{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "8cf15b3c",
"metadata": {},
"outputs": [],
"source": [
"%matplotlib notebook"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d5332cc4",
"metadata": {},
"outputs": [],
"source": [
"import itertools\n",
"import numpy\n",
"import matplotlib\n",
"from matplotlib import patches\n",
"from matplotlib import pyplot\n",
"from pycbc import waveform\n",
"from pycbc.inference import io\n",
"from pycbc import conversions\n",
"\n",
"from scripts import scatter_histograms"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0dd8e4c7",
"metadata": {},
"outputs": [],
"source": [
"pyplot.style.use('seaborn-colorblind')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "29a5dff2",
"metadata": {},
"outputs": [],
"source": [
"color_cycle = [c['color'] for c in matplotlib.rcParams['axes.prop_cycle']] \n",
"colors = itertools.cycle(color_cycle)\n",
"\n",
"# cycle around\n",
"for _ in range(5):\n",
" next(colors)"
]
},
{
"cell_type": "markdown",
"id": "f7551a5f",
"metadata": {},
"source": [
"# PyCBC Inference Read Samples"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "29d23980",
"metadata": {},
"outputs": [],
"source": [
"filenames = {'GW190521': '/work/yifan.wang/gw190521_xrerun/t3-localrun/0_700/result.hdf',\n",
" 'GW191109': '/work/yifan.wang/testingGR-o3b/t2-o3bevents/o3btgr_output/samples_files/H1L1V1-INFERENCE_GW191109_010717-1126259200-400.hdf',\n",
" 'GW200220':'/work/yifan.wang/testingGR-o3b/t4-mpi-massive/t2-200220-mpv1000/result.hdf'}\n",
"samples = {}\n",
"for lbl, fn in filenames.items():\n",
" fp = io.loadfile(fn, 'r')\n",
" s = fp.read_samples(['srcmchirp', 'q', 'parity_mpvinverse'])\n",
" samples[lbl] = s\n",
" fp.close()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "a94f9251",
"metadata": {},
"outputs": [],
"source": [
"filelabels = {'GW190521': 'GW190521',\n",
" 'GW191109': 'GW191109',\n",
" 'GW200220': 'GW200220'\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "fc371486",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'GW190521': rec.array([(-27873.26400656, 83.26078987, 436.04949975, 1.43400538),\n",
" (-27875.2432163 , 71.57790771, 541.07468378, 1.64651789),\n",
" (-27875.26155477, 74.09536189, 440.63653902, 1.73495024), ...,\n",
" (-27880.60033757, 63.38915481, 277.02222192, 1.29027691),\n",
" (-27869.3461176 , 95.44301611, 381.03490203, 1.50594504),\n",
" (-27877.14788405, 79.98899193, 313.76083404, 1.28679349)],\n",
" dtype=[('loglikelihood', '<f8'), ('srcmchirp', '<f8'), ('parity_mpvinverse', '<f8'), ('q', '<f8')]),\n",
" 'GW191109': rec.array([(-17938.0557928 , 48.73074706, 144.75730556, 1.16390601),\n",
" (-17942.74637522, 52.43607729, 329.71287488, 1.02073016),\n",
" (-17942.07580232, 41.9276795 , 180.19740616, 2.19670947), ...,\n",
" (-17940.19991335, 48.96967047, 405.20982651, 1.45601162),\n",
" (-17935.2245303 , 50.17113232, 235.30007999, 1.16774801),\n",
" (-17938.96972275, 53.97280568, 244.20575908, 1.28908152)],\n",
" dtype=[('loglikelihood', '<f8'), ('srcmchirp', '<f8'), ('parity_mpvinverse', '<f8'), ('q', '<f8')]),\n",
" 'GW200220': rec.array([(-81869.72084721, 64.4399525 , 511.48166829, 2.134752 ),\n",
" (-81863.8137886 , 89.29121051, 418.60442017, 3.00415035),\n",
" (-81867.14210206, 67.036858 , 239.90584564, 1.45710404), ...,\n",
" (-81863.32826024, 59.46329134, 606.33211039, 2.63692637),\n",
" (-81864.82296474, 69.8439018 , 467.65624429, 1.19154618),\n",
" (-81866.23224365, 104.03905068, 100.25604593, 2.5061787 )],\n",
