from icing.pireps import pirep_icing
import numpy as np
import pickle
import matplotlib.pyplot as plt
import os
from util.util import get_time_tuple_utc, GenericException, add_time_range_to_filename, is_night, is_day, \
get_grid_values_all, check_oblique, make_times, find_bin_index, get_timestamp
from aeolus.datasource import CLAVRx, CLAVRx_VIIRS, GOESL1B
import h5py
import re
import datetime
from datetime import timezone
import glob
from skyfield import api, almanac
import xarray as xr
goes_date_format = '%Y%j%H'
goes16_directory = '/arcdata/goes/grb/goes16' # /year/date/abi/L1b/RadC
clavrx_dir = '/ships19/cloud/scratch/ICING/'
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 = {}
pirep_file = '/home/rink/data/pireps/pireps_20180101_20200331.csv'
# --- 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 = '/home/rink/data/clavrx/clavrx_OR_ABI-L1b-RadC-M3C01_G16_s20190020002186.level2.nc'
# VIIRS
#a_clvr_file = '/home/rink/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='/home/rink/data/pireps/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, 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
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
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()
#nav = GEOSNavigation(sub_lon=-75.0, CFAC=5.6E-05, COFF=-0.101332, LFAC=-5.6E-05, LOFF=0.128212, num_elems=2500, num_lines=1500)
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:
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 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:])
# 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):
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 = []
for ts in list(neg_ice_dct.keys()):
rpts = neg_ice_dct[ts]
for tup in rpts:
neg_ice_keys.append(ts)
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]
for idx, key in enumerate(ice_keys):
rpts = ice_dct[key]
tup = rpts[ice_tidx[idx]]
n_rpts = new_ice_dct.get(key)
if n_rpts is None:
n_rpts = []
new_ice_dct[key] = n_rpts
n_rpts.append(tup)
# -----------------------------------------------------
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[::12]
no_ice_tidx = no_ice_tidx[::12]
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)
# -------------------------------------------------
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]
return new_ice_dct, new_no_ice_dct, new_neg_ice_dct
def analyze2(filename, filename_l1b):
f = h5py.File(filename, 'r')
icing_alt = f['icing_altitude'][:]
cld_top_hgt = f['cld_height_acha'][:, 10:30, 10:30]
cld_phase = f['cloud_phase'][:, 10:30, 10:30]
cld_opd_dc = f['cld_opd_dcomp'][:, 10:30, 10:30]
cld_opd = f['cld_opd_acha'][:, 10:30, 10:30]
solzen = f['solar_zenith_angle'][:, 10:30, 10:30]
f_l1b = h5py.File(filename_l1b, 'r')
bt_11um = f_l1b['temp_11_0um_nom'][:, 10:30, 10:30]
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]
plt.hist(cld_opd, bins=20)
plt.show()
plt.hist(cld_opd_dc, bins=20)
plt.show()
# --------------------------------------------
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]
num_obs = solzen.shape[0]
idxs = []
for i in range(num_obs):
if day_night == 'NIGHT':
if is_night(solzen[i,]):
idxs.append(i)
elif day_night == 'DAY':
if is_day(solzen[i,]):
idxs.append(i)
else:
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]
print('num pireps all: ', len(icing_alt))
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)
print('num pireps, day_night: ', len(mask), day_night)
keep_idxs = []
for i in range(len(mask)):
frac = np.sum(mask[i]) / nx_x_ny
if icing:
if frac > pass_thresh_frac:
keep_idxs.append(idxs[i])
elif frac > pass_thresh_frac:
keep_idxs.append(idxs[i])
print('num valid pireps: ', len(keep_idxs))
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()
def apply_qc_icing_pireps(icing_alt, cld_top_hgt, cld_phase, cld_opd, cld_mask, bt_11um, solzen, satzen, day_night='ANY'):
opd_thick_threshold = 2
opd_thin_threshold = 0.1
if day_night == 'DAY':
opd_thick_threshold = 20
opd_thin_threshold = 1
closeness = 100.0 # 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':
if is_day(solzen[i,]):
continue
elif day_night == 'DAY':
if 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
# Test1
keep = np.where(keep, cld_top_hgt[i,] > icing_alt[i], False)
# 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) & (cld_top_hgt[i,] > icing_alt[i])), False)
# Test5
keep = np.where(keep, np.invert(bt_11um[i,] > 270.0), False)
# Test6
keep = np.where(keep, np.invert(bt_11um[i,] < 228.0), False)
