from icing.pireps import pirep_icing
import numpy as np
import pickle
import os
from util.util import get_time_tuple_utc, GenericException, add_time_range_to_filename, is_night, is_day, \
check_oblique, get_timestamp, homedir, get_indexes_within_threshold
from aeolus.datasource import CLAVRx, CLAVRx_VIIRS, GOESL1B, CLAVRx_H08
import h5py
import datetime
from datetime import timezone
import glob
from skyfield import api, almanac
goes_date_format = '%Y%j%H'
goes16_directory = '/arcdata/goes/grb/goes16' # /year/date/abi/L1b/RadC
clavrx_dir = '/ships19/cloud/scratch/ICING/'
# next one appears to be gone. Keeping this here for reference.
clavrx_viirs_dir = '/apollo/cloud/scratch/Satellite_Output/NASA-SNPP_VIIRS/global/2019_DNB_for_Rink_wDBfix/level2_h5/'
clavrx_test_dir = '/data/Personal/rink/clavrx/'
dir_fmt = '%Y_%m_%d_%j'
# dir_list = [f.path for f in os.scandir('.') if f.is_dir()]
ds_dct = {}
goes_ds_dct = {}
# --- CLAVRx Radiometric parameters and metadata ------------------------------------------------
l1b_ds_list = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom',
'refl_0_47um_nom', 'refl_0_65um_nom', 'refl_0_86um_nom', 'refl_1_38um_nom', 'refl_1_60um_nom']
l1b_ds_types = ['f4' for ds in l1b_ds_list]
l1b_ds_fill = [-32767 for i in range(10)] + [-32768 for i in range(5)]
l1b_ds_range = ['actual_range' for ds in l1b_ds_list]
# --- CLAVRx L2 parameters and metadata
ds_list = ['cld_height_acha', 'cld_geo_thick', 'cld_press_acha', 'sensor_zenith_angle', 'supercooled_prob_acha',
'supercooled_cloud_fraction', 'cld_temp_acha', 'cld_opd_acha', 'solar_zenith_angle',
'cld_reff_acha', 'cld_reff_dcomp', 'cld_reff_dcomp_1', 'cld_reff_dcomp_2', 'cld_reff_dcomp_3',
'cld_opd_dcomp', 'cld_opd_dcomp_1', 'cld_opd_dcomp_2', 'cld_opd_dcomp_3', 'cld_cwp_dcomp', 'iwc_dcomp',
'lwc_dcomp', 'cld_emiss_acha', 'conv_cloud_fraction', 'cloud_type', 'cloud_phase', 'cloud_mask']
ds_types = ['f4' for i in range(23)] + ['i1' for i in range(3)]
ds_fill = [-32768 for i in range(23)] + [-128 for i in range(3)]
ds_range = ['actual_range' for i in range(23)] + [None for i in range(3)]
# --------------------------------------------
# --- CLAVRx VIIRS L2 parameters and metadata
# ds_list = ['cld_height_acha', 'cld_geo_thick', 'cld_press_acha', 'sensor_zenith_angle', 'supercooled_prob_acha',
# 'supercooled_cloud_fraction', 'cld_temp_acha', 'cld_opd_acha', 'solar_zenith_angle',
# 'cld_reff_acha', 'cld_reff_dcomp', 'cld_reff_dcomp_1', 'cld_reff_dcomp_2', 'cld_reff_dcomp_3',
# 'cld_opd_dcomp', 'cld_opd_dcomp_1', 'cld_opd_dcomp_2', 'cld_opd_dcomp_3', 'cld_cwp_dcomp', 'iwc_dcomp',
# 'lwc_dcomp', 'cld_emiss_acha', 'conv_cloud_fraction', 'cld_opd_nlcomp', 'cld_reff_nlcomp', 'cloud_type',
# 'cloud_phase', 'cloud_mask']
# ds_types = ['f4' for i in range(25)] + ['i1' for i in range(3)]
# ds_fill = [-32768 for i in range(25)] + [-128 for i in range(3)]
# ds_range = ['actual_range' for i in range(25)] + [None for i in range(3)]
# --------------------------------------------
# An example file for accessing and copying metadata
a_clvr_file = homedir+'data/clavrx/clavrx_OR_ABI-L1b-RadC-M3C01_G16_s20190020002186.level2.nc'
#a_clvr_file = homedir+'data/clavrx/RadC/265/clavrx_OR_ABI-L1b-RadC-M6C01_G16_s20212651711172.level2.nc'
# VIIRS
#a_clvr_file = homedir+'data/clavrx/clavrx_snpp_viirs.A2019071.0000.001.2019071061610.uwssec_B00038187.level2.h5'
# Location of files for tile/FOV extraction
# icing_files = [f for f in glob.glob('/data/Personal/rink/icing_ml/icing_2*_DAY.h5')]
# icing_files = [f for f in glob.glob('/data/Personal/rink/icing_ml/icing_l1b_2*_DAY.h5')]
# icing_files = [f for f in glob.glob('/data/Personal/rink/icing_ml/icing_l1b_2*_ANY.h5')]
icing_l1b_files = []
# no_icing_files = [f for f in glob.glob('/data/Personal/rink/icing_ml/no_icing_2*_DAY.h5')]
# no_icing_files = [f for f in glob.glob('/data/Personal/rink/icing_ml/no_icing_l1b_2*_DAY.h5')]
# no_icing_files = [f for f in glob.glob('/data/Personal/rink/icing_ml/no_icing_l1b_2*_ANY.h5')]
no_icing_l1b_files = []
train_params_day = ['cld_height_acha', 'cld_geo_thick', 'supercooled_cloud_fraction', 'cld_temp_acha', 'cld_press_acha',
'solar_zenith_angle', 'cld_reff_dcomp', 'cld_opd_dcomp', 'cld_cwp_dcomp', 'iwc_dcomp', 'lwc_dcomp',
'cloud_phase', 'cloud_mask']
train_params_night = ['cld_height_acha', 'cld_geo_thick', 'supercooled_cloud_fraction', 'cld_temp_acha',
'cld_press_acha', 'cld_reff_acha', 'cld_opd_acha', 'cloud_phase', 'cloud_mask']
# HALF_WIDTH = 20
HALF_WIDTH = 1
# Just a wrapper for the conversion of PIREP reports to in-memory dictionary
def setup(pirep_file=homedir+'data/pirep/pireps_20180101_20200331.csv'):
ice_dict, no_ice_dict, neg_ice_dict = pirep_icing(pirep_file)
return ice_dict, no_ice_dict, neg_ice_dict
def get_clavrx_datasource(timestamp, platform, file_time_span=10):
if platform == 'GOES':
return get_clavrx_datasource_goes(timestamp, file_time_span=file_time_span)
elif platform == 'VIIRS':
return get_clavrx_datasource_viirs(timestamp)
def get_clavrx_datasource_goes(timestamp, file_time_span=10):
dt_obj, time_tup = get_time_tuple_utc(timestamp)
date_dir_str = dt_obj.strftime(dir_fmt)
ds = ds_dct.get(date_dir_str)
if ds is None:
ds = CLAVRx(clavrx_dir + date_dir_str + '/', file_time_span=file_time_span)
ds_dct[date_dir_str] = ds
return ds
def get_clavrx_datasource_viirs(timestamp):
dt_obj, time_tup = get_time_tuple_utc(timestamp)
date_dir_str = dt_obj.strftime('%j')
ds = ds_dct.get(date_dir_str)
if ds is None:
ds = CLAVRx_VIIRS(clavrx_viirs_dir + date_dir_str + '/')
ds_dct[date_dir_str] = ds
return ds
def get_goes_datasource(timestamp):
dt_obj, time_tup = get_time_tuple_utc(timestamp)
yr_dir = str(dt_obj.timetuple().tm_year)
date_dir = dt_obj.strftime(dir_fmt)
files_path = goes16_directory + '/' + yr_dir + '/' + date_dir + '/abi' + '/L1b' + '/RadC/'
ds = goes_ds_dct.get(date_dir)
if ds is None:
ds = GOESL1B(files_path)
goes_ds_dct[date_dir] = ds
return ds
def get_grid_values(h5f, grid_name, j_c, i_c, half_width, num_j=None, num_i=None, scale_factor_name='scale_factor', add_offset_name='add_offset',
fill_value_name='_FillValue', range_name='actual_range', fill_value=None):
try:
hfds = h5f[grid_name]
except Exception:
return None
attrs = hfds.attrs
if attrs is None:
raise GenericException('No attributes object for: '+grid_name)
ylen, xlen = hfds.shape
if half_width is not None:
j_l = j_c-half_width
i_l = i_c-half_width
if j_l < 0 or i_l < 0:
return None
j_r = j_c+half_width+1
i_r = i_c+half_width+1
if j_r >= ylen or i_r >= xlen:
return None
else:
j_l = j_c
j_r = j_c + num_j + 1
i_l = i_c
i_r = i_c + num_i + 1
grd_vals = hfds[j_l:j_r, i_l:i_r]
if fill_value is not None:
grd_vals = np.where(grd_vals == fill_value, np.nan, grd_vals)
if scale_factor_name is not None:
attr = attrs.get(scale_factor_name)
if attr is not None:
if np.isscalar(attr):
scale_factor = attr
else:
scale_factor = attr[0]
grd_vals = grd_vals * scale_factor
if add_offset_name is not None:
attr = attrs.get(add_offset_name)
if attr is not None:
if np.isscalar(attr):
add_offset = attr
else:
add_offset = attr[0]
grd_vals = grd_vals + add_offset
if range_name is not None:
attr = attrs.get(range_name)
if attr is None:
raise GenericException('Attribute: '+range_name+' not found for dataset: '+grid_name)
low = attr[0]
high = attr[1]
grd_vals = np.where(grd_vals < low, np.nan, grd_vals)
grd_vals = np.where(grd_vals > high, np.nan, grd_vals)
elif fill_value_name is not None:
attr = attrs.get(fill_value_name)
if attr is None:
raise GenericException('Attribute: '+fill_value_name+' not found for dataset: '+grid_name)
if np.isscalar(attr):
fill_value = attr
else:
fill_value = attr[0]
grd_vals = np.where(grd_vals == fill_value, np.nan, grd_vals)
return grd_vals
def create_file(filename, data_dct, ds_list, ds_types, lon_c, lat_c, time_s, fl_alt_s, icing_intensity, unq_ids, mask=None):
h5f_expl = h5py.File(a_clvr_file, 'r')
h5f = h5py.File(filename, 'w')
for idx, ds_name in enumerate(ds_list):
data = data_dct[ds_name]
h5f.create_dataset(ds_name, data=data, dtype=ds_types[idx])
lon_ds = h5f.create_dataset('longitude', data=lon_c, dtype='f4')
lon_ds.dims[0].label = 'time'
lon_ds.attrs.create('units', data='degrees_east')
lon_ds.attrs.create('long_name', data='PIREP longitude')
lat_ds = h5f.create_dataset('latitude', data=lat_c, dtype='f4')
lat_ds.dims[0].label = 'time'
lat_ds.attrs.create('units', data='degrees_north')
lat_ds.attrs.create('long_name', data='PIREP latitude')
time_ds = h5f.create_dataset('time', data=time_s)
time_ds.dims[0].label = 'time'
time_ds.attrs.create('units', data='seconds since 1970-1-1 00:00:00')
time_ds.attrs.create('long_name', data='PIREP time')
ice_alt_ds = h5f.create_dataset('icing_altitude', data=fl_alt_s, dtype='f4')
ice_alt_ds.dims[0].label = 'time'
ice_alt_ds.attrs.create('units', data='m')
ice_alt_ds.attrs.create('long_name', data='PIREP altitude')
if icing_intensity is not None:
icing_int_ds = h5f.create_dataset('icing_intensity', data=icing_intensity, dtype='i4')
icing_int_ds.attrs.create('long_name', data='From PIREP. 0:No intensity report, 1:Trace, 2:Light, 3:Light Moderate, 4:Moderate, 5:Moderate Severe, 6:Severe')
unq_ids_ds = h5f.create_dataset('unique_id', data=unq_ids, dtype='i4')
unq_ids_ds.attrs.create('long_name', data='ID mapping to PIREP icing dictionary: see pireps.py')
if mask is not None:
mask = mask.astype(np.byte)
mask_ds = h5f.create_dataset('FOV_mask', data=mask, dtype=np.byte)
mask_ds.attrs.create('long_name', data='The FOVs which pass the cloudy icing report test')
mask_ds.dims[0].label = 'time'
mask_ds.dims[1].label = 'y'
mask_ds.dims[2].label = 'x'
# copy relevant attributes
for ds_name in ds_list:
h5f_ds = h5f[ds_name]
h5f_ds.attrs.create('standard_name', data=h5f_expl[ds_name].attrs.get('standard_name'))
h5f_ds.attrs.create('long_name', data=h5f_expl[ds_name].attrs.get('long_name'))
h5f_ds.attrs.create('units', data=h5f_expl[ds_name].attrs.get('units'))
h5f_ds.dims[0].label = 'time'
h5f_ds.dims[1].label = 'y'
h5f_ds.dims[2].label = 'x'
h5f.close()
h5f_expl.close()
def run(pirep_dct, platform, outfile=None, outfile_l1b=None, dt_str_start=None, dt_str_end=None, skip=True, file_time_span=10):
time_keys = list(pirep_dct.keys())
l1b_grd_dct = {name: [] for name in l1b_ds_list}
ds_grd_dct = {name: [] for name in ds_list}
t_start = None
t_end = None
if (dt_str_start is not None) and (dt_str_end is not None):
dto = datetime.datetime.strptime(dt_str_start, '%Y-%m-%d_%H:%M').replace(tzinfo=timezone.utc)
dto.replace(tzinfo=timezone.utc)
t_start = dto.timestamp()
dto = datetime.datetime.strptime(dt_str_end, '%Y-%m-%d_%H:%M').replace(tzinfo=timezone.utc)
dto.replace(tzinfo=timezone.utc)
t_end = dto.timestamp()
lon_s = np.zeros(1)
lat_s = np.zeros(1)
last_clvr_file = None
last_h5f = None
nav = None
lon_c = []
lat_c = []
time_s = []
fl_alt_s = []
ice_int_s = []
unq_ids = []
for idx, time in enumerate(time_keys):
