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XENON100.ini for pax 4.0.1 ROOT output test
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XENON100.ini
##
# XENON100-specific configuration settings
##
# This is just for setting up pax
[pax]
parent_configuration = "_base"
input = 'XED.ReadXED'
dsp = [
# Do some sanity checks / cleaning on pulses
'CheckPulses.ConcatenateAdjacentPulses',
'CheckPulses.CheckBounds',
# Find individual hits, then sum their waveforms
'HitFinder.FindHits',
'SumWaveform.SumWaveform',
# Combine hits into clusters = peaks
'Cluster.NaturalBreaks',
# Compute properties for each peak
'ComputePeakProperties.BasicProperties',
'ComputePeakProperties.SumWaveformProperties',
'ComputePeakProperties.HitpatternSpread',
# Classify the clusters based on the properties
'ClassifyPeaks.AdHocClassification',
]
transform = [
'PosSimple.PosRecWeightedSum',
'RobustWeightedMean.PosRecRobustWeightedMean',
'NeuralNet.PosRecNeuralNet',
'PosRecChiSquareGamma.PosRecChiSquareGamma',
'MaxPMT.PosRecMaxPMT',
'BuildInteractions.BuildInteractions',
'BuildInteractions.BasicInteractionProperties',
]
[HitFinder.FindHits]
# For detailed description of what these settings do, see the documentation / plugin docstring.
# Compute baseline on first n samples in pulse:
initial_baseline_samples = 40
# Max hits to look for in each pulse: rest will be ignored
max_hits_per_pulse = 500
# Diagnostic plots settings
make_diagnostic_plots = 'never' # Can be always, never, tricky cases, no hits, hits only, saturated
make_diagnostic_plots_in = 'hitfinder_diagnostic_plots'
# Threshold 1: Height / noise.
height_over_noise_high_threshold = 8 # Reasonable and conservative for run10: see xenon:xenon100:analysis:led_pax
height_over_noise_low_threshold = 1
# Threshold 2: Absolute ADC counts above baseline
absolute_adc_counts_high_threshold = 1 # ADC counts
absolute_adc_counts_low_threshold = 1 # ADC counts
# Threshold 3: - Height / minimum
height_over_min_high_threshold = 2
height_over_min_low_threshold = 0
# Raise low threshold temporarily to fraction of hit height for rest of pulse
dynamic_low_threshold_coeff = 0.01
[Cluster]
# Suspicious channel rejection settings
penalty_per_noise_pulse = 1 # "pe" equivalent penalty
penalty_per_lone_hit = 1 # "pe" equivalent penalty
# Threshold to mark a suspicious channel
penalty_geq_this_is_suspicious = 3 # "pe" equivalent penalty
# If the ratio of noise channels / contributing channels is larger than this, classify peak as 'noise'
# noise channel = a channel in the same detector which shows data, but doesn't contribute to the peak
# (or only hits rejected by the suspicious channel algorithm)
max_noise_channels_over_contributing_channels = 2
[Cluster.MeanShift]
s2_size = 20
# If spe peaks are separated by less than this, they will be clustered together
s2_width = 1.0 * us
p_value = 0.999
cluster_all = True
[Cluster.HitDifference]
max_difference = 800 * ns
[Cluster.GapSize]
# If there is a gap between hits larger than this, it will make a new cluster
large_gap_threshold = 450 * ns
# If the area in a cluster is larger than this, it is certainly not a single electron, so we can use...
transition_point = 50 #pe
# ... a tighter gap threshold:
small_gap_threshold = 100 * ns
[ComputePeakProperties.SumWaveformProperties]
# Length of the peak sum waveform field.
# Must be an even multiple of sample size, pax will add 1 sample width so there is a clear center.
peak_waveform_length = 2.5 * us
[BuildInteractions.BuildInteractions]
# Pair S1s and S2s in order of size, but no more than these:
pair_n_s2s = 5
pair_n_s1s = 3
# Never pair S2s smaller than:
s2_pairing_threshold = 70 # pe
# Preference in algorithms to use for the xy reconstructed position
xy_posrec_preference = ['PosRecChi2Gamma', 'PosRecNeuralNet', 'PosSimple']
[BuildInteractions.BasicInteractionProperties]
s1_correction_map = 's1_xyz_XENON100_xerawdp045.json'
s2_correction_map = 'placeholder_map.json'
[PosRecChiSquareGamma.PosRecChiSquareGamma]
# Mode options: 'full', 'no_reconstruct', 'only_reconstruct'
mode = 'full'
# Minimum area for an S2 to be reconstructed
# Superfluous now since speed is not a problem anymore.
# Temporarily for comparisons with previous code.
area_threshold = -1 # pe
# Use neural net position as a seed, if it is available
seed_from_neural_net = True
# If true, will treat saturated PMTs as if they do not exist
# Note that's very different from assuming they see nothing!
ignore_saturated_PMTs = True
[Cluster.NaturalBreaks]
# Always break if a gap of this size is encountered
max_gap_size_in_cluster = 600 * ns
# Lambda function in string - n = number of hits, result is split goodness threshold
min_split_goodness = 'lambda n : max(0.1, 1.2 - 1.1/150 * n)'
# Limit gaps to test for performance reasons
# Haven't tested if these are actually harmful, needed, or effective...
min_gap_size_for_break = 50 * ns
max_n_gaps_to_test = float('inf')
[RobustWeightedMean.PosRecRobustWeightedMean]
# Remove PMTs that are more than ... away in each step
outlier_threshold = 2.5 # 3 and 2 both seem a little worse, though not much. 1.5 is clearly worse.
# Give up if this number of PMTs (or less) is left
min_pmts_left = 3
# Outer ring PMTs are partially obstructed by the TPC wall, upweigh their areas to compensate
outer_ring_pmts = list(range(1, 30 + 1))
outer_ring_multiplication_factor = 1.5
##
# Simulator-specific settings
##
[WaveformSimulator]
# Waveform building / noise simulation settings
cheap_zle = False # If True, will generate noise only around simulated photons.
# If False, generates noise in all channels everywhere. You may want to activate the software ZLE plugin!
event_padding = 5 * us # Padding in the event before the first and after the last photon.
# if you use the cheap_zle, bad things happen if this is smaller than the zle padding
gauss_noise_sigma = 0 #0.05 #pe/bin # Sigma of Gaussian noise to apply to waveform. Set to 0 if you want only real noise.
real_noise_file = 'noise_120326.npy' # Must be a numpy.savez_compressed file containing 1 numpy array (row per channel) of noise data
# Set to None or False if you don't want to use real noise
real_noise_sample_size = 150 # Data is concatenated in noise file: this specifies original sample size. We'll take these samples and shuffle them.
zle_padding = 500 * ns # For cheap_zle: padding in an pulse left of the first and right of the last photon
# Slow control data
pressure = 2.22 * bar # xenon:xenon1t:analysis:maxime:liquidlevelandpressureanalysis, also in slow control / run database
temperature = (-89.32 + 273.15) * K # InsideBell temperature, xenon:xenon100:analysis:stability_run10
anode_voltage = 4.0 * kV # Slow control / run database (early run10)
# PMT characteristics
pmt_transit_time_mean = 50 * ns # PLACEHOLDER - PMT handbook upper limit for linear focussed pmt type
# Not a big issue I think, this merely shifts the entire waveform. Could even put it 0.
pmt_transit_time_spread = 0.8 * ns # xenon:xenon100:pmtdatasheets, Room temperature
pmt_rise_time = 1.8 * ns # xenon:xenon100:pmtdatasheets, Room temperature
pmt_fall_time = 6.7 * ns # PLACEHOLDER - Can't find this! Now chosen 3.7 * rise time, as for Lung et al. 2012 (X1T PMTs)
# Note this pmt pulse shape is probably not accurate, even besides the uncertain parameters, the amplifier & digitizer also shape the pulse.
# Guillaume's fadc.py / spe.py has 4ns as the RC time for the digitizer, so not a big effect?
# Currently only used for s1 time structure calculations:
drift_field = 530 * V /cm # TODO add ref ('The xenon100 experiment'?)
liquid_density = 3 * g / cm**3 # PLACEHOLDER
# S1
maximum_recombination_time = 50 * ns # Prevents crazy recombination times from tail of hyperbolic distribution
s1_detection_efficiency = 1 #0.08 # % photons detected, NSort
singlet_lifetime_liquid = 3.1 * ns # Nest 2014 p2
triplet_lifetime_liquid = 24 * ns # Nest 2014 p2
s1_ER_recombination_fraction = 0.9
#s1_ER_recombination_fraction = 0.6 # Only used for primary/secondary split, we don't do yield calculations here!
