#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2016-2022 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/>.
"""Base classes and utilities for all readers configured by YAML files."""
import glob
import itertools
import logging
import os
import warnings
from abc import ABCMeta, abstractmethod
from collections import OrderedDict, deque
from contextlib import suppress
from fnmatch import fnmatch
from weakref import WeakValueDictionary
import numpy as np
import xarray as xr
import yaml
from pyresample.boundary import AreaDefBoundary, Boundary
from pyresample.geometry import AreaDefinition, StackedAreaDefinition, SwathDefinition
from trollsift.parser import globify, parse
try:
from yaml import CLoader as Loader
except ImportError:
from yaml import Loader # type: ignore
from satpy import DatasetDict
from satpy._compat import cache
from satpy.aux_download import DataDownloadMixin
from satpy.dataset import DataID, DataQuery, get_key
from satpy.dataset.dataid import default_co_keys_config, default_id_keys_config, get_keys_from_config
from satpy.resample import add_crs_xy_coords, get_area_def
from satpy.utils import recursive_dict_update
logger = logging.getLogger(__name__)
[docs]
def listify_string(something):
"""Take *something* and make it a list.
*something* is either a list of strings or a string, in which case the
function returns a list containing the string.
If *something* is None, an empty list is returned.
"""
if isinstance(something, str):
return [something]
if something is not None:
return list(something)
return list()
[docs]
def _get_filebase(path, pattern):
"""Get the end of *path* of same length as *pattern*."""
# convert any `/` on Windows to `\\`
path = os.path.normpath(path)
# A pattern can include directories
tail_len = len(pattern.split(os.path.sep))
return os.path.join(*str(path).split(os.path.sep)[-tail_len:])
[docs]
def _match_filenames(filenames, pattern):
"""Get the filenames matching *pattern*."""
matching = set()
glob_pat = globify(pattern)
for filename in filenames:
if fnmatch(_get_filebase(filename, pattern), glob_pat):
matching.add(filename)
return matching
[docs]
def _verify_reader_info_assign_config_files(config, config_files):
try:
reader_info = config["reader"]
except KeyError:
raise KeyError(
"Malformed config file {}: missing reader 'reader'".format(
config_files))
else:
reader_info["config_files"] = config_files
[docs]
def load_yaml_configs(*config_files, loader=Loader):
"""Merge a series of YAML reader configuration files.
Args:
*config_files (str): One or more pathnames
to YAML-based reader configuration files that will be merged
to create a single configuration.
loader: Yaml loader object to load the YAML with. Defaults to
`CLoader` if libyaml is available, `Loader` otherwise.
Returns: dict
Dictionary representing the entire YAML configuration with the
addition of `config['reader']['config_files']` (the list of
YAML pathnames that were merged).
"""
config = {}
logger.debug("Reading %s", str(config_files))
for config_file in config_files:
with open(config_file, "r", encoding="utf-8") as fd:
config = recursive_dict_update(config, yaml.load(fd, Loader=loader))
_verify_reader_info_assign_config_files(config, config_files)
return config
[docs]
class AbstractYAMLReader(metaclass=ABCMeta):
"""Base class for all readers that use YAML configuration files.
This class should only be used in rare cases. Its child class
`FileYAMLReader` should be used in most cases.
"""
def __init__(self, config_dict):
"""Load information from YAML configuration file about how to read data files."""
if isinstance(config_dict, str):
raise ValueError("Passing config files to create a Reader is "
"deprecated. Use ReaderClass.from_config_files "
"instead.")
self.config = config_dict
self.info = self.config["reader"]
self.name = self.info["name"]
self.file_patterns = []
for file_type, filetype_info in self.config["file_types"].items():
filetype_info.setdefault("file_type", file_type)
# correct separator if needed
file_patterns = [os.path.join(*pattern.split("/"))
for pattern in filetype_info["file_patterns"]]
filetype_info["file_patterns"] = file_patterns
self.file_patterns.extend(file_patterns)
if "sensors" in self.info and not isinstance(self.info["sensors"], (list, tuple)):
self.info["sensors"] = [self.info["sensors"]]
self.datasets = self.config.get("datasets", {})
self._id_keys = self.info.get("data_identification_keys", default_id_keys_config)
self._co_keys = self.info.get("coord_identification_keys", default_co_keys_config)
self.info["filenames"] = []
self.all_ids = {}
self.load_ds_ids_from_config()
[docs]
@classmethod
def from_config_files(cls, *config_files, **reader_kwargs):
"""Create a reader instance from one or more YAML configuration files."""
config_dict = load_yaml_configs(*config_files)
return config_dict["reader"]["reader"](config_dict, **reader_kwargs)
@property
def sensor_names(self):
"""Names of sensors whose data is being loaded by this reader."""
return self.info["sensors"] or []
@property
def all_dataset_ids(self):
"""Get DataIDs of all datasets known to this reader."""
return self.all_ids.keys()
@property
def all_dataset_names(self):
"""Get names of all datasets known to this reader."""
# remove the duplicates from various calibration and resolutions
return set(ds_id["name"] for ds_id in self.all_dataset_ids)
@property
def available_dataset_ids(self):
"""Get DataIDs that are loadable by this reader."""
logger.warning(
"Available datasets are unknown, returning all datasets...")
return self.all_dataset_ids
@property
def available_dataset_names(self):
"""Get names of datasets that are loadable by this reader."""
return (ds_id["name"] for ds_id in self.available_dataset_ids)
@property
@abstractmethod
def start_time(self):
"""Start time of the reader."""
@property
@abstractmethod
def end_time(self):
"""End time of the reader."""
[docs]
@abstractmethod
def filter_selected_filenames(self, filenames):
"""Filter provided filenames by parameters in reader configuration.
Returns: iterable of usable files
"""
[docs]
@abstractmethod
def load(self, dataset_keys):
"""Load *dataset_keys*."""
[docs]
def supports_sensor(self, sensor):
"""Check if *sensor* is supported.
Returns True is *sensor* is None.
"""
if sensor and not (set(self.info.get("sensors")) &
set(listify_string(sensor))):
return False
return True
[docs]
def select_files_from_directory(
self, directory=None, fs=None):
"""Find files for this reader in *directory*.
If directory is None or '', look in the current directory.
Searches the local file system by default. Can search on a remote
filesystem by passing an instance of a suitable implementation of
``fsspec.spec.AbstractFileSystem``.
Args:
directory (Optional[str]): Path to search.
fs (Optional[FileSystem]): fsspec FileSystem implementation to use.
Defaults to None, using local file
system.
