upload notebooks

bda75d0e · Yifan Wang · d3dbcce1 · bda75d0e · bda75d0e · bda75d0e
Commit bda75d0e authored 3 years ago by Yifan Wang
--- a/notebook/1-plot-GW190521-GW191109-GW200220.ipynb
+++ b/notebook/1-plot-GW190521-GW191109-GW200220.ipynb
@@ -377,23 +377,26 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 1,
   "id": "2b6d8f06",
   "metadata": {},
   "outputs": [
    {
-     "data": {
-      "text/plain": [
-       "['#0072B2', '#009E73', '#D55E00', '#CC79A7', '#F0E442', '#56B4E9']"
+     "ename": "NameError",
+     "evalue": "name 'matplotlib' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m/local/user/yifan.wang/ipykernel_3285978/1511099479.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'color'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'axes.prop_cycle'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'matplotlib' is not defined"
     ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
    }
   ],
   "source": [
-    "[c['color'] for c in matplotlib.rcParams['axes.prop_cycle']]      "
+    "[c['color'] for c in matplotlib.rcParams['axes.prop_cycle']]  \n",
+    "plt.figure()\n",
+    "plt.plot([1,2],color=c)"
   ]
  },
  {

 %% Cell type:code id:8cf15b3c tags:

 ``` python
 %matplotlib notebook
 ```

 %% Cell type:code id:d5332cc4 tags:

 ``` python
 import itertools
 import numpy
 import matplotlib
 from matplotlib import patches
 from matplotlib import pyplot
 from pycbc import waveform
 from pycbc.inference import io
 from pycbc import conversions

 from scripts import scatter_histograms
 ```

 %% Cell type:code id:0dd8e4c7 tags:

 ``` python
 pyplot.style.use('seaborn-colorblind')
 ```

 %% Cell type:code id:29a5dff2 tags:

 ``` python
 color_cycle = [c['color'] for c in matplotlib.rcParams['axes.prop_cycle']]
 colors = itertools.cycle(color_cycle)

 # cycle around
 for _ in range(5):
    next(colors)
 ```

 %% Cell type:markdown id:f7551a5f tags:

 # PyCBC Inference Read Samples

 %% Cell type:code id:29d23980 tags:

 ``` python
 filenames = {'GW190521': '/work/yifan.wang/gw190521_xrerun/t3-localrun/0_700/result.hdf',
             'GW191109': '/work/yifan.wang/testingGR-o3b/t2-o3bevents/o3btgr_output/samples_files/H1L1V1-INFERENCE_GW191109_010717-1126259200-400.hdf',
            'GW200220':'/work/yifan.wang/testingGR-o3b/t4-mpi-massive/t2-200220-mpv1000/result.hdf'}
 samples = {}
 for lbl, fn in filenames.items():
    fp = io.loadfile(fn, 'r')
    s = fp.read_samples(['srcmchirp', 'q', 'parity_mpvinverse'])
    samples[lbl] = s
    fp.close()
 ```

 %% Cell type:code id:a94f9251 tags:

 ``` python
 filelabels = {'GW190521': 'GW190521',
              'GW191109': 'GW191109',
              'GW200220': 'GW200220'
             }
 ```

 %% Cell type:code id:fc371486 tags:

 ``` python
 samples
 ```

 %% Output

    {'GW190521': rec.array([(-27873.26400656, 83.26078987, 436.04949975, 1.43400538),
                (-27875.2432163 , 71.57790771, 541.07468378, 1.64651789),
                (-27875.26155477, 74.09536189, 440.63653902, 1.73495024), ...,
                (-27880.60033757, 63.38915481, 277.02222192, 1.29027691),
                (-27869.3461176 , 95.44301611, 381.03490203, 1.50594504),
                (-27877.14788405, 79.98899193, 313.76083404, 1.28679349)],
               dtype=[('loglikelihood', '<f8'), ('srcmchirp', '<f8'), ('parity_mpvinverse', '<f8'), ('q', '<f8')]),
     'GW191109': rec.array([(-17938.0557928 , 48.73074706, 144.75730556, 1.16390601),
                (-17942.74637522, 52.43607729, 329.71287488, 1.02073016),
                (-17942.07580232, 41.9276795 , 180.19740616, 2.19670947), ...,
                (-17940.19991335, 48.96967047, 405.20982651, 1.45601162),
                (-17935.2245303 , 50.17113232, 235.30007999, 1.16774801),
                (-17938.96972275, 53.97280568, 244.20575908, 1.28908152)],
               dtype=[('loglikelihood', '<f8'), ('srcmchirp', '<f8'), ('parity_mpvinverse', '<f8'), ('q', '<f8')]),
     'GW200220': rec.array([(-81869.72084721,  64.4399525 , 511.48166829, 2.134752  ),
                (-81863.8137886 ,  89.29121051, 418.60442017, 3.00415035),
                (-81867.14210206,  67.036858  , 239.90584564, 1.45710404), ...,
                (-81863.32826024,  59.46329134, 606.33211039, 2.63692637),
                (-81864.82296474,  69.8439018 , 467.65624429, 1.19154618),
                (-81866.23224365, 104.03905068, 100.25604593, 2.5061787 )],
               dtype=[('loglikelihood', '<f8'), ('srcmchirp', '<f8'), ('parity_mpvinverse', '<f8'), ('q', '<f8')])}

 %% Cell type:code id:ea533ca3 tags:

 ``` python
 labels = {'srcmchirp': waveform.parameters.srcmchirp.label,
          'q': waveform.parameters.q.label,
          'parity_mpvinverse':'$M_\mathrm{PV}^{-1} (\mathrm{GeV}^{-1})$'
         }
 ```

 %% Cell type:code id:d1fc164a tags:

 ``` python
 x = 'srcmchirp'
 y = 'q'
 z = 'parity_mpvinverse'
 mins = {x: min(s[x].min() for s in samples.values()),
        y: min(s[y].min() for s in samples.values()),
        z: min(s[z].min() for s in samples.values())
       }
 maxs = {x: max(s[x].max() for s in samples.values()),
        y: min(max(s[y].max() for s in samples.values()),6),
        z: max(s[z].max() for s in samples.values())
       }
 ```

 %% Cell type:code id:e95d473c tags:

 ``` python
 mins
 ```

 %% Output

    {'srcmchirp': 31.170769850622765,
     'q': 1.0000229039873965,
     'parity_mpvinverse': 0.006938599419005487}

 %% Cell type:code id:6ed4aeed tags:

 ``` python
 maxs
 ```

 %% Output

    {'srcmchirp': 149.1743322543928,
     'q': 6,
     'parity_mpvinverse': 999.9916720638518}

 %% Cell type:code id:e84b1343 tags:

 ``` python
 # create a pool for evaluating the kombine kde
 from pycbc.pool import choose_pool
 pool = choose_pool(10)
 ```

 %% Cell type:code id:37a7a798 tags:

 ``` python
 samples
 ```

 %% Output

    {'GW190521': rec.array([(-27873.26400656, 83.26078987, 436.04949975, 1.43400538),
                (-27875.2432163 , 71.57790771, 541.07468378, 1.64651789),
                (-27875.26155477, 74.09536189, 440.63653902, 1.73495024), ...,
                (-27880.60033757, 63.38915481, 277.02222192, 1.29027691),
                (-27869.3461176 , 95.44301611, 381.03490203, 1.50594504),
                (-27877.14788405, 79.98899193, 313.76083404, 1.28679349)],
               dtype=[('loglikelihood', '<f8'), ('srcmchirp', '<f8'), ('parity_mpvinverse', '<f8'), ('q', '<f8')]),
     'GW191109': rec.array([(-17938.0557928 , 48.73074706, 144.75730556, 1.16390601),
                (-17942.74637522, 52.43607729, 329.71287488, 1.02073016),
                (-17942.07580232, 41.9276795 , 180.19740616, 2.19670947), ...,
                (-17940.19991335, 48.96967047, 405.20982651, 1.45601162),
                (-17935.2245303 , 50.17113232, 235.30007999, 1.16774801),
                (-17938.96972275, 53.97280568, 244.20575908, 1.28908152)],
               dtype=[('loglikelihood', '<f8'), ('srcmchirp', '<f8'), ('parity_mpvinverse', '<f8'), ('q', '<f8')]),
     'GW200220': rec.array([(-81869.72084721,  64.4399525 , 511.48166829, 2.134752  ),
                (-81863.8137886 ,  89.29121051, 418.60442017, 3.00415035),
                (-81867.14210206,  67.036858  , 239.90584564, 1.45710404), ...,
                (-81863.32826024,  59.46329134, 606.33211039, 2.63692637),
                (-81864.82296474,  69.8439018 , 467.65624429, 1.19154618),
                (-81866.23224365, 104.03905068, 100.25604593, 2.5061787 )],
               dtype=[('loglikelihood', '<f8'), ('srcmchirp', '<f8'), ('parity_mpvinverse', '<f8'), ('q', '<f8')])}