# Nest 2011 p4 for E = about 500 V/cm and LET 10 MeV cm^2 /g (which acc to Chepel&Araujo is for 30 keV ER (higher E, less rec.)
s1_ER_primary_singlet_fraction = 1/(1+1/0.17) # Nest 2014 p2, converted from s/t ratio to s fraction. 0.17 +-0.05
s1_ER_secondary_singlet_fraction = 1/(1+1/0.8) # Nest 2014 p2, assuming gamma-induced ER. 0.8 +- 0.2
s1_NR_singlet_fraction = 1/(1+1/7.8) # Nest 2014 page 2. 7.8 +- 1.5
# S2 electron drift and extraction
drift_velocity_liquid_above_gate = 0.272*cm/us # From the single electron paper
diffusion_constant_liquid = 12*cm**(2)/s # Sorensen 2011, longitudinal diffusion. Ethan's code uses 70*cm**(2)/s! (0.007*mm**2/us)
electron_trapping_time = 140*ns # Nest 2014, but was obtained through fitting data
electron_extraction_yield = 1 # "above 0.96" xenon:xenon100:analysis:maxime:s2afterpulses
gate_to_anode_distance = 5 * mm # See e.g. single electron paper, several other places
# S2 electroluminescence
gas_drift_velocity_slope = 0.54 * mm / us / Td # Fit to Brooks et al 1982 in the 5 Td - 40 Td range
elr_gas_gap_length = 4.0 * mm # Jelle: Fit to Xenon100 single-e S2s xenon:xenon100:analysis:single_e_waveform_model
# Xenon100 Analysis paper, page 4, "h_g ~ 2.5 mm"
s2_secondary_sc_gain_density = 19.7/(4.0*mm) # "secondary scintillation gain" per length unit. 19.7 from NSort. This automatically includes detection efficiencies.
lxe_dielectric_constant = 1.874 # Wikipedia (which cites some chemistry handbook), unitless
# Distance from anode where field becomes wire-like (~1/r) rather than uniform:
anode_field_domination_distance = 0.65 * mm # Jelle: Fit to Xenon100 single-e S2s xenon:xenon100:analysis:single_e_waveform_model
# Distance from anode where field stops:
anode_wire_radius = 125/2 * um # GPlante p 98
singlet_lifetime_gas = 5.88*ns # Nest 2014. +- 5.5 (!!)
triplet_lifetime_gas = 115*ns # Jelle: Fit to Xenon100 single-e S2s xenon:xenon100:analysis:single_e_waveform_model
# Nest 2014: 100.1*ns +- 7.9
singlet_fraction_gas = 0 # Jelle: Fit to Xenon100 single-e S2s xenon:xenon100:analysis:single_e_waveform_model
# Light distribution
s2_lce_map = 's2_xy_lce_map_XENON100_Xerawdp0.4.5.json.gz'
s2_lce_map_zoom_factor = 2 # Upsample LCE map by this factor (in both dimensions, so memory usage grows quadratically)
s2_mean_area_fraction_top = 0.555 # S2 asymmetry average = 0.11, top fraction = (1 + A)/2. Todo: check / add ref!
# Compensate for channel 1 and 2 problem visible in LED calibration, but not in real data
adjust_noise_amplitude = {'1': 0.5, '2': 0.5} # Multiply noise amplitude in specific channels with this amount
# Global settings, passed to every plugin
[DEFAULT]
tpc_name = "XENON100"
tpc_length = 30.5 * cm # G. Plante page 95
tpc_radius = 15.3 * cm # G. Plante page 95
# Signal generatio nsettings
electron_lifetime_liquid = 450 * us # AmBe Run12 mean value, see e.g. xenon1t:sim:notes:morana:ambe:nest
drift_velocity_liquid = 1.73 * um / ns # Andrea says 1.73 um/ns. Ethan's code has 1.8 mm/us.
# Time in the event at which trigger occurs. Set to None or leave out if there is no trigger
trigger_time_in_event = 200 * us # G. Plante page 114
pmt_0_is_fake = True
# Detector specification
# PlotChannelWaveform2D expects the detector names' lexical order to be the same as the channel order
channels_in_detector = {
'tpc': list(range(0, 178+1)),
'veto': list(range(179, 242+1)),
}
n_channels = 242 + 1 # +1 for the fake pmt 0
# PMT numbers for tpc, specified as lists
# Remember python range does not include endpoint!
# PMT 0 does not exist, its gain is set to 0 later
channels_top = list(range(0, 98 + 1))
channels_bottom = list(range(99, 178 + 1))
