util.py 31.21 KiB
import metpy
import numpy as np
import xarray as xr
import datetime
from datetime import timezone
from metpy.units import units
from metpy.calc import thickness_hydrostatic
from collections import namedtuple
import os
import h5py
import pickle
from netCDF4 import Dataset
from util.setup import ancillary_path
from scipy.interpolate import RectBivariateSpline, interp2d
from scipy.ndimage import gaussian_filter
from scipy.signal import medfilt2d
from cartopy.crs import Geostationary, Globe
LatLonTuple = namedtuple('LatLonTuple', ['lat', 'lon'])
FGFTuple = namedtuple('FGFTuple', ['line', 'elem'])
homedir = os.path.expanduser('~') + '/'
# --- 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 = {ds: 'f4' for ds in l1b_ds_list}
l1b_ds_fill = {l1b_ds_list[i]: -32767 for i in range(10)}
l1b_ds_fill.update({l1b_ds_list[i+10]: -32768 for i in range(5)})
l1b_ds_range = {ds: '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 = {ds_list[i]: 'f4' for i in range(23)}
ds_types.update({ds_list[i+23]: 'i1' for i in range(3)})
ds_fill = {ds_list[i]: -32768 for i in range(23)}
ds_fill.update({ds_list[i+23]: -128 for i in range(3)})
ds_range = {ds_list[i]: 'actual_range' for i in range(23)}
ds_range.update({ds_list[i]: None for i in range(3)})
ds_types.update(l1b_ds_types)
ds_fill.update(l1b_ds_fill)
ds_range.update(l1b_ds_range)
ds_types.update({'temp_3_9um_nom': 'f4'})
ds_types.update({'cloud_fraction': 'f4'})
ds_fill.update({'temp_3_9um_nom': -32767})
ds_fill.update({'cloud_fraction': -32768})
ds_range.update({'temp_3_9um_nom': 'actual_range'})
ds_range.update({'cloud_fraction': 'actual_range'})
class MyGenericException(Exception):
def __init__(self, message):
self.message = message
def make_tf_callable_generator(the_generator):
class MyCallable:
def __init__(self, gen):
self.gen = gen
def __call__(self):
return self.gen
the_callable = MyCallable(the_generator)
return the_callable
def get_fill_attrs(name):
if name in ds_fill:
v = ds_fill[name]
if v is None:
return None, '_FillValue'
else:
return v, None
else:
return None, '_FillValue'
class GenericException(Exception):
def __init__(self, message):
self.message = message
class EarlyStop:
def __init__(self, window_length=3, patience=5):
self.patience = patience
self.min = np.finfo(np.single).max
self.cnt = 0
self.cnt_wait = 0
self.window = np.zeros(window_length, dtype=np.single)
self.window.fill(np.nan)
def check_stop(self, value):
self.window[:-1] = self.window[1:]
self.window[-1] = value
if np.any(np.isnan(self.window)):
return False
ave = np.mean(self.window)
if ave < self.min:
self.min = ave
self.cnt_wait = 0
return False
else:
self.cnt_wait += 1
if self.cnt_wait > self.patience:
return True
else:
return False
def get_time_tuple_utc(timestamp):
dt_obj = datetime.datetime.fromtimestamp(timestamp, timezone.utc)
return dt_obj, dt_obj.timetuple()
def get_datetime_obj(dt_str, format_code='%Y-%m-%d_%H:%M'):
dto = datetime.datetime.strptime(dt_str, format_code).replace(tzinfo=timezone.utc)
return dto
def get_timestamp(dt_str, format_code='%Y-%m-%d_%H:%M'):
dto = datetime.datetime.strptime(dt_str, format_code).replace(tzinfo=timezone.utc)
ts = dto.timestamp()
return ts
def add_time_range_to_filename(pathname, tstart, tend=None):
filename = os.path.split(pathname)[1]
w_o_ext, ext = os.path.splitext(filename)
dt_obj, _ = get_time_tuple_utc(tstart)
str_start = dt_obj.strftime('%Y%m%d%H')
filename = w_o_ext+'_'+str_start
if tend is not None:
dt_obj, _ = get_time_tuple_utc(tend)
str_end = dt_obj.strftime('%Y%m%d%H')
filename = filename+'_'+str_end
filename = filename+ext
path = os.path.split(pathname)[0]
path = path+'/'+filename
return path
def haversine_np(lon1, lat1, lon2, lat2, earth_radius=6367.0):
"""
Calculate the great circle distance between two points
on the earth (specified in decimal degrees)
(lon1, lat1) must be broadcastable with (lon2, lat2).
