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diag.py
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diag.py
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import matplotlib.pylab as pl
import numpy as np
import common.utilities as utl
import common.Product as prd
import common.Collection as clc
import common.Comparison as cmp
import sys
import argparse
from mpl_toolkits.axes_grid1 import make_axes_locatable
def main():
# Argument parser
parser = argparse.ArgumentParser()
parser.add_argument("list", help="List of paths of collection")
parser.add_argument("--saveto", help="subdirectory to save figs to", type=str)
parser.add_argument("--hist", help="Display quicklooks with histograms", action="store_true", default=False)
parser.add_argument("--keepall", help="Display quicklooks with histograms with keep_all", action="store_true",
default=False)
parser.add_argument("-v", "--verbose", help="Set verbosity to DEBUG level", action="store_true", default=False)
args = parser.parse_args()
gain_true = 3.
gain_false = 2.5
if args.keepall:
l_stats = []
# Create the logger
logger = utl.get_logger('Diag', args.verbose)
bdef_acix = (
["band02", "SRE_B2.", "R1", []],
["band03", "SRE_B3.", "R1", []],
["band04", "SRE_B4.", "R1", []],
["band05", "SRE_B5.", "R2", []],
["band06", "SRE_B6.", "R2", []],
["band07", "SRE_B7.", "R2", []],
["band08", "SRE_B8.", "R1", []],
["band8a", "SRE_B8A.", "R2", []],
["band11", "SRE_B11.", "R2", []],
["band12", "SRE_B12.", "R2", []])
f = open(args.list, 'r')
paths_list = f.read().splitlines()
for p in paths_list:
paths = p.split(',')
location_name = paths[0].split('/')[-1]
acix_vermote_collection = clc.Collection(paths[0], logger)
acix_maja_collection = clc.Collection(paths[1], logger)
compare = cmp.Comparison(acix_vermote_collection, acix_maja_collection, logger)
for match in compare.matching_products:
logger.info("One-by-one for %s between %s and %s" % (match[0], match[1], match[2]))
p_ref = prd.Product_hdf_acix(match[1], logger)
p_maja = prd.Product_dir_maja(match[2], logger)
timestamp = match[0]
try:
b_ref_b2 = p_ref.get_band(p_ref.find_band(bdef_acix[0][0]), scalef=p_ref.sre_scalef)
b_ref_b3 = p_ref.get_band(p_ref.find_band(bdef_acix[1][0]), scalef=p_ref.sre_scalef)
b_ref_b4 = p_ref.get_band(p_ref.find_band(bdef_acix[2][0]), scalef=p_ref.sre_scalef)
b_ref_b8 = p_ref.get_band(p_ref.find_band(bdef_acix[6][0]), scalef=p_ref.sre_scalef)
m_ref_qa = p_ref.get_band(p_ref.find_band("refqa"))
b_maja_b2 = p_maja.get_band(p_maja.find_band(bdef_acix[0][1]), scalef=p_maja.sre_scalef)
b_maja_b3 = p_maja.get_band(p_maja.find_band(bdef_acix[1][1]), scalef=p_maja.sre_scalef)
b_maja_b4 = p_maja.get_band(p_maja.find_band(bdef_acix[2][1]), scalef=p_maja.sre_scalef)
b_maja_b8 = p_maja.get_band(p_maja.find_band(bdef_acix[6][1]), scalef=p_maja.sre_scalef)
