Content

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import sys,os
base_dir = '/u/home/abzoghbi/data/ngc4151/spec_analysis'
sys.path.append(base_dir)
from spec_helpers import *
%load_ext autoreload
%autoreload 2

### Read useful data from data notebook
data_dir = 'data/xmm'
spec_dir = 'data/xmm_spec'
os.chdir('%s/%s'%(base_dir, data_dir))
data = np.load('log/data.npz')
spec_obsids = data['spec_obsids']
obsids = data['obsids']
spec_data = data['spec_data']
spec_ids = [i+1 for i,o in enumerate(obsids) if o in spec_obsids]

Redo the main fit (fit_4c) and include errors of the two strongest lines: fit_4e

os.chdir('%s/%s'%(base_dir, spec_dir))
suff = '4e'
fit_4e = fit_xspec_model('fit_%s'%suff, spec_ids, base_dir)

# plot the result #
par_names = ['xl_nh', 'xl_xi', 'xh_nh', 'xh_xi', 'nh', 'cf', 'pflx', 'gam', 'xflx', 
             'bT', 'bnrm', 'g5e', 'g5nrm', 'g9e', 'g9nrm']
fit = fit_4e
fig = plt.figure(figsize=(12,7))
idx = [0,1,2,3,4,5,7,8,9,10,11,12,13,14]; iref = 6
for i,ix in enumerate(idx):
    ax = plt.subplot(3,len(idx)//3+1,i+1)
    plt.errorbar(fit[:,iref,0], fit[:,ix,0], fit[:,ix,1], xerr=fit[:,iref,1], 
                 fmt='o', ms=8, lw=0.5)
    ax.set_xlabel(par_names[iref]); ax.set_ylabel(par_names[ix])
plt.tight_layout(pad=0)

png

x,xe = fit[:,iref,0], fit[:,iref,1]
y,ye = fit[:,1,0], fit[:,1,1]
plt.errorbar(x,y,yerr=ye, fmt='o', ms=8, lw=0.5)

<ErrorbarContainer object of 3 artists>

png



Estimate the PSDs

Do it for the powerlaw flux. Compare bending powerlaw to 0-centered lorentzian. The latter can be used to fit the other parameters, for which the former cannot be constrained. The bend frequency and lorentzian width are different, so we obtain some calibration factor so for other lorentzian fit, we use it to scale to the commonly used break frequency


fit = fit_4e
ipar = 6
xtime = spec_data[:,0]
param = fit[:,ipar,:2]
isort = np.argsort(xtime)
xtime, param = xtime[isort], param[isort,:].T



fqL = [0.1/(xtime[-1]-xtime[0]), 2./np.diff(xtime).min()]
# bending powerlaw
pm_bpl = plag.PLag('psdf', [xtime], [param[0]], [param[1]], 0.1, fqL, 'rms', 0, 2)
p0 = [-9.4,-2, -3.6]
par_bpl = plag.optimize(pm_bpl, p0)[0]

# 0-centered lorentzian
pm_l0 = plag.PLag('psdf', [xtime], [param[0]], [param[1]], 0.1, fqL, 'rms', 0, 4)
p0 = [-5,-4.]
par_l0 = plag.optimize(pm_l0, p0)[0]

scale_shift = par_bpl[2] - par_l0[1]
print('scale shift: %g'%scale_shift)
# so we have: break = lore_width + scale_shift

   1 3.026e-01 3.703e+00   inf | 1.020e+01 |  -9.4    -2  -3.6
   2 1.305e-01 1.951e+00 3.116e-01 | 1.051e+01 | -9.24 -2.46 -2.51
   3 3.142e-02 2.861e-01 3.427e-01 | 1.085e+01 | -9.25 -2.44 -2.84
   4 7.469e-03 1.274e-02 7.924e-03 | 1.086e+01 | -9.24 -2.42 -2.93
   5 1.545e-03 4.994e-03 1.716e-04 | 1.086e+01 | -9.23  -2.4 -2.95
   6 3.114e-04 4.340e-04 7.495e-06 | 1.086e+01 | -9.23  -2.4 -2.95
********************
-9.22946 -2.40171 -2.9538
0.572789 0.893383 1.35897
-0.000117633 0.000179097 -0.000433978
********************
   1 1.637e-01 9.314e+00   inf | -7.170e-01 |    -5    -4
   2 1.007e-01 7.093e+00 7.107e+00 | 6.390e+00 | -5.82 -4.06
   3 4.256e-02 3.628e+00 3.651e+00 | 1.004e+01 |  -6.4 -4.19
   4 2.749e-02 8.256e-01 8.487e-01 | 1.089e+01 | -6.65 -4.37
   5 8.265e-03 1.323e-01 6.134e-02 | 1.095e+01 | -6.66 -4.49
   6 1.919e-03 2.545e-02 2.535e-03 | 1.095e+01 | -6.65 -4.53
   7 4.407e-04 5.682e-03 1.308e-04 | 1.095e+01 | -6.65 -4.54
   8 1.014e-04 1.299e-03 6.910e-06 | 1.095e+01 | -6.65 -4.54
********************
-6.64514 -4.53801
0.434769 0.596516
-1.14667e-05 -0.00129888
********************
scale shift: 1.5842