# PMT mappings - daq (module, digitizer channel) -> pmt number
pmt_mappings = {'(54, 0)': 1,
'(54, 1)': 2,
'(54, 2)': 3,
'(54, 3)': 4,
'(54, 4)': 5,
'(54, 5)': 6,
'(54, 6)': 7,
'(54, 7)': 8,
'(78, 0)': 131,
'(78, 1)': 132,
'(78, 2)': 133,
'(78, 3)': 134,
'(78, 4)': 135,
'(78, 5)': 136,
'(78, 6)': 137,
'(78, 7)': 138,
'(79, 0)': 139,
'(79, 1)': 140,
'(79, 2)': 141,
'(79, 3)': 142,
'(79, 4)': 143,
'(79, 5)': 144,
'(79, 6)': 145,
'(79, 7)': 146,
'(80, 0)': 235,
'(80, 1)': 236,
'(80, 2)': 237,
'(80, 3)': 238,
'(80, 4)': 239,
'(80, 5)': 240,
'(80, 6)': 241,
'(80, 7)': 242,
'(89, 0)': 123,
'(89, 1)': 124,
'(89, 2)': 125,
'(89, 3)': 126,
'(89, 4)': 127,
'(89, 5)': 128,
'(89, 6)': 129,
'(89, 7)': 130,
'(95, 0)': 147,
'(95, 1)': 148,
'(95, 2)': 149,
'(95, 3)': 150,
'(95, 4)': 151,
'(95, 5)': 152,
'(95, 6)': 153,
'(95, 7)': 154,
'(102, 0)': 16,
'(102, 1)': 17,
'(102, 2)': 18,
'(102, 3)': 19,
'(102, 4)': 20,
'(102, 5)': 21,
'(102, 6)': 22,
'(102, 7)': 23,
'(106, 0)': 9,
'(106, 1)': 10,
'(106, 2)': 11,
'(106, 3)': 12,
'(106, 4)': 13,
'(106, 5)': 14,
'(106, 6)': 15,
'(107, 0)': 24,
'(107, 1)': 25,
'(107, 2)': 26,
'(107, 3)': 27,
'(107, 4)': 28,
'(107, 5)': 29,
'(107, 6)': 30,
'(108, 0)': 219,
'(108, 1)': 220,
'(108, 2)': 221,
'(108, 3)': 222,
'(108, 4)': 223,
'(108, 5)': 224,
'(108, 6)': 225,
'(108, 7)': 226,
'(110, 0)': 211,
'(110, 1)': 212,
'(110, 2)': 213,
'(110, 3)': 214,
'(110, 4)': 215,
'(110, 5)': 216,
'(110, 6)': 217,
'(110, 7)': 218,
'(115, 0)': 31,
'(115, 1)': 32,
'(115, 2)': 33,
'(115, 3)': 34,
'(115, 4)': 35,
'(115, 5)': 36,
'(115, 6)': 37,
'(115, 7)': 38,
'(117, 0)': 187,
'(117, 1)': 188,
'(117, 2)': 189,
'(117, 3)': 190,
'(117, 4)': 191,
'(117, 5)': 192,
'(117, 6)': 193,
'(117, 7)': 194,
'(121, 0)': 39,
'(121, 1)': 40,
'(121, 2)': 41,
'(121, 3)': 42,
'(121, 4)': 43,
'(121, 5)': 44,
'(121, 6)': 45,
'(121, 7)': 46,
'(124, 0)': 55,
'(124, 1)': 56,
'(124, 2)': 57,
'(124, 3)': 58,
'(124, 4)': 59,
'(124, 5)': 60,
'(124, 6)': 61,
'(127, 0)': 227,
'(127, 1)': 228,
'(127, 2)': 229,
'(127, 3)': 230,
'(127, 4)': 231,
'(127, 5)': 232,
'(127, 6)': 233,
'(127, 7)': 234,
'(136, 0)': 99,
'(136, 1)': 100,
'(136, 2)': 101,
'(136, 3)': 102,
'(136, 4)': 103,
'(136, 5)': 104,
'(136, 6)': 105,
'(136, 7)': 106,
'(139, 0)': 62,
'(139, 1)': 63,
'(139, 2)': 64,
'(139, 3)': 65,
'(139, 4)': 66,
'(139, 5)': 67,
'(140, 0)': 68,
'(140, 1)': 69,
'(140, 2)': 70,
'(140, 3)': 71,
'(140, 4)': 72,
'(140, 5)': 73,
'(140, 6)': 74,
'(143, 0)': 155,
'(143, 1)': 156,
'(143, 2)': 157,
'(143, 3)': 158,
'(143, 4)': 159,
'(143, 5)': 160,
'(143, 6)': 161,
'(143, 7)': 162,
'(144, 0)': 75,
'(144, 1)': 76,
'(144, 2)': 77,
'(144, 3)': 78,
'(144, 4)': 79,
'(144, 5)': 80,
'(144, 6)': 81,
'(144, 7)': 82,
'(147, 0)': 91,
'(147, 1)': 92,
'(147, 2)': 93,
'(147, 3)': 94,
'(147, 4)': 95,
'(147, 5)': 96,
'(147, 6)': 97,
'(147, 7)': 98,
'(150, 0)': 163,
'(150, 1)': 164,
'(150, 2)': 165,
'(150, 3)': 166,
'(150, 4)': 167,
'(150, 5)': 168,
'(150, 6)': 169,
'(150, 7)': 170,
'(153, 0)': 179,
'(153, 1)': 180,
'(153, 2)': 181,
'(153, 3)': 182,
'(153, 4)': 183,
'(153, 5)': 184,
'(153, 6)': 185,
'(153, 7)': 186,
'(154, 0)': 195,
'(154, 1)': 196,
'(154, 2)': 197,
'(154, 3)': 198,
'(154, 4)': 199,
'(154, 5)': 200,
'(154, 6)': 201,
'(154, 7)': 202,
'(156, 0)': 203,
'(156, 1)': 204,
'(156, 2)': 205,
'(156, 3)': 206,
'(156, 4)': 207,
'(156, 5)': 208,
'(156, 6)': 209,
'(156, 7)': 210,
'(160, 0)': 171,
'(160, 1)': 172,
'(160, 2)': 173,
'(160, 3)': 174,
'(160, 4)': 175,
'(160, 5)': 176,
'(160, 6)': 177,
'(160, 7)': 178,
'(162, 0)': 107,
'(162, 1)': 108,
'(162, 2)': 109,
'(162, 3)': 110,
'(162, 4)': 111,
'(162, 5)': 112,
'(162, 6)': 113,
'(162, 7)': 114,
'(165, 0)': 115,
'(165, 1)': 116,
'(165, 2)': 117,
'(165, 3)': 118,
'(165, 4)': 119,
'(165, 5)': 120,
'(165, 6)': 121,
'(165, 7)': 122,
'(167, 0)': 83,
'(167, 1)': 84,
'(167, 2)': 85,
'(167, 3)': 86,
'(167, 4)': 87,
'(167, 5)': 88,
'(167, 6)': 89,
'(167, 7)': 90,
'(171, 0)': 47,
'(171, 1)': 48,
'(171, 2)': 49,
'(171, 3)': 50,
'(171, 4)': 51,
'(171, 5)': 52,
'(171, 6)': 53,
'(171, 7)': 54}
# PMT locations taken from Marc Schumann's pmtpattern code. Agrees also
# with the top PMT locations used by xerawdp.
# Note: don't forget the units...
pmt_locations = [
{'x': 0.000 * cm, 'y': 0.000 * cm}, # 0
{'x': -16.684 * cm, 'y': 0.000 * cm}, # 1
{'x': -16.319 * cm, 'y': 3.469 * cm}, # 2
{'x': -15.242 * cm, 'y': 6.786 * cm}, # 3
{'x': -13.498 * cm, 'y': 9.807 * cm}, # 4
{'x': -11.164 * cm, 'y': 12.399 * cm}, # 5
{'x': -8.342 * cm, 'y': 14.449 * cm}, # 6
{'x': -5.156 * cm, 'y': 15.867 * cm}, # 7
{'x': -1.744 * cm, 'y': 16.593 * cm}, # 8
{'x': 1.744 * cm, 'y': 16.593 * cm}, # 9
{'x': 5.156 * cm, 'y': 15.867 * cm}, # 10
{'x': 8.342 * cm, 'y': 14.449 * cm}, # 11
{'x': 11.164 * cm, 'y': 12.399 * cm}, # 12
{'x': 13.498 * cm, 'y': 9.807 * cm}, # 13
{'x': 15.242 * cm, 'y': 6.786 * cm}, # 14
{'x': 16.319 * cm, 'y': 3.469 * cm}, # 15
{'x': 16.684 * cm, 'y': 0.000 * cm}, # 16
{'x': 16.319 * cm, 'y': -3.469 * cm}, # 17
{'x': 15.242 * cm, 'y': -6.786 * cm}, # 18
{'x': 13.498 * cm, 'y': -9.807 * cm}, # 19
{'x': 11.164 * cm, 'y': -12.399 * cm}, # 20
{'x': 8.342 * cm, 'y': -14.449 * cm}, # 21
{'x': 5.156 * cm, 'y': -15.867 * cm}, # 22
{'x': 1.744 * cm, 'y': -16.593 * cm}, # 23
{'x': -1.744 * cm, 'y': -16.593 * cm}, # 24
{'x': -5.156 * cm, 'y': -15.867 * cm}, # 25
{'x': -8.342 * cm, 'y': -14.449 * cm}, # 26
{'x': -11.164 * cm, 'y': -12.399 * cm}, # 27
{'x': -13.498 * cm, 'y': -9.807 * cm}, # 28
{'x': -15.242 * cm, 'y': -6.786 * cm}, # 29
{'x': -16.319 * cm, 'y': -3.469 * cm}, # 30
{'x': -13.653 * cm, 'y': 0.000 * cm}, # 31
{'x': -13.188 * cm, 'y': 3.534 * cm}, # 32
{'x': -11.824 * cm, 'y': 6.827 * cm}, # 33
{'x': -9.654 * cm, 'y': 9.654 * cm}, # 34
{'x': -6.827 * cm, 'y': 11.824 * cm}, # 35
{'x': -3.534 * cm, 'y': 13.188 * cm}, # 36
{'x': 0.000 * cm, 'y': 13.653 * cm}, # 37
{'x': 3.534 * cm, 'y': 13.188 * cm}, # 38
{'x': 6.827 * cm, 'y': 11.824 * cm}, # 39
{'x': 9.654 * cm, 'y': 9.654 * cm}, # 40
{'x': 11.824 * cm, 'y': 6.827 * cm}, # 41
{'x': 13.188 * cm, 'y': 3.534 * cm}, # 42
{'x': 13.653 * cm, 'y': 0.000 * cm}, # 43
{'x': 13.188 * cm, 'y': -3.534 * cm}, # 44
{'x': 11.824 * cm, 'y': -6.827 * cm}, # 45
{'x': 9.654 * cm, 'y': -9.654 * cm}, # 46
{'x': 6.827 * cm, 'y': -11.824 * cm}, # 47
{'x': 3.534 * cm, 'y': -13.188 * cm}, # 48
{'x': 0.000 * cm, 'y': -13.653 * cm}, # 49
{'x': -3.534 * cm, 'y': -13.188 * cm}, # 50
{'x': -6.827 * cm, 'y': -11.824 * cm}, # 51
{'x': -9.654 * cm, 'y': -9.654 * cm}, # 52
{'x': -11.824 * cm, 'y': -6.827 * cm}, # 53
{'x': -13.188 * cm, 'y': -3.534 * cm}, # 54
{'x': -10.620 * cm, 'y': 0.000 * cm}, # 55
{'x': -10.100 * cm, 'y': 3.282 * cm}, # 56
{'x': -8.592 * cm, 'y': 6.242 * cm}, # 57
{'x': -6.242 * cm, 'y': 8.592 * cm}, # 58
{'x': -3.282 * cm, 'y': 10.100 * cm}, # 59
{'x': 0.000 * cm, 'y': 10.620 * cm}, # 60
{'x': 3.282 * cm, 'y': 10.100 * cm}, # 61
{'x': 6.242 * cm, 'y': 8.592 * cm}, # 62
{'x': 8.592 * cm, 'y': 6.242 * cm}, # 63
{'x': 10.100 * cm, 'y': 3.282 * cm}, # 64
{'x': 10.620 * cm, 'y': 0.000 * cm}, # 65
{'x': 10.100 * cm, 'y': -3.282 * cm}, # 66
{'x': 8.592 * cm, 'y': -6.242 * cm}, # 67
{'x': 6.242 * cm, 'y': -8.592 * cm}, # 68
{'x': 3.282 * cm, 'y': -10.100 * cm}, # 69
{'x': 0.000 * cm, 'y': -10.620 * cm}, # 70
{'x': -3.282 * cm, 'y': -10.100 * cm}, # 71
{'x': -6.242 * cm, 'y': -8.592 * cm}, # 72
{'x': -8.592 * cm, 'y': -6.242 * cm}, # 73
