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visible_predictors.py
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visible_predictors.py
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from angles_geom import get_likelihood_variable_cos_zen
from get_data import compute_variability
from get_data import mask_channels
from static_tests import dawn_day_test, sea_coasts_cloud_test
from utils import *
def visible_outputs(
times,
latitudes,
longitudes,
is_land,
content_visible,
satellite_step,
slot_step,
output_level="abstract",
gray_scale=False,
):
content_visible, mask_input = mask_channels(content_visible)
if output_level == "channel":
if not gray_scale:
return content_visible
else:
nb_channels = np.shape(content_visible)[-1]
for chan in range(nb_channels):
content_visible[:, :, :, chan] = normalize(
content_visible[:, :, :, chan], mask_input, "gray-scale"
)
return np.asarray(content_visible, dtype=np.uint8)
elif output_level == "ndsi":
zen, vis, ndsi = get_zen_vis_ndsi(times, latitudes, longitudes, content_visible)
return zen, vis, ndsi, mask_input
else:
zen, vis, ndsi = get_zen_vis_ndsi(times, latitudes, longitudes, content_visible)
del content_visible
return visible_abstract_predictors(
zen, is_land, vis, mask_input, ndsi, satellite_step, slot_step, gray_scale
)
def visible_abstract_predictors(
zen, is_land, vis, mask_input, ndsi, satellite_step, slot_step, gray_scale
):
mask_output = ~dawn_day_test(zen) | mask_input | ~is_land
(nb_slots, nb_latitudes, nb_longitudes) = np.shape(vis)
nb_features = (
4 # snow index, negative variability, positive variability, cloudy sea
)
if not gray_scale:
array_indexes = np.empty(
shape=(nb_slots, nb_latitudes, nb_longitudes, nb_features)
)
array_indexes[:, :, :, 3] = sea_coasts_cloud_test(zen, is_land, vis)
array_indexes[:, :, :, 1] = get_bright_negative_variability_5d(
ndsi, mask_output, satellite_step, slot_step
)
array_indexes[:, :, :, 2] = get_bright_positive_variability_5d(
ndsi, mask_output, satellite_step, slot_step
)
ndsi[mask_output] = -10
array_indexes[:, :, :, 0] = ndsi
else:
array_indexes = np.empty(
shape=(nb_slots, nb_latitudes, nb_longitudes, nb_features), dtype=np.uint8
)
array_indexes[:, :, :, 3] = sea_coasts_cloud_test(zen, is_land, vis)
array_indexes[:, :, :, 2] = normalize(
get_bright_negative_variability_5d(
ndsi, mask_output, satellite_step, slot_step
),
mask_output,
normalization="gray-scale",
)
array_indexes[:, :, :, 2][mask_output] = 0
array_indexes[:, :, :, 1] = normalize(
get_bright_positive_variability_5d(
ndsi, mask_output, satellite_step, slot_step
),
mask_output,
normalization="gray-scale",
)
array_indexes[:, :, :, 1][mask_output] = 0
array_indexes[:, :, :, 0] = normalize(
ndsi, mask_output, normalization="gray-scale"
)
array_indexes[:, :, :, 2][mask_output] = 0
return array_indexes
def get_zen_vis_ndsi(times, latitudes, longitudes, content_visible):
"""
all useful data for static snow test (except cli)
:param times:
:param latitudes:
:param longitudes:
:param content_visible:
:return:
"""
from angles_geom import get_zenith_angle
zen = get_zenith_angle(times, latitudes, longitudes)
ndsi = get_snow_index(
vis=content_visible[:, :, :, 1],
sir=content_visible[:, :, :, 0],
zen=zen,
threshold_denominator=0.02,
index="ndsi-zenith",
)
return zen, content_visible[:, :, :, 1], ndsi
def get_snow_index(vis, sir, zen, threshold_denominator, index):
if index == "ndsi":
# sir *= 5
ndsi = (vis - sir) / np.maximum(sir + vis, threshold_denominator)
elif index == "ndsi-zenith":
ndsi = (vis - sir) / np.maximum(
sir + vis, threshold_denominator
) + 0.15 * np.square(1 - np.cos(zen))
else:
ndsi = vis / np.maximum(sir, threshold_denominator)
return ndsi
def get_bright_negative_variability_5d(
index, definition_mask, satellite_step, slot_step
):
"""
To recognize covered snow (drop of ndsi compared to normal)
NB: we loose information about the first slot (resp the last slot) if night is 1 slot longer during 1 of the 5 days
:param index:
:param definition_mask:
:param satellite_step: the satellite characteristic time step between two slots (10 minutes for Himawari 8)
:param slot_step: the chosen sampling of slots. if slot_step = n, the sampled slots are s[0], s[n], s[2*n]...