"""
lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])
dlon = lon2 - lon1
dlat = lat2 - lat1
a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2
c = 2.0 * np.arcsin(np.sqrt(a))
km = earth_radius * c
return km
def bin_data_by(a, b, bin_ranges):
nbins = len(bin_ranges)
binned_data = []
for i in range(nbins):
rng = bin_ranges[i]
idxs = (b >= rng[0]) & (b < rng[1])
binned_data.append(a[idxs])
return binned_data
def bin_data_by_edges(a, b, edges):
nbins = len(edges) - 1
binned_data = []
for i in range(nbins):
idxs = (b >= edges[i]) & (b < edges[i+1])
binned_data.append(a[idxs])
return binned_data
def get_bin_ranges(lop, hip, bin_size=100):
bin_ranges = []
delp = hip - lop
nbins = int(delp/bin_size)
for i in range(nbins):
rng = [lop + i*bin_size, lop + i*bin_size + bin_size]
bin_ranges.append(rng)
return bin_ranges
# t must be monotonic increasing
def get_breaks(t, threshold):
t_0 = t[0:t.shape[0]-1]
t_1 = t[1:t.shape[0]]
d = t_1 - t_0
idxs = np.nonzero(d > threshold)
return idxs
# return indexes of ts where value is within ts[i] - threshold < value < ts[i] + threshold
# eventually, if necessary, fully vectorize (numpy) this is possible
# threshold units: seconds
def get_indexes_within_threshold(ts, value, threshold):
idx_s = []
t_s = []
for k, v in enumerate(ts):
if (ts[k] - threshold) <= value <= (ts[k] + threshold):
idx_s.append(k)
t_s.append(v)
return idx_s, t_s
def pressure_to_altitude(pres, temp, prof_pres, prof_temp, sfc_pres=None, sfc_temp=None, sfc_elev=0):
if not np.all(np.diff(prof_pres) > 0):
raise GenericException("target pressure profile must be monotonic increasing")
if pres < prof_pres[0]:
raise GenericException("target pressure less than top of pressure profile")
if temp is None:
temp = np.interp(pres, prof_pres, prof_temp)
i_top = np.argmax(np.extract(prof_pres <= pres, prof_pres)) + 1
pres_s = prof_pres.tolist()
temp_s = prof_temp.tolist()
pres_s = [pres] + pres_s[i_top:]
temp_s = [temp] + temp_s[i_top:]
if sfc_pres is not None:
if pres > sfc_pres: # incoming pressure below surface
return -999.0
prof_pres = np.array(pres_s)
prof_temp = np.array(temp_s)
i_bot = prof_pres.shape[0] - 1
if sfc_pres > prof_pres[i_bot]: # surface below profile bottom
pres_s = pres_s + [sfc_pres]
temp_s = temp_s + [sfc_temp]
else:
idx = np.argmax(np.extract(prof_pres < sfc_pres, prof_pres))
if sfc_temp is None:
sfc_temp = np.interp(sfc_pres, prof_pres, prof_temp)
pres_s = prof_pres.tolist()
temp_s = prof_temp.tolist()
pres_s = pres_s[0:idx+1] + [sfc_pres]
temp_s = temp_s[0:idx+1] + [sfc_temp]
prof_pres = np.array(pres_s)
prof_temp = np.array(temp_s)
prof_pres = prof_pres[::-1]
prof_temp = prof_temp[::-1]
prof_pres = prof_pres * units.hectopascal
prof_temp = prof_temp * units.kelvin
sfc_elev = sfc_elev * units.meter
z = thickness_hydrostatic(prof_pres, prof_temp) + sfc_elev
return z.magnitude
# http://fourier.eng.hmc.edu/e176/lectures/NM/node25.html
def minimize_quadratic(xa, xb, xc, ya, yb, yc):
x_m = xb + 0.5*(((ya-yb)*(xc-xb)*(xc-xb) - (yc-yb)*(xb-xa)*(xb-xa)) / ((ya-yb)*(xc-xb) + (yc-yb)*(xb-xa)))
return x_m
# Return index of nda closest to value. nda must be 1d
def value_to_index(nda, value):
diff = np.abs(nda - value)
idx = np.argmin(diff)
return idx
def find_bin_index(nda, value_s):
idxs = np.arange(nda.shape[0])
iL_s = np.zeros(value_s.shape[0])
iL_s[:,] = -1
for k, v in enumerate(value_s):
above = v >= nda
if not above.any():
continue
below = v < nda
