#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2014-2018 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/>.
"""HRIT/LRIT format reader.
This module is the base module for all HRIT-based formats. Here, you will find
the common building blocks for hrit reading.
One of the features here is the on-the-fly decompression of hrit files when
compressed hrit files are encountered (files finishing with `.C_`).
"""
import datetime as dt
import logging
import os
import dask
import dask.array as da
import numpy as np
import xarray as xr
from pyresample import geometry
import satpy.readers.utils as utils
from satpy.readers import FSFile
from satpy.readers.eum_base import time_cds_short
from satpy.readers.file_handlers import BaseFileHandler
from satpy.readers.seviri_base import dec10216
logger = logging.getLogger("hrit_base")
common_hdr = np.dtype([("hdr_id", "u1"),
("record_length", ">u2")])
primary_header = np.dtype([("file_type", "u1"),
("total_header_length", ">u4"),
("data_field_length", ">u8")])
image_structure = np.dtype([("number_of_bits_per_pixel", "u1"),
("number_of_columns", ">u2"),
("number_of_lines", ">u2"),
("compression_flag_for_data", "u1")])
image_navigation = np.dtype([("projection_name", "S32"),
("cfac", ">i4"),
("lfac", ">i4"),
("coff", ">i4"),
("loff", ">i4")])
image_data_function = np.dtype([("function", "|S1")])
annotation_header = np.dtype([("annotation", "|S1")])
timestamp_record = np.dtype([("cds_p_field", "u1"),
("timestamp", time_cds_short)])
ancillary_text = np.dtype([("ancillary", "|S1")])
key_header = np.dtype([("key", "|S1")])
base_text_headers = {image_data_function: "image_data_function",
annotation_header: "annotation_header",
ancillary_text: "ancillary_text",
key_header: "key_header"}
base_hdr_map = {0: primary_header,
1: image_structure,
2: image_navigation,
3: image_data_function,
4: annotation_header,
5: timestamp_record,
6: ancillary_text,
7: key_header,
}
[docs]
def decompress(infile):
"""Decompress an XRIT data file and return the decompressed buffer."""
from pyPublicDecompWT import xRITDecompress
# decompress in-memory
with open(infile, mode="rb") as fh:
xrit = xRITDecompress()
xrit.decompress(fh.read())
return xrit.data()
[docs]
class HRITFileHandler(BaseFileHandler):
"""HRIT standard format reader."""
def __init__(self, filename, filename_info, filetype_info, hdr_info):
"""Initialize the reader."""
super(HRITFileHandler, self).__init__(filename, filename_info,
filetype_info)
self.mda = {}
self.hdr_info = hdr_info
self._get_hd(self.hdr_info)
self._start_time = filename_info["start_time"]
self._end_time = self._start_time + dt.timedelta(minutes=15)
[docs]
def _get_hd(self, hdr_info):
"""Open the file, read and get the basic file header info and set the mda dictionary."""
hdr_map, variable_length_headers, text_headers = hdr_info
with utils.generic_open(self.filename, mode="rb") as fp:
total_header_length = 16
while fp.tell() < total_header_length:
hdr_id = get_header_id(fp)
the_type = hdr_map[hdr_id["hdr_id"]]
if the_type in variable_length_headers:
field_length = int((hdr_id["record_length"] - 3) /
the_type.itemsize)
current_hdr = get_header_content(fp, the_type, field_length)
key = variable_length_headers[the_type]
if key in self.mda:
if not isinstance(self.mda[key], list):
self.mda[key] = [self.mda[key]]
self.mda[key].append(current_hdr)
else:
self.mda[key] = current_hdr
elif the_type in text_headers:
field_length = int((hdr_id["record_length"] - 3) /
the_type.itemsize)
char = list(the_type.fields.values())[0][0].char
new_type = np.dtype(char + str(field_length))
current_hdr = get_header_content(fp, new_type)[0]
self.mda[text_headers[the_type]] = current_hdr
else:
current_hdr = get_header_content(fp, the_type)[0]
self.mda.update(
dict(zip(current_hdr.dtype.names, current_hdr)))
total_header_length = self.mda["total_header_length"]
self.mda.setdefault("number_of_bits_per_pixel", 10)
self.mda["projection_parameters"] = {"a": 6378169.00,
"b": 6356583.80,
"h": 35785831.00,
# FIXME: find a reasonable SSP
"SSP_longitude": 0.0}
self.mda["orbital_parameters"] = {}
@property
def observation_start_time(self):
"""Get observation start time."""
return self._start_time
@property
def observation_end_time(self):
"""Get observation end time."""
return self._end_time
@property
def start_time(self):
"""Get start time."""
return self._start_time
@property
def end_time(self):
"""Get end time."""
return self._end_time
[docs]
def get_dataset(self, key, info):
"""Load a dataset."""