# Test3
keep = np.where(keep, (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 apply_qc_no_icing_pireps(icing_alt, cld_top_hgt, cld_phase, cld_opd, cld_mask, bt_11um, solzen, satzen, day_night='ANY'):
opd_thick_threshold = 2
opd_thin_threshold = 0.1
if day_night == 'DAY':
opd_thick_threshold = 20
opd_thin_threshold = 1
closeness = 100.0 # 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':
if is_day(solzen[i,]):
continue
elif day_night == 'DAY':
if 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
# Test1
keep = np.where(keep, cld_top_hgt[i,] > icing_alt[i], False)
# # Test2
# keep = np.where(keep,
# ((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, ((cld_phase[i,] == 4) & (cld_opd[i,] < opd_thin_threshold) & (cld_top_hgt[i,] > icing_alt[i])), False)
#
# # Test5
# keep = np.where(keep, (bt_11um[i,] > 270.0), False)
#
# # Test6
# keep = np.where(keep, (bt_11um[i,] < 228.0), 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(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()
# --------- Throw away code --------------------------------------------------------------------
jdays= [['070', '071', '072', '073', '074', '075'], ['078', '079', '080', '081', '082', '084', '085', '086', '087', '088'],
['106', '107', '108', '109', '110', '111', '112', '113', '114'], ['116', '117', '118', '119'],
['128', '129', '130', '131', '132', '133', '134', '135'], ['138', '139', '140'], ['149', '150', '151'],
['163', '164', '165'], ['202', '203', '204', '205', '206'], ['231', '232', '233'], ['251', '252', '253', '254']]
def make_ts_bins():
nbins = len(jdays)
edges = []
for k in range(nbins):
num = len(jdays[k])
jd_a = jdays[k][0]
ts_a = get_timestamp('2019-'+jd_a+'_00:00', format_code='%Y-%j_%H:%M')
jd_b = jdays[k][num-1]
ts_b = get_timestamp('2019-'+jd_b+'_23:59', format_code='%Y-%j_%H:%M')
edges.append(ts_a)
edges.append(ts_b)
return edges
keep_out_edges = make_ts_bins()
keep_out_edges = np.array(keep_out_edges)
def check_times(ts):
bidx = (find_bin_index(keep_out_edges, np.array([ts])))[0]
if bidx != -1 and bidx % 2 == 0:
return True
return False
def tile_extract(trnfile='/home/rink/tiles_l1b_train.h5', tstfile='/home/rink/tiles_l1b_test.h5', L1B_or_L2='L1B',
cld_mask_name='cloud_mask', augment=False, split=0.2, check_keep_out=False):
icing_int_s = []
ice_time_s = []
no_ice_time_s = []
ice_lon_s = []
no_ice_lon_s = []
ice_lat_s = []
no_ice_lat_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'][:]
for i in range(num_obs):
if check_keep_out and check_times(times[i]):
continue
cld_msk = f[cld_mask_name][i, 12:28, 12:28]
for ds_name in params:
dat = f[ds_name][i, 12:28, 12:28]
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])
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)
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']
for i in range(num_obs):
if check_keep_out and not check_times(times[i]):
continue
cld_msk = f[cld_mask_name][i, 12:28, 12:28]
for ds_name in params:
dat = f[ds_name][i, 12:28, 12:28]
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])
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)
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])
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]
#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,]
# 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 = []
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]
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)
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)
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])
# 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]
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 write_file(outfile, params, param_types, data_dct, icing_intensity, icing_times, icing_lons, icing_lats):
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')
# 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, no_icing_to_icing_ratio=5):
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)
data_i -= mean_i
std_i = np.nanstd(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)
data_ni -= mean_ni
std_ni = np.nanstd(data_ni)
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)
print(dname,': (', mean, mean_i, mean_ni, ') (', std, std_i, std_ni, ')')
mean_std_dct[dname] = (mean_ni, std_ni)
[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.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()
# return mean_std_dct
def split_data(num_obs, perc=0.2, skip=1, shuffle=True, seed=None):
