cdf_to_xarray

cdflib.xarray.cdf_to_xarray(filename, to_datetime=False, to_unixtime=False, fillval_to_nan=False)[source][source]

This function converts CDF files into XArray Dataset Objects.

Parameters:

  • 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 datetime64, 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

Return type:

Dataset

Example MMS:
>>> # Import necessary libraries
>>> import cdflib
>>> import xarray as xr
>>> import os
>>> import urllib.request

>>> # Download a CDF file
>>> fname = 'mms2_fgm_srvy_l2_20160809_v4.47.0.cdf'
>>> url = ("https://lasp.colorado.edu/maven/sdc/public/data/sdc/web/cdflib_testing/mms2_fgm_srvy_l2_20160809_v4.47.0.cdf")
>>> if not os.path.exists(fname):
>>>     urllib.request.urlretrieve(url, fname)

>>> # 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)

>>> # 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)
Processing Steps:
  1. For each variable in the CDF file
    1. Determine the name of the dimension that spans the data “records”

      • Check if the variable itself might be a dimension

      • The DEPEND_0 likely points to the approrpiate dimensions

      • If neither of the above, we create a new dimensions named “recordX”

    2. Determine the name of the other dimensions of the variable, if they exist

      • Check if the variable name itself might be a dimension

      • The DEPEND_X probably points to the appropriate dimensions for that variable, so we check those

      • If either of the above are time varying, the code appends “_dim” to the end of the name

      • If no dimensions are found through the above checks, create a dumension named “dimX”

    3. Gather all attributes that belong to the variable

    4. Add a few attributes that enable better plotting with built-in xarray functions (name, units, etc)

    5. Optionally, convert FILLVALs to NaNs in the data

    6. Optionally, convert CDF_EPOCH/EPOCH16/TT2000 variables to unixtime or datetime

    7. Create an XArray Variable object using the dimensions determined in steps 1 and 2, the attributes from steps 3 and 4, and then the variable data

  2. Gather all the Variable objects created in the first step, and separate them into data variables or coordinate variables

  3. Gather all global scope attributes in the CDF file

  4. Create an XArray Dataset objects with the data variables, coordinate variables, and global attributes.