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MPI_ICESat2_ATL03_histogram.py
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#!/usr/bin/env python
u"""
MPI_ICESat2_ATL03_histogram.py (05/2024)
Read ICESat-2 ATL03 and ATL09 data files to calculate average segment surfaces
ATL03 datasets: Global Geolocated Photons
ATL09 datasets: Atmospheric Characteristics
Alternative algorithm that uses gaussian/generalized gaussian decomposition to
extract possibly multiple height surfaces from a histogram of photon events
CALLING SEQUENCE:
mpiexec -np 6 python MPI_ICESat2_ATL03_histogram.py ATL03_file ATL09_file
COMMAND LINE OPTIONS:
-O X, --output X: Name and path of output file
-F X, --fit X: Histogram decomposition functional fit
gaussian: summation of gaussian functions with parameters
amplitude, range and width
general: summation of generalized gaussian functions with parameters
amplitude, range, width and shape
-V, --verbose: Verbose output to track progress
-M X, --mode X: Permission mode of files created
REQUIRES MPI PROGRAM
MPI: standardized and portable message-passing system
https://www.open-mpi.org/
http://mpitutorial.com/
PYTHON DEPENDENCIES:
numpy: Scientific Computing Tools For Python
https://numpy.org
https://numpy.org/doc/stable/user/numpy-for-matlab-users.html
scipy: Scientific Tools for Python
https://docs.scipy.org/doc/
mpi4py: MPI for Python
http://pythonhosted.org/mpi4py/
http://mpi4py.readthedocs.org/en/stable/
h5py: Python interface for Hierarchal Data Format 5 (HDF5)
https://h5py.org
http://docs.h5py.org/en/stable/mpi.html
scikit-learn: Machine Learning in Python
http://scikit-learn.org/stable/index.html
https://github.com/scikit-learn/scikit-learn
timescale: Python tools for time and astronomical calculations
https://pypi.org/project/timescale/
PROGRAM DEPENDENCIES:
fit.py: Utilities for calculating fits from ATL03 Geolocated Photon Data
utilities.py: download and management utilities for syncing files
classify_photons.py: Yet Another Photon Classifier for Geolocated Photon Data
REFERENCES:
A Chauve, C Mallet, F Bretar, S Durrieu, M P Deseilligny and W Puech.
"Processing full-waveform lidar data: Modelling raw signals"
International Archives of Photogrammetry
Remote Sensing and Spatial Information Sciences, 36, 102--107 (2007)
C Mallet and F Bretar.
"Full-waveform topographic lidar: State-of-the-art"
ISPRS Journal of Photogrammetry and Remote Sensing, 64, 1--16 (2009)
J B Blair, D L Rabine, and M A Hofton.
"The Laser Vegetation Imaging Sensor: A Medium-Altitude, Digitisation-Only,
Airborne Laser Altimeter for Mapping Vegetation and Topography"
ISPRS Journal of Photogrammetry and Remote Sensing, 54, 115--122 (1999)
M A Hofton, J B Blair, J B Minster, J R Ridgway, N P Williams, J L Bufton,
and D L Rabine. "An Airborne Scanning Laser Altimetry Survey of Long Valley,
California" International Journal of Remote Sensing, 21(12), 2413--2437 (2000)
M A Hofton, J B Minster, and J B Blair.
"Decomposition of Laser Altimeter Waveforms"
IEEE Transactions on Geoscience and Remote Sensing, 38(4), 1899--1996 (2000)
M A Hofton, J B Blair, S B Luthcke, and D L Rabine.
"Assessing the Performance of 20-25 m Footprint Waveform Lidar Data
Collected in ICESat Data Corridors in Greenland"
Geophysical Research Letters, 35, L24501 (2008), doi:10.1029/2008GL035774
J R Ridgway, J B Minster, N Williams, J L Bufton and W B Krabill.
"Airborne laser altimeter survey of Long Valley, California"
Geophysical Journal International (1997) 131, 267-280
UPDATE HISTORY:
Updated 05/2024: use wrapper to importlib for optional dependencies
Updated 04/2024: use timescale for temporal operations
Updated 03/2024: use pathlib to define and operate on paths
Updated 12/2022: single implicit import of altimetry tools
Updated 06/2022: update classify photons to match current GSFC version
Updated 05/2022: use argparse descriptions within sphinx documentation
Updated 10/2021: using python logging for handling verbose output
do not use possible TEP photons in photon classification calculation
added parsing for converting file lines to arguments
use reference photon geolocation as default in case of no valid segments
Updated 08/2021: update classify photons to match current GSFC version
Updated 05/2021: add photon classifier based on GSFC YAPC algorithms
move histogram fit operations into separate module
Updated 02/2021: replaced numpy bool/int to prevent deprecation warnings
Updated 01/2021: time utilities for converting times from JD and to decimal
Updated 12/2020: H5py deprecation warning change to use make_scale
add option to define histogram decomposition function
Updated 11/2020: use number of binned background photons in fits
Updated 10/2020: using argparse to set parameters
Updated 09/2020: using reference photon delta time to interpolate ATL09
Updated 08/2020: using convert delta time function to convert to Julian days
Updated 07/2020: "re-tiding" is no longer unnecessary
Updated 06/2020: reduce the maximum number of peaks to fit and reduce threshold
verify that complementary beam pair is in list of beams
set masks of output arrays after reading from HDF5
save histogram fit amplitudes to output HDF5 file
add additional beam check within heights groups
Updated 05/2020: add mean median difference of histogram fit residuals
Updated 10/2019: changing Y/N flags to True/False
Updated 09/2019: adding segment quality summary variable