import emcee
def lnprob(x, mod):
    if x[0] < -11 or x[1]<-11: return -np.inf
    if x[0] >1.7 or x[1]>1.7: return -np.inf
    return mod.logLikelihood(x)

def get_time_scale(ipar, use_opt=True):
    # ipar is the index in fit array
    xtime = spec_data[:,0]
    if isinstance(ipar, list):
        param = fit[:,ipar[0],:2]
        for i in ipar[1:]:
            param[:,0] += fit[:,ipar[i],0]
            param[:,1] = (param[:,1]**2 + fit[:,ipar[i],1]**2)**0.5
    else:
        param = fit[:,ipar,:2]
    isort = np.argsort(xtime)
    xtime, param = xtime[isort], param[isort,:].T
        
    pm_l0 = plag.PLag('psdf', [xtime], [param[0]], [param[1]], 0.1, fqL, 'rms', 0, 4)
    p0 = [-5,-4.]
    p,pe = plag.optimize(pm_l0, p0)[:2]

    # mcmc #
    ndim, nwalkers = 2, 100
    if not use_opt:
        p = np.array([-2., -4])
        pe = np.array([0.1, 0.1])
    p0 = [np.random.rand(ndim)*pe+p for i in range(nwalkers)]

    sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob, args=[pm_l0])
    _ = sampler.run_mcmc(p0, 1000)
    chains = sampler.flatchain[len(sampler.flatchain)//2:]
    return chains

# powerlaw flux #
hist = {}
ipar = 6
chains = get_time_scale(ipar)
ts = np.log10(1./(np.exp(chains[:,1] + scale_shift) ) )
h1 = np.histogram(chains[:,0], 60)
plt.subplot(121)
h2 = plt.hist(chains[:,1], 60)
plt.subplot(122)
hs = plt.hist(ts, 60)
hist[par_names[ipar]] = [h1[:2], h2[:2], hs[:2], chains]

   1 1.637e-01 9.314e+00   inf | -7.170e-01 |    -5    -4
   2 1.007e-01 7.093e+00 7.107e+00 | 6.390e+00 | -5.82 -4.06
   3 4.256e-02 3.628e+00 3.651e+00 | 1.004e+01 |  -6.4 -4.19
   4 2.749e-02 8.256e-01 8.487e-01 | 1.089e+01 | -6.65 -4.37
   5 8.265e-03 1.323e-01 6.134e-02 | 1.095e+01 | -6.66 -4.49
   6 1.919e-03 2.545e-02 2.535e-03 | 1.095e+01 | -6.65 -4.53
   7 4.407e-04 5.682e-03 1.308e-04 | 1.095e+01 | -6.65 -4.54
   8 1.014e-04 1.299e-03 6.910e-06 | 1.095e+01 | -6.65 -4.54
********************
-6.64514 -4.53801
0.434769 0.596516
-1.14667e-05 -0.00129888
********************

png

# xillver flux #
ipar = 8
chains = get_time_scale(ipar)
ts = np.log10(1./(np.exp(chains[:,1] + scale_shift) ) )
h1 = np.histogram(chains[:,0], 60)
plt.subplot(121)
h2 = plt.hist(chains[:,1], 60)
plt.subplot(122)
hs = plt.hist(ts, 60)
hist[par_names[ipar]] = [h1[:2], h2[:2], hs[:2], chains]

