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Commit 6791d1b5 authored by tomrink's avatar tomrink
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......@@ -724,36 +724,44 @@ def run_restore_static(directory, ckpt_dir, out_file=None):
def run_evaluate_static(in_file, out_file, ckpt_dir):
N = 8
sub_y, sub_x = (N * 128) + 6, (N * 128) + 6
y_0, x_0, = 2432 - int(sub_y/2), 2432 - int(sub_x/2)
x_130 = slice(2, (N * 128) + 4)
y_130 = slice(2, (N * 128) + 4)
slc_x = slice(2, N*128 + 4)
slc_y = slice(2, N*128 + 4)
slc_x_2 = slice(1, N*128 + 6, 2)
slc_y_2 = slice(1, N*128 + 6, 2)
x_2 = np.arange(int((N*128)/2) + 3)
y_2 = np.arange(int((N*128)/2) + 3)
t = np.arange(0, int((N*128)/2) + 3, 0.5)
s = np.arange(0, int((N*128)/2) + 3, 0.5)
x_k = slice(1, N*128 + 3)
y_k = slice(1, N*128 + 3)
x_128 = slice(3, N*128 + 3)
y_128 = slice(3, N*128 + 3)
slc_y_2, slc_x_2 = slice(1, 128*N + 6, 2), slice(1, 128*N + 6, 2)
y_2, x_2 = np.arange((128*N)/2 + 3), np.arange((128*N)/2 + 3)
t, s = np.arange(1, (128*N)/2 + 2, 0.5), np.arange(1, (128*N)/2 + 2, 0.5)
sub_y, sub_x = (N * 128) + 10, (N * 128) + 10
y_0, x_0, = 2432 - int(sub_y/2), 2432 - int(sub_x/2)
h5f = h5py.File(in_file, 'r')
grd_a = get_grid_values_all(h5f, 'temp_11_0um_nom')
grd_a = grd_a[y_0:y_0+sub_y, x_0:x_0+sub_x]
grd_a = grd_a[y_130, x_130]
bt = grd_a
grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
grd_b = get_grid_values_all(h5f, 'refl_0_65um_nom')
grd_b = grd_b[y_0:y_0+sub_y, x_0:x_0+sub_x]
grd_b = grd_b[y_130, x_130]
refl = grd_b
grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
# grd_a = get_grid_values_all(h5f, 'temp_11_0um_nom')
# grd_a = grd_a[y_0:y_0+sub_y, x_0:x_0+sub_x]
# grd_a = grd_a[y_130, x_130]
# bt = grd_a
# grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
#
# grd_b = get_grid_values_all(h5f, 'refl_0_65um_nom')
# grd_b = grd_b[y_0:y_0+sub_y, x_0:x_0+sub_x]
# grd_b = grd_b[y_130, x_130]
# refl = grd_b
# grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
grd_c = get_grid_values_all(h5f, label_param)
grd_c = grd_c[y_0:y_0+sub_y, x_0:x_0+sub_x]
hr_grd_c = grd_c.copy()
hr_grd_c = hr_grd_c[y_128, x_128]
grd_c = grd_c[slc_y_2, slc_x_2]
if label_param != 'cloud_probability':
grd_c = normalize(grd_c, label_param, mean_std_dct)
grd_c = resample_2d_linear_one(x_2, y_2, grd_c, t, s)
print(grd_a.shape, grd_b.shape, grd_c.shape)
grd_c = grd_c[y_k, x_k]
# data = np.stack([grd_a, grd_b, grd_c], axis=2)
data = np.stack([grd_c], axis=2)
......@@ -766,40 +774,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
if out_file is not None:
np.save(out_file, [out_sr, hr_grd_c])
else:
return out_sr, bt, refl
def run_evaluate_static_2(in_file, out_file, ckpt_dir):
nda = np.load(in_file)
grd_a = nda[:, 0, :, :]
grd_a = grd_a[:, slc_y_2, slc_x_2]
grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
grd_a = resample_2d_linear(x_2, y_2, grd_a, t, s)
grd_b = nda[:, 2, :, :]
grd_b = grd_b[:, slc_y_2, slc_x_2]
grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
grd_b = resample_2d_linear(x_2, y_2, grd_b, t, s)
grd_c = nda[:, 3, :, :]
grd_c = grd_c[:, slc_y_2, slc_x_2]
if label_param != 'cloud_probability':
grd_c = normalize(grd_c, label_param, mean_std_dct)
grd_c = resample_2d_linear(x_2, y_2, grd_c, t, s)
data = np.stack([grd_a, grd_b, grd_c], axis=3)
print(data.shape)
nn = SRCNN()
out_sr = nn.run_evaluate(data, ckpt_dir)
if label_param != 'cloud_probability':
out_sr = denormalize(out_sr, label_param, mean_std_dct)
pass
if out_file is not None:
np.save(out_file, out_sr)
else:
return out_sr
return out_sr, None, None
def analyze(fpath='/Users/tomrink/clavrx_snpp_viirs.A2019080.0100.001.2019080064252.uwssec_B00038315.level2.h5', param='cloud_probability'):
......
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