{'x': -10.100 * cm, 'y': -3.282 * cm}, # 74
{'x': -7.587 * cm, 'y': 0.000 * cm}, # 75
{'x': -6.876 * cm, 'y': 3.206 * cm}, # 76
{'x': -4.775 * cm, 'y': 5.896 * cm}, # 77
{'x': -1.707 * cm, 'y': 7.393 * cm}, # 78
{'x': 1.577 * cm, 'y': 7.421 * cm}, # 79
{'x': 4.671 * cm, 'y': 5.979 * cm}, # 80
{'x': 6.819 * cm, 'y': 3.326 * cm}, # 81
{'x': 7.587 * cm, 'y': 0.000 * cm}, # 82
{'x': 6.876 * cm, 'y': -3.206 * cm}, # 83
{'x': 4.775 * cm, 'y': -5.896 * cm}, # 84
{'x': 1.707 * cm, 'y': -7.393 * cm}, # 85
{'x': -1.577 * cm, 'y': -7.421 * cm}, # 86
{'x': -4.671 * cm, 'y': -5.979 * cm}, # 87
{'x': -6.819 * cm, 'y': -3.326 * cm}, # 88
{'x': -4.500 * cm, 'y': 0.000 * cm}, # 89
{'x': -3.000 * cm, 'y': 3.000 * cm}, # 90
{'x': 0.000 * cm, 'y': 4.500 * cm}, # 91
{'x': 3.000 * cm, 'y': 3.000 * cm}, # 92
{'x': 4.500 * cm, 'y': 0.000 * cm}, # 93
{'x': 3.000 * cm, 'y': -3.000 * cm}, # 94
{'x': 0.000 * cm, 'y': -4.500 * cm}, # 95
{'x': -3.000 * cm, 'y': -3.000 * cm}, # 96
{'x': -1.500 * cm, 'y': 0.000 * cm}, # 97
{'x': 1.500 * cm, 'y': 0.000 * cm}, # 98
{'x': -4.115 * cm, 'y': 12.344 * cm}, # 99
{'x': -1.371 * cm, 'y': 12.344 * cm}, # 100
{'x': 1.371 * cm, 'y': 12.344 * cm}, # 101
{'x': 4.115 * cm, 'y': 12.344 * cm}, # 102
{'x': -8.229 * cm, 'y': 9.600 * cm}, # 103
{'x': -5.486 * cm, 'y': 9.600 * cm}, # 104
{'x': -2.743 * cm, 'y': 9.600 * cm}, # 105
{'x': -0.000 * cm, 'y': 9.600 * cm}, # 106
{'x': 2.743 * cm, 'y': 9.600 * cm}, # 107
{'x': 5.486 * cm, 'y': 9.600 * cm}, # 108
{'x': 8.229 * cm, 'y': 9.600 * cm}, # 109
{'x': -10.972 * cm, 'y': 6.858 * cm}, # 110
{'x': -8.229 * cm, 'y': 6.858 * cm}, # 111
{'x': -5.486 * cm, 'y': 6.858 * cm}, # 112
{'x': -2.743 * cm, 'y': 6.858 * cm}, # 113
{'x': -0.000 * cm, 'y': 6.858 * cm}, # 114
{'x': 2.743 * cm, 'y': 6.858 * cm}, # 115
{'x': 5.486 * cm, 'y': 6.858 * cm}, # 116
{'x': 8.229 * cm, 'y': 6.858 * cm}, # 117
{'x': 10.972 * cm, 'y': 6.858 * cm}, # 118
{'x': -12.344 * cm, 'y': 4.115 * cm}, # 119
{'x': -9.600 * cm, 'y': 4.115 * cm}, # 120
{'x': -6.858 * cm, 'y': 4.115 * cm}, # 121
{'x': -4.115 * cm, 'y': 4.115 * cm}, # 122
{'x': -1.371 * cm, 'y': 4.115 * cm}, # 123
{'x': 1.371 * cm, 'y': 4.115 * cm}, # 124
{'x': 4.115 * cm, 'y': 4.115 * cm}, # 125
{'x': 6.858 * cm, 'y': 4.115 * cm}, # 126
{'x': 9.600 * cm, 'y': 4.115 * cm}, # 127
{'x': 12.344 * cm, 'y': 4.115 * cm}, # 128
{'x': -12.344 * cm, 'y': 1.371 * cm}, # 129
{'x': -9.600 * cm, 'y': 1.371 * cm}, # 130
{'x': -6.858 * cm, 'y': 1.371 * cm}, # 131
{'x': -4.115 * cm, 'y': 1.371 * cm}, # 132
{'x': -1.371 * cm, 'y': 1.371 * cm}, # 133
{'x': 1.371 * cm, 'y': 1.371 * cm}, # 134
{'x': 4.115 * cm, 'y': 1.371 * cm}, # 135
{'x': 6.858 * cm, 'y': 1.371 * cm}, # 136
{'x': 9.600 * cm, 'y': 1.371 * cm}, # 137
{'x': 12.344 * cm, 'y': 1.371 * cm}, # 138
{'x': -12.344 * cm, 'y': -1.371 * cm}, # 139
{'x': -9.600 * cm, 'y': -1.371 * cm}, # 140
{'x': -6.858 * cm, 'y': -1.371 * cm}, # 141
{'x': -4.115 * cm, 'y': -1.371 * cm}, # 142
{'x': -1.371 * cm, 'y': -1.371 * cm}, # 143
{'x': 1.371 * cm, 'y': -1.371 * cm}, # 144
{'x': 4.115 * cm, 'y': -1.371 * cm}, # 145
{'x': 6.858 * cm, 'y': -1.371 * cm}, # 146
{'x': 9.600 * cm, 'y': -1.371 * cm}, # 147
{'x': 12.344 * cm, 'y': -1.371 * cm}, # 148
{'x': -12.344 * cm, 'y': -4.115 * cm}, # 149
{'x': -9.600 * cm, 'y': -4.115 * cm}, # 150
{'x': -6.858 * cm, 'y': -4.115 * cm}, # 151
{'x': -4.115 * cm, 'y': -4.115 * cm}, # 152
{'x': -1.371 * cm, 'y': -4.115 * cm}, # 153
{'x': 1.371 * cm, 'y': -4.115 * cm}, # 154
{'x': 4.115 * cm, 'y': -4.115 * cm}, # 155
{'x': 6.858 * cm, 'y': -4.115 * cm}, # 156
{'x': 9.600 * cm, 'y': -4.115 * cm}, # 157
{'x': 12.344 * cm, 'y': -4.115 * cm}, # 158
{'x': -10.972 * cm, 'y': -6.858 * cm}, # 159
{'x': -8.229 * cm, 'y': -6.858 * cm}, # 160
{'x': -5.486 * cm, 'y': -6.858 * cm}, # 161
{'x': -2.743 * cm, 'y': -6.858 * cm}, # 162
{'x': -0.000 * cm, 'y': -6.858 * cm}, # 163
{'x': 2.743 * cm, 'y': -6.858 * cm}, # 164
{'x': 5.486 * cm, 'y': -6.858 * cm}, # 165
{'x': 8.229 * cm, 'y': -6.858 * cm}, # 166
{'x': 10.972 * cm, 'y': -6.858 * cm}, # 167
{'x': -8.229 * cm, 'y': -9.600 * cm}, # 168
{'x': -5.486 * cm, 'y': -9.600 * cm}, # 169
{'x': -2.743 * cm, 'y': -9.600 * cm}, # 170
{'x': -0.000 * cm, 'y': -9.600 * cm}, # 171
{'x': 2.743 * cm, 'y': -9.600 * cm}, # 172
{'x': 5.486 * cm, 'y': -9.600 * cm}, # 173
{'x': 8.229 * cm, 'y': -9.600 * cm}, # 174
{'x': -4.115 * cm, 'y': -12.344 * cm}, # 175
{'x': -1.371 * cm, 'y': -12.344 * cm}, # 176
{'x': 1.371 * cm, 'y': -12.344 * cm}, # 177
{'x': 4.115 * cm, 'y': -12.344 * cm}, # 178
{'x': -19.715 * cm, 'y': 0.000 * cm}, # 179
{'x': -19.353 * cm, 'y': 3.762 * cm}, # 180
{'x': -18.279 * cm, 'y': 7.385 * cm}, # 181
{'x': -16.534 * cm, 'y': 10.738 * cm}, # 182
{'x': -13.941 * cm, 'y': 13.941 * cm}, # 183
{'x': -11.025 * cm, 'y': 16.344 * cm}, # 184
{'x': -7.703 * cm, 'y': 18.148 * cm}, # 185
{'x': -4.099 * cm, 'y': 19.284 * cm}, # 186
{'x': 0.000 * cm, 'y': 19.715 * cm}, # 187
{'x': 3.762 * cm, 'y': 19.353 * cm}, # 188
{'x': 7.385 * cm, 'y': 18.279 * cm}, # 189
{'x': 10.738 * cm, 'y': 16.534 * cm}, # 190
{'x': 13.941 * cm, 'y': 13.941 * cm}, # 191
{'x': 16.344 * cm, 'y': 11.025 * cm}, # 192
{'x': 18.148 * cm, 'y': 7.703 * cm}, # 193
{'x': 19.284 * cm, 'y': 4.099 * cm}, # 194
{'x': 19.715 * cm, 'y': 0.000 * cm}, # 195
{'x': 19.353 * cm, 'y': -3.762 * cm}, # 196
{'x': 18.279 * cm, 'y': -7.385 * cm}, # 197
{'x': 16.534 * cm, 'y': -10.738 * cm}, # 198
{'x': 13.941 * cm, 'y': -13.941 * cm}, # 199
{'x': 11.025 * cm, 'y': -16.344 * cm}, # 200
{'x': 7.703 * cm, 'y': -18.148 * cm}, # 201
{'x': 4.099 * cm, 'y': -19.284 * cm}, # 202
{'x': 0.000 * cm, 'y': -19.715 * cm}, # 203
{'x': -3.762 * cm, 'y': -19.353 * cm}, # 204
{'x': -7.385 * cm, 'y': -18.279 * cm}, # 205
{'x': -10.738 * cm, 'y': -16.534 * cm}, # 206
{'x': -13.941 * cm, 'y': -13.941 * cm}, # 207
{'x': -16.344 * cm, 'y': -11.025 * cm}, # 208
{'x': -18.148 * cm, 'y': -7.703 * cm}, # 209
{'x': -19.284 * cm, 'y': -4.099 * cm}, # 210
{'x': -19.715 * cm, 'y': 0.000 * cm}, # 211
{'x': -19.353 * cm, 'y': 3.762 * cm}, # 212
{'x': -18.279 * cm, 'y': 7.385 * cm}, # 213
{'x': -16.534 * cm, 'y': 10.738 * cm}, # 214
{'x': -13.941 * cm, 'y': 13.941 * cm}, # 215
{'x': -11.025 * cm, 'y': 16.344 * cm}, # 216
{'x': -7.703 * cm, 'y': 18.148 * cm}, # 217
{'x': -4.099 * cm, 'y': 19.284 * cm}, # 218
{'x': -0.000 * cm, 'y': 19.715 * cm}, # 219
{'x': 3.762 * cm, 'y': 19.353 * cm}, # 220
{'x': 7.385 * cm, 'y': 18.279 * cm}, # 221
{'x': 10.738 * cm, 'y': 16.534 * cm}, # 222
{'x': 13.941 * cm, 'y': 13.941 * cm}, # 223
{'x': 16.344 * cm, 'y': 11.025 * cm}, # 224
{'x': 18.148 * cm, 'y': 7.703 * cm}, # 225
{'x': 19.284 * cm, 'y': 4.099 * cm}, # 226
{'x': 19.715 * cm, 'y': 0.000 * cm}, # 227
{'x': 19.353 * cm, 'y': -3.762 * cm}, # 228
{'x': 18.279 * cm, 'y': -7.385 * cm}, # 229
{'x': 16.534 * cm, 'y': -10.738 * cm}, # 230
{'x': 13.941 * cm, 'y': -13.941 * cm}, # 231
{'x': 11.025 * cm, 'y': -16.344 * cm}, # 232
{'x': 7.703 * cm, 'y': -18.148 * cm}, # 233
{'x': 4.099 * cm, 'y': -19.284 * cm}, # 234
{'x': -0.000 * cm, 'y': -19.715 * cm}, # 235
{'x': -3.762 * cm, 'y': -19.353 * cm}, # 236
{'x': -7.385 * cm, 'y': -18.279 * cm}, # 237
{'x': -10.738 * cm, 'y': -16.534 * cm}, # 238
{'x': -13.941 * cm, 'y': -13.941 * cm}, # 239
{'x': -16.344 * cm, 'y': -11.025 * cm}, # 240
{'x': -18.148 * cm, 'y': -7.703 * cm}, # 241
{'x': -19.284 * cm, 'y': -4.099 * cm}, # 242
]
