mirror of
https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
synced 2024-12-22 03:23:34 +01:00
Why is clk-phase not zero is unresolved
Merge branch 'why-is-clk-phase-not-zero-centered' into main
This commit is contained in:
commit
5ea4a0df17
39 changed files with 1027 additions and 308 deletions
4
.gitmodules
vendored
4
.gitmodules
vendored
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@ -1,6 +1,6 @@
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[submodule "simulations/airshower_beacon_simulation/earsim"]
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path = simulations/airshower_beacon_simulation/lib/earsim
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path = airshower_beacon_simulation/lib/earsim
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url = https://gitlab.com/harmscho/earsim.git
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[submodule "simulations/airshower_beacon_simulation/lib/atmocal"]
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path = simulations/airshower_beacon_simulation/lib/atmocal
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path = airshower_beacon_simulation/lib/atmocal
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url = https://gitlab.com/harmscho/AtmosphereCal
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@ -27,35 +27,56 @@ SEED ?= 12345
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all: beacon clocks phases findks vary-fixes reconstruct
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beacon:
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beacon: generate-beacon signal-to-noise
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phases: beacon-phase clock-phase baseline-phase antenna-phase
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######
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generate-beacon:
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./aa_generate_beacon.py -N ${TRACE_N} -P ${TRACE_PRE} -n ${NOISE_SIGMA} -a ${BEAC_AMP} -f ${BEAC_F} -l ${PB_LOW} -u ${PB_HIGH} -d ${BEAC_DECAY} --data-dir ${DATA_DIR} --input-fname ${INPUT_DIR} | tee ${FIG_DIR}/aa.log
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./ab_modify_clocks.py 0 --data-dir ${DATA_DIR} | tee ${FIG_DIR}/ab.log
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./ab_modify_clocks.py 0 --no-save-clocks --data-dir ${DATA_DIR} | tee ${FIG_DIR}/ab.log
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signal-to-noise:
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./ac_show_signal_to_noise.py --no-show-plots --fig-dir=${FIG_DIR} --data-dir ${DATA_DIR}
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./view_beaconed_antenna.py 72 -p x -p y -p z -p n -p b --ft --no-show-plots --fig-dir=${FIG_DIR} --data-dir ${DATA_DIR}
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clocks:
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#
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new-clocks:
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./ab_modify_clocks.py ${CLK_DEV} --seed ${SEED} --gaussian --data-dir ${DATA_DIR}
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phases:
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clocks:
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./ab_modify_clocks.py ${CLK_DEV} --read-clocks-file --seed ${SEED} --gaussian --data-dir ${DATA_DIR}
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reset-clocks:
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./ab_modify_clocks.py 0 --data-dir ${DATA_DIR}
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#
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beacon-phase:
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./ba_measure_beacon_phase.py --N-mask ${N_MASK} --no-show-plots --fig-dir=${FIG_DIR} --data-dir ${DATA_DIR}
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clock-phase:
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./bb_measure_clock_phase.py --no-show-plots --fig-dir=${FIG_DIR} --data-dir ${DATA_DIR}
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baseline-phase:
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./bc_baseline_phase_deltas.py ${REF_ANTS} --no-show-plots --fig-dir=${FIG_DIR} --data-dir ${DATA_DIR}
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antenna-phase:
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./bd_antenna_phase_deltas.py --no-show-plots --fig-dir=${FIG_DIR} --data-dir ${DATA_DIR}
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#
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findks:
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./ca_period_from_shower.py --input-fname ${INPUT_DIR} --max-k ${MAX_K} --no-show-plots --fig-dir=${FIG_DIR} --data-dir ${DATA_DIR} -l ${PB_LOW} -u ${PB_HIGH}
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./cb_report_measured_antenna_time_offsets.py --no-show-plots --fig-dir=${FIG_DIR} --data-dir ${DATA_DIR}
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#
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vary-fixes:
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./dc_grid_power_time_fixes.py --no-show-plots --fig-dir=${FIG_DIR} --data-dir ${DATA_DIR} --input-fname ${INPUT_DIR}
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#
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reconstruct:
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./da_reconstruction.py --no-show-plots --fig-dir=${FIG_DIR} --data-dir ${DATA_DIR} --input-fname ${INPUT_DIR}
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./db_longitudinal_figure.py --no-show-plots --fig-dir=${FIG_DIR} --data-dir ${DATA_DIR}
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dist-clean:
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rm -vf ${DATA_DIR}/antennas.hdf5
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rm -vf ${DATA_DIR}/ca_breaked_run
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rm -vf ${DATA_DIR}/res.pkl
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rm -vf ${DATA_DIR}/clocks.csv
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rm -vf ${DATA_DIR}/tx.json
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rm -vf ${DATA_DIR}/
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rm -vf ${FIG_DIR}/
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@ -18,6 +18,7 @@ import lib
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# {{{ vim marker
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tx_fname = 'tx.json'
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antennas_fname = 'antennas.hdf5'
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snr_fname = 'snr.json'
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c_light = lib.c_light
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def read_antenna_clock_repair_offsets(antennas, mode='all', freq_name=None):
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@ -49,6 +50,17 @@ def read_antenna_clock_repair_offsets(antennas, mode='all', freq_name=None):
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return time_offsets
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def write_snr_file(fname, snrs):
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with open(fname, 'w') as fp:
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return json.dump(
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{'mean': np.mean(snrs), 'std': np.std(snrs), 'values': snrs},
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fp
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)
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def read_snr_file(fname):
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with open(fname, 'r') as fp:
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return json.load(fp)
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def write_tx_file(fname, tx, f_beacon, **kwargs):
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with open(fname, 'w') as fp:
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return json.dump(
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@ -309,6 +321,7 @@ if __name__ == "__main__":
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# Noise properties
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noise_sigma = args.noise_sigma # mu V/m set to None to ignore
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unique_noise_realisations = True # a new noise realisation per antenna vs. single noise realisation shared across antennas
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# Beacon properties
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beacon_amplitudes = np.array(args.beacon_amplitudes) # mu V/m
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@ -369,6 +382,7 @@ if __name__ == "__main__":
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init_antenna_hdf5(antennas_fname, tx, f_beacon)
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# make beacon per antenna
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noise_realisation = np.array([0])
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for i, antenna in enumerate(ev.antennas):
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#TODO: allow to change the samplerate (2, 4, 8 ns)
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@ -397,9 +411,9 @@ if __name__ == "__main__":
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beacon = 1e-6 * lib.beacon_from(tx, antenna, f_beacon, antenna.t, c_light=c_light, radiate_rsq=beacon_radiate_rsq) # mu V/m
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# noise realisation
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noise_realisation = 0
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if noise_sigma is not None:
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noise_realisation = 1e-6 * rng.normal(0, noise_sigma, size=len(beacon)) # mu V/m
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if unique_noise_realisations or (noise_realisation == 0).all(): # either create one for every antenna, or generate a single one
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print("Noise realisation!")
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noise_realisation = 1e-6 * rng.normal(0, noise_sigma or 0, size=len(antenna.t)) # mu V/m
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# Collect all data to be saved (with the first 3 values the E fields)
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traces = np.array([antenna.Ex, antenna.Ey, antenna.Ez, beacon, noise_realisation])
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@ -26,6 +26,8 @@ if __name__ == "__main__":
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parser.add_argument('-s', '--seed', type=int, nargs='?', default=None, help='Fix seed if supplied.')
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parser.add_argument('--uniform', action='store_const', const='uniform', dest='dist_type')
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parser.add_argument('--gaussian', action='store_const', const='gauss', dest='dist_type')
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parser.add_argument('-r','--read-clocks-file', action='store_true', dest='read_clocks_file')
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parser.add_argument('--no-save-clocks', action='store_false', dest='save_clocks')
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parser.set_defaults(dist_type='gauss')
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parser.add_argument('--data-dir', type=str, default="./data", help='Path to Data Directory. (Default: %(default)s)')
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@ -62,7 +64,11 @@ if __name__ == "__main__":
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N_antennas = len(group.keys())
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if True:
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if args.read_clocks_file and path.isfile(clocks_fname): # read clock deviations from file
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print(f"Reading clocks from {clocks_fname}.")
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clock_offsets = np.loadtxt(clocks_fname)
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elif True: # random clock deviations
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print(f"Modifying clocks upto {max_clock_offset}ns.")
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clock_offsets = np.zeros( N_antennas )
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if args.dist_type == 'uniform': # uniform
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@ -110,6 +116,7 @@ if __name__ == "__main__":
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h5ant['E_AxB'][0, :] += clk_offset
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# save to simple csv
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if args.save_clocks:
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np.savetxt(clocks_fname, clock_offsets)
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print("Antenna clocks modified in " + str(antennas_fname))
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@ -42,6 +42,7 @@ if __name__ == "__main__":
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fname_dir = args.data_dir
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antennas_fname = path.join(fname_dir, beacon.antennas_fname)
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tx_fname = path.join(fname_dir, beacon.tx_fname)
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snr_fname = path.join(fname_dir, beacon.snr_fname)
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# create fig_dir
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if fig_dir:
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@ -92,6 +93,9 @@ if __name__ == "__main__":
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N_samples = len(antennas[0].beacon)
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beacon_snrs = [ lib.signal_to_noise(myfilter(beacon_amp*ant.beacon), myfilter(ant.noise), samplerate=1/dt, signal_band=beacon_pb, noise_band=noise_pb) for i, ant in enumerate(antennas) ]
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# write mean and std to file
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beacon.write_snr_file(snr_fname, beacon_snrs)
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fig, ax = plt.subplots(figsize=figsize)
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ax.set_title(f"Maximum Beacon/Noise SNR (N_samples:{N_samples:.1e})")
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ax.set_xlabel("Antenna no.")
