def interpolate(self, timeseries, **args):
cs = 'corrected_score'
sp = 'speed'
date = 'date'
timestep = args['timestep']
location = args['location']
neighbor_series = args['neighbor_series']
neighbor_locations = args['neighbor_locations']
# override missing on neighbors
lnseries = len(neighbor_series)
ov_neighbor_series = []
ovm = OverrideMissing()
for i in xrange(lnseries):
ov_series = ovm.override(neighbor_series[i], timestep, -1)
ov_neighbor_series.append(ov_series)
# find missing data on target
finder = MissingDataFinder()
new_amount = timeseries.shape[0]
misses = finder.find(timeseries, timestep)
# calucating distances
distances = []
for i in xrange(0, len(neighbor_series)):
d = haversine(location, neighbor_locations[i])
if(d == 0):
raise Exception("distance is 0.")
distances.append(d)
# index start indices
starts = {}
for start, end, amount in misses:
new_amount += amount
starts[start] = [end, amount]
# allocate new numpy array
new_mat = zeros((new_amount,),\
dtype=[('date', int32),\
('corrected_score', float32),\
('speed', float32)])
keys = starts.keys()
current_index = 0
for i in range(len(timeseries)):
if(i in keys):
# missing data starting
# add start measurement
new_mat[current_index] = timeseries[i]
current_index += 1
end, n = starts[i]
w_hat_k = {}
for j in range(1, n + 1):
candidates = []
sum_of_w_hat = 0
sum_of_distances = 0
# search for candidates with no missing data
for k in xrange(len(ov_neighbor_series)):
nseries = ov_neighbor_series[k]
if(nseries[i + j][cs] != -1):
candidates.append(k)
sum_of_distances += distances[k]
# if no candidates available copy old data
if(len(candidates) == 0):
y = timeseries[i][cs]
new_timestep = timeseries[i][d] + j * timestep
new_mat[current_index] = (new_timestep, y, nan)
current_index += 1
else:
# calculate weight and sum, for later use in
# anti-proportional
for k in candidates:
w_hat_k[k] = 1.0 / (distances[k] / sum_of_distances)
sum_of_w_hat += w_hat_k[k]
# calculation of label
y = 0
ws = 0
for k in candidates:
# w_k is anti-proportional
w_k = w_hat_k[k] / sum_of_w_hat
y_k = w_k * ov_neighbor_series[k][i + j][cs]
ws_k = w_k * ov_neighbor_series[k][i + j][sp]
y += y_k
ws += ws_k
new_timestep = timeseries[i][date] + j * timestep
new_mat[current_index] = (new_timestep, y, ws)
current_index += 1
else: # if not missing
new_mat[current_index] = timeseries[i]
current_index += 1
return new_mat
def interpolate(self, timeseries, **args):
cs = 'corrected_score'
sp = 'speed'
date = 'date'
timestep = args['timestep']
location = args['location']
neighbor_series = args['neighbor_series']
neighbor_locations = args['neighbor_locations']
# override missing on neighbors
lnseries = len(neighbor_series)
ov_neighbor_series = []
ovm = OverrideMissing()
for i in range(lnseries):
ov_series = ovm.override(neighbor_series[i], timestep, -1)
ov_neighbor_series.append(ov_series)
# find missing data on target
finder = MissingDataFinder()
new_amount = timeseries.shape[0]
misses = finder.find(timeseries, timestep)
# calucating distances
distances = []
for i in range(0, len(neighbor_series)):
d = haversine(location, neighbor_locations[i])
if d == 0:
raise Exception("distance is 0.")
distances.append(d)
# index start indices
starts = {}
for start, end, amount in misses:
new_amount += int(amount)
starts[start] = [int(end), int(amount)]
# allocate new numpy array
new_mat = zeros((new_amount,),\
dtype=[('date', int32),\
('corrected_score', float32),\
('speed', float32)])
keys = starts.keys()
current_index = 0
for i in range(len(timeseries)):
if i in keys:
# missing data starting
# add start measurement
new_mat[current_index] = timeseries[i]
current_index += 1
end, n = starts[i]
n = int(n)
w_hat_k = {}
for j in range(1, n + 1):
candidates = []
sum_of_w_hat = 0
sum_of_distances = 0
# search for candidates with no missing data
for k in range(len(ov_neighbor_series)):
nseries = ov_neighbor_series[k]
if(nseries[i + j][cs] != -1):
candidates.append(k)
sum_of_distances += distances[k]
# if no candidates available copy old data
if (len(candidates) == 0):
y = timeseries[i][cs]
new_timestep = timeseries[i][d] + j * timestep
new_mat[current_index] = (new_timestep, y, nan)
current_index += 1
else:
# calculate weight and sum, for later use in
# anti-proportional
for k in candidates:
w_hat_k[k] = 1.0 / (distances[k] / sum_of_distances)
sum_of_w_hat += w_hat_k[k]
# calculation of label
y = 0
ws = 0
for k in candidates:
# w_k is anti-proportional
w_k = w_hat_k[k] / sum_of_w_hat
y_k = w_k * ov_neighbor_series[k][i + j][cs]
ws_k = w_k * ov_neighbor_series[k][i + j][sp]
y += y_k
ws += ws_k
new_timestep = timeseries[i][date] + j * timestep
new_mat[current_index] = (new_timestep, y, ws)
current_index += 1
else: # if not missing
new_mat[current_index] = timeseries[i]
current_index += 1
return new_mat
def get_windpark_nearest(self, target_idx, n_nearest,\
year_from=0, year_to=0):
"""This method fetches and returns a windpark from NREL, which consists
of the target turbine with the given target_idx and the surrounding
n-nearest turbines around the target turbine. When called, the wind
measurements for a given range of years are downloaded for every
turbine in the park.
