rex.multi_time_resource.MultiTimeResource

class MultiTimeResource(h5_path, unscale=True, str_decode=True, res_cls=<class 'rex.resource.Resource'>, hsds=False, hsds_kwargs=None)[source]

Bases: BaseDatasetIterable

Class to handle resource data stored temporally accross multiple .h5 files

Examples

Extracting the resource’s Datetime Index

>>> path = '$TESTDATADIR/nsrdb/ri_100_nsrdb_*.h5'
>>> with MultiTimeResource(path) as res:
>>>     ti = res.time_index
>>>
>>> ti
DatetimeIndex(['2012-01-01 00:00:00', '2012-01-01 00:30:00',
               '2012-01-01 01:00:00', '2012-01-01 01:30:00',
               '2012-01-01 02:00:00', '2012-01-01 02:30:00',
               '2012-01-01 03:00:00', '2012-01-01 03:30:00',
               '2012-01-01 04:00:00', '2012-01-01 04:30:00',
               ...
               '2013-12-31 19:00:00', '2013-12-31 19:30:00',
               '2013-12-31 20:00:00', '2013-12-31 20:30:00',
               '2013-12-31 21:00:00', '2013-12-31 21:30:00',
               '2013-12-31 22:00:00', '2013-12-31 22:30:00',
               '2013-12-31 23:00:00', '2013-12-31 23:30:00'],
              dtype='datetime64[ns]', length=35088, freq=None)

NOTE: time_index covers data from 2012 and 2013

>>> with MultiTimeResource(path) as res:
>>>     print(res.h5_files)

[‘/Users/mrossol/Git_Repos/rex/tests/data/nsrdb/ri_100_nsrdb_2012.h5’,: ‘/Users/mrossol/Git_Repos/rex/tests/data/nsrdb/ri_100_nsrdb_2013.h5’]

Data slicing works the same as with “Resource” except axis 0 now covers 2012 and 2013

>>> with MultiTimeResource(path) as res:
>>>     temperature = res['air_temperature']
>>>
>>> temperature
[[ 4.  5.  5. ...  4.  3.  4.]
 [ 4.  4.  5. ...  4.  3.  4.]
 [ 4.  4.  5. ...  4.  3.  4.]
 ...
 [-1. -1.  0. ... -2. -3. -2.]
 [-1. -1.  0. ... -2. -3. -2.]
 [-1. -1.  0. ... -2. -3. -2.]]
>>> temperature.shape
(35088, 100)

>>> with MultiTimeResource(path) as res:
>>>     temperature = res['air_temperature', ::100] # every 100th timestep
>>>
>>> temperature
[[ 4.  5.  5. ...  4.  3.  4.]
 [ 1.  1.  2. ...  0.  0.  1.]
 [-2. -1. -1. ... -2. -4. -2.]
 ...
 [-3. -2. -2. ... -3. -4. -3.]
 [ 0.  0.  1. ...  0. -1.  0.]
 [ 3.  3.  3. ...  2.  2.  3.]]
>>> temperature.shape
(351, 100)

Parameters:

h5_path (str | list) – Unix shell style pattern path with * wildcards to multi-file resource file sets. Files must have the same coordinates but can have different datasets or time indexes. Can also be an explicit list of multi time files, which themselves can contain * wildcards.
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.
res_cls (obj) – Resource handler to us to open individual .h5 files
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
`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

Attributes

`attrs`	Dictionary of all dataset attributes
`chunks`	Dictionary of all dataset chunk sizes
`coordinates`	(lat, lon) pairs
`datasets`	Datasets available
`dsets`	Datasets available
`dtypes`	Dictionary of all dataset dtypes
`global_attrs`	Global (file) attributes
`h5`	Open class instance that handles all .h5 files that data is to be extracted from
`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

property h5

Open class instance that handles all .h5 files that data is to be extracted from

Returns:: h5 (MultiTimeH5 | MultiYearH5)

property datasets

Datasets available

Returns:: list

property dsets

Datasets available

Returns:: list

property resource_datasets

Available resource datasets

Returns:: list

property res_dsets

Available resource datasets

Returns:: list

property shape

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

Returns:: shape (tuple)

property meta

Resource meta data DataFrame

Returns:: meta (pandas.DataFrame)

property time_index

Resource DatetimeIndex

Returns:: time_index (pandas.DatetimeIndex)

property lat_lon

Extract (latitude, longitude) pairs

Returns:: lat_lon (ndarray)

property coordinates

(lat, lon) pairs

Returns:: lat_lon (ndarray)
Type:: Coordinates

property global_attrs

Global (file) attributes

Returns:: global_attrs (dict)

property attrs

Dictionary of all dataset attributes

Returns:: attrs (dict)

property shapes

Dictionary of all dataset shapes

Returns:: shapes (dict)

property dtypes

Dictionary of all dataset dtypes

Returns:: dtypes (dict)

property chunks

Dictionary of all dataset chunk sizes

Returns:: chunks (dict)

property scale_factors

Dictionary of all dataset scale factors

Returns:: scale_factors (dict)

property units

Dictionary of all dataset units

Returns:: units (dict)

get_attrs(dset=None)[source]

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)[source]

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_scale_factor(dset)[source]

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)[source]

Get dataset units

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

get_meta_arr(rec_name, rows=slice(None, None, None))[source]

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.

close()[source]: Close h5 instance