pyresample API

pyresample.geometry

Classes for geometry operations

class pyresample.geometry.AreaDefinition(area_id, name, proj_id, proj_dict, x_size, y_size, area_extent, rotation=None, nprocs=1, lons=None, lats=None, dtype=<class 'numpy.float64'>)

Holds definition of an area.

Parameters:
  • area_id (str) – ID of area
  • name (str) – Name of area
  • proj_id (str) – ID of projection
  • proj_dict (dict) – Dictionary with Proj.4 parameters
  • x_size (int) – x dimension in number of pixels
  • y_size (int) – y dimension in number of pixels
  • rotation (float) – rotation in degrees (negative is cw)
  • area_extent (list) – Area extent as a list (LL_x, LL_y, UR_x, UR_y)
  • nprocs (int, optional) – Number of processor cores to be used
  • lons (numpy array, optional) – Grid lons
  • lats (numpy array, optional) – Grid lats
area_id

str – ID of area

name

str – Name of area

proj_id

str – ID of projection

proj_dict

dict – Dictionary with Proj.4 parameters

x_size

int – x dimension in number of pixels

y_size

int – y dimension in number of pixels

rotation

float – rotation in degrees (negative is cw)

shape

tuple – Corresponding array shape as (rows, cols)

size

int – Number of points in grid

area_extent

tuple – Area extent as a tuple (LL_x, LL_y, UR_x, UR_y)

area_extent_ll

tuple – Area extent in lons lats as a tuple (LL_lon, LL_lat, UR_lon, UR_lat)

pixel_size_x

float – Pixel width in projection units

pixel_size_y

float – Pixel height in projection units

pixel_upper_left

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

pixel_offset_x

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

pixel_offset_y

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

proj4_string

str – Projection defined as Proj.4 string

lons

object – Grid lons

lats

object – Grid lats

cartesian_coords

object – Grid cartesian coordinates

projection_x_coords

object – Grid projection x coordinate

projection_y_coords

object – Grid projection y coordinate

colrow2lonlat(cols, rows)

Return longitudes and latitudes 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).

crop_around(other_area)

Crop this area around other_area.

get_area_slices(area_to_cover)

Compute the slice to read based on an area_to_cover.

get_lonlat(row, col)

Retrieves 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)

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
Returns:

(lons, lats) – Grids of area lons and and lats
Return type:

tuple of numpy arrays
get_lonlats_dask(chunks=4096, dtype=None)

Get the lon lats as a single dask array.

get_proj_coords(data_slice=None, cache=False, dtype=None)

Get projection coordinates of grid.

Parameters:
  • data_slice (slice object, optional) – Calculate only coordinates for specified slice
  • cache (bool, optional) – Store the result. Requires data_slice to be None
Returns:

(target_x, target_y) – Grids of area x- and y-coordinates in projection units
Return type:

tuple of numpy arrays
get_xy_from_lonlat(lon, lat)

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

outer_boundary_corners

Return the lon,lat of the outer edges of the corner points

proj4_string

Return projection definition as Proj.4 string.

update_hash(the_hash=None)

Update a hash, or return a new one if needed.

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 pyresample.utils.check_and_wrap to preprocess your arrays.

corners

Returns the corners of the current area.

get_area()

Get the area of the convex area defined by the corners of the current 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()

Returns 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, **kwargs)

Base method for lon lat retrieval with slicing

get_lonlats_dask(chunks=4096)

Get the lon lats as a single dask array.

intersection(other)

Returns 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)

Tests 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

exception pyresample.geometry.DimensionError
class pyresample.geometry.DynamicAreaDefinition(area_id=None, description=None, proj_dict=None, x_size=None, y_size=None, area_extent=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 size of the area to a given set of longitudes and latitudes, such that e.g. polar satellite granules can be resampled optimaly to a give projection.

compute_domain(corners, resolution=None, size=None)

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

freeze(lonslats=None, resolution=None, size=None, proj_info=None, rotation=None)

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

lonlats:
the geographical coordinates to contain in the resulting area.
resolution:
the resolution of the resulting area.
size:
the size of the resulting area.
proj_info:
complementing parameters to the projection info.
rotation:
rotation in degrees (negative is cw)

Resolution and Size parameters are ignored if the instance is created with the optimize_projection flag set to True.