Updated 04/2019: updated backup algorithm for when the histogram fit fails
estimate both mean and median first photon bias corrections
estimate both mean and median transmit pulse shape corrections
Updated 03/2019: extract a set of ATL09 parameters for each ATL03 segment_ID
Written 05/2017
"""
from __future__ import print_function, division
import sys
import os
import re
import logging
import pathlib
import argparse
import datetime
import warnings
import numpy as np
import scipy.stats
import scipy.signal
import scipy.optimize
import scipy.interpolate
import icesat2_toolkit as is2tk
import timescale.time
# attempt imports
h5py = is2tk.utilities.import_dependency('h5py')
MPI = is2tk.utilities.import_dependency('mpi4py.MPI')
cluster = is2tk.utilities.import_dependency('sklearn.cluster')
yapc = is2tk.utilities.import_dependency('yapc')
# PURPOSE: keep track of MPI threads
def info(rank, size):
logging.info(f'Rank {rank+1:d} of {size:d}')
logging.info(f'module name: {__name__}')
if hasattr(os, 'getppid'):
logging.info(f'parent process: {os.getppid():d}')
logging.info(f'process id: {os.getpid():d}')
# PURPOSE: create argument parser
def arguments():
parser = argparse.ArgumentParser(
description="""Read ICESat-2 ATL03 and ATL09 data files to calculate
average segment surfaces using gaussian/generalized gaussian
decomposition to extract possibly multiple height surfaces from a
histogram of photon events
""",
fromfile_prefix_chars="@"
)
parser.convert_arg_line_to_args = is2tk.utilities.convert_arg_line_to_args
# command line parameters
# first file listed contains the ATL03 file
# second file listed is the associated ATL09 file
parser.add_argument('ATL03',
type=pathlib.Path, nargs='?',
help='ICESat-2 ATL03 file to run')
parser.add_argument('ATL09',
type=pathlib.Path, nargs='?',
help='ICESat-2 ATL09 file to run')
# use default output file name
parser.add_argument('--output','-O',
type=pathlib.Path,
help='Name and path of output file')
# histogram decomposition fit type
# gaussian: summation of gaussian functions
# general: summation of generalized gaussian functions
parser.add_argument('--fit','-F',
type=str, default='gaussian', choices=('gaussian','general'),
help='Histogram decomposition functional fit')
# verbosity settings
# verbose will output information about each output file
parser.add_argument('--verbose','-V',
default=False, action='store_true',
help='Verbose output of run')
# permissions mode of the output files (number in octal)
parser.add_argument('--mode','-M',
type=lambda x: int(x,base=8), default=0o775,
help='Permissions mode of output files')
# return the parser
return parser
# PURPOSE: read ICESat-2 geolocated photon height data (ATL03)
# and backscatter profiles/atmospheric layer characteristics (ATL09)
# Computes heights over segments using the decomposition of histograms
def main():
# start MPI communicator
comm = MPI.COMM_WORLD
# Read the system arguments listed after the program
parser = arguments()
args,_ = parser.parse_known_args()
# create logger
loglevel = logging.INFO if args.verbose else logging.CRITICAL
logging.basicConfig(level=loglevel)
# output module information for process
info(comm.rank,comm.size)
if (comm.rank == 0):
logging.info(f'{str(args.ATL03)} -->')
# compile regular expression operator for extracting data from ATL03 files
rx1 = re.compile(r'(processed_)?(ATL\d+)_(\d{4})(\d{2})(\d{2})(\d{2})(\d{2})'
r'(\d{2})_(\d{4})(\d{2})(\d{2})_(\d{3})_(\d{2})(.*?).h5$')
# universal variables
# speed of light
c = 299792458.0
# associated beam pairs
associated_beam_pair = dict(gt1l='gt1r',gt1r='gt1l',gt2l='gt2r',gt2r='gt2l',
gt3l='gt3r',gt3r='gt3l')
# read ICESat-2 ATL03 HDF5 files (extract base parameters)
SUB,PRD,YY,MM,DD,HH,MN,SS,TRK,CYCL,GRAN,RL,VERS,AUX = \
rx1.findall(args.ATL03.name).pop()
# Open the HDF5 file for reading
fileID = h5py.File(args.ATL03, 'r', driver='mpio', comm=comm)
# read each input beam within the file
IS2_atl03_beams = []
for gtx in [k for k in fileID.keys() if bool(re.match(r'gt\d[lr]',k))]:
# check if subsetted beam contains data
# check in both the geolocation and heights groups
try:
fileID[gtx]['geolocation']['segment_id']
fileID[gtx]['heights']['delta_time']
except KeyError:
pass
else:
IS2_atl03_beams.append(gtx)
# number of GPS seconds between the GPS epoch
# and ATLAS Standard Data Product (SDP) epoch
atlas_sdp_gps_epoch = fileID['ancillary_data']['atlas_sdp_gps_epoch'][:]
# which TEP to use for a given spot (convert to 0-based index)
tep_valid_spot = fileID['ancillary_data']['tep']['tep_valid_spot'][:] - 1
tep_pce = ['pce1_spot1','pce2_spot3']
# valid range of times for each TEP histogram
tep_range_prim = fileID['ancillary_data']['tep']['tep_range_prim'][:]
# save tep parameters for a given beam
tep = {}
# variables of interest for generating corrected elevation estimates
Segment_ID = {}
Segment_Index_begin = {}
Segment_PE_count = {}
Segment_Distance = {}
Segment_Length = {}
Segment_Background = {}
# fit parameters
Segment_delta_time = {}
Segment_Height = {}
Segment_Land_Ice = {}
Segment_Minimum = {}
Segment_Maximum = {}
Segment_Amplitude = {}
Segment_Minimum_Amplitude = {}
Segment_Maximum_Amplitude = {}
Segment_dH_along = {}
Segment_dH_across = {}
Segment_Height_Error = {}
Segment_Land_Ice_Error = {}
Segment_Minimum_Error = {}
Segment_Maximum_Error = {}
Segment_dH_along_Error = {}
Segment_dH_across_Error = {}
Segment_Mean_Median = {}
Segment_X_atc = {}
