rex.sam_resource.SAMResource

class SAMResource(sites, tech, time_index, hub_heights=None, depths=None, require_wind_dir=False, means=False)[source]

Bases: object

Resource container for SAM. Resource handlers preload the datasets needed by SAM for the sites of interest. SAMResource handles all ETL needed before resource data is passed into SAM.

Examples

>>> import os
>>> from rex import TESTDATADIR, WindResource
>>> file = os.path.join(TESTDATADIR, 'wtk/ri_100_wtk_2012.h5')

Here we load a SAM Resource container for a windpower analysis for sites 7 and 8 at a hub height of 90m:

>>> sam = WindResource.preload_SAM(file, sites=[7, 8], hub_heights=90)
>>> sam
SAMResource with 2 windpower sites

You can then use the SAMResource object to easily retrieve the data that is needed to run the SAM windpower module:

>>> sam[7]
                           winddirection  pressure  temperature  windspeed
2012-01-01 00:00:00+00:00            0.0  0.965329        4.270   7.565000
2012-01-01 01:00:00+00:00            0.0  0.965921        3.870   8.040000
2012-01-01 02:00:00+00:00            0.0  0.966612        4.070  10.370000
2012-01-01 03:00:00+00:00            0.0  0.966721        4.060  11.174999
2012-01-01 04:00:00+00:00            0.0  0.967224        3.515   8.570000
...                                  ...       ...          ...        ...
2012-12-31 19:00:00+00:00            0.0  0.967826       -1.965   6.515000
2012-12-31 20:00:00+00:00            0.0  0.967036       -2.095   6.750000
2012-12-31 21:00:00+00:00            0.0  0.966740       -2.495   9.215000
2012-12-31 22:00:00+00:00            0.0  0.966158       -2.735  10.680000
2012-12-31 23:00:00+00:00            0.0  0.965852       -2.460  10.805000

[8784 rows x 4 columns]

>>> sam['meta', 7]
latitude         41.975849
longitude       -71.762329
country      United States
state                   RI
county          Providence
timezone                -5
elevation              208
offshore                 0
Name: 7, dtype: object

>>> sam['meta', 8]
latitude         41.993584
longitude       -71.754852
country      United States
state                   RI
county          Providence
timezone                -5
elevation              180
offshore                 0
Name: 8, dtype: object

>>> sam['meta', 9]
KeyError: 9

Parameters:

sites (int | list | tuple | slice) – int, list, tuple, or slice indicating sites to send to SAM (sites is synonymous with gids aka spatial indices)
tech (str) – SAM technology string. See class attributes for options.
time_index (pandas.DatetimeIndex) – Time-series datetime index
hub_heights (int | float | list, optional) – Hub height(s) to extract wind data at, by default None
depths (int | float | list, optional) – Depth(s) to extract wind data at, by default None
require_wind_dir (bool, optional) – Boolean flag indicating that wind direction is required, by default False
means (bool, optional) – Boolean flag to compute mean resource when res_array is set, by default False

Methods

`append_var_list`(var)	Append a new variable to the SAM resource protected var_list.
`bias_correct`(bc_df)	Bias correct wind or irradiance data using a table of linear correction factors per resource gid.
`check_irradiance_datasets`(datasets[, clearsky])	Check available irradiance datasets
`check_units`(var_name, var_array, tech)	Check units of variable array and convert to SAM units if needed
`compute_irradiance`([clearsky])	Fillin missing irradiance dataset from available values and SZA
`curtail_windspeed`(gids, curtailment)	Apply temporal curtailment mask to windspeed resource at given sites
`enforce_arr_range`(var, arr, valid_range, sites)	Check an array for valid data range, warn, patch, and return.
`load_rex_resource`(rex_res, var_list, ...[, ...])	Load data from a rex Resource handler into this SAMResource container.
`roll_timeseries`(time_series, timezone, ...)	Roll timeseries array to given timezone from UTC
`runnable`()	Check to see if SAMResource iterator is runnable: - Meta must be loaded - Variables in var_list must be loaded
`set_clearsky`()	Make the NSRDB var list for solar based on clearsky irradiance.

