# Copyright (c) 2009-2023 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 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/>.
"""Module defining various utilities."""
from __future__ import annotations
import contextlib
import datetime
import logging
import os
import pathlib
import warnings
from contextlib import contextmanager
from copy import deepcopy
from typing import Literal, Mapping, Optional
from urllib.parse import urlparse
import dask.utils
import numpy as np
import xarray as xr
import yaml
from yaml import BaseLoader, UnsafeLoader
from satpy._compat import DTypeLike
_is_logging_on = False
TRACE_LEVEL = 5
logger = logging.getLogger(__name__)
[docs]
def debug_on(deprecation_warnings=True):
"""Turn debugging logging on.
Sets up a StreamHandler to to `sys.stderr` at debug level for all
loggers, such that all debug messages (and log messages with higher
severity) are logged to the standard error stream.
By default, since Satpy 0.26, this also enables the global visibility
of deprecation warnings. This can be suppressed by passing a false
value.
Args:
deprecation_warnings (Optional[bool]): Switch on deprecation warnings.
Defaults to True.
Returns:
None
"""
logging_on(logging.DEBUG)
if deprecation_warnings:
deprecation_warnings_on()
[docs]
def debug_off():
"""Turn debugging logging off.
This disables both debugging logging and the global visibility of
deprecation warnings.
"""
logging_off()
deprecation_warnings_off()
[docs]
@contextlib.contextmanager
def debug(deprecation_warnings=True):
"""Context manager to temporarily set debugging on.
Example::
>>> with satpy.utils.debug():
... code_here()
Args:
deprecation_warnings (Optional[bool]): Switch on deprecation warnings.
Defaults to True.
"""
debug_on(deprecation_warnings=deprecation_warnings)
yield
debug_off()
[docs]
def trace_on():
"""Turn trace logging on."""
logging_on(TRACE_LEVEL)
[docs]
class _WarningManager:
"""Class to handle switching warnings on and off."""
filt = None
_warning_manager = _WarningManager()
[docs]
def deprecation_warnings_on():
"""Switch on deprecation warnings."""
warnings.filterwarnings("default", category=DeprecationWarning)
_warning_manager.filt = warnings.filters[0]
[docs]
def deprecation_warnings_off():
"""Switch off deprecation warnings."""
if _warning_manager.filt in warnings.filters:
warnings.filters.remove(_warning_manager.filt)
[docs]
def logging_on(level=logging.WARNING):
"""Turn logging on."""
global _is_logging_on
if not _is_logging_on:
console = logging.StreamHandler()
console.setFormatter(logging.Formatter("[%(levelname)s: %(asctime)s :"
" %(name)s] %(message)s",
"%Y-%m-%d %H:%M:%S"))
console.setLevel(level)
logging.getLogger("").addHandler(console)
_is_logging_on = True
log = logging.getLogger("")
log.setLevel(level)
for h in log.handlers:
h.setLevel(level)
[docs]
def logging_off():
"""Turn logging off."""
logging.getLogger("").handlers = [logging.NullHandler()]
[docs]
def get_logger(name):
"""Return logger with null handler added if needed."""
if not hasattr(logging.Logger, "trace"):
logging.addLevelName(TRACE_LEVEL, "TRACE")
def trace(self, message, *args, **kwargs):
if self.isEnabledFor(TRACE_LEVEL):
# Yes, logger takes its '*args' as 'args'.
self._log(TRACE_LEVEL, message, args, **kwargs)
logging.Logger.trace = trace
log = logging.getLogger(name)
return log
[docs]
def in_ipynb():
"""Check if we are in a jupyter notebook."""
try:
return "ZMQ" in get_ipython().__class__.__name__
except NameError:
return False
# Spherical conversions
[docs]
def lonlat2xyz(lon, lat):
"""Convert lon lat to cartesian.
For a sphere with unit radius, convert the spherical coordinates
longitude and latitude to cartesian coordinates.
Args:
lon (number or array of numbers): Longitude in °.
lat (number or array of numbers): Latitude in °.
Returns:
(x, y, z) Cartesian coordinates [1]
"""
lat = np.deg2rad(lat)
lon = np.deg2rad(lon)
x = np.cos(lat) * np.cos(lon)
y = np.cos(lat) * np.sin(lon)
z = np.sin(lat)
return x, y, z
[docs]
def xyz2lonlat(x, y, z, asin=False):
"""Convert cartesian to lon lat.
