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 re
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/'
#clavrx_dir = '/data/Personal/rink/clavrx/'
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']
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):
if platform == 'GOES':
return get_clavrx_datasource_goes(timestamp)
elif platform == 'VIIRS':
return get_clavrx_datasource_viirs(timestamp)
def get_clavrx_datasource_goes(timestamp):
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 + '/')
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):
hfds = h5f[grid_name]
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 None:
raise GenericException('Attribute: '+scale_factor_name+' not found for dataset: '+grid_name)
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 None:
raise GenericException('Attribute: '+add_offset_name+' not found for dataset: '+grid_name)
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):
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')
# 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):
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)
except Exception:
print('run: Problem retrieving Datasource')
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 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)
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 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
def analyze(ice_dct, no_ice_dct):
last_file = None
ice_files = []
ice_times = []
for ts in list(ice_dct.keys()):
try:
ds = get_goes_datasource(ts)
goes_file, t_0, _ = ds.get_file(ts)
if goes_file is not None and goes_file != last_file:
ice_files.append(goes_file)
ice_times.append(t_0)
last_file = goes_file
except Exception:
continue
last_file = None
no_ice_files = []
no_ice_times = []
for ts in list(no_ice_dct.keys()):
try:
ds = get_goes_datasource(ts)
goes_file, t_0, _ = ds.get_file(ts)
if goes_file is not None and goes_file != last_file:
no_ice_files.append(goes_file)
no_ice_times.append(t_0)
last_file = goes_file
except Exception:
continue
ice_times = np.array(ice_times)
no_ice_times = np.array(no_ice_times)
itrsct_vals, comm1, comm2 = np.intersect1d(no_ice_times, ice_times, return_indices=True)
ice_indexes = np.arange(len(ice_times))
ucomm2 = np.setxor1d(comm2, ice_indexes)
np.random.seed(42)
np.random.shuffle(ucomm2)
ucomm2 = ucomm2[0:8000]
files_comm = []
for i in comm2:
files_comm.append(ice_files[i])
files_extra = []
times_extra = []
for i in ucomm2:
files_extra.append(ice_files[i])
times_extra.append(ice_times[i])
files = files_comm + files_extra
times = itrsct_vals.tolist() + times_extra
times = np.array(times)
sidxs = np.argsort(times)
for i in sidxs:
filename = os.path.split(files[i])[1]
so = re.search('_s\\d{11}', filename)
dt_str = so.group()
print(dt_str[2:])
lon_space_hdeg = np.linspace(-180, 180, 721)
lat_space_hdeg = np.linspace(-90, 90, 361)
# grd_bins = np.full((lat_space.shape[0], lon_space.shape[0]), t_delta)
def check_no_overlap(lon, lat, ts, grd_bins, t_delta=600.0):
grd_x_hi = lon_space.shape[0] - 1
grd_y_hi = lat_space.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
# 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 # 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)
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)
print('4: ', ice_keys_4.shape[0])
ice_keys_3 = np.array(ice_keys_3)
ice_tidx_3 = np.array(ice_tidx_3)
print('3: ', ice_keys_3.shape[0])
ice_keys_2 = np.array(ice_keys_2)
ice_tidx_2 = np.array(ice_tidx_2)
print('2: ', ice_keys_2.shape[0])
np.random.seed(42)
ridxs = np.random.permutation(np.arange(ice_keys_2.shape[0]))
ice_keys_2 = ice_keys_2[ridxs]
ice_tidx_2 = ice_tidx_2[ridxs]
num = int(ice_keys_2.shape[0] * 0.7)
ice_keys_2 = ice_keys_2[0:num]
ice_tidx_2 = ice_tidx_2[0:num]