if t_start is not None:
if time < t_start:
continue
if time > t_end:
continue
try:
clvr_ds = get_clavrx_datasource(time, platform, file_time_span=file_time_span)
except Exception:
print('run: Problem retrieving Datasource', get_time_tuple_utc(time))
continue
clvr_file = clvr_ds.get_file(time)[0]
if clvr_file is None:
continue
if clvr_file != last_clvr_file:
try:
h5f = h5py.File(clvr_file, 'r')
nav = clvr_ds.get_navigation(h5f)
except Exception:
if h5f is not None:
h5f.close()
print('Problem with file: ', clvr_file)
continue
if last_h5f is not None:
last_h5f.close()
last_h5f = h5f
last_clvr_file = clvr_file
else:
h5f = last_h5f
cc = ll = -1
reports = pirep_dct[time]
for tup in reports:
lat, lon, fl, I, uid, rpt_str = tup
lat_s[0] = lat
lon_s[0] = lon
cc_a, ll_a = nav.earth_to_lc_s(lon_s, lat_s) # non-navigable, skip
if cc_a[0] < 0 or ll_a[0] < 0:
continue
if skip and (cc_a[0] == cc and ll_a[0] == ll): # time adjacent duplicate, skip
continue
else:
cc = cc_a[0]
ll = ll_a[0]
# test --------------------
lon_grd_vals = get_grid_values(h5f, 'longitude', ll_a[0], cc_a[0], HALF_WIDTH, fill_value_name=None, range_name='valid_range', fill_value=-999.0)
lat_grd_vals = get_grid_values(h5f, 'latitude', ll_a[0], cc_a[0], HALF_WIDTH, fill_value_name=None, range_name='valid_range', fill_value=-999.0)
print(lon_grd_vals[1, 1], lon)
print(lat_grd_vals[1, 1], lat)
continue
# ------------------------------
cnt_a = 0
ds_lst = []
for didx, ds_name in enumerate(ds_list):
gvals = get_grid_values(h5f, ds_name, ll_a[0], cc_a[0], HALF_WIDTH, fill_value_name=None, range_name=ds_range[didx], fill_value=ds_fill[didx])
if gvals is not None:
ds_lst.append(gvals)
cnt_a += 1
cnt_b = 0
l1b_lst = []
for didx, ds_name in enumerate(l1b_ds_list):
gvals = get_grid_values(h5f, ds_name, ll_a[0], cc_a[0], HALF_WIDTH, fill_value_name=None, range_name=l1b_ds_range[didx], fill_value=l1b_ds_fill[didx])
if gvals is not None:
l1b_lst.append(gvals)
cnt_b += 1
if cnt_a > 0 and cnt_a != len(ds_list):
continue
if cnt_b > 0 and cnt_b != len(l1b_ds_list):
continue
if cnt_a == len(ds_list) and cnt_b == len(l1b_ds_list):
for didx, ds_name in enumerate(ds_list):
ds_grd_dct[ds_name].append(ds_lst[didx])
for didx, ds_name in enumerate(l1b_ds_list):
l1b_grd_dct[ds_name].append(l1b_lst[didx])
lon_c.append(lon_s[0])
lat_c.append(lat_s[0])
time_s.append(time)
fl_alt_s.append(fl)
ice_int_s.append(I)
unq_ids.append(uid)
if len(time_s) == 0:
return
t_start = time_s[0]
t_end = time_s[len(time_s)-1]
lon_c = np.array(lon_c)
lat_c = np.array(lat_c)
time_s = np.array(time_s)
fl_alt_s = np.array(fl_alt_s)
ice_int_s = np.array(ice_int_s)
unq_ids = np.array(unq_ids)
data_dct = {}
for ds_name in ds_list:
data_dct[ds_name] = np.array(ds_grd_dct[ds_name])
if outfile is not None:
outfile = add_time_range_to_filename(outfile, t_start, t_end)
create_file(outfile, data_dct, ds_list, ds_types, lon_c, lat_c, time_s, fl_alt_s, ice_int_s, unq_ids)
data_dct = {}
for ds_name in l1b_ds_list:
data_dct[ds_name] = np.array(l1b_grd_dct[ds_name])
if outfile_l1b is not None:
outfile_l1b = add_time_range_to_filename(outfile_l1b, t_start, t_end)
create_file(outfile_l1b, data_dct, l1b_ds_list, l1b_ds_types, lon_c, lat_c, time_s, fl_alt_s, ice_int_s, unq_ids)
def run_viirs(pirep_dct, platform='VIIRS', outfile=None, outfile_l1b=None, dt_str_start=None, dt_str_end=None):
time_keys = list(pirep_dct.keys())
l1b_grd_dct = {name: [] for name in l1b_ds_list}
ds_grd_dct = {name: [] for name in ds_list}
t_start = None
t_end = None
if (dt_str_start is not None) and (dt_str_end is not None):
dto = datetime.datetime.strptime(dt_str_start, '%Y-%m-%d_%H:%M').replace(tzinfo=timezone.utc)
dto.replace(tzinfo=timezone.utc)
t_start = dto.timestamp()
dto = datetime.datetime.strptime(dt_str_end, '%Y-%m-%d_%H:%M').replace(tzinfo=timezone.utc)
dto.replace(tzinfo=timezone.utc)
t_end = dto.timestamp()
lon_s = np.zeros(1)
lat_s = np.zeros(1)
last_clvr_file = None
last_h5f = None
nav = None
lon_c = []
lat_c = []
time_s = []
fl_alt_s = []
ice_int_s = []
unq_ids = []
num_rpts = 0
num_rpts_match = 0
for idx, time in enumerate(time_keys):
if t_start is not None:
if time < t_start:
continue
if time > t_end:
continue
num_rpts += 1
try:
clvr_ds = get_clavrx_datasource(time, platform)
except Exception:
print('run: Problem retrieving Datasource')
continue
clvr_file = clvr_ds.get_file_containing_time(time)[0]
if clvr_file is None:
continue
if clvr_file != last_clvr_file:
try:
h5f = h5py.File(clvr_file, 'r')
nav = clvr_ds.get_navigation(h5f)
except Exception:
if h5f is not None:
h5f.close()
print('Problem with file: ', clvr_file)
continue
if last_h5f is not None:
last_h5f.close()
last_h5f = h5f
last_clvr_file = clvr_file
else:
h5f = last_h5f
cc = ll = -1
reports = pirep_dct[time]
for tup in reports:
lat, lon, fl, I, uid, rpt_str = tup
lat_s[0] = lat
lon_s[0] = lon
print(' ',lon, lat)
if not nav.check_inside(lon, lat):
print('missed range check')
continue
cc_a, ll_a = nav.earth_to_lc_s(lon_s, lat_s) # non-navigable, skip
if cc_a[0] < 0:
print('cant navigate')
continue
if cc_a[0] == cc and ll_a[0] == ll: # time adjacent duplicate, skip
continue
else:
cc = cc_a[0]
ll = ll_a[0]
cnt_a = 0
for didx, ds_name in enumerate(ds_list):
gvals = get_grid_values(h5f, ds_name, ll_a[0], cc_a[0], 20, fill_value_name=None, range_name=ds_range[didx], fill_value=ds_fill[didx])
if gvals is not None:
ds_grd_dct[ds_name].append(gvals)
cnt_a += 1
cnt_b = 0
for didx, ds_name in enumerate(l1b_ds_list):
gvals = get_grid_values(h5f, ds_name, ll_a[0], cc_a[0], 20, fill_value_name=None, range_name=l1b_ds_range[didx], fill_value=l1b_ds_fill[didx])
if gvals is not None:
l1b_grd_dct[ds_name].append(gvals)
cnt_b += 1
if cnt_a > 0 and cnt_a != len(ds_list):
raise GenericException('weirdness')
if cnt_b > 0 and cnt_b != len(l1b_ds_list):
raise GenericException('weirdness')
if cnt_a == len(ds_list) and cnt_b == len(l1b_ds_list):
lon_c.append(lon_s[0])
lat_c.append(lat_s[0])
time_s.append(time)
fl_alt_s.append(fl)
ice_int_s.append(I)
unq_ids.append(uid)
print('num reports: ', num_rpts)
if len(time_s) == 0:
return
t_start = time_s[0]
t_end = time_s[len(time_s)-1]
data_dct = {}
for ds_name in ds_list:
data_dct[ds_name] = np.array(ds_grd_dct[ds_name])
lon_c = np.array(lon_c)
lat_c = np.array(lat_c)
time_s = np.array(time_s)
fl_alt_s = np.array(fl_alt_s)
ice_int_s = np.array(ice_int_s)
unq_ids = np.array(unq_ids)
if outfile is not None:
outfile = add_time_range_to_filename(outfile, t_start, t_end)
create_file(outfile, data_dct, ds_list, ds_types, lon_c, lat_c, time_s, fl_alt_s, ice_int_s, unq_ids)
data_dct = {}
for ds_name in l1b_ds_list:
data_dct[ds_name] = np.array(l1b_grd_dct[ds_name])
if outfile_l1b is not None:
outfile_l1b = add_time_range_to_filename(outfile_l1b, t_start, t_end)
create_file(outfile_l1b, data_dct, l1b_ds_list, l1b_ds_types, lon_c, lat_c, time_s, fl_alt_s, ice_int_s, unq_ids)
def pirep_info(pirep_dct):
time_keys = list(pirep_dct.keys())
lat_s = []
lon_s = []
flt_lvl_s = []
ice_intensity_s = []
for tkey in time_keys:
reports = pirep_dct[tkey]
for tup in reports:
lat, lon, fl, I, uid, rpt_str = tup
lat_s.append(lat)
lon_s.append(lon)
flt_lvl_s.append(fl)
ice_intensity_s.append(I)
lat_s = np.array(lat_s)
lon_s = np.array(lon_s)
flt_lvl_s = np.array(flt_lvl_s)
ice_intensity_s = np.array(ice_intensity_s)
return flt_lvl_s, ice_intensity_s, lat_s, lon_s
lon_space_hdeg = np.linspace(-180, 180, 721)
lat_space_hdeg = np.linspace(-90, 90, 361)
hgt_space_3000 = np.linspace(0, 15000, 3000)
def check_no_overlap(lon, lat, ts, grd_bins, t_delta=600.0):
grd_x_hi = lon_space_hdeg.shape[0] - 1
grd_y_hi = lat_space_hdeg.shape[0] - 1
lon_idx = np.searchsorted(lon_space_hdeg, lon)
lat_idx = np.searchsorted(lat_space_hdeg, lat)
if lon_idx < 0 or lon_idx > grd_x_hi:
return False
if lat_idx < 0 or lat_idx > grd_y_hi:
return False
last_ts = grd_bins[lat_idx, lon_idx]
if ts - last_ts > t_delta:
grd_bins[lat_idx, lon_idx] = ts
return True
else:
return False
def check_no_overlap_alt(lon, lat, hgt, ts, grd_bins, t_delta=600.0):
grd_x_hi = lon_space_hdeg.shape[0] - 1
grd_y_hi = lat_space_hdeg.shape[0] - 1
grd_z_hi = hgt_space_3000.shape[0] - 1
lon_idx = np.searchsorted(lon_space_hdeg, lon)
lat_idx = np.searchsorted(lat_space_hdeg, lat)
hgt_idx = np.searchsorted(hgt_space_3000, hgt)
if lon_idx < 0 or lon_idx > grd_x_hi:
return False
if lat_idx < 0 or lat_idx > grd_y_hi:
return False
if hgt_idx < 0 or hgt_idx > grd_z_hi:
return False
last_ts = grd_bins[hgt_idx, lat_idx, lon_idx]
if ts - last_ts > t_delta:
grd_bins[hgt_idx, lat_idx, lon_idx] = ts
return True
else:
return False
# This mostly reduces some categories for a degree of class balancing and removes no intensity reports
def process(ice_dct, no_ice_dct, neg_ice_dct, t_delta=600):
t_delta = 600 # seconds
new_ice_dct = {}
new_no_ice_dct = {}
new_neg_ice_dct = {}
ice_keys_5_6 = []
ice_tidx_5_6 = []
ice_keys_1 = []
ice_tidx_1 = []
ice_keys_4 = []
ice_tidx_4 = []
ice_keys_3 = []
ice_tidx_3 = []
ice_keys_2 = []
ice_tidx_2 = []
print('num keys ice, no_ice, neg_ice: ', len(ice_dct), len(no_ice_dct), len(neg_ice_dct))
no_intensity_cnt = 0
num_ice_reports = 0
for ts in list(ice_dct.keys()):
rpts = ice_dct[ts]
for idx, tup in enumerate(rpts):
num_ice_reports += 1
if tup[3] == 5 or tup[3] == 6:
ice_keys_5_6.append(ts)
ice_tidx_5_6.append(idx)
elif tup[3] == 1:
ice_keys_1.append(ts)
ice_tidx_1.append(idx)
elif tup[3] == 4:
ice_keys_4.append(ts)
ice_tidx_4.append(idx)
elif tup[3] == 3:
ice_keys_3.append(ts)
ice_tidx_3.append(idx)
elif tup[3] == 2:
ice_keys_2.append(ts)
ice_tidx_2.append(idx)
else:
no_intensity_cnt += 1
no_ice_keys = []
no_ice_tidx = []
for ts in list(no_ice_dct.keys()):
rpts = no_ice_dct[ts]
for idx, tup in enumerate(rpts):
no_ice_keys.append(ts)
no_ice_tidx.append(idx)
neg_ice_keys = []
neg_ice_tidx = []
for ts in list(neg_ice_dct.keys()):
rpts = neg_ice_dct[ts]
for idx, tup in enumerate(rpts):
neg_ice_keys.append(ts)
neg_ice_tidx.append(idx)
print('num ice reports, no ice, neg ice: ', num_ice_reports, len(no_ice_keys), len(neg_ice_keys))
print('------------------------------------------------')
ice_keys_5_6 = np.array(ice_keys_5_6)
ice_tidx_5_6 = np.array(ice_tidx_5_6, dtype='int64')
print('5_6: ', ice_keys_5_6.shape[0])
ice_keys_4 = np.array(ice_keys_4)
ice_tidx_4 = np.array(ice_tidx_4, dtype='int64')
print('4: ', ice_keys_4.shape[0])
ice_keys_3 = np.array(ice_keys_3)
ice_tidx_3 = np.array(ice_tidx_3, dtype='int64')
print('3: ', ice_keys_3.shape[0])
ice_keys_2 = np.array(ice_keys_2)
ice_tidx_2 = np.array(ice_tidx_2, dtype='int64')
print('2: ', ice_keys_2.shape[0])
ice_keys_1 = np.array(ice_keys_1)
ice_tidx_1 = np.array(ice_tidx_1, dtype='int64')
print('1: ', ice_keys_1.shape[0])
print('0: ', no_intensity_cnt)
ice_keys = np.concatenate([ice_keys_1, ice_keys_2, ice_keys_3, ice_keys_4, ice_keys_5_6])