 %% Cell type:code id:737146e4 tags:

 ``` python
 filecolors = {}
 fig, axd = (None, None)
 for run in samples:
    c = next(colors)
    filecolors[run] = c
    s = samples[run]
    fig, axd = scatter_histograms.create_multidim_plot(
        ['parity_mpvinverse', 'srcmchirp'], s, labels=labels,
        mins=mins, maxs=maxs,
        plot_scatter=False, plot_contours=True,
        marginal_percentiles=[], contour_percentiles=[50, 90],
        fill_color=None, contour_color=c, hist_color=c, line_color=c,
        contour_ls=['solid', 'dashed'], contour_labels=False, fold_masses=True,
        use_kombine=True, kdeargs={'pool': pool, 'max_samples': 200},
        fig=fig, axis_dict=axd,
    )

    # add legend
 handles = []
 lbls = []
 for run, c in filecolors.items():
    l = filelabels[run]
    handles.append(patches.Patch(color=c, label=l))
    lbls.append(l)
 fig.legend(loc=(0.7, 0.75), handles=handles, labels=lbls)
 fig.savefig('3events.png')
 ```

 %% Cell type:code id:95c5bcdf tags:

 ``` python
 color_cycle = ['#1f77b4', '#ff7f0e']
 colors = itertools.cycle(color_cycle)

 # cycle around
 for _ in range(5):
    next(colors)
 ```

 %% Cell type:code id:e69aacc6 tags:

 ``` python
 color_cycle = [c['color'] for c in matplotlib.rcParams['axes.prop_cycle']]
 colors = itertools.cycle(color_cycle)

 # cycle around
 #for _ in range(5):
 #    next(colors)
 ```

 %% Cell type:code id:5902c7f1 tags:

 ``` python
 for i in range(5):
    print(i)
 ```

 %% Output

    0
    1
    2
    3
    4

 %% Cell type:code id:4ff63d8a tags:

 ``` python
 color_cycle
 ```

 %% Output

    ['#0072B2', '#009E73', '#D55E00', '#CC79A7', '#F0E442', '#56B4E9']

 %% Cell type:code id:2b6d8f06 tags:

 ``` python
 [c['color'] for c in matplotlib.rcParams['axes.prop_cycle']]
+plt.figure()
+plt.plot([1,2],color=c)
 ```

 %% Output

-    ['#0072B2', '#009E73', '#D55E00', '#CC79A7', '#F0E442', '#56B4E9']
+    ---------------------------------------------------------------------------
+    NameError                                 Traceback (most recent call last)
+    /local/user/yifan.wang/ipykernel_3285978/1511099479.py in <module>
+    ----> 1 [c['color'] for c in matplotlib.rcParams['axes.prop_cycle']]
+          2 plt.figure()
+          3 plt.plot([1,2],color=c)
+    NameError: name 'matplotlib' is not defined

 %% Cell type:code id:87080ba5 tags:

 ``` python
 filecolors = {}
 fig, axd = (None, None)
 for run in samples:
    c = next(colors)
    filecolors[run] = c
    s = samples[run]
    fig, axd = scatter_histograms.create_multidim_plot(
        ['parity_mpvinverse', 'srcmchirp','q'], s, labels=labels,
        mins=mins, maxs=maxs,
        plot_scatter=False, plot_contours=True,
        marginal_percentiles=[], contour_percentiles=[50, 90],
        fill_color=None, contour_color=c, hist_color=c, line_color=c,
        contour_ls=['solid', 'dashed'], contour_labels=False, fold_masses=True,
        use_kombine=True, kdeargs={'pool': pool, 'max_samples': 200},
        fig=fig, axis_dict=axd,
    )

    # add legend
 handles = []
 lbls = []
 for run, c in filecolors.items():
    l = filelabels[run]
    handles.append(patches.Patch(color=c, label=l))
    lbls.append(l)
 fig.legend(loc=(0.7, 0.75), handles=handles, labels=lbls)

 fig.show()
 fig.savefig('./3events-2.png')
 ```

 %% Cell type:code id:132771ea tags:

 ``` python
 ```

--- a/notebook/1-plot-GW190521-GW191109.ipynb
+++ b/notebook/1-plot-GW190521-GW191109.ipynb
--- a/notebook/2-makeMpv.ipynb
+++ b/notebook/2-makeMpv.ipynb
--- a/notebook/3-plot-waveform.ipynb
+++ b/notebook/3-plot-waveform.ipynb
@@ -2,7 +2,7 @@
 "cells": [
  {
   "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 1,
   "id": "expressed-speed",
   "metadata": {},
   "outputs": [],
@@ -40,7 +40,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 2,
   "id": "settled-motel",
   "metadata": {},
   "outputs": [],

