pyresample API¶

pyresample.geometry¶

Classes for geometry operations.

class pyresample.geometry.AreaDefinition(area_id, description, proj_id, projection, width, height, area_extent, rotation=None, nprocs=1, lons=None, lats=None, dtype=<class 'numpy.float64'>)¶

Holds definition of an area.

Parameters:

area_id (str) – Identifier for the area
description (str) – Human-readable description of the area
proj_id (str) – ID of projection
projection (dict or str) – Dictionary or string of Proj.4 parameters
width (int) – x dimension in number of pixels, aka number of grid columns
height (int) – y dimension in number of pixels, aka number of grid rows
rotation (float) – rotation in degrees (negative is cw)
area_extent (list) – Area extent as a list (lower_left_x, lower_left_y, upper_right_x, upper_right_y)
nprocs (int, optional) – Number of processor cores to be used for certain calculations

area_id¶

Identifier for the area

Type:	str

description¶

Human-readable description of the area

Type:	str

proj_id¶

ID of projection

Type:	str

projection¶

Dictionary or string with Proj.4 parameters

Type:	dict or str

width¶

x dimension in number of pixels, aka number of grid columns

Type:	int

height¶

y dimension in number of pixels, aka number of grid rows

Type:	int

rotation¶

rotation in degrees (negative is cw)

Type:	float

shape¶

Corresponding array shape as (height, width)

Type:	tuple

size¶

Number of points in grid

Type:	int

area_extent¶

Area extent as a tuple (lower_left_x, lower_left_y, upper_right_x, upper_right_y)

Type:	tuple

area_extent_ll¶

Area extent in lons lats as a tuple (lower_left_lon, lower_left_lat, upper_right_lon, upper_right_lat)

Type:	tuple

pixel_size_x¶

Pixel width in projection units

Type:	float

pixel_size_y¶

Pixel height in projection units

Type:	float

resolution¶

the resolution of the resulting area as (pixel_size_x, pixel_size_y).

Type:	tuple

upper_left_extent¶

Coordinates (x, y) of upper left corner of upper left pixel in projection units

Type:	tuple

pixel_upper_left¶

Coordinates (x, y) of center of upper left pixel in projection units

Type:	tuple

pixel_offset_x¶

x offset between projection center and upper left corner of upper left pixel in units of pixels.

Type:	float

pixel_offset_y¶

y offset between projection center and upper left corner of upper left pixel in units of pixels..

Type:	float

proj_str¶

Projection defined as Proj.4 string

Type:	str

cartesian_coords¶

Grid cartesian coordinates

Type:	object

projection_x_coords¶

Grid projection x coordinate

Type:	object

projection_y_coords¶

Grid projection y coordinate

Type:	object

aggregate(**dims)¶: Return an aggregated version of the area.

colrow2lonlat(cols, rows)¶

Return lons and lats for the given image columns and rows.

Both scalars and arrays are supported. To be used with scarse data points instead of slices (see get_lonlats).

copy(**override_kwargs)¶

Make a copy of the current area.

This replaces the current values with anything in override_kwargs.

create_areas_def()¶: Generate YAML formatted representation of this area.

create_areas_def_legacy()¶: Create area definition in legacy format.

crop_around(other_area)¶: Crop this area around other_area.

classmethod from_area_of_interest(area_id, projection, shape, center, resolution, units=None, **kwargs)¶

Create an AreaDefinition from center, resolution, and shape.

Parameters:	area_id (str) – ID of area projection (dict or str) – Projection parameters as a proj4_dict or proj4_string shape (list) – Number of pixels in the y and x direction (height, width) center (list) – Center of projection (x, y) resolution (list or float) – Size of pixels: (dx, dy). Can be specified with one value if dx == dy units (str, optional) – Units that provided arguments should be interpreted as. This can be one of ‘deg’, ‘degrees’, ‘meters’, ‘metres’, and any parameter supported by the cs2cs -lu command. Units are determined in the following priority: units expressed with each variable through a DataArray’s attrs attribute. units passed to `units` units used in `projection` meters description (str, optional) – Description/name of area. Defaults to area_id proj_id (str, optional) – ID of projection rotation (float, optional) – rotation in degrees (negative is cw) nprocs (int, optional) – Number of processor cores to be used lons (numpy array, optional) – Grid lons lats (numpy array, optional) – Grid lats
Returns:	AreaDefinition
Return type:	AreaDefinition

classmethod from_circle(area_id, projection, center, radius, shape=None, resolution=None, units=None, **kwargs)¶

Create an AreaDefinition from center, radius, and shape or from center, radius, and resolution.

Parameters:	area_id (str) – ID of area projection (dict or str) – Projection parameters as a proj4_dict or proj4_string center (list) – Center of projection (x, y) radius (list or float) – Length from the center to the edges of the projection (dx, dy) shape (list, optional) – Number of pixels in the y and x direction (height, width) resolution (list or float, optional) – Size of pixels: (dx, dy) units (str, optional) – Units that provided arguments should be interpreted as. This can be one of ‘deg’, ‘degrees’, ‘meters’, ‘metres’, and any parameter supported by the cs2cs -lu command. Units are determined in the following priority: units expressed with each variable through a DataArray’s attrs attribute. units passed to `units` units used in `projection` meters description (str, optional) – Description/name of area. Defaults to area_id proj_id (str, optional) – ID of projection rotation (float, optional) – rotation in degrees (negative is cw) nprocs (int, optional) – Number of processor cores to be used lons (numpy array, optional) – Grid lons lats (numpy array, optional) – Grid lats optimize_projection – Whether the projection parameters have to be optimized for a DynamicAreaDefinition.
Returns:	AreaDefinition or DynamicAreaDefinition – If shape or resolution are provided, an AreaDefinition object is returned. Else a DynamicAreaDefinition object is returned
Return type:	AreaDefinition or DynamicAreaDefinition

Notes

resolution and radius can be specified with one value if dx == dy

classmethod from_extent(area_id, projection, shape, area_extent, units=None, **kwargs)¶

Create an AreaDefinition object from area_extent and shape.

Parameters:	area_id (str) – ID of area projection (dict or str) – Projection parameters as a proj4_dict or proj4_string shape (list) – Number of pixels in the y and x direction (height, width) area_extent (list) – Area extent as a list (lower_left_x, lower_left_y, upper_right_x, upper_right_y) units (str, optional) – Units that provided arguments should be interpreted as. This can be one of ‘deg’, ‘degrees’, ‘meters’, ‘metres’, and any parameter supported by the cs2cs -lu command. Units are determined in the following priority: units expressed with each variable through a DataArray’s attrs attribute. units passed to `units` units used in `projection` meters description (str, optional) – Description/name of area. Defaults to area_id proj_id (str, optional) – ID of projection rotation (float, optional) – rotation in degrees (negative is cw) nprocs (int, optional) – Number of processor cores to be used lons (numpy array, optional) – Grid lons lats (numpy array, optional) – Grid lats
Returns:	AreaDefinition
Return type:	AreaDefinition

classmethod from_ul_corner(area_id, projection, shape, upper_left_extent, resolution, units=None, **kwargs)¶

Create an AreaDefinition object from upper_left_extent, resolution, and shape.