# PMT gains
# Extracted from Zurich's Xenon100 PMT gain database using examples/extract_gain
# File used: all120326_1544.gain
# A few of these gains are zero: we'll assume these PMTs are turned off.
# PMT 0 does not exist (real Xenon100 PMTs start from 0), so it gets gain 0.
gains = [
# 0 -- PMT zero is fake!
0,
# 1 # 2 # 3 # 4 # 5
2675000.0, 2958000.0, 1936000.0, 2326000.0, 1964000.0,
# 6 # 7 # 8 # 9 # 10
1971000.0, 2104000.0, 1999000.0, 0.0, 2102000.0,
# 11 # 12 # 13 # 14 # 15
2044000.0, 0.0, 2177000.0, 2180000.0, 2265000.0,
# 16 # 17 # 18 # 19 # 20
2293000.0, 2177000.0, 2331000.0, 2099000.0, 2096000.0,
# 21 # 22 # 23 # 24 # 25
1899000.0, 2111000.0, 1874000.0, 1948000.0, 2106000.0,
# 26 # 27 # 28 # 29 # 30
2121000.0, 1987000.0, 1889000.0, 2473000.0, 2161000.0,
# 31 # 32 # 33 # 34 # 35
2192000.0, 2329000.0, 1112000.0, 2157000.0, 2106000.0,
# 36 # 37 # 38 # 39 # 40
2182000.0, 2001000.0, 1921000.0, 0.0, 2121000.0,
# 41 # 42 # 43 # 44 # 45
1852000.0, 1878000.0, 2088000.0, 1974000.0, 1940000.0,
# 46 # 47 # 48 # 49 # 50
2134000.0, 2132000.0, 2018000.0, 2207000.0, 2237000.0,
# 51 # 52 # 53 # 54 # 55
2201000.0, 1985000.0, 2173000.0, 2126000.0, 2288000.0,
# 56 # 57 # 58 # 59 # 60
2140000.0, 2170000.0, 0.0, 2408000.0, 2253000.0,
# 61 # 62 # 63 # 64 # 65
2109000.0, 2134000.0, 1979000.0, 2267000.0, 2149000.0,
# 66 # 67 # 68 # 69 # 70
2164000.0, 2077000.0, 2170000.0, 2223000.0, 2325000.0,
# 71 # 72 # 73 # 74 # 75
2122000.0, 2343000.0, 2312000.0, 2090000.0, 1944000.0,
# 76 # 77 # 78 # 79 # 80
2091000.0, 1948000.0, 1974000.0, 2098000.0, 2134000.0,
# 81 # 82 # 83 # 84 # 85
2184000.0, 1992000.0, 2150000.0, 1980000.0, 1878000.0,
# 86 # 87 # 88 # 89 # 90
2093000.0, 2162000.0, 1901000.0, 2120000.0, 2059000.0,
# 91 # 92 # 93 # 94 # 95
2281000.0, 2214000.0, 2143000.0, 1943000.0, 1934000.0,
# 96 # 97 # 98 # 99 # 100
2410000.0, 2227000.0, 1843000.0, 1881000.0, 0.0,
# 101 # 102 # 103 # 104 # 105
1965000.0, 2368000.0, 1938000.0, 1981000.0, 0.0,
# 106 # 107 # 108 # 109 # 110
1732000.0, 2091000.0, 1932000.0, 2080000.0, 2145000.0,
# 111 # 112 # 113 # 114 # 115
1932000.0, 1806000.0, 1939000.0, 1765000.0, 2111000.0,
# 116 # 117 # 118 # 119 # 120
2001000.0, 1917000.0, 2082000.0, 2043000.0, 2027000.0,
# 121 # 122 # 123 # 124 # 125
1833000.0, 1972000.0, 2030000.0, 2139000.0, 1946000.0,
# 126 # 127 # 128 # 129 # 130
1988000.0, 1967000.0, 2190000.0, 2217000.0, 2092000.0,
# 131 # 132 # 133 # 134 # 135
2252000.0, 2170000.0, 2014000.0, 1953000.0, 1997000.0,
# 136 # 137 # 138 # 139 # 140
1966000.0, 1854000.0, 2098000.0, 1639000.0, 2229000.0,
# 141 # 142 # 143 # 144 # 145
1759000.0, 1987000.0, 1911000.0, 1858000.0, 1653000.0,
# 146 # 147 # 148 # 149 # 150
2036000.0, 1716000.0, 0.0, 1907000.0, 2165000.0,
# 151 # 152 # 153 # 154 # 155
1833000.0, 2126000.0, 2119000.0, 1763000.0, 1990000.0,
# 156 # 157 # 158 # 159 # 160
1869000.0, 1874000.0, 2030000.0, 2152000.0, 1917000.0,
# 161 # 162 # 163 # 164 # 165
1749000.0, 1668000.0, 2085000.0, 1886000.0, 1692000.0,
# 166 # 167 # 168 # 169 # 170
2094000.0, 1446000.0, 1773000.0, 2023000.0, 1737000.0,
# 171 # 172 # 173 # 174 # 175
1859000.0, 1887000.0, 1872000.0, 2013000.0, 2078000.0,
# 176 # 177 # 178 # 179 # 180
1796000.0, 0.0, 1770000.0, 2018000.0, 2220000.0,
# 181 # 182 # 183 # 184 # 185
2544000.0, 978900.0, 1795000.0, 1445000.0, 1988000.0,
# 186 # 187 # 188 # 189 # 190
2032000.0, 1915000.0, 2143000.0, 2096000.0, 0.0,
# 191 # 192 # 193 # 194 # 195
0.0, 2081000.0, 1730000.0, 1637000.0, 0.0,
# 196 # 197 # 198 # 199 # 200
1936000.0, 1665000.0, 1958000.0, 1976000.0, 1975000.0,
# 201 # 202 # 203 # 204 # 205
1885000.0, 2101000.0, 2014000.0, 1997000.0, 2001000.0,
# 206 # 207 # 208 # 209 # 210
1993000.0, 1915000.0, 2113000.0, 1985000.0, 1813000.0,
# 211 # 212 # 213 # 214 # 215
2156000.0, 2041000.0, 2060000.0, 1890000.0, 2162000.0,
# 216 # 217 # 218 # 219 # 220
1810000.0, 1988000.0, 1983000.0, 1946000.0, 1941000.0,
# 221 # 222 # 223 # 224 # 225
2134000.0, 1829000.0, 1996000.0, 0.0, 1903000.0,
# 226 # 227 # 228 # 229 # 230
2096000.0, 2150000.0, 1990000.0, 1949000.0, 1870000.0,
# 231 # 232 # 233 # 234 # 235
2070000.0, 1946000.0, 1902000.0, 2128000.0, 0.0,
# 236 # 237 # 238 # 239 # 240
1946000.0, 1652000.0, 1986000.0, 1852000.0, 1909000.0,
# 241 # 242
1932000.0, 2013000.0,
]
