rex.renewable_resource.NSRDB

class NSRDB(h5_file, mode='r', unscale=True, str_decode=True, group=None, use_lapse_rate=True, hsds=False, hsds_kwargs=None)

Bases: SolarResource

Class to handle NSRDB .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

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, downscale=None)

Pre-load project_points for SAM

h5_filestr

h5_file to extract resource from

siteslist

List of sites to be provided to SAM (sites is synonymous with gids aka spatial indices)

unscalebool

Boolean flag to automatically unscale variables on extraction

str_decodebool

Boolean flag to decode the bytestring meta data into normal strings. Setting this to False will speed up the meta data read.

groupstr

Group within .h5 resource file to open

hsdsbool, optional

Boolean flag to use h5pyd to handle .h5 ‘files’ hosted on AWS behind HSDS, by default False

hsds_kwargsdict, optional

Dictionary of optional kwargs for h5pyd, e.g., bucket, username, password, by default None

techstr, 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

meansbool, optional

Boolean flag to compute mean resource when res_array is set, by default False

clearskybool

Boolean flag to pull clearsky instead of real irradiance

bifacialbool

Boolean flag to pull surface albedo for bifacial modeling.

downscaleNoneType | dict

Option for NSRDB resource downscaling to higher temporal resolution. Expects a dict of downscaling kwargs with a minimum requirement of the desired frequency e.g. ‘frequency’: ‘5min’ and an option to add “variability_kwargs”.

SAM_resSAMResource

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_SAM_df(site, extra_cols=None)

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

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)