idxs = np.arange(num_obs)
idxs = list(idxs)
num_test = int(num_obs * perc)
test_idxs = idxs[::int(num_obs / num_test)]
test_set = set(test_idxs)
train_set = (set(idxs)).difference(test_set)
train_idxs = list(train_set)
test_idxs = np.array(test_idxs)
train_idxs = np.array(train_idxs)
if seed is not None:
np.random.seed(seed)
if shuffle:
np.random.shuffle(test_idxs)
np.random.shuffle(train_idxs)
return train_idxs[::skip], test_idxs[::skip]
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 = 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, 361)
lat_space = np.linspace(-90, 90, 181)
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)
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
return grd_bins
# 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 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 collect(icing_times, icing_intensity, jdays=['071', '072', '074', '079', '080', '081', '082', '084', '085', '088',
'107', '110', '113', '117', '119', '129', '132', '134', '139', '150', '164', '203', '205', '232', '252', '254']):
nbins = 6
keep_idxs_yes = [[[] for j in range(nbins)] for i in range(len(jdays))]
keep_idxs_no = [[[] for j in range(nbins)] for i in range(len(jdays))]
ice_intsy = [[[] for j in range(7)] for i in range(len(jdays))]
for jidx, jd in enumerate(jdays):
_, ts_edges = make_times('2019-' + jd + '_00:00', None, num_steps=6, format_code='%Y-%j_%H:%M', hours=4)
fltr_times, fltr_idxs = time_filter_2(icing_times, '2019-'+jd+'_00:00', '2019-'+jd+'_23:59', format_code='%Y-%j_%H:%M')
bin_idxs = find_bin_index(np.array(ts_edges), np.array(fltr_times))
for cnt, bi in enumerate(bin_idxs):
#keep_idxs[jidx][bi].append(fltr_idxs[cnt])
k = fltr_idxs[cnt]
if icing_intensity[k] == -1:
keep_idxs_no[jidx][bi].append(k)
else:
keep_idxs_yes[jidx][bi].append(k)
ice_intsy[jidx][0] = np.sum(icing_intensity[fltr_idxs] == -1)
ice_intsy[jidx][1] = np.sum(icing_intensity[fltr_idxs] == 1)
ice_intsy[jidx][2] = np.sum(icing_intensity[fltr_idxs] == 2)
ice_intsy[jidx][3] = np.sum(icing_intensity[fltr_idxs] == 3)
ice_intsy[jidx][4] = np.sum(icing_intensity[fltr_idxs] == 4)
ice_intsy[jidx][5] = np.sum(icing_intensity[fltr_idxs] == 5)
ice_intsy[jidx][6] = np.sum(icing_intensity[fltr_idxs] == 6)
print(jd, ' --------------------')
for j in range(nbins):
print(len(keep_idxs_no[jidx][j]), len(keep_idxs_yes[jidx][j]))
print('no icing: ', ice_intsy[jidx][0])
print('icing 1: ', ice_intsy[jidx][1])
print('icing 2: ', ice_intsy[jidx][2])
print('icing 3: ', ice_intsy[jidx][3])
print('icing 4: ', ice_intsy[jidx][4])
print('icing 5: ', ice_intsy[jidx][5])
print('icing 6: ', ice_intsy[jidx][6])
return keep_idxs_no, keep_idxs_yes, ice_intsy
# ------------ This code will not be needed when we implement a Fully Connected CNN -----------------------------------
# Example GOES file to retrieve GEOS parameters in MetPy form (CONUS)
exmp_file_conus = '/Users/tomrink/data/OR_ABI-L1b-RadC-M6C14_G16_s20193140811215_e20193140813588_c20193140814070.nc'
# Full Disk
exmp_file_fd = '/Users/tomrink/data/OR_ABI-L1b-RadF-M6C16_G16_s20212521800223_e20212521809542_c20212521809596.nc'
def make_for_full_domain_predict(clvrx_file, name_list=l1b_ds_list, domain='FD'):
w_x = 16
w_y = 16
if domain == 'CONUS':
exmpl_ds = xr.open_dataset(exmp_file_conus)
elif domain == 'FD':
exmpl_ds = xr.open_dataset(exmp_file_fd)
mdat = exmpl_ds.metpy.parse_cf('Rad')
geos = mdat.metpy.cartopy_crs
xlen = mdat.x.values.size
ylen = mdat.y.values.size
exmpl_ds.close()
h5f = h5py.File(clvrx_file, 'r')
grd_dct = {name: None for name in name_list}
cnt_a = 0
for didx, ds_name in enumerate(name_list):
gvals = get_grid_values_all(h5f, ds_name)
if gvals is not None:
grd_dct[ds_name] = gvals
cnt_a += 1
if cnt_a > 0 and cnt_a != len(name_list):
raise GenericException('weirdness')
grd_dct_n = {name: [] for name in name_list}
n_x = int(xlen/w_x)
n_y = int(ylen/w_y)
i_0 = 0
j_0 = 0
cc = []
ll = []
for didx, ds_name in enumerate(name_list):
for j in range(4, n_y-4, 1):
j_ul = j_0 + j * w_y
for i in range(4, n_x-4, 1):
i_ul = i_0 + i * w_x
if didx == 0:
ll.append(j_ul)
cc.append(i_ul)
grd_dct_n[ds_name].append(grd_dct[ds_name][j_ul:j_ul+w_y, i_ul:i_ul+w_x])
grd_dct = {name: None for name in name_list}
for didx, ds_name in enumerate(name_list):
grd_dct[ds_name] = np.stack(grd_dct_n[ds_name])
h5f.close()
return grd_dct, ll, cc
# -------------------------------------------------------------------------------------------