DMvsDrift.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drift normalized by observing frequency vs. Burst Duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "b:\\dev\\frbrepeaters\\driftlaw.py:10: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  tauwerror_expr = lambda r: 1e3*r['time_res']*np.sqrt(r['max_sigma']**6*r['min_sigma_error']**2*np.cos(r['angle']-np.pi/2)**4 + r['angle_error']**2*r['max_sigma']**2*r['min_sigma']**2*(-r['max_sigma']**2 + r['min_sigma']**2)**2*np.cos(r['angle']-np.pi/2)**2*np.sin(r['angle']-np.pi/2)**2 + r['max_sigma_error']**2*r['min_sigma']**6*np.sin(r['angle']-np.pi/2)**4)/(r['max_sigma']**2*np.cos(r['angle']-np.pi/2)**2 + r['min_sigma']**2*np.sin(r['angle']-np.pi/2)**2)**1.5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "# FRB20121102A Michilli et al. (2018):\n",
      ">> num bursts remaining = 14\n",
      "\n",
      "# FRB20121102A Gajjar et al. (2018):\n",
      ">> num bursts remaining = 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hmsmo\\AppData\\Roaming\\Python\\Python38\\site-packages\\scipy\\optimize\\minpack.py:828: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  warnings.warn('Covariance of the parameters could not be estimated',\n",
      "b:\\dev\\frbrepeaters\\driftlaw.py:271: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  red_chisq = chisq / (len(frame) - 1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "# FRB20180916B bursts:\n",
      ">> num bursts remaining = 16\n",
      "\n",
      "# FRB20180814A bursts:\n",
      ">> num bursts remaining = 6\n",
      "FRB20121102A Michilli et al. (2018) (0.050 - 0.133) $t_w^{-1}$\n",
      "FRB20121102A Gajjar et al. (2018) (0.042 - 0.138) $t_w^{-1}$\n",
      "FRB20180916B bursts (0.032 - 0.153) $t_w^{-1}$\n",
      "FRB20180814A bursts (0.071 - 0.152) $t_w^{-1}$\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from frbrepeaters import driftlaw\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import cycle\n",
    "import scipy\n",
    "\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "pd.set_option('display.max_rows', None)\n",
    "%matplotlib inline\n",
    "\n",
    "sources_filenames = [\n",
    "    \"frb121102_michilli_dmvariations.csv\",\n",
    "    'gajjar11a_dmvariations.csv',\n",
    "    \"universal/data/CHIME_180916.J0158+65/dmvars_180916.csv\",\n",
    "    \"universal/data/CHIME_FRB180814.J0422+73/dmvars_180814.csv\"\n",
    "]\n",
    "\n",
    "sources = []\n",
    "for sfile in sources_filenames:\n",
    "    df = pd.read_csv(sfile)\n",
    "    df = df.set_index(\"name\")\n",
    "    df = driftlaw.computeModelDetails(df)\n",
    "    sources.append(df)\n",
    "\n",
    "names = [\n",
    "    'FRB20121102A Michilli et al. (2018) DM Variations', \n",
    "    'FRB20121102A Gajjar et al. (2018) DM Variations', \n",
    "    'FRB20180916B bursts DM Variations', \n",
    "    'FRB20180814A bursts DM Variations'\n",
    "]\n",
    "annotations = [\n",
    "    [0, 1, 2, 3, 4, 5],\n",
    "    [],\n",
    "    [3],\n",
    "    [0, 1, 2, 3, 4, 5]\n",
    "]\n",
    "dmlimited = [\n",
    "    [3, 5, 6, 14],\n",
    "    [],\n",
    "    [15.5, 16, 23, 24, 29, 31, 32, 33],\n",
    "    [180919.0]\n",
    "]\n",
    "exclusions = [\n",
    "    [],\n",
    "    [],\n",
    "    [],\n",
    "    [180917.0], # first is a multi burst, second has negative drift (which should better be corrected by limiting 180814's DM range)\n",
    "]\n",
    "\n",
    "def exclusionLogger(exclusionstr, frame):\n",
    "    return # short \n",
    "    print(exclusionstr)\n",
    "    count = 0\n",
    "    if frame.empty:\n",
    "        print(\">> bursts excluded: \\n\\tnone\")\n",
    "    else:\n",
    "        print('>> bursts excluded:', *[f\"\\n\\t{exname} DM = {row['DM']}\" for exname, row in frame.iterrows()])\n",
    "        count += len(frame)\n",
    "    print(f'>> # of measurements excluded: {count}')\n",
    "    print(f'>> # of bursts limited: {len(frame.index.unique())}')\n",
    "    return count\n",
    "\n",
    "def DMrangeLogger(sname, frame, burstwise=False):\n",
    "    return # short \n",
    "    if not burstwise:\n",
    "        print(f\"{sname}\\t DM Range: {min(frame['DM'])} - {max(frame['DM'])} pc/cm3\")\n",
    "    else:\n",
    "        print(f'>>> {sname} DM Range by burst:')\n",
    "        for bname in frame.index.unique():\n",
    "            DMrangeLogger(f'\\tburst {bname}:', frame.loc[bname])\n",
    "    return\n",
    "\n",
    "def getFRB20121102AFit(pltdata):\n",
    "    \"\"\" merge michilli and gajjar and fit together\"\"\"\n",
    "    frb121102frame = pd.concat([pltdata['frames'][0],pltdata['frames'][1]])\n",
    "    _, fits = driftlaw.plotDriftVsDuration([frb121102frame], ['merged'], logscale=True, fiterrorfunc=driftlaw.log_error)\n",
    "    return fits[0][1], fits[0][2]\n",
    "\n",
    "# targetDMs   = [559.7, 565, 348.82, 188.9]     # assuming different sources match, by eye\n",
    "# targetDMs   = [554.1030000000002, 563.3333333333334, 346.82, 188.7] # DMs with minimal standard deviations on fits. requires excluding 180919 completely\n",
    "targetDMs   = [558.8162631578948, 568.333333333333, 348.82, 188.8] # redone: DM with reduced chi squared closest to 1, and ensuring all bursts are included\n",
    "\n",
    "fig1pltdata = { 'frames': [], 'labels': [], 'colors': cycle(['r', 'r', 'b', 'g']) }\n",
    "fitdata = pd.DataFrame()\n",
    "Bconstants = []\n",
    "for source, name, annotate, exclude, targetDM in zip(sources, names, annotations, exclusions, targetDMs):\n",
    "    frames = []\n",
    "    labels = []\n",
    "    \n",
    "    print('\\n#', name.split(' DM')[0]+':')\n",
    "    DMrangeLogger('', source)\n",
    "\n",
    "    ## Burst Exclusions\n",
    "    # exclude bursts that where a fit was not found\n",
    "    exclusionLogger('> exclusion rule: no fit found', source[~(source.amplitude > 0)])\n",
    "    source = source[source.amplitude > 0]\n",
    "       \n",
    "    # exclude positive drifts, we assume they are non-physical and an artifact of over dedispersion\n",
    "    exclusionLogger('> exclusion rule: drift_abs < 0', source[~(source.drift_abs > 0)])\n",
    "    source = source[source.drift_abs > 0] \n",
    "    \n",
    "    # exclude drifts with huge errors, they are vertical and poorly measured\n",
    "    exclusionLogger('> exclusion rule: drift error > 1e30', source[~(source['drift error (mhz/ms)'] < 1e30)])\n",
    "    source = source[source['drift error (mhz/ms)'] < 1e30] \n",
    "    \n",
    "    source = source.drop(exclude)\n",
    "    DMrangeLogger(name.split(' DM')[0], source, burstwise=True) # log the dm range after we're done excluding\n",
    "    source = driftlaw.driftranges(source) # compute drift ranges after burst exclusions\n",
    "\n",
    "    # special case: exclude 180911 at 190 pc/cm3 cuz drift range switches sign\n",
    "    if name == names[3]:\n",
    "        source = source[~((source.DM == 190) & (source.drift_nu_min < 0))]\n",
    "    source = driftlaw.driftranges(source) # recompute range \n",
    "\n",
    "    for dm, color in zip(source.DM.unique(), cycle(['r', 'y', 'b', 'k', 'g', 'c'])):\n",
    "        df = source[source.DM == dm]\n",
    "        df['color'] = color\n",
    "        frames.append(df)\n",
    "        labels.append(dm)\n",
    "        \n",
    "        # Figure 1\n",
    "        if dm == targetDM:\n",
    "            print(f'>> num bursts remaining = {len(df.index.unique())}')\n",
    "            df['color'] = next(fig1pltdata['colors'])\n",
    "            fig1pltdata['frames'].append(df)\n",
    "            fig1pltdata['labels'].append(name.split(' DM')[0])\n",
    "            \n",
    "        # For Ref figure A, turn off otherwise\n",
    "        # if dm == 565:\n",
    "        #     df['color'] = 'b'\n",
    "        # if dm == 348.82:\n",
    "        #     df['color'] = 'r'\n",
    "            \n",
    "        # Figure 5: compute drift/nu^2 vs nu range of fits\n",
    "        nus = np.linspace(0, 20000, num=2000)   \n",
    "        popt, pcov = scipy.optimize.curve_fit(lambda x, c: c, nus, df['drift_abs_nuobssq'])\n",
    "        Bconstants.append((popt[0], np.sqrt(pcov[0])))\n",
    "        \n",
    "    # driftlaw.plotDriftVsDuration(frames, labels, title=name, annotatei=annotate, logscale=True)\n",
    "    markcolors = []\n",
    "    for f in frames: \n",
    "        markcolors.append(f['color'].iloc[0])\n",
    "    markcolors = cycle(markcolors)\n",
    "    # manylines = ['r--', 'y-', 'b-.', 'k--', 'g--', 'c-.', 'r-', 'b-', 'k-', 'g-', 'c-']\n",
    "    linestyles = ['--', '-', '-.', '--', '--', '-.', '-', '-', '-', '-', '-']\n",