print('2: reduced: ', ice_tidx_2.shape)
ice_keys_1 = np.array(ice_keys_1)
ice_tidx_1 = np.array(ice_tidx_1)
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)
sidxs = np.argsort(ice_keys)
ice_keys = ice_keys[sidxs]
ice_tidx = ice_tidx[sidxs]
grd_bins = np.full((lat_space_hdeg.shape[0], lon_space_hdeg.shape[0]), -(t_delta+1))
cnt = 0
for idx, key in enumerate(ice_keys):
rpts = ice_dct[key]
tup = rpts[ice_tidx[idx]]
lat, lon = tup[0], tup[1]
if not check_no_overlap(lon, lat, key, grd_bins, t_delta=t_delta):
continue
cnt += 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('icing total no overlap: ', cnt)
# -----------------------------------------------------
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[::5]
no_ice_tidx = no_ice_tidx[::5]
print('no ice reduced: ', no_ice_keys.shape[0])
sidxs = np.argsort(no_ice_keys)
no_ice_keys = no_ice_keys[sidxs]
no_ice_tidx = no_ice_tidx[sidxs]
grd_bins = np.full((lat_space_hdeg.shape[0], lon_space_hdeg.shape[0]), -(t_delta+1))
cnt = 0
for idx, key in enumerate(no_ice_keys):
rpts = no_ice_dct[key]
tup = rpts[no_ice_tidx[idx]]
lat, lon = tup[0], tup[1]
if not check_no_overlap(lon, lat, key, grd_bins, t_delta=t_delta):
continue
cnt += 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)
print('no icing total no overlap: ', cnt)
# -------------------------------------------------
# Previous for neg icing
# neg_ice_keys = np.array(neg_ice_keys)
# print('neg ice total: ', neg_ice_keys.shape[0])
# np.random.seed(42)
# np.random.shuffle(neg_ice_keys)
# neg_ice_keys = neg_ice_keys[0:12000]
# uniq_sorted_neg_ice = np.unique(neg_ice_keys)
# print('neg ice reduced: ', uniq_sorted_neg_ice.shape)
#
# for key in uniq_sorted_neg_ice:
# new_neg_ice_dct[key] = neg_ice_dct[key]
# -----------------------------------------------------
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((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 = tup[0], tup[1]
if not check_no_overlap(lon, lat, key, grd_bins, t_delta=t_delta):
continue
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 process_boeing(ice_dct, no_ice_dct):
new_no_ice_dct = {}
print('num keys ice, no_ice: ', len(ice_dct), len(no_ice_dct))
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)
# -----------------------------------------------------
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[::10]
no_ice_tidx = no_ice_tidx[::10]
print('no ice reduced: ', no_ice_keys.shape[0])
sidxs = np.argsort(no_ice_keys)
no_ice_keys = no_ice_keys[sidxs]
no_ice_tidx = no_ice_tidx[sidxs]
for idx, key in enumerate(no_ice_keys):
rpts = no_ice_dct[key]
tup = rpts[no_ice_tidx[idx]]
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)
# -------------------------------------------------
return ice_dct, new_no_ice_dct
def analyze2(filename, filename_l1b):
f = h5py.File(filename, 'r')
icing_alt = f['icing_altitude'][:]
cld_top_hgt = f['cld_height_acha'][:, 12:28, 12:28]
cld_phase = f['cloud_phase'][:, 12:28, 12:28]
cld_opd_dc = f['cld_opd_dcomp'][:, 12:28, 12:28]
cld_opd = f['cld_opd_acha'][:, 12:28, 12:28]
solzen = f['solar_zenith_angle'][:, 12:28, 12:28]
f_l1b = h5py.File(filename_l1b, 'r')
bt_11um = f_l1b['temp_11_0um_nom'][:, 12:28, 12:28]
bt_11um = bt_11um.flatten()
cld_opd = cld_opd.flatten()
cld_opd_dc = cld_opd_dc.flatten()
solzen = solzen.flatten()
keep1 = np.invert(np.isnan(cld_opd))
keep2 = np.invert(np.isnan(solzen))
keep = keep1 & keep2
cld_opd = cld_opd[np.invert(np.isnan(cld_opd))]
cld_opd_dc = cld_opd_dc[keep]
solzen = solzen[keep]
bt_11um = bt_11um[np.invert(np.isnan(bt_11um))]
return cld_opd, bt_11um
# --------------------------------------------
x_a = 10
x_b = 30
