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feature_handling.py
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feature_handling.py
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"""Sup3r feature handling module.
@author: bbenton
"""
import logging
import re
from abc import ABC, abstractmethod
from collections import defaultdict
from concurrent.futures import as_completed
from typing import ClassVar
import numpy as np
import psutil
import xarray as xr
from rex import Resource
from rex.utilities.execution import SpawnProcessPool
from sup3r.utilities.utilities import (
bvf_squared,
get_raster_shape,
inverse_mo_length,
invert_pot_temp,
invert_uv,
rotor_equiv_ws,
transform_rotate_wind,
vorticity_calc,
)
np.random.seed(42)
logger = logging.getLogger(__name__)
class DerivedFeature(ABC):
"""Abstract class for special features which need to be derived from raw
features
"""
@classmethod
@abstractmethod
def inputs(cls, feature):
"""Required inputs for derived feature"""
@classmethod
@abstractmethod
def compute(cls, data, height):
"""Compute method for derived feature"""
class ClearSkyRatioH5(DerivedFeature):
"""Clear Sky Ratio feature class for computing from H5 data"""
@classmethod
def inputs(cls, feature):
"""Get list of raw features used in calculation of the clearsky ratio
Parameters
----------
feature : str
Clearsky ratio feature name, needs to be "clearsky_ratio"
Returns
-------
list
List of required features for clearsky_ratio: clearsky_ghi, ghi
"""
assert feature == 'clearsky_ratio'
return ['clearsky_ghi', 'ghi']
@classmethod
def compute(cls, data, height=None):
"""Compute the clearsky ratio
Parameters
----------
data : dict
dictionary of feature arrays used for this compuation, must include
clearsky_ghi and ghi
height : str | int
Placeholder to match interface with other compute methods
Returns
-------
cs_ratio : ndarray
Clearsky ratio, e.g. the all-sky ghi / the clearsky ghi. NaN where
nighttime.
"""
# need to use a nightime threshold of 1 W/m2 because cs_ghi is stored
# in integer format and weird binning patterns happen in the clearsky
# ratio and cloud mask between 0 and 1 W/m2 and sunrise/sunset
night_mask = data['clearsky_ghi'] <= 1
# set any timestep with any nighttime equal to NaN to avoid weird
# sunrise/sunset artifacts.
night_mask = night_mask.any(axis=(0, 1))
data['clearsky_ghi'][..., night_mask] = np.nan
cs_ratio = data['ghi'] / data['clearsky_ghi']
cs_ratio = cs_ratio.astype(np.float32)
return cs_ratio
class ClearSkyRatioCC(DerivedFeature):
"""Clear Sky Ratio feature class for computing from climate change netcdf
data
"""
@classmethod
def inputs(cls, feature):
"""Get list of raw features used in calculation of the clearsky ratio
Parameters
----------
feature : str
Clearsky ratio feature name, needs to be "clearsky_ratio"
Returns
-------
list
List of required features for clearsky_ratio: clearsky_ghi, rsds
(rsds==ghi for cc datasets)
"""
assert feature == 'clearsky_ratio'
return ['clearsky_ghi', 'rsds']
@classmethod
def compute(cls, data, height=None):
"""Compute the daily average climate change clearsky ratio
Parameters
----------
data : dict
dictionary of feature arrays used for this compuation, must include
clearsky_ghi and rsds (rsds==ghi for cc datasets)
height : str | int
Placeholder to match interface with other compute methods
Returns
-------
cs_ratio : ndarray
Clearsky ratio, e.g. the all-sky ghi / the clearsky ghi. This is
assumed to be daily average data for climate change source data.
"""
cs_ratio = data['rsds'] / data['clearsky_ghi']
cs_ratio = np.minimum(cs_ratio, 1)
cs_ratio = np.maximum(cs_ratio, 0)
return cs_ratio
class CloudMaskH5(DerivedFeature):
"""Cloud Mask feature class for computing from H5 data"""
@classmethod
def inputs(cls, feature):
"""Get list of raw features used in calculation of the cloud mask
Parameters
----------
feature : str
Cloud mask feature name, needs to be "cloud_mask"
Returns
-------
list
List of required features for cloud_mask: clearsky_ghi, ghi
"""
assert feature == 'cloud_mask'
return ['clearsky_ghi', 'ghi']
@classmethod
def compute(cls, data, height=None):
"""Compute the cloud mask
Parameters
----------
data : dict
dictionary of feature arrays used for this compuation, must include
clearsky_ghi and ghi
height : str | int
Placeholder to match interface with other compute methods
Returns
-------
cloud_mask : ndarray
Cloud mask, e.g. 1 where cloudy, 0 where clear. NaN where
nighttime. Data is float32 so it can be normalized without any
integer weirdness.
