.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/quickstart_functional_api.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_quickstart_functional_api.py: Quickstart guide functional api =============================== This example is designed as quickstart guide for the functional api of :py:mod:`pylawr`. .. GENERATED FROM PYTHON SOURCE LINES 11-16 Introduction ------------ In the following, we will use the functional api to process netCDF data from the ``HHG`` X-band weather radar. These steps are almost the same as for the low-level api. .. GENERATED FROM PYTHON SOURCE LINES 18-23 The following processing steps are: 1. Read in data with pre-defined data handler for netcdf data 2. Remove noise 3. Filter out clutter 4. Interpolate missing values 5. Plot the data .. GENERATED FROM PYTHON SOURCE LINES 23-30 .. code-block:: default import pylawr.functions.input as input_funcs import pylawr.functions.transform as transform_funcs import pylawr.functions.plot as plot_funcs from pylawr.grid import PolarGrid .. GENERATED FROM PYTHON SOURCE LINES 31-38 1. Read in data --------------- To read in the ascii data, we will use a pre-defined function to read in ``HHG`` X-band weather radar netCDF data. This functions takes a file name and returns read reflectivity. For this, we need to open a file handler. We need to set the grid manually, because we cannot extract the grid from given netCDF data at the moment. .. GENERATED FROM PYTHON SOURCE LINES 38-44 .. code-block:: default file_path = '../tests/data/lawr_l0_example.nc' reflectivity, _ = input_funcs.read_lawr_nc_level0(file_path) grid = PolarGrid() reflectivity = reflectivity.lawr.set_grid_coordinates(grid) .. GENERATED FROM PYTHON SOURCE LINES 45-47 .. code-block:: default print(reflectivity) .. rst-class:: sphx-glr-script-out .. code-block:: none array([[[-25.083254, 43.722687, 35.157005, ..., 21.714653, 21.636692, 21.650703], [-25.132263, 43.60427 , 37.33281 , ..., 21.870094, 22.084316, 21.731308], [-25.14114 , 43.791496, 37.9972 , ..., 21.80893 , 21.812252, 21.956898], ..., [-25.39464 , 43.42102 , 37.75023 , ..., 21.459068, 22.672329, 22.397419], [-25.292507, 43.703377, 36.075798, ..., 21.719315, 22.868332, 22.19233 ], [-25.27496 , 43.455482, 36.701538, ..., 21.591888, 22.23751 , 21.603336]]], dtype=float32) Coordinates: * time (time) datetime64[ns] 2016-06-07T16:21:30 * azimuth (azimuth) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5 * range (range) float64 29.98 89.94 149.9 ... 1.982e+04 1.988e+04 1.994e+04 Attributes: (12/17) units: dBZ standard_name: equivalent_reflectivity_factor short_name: dBZ Convections: CF-1.0 History: 2016-06-08 09:32 generated from orignal data Sample_Frequency_Hz: 5000000 ... ... institution: Meteorological Institute, Hamburg, Germany title: LAWR data latitude: 53.56833 longitude: 9.973997 corr_offset: 29.5364 tags: beam-expansion-corr .. GENERATED FROM PYTHON SOURCE LINES 48-50 .. code-block:: default print(grid) .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 51-56 2. Remove noise --------------- As next step, we want to remove existing noise from reflectivity. For this, we will use ``remove_noise`` from ``pylawr.functions.transform``, which fits a ``NoiseRemover`` and uses this remover to subtract the noise. .. GENERATED FROM PYTHON SOURCE LINES 56-62 .. code-block:: default reflectivity, noise_remover = transform_funcs.remove_noise(reflectivity) print(reflectivity) print('Determined noiselevel: {0:.3e}'.format(noise_remover.noiselevel)) .. rst-class:: sphx-glr-script-out .. code-block:: none array([[[2.75846010e-03, 2.35650633e+04, 3.27868306e+03, ..., 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [2.72364866e-03, 2.29312039e+04, 5.41103340e+03, ..., 3.61638714e+00, 1.04837054e+01, 