Attributes

`ALIASES`
`CSP_DATA_RANGES`
`DATA_RANGES`
`FLAT_VARS`
`GEOTHERMAL_DATA_RANGES`
`LF_DATA_RANGES`
`PV_DATA_RANGES`
`RES_VARS`
`SWH_DATA_RANGES`
`TPPH_DATA_RANGES`
`WAVE_DATA_RANGES`
`WIND_DATA_RANGES`
`d`	Get depths for geothermal sites
`h`	Get heights for wind sites
`lat_lon`	site latitudes and longitudes
`meta`	Return sites meta
`shape`	Shape of variable arrays
`sites`	Sites being pre-loaded for SAM
`sites_slice`	Get the sites in slice format if possible
`sza`	Solar zenith angle for sites of interest
`time_index`	Return time_index
`var_list`	Return variable list associated with SAMResource type

property sites

Sites being pre-loaded for SAM

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

property sites_slice

Get the sites in slice format if possible

Returns:: sites (list | slice) – Sites slice belonging to this instance of ProjectPoints. The type is slice if possible. Will be a list only if sites are non-sequential. (sites is synonymous with gids aka spatial indices)

property shape

Shape of variable arrays

Returns:: self._shape (tuple) – Shape (time_index, sites) of variable arrays

property var_list

Return variable list associated with SAMResource type

Returns:: _var_list (list) – List of resource variables associated with resource type (‘solar’ or ‘wind’)

property time_index

Return time_index

Returns:: self._time_index (pandas.DatetimeIndex) – Time-series datetime index

property meta

Return sites meta

Returns:: self._meta (pandas.DataFrame) – DataFrame of sites meta data

property h

Get heights for wind sites

Returns:: self._h (int | float | list) – Hub height or height(s) for wind resource, None for other resource

property d

Get depths for geothermal sites

Returns:: self._d (int | float | list) – Depth(s) for geothermal resource, None for other resource

property lat_lon

site latitudes and longitudes

Returns:: ndarray

property sza

Solar zenith angle for sites of interest

Returns:: ndarray

static check_units(var_name, var_array, tech)[source]

Check units of variable array and convert to SAM units if needed

Parameters:

var_name (str) – Variable name
var_array (ndarray) – Variable data
tech (str) – SAM technology string (windpower, pvwattsv5, solarwaterheat, etc..)

Returns:

var_array (ndarray) – Variable data with updated units if needed

static enforce_arr_range(var, arr, valid_range, sites)[source]

Check an array for valid data range, warn, patch, and return.

Parameters:

var (str) – variable name
arr (np.ndarray) – Array to be checked and patched
valid_range (np.ndarray | tuple | list) – arr data will be ensured within the min/max values of valid_range
sites (list) – Resource gid site list for warning printout. (sites is synonymous with gids aka spatial indices)

Returns:

arr (np.ndarray) – Patched array with valid range.

static roll_timeseries(time_series, timezone, time_interval)[source]

Roll timeseries array to given timezone from UTC

Parameters:

time_series (ndarray) – time_series array to roll
timezone (int) – Time zone as UTC offset
time_interval (int) – Number of step-steps in an hour, needed to compute time shift

Returns:

time_series (ndarray) – Time series in local time

check_irradiance_datasets(datasets, clearsky=False)[source]

Check available irradiance datasets

Parameters:

datasets (list) – List of available datasets in resource .h5 file
clearsky (bool, optional) – Flag to check for clearsky irradiance datasets, by default False

compute_irradiance(clearsky=False)[source]

Fillin missing irradiance dataset from available values and SZA

Parameters:: clearsky (bool, optional) – Flag to check for clearsky irradiance datasets, by default False

set_clearsky()[source]: Make the NSRDB var list for solar based on clearsky irradiance.

append_var_list(var)[source]

Append a new variable to the SAM resource protected var_list.

Parameters:: var (str) – New resource variable to be added to the protected var_list property.

bias_correct(bc_df)[source]

Bias correct wind or irradiance data using a table of linear correction factors per resource gid.

Parameters:: bc_df (pd.DataFrame) – DataFrame with wind or solar resource bias correction table. This must have columns “gid” and “method”, where “gid” is the resource file indices, and “method” is a function name from the rex.bias_correction module. Only windspeed or GHI+DNI+DHI are corrected, depending on the technology. See the rex.bias_correction module for more details on available bias correction methods.

runnable()[source]

Check to see if SAMResource iterator is runnable: - Meta must be loaded - Variables in var_list must be loaded

Returns:: bool – Returns True if runnable check passes

curtail_windspeed(gids, curtailment)[source]

Apply temporal curtailment mask to windspeed resource at given sites

Parameters:

gids (int | list) – gids for site or list of sites to curtail
curtailment (ndarray) – Temporal multiplier for curtailment

load_rex_resource(rex_res, var_list, time_slice, sites, hh=None, hh_unit='m')[source]

Load data from a rex Resource handler into this SAMResource container.

Parameters:

rex_res (rex.Resource) – rex Resource handler or similar (NSRDB, WindResource, MultiFileResource, etc…)
var_list (list) – List of variables to retrieve from rex_res. These names may be manipulated with suffixes such as _100m (for a 100m hh input)
time_slice (slice) – Slicing argument for the resource temporal dimension (axis=0)
sites (list | slice | int) – List of site indices (axis=1) (sites is synonymous with gids aka spatial indices)
hh (None | int) – Optional single hub height in meters that datasets are to be loaded from rex_res at
hh_unit (str) – Unit suffix for the hub height input.