Source code for satpy.resample

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2015-2018 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 <>.
"""Resampling in Satpy.

Satpy provides multiple resampling algorithms for resampling geolocated
data to uniform projected grids. The easiest way to perform resampling in
Satpy is through the :class:`~satpy.scene.Scene` object's
:meth:`~satpy.scene.Scene.resample` method. Additional utility functions are
also available to assist in resampling data. Below is more information on
resampling with Satpy as well as links to the relevant API documentation for
available keyword arguments.

Resampling algorithms

.. csv-table:: Available Resampling Algorithms
    :header-rows: 1
    :align: center

    "Resampler", "Description", "Related"
    "nearest", "Nearest Neighbor", :class:`~satpy.resample.KDTreeResampler`
    "ewa", "Elliptical Weighted Averaging", :class:`~pyresample.ewa.DaskEWAResampler`
    "ewa_legacy", "Elliptical Weighted Averaging (Legacy)", :class:`~pyresample.ewa.LegacyDaskEWAResampler`
    "native", "Native", :class:`~satpy.resample.NativeResampler`
    "bilinear", "Bilinear", :class:`~satpy.resample.BilinearResampler`
    "bucket_avg", "Average Bucket Resampling", :class:`~satpy.resample.BucketAvg`
    "bucket_sum", "Sum Bucket Resampling", :class:`~satpy.resample.BucketSum`
    "bucket_count", "Count Bucket Resampling", :class:`~satpy.resample.BucketCount`
    "bucket_fraction", "Fraction Bucket Resampling", :class:`~satpy.resample.BucketFraction`
    "gradient_search", "Gradient Search Resampling", :class:`~pyresample.gradient.GradientSearchResampler`

The resampling algorithm used can be specified with the ``resampler`` keyword
argument and defaults to ``nearest``:

.. code-block:: python

    >>> scn = Scene(...)
    >>> euro_scn = scn.resample('euro4', resampler='nearest')

.. warning::

    Some resampling algorithms expect certain forms of data. For example, the
    EWA resampling expects polar-orbiting swath data and prefers if the data
    can be broken in to "scan lines". See the API documentation for a specific
    algorithm for more information.

Resampling for comparison and composites

While all the resamplers can be used to put datasets of different resolutions
on to a common area, the 'native' resampler is designed to match datasets to
one resolution in the dataset's original projection. This is extremely useful
when generating composites between bands of different resolutions.

.. code-block:: python

    >>> new_scn = scn.resample(resampler='native')

By default this resamples to the
:meth:`highest resolution area <satpy.scene.Scene.finest_area>` (smallest footprint per
pixel) shared between the loaded datasets. You can easily specify the lowest
resolution area:

.. code-block:: python

    >>> new_scn = scn.resample(scn.coarsest_area(), resampler='native')

Providing an area that is neither the minimum or maximum resolution area
may work, but behavior is currently undefined.

Caching for geostationary data

Satpy will do its best to reuse calculations performed to resample datasets,
but it can only do this for the current processing and will lose this
information when the process/script ends. Some resampling algorithms, like
``nearest`` and ``bilinear``, can benefit by caching intermediate data on disk in the directory
specified by `cache_dir` and using it next time. This is most beneficial with
geostationary satellite data where the locations of the source data and the
target pixels don't change over time.

    >>> new_scn = scn.resample('euro4', cache_dir='/path/to/cache_dir')

See the documentation for specific algorithms to see availability and
limitations of caching for that algorithm.

Create custom area definition

See :class:`pyresample.geometry.AreaDefinition` for information on creating
areas that can be passed to the resample method::

    >>> from pyresample.geometry import AreaDefinition
    >>> my_area = AreaDefinition(...)
    >>> local_scene = scn.resample(my_area)

Create dynamic area definition

See :class:`pyresample.geometry.DynamicAreaDefinition` for more information.

Examples coming soon...

Store area definitions

Area definitions can be saved to a custom YAML file (see
`pyresample's writing to disk <>`_)
and loaded using pyresample's utility methods
(`pyresample's loading from disk <>`_)::

    >>> from pyresample import load_area
    >>> my_area = load_area('my_areas.yaml', 'my_area')

Or using :func:`satpy.resample.get_area_def`, which will search through all
``areas.yaml`` files in your ``SATPY_CONFIG_PATH``::

    >>> from satpy.resample import get_area_def
    >>> area_eurol = get_area_def("eurol")

For examples of area definitions, see the file ``etc/areas.yaml`` that is
included with Satpy and where all the area definitions shipped with Satpy are

import hashlib
import json
import os
import warnings
from logging import getLogger
from math import lcm  # type: ignore
from weakref import WeakValueDictionary

import dask
import dask.array as da
import numpy as np
import pyresample
import xarray as xr
import zarr
from packaging import version
from pyresample.ewa import fornav, ll2cr
from pyresample.geometry import SwathDefinition

from satpy.utils import PerformanceWarning, get_legacy_chunk_size

    from pyresample.resampler import BaseResampler as PRBaseResampler
except ImportError:
    PRBaseResampler = None
    from pyresample.gradient import GradientSearchResampler
except ImportError:
    GradientSearchResampler = None
    from pyresample.ewa import DaskEWAResampler, LegacyDaskEWAResampler
except ImportError:
    DaskEWAResampler = LegacyDaskEWAResampler = None

from satpy._config import config_search_paths, get_config_path

LOG = getLogger(__name__)

CHUNK_SIZE = get_legacy_chunk_size()
NN_COORDINATES = {'valid_input_index': ('y1', 'x1'),
                  'valid_output_index': ('y2', 'x2'),
                  'index_array': ('y2', 'x2', 'z2')}
BIL_COORDINATES = {'bilinear_s': ('x1', ),
                   'bilinear_t': ('x1', ),
                   'slices_x': ('x1', 'n'),
                   'slices_y': ('x1', 'n'),
                   'mask_slices': ('x1', 'n'),
                   'out_coords_x': ('x2', ),
                   'out_coords_y': ('y2', )}

resamplers_cache: "WeakValueDictionary[tuple, object]" = WeakValueDictionary()