Parameters:	area_id (str) – ID of area projection (dict or str) – Projection parameters as a proj4_dict or proj4_string shape (list) – Number of pixels in the y and x direction (height, width) upper_left_extent (list) – Upper left corner of upper left pixel (x, y) resolution (list or float) – Size of pixels in meters: (dx, dy). Can be specified with one value if dx == dy units (str, optional) – Units that provided arguments should be interpreted as. This can be one of ‘deg’, ‘degrees’, ‘meters’, ‘metres’, and any parameter supported by the cs2cs -lu command. Units are determined in the following priority: units expressed with each variable through a DataArray’s attrs attribute. units passed to `units` units used in `projection` meters description (str, optional) – Description/name of area. Defaults to area_id proj_id (str, optional) – ID of projection rotation (float, optional) – rotation in degrees (negative is cw) nprocs (int, optional) – Number of processor cores to be used lons (numpy array, optional) – Grid lons lats (numpy array, optional) – Grid lats
Returns:	AreaDefinition
Return type:	AreaDefinition

geocentric_resolution(ellps='WGS84', radius=None)¶: Find best estimate for overall geocentric resolution.

get_area_slices(area_to_cover, shape_divisible_by=None)¶: Compute the slice to read based on an area_to_cover.

get_lonlat(row, col)¶

Retrieve lon and lat values of single point in area grid.

Parameters:	row (int) – col (int) –
Returns:	(lon, lat)
Return type:	tuple of floats

get_lonlats(nprocs=None, data_slice=None, cache=False, dtype=None, chunks=None)¶

Return lon and lat arrays of area.

Parameters:	nprocs (int, optional) – Number of processor cores to be used. Defaults to the nprocs set when instantiating object data_slice (slice object, optional) – Calculate only coordinates for specified slice cache (bool, optional) – Store result the result. Requires data_slice to be None dtype (numpy.dtype, optional) – Data type of the returned arrays chunks (int or tuple, optional) – Create dask arrays and use this chunk size
Returns:	(lons, lats) – Grids of area lons and and lats
Return type:	tuple of numpy arrays

get_lonlats_dask(chunks=None, dtype=None)¶: Get longitudes and latitudes.

get_proj_coords(data_slice=None, dtype=None, chunks=None)¶

Get projection coordinates of grid.

Parameters:

data_slice (slice object, optional) – Calculate only coordinates for specified slice
dtype (numpy.dtype, optional) – Data type of the returned arrays
chunks (int or tuple, optional) – Create dask arrays and use this chunk size

Returns:

(target_x, target_y) (tuple of numpy arrays) – Grids of area x- and y-coordinates in projection units
.. versionchanged:: 1.11.0 – Removed ‘cache’ keyword argument and add ‘chunks’ for creating dask arrays.

get_proj_coords_dask(chunks=None, dtype=None)¶: Get projection coordinates.

get_proj_vectors(dtype=None, chunks=None)¶

Calculate 1D projection coordinates for the X and Y dimension.

Parameters:

dtype (numpy.dtype) – Numpy data type for the returned arrays
chunks (int or tuple) – Return dask arrays with the chunk size specified. If this is a tuple then the first element is the Y array’s chunk size and the second is the X array’s chunk size.

Returns:

tuple ((X, Y) where X and Y are 1-dimensional numpy arrays)
The data type of the returned arrays can be controlled with the
dtype keyword argument. If chunks is provided then dask arrays
are returned instead.

get_proj_vectors_dask(chunks=None, dtype=None)¶: Get projection vectors.

get_xy_from_lonlat(lon, lat)¶

Retrieve closest x and y coordinates.

Retrieve closest x and y coordinates (column, row indices) for the specified geolocation (lon,lat) if inside area. If lon,lat is a point a ValueError is raised if the return point is outside the area domain. If lon,lat is a tuple of sequences of longitudes and latitudes, a tuple of masked arrays are returned.

Input:

lon : point or sequence (list or array) of longitudes lat : point or sequence (list or array) of latitudes

Returns:

(x, y) : tuple of integer points/arrays

get_xy_from_proj_coords(xm, ym)¶

Find closest grid cell index for a specified projection coordinate.

If xm, ym is a tuple of sequences of projection coordinates, a tuple of masked arrays are returned.

Parameters:	xm (list or array) – point or sequence of x-coordinates in meters (map projection) ym (list or array) – point or sequence of y-coordinates in meters (map projection)
Returns:	column and row grid cell indexes as 2 scalars or arrays
Return type:	x, y
Raises:	`ValueError` – if the return point is outside the area domain

lonlat2colrow(lons, lats)¶

Return image columns and rows for the given lons and lats.

Both scalars and arrays are supported. Same as get_xy_from_lonlat, renamed for convenience.

name¶: Return area name.

outer_boundary_corners¶: Return the lon,lat of the outer edges of the corner points.

proj4_string¶: Return projection definition as Proj.4 string.

proj_dict¶: Return the projection dictionary.

proj_str: Return PROJ projection string.

projection_x_coords: Return projection X coordinates.

projection_y_coords: Return projection Y coordinates.

resolution: Return area resolution in X and Y direction.

shape: Return area shape.

to_cartopy_crs()¶: Convert projection to cartopy CRS object.

update_hash(the_hash=None)¶: Update a hash, or return a new one if needed.

x_size¶: Return area width.

y_size¶: Return area height.

class pyresample.geometry.BaseDefinition(lons=None, lats=None, nprocs=1)¶

Base class for geometry definitions.

Changed in version 1.8.0: BaseDefinition no longer checks the validity of the provided longitude and latitude coordinates to improve performance. Longitude arrays are expected to be between -180 and 180 degrees, latitude -90 to 90 degrees. Use check_and_wrap() to preprocess your arrays.

corners¶: Return the corners of the current area.

get_area()¶: Get the area of the convex area defined by the corners of the curren area.

get_area_extent_for_subset(row_LR, col_LR, row_UL, col_UL)¶

Calculate extent for a subdomain of this area.

Rows are counted from upper left to lower left and columns are counted from upper left to upper right.

Parameters:	row_LR (int) – row of the lower right pixel col_LR (int) – col of the lower right pixel row_UL (int) – row of the upper left pixel col_UL (int) – col of the upper left pixel
Returns:	Area extent (LL_x, LL_y, UR_x, UR_y) of the subset
Return type:	area_extent (tuple)

Author:: Ulrich Hamann

get_area_slices(area_to_cover)¶: Compute the slice to read based on an area_to_cover.

get_bbox_lonlats()¶: Return the bounding box lons and lats.

get_boundary_lonlats()¶: Return Boundary objects.

get_cartesian_coords(nprocs=None, data_slice=None, cache=False)¶

Retrieve cartesian coordinates of geometry definition.

Parameters:	nprocs (int, optional) – Number of processor cores to be used. Defaults to the nprocs set when instantiating object data_slice (slice object, optional) – Calculate only cartesian coordnates for the defined slice cache (bool, optional) – Store result the result. Requires data_slice to be None
Returns:	cartesian_coords
Return type:	numpy array

get_lonlat(row, col)¶

Retrieve lon and lat of single pixel.

