#!/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 <http://www.gnu.org/licenses/>.
#
#
"""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
http://tropic.ssec.wisc.edu/real-time/mtpw2/credits.html
"""
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
@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"]