reVX.wind_dirs.prominent_wind_dirs.ProminentWindDirections

class ProminentWindDirections(power_rose_h5_fpath, excl_fpath, agg_dset='powerrose_100m', tm_dset='techmap_wtk', resolution=128, excl_area=None)

Bases: Aggregation

Aggregate PowerRose to Supply Curve points and sort directions in order of prominence. Then convert to equivalent sc_point_gid

Parameters:

• power_rose_h5_fpath (str) – Filepath to .h5 file containing powerrose data

• excl_fpath (str) – Filepath to exclusions h5 with techmap dataset.

• agg_dset (str, optional) – Powerrose dataset to aggreate, by default ‘powerrose_100m’

• tm_dset (str, optional) – Dataset name in the techmap file containing the exclusions-to-resource mapping data, by default ‘techmap_wtk’

• resolution (int, optional) – SC resolution, must be input in combination with gid. Prefered option is to use the row/col slices to define the SC point instead, by default 128

• excl_area (float | None) – Area of an exclusion pixel in km2. None will try to infer the area from the profile transform attribute in excl_fpath.

Methods

aggregate(h5_fpath[, agg_method, ...])	Aggregate with given agg_method
prominent_directions([max_workers, ...])	Aggregate power rose data to supply curve points, find all neighboring supply curve points, sort neighbors in order of prominent powerrose directions
run(power_rose_h5_fpath, excl_fpath[, ...])	Aggregate powerrose to supply curve points, find neighboring supply curve point gids and rank them based on prominent powerrose direction
run_parallel(h5_fpath[, agg_method, ...])	Aggregate in parallel
run_serial(excl_fpath, h5_fpath, tm_dset, ...)	Standalone method to aggregate - can be parallelized.
save_agg_to_h5(h5_fpath, out_fpath, aggregation)	Save aggregated data to disc in .h5 format

Attributes

DIR_ORDER
gids	1D array of supply curve point gids to aggregate
shape	Get the shape of the full exclusions raster.

prominent_directions(max_workers=None, sites_per_worker=1000)

Aggregate power rose data to supply curve points, find all neighboring supply curve points, sort neighbors in order of prominent powerrose directions

Parameters:

• max_workers (int | None, optional) – Number of cores to run summary on. None is all available cpus, by default None

• sites_per_worker (int, optional) – Number of SC points to process on a single parallel worker, by default 1000

Returns:

sc_pr (pandas.DataFrame) – Update meta data table with neighboring supply curve point gids and power-rose value at each cardinal direction

classmethod run(power_rose_h5_fpath, excl_fpath, agg_dset='powerrose_100m', tm_dset='techmap_wtk', resolution=128, excl_area=None, max_workers=None, sites_per_worker=1000, out_fpath=None)

Aggregate powerrose to supply curve points, find neighboring supply curve point gids and rank them based on prominent powerrose direction

Parameters:

• power_rose_h5_fpath (str) – Filepath to .h5 file containing power rose data, of same format as WTK data with directions on axis 0 (rows) and sites on axis 1 (columns)

• excl_fpath (str) – Filepath to exclusions h5 with techmap dataset.

• agg_dset (str, optional) – Powerrose dataset to aggreate, by default ‘powerrose_100m’

• tm_dset (str, optional) – Dataset name in the techmap file containing the exclusions-to-resource mapping data, by default ‘techmap_wtk’

• resolution (int, optional) – SC resolution, must be input in combination with gid. Prefered option is to use the row/col slices to define the SC point instead, by default 128

• excl_area (float | None) – Area of an exclusion pixel in km2. None will try to infer the area from the profile transform attribute in excl_fpath.