PR_USE_SKIPNA = version.parse(pyresample.__version__) > version.parse("1.17.0")

[docs] def hash_dict(the_dict, the_hash=None): """Calculate a hash for a dictionary.""" if the_hash is None: the_hash = hashlib.sha1() # nosec the_hash.update(json.dumps(the_dict, sort_keys=True).encode('utf-8')) return the_hash
[docs] def get_area_file(): """Find area file(s) to use. The files are to be named `areas.yaml` or `areas.def`. """ paths = config_search_paths('areas.yaml') if paths: return paths else: return get_config_path('areas.def')
[docs] def get_area_def(area_name): """Get the definition of *area_name* from file. The file is defined to use is to be placed in the $SATPY_CONFIG_PATH directory, and its name is defined in satpy's configuration file. """ try: from pyresample import parse_area_file except ImportError: from pyresample.utils import parse_area_file return parse_area_file(get_area_file(), area_name)[0]
[docs] def add_xy_coords(data_arr, area, crs=None): """Assign x/y coordinates to DataArray from provided area. If 'x' and 'y' coordinates already exist then they will not be added. Args: data_arr (xarray.DataArray): data object to add x/y coordinates to area (pyresample.geometry.AreaDefinition): area providing the coordinate data. crs ( or None): CRS providing additional information about the area's coordinate reference system if available. Requires pyproj 2.0+. Returns (xarray.DataArray): Updated DataArray object """ if 'x' in data_arr.coords and 'y' in data_arr.coords: # x/y coords already provided return data_arr if 'x' not in data_arr.dims or 'y' not in data_arr.dims: # no defined x and y dimensions return data_arr if not hasattr(area, 'get_proj_vectors'): return data_arr x, y = area.get_proj_vectors() # convert to DataArrays y_attrs = {} x_attrs = {} if crs is not None: units = crs.axis_info[0].unit_name # fix udunits/CF standard units units = units.replace('metre', 'meter') if units == 'degree': y_attrs['units'] = 'degrees_north' x_attrs['units'] = 'degrees_east' else: y_attrs['units'] = units x_attrs['units'] = units y = xr.DataArray(y, dims=('y',), attrs=y_attrs) x = xr.DataArray(x, dims=('x',), attrs=x_attrs) return data_arr.assign_coords(y=y, x=x)
[docs] def add_crs_xy_coords(data_arr, area): """Add :class:`` and x/y or lons/lats to coordinates. For SwathDefinition or GridDefinition areas this will add a `crs` coordinate and coordinates for the 2D arrays of `lons` and `lats`. For AreaDefinition areas this will add a `crs` coordinate and the 1-dimensional `x` and `y` coordinate variables. Args: data_arr (xarray.DataArray): DataArray to add the 'crs' coordinate. area (pyresample.geometry.AreaDefinition): Area to get CRS information from. """ # add CRS object if pyproj 2.0+ try: from pyproj import CRS except ImportError: LOG.debug("Could not add 'crs' coordinate with pyproj<2.0") crs = None else: # default lat/lon projection latlon_proj = "+proj=latlong +datum=WGS84 +ellps=WGS84" # otherwise get it from the area definition if hasattr(area, 'crs'): crs = else: proj_str = getattr(area, 'proj_str', latlon_proj) crs = CRS.from_string(proj_str) data_arr = data_arr.assign_coords(crs=crs) # Add x/y coordinates if possible if isinstance(area, SwathDefinition): # add lon/lat arrays for swath definitions # SwathDefinitions created by Satpy should be assigning DataArray # objects as the lons/lats attributes so use those directly to # maintain original .attrs metadata (instead of converting to dask # array). lons = area.lons lats = area.lats lons.attrs.setdefault('standard_name', 'longitude') lons.attrs.setdefault('long_name', 'longitude') lons.attrs.setdefault('units', 'degrees_east') lats.attrs.setdefault('standard_name', 'latitude') lats.attrs.setdefault('long_name', 'latitude') lats.attrs.setdefault('units', 'degrees_north') # See # data_arr = data_arr.assign_coords(longitude=lons, latitude=lats) else: # Gridded data (AreaDefinition/StackedAreaDefinition) data_arr = add_xy_coords(data_arr, area, crs=crs) return data_arr
[docs] def update_resampled_coords(old_data, new_data, new_area): """Add coordinate information to newly resampled DataArray. Args: old_data (xarray.DataArray): Old data before resampling. new_data (xarray.DataArray): New data after resampling. new_area (pyresample.geometry.BaseDefinition): Area definition for the newly resampled data. """ # copy over other non-x/y coordinates # this *MUST* happen before we set 'crs' below otherwise any 'crs' # coordinate in the coordinate variables we are copying will overwrite the # 'crs' coordinate we just assigned to the data ignore_coords = ('y', 'x', 'crs') new_coords = {} for cname, cval in old_data.coords.items(): # we don't want coordinates that depended on the old x/y dimensions has_ignored_dims = any(dim in cval.dims for dim in ignore_coords) if cname in ignore_coords or has_ignored_dims: continue new_coords[cname] = cval new_data = new_data.assign_coords(**new_coords) # add crs, x, and y coordinates new_data = add_crs_xy_coords(new_data, new_area) return new_data
[docs] class BaseResampler(object): """Base abstract resampler class.""" def __init__(self, source_geo_def, target_geo_def): """Initialize resampler with geolocation information. Args: source_geo_def (SwathDefinition, AreaDefinition): Geolocation definition for the data to be resampled target_geo_def (CoordinateDefinition, AreaDefinition): Geolocation definition for the area to resample data to. """ self.source_geo_def = source_geo_def self.target_geo_def = target_geo_def
[docs] def get_hash(self, source_geo_def=None, target_geo_def=None, **kwargs): """Get hash for the current resample with the given *kwargs*.""" if source_geo_def is None: source_geo_def = self.source_geo_def if target_geo_def is None: target_geo_def = self.target_geo_def the_hash = source_geo_def.update_hash() target_geo_def.update_hash(the_hash) hash_dict(kwargs, the_hash) return the_hash.hexdigest()
[docs] def precompute(self, **kwargs): """Do the precomputation. This is an optional step if the subclass wants to implement more complex features like caching or can share some calculations between multiple datasets to be processed. """ return None
[docs] def compute(self, data, **kwargs): """Do the actual resampling. This must be implemented by subclasses. """ raise NotImplementedError
[docs] def resample(self, data, cache_dir=None, mask_area=None, **kwargs): """Resample `data` by calling `precompute` and `compute` methods. Only certain resampling classes may use `cache_dir` and the `mask` provided when `mask_area` is True. The return value of calling the `precompute` method is passed as the `cache_id` keyword argument of the `compute` method, but may not be used directly for caching. It is up to the individual resampler subclasses to determine how this is used. Args: data (xarray.DataArray): Data to be resampled cache_dir (str): directory to cache precomputed results (default False, optional) mask_area (bool): Mask geolocation data where data values are invalid. This should be used when data values may affect what neighbors are considered valid. Returns (xarray.DataArray): Data resampled to the target area """ # default is to mask areas for SwathDefinitions if mask_area is None and isinstance( self.source_geo_def, SwathDefinition): mask_area = True if mask_area: if isinstance(self.source_geo_def, SwathDefinition): geo_dims = self.source_geo_def.lons.dims else: geo_dims = ('y', 'x') flat_dims = [dim for dim in data.dims if dim not in geo_dims] if np.issubdtype(data.dtype, np.integer): kwargs['mask'] = data == data.attrs.get('_FillValue', np.iinfo(data.dtype.type).max) else: kwargs['mask'] = data.isnull() kwargs['mask'] = kwargs['mask'].all(dim=flat_dims) cache_id = self.precompute(cache_dir=cache_dir, **kwargs) return self.compute(data, cache_id=cache_id, **kwargs)
[docs] def _create_cache_filename(self, cache_dir, prefix='', fmt='.zarr', **kwargs): """Create filename for the cached resampling parameters.""" hash_str = self.get_hash(**kwargs) return os.path.join(cache_dir, prefix + hash_str + fmt)
[docs] class KDTreeResampler(BaseResampler): """Resample using a KDTree-based nearest neighbor algorithm. This resampler implements on-disk caching when the `cache_dir` argument is provided to the `resample` method. This should provide significant performance improvements on consecutive resampling of geostationary data. It is not recommended to provide `cache_dir` when the `mask` keyword argument is provided to `precompute` which occurs by default for `SwathDefinition` source areas. Args: cache_dir (str): Long term storage directory for intermediate results. mask (bool): Force resampled data's invalid pixel mask to be used when searching for nearest neighbor pixels. By default this is True for SwathDefinition source areas and False for all other area definition types. radius_of_influence (float): Search radius cut off distance in meters epsilon (float): Allowed uncertainty in meters. Increasing uncertainty reduces execution time. """ def __init__(self, source_geo_def, target_geo_def): """Init KDTreeResampler.""" super(KDTreeResampler, self).__init__(source_geo_def, target_geo_def) self.resampler = None self._index_caches = {}
[docs] def precompute(self, mask=None, radius_of_influence=None, epsilon=0, cache_dir=None, **kwargs): """Create a KDTree structure and store it for later use. Note: The `mask` keyword should be provided if geolocation may be valid where data points are invalid. """ from pyresample.kd_tree import XArrayResamplerNN del kwargs if mask is not None and cache_dir is not None: LOG.warning("Mask and cache_dir both provided to nearest " "resampler. Cached parameters are affected by " "masked pixels. Will not cache results.") cache_dir = None if radius_of_influence is None and not hasattr(self.source_geo_def, 'geocentric_resolution'): radius_of_influence = self._adjust_radius_of_influence(radius_of_influence) kwargs = dict(source_geo_def=self.source_geo_def, target_geo_def=self.target_geo_def, radius_of_influence=radius_of_influence, neighbours=1, epsilon=epsilon) if self.resampler is None: # FIXME: We need to move all of this caching logic to pyresample self.resampler = XArrayResamplerNN(**kwargs) try: self.load_neighbour_info(cache_dir, mask=mask, **kwargs) LOG.debug("Read pre-computed kd-tree parameters") except IOError: LOG.debug("Computing kd-tree parameters") self.resampler.get_neighbour_info(mask=mask) self.save_neighbour_info(cache_dir, mask=mask, **kwargs)
[docs] def _adjust_radius_of_influence(self, radius_of_influence): """Adjust radius of influence.""" warnings.warn( "Upgrade 'pyresample' for a more accurate default 'radius_of_influence'.", stacklevel=3 ) try: radius_of_influence = self.source_geo_def.lons.resolution * 3 except AttributeError: try: radius_of_influence = max(abs(self.source_geo_def.pixel_size_x), abs(self.source_geo_def.pixel_size_y)) * 3 except AttributeError: radius_of_influence = 1000 except TypeError: radius_of_influence = 10000 return radius_of_influence
[docs] def _apply_cached_index(self, val, idx_name, persist=False): """Reassign resampler index attributes.""" if isinstance(val, np.ndarray): val = da.from_array(val, chunks=CHUNK_SIZE) elif persist and isinstance(val, da.Array): val = val.persist() setattr(self.resampler, idx_name, val) return val
[docs] def _check_numpy_cache(self, cache_dir, mask=None, **kwargs): """Check if there's Numpy cache file and convert it to zarr.""" if cache_dir is None: return fname_np = self._create_cache_filename(cache_dir, prefix='resample_lut-', mask=mask, fmt='.npz', **kwargs) fname_zarr = self._create_cache_filename(cache_dir, prefix='nn_lut-', mask=mask, fmt='.zarr', **kwargs) LOG.debug("Check if %s exists", fname_np) if os.path.exists(fname_np) and not os.path.exists(fname_zarr): import warnings warnings.warn( "Using Numpy files as resampling cache is deprecated.", stacklevel=3 ) LOG.warning("Converting resampling LUT from .npz to .zarr") zarr_out = xr.Dataset() with np.load(fname_np, 'r') as fid: for idx_name, coord in NN_COORDINATES.items(): zarr_out[idx_name] = (coord, fid[idx_name]) # Write indices to Zarr file zarr_out.to_zarr(fname_zarr) LOG.debug("Resampling LUT saved to %s", fname_zarr)
[docs] def load_neighbour_info(self, cache_dir, mask=None, **kwargs): """Read index arrays from either the in-memory or disk cache.""" mask_name = getattr(mask, 'name', None) cached = {} self._check_numpy_cache(cache_dir, mask=mask_name, **kwargs) for idx_name in NN_COORDINATES: if mask_name in self._index_caches: cached[idx_name] = self._apply_cached_index( self._index_caches[mask_name][idx_name], idx_name) elif cache_dir: try: filename = self._create_cache_filename( cache_dir, prefix='nn_lut-', mask=mask_name, **kwargs) fid =, 'r') cache = np.array(fid[idx_name]) if idx_name == 'valid_input_index': # valid input index array needs to be boolean cache = cache.astype(bool) except ValueError: raise IOError cache = self._apply_cached_index(cache, idx_name) cached[idx_name] = cache else: raise IOError self._index_caches[mask_name] = cached
[docs] def save_neighbour_info(self, cache_dir, mask=None, **kwargs): """Cache resampler's index arrays if there is a cache dir.""" if cache_dir: mask_name = getattr(mask, 'name', None) cache = self._read_resampler_attrs() filename = self._create_cache_filename( cache_dir, prefix='nn_lut-', mask=mask_name, **kwargs)'Saving kd_tree neighbour info to %s', filename) zarr_out = xr.Dataset() for idx_name, coord in NN_COORDINATES.items(): # update the cache in place with persisted dask arrays cache[idx_name] = self._apply_cached_index(cache[idx_name], idx_name, persist=True) zarr_out[idx_name] = (coord, cache[idx_name]) # Write indices to Zarr file zarr_out.to_zarr(filename) self._index_caches[mask_name] = cache # Delete the kdtree, it's not needed anymore self.resampler.delayed_kdtree = None
[docs] def _read_resampler_attrs(self): """Read certain attributes from the resampler for caching.""" return {attr_name: getattr(self.resampler, attr_name) for attr_name in NN_COORDINATES}
[docs] def compute(self, data, weight_funcs=None, fill_value=np.nan, with_uncert=False, **kwargs): """Resample data.""" del kwargs LOG.debug("Resampling %s", str( res = self.resampler.get_sample_from_neighbour_info(data, fill_value) return update_resampled_coords(data, res, self.target_geo_def)
[docs] class _LegacySatpyEWAResampler(BaseResampler): """Resample using an elliptical weighted averaging algorithm. This algorithm does **not** use caching or any externally provided data mask (unlike the 'nearest' resampler). This algorithm works under the assumption that the data is observed one scan line at a time. However, good results can still be achieved for non-scan based data provided `rows_per_scan` is set to the number of rows in the entire swath or by setting it to `None`. Args: rows_per_scan (int, None): Number of data rows for every observed scanline. If None then the entire swath is treated as one large scanline. weight_count (int): number of elements to create in the gaussian weight table. Default is 10000. Must be at least 2 weight_min (float): the minimum value to store in the last position of the weight table. Default is 0.01, which, with a `weight_distance_max` of 1.0 produces a weight of 0.01 at a grid cell distance of 1.0. Must be greater than 0. weight_distance_max (float): distance in grid cell units at which to apply a weight of `weight_min`. Default is 1.0. Must be greater than 0. weight_delta_max (float): maximum distance in grid cells in each grid dimension over which to distribute a single swath cell. Default is 10.0. weight_sum_min (float): minimum weight sum value. Cells whose weight sums are less than `weight_sum_min` are set to the grid fill value. Default is EPSILON. maximum_weight_mode (bool): If False (default), a weighted average of all swath cells that map to a particular grid cell is used. If True, the swath cell having the maximum weight of all swath cells that map to a particular grid cell is used. This option should be used for coded/category data, i.e. snow cover. """ def __init__(self, source_geo_def, target_geo_def): """Init _LegacySatpyEWAResampler.""" warnings.warn( "A new version of pyresample is available. Please " "upgrade to get access to a newer 'ewa' and " "'ewa_legacy' resampler.", stacklevel=2 ) super(_LegacySatpyEWAResampler, self).__init__(source_geo_def, target_geo_def) self.cache = {}
[docs] def resample(self, *args, **kwargs): """Run precompute and compute methods. .. note:: This sets the default of 'mask_area' to False since it is not needed in EWA resampling currently. """ kwargs.setdefault('mask_area', False) return super(_LegacySatpyEWAResampler, self).resample(*args, **kwargs)
[docs] def _call_ll2cr(self, lons, lats, target_geo_def, swath_usage=0): """Wrap ll2cr() for handling dask delayed calls better.""" new_src = SwathDefinition(lons, lats) swath_points_in_grid, cols, rows = ll2cr(new_src, target_geo_def) # FIXME: How do we check swath usage/coverage if we only do this # per-block # # Determine if enough of the input swath was used # grid_name = getattr(self.target_geo_def, "name", "N/A") # fraction_in = swath_points_in_grid / float(lons.size) # swath_used = fraction_in > swath_usage # if not swath_used: #"Data does not fit in grid %s because it only %f%% of " # "the swath is used" % # (grid_name, fraction_in * 100)) # raise RuntimeError("Data does not fit in grid %s" % (grid_name,)) # else: # LOG.debug("Data fits in grid %s and uses %f%% of the swath", # grid_name, fraction_in * 100) return np.stack([cols, rows], axis=0)
[docs] def precompute(self, cache_dir=None, swath_usage=0, **kwargs): """Generate row and column arrays and store it for later use.""" if self.cache: # this resampler should be used for one SwathDefinition # no need to recompute ll2cr output again return None if kwargs.get('mask') is not None: LOG.warning("'mask' parameter has no affect during EWA " "resampling") del kwargs source_geo_def = self.source_geo_def target_geo_def = self.target_geo_def if cache_dir: LOG.warning("'cache_dir' is not used by EWA resampling") # Satpy/PyResample don't support dynamic grids out of the box yet lons, lats = source_geo_def.get_lonlats() if isinstance(lons, xr.DataArray): # get dask arrays lons = lats = # we are remapping to a static unchanging grid/area with all of # its parameters specified chunks = (2,) + lons.chunks res = da.map_blocks(self._call_ll2cr, lons, lats, target_geo_def, swath_usage, dtype=lons.dtype, chunks=chunks, new_axis=[0]) cols = res[0] rows = res[1] # save the dask arrays in the class instance cache # the on-disk cache will store the numpy arrays self.cache = { "rows": rows, "cols": cols, } return None
[docs] def _call_fornav(self, cols, rows, target_geo_def, data, grid_coverage=0, **kwargs): """Wrap fornav() to run as a dask delayed.""" num_valid_points, res = fornav(cols, rows, target_geo_def, data, **kwargs) if isinstance(data, tuple): # convert 'res' from tuple of arrays to one array res = np.stack(res) num_valid_points = sum(num_valid_points) grid_covered_ratio = num_valid_points / float(res.size) grid_covered = grid_covered_ratio > grid_coverage if not grid_covered: msg = "EWA resampling only found %f%% of the grid covered " \ "(need %f%%)" % (grid_covered_ratio * 100, grid_coverage * 100) raise RuntimeError(msg) LOG.debug("EWA resampling found %f%% of the grid covered" % (grid_covered_ratio * 100)) return res
[docs] def compute(self, data, cache_id=None, fill_value=0, weight_count=10000, weight_min=0.01, weight_distance_max=1.0, weight_delta_max=1.0, weight_sum_min=-1.0, maximum_weight_mode=False, grid_coverage=0, **kwargs): """Resample the data according to the precomputed X/Y coordinates.""" rows = self.cache["rows"] cols = self.cache["cols"] # if the data is scan based then check its metadata or the passed # kwargs otherwise assume the entire input swath is one large # "scanline" rows_per_scan = kwargs.get('rows_per_scan', data.attrs.get("rows_per_scan", data.shape[0])) if data.ndim == 3 and 'bands' in data.dims: data_in = tuple(data.sel(bands=band).data for band in data['bands']) elif data.ndim == 2: data_in = else: raise ValueError("Unsupported data shape for EWA resampling.") res = dask.delayed(self._call_fornav)( cols, rows, self.target_geo_def, data_in, grid_coverage=grid_coverage, rows_per_scan=rows_per_scan, weight_count=weight_count, weight_min=weight_min, weight_distance_max=weight_distance_max, weight_delta_max=weight_delta_max, weight_sum_min=weight_sum_min, maximum_weight_mode=maximum_weight_mode) if isinstance(data_in, tuple): new_shape = (len(data_in),) + self.target_geo_def.shape else: new_shape = self.target_geo_def.shape data_arr = da.from_delayed(res, new_shape, data.dtype) # from delayed creates one large chunk, break it up a bit if we can data_arr = data_arr.rechunk([CHUNK_SIZE] * data_arr.ndim) if data.ndim == 3 and data.dims[0] == 'bands': dims = ('bands', 'y', 'x') elif data.ndim == 2: dims = ('y', 'x') else: dims = data.dims res = xr.DataArray(data_arr, dims=dims, attrs=data.attrs.copy()) return update_resampled_coords(data, res, self.target_geo_def)
[docs] class BilinearResampler(BaseResampler): """Resample using bilinear interpolation. This resampler implements on-disk caching when the `cache_dir` argument is provided to the `resample` method. This should provide significant performance improvements on consecutive resampling of geostationary data. Args: cache_dir (str): Long term storage directory for intermediate results. radius_of_influence (float): Search radius cut off distance in meters epsilon (float): Allowed uncertainty in meters. Increasing uncertainty reduces execution time. reduce_data (bool): Reduce the input data to (roughly) match the target area. """ def __init__(self, source_geo_def, target_geo_def): """Init BilinearResampler.""" super(BilinearResampler, self).__init__(source_geo_def, target_geo_def) self.resampler = None
[docs] def precompute(self, mask=None, radius_of_influence=50000, epsilon=0, reduce_data=True, cache_dir=False, **kwargs): """Create bilinear coefficients and store them for later use.""" try: from pyresample.bilinear import XArrayBilinearResampler except ImportError: from pyresample.bilinear import XArrayResamplerBilinear as XArrayBilinearResampler del kwargs del mask if self.resampler is None: kwargs = dict(source_geo_def=self.source_geo_def, target_geo_def=self.target_geo_def, radius_of_influence=radius_of_influence, neighbours=32, epsilon=epsilon) self.resampler = XArrayBilinearResampler(**kwargs) try: self.load_bil_info(cache_dir, **kwargs) LOG.debug("Loaded bilinear parameters") except IOError: LOG.debug("Computing bilinear parameters") self.resampler.get_bil_info() LOG.debug("Saving bilinear parameters.") self.save_bil_info(cache_dir, **kwargs)
