* Add classmethod `from_raster_netcdf` that loads a dataset from a
  NetCDF file and reads the appropriate data into a Hazard instance.
* Add a new test file for testing this method.

This is still WIP.

Make `Hazard.from_raster_netcdf` handle cases where coordinates with
other names than dimensions are supposed to be read, and where
coordinates are not flattened. The first is achieved by adding another
method parameter so that users may specify *dimensions* and
*coordinates* separately. The second is achieved by stacking the entire
dataset, which also applies to possibly multi-dimensional coordinates,
instead of flattening only the respective array.

Add a new test case to cover the new capabilities.

This avoids relying on user input which dimensions to use.

This fixes some linter complaints.

…-raster-like-data-from-netcdf-file

Move more complicated test case down

np.testing.assert_array_equal already checks for matching array shapes.

Add capability of reading all "optional" Hazard data through the
`data_vars` parameter. "Optional" means that default values can be
provided and hence the data is not strictly necessary. Changes:

* Add possibility to read `date`, `event_id`, `event_name`, and
  `frequency` from data.
* Add possibility to supply `haz_type` and `unit` through method
  parameters.
* Provide defaults for all optionals.
* Update docstrings.
* Update tests.

* Make 'fraction' an optional argument and move it into `data_vars`.
* Update tests.

Use pathlib for more elegant file handling in tests

Co-authored-by: Chahan M. Kropf <chahan.kropf@usys.ethz.ch>

* Improve docstrings of "internal" functions.
* Rename `read_or default` to `load_data_or_default` and use argument
  list instead of `pd.Series` as single argument.
* Avoid single letter variables.
* Avoid unnecessary local variables.
* Add more type hints.

This hopefully decreases code complexity slightly.

(Hopefully) improve the readability of the method and its signature.

* Move default coordinate keys and attribute keys out of the `Hazard`
  class by defining constants.
* Rename `time` to `event` in `coordinate_vars` argument, as CLIMADA
  operates on events.
* Rename `load_data_or_default` to `load_from_xarray_or_return_default`.
* Rename `identifier` to `default_key`.
* Update docstrings and extend comments.

Skip test for now as it is unclear how this can be tested

…-raster-like-data-from-netcdf-file

Type: Hazard.unit was set instead of Hazard.units. This was not catched
by the test because they were consistent.

…-raster-like-data-from-netcdf-file

In Hazard.from_raster_xarray, promote coordinates to dimensions if they
have a single value. This enables loading datasets with lower dimensions
as long as the "missing" dimensions are specified as coordinates.

Update docstring and tests.

…ata-from-netcdf-file' of https://github.com/CLIMADA-project/climada_python into 487-add-classmethod-to-hazard-for-reading-raster-like-data-from-netcdf-file