:return:
"""
nb_slots_per_day = get_nb_slots_per_day(satellite_step, slot_step)
nb_days = np.shape(index)[0] / nb_slots_per_day
to_return = np.full_like(index, -10)
if nb_days >= 2:
var_ndsi_1d_past = compute_variability(
cloud_index=index,
mask=definition_mask,
step=nb_slots_per_day,
negative_variation_only=True,
abs_value=False,
)
var_ndsi_1d_future = compute_variability(
cloud_index=index,
mask=definition_mask,
step=-nb_slots_per_day,
negative_variation_only=True,
abs_value=False,
)
if nb_days == 2:
to_return[:nb_slots_per_day] = var_ndsi_1d_future[:nb_slots_per_day]
to_return[nb_slots_per_day:] = var_ndsi_1d_past[nb_slots_per_day:]
else: # nb_days >=3
var_ndsi_2d_past = compute_variability(
cloud_index=index,
mask=definition_mask,
step=nb_slots_per_day * 2,
negative_variation_only=True,
abs_value=False,
)
var_ndsi_2d_future = compute_variability(
cloud_index=index,
mask=definition_mask,
step=-2 * nb_slots_per_day,
negative_variation_only=True,
abs_value=False,
)
# first day
to_return[:nb_slots_per_day] = np.maximum(
var_ndsi_1d_future[:nb_slots_per_day],
var_ndsi_2d_future[:nb_slots_per_day],
)
# last day
to_return[-nb_slots_per_day:] = np.maximum(
var_ndsi_1d_past[-nb_slots_per_day:],
var_ndsi_2d_past[-nb_slots_per_day:],
)
if nb_days == 3:
# second day
to_return[nb_slots_per_day : 2 * nb_slots_per_day] = np.maximum(
var_ndsi_1d_past[nb_slots_per_day : 2 * nb_slots_per_day],
var_ndsi_1d_future[nb_slots_per_day : 2 * nb_slots_per_day],
)
else: # nb_days >= 4
# the day previous the last one
to_return[-2 * nb_slots_per_day : -nb_slots_per_day] = np.maximum(
np.maximum(
var_ndsi_1d_past[-2 * nb_slots_per_day : -nb_slots_per_day],
var_ndsi_2d_past[-2 * nb_slots_per_day : -nb_slots_per_day],
),
var_ndsi_1d_future[-2 * nb_slots_per_day : -nb_slots_per_day],
)
# second day
to_return[nb_slots_per_day : 2 * nb_slots_per_day] = np.maximum(
np.maximum(
var_ndsi_1d_future[nb_slots_per_day : 2 * nb_slots_per_day],
var_ndsi_2d_future[nb_slots_per_day : 2 * nb_slots_per_day],
),
var_ndsi_1d_past[nb_slots_per_day : 2 * nb_slots_per_day],
)
if nb_days >= 5:
to_return[
2 * nb_slots_per_day : -2 * nb_slots_per_day
] = np.maximum(
np.maximum(
var_ndsi_1d_past[
2 * nb_slots_per_day : -2 * nb_slots_per_day
],
var_ndsi_2d_past[
2 * nb_slots_per_day : -2 * nb_slots_per_day
],
),
np.maximum(
var_ndsi_1d_future[
2 * nb_slots_per_day : -2 * nb_slots_per_day
],
var_ndsi_2d_future[
2 * nb_slots_per_day : -2 * nb_slots_per_day
],
),
)
to_return[to_return < 0] = 0
return to_return
def get_bright_positive_variability_5d(
index, definition_mask, satellite_step, slot_step
):
"""
To recognize some icy clouds (peaks of ndsi)
NB: we loose information about the first slot (resp the last slot) if night is 1 slot longer during 1 of the 5 days
:param index:
:param definition_mask:
:param satellite_step: the satellite characteristic time step between two slots (10 minutes for Himawari 8)
:param slot_step: the chosen sampling of slots. if slot_step = n, the sampled slots are s[0], s[n], s[2*n]...
