35. Appendix F Python Embedding

35.1. Introduction

MET includes the ability to embed Python to a limited degree. Users may use Python scripts and whatever associated Python packages they wish in order to prepare 2D gridded data fields, point observations, and matched pairs as input to the MET tools. We fully expect that this degree of embedding will increase in the future. In addition, plans are in place to extend Python with MET in upcoming releases, allowing users to invoke MET tools directly from their Python script. While MET version 8.0 was built on Python 2.x, MET versions 9.0 and beyond are built on Python 3.6+.

35.2. Compiling Python Support

In order to use Python embedding, the user’s local Python installation must have the C-language Python header files and libraries. Sometimes when Python is installed locally, these header files and libraries are deleted at the end of the installation process, leaving only the binary executable and run-time shared object files. But the Python header files and libraries must be present to compile support in MET for Python embedding. Assuming the requisite Python files are present, and that Python embedding is enabled when building MET (which is done by passing the –enable-python option to the configure command line), the MET C++ code will use these in the compilation process to link directly to the Python libraries.

The local Python installation must also support a minimum set of required packages. The MET build includes some python wrapper scripts to facilitate the passing of data in memory as well as the reading and writing of temporary files. The packages required by those wrapper scripts are sys, os, argparse, importlib, numpy and netCDF4. While most of these are standard packages and readily available, numpy and netCDF4 may not be. Users are advised to confirm their availability prior to compiling MET with python embedding support.

In addition to the configure option mentioned above, two variables, MET_PYTHON_CC and MET_PYTHON_LD, must also be set for the configuration process. These may either be set as environment variables or as command line options to configure. These constants are passed as compiler command line options when building MET to enable the compiler to find the requisite Python header files and libraries in the user’s local filesystem. Fortunately, Python provides a way to set these variables properly. This frees the user from the necessity of having any expert knowledge of the compiling and linking process. Along with the Python executable, there should be another executable called python3-config, whose output can be used to set these environment variables as follows:

  • On the command line, run “python3-config –cflags”. Set the value of MET_PYTHON_CC to the output of that command.

  • Again on the command line, run “python3-config –ldflags”. Set the value of MET_PYTHON_LD to the output of that command.

Make sure that these are set as environment variables or that you have included them on the command line prior to running configure.

35.3. MET_PYTHON_EXE

When Python embedding support is compiled, MET instantiates the Python interpreter directly. However, for users of highly configurable Conda environments, the Python instance set at compilation time may not be sufficient. Users may want to switch between Conda environments for which different packages are available. MET version 9.0 has been enhanced to address this need.

The types of Python embedding supported in MET are described below. In all cases, by default, the compiled Python instance is used to execute the Python script. If the packages that script imports are not available for the compiled Python instance, users will encounter a runtime error. In the event of a runtime error, users are advised to set the MET_PYTHON_EXE environment variable and rerun. This environment variable should be set to the full path to the version of Python you would like to use. See an example below.

export MET_PYTHON_EXE=/usr/local/python3/bin/python3

Setting this environment variable triggers slightly different processing logic in MET. Rather than executing the user-specified script with compiled Python instance directly, MET does the following:

  1. Wrap the user’s Python script and arguments with a wrapper script (write_tmp_mpr.py, write_tmp_point.py, or write_tmp_dataplane.py) and specify the name of a temporary file to be written.

  2. Use a system call to the MET_PYTHON_EXE Python instance to execute these commands and write the resulting data objects to a temporary ASCII or NetCDF file.

  3. Use the compiled Python instance to run a wrapper script (read_tmp_ascii.py or read_tmp_dataplane.py) to read data from that temporary file.

With this approach, users should be able to execute Python scripts in their own custom environments.

35.4. Python Embedding for 2D data

We now describe how to write Python scripts so that the MET tools may extract 2D gridded data fields from them. Currently, MET offers two ways to interact with Python scripts: by using NumPy N-dimensional arrays (ndarrays) or by using Xarray DataArrays. The interface to be used (NumPy or Xarray) is specified on the command line (more on this later). The user’s scripts can use any Python libraries that are supported by the local Python installation, or any personal or institutional libraries or code that are desired in order to implement the Python script, so long as the data has been loaded into either a NumPy ndarray or an Xarray DataArray by the end of the script. This offers advantages when using data file formats that MET does not directly support. If there is Python code to read the data format, the user can use those tools to read the data, and then copy the data into a NumPy ndarray or an Xarray DataArray. MET can then ingest the data via the Python script. Note that whether a NumPy ndarray or an Xarray DataArray is used, the data should be stored as double precision floating point numbers. Using different data types, such as integers or single precision floating point numbers, will lead to unexpected results in MET.