[docs] def load_bil_info(self, cache_dir, **kwargs): """Load bilinear resampling info from cache directory.""" if cache_dir: filename = self._create_cache_filename(cache_dir, prefix='bil_lut-', **kwargs) try: self.resampler.load_resampling_info(filename) except AttributeError: warnings.warn( "Bilinear resampler can't handle caching, " "please upgrade Pyresample to 0.17.0 or newer.", stacklevel=2 ) raise IOError else: raise IOError
[docs] def save_bil_info(self, cache_dir, **kwargs): """Save bilinear resampling info to cache directory.""" if cache_dir: filename = self._create_cache_filename(cache_dir, prefix='bil_lut-', **kwargs) # There are some old caches, move them out of the way if os.path.exists(filename): _move_existing_caches(cache_dir, filename)'Saving BIL neighbour info to %s', filename) try: self.resampler.save_resampling_info(filename) except AttributeError: warnings.warn( "Bilinear resampler can't handle caching, " "please upgrade Pyresample to 0.17.0 or newer.", stacklevel=2 )
[docs] def compute(self, data, fill_value=None, **kwargs): """Resample the given data using bilinear interpolation.""" del kwargs if fill_value is None: fill_value = data.attrs.get('_FillValue') target_shape = self.target_geo_def.shape res = self.resampler.get_sample_from_bil_info(data, fill_value=fill_value, output_shape=target_shape) return update_resampled_coords(data, res, self.target_geo_def)
[docs] def _move_existing_caches(cache_dir, filename): """Move existing cache files out of the way.""" import os import shutil old_cache_dir = os.path.join(cache_dir, 'moved_by_satpy') try: os.makedirs(old_cache_dir) except FileExistsError: pass try: shutil.move(filename, old_cache_dir) except shutil.Error: os.remove(os.path.join(old_cache_dir, os.path.basename(filename))) shutil.move(filename, old_cache_dir) LOG.warning("Old cache file was moved to %s", old_cache_dir)
[docs] def _mean(data, y_size, x_size): rows, cols = data.shape new_shape = (int(rows / y_size), int(y_size), int(cols / x_size), int(x_size)) data_mean = np.nanmean(data.reshape(new_shape), axis=(1, 3)) return data_mean
[docs] def _repeat_by_factor(data, block_info=None): if block_info is None: return data out_shape = block_info[None]['chunk-shape'] out_data = data for axis, axis_size in enumerate(out_shape): in_size = data.shape[axis] out_data = np.repeat(out_data, int(axis_size / in_size), axis=axis) return out_data
[docs] class NativeResampler(BaseResampler): """Expand or reduce input datasets to be the same shape. If data is higher resolution (more pixels) than the destination area then data is averaged to match the destination resolution. If data is lower resolution (less pixels) than the destination area then data is repeated to match the destination resolution. This resampler does not perform any caching or masking due to the simplicity of the operations. """
[docs] def resample(self, data, cache_dir=None, mask_area=False, **kwargs): """Run NativeResampler.""" # use 'mask_area' with a default of False. It wouldn't do anything. return super(NativeResampler, self).resample(data, cache_dir=cache_dir, mask_area=mask_area, **kwargs)
[docs] @classmethod def _expand_reduce(cls, d_arr, repeats): """Expand reduce.""" if not isinstance(d_arr, da.Array): d_arr = da.from_array(d_arr, chunks=CHUNK_SIZE) if all(x == 1 for x in repeats.values()): return d_arr if all(x >= 1 for x in repeats.values()): return _replicate(d_arr, repeats) if all(x <= 1 for x in repeats.values()): # reduce y_size = 1. / repeats[0] x_size = 1. / repeats[1] return _aggregate(d_arr, y_size, x_size) raise ValueError("Must either expand or reduce in both " "directions")
[docs] def compute(self, data, expand=True, **kwargs): """Resample data with NativeResampler.""" if isinstance(self.target_geo_def, (list, tuple)): # find the highest/lowest area among the provided test_func = max if expand else min target_geo_def = test_func(self.target_geo_def, key=lambda x: x.shape) else: target_geo_def = self.target_geo_def # convert xarray backed with numpy array to dask array if 'x' not in data.dims or 'y' not in data.dims: if data.ndim not in [2, 3]: raise ValueError("Can only handle 2D or 3D arrays without dimensions.") # assume rows is the second to last axis y_axis = data.ndim - 2 x_axis = data.ndim - 1 else: y_axis = data.dims.index('y') x_axis = data.dims.index('x') out_shape = target_geo_def.shape in_shape = data.shape y_repeats = out_shape[0] / float(in_shape[y_axis]) x_repeats = out_shape[1] / float(in_shape[x_axis]) repeats = {axis_idx: 1. for axis_idx in range(data.ndim) if axis_idx not in [y_axis, x_axis]} repeats[y_axis] = y_repeats repeats[x_axis] = x_repeats d_arr = self._expand_reduce(, repeats) new_data = xr.DataArray(d_arr, dims=data.dims) return update_resampled_coords(data, new_data, target_geo_def)
[docs] def _aggregate(d, y_size, x_size): """Average every 4 elements (2x2) in a 2D array.""" if d.ndim != 2: # we can't guarantee what blocks we are getting and how # it should be reshaped to do the averaging. raise ValueError("Can't aggregrate (reduce) data arrays with " "more than 2 dimensions.") if not (x_size.is_integer() and y_size.is_integer()): raise ValueError("Aggregation factors are not integers") y_size = int(y_size) x_size = int(x_size) d = _rechunk_if_nonfactor_chunks(d, y_size, x_size) new_chunks = (tuple(int(x / y_size) for x in d.chunks[0]), tuple(int(x / x_size) for x in d.chunks[1])) return da.core.map_blocks(_mean, d, y_size, x_size, meta=np.array((), dtype=d.dtype), dtype=d.dtype, chunks=new_chunks)