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@ -12,6 +12,8 @@ import numpy as np
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import aa_generate_beacon as beacon
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import lib
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from lib import figlib
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if __name__ == "__main__":
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from os import path
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@ -49,6 +51,7 @@ if __name__ == "__main__":
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####
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fname_dir = args.data_dir
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antennas_fname = path.join(fname_dir, beacon.antennas_fname)
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snr_fname = path.join(fname_dir, beacon.snr_fname)
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fig_dir = args.fig_dir # set None to disable saving
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@ -83,7 +86,8 @@ if __name__ == "__main__":
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N_antennas = len(group.keys())
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# just for funzies
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found_data = np.zeros((N_antennas, 3))
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found_data = np.zeros((N_antennas, 3)) # freq, phase, amp
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noise_data = np.zeros((N_antennas, 2)) # phase, amp
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# Determine frequency and phase
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for i, name in enumerate(group.keys()):
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@ -133,6 +137,7 @@ if __name__ == "__main__":
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# TODO: refine masking
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# use beacon but remove where E_AxB-Beacon != 0
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# Uses the first traces as reference
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t_mask = 0
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if N_mask and orients[0] != 'B':
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N_pre, N_post = N_mask//2, N_mask//2
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@ -166,6 +171,18 @@ if __name__ == "__main__":
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beacon_phases = [ 2*np.pi*t0*f_beacon ]
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amps = [ 3e-7 ]
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# Also try to find the phase from the noise trace if available
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if len(h5ant[traces_key]) > 4:
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noise_trace = h5ant[traces_key][5]
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if np.any(t_mask): # Mask the same area
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noise_trace = noise_trace[t_mask]
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real, imag = lib.direct_fourier_transform(f_beacon, t_trace, noise_trace)
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noise_phase = np.arctan2(imag, real)
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noise_amp = (real**2 + imag**2)**0.5
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noise_data[i] = noise_phase, noise_amp
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# choose highest amp
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idx = np.argmax(amps)
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if False and len(beacon_phases) > 1:
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@ -246,10 +263,16 @@ if __name__ == "__main__":
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h5attrs['amplitude'] = amplitude
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h5attrs['orientation'] = orientation
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if noise_phase:
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h5attrs['noise_phase'] = noise_phase
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h5attrs['noise_amp'] = noise_amp
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print("Beacon Phases, Amplitudes and Frequencies written to", antennas_fname)
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# show histogram of found frequencies
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if show_plots or fig_dir:
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snrs = beacon.read_snr_file(snr_fname)
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if True or allow_frequency_fitting:
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fig, ax = plt.subplots(figsize=figsize)
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ax.set_xlabel("Frequency")
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@ -260,12 +283,45 @@ if __name__ == "__main__":
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fig.savefig(path.join(fig_dir, path.basename(__file__) + f".hist_freq.pdf"))
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if True:
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fig, ax = plt.subplots(figsize=figsize)
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ax.set_xlabel("Amplitudes")
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fig, _ = figlib.fitted_histogram_figure(found_data[:,2], fit_distr=[], mean_snr=snrs['mean'])
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ax = fig.axes[0]
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ax.set_xlabel("Amplitude")
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ax.set_ylabel("Counts")
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ax.hist(found_data[:,2], bins='sqrt', density=False)
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ax.legend()
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if fig_dir:
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fig.savefig(path.join(fig_dir, path.basename(__file__) + f".hist_amp.pdf"))
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if (noise_data[0] != 0).any():
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if True:
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fig, ax = plt.subplots(figsize=figsize)
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ax.set_title("Noise Phases")
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ax.set_xlabel("Phase")
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ax.set_ylabel("#")
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ax.hist(noise_data[:,0], bins='sqrt', density=False)
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ax.legend()
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if fig_dir:
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fig.savefig(path.join(fig_dir, path.basename(__file__) + f".noise.hist_phase.pdf"))
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if True:
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fig, ax = plt.subplots(figsize=figsize)
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ax.set_title("Noise Phase vs Amplitude")
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ax.set_xlabel("Phase")
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ax.set_ylabel("Amplitude [a.u.]")
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ax.plot(noise_data[:,0], noise_data[:,1], ls='none', marker='x')
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if fig_dir:
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fig.savefig(path.join(fig_dir, path.basename(__file__) + f".noise.phase_vs_amp.pdf"))
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if True:
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fig, _ = figlib.fitted_histogram_figure(noise_data[:,1], fit_distr=['rice', 'rayleigh'], mean_snr=snrs['mean'])
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ax = fig.axes[0]
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ax.set_title("Noise Amplitudes")
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ax.set_xlabel("Amplitude [a.u.]")
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ax.set_ylabel("#")
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ax.legend()
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if fig_dir:
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fig.savefig(path.join(fig_dir, path.basename(__file__) + f".noise.hist_amp.pdf"))
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if show_plots:
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plt.show()
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197
airshower_beacon_simulation/bb_measure_clock_phase.py
Executable file
197
airshower_beacon_simulation/bb_measure_clock_phase.py
Executable file
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@ -0,0 +1,197 @@
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#!/usr/bin/env python3
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# vim: fdm=indent ts=4
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import h5py
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from itertools import combinations, zip_longest
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import matplotlib.pyplot as plt
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import numpy as np
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import aa_generate_beacon as beacon
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import lib
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from lib import figlib
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|
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if __name__ == "__main__":
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from os import path
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import sys
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import os
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import matplotlib
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if os.name == 'posix' and "DISPLAY" not in os.environ:
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matplotlib.use('Agg')
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from scriptlib import MyArgumentParser
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parser = MyArgumentParser()
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args = parser.parse_args()
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figsize = (12,8)
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c_light = lib.c_light
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show_plots = args.show_plots
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remove_absolute_phase_offset_first_antenna = True # takes precedence
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remove_absolute_phase_offset_minimum = True
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####
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fname_dir = args.data_dir
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antennas_fname = path.join(fname_dir, beacon.antennas_fname)
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snr_fname = path.join(fname_dir, beacon.snr_fname)
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fig_dir = args.fig_dir # set None to disable saving
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if not path.isfile(antennas_fname):
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print("Antenna file cannot be found, did you try generating a beacon?")
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sys.exit(1)
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# Read in antennas from file
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f_beacon, tx, antennas = beacon.read_beacon_hdf5(antennas_fname)
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# Make sure at least one beacon has been identified
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if not hasattr(antennas[0], 'beacon_info') or len(antennas[0].beacon_info) == 0:
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print(f"No analysed beacon found for {antennas[0].name}, try running the beacon phase analysis script first.")
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sys.exit(1)
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#
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N_beacon_freqs = len(antennas[0].beacon_info)
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for freq_name in antennas[0].beacon_info.keys():
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beacon_phases = np.empty( (len(antennas)) )
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for i, ant in enumerate(antennas):
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beacon_phases[i] = ant.beacon_info[freq_name]['beacon_phase']
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|
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f_beacon = antennas[0].beacon_info[freq_name]['freq']
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clock_phases = lib.remove_antenna_geometry_phase(tx, antennas, f_beacon, beacon_phases, c_light=c_light)
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|
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# Remove the phase from one antenna
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# this is a free parameter
|
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# (only required for absolute timing)
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if remove_absolute_phase_offset_first_antenna or remove_absolute_phase_offset_minimum:
|
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if remove_absolute_phase_offset_first_antenna: # just take the first phase
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minimum_phase = clock_phases[0]
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else: # take the minimum
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minimum_phase = np.min(clock_phases, axis=-1)
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|
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clock_phases -= minimum_phase
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clock_phases = lib.phase_mod(clock_phases)
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# Save to antennas in file
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with h5py.File(antennas_fname, 'a') as fp:
|
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h5group = fp['antennas']
|
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|
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for i, ant in enumerate(antennas):
|
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h5ant = fp['antennas'][ant.name]
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|
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h5beacon_freq = h5ant['beacon_info'][freq_name]
|
||||
h5beacon_freq.attrs['clock_phase'] = clock_phases[i]
|
||||
|
||||
# Plot True Phases at their locations
|
||||
if show_plots or fig_dir:
|
||||
actual_clock_phases = lib.phase_mod(np.array([ -2*np.pi*a.attrs['clock_offset']*f_beacon for a in antennas ]))
|
||||
for i in range(2):
|
||||
plot_residuals = i == 1
|
||||
spatial_unit='m'
|
||||
|
||||
antenna_locs = list(zip(*[(ant.x, ant.y) for ant in antennas]))
|
||||
|
||||
scatter_kwargs = {}
|
||||
scatter_kwargs['cmap'] = 'inferno'
|
||||
|
||||
# Measurements
|
||||
if not plot_residuals:
|
||||
title='Clock phases'
|
||||
color_label='$\\varphi(\\sigma_t)$ [rad]'
|
||||
|
||||
fname_extra='measured.'
|
||||
|
||||
#scatter_kwargs['vmin'] = -np.pi
|
||||
#scatter_kwargs['vmax'] = +np.pi
|
||||
|
||||
# Plot Clock Phases - True Clock Phases at their location
|
||||
else:
|
||||
title='Clock phase Residuals'
|
||||
color_label='$\\Delta\\varphi(\\sigma_t) = \\varphi_{meas} - \\varphi_{true}$ [rad]'
|
||||
|
||||
fname_extra='residuals.'
