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, nprocs=1, lons=None, lats=None, dtype=<type '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
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

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).

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)¶

Returns 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_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 result 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_)¶

Retrieve closest x and y coordinates (column, row indices) for a location specified with projection coordinates (xm_,ym_) in meters. A ValueError is raised, if the return point is outside the area domain. If xm_,ym_ is a tuple of sequences of projection coordinates, a tuple of masked arrays are returned.

Input:

xm_ : point or sequence (list or array) of x-coordinates in m (map projection) ym_ : point or sequence (list or array) of y-coordinates in m (map projection)

Returns:

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

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¶: Returns the lon,lat of the outer edges of the corner points

proj4_string: Returns projection definition as Proj.4 string

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

Base class for geometry definitions

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)¶

Retrieves area_extent for a subdomain rows are counted from upper left to lower left 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 : list: Area extent as a list (LL_x, LL_y, UR_x, UR_y) of the subset

Author:

Ulrich Hamann

get_boundary_lonlats()¶: Returns 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

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.Boundary(side1, side2, side3, side4)¶: Container for geometry boundary. Labelling starts in upper left corner and proceeds clockwise

class pyresample.geometry.CoordinateDefinition(lons, lats, nprocs=1)¶: Base class for geometry definitions defined by lons and lats only

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.

proj4_string¶: 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

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.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.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 resampling 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

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.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.kd_tree.which_kdtree()¶: Returns the name of the kdtree used for resampling

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.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 AreaDefinition object target_area_def (object) – Target area definition as AreaDefinition 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 AreaDefinition object target_area_def (object) – Target area definition as AreaDefinition 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)¶

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 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.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_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.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='c', 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='c', 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 API¶

pyresample.geometry¶

pyresample.image¶

pyresample.grid¶

pyresample.kd_tree¶

pyresample.bilinear¶

pyresample.utils¶

pyresample.data_reduce¶

pyresample.plot¶

pyresample.ewa¶

Table Of Contents

Previous topic

This Page