"""sup3r WindStats module."""
import logging
import os
import pickle
from abc import ABC, abstractmethod
import numpy as np
import pandas as pd
import psutil
from rex.utilities.fun_utils import get_fun_call_str
from scipy.ndimage import gaussian_filter
from sup3r.preprocessing.feature_handling import Feature
from sup3r.qa.utilities import (
direct_dist,
frequency_spectrum,
gradient_dist,
time_derivative_dist,
wavenumber_spectrum,
)
from sup3r.utilities import ModuleName
from sup3r.utilities.cli import BaseCLI
from sup3r.utilities.utilities import (
get_input_handler_class,
get_source_type,
spatial_coarsening,
st_interp,
temporal_coarsening,
vorticity_calc,
)
logger = logging.getLogger(__name__)
[docs]
class Sup3rStatsBase(ABC):
"""Base stats class"""
# Acceptable statistics to request
_DIRECT = 'direct'
_DY_DX = 'gradient'
_DY_DT = 'time_derivative'
_FFT_F = 'spectrum_f'
_FFT_K = 'spectrum_k'
_FLUCT_FFT_F = 'fluctuation_spectrum_f'
_FLUCT_FFT_K = 'fluctuation_spectrum_k'
def __init__(self):
"""Initialize base class for stats"""
self.overwrite_stats = True
def __enter__(self):
return self
def __exit__(self, type, value, traceback):
self.close()
if type is not None:
raise
[docs]
@abstractmethod
def close(self):
"""Close any open file handlers"""
[docs]
@classmethod
def save_cache(cls, array, file_name):
"""Save data to cache file
Parameters
----------
array : ndarray
Wind field data
file_name : str
Path to cache file
"""
os.makedirs(os.path.dirname(file_name), exist_ok=True)
logger.info(f'Saving data to {file_name}')
with open(file_name, 'wb') as f:
pickle.dump(array, f, protocol=4)
[docs]
@classmethod
def load_cache(cls, file_name):
"""Load data from cache file
Parameters
----------
file_name : str
Path to cache file
Returns
-------
array : ndarray
Wind field data
"""
logger.info(f'Loading data from {file_name}')
with open(file_name, 'rb') as f:
arr = pickle.load(f)
return arr
[docs]
def export(self, qa_fp, data):
"""Export stats dictionary to pkl file.
Parameters
----------
qa_fp : str | None
Optional filepath to output QA file (only .h5 is supported)
data : dict
A dictionary with stats for low and high resolution wind fields
overwrite_stats : bool
Whether to overwrite saved stats or not
"""
os.makedirs(os.path.dirname(qa_fp), exist_ok=True)
if not os.path.exists(qa_fp) or self.overwrite_stats:
logger.info('Saving sup3r stats output file: "{}"'.format(qa_fp))
with open(qa_fp, 'wb') as f:
pickle.dump(data, f, protocol=4)
else:
logger.info(
f'{qa_fp} already exists. Delete file or run with '
'overwrite_stats=True.'
)
[docs]
@classmethod
def get_node_cmd(cls, config):
"""Get a CLI call to initialize Sup3rStats and execute the
Sup3rStats.run() method based on an input config
Parameters
----------
config : dict
sup3r wind stats config with all necessary args and kwargs to
initialize Sup3rStats and execute Sup3rStats.run()
"""
import_str = 'import time;\n'
import_str += 'from gaps import Status;\n'
import_str += 'from rex import init_logger;\n'
import_str += f'from sup3r.qa.stats import {cls.__name__};\n'
qa_init_str = get_fun_call_str(cls, config)
log_file = config.get('log_file', None)
log_level = config.get('log_level', 'INFO')
log_arg_str = f'"sup3r", log_level="{log_level}"'
if log_file is not None:
log_arg_str += f', log_file="{log_file}"'
cmd = (
f"python -c \'{import_str}\n"
"t0 = time.time();\n"
f"logger = init_logger({log_arg_str});\n"
f"qa = {qa_init_str};\n"
"qa.run();\n"
"t_elap = time.time() - t0;\n"
)
pipeline_step = config.get('pipeline_step') or ModuleName.STATS
cmd = BaseCLI.add_status_cmd(config, pipeline_step, cmd)
cmd += ";\'\n"
return cmd.replace('\\', '/')
[docs]
class Sup3rStatsCompute(Sup3rStatsBase):
"""Base class for computing stats on input data arrays"""
def __init__(
self,
input_data=None,
s_enhance=1,
t_enhance=1,
compute_features=None,
input_features=None,
cache_pattern=None,
overwrite_cache=False,
overwrite_stats=True,
get_interp=False,
include_stats=None,
max_values=None,
smoothing=None,
spatial_res=None,
temporal_res=None,
n_bins=40,
qa_fp=None,
interp_dists=True,
time_chunk_size=100,
):
"""Parameters
----------
input_data : ndarray
An array of feature data to use for computing statistics
(spatial_1, spatial_2, temporal, features)
s_enhance : int
Factor by which the Sup3rGan model enhanced the spatial
dimensions of the input data
t_enhance : int
Factor by which the Sup3rGan model enhanced the temporal dimension
of the input data
compute_features : list
Features for which to compute wind stats. e.g. ['pressure_100m',
'temperature_100m', 'windspeed_100m']
input_features : list
List of features available in input_data, with same order as the
last channel of input_data.
cache_pattern : str | None
Pattern for files for saving feature data. e.g.
file_path_{feature}.pkl Each feature will be saved to a file with
the feature name replaced in cache_pattern. If not None
feature arrays will be saved here and not stored in self.data until
load_cached_data is called. The cache_pattern can also include
{shape}, {target}, {times} which will help ensure unique cache
files for complex problems.
overwrite_cache : bool
Whether to overwrite cache files storing the interpolated feature
data
get_interp : bool
Whether to include interpolated baseline stats in output
include_stats : list | None
List of stats to include in output. e.g. ['time_derivative',
'gradient', 'vorticity', 'avg_spectrum_k', 'avg_spectrum_f',
'direct']. 'direct' means direct distribution, as opposed to a
distribution of the gradient or time derivative.
max_values : dict | None
Dictionary of max values to keep for stats. e.g.