Using NumPy N-dimensional Arrays

The data must be loaded into a 2D NumPy ndarray named met_data. In addition there must be a Python dictionary named attrs which contains metadata such as timestamps, grid projection and other information. Here is an example attrs dictionary:

attrs = {

   'valid':     '20050807_120000',
   'init':      '20050807_000000',
   'lead':      '120000',
   'accum':     '120000',

   'name':      'Foo',
   'long_name': 'FooBar',
   'level':     'Surface',
   'units':     'None',

   # Define 'grid' as a string or a dictionary

   'grid': {
      'type': 'Lambert Conformal',
      'hemisphere': 'N',
      'name': 'FooGrid',
      'scale_lat_1': 25.0,
      'scale_lat_2': 25.0,
      'lat_pin': 12.19,
      'lon_pin': -135.459,
      'x_pin': 0.0,
      'y_pin': 0.0,
      'lon_orient': -95.0,
      'd_km': 40.635,
      'r_km': 6371.2,
      'nx': 185,
      'ny': 129,
    }

}

In the attrs dictionary, valid time, initialization time, lead time and accumulation time (if any) must be indicated by strings. Valid and initialization times must be given in YYYYMMDD[_HH[MMSS]] format, and lead and accumulation times must be given in HH[MMSS] format, where the square brackets indicate optional elements. The dictionary must also include strings for the name, long_name, level, and units to describe the data. The rest of the attrs dictionary gives the grid size and projection information in the same format that is used in the netCDF files written out by the MET tools. Those entries are also listed below. Note that the grid entry in the attrs dictionary can either be defined as a string or as a dictionary itself.

If specified as a string, grid can be defined as follows:

  • As a named grid:

'grid': 'G212'
'grid': 'lambert 185 129 12.19 -133.459 -95 40.635 6371.2 25 25 N'
  • As the path to an existing gridded data file:

'grid': '/path/to/sample_data.grib'

When specified as a dictionary, the contents of the grid dictionary vary based on the grid type string. The entries for the supported grid types are described below:

  • Lambert Conformal grid dictionary entries:

    • type (“Lambert Conformal”)

    • name (string)

    • hemisphere (string: “N” or “S”)

    • scale_lat_1, scale_lat_2 (double)

    • lat_pin, lon_pin, x_pin, y_pin (double)

    • lon_orient (double)

    • d_km, r_km (double)

    • nx, ny (int)

  • Polar Stereographic grid dictionary entries:

    • type (“Polar Stereographic”)

    • name (string)

    • hemisphere (string: “N” or “S”)

    • scale_lat (double)

    • lat_pin, lon_pin, x_pin, y_pin (double)

    • lon_orient (double)

    • d_km, r_km (double)

    • nx, ny (int)

  • Mercator grid dictionary entries:

    • type (“Mercator”)

    • name (string)

    • lat_ll (double)

    • lon_ll (double)

    • lat_ur (double)

    • lon_ur (double)

    • nx, ny (int)

  • LatLon grid dictionary entries:

    • type (“LatLon”)

    • name (string)

    • lat_ll, lon_ll (double)

    • delta_lat, delta_lon (double)

    • Nlat, Nlon (int)

  • Rotated LatLon grid dictionary entries:

    • type (“Rotated LatLon”)

    • name (string)

    • rot_lat_ll, rot_lon_ll (double)

    • delta_rot_lat, delta_rot_lon (double)

    • Nlat, Nlon (int)

    • true_lat_south_pole, true_lon_south_pole (double)

    • aux_rotation (double)

  • Gaussian grid dictionary entries:

    • type (“Gaussian”)

    • name (string)

    • lon_zero (double)

    • nx, ny (int)

Additional information about supported grids can be found in Appendix B Map Projections, Grids, and Polylines.

Using Xarray DataArrays

To use Xarray DataArrays, a similar procedure to the NumPy case is followed. The Xarray DataArray can be represented as a NumPy N-dimensional array (ndarray) via the values property of the DataArray, and an attrs property that contains a dictionary of attributes. The user must name the Xarray DataArray to be met_data. When one of the MET tools runs the Python script, it will look for an Xarray DataArray named met_data, and will retrieve the data and metadata from the values and attrs properties, respectively, of the Xarray DataArray. The Xarray DataArray attrs dictionary is populated in the same way as for the NumPy interface (please see Python Embedding for 2D data for requirements of each entry in the attrs dictionary). The values NumPy ndarray property of the Xarray DataArray is also populated in the same way as the NumPy case.

Note

Currently, MET does not support Xarray Dataset structures. If you have a Dataset in Xarray, you can create a DataArray of a single variable using:

met_data = xr.DataArray(ds.varname,attrs=ds.attrs)

ds = your Dataset name
varname = variable name in the Dataset you’d like to use in MET

It remains to discuss command lines and config files. Two methods for specifying the Python command and input file name are supported.