Parameters:	row (int) – col (int) –
Returns:	(lon, lat)
Return type:	tuple of floats

get_lonlats(data_slice=None, chunks=None, **kwargs)¶

Get longitude and latitude arrays representing this geometry.

Returns:	(lon, lat) – If chunks is provided then the arrays will be dask arrays with the provided chunk size. If chunks is not provided then the returned arrays are the same as the internal data types of this geometry object (numpy or dask).
Return type:	tuple of numpy arrays

get_lonlats_dask(chunks=None)¶: Get the lon lats as a single dask array.

intersection(other)¶

Return the corners of the intersection polygon of the current area with other.

Parameters:	other (object) – Instance of subclass of BaseDefinition
Returns:	(corner1, corner2, corner3, corner4)
Return type:	tuple of points

overlap_rate(other)¶

Get how much the current area overlaps an other area.

Parameters:	other (object) – Instance of subclass of BaseDefinition
Returns:	overlap_rate
Return type:	float

overlaps(other)¶

Test if the current area overlaps the other area.

This is based solely on the corners of areas, assuming the boundaries to be great circles.

Parameters:	other (object) – Instance of subclass of BaseDefinition
Returns:	overlaps
Return type:	bool

class pyresample.geometry.CoordinateDefinition(lons, lats, nprocs=1)¶

Base class for geometry definitions defined by lons and lats only.

append(other)¶: Append another coordinate definition to existing one.

concatenate(other)¶: Concatenate coordinate definitions.

geocentric_resolution(ellps='WGS84', radius=None, nadir_factor=2)¶

Calculate maximum geocentric pixel resolution.

If lons is a xarray.DataArray object with a resolution attribute, this will be used instead of loading the longitude and latitude data. In this case the resolution attribute is assumed to mean the nadir resolution of a swath and will be multiplied by the nadir_factor to adjust for increases in the spatial resolution towards the limb of the swath.

Parameters:

ellps (str) – PROJ Ellipsoid for the Cartographic projection used as the target geocentric coordinate reference system. Default: ‘WGS84’. Ignored if radius is provided.
radius (float) – Spherical radius of the Earth to use instead of the definitions in ellps.
nadir_factor (int) – Number to multiply the nadir resolution attribute by to reflect pixel size on the limb of the swath.

Returns: Estimated maximum pixel size in meters on a geocentric: coordinate system (X, Y, Z) representing the Earth.
Raises: RuntimeError if a simple search for valid longitude/latitude: data points found no valid data points.

exception pyresample.geometry.DimensionError¶: Wrap ValueError.

class pyresample.geometry.DynamicAreaDefinition(area_id=None, description=None, projection=None, width=None, height=None, area_extent=None, resolution=None, optimize_projection=False, rotation=None)¶

An AreaDefintion containing just a subset of the needed parameters.

The purpose of this class is to be able to adapt the area extent and shape of the area to a given set of longitudes and latitudes, such that e.g. polar satellite granules can be resampled optimally to a given projection.

Parameters:

area_id – The name of the area.
description – The description of the area.
projection – The dictionary or string of projection parameters. Doesn’t have to be complete. If not complete, proj_info must be provided to freeze to “fill in” any missing parameters.
width – x dimension in number of pixels, aka number of grid columns
height – y dimension in number of pixels, aka number of grid rows
shape – Corresponding array shape as (height, width)
area_extent – The area extent of the area.
pixel_size_x – Pixel width in projection units
pixel_size_y – Pixel height in projection units
resolution – Resolution of the resulting area as (pixel_size_x, pixel_size_y) or a scalar if pixel_size_x == pixel_size_y.
optimize_projection – Whether the projection parameters have to be optimized.
rotation – Rotation in degrees (negative is cw)

compute_domain(corners, resolution=None, shape=None)¶

Compute shape and area_extent from corners and [shape or resolution] info.

Corners represents the center of pixels, while area_extent represents the edge of pixels.

Note that shape is (rows, columns) and resolution is (x_size, y_size); the dimensions are flipped.

freeze(lonslats=None, resolution=None, shape=None, proj_info=None)¶

Create an AreaDefinition from this area with help of some extra info.

Parameters:

lonlats (SwathDefinition or tuple) – The geographical coordinates to contain in the resulting area. A tuple should be (lons, lats).
resolution – the resolution of the resulting area.
shape – the shape of the resulting area.
proj_info – complementing parameters to the projection info.
and shape parameters are ignored if the instance is created (Resolution) –
the optimize_projection flag set to True. (with) –

pixel_size_x¶: Return pixel size in X direction.

pixel_size_y¶: Return pixel size in Y direction.

class pyresample.geometry.GridDefinition(lons, lats, nprocs=1)¶

Grid defined by lons and lats.

Parameters:	lons (numpy array) – lats (numpy array) – nprocs (int, optional) – Number of processor cores to be used for calculations.

shape¶

Grid shape as (rows, cols)

Type:	tuple

size¶

Number of elements in grid

Type:	int

lons¶

Grid lons

Type:	object

lats¶

Grid lats

Type:	object

cartesian_coords¶

Grid cartesian coordinates

Type:	object

exception pyresample.geometry.IncompatibleAreas¶: Error when the areas to combine are not compatible.

class pyresample.geometry.StackedAreaDefinition(*definitions, **kwargs)¶

Definition based on muliple vertically stacked AreaDefinitions.

append(definition)¶: Append another definition to the area.

get_lonlats(nprocs=None, data_slice=None, cache=False, dtype=None, chunks=None)¶: Return lon and lat arrays of the area.

get_lonlats_dask(chunks=None, dtype=None)¶: Return lon and lat dask arrays of the area.

height¶: Return height of the area definition.

proj4_string¶: Return projection definition as Proj.4 string.

proj_str¶: Return projection definition as Proj.4 string.

shape¶: Return shape of the area definition.

size¶: Return size of the area definition.

squeeze()¶: Generate a single AreaDefinition if possible.

update_hash(the_hash=None)¶: Update the hash.

width¶: Return width of the area definition.

x_size¶: Return width of the area definition.

y_size¶: Return height of the area definition.

class pyresample.geometry.SwathDefinition(lons, lats, nprocs=1)¶

Swath defined by lons and lats.

Parameters:	lons (numpy array) – lats (numpy array) – nprocs (int, optional) – Number of processor cores to be used for calculations.

shape¶

Swath shape

Type:	tuple

size¶

Number of elements in swath

Type:	int

ndims¶

Swath dimensions

Type:	int

lons¶

Swath lons

Type:	object

lats¶

Swath lats

Type:	object

cartesian_coords¶

Swath cartesian coordinates

Type:	object

aggregate(**dims)¶

Aggregate the current swath definition by averaging.

For example, averaging over 2x2 windows: sd.aggregate(x=2, y=2)

compute_bb_proj_params(proj_dict)¶: Compute BB projection parameters.

compute_optimal_bb_area(proj_dict=None)¶

Compute the “best” bounding box area for this swath with proj_dict.

