rex.joint_pd.joint_pd.JointPD

class JointPD(res_h5, res_cls=<class 'rex.resource.Resource'>, hsds=False)

Bases: object

Compute the joint probability distribution between the desired variables

Parameters:

• res_h5 (str) – Path to resource h5 file(s)

• res_cls (Class, optional) – Resource handler class to use to access res_h5, by default Resource

• hsds (bool, optional) – Boolean flag to use h5pyd to handle .h5 ‘files’ hosted on AWS behind HSDS, by default False

Methods

compute(dset1, dset2, bins1, bins2[, sites, ...])	Compute joint probability distribution between given datasets using given bins for all sites.
compute_joint_pd(res_h5, dset1, dset2, ...)	Compute the joint probability distribution between the two given datasets using the given bins for given sites
plot_joint_pd(jpd[, site])	Plot the mean joint probability distribution accross all sites (site=None), or the distribution for the single given site
run(res_h5, dset1, dset2, bins1, bins2[, ...])	Compute joint probability distribution between given datasets using given bins
save(jpd, out_fpath)	Save joint probability distribution to disk
wind_rose(wind_h5, hub_height[, wspd_bins, ...])	Compute wind rose at given hub height

Attributes

res_cls	Resource class to use to access wind_h5
res_h5	Path to resource h5 file(s)

property res_h5

Path to resource h5 file(s)

Returns:

str

property res_cls

Resource class to use to access wind_h5

Returns:

Class

classmethod compute_joint_pd(res_h5, dset1, dset2, bins1, bins2, res_cls=<class 'rex.resource.Resource'>, hsds=False, sites_slice=None)

Compute the joint probability distribution between the two given datasets using the given bins for given sites

Parameters:

• res_h5 (str) – Path to resource h5 file(s)

• dset1 (str) – Dataset 1 to generate joint probability distribution for

• dset2 (str) – Dataset 2 to generate joint probabilty distribution for

• bins1 (tuple) – (start, stop, step) for dataset 1 bins. The stop value is inclusive, so (0, 6, 2) would yield three bins with edges (0, 2, 4, 6). If the stop value is not perfectly divisible by the step, the last bin will overshoot the stop value.

• bins2 (tuple) – (start, stop, step) for dataset 2 bins. The stop value is inclusive, so (0, 6, 2) would yield three bins with edges (0, 2, 4, 6). If the stop value is not perfectly divisible by the step, the last bin will overshoot the stop value.

• res_cls (Class, optional) – Resource handler class to use to access res_h5, by default Resource

• hsds (bool, optional) – Boolean flag to use h5pyd to handle .h5 ‘files’ hosted on AWS behind HSDS, by default False

• sites_slice (slice | None, optional) – Sites to extract, if None all, by default None (sites is synonymous with gids aka spatial indices)

Returns:

jpd (dict) – Dictionary of joint probabilty distribution densities for given sites

compute(dset1, dset2, bins1, bins2, sites=None, max_workers=None, chunks_per_worker=5)

Compute joint probability distribution between given datasets using given bins for all sites.

Parameters:

• dset1 (str) – Dataset 1 to generate joint probability distribution for

• dset2 (str) – Dataset 2 to generate joint probabilty distribution for

• bins1 (tuple) – (start, stop, step) for dataset 1 bins. The stop value is inclusive, so (0, 6, 2) would yield three bins with edges (0, 2, 4, 6). If the stop value is not perfectly divisible by the step, the last bin will overshoot the stop value.

• bins2 (tuple) – (start, stop, step) for dataset 2 bins. The stop value is inclusive, so (0, 6, 2) would yield three bins with edges (0, 2, 4, 6). If the stop value is not perfectly divisible by the step, the last bin will overshoot the stop value.

