import numpy as np
import pandas as pd
from tqdm import tqdm
from prereise.gather.winddata.power_curves import (
get_power,
get_state_power_curves,
get_turbine_power_curves,
)
def _check_curve(curve):
allowed_curves = ["state", "IEC class 2"]
if curve not in allowed_curves:
err_msg = "curve not in allowed: " + ", ".join(allowed_curves)
raise ValueError(err_msg)
def _find_to_impute(data):
# Locate missing data
to_impute = data[data.U.isna()].index
if len(to_impute) == 0:
print("No missing data")
return
else:
return to_impute
def _select_similar(data, dates, j):
year = dates[j].year
month = dates[j].month
hour = dates[j].hour
select = data[
(dates.year == year)
& (dates.month == month)
& (dates.hour == hour)
& (pd.notna(data.Pout))
]
return select
[docs]def simple(data, wind_farm, inplace=True, curve="state"):
"""Impute missing data using a simple procedure. For each missing entry,
the extrema of the U and V components of the wind speed of all non missing
entries that have the same location, same month, same hour are first found
for each missing entry. Then, a U and V value are randomly generated
between the respective derived ranges.
:param pandas.DataFrame data: data frame as returned by
:py:func:`prereise.gather.winddata.rap.rap.retrieve_data`.
:param pandas.DataFrame wind_farm: data frame of wind farms.
:param bool inplace: should the imputation be done in place.
:param str curve: 'state' to use the state average, otherwise named curve.
:return: (*pandas.DataFrame*) -- data frame with missing entries imputed.
"""
_check_curve(curve)
data_impute = data if inplace else data.copy()
to_impute = _find_to_impute(data)
if to_impute is None:
return
# Information on wind turbines & state average tubrine curves
tpc = get_turbine_power_curves()
spc = get_state_power_curves()
# Timestamp of all entries in data frame
dates = pd.DatetimeIndex(data.index.values)
n_target = len(wind_farm)
select = None
for i, j in tqdm(enumerate(to_impute), total=len(to_impute)):
if i % n_target == 0:
select = _select_similar(data, dates, j)
k = data.loc[j].plant_id
select_plant = select[select.plant_id == k]
min_u, max_u = select_plant["U"].min(), select_plant["U"].max()
min_v, max_v = select_plant["V"].min(), select_plant["V"].max()
data_impute.at[j, "U"] = min_u + (max_u - min_u) * np.random.random()
data_impute.at[j, "V"] = min_v + (max_v - min_v) * np.random.random()
wspd = np.sqrt(data.loc[j].U ** 2 + data.loc[j].V ** 2)
normalized_power = get_power(tpc, spc, wspd, "IEC class 2")
data_impute.at[j, "Pout"] = normalized_power
if not inplace:
return data_impute
[docs]def gaussian(data, wind_farm, inplace=True, curve="state"):
"""Impute missing data using gaussian distributions of U & V. For each
missing entry, sample U & V based on mean and covariance of non-missing
entries that have the same location, same month, and same hour.
:param pandas.DataFrame data: data frame as returned by
:py:func:`prereise.gather.winddata.rap.rap.retrieve_data`.
:param pandas.DataFrame wind_farm: data frame of wind farms.
:param bool inplace: should the imputation be done in place.
:param str curve: 'state' to use the state average, otherwise named curve.
:return: (*pandas.DataFrame*) -- data frame with missing entries imputed.
"""
_check_curve(curve)
data_impute = data if inplace else data.copy()
to_impute = _find_to_impute(data)
if to_impute is None:
return
# Information on wind turbines & state average tubrine curves
tpc = get_turbine_power_curves()
spc = get_state_power_curves()
# Timestamp of all entries in data frame
dates = pd.DatetimeIndex(data.index.values)
n_target = len(wind_farm)
select = None
for i, hour in tqdm(enumerate(to_impute), total=len(to_impute)):
# Only run the similar-selection function the first time
if i % n_target == 0:
select = _select_similar(data, dates, hour)
plant_id = data.loc[hour].plant_id
select_plant = select[select.plant_id == plant_id]
uv_data = np.array([select_plant["U"].to_numpy(), select_plant["V"].to_numpy()])
cov = np.cov(uv_data)
mean = np.mean(uv_data, axis=1)
sample = np.random.multivariate_normal(mean=mean, cov=cov, size=1)
data_impute.at[hour, "U"] = sample[0][0]
data_impute.at[hour, "V"] = sample[0][1]
wspd = np.sqrt(data.loc[hour].U ** 2 + data.loc[hour].V ** 2)
normalized_power = get_power(tpc, spc, wspd, "IEC class 2")
data_impute.at[hour, "Pout"] = normalized_power
if not inplace:
return data_impute
[docs]def linear(data, inplace=True):
"""Given a 2D array, linearly interpolate any missing values column-wise.
:param numpy.array/pandas.DataFrame data: data to interpolate.
:param bool inplace: whether to modify the data inplace or return a modified copy.
:return: (*None/pandas.DataFrame*) -- if ``inplace`` is False, data frame with
missing entries imputed.
"""
data_impute = data if inplace else data.copy()
data_impute[:] = pd.DataFrame(data_impute).interpolate()
if not inplace:
return data_impute
