rex.multi_file_resource.MultiFileWTK

class MultiFileWTK(h5_source, unscale=True, str_decode=True, check_files=False)[source]

Bases: MultiFileResource, WindResource

Class to handle 5min WIND Toolkit data

See also

resource.MultiFileResource: Parent class
resource.WindResource: Parent class

Examples

MultiFileWTK automatically searches for files of the form *m.h5

>>> file = '$TESTDATADIR/wtk'
>>> with MultiFileWTK(file) as res:
>>>     print(list(res._h5_files)
>>>     print(res.datasets)
['$TESTDATADIR/wtk_2010_200m.h5',
 '$TESTDATADIR/wtk_2010_100m.h5']
['coordinates', 'meta', 'pressure_100m', 'pressure_200m',
 'temperature_100m', 'temperature_200m', 'time_index',
 'winddirection_100m', 'winddirection_200m', 'windspeed_100m',
 'windspeed_200m']

MultiFileWTK, like WindResource can interpolate / extrapolate hub-heights

>>> with MultiFileWTK(file) as res:
>>>     wspd = res['windspeed_150m']
>>>
>>> wspd
[[16.19     16.25     16.305    ... 16.375    16.39     16.39    ]
 [16.15     16.205    16.255001 ... 16.35     16.365    16.39    ]
 [16.154999 16.195    16.23     ... 16.335    16.32     16.34    ]
 ...
 [10.965    11.675    12.08     ... 15.18     14.805    14.42    ]
 [11.66     11.91     12.535    ... 13.31     12.23     12.335   ]
 [12.785    13.295    14.014999 ... 12.205    11.360001 11.64    ]]

Parameters

h5_source (str | list) – Unix shell style pattern path with * wildcards to multi-file resource file sets. Files must have the same time index and coordinates but can have different datasets. Can also be an explicit list of complete filepaths.
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.
check_files (bool) – Check to ensure files have the same coordinates and time_index

Methods

`circular_interp`(ts_1, h_1, ts_2, h_2, h)	Circular interpolate/extrapolate time-series data to height h
`close`()	Close h5 instance
`df_str_decode`(df)	Decode a dataframe with byte string columns into ordinary str cols.
`get_SAM_df`(site, height[, require_wind_dir, ...])	Get SAM wind 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_nearest_h`(h, heights)	Get two nearest h values in heights.
`get_scale_factor`(dset)	Get dataset scale factor
`get_units`(dset)	Get dataset units
`linear_interp`(ts_1, h_1, ts_2, h_2, h)	Linear interpolate/extrapolate time-series data to height h
`monin_obukhov_extrapolation`(ts_1, h_1, z0, L, h)	Monin-Obukhov extrapolation
`open_dataset`(ds_name)	Open resource dataset
`power_law_interp`(ts_1, h_1, ts_2, h_2, h[, mean])	Power-law interpolate/extrapolate time-series data to height h
`preload_SAM`(h5_source, sites, hub_heights[, ...])	Placeholder for classmethod that will pre-load project_points for SAM
`shortest_angle`(a0, a1)	Calculate the shortest angle distance between a0 and a1
`stability_function`(zeta)	Calculate stability function depending on sign of L (negative is unstable, positive is stable)

Attributes

`ADD_ATTR`
`SCALE_ATTR`
`UNIT_ATTR`
`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.
`heights`	Extract available heights for pressure, temperature, windspeed, precip, and winddirection variables.
`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_source, sites, hub_heights, unscale=True, str_decode=True, time_index_step=None, means=False, require_wind_dir=False, precip_rate=False, icing=False, check_files=False)[source]

Placeholder for classmethod that will pre-load project_points for SAM

Parameters

h5_source (str | list) – Unix shell style pattern path with * wildcards to multi-file resource file sets. Files must have the same time index and coordinates but can have different datasets. Can also be an explicit list of complete filepaths.
sites (list) – List of sites to be provided to SAM
hub_heights (int | float | list) – Hub heights to extract for SAM
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.
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
require_wind_dir (bool) – Boolean flag as to whether wind direction will be loaded.
precip_rate (bool) – Boolean flag as to whether precipitationrate_0m will be preloaded
icing (bool) – Boolean flag as to whether icing is analyzed. This will preload relative humidity.
check_files (bool) – Check to ensure files have the same coordinates and time_index