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

tuple – Grid shape as (rows, cols)

size

int – Number of elements in grid

lons

object – Grid lons

lats

object – Grid lats

cartesian_coords

object – Grid cartesian coordinates

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)

Return lon and lat arrays of the area.

get_lonlats_dask(chunks=4096, dtype=None)

“Return lon and lat dask arrays of the area.

proj4_string

Returns projection definition as Proj.4 string

proj_str

Returns projection definition as Proj.4 string

squeeze()

Generate a single AreaDefinition if possible.

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

tuple – Swath shape

size

int – Number of elements in swath

ndims

int – Swath dimensions

lons

object – Swath lons

lats

object – Swath lats

cartesian_coords

object – Swath cartesian coordinates

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.

get_edge_lonlats()

Get the concatenated boundary of the current swath.

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.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

numpy array – Image data

geo_def

object – Geometry definition

fill_value

int or None – Resample result fill value

nprocs

int – Number of processor cores to be used for geometry operations

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

numpy array – Image data

geo_def

object – Geometry definition

radius_of_influence

float – Cut off distance in meters

epsilon

float – Allowed uncertainty in meters

fill_value

int or None – Resample result fill value

reduce_data

bool – Perform coarse data reduction before resampling

nprocs

int – Number of processor cores to be used

segments

int or None – Number of segments to use when resampling

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

numpy array – Image data

geo_def

object – Geometry definition

radius_of_influence

float – Cut off distance in meters

epsilon

float – Allowed uncertainty in meters

fill_value

int or None – Resample result fill value

reduce_data

bool – Perform coarse data reduction before resampling

nprocs

int – Number of processor cores to be used

segments

int or None – Number of segments to use when resampling

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

numpy array – Image data

geo_def

object – Area definition as AreaDefinition object

fill_value

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

nprocs

int – Number of processor cores to be used

segments

int or None – Number of segments to use when resampling

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.atop 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

Utility functions for pyresample

exception pyresample.utils.AreaNotFound

Exception raised when specified are is no found in file

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.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.utils.convert_proj_floats(proj_pairs)

Convert PROJ.4 parameters to floats if possible.

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.get_area_def(area_id, area_name, proj_id, proj4_args, x_size, y_size, area_extent, rotation=0)

Construct AreaDefinition object from arguments

Parameters:
  • area_id (str) – ID of area
  • proj_id (str) – ID of projection
  • area_name (str) – Description of area
  • proj4_args (list or str) – Proj4 arguments as list of arguments or string
  • x_size (int) – Number of pixel in x dimension
  • y_size (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.utils.get_area_def_from_raster(source, area_id=None, name=None, proj_id=None, proj_dict=None)

Construct AreaDefinition object from raster

Parameters:
  • source (str, Dataset, DatasetReader or DatasetWriter) – A file name. Also it can be osgeo.gdal.Dataset, rasterio.io.DatasetReader or rasterio.io.DatasetWriter
  • area_id (str, optional) – ID of area
  • name (str, optional) – Name of area
  • proj_id (str, optional) – ID of projection
  • proj_dict (dict, optional) – PROJ.4 parameters
Returns:

area_def – AreaDefinition object
Return type:

object
pyresample.utils.load_area(area_file_name, *regions)

Load area(s) from area file

Parameters:
  • area_file_name (str) – Path to area definition file
  • 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:

object or list
Raises:

AreaNotFound: – If a specified area name is not found
pyresample.utils.parse_area_file(area_file_name, *regions)

Parse area information from area file

Parameters:
  • area_file_name (str) – Path to area definition file
  • 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.utils.proj4_dict_to_str(proj4_dict, sort=False)

Convert a dictionary of PROJ.4 parameters to a valid PROJ.4 string

pyresample.utils.proj4_radius_parameters(proj4_dict)

Calculate ‘a’ and ‘b’ radius parameters.

Parameters:proj4_dict (str or dict) – PROJ.4 parameters
Returns:equatorial and polar radius
Return type:a (float), b (float)
pyresample.utils.proj4_str_to_dict(proj4_str)

Convert PROJ.4 compatible string definition to dict

Note: Key only parameters will be assigned a value of True.

pyresample.utils.recursive_dict_update(d, u)

Recursive dictionary update using

Copied from:

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.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

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

Get Basemap object from AreaDefinition

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='jet')

Display 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='jet')

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