For a sphere with unit radius, convert cartesian coordinates to spherical
coordinates longitude and latitude.
Args:
x (number or array of numbers): x-coordinate, unitless
y (number or array of numbers): y-coordinate, unitless
z (number or array of numbers): z-coordinate, unitless
asin (optional, bool): If true, use arcsin for calculations.
If false, use arctan2 for calculations.
Returns:
(lon, lat): Longitude and latitude in °.
"""
lon = np.rad2deg(np.arctan2(y, x))
if asin:
lat = np.rad2deg(np.arcsin(z))
else:
lat = np.rad2deg(np.arctan2(z, np.sqrt(x ** 2 + y ** 2)))
return lon, lat
[docs]
def angle2xyz(azi, zen):
"""Convert azimuth and zenith to cartesian."""
azi = np.deg2rad(azi)
zen = np.deg2rad(zen)
x = np.sin(zen) * np.sin(azi)
y = np.sin(zen) * np.cos(azi)
z = np.cos(zen)
return x, y, z
[docs]
def xyz2angle(x, y, z, acos=False):
"""Convert cartesian to azimuth and zenith."""
azi = np.rad2deg(np.arctan2(x, y))
if acos:
zen = np.rad2deg(np.arccos(z))
else:
zen = 90 - np.rad2deg(np.arctan2(z, np.sqrt(x ** 2 + y ** 2)))
return azi, zen
[docs]
def proj_units_to_meters(proj_str):
"""Convert projection units from kilometers to meters."""
proj_parts = proj_str.split()
new_parts = []
for itm in proj_parts:
key, val = itm.split("=")
key = key.strip("+")
if key in ["a", "b", "h"]:
val = float(val)
if val < 6e6:
val *= 1000.
val = "%.3f" % val
if key == "units" and val == "km":
continue
new_parts.append("+%s=%s" % (key, val))
return " ".join(new_parts)
[docs]
def _get_sunz_corr_li_and_shibata(cos_zen):
return 24.35 / (2. * cos_zen + np.sqrt(498.5225 * cos_zen**2 + 1))
[docs]
def atmospheric_path_length_correction(data, cos_zen, limit=88., max_sza=95.):
"""Perform Sun zenith angle correction.
This function uses the correction method proposed by
Li and Shibata (2006): https://doi.org/10.1175/JAS3682.1
The correction is limited to ``limit`` degrees (default: 88.0 degrees). For
larger zenith angles, the correction is the same as at the ``limit`` if
``max_sza`` is `None`. The default behavior is to gradually reduce the
correction past ``limit`` degrees up to ``max_sza`` where the correction
becomes 0. Both ``data`` and ``cos_zen`` should be 2D arrays of the same
shape.
"""
# Convert the zenith angle limit to cosine of zenith angle
limit_rad = np.deg2rad(limit)
limit_cos = np.cos(limit_rad)
max_sza_rad = np.deg2rad(max_sza) if max_sza is not None else max_sza
# Cosine correction
corr = _get_sunz_corr_li_and_shibata(cos_zen)
# Use constant value (the limit) for larger zenith angles
corr_lim = _get_sunz_corr_li_and_shibata(limit_cos)
if max_sza is not None:
# gradually fall off for larger zenith angle
grad_factor = (np.arccos(cos_zen) - limit_rad) / (max_sza_rad - limit_rad)
# invert the factor so maximum correction is done at `limit` and falls off later
grad_factor = 1. - np.log(grad_factor + 1) / np.log(2)
# make sure we don't make anything negative
grad_factor = grad_factor.clip(0.)
else:
# Use constant value (the limit) for larger zenith angles
grad_factor = 1.
corr = corr.where(cos_zen > limit_cos, grad_factor * corr_lim)
# Force "night" pixels to 0 (where SZA is invalid)
corr = corr.where(cos_zen.notnull(), 0)
return data * corr
[docs]
def get_satpos(
data_arr: xr.DataArray,
preference: Optional[str] = None,
use_tle: bool = False
) -> tuple[float, float, float]:
"""Get satellite position from dataset attributes.
