# Copyright (c) 2017-2023 Satpy developers
#
# This file is part of satpy.
#
# satpy is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.
#
# satpy is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
# A PARTICULAR PURPOSE. See the GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along with
# satpy. If not, see <http://www.gnu.org/licenses/>.
"""Enhancements."""
import logging
import os
from collections import namedtuple
from functools import wraps
from numbers import Number
from typing import Optional
import dask
import dask.array as da
import numpy as np
import xarray as xr
from trollimage.colormap import Colormap
from trollimage.xrimage import XRImage
from satpy._compat import ArrayLike
from satpy._config import get_config_path
from satpy.utils import find_in_ancillary
LOG = logging.getLogger(__name__)
[docs]
def stretch(img, **kwargs):
"""Perform stretch."""
return img.stretch(**kwargs)
[docs]
def gamma(img, **kwargs):
"""Perform gamma correction."""
return img.gamma(**kwargs)
[docs]
def invert(img, *args):
"""Perform inversion."""
return img.invert(*args)
[docs]
def exclude_alpha(func):
"""Exclude the alpha channel from the DataArray before further processing."""
@wraps(func)
def wrapper(data, **kwargs):
bands = data.coords["bands"].values
exclude = ["A"] if "A" in bands else []
band_data = data.sel(bands=[b for b in bands
if b not in exclude])
band_data = func(band_data, **kwargs)
attrs = data.attrs
attrs.update(band_data.attrs)
# combine the new data with the excluded data
new_data = xr.concat([band_data, data.sel(bands=exclude)],
dim="bands")
data.data = new_data.sel(bands=bands).data
data.attrs = attrs
return data
return wrapper
[docs]
def on_separate_bands(func):
"""Apply `func` one band of the DataArray at a time.
If this decorator is to be applied along with `on_dask_array`, this decorator has to be applied first, eg::
@on_separate_bands
@on_dask_array
def my_enhancement_function(data):
...
"""
@wraps(func)
def wrapper(data, **kwargs):
attrs = data.attrs
data_arrs = []
for idx, band in enumerate(data.coords["bands"].values):
band_data = func(data.sel(bands=[band]), index=idx, **kwargs)
data_arrs.append(band_data)
# we assume that the func can add attrs
attrs.update(band_data.attrs)
data.data = xr.concat(data_arrs, dim="bands").data
data.attrs = attrs
return data
return wrapper
[docs]
def on_dask_array(func):
"""Pass the underlying dask array to *func* instead of the xarray.DataArray."""
@wraps(func)
def wrapper(data, **kwargs):
dims = data.dims
coords = data.coords
d_arr = func(data.data, **kwargs)
return xr.DataArray(d_arr, dims=dims, coords=coords)
return wrapper
[docs]
def using_map_blocks(func):
"""Run the provided function using :func:`dask.array.core.map_blocks`.
This means dask will call the provided function with a single chunk
as a numpy array.
"""
@wraps(func)
def wrapper(data, **kwargs):
return da.map_blocks(func, data, meta=np.array((), dtype=data.dtype), dtype=data.dtype, chunks=data.chunks,
**kwargs)
return on_dask_array(wrapper)
[docs]
def piecewise_linear_stretch( # noqa: D417
img: XRImage,
xp: ArrayLike,
fp: ArrayLike,
reference_scale_factor: Optional[Number] = None,
**kwargs) -> xr.DataArray:
"""Apply 1D linear interpolation.
This uses :func:`numpy.interp` mapped over the provided dask array chunks.
Args:
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 :func:`numpy.interp`.
fp: Target reference values of the output image data points used for
interpolation. This is passed directly to :func:`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.
.. code-block:: yaml
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).