--- a/notebook/GW190521plot.ipynb
+++ b/notebook/GW190521plot.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "expressed-speed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pycbc.inference import io, models\n",
+    "from pycbc import waveform\n",
+    "from pycbc.detector import Detector\n",
+    "from pycbc import cosmology\n",
+    "\n",
+    "# PLOTTING OPTIONS\n",
+    "fig_width_pt = 3*246.0  # Get this from LaTeX using \\showthe\\columnwidth\n",
+    "inches_per_pt = 1.0/72.27               # Convert pt to inch\n",
+    "golden_mean = (np.sqrt(5)-1.0)/2.0         # Aesthetic ratio\n",
+    "fig_width = fig_width_pt*inches_per_pt  # width in inches\n",
+    "fig_height = fig_width*golden_mean      # height in inches\n",
+    "fig_size =  [fig_width,fig_height]\n",
+    "\n",
+    "params = { 'axes.labelsize': 24,\n",
+    "          'font.family': 'serif',\n",
+    "          'font.serif': 'Computer Modern Raman',\n",
+    "          'font.size': 24,\n",
+    "          'legend.fontsize': 20,\n",
+    "          'xtick.labelsize': 24,\n",
+    "          'ytick.labelsize': 24,\n",
+    "          'axes.grid' : True,\n",
+    "          'text.usetex': True,\n",
+    "          'savefig.dpi' : 100,\n",
+    "          'lines.markersize' : 14,\n",
+    "          'figure.figsize': fig_size}\n",
+    "\n",
+    "mpl.rcParams.update(params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "settled-motel",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fn_to_waveform(fn):       \n",
+    "    # read in the raw posterior file and extract the max loglikelihood parameter value\n",
+    "    with io.loadfile(fn, 'r') as fp:   \n",
+    "        data = fp.read_data()                                                           \n",
+    "        psds = fp.read_psds()                                                           \n",
+    "        cp = fp.read_config_file()\n",
+    "        samples = fp.read_samples(list(fp['samples'].keys()))\n",
+    "        idx = samples['loglikelihood'].argmax()\n",
+    "        params = {p: samples[p][idx] for p in samples.fieldnames}\n",
+    "        redshift = cosmology.redshift(params['distance'])\n",
+    "        params['comoving_volume'] = cosmology.cosmological_quantity_from_redshift(redshift, 'comoving_volume')\n",
+    "    # set up the model\n",
+    "    model = models.read_from_config(cp, data=data, psds=psds)\n",
+    "    model.update(**params)\n",
+    "    _ = model.loglikelihood\n",
+    "    pol = model.current_stats['maxl_polarization']\n",
+    "    #compute the max loglikelihood waveform\n",
+    "    wfs = model.waveform_generator.generate(polarization=pol,**model.current_params)\n",
+    "    \n",
+    "    whdata = {}\n",
+    "    for det in model.data:\n",
+    "        d = model.data[det]\n",
+    "        d = d.copy()\n",
+    "        d *= model.weight[det] / (4*model.psds[det].delta_f)**0.5\n",
+    "        whdata[det] = d.to_timeseries()\n",
+    "    \n",
+    "    waveforms = {}\n",
+    "    whitened_waveforms = {}\n",
+    "    for detname in wfs:\n",
+    "        hs = {}\n",
+    "        whitend_hs = {}\n",
+    "        det = Detector(detname)\n",
+    "        fp, fc = det.antenna_pattern(model.current_params['ra'],\n",
+    "                                     model.current_params['dec'],\n",
+    "                                     pol,\n",
+    "                                     model.current_params['tc'])\n",
+    "        hp, hc = wfs[detname]\n",
+    "        h = fp*hp + fc*hc\n",
+    "        h.resize(len(model.weight[detname]))\n",
+    "        wh = h.copy()\n",
+    "        wh *= model.weight[detname] / (4*model.psds[detname].delta_f)**0.5\n",
+    "        whitened_waveforms[detname] = wh.to_timeseries() \n",
+    "    return whdata, whitened_waveforms,model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8691a3c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:No sampling_params section read from config file\n",
+      "WARNING:root:WARNING: The following args are not being used for waveform generation: redshift, srcmchirp, comoving_volume, delta_tc, parity_mpvinverse, q, baseapprox, trigger_time\n",
+      "WARNING: fval is not bracketed by func(zmin)=3.630907107506232e-13 Mpc3 and func(zmax)=164042102234.23068 Mpc3. This means either there is no solution, or that there is more than one solution between zmin and zmax satisfying fval = func(z). [astropy.cosmology.funcs]\n",
+      "WARNING:astropy:fval is not bracketed by func(zmin)=3.630907107506232e-13 Mpc3 and func(zmax)=164042102234.23068 Mpc3. This means either there is no solution, or that there is more than one solution between zmin and zmax satisfying fval = func(z).\n",
+      "WARNING: fval is not bracketed by func(zmin)=3.630907107506232e-13 Mpc3 and func(zmax)=3763143105730.4414 Mpc3. This means either there is no solution, or that there is more than one solution between zmin and zmax satisfying fval = func(z). [astropy.cosmology.funcs]\n",
+      "WARNING:astropy:fval is not bracketed by func(zmin)=3.630907107506232e-13 Mpc3 and func(zmax)=3763143105730.4414 Mpc3. This means either there is no solution, or that there is more than one solution between zmin and zmax satisfying fval = func(z).\n",
+      "WARNING:root:No sampling_params section read from config file\n",
+      "WARNING:root:WARNING: The following args are not being used for waveform generation: redshift, srcmchirp, comoving_volume, delta_tc, q, trigger_time\n"
+     ]
+    }
+   ],
+   "source": [
+    "parity_path = '/work/yifan.wang/testingGR-o3b/snr/GW190521/convert.hdf'\n",
+    "parity_whdata, parity_whwaveform, parity_model = fn_to_waveform(parity_path)\n",
+    "\n",
+    "gr_path = '/work/yifan.wang/gw190521_xrerun/t1-mpi-gr/mpirun_output/config_files/convert.hdf'\n",
+    "_, gr_whwaveform,gr_model = fn_to_waveform(gr_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d27e743c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xlim=(-0.1, 0.1)\n",
+    "fig = plt.figure(figsize=(10.211706102117061, 6.311181454233049*1.6))\n",
+    "fig.subplots_adjust(hspace=.2)\n",
+    "ax = fig.add_subplot(311);\n",
+    "\n",
+    "tc = parity_model.static_params['trigger_time']\n",
+    "det = list(gr_whwaveform.keys())\n",
+    "wdata = parity_whdata[det[0]]\n",
+    "ax.plot(wdata.sample_times-tc, wdata, c='k', alpha=0.2, label='whitened data')\n",
+    "\n",
+    "wwf = parity_whwaveform[det[0]]\n",
+    "ax.plot(wwf.sample_times-tc, wwf,label='birefringence')\n",
+    "ax.plot(gr_whwaveform[det[0]].sample_times-tc, gr_whwaveform[det[0]],label='GR')\n",
+    "\n",
+    "ax.grid(True)\n",
+    "ax.set_xticklabels([])\n",
+    "ax.set_xlim(*xlim)\n",
+    "ax.set_ylim(-100,100)\n",
+    "ax.text(0.054, 70, 'LIGO Hanford', style='italic',fontsize=20,\n",
+    "        bbox={'facecolor':'white', 'alpha':0.5, 'pad':5,'edgecolor':'none'})\n",
+    "ax.set_title('GW190521')\n",
+    "################\n",
+    "bx = fig.add_subplot(312);\n",
+    "wdata = parity_whdata[det[1]]\n",
+    "bx.plot(wdata.sample_times-tc, wdata, c='k', alpha=0.2, label='whitened data')\n",
+    "    \n",
+    "wwf = parity_whwaveform[det[1]]\n",
+    "bx.plot(wwf.sample_times-tc, wwf,label='birefringence')\n",
+    "bx.plot(gr_whwaveform[det[1]].sample_times-tc, gr_whwaveform[det[1]],label='GR')\n",
+    "\n",
+    "bx.set_ylabel('Whitened Strain')\n",
+    "bx.set_xlim(*xlim)\n",
+    "bx.set_ylim(-100,100)\n",
+    "bx.text(0.0515, 70, 'LIGO Livingston', style='italic',fontsize=20,\n",
+    "        bbox={'facecolor':'white', 'alpha':0.5, 'pad':0,'edgecolor':'none'})\n",