Python Embedding Option 1:

On the command line for any of the MET tools which will be obtaining its data from a Python script rather than directly from a data file, the user should specify either PYTHON_NUMPY or PYTHON_XARRAY wherever a (forecast or observation) data file name would normally be given. Then in the name entry of the config file dictionaries for the forecast or observation data, the user should list the Python script to be run followed by any command line arguments for that script. Note that for tools like MODE that take two data files, it would be entirely possible to use the NumPy interface for one file and the Xarray interface for the other.


Listed below is an example of running the Plot-Data-Plane tool to call a Python script for data that is included with the MET release tarball. Assuming the MET executables are in your path, this example may be run from the top-level MET source code directory.

plot_data_plane PYTHON_NUMPY fcst.ps \
  'name="scripts/python/read_ascii_numpy.py data/python/fcst.txt FCST";' \
  -title "Python enabled plot_data_plane"

The first argument for the Plot-Data-Plane tool is the gridded data file to be read. When calling a NumPy Python script, set this to the constant string PYTHON_NUMPY. The second argument is the name of the output PostScript file to be written. The third argument is a string describing the data to be plotted. When calling a Python script, set name to the Python script to be run along with command line arguments. Lastly, the -title option is used to add a title to the plot. Note that any print statements included in the Python script will be printed to the screen. The above example results in the following log messages.

DEBUG 1: Opening data file: PYTHON_NUMPY
Input File: 'data/python/fcst.txt'
Data Name : 'FCST'
Data Shape: (129, 185)
Data Type:  dtype('float64')
Attributes: {'name': 'FCST',  'long_name': 'FCST_word',
             'level': 'Surface', 'units': 'None',
             'init': '20050807_000000', 'valid': '20050807_120000',
             'lead': '120000',  'accum': '120000'
             'grid': {...} }
DEBUG 1: Creating postscript file: fcst.ps

Python Embedding Option 2 using MET_PYTHON_INPUT_ARG:

The second option was added to support the use of Python embedding in tools which read multiple input files. Option 1 reads a single field of data from a single source, whereas tools like Ensemble-Stat, Series-Analysis, and MTD read data from multiple input files. While option 2 can be used in any of the MET tools, it is required for Python embedding in Ensemble-Stat, Series-Analysis, and MTD.

On the command line for any of the MET tools, specify the path to the input gridded data file(s) as the usage statement for the tool indicates. Do not substitute in PYTHON_NUMPY or PYTHON_XARRAY on the command line. In the config file dictionary set the file_type entry to either PYTHON_NUMPY or PYTHON_XARRAY to activate the Python embedding logic. Then, in the name entry of the config file dictionaries for the forecast or observation data, list the Python script to be run followed by any command line arguments for that script. However, in the Python command, replace the name of the input gridded data file with the constant string MET_PYTHON_INPUT_ARG. When looping over multiple input files, the MET tools will replace that constant MET_PYTHON_INPUT_ARG with the path to the file currently being processed. The example plot_data_plane command listed below yields the same result as the example shown above, but using the option 2 logic instead.

The Ensemble-Stat, Series-Analysis, and MTD tools support the use of file lists on the command line, as do some other MET tools. Typically, the ASCII file list contains a list of files which actually exist on your machine and should be read as input. For Python embedding, these tools loop over the ASCII file list entries, set MET_PYTHON_INPUT_ARG to that string, and execute the Python script. This only allows a single command line argument to be passed to the Python script. However multiple arguments may be concatenated together using some delimiter, and the Python script can be defined to parse arguments using that delimiter. When file lists are constructed in this way, the entries will likely not be files which actually exist on your machine. In this case, users should place the constant string “file_list” on the first line of their ASCII file lists. This will ensure that the MET tools will parse the file list properly.

plot_data_plane data/python/fcst.txt fcst.ps \
  'name="scripts/python/read_ascii_numpy.py MET_PYTHON_INPUT_ARG FCST"; \
   file_type=PYTHON_NUMPY;' \
  -title "Python enabled plot_data_plane"

35.5. Python Embedding for Point Observations

The ASCII2NC tool supports the “-format python” option. With this option, point observations may be passed as input. An example of this is provided in Section 6.2.1.3. That example uses the read_ascii_point.py sample script which is included with the MET code. It reads ASCII data in MET’s 11-column point observation format and stores it in a Pandas dataframe to be read by the ASCII2NC tool with Python.

The read_ascii_point.py sample script can be found in:

35.6. Python Embedding for MPR data

The Stat-Analysis tool supports the “-lookin python” option. With this option, matched pair (MPR) data may be passed as input. An example of this is provided in Section 15.3.1.3. That example uses the read_ascii_mpr.py sample script which is included with the MET code. It reads MPR data and stores it in a Pandas dataframe to be read by the Stat-Analysis tool with Python.

The read_ascii_mpr.py sample script can be found in: