Source code for satpy.readers.nwcsaf_nc

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2017-2023 Satpy developers
# This file is part of satpy.
# satpy is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software
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"""Nowcasting SAF common PPS&MSG NetCDF/CF format reader.

   - The NWCSAF GEO 2018 products documentation:


import functools
import logging
import os
from contextlib import suppress
from datetime import datetime

import dask.array as da
import numpy as np
import xarray as xr
from pyproj import CRS
from pyresample.geometry import AreaDefinition

from satpy.readers.file_handlers import BaseFileHandler
from satpy.readers.utils import unzip_file
from satpy.utils import get_legacy_chunk_size

logger = logging.getLogger(__name__)

CHUNK_SIZE = get_legacy_chunk_size()

SENSOR = {"NOAA-19": "avhrr-3",
          "NOAA-18": "avhrr-3",
          "NOAA-15": "avhrr-3",
          "Metop-A": "avhrr-3",
          "Metop-B": "avhrr-3",
          "Metop-C": "avhrr-3",
          "EOS-Aqua": "modis",
          "EOS-Terra": "modis",
          "Suomi-NPP": "viirs",
          "NOAA-20": "viirs",
          "NOAA-21": "viirs",
          "NOAA-22": "viirs",
          "NOAA-23": "viirs",
          "JPSS-1": "viirs",
          "Metop-SG-A1": "metimage",
          "Metop-SG-A2": "metimage",
          "Metop-SG-A3": "metimage",
          "GOES-16": "abi",
          "GOES-17": "abi",
          "Himawari-8": "ahi",
          "Himawari-9": "ahi",

PLATFORM_NAMES = {"MSG1": "Meteosat-8",
                  "MSG2": "Meteosat-9",
                  "MSG3": "Meteosat-10",
                  "MSG4": "Meteosat-11",
                  "GOES16": "GOES-16",
                  "GOES17": "GOES-17",