b_maja_aot = p_maja.get_band(p_maja.find_band("ATB_R1"), layer=1, scalef=p_maja.aot_scalef)
b_maja_vap = p_maja.get_band(p_maja.find_band("ATB_R1"), layer=0, scalef=p_maja.vap_scalef)
clm = p_maja.get_band(p_maja.find_band("CLM_" + bdef_acix[0][2]))
edg = p_maja.get_band(p_maja.find_band("EDG_" + bdef_acix[0][2]))
m_maja_qa, ratio = p_maja.get_mask(clm, edg, stats=True)
if args.hist:
fig, axs = pl.subplots(nrows=3, ncols=3, figsize=[12, 12])
fig.suptitle(location_name + ' ' + timestamp[0:4] + '-' + timestamp[4:6] + '-' + timestamp[6:8],
fontsize=16)
cset_true = axs[0, 0].imshow(
np.dstack((b_maja_b4 * gain_true, b_maja_b3 * gain_true, b_maja_b2 * gain_true)),
interpolation='none', aspect='equal')
axs[0, 0].set_title("Maja quicklook (B4, B3, B2)")
cset_maja_cloud_contour = axs[0, 0].contour(m_maja_qa)
# axs[0, 0].clabel(cset_maja_cloud_contour, inline=1, fontsize=10)
cset_maja_qa = axs[1, 0].imshow(m_maja_qa, interpolation='none', aspect='equal', vmin=0, vmax=1,
cmap='gray')
axs[1, 0].set_title("Maja CLM & EDG (valid=1)")
divider = make_axes_locatable(axs[1, 0])
cax = divider.append_axes("right", size="5%", pad=0.05)
pl.colorbar(cset_maja_qa, cax=cax) # , orientation='horizontal')
# cset_ref_qa = axs[2, 0].imshow(m_ref_qa, interpolation='none', aspect='equal', vmin=0, vmax=1, cmap='gray')
# axs[2, 0].set_title("Reference QA (valid=1)")
# divider = make_axes_locatable(axs[2, 0])
# cax = divider.append_axes("right", size="5%", pad=0.05)
# pl.colorbar(cset_ref_qa, cax=cax) # , orientation='horizontal')
cset_true = axs[2, 0].imshow(
np.dstack((b_ref_b4 * gain_true, b_ref_b3 * gain_true, b_ref_b2 * gain_true)),
interpolation='none', aspect='equal')
axs[2, 0].set_title("Ref quicklook (B4, B3, B2)")
axs[2, 0].contour(m_ref_qa)
# cset_false = axs[0, 1].imshow(np.dstack((b_maja_b8*gain_false, b_maja_b3*gain_false, b_maja_b2*gain_false)), interpolation='none', aspect='equal')
# axs[0, 1].set_title("%s %s (B8,B3,B2)" % (location_name, timestamp))
cset_maja_vap = axs[0, 1].imshow(b_maja_vap, interpolation='none', aspect='equal', cmap='RdBu')
axs[0, 1].set_title("Maja VAP $(g.cm^{-2})$")
divider = make_axes_locatable(axs[0, 1])
cax = divider.append_axes("right", size="5%", pad=0.05)
pl.colorbar(cset_maja_vap, cax=cax, format='%4.2f') # , orientation='horizontal')
# cset_maja_vap = axs[0, 1].imshow(np.dstack((b_maja_b4*gain_true, b_maja_b3*gain_true, b_maja_b2*gain_true)), interpolation='none', aspect='equal')
# axs[0, 1].set_title("Maja VAP")
# divider = make_axes_locatable(axs[0, 1])
# cax = divider.append_axes("right", size="5%", pad=0.05)
# pl.colorbar(cset_maja_vap, cax=cax) # , orientation='horizontal')
# cset_maja_vap_contour = axs[0, 1].contour(b_maja_vap)
# axs[0, 1].clabel(cset_maja_vap_contour, inline=1, fontsize=10)
cset_maja_aot = axs[0, 2].imshow(b_maja_aot, cmap='Wistia')