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, 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_phase = f['cloud_phase'][:, 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_phase, cld_opd, cld_mask, bt_11um, sol_zen, sat_zen, day_night=day_night)
else:
mask, idxs, num_tested = apply_qc_no_icing_pireps(icing_alt, cld_top_hgt, cld_phase, cld_opd, cld_mask, bt_11um, sol_zen, sat_zen, day_night=day_night)
keep_idxs = []
for i in range(len(mask)):
# frac = np.sum(mask[i]) / nx_x_ny
frac = np.sum(mask[i]) / num_tested[i]
if icing:
if frac > pass_thresh_frac:
keep_idxs.append(idxs[i])
elif frac > pass_thresh_frac:
keep_idxs.append(idxs[i])
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)
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 + '_' + '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)
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 + '_' + '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)
f.close()
f_l1b.close()
return len(icing_alt), len(mask), len(keep_idxs)
def apply_qc_icing_pireps(icing_alt, cld_top_hgt, cld_phase, cld_opd, cld_mask, bt_11um, solzen, satzen, day_night='ANY'):
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 = 50.0 # meters
num_obs = len(icing_alt)
cld_mask = cld_mask.reshape((num_obs, -1))
cld_top_hgt = cld_top_hgt.reshape((num_obs, -1))
cld_phase = cld_phase.reshape((num_obs, -1))
cld_opd = cld_opd.reshape((num_obs, -1))
bt_11um = bt_11um.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
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_opd[i,]))
keep = keep_0 & keep_1 & keep_2 & keep_3
num_keep = np.sum(keep)
if num_keep == 0:
continue
# Test 1
keep = np.where(keep, (cld_top_hgt[i,] + closeness) > icing_alt[i], False)
# if (np.sum(keep) / num_keep) < 0.80:
# continue
# Test2
keep = np.where(keep,
np.invert((cld_phase[i,] == 4) &
np.logical_and(cld_top_hgt[i,]+closeness > icing_alt[i], cld_top_hgt[i,]-closeness < icing_alt[i])),
False)
# Test4
keep = np.where(keep, np.invert((cld_phase[i,] == 4) & (cld_opd[i,] < opd_thin_threshold)), False)
# Test5 and Test6
keep = np.where(keep, np.logical_and(bt_11um[i,] > 228.0, bt_11um[i,] < 270.0), False)
# Test3
keep = np.where(keep, (cld_opd[i,] >= opd_thick_threshold) & (cld_phase[i,] == 4), 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_phase, cld_opd, cld_mask, bt_11um, solzen, satzen, day_night='ANY'):
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 = 50.0 # meters
num_obs = len(icing_alt)
cld_mask = cld_mask.reshape((num_obs, -1))
cld_top_hgt = cld_top_hgt.reshape((num_obs, -1))
cld_phase = cld_phase.reshape((num_obs, -1))
cld_opd = cld_opd.reshape((num_obs, -1))
bt_11um = bt_11um.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
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_opd[i,]))
# keep = keep_0 & keep_1 & keep_2 & keep_3
keep = keep_0 & keep_1 & keep_2
num_keep = np.sum(keep)
if num_keep == 0:
continue
# Exp
keep = np.where(keep, (cld_top_hgt[i,] + closeness) > icing_alt[i], False)
#if (np.sum(keep) / num_keep) < 0.80:
# continue
# keep = np.where(keep, np.logical_and(bt_11um[i,] > 228.0, bt_11um[i,] < 273.0), False)
# Test6
# keep = np.where(keep, (bt_11um[i,] < 228.0), False)
# Test1
# keep = np.where(keep, cld_top_hgt[i,] > icing_alt[i], False)
# Test3
# keep = np.where(keep, np.invert((cld_opd[i,] >= opd_thick_threshold) & (cld_phase[i,] == 4) & (cld_top_hgt[i,] > icing_alt[i])), 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):
# 16x16
n_a, n_b = 12, 28
m_a, m_b = 12, 28
# 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
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)
num_ice = icing_int_s.shape[0]
# 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)
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:
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]
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)