   1 1.977e-01 1.068e+01   inf | -1.712e+00 |    -5    -4
   2 1.619e-01 1.018e+01 1.025e+01 | 8.537e+00 | -5.99 -3.98
   3 1.324e-01 9.077e+00 9.185e+00 | 1.772e+01 | -6.96 -3.93
   4 1.022e-01 7.087e+00 7.165e+00 | 2.489e+01 | -7.88  -3.8
   5 8.115e-02 4.352e+00 4.332e+00 | 2.922e+01 | -8.68 -3.58
   6 7.104e-02 1.665e+00 1.649e+00 | 3.087e+01 | -9.26 -3.29
   7 4.090e-02 2.357e-01 2.791e-01 | 3.115e+01 | -9.52 -3.05
   8 2.141e-02 1.319e-01 2.707e-02 | 3.117e+01 | -9.59 -2.93
   9 1.167e-02 7.228e-02 6.106e-03 | 3.118e+01 |  -9.6 -2.87
  10 6.431e-03 3.931e-02 1.764e-03 | 3.118e+01 | -9.61 -2.83
  11 3.549e-03 2.152e-02 5.238e-04 | 3.118e+01 | -9.61 -2.82
  12 1.960e-03 1.183e-02 1.575e-04 | 3.118e+01 | -9.61 -2.81
  13 1.082e-03 6.517e-03 4.770e-05 | 3.118e+01 | -9.61  -2.8
********************
-9.61488 -2.79961
0.387752 0.708842
0.00187241 0.00651705
********************

png

# neutral nh #
ipar = 4
chains = get_time_scale(ipar)
ts = np.log10(1./(np.exp(chains[:,1] + scale_shift) ) )
h1 = np.histogram(chains[:,0], 60)
plt.subplot(121)
h2 = plt.hist(chains[:,1], 60)
plt.subplot(122)
hs = plt.hist(ts, 60)
hist[par_names[ipar]] = [h1[:2], h2[:2], hs[:2], chains]

   1 7.280e+01 7.717e+02   inf | -8.979e+02 |    -5    -4
   2 7.995e+00 1.061e+02 7.280e+02 | -1.698e+02 |    -3    -6
   3 2.974e+00 2.950e+01 7.013e+01 | -9.971e+01 |    -1    -8
   4 5.483e-01 5.918e+00 3.517e+01 | -6.455e+01 |     1    -6
   5 4.811e-01 2.500e+00 2.460e+00 | -6.209e+01 | 0.452 -6.03
   6 1.074e-01 4.645e-01 3.799e-01 | -6.171e+01 | 0.234 -6.08
   7 6.397e-03 4.865e-02 1.405e-02 | -6.169e+01 | 0.209 -6.12
   8 2.084e-03 6.166e-03 2.254e-04 | -6.169e+01 | 0.211 -6.12
   9 3.243e-04 8.187e-04 4.012e-06 | -6.169e+01 | 0.211 -6.13
********************
0.211003 -6.1263
0.498517 0.635167
-0.000526084 -0.000818706
********************

png

# xi_l nh #
ipar = 0
chains = get_time_scale(ipar)
ts = np.log10(1./(np.exp(chains[:,1] + scale_shift) ) )
h1 = np.histogram(chains[:,0], 60)
plt.subplot(121)
h2 = plt.hist(chains[:,1], 60)
plt.subplot(122)
hs = plt.hist(ts, 60)
hist[par_names[ipar]] = [h1[:2], h2[:2], hs[:2], chains]

   1 6.918e+00 6.577e+01   inf | -1.981e+02 |    -5    -4
   2 2.060e+00 3.353e+01 1.274e+02 | -7.072e+01 |    -3    -2
   3   inf 2.297e+00 2.478e+01 | -4.594e+01 |    -1     0
   4 1.429e+00 3.119e-01 3.158e-01 | -4.562e+01 | -1.24 -0.0543
   5 5.603e-01 1.961e-02 6.727e-03 | -4.562e+01 | -1.28 -0.132
   6 3.602e-01 2.202e-02 1.535e-03 | -4.561e+01 | -1.29 -0.206
   7 2.876e-01 2.623e-02 1.775e-03 | -4.561e+01 | -1.29 -0.28
   8 2.550e-01 3.301e-02 2.372e-03 | -4.561e+01 |  -1.3 -0.36
   9 2.359e-01 4.276e-02 3.474e-03 | -4.561e+01 |  -1.3 -0.452
  10 2.153e-01 5.476e-02 5.217e-03 | -4.560e+01 | -1.31 -0.559
  11 1.817e-01 6.490e-02 7.273e-03 | -4.559e+01 | -1.31 -0.679
  12 1.334e-01 6.538e-02 8.187e-03 | -4.559e+01 | -1.31 -0.802
  13 8.311e-02 5.246e-02 6.444e-03 | -4.558e+01 | -1.31 -0.909
  14 4.512e-02 3.368e-02 3.310e-03 | -4.558e+01 | -1.31 -0.985
  15 2.249e-02 1.845e-02 1.170e-03 | -4.557e+01 | -1.31 -1.03
  16 1.072e-02 9.231e-03 3.218e-04 | -4.557e+01 | -1.31 -1.05
  17 5.005e-03 4.411e-03 7.707e-05 | -4.557e+01 | -1.31 -1.06
********************
-1.31356 -1.0638
0.326303 1.10023
-0.000528733 -0.00441064
********************