Returns:
list of strings describing matching files
"""
filenames = set()
if directory is None:
directory = ""
# all the glob patterns that we are going to look at
all_globs = {os.path.join(directory, globify(pattern))
for pattern in self.file_patterns}
# custom filesystem or not
if fs is None:
matcher = glob.iglob
else:
matcher = fs.glob
# get all files matching these patterns
for glob_pat in all_globs:
filenames.update(matcher(glob_pat))
return filenames
[docs]
def select_files_from_pathnames(self, filenames):
"""Select the files from *filenames* this reader can handle."""
selected_filenames = []
filenames = set(filenames) # make a copy of the inputs
for pattern in self.file_patterns:
matching = _match_filenames(filenames, pattern)
filenames -= matching
for fname in matching:
if fname not in selected_filenames:
selected_filenames.append(fname)
if len(selected_filenames) == 0:
logger.warning("No filenames found for reader: %s", self.name)
return selected_filenames
[docs]
def get_dataset_key(self, key, **kwargs):
"""Get the fully qualified `DataID` matching `key`.
See `satpy.readers.get_key` for more information about kwargs.
"""
return get_key(key, self.all_ids.keys(), **kwargs)
[docs]
def load_ds_ids_from_config(self):
"""Get the dataset ids from the config."""
ids = []
for dataset in self.datasets.values():
# xarray doesn't like concatenating attributes that are lists
# https://github.com/pydata/xarray/issues/2060
if "coordinates" in dataset and \
isinstance(dataset["coordinates"], list):
dataset["coordinates"] = tuple(dataset["coordinates"])
id_keys = get_keys_from_config(self._id_keys, dataset)
# Build each permutation/product of the dataset
id_kwargs = self._build_id_permutations(dataset, id_keys)
for id_params in itertools.product(*id_kwargs):
dsid = DataID(id_keys, **dict(zip(id_keys, id_params)))
ids.append(dsid)
# create dataset infos specifically for this permutation
ds_info = dataset.copy()
for key in dsid.keys():
if isinstance(ds_info.get(key), dict):
with suppress(KeyError):
# KeyError is suppressed in case the key does not represent interesting metadata,
# eg a custom type
ds_info.update(ds_info[key][dsid.get(key)])
# this is important for wavelength which was converted
# to a tuple
ds_info[key] = dsid.get(key)
self.all_ids[dsid] = ds_info
return ids
[docs]
def _build_id_permutations(self, dataset, id_keys):
"""Build each permutation/product of the dataset."""
id_kwargs = []
for key, idval in id_keys.items():
val = dataset.get(key, idval.get("default") if idval is not None else None)
val_type = None
if idval is not None:
val_type = idval.get("type")
if val_type is not None and issubclass(val_type, tuple):
# special case: wavelength can be [min, nominal, max]
# but is still considered 1 option
id_kwargs.append((val,))
elif isinstance(val, (list, tuple, set)):
# this key has multiple choices
# (ex. 250 meter, 500 meter, 1000 meter resolutions)
id_kwargs.append(val)
elif isinstance(val, dict):
id_kwargs.append(val.keys())
else:
# this key only has one choice so make it a one
# item iterable
id_kwargs.append((val,))
return id_kwargs
[docs]
class FileYAMLReader(AbstractYAMLReader, DataDownloadMixin):
"""Primary reader base class that is configured by a YAML file.
This class uses the idea of per-file "file handler" objects to read file
contents and determine what is available in the file. This differs from
the base :class:`AbstractYAMLReader` which does not depend on individual
file handler objects. In almost all cases this class should be used over
its base class and can be used as a reader by itself and requires no
subclassing.
"""
# WeakValueDictionary objects must be created at the class level or else
# dask will not be able to serialize them on a distributed environment
_coords_cache: WeakValueDictionary = WeakValueDictionary()
def __init__(self,
config_dict,
filter_parameters=None,
filter_filenames=True,
**kwargs):
"""Set up initial internal storage for loading file data."""
super(FileYAMLReader, self).__init__(config_dict)
self.file_handlers = {}
self.available_ids = {}
self.filter_filenames = self.info.get("filter_filenames", filter_filenames)
self.filter_parameters = filter_parameters or {}
self.register_data_files()
@property
def sensor_names(self):
"""Names of sensors whose data is being loaded by this reader."""
if not self.file_handlers:
return self.info["sensors"]
file_handlers = (handlers[0] for handlers in
self.file_handlers.values())
sensor_names = set()
for fh in file_handlers:
try:
sensor_names.update(fh.sensor_names)
except NotImplementedError:
continue
if not sensor_names:
return self.info["sensors"]
return sorted(sensor_names)
@property
def available_dataset_ids(self):
"""Get DataIDs that are loadable by this reader."""
return self.available_ids.keys()
@property
def start_time(self):
"""Start time of the earlier file used by this reader."""
if not self.file_handlers:
raise RuntimeError("Start time unknown until files are selected")
return min(x[0].start_time for x in self.file_handlers.values())
@property
def end_time(self):
"""End time of the latest file used by this reader."""
if not self.file_handlers:
raise RuntimeError("End time unknown until files are selected")
return max(x[-1].end_time for x in self.file_handlers.values())
[docs]
@staticmethod
def check_file_covers_area(file_handler, check_area):
"""Check if the file covers the current area.
If the file doesn't provide any bounding box information or 'area'
was not provided in `filter_parameters`, the check returns True.
"""
try:
gbb = Boundary(*file_handler.get_bounding_box())
except NotImplementedError as err:
logger.debug("Bounding box computation not implemented: %s",
str(err))
else:
abb = AreaDefBoundary(get_area_def(check_area), frequency=1000)
intersection = gbb.contour_poly.intersection(abb.contour_poly)
if not intersection:
return False
return True
[docs]
def find_required_filehandlers(self, requirements, filename_info):
"""Find the necessary file handlers for the given requirements.
We assume here requirements are available.
Raises:
KeyError, if no handler for the given requirements is available.
RuntimeError, if there is a handler for the given requirements,
but it doesn't match the filename info.
