Composites are defined as arrays of data that are created by processing and/or combining one or multiple data arrays (prerequisites) together.
Composites are generated in satpy using Compositor classes. The attributes of the resulting composites are usually a combination of the prerequisites' attributes and the key/values of the DataID used to identify it.
.. py:currentmodule:: satpy.composites
There are many built-in compositors available in Satpy. The majority use the :class:`GenericCompositor` base class which handles various image modes (L, LA, RGB, and RGBA at the moment) and updates attributes.
The below sections summarize the composites that come with Satpy and
show basic examples of creating and using them with an existing
:class:`~satpy.scene.Scene` object. It is recommended that any composites
that are used repeatedly be configured in YAML configuration files.
General-use compositor code dealing with visible or infrared satellite
data can be put in a configuration file called visir.yaml
. Composites
that are specific to an instrument can be placed in YAML config files named
accordingly (e.g., seviri.yaml
or viirs.yaml
). See the
satpy repository
for more examples.
:class:`GenericCompositor` class can be used to create basic single channel and RGB composites. For example, building an overview composite can be done manually within Python code with:
>>> from satpy.composites import GenericCompositor >>> compositor = GenericCompositor("overview") >>> composite = compositor([local_scene[0.6], ... local_scene[0.8], ... local_scene[10.8]])
One important thing to notice is that there is an internal difference between a composite and an image. A composite is defined as a special dataset which may have several bands (like R, G and B bands). However, the data isn't stretched, or clipped or gamma filtered until an image is generated. To get an image out of the above composite:
>>> from satpy.writers import to_image >>> img = to_image(composite) >>> img.invert([False, False, True]) >>> img.stretch("linear") >>> img.gamma(1.7) >>> img.show()
This part is called enhancement, and is covered in more detail in :doc:`enhancements`.
Single channel composites can also be generated with the
:class:`GenericCompositor`, but in some cases, the
:class:`SingleBandCompositor` may be more appropriate. For example,
the :class:`GenericCompositor` removes attributes such as units
because they are typically not meaningful for an RGB image. Such attributes
are retained in the :class:`SingleBandCompositor`.
:class:`DifferenceCompositor` calculates a difference of two datasets:
>>> from satpy.composites import DifferenceCompositor >>> compositor = DifferenceCompositor("diffcomp") >>> composite = compositor([local_scene[10.8], local_scene[12.0]])
:class:`FillingCompositor`:: fills the missing values in three datasets with the values of another dataset::
>>> from satpy.composites import FillingCompositor >>> compositor = FillingCompositor("fillcomp") >>> filler = local_scene[0.6] >>> data_with_holes_1 = local_scene['ch_a'] >>> data_with_holes_2 = local_scene['ch_b'] >>> data_with_holes_3 = local_scene['ch_c'] >>> composite = compositor([filler, data_with_holes_1, data_with_holes_2, ... data_with_holes_3])
:class:`PaletteCompositor` creates a color version of a single channel categorical dataset using a colormap:
>>> from satpy.composites import PaletteCompositor >>> compositor = PaletteCompositor("palcomp") >>> composite = compositor([local_scene['cma'], local_scene['cma_pal']])
The palette should have a single entry for all the (possible) values in the dataset mapping the value to an RGB triplet. Typically the palette comes with the categorical (e.g. cloud mask) product that is being visualized.
.. deprecated:: 0.40 Composites produced with :class:`PaletteCompositor` will result in an image with mode RGB when enhanced. To produce an image with mode P, use the :class:`SingleBandCompositor` with an associated :func:`~satpy.enhancements.palettize` enhancement and pass ``keep_palette=True`` to :meth:`~satpy.Scene.save_datasets`. If the colormap is sourced from the same dataset as the dataset to be palettized, it must be contained in the auxiliary datasets. Since Satpy 0.40, all built-in composites that used :class:`PaletteCompositor` have been migrated to use :class:`SingleBandCompositor` instead. This has no impact on resulting images unless ``keep_palette=True`` is passed to :meth:`~satpy.Scene.save_datasets`, but the loaded composite now has only one band (previously three).
