def setUpClass(cls):
ds = NREL()
cls.turbine = ds.get_turbine(NREL.park_id['tehachapi'], 2004, 2005)
cls.windpark = ds.get_windpark(NREL.park_id['tehachapi'], 3, 2004,
2005)
cls.pmapping = PowerMapping()
cls.pdmapping = PowerDiffMapping()
def test_nrel_repair(self):
ds = NREL()
target = ds.get_turbine(NREL.park_id['tehachapi'], 2005)
measurements = target.get_measurements()[:43504]
measurements = NRELRepair().repair(measurements)
assert(NRELRepair().validate(measurements))
""" Histogram of Wind Speeds ------------------------------------------------------------- Histograms of wind speeds of a turbine near Cheyenne in the year 2004. """ # Author: Jendrik Poloczek <*****@*****.**> # License: BSD 3 clause import matplotlib.pyplot as plt from pylab import plt from windml.datasets.nrel import NREL ds = NREL() turbine = ds.get_turbine(NREL.park_id['cheyenne'], 2004) speeds = list(map(lambda x : x[2], turbine.measurements)) plt.hist(speeds, color="#c4d8eb", bins=10, normed = 1) plt.show()
""" Histogram of Wind Speeds ------------------------------------------------------------- Histograms of wind speeds of a turbine near Cheyenne in the year 2004. """ # Author: Jendrik Poloczek <*****@*****.**> # License: BSD 3 clause import matplotlib.pyplot as plt from pylab import plt from windml.datasets.nrel import NREL ds = NREL() turbine = ds.get_turbine(NREL.park_id['cheyenne'], 2004) speeds = list(map(lambda x: x[2], turbine.measurements)) plt.hist(speeds, color="#c4d8eb", bins=10, normed=1) plt.show()
This examples shows the topology of a turbine and gives a statistical overview
for different characteristics of its time series.
"""
# Author: Oliver Kramer <*****@*****.**>
# License: BSD 3 clause
import matplotlib.pyplot as plt
import numpy as np
import windml.util.features
from windml.datasets.nrel import NREL
from windml.visualization.show_coord_topo_turbine import show_coord_topo_turbine
ds = NREL()
turbine = ds.get_turbine(NREL.park_id['tehachapi'], 2004)
feat, month_power, ramps_up, ramps_down, power_freq = windml.util.features.compute_highlevel_features(
turbine)
month = [
'jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct',
'nov', 'dec'
]
figure = plt.figure(figsize=(15, 10))
# plot 1
plot1 = plt.subplot(2, 2, 1)
plt.title("Turbine Location")
show_coord_topo_turbine(turbine, show=False)
This examples shows the topology of a turbine and gives a statistical overview
for different characteristics of its time series.
'''
# Author: Oliver Kramer <*****@*****.**>
# License: BSD 3 clause
import matplotlib.pyplot as plt
import numpy as np
import windml.util.features
from windml.datasets.nrel import NREL
from windml.visualization.show_coord_topo_turbine import show_coord_topo_turbine
ds = NREL()
turbine = ds.get_turbine(NREL.park_id['tehachapi'], 2004)
feat, month_power, ramps_up, ramps_down, power_freq = windml.util.features.compute_highlevel_features(
turbine)
month = ['jan', 'feb', 'mar', 'apr', 'may', 'jun',
'jul', 'aug', 'sep', 'oct', 'nov', 'dec']
figure = plt.figure(figsize=(15, 10))
# plot 1
plot1 = plt.subplot(2, 2, 1)
plt.title('Turbine Location')
show_coord_topo_turbine(turbine, show=False)
# plot 2
plot2 = plt.subplot(2, 2, 2)
import matplotlib.pylab as plt import datetime, time import numpy as np from numpy import array, matrix from sklearn.model_selection import GridSearchCV from sklearn.model_selection import KFold from sklearn import __version__ as sklearn_version from sklearn.svm import SVR from sklearn.neighbors import KNeighborsRegressor from windml.datasets.nrel import NREL from windml.visualization.plot_response_curve import plot_response_curve ds = NREL() turbine = ds.get_turbine(NREL.park_id['palmsprings'], 2004, 2006) timeseries = turbine.get_measurements() max_speed = 40 skip = 1 # plot true values as blue points speed = [m[2] for m in timeseries[::skip]] score = [m[1] for m in timeseries[::skip]] # Second Plot: KNN-Interpolation # Built patterns und labels X_train = speed[0:len(speed):1] Y_train = score[0:len(score):1] X_train_array = array([[element] for element in X_train]) # initialize KNN regressor from sklearn.
import datetime, time import numpy as np from numpy import array, matrix from sklearn.grid_search import GridSearchCV from sklearn.cross_validation import KFold from sklearn import __version__ as sklearn_version from sklearn.svm import SVR from sklearn.neighbors import KNeighborsRegressor from windml.datasets.nrel import NREL from windml.visualization.plot_response_curve import plot_response_curve ds = NREL() turbine = ds.get_turbine(NREL.park_id['palmsprings'], 2004, 2006) timeseries = turbine.get_measurements() max_speed = 40 skip = 1 # plot true values as blue points speed = [m[2] for m in timeseries[::skip]] score = [m[1] for m in timeseries[::skip]] # Second Plot: KNN-Interpolation # Built patterns und labels X_train = speed[0:len(speed):1] Y_train = score[0:len(score):1] X_train_array = array([[element] for element in X_train])
def test_get_turbine(self):
ds = NREL()
target = ds.get_turbine(NREL.park_id['tehachapi'], 2004, 2005)
t = target.get_measurements()[0]
assert (len(t) == 3)
def setUpClass(cls):
ds = NREL()
cls.turbine = ds.get_turbine(NREL.park_id['tehachapi'], 2004)
cls.windpark = ds.get_windpark(NREL.park_id['tehachapi'], 3, 2004)
def test_nrel_repair(self):
ds = NREL()
target = ds.get_turbine(NREL.park_id['tehachapi'], 2004)
measurements = target.get_measurements()[:43504]
measurements = NRELRepair().repair(measurements)
assert(NRELRepair().validate(measurements))
def test_get_turbine(self):
ds = NREL()
target = ds.get_turbine(NREL.park_id['tehachapi'], 2004, 2005)
t = target.get_measurements()[0]
assert(len(t) == 3)
def setUpClass(cls):
ds = NREL()
cls.turbine = ds.get_turbine(NREL.park_id['tehachapi'], 2004)
cls.windpark = ds.get_windpark(NREL.park_id['tehachapi'], 3, 2004)
def setUpClass(cls):
ds = NREL()
cls.turbine = ds.get_turbine(NREL.park_id['tehachapi'], 2004, 2005)
cls.windpark = ds.get_windpark(NREL.park_id['tehachapi'], 3, 2004, 2005)
cls.pmapping = PowerMapping()
cls.pdmapping = PowerDiffMapping()