By default, the projection is Oblique Mercator (omerc in proj.4), in which case the right projection angle alpha is computed from the swath centerline. For other projections, only the appropriate center of projection and area extents are computed.

The height and width are computed so that the resolution is approximately the same across dimensions.

copy()¶: Copy the current swath.

get_edge_lonlats()¶: Get the concatenated boundary of the current swath.

update_hash(the_hash=None)¶: Update the hash.

pyresample.geometry.combine_area_extents_vertical(area1, area2)¶: Combine the area extents of areas 1 and 2.

pyresample.geometry.concatenate_area_defs(area1, area2, axis=0)¶: Append area2 to area1 and return the results.

pyresample.geometry.get_array_hashable(arr)¶

Compute a hashable form of the array arr.

Works with numpy arrays, dask.array.Array, and xarray.DataArray.

pyresample.geometry.get_geostationary_angle_extent(geos_area)¶: Get the max earth (vs space) viewing angles in x and y.

pyresample.geometry.get_geostationary_bounding_box(geos_area, nb_points=50)¶

Get the bbox in lon/lats of the valid pixels inside geos_area.

Parameters:	nb_points – Number of points on the polygon

pyresample.geometry.invproj(data_x, data_y, proj_dict)¶: Perform inverse projection.

pyresample.geometry.ordered_dump(data, stream=None, Dumper=<class 'yaml.dumper.Dumper'>, **kwds)¶: Dump the data to YAML in ordered fashion.

pyresample.image¶

Handles resampling of images with assigned geometry definitions

class pyresample.image.ImageContainer(image_data, geo_def, fill_value=0, nprocs=1)¶

Holds image with geometry definition. Allows indexing with linesample arrays.

Parameters:	image_data (numpy array) – Image data geo_def (object) – Geometry definition fill_value (int or None, optional) – Set undetermined pixels to this value. If fill_value is None a masked array is returned with undetermined pixels masked nprocs (int, optional) – Number of processor cores to be used

image_data¶

Image data

Type:	numpy array

geo_def¶

Geometry definition

Type:	object

fill_value¶

Resample result fill value

Type:	int or None

nprocs¶

Number of processor cores to be used for geometry operations

Type:	int

get_array_from_linesample(row_indices, col_indices)¶

Samples from image based on index arrays.

Parameters:	row_indices (numpy array) – Row indices. Dimensions must match col_indices col_indices (numpy array) – Col indices. Dimensions must match row_indices
Returns:	image_data – Resampled image data
Return type:	numpy_array

get_array_from_neighbour_info(*args, **kwargs)¶: Base method for resampling from preprocessed data.

resample(target_geo_def)¶: Base method for resampling

class pyresample.image.ImageContainerBilinear(image_data, geo_def, radius_of_influence, epsilon=0, fill_value=0, reduce_data=False, nprocs=1, segments=None, neighbours=32)¶

Holds image with geometry definition. Allows bilinear to new geometry definition.

Parameters:

image_data (numpy array) – Image data
geo_def (object) – Geometry definition
radius_of_influence (float) – Cut off distance in meters
epsilon (float, optional) – Allowed uncertainty in meters. Increasing uncertainty reduces execution time
fill_value (int or None, optional) – Set undetermined pixels to this value. If fill_value is None a masked array is returned with undetermined pixels masked
reduce_data (bool, optional) – Perform coarse data reduction before resampling in order to reduce execution time
nprocs (int, optional) – Number of processor cores to be used for geometry operations
segments (int or None) – Number of segments to use when resampling. If set to None an estimate will be calculated

image_data¶

Image data

Type:	numpy array

geo_def¶

Geometry definition

Type:	object

radius_of_influence¶

Cut off distance in meters

Type:	float

epsilon¶

Allowed uncertainty in meters

Type:	float

fill_value¶

Resample result fill value

Type:	int or None

reduce_data¶

Perform coarse data reduction before resampling

Type:	bool

nprocs¶

Number of processor cores to be used

Type:	int

segments¶

Number of segments to use when resampling

Type:	int or None

resample(target_geo_def)¶

Resamples image to area definition using bilinear approach

Parameters:	target_geo_def (object) – Target geometry definition
Returns:	image_container – ImageContainerBilinear object of resampled geometry
Return type:	object

class pyresample.image.ImageContainerNearest(image_data, geo_def, radius_of_influence, epsilon=0, fill_value=0, reduce_data=True, nprocs=1, segments=None)¶

Holds image with geometry definition. Allows nearest neighbour to new geometry definition.

Parameters:

image_data (numpy array) – Image data
geo_def (object) – Geometry definition
radius_of_influence (float) – Cut off distance in meters
epsilon (float, optional) – Allowed uncertainty in meters. Increasing uncertainty reduces execution time
fill_value (int or None, optional) – Set undetermined pixels to this value. If fill_value is None a masked array is returned with undetermined pixels masked
reduce_data (bool, optional) – Perform coarse data reduction before resampling in order to reduce execution time
nprocs (int, optional) – Number of processor cores to be used for geometry operations
segments (int or None) – Number of segments to use when resampling. If set to None an estimate will be calculated

image_data¶

Image data

Type:	numpy array

geo_def¶

Geometry definition

Type:	object

radius_of_influence¶

Cut off distance in meters

Type:	float

epsilon¶

Allowed uncertainty in meters

Type:	float

fill_value¶

Resample result fill value

Type:	int or None

reduce_data¶

Perform coarse data reduction before resampling

Type:	bool

nprocs¶

Number of processor cores to be used

Type:	int

segments¶

Number of segments to use when resampling

Type:	int or None

resample(target_geo_def)¶

Resamples image to area definition using nearest neighbour approach

Parameters:	target_geo_def (object) – Target geometry definition
Returns:	image_container – ImageContainerNearest object of resampled geometry
Return type:	object

class pyresample.image.ImageContainerQuick(image_data, geo_def, fill_value=0, nprocs=1, segments=None)¶

Holds image with area definition. ‘ Allows quick resampling within area.

Parameters:

image_data (numpy array) – Image data
geo_def (object) – Area definition as AreaDefinition object
fill_value (int or None, optional) – Set undetermined pixels to this value. If fill_value is None a masked array is returned with undetermined pixels masked
nprocs (int, optional) – Number of processor cores to be used for geometry operations
segments (int or None) – Number of segments to use when resampling. If set to None an estimate will be calculated

image_data¶

Image data

Type:	numpy array

geo_def¶

Area definition as AreaDefinition object

Type:	object

fill_value¶

Resample result fill value If fill_value is None a masked array is returned with undetermined pixels masked

Type:	int or None

nprocs¶

Number of processor cores to be used

Type:	int

segments¶

Number of segments to use when resampling

Type:	int or None

resample(target_area_def)¶

Resamples image to area definition using nearest neighbour approach in projection coordinates.

