Colored_Surface_Temperature_Plot.html | 2 +- examples/generated/Colorized_Grid_Data.html | 2 +- .../Forecast_Model_Vertical_Sounding.html | 2 +- .../generated/GOES_CIRA_Product_Writer.html | 2 +- .../generated/Grid_Levels_and_Parameters.html | 2 +- .../METAR_Station_Plot_with_MetPy.html | 2 +- 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LONGITUDE_FORMATTER, LATITUDE_FORMATTER - from metpy.units import units - import numpy as np - from shapely.geometry import Point, Polygon - - %matplotlib inline - - conus=[-120, -65, 28, 50] - conus_envelope = Polygon([(conus[0],conus[2]),(conus[0],conus[3]), - (conus[1],conus[3]),(conus[1],conus[2]), - (conus[0],conus[2])]) - - DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu") - request = DataAccessLayer.newDataRequest("grid", envelope=conus_envelope) - request.setLocationNames("NAM40") - request.setLevels("0.0SFC") - request.setParameters("TP") - - cycles = DataAccessLayer.getAvailableTimes(request, True) - times = DataAccessLayer.getAvailableTimes(request) - fcstRun = DataAccessLayer.getForecastRun(cycles[-2], times) - -Calculate accumulated precipitation - -.. code:: ipython3 - - for i, tt in enumerate(fcstRun): - response = DataAccessLayer.getGridData(request, [tt]) - grid = response[0] - if i>0: - data += grid.getRawData() - else: - data = grid.getRawData() - data[data <= -9999] = 0 - print(data.min(), data.max(), grid.getDataTime().getFcstTime()/3600) - - - lons, lats = grid.getLatLonCoords() - bbox = [lons.min(), lons.max(), lats.min(), lats.max()] - fcstHr = int(grid.getDataTime().getFcstTime()/3600) - - tp_inch = data * (0.0393701) - print(tp_inch.min(), tp_inch.max()) - - -.. parsed-literal:: - - 0.0 0.0 0.0 - 0.0 32.1875 3.0 - 0.0 52.125 6.0 - 0.0 74.375 9.0 - 0.0 77.125 12.0 - 0.0 78.625 15.0 - 0.0 78.75 18.0 - 0.0 78.75 21.0 - 0.0 79.375 24.0 - 0.0 82.25 27.0 - 0.0 84.0 30.0 - 0.0 84.6875 33.0 - 0.0 85.625 36.0 - 0.0 87.3125 39.0 - 0.0 87.75 42.0 - 0.0 87.75 45.0 - 0.0 89.375 48.0 - 0.0 127.875 51.0 - 0.0 139.5625 54.0 - 0.0 139.6875 57.0 - 0.0 140.5625 60.0 - 0.0 140.625 63.0 - 0.0 140.625 66.0 - 0.0 140.625 69.0 - 0.0 140.625 72.0 - 0.0 140.625 75.0 - 0.0 140.625 78.0 - 0.0 140.625 81.0 - 0.0 140.625 84.0 - 0.0 5.5364203 - - -Determine lat/lon of maximum rainfall value: - -.. code:: ipython3 - - ii,jj = np.where(tp_inch==tp_inch.max()) - i=ii[0] - j=jj[0] - point = Point(lons[i][j], lats[i][j]) - -Draw CONUS map - -.. code:: ipython3 - - def make_map(bbox, projection=ccrs.PlateCarree()): - fig, ax = plt.subplots(figsize=(20, 14), - subplot_kw=dict(projection=projection)) - ax.set_extent(bbox) - ax.coastlines(resolution='50m') - return fig, ax - - cmap = plt.get_cmap('rainbow') - fig, ax = make_map(bbox=bbox) - cs = ax.pcolormesh(lons, lats, tp_inch, cmap=cmap) - cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal') - cbar.set_label(grid.getLocationName() + " Total precipitation in inches, " \ - + str(fcstHr) + "-hr fcst valid " + str(grid.getDataTime().getRefTime())) - - ax.scatter(point.x, point.y, s=300, - transform=ccrs.PlateCarree(),marker="+",facecolor='black') - - inc = 3.5 - box=[point.x-inc, point.x+inc, point.y-inc, point.y+inc] - polygon = Polygon([(box[0],box[2]),(box[0],box[3]), - (box[1],box[3]),(box[1],box[2]), - (box[0],box[2])]) - ax.add_geometries([polygon], ccrs.PlateCarree(), facecolor='none', edgecolor='white', linewidth=2) - - - - -.. parsed-literal:: - - - - - - -.. image:: Precip_Accumulation-Region_Of_Interest_files/Precip_Accumulation-Region_Of_Interest_7_1.png - - -Now create a new gridded data request with a geometry envelope for our -Region of Interest - -.. code:: ipython3 - - request = DataAccessLayer.newDataRequest("grid", envelope=polygon) - request.setLocationNames("HRRR") - request.setLevels("0.0SFC") - request.setParameters("TP") - - cycles = DataAccessLayer.getAvailableTimes(request, True) - times = DataAccessLayer.getAvailableTimes(request) - fcstRun = DataAccessLayer.getForecastRun(cycles[-2], times) - - - for i, tt in enumerate(fcstRun): - response = DataAccessLayer.getGridData(request, [tt]) - grid = response[0] - if i>0: - data += grid.getRawData() - else: - data = grid.getRawData() - data[data <= -9999] = 0 - print(data.min(), data.max(), grid.getDataTime().getFcstTime()/3600) - - - lons, lats = grid.getLatLonCoords() - bbox = [lons.min(), lons.max(), lats.min(), lats.max()] - fcstHr = int(grid.getDataTime().getFcstTime()/3600) - - tp_inch = data * (0.0393701) - print(tp_inch.min(), tp_inch.max()) - - def make_map(bbox, projection=ccrs.PlateCarree()): - fig, ax = plt.subplots(figsize=(20, 14), - subplot_kw=dict(projection=projection)) - ax.set_extent(bbox) - ax.coastlines(resolution='50m') - return fig, ax - - cmap = plt.get_cmap('rainbow') - fig, ax = make_map(bbox=box) - cs = ax.pcolormesh(lons, lats, tp_inch, cmap=cmap) - cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal') - cbar.set_label(grid.getLocationName() + " Total precipitation in inches, " \ - + str(fcstHr) + "-hr fcst valid " + str(grid.getDataTime().getRefTime())) - - -.. parsed-literal:: - - 0.0 1.853 1.0 - 0.0 3.5290003 2.0 - 0.0 5.0290003 3.0 - 0.0 5.051 4.0 - 0.0 5.2960005 5.0 - 0.0 5.2960005 6.0 - 0.0 5.8269997 7.0 - 0.0 6.1790004 8.0 - 0.0 6.1890006 9.0 - 0.0 9.071 10.0 - 0.0 10.812 11.0 - 0.0 14.718 12.0 - 0.0 18.295 13.0 - 0.0 21.339 14.0 - 0.0 22.626 15.0 - 0.0 28.670002 16.0 - 0.0 32.334 17.0 - 0.0 36.628002 18.0 - 0.0 1.4420482 - - - -.. image:: Precip_Accumulation-Region_Of_Interest_files/Precip_Accumulation-Region_Of_Interest_9_1.png - diff --git a/_sources/examples/generated/Precip_Accumulation_Region_of_Interest.rst.txt b/_sources/examples/generated/Precip_Accumulation_Region_of_Interest.rst.txt new file mode 100644 index 0000000..a47a729 --- /dev/null +++ b/_sources/examples/generated/Precip_Accumulation_Region_of_Interest.rst.txt @@ -0,0 +1,479 @@ +====================================== +Precip Accumulation Region of Interest +====================================== +`Notebook `_ +Python-AWIPS Tutorial Notebook + +-------------- + +Objectives +========== + +- Access the model data from an EDEX server and limit the data returned + by using model specific parameters +- Calculate the total precipitation over several model runs +- Create a colorized plot for the continental US of the accumulated + precipitation data +- Calculate and identify area of highest of precipitation +- Use higher resolution data to draw region of interest + +-------------- + +Table of Contents +----------------- + +| `1 + Imports `__\ +| `2 Initial + Setup `__\ +|     `2.1 Geographic + Filter `__\ +|     `2.2 EDEX + Connnection `__\ +|     `2.3 Refine the + Request `__\ +|     `2.4 Get + Times `__\ +| `3 Function: + calculate_accumulated_precip() `__\ +| `4 Function: + make_map() `__\ +| `5 Get the + Data! `__\ +| `6 Plot the + Data! `__\ +|     `6.1 Create CONUS + Image `__\ +|     `6.2 Create Region of Interest + Image `__\ +| `7 High Resolution + ROI `__\ +|     `7.1 New Data + Request `__\ +|     `7.2 Calculate + Data `__\ +|     `7.3 Plot + ROI `__\ +| `8 See + Also `__\ +|     `8.1 Related + Notebooks `__\ +|     `8.2 Additional + Documentation `__\ + +1 Imports +--------- + +The imports below are used throughout the notebook. Note the first +import is coming directly from python-awips and allows us to connect to +an EDEX server. The subsequent imports are for data manipulation and +visualization. + +.. code:: ipython3 + + from awips.dataaccess import DataAccessLayer + import cartopy.crs as ccrs + import matplotlib.pyplot as plt + from metpy.units import units + import numpy as np + from shapely.geometry import Point, Polygon + +`Top `__ + +-------------- + +2 Initial Setup +--------------- + +2.1 Geographic Filter +~~~~~~~~~~~~~~~~~~~~~ + +By defining a bounding box for the Continental US (CONUS), we’re able to +optimize the data request sent to the EDEX server. + +.. code:: ipython3 + + conus=[-125, -65, 25, 55] + conus_envelope = Polygon([(conus[0],conus[2]),(conus[0],conus[3]), + (conus[1],conus[3]),(conus[1],conus[2]), + (conus[0],conus[2])]) + +2.2 EDEX Connection +~~~~~~~~~~~~~~~~~~~ + +First we establish a connection to Unidata’s public EDEX server. With +that connection made, we can create a `new data request +object `__ +and set the data type to **grid**, and use the geographic envelope we +just created. + +.. code:: ipython3 + + DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu") + request = DataAccessLayer.newDataRequest("grid", envelope=conus_envelope) + +2.3 Refine the Request +~~~~~~~~~~~~~~~~~~~~~~ + +Here we specify which model we’re interested in by setting the +*LocationNames*, and the specific data we’re interested in by setting +the *Levels* and *Parameters*. + +.. code:: ipython3 + + request.setLocationNames("GFS1p0") + request.setLevels("0.0SFC") + request.setParameters("TP") + +2.4 Get Times +~~~~~~~~~~~~~ + +We need to get the available times and cycles for our model data + +.. code:: ipython3 + + cycles = DataAccessLayer.getAvailableTimes(request, True) + times = DataAccessLayer.getAvailableTimes(request) + fcstRun = DataAccessLayer.getForecastRun(cycles[-1], times) + +`Top `__ + +-------------- + +3 Function: calculate_accumulated_precip() +------------------------------------------ + +Since we’ll want to calculate the accumulated precipitation of our data +more than once, it makes sense to create a function that we can call +instead of duplicating the logic. + +This function cycles through all the grid data responses and adds up all +of the rainfall to produce a numpy array with the total ammount of +rainfall for the given data request. It also finds the maximum rainfall +point in x and y coordinates. + +.. code:: ipython3 + + def calculate_accumulated_precip(dataRequest, forecastRun): + + for i, tt in enumerate(forecastRun): + response = DataAccessLayer.getGridData(dataRequest, [tt]) + grid = response[0] + if i>0: + data += grid.getRawData() + else: + data = grid.getRawData() + data[data <= -9999] = 0 + print(data.min(), data.max(), grid.getDataTime().getFcstTime()/3600) + + # Convert from mm to inches + result = data * (0.0393701) + + ii,jj = np.where(result==result.max()) + i=ii[0] + j=jj[0] + + return result, i, j + +`Top `__ + +-------------- + +4 Fuction: make_map() +--------------------- + +This function creates the basics of the map we’re going to plot our data +on. It takes in a bounding box to determine the extent and then adds +coastlines for easy frame of reference. + +.. code:: ipython3 + + def make_map(bbox, projection=ccrs.PlateCarree()): + fig, ax = plt.subplots(figsize=(20, 14), + subplot_kw=dict(projection=projection)) + ax.set_extent(bbox) + ax.coastlines(resolution='50m') + return fig, ax + +`Top `__ + +-------------- + +5 Get the Data! +--------------- + +Access the data from the DataAccessLayer interface using the +*getGridData* function. Use that data to calculate the accumulated +rainfall, the maximum rainfall point, and the region of interest +bounding box. + +.. code:: ipython3 + + ## get the grid response from edex + response = DataAccessLayer.getGridData(request, [fcstRun[-1]]) + ## take the first result to get the location information from + grid = response[0] + + ## get the location coordinates and create a bounding box for our map + lons, lats = grid.getLatLonCoords() + bbox = [lons.min(), lons.max(), lats.min(), lats.max()] + fcstHr = int(grid.getDataTime().getFcstTime()/3600) + + ## calculate the total precipitation + tp_inch, i, j = calculate_accumulated_precip(request, fcstRun) + print(tp_inch.min(), tp_inch.max()) + + ## use the max points coordinates to get the max point in lat/lon coords + maxPoint = Point(lons[i][j], lats[i][j]) + inc = 3.5 + ## create a region of interest bounding box + roi_box=[maxPoint.x-inc, maxPoint.x+inc, maxPoint.y-inc, maxPoint.y+inc] + roi_polygon = Polygon([(roi_box[0],roi_box[2]),(roi_box[0],roi_box[3]), + (roi_box[1],roi_box[3]),(roi_box[1],roi_box[2]),(roi_box[0],roi_box[2])]) + + print(maxPoint) + + +.. parsed-literal:: + + 0.0 10.0625 6.0 + 0.0 21.75 12.0 + 0.0 35.1875 18.0 + 0.0 43.5 24.0 + 0.0 45.5625 42.0 + 0.0 47.9375 48.0 + 0.0 52.0625 54.0 + 0.0 56.375 60.0 + 0.0 86.625 66.0 + 0.0 92.4375 72.0 + 0.0 94.375 78.0 + 0.0 95.375 84.0 + 0.0 98.3125 90.0 + 0.0 100.125 96.0 + 0.0 101.6875 102.0 + 0.0 104.0 108.0 + 0.0 107.1875 114.0 + 0.0 115.25 120.0 + 0.0 129.0 126.0 + 0.0 136.375 132.0 + 0.0 141.125 138.0 + 0.0 145.25 144.0 + 0.0 147.375 150.0 + 0.0 5.802169 + POINT (-124 42) + + +`Top `__ + +-------------- + +6 Plot the Data! +---------------- + +6.1 Create CONUS Image +~~~~~~~~~~~~~~~~~~~~~~ + +Plot our data on our CONUS map. + +.. code:: ipython3 + + cmap = plt.get_cmap('rainbow') + fig, ax = make_map(bbox=bbox) + cs = ax.pcolormesh(lons, lats, tp_inch, cmap=cmap) + cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal') + cbar.set_label(grid.getLocationName() + " Total precipitation in inches, " \ + + str(fcstHr) + "-hr fcst valid " + str(grid.getDataTime().getRefTime())) + + ax.scatter(maxPoint.x, maxPoint.y, s=300, + transform=ccrs.PlateCarree(),marker="+",facecolor='black') + + ax.add_geometries([roi_polygon], ccrs.PlateCarree(), facecolor='none', edgecolor='white', linewidth=2) + + + + +.. parsed-literal:: + + + + + + +.. image:: Precip_Accumulation_Region_of_Interest_files/Precip_Accumulation_Region_of_Interest_27_1.png + + +6.2 Create Region of Interest Image +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Now crop the data and zoom in on the region of interest (ROI) to create +a new plot. + +.. code:: ipython3 + + # cmap = plt.get_cmap('rainbow') + fig, ax = make_map(bbox=roi_box) + + cs = ax.pcolormesh(lons, lats, tp_inch, cmap=cmap) + cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal') + cbar.set_label(grid.getLocationName() + " Total precipitation in inches, " \ + + str(fcstHr) + "-hr fcst valid " + str(grid.getDataTime().getRefTime())) + + ax.scatter(maxPoint.x, maxPoint.y, s=300, + transform=ccrs.PlateCarree(),marker="+",facecolor='black') + + + + +.. parsed-literal:: + + + + + + +.. image:: Precip_Accumulation_Region_of_Interest_files/Precip_Accumulation_Region_of_Interest_29_1.png + + +`Top `__ + +-------------- + +7 High Resolution ROI +--------------------- + +7.1 New Data Request +~~~~~~~~~~~~~~~~~~~~ + +To see the region of interest more clearly, we can redo the process with +a higher resolution model (GFS20 vs. GFS1.0). + +.. code:: ipython3 + + roiRequest = DataAccessLayer.newDataRequest("grid", envelope=conus_envelope) + roiRequest.setLocationNames("GFS20") + roiRequest.setLevels("0.0SFC") + roiRequest.setParameters("TP") + + roiCycles = DataAccessLayer.getAvailableTimes(roiRequest, True) + roiTimes = DataAccessLayer.getAvailableTimes(roiRequest) + roiFcstRun = DataAccessLayer.getForecastRun(roiCycles[-1], roiTimes) + +7.2 Calculate Data +~~~~~~~~~~~~~~~~~~ + +.. code:: ipython3 + + roiResponse = DataAccessLayer.getGridData(roiRequest, [roiFcstRun[-1]]) + print(roiResponse) + roiGrid = roiResponse[0] + + roiLons, roiLats = roiGrid.getLatLonCoords() + + roi_data, i, j = calculate_accumulated_precip(roiRequest, roiFcstRun) + + roiFcstHr = int(roiGrid.getDataTime().getFcstTime()/3600) + + +.. parsed-literal:: + + [] + 0.0 22.5625 3.0 + 0.0 35.375 6.0 + 0.0 38.375 9.0 + 0.0 38.375 12.0 + 0.0 41.375 15.0 + 0.0 48.625 18.0 + 0.0 73.0625 30.0 + 0.0 94.9375 33.0 + 0.0 96.125 36.0 + 0.0 97.0 39.0 + 0.0 99.375 45.0 + 0.0 100.0625 48.0 + 0.0 100.25 51.0 + 0.0 100.4375 57.0 + 0.0 100.4375 63.0 + 0.0 118.25 66.0 + 0.0 127.625 69.0 + 0.0 131.125 75.0 + 0.0 131.375 78.0 + 0.0 131.5 81.0 + 0.0 131.875 84.0 + 0.0 132.875 90.0 + 0.0 133.375 96.0 + 0.0 139.1875 102.0 + 0.0 141.625 120.0 + 0.0 141.75 126.0 + 0.0 142.1875 132.0 + 0.0 143.375 138.0 + 0.0 148.6875 144.0 + 0.0 156.25 150.0 + + +7.3 Plot ROI +~~~~~~~~~~~~ + +.. code:: ipython3 + + # cmap = plt.get_cmap('rainbow') + fig, ax = make_map(bbox=roi_box) + + cs = ax.pcolormesh(roiLons, roiLats, roi_data, cmap=cmap) + cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal') + cbar.set_label(roiGrid.getLocationName() + " Total precipitation in inches, " \ + + str(roiFcstHr) + "-hr fcst valid " + str(roiGrid.getDataTime().getRefTime())) + + ax.scatter(maxPoint.x, maxPoint.y, s=300, + transform=ccrs.PlateCarree(),marker="+",facecolor='black') + + +.. parsed-literal:: + + /Users/scarter/opt/miniconda3/envs/python3-awips/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:1702: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh. + X, Y, C, shading = self._pcolorargs('pcolormesh', *args, + + + + +.. parsed-literal:: + + + + + + +.. image:: Precip_Accumulation_Region_of_Interest_files/Precip_Accumulation_Region_of_Interest_37_2.png + + +`Top `__ + +-------------- + +8 See Also +---------- + +8.1 Related Notebooks +~~~~~~~~~~~~~~~~~~~~~ + +- `Colorized Grid + Data `__ +- `Grid Levels and + Parameters `__ + +8.2 Additional Documentation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +**python-awips:** \* +`awips.DataAccessLayer `__ +\* +`awips.PyGridData `__ + +**matplotlib:** \* +`matplotlib.pyplot `__ +\* +`matplotlib.pyplot.subplot `__ +\* +`matplotlib.pyplot.pcolormesh `__ + +`Top `__ + +-------------- diff --git a/examples/generated/Colored_Surface_Temperature_Plot.html b/examples/generated/Colored_Surface_Temperature_Plot.html index 6788276..4dfb3c8 100644 --- a/examples/generated/Colored_Surface_Temperature_Plot.html +++ b/examples/generated/Colored_Surface_Temperature_Plot.html @@ -72,7 +72,7 @@