{'time_derivative': 10, 'gradient': 14, 'vorticity': 7}
smoothing : float | None
Value passed to gaussian filter used for smoothing source data
spatial_res : float | None
Spatial resolution for source data in meters. e.g. 2000. This is
used to determine the wavenumber range for spectra calculations and
to scale spatial derivatives.
temporal_res : float | None
Temporal resolution for source data in seconds. e.g. 60. This is
used to determine the frequency range for spectra calculations and
to scale temporal derivatives.
n_bins : int
Number of bins to use for constructing probability distributions
qa_fp : str
File path for saving statistics. Only .pkl supported.
interp_dists : bool
Whether to interpolate distributions over bins with count=0.
time_chunk_size : int
Size of temporal chunks to interpolate. e.g. If time_chunk_size=10
then the temporal axis of low_res will be split into chunks with 10
time steps, each chunk interpolated, and then the interpolated
chunks will be concatenated.
"""
msg = 'Preparing to compute statistics.'
if input_data is None:
msg = (
'Received empty input array. Skipping statistics '
'computations.'
)
logger.info(msg)
self.max_values = max_values or {}
self.n_bins = n_bins
self.direct_max = self.max_values.get(self._DIRECT, None)
self.time_derivative_max = self.max_values.get(self._DY_DT, None)
self.gradient_max = self.max_values.get(self._DY_DX, None)
self.include_stats = include_stats or [
self._DIRECT,
self._DY_DX,
self._DY_DT,
self._FFT_K,
]
self.s_enhance = s_enhance
self.t_enhance = t_enhance
self._features = compute_features
self._k_range = None
self._f_range = None
self.input_features = input_features
self.smoothing = smoothing
self.get_interp = get_interp
self.cache_pattern = cache_pattern
self.overwrite_cache = overwrite_cache
self.overwrite_stats = overwrite_stats
self.spatial_res = spatial_res or 1
self.temporal_res = temporal_res or 1
self.source_data = input_data
self.qa_fp = qa_fp
self.interp_dists = interp_dists
self.time_chunk_size = time_chunk_size
@property
def k_range(self):
"""Get range of wavenumbers to use for wavenumber spectrum
calculation"""
if self.spatial_res is not None:
domain_size = self.spatial_res * self.source_data.shape[1]
self._k_range = [1 / domain_size, 1 / self.spatial_res]
return self._k_range
@property
def f_range(self):
"""Get range of frequencies to use for frequency spectrum
calculation"""
if self.temporal_res is not None:
domain_size = self.temporal_res * self.source_data.shape[2]
self._f_range = [1 / domain_size, 1 / self.temporal_res]
return self._f_range
@property
def features(self):
"""Get a list of requested feature names
Returns
-------
list
"""
return self._features
def _compute_spectra_type(self, var, stat_type, interp=False):
"""Select the appropriate method and parameters for the given stat_type
and compute that spectrum
Parameters
----------
var: ndarray
Variable for which to compute given spectrum type.
(lat, lon, temporal)
stat_type: str
Spectrum type to compute. e.g. avg_fluctuation_spectrum_k will
compute the wavenumber spectrum of the difference between the var
and mean var.
interp : bool
Whether or not this is interpolated data. If True then this means
that the spatial_res and temporal_res is different than the input
data and needs to be scaled to get accurate wavenumber/frequency
ranges.
Returns
-------
ndarray
wavenumber/frequency values
ndarray
amplitudes corresponding to the wavenumber/frequency values
"""
tmp = var.copy()
if self._FFT_K in stat_type:
method = wavenumber_spectrum
x_range = [self.k_range[0], self.k_range[1]]
if interp:
x_range[1] = x_range[1] * self.s_enhance
if stat_type == self._FLUCT_FFT_K:
tmp = self.get_fluctuation(tmp)
tmp = np.mean(tmp[..., :-1], axis=-1)
elif self._FFT_F in stat_type:
method = frequency_spectrum
x_range = [self.f_range[0], self.f_range[1]]
if interp:
x_range[1] = x_range[1] * self.t_enhance
if stat_type == self._FLUCT_FFT_F:
tmp = tmp - np.mean(tmp)
else:
return None
kwargs = dict(var=tmp, x_range=x_range)
return method(**kwargs)
[docs]
@staticmethod
def get_fluctuation(var):
"""Get difference between array and temporal average of the same array
Parameters
----------
var : ndarray
Array of data to calculate flucation for
(spatial_1, spatial_2, temporal)
Returns
-------
dvar : ndarray
Array with fluctuation data
(spatial_1, spatial_2, temporal)
"""
avg = np.mean(var, axis=-1)
return var - np.repeat(
np.expand_dims(avg, axis=-1), var.shape[-1], axis=-1
)
[docs]
def interpolate_data(self, feature, low_res):
"""Get interpolated low res field
Parameters
----------
feature : str
Name of feature to interpolate
low_res : ndarray
Array of low resolution data to interpolate
(spatial_1, spatial_2, temporal)
Returns
-------
var_itp : ndarray
Array of interpolated data
(spatial_1, spatial_2, temporal)
"""
var_itp, file_name = self.check_return_cache(feature, low_res.shape)
if var_itp is None:
logger.info(f'Interpolating low res {feature}.')