axs[0, 2].imshow(np.dstack((b_maja_b4 * gain_true, b_maja_b3 * gain_true, b_maja_b2 * gain_true)),
interpolation='none', aspect='equal')
axs[0, 2].set_title("Maja AOT $(-)$")
divider = make_axes_locatable(axs[0, 2])
cax = divider.append_axes("right", size="5%", pad=0.05)
pl.colorbar(cset_maja_aot, cax=cax, format='%4.2f') # , orientation='horizontal')
cset_maja_aot_contour = axs[0, 2].contour(b_maja_aot, cmap='Wistia')
axs[0, 2].clabel(cset_maja_aot_contour, inline=1, fontsize=10)
# B2
if args.keepall:
is_valid = np.where(
(m_ref_qa == 1)
& (m_maja_qa == 1)
)
min_sr = -0.1
max_sr = 0.7
is_log = False
filter_label = "(QA=1)"
b_ref_b2_is_valid_count = len(b_ref_b2[is_valid].flatten())
search = np.where(b_ref_b2[is_valid] < 0)
b_ref_b2_is_valid_and_lt0_count = len(b_ref_b2[is_valid][search].flatten())
search = np.where(b_maja_b2[is_valid] < 0)
b_maja_b2_is_valid_and_lt0_count = len(b_maja_b2[is_valid][search].flatten())
else:
is_valid = np.where(
(b_ref_b2 > 0)
& (b_maja_b2 > 0)
& (m_ref_qa == 1)
& (m_maja_qa == 1)
)
min_sr = 0
max_sr = 1
is_log = False
filter_label = "(QA=1 & sr>0)"
axs[1, 1].hist(b_ref_b2[is_valid].flatten(),
bins=200,
histtype='step',
log=is_log,
label='Ref',
range=(min_sr, max_sr),
density=False
)
axs[1, 1].hist(b_maja_b2[is_valid].flatten(),
bins=200,
histtype='step',
log=is_log,
label='Maja',
range=(min_sr, max_sr),
density=False
)
axs[1, 1].set_title("B2 " + filter_label + " RMSE=%8.6f" % utl.rmse(b_ref_b2[is_valid].flatten(),
b_maja_b2[is_valid].flatten()))
axs[1, 1].legend()
# B3
if args.keepall:
is_valid = np.where(
(m_ref_qa == 1)
& (m_maja_qa == 1)
)
min_sr = -0.1
max_sr = 0.7
is_log = False
filter_label = "(QA=1)"
b_ref_b3_is_valid_count = len(b_ref_b3[is_valid].flatten())
search = np.where(b_ref_b3[is_valid] < 0)
b_ref_b3_is_valid_and_lt0_count = len(b_ref_b3[is_valid][search].flatten())
search = np.where(b_maja_b3[is_valid] < 0)
b_maja_b3_is_valid_and_lt0_count = len(b_maja_b3[is_valid][search].flatten())
else:
is_valid = np.where(
(b_ref_b3 > 0)
& (b_maja_b3 > 0)
& (m_ref_qa == 1)
& (m_maja_qa == 1)
)
min_sr = 0
max_sr = 1
is_log = False
filter_label = "(QA=1 & sr>0)"
axs[1, 2].hist(b_ref_b3[is_valid].flatten(),
bins=200,
histtype='step',
log=is_log,
label='Ref',
range=(min_sr, max_sr)
)
axs[1, 2].hist(b_maja_b3[is_valid].flatten(),
bins=200,
histtype='step',
log=is_log,
label='Maja',
range=(min_sr, max_sr)
)
axs[1, 2].set_title("B3 " + filter_label + " RMSE=%8.6f" % utl.rmse(b_ref_b3[is_valid].flatten(),
b_maja_b3[is_valid].flatten()))
axs[1, 2].legend()
# B4
if args.keepall:
is_valid = np.where(
(m_ref_qa == 1)
& (m_maja_qa == 1)
)
min_sr = -0.1
max_sr = 0.7
is_log = False
filter_label = "(QA=1)"
b_ref_b4_is_valid_count = len(b_ref_b4[is_valid].flatten())
search = np.where(b_ref_b4[is_valid] < 0)
b_ref_b4_is_valid_and_lt0_count = len(b_ref_b4[is_valid][search].flatten())