"""
req_fh = []
filename_info = set(filename_info.items())
if requirements:
for requirement in requirements:
for fhd in self.file_handlers[requirement]:
if set(fhd.filename_info.items()).issubset(filename_info):
req_fh.append(fhd)
break
else:
raise RuntimeError("No matching requirement file of type "
"{}".format(requirement))
# break everything and continue to next
# filetype!
return req_fh
[docs]
def sorted_filetype_items(self):
"""Sort the instance's filetypes in using order."""
processed_types = []
file_type_items = deque(self.config["file_types"].items())
while len(file_type_items):
filetype, filetype_info = file_type_items.popleft()
requirements = filetype_info.get("requires")
if requirements is not None:
# requirements have not been processed yet -> wait
missing = [req for req in requirements
if req not in processed_types]
if missing:
file_type_items.append((filetype, filetype_info))
continue
processed_types.append(filetype)
yield filetype, filetype_info
[docs]
@staticmethod
def filename_items_for_filetype(filenames, filetype_info):
"""Iterate over the filenames matching *filetype_info*."""
if not isinstance(filenames, set):
# we perform set operations later on to improve performance
filenames = set(filenames)
for pattern in filetype_info["file_patterns"]:
matched_files = set()
matches = _match_filenames(filenames, pattern)
for filename in matches:
try:
filename_info = parse(
pattern, _get_filebase(filename, pattern))
except ValueError:
logger.debug("Can't parse %s with %s.", filename, pattern)
continue
matched_files.add(filename)
yield filename, filename_info
filenames -= matched_files
[docs]
def _new_filehandler_instances(self, filetype_info, filename_items, fh_kwargs=None):
"""Generate new filehandler instances."""
requirements = filetype_info.get("requires")
filetype_cls = filetype_info["file_reader"]
if fh_kwargs is None:
fh_kwargs = {}
for filename, filename_info in filename_items:
try:
req_fh = self.find_required_filehandlers(requirements,
filename_info)
except KeyError as req:
msg = "No handler for reading requirement {} for {}".format(
req, filename)
warnings.warn(msg, stacklevel=4)
continue
except RuntimeError as err:
warnings.warn(str(err) + " for {}".format(filename), stacklevel=4)
continue
yield filetype_cls(filename, filename_info, filetype_info, *req_fh, **fh_kwargs)
[docs]
def time_matches(self, fstart, fend):
"""Check that a file's start and end time mtach filter_parameters of this reader."""
start_time = self.filter_parameters.get("start_time")
end_time = self.filter_parameters.get("end_time")
fend = fend or fstart
if start_time and fend and fend < start_time:
return False
if end_time and fstart and fstart > end_time:
return False
return True
[docs]
def filter_filenames_by_info(self, filename_items):
"""Filter out file using metadata from the filenames.
Currently only uses start and end time. If only start time is available
from the filename, keep all the filename that have a start time before
the requested end time.
"""
for filename, filename_info in filename_items:
fend = filename_info.get("end_time")
fstart = filename_info.setdefault("start_time", fend)
if fend and fend < fstart:
# correct for filenames with 1 date and 2 times
fend = fend.replace(year=fstart.year,
month=fstart.month,
day=fstart.day)
filename_info["end_time"] = fend
if self.metadata_matches(filename_info):
yield filename, filename_info
[docs]
def filter_selected_filenames(self, filenames):
"""Filter provided files based on metadata in the filename."""
if not isinstance(filenames, set):
# we perform set operations later on to improve performance
filenames = set(filenames)
for _, filetype_info in self.sorted_filetype_items():
filename_iter = self.filename_items_for_filetype(filenames,
filetype_info)
if self.filter_filenames:
filename_iter = self.filter_filenames_by_info(filename_iter)
for fn, _ in filename_iter:
yield fn
[docs]
def _new_filehandlers_for_filetype(self, filetype_info, filenames, fh_kwargs=None):
"""Create filehandlers for a given filetype."""
filename_iter = self.filename_items_for_filetype(filenames,
filetype_info)
if self.filter_filenames:
# preliminary filter of filenames based on start/end time
# to reduce the number of files to open
filename_iter = self.filter_filenames_by_info(filename_iter)
filehandler_iter = self._new_filehandler_instances(filetype_info,
filename_iter,
fh_kwargs=fh_kwargs)
filtered_iter = self.filter_fh_by_metadata(filehandler_iter)
return list(filtered_iter)
[docs]
def create_filehandlers(self, filenames, fh_kwargs=None):
"""Organize the filenames into file types and create file handlers."""
filenames = list(OrderedDict.fromkeys(filenames))
logger.debug("Assigning to %s: %s", self.info["name"], filenames)
self.info.setdefault("filenames", []).extend(filenames)
filename_set = set(filenames)
created_fhs = {}
# load files that we know about by creating the file handlers
for filetype, filetype_info in self.sorted_filetype_items():
filehandlers = self._new_filehandlers_for_filetype(filetype_info,
filename_set,
fh_kwargs=fh_kwargs)
if filehandlers:
created_fhs[filetype] = filehandlers
self.file_handlers[filetype] = sorted(
self.file_handlers.get(filetype, []) + filehandlers,
key=lambda fhd: (fhd.start_time, fhd.filename))
# load any additional dataset IDs determined dynamically from the file
# and update any missing metadata that only the file knows
self.update_ds_ids_from_file_handlers()
return created_fhs
[docs]
def _file_handlers_available_datasets(self):
"""Generate a series of available dataset information.
This is done by chaining file handler's
:meth:`satpy.readers.file_handlers.BaseFileHandler.available_datasets`
together. See that method's documentation for more information.
Returns:
Generator of (bool, dict) where the boolean tells whether the
current dataset is available from any of the file handlers. The
boolean can also be None in the case where no loaded file handler
is configured to load the dataset. The
dictionary is the metadata provided either by the YAML
configuration files or by the file handler itself if it is a new
dataset. The file handler may have also supplemented or modified
the information.
"""
# flatten all file handlers in to one list
flat_fhs = (fh for fhs in self.file_handlers.values() for fh in fhs)
id_values = list(self.all_ids.values())
configured_datasets = ((None, ds_info) for ds_info in id_values)
for fh in flat_fhs:
# chain the 'available_datasets' methods together by calling the
# current file handler's method with the previous ones result
configured_datasets = fh.available_datasets(configured_datasets=configured_datasets)
return configured_datasets
[docs]
def update_ds_ids_from_file_handlers(self):
"""Add or modify available dataset information.
Each file handler is consulted on whether or not it can load the
dataset with the provided information dictionary.
See
:meth:`satpy.readers.file_handlers.BaseFileHandler.available_datasets`
for more information.