chunks = []
slices = np.arange(low_res.shape[-1])
n_chunks = low_res.shape[-1] // self.time_chunk_size + 1
slices = np.array_split(slices, n_chunks)
slices = [slice(s[0], s[-1] + 1) for s in slices]
for i, s in enumerate(slices):
chunks.append(
st_interp(low_res[..., s], self.s_enhance, self.t_enhance)
)
mem = psutil.virtual_memory()
logger.info(
f'Finished interpolating {i+1} / {len(slices)} '
'chunks. Current memory usage is '
f'{mem.used / 1e9:.3f} GB out of '
f'{mem.total / 1e9:.3f} GB total.'
)
var_itp = np.concatenate(chunks, axis=-1)
if 'direction' in feature:
var_itp = (var_itp + 360) % 360
if file_name is not None:
self.save_cache(var_itp, file_name)
return var_itp
[docs]
def check_return_cache(self, feature, shape):
"""Check if interpolated data is cached and return data if it is.
Returns cache file name if cache_pattern is not None
Parameters
----------
feature : str
Name of interpolated feature to check for cache
shape : tuple
Shape of low resolution data. Used to define cache file_name.
Returns
-------
var_itp : ndarray | None
Array of interpolated data if data exists. Otherwise returns None
file_name : str
Name of cache file for interpolated data. If cache_pattern is None
this returns None
"""
var_itp = None
file_name = None
shape_str = f'{shape[0]}x{shape[1]}x{shape[2]}'
if self.cache_pattern is not None:
file_name = self.cache_pattern.replace('{shape}', f'{shape_str}')
file_name = file_name.replace(
'{feature}', f'{feature.lower()}_interp'
)
if file_name is not None and os.path.exists(file_name):
var_itp = self.load_cache(file_name)
return var_itp, file_name
def _compute_dist_type(self, var, stat_type, interp=False, period=None):
"""Select the appropriate method and parameters for the given stat_type
and compute that distribution
Parameters
----------
var: ndarray
Variable for which to compute distribution.
(lat, lon, temporal)
stat_type: str
Distribution type to compute. e.g. mean_gradient will compute the
gradient distribution of the temporal mean of var
interp : bool
Whether or not this is interpolated data. If True then this means
that the spatial_res and temporal_res is different than the input
data and needs to be scaled to get accurate derivatives.
period : float | None
If variable is periodic this gives that period. e.g. If the
variable is winddirection the period is 360 degrees and we need to
account for 0 and 360 being close.
Returns
-------
ndarray
Distribution values at bin centers
ndarray
Distribution value counts
float
Normalization factor
"""
tmp = var.copy()
if 'mean' in stat_type:
tmp = (
np.mean(tmp, axis=-1)
if 'time' not in stat_type
else np.mean(tmp, axis=(0, 1))
)
if self._DIRECT in stat_type:
max_val = self.direct_max
method = direct_dist
scale = 1
elif self._DY_DX in stat_type:
max_val = self.gradient_max
method = gradient_dist
scale = (
self.spatial_res
if not interp
else self.spatial_res / self.s_enhance
)
elif self._DY_DT in stat_type:
max_val = self.time_derivative_max
method = time_derivative_dist
scale = (
self.temporal_res
if not interp
else self.temporal_res / self.t_enhance
)
else:
return None
kwargs = dict(
var=tmp,
diff_max=max_val,
bins=self.n_bins,
scale=scale,
interpolate=self.interp_dists,
period=period,
)
return method(**kwargs)
[docs]
def get_stats(self, var, interp=False, period=None):
"""Get stats for wind fields
Parameters
----------
var: ndarray
(lat, lon, temporal)
interp : bool
Whether or not this is interpolated data. If True then this means
that the spatial_res and temporal_res is different than the input
data and needs to be scaled to get accurate derivatives.
period : float | None
If variable is periodic this gives that period. e.g. If the
variable is winddirection the period is 360 degrees and we need to
account for 0 and 360 being close.
Returns
-------
stats : dict
Dictionary of stats for wind fields
"""
stats_dict = {}
for stat_type in self.include_stats:
if 'spectrum' in stat_type:
out = self._compute_spectra_type(var, stat_type, interp=interp)
else:
out = self._compute_dist_type(
var, stat_type, interp=interp, period=period
)
if out is not None:
mem = psutil.virtual_memory()
logger.info(
f'Computed {stat_type}. Current memory usage is '
f'{mem.used / 1e9:.3f} GB out of '
f'{mem.total / 1e9:.3f} GB total.'