search = np.where(b_maja_b4[is_valid] < 0)
b_maja_b4_is_valid_and_lt0_count = len(b_maja_b4[is_valid][search].flatten())
else:
is_valid = np.where(
(b_ref_b4 > 0)
& (b_maja_b4 > 0)
& (m_ref_qa == 1)
& (m_maja_qa == 1)
)
min_sr = 0
max_sr = 1
is_log = False
filter_label = "(QA=1 & sr>0)"
axs[2, 1].hist(b_ref_b4[is_valid].flatten(),
bins=200,
histtype='step',
log=is_log,
label='Ref',
range=(min_sr, max_sr)
)
axs[2, 1].hist(b_maja_b4[is_valid].flatten(),
bins=200,
histtype='step',
log=is_log,
label='Maja',
range=(min_sr, max_sr)
)
axs[2, 1].set_title("B4 " + filter_label + " RMSE=%8.6f" % utl.rmse(b_ref_b4[is_valid].flatten(),
b_maja_b4[is_valid].flatten()))
axs[2, 1].legend()
# B8
if args.keepall:
is_valid = np.where(
(m_ref_qa == 1)
& (m_maja_qa == 1)
)
min_sr = -0.1
max_sr = 0.7
is_log = False
filter_label = "(QA=1)"
b_ref_b8_is_valid_count = len(b_ref_b8[is_valid].flatten())
search = np.where(b_ref_b8[is_valid] < 0)
b_ref_b8_is_valid_and_lt0_count = len(b_ref_b8[is_valid][search].flatten())
search = np.where(b_maja_b8[is_valid] < 0)
b_maja_b8_is_valid_and_lt0_count = len(b_maja_b8[is_valid][search].flatten())
else:
is_valid = np.where(
(b_ref_b8 > 0)
& (b_maja_b8 > 0)
& (m_ref_qa == 1)
& (m_maja_qa == 1)
)
min_sr = 0
max_sr = 1
is_log = False
filter_label = "(QA=1 & sr>0)"
axs[2, 2].hist(b_ref_b8[is_valid].flatten(),
bins=200,
histtype='step',
log=is_log,
label='Ref',
range=(min_sr, max_sr)
)
axs[2, 2].hist(b_maja_b8[is_valid].flatten(),
bins=200,
histtype='step',
log=is_log,
label='Maja',
range=(min_sr, max_sr)
)
axs[2, 2].set_title("B8 " + filter_label + " RMSE=%8.6f" % utl.rmse(b_ref_b8[is_valid].flatten(),
b_maja_b8[is_valid].flatten()))
axs[2, 2].legend()
fig.tight_layout()
fig.subplots_adjust(top=0.88)
pl.savefig(location_name + '_' + timestamp + '_All_quicklooks.png')
pl.close('all')
if args.keepall:
l_stats = l_stats + [[location_name + timestamp,
b_ref_b2_is_valid_count, b_ref_b3_is_valid_count,
b_ref_b4_is_valid_count, b_ref_b8_is_valid_count,
b_ref_b2_is_valid_and_lt0_count,
b_ref_b3_is_valid_and_lt0_count,
b_ref_b4_is_valid_and_lt0_count,
b_ref_b8_is_valid_and_lt0_count,
b_maja_b2_is_valid_and_lt0_count,
b_maja_b3_is_valid_and_lt0_count,
b_maja_b4_is_valid_and_lt0_count,
b_maja_b8_is_valid_and_lt0_count]]
else:
try:
fig, axs = pl.subplots(figsize=[12, 12])
pl.title(location_name + ' ' + timestamp[0:4] + '-' + timestamp[4:6] + '-' + timestamp[6:8],
fontsize=16)
axs.imshow(
np.dstack((b_maja_b4 * gain_true, b_maja_b3 * gain_true, b_maja_b2 * gain_true)),
interpolation='none', aspect='equal')
axs.set_title("MAJA Quicklook (B4, B3, B2)")
axs.contour(m_maja_qa)
pl.savefig(location_name + '_' + timestamp + '_Maja_quicklooks.png')
pl.close('all')
fig, axs = pl.subplots(figsize=[12, 12])
pl.title(location_name + ' ' + timestamp[0:4] + '-' + timestamp[4:6] + '-' + timestamp[6:8],