+    "bx.grid(True)\n",
+    "bx.set_xticklabels([])\n",
+    "\n",
+    "################\n",
+    "cx = fig.add_subplot(313);\n",
+    "wdata = parity_whdata[det[2]]\n",
+    "cx.plot(wdata.sample_times-tc, wdata, c='k', alpha=0.2, label='whitened data')\n",
+    "    \n",
+    "wwf = parity_whwaveform[det[2]]\n",
+    "cx.plot(wwf.sample_times-tc, wwf,label='birefringence')\n",
+    "cx.plot(gr_whwaveform[det[2]].sample_times-tc, gr_whwaveform[det[2]],label='GR')\n",
+    "cx.text(0.07, 70, 'Virgo', style='italic',fontsize=20,\n",
+    "        bbox={'facecolor':'white', 'alpha':0.5, 'pad':5,'edgecolor':'none'})\n",
+    "\n",
+    "cx.set_xlim(*xlim)\n",
+    "cx.set_ylim(-100,100)\n",
+    "cx.set_xlabel('Time - '+str(tc)+' [s]')\n",
+    "cx.legend(loc='lower right', ncol=3,framealpha=0)\n",
+    "fig.savefig('0521_gw190521.pdf',bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a2769cdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'inclination': 1.3773388232444717,\n",
+       " 'maxl_polarization': 5.132211422080623,\n",
+       " 'srcmchirp': 59.988695836431965,\n",
+       " 'logwt': -28046.517628006437,\n",
+       " 'V1_optimal_snrsq': 4.645117051883857,\n",
+       " 'parity_mpvinverse': 571.5796601060491,\n",
+       " 'distance': 1333.6349776943568,\n",
+       " 'spin1_a': 0.9116850950619036,\n",
+       " 'dec': 0.5513436858058255,\n",
+       " 'delta_tc': 0.0061986454908000005,\n",
+       " 'spin1_polar': 2.076365152215487,\n",
+       " 'spin2_azimuthal': 6.273223231326342,\n",
+       " 'spin2_polar': 0.9148528363767893,\n",
+       " 'ra': 3.3490655420980504,\n",
+       " 'logprior': -49.260229458075116,\n",
+       " 'spin2_a': 0.09566031928959656,\n",
+       " 'coa_phase': 2.930192323180816,\n",
+       " 'loglr': 122.35747765652349,\n",
+       " 'L1_optimal_snrsq': 163.8200365758998,\n",
+       " 'loglikelihood': -28001.80399824368,\n",
+       " 'logjacobian': 0.0,\n",
+       " 'q': 4.542030730356726,\n",
+       " 'maxl_loglr': 125.90973288111286,\n",
+       " 'spin1_azimuthal': 2.6322431174435468,\n",
+       " 'H1_optimal_snrsq': 69.48418153589377,\n",
+       " 'comoving_volume': 5018380778.811694,\n",
+       " 'approximant': 'mpvnosmall',\n",
+       " 'baseapprox': 'IMRPhenomXPHM',\n",
+       " 'f_lower': 20.0,\n",
+       " 'f_ref': 20.0,\n",
+       " 'trigger_time': 1242442967.4473,\n",
+       " 'spin1x': -0.6963825154031204,\n",
+       " 'spin1y': 0.3889331472174398,\n",
+       " 'spin1z': -0.44153381727155666,\n",
+       " 'spin2x': 0.07580444315196702,\n",
+       " 'spin2y': -0.0007551945954393175,\n",
+       " 'spin2z': 0.05834391798748846,\n",
+       " 'tc': 1242442967.4534986,\n",
+       " 'redshift': 0.2556724782843754,\n",
+       " 'mass1': 194.34779119906074,\n",
+       " 'mass2': 42.78874422846471}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "parity_model.current_params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7476300f",
+   "metadata": {},
+   "source": [
+    "# SNR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6bd3b807",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import h5py\n",
+    "from pycbc.conversions import snr_from_loglr\n",
+    "nongr = h5py.File(parity_path,'r')\n",
+    "gr = h5py.File(gr_path,'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2b6eb9a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15.643367774013592"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snr_from_loglr(np.max(nongr['samples']['loglr']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "358ab6ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15.312425524512493"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snr_from_loglr(np.max(gr['samples']['loglr']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eae483fd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
+%% Cell type:code id:expressed-speed tags:
+
+``` python
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from pycbc.inference import io, models
+from pycbc import waveform
+from pycbc.detector import Detector
+from pycbc import cosmology
+
+# PLOTTING OPTIONS
+fig_width_pt = 3*246.0  # Get this from LaTeX using \showthe\columnwidth
+inches_per_pt = 1.0/72.27               # Convert pt to inch
+golden_mean = (np.sqrt(5)-1.0)/2.0         # Aesthetic ratio
+fig_width = fig_width_pt*inches_per_pt  # width in inches
+fig_height = fig_width*golden_mean      # height in inches
+fig_size =  [fig_width,fig_height]
+
+params = { 'axes.labelsize': 24,
+          'font.family': 'serif',
+          'font.serif': 'Computer Modern Raman',
+          'font.size': 24,
+          'legend.fontsize': 20,
+          'xtick.labelsize': 24,
+          'ytick.labelsize': 24,
+          'axes.grid' : True,
+          'text.usetex': True,
+          'savefig.dpi' : 100,
+          'lines.markersize' : 14,
+          'figure.figsize': fig_size}
+
+mpl.rcParams.update(params)
+```
+
+%% Cell type:code id:settled-motel tags:
+
+``` python
+def fn_to_waveform(fn):
+    # read in the raw posterior file and extract the max loglikelihood parameter value
+    with io.loadfile(fn, 'r') as fp:
+        data = fp.read_data()
+        psds = fp.read_psds()
+        cp = fp.read_config_file()
+        samples = fp.read_samples(list(fp['samples'].keys()))
+        idx = samples['loglikelihood'].argmax()
+        params = {p: samples[p][idx] for p in samples.fieldnames}
+        redshift = cosmology.redshift(params['distance'])
+        params['comoving_volume'] = cosmology.cosmological_quantity_from_redshift(redshift, 'comoving_volume')
+    # set up the model
+    model = models.read_from_config(cp, data=data, psds=psds)
+    model.update(**params)
+    _ = model.loglikelihood
+    pol = model.current_stats['maxl_polarization']
+    #compute the max loglikelihood waveform
+    wfs = model.waveform_generator.generate(polarization=pol,**model.current_params)
+
+    whdata = {}
+    for det in model.data:
+        d = model.data[det]
+        d = d.copy()
+        d *= model.weight[det] / (4*model.psds[det].delta_f)**0.5
+        whdata[det] = d.to_timeseries()
+
+    waveforms = {}
+    whitened_waveforms = {}
+    for detname in wfs:
+        hs = {}
+        whitend_hs = {}
+        det = Detector(detname)
+        fp, fc = det.antenna_pattern(model.current_params['ra'],
+                                     model.current_params['dec'],
+                                     pol,
+                                     model.current_params['tc'])
+        hp, hc = wfs[detname]
+        h = fp*hp + fc*hc
+        h.resize(len(model.weight[detname]))
+        wh = h.copy()
+        wh *= model.weight[detname] / (4*model.psds[detname].delta_f)**0.5
+        whitened_waveforms[detname] = wh.to_timeseries()
+    return whdata, whitened_waveforms,model
+```
+
+%% Cell type:code id:8691a3c5 tags:
+
+``` python
+parity_path = '/work/yifan.wang/testingGR-o3b/snr/GW190521/convert.hdf'
+parity_whdata, parity_whwaveform, parity_model = fn_to_waveform(parity_path)
+
+gr_path = '/work/yifan.wang/gw190521_xrerun/t1-mpi-gr/mpirun_output/config_files/convert.hdf'
+_, gr_whwaveform,gr_model = fn_to_waveform(gr_path)
+```
+
+%% Output
+
+    WARNING:root:No sampling_params section read from config file
+    WARNING:root:WARNING: The following args are not being used for waveform generation: redshift, srcmchirp, comoving_volume, delta_tc, parity_mpvinverse, q, baseapprox, trigger_time