)
stats_dict[stat_type] = out
return stats_dict
[docs]
def get_feature_data(self, feature):
"""Get data for requested feature
Parameters
----------
feature : str
Name of feature to get stats for
Returns
-------
ndarray
Array of data for requested feature
"""
if self.source_data is None:
return None
if 'vorticity' in feature:
height = Feature.get_height(feature)
lower_features = [f.lower() for f in self.input_features]
uidx = lower_features.index(f'u_{height}m')
vidx = lower_features.index(f'v_{height}m')
out = vorticity_calc(
self.source_data[..., uidx],
self.source_data[..., vidx],
scale=self.spatial_res,
)
else:
idx = self.input_features.index(feature)
out = self.source_data[..., idx]
return out
[docs]
def get_feature_stats(self, feature):
"""Get stats for high and low resolution fields
Parameters
----------
feature : str
Name of feature to get stats for
Returns
-------
source_stats : dict
Dictionary of stats for input fields
interp : dict
Dictionary of stats for spatiotemporally interpolated fields
"""
source_stats = {}
period = None
if 'direction' in feature:
period = 360
if self.source_data is not None:
out = self.get_feature_data(feature)
source_stats = self.get_stats(out, period=period)
interp = {}
if self.get_interp:
logger.info(f'Getting interpolated baseline stats for {feature}')
itp = self.interpolate_data(feature, out)
interp = self.get_stats(itp, interp=True, period=period)
return source_stats, interp
[docs]
def run(self):
"""Go through all requested features and get the dictionary of
statistics.
Returns
-------
stats : dict
Dictionary of statistics, where keys are source/interp appended
with the feature name. Values are dictionaries of statistics, such
as gradient, avg_spectrum, time_derivative, etc
"""
source_stats = {}
interp_stats = {}
for _, feature in enumerate(self.features):
logger.info(f'Running Sup3rStats for {feature}')
source, interp = self.get_feature_stats(feature)
mem = psutil.virtual_memory()
logger.info(
f'Current memory usage is {mem.used / 1e9:.3f} '
f'GB out of {mem.total / 1e9:.3f} GB total.'
)
if self.source_data is not None:
source_stats[feature] = source
if self.get_interp:
interp_stats[feature] = interp
stats = {'source': source_stats, 'interp': interp_stats}
if self.qa_fp is not None:
logger.info(f'Saving stats to {self.qa_fp}')
self.export(self.qa_fp, stats)
logger.info('Finished Sup3rStats run method.')
return stats
[docs]
class Sup3rStatsSingle(Sup3rStatsCompute):
"""Base class for doing statistical QA on single file set."""
def __init__(
self,
source_file_paths=None,
s_enhance=1,
t_enhance=1,
features=None,
temporal_slice=slice(None),
target=None,
shape=None,
raster_file=None,
time_chunk_size=None,
cache_pattern=None,
overwrite_cache=False,
overwrite_stats=False,
source_handler=None,
worker_kwargs=None,
get_interp=False,
include_stats=None,
max_values=None,
smoothing=None,
coarsen=False,
spatial_res=None,
temporal_res=None,
n_bins=40,
max_delta=10,
qa_fp=None,
):
"""Parameters
----------
source_file_paths : list | str
A list of source files to compute statistics on. Either .nc or .h5
s_enhance : int
Factor by which the Sup3rGan model enhanced the spatial
dimensions of low resolution data
t_enhance : int
Factor by which the Sup3rGan model enhanced temporal dimension
of low resolution data
features : list
Features for which to compute wind stats. e.g. ['pressure_100m',
'temperature_100m', 'windspeed_100m', 'vorticity_100m']
temporal_slice : slice | tuple | list
Slice defining size of full temporal domain. e.g. If we have 5
files each with 5 time steps then temporal_slice = slice(None) will
select all 25 time steps. This can also be a tuple / list with
length 3 that will be interpreted as slice(*temporal_slice)
target : tuple
(lat, lon) lower left corner of raster. You should provide
target+shape or raster_file, or if all three are None the full
source domain will be used.
shape : tuple
(rows, cols) grid size. You should provide target+shape or
raster_file, or if all three are None the full source domain will
be used.
raster_file : str | None
File for raster_index array for the corresponding target and
shape. If specified the raster_index will be loaded from the file
if it exists or written to the file if it does not yet exist.
If None raster_index will be calculated directly. You should
provide target+shape or raster_file, or if all three are None the
full source domain will be used.
time_chunk_size : int
Size of chunks to split time dimension into for parallel data
extraction. If running in serial this can be set to the size
of the full time index for best performance.
cache_pattern : str | None
Pattern for files for saving feature data. e.g.
file_path_{feature}.pkl Each feature will be saved to a file with
the feature name replaced in cache_pattern. If not None
feature arrays will be saved here and not stored in self.data until
load_cached_data is called. The cache_pattern can also include
{shape}, {target}, {times} which will help ensure unique cache
files for complex problems.
overwrite_cache : bool
Whether to overwrite cache files storing the computed/extracted
feature data
overwrite_stats : bool
Whether to overwrite saved stats
input_handler : str | None
data handler class to use for input data. Provide a string name to
match a class in data_handling.py. If None the correct handler will
be guessed based on file type and time series properties.
worker_kwargs : dict | None
Dictionary of worker values. Can include max_workers,
extract_workers, compute_workers, load_workers, norm_workers,
and ti_workers. Each argument needs to be an integer or None.
The value of `max workers` will set the value of all other worker
args. If max_workers == 1 then all processes will be serialized. If
max_workers == None then other worker args will use their own
provided values.
`extract_workers` is the max number of workers to use for
extracting features from source data. If None it will be estimated
based on memory limits. If 1 processes will be serialized.