# Read bands
data = self.read_band(key, info)
# Convert to xarray
xdata = xr.DataArray(data, dims=["y", "x"])
return xdata
[docs]
def get_xy_from_linecol(self, line, col, offsets, factors):
"""Get the intermediate coordinates from line & col.
Intermediate coordinates are actually the instruments scanning angles.
"""
loff, coff = offsets
lfac, cfac = factors
x__ = (col - coff) / cfac * 2**16
y__ = (line - loff) / lfac * 2**16
return x__, y__
[docs]
def get_area_extent(self, size, offsets, factors, platform_height):
"""Get the area extent of the file."""
nlines, ncols = size
h = platform_height
# count starts at 1
cols = 1 - 0.5
lines = 1 - 0.5
ll_x, ll_y = self.get_xy_from_linecol(lines, cols, offsets, factors)
cols += ncols
lines += nlines
ur_x, ur_y = self.get_xy_from_linecol(lines, cols, offsets, factors)
return (np.deg2rad(ll_x) * h, np.deg2rad(ll_y) * h,
np.deg2rad(ur_x) * h, np.deg2rad(ur_y) * h)
[docs]
def get_area_def(self, dsid):
"""Get the area definition of the band."""
cfac = np.int32(self.mda["cfac"])
lfac = np.int32(self.mda["lfac"])
coff = np.float32(self.mda["coff"])
loff = np.float32(self.mda["loff"])
a = self.mda["projection_parameters"]["a"]
b = self.mda["projection_parameters"]["b"]
h = self.mda["projection_parameters"]["h"]
lon_0 = self.mda["projection_parameters"]["SSP_longitude"]
nlines = int(self.mda["number_of_lines"])
ncols = int(self.mda["number_of_columns"])
area_extent = self.get_area_extent((nlines, ncols),
(loff, coff),
(lfac, cfac),
h)
proj_dict = {"a": float(a),
"b": float(b),
"lon_0": float(lon_0),
"h": float(h),
"proj": "geos",
"units": "m"}
area = geometry.AreaDefinition(
"some_area_name",
"On-the-fly area",
"geosmsg",
proj_dict,
ncols,
nlines,
area_extent)
self.area = area
return area
[docs]
def read_band(self, key, info):
"""Read the data."""
output_dtype, output_shape = self._get_output_info()
return da.from_delayed(_read_data(self.filename, self.mda),
shape=output_shape,
dtype=output_dtype)
[docs]
def _get_output_info(self):
bpp = self.mda["number_of_bits_per_pixel"]
if bpp in [10, 16]:
output_dtype = np.uint16
elif bpp == 8:
output_dtype = np.uint8
else:
raise ValueError(f"Unexpected number of bits per pixel: {bpp}")
output_shape = (self.mda["number_of_lines"], self.mda["number_of_columns"])
return output_dtype, output_shape
@dask.delayed
def _read_data(filename, mda):
return HRITSegment(filename, mda).read_data()
[docs]
class HRITSegment:
"""An HRIT segment with data."""
def __init__(self, filename, mda):
"""Set up the segment."""
self.filename = filename
self.mda = mda
self.lines = mda["number_of_lines"]
self.cols = mda["number_of_columns"]
self.bpp = mda["number_of_bits_per_pixel"]
self.compressed = mda["compression_flag_for_data"] == 1
self.offset = mda["total_header_length"]
self.zipped = os.fspath(filename).endswith(".bz2")
[docs]
def read_data(self):
"""Read the data."""
data = self._read_data_from_file()
if self.bpp == 10:
data = dec10216(data)
data = data.reshape((self.lines, self.cols))
return data
[docs]
def _read_data_from_file(self):
if self._is_file_like():
return self._read_file_like()
return self._read_data_from_disk()
[docs]
def _is_file_like(self):
return isinstance(self.filename, FSFile)
[docs]
def _read_data_from_disk(self):
# For reading the image data, unzip_context is faster than generic_open
dtype, shape = self._get_input_info()
with utils.unzip_context(self.filename) as fn:
if self.compressed:
return np.frombuffer(
decompress(fn),
offset=self.offset,
dtype=dtype,
count=np.prod(shape)
)
else:
return np.fromfile(
fn,
offset=self.offset,
dtype=dtype,
count=np.prod(shape)
)
[docs]
def _read_file_like(self):
# filename is likely to be a file-like object, already in memory
dtype, shape = self._get_input_info()
with utils.generic_open(self.filename, mode="rb") as fp:
no_elements = np.prod(shape)
fp.seek(self.offset)
return np.frombuffer(
fp.read(np.dtype(dtype).itemsize * no_elements),
dtype=np.dtype(dtype),
count=no_elements.item()
).reshape(shape)