From e14d497b9c7f8850b8795e75936de4d136b001a6 Mon Sep 17 00:00:00 2001
From: "gregory.ashton" <gregory.ashton@ligo.org>
Date: Wed, 30 Nov 2016 13:21:00 +0100
Subject: [PATCH] Adds initial results from AllSky MC study
Also adds data from directed study
---
Paper/AllSkyMC/MCResults.txt | 693 +++
Paper/AllSkyMC/generate_data.py | 102 +
Paper/AllSkyMC/generate_table.py | 87 +
Paper/AllSkyMC/plot_data.py | 73 +
Paper/AllSkyMC/repeat.sh | 12 +
Paper/AllSkyMC/submitfile | 12 +
Paper/AllSky_run_setup.tex | 8 +
Paper/DirectedMC/MCResults.txt | 7442 ++++++++++++++++++++++++++++++
Paper/allsky_recovery.png | Bin 0 -> 52016 bytes
9 files changed, 8429 insertions(+)
create mode 100644 Paper/AllSkyMC/MCResults.txt
create mode 100644 Paper/AllSkyMC/generate_data.py
create mode 100644 Paper/AllSkyMC/generate_table.py
create mode 100644 Paper/AllSkyMC/plot_data.py
create mode 100755 Paper/AllSkyMC/repeat.sh
create mode 100644 Paper/AllSkyMC/submitfile
create mode 100644 Paper/AllSky_run_setup.tex
create mode 100644 Paper/DirectedMC/MCResults.txt
create mode 100644 Paper/allsky_recovery.png
diff --git a/Paper/AllSkyMC/MCResults.txt b/Paper/AllSkyMC/MCResults.txt
new file mode 100644
index 0000000..561dd86
--- /dev/null
+++ b/Paper/AllSkyMC/MCResults.txt
@@ -0,0 +1,693 @@
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+400.0 5.00000000e-26 -1.64977097e-06 -2.51174146e-13 1.05000000e+01 2.88038750e+01
diff --git a/Paper/AllSkyMC/generate_data.py b/Paper/AllSkyMC/generate_data.py
new file mode 100644
index 0000000..9ec61c1
--- /dev/null
+++ b/Paper/AllSkyMC/generate_data.py
@@ -0,0 +1,102 @@
+import pyfstat
+import numpy as np
+import os
+import sys
+
+ID = sys.argv[1]
+outdir = sys.argv[2]
+
+label = 'run_{}'.format(ID)
+data_label = '{}_data'.format(label)
+results_file_name = '{}/MCResults_{}.txt'.format(outdir, ID)
+
+# Properties of the GW data
+sqrtSX = 2e-23
+tstart = 1000000000
+Tspan = 100*86400
+tend = tstart + Tspan
+
+# Fixed properties of the signal
+F0_center = 30
+F1_center = 1e-10
+F2 = 0
+tref = .5*(tstart+tend)
+
+
+VF0 = VF1 = 100
+DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan)
+DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
+
+depths = np.linspace(100, 400, 7)
+
+run_setup = [((100, 0), 27, False),
+ ((100, 0), 15, False),
+ ((100, 0), 8, False),
+ ((100, 0), 4, False),
+ ((50, 50), 1, False)]
+
+DeltaAlpha = 0.05
+DeltaDelta = 0.05
+
+for depth in depths:
+ h0 = sqrtSX / float(depth)
+ r = np.random.uniform(0, 1)
+ theta = np.random.uniform(0, 2*np.pi)
+ F0 = F0_center + 3*np.sqrt(r)*np.cos(theta)/(np.pi**2 * Tspan**2)
+ F1 = F1_center + 45*np.sqrt(r)*np.sin(theta)/(4*np.pi**2 * Tspan**4)
+ Alpha = np.random.uniform(0, 2*np.pi)
+ Delta = np.arccos(2*np.random.uniform(0, 1)-1)-np.pi/2
+ fAlpha = np.random.uniform(0, 1)
+ Alpha_min = Alpha - DeltaAlpha*(1-fAlpha)
+ Alpha_max = Alpha + DeltaAlpha*fAlpha