Args:
data_arr: DataArray object to access ``.attrs`` metadata
from.
preference: Optional preference for one of the available types of
position information. If not provided or ``None`` then the default
preference is:
* Longitude & Latitude: nadir, actual, nominal, projection
* Altitude: actual, nominal, projection
The provided ``preference`` can be any one of these individual
strings (nadir, actual, nominal, projection). If the
preference is not available then the original preference list is
used. A warning is issued when projection values have to be used because
nothing else is available and it wasn't provided as the ``preference``.
use_tle: If true, try to obtain position via satellite name
and TLE if it can't be determined otherwise. This requires pyorbital, skyfield,
and astropy to be installed and may need network access to obtain the TLE.
Note that even if ``use_tle`` is true, the TLE will not be used if
the dataset metadata contain the satellite position directly.
Returns:
Geodetic longitude, latitude, altitude [km]
"""
if preference is not None and preference not in ("nadir", "actual", "nominal", "projection"):
raise ValueError(f"Unrecognized satellite coordinate preference: {preference}")
lonlat_prefixes = ("nadir_", "satellite_actual_", "satellite_nominal_", "projection_")
alt_prefixes = _get_prefix_order_by_preference(lonlat_prefixes[1:], preference)
lonlat_prefixes = _get_prefix_order_by_preference(lonlat_prefixes, preference)
try:
lon, lat = _get_sat_lonlat(data_arr, lonlat_prefixes)
alt = _get_sat_altitude(data_arr, alt_prefixes)
except KeyError:
if use_tle:
logger.warning(
"Orbital parameters missing from metadata. "
"Calculating from TLE using skyfield and astropy.")
return _get_satpos_from_platform_name(data_arr)
raise KeyError("Unable to determine satellite position. Either the "
"reader doesn't provide that information or "
"geolocation datasets were not available.")
return lon, lat, alt
[docs]
def _get_prefix_order_by_preference(prefixes, preference):
preferred_prefixes = [prefix for prefix in prefixes if preference and preference in prefix]
nonpreferred_prefixes = [prefix for prefix in prefixes if not preference or preference not in prefix]
if nonpreferred_prefixes[-1] == "projection_":
# remove projection as a prefix as it is our fallback
nonpreferred_prefixes = nonpreferred_prefixes[:-1]
return preferred_prefixes + nonpreferred_prefixes
[docs]
def _get_sat_altitude(data_arr, key_prefixes):
orb_params = data_arr.attrs["orbital_parameters"]
alt_keys = [prefix + "altitude" for prefix in key_prefixes]
try:
alt = _get_first_available_item(orb_params, alt_keys)
except KeyError:
alt = orb_params["projection_altitude"]
warnings.warn(
"Actual satellite altitude not available, using projection altitude instead.",
stacklevel=3
)
return alt
[docs]
def _get_sat_lonlat(data_arr, key_prefixes):
orb_params = data_arr.attrs["orbital_parameters"]
lon_keys = [prefix + "longitude" for prefix in key_prefixes]
lat_keys = [prefix + "latitude" for prefix in key_prefixes]
try:
lon = _get_first_available_item(orb_params, lon_keys)
lat = _get_first_available_item(orb_params, lat_keys)
except KeyError:
lon = orb_params["projection_longitude"]
lat = orb_params["projection_latitude"]
warnings.warn(
"Actual satellite lon/lat not available, using projection center instead.",
stacklevel=3
)
return lon, lat
[docs]
def _get_first_available_item(data_dict, possible_keys):
for possible_key in possible_keys:
try:
return data_dict[possible_key]
except KeyError:
continue
raise KeyError("None of the possible keys found: {}".format(", ".join(possible_keys)))
[docs]
def recursive_dict_update(d, u):
"""Recursive dictionary update.
Copied from:
http://stackoverflow.com/questions/3232943/update-value-of-a-nested-dictionary-of-varying-depth
"""
for k, v in u.items():
if isinstance(v, Mapping):
r = recursive_dict_update(d.get(k, {}), v)
d[k] = r
else:
d[k] = u[k]
return d
[docs]
def _check_yaml_configs(configs, key):
"""Get a diagnostic for the yaml *configs*.
*key* is the section to look for to get a name for the config at hand.
