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Check for input nans
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@paulf81
paulf81 committed Oct 21, 2024
1 parent d6fddb3 commit f47b22d
Showing 1 changed file with 51 additions and 31 deletions.
82 changes: 51 additions & 31 deletions floris/wind_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -174,6 +174,13 @@ def __init__(
if not isinstance(wind_speeds, np.ndarray):
raise TypeError("wind_speeds must be a NumPy array")

# Confirm that none of wind_directions or wind_speeds contain NaN values
if np.isnan(wind_directions).any():
raise ValueError("wind_directions contains NaN values")

if np.isnan(wind_speeds).any():
raise ValueError("wind_speeds contains NaN values")

# Confirm that both wind_directions and wind_speeds are monitonically
# increasing and evenly spaced
if len(wind_directions) > 1:
Expand Down Expand Up @@ -589,15 +596,17 @@ def upsample(self, wd_step=None, ws_step=None, method="linear", inplace=False):
# This is the case when wind directions doesn't cover the full range of possible
# degrees (0-360)
if np.abs((wd_range_min_current % 360.0) - (wd_range_max_current % 360.0)) > 1e-6:
wind_direction_column = np.concatenate((
np.array([wd_range_min_current]),
wind_direction_column,
np.array([wd_range_max_current])
))
ti_matrix = ti_matrix = np.vstack((ti_matrix[0, :], ti_matrix, ti_matrix[-1,:]))
freq_matrix = np.vstack((freq_matrix[0, :], freq_matrix, freq_matrix[-1,:]))
wind_direction_column = np.concatenate(
(
np.array([wd_range_min_current]),
wind_direction_column,
np.array([wd_range_max_current]),
)
)
ti_matrix = ti_matrix = np.vstack((ti_matrix[0, :], ti_matrix, ti_matrix[-1, :]))
freq_matrix = np.vstack((freq_matrix[0, :], freq_matrix, freq_matrix[-1, :]))
if self.value_table is not None:
value_matrix = np.vstack((value_matrix[0, :], value_matrix, value_matrix[-1,:]))
value_matrix = np.vstack((value_matrix[0, :], value_matrix, value_matrix[-1, :]))

# In the alternative case, where the wind directions cover the full range
# ie, 0, 10, 20 30, ...350, then need to place 0 at 360 and 350 at -10
Expand All @@ -620,11 +629,7 @@ def upsample(self, wd_step=None, ws_step=None, method="linear", inplace=False):

# Pad out the wind speeds
wind_speed_column = np.concatenate(
(
np.array([ws_range_min_current]),
wind_speed_column,
np.array([ws_range_max_current])
)
(np.array([ws_range_min_current]), wind_speed_column, np.array([ws_range_max_current]))
)
ti_matrix = np.hstack(
(ti_matrix[:, 0].reshape((-1, 1)), ti_matrix, ti_matrix[:, -1].reshape((-1, 1)))
Expand All @@ -637,7 +642,7 @@ def upsample(self, wd_step=None, ws_step=None, method="linear", inplace=False):
(
value_matrix[:, 0].reshape((-1, 1)),
value_matrix,
value_matrix[:, -1].reshape((-1, 1))
value_matrix[:, -1].reshape((-1, 1)),
)
)

Expand Down Expand Up @@ -1121,6 +1126,14 @@ def __init__(
if not isinstance(turbulence_intensities, np.ndarray):
raise TypeError("turbulence_intensities must be a NumPy array")

# Check that wind_directions, wind_speeds, and turbulence_intensities do not contain NaNs
if np.isnan(wind_directions).any():
raise ValueError("wind_directions must not contain NaNs")
if np.isnan(wind_speeds).any():
raise ValueError("wind_speeds must not contain NaNs")
if np.isnan(turbulence_intensities).any():
raise ValueError("turbulence_intensities must not contain NaNs")