`compute_workers` is the max number of workers to use for computing
derived features from raw features in source data. `load_workers`
is the max number of workers to use for loading cached feature
data. `norm_workers` is the max number of workers to use for
normalizing feature data. `ti_workers` is the max number of
workers to use to get full time index. Useful when there are many
input files each with a single time step. If this is greater than
one, time indices for input files will be extracted in parallel
and then concatenated to get the full time index. If input files
do not all have time indices or if there are few input files this
should be set to one.
get_interp : bool
Whether to include interpolated baseline stats in output
include_stats : list | None
List of stats to include in output. e.g. ['time_derivative',
'gradient', 'vorticity', 'avg_spectrum_k', 'avg_spectrum_f',
'direct']. 'direct' means direct distribution, as opposed to a
distribution of the gradient or time derivative.
max_values : dict | None
Dictionary of max values to keep for stats. e.g.
{'time_derivative': 10, 'gradient': 14, 'vorticity': 7}
smoothing : float | None
Value passed to gaussian filter used for smoothing source data
spatial_res : float | None
Spatial resolution for source data in meters. e.g. 2000. This is
used to determine the wavenumber range for spectra calculations.
temporal_res : float | None
Temporal resolution for source data in seconds. e.g. 60. This is
used to determine the frequency range for spectra calculations and
to scale temporal derivatives.
coarsen : bool
Whether to coarsen data or not
max_delta : int, optional
Optional maximum limit on the raster shape that is retrieved at
once. If shape is (20, 20) and max_delta=10, the full raster will
be retrieved in four chunks of (10, 10). This helps adapt to
non-regular grids that curve over large distances, by default 20
n_bins : int
Number of bins to use for constructing probability distributions
qa_fp : str
File path for saving statistics. Only .pkl supported.
"""
logger.info(
'Initializing Sup3rStatsSingle and retrieving source data'
f' for features={features}.'
)
worker_kwargs = worker_kwargs or {}
max_workers = worker_kwargs.get('max_workers', None)
extract_workers = compute_workers = load_workers = ti_workers = None
if max_workers is not None:
extract_workers = compute_workers = load_workers = max_workers
ti_workers = max_workers
extract_workers = worker_kwargs.get('extract_workers', extract_workers)
compute_workers = worker_kwargs.get('compute_workers', compute_workers)
load_workers = worker_kwargs.get('load_workers', load_workers)
ti_workers = worker_kwargs.get('ti_workers', ti_workers)
self.ti_workers = ti_workers
self.s_enhance = s_enhance
self.t_enhance = t_enhance
self.smoothing = smoothing
self.coarsen = coarsen
self.get_interp = get_interp
self.cache_pattern = cache_pattern
self.overwrite_cache = overwrite_cache
self.overwrite_stats = overwrite_stats
self.source_file_paths = source_file_paths
self.spatial_res = spatial_res
self.temporal_res = temporal_res
self.temporal_slice = temporal_slice
self._shape = shape
self._target = target
self._source_handler = None
self._source_handler_class = source_handler
self._features = features
self._input_features = None
self._k_range = None
self._f_range = None
source_handler_kwargs = dict(
target=target,
shape=shape,
temporal_slice=temporal_slice,
raster_file=raster_file,
cache_pattern=cache_pattern,
time_chunk_size=time_chunk_size,
overwrite_cache=overwrite_cache,
worker_kwargs=worker_kwargs,
max_delta=max_delta,
)
self.source_data = self.get_source_data(
source_file_paths, source_handler_kwargs
)
super().__init__(
self.source_data,
s_enhance=s_enhance,
t_enhance=t_enhance,
compute_features=self.compute_features,
input_features=self.input_features,
cache_pattern=cache_pattern,
overwrite_cache=overwrite_cache,
overwrite_stats=overwrite_stats,
get_interp=get_interp,
include_stats=include_stats,
max_values=max_values,
smoothing=smoothing,
spatial_res=spatial_res,
temporal_res=self.temporal_res,
n_bins=n_bins,
qa_fp=qa_fp,
)
[docs]
def close(self):
"""Close any open file handlers"""
if hasattr(self.source_handler, 'close'):
self.source_handler.close()
@property
def source_type(self):
"""Get output data type
Returns
-------
output_type
e.g. 'nc' or 'h5'
"""
if self.source_file_paths is None:
return None
ftype = get_source_type(self.source_file_paths)
if ftype not in ('nc', 'h5'):
msg = (
'Did not recognize source file type: '
f'{self.source_file_paths}'
)
logger.error(msg)
raise TypeError(msg)
return ftype
@property
def source_handler_class(self):
"""Get source handler class"""
HandlerClass = get_input_handler_class(
self.source_file_paths, self._source_handler_class
)
return HandlerClass
@property
def source_handler(self):
"""Get source data handler"""
return self._source_handler
# pylint: disable=E1102
[docs]
def get_source_data(self, file_paths, handler_kwargs=None):
"""Get source data using provided source file paths
Parameters
----------
file_paths : list | str
A list of source files to extract raster data from. Each file must
have the same number of timesteps. Can also pass a string with a
unix-style file path which will be passed through glob.glob
handler_kwargs : dict
Dictionary of keyword arguments passed to
`sup3r.preprocessing.data_handling.DataHandler`
Returns
-------
ndarray
Array of data from source file paths
(spatial_1, spatial_2, temporal, features)
"""
if file_paths is None:
return None
self._source_handler = self.source_handler_class(
file_paths, self.input_features, val_split=0.0, **handler_kwargs
)
self._source_handler.load_cached_data()
if self.coarsen:
logger.info(
'Coarsening data with shape='