"""
avail_datasets = self._file_handlers_available_datasets()
new_ids = {}
for is_avail, ds_info in avail_datasets:
# especially from the yaml config
coordinates = ds_info.get("coordinates")
if isinstance(coordinates, list):
# xarray doesn't like concatenating attributes that are
# lists: https://github.com/pydata/xarray/issues/2060
ds_info["coordinates"] = tuple(ds_info["coordinates"])
ds_info.setdefault("modifiers", tuple()) # default to no mods
# Create DataID for this dataset
ds_id = DataID(self._id_keys, **ds_info)
# all datasets
new_ids[ds_id] = ds_info
# available datasets
# False == we have the file type but it doesn't have this dataset
# None == we don't have the file type object to ask
if is_avail:
self.available_ids[ds_id] = ds_info
self.all_ids = new_ids
[docs]
@staticmethod
def _load_dataset(dsid, ds_info, file_handlers, dim="y", **kwargs):
"""Load only a piece of the dataset."""
slice_list = []
failure = True
for fh in file_handlers:
try:
projectable = fh.get_dataset(dsid, ds_info)
if projectable is not None:
slice_list.append(projectable)
failure = False
except KeyError:
logger.warning("Failed to load {} from {}".format(dsid, fh),
exc_info=True)
if failure:
raise KeyError(
"Could not load {} from any provided files".format(dsid))
if dim not in slice_list[0].dims:
return slice_list[0]
res = xr.concat(slice_list, dim=dim)
combined_info = file_handlers[0].combine_info(
[p.attrs for p in slice_list])
res.attrs = combined_info
return res
[docs]
def _load_dataset_data(self, file_handlers, dsid, **kwargs):
ds_info = self.all_ids[dsid]
proj = self._load_dataset(dsid, ds_info, file_handlers, **kwargs)
# FIXME: areas could be concatenated here
# Update the metadata
proj.attrs["start_time"] = file_handlers[0].start_time
proj.attrs["end_time"] = file_handlers[-1].end_time
proj.attrs["reader"] = self.name
return proj
[docs]
def _preferred_filetype(self, filetypes):
"""Get the preferred filetype out of the *filetypes* list.
At the moment, it just returns the first filetype that has been loaded.
"""
if not isinstance(filetypes, list):
filetypes = [filetypes]
# look through the file types and use the first one that we have loaded
for filetype in filetypes:
if filetype in self.file_handlers:
return filetype
return None
[docs]
def _load_area_def(self, dsid, file_handlers, **kwargs):
"""Load the area definition of *dsid*."""
return _load_area_def(dsid, file_handlers)
[docs]
def _get_file_handlers(self, dsid):
"""Get the file handler to load this dataset."""
ds_info = self.all_ids[dsid]
filetype = self._preferred_filetype(ds_info["file_type"])
if filetype is None:
logger.warning("Required file type '%s' not found or loaded for "
"'%s'", ds_info["file_type"], dsid["name"])
else:
return self.file_handlers[filetype]
[docs]
def _load_dataset_area(self, dsid, file_handlers, coords, **kwargs):
"""Get the area for *dsid*."""
try:
return self._load_area_def(dsid, file_handlers, **kwargs)
except NotImplementedError:
if any(x is None for x in coords):
logger.warning(
"Failed to load coordinates for '{}'".format(dsid))
return None
area = self._make_area_from_coords(coords)
if area is None:
logger.debug("No coordinates found for %s", str(dsid))
return area
[docs]
def _make_area_from_coords(self, coords):
"""Create an appropriate area with the given *coords*."""
if len(coords) == 2:
lons, lats = self._get_lons_lats_from_coords(coords)
sdef = self._make_swath_definition_from_lons_lats(lons, lats)
return sdef
if len(coords) != 0:
raise NameError("Don't know what to do with coordinates " + str(
coords))
[docs]
def _get_lons_lats_from_coords(self, coords):
"""Get lons and lats from the coords list."""
lons, lats = None, None
for coord in coords:
if coord.attrs.get("standard_name") == "longitude":
lons = coord
elif coord.attrs.get("standard_name") == "latitude":
lats = coord
if lons is None or lats is None:
raise ValueError("Missing longitude or latitude coordinate: " + str(coords))
return lons, lats
[docs]
def _make_swath_definition_from_lons_lats(self, lons, lats):
"""Make a swath definition instance from lons and lats."""
key = None
try:
key = (lons.data.name, lats.data.name)
sdef = FileYAMLReader._coords_cache.get(key)
except AttributeError:
sdef = None
if sdef is None:
sdef = SwathDefinition(lons, lats)
sensor_str = "_".join(self.info["sensors"])
shape_str = "_".join(map(str, lons.shape))
sdef.name = "{}_{}_{}_{}".format(sensor_str, shape_str,
lons.attrs.get("name", lons.name),
lats.attrs.get("name", lats.name))
if key is not None:
FileYAMLReader._coords_cache[key] = sdef
return sdef
[docs]
def _load_dataset_with_area(self, dsid, coords, **kwargs):
"""Load *dsid* and its area if available."""
file_handlers = self._get_file_handlers(dsid)
if not file_handlers:
return
try:
ds = self._load_dataset_data(file_handlers, dsid, **kwargs)
except (KeyError, ValueError) as err:
logger.exception("Could not load dataset '%s': %s", dsid, str(err))
return None
coords = self._assign_coords_from_dataarray(coords, ds)
area = self._load_dataset_area(dsid, file_handlers, coords, **kwargs)
if area is not None:
ds.attrs["area"] = area
ds = add_crs_xy_coords(ds, area)
return ds
[docs]
@staticmethod
def _assign_coords_from_dataarray(coords, ds):
"""Assign coords from the *ds* dataarray if needed."""
if not coords:
coords = []
for coord in ds.coords.values():
if coord.attrs.get("standard_name") in ["longitude", "latitude"]:
coords.append(coord)
return coords
[docs]
def _load_ancillary_variables(self, datasets, **kwargs):
"""Load the ancillary variables of `datasets`."""
all_av_ids = self._gather_ancillary_variables_ids(datasets)
loadable_av_ids = [av_id for av_id in all_av_ids if av_id not in datasets]
if not all_av_ids:
return
if loadable_av_ids:
self.load(loadable_av_ids, previous_datasets=datasets, **kwargs)
for dataset in datasets.values():
new_vars = []
for av_id in dataset.attrs.get("ancillary_variables", []):
if isinstance(av_id, DataID):
new_vars.append(datasets[av_id])
else:
new_vars.append(av_id)
dataset.attrs["ancillary_variables"] = new_vars
[docs]
def _gather_ancillary_variables_ids(self, datasets):
"""Gather ancillary variables' ids.
This adds/modifies the dataset's `ancillary_variables` attr.
"""
all_av_ids = set()
for dataset in datasets.values():
ancillary_variables = dataset.attrs.get("ancillary_variables", [])
if not isinstance(ancillary_variables, (list, tuple, set)):
ancillary_variables = ancillary_variables.split(" ")
av_ids = []
for key in ancillary_variables:
try:
av_ids.append(self.get_dataset_key(key))
except KeyError:
logger.warning("Can't load ancillary dataset %s", str(key))
all_av_ids |= set(av_ids)
dataset.attrs["ancillary_variables"] = av_ids
return all_av_ids
[docs]
def get_dataset_key(self, key, available_only=False, **kwargs):
"""Get the fully qualified `DataID` matching `key`.
