def get_windpark(self, target_idx, radius):
"""
This method fetches and returns a windpark from AEMO, which consists of
the target turbine with the given target_idx and the surrounding turbine
within a given radius 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.
Returns
-------
Windpark
An according windpark for target id, radius.
"""
self.check_availability(target_idx=target_idx)
result = Windpark(target_idx, radius)
target_turbine = self.get_turbine(target_idx)
lat_target = radians(target_turbine.latitude)
lon_target = radians(target_turbine.longitude)
turbines = self.get_all_turbines()
Earth_Radius = 6371
for turbine in turbines:
# because we append the target as last element
if(turbine.idx == target_idx):
continue
lat_act = radians(turbine.latitude)
lon_act = radians(turbine.longitude)
dLat = (lat_act - lat_target)
dLon = (lon_act - lon_target)
# Haversine formula:
a = sin(dLat/2) * sin(dLat/2) + cos(lat_target) *\
cos(lat_act) * sin(dLon/2) * sin(dLon/2)
c = 2 * atan2(sqrt(a), sqrt(1-a))
distance_act = Earth_Radius * c;
if(distance_act < radius):
result.add_turbine(turbine)
result.add_turbine(target_turbine)
return result
def get_windpark(self, target_idx, radius):
"""
This method fetches and returns a windpark from AEMO, which consists of
the target turbine with the given target_idx and the surrounding turbine
within a given radius 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.
Returns
-------
Windpark
An according windpark for target id, radius.
"""
self.check_availability(target_idx=target_idx)
result = Windpark(target_idx, radius)
target_turbine = self.get_turbine(target_idx)
lat_target = radians(target_turbine.latitude)
lon_target = radians(target_turbine.longitude)
turbines = self.get_all_turbines()
Earth_Radius = 6371
for turbine in turbines:
# because we append the target as last element
if (turbine.idx == target_idx):
continue
lat_act = radians(turbine.latitude)
lon_act = radians(turbine.longitude)
dLat = (lat_act - lat_target)
dLon = (lon_act - lon_target)
# Haversine formula:
a = sin(dLat/2) * sin(dLat/2) + cos(lat_target) *\
cos(lat_act) * sin(dLon/2) * sin(dLon/2)
c = 2 * atan2(sqrt(a), sqrt(1 - a))
distance_act = Earth_Radius * c
if (distance_act < radius):
result.add_turbine(turbine)
result.add_turbine(target_turbine)
return result
def get_windpark(self, target_idx, radius, 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 turbine
within a given radius 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, radius, and time span.
"""
#if only one year is desired
if year_to == 0:
year_to = year_from
result = Windpark(target_idx, radius)
# determine the coordinates of the target
target = self.fetch_nrel_meta_data(target_idx)
Earth_Radius = 6371
lat_target = math.radians(np.float64(target[1]))
lon_target = math.radians(np.float64(target[2]))
rel_input_lat = []
rel_input_lon = []
#fetch all turbines
turbines = self.fetch_nrel_meta_data_all()
for row in turbines:
turbine_index = np.int(row[0])
if (turbine_index != target_idx):
lat_act = math.radians(np.float64(row[1]))
lon_act = math.radians(np.float64(row[2]))
dLat = (lat_act - lat_target)
dLon = (lon_act - lon_target)
# Haversine formula:
a = math.sin(dLat / 2) * math.sin(dLat / 2) + math.cos(
lat_target) * math.cos(lat_act) * math.sin(
dLon / 2) * math.sin(dLon / 2)
c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
distance_act = Earth_Radius * c
if (distance_act < radius):
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 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
year_from, year_to = 2004, 2006
if sys.version_info < (3, ):
mode = 'rU'
else:
mode = 'r'
csvfile = 'cluster/' + method[methodnum] + '200.csv'
pick = []
with open(csvfile, mode) as csv_arch:
reader = csv.reader(csv_arch, delimiter=',')
for row in reader:
pick.append(int(row[0]))
nrel = NREL()
d = 0
turbines = nrel.fetch_nrel_meta_data_all()
result = Windpark(target_idx, 0)
for row in turbines:
turbine_index = np.int(row[0])
if (turbine_index != target_idx):
if (pick[turbine_index - 1] == pick[target_idx - 1]):
d = d + 1
newturbine = Turbine(row[0], row[1], row[2], row[3], row[4],
row[5], row[6])
for y in range(year_from, year_to + 1):
measurement = nrel.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)
def get_windpark(self, target_idx, radius, year_from=0, year_to=0):
"""This method fetches and returns a windpark from NREL, which consists of
the target mill with the given target_idx and the surrounding wind mill
within a given radius around the target mill. When called, the wind
measurements for a given range of years are downloaded for every mill
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, radius, and time span.
"""
#if only one year is desired
if year_to==0:
year_to=year_from
result = Windpark(target_idx, radius)
# determine the coordinates of the target
target=self.fetch_nrel_meta_data(target_idx)
Earth_Radius = 6371
lat_target = math.radians(np.float64(target[1]))
lon_target = math.radians(np.float64(target[2]))
rel_input_lat = []
rel_input_lon = []
#fetch all mills
mills = self.fetch_nrel_meta_data_all()
for row in mills:
mill_index = np.int(row[0])
if (mill_index != target_idx):
lat_act = math.radians(np.float64(row[1])) # todo "latitude" instead of 1
lon_act = math.radians(np.float64(row[2]))
dLat = (lat_act-lat_target)
dLon = (lon_act-lon_target)
# Haversine formula:
a = math.sin(dLat/2) * math.sin(dLat/2) + math.cos(lat_target) * math.cos(lat_act) * math.sin(dLon/2) * math.sin(dLon/2)
c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))
distance_act = Earth_Radius * c;
if (distance_act < radius):
newmill = Windmill(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))
newmill.add_measurements(measurements)
result.add_windmill(newmill)
#add target mill as last element
newmill = Windmill(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))
newmill.add_measurements(measurements)
result.add_windmill(newmill)
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