if not below.any():
continue
iL = idxs[above].max()
iL_s[k] = iL
return iL_s.astype(np.int32)
# array solzen must be degrees, missing values must NaN. For small roughly 50x50km regions only
def is_day(solzen, test_angle=80.0):
solzen = solzen.flatten()
solzen = solzen[np.invert(np.isnan(solzen))]
if len(solzen) == 0 or np.sum(solzen <= test_angle) < len(solzen):
return False
else:
return True
# array solzen must be degrees, missing values must NaN. For small roughly 50x50km regions only
def is_night(solzen, test_angle=100.0):
solzen = solzen.flatten()
solzen = solzen[np.invert(np.isnan(solzen))]
if len(solzen) == 0 or np.sum(solzen >= test_angle) < len(solzen):
return False
else:
return True
def check_oblique(satzen, test_angle=70.0):
satzen = satzen.flatten()
satzen = satzen[np.invert(np.isnan(satzen))]
if len(satzen) == 0 or np.sum(satzen <= test_angle) < len(satzen):
return False
else:
return True
def get_median(tile_2d):
tile = tile_2d.flatten()
return np.nanmedian(tile)
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', valid_range_name='valid_range', actual_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_name is not None:
attr = attrs.get(fill_value_name)
if attr is not None:
if np.isscalar(attr):
fill_value = attr
else:
fill_value = attr[0]
grd_vals = np.where(grd_vals == fill_value, np.nan, grd_vals)
elif fill_value is not None:
grd_vals = np.where(grd_vals == fill_value, np.nan, grd_vals)
if valid_range_name is not None:
attr = attrs.get(valid_range_name)
if attr is not None:
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)
if scale_factor_name is not None:
attr = attrs.get(scale_factor_name)
if attr is not None:
if np.isscalar(attr):
scale_factor = attr
else:
scale_factor = attr[0]
grd_vals = grd_vals * scale_factor
if add_offset_name is not None:
attr = attrs.get(add_offset_name)
if attr is not None:
if np.isscalar(attr):
add_offset = attr
else:
add_offset = attr[0]
grd_vals = grd_vals + add_offset
if actual_range_name is not None:
attr = attrs.get(actual_range_name)
if attr is not None:
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)
return grd_vals
def get_grid_values_all(h5f, grid_name, scale_factor_name='scale_factor', add_offset_name='add_offset',
fill_value_name='_FillValue', valid_range_name='valid_range', actual_range_name='actual_range', fill_value=None, stride=None):
hfds = h5f[grid_name]
attrs = hfds.attrs
if attrs is None:
raise GenericException('No attributes object for: '+grid_name)
if stride is None:
grd_vals = hfds[:,]
else:
grd_vals = hfds[::stride, ::stride]
if fill_value_name is not None:
attr = attrs.get(fill_value_name)
if attr is not None:
if np.isscalar(attr):
fill_value = attr
else:
fill_value = attr[0]
grd_vals = np.where(grd_vals == fill_value, np.nan, grd_vals)
elif fill_value is not None:
grd_vals = np.where(grd_vals == fill_value, np.nan, grd_vals)
if valid_range_name is not None:
attr = attrs.get(valid_range_name)
if attr is not None:
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)
if scale_factor_name is not None:
attr = attrs.get(scale_factor_name)
if attr is not None:
if np.isscalar(attr):
scale_factor = attr
else:
scale_factor = attr[0]
grd_vals = grd_vals * scale_factor
if add_offset_name is not None:
attr = attrs.get(add_offset_name)
if attr is not None:
if np.isscalar(attr):
add_offset = attr
else:
add_offset = attr[0]
grd_vals = grd_vals + add_offset
if actual_range_name is not None:
