satpy.enhancements package

Submodules

Module contents

Enhancements.

satpy.enhancements.apply_enhancement(data, func, exclude=None, separate=False, pass_dask=False)[source]

Apply func to the provided data.

Parameters

  • data (xarray.DataArray) – Data to be modified inplace.

  • func (callable) – Function to be applied to an xarray

  • exclude (iterable) – Bands in the ‘bands’ dimension to not include in the calculations.

  • separate (bool) – Apply func one band at a time. Default is False.

  • pass_dask (bool) – Pass the underlying dask array instead of the xarray.DataArray.

satpy.enhancements.btemp_threshold(img, min_in, max_in, threshold, threshold_out=None, **kwargs)[source]

Scale data linearly in two separate regions.

This enhancement scales the input data linearly by splitting the data into two regions; min_in to threshold and threshold to max_in. These regions are mapped to 1 to threshold_out and threshold_out to 0 respectively, resulting in the data being “flipped” around the threshold. A default threshold_out is set to 176.0 / 255.0 to match the behavior of the US National Weather Service’s forecasting tool called AWIPS.

Parameters

  • img (XRImage) – Image object to be scaled

  • min_in (float) – Minimum input value to scale

  • max_in (float) – Maximum input value to scale

  • threshold (float) – Input value where to split data in to two regions

  • threshold_out (float) – Output value to map the input threshold to. Optional, defaults to 176.0 / 255.0.

satpy.enhancements.cira_stretch(img, **kwargs)[source]

Logarithmic stretch adapted to human vision.

Applicable only for visible channels.

satpy.enhancements.colorize(img, **kwargs)[source]

Colorize the given image.

Parameters

img – image to be colorized

Kwargs:

palettes: colormap(s) to use

The palettes kwarg can be one of the following:

  • a trollimage.colormap.Colormap object

  • list of dictionaries with each of one of the following forms:
    • {‘filename’: ‘/path/to/colors.npy’,

      ‘min_value’: <float, min value to match colors to>, ‘max_value’: <float, min value to match colors to>, ‘reverse’: <bool, reverse the colormap if True (default: False)}

    • {‘colors’: <trollimage.colormap.Colormap instance>,

      ‘min_value’: <float, min value to match colors to>, ‘max_value’: <float, min value to match colors to>, ‘reverse’: <bool, reverse the colormap if True (default: False)}

    • {‘colors’: <tuple of RGB(A) tuples>,

      ‘min_value’: <float, min value to match colors to>, ‘max_value’: <float, min value to match colors to>, ‘reverse’: <bool, reverse the colormap if True (default: False)}

    • {‘colors’: <tuple of RGB(A) tuples>,

      ‘values’: <tuple of values to match colors to>, ‘min_value’: <float, min value to match colors to>, ‘max_value’: <float, min value to match colors to>, ‘reverse’: <bool, reverse the colormap if True (default: False)}

If multiple palettes are supplied, they are concatenated before applied.

satpy.enhancements.create_colormap(palette)[source]

Create colormap of the given numpy file, color vector, or colormap.

Parameters

palette (dict) – Information describing how to create a colormap object. See below for more details.

From a file

Colormaps can be loaded from .npy files as 2D raw arrays with rows for each color. The filename to load can be provided with the filename key in the provided palette information. The colormap is interpreted as 1 of 4 different “colormap modes”: RGB, RGBA, VRGB, or VRGBA. The colormap mode can be forced with the colormap_mode key in the provided palette information. If it is not provided then a default will be chosen based on the number of columns in the array (3: RGB, 4: VRGB, 5: VRGBA).

The “V” in the possible colormap modes represents the control value of where that color should be applied. If “V” is not provided in the colormap data it defaults to the row index in the colormap array (0, 1, 2, …) divided by the total number of colors to produce a number between 0 and 1. See the “Set Range” section below for more information. The remaining elements in the colormap array represent the Red (R), Green (G), and Blue (B) color to be mapped to.

See the “Color Scale” section below for more information on the value range of provided numbers.

From a list

Colormaps can be loaded from lists of colors provided by the colors key in the provided dictionary. Each element in the list represents a single color to be mapped to and can be 3 (RGB) or 4 (RGBA) elements long. By default the value or control point for a color is determined by the index in the list (0, 1, 2, …) divided by the total number of colors to produce a number between 0 and 1. This can be overridden by providing a values key in the provided dictionary. See the “Set Range” section below for more information.

See the “Color Scale” section below for more information on the value range of provided numbers.