f'{self._source_handler.data.shape}'
)
self._source_handler.data = self.coarsen_data(
self._source_handler.data, smoothing=self.smoothing
)
logger.info(f'Coarsened shape={self._source_handler.data.shape}')
return self._source_handler.data
@property
def shape(self):
"""Shape of source data"""
return self._shape
@property
def lat_lon(self):
"""Get lat/lon for output data"""
if self.source_type is None:
return None
return self.source_handler.lat_lon
@property
def meta(self):
"""Get the meta data corresponding to the flattened source low-res data
Returns
-------
pd.DataFrame
"""
meta = pd.DataFrame(
{
'latitude': self.lat_lon[..., 0].flatten(),
'longitude': self.lat_lon[..., 1].flatten(),
}
)
return meta
@property
def time_index(self):
"""Get the time index associated with the source data
Returns
-------
pd.DatetimeIndex
"""
return self.source_handler.time_index
@property
def input_features(self):
"""Get a list of requested feature names
Returns
-------
list
"""
self._input_features = [
f for f in self.compute_features if 'vorticity' not in f
]
for feature in self.compute_features:
if 'vorticity' in feature:
height = Feature.get_height(feature)
uf = f'U_{height}m'
vf = f'V_{height}m'
if uf.lower() not in [f.lower() for f in self._input_features]:
self._input_features.append(f'U_{height}m')
if vf.lower() not in [f.lower() for f in self._input_features]:
self._input_features.append(f'V_{height}m')
return self._input_features
@input_features.setter
def input_features(self, input_features):
"""Set input features"""
self._input_features = [
f for f in input_features if 'vorticity' not in f
]
for feature in input_features:
if 'vorticity' in feature:
height = Feature.get_height(feature)
uf = f'U_{height}m'
vf = f'V_{height}m'
if uf.lower() not in [f.lower() for f in self._input_features]:
self._input_features.append(f'U_{height}m')
if vf.lower() not in [f.lower() for f in self._input_features]:
self._input_features.append(f'V_{height}m')
return self._input_features
@property
def compute_features(self):
"""Get list of requested feature names"""
return self._features
[docs]
def coarsen_data(self, data, smoothing=None):
"""Re-coarsen a high-resolution synthetic output dataset
Parameters
----------
data : np.ndarray
A copy of the high-resolution output data as a numpy
array of shape (spatial_1, spatial_2, temporal)
smoothing : float | None
Amount of smoothing to apply using a gaussian filter.
Returns
-------
data : np.ndarray
A spatiotemporally coarsened copy of the input dataset, still with
shape (spatial_1, spatial_2, temporal)
"""
n_lats = self.s_enhance * (data.shape[0] // self.s_enhance)
n_lons = self.s_enhance * (data.shape[1] // self.s_enhance)
data = spatial_coarsening(
data[:n_lats, :n_lons], s_enhance=self.s_enhance, obs_axis=False
)
# t_coarse needs shape to be 5D: (obs, s1, s2, t, f)
data = np.expand_dims(data, axis=0)
data = temporal_coarsening(data, t_enhance=self.t_enhance)
data = data[0]
if smoothing is not None:
for i in range(data.shape[-1]):
for t in range(data.shape[-2]):
data[..., t, i] = gaussian_filter(
data[..., t, i], smoothing, mode='nearest'
)
return data
[docs]
class Sup3rStatsMulti(Sup3rStatsBase):
"""Class for doing statistical QA on multiple datasets. These datasets
are low resolution input to sup3r, the synthetic output, and the true
high resolution corresponding to the low resolution input. This class
will provide statistics used to compare all these datasets."""
def __init__(
self,
lr_file_paths=None,
synth_file_paths=None,
hr_file_paths=None,
s_enhance=1,
t_enhance=1,
features=None,
lr_t_slice=slice(None),
synth_t_slice=slice(None),
hr_t_slice=slice(None),
target=None,
shape=None,
raster_file=None,
qa_fp=None,
time_chunk_size=None,
cache_pattern=None,
overwrite_cache=False,
overwrite_synth_cache=False,
overwrite_stats=False,
source_handler=None,
output_handler=None,
worker_kwargs=None,
get_interp=False,
include_stats=None,
max_values=None,
smoothing=None,
spatial_res=None,
temporal_res=None,
n_bins=40,
max_delta=10,
save_fig_data=False,
):
"""Parameters
----------
lr_file_paths : list | str
A list of low-resolution source files (either .nc or .h5)
to extract raster data from.
synth_file_paths : list | str
Sup3r-resolved output files (either .nc or .h5) with
high-resolution data corresponding to the
lr_file_paths * s_enhance * t_enhance
hr_file_paths : list | str
A list of high-resolution source files (either .nc or .h5)
corresponding to the low-resolution source files in
lr_file_paths
s_enhance : int
Factor by which the Sup3rGan model will enhance the spatial
dimensions of low resolution data
t_enhance : int
Factor by which the Sup3rGan model will enhance temporal dimension
of low resolution data
features : list
Features for which to compute wind stats. e.g. ['pressure_100m',
'temperature_100m', 'windspeed_100m', 'vorticity_100m']
lr_t_slice : slice | tuple | list
Slice defining size of temporal domain for the low resolution data.
synth_t_slice : slice | tuple | list
Slice defining size of temporal domain for the sythetic high
resolution data.
hr_t_slice : slice | tuple | list
Slice defining size of temporal domain for the true high
resolution data.
target : tuple
(lat, lon) lower left corner of raster. You should provide
target+shape or raster_file, or if all three are None the full
source domain will be used.
shape : tuple
Shape of the low resolution grid size. (rows, cols). You should
provide target+shape or raster_file, or if all three are None the
full source domain will be used.
raster_file : str | None
File for raster_index array for the corresponding target and
shape. If specified the raster_index will be loaded from the file
if it exists or written to the file if it does not yet exist.