attr = attrs.get(actual_range_name)
if attr is not None:
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)
return grd_vals
# dt_str_0: start datetime string in format YYYY-MM-DD_HH:MM
# dt_str_1: stop datetime string, if not None num_steps is computed
# format_code: default '%Y-%m-%d_%H:%M'
# num_steps with increment of days, hours, minutes or seconds
# dt_str_1 and num_steps cannot both be None
# return num_steps+1 lists of datetime strings and timestamps (edges of a numpy histogram)
def make_times(dt_str_0, dt_str_1=None, format_code='%Y-%m-%d_%H:%M', num_steps=None, days=None, hours=None, minutes=None, seconds=None):
if days is not None:
inc = 86400*days
elif hours is not None:
inc = 3600*hours
elif minutes is not None:
inc = 60*minutes
else:
inc = seconds
dt_obj_s = []
ts_s = []
dto_0 = datetime.datetime.strptime(dt_str_0, format_code).replace(tzinfo=timezone.utc)
ts_0 = dto_0.timestamp()
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()
num_steps = int((ts_1 - ts_0)/inc)
dt_obj_s.append(dto_0)
ts_s.append(ts_0)
dto_last = dto_0
for k in range(num_steps):
dt_obj = dto_last + datetime.timedelta(seconds=inc)
dt_obj_s.append(dt_obj)
ts_s.append(dt_obj.timestamp())
dto_last = dt_obj
return dt_obj_s, ts_s
def normalize(data, param, mean_std_dict, copy=True):
if mean_std_dict.get(param) is None:
raise MyGenericException(param + ': not found in dictionary')
if copy:
data = data.copy()
shape = data.shape
data = data.flatten()
mean, std, lo, hi = mean_std_dict.get(param)
data -= mean
data /= std
not_valid = np.isnan(data)
data[not_valid] = 0
data = np.reshape(data, shape)
return data
def denormalize(data, param, mean_std_dict, copy=True):
if copy:
data = data.copy()
if mean_std_dict.get(param) is None:
raise MyGenericException(param + ': not found in dictionary')
shape = data.shape
data = data.flatten()
mean, std, lo, hi = mean_std_dict.get(param)
data *= std
data += mean
data = np.reshape(data, shape)
return data
def scale(data, param, mean_std_dict, copy=True):
if copy:
data = data.copy()
if mean_std_dict.get(param) is None:
raise MyGenericException(param + ': not found in dictionary')
shape = data.shape
data = data.flatten()
_, _, lo, hi = mean_std_dict.get(param)
data -= lo
data /= (hi - lo)
not_valid = np.isnan(data)
data[not_valid] = 0
data = np.reshape(data, shape)
return data
def scale2(data, lo, hi, copy=True):
if copy:
data = data.copy()
shape = data.shape
data = data.flatten()
data -= lo
data /= (hi - lo)
not_valid = np.isnan(data)
data[not_valid] = 0
data = np.reshape(data, shape)
return data
def descale2(data, lo, hi, copy=True):
if copy:
data = data.copy()
shape = data.shape
data = data.flatten()
data *= (hi - lo)
data += lo
not_valid = np.isnan(data)
data[not_valid] = 0
data = np.reshape(data, shape)
return data
def descale(data, param, mean_std_dict, copy=True):
if copy:
data = data.copy()
if mean_std_dict.get(param) is None:
raise MyGenericException(param + ': not found in dictionary')
shape = data.shape
data = data.flatten()
_, _, lo, hi = mean_std_dict.get(param)
data *= (hi - lo)
data += lo
not_valid = np.isnan(data)
data[not_valid] = 0
data = np.reshape(data, shape)
return data
def add_noise(data, noise_scale=0.01, seed=None, copy=True):
if copy:
data = data.copy()
shape = data.shape
data = data.flatten()
if seed is not None:
np.random.seed(seed)
rnd = np.random.normal(loc=0, scale=noise_scale, size=data.size)
data += rnd