This will first search through available DataIDs, datasets that
should be possible to load, and fallback to "known" datasets, those
that are configured but aren't loadable from the provided files.
Providing ``available_only=True`` will stop this fallback behavior
and raise a ``KeyError`` exception if no available dataset is found.
Args:
key (str, float, DataID, DataQuery): Key to search for in this reader.
available_only (bool): Search only loadable datasets for the
provided key. Loadable datasets are always searched first,
but if ``available_only=False`` (default) then all known
datasets will be searched.
kwargs: See :func:`satpy.readers.get_key` for more information about
kwargs.
Returns:
Best matching DataID to the provided ``key``.
Raises:
KeyError: if no key match is found.
"""
try:
return get_key(key, self.available_dataset_ids, **kwargs)
except KeyError:
if available_only:
raise
return get_key(key, self.all_dataset_ids, **kwargs)
[docs]
def load(self, dataset_keys, previous_datasets=None, **kwargs):
"""Load `dataset_keys`.
If `previous_datasets` is provided, do not reload those.
"""
all_datasets = previous_datasets or DatasetDict()
datasets = DatasetDict()
# Include coordinates in the list of datasets to load
dsids = [self.get_dataset_key(ds_key) for ds_key in dataset_keys]
coordinates = self._get_coordinates_for_dataset_keys(dsids)
all_dsids = list(set().union(*coordinates.values())) + dsids
for dsid in all_dsids:
if dsid in all_datasets:
continue
coords = [all_datasets.get(cid, None)
for cid in coordinates.get(dsid, [])]
ds = self._load_dataset_with_area(dsid, coords, **kwargs)
if ds is not None:
all_datasets[dsid] = ds
if dsid in dsids:
datasets[dsid] = ds
self._load_ancillary_variables(all_datasets, **kwargs)
return datasets
[docs]
def _get_coordinates_for_dataset_keys(self, dsids):
"""Get all coordinates."""
coordinates = {}
for dsid in dsids:
cids = self._get_coordinates_for_dataset_key(dsid)
coordinates.setdefault(dsid, []).extend(cids)
return coordinates
[docs]
def _get_coordinates_for_dataset_key(self, dsid):
"""Get the coordinate dataset keys for *dsid*."""
ds_info = self.all_ids[dsid]
cids = []
for cinfo in ds_info.get("coordinates", []):
if not isinstance(cinfo, dict):
cinfo = {"name": cinfo}
for key in self._co_keys:
if key == "name":
continue
if key in ds_info:
if ds_info[key] is not None:
cinfo[key] = ds_info[key]
cid = DataQuery.from_dict(cinfo)
cids.append(self.get_dataset_key(cid))
return cids
[docs]
def _load_area_def(dsid, file_handlers):
"""Load the area definition of *dsid*."""
area_defs = [fh.get_area_def(dsid) for fh in file_handlers]
area_defs = [area_def for area_def in area_defs
if area_def is not None]
final_area = StackedAreaDefinition(*area_defs)
return final_area.squeeze()
[docs]
def _set_orientation(dataset, upper_right_corner):
"""Set the orientation of geostationary datasets.
Allows to flip geostationary imagery when loading the datasets.
Example call: scn.load(['VIS008'], upper_right_corner='NE')
Args:
dataset: Dataset to be flipped.
upper_right_corner (str): Direction of the upper right corner of the image after flipping.
Possible options are 'NW', 'NE', 'SW', 'SE', or 'native'.
The common upright image orientation corresponds to 'NE'.
Defaults to 'native' (no flipping is applied).
"""
# do some checks and early returns
if upper_right_corner == "native":
logger.debug("Requested orientation for Dataset {} is 'native' (default). "
"No flipping is applied.".format(dataset.attrs.get("name")))
return dataset
if upper_right_corner not in ["NW", "NE", "SE", "SW", "native"]:
raise ValueError("Target orientation for Dataset {} not recognized. "
"Kwarg upper_right_corner should be "
"'NW', 'NE', 'SW', 'SE' or 'native'.".format(dataset.attrs.get("name", "unknown_name")))
if "area" not in dataset.attrs:
logger.info("Dataset {} is missing the area attribute "
"and will not be flipped.".format(dataset.attrs.get("name", "unknown_name")))
return dataset
if isinstance(dataset.attrs["area"], SwathDefinition):
logger.info("Dataset {} is in a SwathDefinition "
"and will not be flipped.".format(dataset.attrs.get("name", "unknown_name")))
return dataset
projection_type = _get_projection_type(dataset.attrs["area"])
accepted_geos_proj_types = ["Geostationary Satellite (Sweep Y)", "Geostationary Satellite (Sweep X)"]
if projection_type not in accepted_geos_proj_types:
logger.info("Dataset {} is not in one of the known geostationary projections {} "
"and cannot be flipped.".format(dataset.attrs.get("name", "unknown_name"),
accepted_geos_proj_types))
return dataset
target_eastright, target_northup = _get_target_scene_orientation(upper_right_corner)
area_extents_to_update = _get_dataset_area_extents_array(dataset.attrs["area"])
current_eastright, current_northup = _get_current_scene_orientation(area_extents_to_update)
if target_northup == current_northup and target_eastright == current_eastright:
logger.info("Dataset {} is already in the target orientation "
"and will not be flipped.".format(dataset.attrs.get("name", "unknown_name")))
return dataset
if target_northup != current_northup:
dataset, area_extents_to_update = _flip_dataset_data_and_area_extents(dataset, area_extents_to_update,
"upsidedown")
if target_eastright != current_eastright:
dataset, area_extents_to_update = _flip_dataset_data_and_area_extents(dataset, area_extents_to_update,
"leftright")
dataset.attrs["area"] = _get_new_flipped_area_definition(dataset.attrs["area"], area_extents_to_update,
flip_areadef_stacking=target_northup != current_northup)
return dataset
[docs]
def _get_projection_type(dataset_area_attr):
"""Get the projection type from the crs coordinate operation method name."""
if isinstance(dataset_area_attr, StackedAreaDefinition):
# assumes all AreaDefinitions in a tackedAreaDefinition have the same projection
area_crs = dataset_area_attr.defs[0].crs
else:
area_crs = dataset_area_attr.crs
return area_crs.coordinate_operation.method_name
[docs]
def _get_target_scene_orientation(upper_right_corner):
"""Get the target scene orientation from the target upper_right_corner.
'NE' corresponds to target_eastright and target_northup being True.