  • Model Sounding Data
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
  • Satellite Imagery
  • Upper Air BUFR Soundings
    • diff --git a/examples/generated/Colorized_Grid_Data.html b/examples/generated/Colorized_Grid_Data.html index cd4be8b..ac15fd1 100644 --- a/examples/generated/Colorized_Grid_Data.html +++ b/examples/generated/Colorized_Grid_Data.html @@ -71,7 +71,7 @@
  • Map Resources and Topography
  • Model Sounding Data
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
  • Satellite Imagery
  • Upper Air BUFR Soundings
    • diff --git a/examples/generated/Forecast_Model_Vertical_Sounding.html b/examples/generated/Forecast_Model_Vertical_Sounding.html index fe3d0c1..532dc0b 100644 --- a/examples/generated/Forecast_Model_Vertical_Sounding.html +++ b/examples/generated/Forecast_Model_Vertical_Sounding.html @@ -75,7 +75,7 @@
  • Map Resources and Topography
  • Model Sounding Data
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
  • Satellite Imagery
  • Upper Air BUFR Soundings
    • diff --git a/examples/generated/GOES_CIRA_Product_Writer.html b/examples/generated/GOES_CIRA_Product_Writer.html index 78c7075..f748f68 100644 --- a/examples/generated/GOES_CIRA_Product_Writer.html +++ b/examples/generated/GOES_CIRA_Product_Writer.html @@ -71,7 +71,7 @@
  • Map Resources and Topography
  • Model Sounding Data
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
  • Satellite Imagery
  • Upper Air BUFR Soundings
    • diff --git a/examples/generated/Grid_Levels_and_Parameters.html b/examples/generated/Grid_Levels_and_Parameters.html index 5429602..8aa512d 100644 --- a/examples/generated/Grid_Levels_and_Parameters.html +++ b/examples/generated/Grid_Levels_and_Parameters.html @@ -75,7 +75,7 @@
  • Map Resources and Topography
  • Model Sounding Data
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
  • Satellite Imagery
  • Upper Air BUFR Soundings
    • diff --git a/examples/generated/METAR_Station_Plot_with_MetPy.html b/examples/generated/METAR_Station_Plot_with_MetPy.html index 624c051..15699dd 100644 --- a/examples/generated/METAR_Station_Plot_with_MetPy.html +++ b/examples/generated/METAR_Station_Plot_with_MetPy.html @@ -72,7 +72,7 @@
  • Map Resources and Topography
  • Model Sounding Data
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
  • Satellite Imagery
  • Upper Air BUFR Soundings
    • diff --git a/examples/generated/Map_Resources_and_Topography.html b/examples/generated/Map_Resources_and_Topography.html index 5f02655..4ba78eb 100644 --- a/examples/generated/Map_Resources_and_Topography.html +++ b/examples/generated/Map_Resources_and_Topography.html @@ -76,7 +76,7 @@
  • Model Sounding Data
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
  • Satellite Imagery
  • Upper Air BUFR Soundings
    • diff --git a/examples/generated/Model_Sounding_Data.html b/examples/generated/Model_Sounding_Data.html index b8c3a73..782ff1e 100644 --- a/examples/generated/Model_Sounding_Data.html +++ b/examples/generated/Model_Sounding_Data.html @@ -73,7 +73,7 @@
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
  • Satellite Imagery
  • Upper Air BUFR Soundings
    • diff --git a/examples/generated/NEXRAD_Level3_Radar.html b/examples/generated/NEXRAD_Level3_Radar.html index 7808db4..26db830 100644 --- a/examples/generated/NEXRAD_Level3_Radar.html +++ b/examples/generated/NEXRAD_Level3_Radar.html @@ -20,7 +20,7 @@ - + @@ -59,7 +59,7 @@
  • Map Resources and Topography
  • Model Sounding Data
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
  • Satellite Imagery
  • Upper Air BUFR Soundings
    • @@ -368,7 +368,7 @@
    diff --git a/examples/generated/Precip_Accumulation-Region_Of_Interest.html b/examples/generated/Precip_Accumulation-Region_Of_Interest.html deleted file mode 100644 index e596d53..0000000 --- a/examples/generated/Precip_Accumulation-Region_Of_Interest.html +++ /dev/null @@ -1,319 +0,0 @@ - - - - - - - Precip Accumulation-Region Of Interest — python-awips documentation - - - - - - - - - - - - - - - - - - -
    - - -
    - -
    -
    -
    - -
    -
    -
    -
    - -
    -