data = np.reshape(data, shape)
return data
crs_goes16_fd = Geostationary(central_longitude=-75.0, satellite_height=35786023.0, sweep_axis='x',
globe=Globe(ellipse=None, semimajor_axis=6378137.0, semiminor_axis=6356752.31414,
inverse_flattening=298.2572221))
crs_goes16_conus = Geostationary(central_longitude=-75.0, satellite_height=35786023.0, sweep_axis='x',
globe=Globe(ellipse=None, semimajor_axis=6378137.0, semiminor_axis=6356752.31414,
inverse_flattening=298.2572221))
crs_h08_fd = Geostationary(central_longitude=140.7, satellite_height=35785.863, sweep_axis='y',
globe=Globe(ellipse=None, semimajor_axis=6378.137, semiminor_axis=6356.7523,
inverse_flattening=298.25702))
def get_cartopy_crs(satellite, domain):
if satellite == 'GOES16':
if domain == 'FD':
geos = crs_goes16_fd
xlen = 5424
xmin = -5433893.0
xmax = 5433893.0
ylen = 5424
ymin = -5433893.0
ymax = 5433893.0
elif domain == 'CONUS':
geos = crs_goes16_conus
xlen = 2500
xmin = -3626269.5
xmax = 1381770.0
ylen = 1500
ymin = 1584175.9
ymax = 4588198.0
elif satellite == 'H08':
geos = crs_h08_fd
xlen = 5500
xmin = -5498.99990119
xmax = 5498.99990119
ylen = 5500
ymin = -5498.99990119
ymax = 5498.99990119
elif satellite == 'H09':
geos = crs_h08_fd
xlen = 5500
xmin = -5498.99990119
xmax = 5498.99990119
ylen = 5500
ymin = -5498.99990119
ymax = 5498.99990119
return geos, xlen, xmin, xmax, ylen, ymin, ymax
def concat_dict_s(t_dct_0, t_dct_1):
keys_0 = list(t_dct_0.keys())
nda_0 = np.array(keys_0)
keys_1 = list(t_dct_1.keys())
nda_1 = np.array(keys_1)
comm_keys, comm0, comm1 = np.intersect1d(nda_0, nda_1, return_indices=True)
comm_keys = comm_keys.tolist()
for key in comm_keys:
t_dct_1.pop(key)
t_dct_0.update(t_dct_1)
return t_dct_0
rho_water = 1000000.0 # g m^-3
rho_ice = 917000.0 # g m^-3
# real(kind=real4), parameter:: Rho_Water = 1.0 !g / m ^ 3
# real(kind=real4), parameter:: Rho_Ice = 0.917 !g / m ^ 3
#
# !--- compute
# cloud
# water
# path
# if (Iphase == 0) then
# Cwp_Dcomp(Elem_Idx, Line_Idx) = 0.55 * Tau * Reff * Rho_Water
# Lwp_Dcomp(Elem_Idx, Line_Idx) = 0.55 * Tau * Reff * Rho_Water
# else
# Cwp_Dcomp(Elem_Idx, Line_Idx) = 0.667 * Tau * Reff * Rho_Ice
# Iwp_Dcomp(Elem_Idx, Line_Idx) = 0.667 * Tau * Reff * Rho_Ice
# endif
def compute_lwc_iwc(iphase, reff, opd, geo_dz):
xy_shape = iphase.shape
iphase = iphase.flatten()
keep_0 = np.invert(np.isnan(iphase))
reff = reff.flatten()
keep_1 = np.invert(np.isnan(reff))
opd = opd.flatten()
keep_2 = np.invert(np.isnan(opd))
geo_dz = geo_dz.flatten()
keep_3 = np.logical_and(np.invert(np.isnan(geo_dz)), geo_dz > 1.0)
keep = keep_0 & keep_1 & keep_2 & keep_3
lwp_dcomp = np.zeros(reff.shape[0])
iwp_dcomp = np.zeros(reff.shape[0])
lwp_dcomp[:] = np.nan
iwp_dcomp[:] = np.nan
ice = iphase == 1 & keep
no_ice = iphase != 1 & keep
# compute ice/liquid water path, g m-2
reff *= 1.0e-06 # convert microns to meters
iwp_dcomp[ice] = 0.667 * opd[ice] * rho_ice * reff[ice]
lwp_dcomp[no_ice] = 0.55 * opd[no_ice] * rho_water * reff[no_ice]
iwp_dcomp /= geo_dz
lwp_dcomp /= geo_dz
lwp_dcomp = lwp_dcomp.reshape(xy_shape)
iwp_dcomp = iwp_dcomp.reshape(xy_shape)
return lwp_dcomp, iwp_dcomp
# 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 get_cf_nav_parameters(satellite='GOES16', domain='FD'):
param_dct = None
if satellite == 'H08': # We presently only have FD
param_dct = {'semi_major_axis': 6378.137,
'semi_minor_axis': 6356.7523,
'perspective_point_height': 35785.863,
