sup3r.postprocessing.file_handling.OutputHandlerH5

class OutputHandlerH5[source]

Bases: OutputHandler

Class to handle writing output to H5 file

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)[source]

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)[source]

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 doesnt 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 max_workers will be estimated based on memory limits.

static invert_uv_single_pair(data, lat_lon, u_idx, v_idx)[source]

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 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

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_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

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.

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

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)

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.

classmethod write_output(data, features, low_res_lat_lon, low_res_times, out_file, meta_data=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
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