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.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 156449e1ebed5c541bc45ca579080211
+tags: 645f666f9bcd5a90fca523b33c5a78b7

.nojekyll

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+# Draw samples from the distribution:
+#
+mu, sigma = 0, 0.1 # mean and standard deviation
+s = np.random.normal(mu, sigma, 1000)
+#
+# Verify the mean and the variance:
+#
+abs(mu - np.mean(s))
+# Expected:
+## 0.0  # may vary
+#
+abs(sigma - np.std(s, ddof=1))
+# Expected:
+## 0.1  # may vary
+#
+# Display the histogram of the samples, along with
+# the probability density function:
+#
+import matplotlib.pyplot as plt
+count, bins, ignored = plt.hist(s, 30, density=True)
+plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
+               np.exp( - (bins - mu)**2 / (2 * sigma**2) ),
+         linewidth=2, color='r')
+plt.show()
+#
+# Two-by-four array of samples from the normal distribution with
+# mean 3 and standard deviation 2.5:
+#
+np.random.normal(3, 2.5, size=(2, 4))
+# Expected:
+## array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],   # random
+##        [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]])  # random

_downloads/01832a403e1fdaf3765fca3887651926/index-3_00.png

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+import numpy as np
+import matplotlib.pyplot as plt
+from scipy import interpolate
+x = np.arange(0, 10)
+y = np.exp(-x/3.0)
+f = interpolate.interp1d(x, y)
+#
+xnew = np.arange(0, 9, 0.1)
+ynew = f(xnew)   # use interpolation function returned by `interp1d`
+plt.plot(x, y, 'o', xnew, ynew, '-')
+plt.show()

_downloads/0577541cb595783e54eb0e4f12517e99/cosmic-sample-cmc-king-2.py

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+from cosmic.sample.initialbinarytable import InitialBinaryTable
+from cosmic.plotting import evolve_and_plot
+single_binary = InitialBinaryTable.InitialBinaries(m1=85.543645, m2=84.99784, porb=446.795757, ecc=0.448872, tphysf=13700.0, kstar1=1, kstar2=1, metallicity=0.002)
+BSEDict = {'xi': 1.0, 'bhflag': 1, 'neta': 0.5, 'windflag': 3, 'wdflag': 1, 'alpha1': 1.0, 'pts1': 0.001, 'pts3': 0.02, 'pts2': 0.01, 'epsnov': 0.001, 'hewind': 0.5, 'ck': 1000, 'bwind': 0.0, 'lambdaf': 0.0, 'mxns': 3.0, 'beta': -1.0, 'tflag': 1, 'acc2': 1.5, 'grflag' : 1, 'remnantflag': 4, 'ceflag': 0, 'eddfac': 1.0, 'ifflag': 0, 'bconst': 3000, 'sigma': 265.0, 'gamma': -2.0, 'pisn': 45.0, 'natal_kick_array' : [[-100.0,-100.0,-100.0,-100.0,0.0], [-100.0,-100.0,-100.0,-100.0,0.0]], 'bhsigmafrac' : 1.0, 'polar_kick_angle' : 90, 'qcrit_array' : [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0], 'cekickflag' : 2, 'cehestarflag' : 0, 'cemergeflag' : 0, 'ecsn' : 2.25, 'ecsn_mlow' : 1.6, 'aic' : 1, 'ussn' : 0, 'sigmadiv' :-20.0, 'qcflag' : 1, 'eddlimflag' : 0, 'fprimc_array' : [2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0], 'bhspinflag' : 0, 'bhspinmag' : 0.0, 'rejuv_fac' : 1.0, 'rejuvflag' : 0, 'htpmb' : 1, 'ST_cr' : 1, 'ST_tide' : 1, 'bdecayfac' : 1, 'rembar_massloss' : 0.5, 'kickflag' : 0, 'zsun' : 0.014, 'bhms_coll_flag' : 0, 'don_lim' : -1, 'acc_lim' : -1, 'rtmsflag' : 0, 'wd_mass_lim': 1}
+fig = evolve_and_plot(single_binary, t_min=None, t_max=None, BSEDict=BSEDict, sys_obs={})