+ fDelta = np.random.uniform(0, 1)
+ Delta_min = Delta - DeltaDelta*(1-fDelta)
+ Delta_max = Delta + DeltaDelta*fDelta
+
+ psi = np.random.uniform(-np.pi/4, np.pi/4)
+ phi = np.random.uniform(0, 2*np.pi)
+ cosi = np.random.uniform(-1, 1)
+
+ data = pyfstat.Writer(
+ label=data_label, outdir=outdir, tref=tref,
+ tstart=tstart, F0=F0, F1=F1, F2=F2, duration=Tspan, Alpha=Alpha,
+ Delta=Delta, h0=h0, sqrtSX=sqrtSX, psi=psi, phi=phi, cosi=cosi,
+ detector='H1,L1')
+ data.make_data()
+ predicted_twoF = data.predict_fstat()
+
+ theta_prior = {'F0': {'type': 'unif',
+ 'lower': F0-DeltaF0/2.,
+ 'upper': F0+DeltaF0/2.},
+ 'F1': {'type': 'unif',
+ 'lower': F1-DeltaF1/2.,
+ 'upper': F1+DeltaF1/2.},
+ 'F2': F2,
+ 'Alpha': {'type': 'unif',
+ 'lower': Alpha_min,
+ 'upper': Alpha_max},
+ 'Delta': {'type': 'unif',
+ 'lower': Delta_min,
+ 'upper': Delta_max},
+ }
+
+ ntemps = 1
+ log10temperature_min = -1
+ nwalkers = 100
+
+ mcmc = pyfstat.MCMCFollowUpSearch(
+ label=label, outdir=outdir,
+ sftfilepath='{}/*{}*sft'.format(outdir, data_label),
+ theta_prior=theta_prior,
+ tref=tref, minStartTime=tstart, maxStartTime=tend,
+ nwalkers=nwalkers, ntemps=ntemps,
+ log10temperature_min=log10temperature_min)
+ mcmc.run(run_setup=run_setup, create_plots=False, log_table=False,
+ gen_tex_table=False)
+ d, maxtwoF = mcmc.get_max_twoF()
+ dF0 = F0 - d['F0']
+ dF1 = F1 - d['F1']
+ with open(results_file_name, 'a') as f:
+ f.write('{} {:1.8e} {:1.8e} {:1.8e} {:1.8e} {:1.8e}\n'
+ .format(depth, h0, dF0, dF1, predicted_twoF, maxtwoF))
+ os.system('rm {}/*{}*'.format(outdir, label))
diff --git a/Paper/AllSkyMC/generate_table.py b/Paper/AllSkyMC/generate_table.py
new file mode 100644
index 0000000..9b34fd6
--- /dev/null
+++ b/Paper/AllSkyMC/generate_table.py
@@ -0,0 +1,87 @@
+import pyfstat
+import numpy as np
+
+outdir = 'data'
+
+label = 'AllSky'
+data_label = '{}_data'.format(label)
+
+# Properties of the GW data
+sqrtSX = 2e-23
+tstart = 1000000000
+Tspan = 100*86400
+tend = tstart + Tspan
+
+# Fixed properties of the signal
+F0_center = 30
+F1_center = 1e-10
+F2 = 0
+tref = .5*(tstart+tend)
+
+
+VF0 = VF1 = 100
+DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan)
+DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
+
+depths = np.linspace(100, 400, 7)
+
+run_setup = [((100, 0), 27, False),
+ ((100, 0), 15, False),
+ ((100, 0), 8, False),
+ ((100, 0), 4, False),
+ ((50, 50), 1, False)]
+
+DeltaAlpha = 0.05
+DeltaDelta = 0.05
+
+depth = 100
+
+h0 = sqrtSX / float(depth)
+F0 = F0_center
+F1 = F1_center
+Alpha = 0
+Delta = 0
+Alpha_min = Alpha - DeltaAlpha/2
+Alpha_max = Alpha + DeltaAlpha/2
+Delta_min = Delta - DeltaDelta/2
+Delta_max = Delta + DeltaDelta/2
+
+psi = 0
+phi = 0
+cosi = 0
+
+data = pyfstat.Writer(
+ label=data_label, outdir=outdir, tref=tref,
+ tstart=tstart, F0=F0, F1=F1, F2=F2, duration=Tspan, Alpha=Alpha,