    Precip Accumulation-Region Of Interest

    -

    Notebook -A way to determine areas of greatest precipitation and generate imagery -for that sector.

    -
    from awips.dataaccess import DataAccessLayer
-import cartopy.crs as ccrs
-import matplotlib.pyplot as plt
-from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
-from metpy.units import units
-import numpy as np
-from shapely.geometry import Point, Polygon
-
-%matplotlib inline
-
-conus=[-120, -65, 28, 50]
-conus_envelope = Polygon([(conus[0],conus[2]),(conus[0],conus[3]),
-                          (conus[1],conus[3]),(conus[1],conus[2]),
-                          (conus[0],conus[2])])
-
-DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu")
-request = DataAccessLayer.newDataRequest("grid", envelope=conus_envelope)
-request.setLocationNames("NAM40")
-request.setLevels("0.0SFC")
-request.setParameters("TP")
-
-cycles = DataAccessLayer.getAvailableTimes(request, True)
-times = DataAccessLayer.getAvailableTimes(request)
-fcstRun = DataAccessLayer.getForecastRun(cycles[-2], times)
-
    -
    -

    Calculate accumulated precipitation

    -
    for i, tt in enumerate(fcstRun):
-    response = DataAccessLayer.getGridData(request, [tt])
-    grid = response[0]
-    if i>0:
-        data += grid.getRawData()
-    else:
-        data = grid.getRawData()
-    data[data <= -9999] = 0
-    print(data.min(), data.max(), grid.getDataTime().getFcstTime()/3600)
-
-
-lons, lats = grid.getLatLonCoords()
-bbox = [lons.min(), lons.max(), lats.min(), lats.max()]
-fcstHr = int(grid.getDataTime().getFcstTime()/3600)
-
-tp_inch = data * (0.0393701)
-print(tp_inch.min(), tp_inch.max())
-
    -
    -
    0.0 0.0 0.0
-0.0 32.1875 3.0
-0.0 52.125 6.0
-0.0 74.375 9.0
-0.0 77.125 12.0
-0.0 78.625 15.0
-0.0 78.75 18.0
-0.0 78.75 21.0
-0.0 79.375 24.0
-0.0 82.25 27.0
-0.0 84.0 30.0
-0.0 84.6875 33.0
-0.0 85.625 36.0
-0.0 87.3125 39.0
-0.0 87.75 42.0
-0.0 87.75 45.0
-0.0 89.375 48.0
-0.0 127.875 51.0
-0.0 139.5625 54.0
-0.0 139.6875 57.0
-0.0 140.5625 60.0
-0.0 140.625 63.0
-0.0 140.625 66.0
-0.0 140.625 69.0
-0.0 140.625 72.0
-0.0 140.625 75.0
-0.0 140.625 78.0
-0.0 140.625 81.0
-0.0 140.625 84.0
-0.0 5.5364203
-
    -
    -

    Determine lat/lon of maximum rainfall value:

    -
    ii,jj = np.where(tp_inch==tp_inch.max())
-i=ii[0]
-j=jj[0]
-point = Point(lons[i][j], lats[i][j])
-
    -
    -

    Draw CONUS map

    -
    def make_map(bbox, projection=ccrs.PlateCarree()):
-    fig, ax = plt.subplots(figsize=(20, 14),
-            subplot_kw=dict(projection=projection))
-    ax.set_extent(bbox)
-    ax.coastlines(resolution='50m')
-    return fig, ax
-
-cmap = plt.get_cmap('rainbow')
-fig, ax = make_map(bbox=bbox)
-cs = ax.pcolormesh(lons, lats, tp_inch, cmap=cmap)
-cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal')
-cbar.set_label(grid.getLocationName() + " Total precipitation in inches, " \
-               + str(fcstHr) + "-hr fcst valid " + str(grid.getDataTime().getRefTime()))
-
-ax.scatter(point.x, point.y, s=300,
-           transform=ccrs.PlateCarree(),marker="+",facecolor='black')
-
-inc = 3.5
-box=[point.x-inc, point.x+inc, point.y-inc, point.y+inc]
-polygon = Polygon([(box[0],box[2]),(box[0],box[3]),
-                   (box[1],box[3]),(box[1],box[2]),
-                   (box[0],box[2])])
-ax.add_geometries([polygon], ccrs.PlateCarree(), facecolor='none', edgecolor='white', linewidth=2)
-
    -
    -
    <cartopy.mpl.feature_artist.FeatureArtist at 0x11b971da0>
-
    -
    -../../_images/Precip_Accumulation-Region_Of_Interest_7_1.png -

    Now create a new gridded data request with a geometry envelope for our -Region of Interest

