def match_synop_imager(synop, cloudproducts, SETTINGS):
retv = TruthImagerTrackObject(truth='synop')
retv.imager_instrument = cloudproducts.instrument.lower()
retv.synop = synop
from atrain_match.utils.common import map_imager_distances
n_neighbours = 250
if config.RESOLUTION == 5:
n_neighbours = 16
mapper_and_dist = map_imager_distances(cloudproducts,
synop.longitude.ravel(),
synop.latitude.ravel(),
radius_of_influence=SETTINGS["SYNOP_RADIUS"],
n_neighbours=n_neighbours)
# pdb.set_trace()
cal, cap = mapper_and_dist["mapper"]
distances = mapper_and_dist["distances"]
cal_1 = cal[:, 0]
cap_1 = cap[:, 0]
calnan = np.where(cal_1 == config.NODATA, np.nan, cal_1)
if (~np.isnan(calnan)).sum() == 0:
logger.warning("No matches within region.")
return None
# check if it is within time limits:
if len(cloudproducts.time.shape) > 1:
imager_time_vector = [cloudproducts.time[line, pixel] for line, pixel in zip(cal_1, cap_1)]
imager_lines_sec_1970 = np.where(cal_1 != config.NODATA, imager_time_vector, np.nan)
else:
imager_lines_sec_1970 = np.where(cal_1 != config.NODATA, cloudproducts.time[cal_1], np.nan)
idx_match = elements_within_range(synop.sec_1970, imager_lines_sec_1970, SETTINGS["sec_timeThr_synop"])
if idx_match.sum() == 0:
logger.warning("No matches in region within time threshold %d s.", SETTINGS["sec_timeThr_synop"])
return None
retv.synop = retv.synop.extract_elements(idx=idx_match)
# Synop line, pixel inside IMAGER swath (one nearest neighbour):
retv.synop.imager_linnum = np.repeat(cal_1, idx_match).astype('i')
retv.synop.imager_pixnum = np.repeat(cap_1, idx_match).astype('i')
# Synop line, pixel inside IMAGER swath (several neighbours):
retv.synop.imager_linnum_nneigh = np.repeat(cal, idx_match, axis=0)
retv.synop.imager_pixnum_nneigh = np.repeat(cap, idx_match, axis=0)
retv.synop.imager_synop_dist = np.repeat(distances, idx_match, axis=0)
# Imager time
retv.imager.sec_1970 = np.repeat(imager_lines_sec_1970, idx_match)
retv.diff_sec_1970 = retv.synop.sec_1970 - retv.imager.sec_1970
do_some_logging(retv, synop)
logger.debug("Extract imager along track!")
retv = imager_track_from_matched(retv, SETTINGS,
cloudproducts,
extract_radiances=False,
extract_ctth=False,
extract_cpp=False,
extract_aux_segments=False,
aux_params=['fractionofland', 'landuse'],
find_mean_data_for_x_neighbours=True)
return retv
def match_calipso_imager(values,
calipso,
calipso_aerosol,
cloudproducts,
SETTINGS,
res=config.RESOLUTION):
from atrain_match.utils.common import map_imager
retv = TruthImagerTrackObject(truth='calipso')
retv.imager_instrument = cloudproducts.instrument.lower()
retv.calipso = calipso
cal, cap = map_imager(cloudproducts,
calipso.longitude.ravel(),
calipso.latitude.ravel(),
radius_of_influence=config.RESOLUTION * 0.7 *
1000.0) # somewhat larger than radius...
# warn if no matches
calnan = np.where(cal == config.NODATA, np.nan, cal)
if (~np.isnan(calnan)).sum() == 0:
logger.warning("No matches within region.")
return None
# check if it is within time limits:
if len(cloudproducts.time.shape) > 1:
imager_time_vector = [
cloudproducts.time[line, pixel] for line, pixel in zip(cal, cap)
]
imager_lines_sec_1970 = np.where(cal != config.NODATA,
imager_time_vector, np.nan)
else:
imager_lines_sec_1970 = np.where(cal != config.NODATA,
cloudproducts.time[cal], np.nan)
idx_match = elements_within_range(calipso.sec_1970, imager_lines_sec_1970,
SETTINGS["sec_timeThr"])
if idx_match.sum() == 0:
logger.warning("No matches in region within time threshold %d s.",
SETTINGS["sec_timeThr"])
return None
retv.calipso = retv.calipso.extract_elements(idx=idx_match)
# Calipso line, pixel inside IMAGER swath:
retv.calipso.imager_linnum = np.repeat(cal, idx_match).astype('i')
retv.calipso.imager_pixnum = np.repeat(cap, idx_match).astype('i')
# Imager time
retv.imager.sec_1970 = imager_lines_sec_1970[idx_match.ravel()]
retv.diff_sec_1970 = retv.calipso.sec_1970 - retv.imager.sec_1970
do_some_logging(retv, calipso)
logger.debug("Generate the latitude, cloudtype tracks!")