|
||||
|
||||
# Modify actual_clock_phases, the same way as clock_phases
|
||||
# was modified
|
||||
if remove_absolute_phase_offset_first_antenna or remove_absolute_phase_offset_minimum:
|
||||
if remove_absolute_phase_offset_first_antenna: # just take the first phase
|
||||
minimum_phase = actual_clock_phases[0]
|
||||
else: # take the minimum
|
||||
minimum_phase = np.min(actual_clock_phases, axis=-1)
|
||||
|
||||
actual_clock_phases -= minimum_phase
|
||||
actual_clock_phases = lib.phase_mod(actual_clock_phases)
|
||||
|
||||
clock_phase_residuals = lib.phase_mod(clock_phases - actual_clock_phases)
|
||||
|
||||
if not plot_residuals:
|
||||
loc_c = clock_phases
|
||||
else:
|
||||
loc_c = clock_phase_residuals
|
||||
|
||||
##
|
||||
## Geometrical Plot
|
||||
##
|
||||
fig, ax = plt.subplots(figsize=figsize)
|
||||
|
||||
ax.set_xlabel('x' if spatial_unit is None else 'x [{}]'.format(spatial_unit))
|
||||
ax.set_ylabel('y' if spatial_unit is None else 'y [{}]'.format(spatial_unit))
|
||||
|
||||
fig.suptitle(title+'\nf_beacon= {:2.0f}MHz'.format(f_beacon*1e3))
|
||||
|
||||
sc = ax.scatter(*antenna_locs, c=loc_c, **scatter_kwargs)
|
||||
fig.colorbar(sc, ax=ax, label=color_label)
|
||||
|
||||
if False:
|
||||
for i, ant in enumerate(antennas):
|
||||
ax.text(ant.x, ant.y, ant.name)
|
||||
|
||||
if not True:
|
||||
ax.plot(tx.x, tx.y, 'X', color='k', markeredgecolor='white')
|
||||
|
||||
if fig_dir:
|
||||
fig.tight_layout()
|
||||
fig.savefig(path.join(fig_dir, path.basename(__file__) + f".geom.{fname_extra}F{freq_name}.pdf"))
|
||||
|
||||
##
|
||||
## Histogram
|
||||
##
|
||||
snrs = beacon.read_snr_file(snr_fname)
|
||||
|
||||
fig = figlib.phase_comparison_figure(
|
||||
loc_c,
|
||||
actual_clock_phases,
|
||||
plot_residuals=plot_residuals,
|
||||
f_beacon=f_beacon,
|
||||
figsize=figsize,
|
||||
fit_gaussian=plot_residuals,
|
||||
mean_snr=snrs['mean']
|
||||
)
|
||||
|
||||
if plot_residuals:
|
||||
fig.suptitle("Difference between Measured and True Clock phases")
|
||||
else:
|
||||
fig.suptitle("Comparison Measured and True Clock Phases")
|
||||
|
||||
axs = fig.get_axes()
|
||||
axs[-1].set_xlabel(f'Antenna {title} {color_label}')
|
||||
|
||||
#
|
||||
i=0
|
||||
secax = axs[i].child_axes[0]
|
||||
secax.set_xlabel('Time $\\Delta\\varphi/(2\\pi f_{beac})$ [ns]')
|
||||
|
||||
#
|
||||
i=1
|
||||
axs[i].set_ylabel("Antenna no.")
|
||||
|
||||
# Save figure
|
||||
if fig_dir:
|
||||
fig.tight_layout()
|
||||
fig.savefig(path.join(fig_dir, path.basename(__file__) + f".{fname_extra}F{freq_name}.pdf"))
|
||||
|
||||
print(f"True phases written to", antennas_fname)
|
||||
|
||||
if show_plots:
|
||||
plt.show()
|
||||
|
|
@ -8,6 +8,7 @@ import numpy as np
|
|||
|
||||
import aa_generate_beacon as beacon
|
||||
import lib
|
||||
from lib import figlib
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
@ -108,45 +109,42 @@ if __name__ == "__main__":
|
|||
|
||||
for i in range(2):
|
||||
plot_residuals = i == 1
|
||||
colors = ['blue', 'orange']
|
||||
|
||||
fig, axs = plt.subplots(2, 1, sharex=True, figsize=figsize)
|
||||
true_phases = my_phase_diffs
|
||||
measured_phases = phase_diffs[:,1]
|
||||
|
||||
if True:
|
||||
phase2time = lambda x: x/(2*np.pi*f_beacon)
|
||||
time2phase = lambda x: 2*np.pi*x*f_beacon
|
||||
secax = axs[0].secondary_xaxis('top', functions=(phase2time, time2phase))
|
||||
secax.set_xlabel('Time $\\Delta\\varphi/(2\\pi f_{beac})$ [ns]')
|
||||
hist_kwargs = {}
|
||||
if plot_residuals:
|
||||
measured_phases = lib.phase_mod(measured_phases - true_phases)
|
||||
hist_kwargs['histtype'] = 'stepfilled'
|
||||
|
||||
fig = figlib.phase_comparison_figure(
|
||||
measured_phases,
|
||||
true_phases,
|
||||
plot_residuals=plot_residuals,
|
||||
f_beacon=f_beacon,
|
||||
figsize=figsize,
|
||||
hist_kwargs=hist_kwargs,
|
||||
fit_gaussian=plot_residuals,
|
||||
)
|
||||
|
||||
axs = fig.get_axes()
|
||||
|
||||
if plot_residuals:
|
||||
phase_residuals = lib.phase_mod(phase_diffs[:,1] - my_phase_diffs)
|
||||
|
||||
fig.suptitle("Difference between Measured and Actual phase difference\n for Baselines (i,j" + (')' if not ref_ant_id else '='+str([ int(a.name) for a in ref_ants])+')'))
|
||||
axs[-1].set_xlabel("Baseline Phase Residual $\\Delta\\varphi_{ij_{meas}} - \\Delta\\varphi_{ij_{true}}$ [rad]")
|
||||
else:
|
||||
fig.suptitle("Comparison Measured and Actual phase difference\n for Baselines (i,j" + (')' if not ref_ant_id else '='+str([ int(a.name) for a in ref_ants])+')'))
|
||||
axs[-1].set_xlabel("Baseline Phase $\\Delta\\varphi_{ij}$ [rad]")
|
||||
|
||||
|
||||
#
|
||||
i=0
|
||||
axs[i].set_ylabel("#")
|
||||
if plot_residuals:
|
||||
axs[i].hist(phase_residuals, bins='sqrt', density=False, alpha=0.8, color=colors[0])
|
||||
else:
|
||||
axs[i].hist(phase_diffs[:,1], bins='sqrt', density=False, alpha=0.8, color=colors[0], ls='solid' , histtype='step', label='Measured')
|
||||
axs[i].hist(my_phase_diffs, bins='sqrt', density=False, alpha=0.8, color=colors[1], ls='dashed', histtype='step', label='Actual')
|
||||
|
||||
secax = axs[i].child_axes[0]
|
||||
secax.set_xlabel('Time $\\Delta\\varphi/(2\\pi f_{beac})$ [ns]')
|
||||
|
||||
#
|
||||
i=1
|
||||
axs[i].set_ylabel("Baseline no.")
|
||||
if plot_residuals:
|
||||
axs[i].plot(phase_residuals, np.arange(N_base), alpha=0.6, ls='none', marker='x', color=colors[0])
|
||||
else:
|
||||
axs[i].plot(phase_diffs[:,1], np.arange(N_base), alpha=0.8, color=colors[0], ls='none', marker='x', label='calculated')
|
||||
axs[i].plot(my_phase_diffs, np.arange(N_base), alpha=0.8, color=colors[1], ls='none', marker='+', label='actual time shifts')
|
||||
|
||||
axs[i].legend()
|
||||
fig.tight_layout()
|
||||
|
||||
if fig_dir:
|
||||
extra_name = "measured"
|
||||
|
|
@ -7,9 +7,11 @@ import matplotlib.pyplot as plt
|
|||
from matplotlib.colors import Normalize
|
||||
import matplotlib as mpl
|
||||
import numpy as np
|
||||
import json
|
||||
|
||||
import aa_generate_beacon as beacon
|
||||
import lib
|
||||
from lib import figlib
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
@ -118,6 +120,11 @@ if __name__ == "__main__":
|
|||
mean_clock_phase = np.nanmean(clock_phase_matrix[my_mask], axis=0)
|
||||
std_clock_phase = np.nanstd( clock_phase_matrix[my_mask], axis=0)
|
||||
|
||||
# Remove the mean from the matrix
|
||||
if False:
|
||||
clock_phase_matrix = clock_phase_matrix - np.mean(mean_clock_phase)
|
||||
mean_clock_phase = np.nanmean(clock_phase_matrix[my_mask], axis=0)
|
||||
std_clock_phase = np.nanstd( clock_phase_matrix[my_mask], axis=0)
|
||||
|
||||
# Show resulting matrix as figure
|
||||
if True:
|
||||
|
|
@ -174,45 +181,40 @@ if __name__ == "__main__":
|
|||
global_phase_shift = actual_antenna_phase_shifts[0] - mean_clock_phase[0]
|
||||
actual_antenna_phase_shifts = lib.phase_mod(actual_antenna_phase_shifts - global_phase_shift )
|
||||
|
||||
fit_info = {}
|
||||
for i in range(2):
|
||||
plot_residuals = i == 1
|
||||
colors = ['blue', 'orange']
|
||||
true_phases = actual_antenna_phase_shifts
|
||||
measured_phases = mean_clock_phase
|
||||
|
||||
fig, axs = plt.subplots(2, 1, sharex=True, figsize=figsize)
|
||||
hist_kwargs = {}
|
||||
if plot_residuals:
|
||||
measured_phases = lib.phase_mod(measured_phases - actual_antenna_phase_shifts)
|
||||
|
||||
if True:
|
||||
phase2time = lambda x: x/(2*np.pi*f_beacon)
|
||||
time2phase = lambda x: 2*np.pi*x*f_beacon
|
||||
secax = axs[0].secondary_xaxis('top', functions=(phase2time, time2phase))
|
||||
secax.set_xlabel('Time $\\Delta\\varphi/(2\\pi f_{beac})$ [ns]')
|
||||
fig, _fit_info = figlib.phase_comparison_figure(
|
||||
measured_phases,
|
||||
true_phases,
|
||||
plot_residuals=plot_residuals,
|
||||
f_beacon=f_beacon,
|
||||
figsize=figsize,
|
||||
hist_kwargs=hist_kwargs,
|
||||
fit_gaussian=plot_residuals,
|
||||
return_fit_info = True,
|
||||
)
|
||||
|
||||
axs = fig.get_axes()
|
||||
|
||||
if plot_residuals:
|
||||
phase_residuals = lib.phase_mod(mean_clock_phase - actual_antenna_phase_shifts)
|
||||
fig.suptitle("Difference between Measured and Actual phases (minus global phase)\n for Antenna $i$")
|
||||
axs[-1].set_xlabel("Antenna Phase Residual $\\Delta_\\varphi$")
|
||||
axs[-1].set_xlabel("Antenna Mean Phase Residual $\\Delta_\\varphi$")
|
||||
else:
|
||||
fig.suptitle("Comparison Measured and Actual phases (minus global phase)\n for Antenna $i$")
|
||||
axs[-1].set_xlabel("Antenna Phase $\\varphi$")
|
||||
|
||||
|
||||
i=0
|
||||
axs[i].set_ylabel("#")
|
||||
if plot_residuals:
|
||||
axs[i].hist(phase_residuals, bins='sqrt', alpha=0.8, color=colors[0])
|
||||
else:
|
||||
axs[i].hist(mean_clock_phase, bins='sqrt', density=False, alpha=0.8, color=colors[0], ls='solid' , histtype='step', label='Measured')
|
||||
axs[i].hist(actual_antenna_phase_shifts, bins='sqrt', density=False, alpha=0.8, color=colors[1], ls='dashed', histtype='step', label='Actual')
|
||||
|
||||
axs[-1].set_xlabel("Antenna Mean Phase $\\varphi$")
|
||||
|
||||
i=1
|
||||
axs[i].set_ylabel("Antenna no.")