+    WARNING: fval is not bracketed by func(zmin)=3.630907107506232e-13 Mpc3 and func(zmax)=164042102234.23068 Mpc3. This means either there is no solution, or that there is more than one solution between zmin and zmax satisfying fval = func(z). [astropy.cosmology.funcs]
+    WARNING:astropy:fval is not bracketed by func(zmin)=3.630907107506232e-13 Mpc3 and func(zmax)=164042102234.23068 Mpc3. This means either there is no solution, or that there is more than one solution between zmin and zmax satisfying fval = func(z).
+    WARNING: fval is not bracketed by func(zmin)=3.630907107506232e-13 Mpc3 and func(zmax)=3763143105730.4414 Mpc3. This means either there is no solution, or that there is more than one solution between zmin and zmax satisfying fval = func(z). [astropy.cosmology.funcs]
+    WARNING:astropy:fval is not bracketed by func(zmin)=3.630907107506232e-13 Mpc3 and func(zmax)=3763143105730.4414 Mpc3. This means either there is no solution, or that there is more than one solution between zmin and zmax satisfying fval = func(z).
+    WARNING:root:No sampling_params section read from config file
+    WARNING:root:WARNING: The following args are not being used for waveform generation: redshift, srcmchirp, comoving_volume, delta_tc, q, trigger_time
+
+%% Cell type:code id:d27e743c tags:
+
+``` python
+xlim=(-0.1, 0.1)
+fig = plt.figure(figsize=(10.211706102117061, 6.311181454233049*1.6))
+fig.subplots_adjust(hspace=.2)
+ax = fig.add_subplot(311);
+
+tc = parity_model.static_params['trigger_time']
+det = list(gr_whwaveform.keys())
+wdata = parity_whdata[det[0]]
+ax.plot(wdata.sample_times-tc, wdata, c='k', alpha=0.2, label='whitened data')
+
+wwf = parity_whwaveform[det[0]]
+ax.plot(wwf.sample_times-tc, wwf,label='birefringence')
+ax.plot(gr_whwaveform[det[0]].sample_times-tc, gr_whwaveform[det[0]],label='GR')
+
+ax.grid(True)
+ax.set_xticklabels([])
+ax.set_xlim(*xlim)
+ax.set_ylim(-100,100)
+ax.text(0.054, 70, 'LIGO Hanford', style='italic',fontsize=20,
+        bbox={'facecolor':'white', 'alpha':0.5, 'pad':5,'edgecolor':'none'})
+ax.set_title('GW190521')
+################
+bx = fig.add_subplot(312);
+wdata = parity_whdata[det[1]]
+bx.plot(wdata.sample_times-tc, wdata, c='k', alpha=0.2, label='whitened data')
+
+wwf = parity_whwaveform[det[1]]
+bx.plot(wwf.sample_times-tc, wwf,label='birefringence')
+bx.plot(gr_whwaveform[det[1]].sample_times-tc, gr_whwaveform[det[1]],label='GR')
+
+bx.set_ylabel('Whitened Strain')
+bx.set_xlim(*xlim)
+bx.set_ylim(-100,100)
+bx.text(0.0515, 70, 'LIGO Livingston', style='italic',fontsize=20,
+        bbox={'facecolor':'white', 'alpha':0.5, 'pad':0,'edgecolor':'none'})
+bx.grid(True)
+bx.set_xticklabels([])
+
+################
+cx = fig.add_subplot(313);
+wdata = parity_whdata[det[2]]
+cx.plot(wdata.sample_times-tc, wdata, c='k', alpha=0.2, label='whitened data')
+
+wwf = parity_whwaveform[det[2]]
+cx.plot(wwf.sample_times-tc, wwf,label='birefringence')
+cx.plot(gr_whwaveform[det[2]].sample_times-tc, gr_whwaveform[det[2]],label='GR')
+cx.text(0.07, 70, 'Virgo', style='italic',fontsize=20,
+        bbox={'facecolor':'white', 'alpha':0.5, 'pad':5,'edgecolor':'none'})
+
+cx.set_xlim(*xlim)
+cx.set_ylim(-100,100)
+cx.set_xlabel('Time - '+str(tc)+' [s]')
+cx.legend(loc='lower right', ncol=3,framealpha=0)
+fig.savefig('0521_gw190521.pdf',bbox_inches='tight')
+```
+
+%% Cell type:code id:a2769cdb tags:
+
+``` python
+parity_model.current_params
+```
+
+%% Output
+
+    {'inclination': 1.3773388232444717,
+     'maxl_polarization': 5.132211422080623,
+     'srcmchirp': 59.988695836431965,
+     'logwt': -28046.517628006437,
+     'V1_optimal_snrsq': 4.645117051883857,
+     'parity_mpvinverse': 571.5796601060491,
+     'distance': 1333.6349776943568,
+     'spin1_a': 0.9116850950619036,
+     'dec': 0.5513436858058255,
+     'delta_tc': 0.0061986454908000005,
+     'spin1_polar': 2.076365152215487,
+     'spin2_azimuthal': 6.273223231326342,
+     'spin2_polar': 0.9148528363767893,
+     'ra': 3.3490655420980504,
+     'logprior': -49.260229458075116,
+     'spin2_a': 0.09566031928959656,
+     'coa_phase': 2.930192323180816,
+     'loglr': 122.35747765652349,
+     'L1_optimal_snrsq': 163.8200365758998,
+     'loglikelihood': -28001.80399824368,
+     'logjacobian': 0.0,
+     'q': 4.542030730356726,
+     'maxl_loglr': 125.90973288111286,
+     'spin1_azimuthal': 2.6322431174435468,
+     'H1_optimal_snrsq': 69.48418153589377,
+     'comoving_volume': 5018380778.811694,
+     'approximant': 'mpvnosmall',
+     'baseapprox': 'IMRPhenomXPHM',
+     'f_lower': 20.0,
+     'f_ref': 20.0,
+     'trigger_time': 1242442967.4473,
+     'spin1x': -0.6963825154031204,
+     'spin1y': 0.3889331472174398,
+     'spin1z': -0.44153381727155666,
+     'spin2x': 0.07580444315196702,
+     'spin2y': -0.0007551945954393175,
+     'spin2z': 0.05834391798748846,
+     'tc': 1242442967.4534986,
+     'redshift': 0.2556724782843754,
+     'mass1': 194.34779119906074,
+     'mass2': 42.78874422846471}
+
+%% Cell type:markdown id:7476300f tags:
+
+# SNR
+
+%% Cell type:code id:6bd3b807 tags:
+
+``` python
+import h5py
+from pycbc.conversions import snr_from_loglr
+nongr = h5py.File(parity_path,'r')
+gr = h5py.File(gr_path,'r')
+```
+
+%% Cell type:code id:2b6eb9a3 tags:
+
+``` python
+snr_from_loglr(np.max(nongr['samples']['loglr']))
+```
+
+%% Output
+
+    15.643367774013592
+
+%% Cell type:code id:358ab6ca tags:
+
+``` python
+snr_from_loglr(np.max(gr['samples']['loglr']))
+```
+
+%% Output
+
+    15.312425524512493
+
+%% Cell type:code id:eae483fd tags:
+
+``` python
+```
--- a/notebook/GW191109-plot.ipynb
+++ b/notebook/GW191109-plot.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "expressed-speed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pycbc.inference import io, models\n",
+    "from pycbc import waveform\n",
+    "from pycbc.detector import Detector\n",
+    "from pycbc import cosmology\n",
+    "\n",
+    "# PLOTTING OPTIONS\n",
+    "fig_width_pt = 3*246.0  # Get this from LaTeX using \\showthe\\columnwidth\n",
+    "inches_per_pt = 1.0/72.27               # Convert pt to inch\n",
+    "golden_mean = (np.sqrt(5)-1.0)/2.0         # Aesthetic ratio\n",
+    "fig_width = fig_width_pt*inches_per_pt  # width in inches\n",
+    "fig_height = fig_width*golden_mean      # height in inches\n",
+    "fig_size =  [fig_width,fig_height]\n",
+    "\n",
+    "params = { 'axes.labelsize': 24,\n",
+    "          'font.family': 'serif',\n",
+    "          'font.serif': 'Computer Modern Raman',\n",
+    "          'font.size': 24,\n",
+    "          'legend.fontsize': 20,\n",
+    "          'xtick.labelsize': 24,\n",
+    "          'ytick.labelsize': 24,\n",
+    "          'axes.grid' : True,\n",
+    "          'text.usetex': True,\n",
+    "          'savefig.dpi' : 100,\n",
+    "          'lines.markersize' : 14,\n",
+    "          'figure.figsize': fig_size}\n",
+    "\n",
+    "mpl.rcParams.update(params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "settled-motel",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fn_to_waveform(fn):       \n",
+    "    # read in the raw posterior file and extract the max loglikelihood parameter value\n",
+    "    with io.loadfile(fn, 'r') as fp:   \n",
+    "        data = fp.read_data()                                                           \n",
+    "        psds = fp.read_psds()                                                           \n",
+    "        cp = fp.read_config_file()\n",
+    "        samples = fp.read_samples(list(fp['samples'].keys()))\n",
+    "        idx = samples['loglikelihood'].argmax()\n",
+    "        params = {p: samples[p][idx] for p in samples.fieldnames}\n",