# Sigmas of the 1pe peak in the gain spectrum, from same file
gain_sigmas = [
# 0 -- PMT zero is fake!
0,
# 1 # 2 # 3 # 4 # 5
931800.0, 891900.0, 786300.0, 1035000.0, 947800.0,
# 6 # 7 # 8 # 9 # 10
932700.0, 1167000.0, 976400.0, 0.0, 1154000.0,
# 11 # 12 # 13 # 14 # 15
1168000.0, 0.0, 956500.0, 1235000.0, 1250000.0,
# 16 # 17 # 18 # 19 # 20
1611000.0, 1203000.0, 1084000.0, 1333000.0, 1006000.0,
# 21 # 22 # 23 # 24 # 25
1051000.0, 935600.0, 1014000.0, 1281000.0, 1147000.0,
# 26 # 27 # 28 # 29 # 30
608800.0, 941200.0, 959700.0, 1464000.0, 1180000.0,
# 31 # 32 # 33 # 34 # 35
1186000.0, 1244000.0, 542500.0, 1146000.0, 951200.0,
# 36 # 37 # 38 # 39 # 40
1274000.0, 1075000.0, 1181000.0, 0.0, 1153000.0,
# 41 # 42 # 43 # 44 # 45
895700.0, 921500.0, 1106000.0, 997000.0, 1030000.0,
# 46 # 47 # 48 # 49 # 50
1116000.0, 1167000.0, 1112000.0, 1172000.0, 1327000.0,
# 51 # 52 # 53 # 54 # 55
1134000.0, 978500.0, 1070000.0, 1114000.0, 1229000.0,
# 56 # 57 # 58 # 59 # 60
1102000.0, 1065000.0, 0.0, 1193000.0, 1194000.0,
# 61 # 62 # 63 # 64 # 65
1142000.0, 1117000.0, 1079000.0, 1333000.0, 1079000.0,
# 66 # 67 # 68 # 69 # 70
1165000.0, 1080000.0, 1107000.0, 1293000.0, 1248000.0,
# 71 # 72 # 73 # 74 # 75
1303000.0, 1227000.0, 1224000.0, 1048000.0, 930300.0,
# 76 # 77 # 78 # 79 # 80
1351000.0, 1056000.0, 997500.0, 1203000.0, 1210000.0,
# 81 # 82 # 83 # 84 # 85
1033000.0, 1050000.0, 1300000.0, 1042000.0, 1110000.0,
# 86 # 87 # 88 # 89 # 90
1106000.0, 1326000.0, 1185000.0, 1261000.0, 1136000.0,
# 91 # 92 # 93 # 94 # 95
1212000.0, 1210000.0, 1281000.0, 1270000.0, 1141000.0,
# 96 # 97 # 98 # 99 # 100
1374000.0, 1220000.0, 993000.0, 1221000.0, 0.0,
# 101 # 102 # 103 # 104 # 105
1010000.0, 1248000.0, 924900.0, 975400.0, 0.0,
# 106 # 107 # 108 # 109 # 110
908800.0, 1026000.0, 1039000.0, 985600.0, 1290000.0,
# 111 # 112 # 113 # 114 # 115
1271000.0, 1054000.0, 941500.0, 988600.0, 1087000.0,
# 116 # 117 # 118 # 119 # 120
1030000.0, 880400.0, 870500.0, 991400.0, 899400.0,
# 121 # 122 # 123 # 124 # 125
878000.0, 991800.0, 1090000.0, 1113000.0, 936000.0,
# 126 # 127 # 128 # 129 # 130
957700.0, 940200.0, 1219000.0, 1326000.0, 1234000.0,
# 131 # 132 # 133 # 134 # 135
1212000.0, 1184000.0, 974400.0, 942600.0, 929500.0,
# 136 # 137 # 138 # 139 # 140
1023000.0, 1037000.0, 950000.0, 1064000.0, 1151000.0,
# 141 # 142 # 143 # 144 # 145
881900.0, 960500.0, 879600.0, 907300.0, 948400.0,
# 146 # 147 # 148 # 149 # 150
834400.0, 966800.0, 0.0, 926600.0, 906700.0,
# 151 # 152 # 153 # 154 # 155
872100.0, 859100.0, 976100.0, 747200.0, 839000.0,
# 156 # 157 # 158 # 159 # 160
943700.0, 1224000.0, 1071000.0, 1123000.0, 814200.0,
# 161 # 162 # 163 # 164 # 165
888900.0, 1000000.0, 991900.0, 873800.0, 917900.0,
# 166 # 167 # 168 # 169 # 170
875000.0, 736700.0, 1083000.0, 822400.0, 1020000.0,
# 171 # 172 # 173 # 174 # 175
1088000.0, 1069000.0, 863000.0, 925700.0, 939000.0,
# 176 # 177 # 178 # 179 # 180
1114000.0, 0.0, 927100.0, 851500.0, 920800.0,
# 181 # 182 # 183 # 184 # 185
1285000.0, 874700.0, 1045000.0, 746600.0, 1415000.0,
# 186 # 187 # 188 # 189 # 190
895900.0, 1191000.0, 982100.0, 900900.0, 0.0,
# 191 # 192 # 193 # 194 # 195
0.0, 1268000.0, 1362000.0, 1308000.0, 0.0,
# 196 # 197 # 198 # 199 # 200
1287000.0, 1156000.0, 1169000.0, 1411000.0, 1127000.0,
# 201 # 202 # 203 # 204 # 205
876800.0, 1350000.0, 1131000.0, 1249000.0, 1477000.0,
# 206 # 207 # 208 # 209 # 210
1218000.0, 1122000.0, 1093000.0, 1275000.0, 894900.0,
# 211 # 212 # 213 # 214 # 215
928500.0, 1045000.0, 1009000.0, 894700.0, 885000.0,
# 216 # 217 # 218 # 219 # 220
947500.0, 991300.0, 1087000.0, 1160000.0, 1138000.0,
# 221 # 222 # 223 # 224 # 225
1124000.0, 1156000.0, 1090000.0, 0.0, 944000.0,
# 226 # 227 # 228 # 229 # 230
1113000.0, 1335000.0, 1182000.0, 983200.0, 1153000.0,
# 231 # 232 # 233 # 234 # 235
1335000.0, 1344000.0, 1103000.0, 1288000.0, 0.0,
# 236 # 237 # 238 # 239 # 240
1173000.0, 1226000.0, 942800.0, 1117000.0, 1182000.0,
# 241 # 242
1042000.0, 1096000.0,
]
[NeuralNet.PosRecNeuralNet]
# Number of neurons in the hidden layer
# Used to check if the number of weights and biases are correct
hidden_layer_neurons = 30
# Neural network outputs position in this unit. Will be converted to pax units.
nn_output_unit = mm
# Biases used by the neuron activation functions, taken from nn-missing_9_12_39_58.c
# Input neuron's don't use a bias, so there should be hidden_layer_neurons + 2 biases
# In nn-missing_9_12_39_58.c input neurons had biases, but they were unusued (random [-1,1]?)
biases = [ 0.89283, 0.89691, -0.93983, -1.07323,
-0.8617 , -1.32323, 0.88486, 1.25378, 0.12575, -0.83402,
-0.76243, 1.08109, -0.75161, 1.03818, 0.26586, 0.37391,
0.31579, -1.16333, -1.06323, -0.80685, 1.24958, -0.00742,
1.14161, 1.24745, -0.10054, 0.4158 , 1.07301, 0.6385 ,
0.23595, 1.05783, 0.96483, -1.72249]
# Weights of the connections, taken from nn-missing_9_12_39_58.c
# The first n_top_pmts * hidden_layer_neurons are for the connections from the input to the hidden layer:
# The first 98 for the first hidden layer neuron, the next 98 of the second hidden layer neuron, etc
# The next hidden_layer_neurons * 2 are for the connections from the hidden to the output layer
# The first hidden_layer_neurons for the x-output neuron, the next hidden_layer_neurons for the y-output neuron, etc
weights = [
2.05493, 0.92636, -0.07739, -5.70877, -2.15994, -1.68442, -1.74991, -3.63212,
0.1146, -2.58555, -2.62432, -0.86721, -2.96008, -2.16185, 0.18565, 0.36426,
1.32725, 1.99249, 2.66945, 1.79211, -0.33068, 2.76363, 1.09043, 1.32375,
1.63071, 1.79625, 0.9494, -0.84298, 2.36222, 0.27201, 0.75649, 2.49042,
-0.92752, -5.57481, -5.11131, -3.1658, -2.43971, -2.61611, 0.0204, -2.58531,
-3.08329, -3.82233, -0.40126, 2.24753, 1.88408, 1.06742, 1.3493, -0.12143,
0.28362, 0.22002, 0.1733, 0.06058, 1.31592, 1.30374, 0.69033, 1.99221,
-1.40047, 0.00156, -5.75333, -5.08705, -4.67929, -4.14427, -3.32261, -4.61612,
-1.08627, 2.78948, 0.97622, 2.01232, 2.19251, 0.67192, 1.84289, 1.8475, 1.7543,
0.56183, 1.24545, 2.75256, -1.77259, -0.4908, -1.42492, -2.49187, -5.3287,
-1.3724, 3.57786, 1.21876, 1.31709, 3.1033, 1.57654, 2.95589, 0.97362, 3.204,
2.88453, -2.37381, -0.52906, 1.34845, 1.05559, 0.87583, 1.81941, 3.21877,
1.43748, 1.65846, 0.42018, 1.86595, 1.33972, 1.23476, 1.28782, 0.72069,
-0.56474, -0.86144, -3.35013, -0.03383, -2.69741, -2.86131, -1.80047, -2.87929,
-2.69217, -2.4848, -3.67064, -2.42264, -2.25647, -4.03773, -2.92267, 1.76435,
2.04828, 1.58122, 1.60254, 1.89119, 1.09514, 1.42131, 0.73343, 0.88368,
0.84474, 0.86234, 0.86201, 0.43219, 0.94879, 1.71763, 0.30748, -4.18852,
-2.77342, -3.07396, -2.3184, -3.03057, -2.80951, -2.91635, -3.08412, -5.5619,
-0.74518, 3.9755, 1.17151, 1.58207, 0.92559, 0.2255, 1.1963, 1.21842, 2.43398,
-0.93156, 2.33953, 1.71595, -0.07261, -2.74021, -4.36349, -5.50416, -5.49852,