[docs] def _rechunk_if_nonfactor_chunks(dask_arr, y_size, x_size): need_rechunk = False new_chunks = list(dask_arr.chunks) for dim_idx, agg_size in enumerate([y_size, x_size]): if dask_arr.shape[dim_idx] % agg_size != 0: raise ValueError("Aggregation requires arrays with shapes divisible by the factor.") for chunk_size in dask_arr.chunks[dim_idx]: if chunk_size % agg_size != 0: need_rechunk = True new_dim_chunk = lcm(chunk_size, agg_size) new_chunks[dim_idx] = new_dim_chunk if need_rechunk: warnings.warn( "Array chunk size is not divisible by aggregation factor. " "Re-chunking to continue native resampling.", PerformanceWarning, stacklevel=5 ) dask_arr = dask_arr.rechunk(tuple(new_chunks)) return dask_arr
[docs] def _replicate(d_arr, repeats): """Repeat data pixels by the per-axis factors specified.""" repeated_chunks = _get_replicated_chunk_sizes(d_arr, repeats) d_arr = d_arr.map_blocks(_repeat_by_factor, meta=np.array((), dtype=d_arr.dtype), dtype=d_arr.dtype, chunks=repeated_chunks) return d_arr
[docs] def _get_replicated_chunk_sizes(d_arr, repeats): repeated_chunks = [] for axis, axis_chunks in enumerate(d_arr.chunks): factor = repeats[axis] if not factor.is_integer(): raise ValueError("Expand factor must be a whole number") repeated_chunks.append(tuple(x * int(factor) for x in axis_chunks)) return tuple(repeated_chunks)
[docs] def _get_arg_to_pass_for_skipna_handling(**kwargs): """Determine if skipna can be passed to the compute functions for the average and sum bucket resampler.""" # FIXME this can be removed once Pyresample 1.18.0 is a Satpy requirement if PR_USE_SKIPNA: if 'mask_all_nan' in kwargs: warnings.warn( 'Argument mask_all_nan is deprecated. Please use skipna for missing values handling. ' 'Continuing with default skipna=True, if not provided differently.', DeprecationWarning, stacklevel=3 ) kwargs.pop('mask_all_nan') else: if 'mask_all_nan' in kwargs: warnings.warn( 'Argument mask_all_nan is deprecated.' 'Please update Pyresample and use skipna for missing values handling.', DeprecationWarning, stacklevel=3 ) kwargs.setdefault('mask_all_nan', False) kwargs.pop('skipna') return kwargs
[docs] class BucketResamplerBase(BaseResampler): """Base class for bucket resampling which implements averaging.""" def __init__(self, source_geo_def, target_geo_def): """Initialize bucket resampler.""" super(BucketResamplerBase, self).__init__(source_geo_def, target_geo_def) self.resampler = None
[docs] def precompute(self, **kwargs): """Create X and Y indices and store them for later use.""" from pyresample import bucket LOG.debug("Initializing bucket resampler.") source_lons, source_lats = self.source_geo_def.get_lonlats( chunks=CHUNK_SIZE) self.resampler = bucket.BucketResampler(self.target_geo_def, source_lons, source_lats)
[docs] def compute(self, data, **kwargs): """Call the resampling.""" raise NotImplementedError("Use the sub-classes")
[docs] def resample(self, data, **kwargs): """Resample `data` by calling `precompute` and `compute` methods. Args: data (xarray.DataArray): Data to be resampled Returns (xarray.DataArray): Data resampled to the target area """ if not PR_USE_SKIPNA and 'skipna' in kwargs: raise ValueError('You are trying to set the skipna argument but you are using an old version of' ' Pyresample that does not support it.' 'Please update Pyresample to 1.18.0 or higher to be able to use this argument.') self.precompute(**kwargs) attrs = data.attrs.copy() data_arr = if data.ndim == 3 and data.dims[0] == 'bands': dims = ('bands', 'y', 'x') # Both one and two dimensional input data results in 2D output elif data.ndim in (1, 2): dims = ('y', 'x') else: dims = data.dims LOG.debug("Resampling %s", str(data.attrs.get('_satpy_id', 'unknown'))) result = self.compute(data_arr, **kwargs) coords = {} if 'bands' in data.coords: coords['bands'] = data.coords['bands'] # Fractions are returned in a dict elif isinstance(result, dict): coords['categories'] = sorted(result.keys()) dims = ('categories', 'y', 'x') new_result = [] for cat in coords['categories']: new_result.append(result[cat]) result = da.stack(new_result) if result.ndim > len(dims): result = da.squeeze(result) # Adjust some attributes if "BucketFraction" in str(self): attrs['units'] = '' attrs['calibration'] = '' attrs['standard_name'] = 'area_fraction' elif "BucketCount" in str(self): attrs['units'] = '' attrs['calibration'] = '' attrs['standard_name'] = 'number_of_observations' result = xr.DataArray(result, dims=dims, coords=coords, attrs=attrs) return update_resampled_coords(data, result, self.target_geo_def)
[docs] class BucketAvg(BucketResamplerBase): """Class for averaging bucket resampling. Bucket resampling calculates the average of all the values that are closest to each bin and inside the target area. Parameters ---------- fill_value : float (default: np.nan) Fill value to mark missing/invalid values in the input data, as well as in the binned and averaged output data. skipna : boolean (default: True) If True, skips missing values (as marked by NaN or `fill_value`) for the average calculation (similarly to Numpy's `nanmean`). Buckets containing only missing values are set to fill_value. If False, sets the bucket to fill_value if one or more missing values are present in the bucket (similarly to Numpy's `mean`). In both cases, empty buckets are set to `fill_value`. """
[docs] def compute(self, data, fill_value=np.nan, skipna=True, **kwargs): """Call the resampling. Args: data (numpy.Array, dask.Array): Data to be resampled fill_value (numpy.nan, int): fill_value. Defaults to numpy.nan skipna (boolean): Skip NA's. Default `True` Returns: dask.Array """ kwargs = _get_arg_to_pass_for_skipna_handling(skipna=skipna, **kwargs) results = [] if data.ndim == 3: for i in range(data.shape[0]): res = self.resampler.get_average(data[i, :, :], fill_value=fill_value, **kwargs) results.append(res) else: res = self.resampler.get_average(data, fill_value=fill_value, **kwargs) results.append(res) return da.stack(results)