"""
diagnostic = {}
for i in configs:
for fname in i:
msg = "ok"
res = None
with open(fname, "r", encoding="utf-8") as stream:
try:
res = yaml.load(stream, Loader=UnsafeLoader)
except yaml.YAMLError as err:
stream.seek(0)
res = yaml.load(stream, Loader=BaseLoader)
if err.context == "while constructing a Python object":
msg = err.problem
else:
msg = "error"
finally:
try:
diagnostic[res[key]["name"]] = msg
except (KeyError, TypeError):
# this object doesn't have a 'name'
pass
return diagnostic
[docs]
def _check_import(module_names):
"""Import the specified modules and provide status."""
diagnostics = {}
for module_name in module_names:
try:
__import__(module_name)
res = "ok"
except ImportError as err:
res = str(err)
diagnostics[module_name] = res
return diagnostics
[docs]
def check_satpy(readers=None, writers=None, extras=None):
"""Check the satpy readers and writers for correct installation.
Args:
readers (list or None): Limit readers checked to those specified
writers (list or None): Limit writers checked to those specified
extras (list or None): Limit extras checked to those specified
Returns: bool
True if all specified features were successfully loaded.
"""
from satpy.readers import configs_for_reader
from satpy.writers import configs_for_writer
print("Readers") # noqa: T201
print("=======") # noqa: T201
for reader, res in sorted(_check_yaml_configs(configs_for_reader(reader=readers), "reader").items()):
print(reader + ": ", res) # noqa: T201
print() # noqa: T201
print("Writers") # noqa: T201
print("=======") # noqa: T201
for writer, res in sorted(_check_yaml_configs(configs_for_writer(writer=writers), "writer").items()):
print(writer + ": ", res) # noqa: T201
print() # noqa: T201
print("Extras") # noqa: T201
print("======") # noqa: T201
module_names = extras if extras is not None else ("cartopy", "geoviews")
for module_name, res in sorted(_check_import(module_names).items()):
print(module_name + ": ", res) # noqa: T201
print() # noqa: T201
[docs]
def unify_chunks(*data_arrays: xr.DataArray) -> tuple[xr.DataArray, ...]:
"""Run :func:`xarray.unify_chunks` if input dimensions are all the same size.
This is mostly used in :class:`satpy.composites.CompositeBase` to safe
guard against running :func:`dask.array.core.map_blocks` with arrays of
different chunk sizes. Doing so can cause unexpected results or errors.
However, xarray's ``unify_chunks`` will raise an exception if dimensions
of the provided DataArrays are different sizes. This is a common case for
Satpy. For example, the "bands" dimension may be 1 (L), 2 (LA), 3 (RGB), or
4 (RGBA) for most compositor operations that combine other composites
together.
"""
if not hasattr(xr, "unify_chunks"):
return data_arrays
if not _all_dims_same_size(data_arrays):
return data_arrays
return tuple(xr.unify_chunks(*data_arrays))
[docs]
def _all_dims_same_size(data_arrays: tuple[xr.DataArray, ...]) -> bool:
known_sizes: dict[str, int] = {}
for data_arr in data_arrays:
for dim, dim_size in data_arr.sizes.items():
known_size = known_sizes.setdefault(dim, dim_size)
if dim_size != known_size:
# this dimension is a different size than previously found
# xarray.unify_chunks will error out if we tried to use it
return False
return True
[docs]
@contextlib.contextmanager
def ignore_invalid_float_warnings():
"""Ignore warnings generated for working with NaN/inf values.
Numpy and dask sometimes don't like NaN or inf values in normal function
calls. This context manager hides/ignores them inside its context.
Examples:
Use around numpy operations that you expect to produce warnings::
with ignore_invalid_float_warnings():
np.nanmean(np.nan)
"""
with np.errstate(invalid="ignore"), warnings.catch_warnings():
warnings.simplefilter("ignore", RuntimeWarning)
yield
[docs]
@contextlib.contextmanager
def ignore_pyproj_proj_warnings():
"""Wrap operations that we know will produce a PROJ.4 precision warning.
Only to be used internally to Pyresample when we have no other choice but
to use PROJ.4 strings/dicts. For example, serialization to YAML or other
human-readable formats or testing the methods that produce the PROJ.4
versions of the CRS.
"""
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
"You will likely lose important projection information",
UserWarning,
)
yield
[docs]
def get_chunk_size_limit(dtype=float):
"""Compute the chunk size limit in bytes given *dtype* (float by default).
It is derived from PYTROLL_CHUNK_SIZE if defined (although deprecated) first, from dask config's `array.chunk-size`
then. It defaults to 128MiB.
Returns:
The recommended chunk size in bytes.