ice_tidx = np.concatenate([ice_tidx_1, ice_tidx_2, ice_tidx_3, ice_tidx_4, ice_tidx_5_6])
print('icing total reduced: ', ice_tidx.shape)
print('----------------------------')
sidxs = np.argsort(ice_keys)
ice_keys = ice_keys[sidxs]
ice_tidx = ice_tidx[sidxs]
# -----------------------------------------------------
no_ice_keys = np.array(no_ice_keys)
no_ice_tidx = np.array(no_ice_tidx)
print('no ice total: ', no_ice_keys.shape[0])
np.random.seed(42)
ridxs = np.random.permutation(np.arange(no_ice_keys.shape[0]))
no_ice_keys = no_ice_keys[ridxs]
no_ice_tidx = no_ice_tidx[ridxs]
no_ice_keys = no_ice_keys[::3]
no_ice_tidx = no_ice_tidx[::3]
print('no ice reduced: ', no_ice_keys.shape[0])
print('---------------------------------')
sidxs = np.argsort(no_ice_keys)
no_ice_keys = no_ice_keys[sidxs]
no_ice_tidx = no_ice_tidx[sidxs]
all_which = np.concatenate([np.full(len(ice_keys), 1), np.full(len(no_ice_keys), 0)])
all_keys = np.concatenate([ice_keys, no_ice_keys])
all_tidx = np.concatenate([ice_tidx, no_ice_tidx])
sidxs = np.argsort(all_keys)
all_keys = all_keys[sidxs]
all_tidx = all_tidx[sidxs]
all_which = all_which[sidxs]
grd_bins = np.full((lat_space_hdeg.shape[0], lon_space_hdeg.shape[0]), -(t_delta+1))
# grd_bins = np.full((hgt_space_3000.shape[0], lat_space_hdeg.shape[0], lon_space_hdeg.shape[0]), -(t_delta+1))
cnt_i = 0
cnt_ni = 0
for idx, key in enumerate(all_keys):
i_ni = all_which[idx]
if i_ni == 0:
rpts = no_ice_dct[key]
tup = rpts[all_tidx[idx]]
elif i_ni == 1:
rpts = ice_dct[key]
tup = rpts[all_tidx[idx]]
lat, lon, falt = tup[0], tup[1], tup[2]
# if not check_no_overlap_alt(lon, lat, falt, key, grd_bins, t_delta=t_delta):
if not check_no_overlap(lon, lat, key, grd_bins, t_delta=t_delta):
# continue
pass
if i_ni == 0:
cnt_ni += 1
n_rpts = new_no_ice_dct.get(key)
if n_rpts is None:
n_rpts = []
new_no_ice_dct[key] = n_rpts
n_rpts.append(tup)
elif i_ni == 1:
cnt_i += 1
n_rpts = new_ice_dct.get(key)
if n_rpts is None:
n_rpts = []
new_ice_dct[key] = n_rpts
n_rpts.append(tup)
print('no overlap ICE: ', cnt_i)
print('no overlap NO ICE: ', cnt_ni)
# -----------------------------------------------------
neg_ice_keys = np.array(neg_ice_keys)
neg_ice_tidx = np.array(neg_ice_tidx)
print('neg ice total: ', neg_ice_keys.shape[0])
# grd_bins = np.full((hgt_space_3000.shape[0], lat_space_hdeg.shape[0], lon_space_hdeg.shape[0]), -(t_delta+1))
grd_bins = np.full((lat_space_hdeg.shape[0], lon_space_hdeg.shape[0]), -(t_delta+1))
cnt = 0
for idx, key in enumerate(neg_ice_keys):
rpts = neg_ice_dct[key]
tup = rpts[neg_ice_tidx[idx]]
lat, lon, falt = tup[0], tup[1], tup[2]
# if not check_no_overlap_alt(lon, lat, falt, key, grd_bins, t_delta=t_delta):
if not check_no_overlap(lon, lat, key, grd_bins, t_delta=t_delta):
# continue
pass
cnt += 1
n_rpts = new_neg_ice_dct.get(key)
if n_rpts is None:
n_rpts = []
new_neg_ice_dct[key] = n_rpts
n_rpts.append(tup)
print('neg icing total no overlap: ', cnt)
# -------------------------------------------------
return new_ice_dct, new_no_ice_dct, new_neg_ice_dct
def analyze2(filename, filename_l1b):
f = h5py.File(filename, 'r')
f_l1b = h5py.File(filename_l1b, 'r')
iint = f['icing_intensity'][:]
icing_alt = f['flight_altitude'][:]
num_obs = iint.size
iint = np.broadcast_to(iint.reshape(num_obs, 1), (num_obs, 256)).flatten()
icing_alt = np.broadcast_to(icing_alt.reshape(num_obs, 1), (num_obs, 256)).flatten()
keep_mask = f['FOV_mask'][:, :, :].reshape([num_obs, -1])
keep_mask = keep_mask.astype(np.bool)
keep_mask = keep_mask.flatten()
iint = iint[keep_mask]
icing_alt = icing_alt[keep_mask]
num_obs = iint.size
print('num obs: ', num_obs)
no_ice = iint == -1 # All no icing
ice = iint >= 1 # All icing
tr_ice = (iint == 1) | (iint == 2) # True for icing cats 1 and 2
tr_plus_ice = iint > 2 # True for icing cats 3 and above
print('num no ice: ', np.sum(no_ice))
print('num ice: ', np.sum(ice))
print('trace ice: ', np.sum(tr_ice))
print('trace plus ice: ', np.sum(tr_plus_ice))
# keep_no_1_2 = (iint == -1) | (iint == 1) | (iint == 2)
# keep_no_3_4_5 = (iint == -1) | (iint > 2)
# # iint = iint[keep_no_1_2]
# iint = iint[keep_no_3_4_5]
# # icing_alt = icing_alt[keep_no_1_2]
# icing_alt = icing_alt[keep_no_3_4_5]
# no_ice = iint == -1
# ice = iint > 2
cld_top_hgt = f['cld_height_acha'][:, :, :].flatten()
cld_top_tmp = f['cld_temp_acha'][:, :, :].flatten()
sc_cld_frac = f['supercooled_cloud_fraction'][:, :, :].flatten()
cld_mask = f['cloud_mask'][:, :, :].flatten()
cld_opd = f['cld_opd_dcomp'][:, :, :].flatten()
# cld_opd = f['cld_opd_acha'][:, :, :].flatten()
cld_reff = f['cld_reff_dcomp'][:, :, :].flatten()
cld_lwc = f['lwc_dcomp'][:, :, :].flatten()
cld_iwc = f['iwc_dcomp'][:, :, :].flatten()
# cld_reff = f['cld_reff_acha'][:, :, :].flatten()
cld_emiss = f['cld_emiss_acha'][:, :, :].flatten()
# cld_phase = f['cloud_phase'][:, :, :].flatten()
# solzen = f['solar_zenith_angle'][:, :, :].flatten()
# bt_10_4 = f_l1b['temp_10_4um_nom'][:, :, :].flatten()
cld_top_hgt = cld_top_hgt[keep_mask]
cld_top_tmp = cld_top_tmp[keep_mask]
sc_cld_frac = sc_cld_frac[keep_mask]
cld_opd = cld_opd[keep_mask]
cld_reff = cld_reff[keep_mask]
cld_lwc = cld_lwc[keep_mask]
cld_iwc = cld_iwc[keep_mask]
cld_mask = cld_mask[keep_mask]
cld_mask = np.where(cld_mask > 1, 1, 0)
# cld_frac = np.sum(cld_mask, axis=1) / 256 # need to do this earlier
# Simple model: Everything below the melting point is icing=True else icing=False ------------------------------
preds = cld_top_tmp <= 273.15
# preds = cld_top_tmp[keep_no_3_4_5] <= 273.15
true_ice = ice & preds
false_ice = no_ice & preds
true_no_ice = no_ice & np.invert(preds)
false_no_ice = ice & np.invert(preds)
tp = np.sum(true_ice).astype(np.float32)
tn = np.sum(true_no_ice).astype(np.float32)
fp = np.sum(false_ice).astype(np.float32)
fn = np.sum(false_no_ice).astype(np.float32)
recall = tp / (tp + fn)
precision = tp / (tp + fp)
f1 = 2 * (precision * recall) / (precision + recall)
mcc = ((tp * tn) - (fp * fn)) / np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
acc = (tp + tn) / (tp + tn + fp + fn)
print('Total (Positive/Icing Prediction: ')
print('True icing: ', np.sum(true_ice))
print('-------------------------')
print('False no icing (False Negative/Miss): ', np.sum(false_no_ice))
print('---------------------------------------------------')
print('---------------------------------------------------')
print('Total (Negative/No Icing Prediction: ')
print('True no icing: ', np.sum(true_no_ice))
print('-------------------------')
print('* False icing (False Positive/False Alarm) *: ', np.sum(false_ice))
print('-------------------------')
print('ACC: ', acc)
print('Recall: ', recall)
print('Precision: ', precision)
print('F1: ', f1)
print('MCC: ', mcc)
# ------------------------------------------------------------------------------------------------------------
f.close()
f_l1b.close()
return (icing_alt[no_ice], icing_alt[ice],
cld_top_hgt[no_ice], cld_top_hgt[ice],
cld_top_tmp[no_ice], cld_top_tmp[ice],
cld_opd[no_ice], cld_opd[ice],
cld_reff[no_ice], cld_reff[ice],
cld_lwc[no_ice], cld_lwc[ice],
cld_iwc[no_ice], cld_iwc[ice],
sc_cld_frac[no_ice], sc_cld_frac[ice])
# --------------------------------------------
# x_a = 10
# x_b = 30
x_a = 12
x_b = 28
y_a = x_a
y_b = x_b
nx = ny = (x_b - x_a)
nx_x_ny = nx * ny
def run_daynight(filename, filename_l1b, day_night='ANY'):
f = h5py.File(filename, 'r')
f_l1b = h5py.File(filename_l1b, 'r')
solzen = f['solar_zenith_angle'][:, y_a:y_b, x_a:x_b]
satzen = f['sensor_zenith_angle'][:, y_a:y_b, x_a:x_b]
num_obs = solzen.shape[0]
idxs = []
for i in range(num_obs):
if not check_oblique(satzen[i,]):
continue
if day_night == 'NIGHT' and is_night(solzen[i,]):
idxs.append(i)
elif day_night == 'DAY' and is_day(solzen[i,]):
idxs.append(i)
elif day_night == 'ANY':
if is_day(solzen[i,]) or is_night(solzen[i,]):
idxs.append(i)
keep_idxs = np.array(idxs)
data_dct = {}
for didx, ds_name in enumerate(ds_list):
data_dct[ds_name] = f[ds_name][keep_idxs,]
lon_c = f['longitude'][keep_idxs]
lat_c = f['latitude'][keep_idxs]
time_s = f['time'][keep_idxs]
fl_alt_s = f['icing_altitude'][keep_idxs]
ice_int_s = f['icing_intensity'][keep_idxs]
unq_ids = f['unique_id'][keep_idxs]
path, fname = os.path.split(filename)
fbase, fext = os.path.splitext(fname)
outfile = path + '/' + fbase + '_' + day_night + fext
create_file(outfile, data_dct, ds_list, ds_types, lon_c, lat_c, time_s, fl_alt_s, ice_int_s, unq_ids)
data_dct = {}
for didx, ds_name in enumerate(l1b_ds_list):
data_dct[ds_name] = f_l1b[ds_name][keep_idxs]
path, fname = os.path.split(filename_l1b)
fbase, fext = os.path.splitext(fname)
outfile_l1b = path + '/' + fbase + '_' + day_night + fext
create_file(outfile_l1b, data_dct, l1b_ds_list, l1b_ds_types, lon_c, lat_c, time_s, fl_alt_s, ice_int_s, unq_ids)
f.close()
f_l1b.close()
def run_qc(filename, filename_l1b, day_night='ANY', pass_thresh_frac=0.20, cloudy_frac=0.5, icing=True):
f = h5py.File(filename, 'r')
icing_alt = f['icing_altitude'][:]
cld_top_hgt = f['cld_height_acha'][:, y_a:y_b, x_a:x_b]
cld_geo_dz = f['cld_geo_thick'][:, y_a:y_b, x_a:x_b]
cld_phase = f['cloud_phase'][:, y_a:y_b, x_a:x_b]
cld_temp = f['cld_temp_acha'][:, y_a:y_b, x_a:x_b]
if day_night == 'DAY':
cld_opd = f['cld_opd_dcomp'][:, y_a:y_b, x_a:x_b]
else:
cld_opd = f['cld_opd_acha'][:, y_a:y_b, x_a:x_b]
cld_mask = f['cloud_mask'][:, y_a:y_b, x_a:x_b]
sol_zen = f['solar_zenith_angle'][:, y_a:y_b, x_a:x_b]
sat_zen = f['sensor_zenith_angle'][:, y_a:y_b, x_a:x_b]
f_l1b = h5py.File(filename_l1b, 'r')
bt_11um = f_l1b['temp_11_0um_nom'][:, y_a:y_b, x_a:x_b]
if icing:
# mask, idxs, num_tested = apply_qc_icing_pireps(icing_alt, cld_top_hgt, cld_geo_dz, cld_phase, cld_opd, cld_mask, bt_11um, sol_zen, sat_zen, cld_temp, day_night=day_night, cloudy_frac=cloudy_frac)
mask, idxs, num_tested = apply_qc_icing_pireps_v3(icing_alt, cld_top_hgt, cld_geo_dz, cld_phase, cld_opd, cld_mask, bt_11um, sol_zen, sat_zen, cld_temp, day_night=day_night, cloudy_frac=cloudy_frac)
else:
# mask, idxs, num_tested = apply_qc_no_icing_pireps(icing_alt, cld_top_hgt, cld_geo_dz, cld_phase, cld_opd, cld_mask, bt_11um, sol_zen, sat_zen, cld_temp, day_night=day_night, cloudy_frac=cloudy_frac)
mask, idxs, num_tested = apply_qc_icing_pireps_v3(icing_alt, cld_top_hgt, cld_geo_dz, cld_phase, cld_opd, cld_mask, bt_11um, sol_zen, sat_zen, cld_temp, day_night=day_night, cloudy_frac=cloudy_frac)
keep_idxs = []
keep_mask = []
for i in range(len(mask)):
# keep_idxs.append(idxs[i])
frac = np.sum(mask[i]) / num_tested[i]
if icing:
if frac > pass_thresh_frac:
keep_idxs.append(idxs[i])
keep_mask.append(mask)
elif frac > pass_thresh_frac:
keep_idxs.append(idxs[i])
keep_mask.append(mask)
if len(keep_idxs) == 0:
f.close()
f_l1b.close()
return len(icing_alt), 0
print('day_night, icing, all, valid, pass: ', day_night, icing, len(icing_alt), len(mask), len(keep_idxs))
print('-----------------------')
keep_idxs = np.array(keep_idxs)