    -
    request = DataAccessLayer.newDataRequest("grid", envelope=polygon)
-request.setLocationNames("HRRR")
-request.setLevels("0.0SFC")
-request.setParameters("TP")
-
-cycles = DataAccessLayer.getAvailableTimes(request, True)
-times = DataAccessLayer.getAvailableTimes(request)
-fcstRun = DataAccessLayer.getForecastRun(cycles[-2], times)
-
-
-for i, tt in enumerate(fcstRun):
-    response = DataAccessLayer.getGridData(request, [tt])
-    grid = response[0]
-    if i>0:
-        data += grid.getRawData()
-    else:
-        data = grid.getRawData()
-    data[data <= -9999] = 0
-    print(data.min(), data.max(), grid.getDataTime().getFcstTime()/3600)
-
-
-lons, lats = grid.getLatLonCoords()
-bbox = [lons.min(), lons.max(), lats.min(), lats.max()]
-fcstHr = int(grid.getDataTime().getFcstTime()/3600)
-
-tp_inch = data * (0.0393701)
-print(tp_inch.min(), tp_inch.max())
-
-def make_map(bbox, projection=ccrs.PlateCarree()):
-    fig, ax = plt.subplots(figsize=(20, 14),
-            subplot_kw=dict(projection=projection))
-    ax.set_extent(bbox)
-    ax.coastlines(resolution='50m')
-    return fig, ax
-
-cmap = plt.get_cmap('rainbow')
-fig, ax = make_map(bbox=box)
-cs = ax.pcolormesh(lons, lats, tp_inch, cmap=cmap)
-cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal')
-cbar.set_label(grid.getLocationName() + " Total precipitation in inches, " \
-               + str(fcstHr) + "-hr fcst valid " + str(grid.getDataTime().getRefTime()))
-
    -
    -
    0.0 1.853 1.0
-0.0 3.5290003 2.0
-0.0 5.0290003 3.0
-0.0 5.051 4.0
-0.0 5.2960005 5.0
-0.0 5.2960005 6.0
-0.0 5.8269997 7.0
-0.0 6.1790004 8.0
-0.0 6.1890006 9.0
-0.0 9.071 10.0
-0.0 10.812 11.0
-0.0 14.718 12.0
-0.0 18.295 13.0
-0.0 21.339 14.0
-0.0 22.626 15.0
-0.0 28.670002 16.0
-0.0 32.334 17.0
-0.0 36.628002 18.0
-0.0 1.4420482
-
    -
    -../../_images/Precip_Accumulation-Region_Of_Interest_9_1.png -
    - - -
    -
    - -
    -
    -
    -
    - - - - \ No newline at end of file diff --git a/examples/generated/Precip_Accumulation_Region_of_Interest.html b/examples/generated/Precip_Accumulation_Region_of_Interest.html new file mode 100644 index 0000000..6a9587a --- /dev/null +++ b/examples/generated/Precip_Accumulation_Region_of_Interest.html @@ -0,0 +1,547 @@ + + + + + + + Precip Accumulation Region of Interest — python-awips documentation + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    +
    +
    + +
    +
    +
    +
    + +
    +

    Precip Accumulation Region of Interest

    +

    Notebook +Python-AWIPS Tutorial Notebook

    +
    +
    +

    Objectives

    +
      +

    • Access the model data from an EDEX server and limit the data returned +by using model specific parameters

      • +

    • Calculate the total precipitation over several model runs

      • +

    • Create a colorized plot for the continental US of the accumulated +precipitation data

      • +

    • Calculate and identify area of highest of precipitation

      • +

    • Use higher resolution data to draw region of interest

      • +
    +
    +
    +

    Table of Contents

    +
    +
    1 +Imports
    +
    2 Initial +Setup
    +
        2.1 Geographic +Filter
    +
        2.2 EDEX +Connnection
    +
        2.3 Refine the +Request
    +
        2.4 Get +Times
    +
    3 Function: +calculate_accumulated_precip()
    +
    4 Function: +make_map()
    +
    5 Get the +Data!
    +
    6 Plot the +Data!
    +
        6.1 Create CONUS +Image
    +
        6.2 Create Region of Interest +Image
    +
    7 High Resolution +ROI
    +
        7.1 New Data +Request
    +
        7.2 Calculate +Data
    +
        7.3 Plot +ROI
    +
    8 See +Also
    +
        8.1 Related +Notebooks
    +
        8.2 Additional +Documentation
    +
    +
    +
    +

    1 Imports

    +

    The imports below are used throughout the notebook. Note the first +import is coming directly from python-awips and allows us to connect to +an EDEX server. The subsequent imports are for data manipulation and +visualization.

    +
    from awips.dataaccess import DataAccessLayer
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
+from metpy.units import units
+import numpy as np
+from shapely.geometry import Point, Polygon
+
    +
    +

    Top

    +
    +
    +
    +

    2 Initial Setup

    +
    +

    2.1 Geographic Filter

    +

    By defining a bounding box for the Continental US (CONUS), we’re able to +optimize the data request sent to the EDEX server.

    +
    conus=[-125, -65, 25, 55]
+conus_envelope = Polygon([(conus[0],conus[2]),(conus[0],conus[3]),
+                          (conus[1],conus[3]),(conus[1],conus[2]),
+                          (conus[0],conus[2])])
+
    +
    +
    +
    +

    2.2 EDEX Connection

    +

    First we establish a connection to Unidata’s public EDEX server. With +that connection made, we can create a new data request +object +and set the data type to grid, and use the geographic envelope we +just created.

    +
    DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu")
+request = DataAccessLayer.newDataRequest("grid", envelope=conus_envelope)
+
    +
    +
    +
    +

    2.3 Refine the Request

    +

    Here we specify which model we’re interested in by setting the +LocationNames, and the specific data we’re interested in by setting +the Levels and Parameters.

    +
    request.setLocationNames("GFS1p0")
+request.setLevels("0.0SFC")
+request.setParameters("TP")
+
    +
    +
    +
    +

    2.4 Get Times

    +

    We need to get the available times and cycles for our model data

    +
    cycles = DataAccessLayer.getAvailableTimes(request, True)
+times = DataAccessLayer.getAvailableTimes(request)
+fcstRun = DataAccessLayer.getForecastRun(cycles[-1], times)
+
    +
    +

    Top

    +
    +
    +
    +
    +

    3 Function: calculate_accumulated_precip()

    +

    Since we’ll want to calculate the accumulated precipitation of our data +more than once, it makes sense to create a function that we can call +instead of duplicating the logic.

    +

    This function cycles through all the grid data responses and adds up all +of the rainfall to produce a numpy array with the total ammount of +rainfall for the given data request. It also finds the maximum rainfall +point in x and y coordinates.

    +
    def calculate_accumulated_precip(dataRequest, forecastRun):
+
+    for i, tt in enumerate(forecastRun):
+        response = DataAccessLayer.getGridData(dataRequest, [tt])
+        grid = response[0]
+        if i>0:
+            data += grid.getRawData()
+        else:
+            data = grid.getRawData()
+        data[data <= -9999] = 0
+        print(data.min(), data.max(), grid.getDataTime().getFcstTime()/3600)
+
+    # Convert from mm to inches
+    result = data * (0.0393701)
+
+    ii,jj = np.where(result==result.max())
+    i=ii[0]
+    j=jj[0]
+
+    return result, i, j
+
    +
    +

    Top

    +
    +
    +
    +

    4 Fuction: make_map()

    +

    This function creates the basics of the map we’re going to plot our data +on. It takes in a bounding box to determine the extent and then adds +coastlines for easy frame of reference.

    +
    def make_map(bbox, projection=ccrs.PlateCarree()):
+    fig, ax = plt.subplots(figsize=(20, 14),
+            subplot_kw=dict(projection=projection))
+    ax.set_extent(bbox)
+    ax.coastlines(resolution='50m')
+    return fig, ax
+
    +
    +

    Top

    +
    +
    +
    +

    5 Get the Data!

    +

    Access the data from the DataAccessLayer interface using the +getGridData function. Use that data to calculate the accumulated +rainfall, the maximum rainfall point, and the region of interest +bounding box.

    +
    ## get the grid response from edex
+response = DataAccessLayer.getGridData(request, [fcstRun[-1]])
+## take the first result to get the location information from
+grid = response[0]
+
+## get the location coordinates and create a bounding box for our map
+lons, lats = grid.getLatLonCoords()
+bbox = [lons.min(), lons.max(), lats.min(), lats.max()]
+fcstHr = int(grid.getDataTime().getFcstTime()/3600)
+
+## calculate the total precipitation
+tp_inch, i, j = calculate_accumulated_precip(request, fcstRun)
+print(tp_inch.min(), tp_inch.max())
+
+## use the max points coordinates to get the max point in lat/lon coords
+maxPoint = Point(lons[i][j], lats[i][j])
+inc = 3.5
+## create a region of interest bounding box
+roi_box=[maxPoint.x-inc, maxPoint.x+inc, maxPoint.y-inc, maxPoint.y+inc]
+roi_polygon = Polygon([(roi_box[0],roi_box[2]),(roi_box[0],roi_box[3]),
+                (roi_box[1],roi_box[3]),(roi_box[1],roi_box[2]),(roi_box[0],roi_box[2])])
+
+print(maxPoint)
+
    +
    +
    0.0 10.0625 6.0
+0.0 21.75 12.0
+0.0 35.1875 18.0
+0.0 43.5 24.0
+0.0 45.5625 42.0
+0.0 47.9375 48.0
+0.0 52.0625 54.0
+0.0 56.375 60.0
+0.0 86.625 66.0
+0.0 92.4375 72.0
+0.0 94.375 78.0
+0.0 95.375 84.0
+0.0 98.3125 90.0
+0.0 100.125 96.0
+0.0 101.6875 102.0
+0.0 104.0 108.0
+0.0 107.1875 114.0
+0.0 115.25 120.0
+0.0 129.0 126.0
+0.0 136.375 132.0
+0.0 141.125 138.0
+0.0 145.25 144.0
+0.0 147.375 150.0
+0.0 5.802169
+POINT (-124 42)
+
    +
    +

    Top

    +
    +
    +
    +

    6 Plot the Data!

    +
    +

    6.1 Create CONUS Image

    +

    Plot our data on our CONUS map.

    +
    cmap = plt.get_cmap('rainbow')
+fig, ax = make_map(bbox=bbox)
+cs = ax.pcolormesh(lons, lats, tp_inch, cmap=cmap)
+cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal')
+cbar.set_label(grid.getLocationName() + " Total precipitation in inches, " \
+               + str(fcstHr) + "-hr fcst valid " + str(grid.getDataTime().getRefTime()))
+
+ax.scatter(maxPoint.x, maxPoint.y, s=300,
+           transform=ccrs.PlateCarree(),marker="+",facecolor='black')
+
+ax.add_geometries([roi_polygon], ccrs.PlateCarree(), facecolor='none', edgecolor='white', linewidth=2)
+
    +
    +
    <cartopy.mpl.feature_artist.FeatureArtist at 0x13eb32340>
+
    +
    +../../_images/Precip_Accumulation_Region_of_Interest_27_1.png +
    +
    +

    6.2 Create Region of Interest Image

    +

    Now crop the data and zoom in on the region of interest (ROI) to create +a new plot.