-4.39466, -3.68777, -4.51162, -4.51409, -1.51865, 1.76933, 0.33857, 2.69841,
2.29655, 2.33987, 1.87652, 2.90099, 0.67494, 1.77809, -1.59501, -1.15055,
0.21857, -0.04932, -0.64459, -0.7841, 2.07596, 1.25851, 1.58492, 2.04072,
0.94734, 1.35939, 3.5689, 3.41048, 2.32757, 1.83878, 2.0014, 1.31464, 0.47773,
0.8291, 0.55422, -0.48389, -1.14893, 0.33774, -6.09616, -1.15041, 4.43764,
-0.15532, 4.68058, 6.07366, -0.65949, 6.57685, 4.33021, 5.83894, 1.70635,
3.50734, 6.66992, 1.1622, -1.78812, 2.30537, 0.73714, 1.03665, -2.6978,
-0.37175, 1.19918, -1.0086, 0.64276, -0.40735, 0.25309, -0.60544, -0.41422,
0.18069, 1.52312, -3.24029, 2.84407, 3.70851, 4.94112, 0.57913, 2.11343,
0.56148, -0.78994, 3.20052, 3.06479, 2.17007, -3.12543, 0.54186, -1.47439,
-0.02879, 1.02874, -0.66741, 0.75127, 0.51892, 0.36454, 0.79795, 1.66235,
0.35742, 0.88059, -3.76658, 0.24887, 1.79626, 2.83274, -1.80493, -3.32972,
-1.02671, -4.3179, -4.26355, -1.09372, -1.74034, 1.53553, -0.03855, 0.98382,
-0.56386, 1.11726, 0.70666, 0.07836, -3.60454, -2.5103, -3.22379, -7.31841,
-3.64629, -4.88984, -2.5733, 3.02269, -0.5127, -0.45519, 1.85344, -2.52977,
1.63422, -4.00975, -3.11833, -0.89919, -5.26051, 2.73604, -1.90026, 0.91214,
-1.74015, 1.79374, -0.7839, -2.44296, -0.306, -2.75093, 1.67354, 0.87866,
4.00013, 3.5341, 0.69297, 3.74312, 3.08947, -0.4588, 1.48949, 2.72438, 3.79018,
2.96984, 3.21476, 4.10788, 6.5633, 1.22366, -0.51689, -1.41498, -0.39634,
-3.3472, -1.07924, -0.69891, -1.86456, -0.6334, -1.3506, -1.03199, -0.52416,
-0.41431, -0.59201, -1.33503, -1.72303, 3.64089, 5.28397, 3.55092, 0.85984,
5.03841, 3.70591, 3.69833, 4.149, 5.1432, 2.82628, 1.77557, -2.89258, -1.01736,
-0.00278, 0.14397, -0.68838, -0.02492, -0.51071, -0.21256, -1.28562, -1.64894,
-3.19531, 0.97157, 0.24302, 1.58204, 4.05524, 2.81764, 0.94074, 1.30125,
0.73577, -0.55402, -0.06123, -1.45868, -1.96723, -1.49383, -1.73205, 0.1076,
-1.45533, -0.60758, -0.11945, -1.6039, -1.93833, -1.99417, 1.70366, -1.57976,
-4.712, -4.04855, -0.31238, -1.88028, -1.43033, -1.54117, -1.18115, -3.535,
-1.868, -1.0212, -2.84762, -1.12519, -1.503, -0.71986, -1.99997, -0.84435,
-2.92716, -2.04643, 6.14439, 6.3825, 4.90892, 5.57035, 5.32348, 4.91583,
4.65557, 5.22237, 0.76719, 6.35304, 13.67053, 0.01745, -0.78189, -2.75848,
-2.06314, 0.3763, 0.69809, -2.35539, 2.02033, 1.67268, 0.48864, -0.03484,
-0.03205, -0.33143, 0.27928, -2.33713, -0.31681, 1.28232, 0.27467, 1.24633,
-0.96309, -0.69952, -0.88223, -0.12653, 1.73243, 0.35691, 0.4683, 3.3198,
-0.46926, 1.93957, -3.92713, 0.57167, -1.08848, -1.23007, 0.14865, -1.81206,
-0.62358, -0.739, 1.31691, -0.12551, 0.97852, 0.0307, 0.36463, -0.47248,
0.70964, -2.14158, -6.21973, -0.15637, -4.84179, -0.03061, 1.92909, 1.48417,
-3.38741, -0.20533, 0.23011, -0.08076, -0.00349, -0.08049, -0.10649, 1.21401,
0.89878, -0.53007, -0.30038, -5.09889, 3.33855, 1.46888, -3.82387, -2.29029,
-1.51487, -1.80343, 0.19447, -0.62742, -0.87609, -0.10855, -1.90236, -4.78419,
-1.35487, -6.08832, -0.69638, 1.64257, -2.53529, -1.78477, -4.27315, -0.53286,
-0.85339, -0.74947, -1.70608, -3.57672, 5.55443, 4.51682, 5.43733, 3.25261,
4.92204, 4.72114, 4.24342, 0.26455, -0.15986, -5.8148, 7.59677, -0.5536,
0.51509, 1.04214, 1.31196, 0.47559, 0.39974, 1.82798, 0.12257, 1.48918,
1.80256, 2.45938, 1.33301, -2.19842, -2.97587, -6.67834, -5.71298, -4.42533,
2.19806, 5.04689, 2.022, 2.59097, 2.73042, 2.82033, 1.3836, -1.71088, -5.54238,
-6.74591, 0.63462, 1.26606, 0.77018, 1.9975, 1.6783, 1.0385, 2.44552, 2.543,
1.09506, 3.07401, -0.49673, -1.96084, -3.74622, -4.6651, -2.20224, 3.2258,
-2.41992, -2.31652, -6.94555, -0.19056, -3.52206, -4.87756, -9.01532, -3.35688,
2.97659, 0.87744, 1.20522, 0.89623, 1.67063, 3.40953, 1.29073, 2.49608, 2.35,
0.11914, -6.30263, -5.3453, -5.08513, -6.40832, -6.77855, -1.48043, 1.77188,
-0.39312, -0.30895, 0.61542, 2.58078, -0.47202, 0.3857, 2.22643, 1.74457,
-7.10406, -3.55734, 1.17486, 4.01168, 2.06692, 1.4474, 1.65711, 1.88018,
-2.79321, -1.1998, 4.68739, -2.70689, -3.69133, -1.72101, -4.237, -2.07569,
3.84586, 0.85898, 2.63201, 0.01103, 1.59658, 0.43651, 0.14737, 0.33271,
2.01717, 0.83741, -0.2098, 2.31423, 1.70646, 0.44969, -0.58627, 1.26013,
-1.20358, -4.79591, -1.68411, -2.53173, -3.32994, -2.90888, -3.63638, -2.03214,
-3.80418, -2.89512, -2.85936, -4.87132, -1.33246, 6.05654, 2.28471, -0.65205,
0.84849, -0.05466, 2.50921, -1.04092, 0.23338, 1.32565, -0.34015, 0.2135,
2.45402, 3.09101, -1.11435, -5.32266, -1.68997, -2.27046, -3.4873, -3.04215,
-2.34693, -4.20661, -3.77492, -1.12436, 0.80441, 2.32792, 2.79601, 2.93304,
2.58529, 1.38062, 2.63633, 0.72175, 2.51243, 2.20223, 3.09686, 0.51905,
-2.70546, -5.86103, -2.17885, -2.47166, -2.85913, -4.08445, -0.11603, 3.82175,
1.55325, 1.7557, 2.18946, 1.63056, 2.66644, 1.92493, 1.7307, 3.11315, 0.32583,
-4.872, -2.57211, -2.30856, 1.69169, 0.82842, 0.69496, 2.74927, 3.09344,
3.61529, -0.66053, 4.7566, 1.04801, -0.08721, -0.68521, 1.42817, 0.61742,
-0.14554, 0.70417, -1.16585, -2.45343, -0.10265, -2.70908, -1.86876, -0.58715,
2.81401, -2.47207, -5.29529, -1.8444, -2.14431, -2.84946, -1.86677, -1.88586,
-5.06853, -4.48385, -2.80776, 3.64595, 5.28144, 5.44263, -0.57837, -0.76381,
-0.7124, 0.67902, -0.35366, 0.04386, -1.39589, -0.84996, -0.85573, 2.09311,
-0.29496, 4.95337, 0.56453, 1.56543, 2.88706, -1.96362, -4.1847, -2.7479,
-3.64716, -4.12477, -1.13199, -1.33668, 2.60744, 3.32449, 1.75552, -1.49981,
0.73553, -1.27907, -1.42302, -1.18681, 0.61544, 0.79715, -0.79416, -2.5138,
5.8801, -2.31851, 2.3982, 0.88249, -2.13926, -3.73237, -1.36781, -2.1828,
3.89276, 3.49023, -1.05882, -1.2832, 3.17102, 0.93372, 2.14919, 1.9577,
3.86366, -0.81659, 0.3385, 2.09788, 2.26227, -3.05568, -2.70478, 4.06836,
3.40815, -4.48194, -2.64789, 2.11881, -2.89103, -0.76763, -0.67738, 1.64314,
0.6195, 2.395, 1.13893, 0.22936, 0.60016, -0.1155, -1.42551, -1.64052,
-4.47337, 2.65422, 2.55187, 2.13893, 2.57819, -2.75235, 0.08723, 2.20188,
1.6766, 0.34554, 0.57507, 1.5151, 1.60332, 2.28665, 3.84268, 1.75466, 2.69237,
1.15172, -0.28155, -1.42227, -1.60604, -1.74375, -2.52948, -2.13479, -1.94634,
-2.08931, -2.89865, -2.61766, -2.11221, -1.73814, -0.83848, 0.29968, 0.97402,
2.96274, 2.04583, 1.91664, -0.51525, 2.22247, 2.18074, 1.4734, 1.43246,
0.72985, 1.03047, 2.55035, -1.10319, -3.37908, -1.83562, -1.53187, -2.0098,
-1.61099, -1.74582, -1.1723, -2.0124, -1.64565, -3.08753, -0.96654, 2.62871,
1.77399, 2.67533, 1.90112, 1.85422, 2.70167, 2.58959, 2.63956, 4.34147,
0.40969, -4.06436, -3.1442, -2.64153, -2.58833, -3.05946, -2.06215, -2.78256,
-3.05691, -0.14405, 3.24387, 2.38556, 2.57474, 1.22044, 2.08193, 5.3872,
-0.67967, -3.22986, -1.57721, -2.40855, -2.93492, -3.16654, 0.8139, 2.51495,
1.28052, 5.80786, -0.83731, -4.35371, -2.8402, -2.41212, -0.60334, -0.56416,
-0.46191, -0.83035, -0.8883, -0.39496, -0.20008, -0.61221, 0.83044, -0.997,