[docs] class NcNWCSAF(BaseFileHandler): """NWCSAF PPS&MSG NetCDF reader.""" def __init__(self, filename, filename_info, filetype_info): """Init method.""" super(NcNWCSAF, self).__init__(filename, filename_info, filetype_info) self._unzipped = unzip_file(self.filename) if self._unzipped: self.filename = self._unzipped self.cache = {} = xr.open_dataset(self.filename, decode_cf=True, mask_and_scale=False, chunks=CHUNK_SIZE) ={"nx": "x", "ny": "y"}) self.sw_version =["source"] self.pps = False self.platform_name = None self.sensor = None self.file_key_prefix = filetype_info.get("file_key_prefix", "") try: # NWCSAF/Geo: try: kwrgs = {"sat_id":["satellite_identifier"]} except KeyError: kwrgs = {"sat_id":["satellite_identifier"].astype(str)} except KeyError: # NWCSAF/PPS: kwrgs = {"platform_name":["platform"]} self.set_platform_and_sensor(**kwrgs) self.upsample_geolocation = functools.lru_cache(maxsize=1)( self._upsample_geolocation_uncached )
[docs] def set_platform_and_sensor(self, **kwargs): """Set some metadata: platform_name, sensors, and pps (identifying PPS or Geo).""" try: # NWCSAF/Geo self.platform_name = PLATFORM_NAMES.get(kwargs["sat_id"], kwargs["sat_id"]) except KeyError: # NWCSAF/PPS self.platform_name = kwargs["platform_name"] self.pps = True self.sensor = set([SENSOR.get(self.platform_name, "seviri")])
[docs] def remove_timedim(self, var): """Remove time dimension from dataset.""" if self.pps and var.dims[0] == "time": data = var[0, :, :] data.attrs = var.attrs var = data return var
[docs] def drop_xycoords(self, variable): """Drop x, y coords when y is scan line number.""" try: if variable.coords["y"].attrs["long_name"] == "scan line number": return variable.drop_vars(["y", "x"]) except KeyError: pass return variable
[docs] def get_dataset(self, dsid, info): """Load a dataset.""" dsid_name = dsid["name"] if dsid_name in self.cache: logger.debug("Get the data set from cache: %s.", dsid_name) return self.cache[dsid_name] if dsid_name in ["lon", "lat"] and dsid_name not in # Get full resolution lon,lat from the reduced (tie points) grid lon, lat = self.upsample_geolocation() if dsid_name == "lon": return lon else: return lat logger.debug("Reading %s.", dsid_name) file_key = self._get_filekeys(dsid_name, info) variable =[file_key] variable = self.remove_timedim(variable) variable = self.scale_dataset(variable, info) variable = self.drop_xycoords(variable) self.get_orbital_parameters(variable) variable.attrs["start_time"] = self.start_time variable.attrs["end_time"] = self.end_time return variable
[docs] def get_orbital_parameters(self, variable): """Get the orbital parameters from the file if possible (geo).""" with suppress(KeyError): gdal_params = dict(elt.strip("+").split("=") for elt in["gdal_projection"].split()) variable.attrs["orbital_parameters"] = dict( satellite_nominal_altitude=float(gdal_params["h"]), satellite_nominal_longitude=float(["sub-satellite_longitude"]), satellite_nominal_latitude=0)
[docs] def _get_varname_in_file(self, info, info_type="file_key"): if isinstance(info[info_type], list): for key in info[info_type]: file_key = self.file_key_prefix + key if file_key in return file_key return self.file_key_prefix + info[info_type]
[docs] def _get_filekeys(self, dsid_name, info): try: file_key = self._get_varname_in_file(info, info_type="file_key") except KeyError: file_key = dsid_name return file_key
[docs] def scale_dataset(self, variable, info): """Scale the data set, applying the attributes from the netCDF file. The scale and offset attributes will then be removed from the resulting variable. """ variable = remove_empties(variable) scale = variable.attrs.get("scale_factor", np.array(1)) offset = variable.attrs.get("add_offset", np.array(0)) if "_FillValue" in variable.attrs: variable.attrs["scaled_FillValue"] = variable.attrs["_FillValue"] * scale + offset if np.issubdtype((scale + offset).dtype, np.floating) or np.issubdtype(variable.dtype, np.floating): variable = self._mask_variable(variable) attrs = variable.attrs.copy() variable = variable * scale + offset variable.attrs = attrs if "valid_range" in variable.attrs: variable.attrs["valid_range"] = variable.attrs["valid_range"] * scale + offset variable.attrs.pop("add_offset", None) variable.attrs.pop("scale_factor", None) variable.attrs.update({"platform_name": self.platform_name, "sensor": self.sensor}) if not variable.attrs.get("standard_name", "").endswith("status_flag"): # TODO: do we really need to add units to everything ? variable.attrs.setdefault("units", "1") ancillary_names = variable.attrs.get("ancillary_variables", "") try: variable.attrs["ancillary_variables"] = ancillary_names.split() except AttributeError: pass if "palette_meanings" in variable.attrs: variable = self._prepare_variable_for_palette(variable, info) if "standard_name" in info: variable.attrs.setdefault("standard_name", info["standard_name"]) variable = self._adjust_variable_for_legacy_software(variable) return variable
[docs] @staticmethod def _mask_variable(variable): if "_FillValue" in variable.attrs: variable = variable.where( variable != variable.attrs["_FillValue"]) variable.attrs["_FillValue"] = np.nan if "valid_range" in variable.attrs: variable = variable.where( variable <= variable.attrs["valid_range"][1]) variable = variable.where( variable >= variable.attrs["valid_range"][0]) if "valid_max" in variable.attrs: variable = variable.where( variable <= variable.attrs["valid_max"]) if "valid_min" in variable.attrs: variable = variable.where( variable >= variable.attrs["valid_min"]) return variable