    "    manylines = [next(markcolors)+lst for lst in linestyles]\n",
    "    labels = [round(lbl, 2) for lbl in labels]\n",
    "    tempax, fits = driftlaw.plotDriftVsDuration(frames, labels, annotatei=[], fitlines=manylines, logscale=True, fiterrorfunc=driftlaw.log_error, dmtrace=True, hidefitlabel=True)\n",
    "    if name.split(' DM')[0] == 'FRB20180916B bursts':\n",
    "        hdls, lbls = tempax.get_legend_handles_labels()\n",
    "        order = [2, 1, 0, 3, 4]\n",
    "        tempax.legend([hdls[idx] for idx in order], [lbls[idx] for idx in order], fontsize='x-large')\n",
    "    if name.split(' DM')[0] == 'FRB20121102A Gajjar et al. (2018)':\n",
    "        tempax.set_xlim(0.073, 0.389)\n",
    "        tempax.legend(fontsize='x-large', loc=1)\n",
    "    \n",
    "    for fit in fits:\n",
    "        fitdata = fitdata.append({'name': name.split(' DM')[0], \n",
    "                                  'DM': fit[0], \n",
    "                                  'param': fit[1], \n",
    "                                  'err': fit[2] ,\n",
    "                                  'red_chisq': fit[3], \n",
    "                                  'numbursts': fit[5]},\n",
    "                                  ignore_index=True)\n",
    "\n",
    "plt.close('all')\n",
    "\n",
    "# fig1pltdata['frames'][0] = pd.concat([fig1pltdata['frames'][0], fig1pltdata['frames'][1]])\n",
    "# del fig1pltdata['frames'][1]\n",
    "# del fig1pltdata['labels'][1]\n",
    "ax, _ = driftlaw.plotDriftVsDuration(fig1pltdata['frames'], fig1pltdata['labels'], fitlines=['r-', 'r--', 'b--', 'g-.'], markers=['o', 'd', 'p', 'X'], title=None, \n",
    "                                     annotatei=[], fitextents=[0.0728, 12.5], hidefit=[0, 1],\n",
    "                                     logscale=True, errorfunc=driftlaw.rangeerror, fiterrorfunc=driftlaw.log_error)\n",
    "\n",
    "## Mark all bursts whose DM ranges were limited by the exclusion process\n",
    "for i, limitedpoints in enumerate(dmlimited):\n",
    "    for lp in limitedpoints:\n",
    "        v = fig1pltdata['frames'][i].loc[lp]\n",
    "        plt.scatter(v['tau_w_ms'], v['drift_over_nuobs'], facecolors='none', edgecolors='k', linewidth=1.0, s=400)\n",
    "        # ax.annotate('o', (v['tau_w_ms'], v['drift_over_nuobs']), xytext=(-10,-18), textcoords='offset pixels', weight='bold', size=25)\n",
    "\n",
    "# see Josephy et al. 2019        \n",
    "C1data = ['C1', 1.9077, 184, 63, -3.92, 0.01, 630, 0.003, 3, 11.4, 11.4/1000, 0.12, 1]\n",
    "josephyburst = pd.DataFrame([C1data], columns=['name', 'angle', 'sigmax', 'sigmay', 'drift (mhz/ms)', 'drift error (mhz/ms)', 'center_f', 'time_res', 'freq_res', 'tau_w_ms', 'tau_w', 'tau_w_error', 'red_chisq']).set_index('name')\n",
    "josephyburst['drift_over_nuobs'] = -1*josephyburst['drift (mhz/ms)']/josephyburst['center_f']\n",
    "xerr, yerr = driftlaw.modelerror(josephyburst)\n",
    "ax.scatter(josephyburst['tau_w_ms'], josephyburst['drift_over_nuobs'], c='r', s=125, edgecolors='k', marker='s', label='FRB20121102A Josephy et al. (2019)')\n",
    "ax.errorbar(josephyburst['tau_w_ms'], josephyburst['drift_over_nuobs'], xerr=xerr[0], yerr=yerr[0], ecolor='r')\n",
    "\n",
    "# Plot shaded regions\n",
    "alldata = pd.concat([f for f in fig1pltdata['frames']+[josephyburst]])\n",
    "x = np.linspace(min(alldata['tau_w_ms'])*0.9, max(alldata['tau_w_ms'])*1.1, num=1200)\n",
    "# plt.rcParams['hatch.linewidth'] = 1\n",
    "for name, color, hatch in zip(fitdata.name.unique(), fig1pltdata['colors'], ['', '/', '\\\\', '-']):\n",
    "    params = fitdata.loc[(fitdata.name == name) & (fitdata.numbursts > 1.0)]['param']\n",
    "    print(f\"{name} ({min(params):.3f} - {max(params):.3f}) $t_w^{{-1}}$\")\n",
    "    if 'Michilli' not in name: # michilli region is contained in gajjar region so no need to plot it\n",
    "        lstr = f\"{name.split(' ')[0]} range of fits\"# \n",
    "        ax.fill_between(x, min(params)/x, max(params)/x, alpha=0.1, color=color, edgecolor=color, label=lstr, zorder=-10)\n",
    "        # ax.fill_between(x, min(params)/x, max(params)/x, alpha=0.1, color='None', edgecolor='w', hatch=hatch, zorder=-9)\n",
    "\n",
    "## Combine michilli and gajjar fits. See `getFRB20121102AFit()`. the result of the fit when merging frames is 0.083 +- 0.005\n",
    "# frb121102_A = getFRB20121102AFit(fig1pltdata) # opens a figure\n",
    "frb121102_A = (0.07816130353202802, 0.005971946986254264)\n",
    "plt.plot(x, frb121102_A[0]/x, 'r-', label='{} fit ({:.3f} $\\\\pm$ {:.3f}) $t_w^{{-1}}$'.format('FRB20121102A', frb121102_A[0], frb121102_A[1]))\n",
    "\n",
    "# Plot tweaks\n",
    "hdls, lbls = ax.get_legend_handles_labels()\n",
    "order = [3, 4, 7, 5, 6, 2, 0, 1, 8, 9, 10]\n",
    "ax.legend([hdls[idx] for idx in order], [lbls[idx] for idx in order], fontsize='x-large')\n",
    "# hdls[0], hdls[1], hdls[2], hdls[5], hdls[6], hdls[7] = hdls[2], hdls[0], hdls[1], hdls[7], hdls[5], hdls[6]\n",
    "# lbls[0], lbls[1], lbls[2], lbls[5], lbls[6], lbls[7] = lbls[2], lbls[0], lbls[1], lbls[7], lbls[5], lbls[6]\n",
    "# ax.legend(hdls, lbls, fontsize='x-large')\n",
    "\n",
    "ax.set_xlim(0.0737, 12.5)\n",
    "# ax.set_ylim(0.0013395279509532563, 508.64764364084175)\n",
    "plt.savefig('universal/figures/driftvnuobs_dmvars.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>err</th>\n",
       "      <th>name</th>\n",
       "      <th>numbursts</th>\n",
       "      <th>param</th>\n",
       "      <th>red_chisq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>554.10</th>\n",
       "      <td>0.004069</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.050158</td>\n",
       "      <td>136733.552395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554.69</th>\n",
       "      <td>0.004161</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.052501</td>\n",
       "      <td>113114.495784</td>\n",
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       "    <tr>\n",
       "      <th>555.28</th>\n",
       "      <td>0.004275</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.055074</td>\n",
       "      <td>89422.555439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555.87</th>\n",
       "      <td>0.004645</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.057947</td>\n",
       "      <td>76925.879224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556.46</th>\n",
       "      <td>0.004670</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.060830</td>\n",
       "      <td>55890.270226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557.05</th>\n",
       "      <td>0.004986</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.064214</td>\n",
       "      <td>42491.426906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557.64</th>\n",
       "      <td>0.005429</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.067820</td>\n",
       "      <td>33967.374134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558.23</th>\n",
       "      <td>0.006000</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.071771</td>\n",
       "      <td>28630.884037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558.82</th>\n",
       "      <td>0.006840</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.076383</td>\n",
       "      <td>26339.448829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559.41</th>\n",
       "      <td>0.007934</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.080863</td>\n",
       "      <td>26866.571041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559.99</th>\n",
       "      <td>0.009617</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.086959</td>\n",
       "      <td>27952.082321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560.58</th>\n",
       "      <td>0.011847</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.092574</td>\n",
       "      <td>30286.464641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561.17</th>\n",
       "      <td>0.015606</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.099499</td>\n",
       "      <td>31968.278305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561.76</th>\n",
       "      <td>0.016994</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.103130</td>\n",
       "      <td>38108.851449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562.35</th>\n",
       "      <td>0.020562</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.110014</td>\n",
       "      <td>33649.910415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562.94</th>\n",
       "      <td>0.021408</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.110467</td>\n",
       "      <td>35441.135086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563.53</th>\n",
