rex.renewable_resource.SolarResource

class SolarResource(h5_file, mode='r', unscale=True, str_decode=True, group=None, use_lapse_rate=True, hsds=False, hsds_kwargs=None)[source]

Bases: AbstractInterpolatedResource

Class to handle Solar BaseResource .h5 files

See also

resource.BaseResource: Parent class

Parameters:

h5_file (str) – Path to .h5 resource file
mode (str, optional) – Mode to instantiate h5py.File instance, by default ‘r’
unscale (bool) – Boolean flag to automatically unscale variables on extraction
str_decode (bool) – Boolean flag to decode the bytestring meta data into normal strings. Setting this to False will speed up the meta data read.
group (str) – Group within .h5 resource file to open
use_lapse_rate (bool) – If a dataset is only available at a single hub-height and this flag value is set to True, pressure / temperature values will be calculated using linear lapse rate adjustment from the available hub height to the requested one. If the flag value is set to False, the value of these variables at the single available hub-height will be returned for all requested heights. This option has no effect if data is available at multiple hub-heights.
hsds (bool, optional) – Boolean flag to use h5pyd to handle .h5 ‘files’ hosted on AWS behind HSDS, by default False
hsds_kwargs (dict, optional) – Dictionary of optional kwargs for h5pyd, e.g., bucket, username, password, by default None

Methods

`close`()	Close h5 instance
`df_str_decode`(df)	Decode a dataframe with byte string columns into ordinary str cols.
`get_SAM_df`(site[, extra_cols])	Get SAM solar resource DataFrame for given site
`get_attrs`([dset])	Get h5 attributes either from file or dataset
`get_dset_properties`(dset)	Get dataset properties (shape, dtype, chunks)
`get_meta_arr`(rec_name[, rows])	Get a meta array by name (faster than DataFrame extraction).
`get_scale_factor`(dset)	Get dataset scale factor
`get_units`(dset)	Get dataset units
`open_dataset`(ds_name)	Open resource dataset
`preload_SAM`(h5_file, sites[, unscale, ...])	Pre-load project_points for SAM

Attributes

`ADD_ATTR`
`INTERPOLABLE_DSETS`
`LAPSE_RATES`	Air Temperature and Pressure lapse rate in C/km and Pa/km
`SCALE_ATTR`
`UNIT_ATTR`
`VARIABLE_NAME`
`VARIABLE_UNIT`
`adders`	Dictionary of all dataset add offset factors
`attrs`	Dictionary of all dataset attributes
`chunks`	Dictionary of all dataset chunk sizes
`coordinates`	(lat, lon) pairs
`data_version`	Get the version attribute of the data.
`datasets`	Datasets available
`dsets`	Datasets available
`dtypes`	Dictionary of all dataset dtypes
`global_attrs`	Global (file) attributes
`groups`	Groups available
`h5`	Open h5py File instance.
`lat_lon`	Extract (latitude, longitude) pairs
`meta`	Resource meta data DataFrame
`res_dsets`	Available resource datasets
`resource_datasets`	Available resource datasets
`scale_factors`	Dictionary of all dataset scale factors
`shape`	Resource shape (timesteps, sites) shape = (len(time_index), len(meta))
`shapes`	Dictionary of all dataset shapes
`time_index`	Resource DatetimeIndex
`units`	Dictionary of all dataset units

get_SAM_df(site, extra_cols=None)[source]

Get SAM solar resource DataFrame for given site

Parameters:

site (int) – Site to extract SAM DataFrame for.
extra_cols (dict, optional) – A dictionary where they keys are extra columns to extract from the SAM solar resource DataFrame and the values are the names the new columns should have (e.g. extra_cols={‘surface_albedo’: ‘Surface Albedo’} will extract the ‘surface_albedo’ from the resource file and call it ‘Surface Albedo’ in the output).