.. code-block:: yaml
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]
"""
LOG.debug("Applying the piecewise_linear_stretch")
if reference_scale_factor is not None:
xp = np.asarray(xp) / reference_scale_factor
fp = np.asarray(fp) / reference_scale_factor
return _piecewise_linear(img.data, xp=xp, fp=fp)
[docs]
@exclude_alpha
@using_map_blocks
def _piecewise_linear(band_data, xp, fp):
# Interpolate band on [0,1] using "lazy" arrays (put calculations off until the end).
interp_data = np.interp(band_data, xp=xp, fp=fp)
interp_data = np.clip(interp_data, 0, 1, out=interp_data)
return interp_data
[docs]
def cira_stretch(img, **kwargs):
"""Logarithmic stretch adapted to human vision.
Applicable only for visible channels.
"""
LOG.debug("Applying the cira-stretch")
return _cira_stretch(img.data)
[docs]
@exclude_alpha
def _cira_stretch(band_data):
log_root = np.log10(0.0223)
denom = (1.0 - log_root) * 0.75
band_data *= 0.01
band_data = band_data.clip(np.finfo(float).eps)
band_data = np.log10(band_data)
band_data -= log_root
band_data /= denom
return band_data
[docs]
def reinhard_to_srgb(img, saturation=1.25, white=100, **kwargs): # noqa: D417
"""Stretch method based on the Reinhard algorithm, using luminance.
Args:
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`
"""
with xr.set_options(keep_attrs=True):
# scale the data to [0, 1] interval
rgb = img.data / 100
white /= 100
# extract color components
r = rgb.sel(bands="R").data
g = rgb.sel(bands="G").data
b = rgb.sel(bands="B").data
# saturate
luma = _compute_luminance_from_rgb(r, g, b)
rgb = (luma + (rgb - luma) * saturation).clip(0)
# reinhard
reinhard_luma = (luma / (1 + luma)) * (1 + luma / (white ** 2))
coef = reinhard_luma / luma
rgb = rgb * coef
# srgb gamma
rgb.data = _srgb_gamma(rgb.data)
img.data = rgb
return img.data
[docs]
def _compute_luminance_from_rgb(r, g, b):
"""Compute the luminance of the image."""
return r * 0.2126 + g * 0.7152 + b * 0.0722
[docs]
def _srgb_gamma(arr):
"""Apply the srgb gamma."""
return da.where(arr < 0.0031308, arr * 12.92, 1.055 * arr ** 0.41666 - 0.055)
[docs]
def lookup(img, **kwargs):
"""Assign values to channels based on a table."""
luts = np.array(kwargs["luts"], dtype=np.float32) / 255.0
return _lookup_table(img.data, luts=luts)
[docs]
@exclude_alpha
@on_separate_bands
@using_map_blocks
def _lookup_table(band_data, luts=None, index=-1):
# NaN/null values will become 0
lut = luts[:, index] if len(luts.shape) == 2 else luts
band_data = band_data.clip(0, lut.size - 1).astype(np.uint8)
return lut[band_data]
[docs]
def colorize(img, **kwargs): # noqa: D417
"""Colorize the given image.
Args:
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)}
- {'dataset': <str, referring to dataset containing palette>,
'color_scale': <int, value to be interpreted as white>,
'min_value': <float, see above>,
'max_value': <float, see above>}
If multiple palettes are supplied, they are concatenated before applied.
"""
full_cmap = _merge_colormaps(kwargs, img)
img.colorize(full_cmap)
[docs]
def palettize(img, **kwargs):
"""Palettize the given image (no color interpolation).
Arguments as for :func:`colorize`.
NB: to retain the palette when saving the resulting image, pass
``keep_palette=True`` to the save method (either via the Scene class or
directly in trollimage).
"""
full_cmap = _merge_colormaps(kwargs, img)
img.palettize(full_cmap)
[docs]
def _merge_colormaps(kwargs, img=None):
"""Merge colormaps listed in kwargs."""
from trollimage.colormap import Colormap
full_cmap = None
palette = kwargs["palettes"]
if isinstance(palette, Colormap):
full_cmap = palette
else:
for itm in palette:
cmap = create_colormap(itm, img)
if full_cmap is None:
full_cmap = cmap
else:
full_cmap = full_cmap + cmap
return full_cmap
[docs]
def create_colormap(palette, img=None): # noqa: D417
"""Create colormap of the given numpy file, color vector, or colormap.