mask = np.concatenate(mask).reshape((-1, ny, nx))
data_dct = {}
for didx, ds_name in enumerate(ds_list):
data_dct[ds_name] = f[ds_name][keep_idxs, y_a:y_b, x_a:x_b]
lon_c = f['longitude'][keep_idxs]
lat_c = f['latitude'][keep_idxs]
time_s = f['time'][keep_idxs]
fl_alt_s = f['icing_altitude'][keep_idxs]
ice_int_s = f['icing_intensity'][keep_idxs]
unq_ids = f['unique_id'][keep_idxs]
path, fname = os.path.split(filename)
fbase, fext = os.path.splitext(fname)
outfile = path + '/' + fbase + '_' + 'QC' + '_' + day_night + fext
create_file(outfile, data_dct, ds_list, ds_types, lon_c, lat_c, time_s, fl_alt_s, ice_int_s, unq_ids, mask)
data_dct = {}
for didx, ds_name in enumerate(l1b_ds_list):
data_dct[ds_name] = f_l1b[ds_name][keep_idxs, y_a:y_b, x_a:x_b]
path, fname = os.path.split(filename_l1b)
fbase, fext = os.path.splitext(fname)
outfile_l1b = path + '/' + fbase + '_' + 'QC' + '_' + day_night + fext
create_file(outfile_l1b, data_dct, l1b_ds_list, l1b_ds_types, lon_c, lat_c, time_s, fl_alt_s, ice_int_s, unq_ids, mask)
f.close()
f_l1b.close()
return len(icing_alt), len(keep_idxs)
def apply_qc_icing_pireps(icing_alt, cld_top_hgt, cld_geo_dz, cld_phase, cld_opd, cld_mask, bt_11um, solzen, satzen, cld_top_temp, day_night='ANY', cloudy_frac=0.5):
if day_night == 'DAY':
opd_thick_threshold = 20
opd_thin_threshold = 1
elif day_night == 'NIGHT' or day_night == 'ANY':
opd_thick_threshold = 2
opd_thin_threshold = 0.1
closeness_top = 200.0 # meters
max_cld_depth = 1000.0
max_altitude = 4000.0
max_cld_altitude = 4000.0
num_obs = len(icing_alt)
cld_mask = cld_mask.reshape((num_obs, -1))
cld_top_hgt = cld_top_hgt.reshape((num_obs, -1))
cld_geo_dz = cld_geo_dz.reshape((num_obs, -1))
bt_11um = bt_11um.reshape((num_obs, -1))
cld_top_temp = cld_top_temp.reshape((num_obs, -1))
mask = []
idxs = []
num_tested = []
for i in range(num_obs):
if not check_oblique(satzen[i,]):
continue
if day_night == 'NIGHT' and not is_night(solzen[i,]):
continue
elif day_night == 'DAY' and not is_day(solzen[i,]):
continue
elif day_night == 'ANY':
pass
# if not (is_day(solzen[i,]) or is_night(solzen[i,])):
# continue
# if not (icing_alt[i] < max_altitude):
# continue
keep_0 = np.logical_or(cld_mask[i,] == 2, cld_mask[i,] == 3) # cloudy
keep_1 = np.invert(np.isnan(cld_top_hgt[i,]))
keep_2 = np.invert(np.isnan(bt_11um[i,]))
keep_3 = np.invert(np.isnan(cld_geo_dz[i,]))
keep = keep_0 & keep_1 & keep_2 & keep_3
num_keep = np.sum(keep)
if num_keep == 0:
continue
if num_keep / nx_x_ny < cloudy_frac: # At least this fraction cloudy
continue
keep = np.where(keep, (cld_top_hgt[i,] + closeness_top) > icing_alt[i], False)
# keep = np.where(keep, cld_top_hgt[i,] < max_cld_altitude, False)
# keep = np.where(keep, cld_top_hgt[i,] > max_cld_depth, False)
# keep = np.where(keep, (cld_top_hgt[i,] - max_cld_depth) < icing_alt[i], False)
keep = np.where(keep, np.logical_and(bt_11um[i,] > 228.0, bt_11um[i,] < 274.0), False)
# keep = np.where(keep, bt_11um[i,] < 275.0, False)
# keep = np.where(keep, cld_top_temp[i,] < 274.2, False)
# cld_hgt = cld_top_hgt[i, ].flatten()
# med_cld_hgt = np.median(cld_hgt[keep])
# if icing_alt[i] > med_cld_hgt:
# continue
#
# cld_tmp = cld_top_temp[i, ].flatten()
# med_cld_tmp = np.median(cld_tmp[keep])
# if med_cld_tmp > 275.0:
# continue
#
# if icing_alt[i] < med_cld_hgt - max_cld_depth:
# continue
mask.append(keep)
idxs.append(i)
num_tested.append(num_keep)
return mask, idxs, num_tested
def apply_qc_icing_pireps_exp1(icing_alt, cld_top_hgt, cld_geo_dz, cld_phase, cld_opd, cld_mask, bt_11um, solzen, satzen, cld_top_temp, day_night='ANY', cloudy_frac=0.5):
if day_night == 'DAY':
opd_thick_threshold = 20
opd_thin_threshold = 1
elif day_night == 'NIGHT' or day_night == 'ANY':
opd_thick_threshold = 2
opd_thin_threshold = 0.1
closeness_top = 200.0 # meters
max_cld_depth = 1000.0
max_altitude = 4000.0
max_cld_altitude = 4000.0
num_obs = len(icing_alt)
cld_mask = cld_mask.reshape((num_obs, -1))
cld_top_hgt = cld_top_hgt.reshape((num_obs, -1))
cld_geo_dz = cld_geo_dz.reshape((num_obs, -1))
bt_11um = bt_11um.reshape((num_obs, -1))
cld_top_temp = cld_top_temp.reshape((num_obs, -1))
mask = []
idxs = []
num_tested = []
for i in range(num_obs):
if not check_oblique(satzen[i,]):
continue
if day_night == 'NIGHT' and not is_night(solzen[i,]):
continue
elif day_night == 'DAY' and not is_day(solzen[i,]):
continue
elif day_night == 'ANY':
pass
# keep_0 = np.logical_or(cld_mask[i,] == 2, cld_mask[i,] == 3) # cloudy
keep_0 = cld_mask[i, ] == 3 # Confident cloudy
keep_1 = np.invert(np.isnan(cld_top_hgt[i,]))
keep_2 = np.invert(np.isnan(bt_11um[i,]))
keep_3 = np.invert(np.isnan(cld_geo_dz[i,]))
keep = keep_0 & keep_1 & keep_2 & keep_3
num_keep = np.sum(keep)
if num_keep == 0:
continue
if num_keep / nx_x_ny < cloudy_frac: # At least this fraction cloudy
continue
keep = np.where(keep, np.logical_and(cld_top_hgt[i,] > icing_alt[i,],
(cld_top_hgt[i,] - 1500.0) < icing_alt[i,]), False)
keep = np.where(keep, np.logical_and(cld_top_temp[i,] > 228.0, cld_top_temp[i,] < 274.0), False)
mask.append(keep)
idxs.append(i)
num_tested.append(num_keep)
return mask, idxs, num_tested
def apply_qc_icing_pireps_exp2(icing_alt, cld_top_hgt, cld_geo_dz, cld_phase, cld_opd, cld_mask, bt_11um, solzen, satzen, cld_top_temp, day_night='ANY', cloudy_frac=0.5):
closeness_top = 200.0 # meters
max_cld_depth = 1000.0
max_altitude = 4000.0
max_cld_altitude = 4000.0
num_obs = len(icing_alt)
cld_mask = cld_mask.reshape((num_obs, -1))
cld_top_hgt = cld_top_hgt.reshape((num_obs, -1))
cld_geo_dz = cld_geo_dz.reshape((num_obs, -1))
bt_11um = bt_11um.reshape((num_obs, -1))
cld_top_temp = cld_top_temp.reshape((num_obs, -1))
mask = []
idxs = []
num_tested = []
for i in range(num_obs):
if not check_oblique(satzen[i,]):
continue
if day_night == 'NIGHT' and not is_night(solzen[i,]):
continue
elif day_night == 'DAY' and not is_day(solzen[i,]):
continue
elif day_night == 'ANY':
pass
# keep_0 = np.logical_or(cld_mask[i,] == 2, cld_mask[i,] == 3) # cloudy
keep_0 = cld_mask[i, ] == 3 # Confident cloudy
keep_1 = np.invert(np.isnan(cld_top_hgt[i,]))
keep_2 = np.invert(np.isnan(bt_11um[i,]))
keep_3 = np.invert(np.isnan(cld_geo_dz[i,]))
keep = keep_0 & keep_1 & keep_2 & keep_3
num_keep = np.sum(keep)
if num_keep == 0:
continue
if num_keep / nx_x_ny < cloudy_frac: # At least this fraction cloudy
continue
keep = np.where(keep, np.logical_and(cld_top_hgt[i,] > icing_alt[i,],
(cld_top_hgt[i,] - 3000.0) < icing_alt[i,]), False)
keep = np.where(keep, np.logical_and(cld_top_temp[i,] > 228.0, cld_top_temp[i,] < 274.0), False)
mask.append(keep)
idxs.append(i)
num_tested.append(num_keep)
return mask, idxs, num_tested
def apply_qc_icing_pireps_v3(icing_alt, cld_top_hgt, cld_geo_dz, cld_phase, cld_opd, cld_mask, bt_11um, solzen, satzen, cld_top_temp, day_night='ANY', cloudy_frac=0.5):
closeness_top = 100.0 # meters
max_cld_depth = 1500.0
max_altitude = 5000.0
max_cld_altitude = 5000.0
num_obs = len(icing_alt)
cld_mask = cld_mask.reshape((num_obs, -1))
cld_top_hgt = cld_top_hgt.reshape((num_obs, -1))
cld_geo_dz = cld_geo_dz.reshape((num_obs, -1))
bt_11um = bt_11um.reshape((num_obs, -1))
cld_top_temp = cld_top_temp.reshape((num_obs, -1))
mask = []
idxs = []
num_tested = []
for i in range(num_obs):
if not check_oblique(satzen[i,]):
continue
if day_night == 'NIGHT' and not is_night(solzen[i,]):
continue
elif day_night == 'DAY' and not is_day(solzen[i,]):
continue
elif day_night == 'ANY':
pass
# keep_0 = cld_mask[i, ] == 3 # Confident cloudy
keep_0 = np.logical_or(cld_mask[i,] == 2, cld_mask[i,] == 3) # cloudy
keep_1 = np.invert(np.isnan(cld_top_hgt[i,]))
# keep_2 = np.invert(np.isnan(cld_geo_dz[i,]))
# keep_3 = np.invert(np.isnan(bt_11um[i,]))
# keep = keep_0 & keep_1 & keep_2 & keep_3
keep = keep_0 & keep_1
num_keep = np.sum(keep)
if num_keep == 0:
continue
if num_keep / nx_x_ny < cloudy_frac: # At least this fraction cloudy
continue
# if icing_alt[i,] > max_altitude:
# continue
# ctt_mean = np.nanmean(cld_top_temp[i,])
# if ctt_mean > 274.0:
# continue
# cth_mean = np.nanmean(cld_top_hgt[i,])
# if not (closeness_top+cth_mean > icing_alt[i,] > cth_mean - max_cld_depth):
# continue
# keep = np.where(keep, cld_top_hgt[i,] < max_cld_altitude, False)
keep = np.where(keep, np.logical_and((cld_top_hgt[i,] + closeness_top) > icing_alt[i,],
(cld_top_hgt[i,] - max_cld_depth) < icing_alt[i,]), False)
# keep = np.where(keep, np.logical_and(cld_top_temp[i,] > 228.0, cld_top_temp[i,] < 274.0), False)
mask.append(keep)
idxs.append(i)
num_tested.append(num_keep)
return mask, idxs, num_tested
def apply_qc_no_icing_pireps(icing_alt, cld_top_hgt, cld_geo_dz, cld_phase, cld_opd, cld_mask, bt_11um, solzen, satzen, cld_top_temp, day_night='ANY', cloudy_frac=0.5):
if day_night == 'DAY':
opd_thick_threshold = 20
opd_thin_threshold = 1
elif day_night == 'NIGHT' or day_night == 'ANY':
opd_thick_threshold = 2
opd_thin_threshold = 0.1
closeness_top = 200.0 # meters
max_cld_depth = 1000.0
max_altitude = 5000.0
max_cld_altitude = 5000.0
num_obs = len(icing_alt)
cld_mask = cld_mask.reshape((num_obs, -1))
cld_top_hgt = cld_top_hgt.reshape((num_obs, -1))
cld_geo_dz = cld_geo_dz.reshape((num_obs, -1))
bt_11um = bt_11um.reshape((num_obs, -1))
cld_top_temp = cld_top_temp.reshape((num_obs, -1))
mask = []
idxs = []
num_tested = []
for i in range(num_obs):
if not check_oblique(satzen[i,]):
continue
if day_night == 'NIGHT' and not is_night(solzen[i,]):
continue
elif day_night == 'DAY' and not is_day(solzen[i,]):
continue
elif day_night == 'ANY':
pass
# if not (is_day(solzen[i,]) or is_night(solzen[i,])):
# continue
# if not (icing_alt[i] < max_altitude):
# continue
keep_0 = np.logical_or(cld_mask[i,] == 2, cld_mask[i,] == 3) # cloudy
keep_1 = np.invert(np.isnan(cld_top_hgt[i,]))
keep_2 = np.invert(np.isnan(bt_11um[i,]))
keep_3 = np.invert(np.isnan(cld_geo_dz[i,]))
keep = keep_0 & keep_1 & keep_2 & keep_3
num_keep = np.sum(keep)
if num_keep == 0:
continue
if num_keep / nx_x_ny < cloudy_frac: # At least this fraction cloudy
continue
keep = np.where(keep, (cld_top_hgt[i,] + closeness_top) > icing_alt[i], False)
# keep = np.where(keep, cld_top_hgt[i,] < max_cld_altitude, False)
# keep = np.where(keep, cld_top_hgt[i,] > max_cld_depth, False)
# keep = np.where(keep, (cld_top_hgt[i,] - max_cld_depth) < icing_alt[i], False)
keep = np.where(keep, np.logical_and(bt_11um[i,] > 228.0, bt_11um[i,] < 274.0), False)
# keep = np.where(keep, bt_11um[i,] < 275.0, False)
# keep = np.where(keep, cld_top_temp[i,] < 274.2, False)
# cld_hgt = cld_top_hgt[i, ].flatten()
# med_cld_hgt = np.median(cld_hgt[keep])
# if icing_alt[i] > med_cld_hgt:
# continue
#
# cld_tmp = cld_top_temp[i, ].flatten()
# med_cld_tmp = np.median(cld_tmp[keep])
# if med_cld_tmp > 275.0:
# continue
#
# if icing_alt[i] < med_cld_hgt - max_cld_depth:
# continue
mask.append(keep)
idxs.append(i)
num_tested.append(num_keep)
return mask, idxs, num_tested