+ Delta=Delta, h0=h0, sqrtSX=sqrtSX, psi=psi, phi=phi, cosi=cosi,
+ detector='H1,L1')
+data.make_data()
+predicted_twoF = data.predict_fstat()
+
+theta_prior = {'F0': {'type': 'unif',
+ 'lower': F0-DeltaF0/2.,
+ 'upper': F0+DeltaF0/2.},
+ 'F1': {'type': 'unif',
+ 'lower': F1-DeltaF1/2.,
+ 'upper': F1+DeltaF1/2.},
+ 'F2': F2,
+ 'Alpha': {'type': 'unif',
+ 'lower': Alpha_min,
+ 'upper': Alpha_max},
+ 'Delta': {'type': 'unif',
+ 'lower': Delta_min,
+ 'upper': Delta_max},
+ }
+
+ntemps = 1
+log10temperature_min = -1
+nwalkers = 100
+
+mcmc = pyfstat.MCMCFollowUpSearch(
+ label=label, outdir=outdir,
+ sftfilepath='{}/*{}*sft'.format(outdir, data_label),
+ theta_prior=theta_prior,
+ tref=tref, minStartTime=tstart, maxStartTime=tend,
+ nwalkers=nwalkers, ntemps=ntemps,
+ log10temperature_min=log10temperature_min)
+mcmc.run(run_setup=run_setup)
diff --git a/Paper/AllSkyMC/plot_data.py b/Paper/AllSkyMC/plot_data.py
new file mode 100644
index 0000000..e2f6f45
--- /dev/null
+++ b/Paper/AllSkyMC/plot_data.py
@@ -0,0 +1,73 @@
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import os
+from tqdm import tqdm
+from oct2py import octave
+
+plt.style.use('paper')
+
+Tspan = 100 * 86400
+
+
+def Recovery(Tspan, Depth, twoFstar=60, detectors='H1,L1'):
+ numDetectors = len(detectors.split(','))
+ cmd = ("DetectionProbabilityStackSlide('Nseg', 1, 'Tdata', {},"
+ "'misHist', createDeltaHist(0), 'avg2Fth', {}, 'detectors', '{}',"
+ "'Depth', {})"
+ ).format(numDetectors*Tspan, twoFstar, detectors, Depth)
+ return octave.eval(cmd, verbose=False)
+
+
+def binomialConfidenceInterval(N, K, confidence=0.95):
+ cmd = '[fLow, fUpper] = binomialConfidenceInterval({}, {}, {})'.format(
+ N, K, confidence)
+ [l, u] = octave.eval(cmd, verbose=False, return_both=True)[0].split('\n')
+ return float(l.split('=')[1]), float(u.split('=')[1])
+
+results_file_name = 'MCResults.txt'
+
+df = pd.read_csv(
+ results_file_name, sep=' ', names=['depth', 'h0', 'dF0', 'dF1',
+ 'twoF_predicted', 'twoF'])
+
+twoFstar = 60
+depths = np.unique(df.depth.values)
+recovery_fraction = []
+recovery_fraction_CI = []
+for d in depths:
+ twoFs = df[df.depth == d].twoF.values
+ N = len(twoFs)
+ K = np.sum(twoFs > twoFstar)
+ print d, N, K
+ recovery_fraction.append(K/float(N))
+ [fLower, fUpper] = binomialConfidenceInterval(N, K)
+ recovery_fraction_CI.append([fLower, fUpper])
+
+yerr = np.abs(recovery_fraction - np.array(recovery_fraction_CI).T)
+fig, ax = plt.subplots()
+ax.errorbar(depths, recovery_fraction, yerr=yerr, fmt='sk', marker='s', ms=2,
+ capsize=1, capthick=0.5, elinewidth=0.5,
+ label='Monte-Carlo result')
+
+fname = 'analytic_data.txt'
+if os.path.isfile(fname):
+ depths_smooth, recovery_analytic = np.loadtxt(fname)
+else:
+ depths_smooth = np.linspace(10, 550, 100)
+ recovery_analytic = []
+ for d in tqdm(depths_smooth):