Parameters
----------
target_idx : int
see windml.datasets.nrel.park_id for example ids.
year_from : int
2004 - 2006
year_to : int
2004 - 2006
Returns
-------
Windpark
An according windpark for target id, n-nearest, and time span.
"""
#if only one year is desired
if year_to == 0:
year_to = year_from
meta = self.fetch_nrel_meta_data_all()
target = self.fetch_nrel_meta_data(target_idx)
tlat, tlon = target[1], target[2]
marked = []
nearest = []
distances = []
for i in xrange(n_nearest):
smallest = None
for t in xrange(meta.shape[0]):
d = haversine((tlat, tlon), (meta[t][1], meta[t][2]))
if (smallest == None and t != target_idx - 1
and t not in marked):
smallest = t
smallest_d = d
else:
if (d <= smallest_d and t != target_idx - 1
and t not in marked):
smallest = t
smallest_d = d
marked.append(smallest)
nearest.append(meta[smallest])
distances.append(smallest_d)
result = Windpark(target_idx, distances[-1])
for row in nearest:
newturbine = Turbine(row[0], row[1] , row[2] , row[3] , row[4],\
row[5], row[6])
if year_from != 0:
for y in range(year_from, year_to + 1):
measurement = self.fetch_nrel_data(row[0], y,\
['date','corrected_score','speed'])
if y == year_from:
measurements = measurement
else:
measurements = np.concatenate(
(measurements, measurement))
newturbine.add_measurements(measurements)
result.add_turbine(newturbine)
#add target turbine as last element
newturbine = Turbine(target[0], target[1] , target[2] , target[3],\
target[4] , target[5], target[6])
if year_from != 0:
for y in range(year_from, year_to + 1):
measurement = self.fetch_nrel_data(target[0], y,\
['date','corrected_score','speed'])
if y == year_from:
measurements = measurement
else:
measurements = np.concatenate((measurements, measurement))
newturbine.add_measurements(measurements)
result.add_turbine(newturbine)
return result
def get_windpark_nearest(self, target_idx, n_nearest,\
year_from=0, year_to=0):
"""This method fetches and returns a windpark from NREL, which consists
of the target turbine with the given target_idx and the surrounding
n-nearest turbines around the target turbine. When called, the wind
measurements for a given range of years are downloaded for every
turbine in the park.
Parameters
----------
target_idx : int
see windml.datasets.nrel.park_id for example ids.
year_from : int
2004 - 2006
year_to : int
2004 - 2006
Returns
-------
Windpark
An according windpark for target id, n-nearest, and time span.
"""
#if only one year is desired
if year_to == 0:
year_to=year_from
meta = self.fetch_nrel_meta_data_all()
target = self.fetch_nrel_meta_data(target_idx)
tlat, tlon = target[1], target[2]
marked = []
nearest = []
distances = []
for i in range(n_nearest):
smallest = None
for t in range(meta.shape[0]):
d = haversine((tlat, tlon), (meta[t][1], meta[t][2]))
if(smallest == None and t != target_idx - 1 and t not in marked):
smallest = t
smallest_d = d
else:
if(d <= smallest_d and t != target_idx - 1 and t not in marked):
smallest = t
smallest_d = d
marked.append(smallest)
nearest.append(meta[smallest])
distances.append(smallest_d)
result = Windpark(target_idx, distances[-1])
for row in nearest:
newturbine = Turbine(row[0], row[1] , row[2] , row[3] , row[4],\
row[5], row[6])
if year_from != 0:
for y in range(year_from, year_to+1):
measurement = self.fetch_nrel_data(row[0], y,\
['date','corrected_score','speed'])
if y==year_from:
measurements = measurement
else:
measurements = np.concatenate((measurements, measurement))
newturbine.add_measurements(measurements)
result.add_turbine(newturbine)
#add target turbine as last element
newturbine = Turbine(target[0], target[1] , target[2] , target[3],\
target[4] , target[5], target[6])
if year_from != 0:
for y in range(year_from, year_to+1):
measurement = self.fetch_nrel_data(target[0], y,\
['date','corrected_score','speed'])
if y == year_from:
measurements = measurement
else:
measurements = np.concatenate((measurements, measurement))
newturbine.add_measurements(measurements)
result.add_turbine(newturbine)
return result