def apply_qc_no_icing_pireps_exp2(icing_alt, cld_top_hgt, cld_geo_dz, cld_phase, cld_opd, cld_mask, bt_11um, solzen, satzen, cld_top_temp, day_night='ANY', cloudy_frac=0.5):
closeness_top = 200.0 # meters
max_cld_depth = 1000.0
max_altitude = 4000.0
max_cld_altitude = 4000.0
num_obs = len(icing_alt)
cld_mask = cld_mask.reshape((num_obs, -1))
cld_top_hgt = cld_top_hgt.reshape((num_obs, -1))
cld_geo_dz = cld_geo_dz.reshape((num_obs, -1))
bt_11um = bt_11um.reshape((num_obs, -1))
cld_top_temp = cld_top_temp.reshape((num_obs, -1))
mask = []
idxs = []
num_tested = []
for i in range(num_obs):
if not check_oblique(satzen[i,]):
continue
if day_night == 'NIGHT' and not is_night(solzen[i,]):
continue
elif day_night == 'DAY' and not is_day(solzen[i,]):
continue
elif day_night == 'ANY':
pass
# keep_0 = np.logical_or(cld_mask[i,] == 2, cld_mask[i,] == 3) # cloudy
keep_0 = cld_mask[i, ] == 3 # Confident cloudy
keep_1 = np.invert(np.isnan(cld_top_hgt[i,]))
keep_2 = np.invert(np.isnan(bt_11um[i,]))
keep_3 = np.invert(np.isnan(cld_geo_dz[i,]))
keep = keep_0 & keep_1 & keep_2 & keep_3
num_keep = np.sum(keep)
if num_keep == 0:
continue
if num_keep / nx_x_ny < cloudy_frac: # At least this fraction cloudy
continue
keep = np.where(keep, np.logical_and(cld_top_hgt[i,] > icing_alt[i,],
(cld_top_hgt[i,] - 3000.0) < icing_alt[i,]), False)
# keep = np.where(keep, np.logical_and(cld_top_temp[i,] > 228.0, cld_top_temp[i,] < 274.0), False)
mask.append(keep)
idxs.append(i)
num_tested.append(num_keep)
return mask, idxs, num_tested
def fov_extract(icing_files, no_icing_files, trnfile='/home/rink/fovs_l1b_train.h5', tstfile='/home/rink/fovs_l1b_test.h5', L1B_or_L2='L1B', split=0.2):
ice_times = []
icing_int_s = []
ice_lons = []
ice_lats = []
no_ice_times = []
no_ice_lons = []
no_ice_lats = []
h5_s_icing = []
h5_s_no_icing = []
if L1B_or_L2 == 'L1B':
params = l1b_ds_list
param_types = l1b_ds_types
elif L1B_or_L2 == 'L2':
params = ds_list
param_types = ds_types
icing_data_dct = {ds: [] for ds in params}
no_icing_data_dct = {ds: [] for ds in params}
sub_indexes = np.arange(400)
num_ice = 0
for fidx in range(len(icing_files)):
fname = icing_files[fidx]
f = h5py.File(fname, 'r')
h5_s_icing.append(f)
times = f['time'][:]
num_obs = len(times)
icing_int = f['icing_intensity'][:]
lons = f['longitude'][:]
lats = f['latitude'][:]
cld_mask = f['cloud_mask'][:, 10:30, 10:30]
cld_mask = cld_mask.reshape((num_obs, -1))
cld_top_temp = f['cld_temp_acha'][:, 10:30, 10:30]
cld_top_temp = cld_top_temp.reshape((num_obs, -1))
for i in range(num_obs):
keep_0 = np.logical_or(cld_mask[i,] == 2, cld_mask[i,] == 3) # cloudy
keep_1 = np.invert(np.isnan(cld_top_temp[i,]))
keep = keep_0 & keep_1
keep = np.where(keep, cld_top_temp[i,] < 273.0, False)
k_idxs = sub_indexes[keep]
np.random.shuffle(k_idxs)
if len(k_idxs) > 20:
k_idxs = k_idxs[0:20]
else:
k_idxs = k_idxs[0:len(k_idxs)]
num_ice += len(k_idxs)
for ds_name in params:
dat = f[ds_name][i, 10:30, 10:30].flatten()
icing_data_dct[ds_name].append(dat[k_idxs])
icing_int_s.append(np.full(len(k_idxs), icing_int[i]))
ice_times.append(np.full(len(k_idxs), times[i]))
ice_lons.append(np.full(len(k_idxs), lons[i]))
ice_lats.append(np.full(len(k_idxs), lats[i]))
print(fname)
for ds_name in params:
lst = icing_data_dct[ds_name]
icing_data_dct[ds_name] = np.concatenate(lst)
icing_int_s = np.concatenate(icing_int_s)
ice_times = np.concatenate(ice_times)
ice_lons = np.concatenate(ice_lons)
ice_lats = np.concatenate(ice_lats)
num_no_ice = 0
for fidx in range(len(no_icing_files)):
fname = no_icing_files[fidx]
f = h5py.File(fname, 'r')
h5_s_no_icing.append(f)
times = f['time']
num_obs = len(times)
lons = f['longitude']
lats = f['latitude']
cld_mask = f['cloud_mask'][:, 10:30, 10:30]
cld_mask = cld_mask.reshape((num_obs, -1))
cld_top_temp = f['cld_temp_acha'][:, 10:30, 10:30]
cld_top_temp = cld_top_temp.reshape((num_obs, -1))
for i in range(num_obs):
keep_0 = np.logical_or(cld_mask[i,] == 2, cld_mask[i,] == 3) # cloudy
keep_1 = np.invert(np.isnan(cld_top_temp[i,]))
keep = keep_0 & keep_1
keep = np.where(keep, cld_top_temp[i,] < 273.0, False)
k_idxs = sub_indexes[keep]
np.random.shuffle(k_idxs)
if len(k_idxs) > 10:
k_idxs = k_idxs[0:10]
else:
k_idxs = k_idxs[0:len(k_idxs)]
num_no_ice += len(k_idxs)
no_ice_times.append(np.full(len(k_idxs), times[i]))
no_ice_lons.append(np.full(len(k_idxs), lons[i]))
no_ice_lats.append(np.full(len(k_idxs), lats[i]))
for ds_name in params:
dat = f[ds_name][i, 10:30, 10:30].flatten()
no_icing_data_dct[ds_name].append(dat[k_idxs])
print(fname)
for ds_name in params:
lst = no_icing_data_dct[ds_name]
no_icing_data_dct[ds_name] = np.concatenate(lst)
no_icing_int_s = np.full(num_no_ice, -1)
no_ice_times = np.concatenate(no_ice_times)
no_ice_lons = np.concatenate(no_ice_lons)
no_ice_lats = np.concatenate(no_ice_lats)
icing_intensity = np.concatenate([icing_int_s, no_icing_int_s])
icing_times = np.concatenate([ice_times, no_ice_times])
icing_lons = np.concatenate([ice_lons, no_ice_lons])
icing_lats = np.concatenate([ice_lats, no_ice_lats])
data_dct = {}
for ds_name in params:
data_dct[ds_name] = np.concatenate([icing_data_dct[ds_name], no_icing_data_dct[ds_name]])
# apply shuffle indexes
# ds_indexes = np.arange(num_ice + num_no_ice)
# np.random.shuffle(ds_indexes)
#
# for ds_name in train_params:
# data_dct[ds_name] = data_dct[ds_name][ds_indexes]
# icing_intensity = icing_intensity[ds_indexes]
# icing_times = icing_times[ds_indexes]
# icing_lons = icing_lons[ds_indexes]
# icing_lats = icing_lats[ds_indexes]
# do sort
ds_indexes = np.argsort(icing_times)
for ds_name in params:
data_dct[ds_name] = data_dct[ds_name][ds_indexes]
icing_intensity = icing_intensity[ds_indexes]
icing_times = icing_times[ds_indexes]
icing_lons = icing_lons[ds_indexes]
icing_lats = icing_lats[ds_indexes]
#trn_idxs, tst_idxs = split_data(icing_intensity.shape[0], shuffle=False, perc=split)
all_idxs = np.arange(icing_intensity.shape[0])
splt_idx = int(icing_intensity.shape[0] * (1-split))
trn_idxs = all_idxs[0:splt_idx]
tst_idxs = all_idxs[splt_idx:]
trn_data_dct = {}
for ds_name in params:
trn_data_dct[ds_name] = data_dct[ds_name][trn_idxs,]
trn_icing_intensity = icing_intensity[trn_idxs,]
trn_icing_times = icing_times[trn_idxs,]
trn_icing_lons = icing_lons[trn_idxs,]
trn_icing_lats = icing_lats[trn_idxs,]
write_file(trnfile, params, param_types, trn_data_dct, trn_icing_intensity, trn_icing_times, trn_icing_lons, trn_icing_lats)
tst_data_dct = {}
for ds_name in params:
tst_data_dct[ds_name] = data_dct[ds_name][tst_idxs,]
tst_icing_intensity = icing_intensity[tst_idxs,]
tst_icing_times = icing_times[tst_idxs,]
tst_icing_lons = icing_lons[tst_idxs,]
tst_icing_lats = icing_lats[tst_idxs,]
# do sort
ds_indexes = np.argsort(tst_icing_times)
for ds_name in params:
tst_data_dct[ds_name] = tst_data_dct[ds_name][ds_indexes]
tst_icing_intensity = tst_icing_intensity[ds_indexes]
tst_icing_times = tst_icing_times[ds_indexes]
tst_icing_lons = tst_icing_lons[ds_indexes]
tst_icing_lats = tst_icing_lats[ds_indexes]
write_file(tstfile, params, param_types, tst_data_dct, tst_icing_intensity, tst_icing_times, tst_icing_lons, tst_icing_lats)
# --- close files
for h5f in h5_s_icing:
h5f.close()
for h5f in h5_s_no_icing:
h5f.close()
def tile_extract(icing_files, no_icing_files, trnfile='/home/rink/tiles_train.h5', vldfile='/home/rink/tiles_valid.h5', tstfile='/home/rink/tiles_test.h5', L1B_or_L2='L1B',
cld_mask_name='cloud_mask', augment=False, do_split=True, has_test=True):
# 16x16
# n_a, n_b = 12, 28
# m_a, m_b = 12, 28
n_a, n_b = 0, 16
m_a, m_b = 0, 16
# 10x10
# n_a, n_b = 15, 25
# m_a, m_b = 15, 25
icing_int_s = []
ice_time_s = []
no_ice_time_s = []
ice_lon_s = []
no_ice_lon_s = []
ice_lat_s = []
no_ice_lat_s = []
ice_flt_alt_s = []
no_ice_flt_alt_s = []
h5_s_icing = []
h5_s_no_icing = []
if L1B_or_L2 == 'L1B':
params = l1b_ds_list
param_types = l1b_ds_types
elif L1B_or_L2 == 'L2':
params = ds_list
param_types = ds_types
params.append('FOV_mask')
param_types.append('i1')
icing_data_dct = {ds: [] for ds in params}
no_icing_data_dct = {ds: [] for ds in params}
for fidx in range(len(icing_files)):
fname = icing_files[fidx]
f = h5py.File(fname, 'r')
h5_s_icing.append(f)
times = f['time'][:]
num_obs = len(times)
lons = f['longitude']
lats = f['latitude']
icing_int = f['icing_intensity'][:]
flt_altitude = f['icing_altitude'][:]
for i in range(num_obs):
cld_msk = f[cld_mask_name][i, n_a:n_b, m_a:m_b]
for ds_name in params:
dat = f[ds_name][i, n_a:n_b, m_a:m_b]
if L1B_or_L2 == 'L2':
keep = np.logical_or(cld_msk == 2, cld_msk == 3) # cloudy
np.where(keep, dat, np.nan)
icing_data_dct[ds_name].append(dat)
icing_int_s.append(icing_int[i])
ice_time_s.append(times[i])
ice_lon_s.append(lons[i])
ice_lat_s.append(lats[i])
ice_flt_alt_s.append(flt_altitude[i])
print(fname)
for ds_name in params:
lst = icing_data_dct[ds_name]
icing_data_dct[ds_name] = np.stack(lst, axis=0)
icing_int_s = np.array(icing_int_s)
ice_time_s = np.array(ice_time_s)
ice_lon_s = np.array(ice_lon_s)
ice_lat_s = np.array(ice_lat_s)
ice_flt_alt_s = np.array(ice_flt_alt_s)
# No icing ------------------------------------------------------------
num_no_ice = 0
for fidx in range(len(no_icing_files)):
fname = no_icing_files[fidx]
f = h5py.File(fname, 'r')
h5_s_no_icing.append(f)
times = f['time']
num_obs = len(times)
lons = f['longitude']
lats = f['latitude']
flt_altitude = f['icing_altitude'][:]
for i in range(num_obs):
cld_msk = f[cld_mask_name][i, n_a:n_b, m_a:m_b]
for ds_name in params:
dat = f[ds_name][i, n_a:n_b, m_a:m_b]
if L1B_or_L2 == 'L2':
keep = np.logical_or(cld_msk == 2, cld_msk == 3) # cloudy
np.where(keep, dat, np.nan)
no_icing_data_dct[ds_name].append(dat)
num_no_ice += 1
no_ice_time_s.append(times[i])
no_ice_lon_s.append(lons[i])
no_ice_lat_s.append(lats[i])
no_ice_flt_alt_s.append(flt_altitude[i])
print(fname)
for ds_name in params:
lst = no_icing_data_dct[ds_name]
no_icing_data_dct[ds_name] = np.stack(lst, axis=0)
no_icing_int_s = np.full(num_no_ice, -1)
no_ice_time_s = np.array(no_ice_time_s)
no_ice_lon_s = np.array(no_ice_lon_s)
no_ice_lat_s = np.array(no_ice_lat_s)
no_ice_flt_alt_s = np.array(no_ice_flt_alt_s)
icing_intensity = np.concatenate([icing_int_s, no_icing_int_s])
icing_times = np.concatenate([ice_time_s, no_ice_time_s])
icing_lons = np.concatenate([ice_lon_s, no_ice_lon_s])
icing_lats = np.concatenate([ice_lat_s, no_ice_lat_s])
icing_alt = np.concatenate([ice_flt_alt_s, no_ice_flt_alt_s])
data_dct = {}
for ds_name in params:
data_dct[ds_name] = np.concatenate([icing_data_dct[ds_name], no_icing_data_dct[ds_name]])
# do sort -------------------------------------
ds_indexes = np.argsort(icing_times)
for ds_name in params:
data_dct[ds_name] = data_dct[ds_name][ds_indexes]
icing_intensity = icing_intensity[ds_indexes]
icing_times = icing_times[ds_indexes]
icing_lons = icing_lons[ds_indexes]