'latitude_of_projection_origin': 0.0,
'longitude_of_projection_origin': 140.7,
'inverse_flattening': 298.257,
'sweep_angle_axis': 'y',
'x_scale_factor': 5.58879902955962e-05,
'x_add_offset': -0.153719917308037,
'y_scale_factor': -5.58879902955962e-05,
'y_add_offset': 0.153719917308037}
elif satellite == 'H09':
param_dct = {'semi_major_axis': 6378.137,
'semi_minor_axis': 6356.7523,
'perspective_point_height': 35785.863,
'latitude_of_projection_origin': 0.0,
'longitude_of_projection_origin': 140.7,
'inverse_flattening': 298.257,
'sweep_angle_axis': 'y',
'x_scale_factor': 5.58879902955962e-05,
'x_add_offset': -0.153719917308037,
'y_scale_factor': -5.58879902955962e-05,
'y_add_offset': 0.153719917308037}
elif satellite == 'GOES16':
if domain == 'CONUS':
param_dct = {'semi_major_axis': 6378137.0,
'semi_minor_axis': 6356752.31414,
'perspective_point_height': 35786023.0,
'latitude_of_projection_origin': 0.0,
'longitude_of_projection_origin': -75,
'inverse_flattening': 298.257,
'sweep_angle_axis': 'x',
'x_scale_factor': 5.6E-05,
'x_add_offset': -0.101332,
'y_scale_factor': -5.6E-05,
'y_add_offset': 0.128212}
elif domain == 'FD':
param_dct = {'semi_major_axis': 6378137.0,
'semi_minor_axis': 6356752.31414,
'perspective_point_height': 35786023.0,
'latitude_of_projection_origin': 0.0,
'longitude_of_projection_origin': -75,
'inverse_flattening': 298.257,
'sweep_angle_axis': 'x',
'x_scale_factor': 5.6E-05,
'x_add_offset': -0.151844,
'y_scale_factor': -5.6E-05,
'y_add_offset': 0.151844}
return param_dct
def write_cld_prods_file_nc4(clvrx_str_time, outfile_name, cloud_fraction, cloud_frac_opd,
x, y, elems, lines, satellite='GOES16', domain='CONUS',
has_time=False):
rootgrp = Dataset(outfile_name, 'w', format='NETCDF4')
rootgrp.setncattr('Conventions', 'CF-1.7')
dim_0_name = 'x'
dim_1_name = 'y'
time_dim_name = 'time'
geo_coords = 'time y x'
dim_0 = rootgrp.createDimension(dim_0_name, size=x.shape[0])
dim_1 = rootgrp.createDimension(dim_1_name, size=y.shape[0])
dim_time = rootgrp.createDimension(time_dim_name, size=1)
tvar = rootgrp.createVariable('time', 'f8', time_dim_name)
tvar[0] = get_timestamp(clvrx_str_time)
tvar.units = 'seconds since 1970-01-01 00:00:00'
if not has_time:
var_dim_list = [dim_1_name, dim_0_name]
else:
var_dim_list = [time_dim_name, dim_1_name, dim_0_name]
cld_frac_ds = rootgrp.createVariable('cloud_fraction', 'i1', var_dim_list)
cld_frac_ds.setncattr('long_name', 'FCNN inferred cloud fraction')
cld_frac_ds.setncattr('coordinates', geo_coords)
cld_frac_ds.setncattr('grid_mapping', 'Projection')
cld_frac_ds.setncattr('missing', -1)
if has_time:
cloud_fraction = cloud_fraction.reshape((1, y.shape[0], x.shape[0]))
cld_frac_ds[:, ] = cloud_fraction
cld_frac_opd_ds = rootgrp.createVariable('cldy_fraction_opd', 'f4', var_dim_list)
cld_frac_opd_ds.setncattr('long_name', 'FCNN inferred fractional OPD')
cld_frac_opd_ds.setncattr('coordinates', geo_coords)
cld_frac_opd_ds.setncattr('grid_mapping', 'Projection')
cld_frac_opd_ds.setncattr('missing', -1.0)
if has_time:
cloud_frac_opd = cloud_frac_opd.reshape((1, y.shape[0], x.shape[0]))
cld_frac_opd_ds[:, ] = cloud_frac_opd
cf_nav_dct = get_cf_nav_parameters(satellite, domain)
if satellite == 'H08':
long_name = 'Himawari Imagery Projection'
elif satellite == 'H09':
long_name = 'Himawari Imagery Projection'
elif satellite == 'GOES16':
long_name = 'GOES-16/17 Imagery Projection'
proj_ds = rootgrp.createVariable('Projection', 'b')