|
||||
if plot_residuals:
|
||||
axs[i].plot(phase_residuals, np.arange(N_ant), alpha=0.6, ls='none', marker='x', color=colors[0])
|
||||
else:
|
||||
axs[i].errorbar(mean_clock_phase, np.arange(N_ant), yerr=std_clock_phase, marker='4', alpha=0.7, ls='none', color=colors[0], label='Measured')
|
||||
axs[i].plot(actual_antenna_phase_shifts, antenna_names, ls='none', marker='3', alpha=0.8, color=colors[1], label='Actual')
|
||||
#axs[i].errorbar(mean_clock_phase, np.arange(N_ant), yerr=std_clock_phase, marker='4', alpha=0.7, ls='none', color=colors[0], label='Measured')
|
||||
|
||||
axs[i].legend()
|
||||
fig.tight_layout()
|
||||
|
||||
if fig_dir:
|
||||
|
|
@ -221,6 +223,19 @@ if __name__ == "__main__":
|
|||
extra_name = "residuals"
|
||||
fig.savefig(path.join(fig_dir, path.basename(__file__) + f".phase.{extra_name}.pdf"))
|
||||
|
||||
# Save fit_info to data file
|
||||
if fname_dir and plot_residuals:
|
||||
with open(path.join(fname_dir, 'phase_time_residuals.json'), 'w') as fp:
|
||||
json.dump(
|
||||
{
|
||||
'mean': np.mean(measured_phases),
|
||||
'std': np.std(measured_phases),
|
||||
'values': measured_phases.tolist(),
|
||||
},
|
||||
fp)
|
||||
|
||||
|
||||
|
||||
##########################
|
||||
##########################
|
||||
##########################
|
||||
|
|
@ -10,6 +10,7 @@ import numpy as np
|
|||
from os import path
|
||||
|
||||
import aa_generate_beacon as beacon
|
||||
from lib import figlib
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
|
|
@ -76,15 +77,28 @@ if __name__ == "__main__":
|
|||
|
||||
for i in range(2):
|
||||
plot_residuals = i == 1
|
||||
colors = ['blue', 'orange']
|
||||
|
||||
fig, axs = plt.subplots(2, 1, sharex=True, figsize=figsize)
|
||||
|
||||
if True:
|
||||
phase2time = lambda x: x/(2*np.pi*f_beacon)
|
||||
time2phase = lambda x: 2*np.pi*x*f_beacon
|
||||
secax = axs[0].secondary_xaxis('top', functions=(time2phase, phase2time))
|
||||
secax.set_xlabel('Phase $2\\pi t f_{beac}$ [rad]')
|
||||
true_phases = actual_time_shifts
|
||||
measured_phases = measured_time_shifts
|
||||
|
||||
hist_kwargs = {}
|
||||
if plot_residuals:
|
||||
measured_phases = measured_phases - true_phases
|
||||
hist_kwargs['histtype'] = 'stepfilled'
|
||||
|
||||
fig = figlib.phase_comparison_figure(
|
||||
measured_phases,
|
||||
true_phases,
|
||||
plot_residuals=plot_residuals,
|
||||
f_beacon=f_beacon,
|
||||
figsize=figsize,
|
||||
hist_kwargs=hist_kwargs,
|
||||
secondary_axis='phase',
|
||||
fit_gaussian=True,
|
||||
)
|
||||
|
||||
axs = fig.get_axes()
|
||||
|
||||
if plot_residuals:
|
||||
time_shift_residuals = measured_time_shifts - actual_time_shifts
|
||||
|
|
@ -94,26 +108,6 @@ if __name__ == "__main__":
|
|||
fig.suptitle("Comparison Measured and Actual clock offset")
|
||||
axs[-1].set_xlabel("Antenna Time Offset $t_c = \\left(\\frac{\\Delta\\varphi}{2\\pi} + k\\right) / f_{beac}$ [ns]")
|
||||
|
||||
i=0
|
||||
axs[i].set_ylabel("#")
|
||||
if plot_residuals:
|
||||
axs[i].hist(time_shift_residuals, bins='sqrt', alpha=0.8, color=colors[0])
|
||||
else:
|
||||
axs[i].hist(measured_time_shifts, bins='sqrt', density=False, alpha=0.8, color=colors[0], ls='solid' , histtype='step', label='Measured')
|
||||
axs[i].hist(actual_time_shifts, bins='sqrt', density=False, alpha=0.8, color=colors[1], ls='dashed', histtype='step', label='Actual')
|
||||
|
||||
|
||||
i=1
|
||||
axs[i].set_ylabel("Antenna no.")
|
||||
if plot_residuals:
|
||||
axs[i].plot(time_shift_residuals, np.arange(N_ant), alpha=0.6, ls='none', marker='x', color=colors[0])
|
||||
else:
|
||||
axs[i].errorbar(measured_time_shifts, np.arange(N_ant), yerr=None, marker='4', alpha=0.7, ls='none', color=colors[0], label='Measured')
|
||||
axs[i].plot(actual_time_shifts, antenna_names, ls='none', marker='3', alpha=0.8, color=colors[1], label='Actual')
|
||||
|
||||
axs[i].legend()
|
||||
fig.tight_layout()
|
||||
|
||||
if fig_dir:
|
||||
extra_name = "comparison"
|
||||
if plot_residuals:
|
||||
|
|
@ -46,6 +46,7 @@ if __name__ == "__main__":
|
|||
fig_subdir = path.join(fig_dir, 'reconstruction')
|
||||
show_plots = args.show_plots
|
||||
|
||||
apply_signal_window_from_max = True
|
||||
remove_beacon_from_traces = True
|
||||
|
||||
####
|
||||
|
|
@ -19,6 +19,65 @@ import lib
|
|||
from lib import rit
|
||||
|
||||
|
||||
def save_overlapping_traces_figure(test_location, ev, N_plot = 30, wx=200, title_extra=None, fname_distinguish='', fig_dir=None, **fig_kwargs):
|
||||
P, t_, a_, a_sum, t_sum = rit.pow_and_time(test_location, ev, dt=1)
|
||||
|
||||
fig, axs = plt.subplots(**fig_kwargs)
|
||||
axs.set_title("Antenna traces" + (("\n" + title_extra) if title_extra is not None else '') )
|
||||
axs.set_xlabel("Time [ns]")
|
||||
axs.set_ylabel("Amplitude [$\\mu V/m$]")
|
||||
if False:
|
||||
text_loc = (0.02, 0.95)
|
||||
axs.text(*text_loc, '[' + ', '.join(['{:.2e}'.format(x) for x in test_location]) + ']', ha='left', transform=axs.transAxes)
|
||||
|
||||
a_max = [ np.amax(ant.E_AxB) for ant in ev.antennas ]
|
||||
power_sort_idx = np.argsort(a_max)
|
||||
|
||||
for i, idx in enumerate(reversed(power_sort_idx)):
|
||||
if i >= N_plot:
|
||||
break
|
||||
|
||||
alpha = max(0.4, 1/N_plot)
|
||||
|
||||
axs.plot(t_[idx], a_[idx], color='r', alpha=alpha, lw=2)
|
||||
|
||||
if fig_dir:
|
||||
if fname_distinguish:
|
||||
fname_distinguish = "." + fname_distinguish
|
||||
|
||||
fig.tight_layout()
|
||||
|
||||
fig.savefig(path.join(fig_dir, path.basename(__file__) + f'{fname_distinguish}.trace_overlap.{case}.pdf'))
|
||||
fig.savefig(path.join(fig_dir, path.basename(__file__) + f'{fname_distinguish}.trace_overlap.{case}.png'), transparent=True)
|
||||
|
||||
# Take center between t_low and t_high
|
||||
if True:
|
||||
orig_xlims = axs.get_xlim()
|
||||
|
||||
if not True: # t_high and t_low from strongest signal
|
||||
t_low = np.min(t_[power_sort_idx[-1]])
|
||||
t_high = np.max(t_[power_sort_idx[-1]])
|
||||
else: # take t_high and t_low from plotted signals
|
||||
a = [np.min(t_[idx]) for idx in power_sort_idx[-N_plot:]]
|
||||
t_low = np.nanmin(a)
|
||||
b = [np.max(t_[idx]) for idx in power_sort_idx[-N_plot:]]
|
||||
t_high = np.nanmax(b)
|
||||
|
||||
if False:
|
||||
axs.plot(a, [0]*N_plot, 'gx', ms=10)
|
||||
axs.plot(b, [0]*N_plot, 'b+', ms=10)
|
||||
|
||||
center_x = (t_high - t_low)/2 + t_low
|
||||
|
||||
low_xlim = max(orig_xlims[0], center_x - wx)
|
||||
high_xlim = min(orig_xlims[1], center_x + wx)
|
||||
|
||||
axs.set_xlim(low_xlim, high_xlim)
|
||||
fig.savefig(path.join(fig_dir, path.basename(__file__) + f'{fname_distinguish}.trace_overlap.zoomed.{case}.pdf'))
|
||||