:return:
"""
nb_slots_per_day = get_nb_slots_per_day(satellite_step, slot_step)
nb_days = np.shape(index)[0] / nb_slots_per_day
to_return = np.full_like(index, -10)
if nb_days >= 2:
var_ndsi_1d_past = compute_variability(
cloud_index=index,
mask=definition_mask,
step=nb_slots_per_day,
negative_variation_only=False,
abs_value=False,
)
var_ndsi_1d_future = compute_variability(
cloud_index=index,
mask=definition_mask,
step=-nb_slots_per_day,
negative_variation_only=False,
abs_value=False,
)
if nb_days == 2:
to_return[:nb_slots_per_day] = var_ndsi_1d_future[:nb_slots_per_day]
to_return[nb_slots_per_day:] = var_ndsi_1d_past[nb_slots_per_day:]
else: # nb_days >=3
var_ndsi_2d_past = compute_variability(
cloud_index=index,
mask=definition_mask,
step=nb_slots_per_day * 2,
negative_variation_only=False,
abs_value=False,
)
var_ndsi_2d_future = compute_variability(
cloud_index=index,
mask=definition_mask,
step=-2 * nb_slots_per_day,
negative_variation_only=False,
abs_value=False,
)
# first day
to_return[:nb_slots_per_day] = np.minimum(
var_ndsi_1d_future[:nb_slots_per_day],
var_ndsi_2d_future[:nb_slots_per_day],
)
# last day
to_return[-nb_slots_per_day:] = np.minimum(
var_ndsi_1d_past[-nb_slots_per_day:],
var_ndsi_2d_past[-nb_slots_per_day:],
)
if nb_days == 3:
# second day
to_return[nb_slots_per_day : 2 * nb_slots_per_day] = np.minimum(
var_ndsi_1d_past[nb_slots_per_day : 2 * nb_slots_per_day],
var_ndsi_1d_future[nb_slots_per_day : 2 * nb_slots_per_day],
)
else: # nb_days >= 4
# the day previous the last one
to_return[-2 * nb_slots_per_day : -nb_slots_per_day] = np.minimum(
np.minimum(
var_ndsi_1d_past[-2 * nb_slots_per_day : -nb_slots_per_day],
var_ndsi_2d_past[-2 * nb_slots_per_day : -nb_slots_per_day],
),
var_ndsi_1d_future[-2 * nb_slots_per_day : -nb_slots_per_day],
)
# second day
to_return[nb_slots_per_day : 2 * nb_slots_per_day] = np.minimum(
np.minimum(
var_ndsi_1d_future[nb_slots_per_day : 2 * nb_slots_per_day],
var_ndsi_2d_future[nb_slots_per_day : 2 * nb_slots_per_day],
),
var_ndsi_1d_past[nb_slots_per_day : 2 * nb_slots_per_day],
)
if nb_days >= 5:
to_return[
2 * nb_slots_per_day : -2 * nb_slots_per_day
] = np.minimum(
np.minimum(
var_ndsi_1d_past[
2 * nb_slots_per_day : -2 * nb_slots_per_day
],
var_ndsi_2d_past[
2 * nb_slots_per_day : -2 * nb_slots_per_day
],
),
np.minimum(
var_ndsi_1d_future[
2 * nb_slots_per_day : -2 * nb_slots_per_day
],
var_ndsi_2d_future[
2 * nb_slots_per_day : -2 * nb_slots_per_day
],
),
)
to_return[to_return < 0] = 0
return to_return
def get_flat_sir(
variable,
cos_zen,
mask,
nb_slots_per_day,
slices_per_day,
tolerance,
persistence_sigma,
mask_not_proper_weather=None,
):
if mask_not_proper_weather is not None:
mask = mask | mask_not_proper_weather
return get_likelihood_variable_cos_zen(
variable=variable,
cos_zen=cos_zen,
mask=mask,
nb_slots_per_day=nb_slots_per_day,
nb_slices_per_day=slices_per_day,
under_bound=-tolerance,
upper_bound=tolerance,
persistence_sigma=persistence_sigma,
)
def get_tricky_transformed_ndsi(snow_index, summit, gamma=4):
recentered = np.abs(snow_index - summit)
# beta = full_like(snow_index, 0.5)
# alpha = -0.5/(max(1-summit, summit)**2)
# return beta + alpha * recentered * recentered
return normalize(np.exp(-gamma * recentered) - np.exp(-gamma * summit))
def get_cloudy_sea(vis, is_land, thresholds):
to_return = np.zeros_like(vis)
for slot in range(len(to_return)):
to_return[slot, :, :][~is_land & (vis[slot, :, :] > thresholds[slot])] = 1
return to_return