1.54848, 0.48968, -0.01574, 1.51981, -1.4332, 2.89194, 4.42584, 3.55539,
5.34567, 5.34856, 5.41014, 5.79346, 3.75278, 5.13919, 4.32107, 4.31268,
5.25549, 4.03244, 4.91588, -0.29055, -1.86427, -1.06197, -0.73345, 0.71211,
0.69027, 0.12605, 0.93826, 0.65154, -1.07943, 0.25199, -1.51703, 1.41456,
-0.14109, 0.80665, 1.81983, 1.36069, 1.28343, 0.71867, 1.8179, 0.34527,
0.24311, -0.65549, 1.9419, -0.33225, 0.60158, 0.37659, 1.0496, 0.467, -0.19889,
0.36592, 0.17139, -0.81724, -1.31459, -0.07801, -3.9996, -3.54007, -3.05173,
-3.15674, -3.6798, -1.60512, -2.53306, -2.77279, -1.2987, -6.00449, -2.20251,
-4.2321, -2.13047, 1.18868, 1.05991, 1.14005, 0.43853, 0.42365, -1.48469,
-3.35034, -2.54516, -1.83545, -2.48713, 0.45345, -5.82833, -5.40064, -3.7851,
0.08152, 2.20177, -0.2457, -0.93849, -3.95727, -1.24757, -3.67444, -0.09931,
2.43026, 1.96308, 0.64327, 0.71157, 1.82354, 2.50433, 2.76126, 4.14769,
-0.94368, 4.36976, 1.96745, -0.72566, -1.59951, -1.9174, -1.75092, -1.79065,
-1.61344, -0.44682, 0.08684, -2.47371, 0.82704, -1.05536, 0.96145, -2.36334,
-1.08366, -1.58099, 1.78159, 2.43678, 2.49142, 1.75151, 2.85084, 3.34495,
5.40444, 4.05012, 3.08421, 3.81422, 4.09455, -0.28485, 0.26049, -4.47407,
0.01104, -0.42882, -0.06541, -0.10722, -0.8632, -0.0911, -0.50095, -0.14736,
-0.79225, -1.73046, -2.41215, 1.93876, 3.8265, 2.46886, 3.45829, 1.31372,
0.30394, -0.40838, 1.26222, 1.03679, -2.8102, -3.07147, 0.65796, -1.91783,
-1.60471, 0.41382, 0.17039, -0.08944, -0.5324, -3.71262, -4.12422, 1.56288,
4.49004, 4.17817, 2.30751, -3.97398, -0.59138, -2.62504, -1.75521, -1.74857,
-2.76388, -2.19118, 0.31663, 0.20776, -3.08574, -3.23907, 2.38241, 0.71136,
-1.48706, -1.90553, -1.15995, -1.62553, -2.99514, -2.24109, -5.1031, -0.85948,
-2.02965, -1.41328, -2.54792, -2.2821, -2.3377, -2.75363, -1.54158, -2.8315,
-4.0539, -1.50306, 0.17413, -1.4617, -1.98083, -0.52462, -1.05712, -0.5721,
-0.67691, -0.58035, 1.8781, 0.167, -0.04213, 1.47516, -0.52573, -0.1906,
-1.3704, 0.66433, -1.6388, -4.42057, -3.99757, -4.467, -4.24728, -2.5316,
-3.77159, -4.09488, -2.92313, -3.12497, -3.3717, -1.93156, 1.79281, 3.53263,
-0.49757, 0.16245, 0.47082, 0.66972, 0.41125, -1.03667, 0.43934, -0.55653,
-0.18251, -0.44368, -0.23611, 1.09877, -0.63286, -1.62049, -0.03247, -3.11513,
-1.99051, -1.69124, 1.82955, 0.60502, 0.05813, 4.247, 4.46925, 2.44435,
0.82396, -0.93969, -1.12996, -0.0676, -1.24089, -1.95321, 2.01073, 1.11989,
1.00825, 2.05424, 4.49073, 1.73188, 5.76164, 3.50844, 4.181, 1.651, 1.57012,
-2.23075, -1.54836, -0.64741, 1.42485, 3.14146, 2.52179, 1.49627, -0.10197,
2.88171, 3.21389, 3.77959, 4.37829, 1.35221, 3.49907, -0.13232, -1.384,
-1.5205, 3.84703, -0.85033, 3.56933, 5.69129, 4.1853, 4.02399, 1.47016,
2.00142, -1.33928, -0.306, -0.29297, -3.24281, -4.07661, -0.46215, -3.98544,
-1.41629, -1.28636, -1.04462, -1.97155, -0.04841, 0.78669, 3.23605, 2.45647,
1.79198, 1.56579, 1.20185, 2.40744, 3.01335, 2.96843, 2.94955, 2.08972,
2.31858, 2.21181, -0.15512, -0.29904, -2.20224, -2.76205, 0.65431, -0.58296,
-0.07764, -0.91801, 1.46127, -0.79129, -0.69313, -0.55906, -0.44745, -3.42054,
1.37992, 4.87406, 3.72284, 3.90719, 3.27571, 2.64693, 2.70081, 2.28209,
2.15081, 2.56865, -2.33965, -3.09012, -0.40034, -0.69942, -4.87996, -5.26152,
-0.17852, -0.57029, -2.46044, -3.75503, -2.0827, -0.17133, 1.07512, 0.94858,
1.82324, 2.05516, 3.01986, 4.24404, 6.39181, 1.00692, -3.92104, -1.77295,
-2.57603, -2.95304, -3.69151, -2.78162, -4.10832, -1.95045, -3.50363, -2.1194,
-3.27523, 1.15137, 1.05404, -2.2172, -1.79699, -0.16005, 3.91104, -2.54182,
-2.63845, -2.80233, -3.78492, -4.84319, -4.94107, -0.43145, 0.03589, -0.781,
0.11279, 0.01245, 1.36412, -1.23474, -1.68936, -0.26118, 5.49837, -6.48935,
-0.7096, -5.89413, -1.92867, -2.04818, -3.07532, -1.29516, -1.37209, -2.32343,
-2.67771, -1.65336, -3.30936, -1.47043, 2.34386, 0.58621, 0.55214, -0.1112,
-0.4248, -0.44396, 0.21701, -0.03093, -0.08972, -0.18645, -0.41148, -1.48662,
0.55831, 3.63264, 6.63129, -0.6499, -1.3027, -3.44807, -3.34776, -3.28381,
-4.1403, -4.09503, -2.69911, -4.02994, -3.01103, 1.12422, 1.76681, -0.04628,
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-1.85891, 0.98656, 2.13995, 3.03435, 3.08202, 2.54797, 2.7987, 1.41019, 1.8994,
-0.78957, -4.92386, -3.73032, -3.69251, -3.2709, -3.84095, -0.3063, 2.24814,
2.2428, 2.21266, 2.24599, 3.31068, -0.71537, -3.02572, -1.53224, -0.81774,
3.93321, 2.74273, -1.03888, -0.69141, 2.80057, 2.89669, 0.95013, -0.02601,
2.62203, 2.34622, 1.13198, -0.03699, 2.01032, 1.37689, -0.72448, 0.5328,
2.05585, 2.74363, -0.39109, -2.97533, -3.80135, -3.27915, -1.98071, -3.70871,
-2.49118, -2.10892, -2.38706, -2.18955, -2.75272, -2.71216, -0.74462, -3.33469,
-4.61839, -0.50497, 3.84662, 1.17609, 1.94042, 1.32774, 1.03301, 1.38139,
1.61179, -0.10425, 1.79719, 1.74973, 3.77723, -0.37339, -4.95555, -3.42642,
-3.30158, -2.25685, -2.58562, -2.04707, -2.23659, -1.79241, -1.22889, -3.04823,
-5.83301, -0.43852, 4.16566, 2.59217, -0.26189, 2.7035, 2.30363, 1.75037,
2.7182, 2.33108, 2.91146, 0.33496, 1.17133, -1.9686, -2.86721, -2.7084,
-2.29512, -1.96403, -3.30011, -2.52237, -2.54625, 1.50824, 1.97314, 2.75205,
2.38544, 2.19122, 2.22088, 3.22098, 4.92459, 0.00306, -4.01066, -2.72837,
-4.20632, -3.52076, 1.26478, 3.9567, 3.24085, 1.858, 2.49864, 3.50685, -0.6891,
-0.09233, -0.01044, 5.1132, 2.51167, 0.41528, 0.53697, 2.03567, 1.38487,
1.3119, -0.36639, 0.21435, 0.61629, -0.8023, 1.3828, 1.41108, -0.26537,
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-2.84438, -2.90179, -3.24763, -0.65203, 2.04105, 3.72178, 3.85015, 1.5123,
1.31258, 1.17769, -0.10791, -0.40241, -0.36569, -0.30306, 0.80425, -0.37148,
1.62677, 1.97281, -0.91247, 1.85394, -0.49302, -3.64728, -3.22082, -2.38874,
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1.3242, -0.61682, 3.23529, 1.06294, 2.3079, -0.80074, 1.1969, 2.22098, 2.83055,
1.77316, 1.36303, -0.79662, -4.44942, -3.24016, -3.92862, -3.2139, -1.0077,
-1.73936, 0.208, 1.55955, 1.85464, 1.89073, -3.01616, 3.99889, 3.54752,
1.61988, -0.58074, 2.38716, -0.69976, -5.18925, -3.62823, 0.50946, 2.57458,
1.73329, 0.9524, 3.911, -0.23675, -0.08911, 0.52023, 1.23378, -1.56839,
-0.92231, 1.43248, 3.27169, 3.06036, 1.79769, -2.38705, -10.10739, 5.65569,
8.56688, -2.15854, -6.88785, 10.60706, -6.91998, 7.58699, 2.41143, -7.41914,
10.35986, -4.24221, -10.97352, -1.8979, 3.0797, -0.4785, 3.99186, -7.17299,
-2.68266, 0.47653, -11.77108, -2.19055, 9.52314, 9.04334, -3.99081, 4.32208,
-11.6703, 0.7666, -9.5206, -11.55083, 0.95402, 4.88098, 7.4705, 7.80808,
2.87936, 6.37226, 5.03745, 7.03276, -7.75866, 5.69802, -4.01009, -7.56241,
1.785, -13.49067, -10.12091, -9.79866, 3.43612, 1.98439, -13.44981, 5.00885,
4.28017, -5.46968, -3.70556, 7.26915, -1.77214, 7.58859, 7.63089, 11.19632,
6.44065
]
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