fig.savefig(path.join(fig_dir, path.basename(__file__) + f'{fname_distinguish}.trace_overlap.zoomed.{case}.png'), transparent=True)
|
||||
|
||||
return fig
|
||||
|
||||
if __name__ == "__main__":
|
||||
valid_cases = ['no_offset', 'repair_none', 'repair_phases', 'repair_all']
|
||||
|
||||
|
|
@ -95,6 +154,9 @@ if __name__ == "__main__":
|
|||
'scale02d': scale02d,
|
||||
}
|
||||
|
||||
N_plot = 30
|
||||
trace_zoom_wx = 100
|
||||
|
||||
plot_titling = {
|
||||
'no_offset': "no clock offset",
|
||||
'repair_none': "unrepaired clock offset",
|
||||
|
|
@ -175,6 +237,9 @@ if __name__ == "__main__":
|
|||
backup_antenna_t = [ ant.t for ant in ev.antennas ]
|
||||
backup_antenna_t_AxB = [ ant.t_AxB for ant in ev.antennas ]
|
||||
|
||||
fig = save_overlapping_traces_figure([0,0,0], ev, N_plot=1, wx=trace_zoom_wx, title_extra = plot_titling[case], fname_distinguish=f'single', fig_dir=fig_dir, figsize=figsize)
|
||||
plt.close(fig)
|
||||
|
||||
with joblib.parallel_backend("loky"):
|
||||
for case in wanted_cases:
|
||||
print(f"Starting {case} figure")
|
||||
|
|
@ -195,73 +260,29 @@ if __name__ == "__main__":
|
|||
|
||||
if i == 0:
|
||||
# Specifically compare the times
|
||||
print(bak_ants[i].t[0], ev.antennas[i].t[0], ev.antennas[i].t[0], ev.antennas[i].attrs['clock_offset'], measured_offsets[i])
|
||||
print("backup time, time with measured_offset, true clock offset, measured clock offset")
|
||||
print(bak_ants[i].t[0], ev.antennas[i].t[0], ev.antennas[i].attrs['clock_offset'], measured_offsets[i])
|
||||
|
||||
#
|
||||
# Plot overlapping traces at 0,0,0
|
||||
#
|
||||
if True:
|
||||
P, t_, a_, a_sum, t_sum = rit.pow_and_time([0,0,0], ev, dt=1)
|
||||
|
||||
fig, axs = plt.subplots(figsize=figsize)
|
||||
axs.set_title("Antenna traces" + "\n" + plot_titling[case])
|
||||
axs.set_xlabel("Time [ns]")
|
||||
axs.set_ylabel("Amplitude [$\\mu V/m$]")
|
||||
|
||||
a_max = [ np.amax(ant.E_AxB) for ant in ev.antennas ]
|
||||
power_sort_idx = np.argsort(a_max)
|
||||
|
||||
N_plot = 30
|
||||
for i, idx in enumerate(reversed(power_sort_idx)):
|
||||
if i > N_plot:
|
||||
break
|
||||
|
||||
alpha = max(0.4, 1/len(a_))
|
||||
|
||||
axs.plot(t_[idx], a_[idx], color='r', alpha=alpha, lw=2)
|
||||
|
||||
if fig_dir:
|
||||
fig.tight_layout()
|
||||
|
||||
fig.savefig(path.join(fig_dir, path.basename(__file__) + f'.trace_overlap.{case}.pdf'))
|
||||
fig.savefig(path.join(fig_dir, path.basename(__file__) + f'.trace_overlap.{case}.png'), transparent=True)
|
||||
|
||||
# Take center between t_low and t_high
|
||||
if True:
|
||||
orig_xlims = axs.get_xlim()
|
||||
|
||||
if not True: # t_high and t_low from strongest signal
|
||||
t_low = np.min(t_[power_sort_idx[-1]])
|
||||
t_high = np.max(t_[power_sort_idx[-1]])
|
||||
else: # take t_high and t_low from plotted signals
|
||||
a = [np.min(t_[idx]) for idx in power_sort_idx[-N_plot:]]
|
||||
axs.plot(a, [0]*N_plot, 'gx', ms=10)
|
||||
t_low = np.nanmin(a)
|
||||
b = [np.max(t_[idx]) for idx in power_sort_idx[-N_plot:]]
|
||||
axs.plot(b, [0]*N_plot, 'b+', ms=10)
|
||||
t_high = np.nanmax(b)
|
||||
|
||||
center_x = (t_high - t_low)/2 + t_low
|
||||
wx = 200
|
||||
|
||||
low_xlim = max(orig_xlims[0], center_x - wx)
|
||||
high_xlim = min(orig_xlims[1], center_x + wx)
|
||||
|
||||
axs.set_xlim(low_xlim, high_xlim)
|
||||
fig.savefig(path.join(fig_dir, path.basename(__file__) + f'.trace_overlap.zoomed.{case}.pdf'))
|
||||
fig.savefig(path.join(fig_dir, path.basename(__file__) + f'.trace_overlap.zoomed.{case}.png'), transparent=True)
|
||||
|
||||
|
||||
if True:
|
||||
continue
|
||||
fig = save_overlapping_traces_figure([0,0,0], ev, N_plot=N_plot, wx=trace_zoom_wx, title_extra = plot_titling[case], fname_distinguish=f'{case}.0', fig_dir=fig_dir, figsize=figsize)
|
||||
plt.close(fig)
|
||||
|
||||
# Measure power on grid
|
||||
# and plot overlapping traces at position with highest power
|
||||
for scalename, scale in scales.items():
|
||||
wx, wy = scale, scale
|
||||
print(f"Starting grid measurement for figure {case} with {scalename}")
|
||||
xx, yy, p, maxp = rit.shower_plane_slice(ev, X=X, Nx=Nx, Ny=Nx, wx=wx, wy=wy)
|
||||
xx, yy, p, maxp_loc = rit.shower_plane_slice(ev, X=X, Nx=Nx, Ny=Nx, wx=wx, wy=wy, zgr=zgr)
|
||||
|
||||
fig, axs = rit.slice_figure(ev, X, xx, yy, p, mode='sp')
|
||||
fig, axs = rit.slice_figure(ev, X, xx, yy, p, mode='sp', scatter_kwargs=dict(
|
||||
vmax=1e5,
|
||||
vmin=0,
|
||||
s=150,
|
||||
cmap='inferno',
|
||||
# edgecolor='black',
|
||||
))
|
||||
|
||||
suptitle = fig._suptitle.get_text()
|
||||
fig.suptitle("")
|
||||
|
|
@ -271,7 +292,24 @@ if __name__ == "__main__":
|
|||
if fig_dir:
|
||||
fig.tight_layout()
|
||||
fig.savefig(path.join(fig_dir, path.basename(__file__) + f'.X{X}.{case}.{scalename}.pdf'))
|
||||
plt.close(fig)
|
||||
|
||||
#
|
||||
# Plot overlapping traces at highest power of each scale
|
||||
#
|
||||
fig = save_overlapping_traces_figure(maxp_loc, ev, N_plot=N_plot, wx=trace_zoom_wx, title_extra = plot_titling[case] + ', ' + scalename + ' best', fname_distinguish=scalename+'.best', fig_dir=fig_dir, figsize=figsize)
|
||||
|
||||
#
|
||||
# and plot overlapping traces at two other locations
|
||||
#
|
||||
if True:
|
||||
for dist in [ 0.5, 5, 10, 50, 100]:
|
||||
# only add distance horizontally
|
||||
location = maxp_loc + np.sqrt(dist*1e3)*np.array([1,1,0])
|
||||
|
||||
fig = save_overlapping_traces_figure(location, ev, N_plot=N_plot, wx=wx, title_extra = plot_titling[case] + ', ' + scalename + f', x + {dist}km', fname_distinguish=f'{scalename}.{dist}', fig_dir=fig_dir, figsize=figsize)
|
||||
plt.close(fig)
|
||||
|
||||
|
||||
if args.show_plots:
|
||||
plt.show()
|
||||
|
||||
1
airshower_beacon_simulation/lib/earsim
Submodule
1
airshower_beacon_simulation/lib/earsim
Submodule
|
|
@ -0,0 +1 @@
|
|||
Subproject commit 6ef809020477cf78415b9fa733d4fbb4a74102a1
|
||||
219
airshower_beacon_simulation/lib/figlib.py
Normal file
219
airshower_beacon_simulation/lib/figlib.py
Normal file
|
|
@ -0,0 +1,219 @@
|
|||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
import scipy.stats as stats
|
||||
from itertools import zip_longest
|
||||
|
||||
def phase_comparison_figure(
|
||||
measured_phases,
|
||||
true_phases,
|
||||
plot_residuals=True,
|
||||
f_beacon=None,
|
||||
hist_kwargs={},
|
||||
sc_kwargs={},
|
||||
text_kwargs={},
|
||||
colors=['blue', 'orange'],
|
||||
legend_on_scatter=True,
|
||||
secondary_axis='time',
|
||||
fit_gaussian=False,
|
||||
mean_snr=None,
|
||||
return_fit_info=False,
|
||||
**fig_kwargs
|
||||
):
|
||||
"""
|
||||
Create a figure comparing measured_phase against true_phase
|
||||
by both plotting the values, and the residuals.