"""
# need to use a nightime threshold of 1 W/m2 because cs_ghi is stored
# in integer format and weird binning patterns happen in the clearsky
# ratio and cloud mask between 0 and 1 W/m2 and sunrise/sunset
night_mask = data['clearsky_ghi'] <= 1
# set any timestep with any nighttime equal to NaN to avoid weird
# sunrise/sunset artifacts.
night_mask = night_mask.any(axis=(0, 1))
cloud_mask = data['ghi'] < data['clearsky_ghi']
cloud_mask = cloud_mask.astype(np.float32)
cloud_mask[night_mask] = np.nan
cloud_mask = cloud_mask.astype(np.float32)
return cloud_mask
class PotentialTempNC(DerivedFeature):
"""Potential Temperature feature class for NETCDF data. Needed since T is
perturbation potential temperature.
"""
@classmethod
def inputs(cls, feature):
"""Get list of inputs needed for compute method."""
height = Feature.get_height(feature)
features = [f'T_{height}m']
return features
@classmethod
def compute(cls, data, height):
"""Method to compute Potential Temperature from NETCDF data
Parameters
----------
data : dict
Dictionary of raw feature arrays to use for derivation
height : str | int
Height at which to compute the derived feature
Returns
-------
ndarray
Derived feature array
"""
return data[f'T_{height}m'] + 300
class TempNC(DerivedFeature):
"""Temperature feature class for NETCDF data. Needed since T is potential
temperature not standard temp.
"""
@classmethod
def inputs(cls, feature):
"""Get list of inputs needed for compute method."""
height = Feature.get_height(feature)
features = [f'PotentialTemp_{height}m', f'Pressure_{height}m']
return features
@classmethod
def compute(cls, data, height):
"""Method to compute T from NETCDF data
Parameters
----------
data : dict
Dictionary of raw feature arrays to use for derivation
height : str | int
Height at which to compute the derived feature
Returns
-------
ndarray
Derived feature array
"""
return invert_pot_temp(data[f'PotentialTemp_{height}m'],
data[f'Pressure_{height}m'])
class PressureNC(DerivedFeature):
"""Pressure feature class for NETCDF data. Needed since P is perturbation
pressure.
"""
@classmethod
def inputs(cls, feature):
"""Get list of inputs needed for compute method."""
height = Feature.get_height(feature)
features = [f'P_{height}m', f'PB_{height}m']
return features
@classmethod
def compute(cls, data, height):
"""Method to compute pressure from NETCDF data
Parameters
----------
data : dict
Dictionary of raw feature arrays to use for derivation
height : str | int
Height at which to compute the derived feature
Returns
-------
ndarray
Derived feature array
"""
return data[f'P_{height}m'] + data[f'PB_{height}m']
class BVFreqSquaredNC(DerivedFeature):
"""BVF Squared feature class with needed inputs method and compute
method
"""
@classmethod
def inputs(cls, feature):
"""Get list of inputs needed for compute method."""