" dtype=[('loglikelihood', '<f8'), ('srcmchirp', '<f8'), ('parity_mpvinverse', '<f8'), ('q', '<f8')])}"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"samples"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "ea533ca3",
"metadata": {},
"outputs": [],
"source": [
"labels = {'srcmchirp': waveform.parameters.srcmchirp.label,\n",
" 'q': waveform.parameters.q.label,\n",
" 'parity_mpvinverse':'$M_\\mathrm{PV}^{-1} (\\mathrm{GeV}^{-1})$'\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "d1fc164a",
"metadata": {},
"outputs": [],
"source": [
"x = 'srcmchirp'\n",
"y = 'q'\n",
"z = 'parity_mpvinverse'\n",
"mins = {x: min(s[x].min() for s in samples.values()),\n",
" y: min(s[y].min() for s in samples.values()),\n",
" z: min(s[z].min() for s in samples.values())\n",
" }\n",
"maxs = {x: max(s[x].max() for s in samples.values()),\n",
" y: min(max(s[y].max() for s in samples.values()),6),\n",
" z: max(s[z].max() for s in samples.values())\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "e95d473c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'srcmchirp': 31.170769850622765,\n",
" 'q': 1.0000229039873965,\n",
" 'parity_mpvinverse': 0.006938599419005487}"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mins"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "6ed4aeed",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'srcmchirp': 149.1743322543928,\n",
" 'q': 6,\n",
" 'parity_mpvinverse': 999.9916720638518}"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"maxs"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "e84b1343",
"metadata": {},
"outputs": [],
"source": [
"# create a pool for evaluating the kombine kde\n",
"from pycbc.pool import choose_pool\n",
"pool = choose_pool(10)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "37a7a798",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'GW190521': rec.array([(-27873.26400656, 83.26078987, 436.04949975, 1.43400538),\n",
" (-27875.2432163 , 71.57790771, 541.07468378, 1.64651789),\n",
" (-27875.26155477, 74.09536189, 440.63653902, 1.73495024), ...,\n",
" (-27880.60033757, 63.38915481, 277.02222192, 1.29027691),\n",
" (-27869.3461176 , 95.44301611, 381.03490203, 1.50594504),\n",
" (-27877.14788405, 79.98899193, 313.76083404, 1.28679349)],\n",
" dtype=[('loglikelihood', '<f8'), ('srcmchirp', '<f8'), ('parity_mpvinverse', '<f8'), ('q', '<f8')]),\n",
" 'GW191109': rec.array([(-17938.0557928 , 48.73074706, 144.75730556, 1.16390601),\n",
" (-17942.74637522, 52.43607729, 329.71287488, 1.02073016),\n",
" (-17942.07580232, 41.9276795 , 180.19740616, 2.19670947), ...,\n",
" (-17940.19991335, 48.96967047, 405.20982651, 1.45601162),\n",
" (-17935.2245303 , 50.17113232, 235.30007999, 1.16774801),\n",
" (-17938.96972275, 53.97280568, 244.20575908, 1.28908152)],\n",
" dtype=[('loglikelihood', '<f8'), ('srcmchirp', '<f8'), ('parity_mpvinverse', '<f8'), ('q', '<f8')]),\n",
" 'GW200220': rec.array([(-81869.72084721, 64.4399525 , 511.48166829, 2.134752 ),\n",
" (-81863.8137886 , 89.29121051, 418.60442017, 3.00415035),\n",
" (-81867.14210206, 67.036858 , 239.90584564, 1.45710404), ...,\n",
" (-81863.32826024, 59.46329134, 606.33211039, 2.63692637),\n",
" (-81864.82296474, 69.8439018 , 467.65624429, 1.19154618),\n",
" (-81866.23224365, 104.03905068, 100.25604593, 2.5061787 )],\n",
" dtype=[('loglikelihood', '<f8'), ('srcmchirp', '<f8'), ('parity_mpvinverse', '<f8'), ('q', '<f8')])}"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"samples"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "737146e4",
