.. image:: _static/logo.png
    :align: center
    :scale: 16%
    :alt: pymt_era5
    :target: https://pymt-era5.readthedocs.io/en/latest/


`pymt_era5 <https://github.com/gantian127/pymt_era5/>`_ is a package that converts `ERA5 datasets <https://confluence.ecmwf.int/display/CKB/ERA5>`_ into a reusable,
plug-and-play data component for `PyMT <https://pymt.readthedocs.io/en/latest/?badge=latest>`_ modeling framework
developed by Community Surface Dynamics Modeling System (`CSDMS <https://csdms.colorado.edu/wiki/Main_Page>`_).
This allows ERA5 datasets (currently support 3 dimensional data) to be easily coupled with other datasets or models that expose
a `Basic Model Interface <https://bmi.readthedocs.io/en/latest/>`_.

---------------
Installation
---------------

Install the pymt in a new environment:

.. code::

    $ conda config --add channels conda-forge
    $ conda create -n pymt -c conda-forge python=3 pymt
    $ conda activate pymt

Please make sure to first install the `CDS API <https://cds.climate.copernicus.eu/api-how-to>`_
and then install the pymt_era5 using pip:

.. code::

  pip install pymt_era5

or conda

.. code::

  conda install -c conda-forge pymt_era5

--------------
Coding Example
--------------
You can learn more details about the coding example from the
`tutorial notebook <https://github.com/gantian127/pymt_era5/blob/master/notebooks/pymt_era5.ipynb>`_.

.. code-block:: python

    import matplotlib.pyplot as plt
    import numpy as np

    from pymt.models import Era5

    # initiate a data component
    data_comp = Era5()
    data_comp.initialize('config_file.yaml')

    # get variable info
    for var_name in data_comp.output_var_names:
        var_unit = data_comp.var_units(var_name)
        var_location = data_comp.var_location(var_name)
        var_type = data_comp.var_type(var_name)
        var_grid = data_comp.var_grid(var_name)
        var_itemsize = data_comp.var_itemsize(var_name)
        var_nbytes = data_comp.var_nbytes(var_name)

        print('variable_name: {} \nvar_unit: {} \nvar_location: {} \nvar_type: {} \nvar_grid: {} \nvar_itemsize: {}'
            '\nvar_nbytes: {} \n'. format(var_name, var_unit, var_location, var_type, var_grid, var_itemsize, var_nbytes))

    # get time info
    start_time = data_comp.start_time
    end_time = data_comp.end_time
    time_step = data_comp.time_step
    time_units = data_comp.time_units
    time_steps = int((end_time - start_time)/time_step) + 1

    print('start_time: {} \nend_time: {} \ntime_step: {} \ntime_units: {} \ntime_steps: {}'.format(
        start_time, end_time, time_step, time_units, time_steps))

    # get variable grid info
    grid_type = data_comp.grid_type(var_grid)
    grid_rank = data_comp.grid_ndim(var_grid)
    grid_shape = data_comp.grid_shape(var_grid)
    grid_spacing = data_comp.grid_spacing(var_grid)
    grid_origin = data_comp.grid_origin(var_grid)

    print('grid_type: {} \ngrid_rank: {} \ngrid_shape: {} \ngrid_spacing: {} \ngrid_origin: {}'.format(
    grid_type, grid_rank, grid_shape, grid_spacing, grid_origin))

    # get variable data
    data = data_comp.get_value('2 metre temperature')
    data_2D = data.reshape(grid_shape)
    print(data.shape, data_2D.shape)

    # get X, Y extent for plot
    min_y, min_x = grid_origin
    max_y = min_y + grid_spacing[0]*(grid_shape[0]-1)
    max_x = min_x + grid_spacing[1]*(grid_shape[1]-1)
    dy = grid_spacing[0]/2
    dx = grid_spacing[1]/2
    extent = [min_x - dx, max_x + dx, min_y - dy, max_y + dy]

    # plot data
    fig, ax = plt.subplots(1,1, figsize=(9,5))
    im = ax.imshow(data_2D, extent=extent)
    cbar = fig.colorbar(im)
    cbar.set_label('2 metre temperature [K]')
    plt.xlabel('longitude [degree_east]')
    plt.ylabel('latitude [degree_north]')
    plt.title('2 metre temperature in Colorado on Jan 1st, 2021 at 00:00')

    # complete the example by finalizing the component
    data_comp.finalize()

|tif_plot|

.. links:

.. |tif_plot| image:: _static/tif_plot.png