[docs] def _prepare_variable_for_palette(self, variable, info): try: so_dataset =[self._get_varname_in_file(info, info_type="scale_offset_dataset")] except KeyError: scale = 1 offset = 0 fill_value = 255 else: scale = so_dataset.attrs["scale_factor"] offset = so_dataset.attrs["add_offset"] fill_value = so_dataset.attrs["_FillValue"] variable.attrs["palette_meanings"] = [int(val) for val in variable.attrs["palette_meanings"].split()] if fill_value not in variable.attrs["palette_meanings"] and "fill_value_color" in variable.attrs: variable.attrs["palette_meanings"] = [fill_value] + variable.attrs["palette_meanings"] variable = xr.DataArray(da.vstack((np.array(variable.attrs["fill_value_color"]),, coords=variable.coords, dims=variable.dims, attrs=variable.attrs) val, idx = np.unique(variable.attrs["palette_meanings"], return_index=True) variable.attrs["palette_meanings"] = val * scale + offset variable = variable[idx] return variable
[docs] def _adjust_variable_for_legacy_software(self, variable): if self.sw_version == "NWC/PPS version v2014" and variable.attrs.get("standard_name") == "cloud_top_altitude": # pps 2014 valid range and palette don't match variable.attrs["valid_range"] = (0., 9000.) if (self.sw_version == "NWC/PPS version v2014" and variable.attrs.get("long_name") == "RGB Palette for ctth_alti"): # pps 2014 palette has the nodata color (black) first variable = variable[1:, :] return variable
[docs] def _upsample_geolocation_uncached(self): """Upsample the geolocation (lon,lat) from the tiepoint grid.""" from geotiepoints import SatelliteInterpolator # Read the fields needed: col_indices =["nx_reduced"].values row_indices =["ny_reduced"].values lat_reduced = self.scale_dataset(["lat_reduced"], {}) lon_reduced = self.scale_dataset(["lon_reduced"], {}) shape = (["y"].shape[0],["x"].shape[0]) cols_full = np.arange(shape[1]) rows_full = np.arange(shape[0]) satint = SatelliteInterpolator((lon_reduced.values, lat_reduced.values), (row_indices, col_indices), (rows_full, cols_full)) lons, lats = satint.interpolate() lon = xr.DataArray(lons, attrs=lon_reduced.attrs, dims=["y", "x"]) lat = xr.DataArray(lats, attrs=lat_reduced.attrs, dims=["y", "x"]) lat = self.drop_xycoords(lat) lon = self.drop_xycoords(lon) return lon, lat
[docs] def get_area_def(self, dsid): """Get the area definition of the datasets in the file. Only applicable for MSG products! """ if self.pps: # PPS: raise NotImplementedError if dsid["name"].endswith("_pal"): raise NotImplementedError crs, area_extent = self._get_projection() crs, area_extent = self._ensure_crs_extents_in_meters(crs, area_extent) nlines, ncols =[dsid["name"]].shape area = AreaDefinition("some_area_name", "On-the-fly area", "geosmsg", crs, ncols, nlines, area_extent) return area
[docs] @staticmethod def _ensure_crs_extents_in_meters(crs, area_extent): """Fix units in Earth shape, satellite altitude and 'units' attribute.""" if "kilo" in crs.axis_info[0].unit_name: proj_dict = crs.to_dict() proj_dict["units"] = "m" if "a" in proj_dict: proj_dict["a"] *= 1000. if "b" in proj_dict: proj_dict["b"] *= 1000. if "R" in proj_dict: proj_dict["R"] *= 1000. proj_dict["h"] *= 1000. area_extent = tuple([val * 1000. for val in area_extent]) crs = CRS.from_dict(proj_dict) return crs, area_extent
def __del__(self): """Delete the instance.""" if self._unzipped: try: os.remove(self._unzipped) except OSError: pass @property def start_time(self): """Return the start time of the object.""" try: return read_nwcsaf_time(["nominal_product_time"]) except KeyError: return read_nwcsaf_time(["time_coverage_start"]) @property def end_time(self): """Return the end time of the object.""" return read_nwcsaf_time(["time_coverage_end"]) @property def sensor_names(self): """List of sensors represented in this file.""" return self.sensor
[docs] def _get_projection(self): """Get projection from the NetCDF4 attributes.""" try: proj_str =["gdal_projection"] except TypeError: proj_str =["gdal_projection"].decode() # Check the a/b/h units radius_a = proj_str.split("+a=")[-1].split()[0] if float(radius_a) > 10e3: units = "m" scale = 1.0 else: units = "km" scale = 1e3 if "units" not in proj_str: proj_str = proj_str + " +units=" + units area_extent = (float(["gdal_xgeo_up_left"]) / scale, float(["gdal_ygeo_low_right"]) / scale, float(["gdal_xgeo_low_right"]) / scale, float(["gdal_ygeo_up_left"]) / scale) crs = CRS.from_string(proj_str) return crs, area_extent
[docs] def remove_empties(variable): """Remove empty objects from the *variable*'s attrs.""" import h5py for key, val in variable.attrs.items(): if isinstance(val, h5py._hl.base.Empty): variable.attrs.pop(key) return variable
[docs] def read_nwcsaf_time(time_value): """Read the time, nwcsaf-style.""" try: # MSG: try: return datetime.strptime(time_value, "%Y-%m-%dT%H:%M:%SZ") except TypeError: # Remove this in summer 2024 (this is not needed since h5netcdf 0.14) return datetime.strptime(time_value.astype(str), "%Y-%m-%dT%H:%M:%SZ") except ValueError: # PPS: return datetime.strptime(time_value, "%Y%m%dT%H%M%S%fZ")