# -*- coding: utf-8 -*-
"""
Classes to handle reV h5 output files.
"""
import logging
import NRWAL
import PySAM
import rex
import sys
import json
from reV.version import __version__
from rex.outputs import Outputs as rexOutputs
logger = logging.getLogger(__name__)
[docs]class Outputs(rexOutputs):
"""
Base class to handle reV output data in .h5 format
Examples
--------
The reV Outputs handler can be used to initialize h5 files in the standard
reV/rex resource data format.
>>> from reV import Outputs
>>> import pandas as pd
>>> import numpy as np
>>>
>>> meta = pd.DataFrame({'latitude': np.ones(100),
>>> 'longitude': np.ones(100)})
>>>
>>> time_index = pd.date_range('20210101', '20220101', freq='1h',
>>> closed='right')
>>>
>>> with Outputs('test.h5', 'w') as f:
>>> f.meta = meta
>>> f.time_index = time_index
You can also use the Outputs handler to read output h5 files from disk.
The Outputs handler will automatically parse the meta data and time index
into the expected pandas objects (DataFrame and DatetimeIndex,
respectively).
>>> with Outputs('test.h5') as f:
>>> print(f.meta.head())
>>>
latitude longitude
gid
0 1.0 1.0
1 1.0 1.0
2 1.0 1.0
3 1.0 1.0
4 1.0 1.0
>>> with Outputs('test.h5') as f:
>>> print(f.time_index)
DatetimeIndex(['2021-01-01 01:00:00+00:00', '2021-01-01 02:00:00+00:00',
'2021-01-01 03:00:00+00:00', '2021-01-01 04:00:00+00:00',
'2021-01-01 05:00:00+00:00', '2021-01-01 06:00:00+00:00',
'2021-01-01 07:00:00+00:00', '2021-01-01 08:00:00+00:00',
'2021-01-01 09:00:00+00:00', '2021-01-01 10:00:00+00:00',
...
'2021-12-31 15:00:00+00:00', '2021-12-31 16:00:00+00:00',
'2021-12-31 17:00:00+00:00', '2021-12-31 18:00:00+00:00',
'2021-12-31 19:00:00+00:00', '2021-12-31 20:00:00+00:00',
'2021-12-31 21:00:00+00:00', '2021-12-31 22:00:00+00:00',
'2021-12-31 23:00:00+00:00', '2022-01-01 00:00:00+00:00'],
dtype='datetime64[ns, UTC]', length=8760, freq=None)
There are a few ways to use the Outputs handler to write data to a file.
Here is one example using the pre-initialized file we created earlier.
Note that the Outputs handler will automatically scale float data using
the "scale_factor" attribute. The Outputs handler will unscale the data
while being read unless the unscale kwarg is explicityly set to False.
This behavior is intended to reduce disk storage requirements for big
data and can be disabled by setting dtype=np.float32 or dtype=np.float64
when writing data.
>>> Outputs.add_dataset(h5_file='test.h5', dset_name='dset1',
>>> dset_data=np.ones((8760, 100)) * 42.42,
>>> attrs={'scale_factor': 100}, dtype=np.int32)
>>> with Outputs('test.h5') as f:
>>> print(f['dset1'])
>>> print(f['dset1'].dtype)
[[42.42 42.42 42.42 ... 42.42 42.42 42.42]
[42.42 42.42 42.42 ... 42.42 42.42 42.42]
[42.42 42.42 42.42 ... 42.42 42.42 42.42]
...
[42.42 42.42 42.42 ... 42.42 42.42 42.42]
[42.42 42.42 42.42 ... 42.42 42.42 42.42]
[42.42 42.42 42.42 ... 42.42 42.42 42.42]]
float32
>>> with Outputs('test.h5', unscale=False) as f:
>>> print(f['dset1'])
>>> print(f['dset1'].dtype)
[[4242 4242 4242 ... 4242 4242 4242]
[4242 4242 4242 ... 4242 4242 4242]
[4242 4242 4242 ... 4242 4242 4242]
...
[4242 4242 4242 ... 4242 4242 4242]
[4242 4242 4242 ... 4242 4242 4242]
[4242 4242 4242 ... 4242 4242 4242]]
int32
Note that the reV Outputs handler is specifically designed to read and
write spatiotemporal data. It is therefore important to intialize the meta
data and time index objects even if your data is only spatial or only
temporal. Furthermore, the Outputs handler will always assume that 1D
datasets represent scalar data (non-timeseries) that corresponds to the
meta data shape, and that 2D datasets represent spatiotemporal data whose
shape corresponds to (len(time_index), len(meta)). You can see these
constraints here:
>>> Outputs.add_dataset(h5_file='test.h5', dset_name='bad_shape',
dset_data=np.ones((1, 100)) * 42.42,
attrs={'scale_factor': 100}, dtype=np.int32)
HandlerValueError: 2D data with shape (1, 100) is not of the proper
spatiotemporal shape: (8760, 100)
>>> Outputs.add_dataset(h5_file='test.h5', dset_name='bad_shape',
dset_data=np.ones((8760,)) * 42.42,
attrs={'scale_factor': 100}, dtype=np.int32)
HandlerValueError: 1D data with shape (8760,) is not of the proper
spatial shape: (100,)
"""
@property
def full_version_record(self):
"""Get record of versions for dependencies
Returns
-------
dict
Dictionary of package versions for dependencies
"""
rev_versions = {'reV': __version__,
'rex': rex.__version__,
'pysam': PySAM.__version__,
'python': sys.version,
'nrwal': NRWAL.__version__,
}
versions = super().full_version_record
versions.update(rev_versions)
return versions
[docs] def set_version_attr(self):
"""Set the version attribute to the h5 file."""
self.h5.attrs['version'] = __version__
self.h5.attrs['full_version_record'] = json.dumps(
self.full_version_record)
self.h5.attrs['package'] = 'reV'