Segment_X_spread = {}
Segment_Y_atc = {}
Segment_sigma_geo = {}
Segment_Longitude = {}
Segment_Latitude = {}
Segment_N_Fit = {}
Segment_N_Peaks = {}
Segment_Window = {}
Segment_RDE = {}
Segment_SNR = {}
Segment_Photon_SNR = {}
Segment_Summary = {}
Segment_Iterations = {}
Segment_Clusters = {}
Segment_Source = {}
Segment_Pulses = {}
# correction parameters
FPB_mean_corr = {}
FPB_mean_sigma = {}
FPB_median_corr = {}
FPB_median_sigma = {}
mean_dead_time = {}
FPB_n_corr = {}
FPB_cal_corr = {}
TPS_mean_corr = {}
TPS_median_corr = {}
# for each input beam within the file
for gtx in sorted(IS2_atl03_beams):
logging.info(gtx) if (comm.rank == 0) else None
# beam type (weak versus strong) for time
atlas_beam_type = fileID[gtx].attrs['atlas_beam_type'].decode('utf-8')
n_pixels = 16.0 if (atlas_beam_type == "strong") else 4.0
# ATL03 Segment ID
Segment_ID[gtx] = fileID[gtx]['geolocation']['segment_id'][:]
# number of valid overlapping ATL03 segments
n_seg = len(Segment_ID[gtx]) - 1
# number of photon events
n_pe, = fileID[gtx]['heights']['delta_time'].shape
# first photon in the segment (convert to 0-based indexing)
Segment_Index_begin[gtx] = fileID[gtx]['geolocation']['ph_index_beg'][:] - 1
# number of photon events in the segment
Segment_PE_count[gtx] = fileID[gtx]['geolocation']['segment_ph_cnt'][:]
# along-track distance for each ATL03 segment
Segment_Distance[gtx] = fileID[gtx]['geolocation']['segment_dist_x'][:]
# along-track length for each ATL03 segment
Segment_Length[gtx] = fileID[gtx]['geolocation']['segment_length'][:]
# ocean tide
fv = fileID[gtx]['geophys_corr']['tide_ocean'].attrs['_FillValue']
tide_ocean = np.ma.array(fileID[gtx]['geophys_corr']['tide_ocean'][:],
fill_value=fv)
tide_ocean.mask = tide_ocean.data == tide_ocean.fill_value
# interpolate background photon rate based on 50-shot summation
background_delta_time = fileID[gtx]['bckgrd_atlas']['delta_time'][:]
SPL = scipy.interpolate.UnivariateSpline(background_delta_time,
fileID[gtx]['bckgrd_atlas']['bckgrd_rate'][:],k=3,s=0)
Segment_Background[gtx] = SPL(fileID[gtx]['geolocation']['delta_time'][:])
# ATLAS spot number for beam in current orientation
spot = int(fileID[gtx].attrs['atlas_spot_number'])
# get ATLAS impulse response variables for the transmitter echo path (TEP)
tep1,tep2 = ('atlas_impulse_response','tep_histogram')
# get appropriate transmitter-echo-path histogram for spot
associated_pce = tep_valid_spot[spot-1]
pce = tep_pce[associated_pce]
# delta time of TEP histogram
tep_tod, = fileID[tep1][pce][tep2]['tep_tod'][:]
# truncate tep to primary histogram (reflection 43-50 ns)
# and extract signal tep from noise tep. calculate width of tep
# ATL03 recommends subsetting between 15-30 ns to avoid secondary
tep_hist_time = np.copy(fileID[tep1][pce][tep2]['tep_hist_time'][:])
tep_hist = np.copy(fileID[tep1][pce][tep2]['tep_hist'][:])
t_TX,p_TX,W_TX,FWHM,TXs,TXe = is2tk.fit.extract_tep_histogram(
tep_hist_time, tep_hist, tep_range_prim)
# save tep information and statistics
tep[gtx] = {}
tep[gtx]['pce'] = pce
tep[gtx]['tep_tod'] = tep_tod
tep[gtx]['tx_start'] = TXs
tep[gtx]['tx_end'] = TXe
tep[gtx]['tx_robust_sprd'] = W_TX
tep[gtx]['sigma_tx'] = FWHM
# channel dead time and first photon bias table for beam
cal1,cal2 = ('ancillary_data','calibrations')
channel_dead_time = fileID[cal1][cal2]['dead_time'][gtx]['dead_time'][:]
mean_dead_time[gtx] = np.mean(channel_dead_time)
fpb_dead_time = fileID[cal1][cal2]['first_photon_bias'][gtx]['dead_time'][:]
fpb_strength = fileID[cal1][cal2]['first_photon_bias'][gtx]['strength'][:]
fpb_width = fileID[cal1][cal2]['first_photon_bias'][gtx]['width'][:]
fpb_corr = fileID[cal1][cal2]['first_photon_bias'][gtx]['ffb_corr'][:]
# calculate first photon bias as a function of strength and width
# for the calculated mean dead time of the beam
ndt,ns,nw = np.shape(fpb_corr)
fpb_corr_dead_time = np.zeros((ns,nw))
for s in range(ns):
for w in range(nw):
SPL = scipy.interpolate.UnivariateSpline(fpb_dead_time/1e9,
fpb_corr[:,s,w],k=3,s=0)
fpb_corr_dead_time[s,w] = SPL(mean_dead_time[gtx])
# bivariate spline for estimating first-photon bias using CAL-19
CAL19 = scipy.interpolate.RectBivariateSpline(fpb_strength[0,:],
fpb_width[0,:]/1e9, fpb_corr_dead_time/1e12, kx=1, ky=1)
# allocate for output segment fit data
fill_value = fileID[gtx]['geolocation']['sigma_h'].attrs['_FillValue']
# delta time of fit photons
# use reference photon delta time as initial guess
# will fill in with segment center delta times
Distributed_delta_time = is2tk.fit.segment_mean(
fileID[gtx]['geolocation']['delta_time'][:])
# segment fit heights
Distributed_Height = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_Height.mask = np.ones((n_seg),dtype=bool)
Distributed_Minimum = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_Minimum.mask = np.ones((n_seg),dtype=bool)
Distributed_Maximum = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_Maximum.mask = np.ones((n_seg),dtype=bool)
# land ice height corrected for first photon bias and transmit-pulse shape
Distributed_Land_Ice = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_Land_Ice.mask = np.ones((n_seg),dtype=bool)
# segment fit amplitudes
Distributed_Amplitude = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_Amplitude.mask = np.ones((n_seg),dtype=bool)
Distributed_Minimum_Amplitude = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_Minimum_Amplitude.mask = np.ones((n_seg),dtype=bool)