/u/home/abzoghbi/soft/etc/python/plag/plag.py:545: RuntimeWarning: divide by zero encountered in true_divide
  absmax = np.max(np.abs(dpar/tmpp))

png

# xi_l xi #
ipar = 1
chains = get_time_scale(ipar, use_opt=False)
ts = np.log10(1./(np.exp(chains[:,1] + scale_shift) ) )
h1 = np.histogram(chains[:,0], 60)
plt.subplot(121)
h2 = plt.hist(chains[:,1], 60)
plt.subplot(122)
hs = plt.hist(ts, 60)
hist[par_names[ipar]] = [h1[:2], h2[:2], hs[:2], chains]

   1 4.930e+01 4.911e+01   inf | -2.813e+02 |    -5    -4
   2 8.638e+00 5.497e+01 1.103e+02 | -1.711e+02 |    -3    -2
   3   inf 2.616e+01 1.285e+02 | -4.257e+01 |    -1     0
   4 3.413e+01 4.926e-01 1.895e+01 | -2.362e+01 |     1     2
   5 5.654e+02 2.192e+00 -2.692e-01 | -2.389e+01 |     3     4
   6 1.773e+04 2.265e+00 -2.046e-02 | -2.391e+01 |     5     6
   7 6.796e+05 2.267e+00 -3.868e-04 | -2.391e+01 |     7     8
   8 7.067e+05 2.267e+00 -7.088e-06 | -2.391e+01 |     9    10
********************
   9   10
2.37266e+07 2.37266e+07
-2.26664 2.26664
********************

png

# xi_h nh #
ipar = 2
chains = get_time_scale(ipar, use_opt=True)
ts = np.log10(1./(np.exp(chains[:,1] + scale_shift) ) )
h1 = np.histogram(chains[:,0], 60)
plt.subplot(121)
h2 = plt.hist(chains[:,1], 60)
plt.subplot(122)
hs = plt.hist(ts, 60)
hist[par_names[ipar]] = [h1[:2], h2[:2], hs[:2], chains]

   1 4.956e+01 2.095e+02   inf | -3.836e+02 |    -5    -4
   2 1.858e+01 6.386e+01 2.455e+02 | -1.381e+02 |    -3    -2
   3 2.219e+00 7.147e+00 6.263e+01 | -7.548e+01 |    -1    -4
   4 5.446e-01 2.801e+00 3.156e+00 | -7.233e+01 |     1    -6
   5 4.852e-01 9.756e-01 1.123e+00 | -7.121e+01 | 0.455 -6.12
   6 1.003e-01 9.859e-02 1.264e-01 | -7.108e+01 | 0.234 -6.17
   7 2.271e-03 1.308e-03 1.205e-03 | -7.108e+01 | 0.211 -6.17
   8 1.881e-04 1.777e-04 5.056e-07 | -7.108e+01 | 0.211 -6.17
********************
0.21141 -6.17164
0.548277 1.12758
-1.7852e-05 -0.000177658
********************

png

# xi_h xi #
ipar = 3
chains = get_time_scale(ipar, use_opt=False)
ts = np.log10(1./(np.exp(chains[:,1] + scale_shift) ) )
h1 = np.histogram(chains[:,0], 60)
plt.subplot(121)
h2 = plt.hist(chains[:,1], 60)
plt.subplot(122)
hs = plt.hist(ts, 60)
hist[par_names[ipar]] = [h1[:2], h2[:2], hs[:2], chains]