:class:`DayNightCompositor` merges two different composites. The first composite will be placed on the day-side of the scene, and the second one on the night side. The transition from day to night is done by calculating solar zenith angle (SZA) weighed average of the two composites. The SZA can optionally be given as third dataset, and if not given, the angles will be calculated. Four arguments are used to generate the image (default values shown in the example below). They can be defined when initializing the compositor:
- lim_low (float): lower limit of Sun zenith angle for the blending of the given channels - lim_high (float): upper limit of Sun zenith angle for the blending of the given channels Together with `lim_low` they define the width of the blending zone - day_night (string): "day_night" means both day and night portions will be kept "day_only" means only day portion will be kept "night_only" means only night portion will be kept - include_alpha (bool): This only affects the "day only" or "night only" result. True means an alpha band will be added to the output image for transparency. False means the output is a single-band image with undesired pixels being masked out (replaced with NaNs).
Usage (with default values):
>>> from satpy.composites import DayNightCompositor >>> compositor = DayNightCompositor("dnc", lim_low=85., lim_high=88., day_night="day_night") >>> composite = compositor([local_scene['true_color'], ... local_scene['night_fog']])
As above, with day_night flag it is also available to use only a day product or only a night product and mask out (make transparent) the opposite portion of the image (night or day). The example below provides only a day product with night portion masked-out:
>>> from satpy.composites import DayNightCompositor >>> compositor = DayNightCompositor("dnc", lim_low=85., lim_high=88., day_night="day_only") >>> composite = compositor([local_scene['true_color'])
By default, the image under day_only or night_only flag will come out with an alpha band to display its transparency. It could be changed by setting include_alpha to False if there's no need for that alpha band. In such cases, it is recommended to use it together with fill_value=0 when saving to geotiff to get a single-band image with black background. In the case below, the image shows its day portion and day/night transition with night portion blacked-out instead of transparent:
>>> from satpy.composites import DayNightCompositor >>> compositor = DayNightCompositor("dnc", lim_low=85., lim_high=88., day_night="day_only", include_alpha=False) >>> composite = compositor([local_scene['true_color'])
:class:`RealisticColors` compositor is a special compositor that is used to create realistic near-true-color composite from MSG/SEVIRI data:
>>> from satpy.composites import RealisticColors >>> compositor = RealisticColors("realcols", lim_low=85., lim_high=95.) >>> composite = compositor([local_scene['VIS006'], ... local_scene['VIS008'], ... local_scene['HRV']])
:class:`CloudCompositor` can be used to threshold the data so that "only" clouds are visible. These composites can be used as an overlay on top of e.g. static terrain images to show a rough idea where there are clouds. The data are thresholded using three variables:
- `transition_min`: values below or equal to this are clouds -> opaque white - `transition_max`: values above this are cloud free -> transparent - `transition_gamma`: gamma correction applied to clarify the clouds
Usage (with default values):
>>> from satpy.composites import CloudCompositor >>> compositor = CloudCompositor("clouds", transition_min=258.15, ... transition_max=298.15, ... transition_gamma=3.0) >>> composite = compositor([local_scene[10.8]])
Support for using this compositor for VIS data, where the values for high/thick clouds tend to be in reverse order to brightness temperatures, is to be added.
:class:`SelfSharpenedRGB` sharpens the RGB with ratio of a band with a strided version of itself.
:class:`LuminanceSharpeningCompositor` replaces the luminance from an RGB composite with luminance created from reflectance data. If the resolutions of the reflectance data _and_ of the target area definition are higher than the base RGB, more details can be retrieved. This compositor can be useful also with matching resolutions, e.g. to highlight shadowing at cloudtops in colorized infrared composite.
>>> from satpy.composites import LuminanceSharpeningCompositor >>> compositor = LuminanceSharpeningCompositor("vis_sharpened_ir") >>> vis_data = local_scene['HRV'] >>> colorized_ir_clouds = local_scene['colorized_ir_clouds'] >>> composite = compositor([vis_data, colorized_ir_clouds])
Similar to :class:`LuminanceSharpeningCompositor`, :class:`SandwichCompositor` uses reflectance data to bring out more details out of infrared or low-resolution composites. :class:`SandwichCompositor` multiplies the RGB channels with (scaled) reflectance.
>>> from satpy.composites import SandwichCompositor >>> compositor = SandwichCompositor("ir_sandwich") >>> vis_data = local_scene['HRV'] >>> colorized_ir_clouds = local_scene['colorized_ir_clouds'] >>> composite = compositor([vis_data, colorized_ir_clouds])
:class:`StaticImageCompositor` can be used to read an image from disk and used just like satellite data, including resampling and using as a part of other composites.
