0.5 geotiffs and __utilities files

conda.recipes/meta.yaml

@@ -1,6 +1,6 @@
 package:
   name: geomatics
-  version: 0.4
+  version: 0.5
 
 about:
   author: Riley Hales
@@ -30,6 +30,7 @@ requirements:
     - python-dateutil
     - numpy
     - pandas
+    - pyshp
 
 outputs:
   - name: geomatics

docs/API Documentation/geotiffs.rst

@@ -0,0 +1,9 @@
+========
+geotiffs
+========
+
+Convert other grids to GeoTiffs
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. automodule:: geomatics.geotiffs
+	:members: convert_netcdf

docs/API Documentation/index.rst

@@ -7,3 +7,4 @@ API Documentation
 
    netcdfs
    geojsons
+   geotiffs

docs/API Documentation/netcdfs.rst

@@ -9,10 +9,3 @@ a single timestep.
 
 .. automodule:: geomatics.netcdfs
 	:members: point_series, box_series,	shp_series
-
-netCDFs and GeoTIFFs
-~~~~~~~~~~~~~~~~~~~~
-
-.. automodule:: geomatics.netcdfs
-	:members: convert_to_geotiff
-	:noindex:

docs/conf.py

@@ -13,7 +13,7 @@
 author = 'Riley Hales'
 
 # The full version, including alpha/beta/rc tags
-release = '0.4'
+release = '0.5'
 master_doc = 'index'
 
 # -- General configuration ---------------------------------------------------

geomatics/__init__.py

@@ -1,2 +1,4 @@
 import geomatics.netcdfs
 import geomatics.geojsons
+import geomatics.geotiffs
+import geomatics.__utilities

geomatics/__utilities.py

@@ -0,0 +1,31 @@
+import os
+import glob
+
+
+def path_to_file_list(path, filetype):
+    if filetype in ('netcdf', 'nc', 'nc4'):
+        filters = ['*.nc', '*.nc4']
+    elif filetype in ('grib', 'grb'):
+        filters = ['*.grib', '*.grb']
+    elif filetype in ('geotiff', 'tiff'):
+        filters = ['*.geotiff', '*.gtiff', '*.tiff']
+    else:
+        raise ValueError('Unconfigured filter type')
+
+    # check that a valid path was provided
+    if isinstance(path, str):
+        if not os.path.exists(path):
+            raise FileNotFoundError('No files or directory found at this path')
+        elif os.path.isfile(path):
+            return [path]
+        elif os.path.isdir(path):
+            files = []
+            for filter in filters:
+                files += glob.glob(os.path.join(path, filter))
+            if len(files) == 0:
+                raise FileNotFoundError('No located within this directory')
+            return files
+    elif isinstance(path, list):
+        return path
+    else:
+        raise ValueError('Provide an absolute file path to a file or directory of files, or a list of paths')