proj_ds.setncattr('long_name', long_name)
proj_ds.setncattr('grid_mapping_name', 'geostationary')
proj_ds.setncattr('sweep_angle_axis', cf_nav_dct['sweep_angle_axis'])
proj_ds.setncattr('semi_major_axis', cf_nav_dct['semi_major_axis'])
proj_ds.setncattr('semi_minor_axis', cf_nav_dct['semi_minor_axis'])
proj_ds.setncattr('inverse_flattening', cf_nav_dct['inverse_flattening'])
proj_ds.setncattr('perspective_point_height', cf_nav_dct['perspective_point_height'])
proj_ds.setncattr('latitude_of_projection_origin', cf_nav_dct['latitude_of_projection_origin'])
proj_ds.setncattr('longitude_of_projection_origin', cf_nav_dct['longitude_of_projection_origin'])
if x is not None:
x_ds = rootgrp.createVariable(dim_0_name, 'f8', [dim_0_name])
x_ds.units = 'rad'
x_ds.setncattr('axis', 'X')
x_ds.setncattr('standard_name', 'projection_x_coordinate')
x_ds.setncattr('long_name', 'fixed grid viewing angle')
x_ds.setncattr('scale_factor', cf_nav_dct['x_scale_factor'])
x_ds.setncattr('add_offset', cf_nav_dct['x_add_offset'])
x_ds[:] = x
y_ds = rootgrp.createVariable(dim_1_name, 'f8', [dim_1_name])
y_ds.units = 'rad'
y_ds.setncattr('axis', 'Y')
y_ds.setncattr('standard_name', 'projection_y_coordinate')
y_ds.setncattr('long_name', 'fixed grid viewing angle')
y_ds.setncattr('scale_factor', cf_nav_dct['y_scale_factor'])
y_ds.setncattr('add_offset', cf_nav_dct['y_add_offset'])
y_ds[:] = y
if elems is not None:
elem_ds = rootgrp.createVariable('elems', 'i2', [dim_0_name])
elem_ds[:] = elems
line_ds = rootgrp.createVariable('lines', 'i2', [dim_1_name])
line_ds[:] = lines
pass
rootgrp.close()
def downscale_2x(original, smoothing=False, samples_axis_first=False):
# if smoothing:
# original = scipy.ndimage.gaussian_filter(original, sigma = 1/2)
if not samples_axis_first:
lr = np.nanmean(np.array([original[0::2,0::2],
original[1::2,0::2],
original[0::2,1::2],
original[1::2,1::2]]),axis=0).squeeze()
elif samples_axis_first:
lr = np.nanmean(np.array([original[:,0::2,0::2],
original[:,1::2,0::2],
original[:,0::2,1::2],
original[:,1::2,1::2]]),axis=0).squeeze()
return lr
def resample(x, y, z, x_new, y_new):
z_intrp = []
for k in range(z.shape[0]):
z_k = z[k, :, :]
f = RectBivariateSpline(x, y, z_k)
z_intrp.append(f(x_new, y_new))
return np.stack(z_intrp)
def resample_one(x, y, z, x_new, y_new):
f = RectBivariateSpline(x, y, z)
return f(x_new, y_new)
def resample_2d_linear(x, y, z, x_new, y_new):
z_intrp = []
for k in range(z.shape[0]):
z_k = z[k, :, :]
f = interp2d(x, y, z_k)
z_intrp.append(f(x_new, y_new))
return np.stack(z_intrp)
def resample_2d_linear_one(x, y, z, x_new, y_new):
f = interp2d(x, y, z)
return f(x_new, y_new)
# Gaussian filter suitable for model training Data Pipeline
# z: input array. Must have dimensions: [BATCH_SIZE, Y, X]
# sigma: Standard deviation for Gaussian kernel
# returns stacked 2d arrays of same input dimension
def smooth_2d(z, sigma=1.0):
z_smoothed = []
for j in range(z.shape[0]):
z_j = z[j, :, :]
z_smoothed.append(gaussian_filter(z_j, sigma=sigma))
return np.stack(z_smoothed)
# For [Y, X], see above
def smooth_2d_single(z, sigma=1.0):
return gaussian_filter(z, sigma=sigma)
def median_filter_2d(z, kernel_size=3):
z_filtered = []
for j in range(z.shape[0]):
z_j = z[j, :, :]
z_filtered.append(medfilt2d(z_j, kernel_size=kernel_size))
return np.stack(z_filtered)
def median_filter_2d_single(z, kernel_size=3):
return medfilt2d(z, kernel_size=kernel_size)