Quickstart
Loading and accessing data
To work with weather satellite data you must create a
Scene object. Satpy does not currently provide an
interface to download satellite data, it assumes that the data is on a
local hard disk already. In order for Satpy to get access to the data the
Scene must be told what files to read and what
Satpy Reader should read them:
>>> from satpy import Scene
>>> from glob import glob
>>> filenames = glob("/home/a001673/data/satellite/Meteosat-10/seviri/lvl1.5/2015/04/20/HRIT/*201504201000*")
>>> global_scene = Scene(reader="seviri_l1b_hrit", filenames=filenames)
To load data from the files use the Scene.load
method. Printing the Scene object will list each of the
xarray.DataArray objects currently loaded:
>>> global_scene.load(['0.8', '1.6', '10.8'])
>>> print(global_scene)
<xarray.DataArray 'reshape-d66223a8e05819b890c4535bc7e74356' (y: 3712, x: 3712)>
dask.array<shape=(3712, 3712), dtype=float32, chunksize=(464, 3712)>
Coordinates:
* x (x) float64 5.567e+06 5.564e+06 5.561e+06 5.558e+06 5.555e+06 ...
* y (y) float64 -5.567e+06 -5.564e+06 -5.561e+06 -5.558e+06 ...
Attributes:
orbital_parameters: {'projection_longitude': 0.0, 'pr...
sensor: seviri
platform_name: Meteosat-11
standard_name: brightness_temperature
units: K
wavelength: (9.8, 10.8, 11.8)
start_time: 2018-02-28 15:00:10.814000
end_time: 2018-02-28 15:12:43.956000
area: Area ID: some_area_name\nDescription: On-the-fly ar...
name: IR_108
resolution: 3000.40316582
calibration: brightness_temperature
polarization: None
level: None
modifiers: ()
ancillary_variables: []
<xarray.DataArray 'reshape-1982d32298aca15acb42c481fd74a629' (y: 3712, x: 3712)>
dask.array<shape=(3712, 3712), dtype=float32, chunksize=(464, 3712)>
Coordinates:
* x (x) float64 5.567e+06 5.564e+06 5.561e+06 5.558e+06 5.555e+06 ...
* y (y) float64 -5.567e+06 -5.564e+06 -5.561e+06 -5.558e+06 ...
Attributes:
orbital_parameters: {'projection_longitude': 0.0, 'pr...
sensor: seviri
platform_name: Meteosat-11
standard_name: toa_bidirectional_reflectance
units: %
wavelength: (0.74, 0.81, 0.88)
start_time: 2018-02-28 15:00:10.814000
end_time: 2018-02-28 15:12:43.956000
area: Area ID: some_area_name\nDescription: On-the-fly ar...
name: VIS008
resolution: 3000.40316582
calibration: reflectance
polarization: None
level: None
modifiers: ()
ancillary_variables: []
<xarray.DataArray 'reshape-e86d03c30ce754995ff9da484c0dc338' (y: 3712, x: 3712)>
dask.array<shape=(3712, 3712), dtype=float32, chunksize=(464, 3712)>
Coordinates:
* x (x) float64 5.567e+06 5.564e+06 5.561e+06 5.558e+06 5.555e+06 ...
* y (y) float64 -5.567e+06 -5.564e+06 -5.561e+06 -5.558e+06 ...
Attributes:
orbital_parameters: {'projection_longitude': 0.0, 'pr...
sensor: seviri
platform_name: Meteosat-11
standard_name: toa_bidirectional_reflectance
units: %
wavelength: (1.5, 1.64, 1.78)
start_time: 2018-02-28 15:00:10.814000
end_time: 2018-02-28 15:12:43.956000
area: Area ID: some_area_name\nDescription: On-the-fly ar...
name: VIS006
resolution: 3000.40316582
calibration: reflectance
polarization: None
level: None
modifiers: ()
ancillary_variables: []
Satpy allows loading file data by wavelengths in micrometers (shown above) or by channel name:
>>> global_scene.load(["VIS008", "IR_016", "IR_108"])
To have a look at the available channels for loading from your Scene object use the
available_dataset_names() method:
>>> global_scene.available_dataset_names()
['HRV',
'IR_108',
'IR_120',
'VIS006',
'WV_062',
'IR_039',
'IR_134',
'IR_097',
'IR_087',
'VIS008',
'IR_016',
'WV_073']
To access the loaded data use the wavelength or name:
>>> print(global_scene[0.8])
For more information on loading datasets by resolution, calibration, or other advanced loading methods see the Reading documentation.
Visualizing data
To visualize loaded data in a pop-up window:
>>> global_scene.show(0.8)
Alternatively if working in a Jupyter notebook the scene can be converted to
a geoviews object using the
to_geoviews() method. The geoviews package is not a
requirement of the base satpy install so in order to use this feature the user
needs to install the geoviews package himself.