   1 1.031e+00 1.233e+01   inf | -3.671e+01 |    -5    -4
   2 1.662e-01 9.818e-01 1.067e+01 | -2.604e+01 |    -3 -4.34
   3 1.669e-01 8.963e-01 6.164e-01 | -2.543e+01 | -3.13 -3.62
   4 2.358e-01 9.924e-01 5.412e-01 | -2.489e+01 | -3.16 -3.02
   5 4.553e-01 1.224e+00 7.806e-01 | -2.411e+01 | -3.18 -2.31
   6 1.660e+00 1.123e+00 1.283e+00 | -2.282e+01 |  -3.2 -1.26
   7 2.060e+00 4.153e-01 9.244e-01 | -2.190e+01 | -3.17 0.744
   8 5.270e+00 1.583e+00 -6.401e-01 | -2.254e+01 | -3.43 -0.788
   9 1.840e+01 1.474e-01 6.401e-01 | -2.190e+01 | -3.02  1.21
  10 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  11 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  12 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  13 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  14 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  15 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  16 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  17 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  18 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  19 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  20 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  21 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  22 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  23 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  24 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  25 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  26 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  27 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  28 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  29 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  30 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  31 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  32 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  33 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  34 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  35 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  36 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  37 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  38 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  39 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  40 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  41 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  42 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  43 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  44 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  45 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  46 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  47 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  48 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  49 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  50 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  51 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
  52 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
  53 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
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 252 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 254 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 256 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 258 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 260 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 261 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 262 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 263 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 264 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 265 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 266 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 268 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 269 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 270 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 271 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 272 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 273 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 274 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 275 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 276 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 278 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 280 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 282 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 284 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 286 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 288 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 290 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 291 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 292 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 293 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 294 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 295 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 296 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 297 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 298 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 300 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 301 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 302 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 303 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 304 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 305 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 306 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 308 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 310 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 311 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 312 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 313 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 314 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 315 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 316 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 318 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 320 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 321 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 322 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 324 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 325 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 326 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 327 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 328 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 329 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 330 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 331 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 332 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 333 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 334 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 335 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 336 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 337 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 338 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 339 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 340 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 341 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 342 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 344 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 345 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 346 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 347 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 348 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 349 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 350 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 351 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 352 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 353 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 354 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 355 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 356 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 357 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 358 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 359 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 360 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 361 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 362 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 363 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 364 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 365 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 366 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 367 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 368 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 369 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 370 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 371 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 372 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 373 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 374 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 375 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 376 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 378 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 380 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 381 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 382 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 383 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 384 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 385 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 386 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 387 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 388 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 390 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 391 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 392 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 393 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 394 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 395 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 396 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 397 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 398 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 399 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 400 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 401 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 402 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 403 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 404 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 405 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 406 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 407 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 408 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 409 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 410 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 411 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 412 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 413 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 414 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 415 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 416 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 417 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 418 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 419 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 420 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 421 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 422 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 423 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 424 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 426 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 428 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 430 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 432 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 442 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 444 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 446 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 450 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 456 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 458 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 460 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 462 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
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 464 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 465 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 466 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 467 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 468 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 469 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 470 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 471 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 472 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 473 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 474 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 475 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 476 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 477 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 478 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 479 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 480 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 481 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 482 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 483 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 484 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 485 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 486 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 487 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 488 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 489 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 490 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 491 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 492 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 493 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 494 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 495 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 496 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 497 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 498 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
 499 1.840e+01 1.474e-01 1.343e+01 | -2.190e+01 | -3.02  1.21
 500 2.241e+01 1.516e+01 -1.343e+01 | -3.533e+01 | -5.02 -0.788
********************
-3.01611 1.21166
0.603644 3.39494
15.1631 0.840234
********************

png

# cf #
ipar = 5
chains = get_time_scale(ipar, use_opt=True)
ts = np.log10(1./(np.exp(chains[:,1] + scale_shift) ) )
h1 = np.histogram(chains[:,0], 60)
plt.subplot(121)
h2 = plt.hist(chains[:,1], 60)
plt.subplot(122)
hs = plt.hist(ts, 60)
hist[par_names[ipar]] = [h1[:2], h2[:2], hs[:2], chains]

   1 2.352e-01 4.195e+00   inf | 3.482e+01 |    -5    -4
   2 5.082e-02 9.882e-01 1.330e+00 | 3.615e+01 | -4.78 -3.06
   3 1.082e-02 6.212e-02 9.096e-02 | 3.624e+01 | -4.86 -3.21
   4 1.203e-03 8.126e-03 1.314e-03 | 3.624e+01 | -4.85 -3.25
   5 1.371e-04 8.231e-04 1.670e-05 | 3.624e+01 | -4.85 -3.25
********************
-4.8498 -3.25349
0.419524 0.718029
0.000138241 -0.000823084
********************

png

# gamma #
ipar = 7
chains = get_time_scale(ipar, use_opt=False)
ts = np.log10(1./(np.exp(chains[:,1] + scale_shift) ) )
h1 = np.histogram(chains[:,0], 60)
plt.subplot(121)
h2 = plt.hist(chains[:,1], 60)
plt.subplot(122)
hs = plt.hist(ts, 60)
hist[par_names[ipar]] = [h1[:2], h2[:2], hs[:2], chains]

