sup3r.postprocessing.writers.base.OutputHandler

class OutputHandler

Bases: OutputMixin

Class to handle forward pass output. This includes transforming features back to their original form and outputting to the correct file format.

Methods

enforce_limits(features, data)	Enforce physical limits for feature data
get_dset_attrs(feature)	Get attrributes for output feature
get_lat_lon(low_res_lat_lon, shape)	Get lat lon arrays for high res output file
get_renamed_features(features)	Rename features based on transformation from u/v to windspeed/winddirection
get_time_dim_name(filepath)	Get the name of the time dimension in the given file
get_times(low_res_times, shape)	Get array of times for high res output file
invert_uv_features(data, features, lat_lon)	Invert U/V to windspeed and winddirection.
invert_uv_single_pair(data, lat_lon, u_idx, ...)	Perform inverse transform in place on a single u/v pair.
is_increasing_lons(lat_lon)	Check if longitudes are in increasing order.
pad_lat_lon(lat_lon)	Pad lat lon grid with additional rows and columns to use for interpolation routine
write_data(out_file, dsets, time_index, ...)	Write list of datasets to out_file.
write_output(data, features, ...[, ...])	Write forward pass output to file

classmethod get_renamed_features(features)

Rename features based on transformation from u/v to windspeed/winddirection

Parameters:

features (list) – List of output features

Returns:

list – List of renamed features u/v -> windspeed/winddirection for each height

classmethod invert_uv_features(data, features, lat_lon, max_workers=None)

Invert U/V to windspeed and winddirection. Performed in place.

Parameters:

• data (ndarray) – High res data from forward pass (spatial_1, spatial_2, temporal, features)

• features (list) – List of output features. If this doesn’t contain any names matching u_*m, this method will do nothing.

• lat_lon (ndarray) – High res lat/lon array (spatial_1, spatial_2, 2)

• max_workers (int | None) – Max workers to use for inverse transform. If None the maximum possible will be used

static invert_uv_single_pair(data, lat_lon, u_idx, v_idx)

Perform inverse transform in place on a single u/v pair.

Parameters:

• data (ndarray) – High res data from forward pass (spatial_1, spatial_2, temporal, features)

• lat_lon (ndarray) – High res lat/lon array (spatial_1, spatial_2, 2)

• u_idx (int) – Index in data for U component to transform

• v_idx (int) – Index in data for V component to transform

static enforce_limits(features, data)

Enforce physical limits for feature data

Parameters:

• features (list) – List of features with ordering corresponding to last channel of data array.

• data (ndarray) – Array of feature data

Returns:

data (ndarray) – Array of feature data with physical limits enforced

static pad_lat_lon(lat_lon)

Pad lat lon grid with additional rows and columns to use for interpolation routine

Parameters:

• lat_lon (ndarray) – Array of lat/lon for input data. (spatial_1, spatial_2, 2) Last dimension has ordering (lat, lon)

• shape (tuple) – (lons, lats) Shape of high res grid

Returns:

lat_lon (ndarray) – Array of padded lat lons (spatial_1, spatial_2, 2) Last dimension has ordering (lat, lon)

static is_increasing_lons(lat_lon)

Check if longitudes are in increasing order. Need to check this for interpolation routine. This is primarily to identify whether the lons go through the 180 -> -180 boundary, which creates a discontinuity. For example, [130, 180, -130, -80]. If any lons go from positive to negative the lons need to be shifted to the range 0-360.

Parameters:

lat_lon (ndarray) – Array of lat/lon for input data. (spatial_1, spatial_2, 2) Last dimension has ordering (lat, lon)

Returns:

bool – Whether all lons are in increasing order or not

classmethod get_lat_lon(low_res_lat_lon, shape)

Get lat lon arrays for high res output file

Parameters:

• low_res_lat_lon (ndarray) – Array of lat/lon for input data. Longitudes must be arranged in a counter-clockwise direction (when looking down from above the north pole). e.g. [-50, -25, 25, 50] or [130, 180, -130, -80]. The latter passes through the 180 -> -180 boundary and will be temporarily shifted to the 0-360 range before interpolation. (spatial_1, spatial_2, 2) Last dimension has ordering (lat, lon)

• shape (tuple) – (lons, lats) Shape of high res grid

Returns:

lat_lon (ndarray) – Array of lat lons for high res output file (spatial_1, spatial_2, 2) Last dimension has ordering (lat, lon)

static get_times(low_res_times, shape)

Get array of times for high res output file

Parameters:

• low_res_times (pd.Datetimeindex) – List of times for low res input data. If there is only a single low res timestep, it is assumed the data is daily.

• shape (int) – Number of time steps for high res time array

Returns:

ndarray – Array of times for high res output file.

classmethod write_output(data, features, low_res_lat_lon, low_res_times, out_file, meta_data=None, invert_uv=None, max_workers=None, gids=None)

Write forward pass output to file

Parameters:

• data (ndarray) – (spatial_1, spatial_2, temporal, features) High resolution forward pass output

• features (list) – List of feature names corresponding to the last dimension of data

• low_res_lat_lon (ndarray) – Array of lat/lon for input data. (spatial_1, spatial_2, 2) Last dimension has ordering (lat, lon)

• low_res_times (pd.Datetimeindex) – List of times for low res source data

• out_file (string) – Output file path

• meta_data (dict | None) – Dictionary of meta data from model

• invert_uv (bool | None) – Whether to convert u and v wind components to windspeed and direction

• max_workers (int | None) – Max workers to use for inverse uv transform. If None the max_workers will be estimated based on memory limits.

• gids (list) – List of coordinate indices used to label each lat lon pair and to help with spatial chunk data collection

static get_dset_attrs(feature)

Get attrributes for output feature

Parameters:

feature (str) – Name of feature to write

Returns:

• attrs (dict) – Dictionary of attributes for requested dset

• dtype (str) – Data type for requested dset. Defaults to float32

static get_time_dim_name(filepath)

Get the name of the time dimension in the given file

Parameters:

filepath (str) – Path to the file

Returns:

time_key (str) – Name of the time dimension in the given file

classmethod write_data(out_file, dsets, time_index, data_list, meta, global_attrs=None)

Write list of datasets to out_file.

Parameters:

• out_file (str) – Pre-existing H5 file output path

• dsets (list) – list of datasets to write to out_file

• time_index (pd.DatetimeIndex()) – Pandas datetime index to use for file time_index.

• data_list (list) – List of np.ndarray objects to write to out_file

• meta (pd.DataFrame) – Full meta dataframe for the final output data.

• global_attrs (dict) – Namespace of file-global attributes for the final output data.