>>> import holoviews as hv
>>> import geoviews as gv
>>> import geoviews.feature as gf
>>> gv.extension("bokeh", "matplotlib")
>>> %opts QuadMesh Image [width=600 height=400 colorbar=True] Feature [apply_ranges=False]
>>> %opts Image QuadMesh (cmap='RdBu_r')
>>> gview = global_scene.to_geoviews(vdims=[0.6])
>>> gview[::5,::5] * gf.coastline * gf.borders
Creating new datasets
Calculations based on loaded datasets/channels can easily be assigned to a new dataset:
>>> global_scene.load(['VIS006', 'VIS008'])
>>> global_scene["ndvi"] = (global_scene['VIS008'] - global_scene['VIS006']) / (global_scene['VIS008'] + global_scene['VIS006'])
>>> global_scene.show("ndvi")
When doing calculations Xarray, by default, will drop all attributes so attributes need to be
copied over by hand. The combine_metadata() function can assist with this task.
Assigning additional custom metadata is also possible.
>>> from satpy.dataset import combine_metadata
>>> scene['new_band'] = scene['VIS008'] / scene['VIS006']
>>> scene['new_band'].attrs = combine_metadata(scene['VIS008'], scene['VIS006'])
>>> scene['new_band'].attrs['some_other_key'] = 'whatever_value_you_want'
Generating composites
Satpy comes with many composite recipes built-in and makes them loadable like any other dataset:
>>> global_scene.load(['overview'])
To get a list of all available composites for the current scene:
>>> global_scene.available_composite_names()
['overview_sun',
'airmass',
'natural_color',
'night_fog',
'overview',
'green_snow',
'dust',
'fog',
'natural_color_raw',
'cloudtop',
'convection',
'ash']
Loading composites will load all necessary dependencies to make that composite and unload them after the composite has been generated.
Note
Some composite require datasets to be at the same resolution or shape. When this is the case the Scene object must be resampled before the composite can be generated (see below).
Resampling
In certain cases it may be necessary to resample datasets whether they come from a file or are generated composites. Resampling is useful for mapping data to a uniform grid, limiting input data to an area of interest, changing from one projection to another, or for preparing datasets to be combined in a composite (see above). For more details on resampling, different resampling algorithms, and creating your own area of interest see the Resampling documentation. To resample a Satpy Scene:
>>> local_scene = global_scene.resample("eurol")
This creates a copy of the original global_scene with all loaded datasets
resampled to the built-in “eurol” area. Any composites that were requested,
but could not be generated are automatically generated after resampling. The
new local_scene can now be used like the original global_scene for
working with datasets, saving them to disk or showing them on screen:
>>> local_scene.show('overview')
>>> local_scene.save_dataset('overview', './local_overview.tif')
Saving to disk
To save all loaded datasets to disk as geotiff images:
>>> global_scene.save_datasets()
To save all loaded datasets to disk as PNG images:
>>> global_scene.save_datasets(writer='simple_image')
Or to save an individual dataset:
>>> global_scene.save_dataset('VIS006', 'my_nice_image.png')
Datasets are automatically scaled or “enhanced” to be compatible with the output format and to provide the best looking image. For more information on saving datasets and customizing enhancements see the documentation on Writing.
Slicing and subsetting scenes
Array slicing can be done at the scene level in order to get subsets with consistent navigation throughout. Note that this does not take into account scenes that may include channels at multiple resolutions, i.e. index slicing does not account for dataset spatial resolution.
>>> scene_slice = global_scene[2000:2004, 2000:2004]
>>> vis006_slice = scene_slice['VIS006']
>>> vis006_slice_meas = vis006_slice.values
>>> vis006_slice_lon, vis006_slice_lat = vis006_slice.attrs['area'].get_lonlats()
To subset multi-resolution data consistently, use the crop() method.
>>> scene_llbox = global_scene.crop(ll_bbox=(-4.0, -3.9, 3.9, 4.0))
>>> vis006_llbox = scene_llbox['VIS006']
>>> vis006_llbox_meas = vis006_llbox.values
>>> vis006_llbox_lon, vis006_llbox_lat = vis006_llbox.attrs['area'].get_lonlats()
Troubleshooting
When something goes wrong, a first step to take is check that the latest Version
of satpy and its dependencies are installed. Satpy drags in a few packages as
dependencies per default, but each reader and writer has it’s own dependencies
which can be unfortunately easy to miss when just doing a regular pip install.
To check the missing dependencies for the readers and writers, a utility
function called check_satpy() can be used:
>>> from satpy.utils import check_satpy
>>> check_satpy()
Due to the way Satpy works, producing as many datasets as possible, there are times that behavior can be unexpected but with no exceptions raised. To help troubleshoot these situations log messages can be turned on. To do this run the following code before running any other Satpy code:
>>> from satpy.utils import debug_on
>>> debug_on()