Merge dccd00a into 4a0167e

.travis.yml

@@ -1,3 +1,4 @@
+dist: xenial
 language: python
 sudo: false
 addons:
@@ -10,8 +11,8 @@ notifications:
 python:
   # We don't actually use the Travis Python, but this keeps it organized.
   - "2.7"
-  - "3.4"
-  - "3.5"
+  - "3.6"
+  - "3.7"
 install:
   # You may want to periodically update this, although the conda update
   # conda line below will keep everything up-to-date.  We do this

CHANGES.rst

@@ -2,7 +2,12 @@
 Changelog
 =========
 
-Version 0.X
+Unreleased
+==========
+
+- Add gldas land mask to package (no download necessary)
+
+Version 0.5
 ===========
 
 - Update trollsift parsing

gldas/grid.py

@@ -23,47 +23,55 @@
 import numpy as np
 
 from pygeogrids.grids import BasicGrid
-import pandas as pd
+from netCDF4 import Dataset
+import os
 
 
-def GLDAS025Cellgrid():
-    '''
-    GLDAS-Noah 0.25deg cell grid.
-    :return: global QDEG-CellGrid
-    '''
+def GLDAS025Grids(only_land=False):
+    """
+    Create global 0.25 DEG gldas grids (origin in bottom left)
+
+    Parameters
+    ---------
+    only_land : bool, optional (default: False)
+        Uses the land mask to reduce the GLDAS 0.25DEG land grid to land points
+        only.
+
+    Returns
+    --------
+    grid : pygeogrids.CellGrid
+        Either a land grid or a global grid
+    """
+
     resolution = 0.25
-    lon, lat = np.meshgrid(
-        np.arange(-180 + resolution / 2, 180 + resolution / 2, resolution),
-        np.arange(-90 + resolution / 2, 90 + resolution / 2, resolution))
+    glob_lons = np.arange(-180 + resolution / 2, 180 + resolution / 2, resolution)
+    glob_lats = np.arange(-90 + resolution / 2, 90 + resolution / 2, resolution)
+    lon, lat = np.meshgrid(glob_lons, glob_lats)
+    glob_grid = BasicGrid(lon.flatten(), lat.flatten()).to_cell_grid(cellsize=5.)
 
-    return BasicGrid(lon.flatten(), lat.flatten()).to_cell_grid(cellsize=5.)
+    if only_land:
+        ds = Dataset(os.path.join(os.path.abspath(os.path.dirname(__file__)),
+                                  'GLDASp4_landmask_025d.nc4'))
+        land_lats = ds.variables['lat'][:]
+        land_mask = ds.variables['GLDAS_mask'][:].flatten().filled() == 0.
+        dlat = glob_lats.size - land_lats.size
 
+        land_mask = np.concatenate((np.ones(dlat * glob_lons.size), land_mask))
+        land_points = np.ma.masked_array(glob_grid.get_grid_points()[0], land_mask)
 
-def GLDASLandPoints(grid):
-    '''
-    Load land mask from https://ldas.gsfc.nasa.gov/gldas/data/0.25deg/landmask_mod44w_025.asc
-    and reduce input grid to land points
-    :return: (GPI, dist)
-    '''
-    try:
-        land_mask = pd.read_csv('https://ldas.gsfc.nasa.gov/gldas/data/0.25deg/landmask_mod44w_025.asc',
-                                delim_whitespace=True, header=None, names=['idx', 'gpi', 'lat', 'lon', 'land_flag'])
-    except:
-        raise ImportError(
-            'reading land mask from https://ldas.gsfc.nasa.gov/gldas/data/0.25deg/landmask_mod44w_025.asc failed.')
-    land_mask = land_mask.loc[land_mask['land_flag'] == 1]
-    lat, lon = land_mask['lat'].values, land_mask['lon'].values
+        land_grid = glob_grid.subgrid_from_gpis(land_points[~land_points.mask].filled())
+        return land_grid
+    else:
+        return glob_grid
 
-    return grid.find_nearest_gpi(lon, lat)
+
+def GLDAS025Cellgrid():
+    return GLDAS025Grids(only_land=False)
 
 
 def GLDAS025LandGrid():
-    '''
-    0.25deg cell grid of land points from gldas land mask.
-    :return: global QDEG-LandGrid
-    '''
-    grid = GLDAS025Cellgrid()
-    land_gpis, dist = GLDASLandPoints(grid)
-    if any(dist) > 0:
-        raise Exception('GLDAS grid does not conform with QDEG grid')
-    return grid.subgrid_from_gpis(land_gpis)
+    return GLDAS025Grids(only_land=True)
+
+
+if __name__ == '__main__':
+    GLDAS025LandGrid()

gldas/interface.py

@@ -60,8 +60,7 @@ class GLDAS_Noah_v21_025Img(ImageBase):
         Needed for some legacy code.
     """
 
-
-    def __init__(self, filename, mode='r', parameter='SoilMoi0_10cm_inst', 
+    def __init__(self, filename, mode='r', parameter='SoilMoi0_10cm_inst',
                  subgrid=None, array_1D=False):
 
         super(GLDAS_Noah_v21_025Img, self).__init__(filename, mode=mode)
@@ -107,7 +106,7 @@ def read(self, timestamp=None):
                 np.ma.set_fill_value(param_data, 9999)
                 param_data = np.concatenate((
                     self.fill_values,
-                    np.ma.getdata(param_data.filled()) .flatten()))
+                    np.ma.getdata(param_data.filled()).flatten()))
 
                 return_img.update(
                     {str(parameter): param_data[self.grid.activegpis]})
@@ -130,13 +129,12 @@ def read(self, timestamp=None):
 
         if self.array_1D:
             return Image(self.grid.activearrlon, self.grid.activearrlat,
-                        return_img, return_metadata, timestamp)
+                         return_img, return_metadata, timestamp)
         else:
             for key in return_img:
                 return_img[key] = np.flipud(
                     return_img[key].reshape((720, 1440)))
 
-
             return Image(np.flipud(self.grid.activearrlon.reshape((720, 1440))),
                          np.flipud(self.grid.activearrlat.reshape((720, 1440))),
                          return_img,
@@ -292,9 +290,8 @@ class GLDAS_Noah_v21_025Ds(MultiTemporalImageBase):
         Needed for some legacy code.
     """
 
-    def __init__(self, data_path, parameter='SoilMoi0_10cm_inst', 
+    def __init__(self, data_path, parameter='SoilMoi0_10cm_inst',
                  subgrid=None, array_1D=False):
-
         ioclass_kws = {'parameter': parameter,
                        'subgrid': subgrid,
                        'array_1D': array_1D}
@@ -309,7 +306,6 @@ def __init__(self, data_path, parameter='SoilMoi0_10cm_inst',
                                                    exact_templ=False,
                                                    ioclass_kws=ioclass_kws)
 
-
     def tstamps_for_daterange(self, start_date, end_date):
         """
         return timestamps for daterange,