       "      <td>0.029011</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.116335</td>\n",
       "      <td>36050.376519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564.12</th>\n",
       "      <td>0.024899</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.118765</td>\n",
       "      <td>39679.105507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564.71</th>\n",
       "      <td>0.026749</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.123889</td>\n",
       "      <td>44494.367909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565.30</th>\n",
       "      <td>0.031899</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.133128</td>\n",
       "      <td>45310.558399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559.70</th>\n",
       "      <td>0.008695</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.083802</td>\n",
       "      <td>27377.573036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             err                                 name  numbursts     param  \\\n",
       "DM                                                                           \n",
       "554.10  0.004069  FRB20121102A Michilli et al. (2018)       14.0  0.050158   \n",
       "554.69  0.004161  FRB20121102A Michilli et al. (2018)       14.0  0.052501   \n",
       "555.28  0.004275  FRB20121102A Michilli et al. (2018)       14.0  0.055074   \n",
       "555.87  0.004645  FRB20121102A Michilli et al. (2018)       13.0  0.057947   \n",
       "556.46  0.004670  FRB20121102A Michilli et al. (2018)       14.0  0.060830   \n",
       "557.05  0.004986  FRB20121102A Michilli et al. (2018)       14.0  0.064214   \n",
       "557.64  0.005429  FRB20121102A Michilli et al. (2018)       14.0  0.067820   \n",
       "558.23  0.006000  FRB20121102A Michilli et al. (2018)       14.0  0.071771   \n",
       "558.82  0.006840  FRB20121102A Michilli et al. (2018)       14.0  0.076383   \n",
       "559.41  0.007934  FRB20121102A Michilli et al. (2018)       14.0  0.080863   \n",
       "559.99  0.009617  FRB20121102A Michilli et al. (2018)       14.0  0.086959   \n",
       "560.58  0.011847  FRB20121102A Michilli et al. (2018)       14.0  0.092574   \n",
       "561.17  0.015606  FRB20121102A Michilli et al. (2018)       14.0  0.099499   \n",
       "561.76  0.016994  FRB20121102A Michilli et al. (2018)       12.0  0.103130   \n",
       "562.35  0.020562  FRB20121102A Michilli et al. (2018)       13.0  0.110014   \n",
       "562.94  0.021408  FRB20121102A Michilli et al. (2018)       12.0  0.110467   \n",
       "563.53  0.029011  FRB20121102A Michilli et al. (2018)       12.0  0.116335   \n",
       "564.12  0.024899  FRB20121102A Michilli et al. (2018)       11.0  0.118765   \n",
       "564.71  0.026749  FRB20121102A Michilli et al. (2018)       10.0  0.123889   \n",
       "565.30  0.031899  FRB20121102A Michilli et al. (2018)       10.0  0.133128   \n",
       "559.70  0.008695  FRB20121102A Michilli et al. (2018)       14.0  0.083802   \n",
       "\n",
       "            red_chisq  \n",
       "DM                     \n",
       "554.10  136733.552395  \n",
       "554.69  113114.495784  \n",
       "555.28   89422.555439  \n",
       "555.87   76925.879224  \n",
       "556.46   55890.270226  \n",
       "557.05   42491.426906  \n",
       "557.64   33967.374134  \n",
       "558.23   28630.884037  \n",
       "558.82   26339.448829  \n",
       "559.41   26866.571041  \n",
       "559.99   27952.082321  \n",
       "560.58   30286.464641  \n",
       "561.17   31968.278305  \n",
       "561.76   38108.851449  \n",
       "562.35   33649.910415  \n",
       "562.94   35441.135086  \n",
       "563.53   36050.376519  \n",
       "564.12   39679.105507  \n",
       "564.71   44494.367909  \n",
       "565.30   45310.558399  \n",
       "559.70   27377.573036  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minimal red_chisq:\n"
     ]
    },
    {
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       "      <th>err</th>\n",
       "      <th>name</th>\n",
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       "    <tr>\n",
       "      <th>558.82</th>\n",
       "      <td>0.00684</td>\n",
       "      <td>FRB20121102A Michilli et al. (2018)</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.076383</td>\n",
       "      <td>26339.448829</td>\n",
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       "            err                                 name  numbursts     param  \\\n",
       "DM                                                                          \n",
       "558.82  0.00684  FRB20121102A Michilli et al. (2018)       14.0  0.076383   \n",
       "\n",
       "           red_chisq  \n",
       "DM                    \n",
       "558.82  26339.448829  "
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       "      <th>err</th>\n",
       "      <th>name</th>\n",
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       "      <th>param</th>\n",
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       "    <tr>\n",
       "      <th>DM</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>555.00</th>\n",
       "      <td>0.005434</td>\n",
       "      <td>FRB20121102A Gajjar et al. (2018)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.042264</td>\n",
       "      <td>5013.343836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556.67</th>\n",
       "      <td>0.005440</td>\n",
       "      <td>FRB20121102A Gajjar et al. (2018)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.045907</td>\n",
       "      <td>3847.756474</td>\n",
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       "    <tr>\n",
       "      <th>558.33</th>\n",
       "      <td>0.005107</td>\n",
       "      <td>FRB20121102A Gajjar et al. (2018)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.049414</td>\n",
       "      <td>2556.993140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560.00</th>\n",
       "      <td>0.005190</td>\n",
       "      <td>FRB20121102A Gajjar et al. (2018)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.054845</td>\n",
       "      <td>1947.910914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561.67</th>\n",
       "      <td>0.006241</td>\n",
       "      <td>FRB20121102A Gajjar et al. (2018)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.062340</td>\n",
       "      <td>1409.896320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563.33</th>\n",
       "      <td>0.003865</td>\n",
       "      <td>FRB20121102A Gajjar et al. (2018)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.066472</td>\n",
       "      <td>626.913981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565.00</th>\n",
       "      <td>0.005173</td>\n",
       "      <td>FRB20121102A Gajjar et al. (2018)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.078155</td>\n",
       "      <td>380.301114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566.67</th>\n",
       "      <td>0.013373</td>\n",
       "      <td>FRB20121102A Gajjar et al. (2018)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.095456</td>\n",
       "      <td>663.827857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568.33</th>\n",
       "      <td>0.009128</td>\n",
       "      <td>FRB20121102A Gajjar et al. (2018)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.105224</td>\n",
       "      <td>377.505407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570.00</th>\n",
       "      <td>0.030079</td>\n",
       "      <td>FRB20121102A Gajjar et al. (2018)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.138497</td>\n",
       "      <td>2010.045950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583.00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>FRB20121102A Gajjar et al. (2018)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.354520</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             err                               name  numbursts     param  \\\n",
       "DM                                                                         \n",
       "555.00  0.005434  FRB20121102A Gajjar et al. (2018)        5.0  0.042264   \n",
       "556.67  0.005440  FRB20121102A Gajjar et al. (2018)        5.0  0.045907   \n",
       "558.33  0.005107  FRB20121102A Gajjar et al. (2018)        5.0  0.049414   \n",
       "560.00  0.005190  FRB20121102A Gajjar et al. (2018)        5.0  0.054845   \n",
       "561.67  0.006241  FRB20121102A Gajjar et al. (2018)        5.0  0.062340   \n",
       "563.33  0.003865  FRB20121102A Gajjar et al. (2018)        5.0  0.066472   \n",
       "565.00  0.005173  FRB20121102A Gajjar et al. (2018)        5.0  0.078155   \n",