height = Feature.get_height(feature)
features = [f'PT_{height}m', f'PT_{int(height) - 100}m']
return features
@classmethod
def compute(cls, data, height):
"""Method to compute BVF squared from NETCDF data
Parameters
----------
data : dict
Dictionary of raw feature arrays to use for derivation
height : str | int
Height at which to compute the derived feature
Returns
-------
ndarray
Derived feature array
"""
# T is perturbation potential temperature for wrf and the
# base potential temperature is 300K
bvf2 = np.float32(9.81 / 100)
bvf2 *= (data[f'PT_{height}m'] - data[f'PT_{int(height) - 100}m'])
bvf2 /= (data[f'PT_{height}m'] + data[f'PT_{int(height) - 100}m'])
bvf2 /= np.float32(2)
return bvf2
class InverseMonNC(DerivedFeature):
"""Inverse MO feature class with needed inputs method and compute method"""
@classmethod
def inputs(cls, feature):
"""Required inputs for inverse MO from NETCDF data
Parameters
----------
feature : str
raw feature name. e.g. RMOL
Returns
-------
list
List of required features for computing RMOL
"""
assert feature == 'RMOL'
features = ['UST', 'HFX']
return features
@classmethod
def compute(cls, data, height=None):
"""Method to compute Inverse MO from NC data
Parameters
----------
data : dict
Dictionary of raw feature arrays to use for derivation
height : str | int
Placeholder to match interface with other compute methods
Returns
-------
ndarray
Derived feature array
"""
return inverse_mo_length(data['UST'], data['HFX'])
class BVFreqMon(DerivedFeature):
"""BVF MO feature class with needed inputs method and compute method"""
@classmethod
def inputs(cls, feature):
"""Required inputs for computing BVF times inverse MO from data
Parameters
----------
feature : str
raw feature name. e.g. BVF_MO_100m
Returns
-------
list
List of required features for computing BVF_MO
"""
height = Feature.get_height(feature)
features = [f'BVF2_{height}m', 'RMOL']
return features
@classmethod
def compute(cls, data, height):
"""Method to compute BVF MO from data
Parameters
----------
data : dict
Dictionary of raw feature arrays to use for derivation
height : str | int
Height at which to compute the derived feature
Returns
-------
ndarray
Derived feature array
"""
bvf_mo = data[f'BVF2_{height}m']
mask = data['RMOL'] != 0
bvf_mo[mask] = bvf_mo[mask] / data['RMOL'][mask]
# making this zero when not both bvf and mo are negative
bvf_mo[data['RMOL'] >= 0] = 0
bvf_mo[bvf_mo < 0] = 0
return bvf_mo
class BVFreqSquaredH5(DerivedFeature):
"""BVF Squared feature class with needed inputs method and compute
method
"""
@classmethod
def inputs(cls, feature):
"""Required inputs for computing BVF squared
Parameters
----------
feature : str
raw feature name. e.g. BVF2_100m
Returns
-------
list
List of required features for computing BVF2
"""
height = Feature.get_height(feature)
features = [
f'temperature_{height}m', f'temperature_{int(height) - 100}m',
f'pressure_{height}m', f'pressure_{int(height) - 100}m'
]
return features
@classmethod
def compute(cls, data, height):
"""Method to compute BVF squared from H5 data
Parameters
----------
data : dict
Dictionary of raw feature arrays to use for derivation
height : str | int
Height at which to compute the derived feature
Returns
-------
ndarray
Derived feature array
"""
return bvf_squared(data[f'temperature_{height}m'],
data[f'temperature_{int(height) - 100}m'],
data[f'pressure_{height}m'],
data[f'pressure_{int(height) - 100}m'], 100)
class WindspeedNC(DerivedFeature):
"""Windspeed feature from netcdf data"""
@classmethod
def inputs(cls, feature):
"""Required inputs for computing windspeed from netcdf data
Parameters
----------
feature : str
raw feature name. e.g. BVF_MO_100m
Returns
-------
list
List of required features for computing windspeed
"""
height = Feature.get_height(feature)
features = [f'U_{height}m', f'V_{height}m', 'lat_lon']
return features
@classmethod
def compute(cls, data, height):
"""Compute windspeed
Parameters
----------
data : dict
Dictionary of raw feature arrays to use for derivation
height : str | int
Height at which to compute the derived feature
Returns
-------
ndarray
Derived feature array
"""
ws, _ = invert_uv(data[f'U_{height}m'], data[f'V_{height}m'],
data['lat_lon'])
return ws
class WinddirectionNC(DerivedFeature):
"""Winddirection feature from netcdf data"""
@classmethod
def inputs(cls, feature):
"""Required inputs for computing windspeed from netcdf data
Parameters
----------
feature : str
raw feature name. e.g. BVF_MO_100m
Returns
-------
list