+ recovery_analytic.append(Recovery(Tspan, d, twoFstar))
+ np.savetxt(fname, np.array([depths_smooth, recovery_analytic]))
+depths_smooth = np.concatenate(([0], depths_smooth))
+recovery_analytic = np.concatenate(([1], recovery_analytic))
+ax.plot(depths_smooth, recovery_analytic, '-k', label='Theoretical maximum')
+
+
+ax.set_ylim(0, 1.05)
+ax.set_xlabel(r'Signal depth', size=10)
+ax.set_ylabel(r'Recovered fraction', size=10)
+ax.legend(loc=1, frameon=False)
+
+fig.tight_layout()
+fig.savefig('allsky_recovery.png')
diff --git a/Paper/AllSkyMC/repeat.sh b/Paper/AllSkyMC/repeat.sh
new file mode 100755
index 0000000..6ec0f9b
--- /dev/null
+++ b/Paper/AllSkyMC/repeat.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+
+. /home/gregory.ashton/lalsuite-install/etc/lalapps-user-env.sh
+export PATH="/home/gregory.ashton/anaconda2/bin:$PATH"
+export MPLCONFIGDIR=/home/gregory.ashton/.config/matplotlib
+
+rm /local/user/gregory.ashton/MCResults*txt
+for ((n=0;n<10;n++))
+do
+/home/gregory.ashton/anaconda2/bin/python generate_data.py "$1" /local/user/gregory.ashton --quite --no-template-counting
+done
+cp /local/user/gregory.ashton/MCResults*txt /home/gregory.ashton/PyFstat/Paper/AllSkyMC/CollectedOutput
diff --git a/Paper/AllSkyMC/submitfile b/Paper/AllSkyMC/submitfile
new file mode 100644
index 0000000..5af7178
--- /dev/null
+++ b/Paper/AllSkyMC/submitfile
@@ -0,0 +1,12 @@
+Executable= repeat.sh
+Arguments= $(Cluster)_$(Process)
+Universe=vanilla
+Input=/dev/null
+accounting_group = ligo.dev.o2.cw.explore.test
+Output=CollectedOutput/out.$(Process)
+Error=CollectedOutput/err.$(Process)
+Log=CollectedOutput/log.$(Process)
+request_cpus = 1
+request_memory = 16 GB
+
+Queue 10
diff --git a/Paper/AllSky_run_setup.tex b/Paper/AllSky_run_setup.tex
new file mode 100644
index 0000000..b54e483
--- /dev/null
+++ b/Paper/AllSky_run_setup.tex
@@ -0,0 +1,8 @@
+\begin{tabular}{c|cccccc}
+Stage & $\Nseg$ & $\Tcoh^{\rm days}$ &$\Nsteps$ & $\V$ & $\Vsky$ & $\Vpe$ \\ \hline
+0 & 27 & 3.7 & 100 & 1.0 & 1.0 & 1.0 \\
+1 & 15 & 6.7 & 100 & 1.0 & 1.0 & 1.0 \\
+2 & 8 & 12.5 & 100 & 1.0 & 1.0 & 1.0 \\
+3 & 4 & 25.0 & 100 & 1.0 & 1.0 & 1.0 \\
+4 & 1 & 100.0 & 50,50 & 1.0 & 1.0 & 1.0 \\
+\end{tabular}
diff --git a/Paper/DirectedMC/MCResults.txt b/Paper/DirectedMC/MCResults.txt
new file mode 100644
index 0000000..bcb595f
--- /dev/null
+++ b/Paper/DirectedMC/MCResults.txt
@@ -0,0 +1,7442 @@
+100.0 2.00000000e-25 -2.19465335e-10 2.66537345e-15 1.56400000e+02 1.78618546e+02
+150.0 1.33333333e-25 5.87482418e-09 -2.81695898e-16 2.68600000e+02 3.11732788e+02
+200.0 1.00000000e-25 -6.24926599e-09 -8.14451528e-15 3.30000000e+01 3.38921204e+01
+250.0 8.00000000e-26 -4.52202187e-09 1.67031992e-15 9.51000000e+01 1.02692680e+02
+300.0 6.66666667e-26 -5.15810683e-09 1.68701731e-14 6.63000000e+01 6.81395569e+01
+350.0 5.71428571e-26 1.86775379e-06 -2.37876459e-13 1.22000000e+01 2.23260117e+01