       "566.67  0.013373  FRB20121102A Gajjar et al. (2018)        5.0  0.095456   \n",
       "568.33  0.009128  FRB20121102A Gajjar et al. (2018)        5.0  0.105224   \n",
       "570.00  0.030079  FRB20121102A Gajjar et al. (2018)        5.0  0.138497   \n",
       "583.00  0.000000  FRB20121102A Gajjar et al. (2018)        1.0  0.354520   \n",
       "\n",
       "          red_chisq  \n",
       "DM                   \n",
       "555.00  5013.343836  \n",
       "556.67  3847.756474  \n",
       "558.33  2556.993140  \n",
       "560.00  1947.910914  \n",
       "561.67  1409.896320  \n",
       "563.33   626.913981  \n",
       "565.00   380.301114  \n",
       "566.67   663.827857  \n",
       "568.33   377.505407  \n",
       "570.00  2010.045950  \n",
       "583.00          NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minimal red_chisq:\n"
     ]
    },
    {
     "data": {
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       "      <th>err</th>\n",
       "      <th>name</th>\n",
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       "      <th>param</th>\n",
       "      <th>red_chisq</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>568.33</th>\n",
       "      <td>0.009128</td>\n",
       "      <td>FRB20121102A Gajjar et al. (2018)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.105224</td>\n",
       "      <td>377.505407</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "             err                               name  numbursts     param  \\\n",
       "DM                                                                         \n",
       "568.33  0.009128  FRB20121102A Gajjar et al. (2018)        5.0  0.105224   \n",
       "\n",
       "         red_chisq  \n",
       "DM                  \n",
       "568.33  377.505407  "
      ]
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       "      <th>err</th>\n",
       "      <th>name</th>\n",
       "      <th>numbursts</th>\n",
       "      <th>param</th>\n",
       "      <th>red_chisq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>348.82</th>\n",
       "      <td>0.006268</td>\n",
       "      <td>FRB20180916B bursts</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.082300</td>\n",
       "      <td>0.558149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347.82</th>\n",
       "      <td>0.002480</td>\n",
       "      <td>FRB20180916B bursts</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.044417</td>\n",
       "      <td>0.048638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346.82</th>\n",
       "      <td>0.001758</td>\n",
       "      <td>FRB20180916B bursts</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.031789</td>\n",
       "      <td>0.016590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349.32</th>\n",
       "      <td>0.022745</td>\n",
       "      <td>FRB20180916B bursts</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.152874</td>\n",
       "      <td>13.204242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349.82</th>\n",
       "      <td>0.103156</td>\n",
       "      <td>FRB20180916B bursts</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.138476</td>\n",
       "      <td>211.227299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             err                 name  numbursts     param   red_chisq\n",
       "DM                                                                    \n",
       "348.82  0.006268  FRB20180916B bursts       16.0  0.082300    0.558149\n",
       "347.82  0.002480  FRB20180916B bursts       16.0  0.044417    0.048638\n",
       "346.82  0.001758  FRB20180916B bursts       16.0  0.031789    0.016590\n",
       "349.32  0.022745  FRB20180916B bursts       14.0  0.152874   13.204242\n",
       "349.82  0.103156  FRB20180916B bursts        8.0  0.138476  211.227299"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minimal red_chisq:\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>err</th>\n",
       "      <th>name</th>\n",
       "      <th>numbursts</th>\n",
       "      <th>param</th>\n",
       "      <th>red_chisq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>346.82</th>\n",
       "      <td>0.001758</td>\n",
       "      <td>FRB20180916B bursts</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.031789</td>\n",
       "      <td>0.01659</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "             err                 name  numbursts     param  red_chisq\n",
       "DM                                                                   \n",
       "346.82  0.001758  FRB20180916B bursts       16.0  0.031789    0.01659"
      ]
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       "      <th></th>\n",
       "      <th>err</th>\n",
       "      <th>name</th>\n",
       "      <th>numbursts</th>\n",
       "      <th>param</th>\n",
       "      <th>red_chisq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>189.0</th>\n",
       "      <td>0.014946</td>\n",
       "      <td>FRB20180814A bursts</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.084921</td>\n",
       "      <td>0.019496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189.8</th>\n",
       "      <td>0.055402</td>\n",
       "      <td>FRB20180814A bursts</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.152410</td>\n",
       "      <td>0.291529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190.0</th>\n",
       "      <td>0.074712</td>\n",
       "      <td>FRB20180814A bursts</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.150612</td>\n",
       "      <td>0.408035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188.9</th>\n",
       "      <td>0.009991</td>\n",
       "      <td>FRB20180814A bursts</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.071432</td>\n",
       "      <td>0.009745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189.4</th>\n",
       "      <td>0.026126</td>\n",
       "      <td>FRB20180814A bursts</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.108432</td>\n",
       "      <td>0.056874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188.8</th>\n",
       "      <td>0.013460</td>\n",
       "      <td>FRB20180814A bursts</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.075658</td>\n",
       "      <td>0.012456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188.7</th>\n",
       "      <td>0.011983</td>\n",
       "      <td>FRB20180814A bursts</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.076306</td>\n",
       "      <td>0.013196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            err                 name  numbursts     param  red_chisq\n",
       "DM                                                                  \n",
       "189.0  0.014946  FRB20180814A bursts        5.0  0.084921   0.019496\n",
       "189.8  0.055402  FRB20180814A bursts        5.0  0.152410   0.291529\n",
       "190.0  0.074712  FRB20180814A bursts        4.0  0.150612   0.408035\n",
       "188.9  0.009991  FRB20180814A bursts        6.0  0.071432   0.009745\n",
       "189.4  0.026126  FRB20180814A bursts        5.0  0.108432   0.056874\n",
       "188.8  0.013460  FRB20180814A bursts        6.0  0.075658   0.012456\n",
       "188.7  0.011983  FRB20180814A bursts        5.0  0.076306   0.013196"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minimal red_chisq:\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>err</th>\n",
       "      <th>name</th>\n",
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       "      <th>param</th>\n",
       "      <th>red_chisq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DM</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>188.9</th>\n",
       "      <td>0.009991</td>\n",
       "      <td>FRB20180814A bursts</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.071432</td>\n",
       "      <td>0.009745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            err                 name  numbursts     param  red_chisq\n",
       "DM                                                                  \n",
       "188.9  0.009991  FRB20180814A bursts        6.0  0.071432   0.009745"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name in fitdata.name.unique():\n",
    "    df = fitdata.loc[(fitdata.name == name)]# & (fitdata.numbursts > 1.0)]\n",
    "    df = df.set_index('DM')\n",
    "    display(df)\n",
    "    # print('minimal standard deviation:')\n",
    "    # display(df[df.err == df.err.min()])\n",
    "    print('minimal red_chisq:')\n",
    "    display(df[df.red_chisq == df.red_chisq.min()])\n",
    "    \n",
    "# dm at which chisq is minimal and no bursts entirely excluded\n",
