pytesmo.validation_framework.results_manager module

The results manager stores validation results in netcdf format.

class pytesmo.validation_framework.results_manager.PointDataResults(filename, zlib=True, read_only=False)

Bases: Dataset

add_metrics_results(lons, lats, results, attr=None)

Add observations over time to a locations results.

Parameters:

• lons (np.array) – Array of location longitudes, shape must match shape of arrays in data

• lats (np.array) – Array of location latitudes, shape must match shape of arrays in data

• results (dict) – Variable names as dict keys and data arrays as values. As returned by the metric calculators, except the RollingMetrics. Shape of data arrays must match lons/lats.

• attr (dict, optional (default: None)) – Variable names as keys and attributes as dicts for each variable. Only used when the variable is created, not if it is already in the dataset.

Returns:

idx – Indices of the new locations, can be used to add time results

Return type:

indices

add_result(lon, lat, data, ts_vars=None, times=None, attr=None)

Add all results (time series and location metrics) for a single point.

Parameters:

• lon (float) – Longitude of the point

• lat (float) – Latitude of the point

• data (dict) – Dict of metric names and values. For normal (not rolling) metrics this is an array of size 1, otherwise if the same size as time.

• times (np.array, optional (default: None)) – Time values, length must mach all time series (rolling) metrics in data.

• attr (dict, optional (default: None)) –

add_ts_results(idx, times, results, attr=None)

Add observations over time to previously added locations results.

Parameters:

• idx (int) – Location index, as returned when adding metrics results.

• times (pd.DatetimeIndex or np.array) – Datetime index as in the validation results from rolling metrics

• results (dict) – Variable names as dict keys and data arrays as values. Data arrays must have same size as times.

• attr (dict, optional (default: None)) – Variable names as keys and attributes as dicts for each variable. Only used when the variable is created, not if it is already in the dataset.

read_loc(idx: int | array | None = None) → DataFrame

Read loc data for one/multiple/all point(s)

read_ts(idx: int) → DataFrame

Read time series data for a single point

property variables: array

Names of all variables in the data set

pytesmo.validation_framework.results_manager.build_filename(root, key)

Create savepath/filename that does not exceed 255 characters

Parameters:

• root (str) – Directory where the file should be stored

• key (list of tuples) – The keys are joined to create a filename from them. If the length of the joined keys is too long we shorten it.

Returns:

fname – Full path to the netcdf file to store

Return type:

str

pytesmo.validation_framework.results_manager.netcdf_results_manager(results, save_path, filename: dict | None = None, ts_vars: list | None = None, zlib=True, attr=None)

Write validation results to netcdf file.

Parameters:

• results (dict) – Validation results as returned by the metrics calculator. Keys are tuples that define the dataset names that were used. Values contains ‘lon’ and ‘lat’ keys for defining the points, and optionally ‘time’ which sets the time stamps for each location (if there are metrics over time in the results - e.g due to RollingMetrics)

• save_path (str) – Directory where the netcdf file(s) are are created.

• filename (dict, optional (default: None)) – Filename(s) (value), for each dataset combination in results (key). By default (if None is passed) the keys in results are used to generate a file name.

• ts_vars (list, optional (default: None)) – List of variables in results that are treated as time series

• zlib (bool, optional (default: True)) – Activate compression

• attr (dict, optional (default: None)) – Variable attributes, variable names as keys, attributes as another dict in values.