本文整理汇总了Python中windml.datasets.nrel.NREL.get_turbines方法的典型用法代码示例。如果您正苦于以下问题:Python NREL.get_turbines方法的具体用法?Python NREL.get_turbines怎么用?Python NREL.get_turbines使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类windml.datasets.nrel.NREL
的用法示例。
在下文中一共展示了NREL.get_turbines方法的4个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_mreg_interpolation_multi
# 需要导入模块: from windml.datasets.nrel import NREL [as 别名]
# 或者: from windml.datasets.nrel.NREL import get_turbines [as 别名]
def test_mreg_interpolation_multi(self):
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 3, 2004)
target = windpark.get_target()
timestep = 600
measurements = target.get_measurements()[300:350]
damaged, indices = MARDestroyer().destroy(measurements, percentage=.50)
before_misses = MissingDataFinder().find(damaged, timestep)
neighbors = windpark.get_turbines()[:-1]
count_neighbors = len(neighbors)
reg = 'knn' # KNeighborsRegressor(10, 'uniform')
regargs = {'n' : 10, 'variant' : 'uniform'}
processed = 0
missed = {k : count_neighbors for k in indices}
exclude = []
damaged_nseries = []
for neighbor in neighbors:
nseries = neighbor.get_measurements()[300:350]
damaged, indices = MARDestroyer().destroy(nseries, percentage=.50, exclude=exclude)
for index in indices:
if(index not in missed.keys()):
missed[index] = count_neighbors
missed[index] -= 1
if(missed[index] == 1):
exclude.append(index) # exclude in next iterations
damaged_nseries.append(damaged)
t_hat = MRegInterpolation().interpolate(damaged, timestep=timestep,\
neighbor_series=damaged_nseries, reg=reg, regargs=regargs)
after_misses = MissingDataFinder().find(t_hat, timestep)
assert(len(after_misses) < 1)
示例2: test_mreg_interpolation
# 需要导入模块: from windml.datasets.nrel import NREL [as 别名]
# 或者: from windml.datasets.nrel.NREL import get_turbines [as 别名]
def test_mreg_interpolation(self):
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 3, 2004)
target = windpark.get_target()
timestep = 600
measurements = target.get_measurements()[300:500]
damaged, indices = MARDestroyer().destroy(measurements, percentage=.50)
before_misses = MissingDataFinder().find(damaged, timestep)
neighbors = windpark.get_turbines()[:-1]
reg = 'knn' # KNeighborsRegressor(10, 'uniform')
regargs = {'n' : 10, 'variant' : 'uniform'}
nseries = [t.get_measurements()[300:500] for t in neighbors]
t_hat = MRegInterpolation().interpolate(damaged, timestep=timestep,\
neighbor_series=nseries, reg=reg, regargs=regargs)
after_misses = MissingDataFinder().find(t_hat, timestep)
assert(len(after_misses) < 1)
示例3: test_topological_interpolation
# 需要导入模块: from windml.datasets.nrel import NREL [as 别名]
# 或者: from windml.datasets.nrel.NREL import get_turbines [as 别名]
def test_topological_interpolation(self):
park_id = NREL.park_id['tehachapi']
windpark = NREL().get_windpark(park_id, 10, 2004)
target = windpark.get_target()
timestep = 600
measurements = target.get_measurements()[300:500]
damaged, indices = NMARDestroyer().destroy(measurements, percentage=.80,\
min_length=10, max_length=100)
tloc = (target.longitude, target.latitude)
neighbors = windpark.get_turbines()[:-1]
nseries = [t.get_measurements()[300:500] for t in neighbors]
nlocs = [(t.longitude, t.latitude) for t in neighbors]
t_hat = TopologicInterpolation().interpolate(\
damaged, method="topologic",\
timestep=timestep, location=tloc,\
neighbor_series = nseries,\
neighbor_locations = nlocs)
misses = MissingDataFinder().find(t_hat, timestep)
assert(measurements.shape[0] == t_hat.shape[0])
assert(len(misses) < 1)
示例4: NREL
# 需要导入模块: from windml.datasets.nrel import NREL [as 别名]
# 或者: from windml.datasets.nrel.NREL import get_turbines [as 别名]
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])
y2_hat = array([m[2] for m in damaged])