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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**.
.. doctest::
>>> 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
.. doctest::
>>> 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
.. doctest::
>>> 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:
.. doctest::
>>> 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 :ref:`preproc`
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