icing_lats = icing_lats[ds_indexes]
icing_alt = icing_alt[ds_indexes]
if do_split:
trn_idxs, vld_idxs, tst_idxs = split_data(icing_times, has_test)
# Below for no test data, just train and valid
if has_test: # for no test data, just train and valid
trn_idxs = np.concatenate([trn_idxs, tst_idxs])
else:
trn_idxs = np.arange(icing_intensity.shape[0])
tst_idxs = None
# ---------------------------------------------
trn_data_dct = {}
for ds_name in params:
trn_data_dct[ds_name] = data_dct[ds_name][trn_idxs,]
trn_icing_intensity = icing_intensity[trn_idxs,]
trn_icing_times = icing_times[trn_idxs,]
trn_icing_lons = icing_lons[trn_idxs,]
trn_icing_lats = icing_lats[trn_idxs,]
trn_icing_alt = icing_alt[trn_idxs,]
# Data augmentation -------------------------------------------------------------
if augment:
trn_data_dct_aug = {ds_name: [] for ds_name in params}
trn_icing_intensity_aug = []
trn_icing_times_aug = []
trn_icing_lons_aug = []
trn_icing_lats_aug = []
trn_icing_alt_aug = []
for k in range(trn_icing_intensity.shape[0]):
iceint = trn_icing_intensity[k]
icetime = trn_icing_times[k]
icelon = trn_icing_lons[k]
icelat = trn_icing_lats[k]
icealt = trn_icing_alt[k]
if iceint == 3 or iceint == 4 or iceint == 5 or iceint == 6:
for ds_name in params:
dat = trn_data_dct[ds_name]
trn_data_dct_aug[ds_name].append(np.fliplr(dat[k,]))
trn_data_dct_aug[ds_name].append(np.flipud(dat[k,]))
trn_data_dct_aug[ds_name].append(np.rot90(dat[k,]))
trn_icing_intensity_aug.append(iceint)
trn_icing_intensity_aug.append(iceint)
trn_icing_intensity_aug.append(iceint)
trn_icing_times_aug.append(icetime)
trn_icing_times_aug.append(icetime)
trn_icing_times_aug.append(icetime)
trn_icing_lons_aug.append(icelon)
trn_icing_lons_aug.append(icelon)
trn_icing_lons_aug.append(icelon)
trn_icing_lats_aug.append(icelat)
trn_icing_lats_aug.append(icelat)
trn_icing_lats_aug.append(icelat)
trn_icing_alt_aug.append(icealt)
trn_icing_alt_aug.append(icealt)
trn_icing_alt_aug.append(icealt)
for ds_name in params:
trn_data_dct_aug[ds_name] = np.stack(trn_data_dct_aug[ds_name])
trn_icing_intensity_aug = np.stack(trn_icing_intensity_aug)
trn_icing_times_aug = np.stack(trn_icing_times_aug)
trn_icing_lons_aug = np.stack(trn_icing_lons_aug)
trn_icing_lats_aug = np.stack(trn_icing_lats_aug)
trn_icing_alt_aug = np.stack(trn_icing_alt_aug)
for ds_name in params:
trn_data_dct[ds_name] = np.concatenate([trn_data_dct[ds_name], trn_data_dct_aug[ds_name]])
trn_icing_intensity = np.concatenate([trn_icing_intensity, trn_icing_intensity_aug])
trn_icing_times = np.concatenate([trn_icing_times, trn_icing_times_aug])
trn_icing_lons = np.concatenate([trn_icing_lons, trn_icing_lons_aug])
trn_icing_lats = np.concatenate([trn_icing_lats, trn_icing_lats_aug])
trn_icing_alt = np.concatenate([trn_icing_alt, trn_icing_alt_aug])
# do sort
ds_indexes = np.argsort(trn_icing_times)
for ds_name in params:
trn_data_dct[ds_name] = trn_data_dct[ds_name][ds_indexes]
trn_icing_intensity = trn_icing_intensity[ds_indexes]
trn_icing_times = trn_icing_times[ds_indexes]
trn_icing_lons = trn_icing_lons[ds_indexes]
trn_icing_lats = trn_icing_lats[ds_indexes]
trn_icing_alt = trn_icing_alt[ds_indexes]
write_file(trnfile, params, param_types, trn_data_dct, trn_icing_intensity, trn_icing_times, trn_icing_lons, trn_icing_lats, trn_icing_alt)
if do_split:
if has_test:
tst_data_dct = {}
for ds_name in params:
tst_data_dct[ds_name] = data_dct[ds_name][tst_idxs,]
tst_icing_intensity = icing_intensity[tst_idxs,]
tst_icing_times = icing_times[tst_idxs,]
tst_icing_lons = icing_lons[tst_idxs,]
tst_icing_lats = icing_lats[tst_idxs,]
tst_icing_alt = icing_alt[tst_idxs,]
# do sort
ds_indexes = np.argsort(tst_icing_times)
for ds_name in params:
tst_data_dct[ds_name] = tst_data_dct[ds_name][ds_indexes]
tst_icing_intensity = tst_icing_intensity[ds_indexes]
tst_icing_times = tst_icing_times[ds_indexes]
tst_icing_lons = tst_icing_lons[ds_indexes]
tst_icing_lats = tst_icing_lats[ds_indexes]
tst_icing_alt = tst_icing_alt[ds_indexes]
write_file(tstfile, params, param_types, tst_data_dct, tst_icing_intensity, tst_icing_times, tst_icing_lons, tst_icing_lats, tst_icing_alt)
vld_data_dct = {}
for ds_name in params:
vld_data_dct[ds_name] = data_dct[ds_name][vld_idxs,]
vld_icing_intensity = icing_intensity[vld_idxs,]
vld_icing_times = icing_times[vld_idxs,]
vld_icing_lons = icing_lons[vld_idxs,]
vld_icing_lats = icing_lats[vld_idxs,]
vld_icing_alt = icing_alt[vld_idxs,]
# do sort
ds_indexes = np.argsort(vld_icing_times)
for ds_name in params:
vld_data_dct[ds_name] = vld_data_dct[ds_name][ds_indexes]
vld_icing_intensity = vld_icing_intensity[ds_indexes]
vld_icing_times = vld_icing_times[ds_indexes]
vld_icing_lons = vld_icing_lons[ds_indexes]
vld_icing_lats = vld_icing_lats[ds_indexes]
vld_icing_alt = vld_icing_alt[ds_indexes]
write_file(vldfile, params, param_types, vld_data_dct, vld_icing_intensity, vld_icing_times, vld_icing_lons, vld_icing_lats, vld_icing_alt)
# --- close files
for h5f in h5_s_icing:
h5f.close()
for h5f in h5_s_no_icing:
h5f.close()
def write_file(outfile, params, param_types, data_dct, icing_intensity, icing_times, icing_lons, icing_lats, icing_alt):
h5f_expl = h5py.File(a_clvr_file, 'r')
h5f_out = h5py.File(outfile, 'w')
for idx, ds_name in enumerate(params):
dt = param_types[idx]
data = data_dct[ds_name]
h5f_out.create_dataset(ds_name, data=data, dtype=dt)
icing_int_ds = h5f_out.create_dataset('icing_intensity', data=icing_intensity, dtype='i4')
icing_int_ds.attrs.create('long_name', data='From PIREP. -1:No Icing, 1:Trace, 2:Light, 3:Light Moderate, 4:Moderate, 5:Moderate Severe, 6:Severe')
time_ds = h5f_out.create_dataset('time', data=icing_times, dtype='f4')
time_ds.attrs.create('units', data='seconds since 1970-1-1 00:00:00')
time_ds.attrs.create('long_name', data='PIREP time')
lon_ds = h5f_out.create_dataset('longitude', data=icing_lons, dtype='f4')
lon_ds.attrs.create('units', data='degrees_east')
lon_ds.attrs.create('long_name', data='PIREP longitude')
lat_ds = h5f_out.create_dataset('latitude', data=icing_lats, dtype='f4')
lat_ds.attrs.create('units', data='degrees_north')
lat_ds.attrs.create('long_name', data='PIREP latitude')
alt_ds = h5f_out.create_dataset('flight_altitude', data=icing_alt, dtype='f4')
alt_ds.attrs.create('units', data='meter')
alt_ds.attrs.create('long_name', data='PIREP altitude')
# copy relevant attributes
for ds_name in params:
h5f_ds = h5f_out[ds_name]
try:
h5f_ds.attrs.create('standard_name', data=h5f_expl[ds_name].attrs.get('standard_name'))
h5f_ds.attrs.create('long_name', data=h5f_expl[ds_name].attrs.get('long_name'))
h5f_ds.attrs.create('units', data=h5f_expl[ds_name].attrs.get('units'))
attr = h5f_expl[ds_name].attrs.get('actual_range')
if attr is not None:
h5f_ds.attrs.create('actual_range', data=attr)
attr = h5f_expl[ds_name].attrs.get('flag_values')
if attr is not None:
h5f_ds.attrs.create('flag_values', data=attr)
except Exception:
pass
# --- close files
h5f_out.close()
h5f_expl.close()
def run_mean_std(check_cloudy=False):
ds_list = ['cld_height_acha', 'cld_geo_thick', 'cld_press_acha',
'supercooled_cloud_fraction', 'cld_temp_acha', 'cld_opd_acha',
'cld_reff_acha', 'cld_reff_dcomp', 'cld_reff_dcomp_1', 'cld_reff_dcomp_2', 'cld_reff_dcomp_3',
'cld_opd_dcomp', 'cld_opd_dcomp_1', 'cld_opd_dcomp_2', 'cld_opd_dcomp_3', 'cld_cwp_dcomp', 'iwc_dcomp',
'lwc_dcomp', 'cld_emiss_acha', 'conv_cloud_fraction']
# ds_list = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
# 'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom',
# 'refl_0_47um_nom', 'refl_0_65um_nom', 'refl_0_86um_nom', 'refl_1_38um_nom', 'refl_1_60um_nom']
mean_std_dct = {}
ice_flist = [f for f in glob.glob('/data/Personal/rink/icing/icing_2*.h5')]
no_ice_flist = [f for f in glob.glob('/data/Personal/rink/icing/no_icing_2*.h5')]
ice_h5f_lst = [h5py.File(f, 'r') for f in ice_flist]
no_ice_h5f_lst = [h5py.File(f, 'r') for f in no_ice_flist]
if check_cloudy:
cld_msk_i = []
cld_msk_ni = []
for idx, ice_h5f in enumerate(ice_h5f_lst):
no_ice_h5f = no_ice_h5f_lst[idx]
cld_msk_i.append(ice_h5f['cloud_mask'][:,].flatten())
cld_msk_ni.append(no_ice_h5f['cloud_mask'][:,].flatten())
cld_msk_i = np.concatenate(cld_msk_i)
cld_msk_ni = np.concatenate(cld_msk_ni)
for dname in ds_list:
data = []
data_i = []
data_ni = []
for idx, ice_h5f in enumerate(ice_h5f_lst):
no_ice_h5f = no_ice_h5f_lst[idx]
data.append(ice_h5f[dname][:,].flatten())
data.append(no_ice_h5f[dname][:,].flatten())
data_i.append(ice_h5f[dname][:,].flatten())
data_ni.append(no_ice_h5f[dname][:,].flatten())
data = np.concatenate(data)
mean = np.nanmean(data)
data -= mean
std = np.nanstd(data)
data_i = np.concatenate(data_i)
if check_cloudy:
keep = np.logical_or(cld_msk_i == 2, cld_msk_i == 3)
data_i = data_i[keep]
mean_i = np.nanmean(data_i)
lo_i = np.nanmin(data_i)
hi_i = np.nanmax(data_i)
data_i -= mean_i
std_i = np.nanstd(data_i)
cnt_i = np.sum(np.invert(np.isnan(data_i)))
data_ni = np.concatenate(data_ni)
if check_cloudy:
keep = np.logical_or(cld_msk_ni == 2, cld_msk_ni == 3)
data_ni = data_ni[keep]
mean_ni = np.nanmean(data_ni)
lo_ni = np.nanmin(data_ni)
hi_ni = np.nanmax(data_ni)
data_ni -= mean_ni
std_ni = np.nanstd(data_ni)
cnt_ni = np.sum(np.invert(np.isnan(data_ni)))
no_icing_to_icing_ratio = cnt_ni/cnt_i
mean = (mean_i + no_icing_to_icing_ratio*mean_ni)/(no_icing_to_icing_ratio + 1)
std = (std_i + no_icing_to_icing_ratio*std_ni)/(no_icing_to_icing_ratio + 1)
lo = (lo_i + no_icing_to_icing_ratio*lo_ni)/(no_icing_to_icing_ratio + 1)
hi = (hi_i + no_icing_to_icing_ratio*hi_ni)/(no_icing_to_icing_ratio + 1)
print(dname,': (', mean, mean_i, mean_ni, ') (', std, std_i, std_ni, ') ratio: ', no_icing_to_icing_ratio)
print(dname,': (', lo, lo_i, lo_ni, ') (', hi, hi_i, hi_ni, ') ratio: ', no_icing_to_icing_ratio)
mean_std_dct[dname] = (mean, std, lo, hi)
[h5f.close() for h5f in ice_h5f_lst]
[h5f.close() for h5f in no_ice_h5f_lst]
f = open('/home/rink/data/icing_ml/mean_std_lo_hi.pkl', 'wb')
pickle.dump(mean_std_dct, f)
f.close()
return mean_std_dct
def run_mean_std_2(check_cloudy=False, no_icing_to_icing_ratio=5, params=train_params_day):
params = ['cld_height_acha', 'cld_geo_thick', 'supercooled_cloud_fraction', 'cld_temp_acha', 'cld_press_acha',
'cld_reff_dcomp', 'cld_opd_dcomp', 'cld_cwp_dcomp', 'iwc_dcomp', 'lwc_dcomp']
mean_std_dct = {}
flist = [f for f in glob.glob('/Users/tomrink/data/icing/fov*.h5')]
h5f_lst = [h5py.File(f, 'r') for f in flist]
if check_cloudy:
cld_msk = []
for idx, h5f in enumerate(h5f_lst):
cld_msk.append(h5f['cloud_mask'][:,].flatten())
cld_msk = np.concatenate(cld_msk)
for dname in params:
data = []
for idx, h5f in enumerate(h5f_lst):
data.append(h5f[dname][:,].flatten())
data = np.concatenate(data)
if check_cloudy:
keep = np.logical_or(cld_msk == 2, cld_msk == 3)
data = data[keep]
mean = np.nanmean(data)
data -= mean
std = np.nanstd(data)
print(dname,': ', mean, std)
