def predict(self, x, y) -> wf.WindDataFrame:
r = np.ndarray(shape=(2, len(x.values)))
r[0, :] = x.values
r[1, :] = y.values
f = np.real(self.series.eval(r))
return tools.create_wind_data_frame(x, y, f[0, :], f[1, :])
def predict(self, x, y) -> wf.WindDataFrame:
############
frame = self.lookup_data
frame = frame[['x', 'y', 'altitude']].drop_duplicates()
l1 = len(frame.index)
x_ref = np.array(frame['x']).reshape((l1, 1))
y_ref = np.array(frame['y']).reshape((l1, 1))
def lookup(x, y):
# Copied code from nn_windfield
l2 = len(x)
x_m = np.array(x).reshape((1, l2))
y_m = np.array(y).reshape((1, l2))
d_x = np.repeat(x_ref, l2, axis=1) - np.repeat(x_m, l1, axis=0)
d_y = np.repeat(y_ref, l2, axis=1) - np.repeat(y_m, l1, axis=0)
distance = d_x**2.0 + d_y**2.0
nearest = np.argmin(distance, axis=0)
return frame.iloc[nearest]['altitude'].values
self.altitude_lookup = lookup
#############
df = pd.DataFrame({'x': x, 'y': y})
df['z'] = self.altitude_lookup(df.x, df.y)
uv = self.rf.predict(X=self.pol.fit_transform(df[['x', 'y', 'z']]))
frame = tools.create_wind_data_frame(df.x, df.y, uv[:, 0], uv[:, 1])
frame['altitude'] = df.z
return frame
def predict(self, x, y) -> wf.WindDataFrame:
df = pd.DataFrame({'x': x, 'y': y})
df['z'] = self.altitude_lookup(df.x, df.y)
uv = self.rf.predict(X=self.pol.fit_transform(df[['x', 'y', 'z']]))
frame = tools.create_wind_data_frame(df.x, df.y, uv[:, 0], uv[:, 1])
frame['altitude'] = df.z
return frame
def predict(self, x, y) -> wf.WindDataFrame:
if self.elevation is True:
h = self.altitude_lookup(x, y)
coordinates = np.hstack((x[:, None], y[:, None], h[:, None]))
else:
coordinates = np.hstack((x[:, None], y[:, None]))
predicted_wind_vectors = self.MLP.predict(
self.scaler_data.transform(coordinates))
u = predicted_wind_vectors[:, 0]
v = predicted_wind_vectors[:, 1]
return tools.create_wind_data_frame(x, y, np.real(u), np.real(v))
def predict(self, x, y) -> wf.WindDataFrame:
def calculate_u(x, y):
weight = 1 / ((np.abs(self.x - x))**2 + np.abs((self.y - y))**2)
total_weight = np.sum(weight)
return np.sum(self.u * weight) / total_weight
def calculate_v(x, y):
weight = 1 / ((self.x - x)**2 + (self.y - y)**2)
total_weight = np.sum(weight)
return np.sum(self.v * weight) / total_weight
u = np.array([calculate_u(a, b) for (a, b) in zip(x, y)])
v = np.array([calculate_v(a, b) for (a, b) in zip(x, y)])
return tools.create_wind_data_frame(x, y, u, v)
def predict(self, x, y) -> wf.WindDataFrame:
l2 = len(x)
x_m = np.array(x).reshape((1, l2))
y_m = np.array(y).reshape((1, l2))
d_x = np.repeat(self.x, l2, axis=1) - np.repeat(x_m, self.l, axis=0)
d_y = np.repeat(self.y, l2, axis=1) - np.repeat(y_m, self.l, axis=0)
distance = d_x ** 2.0 + d_y ** 2.0
nearest = np.argmin(distance, axis=0)
u = self.u[nearest]
v = self.v[nearest]
return tools.create_wind_data_frame(x, y, u, v)
def predict(self, x, y) -> wf.WindDataFrame:
############
frame = self.lookup_data
frame = frame[['x', 'y', 'altitude']].drop_duplicates()
l1 = len(frame.index)
x_ref = np.array(frame['x']).reshape((l1, 1))
y_ref = np.array(frame['y']).reshape((l1, 1))
def lookup(x, y):
# Copied code from nn_windfield
l2 = len(x)
x_m = np.array(x).reshape((1, l2))
y_m = np.array(y).reshape((1, l2))
d_x = np.repeat(x_ref, l2, axis=1) - np.repeat(x_m, l1, axis=0)
d_y = np.repeat(y_ref, l2, axis=1) - np.repeat(y_m, l1, axis=0)
distance = d_x**2.0 + d_y**2.0
nearest = np.argmin(distance, axis=0)
return frame.iloc[nearest]['altitude'].values
self.altitude_lookup = lookup
#############
if self.elevation is True:
h = self.altitude_lookup(x, y)
coordinates = np.hstack((x[:, None], y[:, None], h[:, None]))
else:
coordinates = np.hstack((x[:, None], y[:, None]))
predicted_wind_vectors = self.MLP.predict(
self.scaler_data.transform(coordinates))
u = predicted_wind_vectors[:, 0]
v = predicted_wind_vectors[:, 1]
return tools.create_wind_data_frame(x, y, np.real(u), np.real(v))
def predict(self, x, y) -> wf.WindDataFrame:
wdf = tools.create_wind_data_frame(x, y, np.zeros(len(x)), np.zeros(len(x)))
wdf[['u', 'v']] = sum([w*wf.predict(x, y)[['u','v']] for w, wf in zip(self.weights, self.windfields)])
return wdf
def predict(self, x, y) -> wf.WindDataFrame:
return tools.create_wind_data_frame(x, y, np.zeros_like(x),
np.zeros_like(y))
def predict(self, x, y) -> wf.WindDataFrame:
u, _ = self.u_kriging.execute(style='points', xpoints=x, ypoints=y)
v, _ = self.v_kriging.execute(style='points', xpoints=x, ypoints=y)
pred = create_wind_data_frame(x, y, u, v)
return pred