"""
pixel_size = _get_chunk_pixel_size()
if pixel_size is not None:
return pixel_size * np.dtype(dtype).itemsize
return get_dask_chunk_size_in_bytes()
[docs]
def get_dask_chunk_size_in_bytes():
"""Get the dask configured chunk size in bytes."""
return dask.utils.parse_bytes(dask.config.get("array.chunk-size", "128MiB"))
[docs]
def _get_chunk_pixel_size():
"""Compute the maximum chunk size from PYTROLL_CHUNK_SIZE."""
legacy_chunk_size = _get_pytroll_chunk_size()
if legacy_chunk_size is not None:
return legacy_chunk_size ** 2
[docs]
def get_legacy_chunk_size():
"""Get the legacy chunk size.
This function should only be used while waiting for code to be migrated to use satpy.utils.get_chunk_size_limit
instead.
"""
chunk_size = _get_pytroll_chunk_size()
if chunk_size is not None:
return chunk_size
import math
return int(math.sqrt(get_dask_chunk_size_in_bytes() / 8))
[docs]
def _get_pytroll_chunk_size():
try:
chunk_size = int(os.environ["PYTROLL_CHUNK_SIZE"])
warnings.warn(
"The PYTROLL_CHUNK_SIZE environment variable is pending deprecation. "
"You can use the dask config setting `array.chunk-size` (or the DASK_ARRAY__CHUNK_SIZE environment"
" variable) and set it to the square of the PYTROLL_CHUNK_SIZE instead.",
stacklevel=2
)
return chunk_size
except KeyError:
return None
[docs]
def normalize_low_res_chunks(
chunks: tuple[int | Literal["auto"], ...],
input_shape: tuple[int, ...],
previous_chunks: tuple[int, ...],
low_res_multipliers: tuple[int, ...],
input_dtype: DTypeLike,
) -> tuple[int, ...]:
"""Compute dask chunk sizes based on data resolution.
First, chunks are computed for the highest resolution version of the data.
This is done by multiplying the input array shape by the
``low_res_multiplier`` and then using Dask's utility functions and
configuration to produce a chunk size to fit into a specific number of
bytes. See :doc:`dask:array-chunks` for more information.
Next, the same multiplier is used to reduce the high resolution chunk sizes
to the lower resolution of the input data. The end result of reading
multiple resolutions of data is that each dask chunk covers the same
geographic region. This also means replicating or aggregating one
resolution and then combining arrays should not require any rechunking.
Args:
chunks: Requested chunk size for each dimension. This is passed
directly to dask. Use ``"auto"`` for dimensions that should have
chunks determined for them, ``-1`` for dimensions that should be
whole (not chunked), and ``1`` or any other positive integer for
dimensions that have a known chunk size beforehand.
input_shape: Shape of the array to compute dask chunk size for.
previous_chunks: Any previous chunking or structure of the data. This
can also be thought of as the smallest number of high (fine) resolution
elements that make up a single "unit" or chunk of data. This could
be a multiple or factor of the scan size for some instruments and/or
could be based on the on-disk chunk size. This value ensures that
chunks are aligned to the underlying data structure for best
performance. On-disk chunk sizes should be multiplied by the
largest low resolution multiplier if it is the same between all
files (ex. 500m file has 226 chunk size, 1km file has 226 chunk
size, etc).. Otherwise, the resulting low resolution chunks may
not be aligned to the on-disk chunks. For example, if dask decides
on a chunk size of 226 * 3 for 500m data, that becomes 226 * 3 / 2
for 1km data which is not aligned to the on-disk chunk size of 226.
low_res_multipliers: Number of high (fine) resolution pixels that fit
in a single low (coarse) resolution pixel.
input_dtype: Dtype for the final unscaled array. This is usually
32-bit float (``np.float32``) or 64-bit float (``np.float64``)
for non-category data. If this doesn't represent the final data
type of the data then the final size of chunks in memory will not
match the user's request via dask's ``array.chunk-size``
configuration. Sometimes it is useful to keep this as a single
dtype for all reading functionality (ex. ``np.float32``) in order
to keep all read variable chunks the same size regardless of dtype.
Returns:
A tuple where each element is the chunk size for that axis/dimension.