+400.0 5.00000000e-26 3.07087356e-06 3.62556474e-15 1.39000000e+01 2.97755280e+01
+100.0 2.00000000e-25 -2.16599716e-09 -3.82237684e-15 2.65100000e+02 2.64386414e+02
+150.0 1.33333333e-25 -3.63072417e-09 -1.80203328e-15 2.11300000e+02 2.01857880e+02
+200.0 1.00000000e-25 5.75444048e-09 -6.06733702e-15 3.31000000e+01 4.13978004e+01
+250.0 8.00000000e-26 -4.44060078e-09 -2.96929115e-15 3.48000000e+01 3.06079292e+01
+300.0 6.66666667e-26 1.41790935e-09 -9.73065284e-15 4.06000000e+01 4.66478424e+01
+350.0 5.71428571e-26 1.44146526e-08 3.29060962e-15 4.12000000e+01 4.62977943e+01
+400.0 5.00000000e-26 -4.14943234e-07 1.83479869e-13 1.05000000e+01 2.51504650e+01
+100.0 2.00000000e-25 4.43977655e-09 -2.16913514e-15 5.43200000e+02 6.00142822e+02
+150.0 1.33333333e-25 6.24113028e-09 1.28738622e-14 5.85000000e+01 3.70414391e+01
+200.0 1.00000000e-25 1.69545906e-08 -6.76449067e-15 5.85000000e+01 6.39265480e+01
+250.0 8.00000000e-26 -1.43921305e-07 -8.17630628e-14 3.58000000e+01 2.45765285e+01
+300.0 6.66666667e-26 -1.02755386e-08 -2.31766830e-14 6.53000000e+01 4.03263130e+01
+350.0 5.71428571e-26 5.44316947e-09 -8.90818937e-16 5.36000000e+01 8.60384827e+01
+400.0 5.00000000e-26 -1.79037762e-08 6.33831297e-15 2.79000000e+01 4.31176872e+01
+100.0 2.00000000e-25 3.49434970e-09 -5.44167311e-15 2.25400000e+02 2.32451614e+02
+150.0 1.33333333e-25 5.54272006e-09 6.41406965e-15 2.09800000e+02 1.80297318e+02
+200.0 1.00000000e-25 1.05227151e-08 -1.00229287e-14 3.37000000e+01 4.90355606e+01
+250.0 8.00000000e-26 -7.73102471e-09 2.79312574e-14 2.30000000e+01 3.06499615e+01
+300.0 6.66666667e-26 -7.41140838e-09 1.00347006e-14 3.97000000e+01 3.91013145e+01
+350.0 5.71428571e-26 -3.23808891e-09 8.47541785e-15 5.36000000e+01 7.44899521e+01
+400.0 5.00000000e-26 -7.71289312e-07 -2.79672488e-13 9.10000000e+00 1.84311752e+01
+100.0 2.00000000e-25 -1.69833214e-09 4.49591068e-16 5.65200000e+02 5.44850342e+02
+150.0 1.33333333e-25 9.51330037e-09 2.05358369e-15 3.63000000e+01 4.85485382e+01
+200.0 1.00000000e-25 -2.72988387e-09 7.34688111e-15 2.83000000e+01 3.11662083e+01
+250.0 8.00000000e-26 -7.30251770e-09 -2.50233940e-15 4.93000000e+01 7.11912842e+01
+300.0 6.66666667e-26 -1.10587947e-06 1.20305833e-14 3.92000000e+01 3.42653351e+01
+350.0 5.71428571e-26 6.28679331e-09 4.58518118e-16 3.58000000e+01 5.51116982e+01
+400.0 5.00000000e-26 -2.64841899e-06 -7.27950259e-14 8.90000000e+00 1.75188961e+01
+100.0 2.00000000e-25 2.16561702e-09 -1.51897596e-15 5.94600000e+02 5.36020020e+02
+150.0 1.33333333e-25 -4.79854378e-10 -1.68655128e-15 2.41600000e+02 1.98659882e+02
+200.0 1.00000000e-25 -8.36190495e-09 -6.87790482e-15 4.64000000e+01 5.83812332e+01
+250.0 8.00000000e-26 1.29670994e-08 -1.16622723e-14 2.87000000e+01 4.78854294e+01
+300.0 6.66666667e-26 -1.54940775e-07 -1.24893559e-13 1.43000000e+01 2.83098640e+01