• max_workers (int | None, optional) – Number of cores to run summary on. None is all available cpus, by default None

• sites_per_worker (int, optional) – Number of SC points to process on a single parallel worker, by default 1000

• out_fpath (str) – Path to .csv file to save output table to

Returns:

sc_pr (pandas.DataFrame) – Update meta data table with neighboring supply curve point gids and power-rose value at each cardinal direction

aggregate(h5_fpath, agg_method='mean', max_workers=None, sites_per_worker=100)

Aggregate with given agg_method

Parameters:

• h5_fpath (str) – Filepath to .h5 file to aggregate

• agg_method (str, optional) – Aggregation method, either mean or sum/aggregate, by default “mean”

• max_workers (int, optional) – Number of cores to run summary on. None is all available cpus, by default None

• sites_per_worker (int, optional) – Number of SC points to process on a single parallel worker, by default 100

Returns:

agg (dict) – Aggregated values for each aggregation dataset

property gids

1D array of supply curve point gids to aggregate

Returns:

ndarray

run_parallel(h5_fpath, agg_method='mean', excl_area=None, max_workers=None, sites_per_worker=100)

Aggregate in parallel

Parameters:

• h5_fpath (str) – Filepath to .h5 file to aggregate

• agg_method (str, optional) – Aggregation method, either mean or sum/aggregate, by default “mean”

• excl_area (float, optional) – Area of an exclusion cell (square km), by default None

• max_workers (int, optional) – Number of cores to run summary on. None is all available cpus, by default None

• sites_per_worker (int, optional) – Number of SC points to process on a single parallel worker, by default 100

Returns:

agg_out (dict) – Aggregated values for each aggregation dataset

classmethod run_serial(excl_fpath, h5_fpath, tm_dset, *agg_dset, agg_method='mean', excl_dict=None, inclusion_mask=None, area_filter_kernel='queen', min_area=None, resolution=64, excl_area=0.0081, gids=None, gen_index=None)

Standalone method to aggregate - can be parallelized.

Parameters:

• excl_fpath (str | list | tuple) – Filepath to exclusions h5 with techmap dataset (can be one or more filepaths).

• h5_fpath (str) – Filepath to .h5 file to aggregate

• tm_dset (str) – Dataset name in the techmap file containing the exclusions-to-resource mapping data.

• agg_dset (str) – Dataset to aggreate, can supply multiple datasets. The datasets should be scalar values for each site. This method cannot aggregate timeseries data.

• agg_method (str, optional) – Aggregation method, either mean or sum/aggregate, by default “mean”

• excl_dict (dict | None) – Dictionary of exclusion keyword arugments of the format {layer_dset_name: {kwarg: value}} where layer_dset_name is a dataset in the exclusion h5 file and kwarg is a keyword argument to the reV.supply_curve.exclusions.LayerMask class. by default None

• inclusion_mask (np.ndarray, optional) – 2D array pre-extracted inclusion mask where 1 is included and 0 is excluded. This must be either match the full exclusion shape or be a list of single-sc-point exclusion masks corresponding to the gids input, by default None

• area_filter_kernel (str, optional) – Contiguous area filter method to use on final exclusions mask, by default “queen”

• min_area (float, optional) – Minimum required contiguous area filter in sq-km, by default None

• resolution (int, optional) – SC resolution, must be input in combination with gid. Prefered option is to use the row/col slices to define the SC point instead, by default 0.0081

• excl_area (float, optional) – Area of an exclusion pixel in km2. None will try to infer the area from the profile transform attribute in excl_fpath, by default None

• gids (list, optional) – List of supply curve point gids to get summary for (can use to subset if running in parallel), or None for all gids in the SC extent, by default None

• gen_index (np.ndarray, optional) – Array of generation gids with array index equal to resource gid. Array value is -1 if the resource index was not used in the generation run, by default None

Returns:

agg_out (dict) – Aggregated values for each aggregation dataset

static save_agg_to_h5(h5_fpath, out_fpath, aggregation)

Save aggregated data to disc in .h5 format

Parameters:

• out_fpath (str) – Output .h5 file path

• aggregation (dict) – Aggregated values for each aggregation dataset

property shape

Get the shape of the full exclusions raster.

Returns:

tuple