    +
    # cmap = plt.get_cmap('rainbow')
+fig, ax = make_map(bbox=roi_box)
+
+cs = ax.pcolormesh(lons, lats, tp_inch, cmap=cmap)
+cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal')
+cbar.set_label(grid.getLocationName() + " Total precipitation in inches, " \
+               + str(fcstHr) + "-hr fcst valid " + str(grid.getDataTime().getRefTime()))
+
+ax.scatter(maxPoint.x, maxPoint.y, s=300,
+           transform=ccrs.PlateCarree(),marker="+",facecolor='black')
+
    +
    +
    <matplotlib.collections.PathCollection at 0x13ed521c0>
+
    +
    +../../_images/Precip_Accumulation_Region_of_Interest_29_1.png +

    Top

    +
    +
    +
    +
    +

    7 High Resolution ROI

    +
    +

    7.1 New Data Request

    +

    To see the region of interest more clearly, we can redo the process with +a higher resolution model (GFS20 vs. GFS1.0).

    +
    roiRequest = DataAccessLayer.newDataRequest("grid", envelope=conus_envelope)
+roiRequest.setLocationNames("GFS20")
+roiRequest.setLevels("0.0SFC")
+roiRequest.setParameters("TP")
+
+roiCycles = DataAccessLayer.getAvailableTimes(roiRequest, True)
+roiTimes = DataAccessLayer.getAvailableTimes(roiRequest)
+roiFcstRun = DataAccessLayer.getForecastRun(roiCycles[-1], roiTimes)
+
    +
    +
    +
    +

    7.2 Calculate Data

    +
    roiResponse = DataAccessLayer.getGridData(roiRequest, [roiFcstRun[-1]])
+print(roiResponse)
+roiGrid = roiResponse[0]
+
+roiLons, roiLats = roiGrid.getLatLonCoords()
+
+roi_data, i, j = calculate_accumulated_precip(roiRequest, roiFcstRun)
+
+roiFcstHr = int(roiGrid.getDataTime().getFcstTime()/3600)
+
    +
    +
    [<awips.dataaccess.PyGridData.PyGridData object at 0x13ecb4eb0>]
+0.0 22.5625 3.0
+0.0 35.375 6.0
+0.0 38.375 9.0
+0.0 38.375 12.0
+0.0 41.375 15.0
+0.0 48.625 18.0
+0.0 73.0625 30.0
+0.0 94.9375 33.0
+0.0 96.125 36.0
+0.0 97.0 39.0
+0.0 99.375 45.0
+0.0 100.0625 48.0
+0.0 100.25 51.0
+0.0 100.4375 57.0
+0.0 100.4375 63.0
+0.0 118.25 66.0
+0.0 127.625 69.0
+0.0 131.125 75.0
+0.0 131.375 78.0
+0.0 131.5 81.0
+0.0 131.875 84.0
+0.0 132.875 90.0
+0.0 133.375 96.0
+0.0 139.1875 102.0
+0.0 141.625 120.0
+0.0 141.75 126.0
+0.0 142.1875 132.0
+0.0 143.375 138.0
+0.0 148.6875 144.0
+0.0 156.25 150.0
+
    +
    +
    +
    +

    7.3 Plot ROI

    +
    # cmap = plt.get_cmap('rainbow')
+fig, ax = make_map(bbox=roi_box)
+
+cs = ax.pcolormesh(roiLons, roiLats, roi_data, cmap=cmap)
+cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal')
+cbar.set_label(roiGrid.getLocationName() + " Total precipitation in inches, " \
+               + str(roiFcstHr) + "-hr fcst valid " + str(roiGrid.getDataTime().getRefTime()))
+
+ax.scatter(maxPoint.x, maxPoint.y, s=300,
+           transform=ccrs.PlateCarree(),marker="+",facecolor='black')
+
    +
    +
    /Users/scarter/opt/miniconda3/envs/python3-awips/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:1702: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh.
+  X, Y, C, shading = self._pcolorargs('pcolormesh', *args,
+
    +
    +
    <matplotlib.collections.PathCollection at 0x13edc39a0>
+
    +
    +../../_images/Precip_Accumulation_Region_of_Interest_37_2.png +

    Top

    +
    +
    +
    +
    +

    8 See Also

    + +
    +

    8.2 Additional Documentation

    +

    python-awips: * +awips.DataAccessLayer +* +awips.PyGridData

    +

    matplotlib: * +matplotlib.pyplot +* +matplotlib.pyplot.subplot +* +matplotlib.pyplot.pcolormesh

    +

    Top

    +
    +
    +
    +
    +
    + + +
    +
    + +
    +
    +
    +
    + + + + \ No newline at end of file diff --git a/examples/generated/Regional_Surface_Obs_Plot.html b/examples/generated/Regional_Surface_Obs_Plot.html index cd50955..1a046e6 100644 --- a/examples/generated/Regional_Surface_Obs_Plot.html +++ b/examples/generated/Regional_Surface_Obs_Plot.html @@ -21,7 +21,7 @@ - + @@ -59,7 +59,7 @@
  • Map Resources and Topography
  • Model Sounding Data
  • NEXRAD Level3 Radar
    • -
  • Precip Accumulation-Region Of Interest
    • +
  • Precip Accumulation Region of Interest
  • Regional Surface Obs Plot
    • Objectives
      • Table of Contents
        • @@ -695,7 +695,7 @@ list