Distributed_Maximum_Amplitude = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_Maximum_Amplitude.mask = np.ones((n_seg),dtype=bool)
# segment fit along-track slopes
Distributed_dH_along = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_dH_along.mask = np.ones((n_seg),dtype=bool)
# segment fit height errors
Distributed_Height_Error = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_Height_Error.mask = np.ones((n_seg),dtype=bool)
Distributed_Minimum_Error = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_Minimum_Error.mask = np.ones((n_seg),dtype=bool)
Distributed_Maximum_Error = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_Maximum_Error.mask = np.ones((n_seg),dtype=bool)
# land ice height errors (max of fit or first photon bias uncertainties)
Distributed_Land_Ice_Error = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_Land_Ice_Error.mask = np.ones((n_seg),dtype=bool)
# segment fit along-track slope errors
Distributed_dH_along_Error = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_dH_along_Error.mask = np.ones((n_seg),dtype=bool)
# difference between the mean and median of the residuals from fit height
Distributed_Mean_Median = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_Mean_Median.mask = np.ones((n_seg),dtype=bool)
# along-track X coordinates of segment fit
Distributed_X_atc = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_X_atc.mask = np.ones((n_seg),dtype=bool)
# along-track X coordinate spread of points used in segment fit
Distributed_X_spread = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_X_spread.mask = np.ones((n_seg),dtype=bool)
# along-track Y coordinates of segment fit
Distributed_Y_atc = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_Y_atc.mask = np.ones((n_seg),dtype=bool)
# longitude of fit photons
Distributed_Longitude = is2tk.fit.segment_mean(
fileID[gtx]['geolocation']['reference_photon_lon'][:])
# latitude of fit photons
Distributed_Latitude = is2tk.fit.segment_mean(
fileID[gtx]['geolocation']['reference_photon_lat'][:])
# number of photons in fit
Distributed_N_Fit = np.ma.zeros((n_seg),fill_value=-1,dtype=int)
Distributed_N_Fit.mask = np.ones((n_seg),dtype=bool)
# number of peaks in the final histogram fit
Distributed_N_Peaks = np.ma.zeros((n_seg),fill_value=-1,dtype=int)
Distributed_N_Peaks.mask = np.ones((n_seg),dtype=bool)
# size of the window used in the fit
Distributed_Window = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_Window.mask = np.ones((n_seg),dtype=bool)
# robust dispersion estimator
Distributed_RDE = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_RDE.mask = np.ones((n_seg),dtype=bool)
# signal-to-noise ratio
Distributed_SNR = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_SNR.mask = np.ones((n_seg),dtype=bool)
# maximum signal-to-noise ratio from photon classifier
Distributed_Photon_SNR = np.ma.zeros((n_seg),fill_value=0,dtype=int)
Distributed_Photon_SNR.mask = np.ones((n_seg),dtype=bool)
# segment quality summary
Distributed_Summary = np.ma.zeros((n_seg),fill_value=-1,dtype=int)
Distributed_Summary.mask = np.ones((n_seg),dtype=bool)
# number of iterations for fit
Distributed_Iterations = np.ma.zeros((n_seg),fill_value=-1,dtype=int)
Distributed_Iterations.mask = np.ones((n_seg),dtype=bool)
# number of estimated clusters of data
Distributed_Clusters = np.ma.zeros((n_seg),fill_value=0,dtype=int)
Distributed_Clusters.mask = np.ones((n_seg),dtype=bool)
# signal source selection
Distributed_Source = np.ma.zeros((n_seg),fill_value=4,dtype=int)
Distributed_Source.mask = np.ones((n_seg),dtype=bool)
# number of pulses in segment
Distributed_Pulses = np.ma.zeros((n_seg),fill_value=-1,dtype=int)
Distributed_Pulses.mask = np.ones((n_seg),dtype=bool)
# first photon bias estimates
Distributed_FPB_mean_corr = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_FPB_mean_corr.mask = np.ones((n_seg),dtype=bool)
Distributed_FPB_mean_sigma = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_FPB_mean_sigma.mask = np.ones((n_seg),dtype=bool)
Distributed_FPB_median_corr = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_FPB_median_corr.mask = np.ones((n_seg),dtype=bool)
Distributed_FPB_median_sigma = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_FPB_median_sigma.mask = np.ones((n_seg),dtype=bool)
Distributed_FPB_n_corr = np.ma.zeros((n_seg),fill_value=-1,dtype=int)
Distributed_FPB_n_corr.mask = np.ones((n_seg),dtype=bool)
Distributed_FPB_cal_corr = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_FPB_cal_corr.mask = np.ones((n_seg),dtype=bool)
# transmit pulse shape bias estimates
Distributed_TPS_mean_corr = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_TPS_mean_corr.mask = np.ones((n_seg),dtype=bool)
Distributed_TPS_median_corr = np.ma.zeros((n_seg),fill_value=fill_value)
Distributed_TPS_median_corr.mask = np.ones((n_seg),dtype=bool)
# along-track and across-track distance for photon events
x_atc = fileID[gtx]['heights']['dist_ph_along'][:].copy()
y_atc = fileID[gtx]['heights']['dist_ph_across'][:].copy()
# photon event heights
h_ph = fileID[gtx]['heights']['h_ph'][:].copy()
# digital elevation model interpolated to photon events
dem_h = np.zeros((n_pe))
# for each 20m segment
for j,_ in enumerate(Segment_ID[gtx]):
# index for 20m segment j
idx = Segment_Index_begin[gtx][j]
# skip segments with no photon events
if (idx < 0):
continue
# number of photons in 20m segment
cnt = Segment_PE_count[gtx][j]