Parameters:	target_area_def (object) – Target area definition as AreaDefinition object
Returns:	image_container – ImageContainerQuick object of resampled area
Return type:	object

pyresample.grid¶

Resample image from one projection to another using nearest neighbour method in cartesian projection coordinate systems

pyresample.grid.get_image_from_linesample(row_indices, col_indices, source_image, fill_value=0)¶

Samples from image based on index arrays.

Parameters:	row_indices (numpy array) – Row indices. Dimensions must match col_indices col_indices (numpy array) – Col indices. Dimensions must match row_indices source_image (numpy array) – Source image fill_value (int or None, optional) – Set undetermined pixels to this value. If fill_value is None a masked array is returned with undetermined pixels masked
Returns:	image_data – Resampled image
Return type:	numpy array

pyresample.grid.get_image_from_lonlats(lons, lats, source_area_def, source_image_data, fill_value=0, nprocs=1)¶

Samples from image based on lon lat arrays using nearest neighbour method in cartesian projection coordinate systems.

Parameters:	lons (numpy array) – Lons. Dimensions must match lats lats (numpy array) – Lats. Dimensions must match lons source_area_def (object) – Source definition as AreaDefinition object source_image_data (numpy array) – Source image data fill_value (int or None, optional) – Set undetermined pixels to this value. If fill_value is None a masked array is returned with undetermined pixels masked nprocs (int, optional) – Number of processor cores to be used
Returns:	image_data – Resampled image data
Return type:	numpy array

pyresample.grid.get_linesample(lons, lats, source_area_def, nprocs=1)¶

Returns index row and col arrays for resampling

Parameters:	lons (numpy array) – Lons. Dimensions must match lats lats (numpy array) – Lats. Dimensions must match lons source_area_def (object) – Source definition as AreaDefinition object nprocs (int, optional) – Number of processor cores to be used
Returns:	(row_indices, col_indices) – Arrays for resampling area by array indexing
Return type:	tuple of numpy arrays

pyresample.grid.get_resampled_image(target_area_def, source_area_def, source_image_data, fill_value=0, nprocs=1, segments=None)¶

Resamples image using nearest neighbour method in cartesian projection coordinate systems.

Parameters:	target_area_def (object) – Target definition as AreaDefinition object source_area_def (object) – Source definition as AreaDefinition object source_image_data (numpy array) – Source image data fill_value ({int, None} optional) – Set undetermined pixels to this value. If fill_value is None a masked array is returned with undetermined pixels masked nprocs (int, optional) – Number of processor cores to be used segments ({int, None} optional) – Number of segments to use when resampling. If set to None an estimate will be calculated.
Returns:	image_data – Resampled image data
Return type:	numpy array

pyresample.kd_tree¶

Handles reprojection of geolocated data. Several types of resampling are supported

exception pyresample.kd_tree.EmptyResult¶

pyresample.kd_tree.get_neighbour_info(source_geo_def, target_geo_def, radius_of_influence, neighbours=8, epsilon=0, reduce_data=True, nprocs=1, segments=None)¶

Returns neighbour info

Parameters:

source_geo_def (object) – Geometry definition of source
target_geo_def (object) – Geometry definition of target
radius_of_influence (float) – Cut off distance in meters
neighbours (int, optional) – The number of neigbours to consider for each grid point
epsilon (float, optional) – Allowed uncertainty in meters. Increasing uncertainty reduces execution time
reduce_data (bool, optional) – Perform initial coarse reduction of source dataset in order to reduce execution time
nprocs (int, optional) – Number of processor cores to be used
segments (int or None) – Number of segments to use when resampling. If set to None an estimate will be calculated

Returns:

(valid_input_index, valid_output_index,
index_array, distance_array) (tuple of numpy arrays) – Neighbour resampling info

pyresample.kd_tree.get_sample_from_neighbour_info(resample_type, output_shape, data, valid_input_index, valid_output_index, index_array, distance_array=None, weight_funcs=None, fill_value=0, with_uncert=False)¶

Resamples swath based on neighbour info

Parameters:	resample_type ({'nn', 'custom'}) – ‘nn’: Use nearest neighbour resampling ‘custom’: Resample based on weight_funcs output_shape ((int, int)) – Shape of output as (rows, cols) data (numpy array) – Source data valid_input_index (numpy array) – valid_input_index from get_neighbour_info valid_output_index (numpy array) – valid_output_index from get_neighbour_info index_array (numpy array) – index_array from get_neighbour_info distance_array (numpy array, optional) – distance_array from get_neighbour_info Not needed for ‘nn’ resample type weight_funcs (list of function objects or function object, optional) – List of weight functions f(dist) to use for the weighting of each channel 1 to k. If only one channel is resampled weight_funcs is a single function object. Must be supplied when using ‘custom’ resample type fill_value (int or None, optional) – Set undetermined pixels to this value. If fill_value is None a masked array is returned with undetermined pixels masked
Returns:	result – Source data resampled to target geometry
Return type:	numpy array

pyresample.kd_tree.query_no_distance(target_lons, target_lats, valid_output_index, mask=None, valid_input_index=None, neighbours=None, epsilon=None, radius=None, kdtree=None)¶

Query the kdtree. No distances are returned.

NOTE: Dask array arguments must always come before other keyword arguments: for da.blockwise arguments to work.

pyresample.kd_tree.resample_custom(source_geo_def, data, target_geo_def, radius_of_influence, weight_funcs, neighbours=8, epsilon=0, fill_value=0, reduce_data=True, nprocs=1, segments=None, with_uncert=False)¶

Resamples data using kd-tree custom radial weighting neighbour approach

Parameters:

source_geo_def (object) – Geometry definition of source
data (numpy array) – Array of single channel data points or (source_geo_def.shape, k) array of k channels of datapoints
target_geo_def (object) – Geometry definition of target
radius_of_influence (float) – Cut off distance in meters
weight_funcs (list of function objects or function object) – List of weight functions f(dist) to use for the weighting of each channel 1 to k. If only one channel is resampled weight_funcs is a single function object.
neighbours (int, optional) – The number of neigbours to consider for each grid point
epsilon (float, optional) – Allowed uncertainty in meters. Increasing uncertainty reduces execution time
fill_value ({int, None}, optional) – Set undetermined pixels to this value. If fill_value is None a masked array is returned with undetermined pixels masked
reduce_data (bool, optional) – Perform initial coarse reduction of source dataset in order to reduce execution time
nprocs (int, optional) – Number of processor cores to be used
segments ({int, None}) – Number of segments to use when resampling. If set to None an estimate will be calculated

Returns:

data (numpy array (default)) – Source data resampled to target geometry
data, stddev, counts (numpy array, numpy array, numpy array (if with_uncert == True)) – Source data resampled to target geometry. Weighted standard devaition for all pixels having more than one source value Counts of number of source values used in weighting per pixel

pyresample.kd_tree.resample_gauss(source_geo_def, data, target_geo_def, radius_of_influence, sigmas, neighbours=8, epsilon=0, fill_value=0, reduce_data=True, nprocs=1, segments=None, with_uncert=False)¶

Resamples data using kd-tree gaussian weighting neighbour approach.