        diff --git a/examples/generated/Satellite_Imagery.html b/examples/generated/Satellite_Imagery.html index 4f521f7..61c01b1 100644 --- a/examples/generated/Satellite_Imagery.html +++ b/examples/generated/Satellite_Imagery.html @@ -59,7 +59,7 @@
      • Map Resources and Topography
      • Model Sounding Data
      • NEXRAD Level3 Radar
        • -
      • Precip Accumulation-Region Of Interest
        • +
      • Precip Accumulation Region of Interest
      • Regional Surface Obs Plot
      • Satellite Imagery
        • Available Sources, Creating Entities, Sectors, and Products
            diff --git a/examples/generated/Upper_Air_BUFR_Soundings.html b/examples/generated/Upper_Air_BUFR_Soundings.html index 5a04e32..a8438bb 100644 --- a/examples/generated/Upper_Air_BUFR_Soundings.html +++ b/examples/generated/Upper_Air_BUFR_Soundings.html @@ -59,7 +59,7 @@
          • Map Resources and Topography
          • Model Sounding Data
          • NEXRAD Level3 Radar
            • -
          • Precip Accumulation-Region Of Interest
            • +
          • Precip Accumulation Region of Interest
          • Regional Surface Obs Plot
          • Satellite Imagery
          • Upper Air BUFR Soundings
              diff --git a/examples/generated/Watch_Warning_and_Advisory_Plotting.html b/examples/generated/Watch_Warning_and_Advisory_Plotting.html index 5c24014..6decfe7 100644 --- a/examples/generated/Watch_Warning_and_Advisory_Plotting.html +++ b/examples/generated/Watch_Warning_and_Advisory_Plotting.html @@ -59,7 +59,7 @@
            • Map Resources and Topography
            • Model Sounding Data
            • NEXRAD Level3 Radar
              • -
            • Precip Accumulation-Region Of Interest
              • +
            • Precip Accumulation Region of Interest
            • Regional Surface Obs Plot
            • Satellite Imagery
            • Upper Air BUFR Soundings
              • diff --git a/examples/index.html b/examples/index.html index f8e729a..7af4158 100644 --- a/examples/index.html +++ b/examples/index.html @@ -59,7 +59,7 @@
            • Map Resources and Topography
            • Model Sounding Data
            • NEXRAD Level3 Radar
              • -
            • Precip Accumulation-Region Of Interest
              • +
            • Precip Accumulation Region of Interest
            • Regional Surface Obs Plot
            • Satellite Imagery
            • Upper Air BUFR Soundings
              • @@ -108,7 +108,7 @@
            • Map Resources and Topography
            • Model Sounding Data
            • NEXRAD Level3 Radar
              • -
            • Precip Accumulation-Region Of Interest
              • +
            • Precip Accumulation Region of Interest
            • Regional Surface Obs Plot
            • Satellite Imagery
            • Upper Air BUFR Soundings
              • diff --git a/objects.inv b/objects.inv index b0e7364e6d52926999452df69182750e0d78b52e..904bc735d963596336201362b56ca7b281971661 100644 GIT binary patch delta 6509 zcmV-z8ItD9F~BpBjDI~y);dUz?f8+NIesKQ%Zc5x(>XIOnt~*DD3MK4cHE=;5BCr6 zm)xqtg8<)@=$<4N3GQ9QqpDCSfc{gMWN**?T-=8%aTc#1MVgD0CC|UD;@Kt=?BC2! z!grMtZS5Y;k(^7BvW&wp$Gxn8B)_q#vi~U*l9R>3EZn0D7kPRiu4aZ+KgY_UqtSwHxHxZ-jKrfqeA}C zDac;lrhlM6g8b_IMd4p$sG_M0Q!#tND!SVwNepf%bxri8#%Fk5$I#Tl*j;EXp74_8 zRg4A=CjHv~`}sQdXEZk24*D$HJ} z3SH-E8^kX#7mn`Y&3YED7Zp#~x!dgryTv=b;2QKEu&B%}dAG#8HQtr;!t!$Wo2qgJ zimgg*knX3+RW+Kk+>AS5Wg~#$|9XD7BNPp%F>tJbv<+O@yDqiAeY;OTaLQNF-F{I8 zAAgQ&&k$&1s;!}MR$!yatC8U>9v<3xUVx{8Zy7+~eO>FKTAgx&be}K}~ zzb{W$7lr!=XurltD*Nwc7}e?8mmzM5-u(m24MDMgfVn0VRkA*87D;dl3*>7NPU;@hbnE2_hl1OCl7Gf<@ABNJq z?|vREr{Dc_4bz3Gzn)D`XOCe9l{vkQ;ygFO1P!>yuH;|d-oM!_^`BTCC-EZnljUFJ8M1LSTN0Oo(@@7IXKg!}xLhK$>>DLflVecg!0C55W z&^rJQi}{lT;|Rtz*giX#6y&ZcI|sxhk&>`a&3{W0kxoy;bo%kz<-~M1)%g*P zTWI_Ae)@f71?!0BZw%9Lf`2I9%%(y7DALz&x-0wQ{uD@MW0C5bK85*m`UMoas%;I= z0i6>%Cv=eIvYFYD3$sS1D?jNWP;bR*#6ocRCvoH{7(tK_=E#@=1|GsD>6G9W^gx(Q zPXp-2n@Cp7L@e;Xcs{*agJv=za)rz#@;jb%mj#DAgi20-ou=Ib~Bi&XrBttkMqo2UksYgjH}k<-K;n2xzH3c8R$ zsWbxlQuH=6Lpqg&=Kx5&wnU zDnxh0vp5}D27kCx-HvqChdQYDp==3YXihWml6y$dZnX?MAaX=>*b5q9N5$r|2*V5U zoMk$qCEa@&)7o{KiOt}1q~P(K8w%<4ONs+hem=Waa+t`PoCDy}9**~6?q&}U-1RaK zN?zsx)XOT&!Te;KD9_k~pMgZ65c_MJx;YPlas|o>lz&58seyk@loLhyQBx`vpsj+j z2%VKcI#(+kOA2iV4AOy7{#x;S2RQNF)-PyeZgdHduF)kp*umu;&=bnO=0S_!dtkYH z)+0mfsarVRRs~75>Iz@4kg!^Z`#bm*on5KwQrKcuU5fWihM^f~4W{-=u^f!D)IQ^w zb4kI0f`2OtjufPQ*n4(bNT7CLF~lLg0|ExA04~r>Yw_fUJn#wJ>VdCCxpTX!Nl68o z5~!Sj2+59`9P5{;-m^0;*H@6sj+2mFcSF`@bo|@<`Wq9}1j`Ny@(pjp`KPhOem{;DR zo_|l~h1zCvD`*_evXQjEnpIjH+~PRYb4%-Cc5m*ZjJVXZ66pfGuyNESttUxDQjv#p zIq8e!K(%;S8XS?d880_BB!O(E!_v@j$U!ukFH~H@ktPWh#}JzcyG_+A%#AbdC3B+(F2p1O|Hb!y?>d=ziaXvATLSxYW%Q~9#@BzaW!m~)PKFz znJXQZ=1PYc7aC1-0G9oR-xRcdLrt~7Mf-kyjmlY5Ay%1>k^&Uz7Ap?DtF}DpIILNG zz-!7~GJSqWyC7YqqMm`_vS1(&_>h)7hIHf}KAPKqX@SG(!Ehyk=b)J>W0-<9Fn@hn zZr{iV#|e&Ga7cs#LVLSJQiN0_eyVCZe)tpGVb@@|f|NQ%Ly{DViQW<)ladQ^*{U?e!8Qy|75F1%DE}WpzoY$sA1le3TApz>vi1j`}*0XW?Q^iGAxp zu-EYG4WmO#{PmbAH8u-}AH3i|A?_R@?ofy`T>zv!U`d=%w71YuvyilxpFWS#a2mfb z%oaWhaBB-dgX!E9APJ?1fEIiai;RUUf6*tx+1kCOV*r>OF}cD-Ht7;T=YM91z^>?y zcO<>a5N$eC9&Qhk-Rc3yE51t4mF1q!rbVQ>oF>wX%tz7(y{bs`QA?!OosWjj*!#~N z-Ff>_dES0xImI}5rr8hil#lNZ)4(Kbl%Yy_4_@$5$qOE!orbx=7VuHY54M!kkyK4% z`Ce25#ylTMl~h>gqXD^Yt$)MD5MJ{eSaz9Be!IY9PW{z$K_ZLT?P@B;bMuauyM4ZF`UNXsQno6d4bOn z|CoJ7)=9xSxw!&a2HM(YE$x`uG%l8C4V2`xIX8*k74S$E?G5D z2!33j@{PvnxD<;SuO^&;%yB6oL%fyc&oPHzdBKxJC@*Z17{eWv_Wo9NRE}#MmE&C; zm5v0-CP(GC%~3ga9hE-4VUS@j92D6l94|z6iXuXjEX6tt7umlsJ9nfYx$E}Eq}6$mLDQeqs~8R)A@K)?r(&yO=Ah` zG!h`gjLqYeJw5p&wzJ&$2&0OI7dEt{;y}fT3I#gX0^MqXt_y*_e?QH<4{8zJ{}ZmH z;bX5Let*wpbj0Ke^MhJQC!Psi1MXpqSADddQ8XV)>sBAw3WqVv^qE*eyh@;tXQS$dr*!YzI;75Z`u*zmPJflBeb}z(#dE*VpInxa81}Oph1Ul| zm36R6@@!kLQWIi_R+FNehpc@vBypQ-GJ+xU82;V(O20y?7@&{^gmWST0|T&$`iQW*SYqR)1#tfR^$O1ZxjUF<-IvoD|T?8TprVx3L>y zSSF|WWF~?}QTUNDll6en;PDaX?@mi_>5Eu5N$bAb-P2v;WeuC>KJ|fnwyhCP#UCBEh=zqN^ zTrBgo9>q-!ghz%1{-bEo?q{ceTzG*DwK0{z>*SJ>hdfTKPSKboVmqCbXcP=nFit)x z!bm|e5v0gj+dg^3fLTdvrf`w82Bgx`;H>;FW1$?o*Zx(A9-o=0TC*UAnO$f1p;D&H zoY6D7o6rl6OB53^*Y(>_>y~7MYeL}WIf6G~?+bO= zD=H^4cH61pwoh1eeh;H;;mt6y^`kp>>h+lZrcL0u1!X~7KA+U^iK807L8Xud>Wf81 z3Iomv?HF>ltJkRV$|RGOEI~6=JD{BDJTqlgZ(X76FmtBmAAZn7KWVE+Jbx(Js;4|C zNmls5h2DYpeo_#oiMJ~WCuM&!K*N!G@Y4_1=*)fI1^aXTzT+=ABz+n+C~vf zVQv++Q9qi?4)g-adZ5a3HGg922#`uS*tKgRBQHMaS8Avka6Ypv-Tc(4i-k`W+mk8|vOW7Xk7+rGsi+{Aip((f>l60r< zD*K{}$bc~xVN}E$7zXYVrz?U?TTx93xg=N|>DMolqf3T{+>dgswrRY4z4-6s^jtRY z!IY-@9gH_Jji&HSo4x#OM%Wz)A>{Wtm;JCRNNEVn#ZoYd1ek-!`fgKU37C zLMt=)4(|FHKQot^x_|5q$Rg;VY)w93beF+hhIdVmtW^#@LgO8>^bM{vD#%np8k)MG zvALdWmus1=pi!f}Rv8R~6015q7?Y*oq*0lS1SfL}9;b6+t$FP0KmA9a#p~$#O@mja z9VqRk5catX)_KwbdqBY(Uo%%*cUCE(|+Wue!=UOdN_ ze~Qx|4FAd4(^9M%9l{le@DV-r`bd-`K8_Lt#0iKa5T}E;BoHt1O+OFjCUG|zz8(0f z?z1Lw8ynNkU2YMWpl}C;E4fL|#xCRJD}UaC-=n4c)LNjme!qV6rWzBje!9+v;4VVv z`foT-f`4LV(j}@r{J-wA0mZ^_-xK9KwCbC|Y1t3Co+Z;Dq&KNfj=I$K?rnQI zS%-X0CzCauN;d5Hho0mT%suiUnyngjQ#jjW*XLinC=3KxqZS=@ElYUlTcpl(eAHw7 z@mMMYFO!7}_|{MWJefJCere0QYuzHSbHC>2&CtQMYtqrkKmNj-+IlZn)gGwxo!kj7dN4D^|P1u?`C#{{iu3!BB>TXpdU5%ijG5+>bfVN!sEN%1W9!-rT71E?1|noqAIjWMIA zVSof`-HZ<8(+gNL9a@kg$RR?f@M#gKpOTPLzR^UI(i8$n?t*UoN~n43n%C^TL#Yp? 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"Design/Implementation": [[16, "design-implementation"]], "How users of the framework retrieve and use the data": [[16, "how-users-of-the-framework-retrieve-and-use-the-data"]], "How plugin developers contribute support for new datatypes": [[16, "how-plugin-developers-contribute-support-for-new-datatypes"]], "How the framework works when it receives a request": [[16, "how-the-framework-works-when-it-receives-a-request"]], "Request interfaces": [[16, "request-interfaces"]], "Data Interfaces": [[16, "data-interfaces"]], "Factory Interfaces (Java only)": [[16, "factory-interfaces-java-only"]], "Colored Surface Temperature Plot": [[17, "colored-surface-temperature-plot"]], "Objectives": [[17, "objectives"], [18, "objectives"], [19, "objectives"], [20, "objectives"], [21, "objectives"], [22, "objectives"], [23, "objectives"], [24, "objectives"], [27, "objectives"], [29, "objectives"], [30, "objectives"]], "Table of Contents": [[17, "table-of-contents"], [18, "table-of-contents"], [19, "table-of-contents"], [20, "table-of-contents"], [21, "table-of-contents"], [22, "table-of-contents"], [23, "table-of-contents"], [24, "table-of-contents"], [27, "table-of-contents"], [29, "table-of-contents"], [30, "table-of-contents"]], "1 Imports": [[17, "imports"], [18, "imports"], [19, "imports"], [20, "imports"], [21, "imports"], [22, "imports"], [23, "imports"], [24, "imports"], [29, "imports"], [30, "imports"]], "2 Initial Setup": [[17, "initial-setup"], [20, "initial-setup"]], "2.1 Geographic Filter": [[17, "geographic-filter"]], "2.2 EDEX Connection": [[17, "edex-connection"]], "3 Filter by