   1 2.370e+00 1.784e+01   inf | -3.348e+01 |    -5    -4
   2 3.777e+00 1.307e+01 3.630e+01 | 2.813e+00 | -4.85    -2
   3   inf 2.523e+00 1.521e+01 | 1.802e+01 | -4.73     0
   4 2.448e+01 2.526e+00 9.248e-01 | 1.895e+01 | -3.92     2
   5 4.488e+02 4.570e+00 -1.118e+00 | 1.783e+01 | -1.92     4
   6 5.527e+05 4.625e+00 -4.357e-02 | 1.778e+01 | 0.0817     6
   7 1.171e+06 4.626e+00 -8.152e-04 | 1.778e+01 |  2.08     8
   8 9.438e+04 4.626e+00 -1.494e-05 | 1.778e+01 |  4.08    10
********************
4.08171   10
 nan  nan
-4.62579 4.62579
********************


/u/home/abzoghbi/soft/etc/python/plag/plag.py:562: RuntimeWarning: invalid value encountered in sqrt
  p, pe = tmpp, np.sqrt(np.diagonal(hinv))

png

# bnorm #
ipar = 10
chains = get_time_scale(ipar, use_opt=True)
ts = np.log10(1./(np.exp(chains[:,1] + scale_shift) ) )
h1 = np.histogram(chains[:,0], 60)
plt.subplot(121)
h2 = plt.hist(chains[:,1], 60)
plt.subplot(122)
hs = plt.hist(ts, 60)
hist[par_names[ipar]] = [h1[:2], h2[:2], hs[:2], chains]

   1 2.846e+00 4.707e+01   inf | 3.113e+01 |    -5    -4
   2 4.288e-01 1.016e+01 8.766e+01 | 1.188e+02 |    -3    -2
   3 2.268e-01 3.905e+00 2.619e+00 | 1.214e+02 | -1.71 -2.07
   4 3.930e-02 8.188e-01 9.900e-01 | 1.224e+02 |  -2.1 -2.14
   5 2.801e-03 9.682e-03 3.759e-02 | 1.224e+02 | -2.18 -2.18
   6 1.006e-03 3.765e-03 4.736e-05 | 1.224e+02 | -2.18 -2.18
********************
-2.18314 -2.18309
0.356621 0.750248
0.000768923 -0.00376461
********************

png

# g@0.5 #
ipar = 12
chains = get_time_scale(ipar, use_opt=True)
ts = np.log10(1./(np.exp(chains[:,1] + scale_shift) ) )
h1 = np.histogram(chains[:,0], 60)
plt.subplot(121)
h2 = plt.hist(chains[:,1], 60)
plt.subplot(122)
hs = plt.hist(ts, 60)
hist[par_names[ipar]] = [h1[:2], h2[:2], hs[:2], chains]

   1 1.130e+00 1.393e+01   inf | 1.544e+02 |    -5    -4
   2 5.587e-01 2.028e+00 2.342e+01 | 1.778e+02 |    -3    -2
   3 3.114e-02 9.982e-02 1.324e+00 | 1.792e+02 | -3.39 -0.883
   4 2.002e-02 1.355e-02 7.561e-04 | 1.792e+02 | -3.38 -0.855
   5 1.648e-02 8.965e-03 2.169e-05 | 1.792e+02 | -3.38 -0.872
********************
-3.37709 -0.872319
0.343078 1.2661
-0.00343262 0.00896493
********************

png

# g@0.9 #
ipar = 14
chains = get_time_scale(ipar, use_opt=True)
ts = np.log10(1./(np.exp(chains[:,1] + scale_shift) ) )
h1 = np.histogram(chains[:,0], 60)
plt.subplot(121)
h2 = plt.hist(chains[:,1], 60)
plt.subplot(122)
hs = plt.hist(ts, 60)
hist[par_names[ipar]] = [h1[:2], h2[:2], hs[:2], chains]

   1 9.403e-01 1.030e+01   inf | 1.997e+02 |    -5    -4
   2 1.324e-01 3.257e+00 1.191e+01 | 2.117e+02 |    -3    -2
   3 4.187e-02 5.177e-01 7.965e-01 | 2.124e+02 |  -3.4 -2.07
   4 2.210e-02 6.397e-02 2.648e-02 | 2.125e+02 | -3.46 -2.16
   5 8.062e-03 2.655e-02 2.197e-03 | 2.125e+02 | -3.45  -2.2
   6 2.971e-03 9.712e-03 3.140e-04 | 2.125e+02 | -3.45 -2.22
   7 1.068e-03 3.532e-03 4.256e-05 | 2.125e+02 | -3.44 -2.23
********************
-3.44475 -2.22792
0.403349 0.839083
-0.000921179 -0.00353154
********************

png

os.system('mkdir -p results/other_pars')
np.savez('results/other_pars/lor_hist.npz', hist=hist, fit=fit, par_names=par_names)

plt.plot(hist['nh'][2][1][1:], hist['nh'][2][0])