    "# michilli 559.405421\n",
    "# gajjar   565.0\n",
    "# 180916   346.82 (or 348.82 if picking for close to 1)\n",
    "# 180814   188.9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drift vs. Frequency (Discussion Figure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-18217543ed36>:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  Bfits = np.array(Bconstants)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.078964601744387e-08 3.501656829509886e-06\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[3.755319457097987e-08, array([4.62947829e-09])],\n",
       "       [3.958180525027664e-08, array([5.0959902e-09])],\n",
       "       [4.186004466660003e-08, array([5.6489454e-09])],\n",
       "       [4.5082972650862676e-08, array([6.80620332e-09])],\n",
       "       [4.758091700090433e-08, array([7.14720958e-09])],\n",
       "       [5.124433656779428e-08, array([8.19180507e-09])],\n",
       "       [5.550334322804194e-08, array([9.5303914e-09])],\n",
       "       [6.056955240732434e-08, array([1.12315229e-08])],\n",
       "       [6.741532716456259e-08, array([1.37664844e-08])],\n",
       "       [7.603638541932202e-08, array([1.74761348e-08])],\n",
       "       [9.068450727638977e-08, array([2.44005783e-08])],\n",
       "       [1.1731959437042582e-07, array([4.16298612e-08])],\n",
       "       [2.3694669910297258e-07, array([1.44999377e-07])],\n",
       "       [1.2132157911508368e-07, array([4.03948839e-08])],\n",
       "       [1.7911307842075265e-07, array([7.9283062e-08])],\n",
       "       [1.5651949767466468e-07, array([7.17765978e-08])],\n",
       "       [3.501656829509886e-06, array([3.38884067e-06])],\n",
       "       [2.1501480077554237e-07, array([1.34195878e-07])],\n",
       "       [9.239945288527096e-08, array([1.45336584e-08])],\n",
       "       [1.0914982913451715e-07, array([1.90136289e-08])],\n",
       "       [8.239150853041422e-08, array([2.03219339e-08])],\n",
       "       [4.089591910675928e-08, array([5.80216112e-09])],\n",
       "       [4.473755347603959e-08, array([6.69144872e-09])],\n",
       "       [4.7916785266796094e-08, array([7.2663885e-09])],\n",
       "       [5.512171659644952e-08, array([9.45203104e-09])],\n",
       "       [6.556870082035245e-08, array([1.28103873e-08])],\n",
       "       [6.836853788457113e-08, array([1.40104002e-08])],\n",
       "       [8.60056510559134e-08, array([1.94914142e-08])],\n",
       "       [1.2025645680388086e-07, array([3.23648672e-08])],\n",
       "       [1.3096861411417307e-07, array([4.05535707e-08])],\n",
       "       [2.267170714753252e-07, array([8.96697603e-08])],\n",
       "       [1.4343811422639095e-07, array([inf])],\n",
       "       [5.984207779081985e-08, array([8.08561161e-09])],\n",
       "       [3.058189214334344e-08, array([2.24685657e-09])],\n",
       "       [2.078964601744387e-08, array([1.38546068e-09])],\n",
       "       [1.526862544113376e-07, array([3.8810614e-08])],\n",
       "       [1.2490097892334902e-06, array([1.15365793e-06])],\n",
       "       [2.9411633434029666e-08, array([9.54365403e-09])],\n",
       "       [7.765827503563372e-08, array([3.35059077e-08])],\n",
       "       [7.684855755566434e-08, array([5.07049706e-08])],\n",
       "       [2.182765546333594e-08, array([5.82552484e-09])],\n",
       "       [4.21401711660935e-08, array([1.54483977e-08])],\n",
       "       [2.4323963458404415e-08, array([7.68948527e-09])],\n",
       "       [2.4828372913481327e-08, array([7.87162095e-09])]], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Bfits = np.array(Bconstants)\n",
    "print(Bfits[:,0].min(), Bfits[:,0].max())\n",
    "Bfits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.590426024179991e-08 8.446693232174064e-09\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-a5f2cb9c7565>:81: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  Bfits = np.array(Bconstants)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.078964601744387e-08 3.501656829509886e-06\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "def drift_nuobs_expr(B, x):\n",
    "    return B[0]*x**2\n",
    "\n",
    "def drift_nuobs_expr_log(B, x):\n",
    "    return B[0] + 2*x\n",
    "\n",
    "def constantmodel(x, c):\n",
    "    return c\n",
    "\n",
    "def drift_nuobs_sq(B, x):\n",
    "    return B[0]*np.ones(x.shape)\n",
    "\n",
    "def frexp10(x):\n",
    "    \"\"\" \n",
    "    source : github.com/joelfrederico/SciSalt/blob/master/scisalt/numpy/frexp10.py \n",
    "    related: stackoverflow.com/questions/45332056/decompose-a-float-into-mantissa-and-exponent-in-base-10-without-strings \n",
    "    \"\"\"\n",
    "    expon = np.int(np.floor(np.log10(np.abs(x))))\n",
    "    mant = x/np.power(10.0, expon)\n",
    "    return (mant, expon)\n",
    "\n",
    "plt.rcParams[\"errorbar.capsize\"] = 4\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "\n",
    "markersize = 140\n",
    "fontsize = 25\n",
    "annotsize = 14\n",
    "logscale = True\n",
    "figsize = (10, 9)\n",
    "\n",
    "frames = fig1pltdata['frames']\n",
    "for frame in frames: \n",
    "    frame['drift_abs_nuobssq'] = frame['drift_abs']/frame['center_f']**2/1000\n",
    "    \n",
    "def yerror(frame):\n",
    "    \"\"\" the shape of this is (1, 2, len(frame)) because pandas \"\"\"\n",
    "    ey = [np.array([frame['drift_abs_nuobssq'] - frame['drift_nu_min']/frame['center_f']/1000, frame['drift_nu_max']/frame['center_f']/1000 - frame['drift_abs_nuobssq']])]\n",
    "    return ey\n",
    "\n",
    "ax = frames[0].plot.scatter(x='center_f', y='drift_abs_nuobssq', \n",
    "                           # yerr=np.sqrt(frames[0]['red_chisq'])*frames[0]['drift error (mhz/ms)']*(1/frames[0]['center_f']**2)/1000,\n",
    "                           yerr=yerror(frames[0]),\n",
    "                           figsize=figsize, s=markersize, c='color', \n",
    "                           colorbar=False, fontsize=fontsize, \n",
    "                           logy=logscale, logx=logscale, marker='o', edgecolors='k',\n",
    "                           label='FRB20121102A Michilli et al. (2018)')\n",
    "\n",
    "labels = ['FRB20121102A Gajjar et al. (2018)', 'FRB20180916B bursts', 'FRB20180814A bursts']\n",
    "for frame, label, mark in zip(frames[1:], labels, ['d', 'p', 'X']):\n",
    "    frame.plot.scatter(ax=ax, x='center_f', y='drift_abs_nuobssq', \n",
    "                       # yerr=np.sqrt(frame['red_chisq'])*frame['drift error (mhz/ms)']*(1/frame['center_f']**2)/1000,\n",
    "                       yerr=yerror(frame),\n",
    "                       figsize=figsize, s=markersize, c='color', colorbar=False, \n",
    "                       fontsize=fontsize, logy=logscale, logx=logscale, marker=mark, edgecolors='k',\n",
    "                       label=label)\n",
    "\n",
    "# Josephy point\n",
    "ax.scatter(josephyburst['center_f'], -josephyburst['drift (mhz/ms)']/josephyburst['center_f']**2/1000, c='r', s=125, edgecolors='k', marker='s', label='FRB20121102A Josephy et al. (2019)')\n",
    "\n",
    "## Mark all bursts whose DM ranges were limited by the exclusion process\n",
    "for i, limitedpoints in enumerate(dmlimited):\n",
    "    for lp in limitedpoints:\n",
    "        v = fig1pltdata['frames'][i].loc[lp]\n",
    "        plt.scatter(v['center_f'], v['drift_abs_nuobssq'], facecolors='none', edgecolors='k', linewidth=1.0, s=400)\n",
    "        # ax.annotate('o', (v['tau_w_ms'], v['drift_over_nuobs']), xytext=(-10,-18), textcoords='offset pixels', weight='bold', size=25)\n",
    "\n",
    "ax.set_xlabel('Observed Frequency $\\\\nu_{obs}$ (MHz)', size=fontsize)\n",
    "ax.set_ylabel('Sub-burst Slope $\\left|\\\\frac{d\\\\nu_\\\\mathrm{obs}}{dt_\\\\mathrm{D}}\\\\right|\\, (1/\\\\nu_{obs})^{2}$', size=fontsize-3)\n",
    "alldata = pd.concat([f for f in fig1pltdata['frames']])#+[josephyburst]])\n",
    "\n",
    "x = np.linspace(0, 20000, num=2000)   \n",
    "popt, pcov = scipy.optimize.curve_fit(lambda x, c: c, x, alldata['drift_abs_nuobssq'])\n",
    "berr = frexp10(np.sqrt(pcov[0][0]))[0]\n",
    "hacklbl = f'$\\it{{B}}$ = ({frexp10(popt[0])[0]:.2} $\\pm$ {berr/10:.1}) $\\\\times$10$^{{{frexp10(popt[0])[1]}}}$'\n",
    "# hacklbl = '($B = 6.7 \\\\times 10^{-8}$)'\n",
    "plt.plot(x, popt[0]*np.ones(x.shape), 'k--', label=hacklbl)\n",
    "print(popt[0], np.sqrt(pcov[0][0]))#, np.frexp(popt[0]))\n",
    "\n",
    "# range of fits\n",
    "Bfits = np.array(Bconstants)\n",
    "color = 'b'\n",
    "bmin, bmax = Bfits[:,0].min(), Bfits[:,0].max()\n",
    "print(bmin, bmax)\n",
    "lstr = f\"Range of fits: {frexp10(bmin)[0]:.2}$\\\\times$10$^{{{frexp10(bmin)[1]}}}$ to {frexp10(bmax)[0]:.2}$\\\\times$10$^{{{frexp10(bmax)[1]}}}$\"\n",