|
||||
"""
|
||||
default_fig_kwargs = dict(sharex=True)
|
||||
default_hist_kwargs = dict(bins='sqrt', density=False, alpha=0.8, histtype='step')
|
||||
default_text_kwargs = dict(fontsize=14, verticalalignment='top')
|
||||
default_sc_kwargs = dict(alpha=0.6, ls='none')
|
||||
|
||||
fig_kwargs = {**default_fig_kwargs, **fig_kwargs}
|
||||
hist_kwargs = {**default_hist_kwargs, **hist_kwargs}
|
||||
text_kwargs = {**default_text_kwargs, **text_kwargs}
|
||||
sc_kwargs = {**default_sc_kwargs, **sc_kwargs}
|
||||
|
||||
do_hist_plot = hist_kwargs is not False
|
||||
do_scatter_plot = sc_kwargs is not False
|
||||
|
||||
fig, axs = plt.subplots(0+do_hist_plot+do_scatter_plot, 1, **fig_kwargs)
|
||||
|
||||
if not hasattr(axs, '__len__'):
|
||||
axs = [axs]
|
||||
|
||||
if f_beacon and secondary_axis in ['phase', 'time']:
|
||||
phase2time = lambda x: x/(2*np.pi*f_beacon)
|
||||
time2phase = lambda x: 2*np.pi*x*f_beacon
|
||||
|
||||
if secondary_axis == 'time':
|
||||
functions = (phase2time, time2phase)
|
||||
label = 'Time $\\varphi/(2\\pi f_{beac})$ [ns]'
|
||||
else:
|
||||
functions = (time2phase, phase2time)
|
||||
label = 'Phase $2\\pi t f_{beac}$ [rad]'
|
||||
|
||||
secax = axs[0].secondary_xaxis('top', functions=functions)
|
||||
|
||||
# Histogram
|
||||
fit_info = {}
|
||||
if do_hist_plot:
|
||||
i=0
|
||||
|
||||
axs[i].set_ylabel("#")
|
||||
|
||||
this_kwargs = dict(
|
||||
ax = axs[i],
|
||||
text_kwargs=text_kwargs,
|
||||
hist_kwargs={**hist_kwargs, **dict(label='Measured', color=colors[0], ls='solid')},
|
||||
mean_snr=mean_snr,
|
||||
)
|
||||
|
||||
if fit_gaussian:
|
||||
this_kwargs['fit_distr'] = 'gaussian'
|
||||
|
||||
_, fit_info = fitted_histogram_figure(
|
||||
measured_phases,
|
||||
**this_kwargs
|
||||
)
|
||||
|
||||
if not plot_residuals: # also plot the true clock phases
|
||||
_bins = fit_info['bins']
|
||||
axs[i].hist(true_phases, color=colors[1], label='Actual', ls='dashed', **{**hist_kwargs, **dict(bins=_bins)})
|
||||
|
||||
# Scatter plot
|
||||
if do_scatter_plot:
|
||||
i=1
|
||||
axs[i].set_ylabel("Antenna no.")
|
||||
axs[i].plot(measured_phases, np.arange(len(measured_phases)), marker='x' if plot_residuals else '3', color=colors[0], label='Measured', **sc_kwargs)
|
||||
|
||||
if not plot_residuals: # also plot the true clock phases
|
||||
axs[i].plot(true_phases, np.arange(len(true_phases)), marker='4', color=colors[1], label='Actual', **sc_kwargs)
|
||||
|
||||
if not plot_residuals and legend_on_scatter:
|
||||
axs[i].legend()
|
||||
|
||||
fig.tight_layout()
|
||||
|
||||
if return_fit_info:
|
||||
return fig, fit_info
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def fitted_histogram_figure(
|
||||
amplitudes,
|
||||
fit_distr = None,
|
||||
calc_chisq = True,
|
||||
text_kwargs={},
|
||||
hist_kwargs={},
|
||||
mean_snr = None,
|
||||
ax = None,
|
||||
**fig_kwargs
|
||||
):
|
||||
"""
|
||||
Create a figure showing $amplitudes$ as a histogram.
|
||||
If fit_distr is a (list of) string, also fit the respective
|
||||
distribution function and show the parameters on the figure.
|
||||
"""
|
||||
default_hist_kwargs = dict(bins='sqrt', density=False, alpha=0.8, histtype='step', label='hist')
|
||||
default_text_kwargs = dict(fontsize=14, verticalalignment='top')
|
||||
|
||||
if isinstance(fit_distr, str):
|
||||
fit_distr = [fit_distr]
|
||||
|
||||
hist_kwargs = {**default_hist_kwargs, **hist_kwargs}
|
||||
text_kwargs = {**default_text_kwargs, **text_kwargs}
|
||||
|
||||
if ax is None:
|
||||
fig, ax = plt.subplots(1,1, **fig_kwargs)
|
||||
else:
|
||||
fig = ax.get_figure()
|
||||
|
||||
text_kwargs['transform'] = ax.transAxes
|
||||
|
||||
counts, bins, _patches = ax.hist(amplitudes, **hist_kwargs)
|
||||
|
||||
fit_info = []
|
||||
if fit_distr:
|
||||
min_x = min(amplitudes)
|
||||
max_x = max(amplitudes)
|
||||
|
||||
dx = bins[1] - bins[0]
|
||||
scale = len(amplitudes) * dx
|
||||
|
||||
xs = np.linspace(min_x, max_x)
|
||||
|
||||
for distr in fit_distr:
|
||||
fit_params2text_params = lambda x: x
|
||||
|
||||
if 'rice' == distr:
|
||||
name = "Rice"
|
||||
param_names = [ "$\\nu$", "$\\sigma$" ]
|
||||
distr_func = stats.rice
|
||||
|
||||
fit_params2text_params = lambda x: (x[0]*x[1], x[1])
|
||||
|
||||
elif 'gaussian' == distr:
|
||||
name = "Norm"
|
||||
param_names = [ "$\\mu$", "$\\sigma$" ]
|
||||
distr_func = stats.norm
|
||||
|
||||
elif 'rayleigh' == distr:
|
||||
name = "Rayleigh"
|
||||
param_names = [ "$\\sigma$" ]
|
||||
distr_func = stats.rayleigh
|
||||
|
||||
fit_params2text_params = lambda x: (x[0]+x[1]/2,)
|
||||
|
||||
else:
|
||||
raise ValueError('Unknown distribution function '+distr)
|
||||
|
||||
label = name +"(" + ','.join(param_names) + ')'
|
||||
|
||||
fit_params = distr_func.fit(amplitudes)
|
||||
fit_ys = distr_func.pdf(xs, *fit_params)
|
||||
|
||||
ax.plot(xs, fit_ys*scale, label=label)
|
||||
|
||||
chisq_strs = []
|
||||
if calc_chisq:
|
||||
ct = np.diff(distr_func.cdf(bins, *fit_params))*np.sum(counts)
|
||||
c2t = stats.chisquare(counts, ct, ddof=len(fit_params))
|
||||
chisq_strs = [
|
||||
f"$\\chi^2$/dof = {c2t[0]: .2g}/{len(fit_params)}"
|
||||
]
|
||||
|
||||
# change parameters if needed
|
||||
text_fit_params = fit_params2text_params(fit_params)
|
||||
|
||||
text_str = "\n".join(
|
||||
[label]
|
||||
+
|
||||
[ f"{param} = {value: .2e}" for param, value in zip_longest(param_names, text_fit_params, fillvalue='?') ]
|
||||
+
|
||||
chisq_strs
|
||||
)
|
||||
|
||||
this_info = {
|
||||
'name': name,
|
||||
'param_names': param_names,
|
||||
'param_values': text_fit_params,
|
||||
'text_str': text_str,
|
||||
}
|
||||
|
||||
if chisq_strs:
|
||||
this_info['chisq'] = c2t[0]
|
||||
this_info['dof'] = len(fit_params)
|
||||
|
||||
fit_info.append(this_info)
|
||||
|
||||
loc = (0.02, 0.95)
|
||||
ax.text(*loc, "\n\n".join([info['text_str'] for info in fit_info]), **{**text_kwargs, **dict(ha='left')})
|
||||
|
||||
if mean_snr:
|
||||
text_str = f"$\\langle SNR \\rangle$ = {mean_snr: .1e}"
|
||||
loc = (0.98, 0.95)
|
||||
ax.text(*loc, text_str, **{**text_kwargs, **dict(ha='right')})
|
||||
|
||||
return fig, dict(fit_info=fit_info, counts=counts, bins=bins)
|
||||
|
|
@ -58,6 +58,8 @@ def pow_and_time(test_loc,ev,dt=1.0):
|
|||
a_sum = np.add(a_sum,a_int)
|
||||
if len(a_sum) != 0:
|
||||
P = np.sum(np.square(np.absolute(np.fft.fft(a_sum))))
|
||||
# normalise P with the length of the traces
|
||||
P = P/( t_sum[-1] - t_sum[0])
|
||||
else:
|
||||
print("ERROR, a_sum lenght = 0",
|
||||
"tmin ",t_min,
|
||||
|
|
@ -112,11 +114,21 @@ def shower_plane_slice(e,X=750.,Nx=10,Ny=10,wx=1e3,wy=1e3,xoff=0,yoff=0,zgr=0,n_
|
|||
|
||||
return xx,yy,p,locs[np.argmax(p)]
|
||||
|
||||
def slice_figure(e,X,xx,yy,p,mode='horizontal'):
|
||||
def slice_figure(e,X,xx,yy,p,mode='horizontal', scatter_kwargs={}, colorbar_kwargs={'label':'Power'}):
|
||||
scatter_kwargs = {
|
||||
**dict(
|
||||
cmap='Spectral_r',
|
||||
alpha=0.9,
|
||||
s=30
|
||||
),
|
||||
**scatter_kwargs
|
||||
}
|
||||
|
||||
|
||||
fig, axs = plt.subplots(1,figsize=(10,8))
|
||||
fig.suptitle(r'E = %.1f EeV, $\theta$ = %.1f$^\circ$, $\phi$ = %.1f$^\circ$ X = %.f'%(e.energy,e.zenith,e.azimuth,X))
|
||||
sc = axs.scatter(xx/1e3,yy/1e3,c=p,cmap='Spectral_r',alpha=0.6)
|
||||
fig.colorbar(sc,ax=axs)
|
||||
sc = axs.scatter(xx/1e3,yy/1e3,c=p,**scatter_kwargs)
|
||||
fig.colorbar(sc,ax=axs, **colorbar_kwargs)
|
||||
zgr = 0 + e.core[2]
|
||||
dX = atm.distance_to_slant_depth(np.deg2rad(e.zenith),X,zgr)
|
||||
xc = np.sin(np.deg2rad(e.zenith))*np.cos(np.deg2rad(e.azimuth))* dX
|
||||
67
airshower_beacon_simulation/matrix_base/bin/collect_figures.py
Executable file
67
airshower_beacon_simulation/matrix_base/bin/collect_figures.py
Executable file
|
|
@ -0,0 +1,67 @@
|
|||
#!/usr/bin/env python3
|
||||
|
||||
import os
|
||||
import re
|
||||
import tempfile
|
||||
|
||||
def parse_env_file(fname):
|
||||
envs = {}
|
||||
env_reg = re.compile('^(?:\s*export)?\s*(.*)\s*=\s*(.*)\s*$')
|
||||
|
||||
with open(fname, 'r') as f:
|
||||
for line in f:
|
||||
if '=' not in line:
|
||||
continue
|
||||
|
||||
m = env_reg.match(line)
|
||||
envs[m.group(1)] = m.group(2)
|
||||
|
||||
return envs
|
||||
|
||||
def mutilate_figure_name(fig_fname, envs):
|
||||
return fig_fname
|
||||
|
||||
if __name__ == "__main__":
|
||||
from argparse import ArgumentParser
|
||||
|
||||
parser = ArgumentParser()
|
||||
#parser.add_argument('-f', '--figures', nargs='*')
|
||||
parser.add_argument("-d", "--directories", nargs='*')
|
||||
parser.add_argument('out_dir', default='./figures', type=str)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
os.makedirs(args.out_dir, exist_ok=True)
|
||||
|
||||
#with open(args.out_file, 'w') as fp:
|
||||
if True:
|
||||
for d in args.directories:
|
||||
d = os.path.realpath(d)
|
||||
fig_dir = os.path.join(d, 'figures')
|
||||
env_fname = os.path.join(d, 'env.sh')
|
||||
|
||||
if not os.path.exists(fig_dir):
|
||||
print(f"Cannot find {fig_dir}")
|
||||
continue
|
||||
|
||||
## parse properties from env.sh
|
||||
#envs = parse_env_file(env_fname)
|
||||
#print(envs, fig_dir)
|
||||
|
||||
for f in os.listdir(fig_dir):
|
||||
fname, ext = os.path.splitext(f)
|
||||
|
||||
dname = os.path.basename(d)
|
||||
|
||||
if ext not in ['.pdf', '.png']:
|
||||
continue
|
||||
|
||||
link_name = fname + "_" + dname + ext
|
||||
|
||||
target = os.path.realpath(os.path.join(fig_dir, f))
|
||||
tmpLink = tempfile.mktemp(dir=args.out_dir)
|
||||
|
||||
os.symlink(target, tmpLink)
|
||||
os.rename(tmpLink, os.path.join(args.out_dir, link_name))
|
||||
|
||||
|
||||
|
|
@ -17,10 +17,17 @@ for options in product(baselines, clock_devs, noise_sigmas, trace_lengths):
|
|||
|
||||
# Make directory
|
||||
if path.exists(dirname):
|
||||
print(f"{dirname} already exists! continuing..")