# --- 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()
# Special VIIRS date/times -----------------------------------------------------------
# time_ranges = [[get_timestamp('2019-03-12_00:00'), get_timestamp('2019-03-15_23:59')],
# [get_timestamp('2019-03-20_00:00'), get_timestamp('2019-03-26_23:59')],
# [get_timestamp('2019-03-29_00:00'), get_timestamp('2019-03-29_23:59')],
# [get_timestamp('2019-04-17_00:00'), get_timestamp('2019-04-17_23:59')],
# [get_timestamp('2019-04-20_00:00'), get_timestamp('2019-04-20_23:59')],
# [get_timestamp('2019-04-23_00:00'), get_timestamp('2019-04-23_23:59')],
# [get_timestamp('2019-04-27_00:00'), get_timestamp('2019-04-27_23:59')],
# [get_timestamp('2019-04-29_00:00'), get_timestamp('2019-04-29_23:59')],
# [get_timestamp('2019-05-09_00:00'), get_timestamp('2019-05-09_23:59')],
# [get_timestamp('2019-05-12_00:00'), get_timestamp('2019-05-12_23:59')],
# [get_timestamp('2019-05-14_00:00'), get_timestamp('2019-05-14_23:59')],
# [get_timestamp('2019-05-19_00:00'), get_timestamp('2019-05-19_23:59')],
# [get_timestamp('2019-06-13_00:00'), get_timestamp('2019-06-13_23:59')],
# [get_timestamp('2019-07-22_00:00'), get_timestamp('2019-07-22_23:59')],
# [get_timestamp('2019-07-24_00:00'), get_timestamp('2019-07-24_29:59')],
# [get_timestamp('2019-08-20_00:00'), get_timestamp('2019-08-20_23:59')],
# [get_timestamp('2019-09-09_00:00'), get_timestamp('2019-09-09_23:59')],
# [get_timestamp('2019-09-09_00:00'), get_timestamp('2019-09-11_23:59')]]
def split_data(times):
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_lunar = [[get_timestamp('2018-01-01_00:00'), get_timestamp('2018-01-06_23:59')],
# [get_timestamp('2018-03-01_00:00'), get_timestamp('2018-03-06_23:59')],
# [get_timestamp('2018-05-01_00:00'), get_timestamp('2018-05-06_23:59')],
# [get_timestamp('2018-07-01_00:00'), get_timestamp('2018-07-06_23:59')],
# [get_timestamp('2018-09-01_00:00'), get_timestamp('2018-09-06_23:59')],
# [get_timestamp('2018-11-01_00:00'), get_timestamp('2018-11-06_23:59')],
# [get_timestamp('2019-01-01_00:00'), get_timestamp('2019-01-06_23:59')],
# #[get_timestamp('2019-03-01_00:00'), get_timestamp('2019-03-07_23:59')],
# [get_timestamp('2019-03-12_00:00'), get_timestamp('2019-03-15_23:59')],
# [get_timestamp('2019-03-20_00:00'), get_timestamp('2019-03-26_23:59')],
# [get_timestamp('2019-03-29_00:00'), get_timestamp('2019-03-29_23:59')],
# [get_timestamp('2019-05-01_00:00'), get_timestamp('2019-05-03_23:59')],
# [get_timestamp('2019-05-09_00:00'), get_timestamp('2019-05-09_23:59')],
# [get_timestamp('2019-05-12_00:00'), get_timestamp('2019-05-12_23:59')],
# [get_timestamp('2019-05-14_00:00'), get_timestamp('2019-05-14_23:59')],
# [get_timestamp('2019-05-19_00:00'), get_timestamp('2019-05-19_23:59')],
# [get_timestamp('2019-07-01_00:00'), get_timestamp('2019-07-05_23:59')],
# [get_timestamp('2019-07-22_00:00'), get_timestamp('2019-07-22_23:59')],
# [get_timestamp('2019-07-24_00:00'), get_timestamp('2019-07-24_23:59')],
# [get_timestamp('2019-09-01_00:00'), get_timestamp('2019-09-05_23:59')],
# [get_timestamp('2019-09-09_00:00'), get_timestamp('2019-09-09_23:59')],
# [get_timestamp('2019-09-09_00:00'), get_timestamp('2019-09-11_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')]]
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)
# train_time_idxs = time_idxs[np.in1d(time_idxs, vld_time_idxs, invert=True)]
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')]]