>>> from satpy.composites import StaticImageCompositor >>> compositor = StaticImageCompositor("static_image", filename="image.tif") >>> composite = compositor()
:class:`BackgroundCompositor` can be used to stack two composites together. If the composites don't have alpha channels, the background is used where foreground has no data. If foreground has alpha channel, the alpha values are used to weight when blending the two composites.
>>> from satpy import Scene >>> from satpy.composites import BackgroundCompositor >>> compositor = BackgroundCompositor() >>> clouds = local_scene['ir_cloud_day'] >>> background = local_scene['overview'] >>> composite = compositor([clouds, background])
:class:`CategoricalDataCompositor` can be used to recategorize categorical data. This is for example useful to combine comparable categories into a common category. The category remapping from data to composite is done using a look-up-table (lut):
composite = [[lut[data[0,0]], lut[data[0,1]], lut[data[0,Nj]]], [[lut[data[1,0]], lut[data[1,1]], lut[data[1,Nj]], [[lut[data[Ni,0]], lut[data[Ni,1]], lut[data[Ni,Nj]]]
Hence, lut must have a length that is greater than the maximum value in data in orer to avoid an IndexError. Below is an example on how to create a binary clear-sky/cloud mask from a pseodu cloud type product with six categories representing clear sky (cat1/cat5), cloudy features (cat2-cat4) and missing/undefined data (cat0):
>>> cloud_type = local_scene['cloud_type'] # 0 - cat0, 1 - cat1, 2 - cat2, 3 - cat3, 4 - cat4, 5 - cat5, # categories: 0 1 2 3 4 5 >>> lut = [np.nan, 0, 1, 1, 1, 0] >>> compositor = CategoricalDataCompositor('binary_cloud_mask', lut=lut) >>> composite = compositor([cloud_type]) # 0 - cat1/cat5, 1 - cat2/cat3/cat4, nan - cat0
To save the custom composite, follow the :ref:`component_configuration`
documentation. Once your component configuration directory is created
you can create your custom composite YAML configuration files.
Compositors that can be used for multiple instruments can be placed in the
generic $SATPY_CONFIG_PATH/composites/visir.yaml
file. Composites that
are specific to one sensor should be placed in
$SATPY_CONFIG_PATH/composites/<sensor>.yaml
. Custom enhancements for your new
composites can be stored in $SATPY_CONFIG_PATH/enhancements/generic.yaml
or
$SATPY_CONFIG_PATH/enhancements/<sensor>.yaml
.
With that, you should be able to load your new composite directly. Example configuration files can be found in the satpy repository as well as a few simple examples below.
This is the overview composite shown in the first code example above using :class:`GenericCompositor`:
sensor_name: visir composites: overview: compositor: !!python/name:satpy.composites.GenericCompositor prerequisites: - 0.6 - 0.8 - 10.8 standard_name: overview
For an instrument specific version (here MSG/SEVIRI), we should use the channel _names_ instead of wavelengths. Note also that the sensor_name is now combination of visir and seviri, which means that it extends the generic visir composites:
sensor_name: visir/seviri composites: overview: compositor: !!python/name:satpy.composites.GenericCompositor prerequisites: - VIS006 - VIS008 - IR_108 standard_name: overview
In the following examples only the composite receipes are shown, and the header information (sensor_name, composites) and intendation needs to be added.
In many cases the basic datasets that go into the composite need to be adjusted, e.g. for Solar zenith angle normalization. These modifiers can be applied in the following way:
overview: compositor: !!python/name:satpy.composites.GenericCompositor prerequisites: - name: VIS006 modifiers: [sunz_corrected] - name: VIS008 modifiers: [sunz_corrected] - IR_108 standard_name: overview
Here we see two changes:
- channels with modifiers need to have either name or wavelength added in front of the channel name or wavelength, respectively
- a list of modifiers attached to the dictionary defining the channel
The modifier above is a built-in that normalizes the Solar zenith angle to Sun being directly at the zenith.
More examples can be found in Satpy source code directory satpy/etc/composites.
See the :doc:`modifiers` documentation for more information on available built-in modifiers.
