==========
Quickstart
==========

Loading and accessing data
==========================

.. testsetup:: *
    >>> import sys
    >>> reload(sys)
    >>> sys.setdefaultencoding('utf8')

To work with weather satellite data you must create a
:class:`~satpy.scene.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
:ref:`Satpy Reader <reader_table>` 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 :meth:`Scene.load <satpy.scene.Scene.load>`
method. Printing the Scene object will list each of the
:class:`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 :class:`~satpy.scene.Scene` object use the
:meth:`~satpy.scene.Scene.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 :doc:`reading` documentation.


Calculating measurement values and navigation coordinates
=========================================================

Once loaded, measurement values can be calculated from a DataArray within a scene, using .values to get a fully calculated numpy array:

    >>> vis008 = global_scene["VIS008"]
    >>> vis008_meas = vis008.values

Note that for very large images, such as half-kilometer geostationary imagery, calculated measurement arrays may require multiple gigabytes of memory; using deferred computation and/or subsetting of datasets may be preferred in such cases.

The 'area' attribute of the DataArray, if present, can be converted to latitude and longitude arrays. For some instruments (typically polar-orbiters), the get_lonlats() may result in arrays needing an additional .compute() or .values extraction.

    >>> vis008_lon, vis008_lat = vis008.attrs['area'].get_lonlats()


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 <https://geoviews.org>`_ object using the
:meth:`~satpy.scene.Scene.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 :func:`combine_metadata <satpy.dataset.metadata.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
==========

.. todo::

   Explain where and how to define new areas

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
:doc:`resample` 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
:doc:`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 :meth:`~satpy.scene.Scene.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()

.. _user_warnings_errors:

Warnings and Errors
===================

Throughout the calculations that Satpy performs you may see various warnings
or if you have logging enabled (see :ref:`troubleshooting` below) see warning
or error log messages. There are some warnings emitted as part of Satpy's
processing that originate in the libraries that Satpy depends on, but are
mostly expected due to the way Satpy does its calculations and the data that
Satpy is working with. Except for in special cases, Satpy does not generally
hide or ignore these warnigns from dependencies and it is left to the user
to control how they'd like to handle them.

Many of the data arrays that Satpy works with use NaN values to indicate
invalid, masked, or bad quality pixels. Some calculations or libraries
emit a warning or error when they encounter NaNs. For example, Numpy will
often emit warnings like::

    RuntimeWarning: invalid value encountered in multiply

For the most part these warnings are expected. For single Satpy-based scripts
it is recommend to ignore these warnings from numpy globally using
the :func:`numpy.seterr` or :class:`numpy.errstate` functions:

.. code-block:: python

    import numpy as np
    np.seterr(invalid="ignore")

.. _troubleshooting:

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 :func:`~satpy.utils.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()