_downloads/22b309dfafc5e63b67c6440c3b9601db/cosmic-sample-cmc-king-1.png

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+# Draw samples from the distribution:
+#
+s = np.random.uniform(-1,0,1000)
+#
+# All values are within the given interval:
+#
+np.all(s >= -1)
+# Expected:
+## True
+np.all(s < 0)
+# Expected:
+## True
+#
+# Display the histogram of the samples, along with the
+# probability density function:
+#
+import matplotlib.pyplot as plt
+count, bins, ignored = plt.hist(s, 15, density=True)
+plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
+plt.show()

_downloads/259b25c6db6ac866207ff7f3d50b1420/cosmic-sample-cmc-elson-2.pdf

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+# Draw samples from the distribution:
+#
+mu, sigma = 0, 0.1 # mean and standard deviation
+s = np.random.normal(mu, sigma, 1000)
+#
+# Verify the mean and the variance:
+#
+abs(mu - np.mean(s))
+# Expected:
+## 0.0  # may vary
+#
+abs(sigma - np.std(s, ddof=1))
+# Expected:
+## 0.1  # may vary
+#
+# Display the histogram of the samples, along with
+# the probability density function:
+#
+import matplotlib.pyplot as plt
+count, bins, ignored = plt.hist(s, 30, density=True)
+plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
+               np.exp( - (bins - mu)**2 / (2 * sigma**2) ),
+         linewidth=2, color='r')
+plt.show()
+#
+# Two-by-four array of samples from the normal distribution with
+# mean 3 and standard deviation 2.5:
+#
+np.random.normal(3, 2.5, size=(2, 4))
+# Expected:
+## array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],   # random
+##        [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]])  # random

_downloads/28df02138d3c4ff463278474666fdfaa/cosmic-sample-cmc-king-1.py

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+from cosmic.utils import a_from_p
+from cosmic.sample.initialbinarytable import InitialBinaryTable
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+final_kstar = np.linspace(0,14,15)
+colors = {'green' : '#1b9e77', 'purple' : '#d95f02', 'orange' : '#7570b3'}
+final_kstar = np.linspace(0,14,15)
+initC_mult, m_sin_mult, m_bin_mult, n_sin_mult, n_bin_mult = InitialBinaryTable.sampler('multidim',
+                                                                                        final_kstar1=final_kstar,
+                                                                                        final_kstar2=final_kstar,
+                                                                                        rand_seed=2,
+                                                                                        nproc=1,
+                                                                                        SF_start=13700.0,
+                                                                                        SF_duration=0.0,
+                                                                                        met=0.02,
+                                                                                        size=100000)
+initC_mult['sep'] = a_from_p(p=initC_mult.porb, m1=initC_mult.mass_1, m2=initC_mult.mass_2)