+    "        redshift = cosmology.redshift(params['distance'])\n",
+    "        params['comoving_volume'] = cosmology.cosmological_quantity_from_redshift(redshift, 'comoving_volume')\n",
+    "    # set up the model\n",
+    "    model = models.read_from_config(cp, data=data, psds=psds)\n",
+    "    model.update(**params)\n",
+    "    _ = model.loglikelihood\n",
+    "    pol = model.current_stats['maxl_polarization']\n",
+    "    #compute the max loglikelihood waveform\n",
+    "    wfs = model.waveform_generator.generate(polarization=pol,**model.current_params)\n",
+    "    \n",
+    "    whdata = {}\n",
+    "    for det in model.data:\n",
+    "        d = model.data[det]\n",
+    "        d = d.copy()\n",
+    "        d *= model.weight[det] / (4*model.psds[det].delta_f)**0.5\n",
+    "        whdata[det] = d.to_timeseries()\n",
+    "    \n",
+    "    waveforms = {}\n",
+    "    whitened_waveforms = {}\n",
+    "    for detname in wfs:\n",
+    "        hs = {}\n",
+    "        whitend_hs = {}\n",
+    "        det = Detector(detname)\n",
+    "        fp, fc = det.antenna_pattern(model.current_params['ra'],\n",
+    "                                     model.current_params['dec'],\n",
+    "                                     pol,\n",
+    "                                     model.current_params['tc'])\n",
+    "        hp, hc = wfs[detname]\n",
+    "        h = fp*hp + fc*hc\n",
+    "        h.resize(len(model.weight[detname]))\n",
+    "        wh = h.copy()\n",
+    "        wh *= model.weight[detname] / (4*model.psds[detname].delta_f)**0.5\n",
+    "        whitened_waveforms[detname] = wh.to_timeseries() \n",
+    "    return whdata, whitened_waveforms,model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "8691a3c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:No sampling_params section read from config file\n",
+      "WARNING:root:WARNING: The following args are not being used for waveform generation: delta_tc, q, trigger_time, redshift, baseapprox, comoving_volume, parity_mpvinverse, srcmchirp\n",
+      "WARNING:root:No sampling_params section read from config file\n",
+      "WARNING:root:WARNING: The following args are not being used for waveform generation: recalib_phase_l1_2, q, recalib_amplitude_l1_7, redshift, recalib_amplitude_l1_4, recalib_amplitude_l1_6, recalib_amplitude_l1_3, recalib_phase_l1_9, recalib_phase_l1_3, recalib_phase_l1_6, recalib_amplitude_l1_0, recalib_phase_h1_8, recalib_amplitude_h1_8, recalib_phase_h1_7, recalib_phase_h1_9, delta_tc, recalib_phase_h1_4, recalib_phase_h1_0, recalib_amplitude_l1_1, recalib_phase_l1_7, recalib_phase_h1_5, recalib_amplitude_h1_3, recalib_amplitude_h1_1, recalib_amplitude_h1_7, recalib_phase_l1_0, recalib_amplitude_l1_2, recalib_amplitude_h1_5, recalib_amplitude_h1_6, recalib_amplitude_l1_8, recalib_phase_l1_4, trigger_time, recalib_phase_h1_2, recalib_phase_h1_3, comoving_volume, srcmchirp, recalib_phase_h1_1, recalib_amplitude_l1_5, recalib_amplitude_h1_9, recalib_phase_l1_5, recalib_amplitude_l1_9, recalib_phase_l1_8, recalib_amplitude_h1_2, recalib_amplitude_h1_4, recalib_amplitude_h1_0, recalib_phase_h1_6, recalib_phase_l1_1\n"
+     ]
+    }
+   ],
+   "source": [
+    "parity_path = '/work/yifan.wang/testingGR-o3b/snr/GW191109/t2run/convert.hdf'\n",
+    "parity_whdata, parity_whwaveform, parity_model = fn_to_waveform(parity_path)\n",
+    "\n",
+    "#gr_path = '/work/yifan.wang/testingGR-o3b/t4-mpi-massive/t2-gr-191109-lowlive/convert.hdf'\n",
+    "gr_path = '/work/yifan.wang/4ogc/release_prod/convertsnr/1212_posterior/GW191109_010717-PYCBC-POSTERIOR-IMRPhenomXPHM.hdf'\n",
+    "_, gr_whwaveform,gr_model = fn_to_waveform(gr_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "d27e743c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xlim=(-0.1, 0.1)\n",
+    "fig = plt.figure(figsize=(10.211706102117061, 6.311181454233049*1.6))\n",
+    "fig.subplots_adjust(hspace=.2)\n",
+    "ax = fig.add_subplot(311);\n",
+    "\n",
+    "tc = parity_model.static_params['trigger_time']\n",
+    "det = list(gr_whwaveform.keys())\n",
+    "wdata = parity_whdata[det[0]]\n",
+    "ax.plot(wdata.sample_times-tc, wdata, c='k', alpha=0.2, label='whitened data')\n",
+    "\n",
+    "wwf = parity_whwaveform[det[0]]\n",
+    "ax.plot(wwf.sample_times-tc, wwf,label='birefringence')\n",
+    "ax.plot(gr_whwaveform[det[0]].sample_times-tc, gr_whwaveform[det[0]],label='GR')\n",
+    "\n",
+    "ax.grid(True)\n",
+    "ax.set_xticklabels([])\n",
+    "ax.set_xlim(*xlim)\n",
+    "ax.set_ylim(-100,100)\n",
+    "ax.text(0.054, 70, 'LIGO Hanford', style='italic',fontsize=20,\n",
+    "        bbox={'facecolor':'white', 'alpha':0.5, 'pad':5,'edgecolor':'none'})\n",
+    "ax.set_title('GW191109')\n",
+    "################\n",
+    "bx = fig.add_subplot(312);\n",
+    "wdata = parity_whdata[det[1]]\n",
+    "bx.plot(wdata.sample_times-tc, wdata, c='k', alpha=0.2, label='whitened data')\n",
+    "    \n",
+    "wwf = parity_whwaveform[det[1]]\n",
+    "bx.plot(wwf.sample_times-tc, wwf,label='birefringence')\n",
+    "bx.plot(gr_whwaveform[det[1]].sample_times-tc, gr_whwaveform[det[1]],label='GR')\n",
+    "\n",
+    "bx.set_ylabel('Whitened Strain')\n",
+    "bx.set_xlim(*xlim)\n",
+    "bx.set_ylim(-100,100)\n",
+    "bx.text(0.0515, 70, 'LIGO Livingston', style='italic',fontsize=20,\n",
+    "        bbox={'facecolor':'white', 'alpha':0.5, 'pad':0,'edgecolor':'none'})\n",
+    "bx.grid(True)\n",
+    "\n",
+    "bx.set_xlim(*xlim)\n",
+    "bx.set_ylim(-100,100)\n",
+    "bx.set_xlabel('Time - '+str(tc)+' [s]')\n",
+    "ax.legend(loc='lower right', ncol=3,framealpha=0,bbox_to_anchor=(1,-0.1))\n",
+    "fig.savefig('0521_gw191109.pdf',bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7476300f",
+   "metadata": {},
+   "source": [
+    "# SNR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "6bd3b807",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import h5py\n",
+    "from pycbc.conversions import snr_from_loglr\n",
+    "nongr = h5py.File(parity_path,'r')\n",
+    "gr = h5py.File(gr_path,'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "2b6eb9a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16.374853000017954"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snr_from_loglr(np.max(nongr['samples']['maxl_loglr']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "358ab6ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16.461053196773342"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snr_from_loglr(np.max(gr['samples']['maxl_loglr']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cdd6d44c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "eae483fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nongr = h5py.File('/work/yifan.wang/testingGR-o3b/snr/GW191109/convert.hdf','r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "6f366b66",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gr = h5py.File('/work/yifan.wang/testingGR-o3b/t4-mpi-massive/t2-gr-191109-lowlive/convert.hdf','r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "0e541d35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16.340538761683053"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snr_from_loglr(np.max(nongr['samples']['maxl_loglr']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "e7326128",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16.057563122880396"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snr_from_loglr(np.max(gr['samples']['loglr']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "d33a01f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16.521160910966348"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gr=h5py.File('/work/yifan.wang/testingGR-o3b/t4-mpi-massive/gr-191109/convert.hdf','r')\n",