"""
target_northup = upper_right_corner in ["NW", "NE"]
target_eastright = upper_right_corner in ["NE", "SE"]
return target_eastright, target_northup
[docs]
def _get_dataset_area_extents_array(dataset_area_attr):
"""Get dataset area extents in a numpy array for further flipping."""
if isinstance(dataset_area_attr, StackedAreaDefinition):
# array of area extents if the Area is a StackedAreaDefinition
area_extents_to_update = np.asarray([list(area_def.area_extent) for area_def in dataset_area_attr.defs])
else:
# array with a single item if Area is in one piece
area_extents_to_update = np.asarray([list(dataset_area_attr.area_extent)])
return area_extents_to_update
[docs]
def _get_current_scene_orientation(area_extents_to_update):
"""Get the current scene orientation from the area_extents."""
# assumes all AreaDefinitions inside a StackedAreaDefinition have the same orientation
current_northup = area_extents_to_update[0, 3] - area_extents_to_update[0, 1] > 0
current_eastright = area_extents_to_update[0, 2] - area_extents_to_update[0, 0] > 0
return current_eastright, current_northup
[docs]
def _flip_dataset_data_and_area_extents(dataset, area_extents_to_update, flip_direction):
"""Flip the data and area extents array for a dataset."""
logger.info("Flipping Dataset {} {}.".format(dataset.attrs.get("name", "unknown_name"), flip_direction))
if flip_direction == "upsidedown":
dataset = dataset[::-1, :]
area_extents_to_update[:, [1, 3]] = area_extents_to_update[:, [3, 1]]
elif flip_direction == "leftright":
dataset = dataset[:, ::-1]
area_extents_to_update[:, [0, 2]] = area_extents_to_update[:, [2, 0]]
else:
raise ValueError("Flip direction not recognized. Should be either 'upsidedown' or 'leftright'.")
return dataset, area_extents_to_update
[docs]
def _get_new_flipped_area_definition(dataset_area_attr, area_extents_to_update, flip_areadef_stacking):
"""Get a new area definition with updated area_extents for flipped geostationary datasets."""
if len(area_extents_to_update) == 1:
# just update the area extents using the AreaDefinition copy method
new_area_def = dataset_area_attr.copy(area_extent=area_extents_to_update[0])
else:
# update the stacked AreaDefinitions singularly
new_area_defs_to_stack = []
for n_area_def, area_def in enumerate(dataset_area_attr.defs):
new_area_defs_to_stack.append(area_def.copy(area_extent=area_extents_to_update[n_area_def]))
# flip the order of stacking if the area is upside down
if flip_areadef_stacking:
new_area_defs_to_stack = new_area_defs_to_stack[::-1]
# regenerate the StackedAreaDefinition
new_area_def = StackedAreaDefinition(*new_area_defs_to_stack)
return new_area_def
[docs]
class GEOFlippableFileYAMLReader(FileYAMLReader):
"""Reader for flippable geostationary data."""
[docs]
def _load_dataset_with_area(self, dsid, coords, upper_right_corner="native", **kwargs):
ds = super(GEOFlippableFileYAMLReader, self)._load_dataset_with_area(dsid, coords, **kwargs)
if ds is not None:
ds = _set_orientation(ds, upper_right_corner)
return ds
[docs]
class GEOSegmentYAMLReader(GEOFlippableFileYAMLReader):
"""Reader for segmented geostationary data.
This reader pads the data to full geostationary disk if necessary.
This reader uses an optional ``pad_data`` keyword argument that can be
passed to :meth:`Scene.load` to control if padding is done (True by
default). Passing `pad_data=False` will return data unpadded.
When using this class in a reader's YAML configuration, segmented file
types (files that may have multiple segments) should specify an extra
``expected_segments`` piece of file_type metadata. This tells this reader
how many total segments it should expect when padding data. Alternatively,
the file patterns for a file type can include a ``total_segments``
field which will be used if ``expected_segments`` is not defined. This
will default to 1 segment.
"""
[docs]
def create_filehandlers(self, filenames, fh_kwargs=None):
"""Create file handler objects and determine expected segments for each.
Additionally, sort the filehandlers by segment number to avoid
issues with filenames where start_time or alphabetic sorting does not
produce the correct order.
"""
created_fhs = super(GEOSegmentYAMLReader, self).create_filehandlers(
filenames, fh_kwargs=fh_kwargs)
# add "expected_segments" information
for fhs in created_fhs.values():
for fh in fhs:
# check the filename for total_segments parameter as a fallback
ts = fh.filename_info.get("total_segments", 1)
# if the YAML has segments explicitly specified then use that
fh.filetype_info.setdefault("expected_segments", ts)
# add segment key-values for FCI filehandlers
if "segment" not in fh.filename_info:
fh.filename_info["segment"] = fh.filename_info.get("count_in_repeat_cycle", 1)
self._sort_segment_filehandler_by_segment_number()
return created_fhs
[docs]
def _sort_segment_filehandler_by_segment_number(self):
if hasattr(self, "file_handlers"):
for file_type in self.file_handlers.keys():
self.file_handlers[file_type] = sorted(self.file_handlers[file_type],
key=lambda x: x.filename_info.get("segment", 0))
[docs]
def _load_dataset(self, dsid, ds_info, file_handlers, dim="y", pad_data=True):
"""Load only a piece of the dataset."""
if not pad_data:
return FileYAMLReader._load_dataset(dsid, ds_info,
file_handlers)
counter, expected_segments, slice_list, failure, projectable = \
_find_missing_segments(file_handlers, ds_info, dsid)
if projectable is None or failure:
raise KeyError(
"Could not load {} from any provided files".format(dsid))
filetype = file_handlers[0].filetype_info["file_type"]
self.empty_segment = xr.full_like(projectable, np.nan)
for i, sli in enumerate(slice_list):
if sli is None:
slice_list[i] = self._get_empty_segment(dim=dim, idx=i, filetype=filetype)
while expected_segments > counter:
slice_list.append(self._get_empty_segment(dim=dim, idx=counter, filetype=filetype))
counter += 1
if dim not in slice_list[0].dims:
return slice_list[0]
res = xr.concat(slice_list, dim=dim)
combined_info = file_handlers[0].combine_info(
[p.attrs for p in slice_list])
res.attrs = combined_info
return res
[docs]
def _get_empty_segment(self, **kwargs):
return self.empty_segment
[docs]
def _load_area_def(self, dsid, file_handlers, pad_data=True):
"""Load the area definition of *dsid* with padding."""
if not pad_data:
return _load_area_def(dsid, file_handlers)
return self._load_area_def_with_padding(dsid, file_handlers)
[docs]
def _load_area_def_with_padding(self, dsid, file_handlers):
"""Load the area definition of *dsid* with padding."""