_downloads/2a8e995e3b498dddeb433d086b2ab469/cosmic-sample-cmc-king-2_00_00.png

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+from cosmic.sample.initialbinarytable import InitialBinaryTable
+from cosmic.plotting import evolve_and_plot
+import numpy as np
+np.random.seed(5)
+BSEDict = {'xi': 1.0, 'bhflag': 1, 'neta': 0.5, 'windflag': 3, 'wdflag': 1, 'alpha1': 1.0, 'pts1': 0.001, 'pts3': 0.02, 'pts2': 0.01, 'epsnov': 0.001, 'hewind': 0.5, 'ck': 1000, 'bwind': 0.0, 'lambdaf': 0.0, 'mxns': 3.0, 'beta': -1.0, 'tflag': 1, 'acc2': 1.5, 'grflag' : 1, 'remnantflag': 4, 'ceflag': 0, 'eddfac': 1.0, 'ifflag': 0, 'bconst': 3000, 'sigma': 265.0, 'gamma': -2.0, 'pisn': 45.0, 'natal_kick_array' : [[-100.0,-100.0,-100.0,-100.0,0.0], [-100.0,-100.0,-100.0,-100.0,0.0]], 'bhsigmafrac' : 1.0, 'polar_kick_angle' : 90, 'qcrit_array' : [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0], 'cekickflag' : 2, 'cehestarflag' : 0, 'cemergeflag' : 0, 'ecsn' : 2.25, 'ecsn_mlow' : 1.6, 'aic' : 1, 'ussn' : 0, 'sigmadiv' :-20.0, 'qcflag' : 1, 'eddlimflag' : 0, 'fprimc_array' : [2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0], 'bhspinflag' : 0, 'bhspinmag' : 0.0, 'rejuv_fac' : 1.0, 'rejuvflag' : 0, 'htpmb' : 1, 'ST_cr' : 1, 'ST_tide' : 1, 'bdecayfac' : 1, 'rembar_massloss' : 0.5, 'kickflag' : 0, 'zsun' : 0.014, 'bhms_coll_flag' : 0, 'don_lim' : -1, 'acc_lim' : -1, 'rtmsflag' : 0, 'wd_mass_lim': 1}
+binary_set = InitialBinaryTable.InitialBinaries(m1=[85.543645, 11.171469], m2=[84.99784, 9.67305], porb=[446.795757, 370.758343], ecc=[0.448872, 0.370], tphysf=[13700.0, 13700.0], kstar1=[1, 1], kstar2=[1, 1], metallicity=[0.002, 0.02])
+fig = evolve_and_plot(binary_set, t_min=None, t_max=[6.0, 60.0], BSEDict=BSEDict, sys_obs={})

_downloads/367539d56df09a5f5013b98f12c13308/index-1.hires.png

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+# Draw samples from the distribution:
+#
+s = np.random.uniform(-1,0,1000)
+#
+# All values are within the given interval:
+#
+np.all(s >= -1)
+# Expected:
+## True
+np.all(s < 0)
+# Expected:
+## True
+#
+# Display the histogram of the samples, along with the
+# probability density function:
+#
+import matplotlib.pyplot as plt
+count, bins, ignored = plt.hist(s, 15, density=True)
+plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
+plt.show()