0.00000000e+00], [2.71738575e-03, 2.39413914e+04, 6.30549922e+03, ..., 1.46524884e+00, 6.71052823e-01, 4.89833757e+00], ..., [2.54381865e-03, 2.19837606e+04, 5.95692793e+03, ..., 0.00000000e+00, 3.39133624e+01, 2.16510384e+01], [2.61253140e-03, 2.34605086e+04, 4.05115425e+03, ..., 0.00000000e+00, 4.24551867e+01, 1.36400978e+01], [2.62450052e-03, 2.21588953e+04, 4.67899971e+03, ..., 0.00000000e+00, 1.62856158e+01, 0.00000000e+00]]]) Coordinates: * time (time) datetime64[ns] 2016-06-07T16:21:30 * azimuth (azimuth) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5 * range (range) float64 29.98 89.94 149.9 ... 1.982e+04 1.988e+04 1.994e+04 Attributes: (12/17) units: mm**6/m**3 standard_name: linear_equivalent_reflectivity_factor short_name: Z Convections: CF-1.0 History: 2016-06-08 09:32 generated from orignal data Sample_Frequency_Hz: 5000000 ... ... institution: Meteorological Institute, Hamburg, Germany title: LAWR data latitude: 53.56833 longitude: 9.973997 corr_offset: 29.5364 tags: neg-lin-refl-repl-0.0;noise-filtered;beam-expansion... Determined noiselevel: 3.714e-07 .. GENERATED FROM PYTHON SOURCE LINES 63-66 We notice that the logarithmic reflectivity was translated into a linear reflectivity with our noise remover. Thus, we need to retranslate our linear reflectivity to a logarithmic reflectivity. .. GENERATED FROM PYTHON SOURCE LINES 66-69 .. code-block:: default reflectivity = reflectivity.lawr.to_dbz() .. GENERATED FROM PYTHON SOURCE LINES 70-76 3. Filter out clutter --------------------- After we substracted a determined noise level from our reflectivity field, we want to remove clutter. Based on multiple application gradient-based filters andoptical filters, ``remove_clutter`` removes clutter from our reflectivity. .. GENERATED FROM PYTHON SOURCE LINES 76-82 .. code-block:: default reflectivity, cluttermap = transform_funcs.remove_clutter_lawr(reflectivity) print(reflectivity) print(cluttermap) .. rst-class:: sphx-glr-script-out .. code-block:: none array([[[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]]) Coordinates: * time (time) datetime64[ns] 2016-06-07T16:21:30 * azimuth (azimuth) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5 * range (range) float64 29.98 89.94 149.9 ... 1.982e+04 1.988e+04 1.994e+04 Attributes: (12/17) units: dBZ standard_name: equivalent_reflectivity_factor short_name: dBZ Convections: CF-1.0 History: 2016-06-08 09:32 generated from orignal data Sample_Frequency_Hz: 5000000 ... ... institution: Meteorological Institute, Hamburg, Germany title: LAWR data latitude: 53.56833 longitude: 9.973997 corr_offset: 29.5364 tags: neg-lin-refl-repl-0.0;noise-filtered;beam-expansion... ClutterMap(1*TDBZFilter, 1*SPINFilter, 1*SPKFilterOne, 1*SPKFilterTwo, 1*RINGFilterOne, 1*RINGFilterTwo, 1*SpeckleFilterW33, 1*SpeckleFilterW35, 1*SpeckleFilterW55, 1*SpeckleFilterW57, 1*SpeckleFilterW77) .. GENERATED FROM PYTHON SOURCE LINES 83-90 4. Interpolate missing values ----------------------------- We can see that there are missing values within our reflectivity array, which need to be interpolated. For this, we will use ``interpolate_missing`` from our filter functions. This remaps existing values to missing values such that no missing value remains. If a criterion is exceeded, some missing values are filled with no rain to avoid interpolation artifacts. .. GENERATED FROM PYTHON SOURCE LINES 90-96 .. code-block:: default reflectivity, remapper = transform_funcs.interpolate_missing(reflectivity) print(reflectivity) print(remapper) .. rst-class:: sphx-glr-script-out .. code-block:: none /home/docs/checkouts/readthedocs.org/user_builds/pylawr/conda/stable/lib/python3.11/site-packages/xarray/core/coordinates.py:41: FutureWarning: Updating MultiIndexed coordinate 