List of required features for computing windspeed
"""
height = Feature.get_height(feature)
features = [f'U_{height}m', f'V_{height}m', 'lat_lon']
return features
@classmethod
def compute(cls, data, height):
"""Compute winddirection
Parameters
----------
data : dict
Dictionary of raw feature arrays to use for derivation
height : str | int
Height at which to compute the derived feature
Returns
-------
ndarray
Derived feature array
"""
_, wd = invert_uv(data[f'U_{height}m'], data[f'V_{height}m'],
data['lat_lon'])
return wd
class Veer(DerivedFeature):
"""Veer at a given height"""
HEIGHTS: ClassVar[list] = [40, 60, 80, 100, 120]
@classmethod
def inputs(cls, feature):
"""Required inputs for computing Veer
Parameters
----------
feature : str
raw feature name. e.g. BVF_MO_100m
Returns
-------
list
List of required features for computing REWS
"""
rotor_center = Feature.get_height(feature)
if rotor_center is None:
heights = cls.HEIGHTS
else:
heights = [int(rotor_center) - i * 20 for i in [-2, -1, 0, 1, 2]]
features = [f'winddirection_{height}m' for height in heights]
return features
@classmethod
def compute(cls, data, height):
"""Compute Veer
Parameters
----------
data : dict
Dictionary of raw feature arrays to use for derivation
height : str | int
Height at which to compute the derived feature
Returns
-------
ndarray
Derived feature array
"""
if height is None:
heights = cls.HEIGHTS
else:
heights = [int(height) - i * 20 for i in [-2, -1, 0, 1, 2]]
veer = 0
for i in range(0, len(heights), 2):
tmp = np.radians(data[f'winddirection_{height[i + 1]}'])
tmp -= np.radians(data[f'winddirection_{height[i]}'])
veer += np.abs(tmp)
veer /= (heights[-1] - heights[0])
return veer
class Shear(DerivedFeature):
"""Wind shear at a given height"""
@classmethod
def inputs(cls, feature):
"""Required inputs for computing Veer
Parameters
----------
feature : str
raw feature name. e.g. BVF_MO_100m
Returns
-------
list
List of required features for computing Veer
"""
height = Feature.get_height(feature)
heights = [int(height), int(height) + 20]
features = []
for height in heights:
features.append(f'winddirection_{height}m')
return features
@classmethod
def compute(cls, data, height):
"""Compute REWS
Parameters
----------
data : dict
Dictionary of raw feature arrays to use for derivation
height : str | int
Height at which to compute the derived feature
Returns
-------
ndarray
Derived feature array
"""
heights = [int(height), int(height) + 20]
shear = np.cos(np.radians(data[f'winddirection_{int(height) + 20}m']))
shear -= np.cos(np.radians(data[f'winddirection_{int(height)}m']))
shear /= (heights[-1] - heights[0])
return shear
class Rews(DerivedFeature):
"""Rotor equivalent wind speed"""
HEIGHTS: ClassVar[list] = [40, 60, 80, 100, 120]
@classmethod
def inputs(cls, feature):
"""Required inputs for computing REWS
Parameters
----------
feature : str
raw feature name. e.g. BVF_MO_100m
Returns
-------
list
List of required features for computing REWS
"""
rotor_center = Feature.get_height(feature)
if rotor_center is None:
heights = cls.HEIGHTS
else:
heights = [int(rotor_center) - i * 20 for i in [-2, -1, 0, 1, 2]]
features = []
for height in heights:
features.append(f'windspeed_{height}m')
features.append(f'winddirection_{height}m')
return features
@classmethod
def compute(cls, data, height):
"""Compute REWS
Parameters
----------
data : dict
Dictionary of raw feature arrays to use for derivation
height : str | int
Height at which to compute the derived feature
Returns
-------
ndarray
Derived feature array
"""
if height is None:
heights = cls.HEIGHTS
else:
heights = [int(height) - i * 20 for i in [-2, -1, 0, 1, 2]]
rews = rotor_equiv_ws(data, heights)
return rews
class UWindPowerLaw(DerivedFeature):
"""U wind component feature class with needed inputs method and compute
method. Uses power law extrapolation to get values above surface
https://csl.noaa.gov/projects/lamar/windshearformula.html
https://www.tandfonline.com/doi/epdf/10.1080/00022470.1977.10470503
"""
ALPHA = 0.2
NEAR_SFC_HEIGHT = 10
@classmethod
def inputs(cls, feature):
"""Required inputs for computing U wind component
Parameters
----------
feature : str
raw feature name. e.g. U_100m
Returns
-------
list
List of required features for computing U
"""
features = ['uas']
return features
@classmethod
def compute(cls, data, height):
"""Method to compute U wind component from data
Parameters
----------
data : dict
Dictionary of raw feature arrays to use for derivation
height : str | int