"metadata": {},
"outputs": [],
"source": [
"filecolors = {}\n",
"fig, axd = (None, None)\n",
"for run in samples:\n",
" c = next(colors)\n",
" filecolors[run] = c\n",
" s = samples[run]\n",
" fig, axd = scatter_histograms.create_multidim_plot(\n",
" ['parity_mpvinverse', 'srcmchirp'], s, labels=labels,\n",
" mins=mins, maxs=maxs,\n",
" plot_scatter=False, plot_contours=True,\n",
" marginal_percentiles=[], contour_percentiles=[50, 90],\n",
" fill_color=None, contour_color=c, hist_color=c, line_color=c,\n",
" contour_ls=['solid', 'dashed'], contour_labels=False, fold_masses=True,\n",
" use_kombine=True, kdeargs={'pool': pool, 'max_samples': 200},\n",
" fig=fig, axis_dict=axd,\n",
" )\n",
" \n",
" # add legend\n",
"handles = []\n",
"lbls = []\n",
"for run, c in filecolors.items():\n",
" l = filelabels[run]\n",
" handles.append(patches.Patch(color=c, label=l))\n",
" lbls.append(l)\n",
"fig.legend(loc=(0.7, 0.75), handles=handles, labels=lbls)\n",
"fig.savefig('3events.png')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "95c5bcdf",
"metadata": {},
"outputs": [],
"source": [
"color_cycle = ['#1f77b4', '#ff7f0e'] \n",
"colors = itertools.cycle(color_cycle)\n",
"\n",
"# cycle around\n",
"for _ in range(5):\n",
" next(colors)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "e69aacc6",
"metadata": {},
"outputs": [],
"source": [
"color_cycle = [c['color'] for c in matplotlib.rcParams['axes.prop_cycle']] \n",
"colors = itertools.cycle(color_cycle)\n",
"\n",
"# cycle around\n",
"#for _ in range(5):\n",
"# next(colors)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "5902c7f1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n",
"4\n"
]
}
],
"source": [
"for i in range(5):\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "4ff63d8a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['#0072B2', '#009E73', '#D55E00', '#CC79A7', '#F0E442', '#56B4E9']"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"color_cycle"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "2b6d8f06",
"metadata": {},
"outputs": [
{
"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"
]
}
],
"source": [
"[c['color'] for c in matplotlib.rcParams['axes.prop_cycle']] \n",
"plt.figure()\n",
"plt.plot([1,2],color=c)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "87080ba5",
"metadata": {},
"outputs": [],
"source": [
"filecolors = {}\n",
"fig, axd = (None, None)\n",
"for run in samples:\n",
" c = next(colors)\n",
" filecolors[run] = c\n",
" s = samples[run]\n",
" fig, axd = scatter_histograms.create_multidim_plot(\n",
" ['parity_mpvinverse', 'srcmchirp','q'], s, labels=labels,\n",
" mins=mins, maxs=maxs,\n",
" plot_scatter=False, plot_contours=True,\n",
" marginal_percentiles=[], contour_percentiles=[50, 90],\n",
" fill_color=None, contour_color=c, hist_color=c, line_color=c,\n",
" contour_ls=['solid', 'dashed'], contour_labels=False, fold_masses=True,\n",
" use_kombine=True, kdeargs={'pool': pool, 'max_samples': 200},\n",
" fig=fig, axis_dict=axd,\n",
" )\n",
" \n",
" # add legend\n",
"handles = []\n",
"lbls = []\n",
"for run, c in filecolors.items():\n",
" l = filelabels[run]\n",
" handles.append(patches.Patch(color=c, label=l))\n",
" lbls.append(l)\n",
"fig.legend(loc=(0.7, 0.75), handles=handles, labels=lbls)\n",
"\n",
"fig.show()\n",
"fig.savefig('./3events-2.png')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "132771ea",
"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
}