Args:
palette (dict): Information describing how to create a colormap
object. See below for more details.
**From a file**
Colormaps can be loaded from ``.npy``, ``.npz``, or comma-separated text
files. Numpy (npy/npz) files should be 2D arrays with rows for each color.
Comma-separated files should have a row for each color with each column
representing a single value/channel. The filename to load can be provided
with the ``filename`` key in the provided palette information. A filename
ending with ``.npy`` or ``.npz`` is read as a numpy file with
:func:`numpy.load`. All other extensions are
read as a comma-separated file. For ``.npz`` files the data must be stored
as a positional list where the first element represents the colormap to
use. See :func:`numpy.savez` for more information. The path to the
colormap can be relative if it is stored in a directory specified by
:ref:`config_path_setting`. Otherwise it should be an absolute path.
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 :doc:`trollimage:colormap` for the full list of available colormaps.
**From an auxiliary variable**
If the colormap is defined in the same dataset as the data to which the
colormap shall be applied, this can be indicated with
``{'dataset': 'palette_variable'}``, where ``'palette_variable'`` is the
name of the variable containing the palette. This variable must be an
auxiliary variable to the dataset to which the colours are applied. When
using this, it is important that one should **not** set ``min_value`` and
``max_value`` as those will be taken from the ``valid_range`` attribute
on the dataset and if those differ from ``min_value`` and ``max_value``,
the resulting colors will not match the ones in the palette.
**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 :meth:`trollimage.colormap.Colormap.set_range` for more
information.
"""
fname = palette.get("filename", None)
colors = palette.get("colors", None)
dataset = palette.get("dataset", None)
# are colors between 0-255 or 0-1
color_scale = palette.get("color_scale", 255)
if fname:
if not os.path.exists(fname):
fname = get_config_path(fname)
cmap = Colormap.from_file(fname, palette.get("colormap_mode", None), color_scale)
elif isinstance(colors, (tuple, list)):
cmap = Colormap.from_sequence_of_colors(colors, palette.get("values", None), color_scale)
elif isinstance(colors, str):
cmap = Colormap.from_name(colors)
elif isinstance(dataset, str):
cmap = _create_colormap_from_dataset(img, dataset, color_scale)
else:
raise ValueError("Unknown colormap format: {}".format(palette))
if palette.get("reverse", False):
cmap.reverse()
if "min_value" in palette and "max_value" in palette:
cmap.set_range(palette["min_value"], palette["max_value"])
elif "min_value" in palette or "max_value" in palette:
raise ValueError("Both 'min_value' and 'max_value' must be specified (or neither)")
return cmap
[docs]
def _create_colormap_from_dataset(img, dataset, color_scale):
"""Create a colormap from an auxiliary variable in a source file."""
match = find_in_ancillary(img.data, dataset)
return Colormap.from_array_with_metadata(
match, img.data.dtype, color_scale,
valid_range=img.data.attrs.get("valid_range"),
scale_factor=img.data.attrs.get("scale_factor", 1),
add_offset=img.data.attrs.get("add_offset", 0),
remove_last=False)
[docs]
def three_d_effect(img, **kwargs):
"""Create 3D effect using convolution."""
w = kwargs.get("weight", 1)
LOG.debug("Applying 3D effect with weight %.2f", w)
kernel = np.array([[-w, 0, w],
[-w, 1, w],
[-w, 0, w]])
mode = kwargs.get("convolve_mode", "same")
return _three_d_effect(img.data, kernel=kernel, mode=mode)
[docs]
@exclude_alpha
@on_separate_bands
@on_dask_array
def _three_d_effect(band_data, kernel=None, mode=None, index=None):
del index
delay = dask.delayed(_three_d_effect_delayed)(band_data, kernel, mode)
new_data = da.from_delayed(delay, shape=band_data.shape, dtype=band_data.dtype)
return new_data
[docs]
def _three_d_effect_delayed(band_data, kernel, mode):
"""Kernel for running delayed 3D effect creation."""
from scipy.signal import convolve2d
band_data = band_data.reshape(band_data.shape[1:])
new_data = convolve2d(band_data, kernel, mode=mode)
return new_data.reshape((1, band_data.shape[0], band_data.shape[1]))
[docs]
def btemp_threshold(img, min_in, max_in, threshold, threshold_out=None, **kwargs): # noqa: D417
"""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.
