pyresample API

pyresample.geometry

Classes for geometry operations

class geometry.AreaDefinition(area_id, name, proj_id, proj_dict, x_size, y_size, area_extent, nprocs=1, lons=None, lats=None, dtype=<Mock object at 0x7fb2a5137e50>)

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

Attributes:

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..

Properties: 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

get_lonlat(row, col)

Retrieves lon and lat values of single point in area grid

Parameters:

row : int col : int

Returns:

(lon, lat) : 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) : tuple of numpy arrays

Grids of area lons and and lats

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) : tuple of numpy arrays

Grids of area x- and y-coordinates in projection units

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

proj4_string

Returns projection definition as Proj.4 string

class 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_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 : numpy array

get_lonlat(row, col)

Retrieve lon and lat of single pixel

Parameters:

row : int col : int

Returns:

(lon, lat) : 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) : 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 : 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 : bool

class geometry.Boundary(side1, side2, side3, side4)

Container for geometry boundary. Labelling starts in upper left corner and proceeds clockwise

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

Base class for geometry definitions defined by lons and lats only

class 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.

Attributes:

shape : tuple

Grid shape as (rows, cols)

size : int

Number of elements in grid

Properties: lons : object

Grid lons

lats : object

Grid lats

cartesian_coords : object

Grid cartesian coordinates

class 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.

Attributes:

shape : tuple

Swath shape

size : int

Number of elements in swath

ndims : int

Swath dimensions

Properties: lons : object

Swath lons

lats : object

Swath lats

cartesian_coords : object

Swath cartesian coordinates

pyresample.image

Handles resampling of images with assigned geometry definitions

class 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, 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

Attributes:

image_data : numpy array

Image data

geo_def : object

Geometry definition

fill_value : {int, 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 : numpy_array

Resampled image data

get_array_from_neighbour_info(*args, **kwargs)

Base method for resampling from preprocessed data.

resample(target_geo_def)

Base method for resampling

class 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, 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, None}

Number of segments to use when resampling. If set to None an estimate will be calculated

Attributes:

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, 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, 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 : object

ImageContainerNearest object of resampled geometry

class 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, 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, None}

Number of segments to use when resampling. If set to None an estimate will be calculated

Attributes:

image_data : numpy array

Image data

geo_def : object

Area definition as AreaDefinition object

fill_value : {int, 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, 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 : object

ImageContainerQuick object of resampled area

pyresample.grid

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

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, 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 : numpy array

Resampled image

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, 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 : numpy array

Resampled image data

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) : tuple of numpy arrays

Arrays for resampling area by array indexing

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 : numpy array

Resampled image data

pyresample.kd_tree

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

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

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:

(valid_input_index, valid_output_index, index_array, distance_array) : tuple of numpy arrays

Neighbour resampling info

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, None}, optional

Set undetermined pixels to this value. If fill_value is None a masked array is returned with undetermined pixels masked

Returns:

result : numpy array

Source data resampled to target geometry

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

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, 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

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, 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

Source data resampled to target geometry

kd_tree.which_kdtree()

Returns the name of the kdtree used for resampling

pyresample.utils

Utility functions for pyresample

exception utils.AreaNotFound

Exception raised when specified are is no found in file

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 : float

sigma for use in resampling gauss function

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) : tuple of numpy arrays

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) : tuple of numpy arrays

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 : object

AreaDefinition object

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 : object or list

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

Raises:

AreaNotFound

If a specified area name is not found

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

List of AreaDefinition objects

Raises:

AreaNotFound

If a specified area is not found

pyresample.data_reduce

Reduce data sets based on geographical information

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 : numpy array

Boolean array of same size as lons and lats indicating relevant indices

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

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 : numpy array

Boolean array of same size as lon and lat indicating relevant indices

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) : list of numpy arrays

Reduced swath data and coordinate set

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) : list of numpy arrays

Reduced swath data and coordinate set

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) : list of numpy arrays

Reduced swath data and coordinate set

pyresample.plot

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 : Basemap object

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) : semi-major and semi-minor axis

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')

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’

plot.show_quicklook(area_def, data, vmin=None, vmax=None, label='Variable (units)', num_meridians=45, num_parallels=10, coast_res='c')

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 : Basemap object