Source code for satpy.readers.li_l2_nc

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"""MTG Lightning Imager (LI) Level-2 (L2) unified reader.

This reader supports reading all the products from the LI L2
processing level:

Point products:
  * L2-LE Lightning Events
  * L2-LEF Lightning Events Filtered
  * L2-LFL Lightning Flashes
  * L2-LGR Lightning Groups
Accumulated products:
  * L2-AF Accumulated Flashes
  * L2-AFA Accumulated Flash Area
  * L2-AFR Accumulated Flash Radiance

Per default, the unified LI L2 reader returns the data either as an 1-D array
or as a 2-D array depending on the product type.

Point-based products (LE, LEF, LFL, LGR) are "classic" lightning products
consisting of values with attached latitude and longitude coordinates.
Hence, these products are provided by the reader as 1-D arrays,
with a ``pyresample.geometry.SwathDefinition`` area
attribute containing the points lat-lon coordinates.

Accumulated products (AF, AFA, AFR) are the result of temporal accumulation
of events (e.g. over 30 seconds), and are gridded in the FCI 2km geostationary
projection grid, in order to facilitate the synergistic usage together with FCI.
Compared to the point products, the gridded products also give information
about the spatial extent of the lightning activity.
Hence, these products are provided by the reader as 2-D arrays in the FCI 2km
grid as per intended usage, with a ``pyresample.geometry.AreaDefinition`` area
attribute containing the grid geolocation information.
In this way, the products can directly be overlaid to FCI data.
If needed, the accumulated products can also be accessed as 1-d array by
setting the reader kwarg ``with_area_definition=False``,

  scn = Scene(filenames=filenames, reader="li_l2_nc", reader_kwargs={'with_area_definition': False})

For both 1-d and 2-d products, the lat-lon coordinates of the points/grid pixels
can be accessed using e.g.

See the LI L2 Product User Guide `PUG`_ for more information.

.. _PUG:


import logging

import dask.array as da
import numpy as np
import xarray as xr

from satpy.readers.li_base_nc import LINCFileHandler
from satpy.resample import get_area_def
from satpy.utils import get_legacy_chunk_size

logger = logging.getLogger(__name__)
LI_GRID_SHAPE = (5568, 5568)
CHUNK_SIZE = get_legacy_chunk_size()

[docs] class LIL2NCFileHandler(LINCFileHandler): """Implementation class for the unified LI L2 satpy reader.""" def __init__(self, filename, filename_info, filetype_info, with_area_definition=True): """Initialize LIL2NCFileHandler.""" super(LIL2NCFileHandler, self).__init__(filename, filename_info, filetype_info) if with_area_definition and not self.prod_in_accumulation_grid: logger.debug(f"The current product {filetype_info['file_desc']['product_type']} " f"is not an accumulated product so it will not be regridded.") self.with_area_def = False else: self.with_area_def = with_area_definition
[docs] def get_dataset(self, dataset_id, ds_info=None): """Get the dataset and apply gridding if requested.""" data_array = super().get_dataset(dataset_id, ds_info) # variable_patterns are compiled to regex patterns # hence search variable name from swath_coordinate var_with_swath_coord = self.is_var_with_swath_coord(dataset_id) if var_with_swath_coord and self.with_area_def: data_array = self.get_array_on_fci_grid(data_array) return data_array
[docs] def get_area_def(self, dsid): """Compute area definition for a dataset, only supported for accumulated products.""" var_with_swath_coord = self.is_var_with_swath_coord(dsid) if var_with_swath_coord and self.with_area_def: return get_area_def("mtg_fci_fdss_2km") raise NotImplementedError("Area definition is not supported for non-accumulated products.")
[docs] def is_var_with_swath_coord(self, dsid): """Check if the variable corresponding to this dataset is listed as variable with swath coordinates.""" # since the patterns are compiled to regex we use the search() method below to find matches with_swath_coords = any([["name"]) is not None for p in self.swath_coordinates["patterns"]]) return with_swath_coords
[docs] def get_array_on_fci_grid(self, data_array: xr.DataArray): """Obtain the accumulated products as a (sparse) 2-d array. The array has the shape of the FCI 2 km grid (5568x5568px), and will have an AreaDefinition attached. """ # Integer values without the application of scale_factor and add_offset # hence no projection/index calculation. # Note that x and y have origin in the south-west corner of the image # and start with index 1. rows = self.get_measured_variable("y") cols = self.get_measured_variable("x") attrs = data_array.attrs rows, cols = da.compute(rows, cols) # origin is in the south-west corner, so we flip the rows (applying # offset of 1 implicitly) # And we manually offset the columns by 1 too: rows = (LI_GRID_SHAPE[0] - rows.astype(int)) cols = cols.astype(int) - 1 # Create an empyt 1-D array for the results flattened_result = np.nan * da.zeros((LI_GRID_SHAPE[0] * LI_GRID_SHAPE[1]), dtype=data_array.dtype) # Insert the data. Dask doesn't support this for more than one dimension at a time, so ... flattened_result[rows * LI_GRID_SHAPE[0] + cols] = data_array # ... reshape to final 2D grid data_2d = da.reshape(flattened_result, LI_GRID_SHAPE) xarr = xr.DataArray(da.asarray(data_2d, CHUNK_SIZE), dims=("y", "x")) xarr.attrs = attrs return xarr