Args:
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.
"""
threshold_out = threshold_out if threshold_out is not None else (176 / 255.0)
low_factor = (threshold_out - 1.) / (min_in - threshold)
low_offset = 1. + (low_factor * min_in)
high_factor = threshold_out / (max_in - threshold)
high_offset = high_factor * max_in
Coeffs = namedtuple("Coeffs", "factor offset")
high = Coeffs(high_factor, high_offset)
low = Coeffs(low_factor, low_offset)
return _bt_threshold(img.data,
threshold=threshold,
high_coeffs=high,
low_coeffs=low)
[docs]
@exclude_alpha
@using_map_blocks
def _bt_threshold(band_data, threshold, high_coeffs, low_coeffs):
# expects dask array to be passed
return np.where(band_data >= threshold,
high_coeffs.offset - high_coeffs.factor * band_data,
low_coeffs.offset - low_coeffs.factor * band_data)
[docs]
def jma_true_color_reproduction(img):
"""Apply CIE XYZ matrix and return True Color Reproduction data.
Himawari-8 True Color Reproduction Approach Based on the CIE XYZ Color System
Hidehiko MURATA, Kotaro SAITOH, and Yasuhiko SUMIDA
Meteorological Satellite Center, Japan Meteorological Agency
NOAA National Environmental Satellite, Data, and Information Service
Colorado State University—CIRA
https://www.jma.go.jp/jma/jma-eng/satellite/introduction/TCR.html
"""
_jma_true_color_reproduction(img.data,
platform=img.data.attrs["platform_name"])
[docs]
@exclude_alpha
@on_dask_array
def _jma_true_color_reproduction(img_data, platform=None):
"""Convert from AHI RGB space to sRGB space.
The conversion matrices for this are supplied per-platform.
The matrices are computed using the method described in the paper:
'True Color Imagery Rendering for Himawari-8 with a Color Reproduction Approach
Based on the CIE XYZ Color System' (:doi:`10.2151/jmsj.2018-049`).
"""
# Conversion matrix dictionaries specifying sensor and platform.
ccm_dict = {"himawari-8": np.array([[1.1629, 0.1539, -0.2175],
[-0.0252, 0.8725, 0.1300],
[-0.0204, -0.1100, 1.0633]]),
"himawari-9": np.array([[1.1619, 0.1542, -0.2168],
[-0.0271, 0.8749, 0.1295],
[-0.0202, -0.1103, 1.0634]]),
"goes-16": np.array([[1.1425, 0.1819, -0.2250],
[-0.0951, 0.9363, 0.1360],
[-0.0113, -0.1179, 1.0621]]),
"goes-17": np.array([[1.1437, 0.1818, -0.2262],
[-0.0952, 0.9354, 0.1371],
[-0.0113, -0.1178, 1.0620]]),
"goes-18": np.array([[1.1629, 0.1539, -0.2175],
[-0.0252, 0.8725, 0.1300],
[-0.0204, -0.1100, 1.0633]]),
"mtg-i1": np.array([[0.9007, 0.2086, -0.0100],
[-0.0475, 1.0662, -0.0414],
[-0.0123, -0.1342, 1.0794]]),
"geo-kompsat-2a": np.array([[1.1661, 0.1489, -0.2157],
[-0.0255, 0.8745, 0.1282],
[-0.0205, -0.1103, 1.0637]]),
}
# A conversion matrix, sensor name and platform name is required
if platform is None:
raise ValueError("Missing platform name.")
# Get the satellite-specific conversion matrix
try:
ccm = ccm_dict[platform.lower()]
except KeyError:
raise KeyError(f"No conversion matrix found for platform {platform}")
output = da.dot(img_data.T, ccm.T)
return output.T