    "lstr = \"Range of possible fits\"\n",
    "# lstr = f\"Range of fits {Bfits[:,0].min():.1e} - {Bfits[:,0].max():.1e}\"\n",
    "# ax.fill_between(x, Bfits[:,0].min(), Bfits[:,0].max(), alpha=0.1, color=color, edgecolor=color, label=lstr, zorder=-10)\n",
    "\n",
    "hdls, lbls = ax.get_legend_handles_labels()\n",
    "order = [1, 2, 5, 3, 4, 0]\n",
    "hdls = [hdls[i] for i in order]\n",
    "lbls = [lbls[i] for i in order]\n",
    "ax.legend(hdls, lbls, fontsize='xx-large', loc=2)\n",
    "\n",
    "plt.xlim(3*10**2, 10**4)\n",
    "plt.ylim(0.3*10**-8, 1*10**-4)\n",
    "plt.tight_layout()\n",
    "plt.savefig('universal/driftvsnuobs_log.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Observed Frequency (MHz)')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sub-burst slope vs nu_obs to compare with pastor-marazuela et al. 2020\n",
    "ax = frames[2].plot.scatter(x='center_f', y='drift (mhz/ms)', \n",
    "                           # yerr=np.sqrt(frames[0]['red_chisq'])*frames[0]['drift error (mhz/ms)']*(1/frames[0]['center_f']**2)/1000,\n",
    "                           yerr=yerror(frames[2]),\n",
    "                           figsize=figsize, s=markersize, c='color', \n",
    "                           colorbar=False, fontsize=fontsize, \n",
    "                           logy=False, logx=False, marker='o', edgecolors='k',\n",
    "                           label='FRB180916')\n",
    "ax.set_ylabel(\"Sub-burst Slope\")\n",
    "ax.set_xlabel(\"Observed Frequency (MHz)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-31-166b622dec7b>:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  michillibursts['drift_abs_z'] = michillibursts['drift_abs']*(1+michillibursts['z'])**2\n",
      "<ipython-input-31-166b622dec7b>:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  michillibursts['drift_abs_nuobssq'] = michillibursts['drift_abs']*(1/michillibursts['center_f']**2)/1000\n",
      "<ipython-input-31-166b622dec7b>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  michillibursts['center_f_z'] = michillibursts['center_f']*(1+michillibursts['z'])\n",
      "<ipython-input-31-166b622dec7b>:41: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  otherbursts['drift_abs_z'] = otherbursts['drift_abs']*(1+otherbursts['z'])**2\n",
      "<ipython-input-31-166b622dec7b>:42: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  otherbursts['drift_abs_nuobssq'] = otherbursts['drift_abs']*(1/otherbursts['center_f']**2)/1000\n",
      "<ipython-input-31-166b622dec7b>:43: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  otherbursts['center_f_z'] = otherbursts['center_f']*(1+otherbursts['z'])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8.22364449e-08] [1.73154499e-08]\n",
      "[8.22364445e-08] [1.731545e-08]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-31-166b622dec7b>:112: RuntimeWarning: divide by zero encountered in log\n",
      "  xlog = np.log(x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# michili fit\n",
    "def drift_nuobs_expr(B, x):\n",
    "    return B[0]*x**2\n",
    "\n",
    "def drift_nuobs_expr_log(B, x):\n",
    "    return B[0] + 2*x\n",
    "\n",
    "def constantmodel(x, c):\n",
    "    return c\n",
    "\n",
    "def drift_nuobs_sq(B, x):\n",
    "    return B[0]*np.ones(x.shape)\n",
    "\n",
    "drift_nuobs_model = scipy.odr.Model(drift_nuobs_expr)\n",
    "drift_nuobs_model_log = scipy.odr.Model(drift_nuobs_expr_log)\n",
    "drift_nuobs_sq_model = scipy.odr.Model(drift_nuobs_sq)\n",
    "\n",
    "plt.rcParams[\"errorbar.capsize\"] = 4\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "\n",
    "markersize = 140\n",
    "fontsize = 25\n",
    "annotsize = 14\n",
    "logscale = True\n",
    "filename = 'log_drift_over_nu_obsvsduration_inprep' if logscale else 'drift_over_nu_obsvsduration_inprep'\n",
    "figsize = (17, 8)\n",
    "# figsize = (14, 10)\n",
    "\n",
    "michillibursts['drift_abs_z'] = michillibursts['drift_abs']*(1+michillibursts['z'])**2\n",
    "michillibursts['drift_abs_nuobssq'] = michillibursts['drift_abs']*(1/michillibursts['center_f']**2)/1000\n",
    "michillibursts['center_f_z'] = michillibursts['center_f']*(1+michillibursts['z'])\n",
    "\n",
    "selectbursts180916['drift_abs_z'] = selectbursts180916['drift_abs']*(1+selectbursts180916['z'])**2\n",
    "selectbursts180916['drift_abs_nuobssq'] = selectbursts180916['drift_abs']*(1/selectbursts180916['center_f']**2)/1000\n",
    "selectbursts180916['center_f_z'] = selectbursts180916['center_f']*(1+selectbursts180916['z'])\n",
    "\n",
    "selectbursts180814['drift_abs_z'] = selectbursts180814['drift_abs']*(1+selectbursts180814['z'])**2\n",
    "selectbursts180814['drift_abs_nuobssq'] = selectbursts180814['drift_abs']*(1/selectbursts180814['center_f']**2)/1000\n",
    "selectbursts180814['center_f_z'] = selectbursts180814['center_f']*(1+selectbursts180814['z'])\n",
    "\n",
    "otherbursts['drift_abs_z'] = otherbursts['drift_abs']*(1+otherbursts['z'])**2\n",
    "otherbursts['drift_abs_nuobssq'] = otherbursts['drift_abs']*(1/otherbursts['center_f']**2)/1000\n",
    "otherbursts['center_f_z'] = otherbursts['center_f']*(1+otherbursts['z'])\n",
    "\n",
    "ax = michillibursts.plot.scatter(x='center_f', y='drift_abs_nuobssq', \n",
    "                               yerr=np.sqrt(michillibursts['red_chisq'])*michillibursts['drift error (mhz/ms)']*(1/michillibursts['center_f']**2)/1000,\n",
    "                               figsize=figsize, s=markersize, c='color', colorbar=False, fontsize=fontsize, logy=logscale, logx=logscale, marker='o', edgecolors='k',\n",
    "                               label='FRB121102 Michilli et al. (2018)')\n",
    "    \n",
    "selectbursts180916.plot.scatter(ax=ax, x='center_f', y='drift_abs_nuobssq', \n",
    "                               yerr=np.sqrt(selectbursts180916['red_chisq'])*selectbursts180916['drift error (mhz/ms)']*(1/selectbursts180916['center_f']**2)/1000,\n",
    "                               figsize=figsize, s=markersize, c='color', colorbar=False, fontsize=fontsize, logy=logscale, logx=logscale, marker='p', edgecolors='k',\n",
    "                               label='FRB180916.J0158+65 bursts')\n",
    "selectbursts180814.plot.scatter(ax=ax, x='center_f', y='drift_abs_nuobssq', \n",
    "                               yerr=np.sqrt(selectbursts180814['red_chisq'])*selectbursts180814['drift error (mhz/ms)']*(1/selectbursts180814['center_f']**2)/1000,\n",
    "                               figsize=figsize, s=markersize, c='color', colorbar=False, fontsize=fontsize, logy=logscale, logx=logscale, marker='X', edgecolors='k',\n",
    "                               label='FRB180814.J0422+73 bursts')\n",
    "\n",
    "otherbursts.head(5).plot.scatter(ax=ax, x='center_f', y='drift_abs_nuobssq',\n",
    "                         yerr=np.sqrt(otherbursts['red_chisq'])*otherbursts['drift error (mhz/ms)']*(1/otherbursts['center_f']**2)/1000,\n",
    "                         figsize=figsize, s=markersize, c='color', colorbar=False, marker='d', edgecolors='k', label='FRB121102 Gajjar et al. (2018)')\n",
    "otherbursts.tail(1).plot.scatter(ax=ax, x='center_f', y='drift_abs_nuobssq',\n",
    "                         yerr=np.sqrt(otherbursts['red_chisq'])*otherbursts['drift error (mhz/ms)']*(1/otherbursts['center_f']**2)/1000,\n",
    "                         figsize=figsize, s=markersize, c='color', colorbar=False, marker='s', edgecolors='k', label='FRB121102 Josephy et al. (2019)')\n",
    "\n",
    "# for k, v in otherbursts.iterrows():\n",
    "#     ax.annotate(k+'_c', (v['tau_w_ms_corr'], v['drift_corr']), xytext=(-3,5), textcoords='offset points', weight='bold', size=annotsize)\n",
    "# for k, v in michillibursts.iterrows():\n",
    "#     ax.annotate(k, (v['tau_w_ms'], v['drift_abs']), xytext=(-3,5), textcoords='offset points', weight='bold', size=annotsize)\n",
    "# for k, v in selectbursts180916.iterrows():\n",
    "#     ax.annotate(int(k) if k != 15.5 else k, (v['tau_w_ms'], v['drift_over_nuobs']), xytext=(-3,5), textcoords='offset points', weight='bold', size=annotsize)\n",
    "# for k, v in selectbursts180814.iterrows():\n",
    "#     if v['drift_abs'] > 0:\n",
    "#         ax.annotate(k, (v['center_f'], v['drift_abs']), xytext=(-3,5), textcoords='offset points', weight='bold', size=annotsize)\n",
    "\n",
    "# if not logscale:\n",
    "#     ax.set_xlim(-0.1, 20)\n",
    "#     ax.set_ylim(-0.2, 4)\n",
    "# elif logscale:\n",
    "#     ax.set_xlim(0.02, 50)\n",
    "#     ax.set_ylim(10**-3, 10**1)\n",
    "\n",
    "# ax.set_title('Sub-burst Drift Rate vs. Burst Duration (fit to Michilli bursts)', size=fontsize)\n",