_downloads/42839ac0e333dd910660bc1d23750f52/index-3_01.png

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+from cosmic.utils import a_from_p
+from cosmic.sample.initialbinarytable import InitialBinaryTable
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+final_kstar = np.linspace(0,14,15)
+colors = {'green' : '#1b9e77', 'orange' : '#d95f02', 'purple' : '#7570b3'}
+initC_logP, m_sin_logP, m_bin_logP, n_sin_logP, n_bin_logP = InitialBinaryTable.sampler('independent',
+                                                                                        final_kstar1=final_kstar,
+                                                                                        final_kstar2=final_kstar,
+                                                                                        binfrac_model=1.0,
+                                                                                        primary_model='kroupa01',
+                                                                                        ecc_model='thermal',
+                                                                                        porb_model='log_uniform',
+                                                                                        qmin=-1,
+                                                                                        SF_start=13700.0,
+                                                                                        SF_duration=0.0,
+                                                                                        met=0.02,
+                                                                                        size=100000)
+initC_Sana, m_sin_Sana, m_bin_Sana, n_sin_Sana, n_bin_Sana = InitialBinaryTable.sampler('independent',
+                                                                                        final_kstar1=final_kstar,
+                                                                                        final_kstar2=final_kstar,
+                                                                                        binfrac_model=1.0,
+                                                                                        primary_model='kroupa01',
+                                                                                        ecc_model='sana12',
+                                                                                        porb_model='sana12',
+                                                                                        qmin=-1,
+                                                                                        SF_start=13700.0,
+                                                                                        SF_duration=0.0,
+                                                                                        met=0.02,
+                                                                                        size=100000)
+initC_Moe, m_sin_Moe, m_bin_Moe, n_sin_Moe, n_bin_Moe = InitialBinaryTable.sampler('independent',
+                                                                                   final_kstar1=final_kstar,
+                                                                                   final_kstar2=final_kstar,
+                                                                                   binfrac_model=1.0,
+                                                                                   primary_model='kroupa01',
+                                                                                   ecc_model='sana12',
+                                                                                   porb_model='moe19',
+                                                                                   qmin=-1,
+                                                                                   SF_start=13700.0,
+                                                                                   SF_duration=0.0,
+                                                                                   met=0.02,
+                                                                                   size=100000)
+initC_logP['sep'] = a_from_p(p=initC_logP.porb, m1=initC_logP.mass_1, m2=initC_logP.mass_2)
+initC_Sana['sep'] = a_from_p(p=initC_Sana.porb, m1=initC_Sana.mass_1, m2=initC_Sana.mass_2)
+initC_Moe['sep'] = a_from_p(p=initC_Moe.porb, m1=initC_Moe.mass_1, m2=initC_Moe.mass_2)
+fig = plt.figure(figsize = (15,6))
+ax1 = plt.subplot(231)
+ax2 = plt.subplot(232)
+ax3 = plt.subplot(233)
+ax4 = plt.subplot(234)
+ax5 = plt.subplot(235)
+ax6 = plt.subplot(236)
+ax1.hist(np.log10(initC_logP.mass_1), bins = 20, histtype='step', density=True,
+         lw=3, color=colors['purple'], label='independent')
+ax1.hist(np.log10(initC_Sana.mass_1), bins = 20, histtype='step', density=True,
+         lw=3, color=colors['orange'], label='Sana+2012')
+ax1.hist(np.log10(initC_Moe.mass_1), bins = 20, histtype='step', density=True,
+         lw=3, color=colors['green'], label='Moe+2019')
+ax1.set_xlabel(r'Log$_{10}$(M$_1$/M$_{\odot}$)', size=18)
+ax1.set_ylabel('normalized counts', size=18)
+ax1.legend(prop={'size' : 18})
+ax2.hist(np.log10(initC_logP.porb), bins = 20, histtype='step', density=True,
+         lw=3, color=colors['purple'], label='independent')
+ax2.hist(np.log10(initC_Sana.porb), bins = 20, histtype='step', density=True,
+         lw=3, color=colors['orange'], label='Sana+2012')
+ax2.hist(np.log10(initC_Moe.porb), bins = 20, histtype='step', density=True,
+         lw=3, color=colors['green'], label='Moe+2019')
+ax2.set_xlabel(r'Log$_{10}$(P$_{\rm{orb}}$/day)', size=18)
+ax3.hist(initC_logP.ecc, bins = 10, histtype='step', density=True,
+         lw=3, color=colors['purple'], label='independent')
+ax3.hist(initC_Sana.ecc, bins = 10, histtype='step', density=True,
+         lw=3, color=colors['orange'], label='Sana+2012')
+ax3.hist(initC_Moe.ecc, bins = 10, histtype='step', density=True,
+         lw=3, color=colors['green'], label='Moe+2019')
+ax3.set_xlabel('Eccentricity', size=18)
+ax4.hist(initC_logP.mass_2/initC_logP.mass_1, bins = 20, histtype='step', density=True,
+         lw=3, color=colors['purple'], label='independent')
+ax4.hist(initC_Sana.mass_2/initC_Sana.mass_1, bins = 20, histtype='step', density=True,
+         lw=3, color=colors['orange'], label='Sana+2012')
+ax4.hist(initC_Moe.mass_2/initC_Moe.mass_1, bins = 20, histtype='step', density=True,
+         lw=3, color=colors['green'], label='Moe+2019')
+ax4.set_xlabel(r'q=M$_1$/M$_2$', size=18)
+ax4.set_ylabel('normalized counts', size=18)
+ax5.hist(np.log10(initC_logP.sep), bins = 20, histtype='step', density=True,
+         lw=3, color=colors['purple'], label='independent')
+ax5.hist(np.log10(initC_Sana.sep), bins = 20, histtype='step', density=True,
+         lw=3, color=colors['orange'], label='Sana+2012')
+ax5.hist(np.log10(initC_Moe.sep), bins = 20, histtype='step', density=True,
+         lw=3, color=colors['green'], label='Moe+2019')
+ax5.set_xlabel(r'Log$_{10}$(a/R$_{\odot}$)', size=18)
+ax6.hist(np.log10(initC_logP.sep*(1-initC_logP.ecc)), bins = 20, histtype='step', density=True,
+         lw=3, color=colors['purple'], label='independent')
+ax6.hist(np.log10(initC_Sana.sep*(1-initC_Sana.ecc)), bins = 20, histtype='step', density=True,
+         lw=3, color=colors['orange'], label='Sana+2012')
+ax6.hist(np.log10(initC_Moe.sep*(1-initC_Moe.ecc)), bins = 20, histtype='step', density=True,
+         lw=3, color=colors['green'], label='Moe+2019')
+ax6.set_xlabel(r'Log$_{10}$(a(1-e)/R$_{\odot}$)', size=18)
+fig.tight_layout()
+fig.show()

