Docs and unit tests are fixed! Testing the docstrings

cdflib/cdf_to_xarray.py

@@ -596,22 +596,18 @@ def _verify_dimension_sizes(created_data_vars, created_coord_vars):
 
 def cdf_to_xarray(filename, to_datetime=False, to_unixtime=False, fillval_to_nan=False):
     """
-    This function converts CDF files into XArray Dataset Objects
+    This function converts CDF files into XArray Dataset Objects.
 
     Parameters:
-        filename : str
-            The CDF file to read into memory
-        to_datetime : bool, optional
-            Whether or not to convert CDF_EPOCH/EPOCH_16/TT2000 to datetime, or leave them as is
-        to_unixtime : bool, optional
-            Whether or not to convert CDF_EPOCH/EPOCH_16/TT2000 to unixtime, or leave them as is
-        fillval_to_nan : bool, optional
-            If True, any data values that match the FILLVAL attribute for a variable will be set to NaN
+        filename (str):  The path to the CDF file to read
+        to_datetime (bool, optional): Whether or not to convert CDF_EPOCH/EPOCH_16/TT2000 to datetime, or leave them as is
+        to_unixtime (bool, optional): Whether or not to convert CDF_EPOCH/EPOCH_16/TT2000 to unixtime, or leave them as is
+        fillval_to_nan (bool, optional): If True, any data values that match the FILLVAL attribute for a variable will be set to NaN
 
     Returns:
         An XArray Dataset Object
 
-    Example:
+    Example MMS:
         >>> #Import necessary libraries
         >>> import cdflib
         >>> import xarray as xr
@@ -627,6 +623,39 @@ def cdf_to_xarray(filename, to_datetime=False, to_unixtime=False, fillval_to_nan
         >>> #Load in and display the CDF file
         >>> mms_data = cdflib.cdf_to_xarray("mms2_fgm_srvy_l2_20160809_v4.47.0.cdf", to_unixtime=True, fillval_to_nan=True)
         >>> print(mms_data)
+
+        >>> # Show off XArray functionality
+        >>>
+        >>> # Slice the data using built in XArray functions
+        >>> mms_data2 = mms_data.isel(dim0=0)
+        >>> # Plot the sliced data using built in XArray functions
+        >>> mms_data2['mms2_fgm_b_gse_srvy_l2'].plot()
+        >>> # Zoom in on the slices data in time using built in XArray functions
+        >>> mms_data3 = mms_data2.isel(Epoch=slice(716000,717000))
+        >>> # Plot the zoomed in sliced data using built in XArray functionality
+        >>> mms_data3['mms2_fgm_b_gse_srvy_l2'].plot()
+
+    Example THEMIS:
+        >>> #Import necessary libraries
+        >>> import cdflib
+        >>> import xarray as xr
+        >>> import os
+        >>> import urllib.request
+
+        >>> #Download a CDF file
+        >>> fname = 'thg_l2_mag_amd_20070323_v01.cdf'
+        >>> url = ("https://lasp.colorado.edu/maven/sdc/public/data/sdc/web/cdflib_testing/thg_l2_mag_amd_20070323_v01.cdf")
+        >>> if not os.path.exists(fname):
+        >>>     urllib.request.urlretrieve(url, fname)
+
+        >>> #Load in and display the CDF file
+        >>> thg_data = cdflib.cdf_to_xarray(fname, to_unixtime=True, fillval_to_nan=True)
+        >>> print(thg_data)
+
+    Processing Steps:
+        1. asdf
+        2. asdfasdf
+        3. asdfasdfasdf
     """
 
 

doc/modules/cdf_to_xarray.rst

@@ -6,6 +6,6 @@ cdf_to_xarray function
 
 This module is comprised of a single function which takes a CDF file and converts it to an XArray Dataset object.    
 
-This will attempt to determine any `ISTP Complaince <https://spdf.gsfc.nasa.gov/istp_guide/istp_guide.html>`_ within the file, and incorporate that into the Dataset object.   
+This will attempt to determine any `ISTP Compliance <https://spdf.gsfc.nasa.gov/istp_guide/istp_guide.html>`_ within the file, and incorporate that into the Dataset object.
 
 .. autofunction:: cdflib.cdf_to_xarray