mean_std_dct[dname] = (mean, std)
[h5f.close() for h5f in h5f_lst]
f = open('/Users/tomrink/data/icing/fovs_mean_std_day.pkl', 'wb')
pickle.dump(mean_std_dct, f)
f.close()
def run_mean_std_3(train_file_path, check_cloudy=False, params=train_params_day):
# params = ['cld_height_acha', 'cld_geo_thick', 'cld_press_acha', 'supercooled_cloud_fraction', 'cld_temp_acha', 'cld_opd_acha',
# 'cld_reff_acha', 'cld_reff_dcomp', 'cld_reff_dcomp_1', 'cld_reff_dcomp_2', 'cld_reff_dcomp_3',
# 'cld_opd_dcomp', 'cld_opd_dcomp_1', 'cld_opd_dcomp_2', 'cld_opd_dcomp_3', 'cld_cwp_dcomp', 'iwc_dcomp',
# 'lwc_dcomp', 'cld_emiss_acha', 'conv_cloud_fraction']
#check_cloudy = True
params = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom',
'refl_0_47um_nom', 'refl_0_65um_nom', 'refl_0_86um_nom', 'refl_1_38um_nom', 'refl_1_60um_nom']
mean_std_lo_hi_dct = {}
h5f = h5py.File(train_file_path, 'r')
if check_cloudy:
cld_msk = h5f['cloud_mask'][:].flatten()
for dname in params:
data = h5f[dname][:,].flatten()
if check_cloudy:
keep = np.logical_or(cld_msk == 2, cld_msk == 3)
data = data[keep]
lo = np.nanmin(data)
hi = np.nanmax(data)
mean = np.nanmean(data)
data -= mean
std = np.nanstd(data)
print(dname,': ', mean, std, lo, hi)
mean_std_lo_hi_dct[dname] = (mean, std, lo, hi)
h5f.close()
f = open('/Users/tomrink/data/icing/mean_std_lo_hi_test.pkl', 'wb')
pickle.dump(mean_std_lo_hi_dct, f)
f.close()
def split_data(times, has_test):
time_idxs = np.arange(times.shape[0])
# time_ranges = [[get_timestamp('2018-01-01_00:00'), get_timestamp('2018-01-07_23:59')],
# [get_timestamp('2018-03-01_00:00'), get_timestamp('2018-03-07_23:59')],
# [get_timestamp('2018-05-01_00:00'), get_timestamp('2018-05-07_23:59')],
# [get_timestamp('2018-07-01_00:00'), get_timestamp('2018-07-07_23:59')],
# [get_timestamp('2018-09-01_00:00'), get_timestamp('2018-09-07_23:59')],
# [get_timestamp('2018-11-01_00:00'), get_timestamp('2018-11-07_23:59')],
# [get_timestamp('2019-01-01_00:00'), get_timestamp('2019-01-07_23:59')],
# [get_timestamp('2019-03-01_00:00'), get_timestamp('2019-03-07_23:59')],
# [get_timestamp('2019-05-01_00:00'), get_timestamp('2019-05-07_23:59')],
# [get_timestamp('2019-07-01_00:00'), get_timestamp('2019-07-07_23:59')],
# [get_timestamp('2019-09-01_00:00'), get_timestamp('2019-09-07_23:59')],
# [get_timestamp('2019-11-01_00:00'), get_timestamp('2019-11-07_23:59')],
# [get_timestamp('2021-09-24_00:00'), get_timestamp('2021-10-01_23:59')],
# [get_timestamp('2021-11-01_00:00'), get_timestamp('2021-11-07_23:59')],
# [get_timestamp('2022-01-01_00:00'), get_timestamp('2022-01-07_23:59')],
# [get_timestamp('2022-03-01_00:00'), get_timestamp('2022-03-07_23:59')],
# [get_timestamp('2022-04-01_00:00'), get_timestamp('2022-04-04_23:59')]]
time_ranges = [[get_timestamp('2018-01-01_00:00'), get_timestamp('2018-01-04_23:59')],
[get_timestamp('2018-02-01_00:00'), get_timestamp('2018-02-04_23:59')],
[get_timestamp('2018-03-01_00:00'), get_timestamp('2018-03-04_23:59')],
[get_timestamp('2018-04-01_00:00'), get_timestamp('2018-04-04_23:59')],
[get_timestamp('2018-05-01_00:00'), get_timestamp('2018-05-04_23:59')],
[get_timestamp('2018-06-01_00:00'), get_timestamp('2018-06-04_23:59')],
[get_timestamp('2018-07-01_00:00'), get_timestamp('2018-07-04_23:59')],
[get_timestamp('2018-08-01_00:00'), get_timestamp('2018-08-04_23:59')],
[get_timestamp('2018-09-01_00:00'), get_timestamp('2018-09-04_23:59')],
[get_timestamp('2018-10-01_00:00'), get_timestamp('2018-10-04_23:59')],
[get_timestamp('2018-11-01_00:00'), get_timestamp('2018-11-04_23:59')],
[get_timestamp('2018-12-01_00:00'), get_timestamp('2018-12-04_23:59')],
[get_timestamp('2019-01-01_00:00'), get_timestamp('2019-01-04_23:59')],
[get_timestamp('2019-02-01_00:00'), get_timestamp('2019-02-04_23:59')],
[get_timestamp('2019-03-01_00:00'), get_timestamp('2019-03-04_23:59')],
[get_timestamp('2019-04-01_00:00'), get_timestamp('2019-04-04_23:59')],
[get_timestamp('2019-05-01_00:00'), get_timestamp('2019-05-04_23:59')],
[get_timestamp('2019-06-01_00:00'), get_timestamp('2019-06-04_23:59')],
[get_timestamp('2019-07-01_00:00'), get_timestamp('2019-07-04_23:59')],
[get_timestamp('2019-08-01_00:00'), get_timestamp('2019-08-04_23:59')],
[get_timestamp('2019-09-01_00:00'), get_timestamp('2019-09-04_23:59')],
[get_timestamp('2019-10-01_00:00'), get_timestamp('2019-10-04_23:59')],
[get_timestamp('2019-11-01_00:00'), get_timestamp('2019-11-04_23:59')],
[get_timestamp('2019-12-01_00:00'), get_timestamp('2019-12-04_23:59')],
[get_timestamp('2021-12-04_00:00'), get_timestamp('2021-12-08_23:59')],
[get_timestamp('2022-01-01_00:00'), get_timestamp('2022-01-04_23:59')],
[get_timestamp('2022-02-01_00:00'), get_timestamp('2022-02-04_23:59')],
[get_timestamp('2022-03-01_00:00'), get_timestamp('2022-03-04_23:59')],
[get_timestamp('2022-04-01_00:00'), get_timestamp('2022-04-04_23:59')],
[get_timestamp('2022-05-01_00:00'), get_timestamp('2022-05-04_23:59')],
[get_timestamp('2022-10-03_00:00'), get_timestamp('2022-10-07_23:59')],
[get_timestamp('2022-11-01_00:00'), get_timestamp('2022-11-04_23:59')],
[get_timestamp('2022-12-01_00:00'), get_timestamp('2022-12-04_23:59')],
[get_timestamp('2023-12-01_00:00'), get_timestamp('2023-12-04_23:59')]]
keep_out = 10800 # 3 hrs
vld_time_idxs = []
for t_rng in time_ranges:
t_rng[0] -= keep_out
t_rng[1] += keep_out
tidxs = np.searchsorted(times, t_rng)
vld_time_idxs.append(np.arange(tidxs[0], tidxs[1], 1))
vld_time_idxs = np.concatenate(vld_time_idxs, axis=None)
# time_ranges = [[get_timestamp('2018-02-01_00:00'), get_timestamp('2018-02-04_23:59')],
# [get_timestamp('2018-04-01_00:00'), get_timestamp('2018-04-04_23:59')],
# [get_timestamp('2018-06-01_00:00'), get_timestamp('2018-06-04_23:59')],
# [get_timestamp('2018-08-01_00:00'), get_timestamp('2018-08-04_23:59')],
# [get_timestamp('2018-10-01_00:00'), get_timestamp('2018-10-04_23:59')],
# [get_timestamp('2018-12-01_00:00'), get_timestamp('2018-12-04_23:59')],
# [get_timestamp('2019-02-01_00:00'), get_timestamp('2019-02-04_23:59')],
# [get_timestamp('2019-04-01_00:00'), get_timestamp('2019-04-04_23:59')],
# [get_timestamp('2019-06-01_00:00'), get_timestamp('2019-06-04_23:59')],
# [get_timestamp('2019-08-01_00:00'), get_timestamp('2019-08-04_23:59')],
# [get_timestamp('2019-10-01_00:00'), get_timestamp('2019-10-04_23:59')],
# [get_timestamp('2019-12-01_00:00'), get_timestamp('2019-12-04_23:59')],
# [get_timestamp('2021-10-05_00:00'), get_timestamp('2021-10-10_23:59')],
# [get_timestamp('2021-12-01_00:00'), get_timestamp('2021-12-04_23:59')],
# [get_timestamp('2022-02-01_00:00'), get_timestamp('2022-02-04_23:59')],
# [get_timestamp('2022-03-26_00:00'), get_timestamp('2022-03-30_23:59')],
# [get_timestamp('2022-04-07_00:00'), get_timestamp('2022-04-10_23:59')],
# [get_timestamp('2022-05-01_00:00'), get_timestamp('2022-05-06_23:59')],
# [get_timestamp('2022-10-03_00:00'), get_timestamp('2022-10-08_23:59')],
# [get_timestamp('2022-10-17_00:00'), get_timestamp('2022-10-22_20:59')],
# [get_timestamp('2022-11-01_00:00'), get_timestamp('2022-11-05_23:59')],
# [get_timestamp('2022-11-10_00:00'), get_timestamp('2022-12-01_23:59')]]
time_ranges = [[get_timestamp('2018-01-11_00:00'), get_timestamp('2018-01-12_23:59')],
[get_timestamp('2018-02-11_00:00'), get_timestamp('2018-02-13_23:59')],
[get_timestamp('2018-03-11_00:00'), get_timestamp('2018-03-13_23:59')],
[get_timestamp('2018-04-11_00:00'), get_timestamp('2018-04-13_23:59')],
[get_timestamp('2018-05-11_00:00'), get_timestamp('2018-05-13_23:59')],
[get_timestamp('2018-06-11_00:00'), get_timestamp('2018-06-13_23:59')],
[get_timestamp('2018-07-11_00:00'), get_timestamp('2018-07-13_23:59')],
[get_timestamp('2018-08-11_00:00'), get_timestamp('2018-08-13_23:59')],
[get_timestamp('2018-09-11_00:00'), get_timestamp('2018-09-13_23:59')],
[get_timestamp('2018-10-11_00:00'), get_timestamp('2018-10-13_23:59')],
[get_timestamp('2018-11-11_00:00'), get_timestamp('2018-11-13_23:59')],
[get_timestamp('2018-12-11_00:00'), get_timestamp('2018-12-13_23:59')],
[get_timestamp('2019-01-11_00:00'), get_timestamp('2019-01-13_23:59')],
[get_timestamp('2019-02-11_00:00'), get_timestamp('2019-02-13_23:59')],
[get_timestamp('2019-03-11_00:00'), get_timestamp('2019-03-13_23:59')],
[get_timestamp('2019-04-11_00:00'), get_timestamp('2019-04-13_23:59')],
[get_timestamp('2019-05-11_00:00'), get_timestamp('2019-05-13_23:59')],
[get_timestamp('2019-06-11_00:00'), get_timestamp('2019-06-13_23:59')],
[get_timestamp('2019-07-11_00:00'), get_timestamp('2019-07-13_23:59')],
[get_timestamp('2019-08-11_00:00'), get_timestamp('2019-08-13_23:59')],
[get_timestamp('2019-09-11_00:00'), get_timestamp('2019-09-13_23:59')],
[get_timestamp('2019-10-11_00:00'), get_timestamp('2019-10-13_23:59')],
[get_timestamp('2019-11-11_00:00'), get_timestamp('2019-11-13_23:59')],
[get_timestamp('2019-12-11_00:00'), get_timestamp('2019-12-13_23:59')],
[get_timestamp('2021-12-11_00:00'), get_timestamp('2021-12-13_23:59')],
[get_timestamp('2022-01-11_00:00'), get_timestamp('2022-01-13_23:59')],
[get_timestamp('2022-02-11_00:00'), get_timestamp('2022-02-13_23:59')],
[get_timestamp('2022-03-11_00:00'), get_timestamp('2022-03-13_23:59')],
[get_timestamp('2022-04-11_00:00'), get_timestamp('2022-04-13_23:59')],
[get_timestamp('2022-05-11_00:00'), get_timestamp('2022-05-13_23:59')],
[get_timestamp('2022-10-11_00:00'), get_timestamp('2022-10-13_23:59')],
[get_timestamp('2022-11-11_00:00'), get_timestamp('2022-11-13_23:59')],
[get_timestamp('2022-12-11_00:00'), get_timestamp('2022-12-13_23:59')],
[get_timestamp('2023-12-11_00:00'), get_timestamp('2023-12-13_23:59')]]
tst_time_idxs = []
if has_test:
for t_rng in time_ranges:
t_rng[0] -= keep_out
t_rng[1] += keep_out
tidxs = np.searchsorted(times, t_rng)
tst_time_idxs.append(np.arange(tidxs[0], tidxs[1], 1))
tst_time_idxs = np.concatenate(tst_time_idxs, axis=None)
vld_tst_time_idxs = np.concatenate([vld_time_idxs, tst_time_idxs])
vld_tst_time_idxs = np.sort(vld_tst_time_idxs)
vld_tst_time_idxs = np.sort(vld_time_idxs)
train_time_idxs = time_idxs[np.in1d(time_idxs, vld_tst_time_idxs, invert=True)]
return train_time_idxs, vld_time_idxs, tst_time_idxs
def normalize(data, param, mean_std_dict, add_noise=False, noise_scale=1.0, seed=None):
if mean_std_dict.get(param) is None:
return data
shape = data.shape
data = data.flatten()
mean, std, lo, hi = mean_std_dict.get(param)
data -= mean
data /= std
if add_noise:
if seed is not None:
np.random.seed(seed)
rnd = np.random.normal(loc=0, scale=noise_scale, size=data.size)
data += rnd
not_valid = np.isnan(data)
data[not_valid] = 0
data = np.reshape(data, shape)
return data
lon_space = np.linspace(-180, 180, 721)
lat_space = np.linspace(-90, 90, 361)
def spatial_filter(icing_dict):
keys = icing_dict.keys()
grd_x_hi = lon_space.shape[0] - 1
grd_y_hi = lat_space.shape[0] - 1
grd_bins = np.full((lat_space.shape[0], lon_space.shape[0]), 0)