+    "snr_from_loglr(np.max(gr['samples']['maxl_loglr']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "b92e9523",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16.37840081629306"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file=h5py.File('/work/yifan.wang/testingGR-o3b/t4-mpi-massive/highlive2-GW191109-xphm-mpv1000/convert.hdf','r')\n",
+    "snr_from_loglr(np.max(file['samples']['maxl_loglr']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "92155527",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16.375657933055365"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file=h5py.File('/work/yifan.wang/testingGR-o3b/t4-mpi-massive/highlive-GW191109-xphm-mpv1000/convert.hdf','r')\n",
+    "snr_from_loglr(np.max(file['samples']['maxl_loglr']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "16ec631d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fn = '/work/yifan.wang/testingGR-o3b/t4-mpi-massive/t2-gr-191109-lowlive/result.hdf'\n",
+    "def printsnr(fn):\n",
+    "    with io.loadfile(fn, 'r') as fp:   \n",
+    "        data = fp.read_data()                                                           \n",
+    "        psds = fp.read_psds()                                                           \n",
+    "        cp = fp.read_config_file()\n",
+    "        samples = fp.read_samples(list(fp['samples'].keys()))\n",
+    "        idx = samples['loglikelihood'].argmax()\n",
+    "        params = {p: samples[p][idx] for p in samples.fieldnames}\n",
+    "        # set up the model\n",
+    "        model = models.read_from_config(cp, data=data, psds=psds)\n",
+    "        model.update(**params)\n",
+    "        _ = model.loglikelihood\n",
+    "        print(snr_from_loglr(model.loglr))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "5c0a894f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:No sampling_params section read from config file\n",
+      "WARNING:root:WARNING: The following args are not being used for waveform generation: delta_tc, q, trigger_time, redshift, comoving_volume, srcmchirp\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16.05759603653594\n"
+     ]
+    }
+   ],
+   "source": [
+    "printsnr(fn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "bd8c92d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:No sampling_params section read from config file\n",
+      "WARNING:root:WARNING: The following args are not being used for waveform generation: delta_tc, q, trigger_time, redshift, baseapprox, comoving_volume, parity_mpvinverse, srcmchirp\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16.094266974440536\n"
+     ]
+    }
+   ],
+   "source": [
+    "printsnr('/work/yifan.wang/testingGR-o3b/t4-mpi-massive/GW191109-xphm-mpv1000/result.hdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "ce51561b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:No sampling_params section read from config file\n",
+      "WARNING:root:WARNING: The following args are not being used for waveform generation: delta_tc, q, trigger_time, redshift, baseapprox, comoving_volume, parity_mpvinverse, srcmchirp\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16.136603108612462\n"
+     ]
+    }
+   ],
+   "source": [
+    "printsnr('/work/yifan.wang/testingGR-o3b/t4-mpi-massive/highlive2-GW191109-xphm-mpv1000/result.hdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "aa022dfd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:No sampling_params section read from config file\n",
+      "WARNING:root:WARNING: The following args are not being used for waveform generation: delta_tc, q, trigger_time, redshift, baseapprox, comoving_volume, parity_mpvinverse, srcmchirp\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16.13759201817148\n"
+     ]
+    }
+   ],
+   "source": [
+    "printsnr('/work/yifan.wang/testingGR-o3b/t4-mpi-massive/highlive-GW191109-xphm-mpv1000/result.hdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec31e12b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "printsnr('/work/yifan.wang/testingGR-o3b/t4-mpi-massive/highlive2-gr-GW191109/')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
+%% Cell type:code id:expressed-speed tags:
+
+``` python
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from pycbc.inference import io, models
+from pycbc import waveform
+from pycbc.detector import Detector
+from pycbc import cosmology
+
+# PLOTTING OPTIONS
+fig_width_pt = 3*246.0  # Get this from LaTeX using \showthe\columnwidth
+inches_per_pt = 1.0/72.27               # Convert pt to inch
+golden_mean = (np.sqrt(5)-1.0)/2.0         # Aesthetic ratio
+fig_width = fig_width_pt*inches_per_pt  # width in inches
+fig_height = fig_width*golden_mean      # height in inches
+fig_size =  [fig_width,fig_height]
+
+params = { 'axes.labelsize': 24,
+          'font.family': 'serif',
+          'font.serif': 'Computer Modern Raman',
+          'font.size': 24,
+          'legend.fontsize': 20,
+          'xtick.labelsize': 24,
+          'ytick.labelsize': 24,
+          'axes.grid' : True,
+          'text.usetex': True,
+          'savefig.dpi' : 100,
+          'lines.markersize' : 14,
+          'figure.figsize': fig_size}
+
+mpl.rcParams.update(params)
+```
+
+%% Cell type:code id:settled-motel tags:
+
+``` python
+def fn_to_waveform(fn):
+    # read in the raw posterior file and extract the max loglikelihood parameter value
+    with io.loadfile(fn, 'r') as fp:
+        data = fp.read_data()
+        psds = fp.read_psds()
+        cp = fp.read_config_file()
+        samples = fp.read_samples(list(fp['samples'].keys()))
+        idx = samples['loglikelihood'].argmax()
+        params = {p: samples[p][idx] for p in samples.fieldnames}
+        redshift = cosmology.redshift(params['distance'])
+        params['comoving_volume'] = cosmology.cosmological_quantity_from_redshift(redshift, 'comoving_volume')
+    # set up the model
+    model = models.read_from_config(cp, data=data, psds=psds)
+    model.update(**params)
+    _ = model.loglikelihood
+    pol = model.current_stats['maxl_polarization']
+    #compute the max loglikelihood waveform
+    wfs = model.waveform_generator.generate(polarization=pol,**model.current_params)
+
+    whdata = {}
+    for det in model.data:
+        d = model.data[det]
+        d = d.copy()
+        d *= model.weight[det] / (4*model.psds[det].delta_f)**0.5
+        whdata[det] = d.to_timeseries()
+
+    waveforms = {}
+    whitened_waveforms = {}
+    for detname in wfs:
+        hs = {}
+        whitend_hs = {}
+        det = Detector(detname)
+        fp, fc = det.antenna_pattern(model.current_params['ra'],
+                                     model.current_params['dec'],
+                                     pol,
+                                     model.current_params['tc'])
+        hp, hc = wfs[detname]
+        h = fp*hp + fc*hc
+        h.resize(len(model.weight[detname]))
+        wh = h.copy()
+        wh *= model.weight[detname] / (4*model.psds[detname].delta_f)**0.5
+        whitened_waveforms[detname] = wh.to_timeseries()
+    return whdata, whitened_waveforms,model
+```
+
+%% Cell type:code id:8691a3c5 tags:
+
+``` python
+parity_path = '/work/yifan.wang/testingGR-o3b/snr/GW191109/t2run/convert.hdf'
+parity_whdata, parity_whwaveform, parity_model = fn_to_waveform(parity_path)
+
+#gr_path = '/work/yifan.wang/testingGR-o3b/t4-mpi-massive/t2-gr-191109-lowlive/convert.hdf'