From a builtin colormap

Colormaps can be loaded by name from the builtin colormaps in the trollimage` package. Specify the name with the colors key in the provided dictionary (ex. {'colors': 'blues'}). See Colormap for the full list of available colormaps.

Color Scale

By default colors are expected to be in a 0-255 range. This can be overridden by specifying color_scale in the provided colormap information. A common alternative to 255 is 1 to specify floating point numbers between 0 and 1. The resulting Colormap uses the normalized color values (0-1).

Set Range

By default the control points or values of the Colormap are between 0 and 1. This means that data values being mapped to a color must also be between 0 and 1. When this is not the case, the expected input range of the data can be used to configure the Colormap and change the control point values. To do this specify the input data range with min_value and max_value. See trollimage.colormap.Colormap.set_range() for more information.

satpy.enhancements.crefl_scaling(img, **kwargs)[source]

Apply non-linear stretch used by CREFL-based RGBs.

satpy.enhancements.gamma(img, **kwargs)[source]

Perform gamma correction.

satpy.enhancements.invert(img, *args)[source]

Perform inversion.

satpy.enhancements.lookup(img, **kwargs)[source]

Assign values to channels based on a table.

satpy.enhancements.palettize(img, **kwargs)[source]

Palettize the given image (no color interpolation).

satpy.enhancements.piecewise_linear_stretch(img: trollimage.xrimage.XRImage, xp: Union[int, float, complex, str, bytes, numpy.generic, Sequence[Union[int, float, complex, str, bytes, numpy.generic]], Sequence[Sequence[Any]], numpy.typing._array_like._SupportsArray], fp: Union[int, float, complex, str, bytes, numpy.generic, Sequence[Union[int, float, complex, str, bytes, numpy.generic]], Sequence[Sequence[Any]], numpy.typing._array_like._SupportsArray], reference_scale_factor: Optional[numbers.Number] = None, **kwargs) → xarray.core.dataarray.DataArray[source]

Apply 1D linear interpolation.

This uses numpy.interp() mapped over the provided dask array chunks.

Parameters

  • img – Image data to be scaled. It is assumed the data is already normalized between 0 and 1.

  • xp – Input reference values of the image data points used for interpolation. This is passed directly to numpy.interp().

  • fp – Target reference values of the output image data points used for interpolation. This is passed directly to numpy.interp().

  • reference_scale_factor – Divide xp and fp by this value before using them for interpolation. This is a convenience to make matching normalized image data to interp coordinates or to avoid floating point precision errors in YAML configuration files. If not provided, xp and fp will not be modified.

Examples

This example YAML uses a ‘crude’ stretch to pre-scale the RGB data and then uses reference points in a 0-255 range.

true_color_linear_interpolation:
  sensor: abi
  standard_name: true_color
  operations:
  - name: reflectance_range
    method: !!python/name:satpy.enhancements.stretch
    kwargs: {stretch: 'crude', min_stretch: 0., max_stretch: 100.}
  - name: Linear interpolation
    method: !!python/name:satpy.enhancements.piecewise_linear_stretch
    kwargs:
     xp: [0., 25., 55., 100., 255.]
     fp: [0., 90., 140., 175., 255.]
     reference_scale_factor: 255

This example YAML does the same as the above on the C02 channel, but the interpolation reference points are already adjusted for the input reflectance (%) data and the output range (0 to 1).

c02_linear_interpolation:
  sensor: abi
  standard_name: C02
  operations:
  - name: Linear interpolation
    method: !!python/name:satpy.enhancements.piecewise_linear_stretch
    kwargs:
     xp: [0., 9.8039, 21.5686, 39.2157, 100.]
     fp: [0., 0.3529, 0.5490, 0.6863, 1.0]
satpy.enhancements.reinhard_to_srgb(img, saturation=1.25, white=100, **kwargs)[source]

Stretch method based on the Reinhard algorithm, using luminance.

Parameters

  • saturation – Saturation enhancement factor. Less is grayer. Neutral is 1.

  • white – the reflectance luminance to set to white (in %).

Reinhard, Erik & Stark, Michael & Shirley, Peter & Ferwerda, James. (2002). Photographic Tone Reproduction For Digital Images. ACM Transactions on Graphics. :doi: 21. 10.1145/566654.566575

satpy.enhancements.stretch(img, **kwargs)[source]

Perform stretch.

satpy.enhancements.three_d_effect(img, **kwargs)[source]

Create 3D effect using convolution.