# add segment distance to along-track coordinates
x_atc[idx:idx+cnt] += Segment_Distance[gtx][j]
# interpolate digital elevation model to photon events
dem_h[idx:idx+cnt] = fileID[gtx]['geophys_corr']['dem_h'][j]
# iterate over ATLAS major frames
photon_mframes = fileID[gtx]['heights']['pce_mframe_cnt'][:].copy()
# background ATLAS group variables are based upon 50-shot summations
# PCE Major Frames are based upon 200-shot summations
pce_mframe_cnt = fileID[gtx]['bckgrd_atlas']['pce_mframe_cnt'][:].copy()
# find unique major frames and their indices within background ATLAS group
# (there will 4 background ATLAS time steps for nearly every major frame)
unique_major_frames,unique_index = np.unique(pce_mframe_cnt,return_index=True)
# number of unique major frames in granule for beam
major_frame_count = len(unique_major_frames)
# height of each telemetry band for a major frame
tlm_height = {}
tlm_height['band1'] = fileID[gtx]['bckgrd_atlas']['tlm_height_band1'][:].copy()
tlm_height['band2'] = fileID[gtx]['bckgrd_atlas']['tlm_height_band2'][:].copy()
# elevation above ellipsoid of each telemetry band for a major frame
tlm_top = {}
tlm_top['band1'] = fileID[gtx]['bckgrd_atlas']['tlm_top_band1'][:].copy()
tlm_top['band2'] = fileID[gtx]['bckgrd_atlas']['tlm_top_band2'][:].copy()
# buffer to telemetry band to set as valid
tlm_buffer = 100.0
# flag denoting photon events as possible TEP
if (int(RL) < 4):
isTEP = np.any((fileID[gtx]['heights']['signal_conf_ph'][:]==-2),axis=1)
else:
isTEP = (fileID[gtx]['heights']['quality_ph'][:] == 3)
# photon event weights
Distributed_Weights = np.zeros((n_pe), dtype=np.float64)
# run for each major frame (distributed over comm.size # of processes)
for iteration in range(comm.rank, major_frame_count, comm.size):
# background atlas index for iteration
idx = unique_index[iteration]
# photon indices for major frame (buffered by 1 frame on each side)
# do not use possible TEP photons in photon classification
i1, = np.nonzero((photon_mframes >= unique_major_frames[iteration]-1) &
(photon_mframes <= unique_major_frames[iteration]+1) &
np.logical_not(isTEP))
# indices for the major frame within the buffered window
i2, = np.nonzero(photon_mframes[i1] == unique_major_frames[iteration])
# sum of telemetry band widths for major frame
h_win_width = 0.0
# check that each telemetry band is close to DEM
for b in ['band1','band2']:
# bottom of the telemetry band for major frame
tlm_bot_band = tlm_top[b][idx] - tlm_height[b][idx]
if np.any((dem_h[i1[i2]] >= (tlm_bot_band-tlm_buffer)) &
(dem_h[i1[i2]] <= (tlm_top[b][idx]+tlm_buffer))):
# add telemetry height to window width
h_win_width += tlm_height[b][idx]
# calculate photon event weights
Distributed_Weights[i1[i2]] = yapc.classify_photons(x_atc[i1],
h_ph[i1], h_win_width, i2, K=0, min_knn=5, min_ph=3,
min_xspread=1.0, min_hspread=0.01, win_x=15.0, win_h=6.0,
method='linear')
# photon event weights
pe_weights = np.zeros((n_pe), dtype=np.float64)
comm.Allreduce(sendbuf=[Distributed_Weights, MPI.DOUBLE], \
recvbuf=[pe_weights, MPI.DOUBLE], op=MPI.SUM)
Distributed_Weights = None
# wait for all distributed processes to finish for beam
comm.Barrier()
# photon event weights scaled to a single byte
weight_ph = np.array(255*pe_weights,dtype=np.uint8)
# verify photon event weights
np.clip(weight_ph, 0, 255, out=weight_ph)
# iterate over valid ATL03 segments
# in ATL03 1-based indexing: invalid == 0
# here in 0-based indexing: invalid == -1
segment_indices, = np.nonzero((Segment_Index_begin[gtx][:-1] >= 0) &
(Segment_Index_begin[gtx][1:] >= 0))
iteration_count = len(segment_indices)
# run for each geoseg (distributed over comm.size # of processes)
for iteration in range(comm.rank, iteration_count, comm.size):
# indice for iteration (can run through a subset of segments)
j = segment_indices[iteration]
# iterate over valid ATL03 segments
# in ATL03 1-based indexing: invalid == 0
# here in 0-based indexing: invalid == -1
if (Segment_Index_begin[gtx][j] >= 0):
# index for segment j
idx = Segment_Index_begin[gtx][j]
# number of photons in segment (use 2 ATL03 segments)
c1 = int(Segment_PE_count[gtx][j])
c2 = int(Segment_PE_count[gtx][j+1])
cnt = c1 + c2
# time of each Photon event (PE)
segment_times = np.copy(fileID[gtx]['heights']['delta_time'][idx:idx+cnt])
# Photon event lat/lon and elevation (re-tided WGS84)
segment_heights = np.copy(h_ph[idx:idx+cnt])
# ATL03 pe heights no longer apply the ocean tide
# and so "re-tiding" is no longer unnecessary
# segment_heights[:c1] += tide_ocean[j]
# segment_heights[c1:] += tide_ocean[j+1]
segment_lats = np.copy(fileID[gtx]['heights']['lat_ph'][idx:idx+cnt])
segment_lons = np.copy(fileID[gtx]['heights']['lon_ph'][idx:idx+cnt])
# Photon event channel and identification
ID_channel = np.copy(fileID[gtx]['heights']['ph_id_channel'][idx:idx+cnt])
ID_pulse = np.copy(fileID[gtx]['heights']['ph_id_pulse'][idx:idx+cnt])
n_pulses = np.unique(ID_pulse).__len__()
frame_number = np.copy(fileID[gtx]['heights']['pce_mframe_cnt'][idx:idx+cnt])
# vertical noise-photon density
background_density = 2.0*n_pulses*Segment_Background[gtx][j]/c
# along-track X and Y coordinates
distance_along_X = np.copy(x_atc[idx:idx+cnt])
distance_along_Y = np.copy(y_atc[idx:idx+cnt])
# check the spread of photons along-track (must be > 20m)
along_X_spread = distance_along_X.max() - distance_along_X.min()