[<matplotlib.lines.Line2D at 0x7fd1a4f2a208>]

png

d = np.load('results/other_pars/lor_hist.npz')
hist = d['hist'][()]
fit = d['fit']
par_names = d['par_names']
scale_shift = d['scale_shift'][()]

(50000, 2)

q = np.quantile(1./np.exp(hist['pflx'][3][:,1] + scale_shift), [0.5, 0.16, 0.84])
print(q[0], q[1]-q[0], q[2]-q[0])

19.540324055756834 -9.475743146306439 20.324145457800014

q = np.quantile(1./np.exp(hist[‘nh’][3][:,1] + scale_shift), [0.5, 0.16, 0.84])

print(q[0], q[1]-q[0], q[2]-q[0])

q = np.quantile(1./np.exp(hist['cf'][3][:,1] + scale_shift), [0.5, 0.16, 0.84])
print(q[0], q[1]-q[0], q[2]-q[0])

4.831323970781556 -2.9364150632206916 6.039728554021691



fit_4d1: replace PC with PL

os.chdir('%s/%s'%(base_dir, spec_dir))
suff = '4d1'
fit_4d1 = fit_xspec_model('fit_%s'%suff, spec_ids, base_dir)

# plot the result #
par_names = ['xl_nh', 'xl_xi', 'p2flx', 'xh_nh', 'xh_xi', 'nh', 'pflx', 'gam', 'xflx', 
             'bT', 'bnrm']
fit = fit_4d1
fig = plt.figure(figsize=(12,7))
idx = [0,1,2,3,4,5,7,8,9,10]; iref = 6
for i,ix in enumerate(idx):
    ax = plt.subplot(3,len(idx)//3+1,i+1)
    plt.errorbar(fit[:,iref,0], fit[:,ix,0], fit[:,ix,1], xerr=fit[:,iref,1], 
                 fmt='o', ms=8, lw=0.5)
    ax.set_xlabel(par_names[iref]); ax.set_ylabel(par_names[ix])
plt.tight_layout(pad=0)

png

# 2nd powerlaw #
ipar = 2
chains = get_time_scale(ipar)
ts = np.log10(1./(np.exp(chains[:,1] + scale_shift) ) )
h1 = np.histogram(chains[:,0], 60)
plt.subplot(121)
h2 = plt.hist(chains[:,1], 60)
plt.subplot(122)
hs = plt.hist(ts, 60)
hist[par_names[ipar]] = [h1[:2], h2[:2], hs[:2], chains]

   1 1.721e-01 8.202e+00   inf | -4.139e+00 |    -5    -4
   2 1.136e-01 5.478e+00 5.872e+00 | 1.733e+00 | -5.86 -3.95
   3 5.293e-02 2.231e+00 2.483e+00 | 4.216e+00 | -6.53 -3.81
   4 3.209e-02 3.429e-01 4.279e-01 | 4.644e+00 | -6.87 -3.62
   5 1.015e-02 6.647e-02 2.590e-02 | 4.670e+00 | -6.96  -3.5
   6 2.714e-03 1.782e-02 1.611e-03 | 4.672e+00 | -6.97 -3.47
   7 7.214e-04 4.752e-03 1.122e-04 | 4.672e+00 | -6.97 -3.46
   8 1.915e-04 1.262e-03 7.870e-06 | 4.672e+00 | -6.97 -3.46
********************
-6.97462 -3.45557
0.429131 0.708857
-0.00016565 0.00126185
********************

png

q = np.quantile(1./np.exp(chains[:,1] + scale_shift), [0.5, 0.16, 0.84])
print(q[0], q[1]-q[0], q[2]-q[0])

6.633923154126462 -3.8989064638560604 8.319376812510656

fit_4c1: force low xi for 1,2 and explore xi variations

os.chdir('%s/%s'%(base_dir, spec_dir))
suff = '4c1'
fit_4c1 = fit_xspec_model('fit_%s'%suff, spec_ids, base_dir)