# Pad missing segments between the first available and expected
area_defs = self._pad_later_segments_area(file_handlers, dsid)
# Add missing start segments
area_defs = self._pad_earlier_segments_area(file_handlers, dsid, area_defs)
# Stack the area definitions
area_def = _stack_area_defs(area_defs)
return area_def
[docs]
def _pad_later_segments_area(self, file_handlers, dsid):
"""Pad area definitions for missing segments that are later in sequence than the first available."""
expected_segments = file_handlers[0].filetype_info["expected_segments"]
filetype = file_handlers[0].filetype_info["file_type"]
available_segments = [int(fh.filename_info.get("segment", 1)) for
fh in file_handlers]
area_defs = self._get_segments_areadef_with_later_padded(file_handlers, filetype, dsid, available_segments,
expected_segments)
return area_defs
[docs]
def _get_segments_areadef_with_later_padded(self, file_handlers, filetype, dsid, available_segments,
expected_segments):
seg_size = None
area_defs = {}
for segment in range(available_segments[0], expected_segments + 1):
try:
idx = available_segments.index(segment)
fh = file_handlers[idx]
area = fh.get_area_def(dsid)
except ValueError:
area = self._get_new_areadef_for_padded_segment(area, filetype, seg_size, segment, padding_type="later")
area_defs[segment] = area
seg_size = area.shape
return area_defs
[docs]
def _pad_earlier_segments_area(self, file_handlers, dsid, area_defs):
"""Pad area definitions for missing segments that are earlier in sequence than the first available."""
available_segments = [int(fh.filename_info.get("segment", 1)) for
fh in file_handlers]
area = file_handlers[0].get_area_def(dsid)
seg_size = area.shape
filetype = file_handlers[0].filetype_info["file_type"]
for segment in range(available_segments[0] - 1, 0, -1):
area = self._get_new_areadef_for_padded_segment(area, filetype, seg_size, segment, padding_type="earlier")
area_defs[segment] = area
seg_size = area.shape
return area_defs
[docs]
def _get_new_areadef_for_padded_segment(self, area, filetype, seg_size, segment, padding_type):
logger.debug("Padding to full disk with segment nr. %d", segment)
new_height_px, new_ll_y, new_ur_y = self._get_y_area_extents_for_padded_segment(area, filetype, padding_type,
seg_size, segment)
fill_extent = (area.area_extent[0], new_ll_y,
area.area_extent[2], new_ur_y)
area = AreaDefinition("fill", "fill", "fill", area.crs,
seg_size[1], new_height_px,
fill_extent)
return area
[docs]
def _get_y_area_extents_for_padded_segment(self, area, filetype, padding_type, seg_size, segment):
new_height_proj_coord, new_height_px = self._get_new_areadef_heights(area, seg_size,
segment_n=segment,
filetype=filetype)
if padding_type == "later":
new_ll_y = area.area_extent[1] + new_height_proj_coord
new_ur_y = area.area_extent[1]
elif padding_type == "earlier":
new_ll_y = area.area_extent[3]
new_ur_y = area.area_extent[3] - new_height_proj_coord
else:
raise ValueError("Padding type not recognised.")
return new_height_px, new_ll_y, new_ur_y
[docs]
def _get_new_areadef_heights(self, previous_area, previous_seg_size, **kwargs):
new_height_px = previous_seg_size[0]
new_height_proj_coord = previous_area.area_extent[1] - previous_area.area_extent[3]
return new_height_proj_coord, new_height_px
[docs]
def _stack_area_defs(area_def_dict):
"""Stack given dict of area definitions and return a StackedAreaDefinition."""
area_defs = [area_def_dict[area_def] for
area_def in sorted(area_def_dict.keys())
if area_def is not None]
area_def = StackedAreaDefinition(*area_defs)
area_def = area_def.squeeze()
return area_def
[docs]
def _find_missing_segments(file_handlers, ds_info, dsid):
"""Find missing segments."""
slice_list = []
failure = True
counter = 1
expected_segments = 1
projectable = None
for fh in file_handlers:
if fh.filetype_info["file_type"] in ds_info["file_type"]:
expected_segments = fh.filetype_info["expected_segments"]
while int(fh.filename_info.get("segment", 1)) > counter:
slice_list.append(None)
counter += 1
try:
projectable = fh.get_dataset(dsid, ds_info)
if projectable is not None:
slice_list.append(projectable)
failure = False
counter += 1
except KeyError:
logger.warning("Failed to load %s from %s", str(dsid), str(fh),
exc_info=True)
# The last segment is missing?
if len(slice_list) < expected_segments:
slice_list.append(None)
return counter, expected_segments, slice_list, failure, projectable
[docs]
def _get_empty_segment_with_height(empty_segment, new_height, dim):
"""Get a new empty segment with the specified height."""
if empty_segment.shape[0] > new_height:
# if current empty segment is too tall, slice the DataArray
return empty_segment[:new_height, :]
if empty_segment.shape[0] < new_height:
# if current empty segment is too short, concatenate a slice of the DataArray
return xr.concat([empty_segment, empty_segment[:new_height - empty_segment.shape[0], :]], dim=dim)
return empty_segment
[docs]
class GEOVariableSegmentYAMLReader(GEOSegmentYAMLReader):
"""GEOVariableSegmentYAMLReader for handling segmented GEO products with segments of variable height.
This YAMLReader overrides parts of the GEOSegmentYAMLReader to account for formats where the segments can
have variable heights. It computes the sizes of the padded segments using the information available in the
file(handlers), so that gaps of any size can be filled as needed.
This implementation was motivated by the FCI L1c format, where the segments (called chunks in the FCI world)
can have variable heights. It is however generic, so that any future reader can use it. The requirement
for the reader is to have a method called `get_segment_position_info`, returning a dictionary containing
the positioning info for each segment (see example in
:func:`satpy.readers.fci_l1c_nc.FCIL1cNCFileHandler.get_segment_position_info`).
For more information on please see the documentation of :func:`satpy.readers.yaml_reader.GEOSegmentYAMLReader`.