Parameters:

source_geo_def (object) – Geometry definition of source
data (numpy array) – Array of single channel data points or (source_geo_def.shape, k) array of k channels of datapoints
target_geo_def (object) – Geometry definition of target
radius_of_influence (float) – Cut off distance in meters
sigmas (list of floats or float) – List of sigmas to use for the gauss weighting of each channel 1 to k, w_k = exp(-dist^2/sigma_k^2). If only one channel is resampled sigmas is a single float value.
neighbours (int, optional) – The number of neigbours to consider for each grid point
epsilon (float, optional) – Allowed uncertainty in meters. Increasing uncertainty reduces execution time
fill_value ({int, None}, optional) – Set undetermined pixels to this value. If fill_value is None a masked array is returned with undetermined pixels masked
reduce_data (bool, optional) – Perform initial coarse reduction of source dataset in order to reduce execution time
nprocs (int, optional) – Number of processor cores to be used
segments (int or None) – Number of segments to use when resampling. If set to None an estimate will be calculated
with_uncert (bool, optional) – Calculate uncertainty estimates

Returns:

data (numpy array (default)) – Source data resampled to target geometry
data, stddev, counts (numpy array, numpy array, numpy array (if with_uncert == True)) – Source data resampled to target geometry. Weighted standard devaition for all pixels having more than one source value Counts of number of source values used in weighting per pixel

pyresample.kd_tree.resample_nearest(source_geo_def, data, target_geo_def, radius_of_influence, epsilon=0, fill_value=0, reduce_data=True, nprocs=1, segments=None)¶

Resamples data using kd-tree nearest neighbour approach

Parameters:	source_geo_def (object) – Geometry definition of source data (numpy array) – 1d array of single channel data points or (source_size, k) array of k channels of datapoints target_geo_def (object) – Geometry definition of target radius_of_influence (float) – Cut off distance in meters epsilon (float, optional) – Allowed uncertainty in meters. Increasing uncertainty reduces execution time fill_value (int or None, optional) – Set undetermined pixels to this value. If fill_value is None a masked array is returned with undetermined pixels masked reduce_data (bool, optional) – Perform initial coarse reduction of source dataset in order to reduce execution time nprocs (int, optional) – Number of processor cores to be used segments (int or None) – Number of segments to use when resampling. If set to None an estimate will be calculated
Returns:	data – Source data resampled to target geometry
Return type:	numpy array

pyresample.bilinear¶

Code for resampling using bilinear algorithm for irregular grids.

The algorithm is taken from

http://www.ahinson.com/algorithms_general/Sections/InterpolationRegression/InterpolationIrregularBilinear.pdf

pyresample.bilinear.get_bil_info(source_geo_def, target_area_def, radius=50000.0, neighbours=32, nprocs=1, masked=False, reduce_data=True, segments=None, epsilon=0)¶

Calculate information needed for bilinear resampling.

source_geo_def : object: Geometry definition of source data
target_area_def : object: Geometry definition of target area
radius : float, optional: Cut-off distance in meters
neighbours : int, optional: Number of neighbours to consider for each grid point when searching the closest corner points
nprocs : int, optional: Number of processor cores to be used for getting neighbour info
masked : bool, optional: If true, return masked arrays, else return np.nan values for invalid points (default)
reduce_data : bool, optional: Perform initial coarse reduction of source dataset in order to reduce execution time
segments : int or None: Number of segments to use when resampling. If set to None an estimate will be calculated
epsilon : float, optional: Allowed uncertainty in meters. Increasing uncertainty reduces execution time

Returns:	t__ (numpy array) – Vertical fractional distances from corner to the new points s__ (numpy array) – Horizontal fractional distances from corner to the new points input_idxs (numpy array) – Valid indices in the input data idx_arr (numpy array) – Mapping array from valid source points to target points

pyresample.bilinear.get_sample_from_bil_info(data, t__, s__, input_idxs, idx_arr, output_shape=None)¶

Resample data using bilinear interpolation.

Parameters:	data (numpy array) – 1d array to be resampled t (numpy array) – Vertical fractional distances from corner to the new points s (numpy array) – Horizontal fractional distances from corner to the new points input_idxs (numpy array) – Valid indices in the input data idx_arr (numpy array) – Mapping array from valid source points to target points output_shape (tuple, optional) – Tuple of (y, x) dimension for the target projection. If None (default), do not reshape data.
Returns:	result – Source data resampled to target geometry
Return type:	numpy array

pyresample.bilinear.resample_bilinear(data, source_geo_def, target_area_def, radius=50000.0, neighbours=32, nprocs=1, fill_value=0, reduce_data=True, segments=None, epsilon=0)¶

Resample using bilinear interpolation.

data : numpy array: Array of single channel data points or (source_geo_def.shape, k) array of k channels of datapoints
source_geo_def : object: Geometry definition of source data
target_area_def : object: Geometry definition of target area
radius : float, optional: Cut-off distance in meters
neighbours : int, optional: Number of neighbours to consider for each grid point when searching the closest corner points
nprocs : int, optional: Number of processor cores to be used for getting neighbour info
fill_value : {int, None}, optional: Set undetermined pixels to this value. If fill_value is None a masked array is returned with undetermined pixels masked
reduce_data : bool, optional: Perform initial coarse reduction of source dataset in order to reduce execution time
segments : int or None: Number of segments to use when resampling. If set to None an estimate will be calculated
epsilon : float, optional: Allowed uncertainty in meters. Increasing uncertainty reduces execution time

Returns:	data – Source data resampled to target geometry
Return type:	numpy array

pyresample.utils¶

Miscellaneous utility functions for pyresample.

pyresample.utils.check_and_wrap(lons, lats)¶

Wrap longitude to [-180:+180[ and check latitude for validity.

Parameters:	lons (ndarray) – Longitude degrees lats (ndarray) – Latitude degrees
Returns:	Longitude degrees in the range [-180:180[ and the original latitude array
Return type:	lons, lats
Raises:	`ValueError` – If latitude array is not between -90 and 90

pyresample.utils.fwhm2sigma(fwhm)¶

Calculate sigma for gauss function from FWHM (3 dB level)

Parameters:	fwhm (float) – FWHM of gauss function (3 dB level of beam footprint)
Returns:	sigma – sigma for use in resampling gauss function
Return type:	float

pyresample.utils.generate_nearest_neighbour_linesample_arrays(source_area_def, target_area_def, radius_of_influence, nprocs=1)¶

Generate linesample arrays for nearest neighbour grid resampling

Parameters:	source_area_def (object) – Source area definition as geometry definition object target_area_def (object) – Target area definition as geometry definition object radius_of_influence (float) – Cut off distance in meters nprocs (int, optional) – Number of processor cores to be used
Returns:	(row_indices, col_indices)
Return type:	tuple of numpy arrays

pyresample.utils.generate_quick_linesample_arrays(source_area_def, target_area_def, nprocs=1)¶