"""
if any(len(input_shape) != len(param) for param in (low_res_multipliers, chunks, previous_chunks)):
raise ValueError("Input shape, low res multipliers, chunks, and previous chunks must all be the same size")
high_res_shape = tuple(dim_size * lr_mult for dim_size, lr_mult in zip(input_shape, low_res_multipliers))
chunks_for_high_res = dask.array.core.normalize_chunks(
chunks,
shape=high_res_shape,
dtype=input_dtype,
previous_chunks=previous_chunks,
)
low_res_chunks: list[int] = []
for req_chunks, hr_chunks, prev_chunks, lr_mult in zip(
chunks,
chunks_for_high_res,
previous_chunks, low_res_multipliers
):
if req_chunks != "auto":
low_res_chunks.append(req_chunks)
continue
low_res_chunks.append(round(max(hr_chunks[0] / lr_mult, prev_chunks / lr_mult)))
return tuple(low_res_chunks)
[docs]
def convert_remote_files_to_fsspec(filenames, storage_options=None):
"""Check filenames for transfer protocols, convert to FSFile objects if possible."""
if storage_options is None:
storage_options = {}
if isinstance(filenames, dict):
return _check_file_protocols_for_dicts(filenames, storage_options)
return _check_file_protocols(filenames, storage_options)
[docs]
def _check_file_protocols_for_dicts(filenames, storage_options):
res = {}
for reader, files in filenames.items():
opts = storage_options.get(reader, {})
res[reader] = _check_file_protocols(files, opts)
return res
[docs]
def _check_file_protocols(filenames, storage_options):
local_files, remote_files, fs_files = _sort_files_to_local_remote_and_fsfiles(filenames)
if remote_files:
return local_files + fs_files + _filenames_to_fsfile(remote_files, storage_options)
return local_files + fs_files
[docs]
def _sort_files_to_local_remote_and_fsfiles(filenames):
from satpy.readers import FSFile
local_files = []
remote_files = []
fs_files = []
for f in filenames:
if isinstance(f, FSFile):
fs_files.append(f)
elif isinstance(f, pathlib.Path):
local_files.append(f)
elif urlparse(f).scheme in ("", "file") or "\\" in f:
local_files.append(f)
else:
remote_files.append(f)
return local_files, remote_files, fs_files
[docs]
def _filenames_to_fsfile(filenames, storage_options):
import fsspec
from satpy.readers import FSFile
if filenames:
fsspec_files = fsspec.open_files(filenames, **storage_options)
return [FSFile(f) for f in fsspec_files]
return []
[docs]
def get_storage_options_from_reader_kwargs(reader_kwargs):
"""Read and clean storage options from reader_kwargs."""
if reader_kwargs is None:
return None, None
new_reader_kwargs = deepcopy(reader_kwargs) # don't modify user provided dict
storage_options = _get_storage_dictionary_options(new_reader_kwargs)
return storage_options, new_reader_kwargs
[docs]
def _get_storage_dictionary_options(reader_kwargs):
storage_opt_dict = {}
shared_storage_options = reader_kwargs.pop("storage_options", {})
if not reader_kwargs:
# no other reader kwargs
return shared_storage_options
for reader_name, rkwargs in reader_kwargs.items():
if not isinstance(rkwargs, dict):
# reader kwargs are not per-reader, return a single dictionary of storage options
return shared_storage_options
if shared_storage_options:
# set base storage options if there are any
storage_opt_dict[reader_name] = shared_storage_options.copy()
if isinstance(rkwargs, dict) and "storage_options" in rkwargs:
storage_opt_dict.setdefault(reader_name, {}).update(rkwargs.pop("storage_options"))
return storage_opt_dict
[docs]
@contextmanager
def import_error_helper(dependency_name):
"""Give more info on an import error."""
try:
yield
except ImportError as err:
raise ImportError(err.msg + f" It can be installed with the {dependency_name} package.")
[docs]
def find_in_ancillary(data, dataset):
"""Find a dataset by name in the ancillary vars of another dataset.
Args:
data (xarray.DataArray):
Array for which to search the ancillary variables
dataset (str):
Name of ancillary variable to look for.
"""
matches = [x for x in data.attrs["ancillary_variables"] if x.attrs.get("name") == dataset]
cnt = len(matches)
if cnt < 1:
raise ValueError(
f"Could not find dataset named {dataset:s} in ancillary "
f"variables for dataset {data.attrs.get('name')!r}")
if cnt > 1:
raise ValueError(
f"Expected exactly one dataset named {dataset:s} in ancillary "
f"variables for dataset {data.attrs.get('name')!r}, "
f"found {cnt:d}")
return matches[0]