# Confirm that both wind_directions and wind_speeds
# and turbulence intensities are monotonically
# increasing and evenly spaced
Expand Down Expand Up @@ -1579,21 +1592,21 @@ def upsample(self, wd_step=None, ws_step=None, ti_step=None, method="linear", in
(
np.array([wd_range_min_current]),
wind_direction_column,
np.array([wd_range_max_current])
np.array([wd_range_max_current]),
)
)
freq_matrix = np.concatenate(
(freq_matrix[0, :, :][None, :, :], freq_matrix, freq_matrix[-1, :, :][None, :, :]),
axis=0
axis=0,
)
if self.value_table is not None:
value_matrix = np.concatenate(
(
value_matrix[0, :, :][None, :, :],
value_matrix,
value_matrix[-1, :, :][None, :, :]
value_matrix[-1, :, :][None, :, :],
),
axis=0
axis=0,
)

# In the alternative case, where the wind directions cover the full range
Expand Down Expand Up @@ -1624,46 +1637,42 @@ def upsample(self, wd_step=None, ws_step=None, ti_step=None, method="linear", in

# Pad out the wind speeds
wind_speed_column = np.concatenate(
(
np.array([ws_range_min_current]),
wind_speed_column,
np.array([ws_range_max_current])
)
(np.array([ws_range_min_current]), wind_speed_column, np.array([ws_range_max_current]))
)
freq_matrix = np.concatenate(
(freq_matrix[:, 0, :][:, None, :], freq_matrix, freq_matrix[:, -1, :][:, None, :]),
axis=1
axis=1,
)
if self.value_table is not None:
value_matrix = np.concatenate(
(
value_matrix[:, 0, :][:, None, :],
value_matrix,
value_matrix[:, -1, :][:, None, :]
value_matrix[:, -1, :][:, None, :],
),
axis=1
axis=1,
)

# Pad out the turbulence intensities
turbulence_intensity_column = np.concatenate(
(
np.array([ti_range_min_current]),
turbulence_intensity_column,
np.array([ti_range_max_current])
np.array([ti_range_max_current]),
)
)
freq_matrix = np.concatenate(
(freq_matrix[:, :, 0][:, :, None], freq_matrix, freq_matrix[:, :, -1][:, :, None]),
axis=2
axis=2,
)
if self.value_table is not None:
value_matrix = np.concatenate(
(
value_matrix[:, :, 0][:, :, None],
value_matrix,
value_matrix[:, :, -1][:, :, None]
value_matrix[:, :, -1][:, :, None],
),
axis=2
axis=2,
)

# Grid wind directions, wind speeds and turbulence intensities to match the
Expand Down Expand Up @@ -2178,6 +2187,17 @@ def __init__(
if len(wind_directions) != len(values):
raise ValueError("wind_directions and values must be the same length")

# Confirm that none of wind_directions, wind_speeds, turbulence_intensitiess and
# values contain NaNs
if np.isnan(wind_directions).any():
raise ValueError("wind_directions must not contain NaNs")
if np.isnan(wind_speeds).any():
raise ValueError("wind_speeds must not contain NaNs")
if np.isnan(turbulence_intensities).any():
raise ValueError("turbulence_intensities must not contain NaNs")
if values is not None and np.isnan(values).any():
raise ValueError("values must not contain NaNs")

self.wind_directions = wind_directions
self.wind_speeds = wind_speeds
self.turbulence_intensities = turbulence_intensities
Expand Down Expand Up @@ -2985,7 +3005,7 @@ def _generate_wind_speed_frequencies_from_weibull(self, A, k, wind_speeds=None):
wind_speeds = self.wind_speeds
ws_steps = np.diff(wind_speeds)
if not np.all(np.isclose(ws_steps, ws_steps[0])):
raise ValueError("wind_speeds must be equally spaced.")
raise ValueError("wind_speeds must be equally spaced.")
else:
ws_step = ws_steps[0]

Expand Down

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