If None raster_index will be calculated directly. You should
provide target+shape or raster_file, or if all three are None the
full source domain will be used.
qa_fp : str | None
Optional filepath to output QA file when you call
Sup3rStatsWind.run()
(only .pkl is supported)
time_chunk_size : int
Size of chunks to split time dimension into for parallel data
extraction. If running in serial this can be set to the size
of the full time index for best performance.
cache_pattern : str | None
Pattern for files for saving feature data. e.g.
file_path_{feature}.pkl Each feature will be saved to a file with
the feature name replaced in cache_pattern. If not None
feature arrays will be saved here and not stored in self.data until
load_cached_data is called. The cache_pattern can also include
{shape}, {target}, {times} which will help ensure unique cache
files for complex problems.
overwrite_cache : bool
Whether to overwrite cache files storing the computed/extracted
feature data for low-resolution and high-resolution data
overwrite_synth_cache : bool
Whether to overwrite cache files stored computed/extracted data
for synthetic output.
overwrite_stats : bool
Whether to overwrite saved stats
input_handler : str | None
data handler class to use for input data. Provide a string name to
match a class in data_handling.py. If None the correct handler will
be guessed based on file type and time series properties.
output_handler : str | None
data handler class to use for output data. Provide a string name to
match a class in data_handling.py. If None the correct handler will
be guessed based on file type and time series properties.
worker_kwargs : dict | None
Dictionary of worker values. Can include max_workers,
extract_workers, compute_workers, load_workers, norm_workers,
and ti_workers. Each argument needs to be an integer or None.
The value of `max workers` will set the value of all other worker
args. If max_workers == 1 then all processes will be serialized. If
max_workers == None then other worker args will use their own
provided values.
`extract_workers` is the max number of workers to use for
extracting features from source data. If None it will be estimated
based on memory limits. If 1 processes will be serialized.
`compute_workers` is the max number of workers to use for computing
derived features from raw features in source data. `load_workers`
is the max number of workers to use for loading cached feature
data. `norm_workers` is the max number of workers to use for
normalizing feature data. `ti_workers` is the max number of
workers to use to get full time index. Useful when there are many
input files each with a single time step. If this is greater than
one, time indices for input files will be extracted in parallel
and then concatenated to get the full time index. If input files
do not all have time indices or if there are few input files this
should be set to one.
get_interp : bool
Whether to include interpolated baseline stats in output
include_stats : list | None
List of stats to include in output. e.g. ['time_derivative',
'gradient', 'vorticity', 'avg_spectrum_k', 'avg_spectrum_f',
'direct']. 'direct' means direct distribution, as opposed to a
distribution of the gradient or time derivative.
max_values : dict | None
Dictionary of max values to keep for stats. e.g.
{'time_derivative': 10, 'gradient': 14, 'vorticity': 7}
smoothing : float | None
Value passed to gaussian filter used for smoothing source data
spatial_res : float | None
Spatial resolution for source data in meters. e.g. 2000. This is
used to determine the wavenumber range for spectra calculations.
temporal_res : float | None
Temporal resolution for source data in seconds. e.g. 60. This is
used to determine the frequency range for spectra calculations and
to scale temporal derivatives.
max_delta : int, optional
Optional maximum limit on the raster shape that is retrieved at
once. If shape is (20, 20) and max_delta=10, the full raster will
be retrieved in four chunks of (10, 10). This helps adapt to
non-regular grids that curve over large distances, by default 20
n_bins : int
Number of bins to use for constructing probability distributions
"""
logger.info(
'Initializing Sup3rStatsMulti and retrieving source data'
f' for features={features}.'
