Source code for satpy.readers.mimic_TPW2_nc

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2019 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
# Foundation, either version 3 of the License, or (at your option) any later
# version.
# Satpy is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
# A PARTICULAR PURPOSE.  See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with
# Satpy.  If not, see <>.

"""Reader for Mimic TPW data in netCDF format from SSEC.

This module implements reader for MIMIC_TPW2 netcdf files.
MIMIC-TPW2 is an experimental global product of total precipitable water (TPW),
using morphological compositing of the MIRS retrieval from several available
operational microwave-frequency sensors. Originally described in a 2010 paper by
Wimmers and Velden. This Version 2 is developed from an older method that uses simpler,
but more limited TPW retrievals and advection calculations.

More information, data and credits at

import logging

import numpy as np
import xarray as xr
from pyresample.geometry import AreaDefinition

from satpy.readers.netcdf_utils import NetCDF4FileHandler, netCDF4

logger = logging.getLogger(__name__)

[docs] class MimicTPW2FileHandler(NetCDF4FileHandler): """NetCDF4 reader for MIMC TPW.""" def __init__(self, filename, filename_info, filetype_info): """Initialize the reader.""" super(MimicTPW2FileHandler, self).__init__(filename, filename_info, filetype_info, xarray_kwargs={"decode_times": False})
[docs] def available_datasets(self, configured_datasets=None): """Get datasets in file matching gelocation shape (lat/lon).""" lat_shape = self.file_content.get("/dimension/lat") lon_shape = self.file_content.get("/dimension/lon") # Read the lat/lon variables? handled_variables = set() # update previously configured datasets logger.debug("Starting previously configured variables loop...") for is_avail, ds_info in (configured_datasets or []): # some other file handler knows how to load this if is_avail is not None: yield is_avail, ds_info var_name = ds_info.get("file_key", ds_info["name"]) # logger.debug("Evaluating previously configured variable: %s", var_name) matches = self.file_type_matches(ds_info["file_type"]) # we can confidently say that we can provide this dataset and can # provide more info if matches and var_name in self: logger.debug("Handling previously configured variable: %s", var_name) handled_variables.add(var_name) new_info = ds_info.copy() # don't mess up the above yielded yield True, new_info elif is_avail is None: # if we didn't know how to handle this dataset and no one else did # then we should keep it going down the chain yield is_avail, ds_info # Iterate over dataset contents for var_name, val in self.file_content.items(): # Only evaluate variables if isinstance(val, netCDF4.Variable): logger.debug("Evaluating new variable: %s", var_name) var_shape = self[var_name + "/shape"] logger.debug("Dims:{}".format(var_shape)) if var_shape == (lat_shape, lon_shape): logger.debug("Found valid additional dataset: %s", var_name) # Skip anything we have already configured if var_name in handled_variables: logger.debug("Already handled, skipping: %s", var_name) continue handled_variables.add(var_name) # Create new ds_info object new_info = { "name": var_name, "file_key": var_name, "file_type": self.filetype_info["file_type"], } logger.debug(var_name) yield True, new_info
[docs] def get_dataset(self, ds_id, info): """Load dataset designated by the given key from file.""" logger.debug("Getting data for: %s", ds_id["name"]) file_key = info.get("file_key", ds_id["name"]) data = np.flipud(self[file_key]) data = xr.DataArray(data, dims=["y", "x"]) data.attrs = self.get_metadata(data, info) if "lon" in data.dims: data.rename({"lon": "x"}) if "lat" in data.dims: data.rename({"lat": "y"}) return data
[docs] def get_area_def(self, dsid): """Flip data up/down and define equirectangular AreaDefintion.""" flip_lat = np.flipud(self["latArr"]) latlon = np.meshgrid(self["lonArr"], flip_lat) width = self["lonArr/shape"][0] height = self["latArr/shape"][0] lower_left_x = latlon[0][height-1][0] lower_left_y = latlon[1][height-1][0] upper_right_y = latlon[1][0][width-1] upper_right_x = latlon[0][0][width-1] area_extent = (lower_left_x, lower_left_y, upper_right_x, upper_right_y) description = "MIMIC TPW WGS84" area_id = "mimic" proj_id = "World Geodetic System 1984" projection = "EPSG:4326" area_def = AreaDefinition(area_id, description, proj_id, projection, width, height, area_extent, ) return area_def
[docs] def get_metadata(self, data, info): """Get general metadata for file.""" metadata = {} metadata.update(data.attrs) metadata.update(info) metadata.update({ "platform_shortname": "aggregated microwave", "sensor": "mimic", "start_time": self.start_time, "end_time": self.end_time, }) metadata.update(self[info.get("file_key")].variable.attrs) return metadata
@property def start_time(self): """Start timestamp of the dataset determined from yaml.""" return self.filename_info["start_time"] @property def end_time(self): """End timestamp of the dataset same as start_time.""" return self.filename_info.get("end_time", self.start_time) @property def sensor_name(self): """Sensor name.""" return self["sensor"]