Returns

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

property attrs

Dictionary of all dataset attributes

Returns: attrs (dict)

property chunks

Dictionary of all dataset chunk sizes

Returns: chunks (dict)

classmethod circular_interp(ts_1, h_1, ts_2, h_2, h)

Circular interpolate/extrapolate time-series data to height h

Parameters

ts_1 (ndarray) – Time-series array at height h_1
h_1 (int | float) – Height corresponding to time-seris ts_1
ts_2 (ndarray) – Time-series array at height h_2
h_2 (int | float) – Height corresponding to time-seris ts_2
h (int | float) – Height of desired time-series

Returns

out (ndarray) – Time-series array at height h

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, height, require_wind_dir=False, icing=False, add_header=False)

Get SAM wind resource DataFrame for given site

Parameters

site (int) – Site to extract SAM DataFrame for
height (int) – Hub height to extract SAM variables at
require_wind_dir (bool, optional) – Boolean flag as to whether wind direction will be loaded, by default False
icing (bool, optional) – Boolean flag to include relativehumitidy for icing calculation, by default False
add_header (bool, optional) – Add units and hub_height below variable names, needed for SAM .csv, by default False

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.

static get_nearest_h(h, heights)

Get two nearest h values in heights. Determine if h is inside or outside the range of heights (requiring extrapolation instead of interpolation)

Parameters

h (int | float) – Height value of interest
heights (list) – List of available heights

Returns

nearest_h (list) – list of 1st and 2nd nearest height in heights
extrapolate (bool) – Flag as to whether h is inside or outside heights range

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 heights

Extract available heights for pressure, temperature, windspeed, precip, and winddirection variables. Used for interpolation/extrapolation.

Returns: self._heights (dict) – Dictionary of available heights for: windspeed, winddirection, temperature, and pressure

property lat_lon

Extract (latitude, longitude) pairs

Returns: lat_lon (ndarray)

static linear_interp(ts_1, h_1, ts_2, h_2, h)

Linear interpolate/extrapolate time-series data to height h

Parameters

ts_1 (ndarray) – Time-series array at height h_1
h_1 (int | float) – Height corresponding to time-seris ts_1
ts_2 (ndarray) – Time-series array at height h_2
h_2 (int | float) – Height corresponding to time-seris ts_2
h (int | float) – Height of desired time-series

Returns

out (ndarray) – Time-series array at height h

property meta

Resource meta data DataFrame

Returns: meta (pandas.DataFrame)

classmethod monin_obukhov_extrapolation(ts_1, h_1, z0, L, h)

Monin-Obukhov extrapolation

Parameters: ts_1 (ndarray) – Time-series array at height h_1

h_1int | float: Height corresponding to time-seris ts_1
z0: int | float | ndarray: Roughness length
Lndarray: time-series of Obukhov length (m; measure of stability)
hint | float: Desired height

Returns: ndarray – new wind speed from MO extrapolation.

open_dataset(ds_name)

Open resource dataset

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

static power_law_interp(ts_1, h_1, ts_2, h_2, h, mean=True)

Power-law interpolate/extrapolate time-series data to height h

Parameters

ts_1 (ndarray) – Time-series array at height h_1
h_1 (int | float) – Height corresponding to time-seris ts_1
ts_2 (ndarray) – Time-series array at height h_2
h_2 (int | float) – Height corresponding to time-seris ts_2
h (int | float) – Height of desired time-series
mean (bool) – Calculate average alpha versus point by point alpha

Returns

out (ndarray) – Time-series array at height h

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)

static shortest_angle(a0, a1)

Calculate the shortest angle distance between a0 and a1

Parameters

a0 (int | float) – angle 0 in degrees
a1 (int | float) – angle 1 in degrees

Returns

da (int | float) – shortest angle distance between a0 and a1

static stability_function(zeta)

Calculate stability function depending on sign of L (negative is unstable, positive is stable)

Parameters: zeta (ndarray) – Normalized length
Returns: numpy.ndarray – stability measurements.

property time_index

Resource DatetimeIndex

Returns: time_index (pandas.DatetimeIndex)

property units

Dictionary of all dataset units

Returns: units (dict)