#!/usr/bin/env python
# Copyright (c) 2017 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 numpy as np
import xarray as xr
import dask
import dask.array as da
import logging
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 apply_enhancement(data, func, exclude=None, separate=False,
pass_dask=False):
"""Apply `func` to the provided data.
Args:
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.
"""
attrs = data.attrs
bands = data.coords['bands'].values
if exclude is None:
exclude = ['A'] if 'A' in bands else []
if separate:
data_arrs = []
for idx, band_name in enumerate(bands):
band_data = data.sel(bands=[band_name])
if band_name in exclude:
# don't modify alpha
data_arrs.append(band_data)
continue
if pass_dask:
dims = band_data.dims
coords = band_data.coords
d_arr = func(band_data.data, index=idx)
band_data = xr.DataArray(d_arr, dims=dims, coords=coords)
else:
band_data = func(band_data, index=idx)
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
else:
band_data = data.sel(bands=[b for b in bands
if b not in exclude])
if pass_dask:
dims = band_data.dims
coords = band_data.coords
d_arr = func(band_data.data)
band_data = xr.DataArray(d_arr, dims=dims, coords=coords)
else:
band_data = func(band_data)
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
[docs]def crefl_scaling(img, **kwargs):
"""Apply non-linear stretch used by CREFL-based RGBs."""
LOG.debug("Applying the crefl_scaling")
def func(band_data, index=None):
idx = np.array(kwargs['idx']) / 255
sc = np.array(kwargs['sc']) / 255
band_data *= .01
# Interpolate band on [0,1] using "lazy" arrays (put calculations off until the end).
band_data = xr.DataArray(da.clip(band_data.data.map_blocks(np.interp, xp=idx, fp=sc), 0, 1),
coords=band_data.coords, dims=band_data.dims, name=band_data.name,
attrs=band_data.attrs)
return band_data
return apply_enhancement(img.data, func, separate=True)
[docs]def cira_stretch(img, **kwargs):
"""Logarithmic stretch adapted to human vision.
Applicable only for visible channels.
"""
LOG.debug("Applying the cira-stretch")
def func(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
return apply_enhancement(img.data, func)
def _lookup_delayed(luts, band_data):
# can't use luts.__getitem__ for some reason
return luts[band_data]
[docs]def lookup(img, **kwargs):
"""Assign values to channels based on a table."""
luts = np.array(kwargs['luts'], dtype=np.float32) / 255.0
def func(band_data, luts=luts, 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)
new_delay = dask.delayed(_lookup_delayed)(lut, band_data)
new_data = da.from_delayed(new_delay, shape=band_data.shape,
dtype=luts.dtype)
return new_data
return apply_enhancement(img.data, func, separate=True, pass_dask=True)
[docs]def colorize(img, **kwargs):
"""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)}
If multiple palettes are supplied, they are concatenated before applied.
"""
full_cmap = _merge_colormaps(kwargs)
img.colorize(full_cmap)
[docs]def palettize(img, **kwargs):
"""Palettize the given image (no color interpolation)."""
full_cmap = _merge_colormaps(kwargs)
img.palettize(full_cmap)
def _merge_colormaps(kwargs):
"""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)
if full_cmap is None:
full_cmap = cmap
else:
full_cmap = full_cmap + cmap
return full_cmap
[docs]def create_colormap(palette):
"""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`` 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 :doc:`trollimage: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 :meth:`trollimage.colormap.Colormap.set_range` for more
information.
"""
from trollimage.colormap import Colormap
fname = palette.get('filename', None)
colors = palette.get('colors', None)
# are colors between 0-255 or 0-1
color_scale = palette.get('color_scale', 255)
if fname:
data = np.load(fname)
cols = data.shape[1]
default_modes = {
3: 'RGB',
4: 'VRGB',
5: 'VRGBA'
}
default_mode = default_modes.get(cols)
mode = palette.setdefault('colormap_mode', default_mode)
if mode is None or len(mode) != cols:
raise ValueError(
"Unexpected colormap shape for mode '{}'".format(mode))
rows = data.shape[0]
if mode[0] == 'V':
colors = data[:, 1:]
if color_scale != 1:
colors = data[:, 1:] / float(color_scale)
values = data[:, 0]
else:
colors = data
if color_scale != 1:
colors = colors / float(color_scale)
values = np.arange(rows) / float(rows - 1)
cmap = Colormap(*zip(values, colors))
elif isinstance(colors, (tuple, list)):
cmap = []
values = palette.get('values', None)
for idx, color in enumerate(colors):
if values is not None:
value = values[idx]
else:
value = idx / float(len(colors) - 1)
if color_scale != 1:
color = tuple(elem / float(color_scale) for elem in color)
cmap.append((value, tuple(color)))
cmap = Colormap(*cmap)
elif isinstance(colors, str):
from trollimage import colormap
import copy
cmap = copy.copy(getattr(colormap, colors))
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")
return cmap
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 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')
def func(band_data, kernel=kernel, mode=mode, 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
return apply_enhancement(img.data, func, separate=True, pass_dask=True)
[docs]def btemp_threshold(img, min_in, max_in, threshold, threshold_out=None, **kwargs):
"""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
def _bt_threshold(band_data):
# expects dask array to be passed
return da.where(band_data >= threshold,
high_offset - high_factor * band_data,
low_offset - low_factor * band_data)
return apply_enhancement(img.data, _bt_threshold, pass_dask=True)