Returns:

res_df (pandas.DataFrame) – time-series DataFrame of resource variables needed to run SAM

classmethod preload_SAM(h5_file, sites, unscale=True, str_decode=True, group=None, hsds=False, hsds_kwargs=None, tech='pvwattsv8', time_index_step=None, means=False, clearsky=False, bifacial=False)[source]

Pre-load project_points for SAM

Parameters:

h5_file (str) – h5_file to extract resource from
sites (list) – List of sites to be provided to SAM (sites is synonymous with gids aka spatial indices)
unscale (bool) – Boolean flag to automatically unscale variables on extraction
str_decode (bool) – Boolean flag to decode the bytestring meta data into normal strings. Setting this to False will speed up the meta data read.
group (str) – Group within .h5 resource file to open
hsds (bool, optional) – Boolean flag to use h5pyd to handle .h5 ‘files’ hosted on AWS behind HSDS, by default False
hsds_kwargs (dict, optional) – Dictionary of optional kwargs for h5pyd, e.g., bucket, username, password, by default None
tech (str, optional) – SAM technology string, by default ‘pvwattsv8’
time_index_step (int, optional) – Step size for time_index, used to reduce temporal resolution, by default None
means (bool, optional) – Boolean flag to compute mean resource when res_array is set, by default False
clearsky (bool) – Boolean flag to pull clearsky instead of real irradiance
bifacial (bool) – Boolean flag to pull surface albedo for bifacial modeling.

Returns:

SAM_res (SAMResource) – Instance of SAMResource pre-loaded with Solar resource for sites in project_points

LAPSE_RATES = {'pressure': 11109, 'temperature': 6.56}: Air Temperature and Pressure lapse rate in C/km and Pa/km

property adders

Dictionary of all dataset add offset factors

Returns:: adders (dict)

property attrs

Dictionary of all dataset attributes

Returns:: attrs (dict)

property chunks

Dictionary of all dataset chunk sizes

Returns:: chunks (dict)

close(): Close h5 instance

property coordinates

(lat, lon) pairs

Returns:: lat_lon (ndarray)
Type:: Coordinates

property data_version

Get the version attribute of the data. None if not available.

Returns:: version (str | None)

property datasets

Datasets available

Returns:: list

static df_str_decode(df)

Decode a dataframe with byte string columns into ordinary str cols.

Parameters:: df (pd.DataFrame) – Dataframe with some columns being byte strings.
Returns:: df (pd.DataFrame) – DataFrame with str columns instead of byte str columns.

property dsets

Datasets available

Returns:: list

property dtypes

Dictionary of all dataset dtypes

Returns:: dtypes (dict)

get_attrs(dset=None)

Get h5 attributes either from file or dataset

Parameters:: dset (str) – Dataset to get attributes for, if None get file (global) attributes
Returns:: attrs (dict) – Dataset or file attributes

get_dset_properties(dset)

Get dataset properties (shape, dtype, chunks)

Parameters:

dset (str) – Dataset to get scale factor for

Returns:

shape (tuple) – Dataset array shape
dtype (str) – Dataset array dtype
chunks (tuple) – Dataset chunk size

get_meta_arr(rec_name, rows=slice(None, None, None))

Get a meta array by name (faster than DataFrame extraction).

Parameters:

rec_name (str) – Named record from the meta data to retrieve.
rows (slice) – Rows of the record to extract.

Returns:

meta_arr (np.ndarray) – Extracted array from the meta data record name.

get_scale_factor(dset)

Get dataset scale factor

Parameters:: dset (str) – Dataset to get scale factor for
Returns:: float – Dataset scale factor, used to unscale int values to floats

get_units(dset)

Get dataset units

Parameters:: dset (str) – Dataset to get units for
Returns:: str – Dataset units, None if not defined

property global_attrs

Global (file) attributes

Returns:: global_attrs (dict)

property groups

Groups available

Returns:: groups (list) – List of groups

property h5

Open h5py File instance. If _group is not None return open Group

Returns:: h5 (h5py.File | h5py.Group)

property lat_lon

Extract (latitude, longitude) pairs

Returns:: lat_lon (ndarray)

property meta

Resource meta data DataFrame

Returns:: meta (pandas.DataFrame)

open_dataset(ds_name)

Open resource dataset

Parameters:: ds_name (str) – Dataset name to open
Returns:: ds (ResourceDataset) – Resource for open resource dataset

property res_dsets

Available resource datasets

Returns:: list

property resource_datasets

Available resource datasets

Returns:: list

property scale_factors

Dictionary of all dataset scale factors

Returns:: scale_factors (dict)

property shape

Resource shape (timesteps, sites) shape = (len(time_index), len(meta))

Returns:: shape (tuple)

property shapes

Dictionary of all dataset shapes

Returns:: shapes (dict)

property time_index

Resource DatetimeIndex

Returns:: time_index (pandas.DatetimeIndex)

property units

Dictionary of all dataset units

Returns:: units (dict)