geomatics/geotiffs.py

@@ -0,0 +1,93 @@
+import os
+
+import netCDF4
+import rasterio
+import numpy as np
+
+from .__utilities import path_to_file_list
+
+__all__ = ['convert_netcdf']
+
+
+def convert_netcdf(path, variable, **kwargs):
+    """
+    Args:
+        path: Either 1) the absolute path to a directory containing netcdfs named by date or 2) the absolute path to
+            a single netcdf containing many time values for a specified variable
+        variable: The name of a variable as it is stored in the netcdf e.g. 'temp' instead of Temperature
+
+    Keyword Args:
+        xvar: Name of the x coordinate variable used to spatial reference the netcdf array. Default: 'lon' (longitude)
+        yvar: Name of the y coordinate variable used to spatial reference the netcdf array. Default: 'lat' (latitude)
+        save_dir: The directory to store the geotiffs to. Default: directory containing the netcdfs.
+        fill_value: The value used for filling no_data spaces in the array. Default: -9999
+        delete_source: Allows you to delete the source netcdfs as they are converted. Default: False
+
+    Returns:
+        1. A list of paths to the geotiff files created
+        2. A dictionary that contains the affine geotransform information
+    """
+    files = path_to_file_list(path, 'nc')
+
+    # parse the optional argument from the kwargs
+    x_var = kwargs.get('xvar', 'lon')
+    y_var = kwargs.get('yvar', 'lat')
+    save_dir = kwargs.get('save_dir', os.path.dirname(files[0]))
+    delete_sources = kwargs.get('delete_sources', False)
+    fill_value = kwargs.get('fill_value', -9999)
+
+    # open the first netcdf and collect georeferencing information
+    nc_obj = netCDF4.Dataset(files[0], 'r')
+    lat = nc_obj.variables[x_var][:]
+    lon = nc_obj.variables[y_var][:]
+    lon_min = lon.min()
+    lon_max = lon.max()
+    lat_min = lat.min()
+    lat_max = lat.max()
+    data = nc_obj[variable][:]
+    data = data[0]
+    height = data.shape[0]
+    width = data.shape[1]
+    nc_obj.close()
+
+    # Geotransform for each of the netcdf files
+    gt = rasterio.transform.from_bounds(lon_min, lat_min, lon_max, lat_max, width, height)
+
+    # A list of all the files that get written which can be returned
+    output_files = []
+
+    # Create a geotiff for each netcdf in the list of files
+    for file in files:
+        # set the files to open/save
+        save_path = os.path.join(save_dir, os.path.basename(file) + '.tif')
+        output_files.append(save_path)
+
+        # open the netcdf and get the data array
+        nc_obj = netCDF4.Dataset(file, 'r')
+        array = np.asarray(nc_obj[variable][:])
+        array = array[0]
+        array[array == fill_value] = np.nan  # If you have fill values, change the comparator to git rid of it
+        array = np.flip(array, axis=0)
+        nc_obj.close()
+
+        # if you want to delete the source netcdfs as you go
+        if delete_sources:
+            os.remove(file)
+
+        # write it to a geotiff
+        with rasterio.open(
+                save_path,
+                'w',
+                driver='GTiff',
+                height=data.shape[0],
+                width=data.shape[1],
+                count=1,
+                dtype=data.dtype,
+                nodata=np.nan,
+                crs='+proj=latlong',
+                transform=gt,
+        ) as dst:
+            dst.write(array, 1)
+
+    return output_files, dict(
+        lon_min=lon_min, lon_max=lon_max, lat_min=lat_min, lat_max=lat_max, height=height, width=width)

geomatics/netcdfs.py

@@ -8,7 +8,9 @@
 import rasterio
 import rasterstats
 
-__all__ = ['point_series', 'box_series', 'shp_series', 'convert_to_geotiff']
+from .__utilities import path_to_file_list
+
+__all__ = ['point_series', 'box_series', 'shp_series']
 
 
 def point_series(path, variable, coordinates, filename_pattern=None, **kwargs):
@@ -45,7 +47,7 @@ def point_series(path, variable, coordinates, filename_pattern=None, **kwargs):
     fill_value = kwargs.get('fill_value', -9999)
 
     # confirm that a valid path to data was provided
-    files = __path_to_file_list(path)
+    files = path_to_file_list(path, 'nc')
     files.sort()
 
     # get a list of the x&y coordinates in the netcdfs using the first file as a reference
@@ -128,7 +130,7 @@ def box_series(path, variable, coordinates, filename_pattern=None, **kwargs):
     stat = kwargs.get('stat_type', 'mean')
 
     # confirm that a valid path to data was provided
-    files = __path_to_file_list(path)
+    files = path_to_file_list(path, 'nc')
     files.sort()
 
     # get a list of the x&y coordinates using the first file as a reference
@@ -239,7 +241,7 @@ def shp_series(path, variable, shp_path, filename_pattern=None, **kwargs):
     stat = kwargs.get('stat_type', 'mean')
 
     # confirm that a valid path to data was provided
-    files = __path_to_file_list(path)
+    files = path_to_file_list(path, 'nc')
     files.sort()
 