Generate linesample arrays for quick grid resampling

Parameters:	source_area_def (object) – Source area definition as geometry definition object target_area_def (object) – Target area definition as geometry definition object nprocs (int, optional) – Number of processor cores to be used
Returns:	(row_indices, col_indices)
Return type:	tuple of numpy arrays

pyresample.utils.is_pyproj2()¶: Determine whether the current pyproj version is >= 2.0

pyresample.utils.recursive_dict_update(d, u)¶

Recursive dictionary update using

Copied from:

http://stackoverflow.com/questions/3232943/update-value-of-a-nested-dictionary-of-varying-depth

pyresample.utils.wrap_longitudes(lons)¶

Wrap longitudes to the [-180:+180[ validity range (preserves dtype)

Parameters:	lons (numpy array) – Longitudes in degrees
Returns:	lons – Longitudes wrapped into [-180:+180[ validity range
Return type:	numpy array

pyresample.area_config¶

exception pyresample.area_config.AreaNotFound¶: Exception raised when specified are is no found in file.

pyresample.area_config.convert_def_to_yaml(def_area_file, yaml_area_file)¶

Convert a legacy area def file to the yaml counter partself.

yaml_area_file will be overwritten by the operation.

pyresample.area_config.create_area_def(area_id, projection, width=None, height=None, area_extent=None, shape=None, upper_left_extent=None, center=None, resolution=None, radius=None, units=None, **kwargs)¶

Takes data the user knows and tries to make an area definition from what can be found.

Parameters:	area_id (str) – ID of area projection (dict or str) – Projection parameters as a proj4_dict or proj4_string description (str, optional) – Description/name of area. Defaults to area_id proj_id (str, optional) – ID of projection (deprecated) units (str, optional) – Units that provided arguments should be interpreted as. This can be one of ‘deg’, ‘degrees’, ‘meters’, ‘metres’, and any parameter supported by the cs2cs -lu command. Units are determined in the following priority: units expressed with each variable through a DataArray’s attrs attribute. units passed to `units` units used in `projection` meters width (str, optional) – Number of pixels in the x direction height (str, optional) – Number of pixels in the y direction area_extent (list, optional) – Area extent as a list (lower_left_x, lower_left_y, upper_right_x, upper_right_y) shape (list, optional) – Number of pixels in the y and x direction (height, width) upper_left_extent (list, optional) – Upper left corner of upper left pixel (x, y) center (list, optional) – Center of projection (x, y) resolution (list or float, optional) – Size of pixels: (dx, dy) radius (list or float, optional) – Length from the center to the edges of the projection (dx, dy) rotation (float, optional) – rotation in degrees(negative is cw) nprocs (int, optional) – Number of processor cores to be used lons (numpy array, optional) – Grid lons lats (numpy array, optional) – Grid lats optimize_projection – Whether the projection parameters have to be optimized for a DynamicAreaDefinition.
Returns:	AreaDefinition or DynamicAreaDefinition – If shape and area_extent are found, an AreaDefinition object is returned. If only shape or area_extent can be found, a DynamicAreaDefinition object is returned
Return type:	AreaDefinition or DynamicAreaDefinition
Raises:	ValueError: – If neither shape nor area_extent could be found

Notes

resolution and radius can be specified with one value if dx == dy
If resolution and radius are provided as angles, center must be given or findable. In such a case, they represent [projection x distance from center[0] to center[0]+dx, projection y distance from center[1] to center[1]+dy]

pyresample.area_config.get_area_def(area_id, area_name, proj_id, proj4_args, width, height, area_extent, rotation=0)¶

Construct AreaDefinition object from arguments

Parameters:	area_id (str) – ID of area area_name (str) – Description of area proj_id (str) – ID of projection proj4_args (list, dict, or str) – Proj4 arguments as list of arguments or string width (int) – Number of pixel in x dimension height (int) – Number of pixel in y dimension rotation (float) – Rotation in degrees (negative is cw) area_extent (list) – Area extent as a list of ints (LL_x, LL_y, UR_x, UR_y)
Returns:	area_def – AreaDefinition object
Return type:	object

pyresample.area_config.load_area(area_file_name, *regions)¶

Load area(s) from area file.

Parameters:	area_file_name (str, pathlib.Path, stream, or list thereof) – List of paths or streams. Any str or pathlib.Path will be interpreted as a path to a file. Any stream will be interpreted as containing a yaml definition. To read directly from a string, use `load_area_from_string()`. regions (str argument list) – Regions to parse. If no regions are specified all regions in the file are returned
Returns:	area_defs – If one area name is specified a single AreaDefinition object is returned. If several area names are specified a list of AreaDefinition objects is returned
Return type:	AreaDefinition or list
Raises:	AreaNotFound: – If a specified area name is not found

pyresample.area_config.load_area_from_string(area_strs, *regions)¶

Load area(s) from area strings.

Like load_area(), but load from string directly.

Parameters:	area_strs (str or List[str]) – Strings containing yaml definitions. regions (str) – Regions to parse.
Returns:	area_defs – If one area name is specified a single AreaDefinition object is returned. If several area names are specified a list of AreaDefinition objects is returned
Return type:	AreaDefinition or list

pyresample.area_config.parse_area_file(area_file_name, *regions)¶

Parse area information from area file

Parameters:	area_file_name (str or list) – One or more paths to area definition files regions (str argument list) – Regions to parse. If no regions are specified all regions in the file are returned
Returns:	area_defs – List of AreaDefinition objects
Return type:	list
Raises:	AreaNotFound: – If a specified area is not found

pyresample.data_reduce¶

Reduce data sets based on geographical information

pyresample.data_reduce.get_valid_index_from_cartesian_grid(cart_grid, lons, lats, radius_of_influence)¶

Calculates relevant data indices using coarse data reduction of swath data by comparison with cartesian grid

Parameters:	chart_grid (numpy array) – Grid of area cartesian coordinates lons (numpy array) – Swath lons lats (numpy array) – Swath lats data (numpy array) – Swath data radius_of_influence (float) – Cut off distance in meters
Returns:	valid_index – Boolean array of same size as lons and lats indicating relevant indices
Return type:	numpy array

pyresample.data_reduce.get_valid_index_from_lonlat_boundaries(boundary_lons, boundary_lats, lons, lats, radius_of_influence)¶: Find relevant indices from grid boundaries using the winding number theorem

pyresample.data_reduce.get_valid_index_from_lonlat_grid(grid_lons, grid_lats, lons, lats, radius_of_influence)¶

Calculates relevant data indices using coarse data reduction of swath data by comparison with lon lat grid

Parameters:	chart_grid (numpy array) – Grid of area cartesian coordinates lons (numpy array) – Swath lons lats (numpy array) – Swath lats data (numpy array) – Swath data radius_of_influence (float) – Cut off distance in meters
Returns:	valid_index – Boolean array of same size as lon and lat indicating relevant indices
Return type:	numpy array

pyresample.data_reduce.swath_from_cartesian_grid(cart_grid, lons, lats, data, radius_of_influence)¶

Makes coarse data reduction of swath data by comparison with cartesian grid

Parameters:	chart_grid (numpy array) – Grid of area cartesian coordinates lons (numpy array) – Swath lons lats (numpy array) – Swath lats data (numpy array) – Swath data radius_of_influence (float) – Cut off distance in meters
Returns:	(lons, lats, data) – Reduced swath data and coordinate set
Return type:	list of numpy arrays

pyresample.data_reduce.swath_from_lonlat_boundaries(boundary_lons, boundary_lats, lons, lats, data, radius_of_influence)¶

