# Resampling of gridded data

Pyresample can be used to resample from an existing grid to another. Nearest neighbour resampling is used.

## pyresample.image

Note

The pyresample.image module is deprecated. Please use pyresample.kd_tree or pyresample.bilinear instead.

A grid can be stored in an object of type ImageContainer along with its area definition. An object of type ImageContainer allows for calculating resampling using preprocessed arrays using the method get_array_from_linesample

Resampling can be done using descendants of ImageContainer and calling their resample method.

An ImageContainerQuick object allows for the grid to be resampled to a new area defintion using an approximate (but fast) nearest neighbour method. Resampling an object of type ImageContainerQuick returns a new object of type ImageContainerQuick.

An ImageContainerNearest object allows for the grid to be resampled to a new area defintion (or swath definition) using an accurate kd-tree method. Resampling an object of type ImageContainerNearest returns a new object of type ImageContainerNearest.

    >>> import numpy as np
    >>> from pyresample import image, geometry
    >>> area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
    ...                                {'a': '6378144.0', 'b': '6356759.0',
    ...                                 'lat_0': '50.00', 'lat_ts': '50.00',
    ...                                 'lon_0': '8.00', 'proj': 'stere'},
    ...                                800, 800,
    ...                                [-1370912.72, -909968.64,
    ...                                 1029087.28, 1490031.36])
    >>> msg_area = geometry.AreaDefinition('msg_full', 'Full globe MSG image 0 degrees',
    ...                                'msg_full',
    ...                                {'a': '6378169.0', 'b': '6356584.0',
    ...                                 'h': '35785831.0', 'lon_0': '0',
    ...                                 'proj': 'geos'},
    ...                                3712, 3712,
    ...                                [-5568742.4, -5568742.4,
    ...                                 5568742.4, 5568742.4])
    >>> data = np.ones((3712, 3712))
    >>> msg_con_quick = image.ImageContainerQuick(data, msg_area)
    >>> area_con_quick = msg_con_quick.resample(area_def)
    >>> result_data_quick = area_con_quick.image_data
    >>> msg_con_nn = image.ImageContainerNearest(data, msg_area, radius_of_influence=50000)
    >>> area_con_nn = msg_con_nn.resample(area_def)
    >>> result_data_nn = area_con_nn.image_data

Data is assumed to be a numpy array of shape (rows, cols) or (rows, cols, channels).

Masked arrays can be used as data input. In order to have undefined pixels masked out instead of assigned a fill value set fill_value=None when calling resample_area_*.

Using ImageContainerQuick the risk of image artifacts increases as the distance from source projection center increases.

The constructor argument radius_of_influence to ImageContainerNearest specifices the maximum distance to search for a neighbour for each point in the target grid. The unit is meters.

The constructor arguments of an ImageContainer object can be changed as attributes later

    >>> import numpy as np
    >>> from pyresample import image, geometry
    >>> msg_area = geometry.AreaDefinition('msg_full', 'Full globe MSG image 0 degrees',
    ...                                'msg_full',
    ...                                {'a': '6378169.0', 'b': '6356584.0',
    ...                                 'h': '35785831.0', 'lon_0': '0',
    ...                                 'proj': 'geos'},
    ...                                3712, 3712,
    ...                                [-5568742.4, -5568742.4,
    ...                                 5568742.4, 5568742.4])
    >>> data = np.ones((3712, 3712))
    >>> msg_con_nn = image.ImageContainerNearest(data, msg_area, radius_of_influence=50000)
    >>> msg_con_nn.radius_of_influence = 45000
    >>> msg_con_nn.fill_value = -99

### Multi channel images

If the dataset has several channels the last index of the data array specifies the channels

    >>> import numpy as np
    >>> from pyresample import image, geometry
    >>> msg_area = geometry.AreaDefinition('msg_full', 'Full globe MSG image 0 degrees',
    ...                                'msg_full',
    ...                                {'a': '6378169.0', 'b': '6356584.0',
    ...                                 'h': '35785831.0', 'lon_0': '0',
    ...                                 'proj': 'geos'},
    ...                                3712, 3712,
    ...                                [-5568742.4, -5568742.4,
    ...                                 5568742.4, 5568742.4])
    >>> channel1 = np.ones((3712, 3712))
    >>> channel2 = np.ones((3712, 3712)) * 2
    >>> channel3 = np.ones((3712, 3712)) * 3
    >>> data = np.dstack((channel1, channel2, channel3))
    >>> msg_con_nn = image.ImageContainerNearest(data, msg_area, radius_of_influence=50000)

### Segmented resampling

Pyresample calculates the result in segments in order to reduce memory footprint. This is controlled by the segments contructor keyword argument. If no segments argument is given pyresample will estimate the number of segments to use.

Forcing quick resampling to use 4 resampling segments:

    >>> import numpy as np
    >>> from pyresample import image, geometry
    >>> area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
    ...                                {'a': '6378144.0', 'b': '6356759.0',
    ...                                 'lat_0': '50.00', 'lat_ts': '50.00',
    ...                                 'lon_0': '8.00', 'proj': 'stere'},
    ...                                800, 800,
    ...                                [-1370912.72, -909968.64,
    ...                                 1029087.28, 1490031.36])
    >>> msg_area = geometry.AreaDefinition('msg_full', 'Full globe MSG image 0 degrees',
    ...                                'msg_full',
    ...                                {'a': '6378169.0', 'b': '6356584.0',
    ...                                 'h': '35785831.0', 'lon_0': '0',
    ...                                 'proj': 'geos'},
    ...                                3712, 3712,
    ...                                [-5568742.4, -5568742.4,
    ...                                 5568742.4, 5568742.4])
    >>> data = np.ones((3712, 3712))
    >>> msg_con_quick = image.ImageContainerQuick(data, msg_area, segments=4)
    >>> area_con_quick = msg_con_quick.resample(area_def)

### Constructor arguments

The full list of constructor arguments:

> ImageContainerQuick:

- image_data : Dataset. Masked arrays can be used.

- geo_def : Geometry definition.

- fill_value (optional) : Fill value for undefined pixels. Defaults to 0. If set to None they will be masked out.

- nprocs (optional) : Number of processor cores to use. Defaults to 1.

- segments (optional) : Number of segments to split resampling in. Defaults to auto estimation.

> ImageContainerNearest:

- image_data : Dataset. Masked arrays can be used.

- geo_def : Geometry definition.

- radius_of_influence : Cut off radius in meters when considering neighbour pixels.

- epsilon (optional) : The distance to a found value is guaranteed to be no further than (1 + eps) times the distance to the correct neighbour.

- fill_value (optional) : Fill value for undefined pixels. Defaults to 0. If set to None they will be masked out.

- reduce_data (optional) : Apply geographic reduction of dataset before resampling. Defaults to True

- nprocs (optional) : Number of processor cores to use. Defaults to 1.

- segments (optional) : Number of segments to split resampling in. Defaults to auto estimation.

### Preprocessing of grid resampling

For preprocessing of grid resampling see Preprocessing of grids