# time_ranges_lunar = [[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('2019-04-17_00:00'), get_timestamp('2019-04-17_23:59')],
# [get_timestamp('2019-04-20_00:00'), get_timestamp('2019-04-20_23:59')],
# [get_timestamp('2019-04-23_00:00'), get_timestamp('2019-04-23_23:59')],
# [get_timestamp('2019-04-27_00:00'), get_timestamp('2019-04-27_23:59')],
# [get_timestamp('2019-04-29_00:00'), get_timestamp('2019-04-29_23:59')],
# [get_timestamp('2019-06-01_00:00'), get_timestamp('2019-06-03_23:59')],
# [get_timestamp('2019-06-13_00:00'), get_timestamp('2019-06-13_23:59')],
# [get_timestamp('2019-08-01_00:00'), get_timestamp('2019-08-03_23:59')],
# [get_timestamp('2019-08-20_00:00'), get_timestamp('2019-08-20_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')]]
tst_time_idxs = []
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)
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):
h5f = h5py.File(test_file, 'r')
nda = h5f['flight_altitude'][:]
iint = h5f['icing_intensity'][:]
cld_hgt = h5f['cld_height_acha'][:]
cld_dz = h5f['cld_geo_thick'][:]
cld_tmp = h5f['cld_temp_acha'][:]
print('report altitude (m): ', np.histogram(nda, bins=12))
iint = np.where(iint == -1, 0, iint)
iint = np.where(iint != 0, 1, iint)
nda[np.logical_and(nda >= 0, nda < 2000)] = 0
nda[np.logical_and(nda >= 2000, nda < 4000)] = 1
nda[np.logical_and(nda >= 4000, nda < 6000)] = 2
nda[np.logical_and(nda >= 6000, nda < 8000)] = 3
nda[np.logical_and(nda >= 8000, nda < 15000)] = 4
print(np.sum(nda == 0), np.sum(nda == 1), np.sum(nda == 2), np.sum(nda == 3), np.sum(nda == 4))
print('No icing: ', np.histogram(nda[iint == 0], bins=5)[0])
print('---------------------------')
print('Icing: ', np.histogram(nda[iint == 1], bins=5)[0])
print('---------------------------')
print('No Icing(Negative): mean cld_dz, cld_hgt')
print('Icing(Positive): ", "')
print('level 0: ')
print(np.nanmean(cld_dz[(nda == 0) & (iint == 0)]), np.nanmean(cld_hgt[(nda == 0) & (iint == 0)]), np.nanmean(cld_tmp[(nda == 0) & (iint == 0)]))
print(np.nanmean(cld_dz[(nda == 0) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 0) & (iint == 1)]), np.nanmean(cld_tmp[(nda == 0) & (iint == 1)]))
print('------------')
print('level 1: ')
print(np.nanmean(cld_dz[(nda == 1) & (iint == 0)]), np.nanmean(cld_hgt[(nda == 1) & (iint == 0)]), np.nanmean(cld_tmp[(nda == 1) & (iint == 0)]))
print(np.nanmean(cld_dz[(nda == 1) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 1) & (iint == 1)]), np.nanmean(cld_tmp[(nda == 1) & (iint == 1)]))
print('------------')
print('level 2: ')
print(np.nanmean(cld_dz[(nda == 2) & (iint == 0)]), np.nanmean(cld_hgt[(nda == 2) & (iint == 0)]), np.nanmean(cld_tmp[(nda == 2) & (iint == 0)]))
print(np.nanmean(cld_dz[(nda == 2) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 2) & (iint == 1)]), np.nanmean(cld_tmp[(nda == 2) & (iint == 1)]))
print('------------')
print('level 3: ')
print(np.nanmean(cld_dz[(nda == 3) & (iint == 0)]), np.nanmean(cld_hgt[(nda == 3) & (iint == 0)]), np.nanmean(cld_tmp[(nda == 3) & (iint == 0)]))
print(np.nanmean(cld_dz[(nda == 3) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 3) & (iint == 1)]), np.nanmean(cld_tmp[(nda == 3) & (iint == 1)]))
print('------------')
print('level 4: ')
print(np.nanmean(cld_dz[(nda == 4) & (iint == 0)]), np.nanmean(cld_hgt[(nda == 4) & (iint == 0)]), np.nanmean(cld_tmp[(nda == 4) & (iint == 0)]))
print(np.nanmean(cld_dz[(nda == 4) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 4) & (iint == 1)]), np.nanmean(cld_tmp[(nda == 4) & (iint == 1)]))
print('----------------------------------------------------------')
print('----------------------------------------------------------')
if preds_file is not None:
labels, prob_avg, cm_avg = pickle.load(open(preds_file, 'rb'))
preds = np.where(prob_avg > 0.5, 1, 0)
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)
print('Total (Positive/Icing Prediction: ')
print('True icing: ', np.histogram(nda[true_ice], bins=5)[0])
print('-------------------------')
print('False no icing (False Negative/Miss): ', np.histogram(nda[false_no_ice], bins=5)[0])
print('By flight level:')