from atrain_match.libs.extract_imager_along_track import imager_track_from_matched
retv = imager_track_from_matched(retv, SETTINGS, cloudproducts)
if calipso_aerosol is not None:
retv.calipso_aerosol = calipso_aerosol.extract_elements(idx=idx_match)
max_cloud_top_calipso = np.maximum.reduce(
retv.calipso.layer_top_altitude.ravel())
logger.debug("max_cloud_top_calipso: %2.1f", max_cloud_top_calipso)
return retv
def match_mora_imager(mora, cloudproducts, SETTINGS):
retv = TruthImagerTrackObject('mora')
retv.imager_instrument = cloudproducts.instrument.lower()
retv.mora = mora
from atrain_match.utils.common import map_imager
"""
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
from atrain_match.plotting.histogram_plotting import distribution_map
plt.plot(cloudproducts.longitude.ravel(),
cloudproducts.latitude.ravel(), 'r.')
plt.plot(mora.longitude.ravel(), mora.latitude.ravel(), 'b.')
plt.show()
"""
cal, cap = map_imager(cloudproducts,
mora.longitude.ravel(),
mora.latitude.ravel(),
radius_of_influence=config.RESOLUTION*0.7*1000.0)
calnan = np.where(cal == config.NODATA, np.nan, cal)
if (~np.isnan(calnan)).sum() == 0:
logger.warning("No matches within region.")
return None
# check if it is within time limits:
if len(cloudproducts.time.shape) > 1:
imager_time_vector = [cloudproducts.time[line, pixel] for line, pixel in zip(cal, cap)]
imager_lines_sec_1970 = np.where(cal != config.NODATA, imager_time_vector, np.nan)
else:
imager_lines_sec_1970 = np.where(cal != config.NODATA, cloudproducts.time[cal], np.nan)
idx_match = elements_within_range(mora.sec_1970, imager_lines_sec_1970, SETTINGS["sec_timeThr_synop"])
if idx_match.sum() == 0:
logger.warning("No matches in region within time threshold %d s.", SETTINGS["sec_timeThr_synop"])
return None
retv.mora = retv.mora.extract_elements(idx=idx_match)
# Mora line, pixel inside IMAGER swath (one nearest neighbour):
retv.mora.imager_linnum = np.repeat(cal, idx_match).astype('i')
retv.mora.imager_pixnum = np.repeat(cap, idx_match).astype('i')
# Imager time
retv.imager.sec_1970 = np.repeat(imager_lines_sec_1970, idx_match)
retv.diff_sec_1970 = retv.mora.sec_1970 - retv.imager.sec_1970
do_some_logging(retv, mora)
logger.debug("Extract imager along track!")
retv = imager_track_from_matched(retv,
SETTINGS,
cloudproducts,
extract_nwp_segments=False)
return retv
def match_iss_imager(iss, cloudproducts, SETTINGS):
retv = TruthImagerTrackObject(truth='iss')
retv.imager_instrument = cloudproducts.instrument.lower()
retv.iss = iss
from atrain_match.utils.common import map_imager
cal, cap = map_imager(cloudproducts,
iss.longitude.ravel(),
iss.latitude.ravel(),
radius_of_influence=config.RESOLUTION * 0.7 *
1000.0) # larger than radius...
calnan = np.where(cal == config.NODATA, np.nan, cal)
if (~np.isnan(calnan)).sum() == 0:
logger.warning("No matches within region.")