fontsize=16)
axs.imshow(
np.dstack((b_ref_b4 * gain_true, b_ref_b3 * gain_true, b_ref_b2 * gain_true)),
interpolation='none', aspect='equal')
axs.set_title("REF Quicklook (B4, B3, B2)")
axs.contour(m_ref_qa)
pl.savefig(location_name + '_' + timestamp + '_Ref_quicklooks.png')
pl.close('all')
except:
print(sys.exc_info())
except:
e = sys.exc_info()
logger.error(e)
b_common_stats_dataset_count = 0
b_ref_stats_dataset_with_any_sr_lt0_count = 0
b_ref_stats_dataset_with_atmost_025prc_sr_lt0_count = 0
b_ref_stats_dataset_with_atmost_05prc_sr_lt0_count = 0
b_ref_stats_dataset_with_atmost_10prc_sr_lt0_count = 0
b_ref_stats_dataset_with_morethan_10prc_sr_lt0_count = 0
b_maja_stats_dataset_with_any_sr_lt0_count = 0
b_maja_stats_dataset_with_atmost_025prc_sr_lt0_count = 0
b_maja_stats_dataset_with_atmost_05prc_sr_lt0_count = 0
b_maja_stats_dataset_with_atmost_10prc_sr_lt0_count = 0
b_maja_stats_dataset_with_morethan_10prc_sr_lt0_count = 0
for l in range(len(l_stats)):
b_common_stats_dataset_count += 1
if (l_stats[l][5] > 0) or (l_stats[l][6] > 0) or (l_stats[l][7] > 0) or (
l_stats[l][8] > 0):
b_ref_stats_dataset_with_any_sr_lt0_count += 1
if max(l_stats[l][5], l_stats[l][6], l_stats[l][7], l_stats[l][8]) <= 20250:
logger.info("STATS REF_LT025: %s: b2_ratio=%i/%i, b3_ratio=%i/%i, b4_ratio=%i/%i, b8_ratio=%i/%i"
% (l_stats[l][0],
l_stats[l][5], l_stats[l][1],
l_stats[l][6], l_stats[l][2],
l_stats[l][7], l_stats[l][3],
l_stats[l][8], l_stats[l][4]
))
b_ref_stats_dataset_with_atmost_025prc_sr_lt0_count += 1
elif max(l_stats[l][5], l_stats[l][6], l_stats[l][7], l_stats[l][8]) <= 40500:
logger.info("STATS REF_LT05: %s: b2_ratio=%i/%i, b3_ratio=%i/%i, b4_ratio=%i/%i, b8_ratio=%i/%i"
% (l_stats[l][0],
l_stats[l][5], l_stats[l][1],
l_stats[l][6], l_stats[l][2],
l_stats[l][7], l_stats[l][3],
l_stats[l][8], l_stats[l][4]
))
b_ref_stats_dataset_with_atmost_05prc_sr_lt0_count += 1
elif max(l_stats[l][5], l_stats[l][6], l_stats[l][7], l_stats[l][8]) <= 81000:
b_ref_stats_dataset_with_atmost_10prc_sr_lt0_count += 1
logger.info("STATS REF_LT10: %s: b2_ratio=%i/%i, b3_ratio=%i/%i, b4_ratio=%i/%i, b8_ratio=%i/%i"
% (l_stats[l][0],
l_stats[l][5], l_stats[l][1],
l_stats[l][6], l_stats[l][2],
l_stats[l][7], l_stats[l][3],
l_stats[l][8], l_stats[l][4]
))
else:
b_ref_stats_dataset_with_morethan_10prc_sr_lt0_count += 1
logger.info("STATS REF_GT10: %s: b2_ratio=%i/%i, b3_ratio=%i/%i, b4_ratio=%i/%i, b8_ratio=%i/%i"
% (l_stats[l][0],
l_stats[l][5], l_stats[l][1],
l_stats[l][6], l_stats[l][2],
l_stats[l][7], l_stats[l][3],
l_stats[l][8], l_stats[l][4]
))
if (l_stats[l][9] > 0) or (l_stats[l][10] > 0) or (l_stats[l][11] > 0) or (
l_stats[l][12] > 0):
b_maja_stats_dataset_with_any_sr_lt0_count += 1
if max(l_stats[l][9], l_stats[l][10], l_stats[l][11], l_stats[l][12]) <= 20250:
logger.info("STATS MAJA_LT025: %s: b2_ratio=%i/%i, b3_ratio=%i/%i, b4_ratio=%i/%i, b8_ratio=%i/%i"
% (l_stats[l][0],
l_stats[l][9], l_stats[l][1],
l_stats[l][10], l_stats[l][2],
l_stats[l][11], l_stats[l][3],
l_stats[l][12], l_stats[l][4]
))
b_maja_stats_dataset_with_atmost_025prc_sr_lt0_count += 1
elif max(l_stats[l][9], l_stats[l][10], l_stats[l][11], l_stats[l][12]) <= 40500:
logger.info("STATS LT05: %s: b2_ratio=%i/%i, b3_ratio=%i/%i, b4_ratio=%i/%i, b8_ratio=%i/%i"
% (l_stats[l][0],
l_stats[l][9], l_stats[l][1],
l_stats[l][10], l_stats[l][2],
l_stats[l][11], l_stats[l][3],
l_stats[l][12], l_stats[l][4]
))
b_maja_stats_dataset_with_atmost_05prc_sr_lt0_count += 1
elif max(l_stats[l][9], l_stats[l][10], l_stats[l][11], l_stats[l][12]) <= 81000:
b_maja_stats_dataset_with_atmost_10prc_sr_lt0_count += 1
logger.info("STATS LT10: %s: b2_ratio=%i/%i, b3_ratio=%i/%i, b4_ratio=%i/%i, b8_ratio=%i/%i"
% (l_stats[l][0],
l_stats[l][9], l_stats[l][1],
l_stats[l][10], l_stats[l][2],
l_stats[l][11], l_stats[l][3],
l_stats[l][12], l_stats[l][4]
))
else:
b_maja_stats_dataset_with_morethan_10prc_sr_lt0_count += 1
logger.info("STATS GT10: %s: b2_ratio=%i/%i, b3_ratio=%i/%i, b4_ratio=%i/%i, b8_ratio=%i/%i"
% (l_stats[l][0],
l_stats[l][9], l_stats[l][1],
l_stats[l][10], l_stats[l][2],
l_stats[l][11], l_stats[l][3],
l_stats[l][12], l_stats[l][4]
))
logger.info("STATS: Tested %i location and timestamps" % b_common_stats_dataset_count)
logger.info("STATS: Found %i datasets with sr_ref < 0" % b_ref_stats_dataset_with_any_sr_lt0_count)
logger.info(
"STATS: %i datasets with at most 2.5%% of sr_ref < 0" % b_ref_stats_dataset_with_atmost_025prc_sr_lt0_count)
logger.info(
"STATS: %i datasets with 2.5%% to 5%% of sr_ref < 0" % b_ref_stats_dataset_with_atmost_05prc_sr_lt0_count)
logger.info(
"STATS: %i datasets with 5%% to 10%% of sr_ref < 0" % b_ref_stats_dataset_with_atmost_10prc_sr_lt0_count)
logger.info(
"STATS: %i datasets with more than 10%% of sr_ref < 0" % b_ref_stats_dataset_with_morethan_10prc_sr_lt0_count)
logger.info("STATS: Found %i datasets with sr_maja < 0" % b_maja_stats_dataset_with_any_sr_lt0_count)
logger.info(
"STATS: %i datasets with at most 2.5%% of sr_maja < 0" % b_maja_stats_dataset_with_atmost_025prc_sr_lt0_count)
logger.info(
"STATS: %i datasets with 2.5%% to 5%% of sr_maja < 0" % b_maja_stats_dataset_with_atmost_05prc_sr_lt0_count)
logger.info(
"STATS: %i datasets with 5%% to 10%% of sr_maja < 0" % b_maja_stats_dataset_with_atmost_10prc_sr_lt0_count)
logger.info(
"STATS: %i datasets with more than 10%% of sr_maja < 0" % b_maja_stats_dataset_with_morethan_10prc_sr_lt0_count)
print(b_common_stats_dataset_count, b_ref_stats_dataset_with_any_sr_lt0_count, b_ref_stats_dataset_with_atmost_10prc_sr_lt0_count)
print(l_stats)
sys.exit(0)
if __name__ == "__main__":
main()