|
||||
print(f"{dirname} already exists! continuing anyway..")
|
||||
|
||||
os.makedirs(dirname, exist_ok=True)
|
||||
|
||||
# Soft link clock file if available
|
||||
if True:
|
||||
os.makedirs(path.join(dirname, 'data'), exist_ok=True)
|
||||
|
||||
if not path.isfile(path.join(dirname, 'data/clocks.csv')):
|
||||
os.symlink(f'../../c{clk_dev}_clocks.csv', path.join(dirname, 'data/clocks.csv'))
|
||||
|
||||
# Setup config.mk
|
||||
with open(path.join(dirname, 'env.sh'), 'w') as fp:
|
||||
template = f"""
|
||||
217
airshower_beacon_simulation/matrix_base/bin/time_res_vs_snr.py
Executable file
217
airshower_beacon_simulation/matrix_base/bin/time_res_vs_snr.py
Executable file
|
|
@ -0,0 +1,217 @@
|
|||
#!/usr/bin/env python3
|
||||
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import json
|
||||
|
||||
from scipy import special
|
||||
|
||||
|
||||
# Mimic import aa_generate_beacon as beacon
|
||||
beacon = lambda: None
|
||||
|
||||
def read_snr_file(fname):
|
||||
with open(fname, 'r') as fp:
|
||||
return json.load(fp)
|
||||
|
||||
def read_tx_file(fname):
|
||||
with open(fname, 'r') as fp:
|
||||
data = json.load(fp)
|
||||
|
||||
f_beacon = data['f_beacon']
|
||||
tx = data['tx']
|
||||
|
||||
del data['f_beacon']
|
||||
del data['tx']
|
||||
|
||||
return tx, f_beacon, data
|
||||
|
||||
|
||||
beacon.snr_fname = 'snr.json'
|
||||
beacon.tx_fname = 'tx.json'
|
||||
beacon.read_snr_file = read_snr_file
|
||||
beacon.read_tx_file = read_tx_file
|
||||
|
||||
def read_snr_directories(data_directories, tx_fname=beacon.tx_fname, snr_fname=beacon.snr_fname, phase_res_fname='phase_time_residuals.json'):
|
||||
# Read in snr
|
||||
snrs = np.zeros(len(data_directories), dtype=float)
|
||||
time_residuals = np.zeros( (len(snrs), 2), dtype=float)
|
||||
|
||||
|
||||
tx, f_beacon, _ = beacon.read_tx_file(path.join(data_directories[0], tx_fname))
|
||||
for i, data_dir in enumerate(data_directories):
|
||||
# Read SNR file
|
||||
snr_data = read_snr_file(path.join(data_dir, snr_fname))
|
||||
|
||||
# Open antennas file
|
||||
with open(path.join(data_dir, phase_res_fname), 'r') as fp:
|
||||
time_res_data = json.load(fp)
|
||||
|
||||
snrs[i] = snr_data['mean']
|
||||
|
||||
time_residuals[i] = time_res_data['mean'], time_res_data['std']
|
||||
|
||||
return f_beacon, snrs, time_residuals
|
||||
|
||||
|
||||
## Math
|
||||
def expectation(x, pdfx):
|
||||
dx = x[1] - x[0]
|
||||
return np.sum(x*pdfx*dx)
|
||||
|
||||
def variance(x, pdfx):
|
||||
mu = expectation(x, pdfx)
|
||||
dx = x[1] - x[0]
|
||||
return np.sum(x**2 *pdfx*dx) - mu**2
|
||||
|
||||
def phase_distribution(theta, sigma, s):
|
||||
k = s/sigma
|
||||
ct = np.cos(theta)
|
||||
st = np.sin(theta)
|
||||
pipi = 2*np.pi
|
||||
|
||||
return (np.exp(-k**2/2)/pipi) \
|
||||
+ (
|
||||
(pipi**-0.5)*k*np.exp(-(k*st)**2/2)
|
||||
* ((1.+special.erf(k*ct*2**-0.5))*ct/2)
|
||||
)
|
||||
|
||||
def phase_distribution_gauss(theta, sigma, s):
|
||||
k = s/sigma
|
||||
return 1/np.sqrt(2*np.pi) * k *np.exp( -(k*theta)**2/2)
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from os import path
|
||||
import sys
|
||||
|
||||
import os
|
||||
import matplotlib
|
||||
if os.name == 'posix' and "DISPLAY" not in os.environ:
|
||||
matplotlib.use('Agg')
|
||||
|
||||
from argparse import ArgumentParser
|
||||
parser = ArgumentParser()
|
||||
|
||||
figsize = (12,8)
|
||||
|
||||
# Multiple Data Directories
|
||||
parser.add_argument("-d", dest='data_directories', default=[], nargs='*')
|
||||
|
||||
# Whether to show plots
|
||||
group1 = parser.add_mutually_exclusive_group(required=False)
|
||||
group1.add_argument('--show-plots', action='store_true', default=True, help='Default: %(default)s')
|
||||
group1.add_argument('--no-show-plots', dest='show-plots', action='store_false')
|
||||
|
||||
# Figures directory
|
||||
parser.add_argument('--fig-dir', type=str, default='./figures', help='Set None to disable saving figures. (Default: %(default)s)')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
show_plots = args.show_plots
|
||||
|
||||
if not args.data_directories:
|
||||
parser.error("At least one data directory should be specified.")