_downloads/43fc19dd76eb5baf2341ebff3d4d047c/index-1.png

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+from cosmic.sample.initialbinarytable import InitialBinaryTable
+from cosmic.plotting import evolve_and_plot
+single_binary = InitialBinaryTable.InitialBinaries(m1=85.543645, m2=84.99784, porb=446.795757, ecc=0.448872, tphysf=13700.0, kstar1=1, kstar2=1, metallicity=0.002)
+BSEDict = {'xi': 1.0, 'bhflag': 1, 'neta': 0.5, 'windflag': 3, 'wdflag': 1, 'alpha1': 1.0, 'pts1': 0.001, 'pts3': 0.02, 'pts2': 0.01, 'epsnov': 0.001, 'hewind': 0.5, 'ck': 1000, 'bwind': 0.0, 'lambdaf': 0.0, 'mxns': 3.0, 'beta': -1.0, 'tflag': 1, 'acc2': 1.5, 'grflag' : 1, 'remnantflag': 4, 'ceflag': 0, 'eddfac': 1.0, 'ifflag': 0, 'bconst': 3000, 'sigma': 265.0, 'gamma': -2.0, 'pisn': 45.0, 'natal_kick_array' : [[-100.0,-100.0,-100.0,-100.0,0.0], [-100.0,-100.0,-100.0,-100.0,0.0]], 'bhsigmafrac' : 1.0, 'polar_kick_angle' : 90, 'qcrit_array' : [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0], 'cekickflag' : 2, 'cehestarflag' : 0, 'cemergeflag' : 0, 'ecsn' : 2.25, 'ecsn_mlow' : 1.6, 'aic' : 1, 'ussn' : 0, 'sigmadiv' :-20.0, 'qcflag' : 1, 'eddlimflag' : 0, 'fprimc_array' : [2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0,2.0/21.0], 'bhspinflag' : 0, 'bhspinmag' : 0.0, 'rejuv_fac' : 1.0, 'rejuvflag' : 0, 'htpmb' : 1, 'ST_cr' : 1, 'ST_tide' : 1, 'bdecayfac' : 1, 'rembar_massloss' : 0.5, 'kickflag' : 0, 'zsun' : 0.014, 'bhms_coll_flag' : 0, 'don_lim' : -1, 'acc_lim' : -1, 'rtmsflag' : 0, 'wd_mass_lim': 1}
+fig = evolve_and_plot(single_binary, t_min=None, t_max=6.0, BSEDict=BSEDict, sys_obs={})