Makes coarse data reduction of swath data by comparison with lon lat boundary

Parameters:	boundary_lons (numpy array) – Grid of area lons boundary_lats (numpy array) – Grid of area lats lons (numpy array) – Swath lons lats (numpy array) – Swath lats data (numpy array) – Swath data radius_of_influence (float) – Cut off distance in meters
Returns:	(lons, lats, data) – Reduced swath data and coordinate set
Return type:	list of numpy arrays

pyresample.data_reduce.swath_from_lonlat_grid(grid_lons, grid_lats, lons, lats, data, radius_of_influence)¶

Makes coarse data reduction of swath data by comparison with lon lat grid

Parameters:	grid_lons (numpy array) – Grid of area lons grid_lats (numpy array) – Grid of area lats lons (numpy array) – Swath lons lats (numpy array) – Swath lats data (numpy array) – Swath data radius_of_influence (float) – Cut off distance in meters
Returns:	(lons, lats, data) – Reduced swath data and coordinate set
Return type:	list of numpy arrays

pyresample.plot¶

Utility functions for quick and easy display.

pyresample.plot.area_def2basemap(area_def, **kwargs)¶

Get Basemap object from an AreaDefinition object.

Parameters:	area_def (object) – geometry.AreaDefinition object *kwargs (Keyword arguments*) – Additional initialization arguments for Basemap
Returns:	bmap
Return type:	Basemap object

pyresample.plot.ellps2axis(ellps_name)¶

Get semi-major and semi-minor axis from ellipsis definition.

Parameters:	ellps_name (str) – Standard name of ellipsis
Returns:	(a, b)
Return type:	semi-major and semi-minor axis

pyresample.plot.save_quicklook(filename, area_def, data, vmin=None, vmax=None, label='Variable (units)', num_meridians=45, num_parallels=10, coast_res='110m', backend='AGG', cmap='RdBu_r')¶

Display and save default quicklook plot.

Parameters:

filename (str) – path to output file
area_def (object) – geometry.AreaDefinition object
data (numpy array | numpy masked array) – 2D array matching area_def. Use masked array for transparent values
vmin (float, optional) – Min value for luminescence scaling
vmax (float, optional) – Max value for luminescence scaling
label (str, optional) – Label for data
num_meridians (int, optional) – Number of meridians to plot on the globe
num_parallels (int, optional) – Number of parallels to plot on the globe
coast_res ({'c', 'l', 'i', 'h', 'f'}, optional) – Resolution of coastlines
backend (str, optional) – matplotlib backend to use’

pyresample.plot.show_quicklook(area_def, data, vmin=None, vmax=None, label='Variable (units)', num_meridians=45, num_parallels=10, coast_res='110m', cmap='RdBu_r')¶

Display default quicklook plot.

Parameters:	area_def (object) – geometry.AreaDefinition object data (numpy array \| numpy masked array) – 2D array matching area_def. Use masked array for transparent values vmin (float, optional) – Min value for luminescence scaling vmax (float, optional) – Max value for luminescence scaling label (str, optional) – Label for data num_meridians (int, optional) – Number of meridians to plot on the globe num_parallels (int, optional) – Number of parallels to plot on the globe coast_res ({'c', 'l', 'i', 'h', 'f'}, optional) – Resolution of coastlines
Returns:	bmap
Return type:	Basemap object

pyresample.ewa¶

pyresample.bucket¶

Code for resampling using bucket resampling.

class pyresample.bucket.BucketResampler(target_area, source_lons, source_lats)¶

Class for bucket resampling.

Bucket resampling is useful for calculating averages and hit-counts when aggregating data to coarser scale grids.

Below are examples how to use the resampler.

Read data using Satpy. The resampling can also be done (apart from fractions) directly from Satpy, but this demonstrates the direct low-level usage.

>>> from pyresample.bucket import BucketResampler
>>> from satpy import Scene
>>> from satpy.resample import get_area_def
>>> fname = "hrpt_noaa19_20170519_1214_42635.l1b"
>>> glbl = Scene(filenames=[fname])
>>> glbl.load(['4'])
>>> data = glbl['4']
>>> lons, lats = data.area.get_lonlats()
>>> target_area = get_area_def('euro4')

Initialize the resampler

>>> resampler = BucketResampler(adef, lons, lats)

Calculate the sum of all the data in each grid location:

>>> sums = resampler.get_sum(data)

Calculate how many values were collected at each grid location:

>>> counts = resampler.get_count()

The average can be calculated from the above two results, or directly using the helper method:

>>> average = resampler.get_average(data)

Calculate fractions of occurrences of different values in each grid location. The data needs to be categorical (in integers), so we’ll create some categorical data from the brightness temperature data that were read earlier. The data are returned in a dictionary with the categories as keys.

>>> data = da.where(data > 250, 1, 0)
>>> fractions = resampler.get_fractions(data, categories=[0, 1])
>>> import matplotlib.pyplot as plt
>>> plt.imshow(fractions[0]); plt.show()

get_average(data, fill_value=nan, mask_all_nan=False)¶

Calculate bin-averages using bucket resampling.

Parameters:	data (Numpy or Dask array) – Data to be binned and averaged fill_value (float) – Fill value to replace missing values. Default: np.nan
Returns:	average – Binned and averaged data.
Return type:	Dask array

get_count()¶

Count the number of occurrences for each bin using drop-in-a-bucket resampling.

Returns:	data – Bin-wise count of hits for each target grid location
Return type:	Dask array

get_fractions(data, categories=None, fill_value=nan)¶

Get fraction of occurrences for each given categorical value.

Parameters:	data (Numpy or Dask array) – Categorical data to be processed categories (iterable or None) – One dimensional list of categories in the data, or None. If None, categories are determined from the data by fully processing the data and finding the unique category values. fill_value (float) – Fill value to replace missing values. Default: np.nan

get_sum(data, mask_all_nan=False)¶

Calculate sums for each bin with drop-in-a-bucket resampling.

Parameters:	data (Numpy or Dask array) – mask_all_nan (boolean (optional)) – Mask bins that have only NaN results, default: False
Returns:	data – Bin-wise sums in the target grid
Return type:	Numpy or Dask array

pyresample.bucket.round_to_resolution(arr, resolution)¶

Round the values in arr to closest resolution element.

Parameters:	arr (list, tuple, Numpy or Dask array) – Array to be rounded resolution (float) – Resolution unit to which data are rounded
Returns:	data – Source data rounded to the closest resolution unit
Return type:	Numpy or Dask array

pyresample API¶

pyresample.geometry¶

pyresample.image¶

pyresample.grid¶

pyresample.kd_tree¶

pyresample.bilinear¶

pyresample.utils¶

pyresample.area_config¶

pyresample.data_reduce¶

pyresample.plot¶

pyresample.ewa¶

pyresample.bucket¶

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