Time": [[17, "filter-by-time"]], "4 Access and Convert Temp Data": [[17, "access-and-convert-temp-data"]], "5 Define Temperature Thresholds": [[17, "define-temperature-thresholds"]], "6 Plot the Data!": [[17, "plot-the-data"], [22, "plot-the-data"], [29, "plot-the-data"]], "7 See Also": [[17, "see-also"], [22, "see-also"], [29, "see-also"]], "7.1 Additional Documention": [[17, 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"module-awips.dataaccess.ThriftClientRouter"]], "TimeUtil": [[13, "module-awips.TimeUtil"]], "API Documentation": [[14, "api-documentation"]], "Available Data Types": [[15, "available-data-types"]], "satellite": [[15, "satellite"]], "binlightning": [[15, "binlightning"]], "grid": [[15, "grid"]], "warning": [[15, "warning"]], "radar": [[15, "radar"]], "Development Guide": [[16, "development-guide"]], "Writing a New Factory": [[16, "writing-a-new-factory"]], "Registering the Factory with the Framework": [[16, "registering-the-factory-with-the-framework"]], "Retrieving Data Using the Factory": [[16, "retrieving-data-using-the-factory"]], "Development Background": [[16, "development-background"]], "Design/Implementation": [[16, "design-implementation"]], "How users of the framework retrieve and use the data": [[16, "how-users-of-the-framework-retrieve-and-use-the-data"]], "How plugin developers contribute support for new datatypes": [[16, "how-plugin-developers-contribute-support-for-new-datatypes"]], "How the framework works when it receives a request": [[16, "how-the-framework-works-when-it-receives-a-request"]], "Request interfaces": [[16, "request-interfaces"]], "Data Interfaces": [[16, "data-interfaces"]], "Factory Interfaces (Java only)": [[16, "factory-interfaces-java-only"]], "Colored Surface Temperature Plot": [[17, "colored-surface-temperature-plot"]], "Objectives": [[17, "objectives"], [18, "objectives"], [19, "objectives"], [20, "objectives"], [21, "objectives"], [22, "objectives"], [23, "objectives"], [24, "objectives"], [26, "objectives"], [27, "objectives"], [29, "objectives"], [30, "objectives"]], "Table of Contents": [[17, "table-of-contents"], [18, "table-of-contents"], [19, "table-of-contents"], [20, "table-of-contents"], [21, "table-of-contents"], [22, "table-of-contents"], [23, "table-of-contents"], [24, "table-of-contents"], [26, "table-of-contents"], [27, "table-of-contents"], [29, "table-of-contents"], [30, "table-of-contents"]], "1 Imports": [[17, "imports"], [18, "imports"], [19, "imports"], [20, "imports"], [21, "imports"], [22, "imports"], [23, "imports"], [24, "imports"], [26, "imports"], [29, "imports"], [30, "imports"]], "2 Initial Setup": [[17, "initial-setup"], [20, "initial-setup"], [26, "initial-setup"]], "2.1 Geographic Filter": [[17, "geographic-filter"], [26, "geographic-filter"]], "2.2 EDEX Connection": [[17, "edex-connection"], [26, "edex-connection"]], "3 Filter by Time": [[17, "filter-by-time"]], "4 Access and Convert Temp Data": [[17, "access-and-convert-temp-data"]], "5 Define Temperature Thresholds": [[17, "define-temperature-thresholds"]], "6 Plot the Data!": [[17, "plot-the-data"], [22, "plot-the-data"], [26, "plot-the-data"], [29, "plot-the-data"]], "7 See Also": [[17, "see-also"], [22, "see-also"], [29, "see-also"]], "7.1 Additional Documention": [[17, "additional-documention"]], "Colorized Grid Data": [[18, "colorized-grid-data"]], "2 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"use-the-data"], [29, "use-the-data"]], "5.1 Get the Data!": [[22, "get-the-data"]], "5.2 Extract all Parameters": [[22, "extract-all-parameters"]], "5.3 Populate the Data Dictionary": [[22, "populate-the-data-dictionary"]], "7.1 Related Notebooks": [[22, "related-notebooks"], [29, "related-notebooks"]], "7.2 Additional Documentation": [[22, "additional-documentation"], [29, "additional-documentation"]], "Map Resources and Topography": [[23, "map-resources-and-topography"]], "3 Function: make_map()": [[23, "function-make-map"]], "4 Create Initial Map From CWA": [[23, "create-initial-map-from-cwa"]], "5 Draw Merged CWA": [[23, "draw-merged-cwa"]], "6 Draw Interstates using Boundary Filter": [[23, "draw-interstates-using-boundary-filter"]], "7 Draw Nearby Cities": [[23, "draw-nearby-cities"]], "8 Draw Nearby Lakes": [[23, "draw-nearby-lakes"]], "9 Draw Major Rivers": [[23, "draw-major-rivers"]], "10 Draw Topography": [[23, "draw-topography"]], "11 See Also": [[23, "see-also"]], "11.1 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"additional-documentation"]], "NEXRAD Level3 Radar": [[25, "nexrad-level3-radar"]], "Precip Accumulation Region of Interest": [[26, "precip-accumulation-region-of-interest"]], "2.3 Refine the Request": [[26, "refine-the-request"]], "2.4 Get Times": [[26, "get-times"]], "3 Function: calculate_accumulated_precip()": [[26, "function-calculate-accumulated-precip"]], "4 Fuction: make_map()": [[26, "fuction-make-map"]], "6.1 Create CONUS Image": [[26, "create-conus-image"]], "6.2 Create Region of Interest Image": [[26, "create-region-of-interest-image"]], "7 High Resolution ROI": [[26, "high-resolution-roi"]], "7.1 New Data Request": [[26, "new-data-request"]], "7.2 Calculate Data": [[26, "calculate-data"]], "7.3 Plot ROI": [[26, "plot-roi"]], "Regional Surface Obs Plot": [[27, "regional-surface-obs-plot"]], "Imports": [[27, "imports"]], "Function: get_cloud_cover()": [[27, "function-get-cloud-cover"]], "Function: make_map()": [[27, "function-make-map"]], "Function: extract_plotting_data()": 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[[30, "additional-documentation"]], "Data Plotting Examples": [[31, "data-plotting-examples"]], "Python AWIPS Data Access Framework": [[32, "python-awips-data-access-framework"]], "Pre-requisite Software": [[32, "pre-requisite-software"]], "Package-Only Install": [[32, "package-only-install"]], "Pip Install": [[32, "pip-install"]], "Conda Install": [[32, "conda-install"]], "Source Code with Examples Install": [[32, "source-code-with-examples-install"]], "Beta Python-AWIPS Install": [[32, "beta-python-awips-install"]], "Questions \u2013 Contact Us!": [[32, "questions-contact-us"]]}, "indexentries": {"awips.dataaccess.combinedtimequery": [[1, "module-awips.dataaccess.CombinedTimeQuery"]], "getavailabletimes() (in module awips.dataaccess.combinedtimequery)": [[1, "awips.dataaccess.CombinedTimeQuery.getAvailableTimes"]], "module": [[1, "module-awips.dataaccess.CombinedTimeQuery"], [2, "module-awips.dataaccess.DataAccessLayer"], [3, "module-awips.DateTimeConverter"], [5, 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"getavailabletimes() (in module awips.dataaccess.dataaccesslayer)": [[2, "awips.dataaccess.DataAccessLayer.getAvailableTimes"]], "getforecastrun() (in module awips.dataaccess.dataaccesslayer)": [[2, "awips.dataaccess.DataAccessLayer.getForecastRun"]], "getgeometrydata() (in module awips.dataaccess.dataaccesslayer)": [[2, "awips.dataaccess.DataAccessLayer.getGeometryData"]], "getgriddata() (in module awips.dataaccess.dataaccesslayer)": [[2, "awips.dataaccess.DataAccessLayer.getGridData"]], "getidentifiervalues() (in module awips.dataaccess.dataaccesslayer)": [[2, "awips.dataaccess.DataAccessLayer.getIdentifierValues"]], "getmetarobs() (in module awips.dataaccess.dataaccesslayer)": [[2, "awips.dataaccess.DataAccessLayer.getMetarObs"]], "getoptionalidentifiers() (in module awips.dataaccess.dataaccesslayer)": [[2, "awips.dataaccess.DataAccessLayer.getOptionalIdentifiers"]], "getradarproductids() (in module awips.dataaccess.dataaccesslayer)": [[2, "awips.dataaccess.DataAccessLayer.getRadarProductIDs"]], "getradarproductnames() (in module awips.dataaccess.dataaccesslayer)": [[2, "awips.dataaccess.DataAccessLayer.getRadarProductNames"]], "getrequiredidentifiers() (in module awips.dataaccess.dataaccesslayer)": [[2, "awips.dataaccess.DataAccessLayer.getRequiredIdentifiers"]], "getsupporteddatatypes() (in module awips.dataaccess.dataaccesslayer)": [[2, "awips.dataaccess.DataAccessLayer.getSupportedDatatypes"]], "getsynopticobs() (in module awips.dataaccess.dataaccesslayer)": [[2, "awips.dataaccess.DataAccessLayer.getSynopticObs"]], "newdatarequest() (in module awips.dataaccess.dataaccesslayer)": [[2, "awips.dataaccess.DataAccessLayer.newDataRequest"]], "setlazyloadgridlatlon() (in module awips.dataaccess.dataaccesslayer)": [[2, "awips.dataaccess.DataAccessLayer.setLazyLoadGridLatLon"]], "awips.datetimeconverter": [[3, "module-awips.DateTimeConverter"]], "constructtimerange() (in module awips.datetimeconverter)": [[3, "awips.DateTimeConverter.constructTimeRange"]], "converttodatetime() (in module awips.datetimeconverter)": [[3, "awips.DateTimeConverter.convertToDateTime"]], "idatarequest (class in awips.dataaccess)": [[4, "awips.dataaccess.IDataRequest"]], "__weakref__ (awips.dataaccess.idatarequest attribute)": [[4, "awips.dataaccess.IDataRequest.