)
self.qa_fp = qa_fp
self.overwrite_stats = overwrite_stats
self.save_fig_data = save_fig_data
self.features = features
# get low res and interp stats
logger.info('Retrieving source data for low-res and interp stats')
kwargs = dict(
source_file_paths=lr_file_paths,
s_enhance=s_enhance,
t_enhance=t_enhance,
features=features,
temporal_slice=lr_t_slice,
target=target,
shape=shape,
time_chunk_size=time_chunk_size,
cache_pattern=cache_pattern,
overwrite_cache=overwrite_cache,
overwrite_stats=overwrite_stats,
source_handler=source_handler,
worker_kwargs=worker_kwargs,
get_interp=get_interp,
include_stats=include_stats,
max_values=max_values,
smoothing=None,
spatial_res=spatial_res,
temporal_res=temporal_res,
n_bins=n_bins,
max_delta=max_delta,
)
self.lr_stats = Sup3rStatsSingle(**kwargs)
if self.lr_stats.source_data is not None:
self.lr_shape = self.lr_stats.source_handler.grid_shape
target = self.lr_stats.source_handler.target
else:
self.lr_shape = shape
# get high res stats
shape = (self.lr_shape[0] * s_enhance, self.lr_shape[1] * s_enhance)
logger.info(
'Retrieving source data for high-res stats with ' f'shape={shape}'
)
tmp_raster = (
raster_file
if raster_file is None
else raster_file.replace('.txt', '_hr.txt')
)
tmp_cache = (
cache_pattern
if cache_pattern is None
else cache_pattern.replace('.pkl', '_hr.pkl')
)
hr_spatial_res = spatial_res or 1
hr_spatial_res /= s_enhance
hr_temporal_res = temporal_res or 1
hr_temporal_res /= t_enhance
kwargs_new = dict(
source_file_paths=hr_file_paths,
s_enhance=1,
t_enhance=1,
shape=shape,
target=target,
spatial_res=hr_spatial_res,
temporal_res=hr_temporal_res,
get_interp=False,
source_handler=source_handler,
cache_pattern=tmp_cache,
temporal_slice=hr_t_slice,
)
kwargs_hr = kwargs.copy()
kwargs_hr.update(kwargs_new)
self.hr_stats = Sup3rStatsSingle(**kwargs_hr)
# get synthetic stats
shape = (self.lr_shape[0] * s_enhance, self.lr_shape[1] * s_enhance)
logger.info(
'Retrieving source data for synthetic stats with ' f'shape={shape}'
)
tmp_raster = (
raster_file
if raster_file is None
else raster_file.replace('.txt', '_synth.txt')
)
tmp_cache = (
cache_pattern
if cache_pattern is None
else cache_pattern.replace('.pkl', '_synth.pkl')
)
kwargs_new = dict(
source_file_paths=synth_file_paths,
s_enhance=1,
t_enhance=1,
shape=shape,
target=target,
spatial_res=hr_spatial_res,
temporal_res=hr_temporal_res,
get_interp=False,
source_handler=output_handler,
raster_file=tmp_raster,
cache_pattern=tmp_cache,
overwrite_cache=(overwrite_synth_cache),
temporal_slice=synth_t_slice,
)
kwargs_synth = kwargs.copy()
kwargs_synth.update(kwargs_new)
self.synth_stats = Sup3rStatsSingle(**kwargs_synth)
# get coarse stats
logger.info('Retrieving source data for coarse stats')
tmp_raster = (
raster_file
if raster_file is None
else raster_file.replace('.txt', '_coarse.txt')
)
tmp_cache = (
cache_pattern
if cache_pattern is None
else cache_pattern.replace('.pkl', '_coarse.pkl')
)
kwargs_new = dict(
source_file_paths=hr_file_paths,
spatial_res=spatial_res,
temporal_res=temporal_res,
target=target,
shape=shape,
smoothing=smoothing,
coarsen=True,
get_interp=False,
source_handler=output_handler,
cache_pattern=tmp_cache,
temporal_slice=hr_t_slice,
)
kwargs_coarse = kwargs.copy()
kwargs_coarse.update(kwargs_new)
self.coarse_stats = Sup3rStatsSingle(**kwargs_coarse)
[docs]
def export_fig_data(self):
"""Save data fields for data viz comparison"""
for feature in self.features:
fig_data = {}
if self.synth_stats.source_data is not None:
fig_data.update(
{
'time_index': self.synth_stats.time_index,
'synth': self.synth_stats.get_feature_data(feature),
'synth_grid': self.synth_stats.source_handler.lat_lon,
}
)
if self.lr_stats.source_data is not None:
fig_data.update(
{
'low_res': self.lr_stats.get_feature_data(feature),
'low_res_grid': self.lr_stats.source_handler.lat_lon,
}
)
if self.hr_stats.source_data is not None:
fig_data.update(
{
'high_res': self.hr_stats.get_feature_data(feature),
'high_res_grid': self.hr_stats.source_handler.lat_lon,
}
)
if self.coarse_stats.source_data is not None:
fig_data.update(
{'coarse': self.coarse_stats.get_feature_data(feature)}
)
file_name = self.qa_fp.replace('.pkl', f'_{feature}_compare.pkl')
with open(file_name, 'wb') as fp:
pickle.dump(fig_data, fp, protocol=4)
logger.info(f'Saved figure data for {feature} to {file_name}.')
[docs]
def close(self):
"""Close any open file handlers"""
stats = [
self.lr_stats,
self.hr_stats,
self.synth_stats,
self.coarse_stats,
]
for s_handle in stats:
s_handle.close()
[docs]
def run(self):
"""Go through all datasets and get the dictionary of statistics.
Returns
-------
stats : dict
Dictionary of statistics, where keys are lr/hr/interp appended with
the feature name. Values are dictionaries of statistics, such as
gradient, avg_spectrum, time_derivative, etc
"""
stats = {}
if self.lr_stats.source_data is not None:
logger.info('Computing statistics on low-resolution dataset.')
lr_stats = self.lr_stats.run()
stats['low_res'] = lr_stats['source']
if lr_stats['interp']:
stats['interp'] = lr_stats['interp']
if self.synth_stats.source_data is not None:
logger.info(
'Computing statistics on synthetic high-resolution dataset.'
)
synth_stats = self.synth_stats.run()
stats['synth'] = synth_stats['source']
if self.coarse_stats.source_data is not None:
logger.info(
'Computing statistics on coarsened low-resolution dataset.'
)
coarse_stats = self.coarse_stats.run()
stats['coarse'] = coarse_stats['source']
if self.hr_stats.source_data is not None:
logger.info('Computing statistics on high-resolution dataset.')
hr_stats = self.hr_stats.run()
stats['high_res'] = hr_stats['source']
if self.qa_fp is not None:
self.export(self.qa_fp, stats)
if self.save_fig_data:
self.export_fig_data()
logger.info('Finished Sup3rStats run method.')
return stats