     # open the netcdf determine the affine transformation of the netcdf grids
@@ -307,99 +309,6 @@ def shp_series(path, variable, shp_path, filename_pattern=None, **kwargs):
     return pd.DataFrame(timeseries[1], columns=['values'], index=timeseries[0])
 
 
-def convert_to_geotiff(files, variable, **kwargs):
-    """
-
-    Args:
-        files:
-        variable:
-        **kwargs:
-
-    Returns:
-
-    """
-    files = __path_to_file_list(files)
-
-    # parse the optional argument from the kwargs
-    save_dir = kwargs.get('save_dir', os.path.dirname(files[0]))
-    delete_sources = kwargs.get('delete_sources', False)
-    fill_value = kwargs.get('fill_value', -9999)
-
-    # open the first netcdf and collect georeferencing information
-    nc_obj = netCDF4.Dataset(files[0], 'r')
-    lat = nc_obj.variables['lat'][:]
-    lon = nc_obj.variables['lon'][:]
-    lon_min = lon.min()
-    lon_max = lon.max()
-    lat_min = lat.min()
-    lat_max = lat.max()
-    data = nc_obj[variable][:]
-    data = data[0]
-    height = data.shape[0]
-    width = data.shape[1]
-    nc_obj.close()
-
-    # Geotransform for each of the netcdf files
-    gt = rasterio.transform.from_bounds(lon_min, lat_min, lon_max, lat_max, width, height)
-
-    # A list of all the files that get written which can be returned
-    output_files = []
-
-    # Create a geotiff for each netcdf in the list of files
-    for file in files:
-        # set the files to open/save
-        save_path = os.path.join(save_dir, os.path.basename(file) + '.tif')
-        output_files.append(save_path)
-
-        # open the netcdf and get the data array
-        nc_obj = netCDF4.Dataset(file, 'r')
-        array = np.asarray(nc_obj[variable][:])
-        array = array[0]
-        array[array == fill_value] = np.nan  # If you have fill values, change the comparator to git rid of it
-        array = np.flip(array, axis=0)
-        nc_obj.close()
-
-        # if you want to delete the source netcdfs as you go
-        if delete_sources:
-            os.remove(file)
-
-        # write it to a geotiff
-        with rasterio.open(
-                save_path,
-                'w',
-                driver='GTiff',
-                height=data.shape[0],
-                width=data.shape[1],
-                count=1,
-                dtype=data.dtype,
-                nodata=np.nan,
-                crs='+proj=latlong',
-                transform=gt,
-        ) as dst:
-            dst.write(array, 1)
-
-    return output_files, dict(
-        lon_min=lon_min, lon_max=lon_max, lat_min=lat_min, lat_max=lat_max, height=height, width=width)
-
-
-def __path_to_file_list(path):
-    # check that a valid path was provided
-    if isinstance(path, str):
-        if os.path.isfile(path):
-            return [path]
-        elif os.path.isdir(path):
-            files = [os.path.join(path, f) for f in os.listdir(path) if f.endswith('.nc') or f.endswith('.nc4')]
-            if len(files) == 0:
-                raise FileNotFoundError('No netcdfs located within this directory')
-            return files
-        else:
-            raise FileNotFoundError('No netcdf file or directory found at this path')
-    elif isinstance(path, list):
-        return path
-    else:
-        raise ValueError('Provide an absolute file path to a netcdf or directory of netcdf files, or a list of paths')
-
-
 def __guess_timedelta(nc_obj, t_var, step=1):
     units = str(nc_obj[t_var].__dict__['units'])
     units = units.replace(' ', '').lower()

setup.py

@@ -6,7 +6,7 @@
 setup(
     name='geomatics',
     packages=['geomatics'],
-    version='0.4',
+    version='0.5',
     description='GIS tools developed by Riley Hales for the BYU Hydroinformatics Lab',
     long_description=long_description,
     long_description_content_type="text/markdown",