return None
# check if it is within time limits:
if len(cloudproducts.time.shape) > 1:
imager_time_vector = [
cloudproducts.time[line, pixel] for line, pixel in zip(cal, cap)
]
imager_lines_sec_1970 = np.where(cal != config.NODATA,
imager_time_vector, np.nan)
else:
imager_lines_sec_1970 = np.where(cal != config.NODATA,
cloudproducts.time[cal], np.nan)
# Find all matching Iss pixels within +/- sec_timeThr from the IMAGER data
idx_match = elements_within_range(iss.sec_1970, imager_lines_sec_1970,
SETTINGS["sec_timeThr"])
if idx_match.sum() == 0:
logger.warning("No matches in region within time threshold %d s.",
SETTINGS["sec_timeThr"])
return None
retv.iss = retv.iss.extract_elements(idx=idx_match)
# Calipso line, pixel inside IMAGER swath:
retv.iss.imager_linnum = np.repeat(cal, idx_match).astype('i')
retv.iss.imager_pixnum = np.repeat(cap, idx_match).astype('i')
# Imager time
retv.imager.sec_1970 = np.repeat(imager_lines_sec_1970, idx_match)
retv.diff_sec_1970 = retv.iss.sec_1970 - retv.imager.sec_1970
do_some_logging(retv, iss)
logger.info("Generate the latitude, cloudtype tracks!")
retv = imager_track_from_matched(retv, SETTINGS, cloudproducts)
return retv
def match_amsr_imager(amsr, cloudproducts, SETTINGS):
retv = TruthImagerTrackObject(truth='amsr')
retv.imager_instrument = cloudproducts.instrument.lower()
retv.amsr = amsr
if (getattr(cloudproducts.cpp, "cpp_lwp") < 0).all():
logger.warning("Not matching AMSR-E with scene with no lwp.")
return None
# return MatchupError("No imager Lwp.") # if only LWP matching?
from atrain_match.utils.common import map_imager_distances
n_neighbours = 8
if config.RESOLUTION == 5:
n_neighbours = 5
mapper_and_dist = map_imager_distances(cloudproducts,
amsr.longitude.ravel(),
amsr.latitude.ravel(),
radius_of_influence=AMSR_RADIUS,
n_neighbours=n_neighbours)
cal, cap = mapper_and_dist["mapper"]
distances = mapper_and_dist["distances"]
cal_1 = cal[:, 0]
cap_1 = cap[:, 0]
calnan = np.where(cal_1 == config.NODATA, np.nan, cal_1)
if (~np.isnan(calnan)).sum() == 0:
logger.warning("No matches within region.")
return None
# check if it is within time limits:
if len(cloudproducts.time.shape) > 1:
imager_time_vector = [
cloudproducts.time[line, pixel]
for line, pixel in zip(cal_1, cap_1)
]
imager_lines_sec_1970 = np.where(cal_1 != config.NODATA,
imager_time_vector, np.nan)
else:
imager_lines_sec_1970 = np.where(cal_1 != config.NODATA,
cloudproducts.time[cal_1], np.nan)
# Find all matching Amsr pixels within +/- sec_timeThr from the IMAGER data
imager_sunz_vector = np.array([
cloudproducts.imager_angles.sunz.data[line, pixel]
for line, pixel in zip(cal_1, cap_1)
])
idx_match = np.logical_and(
elements_within_range(amsr.sec_1970, imager_lines_sec_1970,
SETTINGS["sec_timeThr"]),
imager_sunz_vector <= 84) # something larger than 84 (max for lwp)
if idx_match.sum() == 0:
logger.warning(
"No light (sunz<84) matches in region within time threshold %d s.",
SETTINGS["sec_timeThr"])
return None
retv.amsr = retv.amsr.extract_elements(idx=idx_match)
# Amsr line, pixel inside IMAGER swath (one neighbour):
retv.amsr.imager_linnum = np.repeat(cal_1, idx_match).astype('i')
retv.amsr.imager_pixnum = np.repeat(cap_1, idx_match).astype('i')
retv.amsr.imager_linnum_nneigh = np.repeat(cal, idx_match, axis=0)
retv.amsr.imager_pixnum_nneigh = np.repeat(cap, idx_match, axis=0)
retv.amsr.imager_amsr_dist = np.repeat(distances, idx_match, axis=0)
# Imager time
retv.imager.sec_1970 = np.repeat(imager_lines_sec_1970, idx_match)
retv.diff_sec_1970 = retv.amsr.sec_1970 - retv.imager.sec_1970
do_some_logging(retv, amsr)
logger.debug("Extract imager lwp along track!")
retv = imager_track_from_matched(retv,
SETTINGS,
cloudproducts,
extract_radiances=False,
extract_aux_segments=False,
extract_ctth=False,
extract_ctype=False,
aux_params=['fractionofland', 'landuse'],
extract_some_data_for_x_neighbours=True)
return retv