|
||||
|
||||
f_beacon, snrs, time_residuals = read_snr_directories(args.data_directories)
|
||||
|
||||
phase2time = lambda x: x/(2*np.pi*f_beacon)
|
||||
time2phase = lambda x: 2*np.pi*x*f_beacon
|
||||
|
||||
fig, ax = plt.subplots(figsize=(8,6))
|
||||
ax.set_title("Beacon ({:.2f}MHz) Simulation".format(f_beacon*1e3))
|
||||
ax.set_xlabel('Signal to Noise ratio')
|
||||
ax.set_ylabel('Time Accuracy $\\sigma(t)$ [ns]')
|
||||
|
||||
# group per directories per N
|
||||
if True:
|
||||
import re
|
||||
|
||||
dirdict = {}
|
||||
N_re = re.compile(r'_N(\d+)_')
|
||||
|
||||
for d in args.data_directories:
|
||||
m = N_re.findall(d)[0]
|
||||
|
||||
if m not in dirdict:
|
||||
dirdict[m] = []
|
||||
|
||||
dirdict[m].append(d)
|
||||
|
||||
|
||||
dirlist = dirdict.values()
|
||||
|
||||
# plot data directories as one group
|
||||
else:
|
||||
dirlist = [args.data_directories]
|
||||
|
||||
for dirlist in dirlist:
|
||||
f_beacon, snrs, time_residuals = read_snr_directories(dirlist)
|
||||
|
||||
# plot data
|
||||
ax.plot(snrs*np.sqrt(np.pi/2), phase2time(time_residuals[:,1]), ls='none', marker='o')
|
||||
|
||||
# Add secondary axis
|
||||
if True:
|
||||
if False and secondary_axis == 'time':
|
||||
functions = (phase2time, time2phase)
|
||||
label = 'Time Accuracy $\\sigma_t\\varphi/(2\\pi f_{beac})$ [ns]'
|
||||
else:
|
||||
functions = (time2phase, phase2time)
|
||||
label = 'Phase Accuracy $\\sigma_\\varphi$ [rad]'
|
||||
|
||||
secax = ax.secondary_yaxis('right', functions=functions)
|
||||
secax.set_ylabel(label)
|
||||
|
||||
# logscales
|
||||
if True:
|
||||
ax.set_xscale('log')
|
||||
ax.set_yscale('log')
|
||||
|
||||
# plot phasor snr
|
||||
if True:
|
||||
thetas = np.linspace(-np.pi, np.pi, 500)
|
||||
sigma = 1
|
||||
_snr_min = min(10, min(snrs))
|
||||
_snr_max = min(100, max(snrs))
|
||||
|
||||
if ax.get_xscale() == 'log': #log
|
||||
_snrs = np.logspace(np.log10(_snr_min), np.log10(_snr_max))
|
||||
else: #linear
|
||||
_snrs = np.linspace(_snr_min, _snr_max)
|
||||
|
||||
# Phasor Rice
|
||||
phasor_pdfs = np.array([phase_distribution(thetas, sigma, s) for s in _snrs ])
|
||||
phasor_sigma = np.sqrt(np.array([variance(thetas, pdf) for pdf in phasor_pdfs]))
|
||||
|
||||
ax.plot(_snrs, phase2time(phasor_sigma), label='Rice')
|
||||
|
||||
if True: # plot a pdf
|
||||
fig2, ax2 = plt.subplots()
|
||||
for idx in [0, len(_snrs)//4, len(_snrs)//2, -1]:
|
||||
ax2.plot(thetas, phasor_pdfs[idx], label='s = {:.1f}'.format(_snrs[idx]))
|
||||
ax2.set_xlabel('$\\theta$')
|
||||
ax2.set_ylabel('$p(\\theta)$')
|
||||
ax2.legend()
|
||||
|
||||
# Gauss Phasor
|
||||
phasor_pdfs = np.array([phase_distribution_gauss(thetas, sigma, s) for s in _snrs ])
|
||||
phasor_sigma = np.sqrt(np.array([variance(thetas, pdf) for pdf in phasor_pdfs]))
|
||||
|
||||
ax.plot(_snrs, phase2time(phasor_sigma), label='Gauss')
|
||||
ax.legend()
|
||||
|
||||
# Set limit on x values
|
||||
if not True or ax.get_xscale() != 'log':
|
||||
ax.set_xlim(0, 100)
|
||||
|
||||
if args.fig_dir:
|
||||
fig.tight_layout()
|
||||
fig.savefig(path.join(args.fig_dir, "time_res_vs_snr.pdf"))
|
||||
|
||||
if args.show_plots:
|
||||
plt.show()
|
||||
|
||||
|
||||
|
|
@ -1,148 +0,0 @@
|
|||
#!/usr/bin/env python3
|
||||
# vim: fdm=indent ts=4
|
||||
|
||||
import h5py
|
||||
from itertools import combinations, zip_longest
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
import aa_generate_beacon as beacon
|
||||
import lib
|
||||
|
||||
if __name__ == "__main__":
|
||||
from os import path
|
||||
import sys
|
||||
|
||||
import os
|
||||
import matplotlib
|
||||
if os.name == 'posix' and "DISPLAY" not in os.environ:
|
||||
matplotlib.use('Agg')
|
||||
|
||||
from scriptlib import MyArgumentParser
|
||||
parser = MyArgumentParser()
|
||||
args = parser.parse_args()
|
||||
|
||||
figsize = (12,8)
|
||||
c_light = lib.c_light
|
||||
show_plots = args.show_plots
|
||||
|
||||
remove_absolute_phase_offset_first_antenna = True # takes precedence
|
||||
remove_absolute_phase_offset_minimum = True
|
||||
|
||||
####
|
||||
fname_dir = args.data_dir
|
||||
antennas_fname = path.join(fname_dir, beacon.antennas_fname)
|
||||
|
||||
fig_dir = args.fig_dir # set None to disable saving
|
||||
|
||||
if not path.isfile(antennas_fname):
|
||||
print("Antenna file cannot be found, did you try generating a beacon?")
|
||||
sys.exit(1)
|
||||
|
||||
# Read in antennas from file
|
||||
f_beacon, tx, antennas = beacon.read_beacon_hdf5(antennas_fname)
|
||||
|
||||
# Make sure at least one beacon has been identified
|
||||
if not hasattr(antennas[0], 'beacon_info') or len(antennas[0].beacon_info) == 0:
|
||||
print(f"No analysed beacon found for {antennas[0].name}, try running the beacon phase analysis script first.")
|
||||
sys.exit(1)
|
||||
|
||||
#
|
||||
N_beacon_freqs = len(antennas[0].beacon_info)
|
||||
|
||||
for freq_name in antennas[0].beacon_info.keys():
|
||||
beacon_phases = np.empty( (len(antennas)) )
|
||||
for i, ant in enumerate(antennas):
|
||||
beacon_phases[i] = ant.beacon_info[freq_name]['beacon_phase']
|
||||
|
||||
f_beacon = antennas[0].beacon_info[freq_name]['freq']
|
||||
|
||||
clock_phases = lib.remove_antenna_geometry_phase(tx, antennas, f_beacon, beacon_phases, c_light=c_light)
|
||||
|
||||
# Remove the phase from one antenna
|
||||
# this is a free parameter
|
||||
# (only required for absolute timing)
|
||||
if remove_absolute_phase_offset_first_antenna or remove_absolute_phase_offset_minimum:
|
||||
if remove_absolute_phase_offset_first_antenna: # just take the first phase
|
||||
minimum_phase = clock_phases[0]
|
||||
else: # take the minimum
|
||||
minimum_phase = np.min(clock_phases, axis=-1)
|
||||
|
||||
clock_phases -= minimum_phase
|
||||
clock_phases = lib.phase_mod(clock_phases)
|
||||
|
||||
# Save to antennas in file
|
||||
with h5py.File(antennas_fname, 'a') as fp:
|
||||
h5group = fp['antennas']
|
||||
|
||||
for i, ant in enumerate(antennas):
|
||||
h5ant = fp['antennas'][ant.name]
|
||||
|
||||
h5beacon_freq = h5ant['beacon_info'][freq_name]
|
||||
h5beacon_freq.attrs['clock_phase'] = clock_phases[i]
|
||||
|
||||
# Plot True Phases at their locations
|
||||
if show_plots or fig_dir:
|
||||
fig, ax = plt.subplots(figsize=figsize)
|
||||
spatial_unit='m'
|
||||
fig.suptitle('Clock phases\nf_beacon= {:2.0f}MHz'.format(f_beacon*1e3))
|
||||
|
||||
antenna_locs = list(zip(*[(ant.x, ant.y) for ant in antennas]))
|
||||
ax.set_xlabel('x' if spatial_unit is None else 'x [{}]'.format(spatial_unit))
|
||||
ax.set_ylabel('y' if spatial_unit is None else 'y [{}]'.format(spatial_unit))
|
||||
scatter_kwargs = {}
|
||||
scatter_kwargs['cmap'] = 'inferno'
|
||||
#scatter_kwargs['vmin'] = -np.pi
|
||||
#scatter_kwargs['vmax'] = +np.pi
|
||||
color_label='$\\varphi(\\sigma_t)$ [rad]'
|
||||
|
||||
sc = ax.scatter(*antenna_locs, c=clock_phases, **scatter_kwargs)
|
||||
fig.colorbar(sc, ax=ax, label=color_label)
|
||||
|
||||
if False:
|
||||
for i, ant in enumerate(antennas):
|
||||
ax.text(ant.x, ant.y, ant.name)
|
||||
|
||||
if not True:
|
||||
ax.plot(tx.x, tx.y, 'X', color='k', markeredgecolor='white')
|
||||
|
||||
if fig_dir:
|
||||
fig.savefig(path.join(fig_dir, path.basename(__file__) + f".F{freq_name}.pdf"))
|
||||
|
||||
# Plot True Phases - Actual True Phases at their location
|
||||
if show_plots or fig_dir:
|
||||
fig, ax = plt.subplots(figsize=figsize)
|
||||
fig.suptitle('Clock phase Residuals\nf_beacon={:2.0f}MHz'.format(f_beacon*1e3))
|
||||
|
||||
actual_clock_phases = np.array([ -2*np.pi*a.attrs['clock_offset']*f_beacon for a in antennas ])
|
||||
|
||||
# Modify actual_clock_phases, the same way as clock_phases
|
||||
# was modified
|
||||
if remove_absolute_phase_offset_first_antenna or remove_absolute_phase_offset_minimum:
|
||||
if remove_absolute_phase_offset_first_antenna: # just take the first phase
|
||||
minimum_phase = actual_clock_phases[0]
|
||||
else: # take the minimum
|
||||
minimum_phase = np.min(actual_clock_phases, axis=-1)
|
||||
|
||||
actual_clock_phases -= minimum_phase
|
||||
actual_clock_phases = lib.phase_mod(actual_clock_phases)
|
||||
|
||||
clock_phase_residuals = lib.phase_mod(clock_phases - actual_clock_phases)
|
||||
|
||||
antenna_locs = list(zip(*[(ant.x, ant.y) for ant in antennas]))
|
||||
ax.set_xlabel('x' if spatial_unit is None else 'x [{}]'.format(spatial_unit))
|
||||
ax.set_ylabel('y' if spatial_unit is None else 'y [{}]'.format(spatial_unit))
|
||||
scatter_kwargs = {}
|
||||
scatter_kwargs['cmap'] = 'inferno'
|
||||
color_label='$\\Delta\\varphi(\\sigma_t) = \\varphi_{meas} - \\varphi_{true}$ [rad]'
|
||||
|
||||
sc = ax.scatter(*antenna_locs, c=clock_phase_residuals, **scatter_kwargs)
|
||||
fig.colorbar(sc, ax=ax, label=color_label)
|
||||
|
||||
if fig_dir:
|
||||
fig.savefig(path.join(fig_dir, path.basename(__file__) + f".residual.F{freq_name}.pdf"))
|
||||
|
||||
print(f"True phases written to", antennas_fname)
|
||||
|
||||
if show_plots:
|
||||
plt.show()
|
||||
|
|
@ -1 +0,0 @@
|
|||
Subproject commit a5abe360fa5210be6ab423ecf2b5f783ae45b675
|
||||
Loading…
Reference in a new issue