    "ax.set_xlabel('Observed Frequency $\\\\nu_{obs}$ (MHz)', size=fontsize)\n",
    "ax.set_ylabel('Sub-burst Drift Rate $\\left|\\\\frac{d\\\\nu_\\\\mathrm{obs}}{dt_\\\\mathrm{D}}\\\\right|\\, (1/\\\\nu_{obs})^{2}$ (s$^{-1}$)', size=fontsize-3)\n",
    "\n",
    "\n",
    "allbursts = pd.concat([michillibursts, otherbursts, selectbursts180916, selectbursts180814])\n",
    "### LINEAR FIT\n",
    "fitbitch = scipy.odr.RealData(allbursts['center_f'], \n",
    "                             allbursts['drift_abs_nuobssq'])#,\n",
    "                             #sy=np.sqrt(allbursts['red_chisq'])*allbursts['drift error (mhz/ms)']*(1/allbursts['center_f']**2)/1000,\n",
    "                             #sx=np.array(len(allbursts['center_f'])*[5000])*np.random.rand(len(allbursts['center_f'])))\n",
    "fitter_driftnuobs = scipy.odr.ODR(fitbitch, drift_nuobs_sq_model, beta0=[0.00003])\n",
    "fitter_driftnuobs.set_job(fit_type=0)\n",
    "fitdriftnuobs = fitter_driftnuobs.run()\n",
    "\n",
    "### LOG FIT\n",
    "# fitbitch = scipy.odr.RealData(np.log(allbursts['center_f']), \n",
    "#                              np.log(allbursts['drift_abs']))\n",
    "#                              #sy=np.log(np.sqrt(allbursts['red_chisq'])*allbursts['drift error (mhz/ms)']))\n",
    "# fitter_driftnuobs = scipy.odr.ODR(fitbitch, drift_nuobs_model_log, beta0=[1000])\n",
    "# fitter_driftnuobs.set_job(fit_type=0)\n",
    "# fitdriftnuobs = fitter_driftnuobs.run()\n",
    "\n",
    "x = np.linspace(0, 20000, num=2000)   \n",
    "popt, pcov = scipy.optimize.curve_fit(constantmodel, x, allbursts['drift_abs_nuobssq'])#, \n",
    "                                      #sigma=np.sqrt(allbursts['red_chisq'])*allbursts['drift error (mhz/ms)']*(1/allbursts['center_f']**2)/1000, \n",
    "                                      #absolute_sigma=True)\n",
    "print(popt, np.sqrt(pcov)[0])\n",
    "### Plot fits\n",
    "xlog = np.log(x)\n",
    "print(fitdriftnuobs.beta, fitdriftnuobs.sd_beta)\n",
    "\n",
    "hacklbl = '(8.2 $\\pm$ 1.7)$\\\\times$10$^{-8}$'\n",
    "lbl = '({:.1e} $\\pm$ {:0.2e})'.format(fitdriftnuobs.beta[0], fitdriftnuobs.sd_beta[0])\n",
    "#plt.plot(x, fitdriftnuobs.beta[0]*np.ones(x.shape), 'k--', label=hacklbl)\n",
    "# plt.plot(x, fitdriftnuobs.beta[0]*np.ones(x.shape), 'k--', label=hacklbl)\n",
    "plt.plot(x, popt[0]*np.ones(x.shape), 'k--', label=hacklbl)\n",
    "#plt.plot(x, fitdriftnuobs.beta[0]+2*x, l, label=str((round(fitdriftnuobs.beta[0], 6))) + ' $\\\\nu_{obs}^{2}$')\n",
    "\n",
    "# plt.xlim(2*10**2, 1*10**4)\n",
    "# plt.ylim(10**-1, 10**5)\n",
    "# plt.xlim(0, 10000); plt.ylim(-500, 20000)\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "handles = [handles[1], handles[4], handles[5], handles[2], handles[3], handles[0]]\n",
    "labels = [labels[1], labels[4], labels[5], labels[2], labels[3], labels[0]]\n",
    "plt.legend(handles, labels, fontsize='xx-large', loc=8)\n",
    "\n",
    "###\n",
    "plt.xlim(3*10**2, 10**4)\n",
    "plt.ylim(10**-9, 2*10**-6)\n",
    "\n",
    "# plt.title(\"Non Redshift Corrected\", size=20)\n",
    "#plt.title(\"FRB121102, FRB180916.J0158+65, and FRB180814.J0422+73\", size=20)\n",
    "plt.tight_layout()\n",
    "# plt.savefig('driftvsnuobs_log.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pasted from other notebook, dont run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats\n",
    "import scipy.odr\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib as mpl\n",
    "from tqdm import tqdm\n",
    "import functools\n",
    "print = functools.partial(print, flush=True) # print doesn't happen til script ends so force it to flush... windows thing?\n",
    "import pandas as pd\n",
    "import driftlaw\n",
    "\n",
    "bursts121102 = pd.read_csv('data/aggregate/burstsFRB121102.csv') \n",
    "bursts121102 = bursts121102.set_index('name')\n",
    "##########\n",
    "bursts180916 = pd.read_csv('data/aggregate/burstsFRB180916.csv') \n",
    "bursts180916 = bursts180916.set_index('name')\n",
    "bursts180916['time_res'] = 0.00098304 # s\n",
    "#########\n",
    "bursts180814 = pd.read_csv('data/aggregate/burstsFRB180814.csv') # DM = 188.9 pc/cm3\n",
    "bursts180814 = bursts180814.set_index('name')\n",
    "bursts180814['time_res'] = 0.00098304 # s, Not true for burst 180917\n",
    "###########\n",
    "burstsSGR1935 = pd.read_csv('data/aggregate/SGR1935+2154bursts.csv')\n",
    "burstsSGR1935 = burstsSGR1935.set_index('name')\n",
    "##########\n",
    "\n",
    "# Set redshifts\n",
    "bursts121102['z']  = 0.19273 # (josephy et al. 2019, frbcat.org)\n",
    "bursts180916['z']  = 0.0337  # (chime 2020b et al.)\n",
    "bursts180814['z']  = 0.05    # not accurate, z < 0.1 (amiri et al. 2019)\n",
    "burstsSGR1935['z'] = 0       # ? galactic\n",
    "\n",
    "#######\n",
    "bursts121102 = driftlaw.computeModelDetails(bursts121102)\n",
    "bursts121102.at['C1', 'tau_w_ms'] = 11.4\n",
    "bursts121102.at['C1', 'tau_w'] = 11.4/1000\n",
    "bursts121102.at['C1', 'tau_w_error'] = 0.12\n",
    "bursts121102_to_include = ['M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'M8', 'M9a', 'M9b', 'M10a', 'M10b', 'M11', 'M12', 'M13', 'M14', 'M15', 'M16', 'G11D', 'G11A1a', 'G11A1b', 'G11A2', 'G11A3', 'C1']\n",
    "selectbursts121102 = bursts121102.loc[bursts121102_to_include]\n",
    "\n",
    "bursts180916 = driftlaw.computeModelDetails(bursts180916)\n",
    "selectbursts180916 = bursts180916\n",
    "\n",
    "bursts180814 = driftlaw.computeModelDetails(bursts180814)\n",
    "selectbursts180814 = bursts180814.drop(180917)\n",
    "\n",
    "burstsSGR1935 = driftlaw.computeModelDetails(burstsSGR1935)\n",
    "#display(bursts121102[['tau_w', 'tau_w_error']], bursts180916[['tau_w', 'tau_w_error']], bursts180814[['tau_w', 'tau_w_error']], burstsSGR1935[['tau_w', 'tau_w_error']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_model = scipy.odr.Model(driftlaw.reciprocal_odr)\n",
    "fit_model_log = scipy.odr.Model(driftlaw.reciprocal_odr_log)\n",
    "# Color by source\n",
    "selectbursts121102['color'] = 'r'#'c'\n",
    "selectbursts121102.at[selectbursts121102.index > 'M', 'color'] = 'r'\n",
    "selectbursts121102.at['C1', 'color'] = 'r'#'b'\n",
    "selectbursts180916['color']          = 'b'\n",
    "selectbursts180814['color']          = 'g'\n",
    "burstsSGR1935['color']               = 'k'\n",
    "michillibursts = selectbursts121102.head(18)\n",
    "otherbursts = selectbursts121102.tail(6)\n",
    "burstsSGR1935['drift (mhz/ms)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"errorbar.capsize\"] = 4\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "\n",
    "markersize = 125#100\n",
    "fontsize = 25 #18\n",
    "annotsize = 14\n",
    "logscale = True\n",
    "filename = 'log_drift_over_nu_obsvsduration' if logscale else 'drift_over_nu_obsvsduration'\n",
    "figsize = (17, 8)\n",
    "figsize = (17, 9)\n",
    "# figsize = (14, 10)\n",
    "\n",
    "\n",
    "ax = michillibursts.plot.scatter(x='tau_w_ms', y='drift_over_nuobs', \n",
    "                               xerr=np.sqrt(michillibursts['red_chisq'])*michillibursts['tau_w_error'], \n",
    "                               yerr=np.sqrt(michillibursts['red_chisq'])*michillibursts['drift error (mhz/ms)']/michillibursts['center_f'], \n",
    "                               figsize=figsize, s=markersize, c='color', colorbar=False, fontsize=fontsize, logy=logscale, logx=logscale, marker='o', edgecolors='k',\n",
    "                               label='FRB121102 Michilli et al. (2018)')\n",
    "    \n",
    "selectbursts180916.plot.scatter(ax=ax, x='tau_w_ms', y='drift_over_nuobs', \n",
    "                               xerr=np.sqrt(selectbursts180916['red_chisq'])*selectbursts180916['tau_w_error'], \n",
    "                               yerr=np.sqrt(selectbursts180916['red_chisq'])*selectbursts180916['drift error (mhz/ms)']/selectbursts180916['center_f'], \n",
    "                               figsize=figsize, s=markersize, c='color', colorbar=False, fontsize=fontsize, logy=logscale, logx=logscale, marker='p', edgecolors='k',\n",
    "                               label='FRB180916.J0158+65 bursts')\n",
    "selectbursts180814.plot.scatter(ax=ax, x='tau_w_ms', y='drift_over_nuobs', \n",
    "                               xerr=np.sqrt(selectbursts180814['red_chisq'])*selectbursts180814['tau_w_error'], \n",
    "                               yerr=np.sqrt(selectbursts180814['red_chisq'])*selectbursts180814['drift error (mhz/ms)']/selectbursts180814['center_f'], \n",
    "                               figsize=figsize, s=markersize+50, c='color', colorbar=False, fontsize=fontsize, logy=logscale, logx=logscale, marker='X', edgecolors='k',\n",