Often it is handy to use other composites as a part of the composite. In this example we have one composite that relies on solar channels on the day side, and another for the night side:
natural_with_night_fog: compositor: !!python/name:satpy.composites.DayNightCompositor prerequisites: - natural_color - night_fog standard_name: natural_with_night_fog
This compositor has three additional keyword arguments that can be defined (shown with the default values, thus identical result as above):
natural_with_night_fog: compositor: !!python/name:satpy.composites.DayNightCompositor prerequisites: - natural_color - night_fog lim_low: 85.0 lim_high: 88.0 day_night: "day_night" standard_name: natural_with_night_fog
It is also possible to define sub-composites in-line. This example is the built-in airmass composite:
airmass: compositor: !!python/name:satpy.composites.GenericCompositor prerequisites: - compositor: !!python/name:satpy.composites.DifferenceCompositor prerequisites: - wavelength: 6.2 - wavelength: 7.3 - compositor: !!python/name:satpy.composites.DifferenceCompositor prerequisites: - wavelength: 9.7 - wavelength: 10.8 - wavelength: 6.2 standard_name: airmass
Below is an example composite config using :class:`StaticImageCompositor`, :class:`DayNightCompositor`, :class:`CloudCompositor` and :class:`BackgroundCompositor` to show how to create a composite with a blended day/night imagery as background for clouds. As the images are in PNG format, and thus not georeferenced, the name of the area definition for the background images are given. When using GeoTIFF images the area parameter can be left out.
Note
The background blending uses the current time if there is no timestamps in the image filenames.
clouds_with_background: compositor: !!python/name:satpy.composites.BackgroundCompositor standard_name: clouds_with_background prerequisites: - ir_cloud_day - compositor: !!python/name:satpy.composites.DayNightCompositor prerequisites: - static_day - static_night static_day: compositor: !!python/name:satpy.composites.StaticImageCompositor standard_name: static_day filename: /path/to/day_image.png area: euro4 static_night: compositor: !!python/name:satpy.composites.StaticImageCompositor standard_name: static_night filename: /path/to/night_image.png area: euro4
To ensure that the images aren't auto-stretched and possibly altered, the following should be added to enhancement config (assuming 8-bit image) for both of the static images:
static_day: standard_name: static_day operations: - name: stretch method: !!python/name:satpy.enhancements.stretch kwargs: stretch: crude min_stretch: [0, 0, 0] max_stretch: [255, 255, 255]
.. todo:: Explain how composite names, composite standard_name, enhancement names, and enhancement standard_name are related to each other Explain what happens when no enhancement is configured for a product (= use the default enhancement). Explain that the methods are often just a wrapper for XRImage methods, but can also be something completely custom. List and explain in detail the built-in enhancements: - stretch - gamma - invert - cira_stretch - lookup - colorize - palettize - three_d_effect - btemp_threshold
.. todo:: Should this be in another file/page?
After the composite is defined and created, it needs to be converted
to an image. To do this, it is necessary to describe how the data
values are mapped to values stored in the image format. This
procedure is called stretching
, and in Satpy it is implemented by
enhancements
.
The first step is to convert the composite to an :class:`~trollimage.xrimage.XRImage` object:
>>> from satpy.writers import to_image >>> img = to_image(composite)
Now it is possible to apply enhancements available in the class:
>>> img.invert([False, False, True]) >>> img.stretch("linear") >>> img.gamma(1.7)
And finally either show or save the image:
>>> img.show() >>> img.save('image.tif')
As pointed out in the composite section, it is better to define
frequently used enhancements in configuration files under
$SATPY_CONFIG_PATH/enhancements/
. The enhancements can either be in
generic.yaml
or instrument-specific file (e.g., seviri.yaml
).
The above enhancement can be written (with the headers necessary for the file) as:
enhancements: overview: standard_name: overview operations: - name: inverse method: !!python/name:satpy.enhancements.invert args: [False, False, True] - name: stretch method: !!python/name:satpy.enhancements.stretch kwargs: stretch: linear - name: gamma method: !!python/name:satpy.enhancements.gamma kwargs: gamma: [1.7, 1.7, 1.7]
Warning
If you define a composite with no matching enhancement, Satpy will by default apply the :func:`~trollimage.xrimage.XRImage.stretch_linear` enhancement with cutoffs of 0.5% and 99.5%. If you want no enhancement at all (maybe you are enhancing a composite based on :class:`DayNightCompositor` where the components have their own enhancements defined), you need to define an enhancement that does nothing:
enhancements: day_x: standard_name: day_x operations: []
It is recommended to define an enhancement even if you intend to use the default, in case the default should change in future versions of Satpy.
More examples can be found in Satpy source code directory
satpy/etc/enhancements/generic.yaml
.
See the :doc:`enhancements` documentation for more information on available built-in enhancements.