print('No Icing(Negative): mean cld_dz, cld_hgt')
print('Icing(Positive): ", "')
print('level 0: ')
print(np.nanmean(cld_dz[(nda == 0) & false_no_ice]), np.nanmean(cld_hgt[(nda == 0) & false_no_ice]), np.nanmean(cld_tmp[(nda == 0) & false_no_ice]))
print(np.nanmean(cld_dz[(nda == 0) & true_ice]), np.nanmean(cld_hgt[(nda == 0) & true_ice]), np.nanmean(cld_tmp[(nda == 0) & true_ice]))
print('------------')
print('level 1: ')
print(np.nanmean(cld_dz[(nda == 1) & false_no_ice]), np.nanmean(cld_hgt[(nda == 1) & false_no_ice]), np.nanmean(cld_tmp[(nda == 1) & false_no_ice]))
print(np.nanmean(cld_dz[(nda == 1) & true_ice]), np.nanmean(cld_hgt[(nda == 1) & true_ice]), np.nanmean(cld_tmp[(nda == 1) & true_ice]))
print('------------')
print('level 2: ')
print(np.nanmean(cld_dz[(nda == 2) & false_no_ice]), np.nanmean(cld_hgt[(nda == 2) & false_no_ice]), np.nanmean(cld_tmp[(nda == 2) & false_no_ice]))
print(np.nanmean(cld_dz[(nda == 2) & true_ice]), np.nanmean(cld_hgt[(nda == 2) & true_ice]), np.nanmean(cld_tmp[(nda == 2) & true_ice]))
print('------------')
print('level 3: ')
print(np.nanmean(cld_dz[(nda == 3) & false_no_ice]), np.nanmean(cld_hgt[(nda == 3) & false_no_ice]), np.nanmean(cld_tmp[(nda == 3) & false_no_ice]))
print(np.nanmean(cld_dz[(nda == 3) & true_ice]), np.nanmean(cld_hgt[(nda == 3) & true_ice]), np.nanmean(cld_tmp[(nda == 3) & true_ice]))
print('------------')
print('level 4: ')
print(np.nanmean(cld_dz[(nda == 4) & false_no_ice]), np.nanmean(cld_hgt[(nda == 4) & false_no_ice]), np.nanmean(cld_tmp[(nda == 4) & false_no_ice]))
print(np.nanmean(cld_dz[(nda == 4) & true_ice]), np.nanmean(cld_hgt[(nda == 4) & true_ice]), np.nanmean(cld_tmp[(nda == 4) & true_ice]))
print('---------------------------------------------------')
print('---------------------------------------------------')
print('Total (Negative/No Icing Prediction: ')
print('True no icing: ', np.histogram(nda[true_no_ice], bins=5)[0])
print('-------------------------')
print('* False icing (False Positive/False Alarm) *: ', np.histogram(nda[false_ice], bins=5)[0])
print('-------------------------')
print('level 0: ')
print(np.nanmean(cld_dz[(nda == 0) & false_ice]), np.nanmean(cld_hgt[(nda == 0) & false_ice]), np.nanmean(cld_tmp[(nda == 0) & false_ice]))
print(np.nanmean(cld_dz[(nda == 0) & true_no_ice]), np.nanmean(cld_hgt[(nda == 0) & true_no_ice]), np.nanmean(cld_tmp[(nda == 0) & true_no_ice]))
print('------------')
print('level 1: ')
print(np.nanmean(cld_dz[(nda == 1) & false_ice]), np.nanmean(cld_hgt[(nda == 1) & false_ice]), np.nanmean(cld_tmp[(nda == 1) & false_ice]))
print(np.nanmean(cld_dz[(nda == 1) & true_no_ice]), np.nanmean(cld_hgt[(nda == 1) & true_no_ice]), np.nanmean(cld_tmp[(nda == 1) & true_no_ice]))
print('------------')
print('level 2: ')
print(np.nanmean(cld_dz[(nda == 2) & false_ice]), np.nanmean(cld_hgt[(nda == 2) & false_ice]), np.nanmean(cld_tmp[(nda == 2) & false_ice]))
print(np.nanmean(cld_dz[(nda == 2) & true_no_ice]), np.nanmean(cld_hgt[(nda == 2) & true_no_ice]), np.nanmean(cld_tmp[(nda == 2) & true_no_ice]))
print('------------')
print('level 3: ')
print(np.nanmean(cld_dz[(nda == 3) & false_ice]), np.nanmean(cld_hgt[(nda == 3) & false_ice]), np.nanmean(cld_tmp[(nda == 3) & false_ice]))
print(np.nanmean(cld_dz[(nda == 3) & true_no_ice]), np.nanmean(cld_hgt[(nda == 3) & true_no_ice]), np.nanmean(cld_tmp[(nda == 3) & true_no_ice]))
print('------------')
print('level 4: ')
print(np.nanmean(cld_dz[(nda == 4) & false_ice]), np.nanmean(cld_hgt[(nda == 4) & false_ice]), np.nanmean(cld_tmp[(nda == 4) & false_ice]))
print(np.nanmean(cld_dz[(nda == 4) & true_no_ice]), np.nanmean(cld_hgt[(nda == 4) & true_no_ice]), np.nanmean(cld_tmp[(nda == 4) & true_no_ice]))
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]