# check confidence level associated with each photon event
# -2: TEP
# -1: Events not associated with a specific surface type
# 0: noise
# 1: buffer but algorithm classifies as background
# 2: low
# 3: medium
# 4: high
# Surface types for signal classification confidence
# 0=Land; 1=Ocean; 2=SeaIce; 3=LandIce; 4=InlandWater
ice_sig_conf = np.copy(fileID[gtx]['heights']['signal_conf_ph'][idx:idx+cnt,3])
ice_sig_low_count = np.count_nonzero(ice_sig_conf > 1)
# indices of TEP classified photons
ice_sig_tep_pe, = np.nonzero(ice_sig_conf == -2)
# photon event weights from photon classifier
segment_weights = weight_ph[idx:idx+cnt]
snr_norm = np.max(segment_weights)
# photon event signal-to-noise ratio from photon classifier
photon_snr = np.zeros((cnt),dtype=int)
if (snr_norm > 0):
photon_snr[:] = 100.0*segment_weights/snr_norm
# copy signal to noise ratio for segment
Distributed_Photon_SNR.data[j] = np.copy(snr_norm)
Distributed_Photon_SNR.mask[j] = (snr_norm > 0)
# photon confidence levels from classifier
pe_sig_conf = np.zeros((cnt),dtype=int)
# calculate confidence levels from photon classifier
pe_sig_conf[photon_snr >= 25] = 2
pe_sig_conf[photon_snr >= 60] = 3
pe_sig_conf[photon_snr >= 80] = 4
# copy classification for TEP photons
pe_sig_conf[ice_sig_tep_pe] = -2
pe_sig_low_count = np.count_nonzero(pe_sig_conf > 1)
# check if segment has photon events classified for land ice
# that are at or above low-confidence threshold
# and that the spread of photons is greater than 20m
if (ice_sig_low_count > 10) & (along_X_spread > 20):
# use density-based spatial clustering in segment
db = cluster.DBSCAN(eps=0.5).fit(
np.c_[distance_along_X, segment_heights],
sample_weight=photon_snr)
labels = db.labels_
# number of noise photons
noise_photons = list(labels).count(-1)
noise_cluster = 1 if noise_photons else 0
# number of photon event clusters in segment
n_clusters = len(set(labels)) - noise_cluster
Distributed_Clusters.data[j] = n_clusters
Distributed_Clusters.mask[j] = (n_clusters > 0)
# perform a histogram fit procedure
Segment_X = Segment_Distance[gtx][j] + Segment_Length[gtx][j]
# step-size for histograms (50 ps ~ 7.5mm height)
valid,fit,centroid = is2tk.fit.try_histogram_fit(
distance_along_X, distance_along_Y, segment_heights,
pe_sig_conf, Segment_X, 5e-11, FIT_TYPE=args.fit,
ITERATE=20, BACKGROUND=background_density,
CONFIDENCE=[2,1,0])
# indices of points used in final iterated fit
ifit = fit['indices'] if valid else None
if bool(valid) & (np.abs(fit['error'][0]) < 20):
# maximum likely height from amplitudes
iamp = np.argmax(fit['amplitude'])
Distributed_Height.data[j] = fit['height'][iamp]
Distributed_Height.mask[j] = False
Distributed_Height_Error.data[j] = fit['error'][iamp]
Distributed_Height_Error.mask[j] = False
Distributed_Amplitude.data[j] = fit['amplitude'][iamp]
Distributed_Amplitude.mask[j] = False
# minimum decomposed height and error
imin = np.argmin(fit['height'])
Distributed_Minimum.data[j] = fit['height'][imin]
Distributed_Minimum.mask[j] = False
Distributed_Minimum_Error.data[j] = fit['error'][imin]
Distributed_Minimum_Error.mask[j] = False
Distributed_Minimum_Amplitude.data[j] = fit['amplitude'][imin]
Distributed_Minimum_Amplitude.mask[j] = False
# maximum decomposed height and error
imax = np.argmax(fit['height'])
Distributed_Maximum.data[j] = fit['height'][imax]
Distributed_Maximum.mask[j] = False
Distributed_Maximum_Error.data[j] = fit['error'][imax]
Distributed_Maximum_Error.mask[j] = False
Distributed_Maximum_Amplitude.data[j] = fit['amplitude'][imax]
Distributed_Maximum_Amplitude.mask[j] = False
# along-track and cross-track coordinates
Distributed_X_atc.data[j] = np.copy(centroid['x'])
Distributed_X_atc.mask[j] = False
Distributed_X_spread.data[j] = np.copy(along_X_spread)
Distributed_X_spread.mask[j] = False
Distributed_Y_atc.data[j] = np.copy(centroid['y'])
Distributed_Y_atc.mask[j] = False
# fit geolocation to the along-track distance of segment
Distributed_delta_time[j] = \
is2tk.fit.fit_geolocation(segment_times[ifit],
distance_along_X[ifit], Distributed_X_atc[j])
Distributed_Longitude[j] = \
is2tk.fit.fit_geolocation(segment_lons[ifit],
distance_along_X[ifit], Distributed_X_atc[j])
Distributed_Latitude[j] = \
is2tk.fit.fit_geolocation(segment_lats[ifit],
distance_along_X[ifit], Distributed_X_atc[j])
# number of photons used in fit
Distributed_N_Fit.data[j] = len(ifit)
Distributed_N_Fit.mask[j] = False
# number of peaks used in histogram fit
Distributed_N_Peaks.data[j] = np.copy(fit['peaks'])
Distributed_N_Peaks.mask[j] = False
# size of the final window
Distributed_Window.data[j] = np.copy(fit['window'])
Distributed_Window.mask[j] = False
# robust dispersion estimator
Distributed_RDE.data[j] = np.copy(fit['RDE'])
Distributed_RDE.mask[j] = False
# signal to noise ratio
N_BG = background_density*Distributed_Window.data[j]
Distributed_SNR.data[j] = Distributed_N_Fit.data[j]/N_BG
Distributed_SNR.mask[j] = False
# number of iterations used in fit
Distributed_Iterations.data[j] = np.copy(fit['iterations'])
Distributed_Iterations.mask[j] = False
Distributed_Source.data[j] = np.copy(valid)
Distributed_Source.mask[j] = False
Distributed_Pulses.data[j] = np.copy(n_pulses)
Distributed_Pulses.mask[j] = False
# calculate difference between the mean and the median from the fit
z_full = -c*fit['time']/2.0
residual_mean = np.sum(z_full*fit['residuals'])/np.sum(fit['residuals'])
residual_cpdf = np.cumsum(fit['residuals']/np.sum(fit['residuals']))