+gr_path = '/work/yifan.wang/4ogc/release_prod/convertsnr/1212_posterior/GW191109_010717-PYCBC-POSTERIOR-IMRPhenomXPHM.hdf'
+_, gr_whwaveform,gr_model = fn_to_waveform(gr_path)
+```
+
+%% Output
+
+    WARNING:root:No sampling_params section read from config file
+    WARNING:root:WARNING: The following args are not being used for waveform generation: delta_tc, q, trigger_time, redshift, baseapprox, comoving_volume, parity_mpvinverse, srcmchirp
+    WARNING:root:No sampling_params section read from config file
+    WARNING:root:WARNING: The following args are not being used for waveform generation: recalib_phase_l1_2, q, recalib_amplitude_l1_7, redshift, recalib_amplitude_l1_4, recalib_amplitude_l1_6, recalib_amplitude_l1_3, recalib_phase_l1_9, recalib_phase_l1_3, recalib_phase_l1_6, recalib_amplitude_l1_0, recalib_phase_h1_8, recalib_amplitude_h1_8, recalib_phase_h1_7, recalib_phase_h1_9, delta_tc, recalib_phase_h1_4, recalib_phase_h1_0, recalib_amplitude_l1_1, recalib_phase_l1_7, recalib_phase_h1_5, recalib_amplitude_h1_3, recalib_amplitude_h1_1, recalib_amplitude_h1_7, recalib_phase_l1_0, recalib_amplitude_l1_2, recalib_amplitude_h1_5, recalib_amplitude_h1_6, recalib_amplitude_l1_8, recalib_phase_l1_4, trigger_time, recalib_phase_h1_2, recalib_phase_h1_3, comoving_volume, srcmchirp, recalib_phase_h1_1, recalib_amplitude_l1_5, recalib_amplitude_h1_9, recalib_phase_l1_5, recalib_amplitude_l1_9, recalib_phase_l1_8, recalib_amplitude_h1_2, recalib_amplitude_h1_4, recalib_amplitude_h1_0, recalib_phase_h1_6, recalib_phase_l1_1
+
+%% Cell type:code id:d27e743c tags:
+
+``` python
+xlim=(-0.1, 0.1)
+fig = plt.figure(figsize=(10.211706102117061, 6.311181454233049*1.6))
+fig.subplots_adjust(hspace=.2)
+ax = fig.add_subplot(311);
+
+tc = parity_model.static_params['trigger_time']
+det = list(gr_whwaveform.keys())
+wdata = parity_whdata[det[0]]
+ax.plot(wdata.sample_times-tc, wdata, c='k', alpha=0.2, label='whitened data')
+
+wwf = parity_whwaveform[det[0]]
+ax.plot(wwf.sample_times-tc, wwf,label='birefringence')
+ax.plot(gr_whwaveform[det[0]].sample_times-tc, gr_whwaveform[det[0]],label='GR')
+
+ax.grid(True)
+ax.set_xticklabels([])
+ax.set_xlim(*xlim)
+ax.set_ylim(-100,100)
+ax.text(0.054, 70, 'LIGO Hanford', style='italic',fontsize=20,
+        bbox={'facecolor':'white', 'alpha':0.5, 'pad':5,'edgecolor':'none'})
+ax.set_title('GW191109')
+################
+bx = fig.add_subplot(312);
+wdata = parity_whdata[det[1]]
+bx.plot(wdata.sample_times-tc, wdata, c='k', alpha=0.2, label='whitened data')
+
+wwf = parity_whwaveform[det[1]]
+bx.plot(wwf.sample_times-tc, wwf,label='birefringence')
+bx.plot(gr_whwaveform[det[1]].sample_times-tc, gr_whwaveform[det[1]],label='GR')
+
+bx.set_ylabel('Whitened Strain')
+bx.set_xlim(*xlim)
+bx.set_ylim(-100,100)
+bx.text(0.0515, 70, 'LIGO Livingston', style='italic',fontsize=20,
+        bbox={'facecolor':'white', 'alpha':0.5, 'pad':0,'edgecolor':'none'})
+bx.grid(True)
+
+bx.set_xlim(*xlim)
+bx.set_ylim(-100,100)
+bx.set_xlabel('Time - '+str(tc)+' [s]')
+ax.legend(loc='lower right', ncol=3,framealpha=0,bbox_to_anchor=(1,-0.1))
+fig.savefig('0521_gw191109.pdf',bbox_inches='tight')
+```
+
+%% Cell type:markdown id:7476300f tags:
+
+# SNR
+
+%% Cell type:code id:6bd3b807 tags:
+
+``` python
+import h5py
+from pycbc.conversions import snr_from_loglr
+nongr = h5py.File(parity_path,'r')
+gr = h5py.File(gr_path,'r')
+```
+
+%% Cell type:code id:2b6eb9a3 tags:
+
+``` python
+snr_from_loglr(np.max(nongr['samples']['maxl_loglr']))
+```
+
+%% Output
+
+    16.374853000017954
+
+%% Cell type:code id:358ab6ca tags:
+
+``` python
+snr_from_loglr(np.max(gr['samples']['maxl_loglr']))
+```
+
+%% Output
+
+    16.461053196773342
+
+%% Cell type:code id:cdd6d44c tags:
+
+``` python
+```
+
+%% Cell type:code id:eae483fd tags:
+
+``` python
+nongr = h5py.File('/work/yifan.wang/testingGR-o3b/snr/GW191109/convert.hdf','r')
+```
+
+%% Cell type:code id:6f366b66 tags:
+
+``` python
+gr = h5py.File('/work/yifan.wang/testingGR-o3b/t4-mpi-massive/t2-gr-191109-lowlive/convert.hdf','r')
+```
+
+%% Cell type:code id:0e541d35 tags:
+
+``` python
+snr_from_loglr(np.max(nongr['samples']['maxl_loglr']))
+```
+
+%% Output
+
+    16.340538761683053
+
+%% Cell type:code id:e7326128 tags:
+
+``` python
+snr_from_loglr(np.max(gr['samples']['loglr']))
+```
+
+%% Output
+
+    16.057563122880396
+
+%% Cell type:code id:d33a01f7 tags:
+
+``` python
+gr=h5py.File('/work/yifan.wang/testingGR-o3b/t4-mpi-massive/gr-191109/convert.hdf','r')
+snr_from_loglr(np.max(gr['samples']['maxl_loglr']))
+```
+
+%% Output
+
+    16.521160910966348
+
+%% Cell type:code id:b92e9523 tags:
+
+``` python
+file=h5py.File('/work/yifan.wang/testingGR-o3b/t4-mpi-massive/highlive2-GW191109-xphm-mpv1000/convert.hdf','r')
+snr_from_loglr(np.max(file['samples']['maxl_loglr']))
+```
+
+%% Output
+
+    16.37840081629306
+
+%% Cell type:code id:92155527 tags:
+
+``` python
+file=h5py.File('/work/yifan.wang/testingGR-o3b/t4-mpi-massive/highlive-GW191109-xphm-mpv1000/convert.hdf','r')
+snr_from_loglr(np.max(file['samples']['maxl_loglr']))
+```
+
+%% Output
+
+    16.375657933055365
+
+%% Cell type:code id:16ec631d tags:
+
+``` python
+fn = '/work/yifan.wang/testingGR-o3b/t4-mpi-massive/t2-gr-191109-lowlive/result.hdf'
+def printsnr(fn):
+    with io.loadfile(fn, 'r') as fp:
+        data = fp.read_data()
+        psds = fp.read_psds()
+        cp = fp.read_config_file()
+        samples = fp.read_samples(list(fp['samples'].keys()))
+        idx = samples['loglikelihood'].argmax()
+        params = {p: samples[p][idx] for p in samples.fieldnames}
+        # set up the model
+        model = models.read_from_config(cp, data=data, psds=psds)
+        model.update(**params)
+        _ = model.loglikelihood
+        print(snr_from_loglr(model.loglr))
+```
+
+%% Cell type:code id:5c0a894f tags:
+
+``` python
+printsnr(fn)
+```
+
+%% Output
+
+    WARNING:root:No sampling_params section read from config file
+    WARNING:root:WARNING: The following args are not being used for waveform generation: delta_tc, q, trigger_time, redshift, comoving_volume, srcmchirp
+
+    16.05759603653594
+
+%% Cell type:code id:bd8c92d1 tags:
+
+``` python
+printsnr('/work/yifan.wang/testingGR-o3b/t4-mpi-massive/GW191109-xphm-mpv1000/result.hdf')
+```
+
+%% Output
+
+    WARNING:root:No sampling_params section read from config file
+    WARNING:root:WARNING: The following args are not being used for waveform generation: delta_tc, q, trigger_time, redshift, baseapprox, comoving_volume, parity_mpvinverse, srcmchirp
+
+    16.094266974440536
+
+%% Cell type:code id:ce51561b tags:
+
+``` python
+printsnr('/work/yifan.wang/testingGR-o3b/t4-mpi-massive/highlive2-GW191109-xphm-mpv1000/result.hdf')
+```
+
+%% Output
+
+    WARNING:root:No sampling_params section read from config file
+    WARNING:root:WARNING: The following args are not being used for waveform generation: delta_tc, q, trigger_time, redshift, baseapprox, comoving_volume, parity_mpvinverse, srcmchirp
+
+    16.136603108612462
+
+%% Cell type:code id:aa022dfd tags:
+
+``` python
+printsnr('/work/yifan.wang/testingGR-o3b/t4-mpi-massive/highlive-GW191109-xphm-mpv1000/result.hdf')
+```
+
+%% Output
+
+    WARNING:root:No sampling_params section read from config file
+    WARNING:root:WARNING: The following args are not being used for waveform generation: delta_tc, q, trigger_time, redshift, baseapprox, comoving_volume, parity_mpvinverse, srcmchirp
+
+    16.13759201817148
+
+%% Cell type:code id:ec31e12b tags:
+
+``` python
+printsnr('/work/yifan.wang/testingGR-o3b/t4-mpi-massive/highlive2-gr-GW191109/')
+```