__weakref__"]], "addidentifier() (awips.dataaccess.idatarequest method)": [[4, "awips.dataaccess.IDataRequest.addIdentifier"]], "getdatatype() (awips.dataaccess.idatarequest method)": [[4, "awips.dataaccess.IDataRequest.getDatatype"]], "getenvelope() (awips.dataaccess.idatarequest method)": [[4, "awips.dataaccess.IDataRequest.getEnvelope"]], "getidentifiers() (awips.dataaccess.idatarequest method)": [[4, "awips.dataaccess.IDataRequest.getIdentifiers"]], "getlevels() (awips.dataaccess.idatarequest method)": [[4, "awips.dataaccess.IDataRequest.getLevels"]], "getlocationnames() (awips.dataaccess.idatarequest method)": [[4, "awips.dataaccess.IDataRequest.getLocationNames"]], "setdatatype() (awips.dataaccess.idatarequest method)": [[4, "awips.dataaccess.IDataRequest.setDatatype"]], "setenvelope() (awips.dataaccess.idatarequest method)": [[4, "awips.dataaccess.IDataRequest.setEnvelope"]], "setlevels() (awips.dataaccess.idatarequest method)": [[4, "awips.dataaccess.IDataRequest.setLevels"]], "setlocationnames() (awips.dataaccess.idatarequest method)": [[4, "awips.dataaccess.IDataRequest.setLocationNames"]], "setparameters() (awips.dataaccess.idatarequest method)": [[4, "awips.dataaccess.IDataRequest.setParameters"]], "ifpclient (class in awips.gfe.ifpclient)": [[5, "awips.gfe.IFPClient.IFPClient"]], "awips.gfe.ifpclient": [[5, "module-awips.gfe.IFPClient"]], "commitgrid() (awips.gfe.ifpclient.ifpclient method)": [[5, "awips.gfe.IFPClient.IFPClient.commitGrid"]], "getgridinventory() (awips.gfe.ifpclient.ifpclient method)": [[5, "awips.gfe.IFPClient.IFPClient.getGridInventory"]], "getparmlist() (awips.gfe.ifpclient.ifpclient method)": [[5, "awips.gfe.IFPClient.IFPClient.getParmList"]], "getselecttr() (awips.gfe.ifpclient.ifpclient method)": [[5, "awips.gfe.IFPClient.IFPClient.getSelectTR"]], "getsiteid() (awips.gfe.ifpclient.ifpclient method)": [[5, "awips.gfe.IFPClient.IFPClient.getSiteID"]], "awips.dataaccess.modelsounding": [[6, "module-awips.dataaccess.ModelSounding"]], "changeedexhost() (in module awips.dataaccess.modelsounding)": [[6, "awips.dataaccess.ModelSounding.changeEDEXHost"]], "getsounding() (in module awips.dataaccess.modelsounding)": [[6, "awips.dataaccess.ModelSounding.getSounding"]], "pydata (class in awips.dataaccess.pydata)": [[7, "awips.dataaccess.PyData.PyData"]], "awips.dataaccess.pydata": [[7, "module-awips.dataaccess.PyData"]], "getattribute() (awips.dataaccess.pydata.pydata method)": [[7, "awips.dataaccess.PyData.PyData.getAttribute"]], "getattributes() (awips.dataaccess.pydata.pydata method)": [[7, "awips.dataaccess.PyData.PyData.getAttributes"]], "getdatatime() (awips.dataaccess.pydata.pydata method)": [[7, "awips.dataaccess.PyData.PyData.getDataTime"]], "getlevel() (awips.dataaccess.pydata.pydata method)": [[7, "awips.dataaccess.PyData.PyData.getLevel"]], "getlocationname() (awips.dataaccess.pydata.pydata method)": [[7, "awips.dataaccess.PyData.PyData.getLocationName"]], "pygeometrydata (class in awips.dataaccess.pygeometrydata)": [[8, "awips.dataaccess.PyGeometryData.PyGeometryData"]], "awips.dataaccess.pygeometrydata": [[8, "module-awips.dataaccess.PyGeometryData"]], "getgeometry() (awips.dataaccess.pygeometrydata.pygeometrydata method)": [[8, "awips.dataaccess.PyGeometryData.PyGeometryData.getGeometry"]], "getnumber() (awips.dataaccess.pygeometrydata.pygeometrydata method)": [[8, "awips.dataaccess.PyGeometryData.PyGeometryData.getNumber"]], "getparameters() (awips.dataaccess.pygeometrydata.pygeometrydata method)": [[8, "awips.dataaccess.PyGeometryData.PyGeometryData.getParameters"]], "getstring() (awips.dataaccess.pygeometrydata.pygeometrydata method)": [[8, "awips.dataaccess.PyGeometryData.PyGeometryData.getString"]], "gettype() (awips.dataaccess.pygeometrydata.pygeometrydata method)": [[8, "awips.dataaccess.PyGeometryData.PyGeometryData.getType"]], "getunit() (awips.dataaccess.pygeometrydata.pygeometrydata method)": [[8, "awips.dataaccess.PyGeometryData.PyGeometryData.getUnit"]], "pygriddata (class in awips.dataaccess.pygriddata)": [[9, "awips.dataaccess.PyGridData.PyGridData"]], "awips.dataaccess.pygriddata": [[9, "module-awips.dataaccess.PyGridData"]], "getlatloncoords() (awips.dataaccess.pygriddata.pygriddata method)": [[9, "awips.dataaccess.PyGridData.PyGridData.getLatLonCoords"]], "getparameter() (awips.dataaccess.pygriddata.pygriddata method)": [[9, "awips.dataaccess.PyGridData.PyGridData.getParameter"]], "getrawdata() (awips.dataaccess.pygriddata.pygriddata method)": [[9, "awips.dataaccess.PyGridData.PyGridData.getRawData"]], "getunit() (awips.dataaccess.pygriddata.pygriddata method)": [[9, "awips.dataaccess.PyGridData.PyGridData.getUnit"]], "awips.radarcommon": [[10, "module-awips.RadarCommon"]], "encode_dep_vals() (in module awips.radarcommon)": [[10, "awips.RadarCommon.encode_dep_vals"]], "encode_radial() (in module awips.radarcommon)": [[10, "awips.RadarCommon.encode_radial"]], "encode_thresh_vals() (in module awips.radarcommon)": [[10, "awips.RadarCommon.encode_thresh_vals"]], "get_data_type() (in module awips.radarcommon)": [[10, "awips.RadarCommon.get_data_type"]], "get_datetime_str() (in module awips.radarcommon)": [[10, "awips.RadarCommon.get_datetime_str"]], "get_hdf5_data() (in module awips.radarcommon)": [[10, "awips.RadarCommon.get_hdf5_data"]], "get_header() (in module awips.radarcommon)": [[10, "awips.RadarCommon.get_header"]], "thriftclient (class in awips.thriftclient)": [[11, "awips.ThriftClient.ThriftClient"]], "thriftrequestexception": [[11, "awips.ThriftClient.ThriftRequestException"]], "awips.thriftclient": [[11, "module-awips.ThriftClient"]], "sendrequest() (awips.thriftclient.thriftclient method)": [[11, "awips.ThriftClient.ThriftClient.sendRequest"]], "lazygridlatlon (class in awips.dataaccess.thriftclientrouter)": [[12, "awips.dataaccess.ThriftClientRouter.LazyGridLatLon"]], "thriftclientrouter (class in awips.dataaccess.thriftclientrouter)": [[12, "awips.dataaccess.ThriftClientRouter.ThriftClientRouter"]], "awips.dataaccess.thriftclientrouter": [[12, "module-awips.dataaccess.ThriftClientRouter"]], "getavailablelevels() (awips.dataaccess.thriftclientrouter.thriftclientrouter method)": [[12, "awips.dataaccess.ThriftClientRouter.ThriftClientRouter.getAvailableLevels"]], "getavailablelocationnames() (awips.dataaccess.thriftclientrouter.thriftclientrouter method)": [[12, "awips.dataaccess.ThriftClientRouter.ThriftClientRouter.getAvailableLocationNames"]], "getavailableparameters() (awips.dataaccess.thriftclientrouter.thriftclientrouter method)": [[12, "awips.dataaccess.ThriftClientRouter.ThriftClientRouter.getAvailableParameters"]], "getavailabletimes() (awips.dataaccess.thriftclientrouter.thriftclientrouter method)": [[12, "awips.dataaccess.ThriftClientRouter.ThriftClientRouter.getAvailableTimes"]], "getgeometrydata() (awips.dataaccess.thriftclientrouter.thriftclientrouter method)": [[12, "awips.dataaccess.ThriftClientRouter.ThriftClientRouter.getGeometryData"]], "getgriddata() (awips.dataaccess.thriftclientrouter.thriftclientrouter method)": [[12, "awips.dataaccess.ThriftClientRouter.ThriftClientRouter.getGridData"]], "getidentifiervalues() (awips.dataaccess.thriftclientrouter.thriftclientrouter method)": [[12, "awips.dataaccess.ThriftClientRouter.ThriftClientRouter.getIdentifierValues"]], "getnotificationfilter() (awips.dataaccess.thriftclientrouter.thriftclientrouter method)": [[12, "awips.dataaccess.ThriftClientRouter.ThriftClientRouter.getNotificationFilter"]], "getoptionalidentifiers() (awips.dataaccess.thriftclientrouter.thriftclientrouter method)": [[12, "awips.dataaccess.ThriftClientRouter.ThriftClientRouter.getOptionalIdentifiers"]], "getrequiredidentifiers() (awips.dataaccess.thriftclientrouter.thriftclientrouter method)": [[12, "awips.dataaccess.ThriftClientRouter.ThriftClientRouter.getRequiredIdentifiers"]], "getsupporteddatatypes() (awips.dataaccess.thriftclientrouter.thriftclientrouter method)": [[12, "awips.dataaccess.ThriftClientRouter.ThriftClientRouter.getSupportedDatatypes"]], "newdatarequest() (awips.dataaccess.thriftclientrouter.thriftclientrouter method)": [[12, "awips.dataaccess.ThriftClientRouter.ThriftClientRouter.newDataRequest"]], "setlazyloadgridlatlon() (awips.dataaccess.thriftclientrouter.thriftclientrouter method)": [[12, "awips.dataaccess.ThriftClientRouter.ThriftClientRouter.setLazyLoadGridLatLon"]], "awips.timeutil": [[13, "module-awips.TimeUtil"]], "determinedrtoffset() (in module awips.timeutil)": [[13, "awips.TimeUtil.determineDrtOffset"]], "maketime() (in module awips.timeutil)": [[13, "awips.TimeUtil.makeTime"]]}}) \ No newline at end of file

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