residual_median = np.interp(0.5,residual_cpdf,z_full)
Distributed_Mean_Median.data[j] = residual_mean - residual_median
Distributed_Mean_Median.mask[j] = False
# calculate flags for quality summary
VPD = Distributed_N_Fit.data[j]/Distributed_Window.data[j]
Distributed_Summary.data[j] = int(
(Distributed_RDE.data[j] >= 1) |
(Distributed_Height_Error.data[j] >= 1) |
(VPD <= (n_pixels/4.0)))
Distributed_Summary.mask[j] = False
# estimate first photon bias corrections
# step-size for histograms (50 ps ~ 7.5mm height)
try:
FPB = is2tk.fit.histogram_first_photon_bias(fit['time'],
fit['residuals'], n_pulses, n_pixels, mean_dead_time[gtx],
5e-11, METHOD='direct', ITERATE=20)
except:
pass
else:
Distributed_FPB_mean_corr.data[j] = -0.5*FPB['mean']*c
Distributed_FPB_mean_corr.mask[j] = False
Distributed_FPB_mean_sigma.data[j] = 0.5*FPB['mean_sigma']*c
Distributed_FPB_mean_sigma.mask[j] = False
Distributed_FPB_median_corr.data[j] = -0.5*FPB['median']*c
Distributed_FPB_median_corr.mask[j] = False
Distributed_FPB_median_sigma.data[j] = 0.5*FPB['median_sigma']*c
Distributed_FPB_median_sigma.mask[j] = False
Distributed_FPB_n_corr.data[j] = np.copy(FPB['count'])
Distributed_FPB_n_corr.mask[j] = False
# first photon bias correction from CAL-19
FPB_calibrated = CAL19.ev(FPB['strength'],FPB['width'])
Distributed_FPB_cal_corr.data[j] = -0.5*FPB_calibrated*c
Distributed_FPB_cal_corr.mask[j] = False
# estimate transmit pulse shape correction
try:
W_RX = 2.0*Distributed_RDE.data[j]/c
dt_W = 2.0*Distributed_Window.data[j]/c
TPS = is2tk.fit.calc_transmit_pulse_shape(t_TX,
p_TX, W_TX, W_RX, dt_W, Distributed_SNR.data[j],
ITERATE=50)
except:
pass
else:
Distributed_TPS_mean_corr.data[j] = 0.5*TPS['mean']*c
Distributed_TPS_mean_corr.mask[j] = False
Distributed_TPS_median_corr.data[j] = 0.5*TPS['median']*c
Distributed_TPS_median_corr.mask[j] = False
# some ATL03 segments will not result in a valid fit
# backup algorithm uses 4 segments to find a valid surface
if (j not in (0,n_seg-2,n_seg-1)) & Distributed_Height.mask[j] & \
(Segment_Index_begin[gtx][j-1] > 0):
# index for segment j
idx = Segment_Index_begin[gtx][j-1]
# number of photons in segment (use 4 ATL03 segments)
c1 = Segment_PE_count[gtx][j-1].astype(int)
c2 = Segment_PE_count[gtx][j].astype(int)
c3 = Segment_PE_count[gtx][j+1].astype(int)
c4 = Segment_PE_count[gtx][j+2].astype(int)
cnt = c1 + c2 + c3 + c4
# time of each Photon event (PE)
segment_times = np.copy(fileID[gtx]['heights']['delta_time'][idx:idx+cnt])
# Photon event lat/lon and elevation (re-tided WGS84)
segment_heights = np.copy(h_ph[idx:idx+cnt])
# ATL03 pe heights no longer apply the ocean tide
# and so "re-tiding" is no longer unnecessary
# segment_heights[:c1] += tide_ocean[j-1]
# segment_heights[c1:c1+c2] += tide_ocean[j]
# segment_heights[c1+c2:c1+c2+c3] += tide_ocean[j+1]
# segment_heights[c1+c2+c3:] += tide_ocean[j+2]
segment_lats = np.copy(fileID[gtx]['heights']['lat_ph'][idx:idx+cnt])
segment_lons = np.copy(fileID[gtx]['heights']['lon_ph'][idx:idx+cnt])
# Photon event channel and identification
ID_channel = np.copy(fileID[gtx]['heights']['ph_id_channel'][idx:idx+cnt])
ID_pulse = np.copy(fileID[gtx]['heights']['ph_id_pulse'][idx:idx+cnt])
n_pulses = np.unique(ID_pulse).__len__()
frame_number = np.copy(fileID[gtx]['heights']['pce_mframe_cnt'][idx:idx+cnt])
# vertical noise-photon density
background_density = 2.0*n_pulses*Segment_Background[gtx][j]/c
# along-track X and Y coordinates
distance_along_X = np.copy(x_atc[idx:idx+cnt])
distance_along_Y = np.copy(y_atc[idx:idx+cnt])
# check the spread of photons along-track (must be > 40m)
along_X_spread = distance_along_X.max() - distance_along_X.min()
# check confidence level associated with each photon event
# -2: TEP
# -1: Events not associated with a specific surface type
# 0: noise
# 1: buffer but algorithm classifies as background
# 2: low
# 3: medium
# 4: high
# Surface types for signal classification confidence
# 0=Land; 1=Ocean; 2=SeaIce; 3=LandIce; 4=InlandWater
ice_sig_conf = np.copy(fileID[gtx]['heights']['signal_conf_ph'][idx:idx+cnt,3])
ice_sig_low_count = np.count_nonzero(ice_sig_conf > 1)
# indices of TEP classified photons
ice_sig_tep_pe, = np.nonzero(ice_sig_conf == -2)
# photon event weights from photon classifier
segment_weights = pe_weights[idx:idx+cnt]
snr_norm = np.max(segment_weights)
# photon event signal-to-noise ratio from photon classifier
photon_snr = np.array(100.0*segment_weights/snr_norm,dtype=int)
Distributed_Photon_SNR.data[j] = np.copy(snr_norm)
Distributed_Photon_SNR.mask[j] = (snr_norm > 0)
# photon confidence levels from classifier
pe_sig_conf = np.zeros((cnt),dtype=int)
# calculate confidence levels from photon classifier
pe_sig_conf[photon_snr >= 25] = 2
pe_sig_conf[photon_snr >= 60] = 3
pe_sig_conf[photon_snr >= 80] = 4
# copy classification for TEP photons
pe_sig_conf[ice_sig_tep_pe] = -2
pe_sig_low_count = np.count_nonzero(pe_sig_conf > 1)
# check if segment has photon events classified for land ice
# that are at or above low-confidence threshold
# and that the spread of photons is greater than 40m
if (pe_sig_low_count > 10) & (along_X_spread > 40):
# use density-based spatial clustering in segment
db = cluster.DBSCAN(eps=0.5).fit(
np.c_[distance_along_X, segment_heights],
sample_weight=photon_snr)
labels = db.labels_
# number of noise photons
noise_photons = list(labels).count(-1)