# plot the result #
par_names = ['xl_nh', 'xl_xi', 'p2flx', 'xh_nh', 'xh_xi', 'nh', 'pflx', 'gam', 'xflx', 
             'bT', 'bnrm']
fit = fit_4c1
fig = plt.figure(figsize=(12,7))
idx = [0,1,2,3,4,5,7,8,9,10]; iref = 6
for i,ix in enumerate(idx):
    ax = plt.subplot(3,len(idx)//3+1,i+1)
    plt.errorbar(fit[:,iref,0], fit[:,ix,0], fit[:,ix,1], xerr=fit[:,iref,1], 
                 fmt='o', ms=8, lw=0.5)
    ax.set_xlabel(par_names[iref]); ax.set_ylabel(par_names[ix])
plt.tight_layout(pad=0)

png

# pflx
xarr, xerr = fit[:,6,0], fit[:,6,1]
# xi
yarr, yerr = fit[:,1,0], fit[:,1,1]
plt.errorbar(xarr, yarr, yerr, xerr=xerr, fmt='o')

<ErrorbarContainer object of 3 artists>

png

X = np.random.randn(200, len(xarr)) * xerr + xarr
Y = np.random.randn(200, len(xarr)) * yerr + yarr
xdat, ydat, xmod, ymod, text = fit_linear_model(X, Y, 'pf_xi', spec_ids)

# pf_xi: spearman r,pvalue:    0.511  0.00754

print(text)

# pf_xi: spearman r,pvalue:    0.511  0.00754
# fit pars:    1.893 +-   0.1365,    20.07 +-    1.388

descriptor pf_xi_x pf_xi_y,+-
-10.8061 -0.404963 0.0997348
-10.7843 -0.363537 0.0971211
-10.7625 -0.322297 0.0945289
-10.7407 -0.281056 0.0919601
-10.7189 -0.239941 0.0894165
-10.6971 -0.19905 0.0869006
-10.6753 -0.157518 0.0844146
-10.6535 -0.117419 0.0819614
-10.6317 -0.0765933 0.0795439
-10.6099 -0.0349582 0.0771657
-10.5881 0.00621111 0.0748303
-10.5663 0.0484322 0.0725419
-10.5445 0.0887713 0.0703051
-10.5227 0.130923 0.068125
-10.5009  0.17165 0.0660072
-10.4791   0.2113 0.0639579
-10.4573 0.252881 0.061984
-10.4355 0.294765 0.0600927
-10.4137 0.336772 0.0582922
-10.3919 0.378032 0.0565912
  -10.37 0.418102 0.0549988
-10.3482 0.459333 0.0535247
-10.3264 0.500512 0.0521791
-10.3046 0.540325 0.050972
-10.2828 0.580577 0.0499135
 -10.261 0.620525 0.0490133
-10.2392 0.662218 0.0482801
-10.2174 0.705674 0.0477218
-10.1956 0.745523 0.0473444
-10.1738 0.786374 0.0471524
 -10.152  0.82581 0.0471479
-10.1302 0.867496 0.0473312
-10.1084 0.910311 0.0476998
-10.0866 0.951733 0.0482498
-10.0648 0.992384 0.0489748
 -10.043  1.03392 0.0498673
-10.0212  1.07529 0.0509185
-9.99939  1.11537 0.0521188
-9.97758  1.15681 0.0534582
-9.95578  1.19784 0.0549264
descriptor id pf_xi_xd,+- pf_xi_yd,+-
   1 -10.8061 0.00565998 -0.993889 0.100214
   2  -10.778 0.00335872 -0.481052 0.310682
   3 -10.7922 0.00696853 -0.375095  0.11419
   4 -10.0709 0.00346941  1.47806 0.150863
   5 -10.0728 0.00438831  1.37328 0.0677261
   6  -9.9961 0.0021622  1.21713 0.102057
   7 -10.7202 0.00660506 -0.179783 0.231239
   8 -10.4155 0.00661477  1.31811 0.148171
  10 -10.1376 0.00947865  1.09424 0.185907
  11  -10.136 0.00519688  1.08682 0.136444
  12 -9.99641 0.0054289   1.1121 0.0496004
  13 -10.3636 0.0086133  1.13198 0.358864
  15 -10.0906 0.00790318  1.52834  0.14184
  16 -10.3755 0.00606473 0.614552 0.201328
  17 -10.2757 0.00767961 0.600153  0.46684
  18 -10.4052 0.00531393  1.12288 0.128568
  19  -10.324 0.00990581 -0.582264 0.573452
  20 -10.1783 0.00536644 -0.159649  0.28637
  21  -10.156 0.00345762 -0.837471 0.183429
  22 -9.99302 0.00625617 0.494651 0.187846
  23 -9.95578 0.00465043  1.14026 0.0915122
  24 -10.0624 0.00567419  1.22119 0.0834203