Height at which to compute the derived feature
Returns
-------
ndarray
Derived feature array
"""
return data['uas'] * (float(height) / cls.NEAR_SFC_HEIGHT)**cls.ALPHA
class VWindPowerLaw(DerivedFeature):
"""V wind component feature class with needed inputs method and compute
method. Uses power law extrapolation to get values above surface
https://csl.noaa.gov/projects/lamar/windshearformula.html
https://www.tandfonline.com/doi/epdf/10.1080/00022470.1977.10470503
"""
ALPHA = 0.2
NEAR_SFC_HEIGHT = 10
@classmethod
def inputs(cls, feature):
"""Required inputs for computing V wind component
Parameters
----------
feature : str
raw feature name. e.g. V_100m
Returns
-------
list
List of required features for computing V
"""
features = ['vas']
return features
@classmethod
def compute(cls, data, height):
"""Method to compute V wind component from data
Parameters
----------
data : dict
Dictionary of raw feature arrays to use for derivation
height : str | int
Height at which to compute the derived feature
Returns
-------
ndarray
Derived feature array
"""
return data['vas'] * (float(height) / cls.NEAR_SFC_HEIGHT)**cls.ALPHA
class UWind(DerivedFeature):
"""U wind component feature class with needed inputs method and compute
method
"""
@classmethod
def inputs(cls, feature):
"""Required inputs for computing U wind component
Parameters
----------
feature : str
raw feature name. e.g. U_100m
Returns
-------
list
List of required features for computing U
"""
height = Feature.get_height(feature)
features = [
f'windspeed_{height}m', f'winddirection_{height}m', 'lat_lon'
]
return features
@classmethod
def compute(cls, data, height):
"""Method to compute U wind component from data
Parameters
----------
data : dict
Dictionary of raw feature arrays to use for derivation
height : str | int
Height at which to compute the derived feature
Returns
-------
ndarray
Derived feature array
"""
u, _ = transform_rotate_wind(data[f'windspeed_{height}m'],
data[f'winddirection_{height}m'],
data['lat_lon'])
return u
class Vorticity(DerivedFeature):
"""Vorticity feature class with needed inputs method and compute
method
"""
@classmethod
def inputs(cls, feature):
"""Required inputs for computing vorticity
Parameters
----------
feature : str
raw feature name. e.g. vorticity_100m
Returns
-------
list
List of required features for computing vorticity
"""
height = Feature.get_height(feature)
features = [f'U_{height}m', f'V_{height}m']
return features
@classmethod
def compute(cls, data, height):
"""Method to compute vorticity
Parameters
----------
data : dict
Dictionary of raw feature arrays to use for derivation
height : str | int
Height at which to compute the derived feature
Returns
-------
ndarray
Derived feature array
"""
vort = vorticity_calc(data[f'U_{height}m'], data[f'V_{height}m'])
return vort
class VWind(DerivedFeature):
"""V wind component feature class with needed inputs method and compute
method
"""
@classmethod
def inputs(cls, feature):
"""Required inputs for computing V wind component
Parameters
----------
feature : str
raw feature name. e.g. V_100m
Returns
-------
list
List of required features for computing V
"""
height = Feature.get_height(feature)
features = [
f'windspeed_{height}m', f'winddirection_{height}m', 'lat_lon'
]
return features
@classmethod
def compute(cls, data, height):
"""Method to compute V wind component from data
Parameters
----------
data : dict
Dictionary of raw feature arrays to use for derivation
height : str | int
Height at which to compute the derived feature
Returns
-------
ndarray
Derived feature array
"""
_, v = transform_rotate_wind(data[f'windspeed_{height}m'],
data[f'winddirection_{height}m'],
data['lat_lon'])
return v
class TempNCforCC(DerivedFeature):
"""Air temperature variable from climate change nc files"""
@classmethod
def inputs(cls, feature):
"""Required inputs for computing ta
Parameters
----------
feature : str
raw feature name. e.g. ta
Returns
-------
list
List of required features for computing ta
"""
height = Feature.get_height(feature)
return [f'ta_{height}m']
@classmethod
def compute(cls, data, height):
"""Method to compute ta in Celsius from ta source in Kelvin
Parameters
----------
data : dict
Dictionary of raw feature arrays to use for derivation
height : str | int
Height at which to compute the derived feature
Returns
-------
ndarray
Derived feature array
"""
return data[f'ta_{height}m'] - 273.15
class Tas(DerivedFeature):
"""Air temperature near surface variable from climate change nc files"""