+350.0 5.71428571e-26 -5.89703504e-07 -2.72876141e-13 1.09000000e+01 1.97143402e+01
+400.0 5.00000000e-26 1.86744268e-06 -2.49942580e-13 1.22000000e+01 1.85077629e+01
+100.0 2.00000000e-25 -2.41374565e-09 1.13700213e-15 3.34800000e+02 3.32381409e+02
+150.0 1.33333333e-25 -2.56176591e-09 4.22825359e-15 4.36000000e+01 4.73697357e+01
+200.0 1.00000000e-25 1.22152244e-09 6.05081570e-15 1.11300000e+02 1.06077988e+02
+250.0 8.00000000e-26 6.37033537e-09 1.82280252e-15 3.25000000e+01 2.76667786e+01
+300.0 6.66666667e-26 9.31637750e-07 1.45530931e-13 1.20000000e+01 1.85442982e+01
+350.0 5.71428571e-26 -6.70360475e-07 3.90878944e-14 1.06000000e+01 2.02619667e+01
+400.0 5.00000000e-26 -3.43252076e-08 -2.46076551e-13 1.79000000e+01 2.04143829e+01
+100.0 2.00000000e-25 -2.84403612e-09 8.12491228e-15 8.06000000e+01 8.02765350e+01
+150.0 1.33333333e-25 4.91436225e-10 2.68642477e-16 7.36000000e+01 9.26393280e+01
+200.0 1.00000000e-25 -5.17120569e-09 -1.06808739e-15 2.59000000e+01 2.54949970e+01
+250.0 8.00000000e-26 1.48673251e-09 -1.50066558e-15 2.08000000e+01 3.11433640e+01
+300.0 6.66666667e-26 -4.09933065e-09 -1.33022377e-14 1.67000000e+01 4.77056465e+01
+350.0 5.71428571e-26 -7.60154606e-09 9.22171606e-15 3.64000000e+01 4.16177483e+01
+400.0 5.00000000e-26 -3.05994579e-06 -3.37890536e-13 1.55000000e+01 1.89266891e+01
+100.0 2.00000000e-25 5.52554980e-09 -5.50624890e-15 1.04500000e+02 8.01520996e+01
+150.0 1.33333333e-25 7.47595053e-09 -1.96912266e-15 1.71300000e+02 1.61900192e+02
+200.0 1.00000000e-25 1.53788804e-09 7.04696730e-15 1.38400000e+02 1.35571640e+02
+250.0 8.00000000e-26 -1.85430810e-06 -4.20801983e-13 2.32000000e+01 2.65614014e+01
+300.0 6.66666667e-26 -8.06765854e-09 1.38506812e-14 4.17000000e+01 2.94134369e+01
+350.0 5.71428571e-26 2.21063679e-07 -1.04789509e-13 2.55000000e+01 2.75246010e+01
+400.0 5.00000000e-26 -1.17525107e-06 -1.45053683e-13 9.60000000e+00 1.86225243e+01
+100.0 2.00000000e-25 5.74488368e-09 -3.70382247e-15 1.43600000e+02 1.22172478e+02
+150.0 1.33333333e-25 -6.71658285e-10 -1.42264757e-15 1.59100000e+02 1.72938705e+02
+200.0 1.00000000e-25 -1.55632733e-08 9.38763847e-15 9.56000000e+01 7.15571442e+01
+250.0 8.00000000e-26 3.58018681e-09 -2.76303429e-15 8.33000000e+01 9.03139954e+01
+300.0 6.66666667e-26 -6.36659081e-09 -3.43069739e-15 4.30000000e+01 7.07415390e+01
+350.0 5.71428571e-26 -2.24433945e-06 -4.81580725e-13 1.88000000e+01 2.55075836e+01
+400.0 5.00000000e-26 -2.16326111e-07 -9.50855315e-14 2.09000000e+01 1.84336681e+01
+100.0 2.00000000e-25 -4.18040713e-09 4.47635450e-16 5.73600000e+02 5.89520813e+02
+150.0 1.33333333e-25 9.35527211e-09 1.18909396e-14 5.93000000e+01 5.49003639e+01
+200.0 1.00000000e-25 -1.11080318e-08 -3.19784327e-14 3.09000000e+01 2.69124584e+01
+250.0 8.00000000e-26 -4.12530454e-10 -3.77288808e-15 5.20000000e+01 7.48358078e+01
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GitLab