'grid_cell' would corrupt indices for other variables: ['azimuth', 'range']. This will raise an error in the future. Use `.drop_vars({'range', 'azimuth', 'grid_cell'})` before assigning new coordinate values. self.update({key: value}) /home/docs/checkouts/readthedocs.org/user_builds/pylawr/conda/stable/lib/python3.11/site-packages/xarray/core/coordinates.py:41: FutureWarning: Updating MultiIndexed coordinate 'grid_cell' would corrupt indices for other variables: ['grid_lat', 'grid_lon']. This will raise an error in the future. Use `.drop_vars({'grid_lat', 'grid_lon', 'grid_cell'})` before assigning new coordinate values. self.update({key: value}) array([[[-32.5, -32.5, -32.5, ..., -32.5, -32.5, -32.5], [-32.5, -32.5, -32.5, ..., -32.5, -32.5, -32.5], [-32.5, -32.5, -32.5, ..., -32.5, -32.5, -32.5], ..., [-32.5, -32.5, -32.5, ..., -32.5, -32.5, -32.5], [-32.5, -32.5, -32.5, ..., -32.5, -32.5, -32.5], [-32.5, -32.5, -32.5, ..., -32.5, -32.5, -32.5]]]) Coordinates: * azimuth (azimuth) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5 * range (range) float64 29.98 89.94 149.9 ... 1.982e+04 1.988e+04 1.994e+04 * time (time) datetime64[ns] 2016-06-07T16:21:30 Attributes: (12/17) units: dBZ standard_name: equivalent_reflectivity_factor short_name: dBZ Convections: CF-1.0 History: 2016-06-08 09:32 generated from orignal data Sample_Frequency_Hz: 5000000 ... ... institution: Meteorological Institute, Hamburg, Germany title: LAWR data latitude: 53.56833 longitude: 9.973997 corr_offset: 29.5364 tags: neg-lin-refl-repl-0.0;noise-filtered;beam-expansion... OrdinaryKriging(n_neighbors=10) .. GENERATED FROM PYTHON SOURCE LINES 97-102 5. Plot the data ---------------- The missing values are interpolated with a nearest neighbor interpolation, where the five nearest neighbors are averaged. After we interpolated our data, we can plot the data. .. GENERATED FROM PYTHON SOURCE LINES 104-108 To plot the data, we need to translate our reflectivity to rain rates. This translation is given by a Z-R relationship, where the scale and ploynomial degree factor can be set. All values without rain are set to NaN, because they will be plotted transparent. .. GENERATED FROM PYTHON SOURCE LINES 108-113 .. code-block:: default rain_rate = reflectivity.lawr.to_z().lawr.zr_convert(a=256, b=1.42) rain_rate = rain_rate.where(rain_rate >= 0.1) rain_rate = rain_rate.lawr.set_grid_coordinates(grid) .. GENERATED FROM PYTHON SOURCE LINES 114-117 After we translated the rain rate, we can use this array as input array to ``plot_rain_clutter`` function to plot this rain rate together with our cluttermap. .. GENERATED FROM PYTHON SOURCE LINES 117-122 .. code-block:: default plotter = plot_funcs.plot_rain_clutter(rain_rate, cluttermap=cluttermap, plot_path='/tmp/quickstart_functional_api.png') plotter.show() .. image-sg:: /examples/images/sphx_glr_quickstart_functional_api_001.png :alt: quickstart functional api :srcset: /examples/images/sphx_glr_quickstart_functional_api_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 123-125 This creates a default plot with an information header, a map with the rainfall rates and a corresponding colorbar. .. GENERATED FROM PYTHON SOURCE LINES 127-130 After we plotted the rain rate and cluttermap, we finished this quickstart tutorial. If you want to get a deeper insight into this radar software, you can also check-out the quickstart tutorial for our low-level api. .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 2.098 seconds) .. _sphx_glr_download_examples_quickstart_functional_api.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: quickstart_functional_api.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: quickstart_functional_api.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_