"""
def __init__(self,
config_dict,
filter_parameters=None,
filter_filenames=True,
**kwargs):
"""Initialise the GEOVariableSegmentYAMLReader object."""
super().__init__(config_dict, filter_parameters, filter_filenames, **kwargs)
self.segment_heights = cache(self._segment_heights)
self.segment_infos = dict()
[docs]
def _collect_segment_position_infos(self, filetype):
# collect the segment positioning infos for all available segments
for fh in self.file_handlers[filetype]:
chk_infos = fh.get_segment_position_info()
chk_infos.update({"segment_nr": fh.filename_info["segment"] - 1})
self.segment_infos[filetype]["available_segment_infos"].append(chk_infos)
[docs]
def _initialise_segment_infos(self, filetype):
# initialise the segment info for this filetype
filetype_fhs_sample = self.file_handlers[filetype][0]
exp_segment_nr = filetype_fhs_sample.filetype_info["expected_segments"]
grid_width_to_grid_type = _get_grid_width_to_grid_type(filetype_fhs_sample.get_segment_position_info())
self.segment_infos.update({filetype: {"available_segment_infos": [],
"expected_segments": exp_segment_nr,
"grid_width_to_grid_type": grid_width_to_grid_type}})
[docs]
def _get_empty_segment(self, dim=None, idx=None, filetype=None):
grid_width = self.empty_segment.shape[1]
segment_height = self.segment_heights(filetype, grid_width)[idx]
return _get_empty_segment_with_height(self.empty_segment, segment_height, dim=dim)
[docs]
def _segment_heights(self, filetype, grid_width):
"""Compute optimal padded segment heights (in number of pixels) based on the location of available segments."""
self._extract_segment_location_dicts(filetype)
grid_type = self.segment_infos[filetype]["grid_width_to_grid_type"][grid_width]
segment_heights = _compute_optimal_missing_segment_heights(self.segment_infos[filetype], grid_type, grid_width)
return segment_heights
[docs]
def _get_new_areadef_heights(self, previous_area, previous_seg_size, segment_n=None, filetype=None):
# retrieve the segment height in number of pixels
grid_width = previous_seg_size[1]
new_height_px = self.segment_heights(filetype, grid_width)[segment_n - 1]
# scale the previous vertical area extent using the new pixel height
prev_area_extent = previous_area.area_extent[1] - previous_area.area_extent[3]
new_height_proj_coord = prev_area_extent * new_height_px / previous_seg_size[0]
return new_height_proj_coord, new_height_px
[docs]
def _get_grid_width_to_grid_type(seg_info):
grid_width_to_grid_type = dict()
for grid_type, grid_type_seg_info in seg_info.items():
grid_width_to_grid_type.update({grid_type_seg_info["grid_width"]: grid_type})
return grid_width_to_grid_type
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def _compute_optimal_missing_segment_heights(seg_infos, grid_type, expected_vertical_size):
# initialise positioning arrays
segment_start_rows, segment_end_rows, segment_heights = _init_positioning_arrays_for_variable_padding(
seg_infos["available_segment_infos"], grid_type, seg_infos["expected_segments"])
# populate start row of first segment and end row of last segment with known values
segment_start_rows[0] = 1
segment_end_rows[seg_infos["expected_segments"] - 1] = expected_vertical_size
# find missing segments and group contiguous missing segments together
missing_segments = np.where(segment_heights == 0)[0]
groups_missing_segments = np.split(missing_segments, np.where(np.diff(missing_segments) > 1)[0] + 1)
for group in groups_missing_segments:
_compute_positioning_data_for_missing_group(segment_start_rows, segment_end_rows, segment_heights, group)
return segment_heights.astype("int")
[docs]
def _compute_positioning_data_for_missing_group(segment_start_rows, segment_end_rows, segment_heights, group):
_populate_group_start_end_row_using_neighbour_segments(group, segment_end_rows, segment_start_rows)
proposed_sizes_missing_segments = _compute_proposed_sizes_of_missing_segments_in_group(group, segment_end_rows,
segment_start_rows)
_populate_start_end_rows_of_missing_segments_with_proposed_sizes(group, proposed_sizes_missing_segments,
segment_start_rows, segment_end_rows,
segment_heights)
[docs]
def _populate_start_end_rows_of_missing_segments_with_proposed_sizes(group, proposed_sizes_missing_segments,
segment_start_rows, segment_end_rows,
segment_heights):
for n in range(len(group)):
# start of first and end of last missing segment have been populated already
if n != 0:
segment_start_rows[group[n]] = segment_start_rows[group[n - 1]] + proposed_sizes_missing_segments[n] + 1
if n != len(group) - 1:
segment_end_rows[group[n]] = segment_start_rows[group[n]] + proposed_sizes_missing_segments[n]
segment_heights[group[n]] = proposed_sizes_missing_segments[n]
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def _compute_proposed_sizes_of_missing_segments_in_group(group, segment_end_rows, segment_start_rows):
size_group_gap = segment_end_rows[group[-1]] - segment_start_rows[group[0]] + 1
proposed_sizes_missing_segments = split_integer_in_most_equal_parts(size_group_gap, len(group))
return proposed_sizes_missing_segments
[docs]
def _populate_group_start_end_row_using_neighbour_segments(group, segment_end_rows, segment_start_rows):
# if group is at the start/end of the full-disk, we know the start/end value already
if segment_start_rows[group[0]] == 0:
_populate_group_start_row_using_previous_segment(group, segment_end_rows, segment_start_rows)
if segment_end_rows[group[-1]] == 0:
_populate_group_end_row_using_later_segment(group, segment_end_rows, segment_start_rows)
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def _populate_group_end_row_using_later_segment(group, segment_end_rows, segment_start_rows):
segment_end_rows[group[-1]] = segment_start_rows[group[-1] + 1] - 1
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def _populate_group_start_row_using_previous_segment(group, segment_end_rows, segment_start_rows):
segment_start_rows[group[0]] = segment_end_rows[group[0] - 1] + 1
[docs]
def _init_positioning_arrays_for_variable_padding(chk_infos, grid_type, exp_segment_nr):
segment_heights = np.zeros(exp_segment_nr)
segment_start_rows = np.zeros(exp_segment_nr)
segment_end_rows = np.zeros(exp_segment_nr)
_populate_positioning_arrays_with_available_segment_info(chk_infos, grid_type, segment_start_rows, segment_end_rows,
segment_heights)
return segment_start_rows, segment_end_rows, segment_heights
[docs]
def _populate_positioning_arrays_with_available_segment_info(chk_infos, grid_type, segment_start_rows, segment_end_rows,
segment_heights):
for chk_info in chk_infos:
current_fh_segment_nr = chk_info["segment_nr"]
segment_heights[current_fh_segment_nr] = chk_info[grid_type]["segment_height"]
segment_start_rows[current_fh_segment_nr] = chk_info[grid_type]["start_position_row"]
segment_end_rows[current_fh_segment_nr] = chk_info[grid_type]["end_position_row"]
[docs]
def split_integer_in_most_equal_parts(x, n):
"""Split an integer number x in n parts that are as equally-sizes as possible."""
if x % n == 0:
return np.repeat(x // n, n).astype("int")
else:
# split the remainder amount over the last remainder parts
remainder = int(x % n)
mod = int(x // n)
ar = np.repeat(mod, n)
ar[-remainder:] = mod + 1
return ar.astype("int")