[docs] class BucketSum(BucketResamplerBase): """Class for bucket resampling which implements accumulation (sum). This resampler calculates the cumulative sum of all the values that are closest to each bin and inside the target area. Parameters ---------- fill_value : float (default: np.nan) Fill value for missing data skipna : boolean (default: True) If True, skips NaN values for the sum calculation (similarly to Numpy's `nansum`). Buckets containing only NaN are set to zero. If False, sets the bucket to NaN if one or more NaN values are present in the bucket (similarly to Numpy's `sum`). In both cases, empty buckets are set to 0. """
[docs] def compute(self, data, skipna=True, **kwargs): """Call the resampling.""" kwargs = _get_arg_to_pass_for_skipna_handling(skipna=skipna, **kwargs) results = [] if data.ndim == 3: for i in range(data.shape[0]): res = self.resampler.get_sum(data[i, :, :], **kwargs) results.append(res) else: res = self.resampler.get_sum(data, **kwargs) results.append(res) return da.stack(results)
[docs] class BucketCount(BucketResamplerBase): """Class for bucket resampling which implements hit-counting. This resampler calculates the number of occurences of the input data closest to each bin and inside the target area. """
[docs] def compute(self, data, **kwargs): """Call the resampling.""" results = [] if data.ndim == 3: for _i in range(data.shape[0]): res = self.resampler.get_count() results.append(res) else: res = self.resampler.get_count() results.append(res) return da.stack(results)
[docs] class BucketFraction(BucketResamplerBase): """Class for bucket resampling to compute category fractions. This resampler calculates the fraction of occurences of the input data per category. """
[docs] def compute(self, data, fill_value=np.nan, categories=None, **kwargs): """Call the resampling.""" if data.ndim > 2: raise ValueError("BucketFraction not implemented for 3D datasets") result = self.resampler.get_fractions(data, categories=categories, fill_value=fill_value) return result
# TODO: move this to pyresample.resampler RESAMPLERS = {"kd_tree": KDTreeResampler, "nearest": KDTreeResampler, "bilinear": BilinearResampler, "native": NativeResampler, "gradient_search": GradientSearchResampler, "bucket_avg": BucketAvg, "bucket_sum": BucketSum, "bucket_count": BucketCount, "bucket_fraction": BucketFraction, } if DaskEWAResampler is not None: RESAMPLERS['ewa'] = DaskEWAResampler RESAMPLERS['ewa_legacy'] = LegacyDaskEWAResampler else: RESAMPLERS['ewa'] = _LegacySatpyEWAResampler # deepcode ignore PythonSameEvalBinaryExpressiontrue: PRBaseResampler is None only on import errors if PRBaseResampler is None: PRBaseResampler = BaseResampler # TODO: move this to pyresample
[docs] def prepare_resampler(source_area, destination_area, resampler=None, **resample_kwargs): """Instantiate and return a resampler.""" if resampler is None:"Using default KDTree resampler") resampler = 'kd_tree' if isinstance(resampler, (BaseResampler, PRBaseResampler)): raise ValueError("Trying to create a resampler when one already " "exists.") if isinstance(resampler, str): resampler_class = RESAMPLERS.get(resampler, None) if resampler_class is None: if resampler == "gradient_search": warnings.warn( 'Gradient search resampler not available. Maybe missing `shapely`?', stacklevel=2 ) raise KeyError("Resampler '%s' not available" % resampler) else: resampler_class = resampler key = (resampler_class, source_area, destination_area, hash_dict(resample_kwargs).hexdigest()) try: resampler_instance = resamplers_cache[key] except KeyError: resampler_instance = resampler_class(source_area, destination_area) resamplers_cache[key] = resampler_instance return key, resampler_instance
# TODO: move this to pyresample
[docs] def resample(source_area, data, destination_area, resampler=None, **kwargs): """Do the resampling.""" if not isinstance(resampler, (BaseResampler, PRBaseResampler)): # we don't use the first argument (cache key) _, resampler_instance = prepare_resampler(source_area, destination_area, resampler) else: resampler_instance = resampler if isinstance(data, list): res = [resampler_instance.resample(ds, **kwargs) for ds in data] else: res = resampler_instance.resample(data, **kwargs) return res
[docs] def get_fill_value(dataset): """Get the fill value of the *dataset*, defaulting to np.nan.""" if np.issubdtype(dataset.dtype, np.integer): return dataset.attrs.get('_FillValue', np.nan) return np.nan
[docs] def resample_dataset(dataset, destination_area, **kwargs): """Resample *dataset* and return the resampled version. Args: dataset (xarray.DataArray): Data to be resampled. destination_area: The destination onto which to project the data, either a full blown area definition or a string corresponding to the name of the area as defined in the area file. **kwargs: The extra parameters to pass to the resampler objects. Returns: A resampled DataArray with updated ``.attrs["area"]`` field. The dtype of the array is preserved. """ # call the projection stuff here try: source_area = dataset.attrs["area"] except KeyError:"Cannot reproject dataset %s, missing area info", dataset.attrs['name']) return dataset fill_value = kwargs.pop('fill_value', get_fill_value(dataset)) new_data = resample(source_area, dataset, destination_area, fill_value=fill_value, **kwargs) new_attrs = new_data.attrs new_data.attrs = dataset.attrs.copy() new_data.attrs.update(new_attrs) new_data.attrs.update(area=destination_area) return new_data