noise_cluster = 1 if noise_photons else 0
# number of photon event clusters in segment
n_clusters = len(set(labels)) - noise_cluster
Distributed_Clusters.data[j] = n_clusters
Distributed_Clusters.mask[j] = (n_clusters > 0)
# perform a histogram fit procedure
Segment_X = Segment_Distance[gtx][j] + Segment_Length[gtx][j]
# step-size for histograms (50 ps ~ 7.5mm height)
valid,fit,centroid = is2tk.fit.try_histogram_fit(
distance_along_X, distance_along_Y, segment_heights,
pe_sig_conf, Segment_X, 5e-11, FIT_TYPE=args.fit,
ITERATE=20, BACKGROUND=background_density, CONFIDENCE=[1,0])
# indices of points used in final iterated fit
ifit = fit['indices'] if valid else None
if bool(valid) & (np.abs(fit['error'][0]) < 20):
# maximum likely height from amplitudes
iamp = np.argmax(fit['amplitude'])
Distributed_Height.data[j] = fit['height'][iamp]
Distributed_Height.mask[j] = False
Distributed_Height_Error.data[j] = fit['error'][iamp]
Distributed_Height_Error.mask[j] = False
Distributed_Amplitude.data[j] = fit['amplitude'][iamp]
Distributed_Amplitude.mask[j] = False
# minimum decomposed height and error
imin = np.argmin(fit['height'])
Distributed_Minimum.data[j] = fit['height'][imin]
Distributed_Minimum.mask[j] = False
Distributed_Minimum_Error.data[j] = fit['error'][imin]
Distributed_Minimum_Error.mask[j] = False
Distributed_Minimum_Amplitude.data[j] = fit['amplitude'][imin]
Distributed_Minimum_Amplitude.mask[j] = False
# maximum decomposed height and error
imax = np.argmax(fit['height'])
Distributed_Maximum.data[j] = fit['height'][imax]
Distributed_Maximum.mask[j] = False
Distributed_Maximum_Error.data[j] = fit['error'][imax]
Distributed_Maximum_Error.mask[j] = False
Distributed_Maximum_Amplitude.data[j] = fit['amplitude'][imax]
Distributed_Maximum_Amplitude.mask[j] = False
# along-track and cross-track coordinates
Distributed_X_atc.data[j] = np.copy(centroid['x'])
Distributed_X_atc.mask[j] = False
Distributed_X_spread.data[j] = np.copy(along_X_spread)
Distributed_X_spread.mask[j] = False
Distributed_Y_atc.data[j] = np.copy(centroid['y'])
Distributed_Y_atc.mask[j] = False
# fit geolocation to the along-track distance of segment
Distributed_delta_time[j] = \
is2tk.fit.fit_geolocation(segment_times[ifit],
distance_along_X[ifit], Distributed_X_atc[j])
Distributed_Longitude[j] = \
is2tk.fit.fit_geolocation(segment_lons[ifit],
distance_along_X[ifit], Distributed_X_atc[j])
Distributed_Latitude[j] = \
is2tk.fit.fit_geolocation(segment_lats[ifit],
distance_along_X[ifit], Distributed_X_atc[j])
# number of photons used in fit
Distributed_N_Fit.data[j] = len(ifit)
Distributed_N_Fit.mask[j] = False
# number of peaks used in histogram fit
Distributed_N_Peaks.data[j] = np.copy(fit['peaks'])
Distributed_N_Peaks.mask[j] = False
# size of the final window
Distributed_Window.data[j] = np.copy(fit['window'])
Distributed_Window.mask[j] = False
# robust dispersion estimator
Distributed_RDE.data[j] = np.copy(fit['RDE'])
Distributed_RDE.mask[j] = False
# signal to noise ratio
N_BG = background_density*Distributed_Window.data[j]
Distributed_SNR.data[j] = Distributed_N_Fit.data[j]/N_BG
Distributed_SNR.mask[j] = False
# number of iterations used in fit
Distributed_Iterations.data[j] = np.copy(fit['iterations'])
Distributed_Iterations.mask[j] = False
Distributed_Source.data[j] = 2 + np.copy(valid)
Distributed_Source.mask[j] = False
Distributed_Pulses.data[j] = np.copy(n_pulses)
Distributed_Pulses.mask[j] = False
# calculate difference between the mean and the median from the fit
z_full = -c*fit['time']/2.0
residual_mean = np.sum(z_full*fit['residuals'])/np.sum(fit['residuals'])
residual_cpdf = np.cumsum(fit['residuals']/np.sum(fit['residuals']))
residual_median = np.interp(0.5,residual_cpdf,z_full)
Distributed_Mean_Median.data[j] = residual_mean - residual_median
Distributed_Mean_Median.mask[j] = False
# calculate flags for quality summary
VPD = Distributed_N_Fit.data[j]/Distributed_Window.data[j]
Distributed_Summary.data[j] = int(
(Distributed_RDE.data[j] >= 1) |
(Distributed_Height_Error.data[j] >= 1) |
(VPD <= (n_pixels/4.0)))
Distributed_Summary.mask[j] = False
# estimate first photon bias corrections
# step-size for histograms (50 ps ~ 7.5mm height)
try:
FPB = is2tk.fit.histogram_first_photon_bias(fit['time'],
fit['residuals'], n_pulses, n_pixels, mean_dead_time[gtx],
5e-11, METHOD='direct', ITERATE=20)
except:
pass
else:
Distributed_FPB_mean_corr.data[j] = -0.5*FPB['mean']*c
Distributed_FPB_mean_corr.mask[j] = False
Distributed_FPB_mean_sigma.data[j] = 0.5*FPB['mean_sigma']*c
Distributed_FPB_mean_sigma.mask[j] = False
Distributed_FPB_median_corr.data[j] = -0.5*FPB['median']*c
Distributed_FPB_median_corr.mask[j] = False
Distributed_FPB_median_sigma.data[j] = 0.5*FPB['median_sigma']*c
Distributed_FPB_median_sigma.mask[j] = False
Distributed_FPB_n_corr.data[j] = np.copy(FPB['count'])
Distributed_FPB_n_corr.mask[j] = False
# first photon bias correction from CAL-19
FPB_calibrated = CAL19.ev(FPB['strength'],FPB['width'])
Distributed_FPB_cal_corr.data[j] = -0.5*FPB_calibrated*c
Distributed_FPB_cal_corr.mask[j] = False
# estimate transmit pulse shape correction
try:
W_RX = 2.0*Distributed_RDE.data[j]/c
dt_W = 2.0*Distributed_Window.data[j]/c
TPS = is2tk.fit.calc_transmit_pulse_shape(t_TX,
p_TX, W_TX, W_RX, dt_W, Distributed_SNR.data[j],
ITERATE=50)
except:
pass
else:
Distributed_TPS_mean_corr.data[j] = 0.5*TPS['mean']*c
Distributed_TPS_mean_corr.mask[j] = False