# generating destroyed measurements which are constant over all
# methods
data = []
for park in parks.keys():
windpark = NREL().get_windpark_nearest(parks[park], 5, 2004)
windpark_test = NREL().get_windpark_nearest(parks[park], 5, 2005)
target = windpark.get_target()
measurements = repair_nrel(target.get_measurements()[:10000])
for i in range(2):
damaged_series = {}
de = lambda rate: (rate, (destroy(
measurements, method=destroy_method, percentage=rate)[0]))
dseries = map(de, rates)
for rate, series in dseries:
damaged_series[rate] = series
# with reconstruction
def run(pars):
method, rate = pars
mse = experiment(method, windpark, windpark_test,
damaged_series[rate], rate)
return method, rate, mse
results = map(run, list(chain(product(methods, rates))))
error, var, std = scores(measurements, reconstructed)
return error, var, std
# generating destroyed measurements which are constant over all
# methods
data = []
park = 'reno'
windpark = NREL().get_windpark_nearest(parks[park], 5, 2004)
target = windpark.get_target()
measurements = repair_nrel(target.get_measurements()[:10000])
for i in range(2):
damaged_series = {}
de = lambda rate : (rate, (destroy(measurements, method=destroy_method, percentage=rate)[0]))
dseries = map(de, rates)
for rate, series in dseries:
damaged_series[rate] = series
def run(pars):
method, rate = pars
error, var, std = experiment(method, windpark, damaged_series[rate], rate)
return method, rate, error, var, std
results = map(run, list(chain(product(methods, rates))))
encoding = lambda method, rate, error, park, var, std :\
{"method": method,\
"rate": rate,\
"rmse": error,\
from windml.preprocessing.preprocessing import interpolate
from windml.visualization.plot_timeseries import plot_timeseries
import matplotlib.pyplot as plt
import matplotlib.dates as md
from pylab import *
from numpy import array, zeros, float32, int32
# get windpark and corresponding target. forecast is for the target turbine
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 3, 2004)
target = windpark.get_target()
measurements = target.get_measurements()[300:1000]
damaged, indices = destroy(measurements, method="nmar", percentage=.80,\
min_length=10, max_length=100)
neighbors = windpark.get_turbines()[:-1]
nseries = [t.get_measurements()[300:1000] for t in neighbors]
tinterpolated = interpolate(damaged, method='mreg',\
timestep=600,\
neighbor_series = nseries,\
reg = 'linear_model')
d = array([m[0] for m in tinterpolated])
y1 = array([m[1] for m in tinterpolated]) #score
y2 = array([m[2] for m in tinterpolated]) #speed
d_hat = array([m[0] for m in damaged])
y1_hat = array([m[1] for m in damaged])
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
import matplotlib.pyplot as plt
import matplotlib.dates as md
from pylab import *
from numpy import array, zeros, float32, int32
# get windpark and corresponding target. forecast is for the target turbine
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 5, 2004)
target = windpark.get_target()
measurements = target.get_measurements()[300:1000]
damaged, indices = destroy(measurements, method='nmar', percentage=.80,\
min_length=10, max_length=100)
neighbors = windpark.get_turbines()[:-1]
nseries = [t.get_measurements()[300:1000] for t in neighbors]
gamma_range = [0.0001, 0.000001]
C_range = [2**i for i in range(-3, 5, 1)]
regargs = {
"epsilon": 0.1,
"cv_method": "kfold",
"cv_args": {
"k_folds": 10
},
"kernel": 'rbf',
"tuned_parameters": [{
'kernel': ['rbf'],
# License: BSD 3 clause
from windml.datasets.nrel import NREL
from windml.visualization.plot_timeseries import plot_timeseries
from windml.preprocessing.preprocessing import destroy
import matplotlib.pyplot as plt
import matplotlib.dates as md
from pylab import *
from numpy import array
ds = NREL()
turbine = ds.get_turbine(NREL.park_id['tehachapi'], 2004)
measurements = turbine.get_measurements()[:1000]
damaged, indices = destroy(measurements, method='mar', percentage=.80)
d = array([m[0] for m in measurements])
y1 = array([m[1] for m in measurements]) #score
y2 = array([m[2] for m in measurements]) #speed
d_hat = array([m[0] for m in damaged])
y1_hat = array([m[1] for m in damaged])
y2_hat = array([m[2] for m in damaged])
d_time = []
for i in range (len(d)):
d_act = datetime.datetime.fromtimestamp(d[i])
d_time.append(d_act)
d_time_hat = []
# License: BSD 3 clause
from windml.datasets.nrel import NREL
from windml.visualization.plot_timeseries import plot_timeseries
from windml.preprocessing.preprocessing import destroy
import matplotlib.pyplot as plt
import matplotlib.dates as md
from pylab import *
from numpy import array
ds = NREL()
turbine = ds.get_turbine(NREL.park_id['tehachapi'], 2004)
measurements = turbine.get_measurements()[:1000]
damaged, indices = destroy(measurements, method='mar', percentage=.80)
d = array([m[0] for m in measurements])
y1 = array([m[1] for m in measurements]) #score
y2 = array([m[2] for m in measurements]) #speed
d_hat = array([m[0] for m in damaged])
y1_hat = array([m[1] for m in damaged])
y2_hat = array([m[2] for m in damaged])
d_time = []
for i in range(len(d)):
d_act = datetime.datetime.fromtimestamp(d[i])
d_time.append(d_act)
d_time_hat = []