grd_bins_keys = [[[] for i in range(lon_space.shape[0])] for j in range(lat_space.shape[0])]
for key in keys:
rpts = icing_dict.get(key)
for tup in rpts:
lat = tup[0]
lon = tup[1]
lon_idx = np.searchsorted(lon_space, lon)
lat_idx = np.searchsorted(lat_space, lat)
if lon_idx < 0 or lon_idx > grd_x_hi:
continue
if lat_idx < 0 or lat_idx > grd_y_hi:
continue
grd_bins[lat_idx, lon_idx] += 1
grd_bins_keys[lat_idx][lon_idx].append(key)
return grd_bins, grd_bins_keys
def remove_common(boeing_dct, pirep_dct, threshold=3000):
boeing_times = list(boeing_dct.keys())
pirep_times = np.array(list(pirep_dct.keys()))
pt_s = []
bt_s = []
bi_s = []
for k, bt in enumerate(boeing_times):
idx_s, v_s = get_indexes_within_threshold(pirep_times, bt, threshold=threshold)
if len(idx_s) > 0:
bt_s.append(bt)
bi_s.append(k)
pt_s.append(v_s[0])
boeing_times = np.array(boeing_times)
sub_pirep_dct = {}
sub_boeing_dct = {}
for key in pt_s:
sub_pirep_dct[key] = pirep_dct.get(key)
for key in bt_s:
sub_boeing_dct[key] = boeing_dct.get(key)
grd_bins, _ = spatial_filter(sub_pirep_dct)
grd_bins_boeing, key_bins = spatial_filter(sub_boeing_dct)
grd_bins = np.where(grd_bins > 0, 1, grd_bins)
grd_bins_boeing = np.where(grd_bins_boeing > 0, 1, grd_bins_boeing)
ovrlp_grd_bins = grd_bins + grd_bins_boeing
ovlp_keys = []
for j in range(lat_space.shape[0]):
for i in range(lon_space.shape[0]):
if ovrlp_grd_bins[j, i] == 2:
keys = key_bins[j][i]
nkeys = len(keys)
for k in range(nkeys):
ovlp_keys.append(keys[k])
set_a = set(ovlp_keys)
set_b = set(boeing_times)
set_b.difference_update(set_a)
no_ovlp_dct = {}
for key in set_b:
no_ovlp_dct[key] = boeing_dct.get(key)
return no_ovlp_dct
# dt_str_0: start datetime string in format YYYY-MM-DD_HH:MM (default)
# dt_str_1: end datetime string in format YYYY-MM-DD_HH:MM (default)
# format_code: Python Datetime format code, default: '%Y-%m-%d_%H:%M'
# return a flatten list of icing reports
def time_filter(icing_dct, dt_str_0=None, dt_str_1=None, format_code='%Y-%m-%d_%H:%M'):
ts_0 = 0
if dt_str_0 is not None:
dto_0 = datetime.datetime.strptime(dt_str_0, format_code).replace(tzinfo=timezone.utc)
ts_0 = dto_0.timestamp()
ts_1 = np.finfo(np.float64).max
if dt_str_1 is not None:
dto_1 = datetime.datetime.strptime(dt_str_1, format_code).replace(tzinfo=timezone.utc)
ts_1 = dto_1.timestamp()
keep_reports = []
keep_times = []
keep_lons = []
keep_lats = []
for ts in list(icing_dct.keys()):
if ts_0 <= ts < ts_1:
rpts = icing_dct[ts]
for idx, tup in enumerate(rpts):
keep_reports.append(tup)
keep_times.append(ts)
keep_lats.append(tup[0])
keep_lons.append(tup[1])
return keep_times, keep_lons, keep_lats, keep_reports
# dt_str_0: start datetime string in format YYYY-MM-DD_HH:MM (default)
# dt_str_1: end datetime string in format YYYY-MM-DD_HH:MM (default)
# format_code: Python Datetime format code, default: '%Y-%m-%d_%H:%M'
# return a flatten list of icing reports
def time_filter_2(times, dt_str_0=None, dt_str_1=None, format_code='%Y-%m-%d_%H:%M'):
ts_0 = 0
if dt_str_0 is not None:
dto_0 = datetime.datetime.strptime(dt_str_0, format_code).replace(tzinfo=timezone.utc)
ts_0 = dto_0.timestamp()
ts_1 = np.finfo(np.float64).max
if dt_str_1 is not None:
dto_1 = datetime.datetime.strptime(dt_str_1, format_code).replace(tzinfo=timezone.utc)
ts_1 = dto_1.timestamp()
keep_idxs = []
keep_times = []
for idx, ts in enumerate(times):
if ts_0 <= ts < ts_1:
keep_times.append(ts)
keep_idxs.append(idx)
return keep_times, keep_idxs
def time_filter_3(icing_dct, ts_0, ts_1, alt_lo=None, alt_hi=None):
keep_reports = []
keep_times = []
keep_lons = []
keep_lats = []
for ts in list(icing_dct.keys()):
if ts_0 <= ts < ts_1:
rpts = icing_dct[ts]
for idx, tup in enumerate(rpts):
falt = tup[2]
if alt_lo is not None and (alt_lo < falt <= alt_hi):
keep_reports.append(tup)
keep_times.append(ts)
keep_lats.append(tup[0])
keep_lons.append(tup[1])
return keep_times, keep_lons, keep_lats, keep_reports
def analyze_moon_phase(icing_dict):
ts = api.load.timescale()
eph = api.load('de421.bsp')
last_date = None
moon_phase = None
cnt = 0
for key in list(icing_dict.keys()):
dt_obj, dt_tup = get_time_tuple_utc(key)
date = datetime.date(dt_tup.tm_year, dt_tup.tm_mon, dt_tup.tm_mday)
if last_date != date:
t = ts.utc(dt_tup.tm_year, dt_tup.tm_mon, dt_tup.tm_mday)
moon_phase = almanac.moon_phase(eph, t)
if 30 < moon_phase.degrees < 330:
cnt += 1
last_date = date
else:
if 30 < moon_phase.degrees < 330:
cnt += 1
print(len(icing_dict), cnt)
def tiles_info(filename):
h5f = h5py.File(filename, 'r')
iint = h5f['icing_intensity'][:]
print('No Icing: ', np.sum(iint == -1))
print('Icing: ', np.sum(iint > 0))
print('Icing 1: ', np.sum(iint == 1))
print('Icing 2: ', np.sum(iint == 2))
print('Icing 3: ', np.sum(iint == 3))
print('Icing 4: ', np.sum(iint == 4))
print('Icing 5: ', np.sum(iint == 5))
print('Icing 6: ', np.sum(iint == 6))
def analyze(preds_file, labels, prob_avg, test_file):
if preds_file is not None:
labels, prob_avg = np.load(preds_file, allow_pickle=True)
h5f = h5py.File(test_file, 'r')
nda = h5f['flight_altitude'][:]
num_obs = nda.shape[0]
iint = h5f['icing_intensity'][:]
cld_hgt = h5f['cld_height_acha'][:]
cld_dz = h5f['cld_geo_thick'][:]
cld_tmp = h5f['cld_temp_acha'][:]
# cld_top_hgt = cld_hgt.reshape((num_obs, -1))
print('report altitude (m): ', np.histogram(nda, bins=12))
flt_alt = nda.copy()
iint = np.where(iint == -1, 0, iint)
iint = np.where(iint != 0, 1, iint)
nda[np.logical_and(nda >= 100, nda < 4000)] = 0
nda[np.logical_and(nda >= 4000, nda < 15000)] = 1
nda[np.logical_and(nda >= 15000, nda < 20000)] = 2
nda[np.logical_and(nda >= 20000, nda < 25000)] = 3
# nda[np.logical_and(nda >= 8000, nda < 15000)] = 4
print(np.sum(nda == 0), np.sum(nda == 1), np.sum(nda == 2))
print('No icing: ', np.sum((iint == 0) & (nda == 0)), np.sum((iint == 0) & (nda == 1)), np.sum((iint == 0) & (nda == 2)), np.sum((iint == 0) & (nda == 3)))
print('---------------------------')
print('Icing: ', np.sum((iint == 1) & (nda == 0)), np.sum((iint == 1) & (nda == 1)), np.sum((iint == 1) & (nda == 2)), np.sum((iint == 1) & (nda == 3)))
print('---------------------------')
print('----------------------------------------------------------')
print('----------------------------------------------------------')
if prob_avg is None:
return
preds = np.where(prob_avg > 0.5, 1, 0)
num_0 = np.sum(nda == 0)
num_1 = np.sum(nda == 1)
num_2 = np.sum(nda == 2)
num_3 = np.sum(nda == 3)
true_ice = (labels == 1) & (preds == 1)
false_ice = (labels == 0) & (preds == 1)
true_no_ice = (labels == 0) & (preds == 0)
false_no_ice = (labels == 1) & (preds == 0)
tp_0 = np.sum(true_ice & (nda == 0))
tp_1 = np.sum(true_ice & (nda == 1))
tp_2 = np.sum(true_ice & (nda == 2))
tp_3 = np.sum(true_ice & (nda == 3))
tn_0 = np.sum(true_no_ice & (nda == 0))
tn_1 = np.sum(true_no_ice & (nda == 1))
tn_2 = np.sum(true_no_ice & (nda == 2))
tn_3 = np.sum(true_no_ice & (nda == 3))
fp_0 = np.sum(false_ice & (nda == 0))
fp_1 = np.sum(false_ice & (nda == 1))
fp_2 = np.sum(false_ice & (nda == 2))
fp_3 = np.sum(false_ice & (nda == 3))
fn_0 = np.sum(false_no_ice & (nda == 0))
fn_1 = np.sum(false_no_ice & (nda == 1))
fn_2 = np.sum(false_no_ice & (nda == 2))
fn_3 = np.sum(false_no_ice & (nda == 3))
recall_0 = tp_0 / (tp_0 + fn_0)
recall_1 = tp_1 / (tp_1 + fn_1)
recall_2 = tp_2 / (tp_2 + fn_2)
recall_3 = tp_3 / (tp_3 + fn_3)
precision_0 = tp_0 / (tp_0 + fp_0)
precision_1 = tp_1 / (tp_1 + fp_1)
precision_2 = tp_2 / (tp_2 + fp_2)
precision_3 = tp_3 / (tp_3 + fp_3)
f1_0 = 2 * (precision_0 * recall_0) / (precision_0 + recall_0)
f1_1 = 2 * (precision_1 * recall_1) / (precision_1 + recall_1)
f1_2 = 2 * (precision_2 * recall_2) / (precision_2 + recall_2)
f1_3 = 2 * (precision_3 * recall_3) / (precision_3 + recall_3)
mcc_0 = ((tp_0 * tn_0) - (fp_0 * fn_0)) / np.sqrt((tp_0 + fp_0) * (tp_0 + fn_0) * (tn_0 + fp_0) * (tn_0 + fn_0))
mcc_1 = ((tp_1 * tn_1) - (fp_1 * fn_1)) / np.sqrt((tp_1 + fp_1) * (tp_1 + fn_1) * (tn_1 + fp_1) * (tn_1 + fn_1))
mcc_2 = ((tp_2 * tn_2) - (fp_2 * fn_2)) / np.sqrt((tp_2 + fp_2) * (tp_2 + fn_2) * (tn_2 + fp_2) * (tn_2 + fn_2))
mcc_3 = ((tp_3 * tn_3) - (fp_3 * fn_3)) / np.sqrt((tp_3 + fp_3) * (tp_3 + fn_3) * (tn_3 + fp_3) * (tn_3 + fn_3))
acc_0 = np.sum(labels[nda == 0] == preds[nda == 0]) / num_0
acc_1 = np.sum(labels[nda == 1] == preds[nda == 1]) / num_1
acc_2 = np.sum(labels[nda == 2] == preds[nda == 2]) / num_2
acc_3 = np.sum(labels[nda == 3] == preds[nda == 3]) / num_3
print('Total (Positive/Icing Prediction: ')
print('True icing: ', np.sum(true_ice & (nda == 0)), np.sum(true_ice & (nda == 1)), np.sum(true_ice & (nda == 2)), np.sum(true_ice & (nda == 3)))
print('-------------------------')
print('False no icing (False Negative/Miss): ', np.sum(false_no_ice & (nda == 0)), np.sum(false_no_ice & (nda == 1)), np.sum(false_no_ice & (nda == 2)), np.sum(false_no_ice & (nda == 3)))
print('---------------------------------------------------')
print('---------------------------------------------------')
print('Total (Negative/No Icing Prediction: ')
print('True no icing: ', np.sum(true_no_ice & (nda == 0)), np.sum(true_no_ice & (nda == 1)), np.sum(true_no_ice & (nda == 2)), np.sum(true_no_ice & (nda == 3)))
print('-------------------------')
print('* False icing (False Positive/False Alarm) *: ', np.sum(false_ice & (nda == 0)), np.sum(false_ice & (nda == 1)), np.sum(false_ice & (nda == 2)), np.sum(false_ice & (nda == 3)))
print('-------------------------')
print('ACC: ', acc_0, acc_1, acc_2, acc_3)
print('Recall: ', recall_0, recall_1, recall_2, recall_3)
print('Precision: ', precision_0, precision_1, precision_2, precision_3)
print('F1: ', f1_0, f1_1, f1_2, f1_3)
print('MCC: ', mcc_0, mcc_1, mcc_2, mcc_3)
return labels[nda == 0], prob_avg[nda == 0], labels[nda == 1], prob_avg[nda == 1], labels[nda == 2], prob_avg[nda == 2]
def get_training_parameters(day_night='DAY', l1b_andor_l2='both'):
if day_night == 'DAY':
train_params_l2 = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'iwc_dcomp', 'lwc_dcomp']
train_params_l1b = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom',
'refl_0_47um_nom', 'refl_0_65um_nom', 'refl_0_86um_nom', 'refl_1_38um_nom', 'refl_1_60um_nom']
else:
train_params_l2 = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
'cld_emiss_acha', 'conv_cloud_fraction', 'cld_reff_acha', 'cld_opd_acha']
train_params_l1b = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom']
if l1b_andor_l2 == 'both':
train_params = train_params_l1b + train_params_l2
elif l1b_andor_l2 == 'l1b':
train_params = train_params_l1b
elif l1b_andor_l2 == 'l2':
train_params = train_params_l2
return train_params
flt_level_ranges = {k: None for k in range(5)}
flt_level_ranges[0] = [0.0, 2000.0]
flt_level_ranges[1] = [2000.0, 4000.0]
flt_level_ranges[2] = [4000.0, 6000.0]
flt_level_ranges[3] = [6000.0, 8000.0]
flt_level_ranges[4] = [8000.0, 15000.0]