• sites (list | slice, optional) – Subset of sites to extract, by default None or all sites (sites is synonymous with gids aka spatial indices)

• max_workers (None | int, optional) – Number of workers to use, if 1 run in serial, if None use all available cores, by default None

• chunks_per_worker (int, optional) – Number of chunks to extract on each worker, by default 5

Returns:

jpd (pandas.DataFrame) – DataFrame of joint probability distribution between given datasets with given bins

save(jpd, out_fpath)

Save joint probability distribution to disk

Parameters:

• jpd (pandas.DataFrame) – Table of joint probability distribution densities to save

• out_fpath (str) – .csv, or .h5 file path to save joint probability distribution to

static plot_joint_pd(jpd, site=None, **kwargs)

Plot the mean joint probability distribution accross all sites (site=None), or the distribution for the single given site

Parameters:

• jpd (pandas.DataFrame) – DataFrame of joint probability distribution between given datasets with given bins

• site (int, optional) – Site to plot distribution for, if None plot mean distribution across all sites, by default None

classmethod run(res_h5, dset1, dset2, bins1, bins2, sites=None, res_cls=<class 'rex.resource.Resource'>, hsds=False, max_workers=None, chunks_per_worker=5, out_fpath=None)

Compute joint probability distribution between given datasets using given bins

Parameters:

• res_h5 (str) – Path to resource h5 file(s)

• dset1 (str) – Dataset 1 to generate joint probability distribution for

• dset2 (str) – Dataset 2 to generate joint probabilty distribution for

• bins1 (tuple) – (start, stop, step) for dataset 1 bins. The stop value is inclusive, so (0, 6, 2) would yield three bins with edges (0, 2, 4, 6). If the stop value is not perfectly divisible by the step, the last bin will overshoot the stop value.

• bins2 (tuple) – (start, stop, step) for dataset 2 bins. The stop value is inclusive, so (0, 6, 2) would yield three bins with edges (0, 2, 4, 6). If the stop value is not perfectly divisible by the step, the last bin will overshoot the stop value.

• sites (list | slice, optional) – Subset of sites to extract, by default None or all sites (sites is synonymous with gids aka spatial indices)

• res_cls (Class, optional) – Resource class to use to access res_h5, by default Resource

• hsds (bool, optional) – Boolean flag to use h5pyd to handle .h5 ‘files’ hosted on AWS behind HSDS, by default False

• max_workers (None | int, optional) – Number of workers to use, if 1 run in serial, if None use all available cores, by default None

• chunks_per_worker (int, optional) – Number of chunks to extract on each worker, by default 5

• out_fpath (str, optional) – .csv, or .h5 file path to save joint probability distribution to

Returns:

out (pandas.DataFrame) – DataFrame of joint probability distribution between given datasets with given bins

classmethod wind_rose(wind_h5, hub_height, wspd_bins=(0, 30, 1), wdir_bins=(0, 360, 5), sites=None, res_cls=<class 'rex.renewable_resource.WindResource'>, hsds=False, max_workers=None, chunks_per_worker=5, out_fpath=None)

Compute wind rose at given hub height

Parameters:

• wind_h5 (str) – Path to resource h5 file(s)

• hub_height (str | int) – Hub-height to compute wind rose at

• wspd_bins (tuple) – (start, stop, step) for wind speed bins

• wdir_bins (tuple) – (start, stop, step) for wind direction bins

• sites (list | slice, optional) – Subset of sites to extract, by default None or all sites (sites is synonymous with gids aka spatial indices)

• res_cls (Class, optional) – Resource class to use to access wind_h5, by default Resource

• hsds (bool, optional) – Boolean flag to use h5pyd to handle .h5 ‘files’ hosted on AWS behind HSDS, by default False

• max_workers (None | int, optional) – Number of workers to use, if 1 run in serial, if None use all available cores, by default None

• chunks_per_worker (int, optional) – Number of chunks to extract on each worker, by default 5

• out_fpath (str, optional) – .csv, or .h5 file path to save wind rose to

Returns:

wind_rose (pandas.DataFrame) – DataFrame of wind rose frequencies at desired hub-height