    "                               label='FRB180814.J0422+73 bursts')\n",
    "                               #label='FRB180814.J0422+73 bursts @ DM={} pc/cm$^3$'.format(dms180814[dm_idx]))\n",
    "burstsSGR1935.plot.scatter(ax=ax, x='tau_w_ms', y='drift_over_nuobs', \n",
    "                               xerr=np.sqrt(burstsSGR1935['red_chisq'])*burstsSGR1935['tau_w_error'], \n",
    "                               yerr=np.sqrt(burstsSGR1935['red_chisq'])*burstsSGR1935['drift error (mhz/ms)']/burstsSGR1935['center_f'], \n",
    "                               figsize=figsize, s=markersize, c='color', colorbar=False, fontsize=fontsize, logy=logscale, logx=logscale, marker='p', edgecolors='k',\n",
    "                               label='SGR1935+2154 bursts')\n",
    "\n",
    "otherbursts.head(5).plot.scatter(ax=ax, x='tau_w_ms', y='drift_over_nuobs',\n",
    "                         xerr=np.sqrt(otherbursts['red_chisq'])*otherbursts['tau_w_error'], \n",
    "                         yerr=np.sqrt(otherbursts['red_chisq'])*otherbursts['drift error (mhz/ms)']/otherbursts['center_f'], edgecolors='k',\n",
    "                         figsize=figsize, s=markersize, c='color', colorbar=False, marker='d', label='FRB121102 Gajjar et al. (2018)') \n",
    "otherbursts.tail(1).plot.scatter(ax=ax, x='tau_w_ms', y='drift_over_nuobs',\n",
    "                         xerr=np.sqrt(otherbursts['red_chisq'])*otherbursts['tau_w_error'], \n",
    "                         yerr=np.sqrt(otherbursts['red_chisq'])*otherbursts['drift error (mhz/ms)']/otherbursts['center_f'], edgecolors='k',\n",
    "                         figsize=figsize, s=markersize, c='color', colorbar=False, marker='s', label='FRB121102 Josephy et al. (2019)') \n",
    "\n",
    "# for k, v in otherbursts.iterrows():\n",
    "#     ax.annotate(k+'_c', (v['tau_w_ms'], v['drift_over_nuobs']), xytext=(-3,5), textcoords='offset points', weight='bold', size=annotsize)\n",
    "# for k, v in michillibursts.iterrows():\n",
    "#     ax.annotate(k, (v['tau_w_ms'], v['drift_over_nuobs']), xytext=(-3,5), textcoords='offset points', weight='bold', size=annotsize)\n",
    "# for k, v in selectbursts180916.iterrows():\n",
    "#     ax.annotate(int(k) if k != 15.5 else k, (v['tau_w_ms'], v['drift_over_nuobs']), xytext=(-3,5), textcoords='offset points', weight='bold', size=annotsize)\n",
    "for k, v in burstsSGR1935.iterrows():\n",
    "    if v['drift_over_nuobs'] > 0 or not logscale:\n",
    "        ax.annotate(k, (v['tau_w_ms'], v['drift_over_nuobs']), xytext=(-3,5), textcoords='offset points', weight='bold', size=annotsize)\n",
    "# for k, v in selectbursts180814.iterrows():\n",
    "#     if v['drift_over_nuobs'] > 0:\n",
    "#         ax.annotate(k, (v['tau_w_ms'], v['drift_over_nuobs']), xytext=(-3,5), textcoords='offset points', weight='bold', size=annotsize)\n",
    "\n",
    "if not logscale:\n",
    "    pass\n",
    "    # ax.set_xlim(-0.1, 20)\n",
    "    # ax.set_ylim(-0.2, 4)\n",
    "elif logscale:\n",
    "    ax.set_xlim(0.04, 20)\n",
    "    ax.set_ylim(10**-3, 10**1)\n",
    "\n",
    "# ax.set_title('Sub-burst Drift Rate vs. Burst Duration (fit to Michilli bursts)', size=fontsize)\n",
    "ax.set_xlabel('Sub-burst Duration $t_\\\\mathrm{w}$ (ms)', size=fontsize)\n",
    "ax.set_ylabel('Sub-burst Drift Rate $\\,(1/\\\\nu_{\\\\mathrm{obs}}) \\left|\\\\frac{d\\\\nu_\\\\mathrm{obs}}{dt_\\\\mathrm{D}}\\\\right|$ (ms$^{-1}$)', size=fontsize)\n",
    "\n",
    "def driftnu_error(frame):\n",
    "    sx = np.log((frame['tau_w_ms'] + np.sqrt(frame['red_chisq'])*frame['tau_w_error']) / frame['tau_w_ms'])\n",
    "    sy = np.log((frame['drift_over_nuobs'] + np.sqrt(frame['red_chisq'])*(frame['drift error (mhz/ms)'])) / frame['drift_over_nuobs'])\n",
    "    return sx, sy\n",
    "\n",
    "# ODR fit log\n",
    "num_to_fit = 24 #23 to exlude chime\n",
    "fitdata_log = scipy.odr.RealData(np.log(selectbursts121102.head(num_to_fit)['tau_w_ms']), \n",
    "                             np.log(selectbursts121102.head(num_to_fit)['drift_over_nuobs']),\n",
    "                             sx=driftnu_error(selectbursts121102.head(num_to_fit))[0],\n",
    "                             sy=driftnu_error(selectbursts121102.head(num_to_fit))[1])\n",
    "                             #sx=np.log(np.sqrt(selectbursts121102.head(num_to_fit)['red_chisq'])*selectbursts121102.head(num_to_fit)['tau_w_error']), \n",
    "                             #sy=np.log(np.sqrt(selectbursts121102.head(num_to_fit)['red_chisq'])*selectbursts121102.head(num_to_fit)['drift error (mhz/ms)']/selectbursts121102.head(num_to_fit)['center_f']))\n",
    "odrfitter_log = scipy.odr.ODR(fitdata_log, fit_model_log, beta0=[500])\n",
    "odrfitter_log.set_job(fit_type=0)\n",
    "odrfit_log = odrfitter_log.run()\n",
    "\n",
    "# ODR fit log 180916\n",
    "fitdata_log_180916 = scipy.odr.RealData(np.log(selectbursts180916['tau_w_ms']), \n",
    "                             np.log(selectbursts180916['drift_over_nuobs']),\n",
    "                             sx=driftnu_error(selectbursts180916)[0],\n",
    "                             sy=driftnu_error(selectbursts180916)[1])\n",
    "                             #sx=np.log(np.sqrt(selectbursts180916['red_chisq'])*selectbursts180916['tau_w_error']), \n",
    "                             #sy=np.log(np.sqrt(selectbursts180916['red_chisq'])*selectbursts180916['drift error (mhz/ms)']/selectbursts180916['center_f'] ))\n",
    "odrfitter_log180916 = scipy.odr.ODR(fitdata_log_180916, fit_model_log, beta0=[1000])\n",
    "odrfitter_log180916.set_job(fit_type=0)\n",
    "odrfit_log180916 = odrfitter_log180916.run()\n",
    "\n",
    "# ODR fit log 180814\n",
    "fitdata_log_180814 = scipy.odr.RealData(np.log(selectbursts180814['tau_w_ms']), \n",
    "                             np.log(selectbursts180814['drift_over_nuobs']),\n",
    "                             sx=driftnu_error(selectbursts180814)[0],\n",
    "                             sy=driftnu_error(selectbursts180814)[1])\n",
    "                             #sx=np.log(np.sqrt(selectbursts180814['red_chisq'])*selectbursts180814['tau_w_error']), \n",
    "                             #sy=np.log(np.sqrt(selectbursts180814['red_chisq'])*selectbursts180814['drift error (mhz/ms)']/selectbursts180814['center_f'] ))\n",
    "odrfitter_log180814 = scipy.odr.ODR(fitdata_log_180814, fit_model_log, beta0=[1000])\n",
    "odrfitter_log180814.set_job(fit_type=0)\n",
    "odrfit_log180814 = odrfitter_log180814.run()\n",
    "\n",
    "### Plot fits\n",
    "x = np.linspace(0, 20, num=1200)\n",
    "opts  = [np.exp(odrfit_log.beta), np.exp(odrfit_log180916.beta), np.exp(odrfit_log180814.beta)]\n",
    "errs  = [opts[0]*(np.exp(odrfit_log.sd_beta)-1), opts[1]*(np.exp(odrfit_log180916.sd_beta)-1), opts[2]*(np.exp(odrfit_log180814.sd_beta)-1)]\n",
    "#errs  = [np.exp(odrfit_log.sd_beta), np.exp(odrfit_log180916.sd_beta), np.exp(odrfit_log180814.sd_beta)]\n",
    "#names = ['Fit from FRB121102 bursts', 'Fit from FRB180916.J0158+65 bursts', 'Fit from FRB180814.J0422+73 bursts']\n",
    "\n",
    "names = ['FRB121102 fit', 'FRB180916.J0158+65 fit', 'FRB180814.J0422+73 fit']\n",
    "ls    = ['r-', 'b--', 'g-.']\n",
    "for opt, err, name, l in zip(opts, errs, names, ls):\n",
    "    print(opt, err)\n",
    "    lstr = '{} ({:.3f} $\\pm$ {:.3f}) / $t_w$'.format(name, opt[0], err[0])\n",
    "    plt.plot(x, opt[0]/x, l, label=lstr)\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "#handles = [handles[0], handles[2], handles[4], handles[5], handles[1], handles[3]]\n",
    "#labels = [labels[0], labels[2], labels[4], labels[5], labels[1], labels[3]]\n",
    "# handles = [handles[2], handles[5], handles[6], handles[4], handles[3], handles[0], handles[1]]\n",
    "# labels = [labels[2], labels[5], labels[6], labels[4], labels[3], labels[0], labels[1]]\n",
    "handles = [handles[3], handles[6], handles[7], handles[5], handles[4], handles[0], handles[1], handles[2]]\n",
    "labels = [labels[3], labels[6], labels[7], labels[5], labels[4], labels[0], labels[1], labels[2]]\n",
    "plt.legend(handles, labels, fontsize='xx-large')\n",
    "# 2 digits, ytitle\n",
    "\n",
    "# plt.title(\"Non Redshift Corrected\", size=20)\n",
    "#plt.title(\"FRB121102, FRB180916.J0158+65, and FRB180814.J0422+73\", size=20)\n",
    "plt.tight_layout()\n",
    "# plt.savefig('180814_dm{}.png'.format(dms180814[dm_idx]))\n",
    "# print('180814_dm{}.png'.format(dms180814[dm_idx]))\n",
    "# for f in ['png', 'pdf', 'eps']: plt.savefig('figures/{}.{}'.format(filename, f))\n",
    "# for f in ['png']: plt.savefig('figures/{}.{}'.format(filename, f))"
   ]
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