embedding_generation_indicator_reconstruction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kyle/supernova/snfactory/manifold_twins/analysis/twins_embedding.py:14: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  from tqdm.autonotebook import tqdm\n"
     ]
    }
   ],
   "source": [
    "from idrtools import math\n",
    "from twins_embedding import TwinsEmbeddingAnalysis\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import colors\n",
    "from tqdm.notebook import tqdm\n",
    "from astropy.table import Table\n",
    "import utils\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading dataset...\n",
      "    IDR:          BLACKSTON\n",
      "    Phase range: [-5.0, 5.0] days\n",
      "    Bin velocity: 1000.0\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1f8af05f1be74df2b2ac041316896218",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating the spectra at maximum light...\n",
      "    Loaded cached stan model\n",
      "    Using saved stan result\n",
      "Reading between the lines...\n",
      "    Loaded cached stan model\n",
      "    Using saved stan result\n",
      "Building masks...\n",
      "    Masking 30/203 targets whose uncertainty power is \n",
      "    more than 0.100 of the intrinsic power.\n",
      "Generating the manifold learning embedding...\n",
      "Loading other indicators of diversity...\n",
      "Fitting RBTL Twins Manifold GP...\n",
      "GP magnitude residuals fit:\n",
      "    Fit result:           CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "    intrinsic_dispersion      0.073 ± 0.008\n",
      "    gp_kernel_amplitude       0.164 ± 0.082\n",
      "    gp_length_scale           5.541 ± 3.315\n",
      "    offset                    -0.116 ± 0.124\n",
      "    covariate_slope_0         -0.122 ± 0.051\n",
      "    Fit NMAD                  0.083 mag\n",
      "    Fit std                   0.101 mag\n",
      "Fitting SALT2 Twins Manifold GP...\n",
      "GP magnitude residuals fit:\n",
      "    Fit result:           CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n",
      "    intrinsic_dispersion      0.085 ± 0.010\n",
      "    gp_kernel_amplitude       0.380 ± 0.230\n",
      "    gp_length_scale           8.631 ± 5.328\n",
      "    offset                    6.956 ± 0.299\n",
      "    covariate_slope_0         2.810 ± 0.153\n",
      "    Fit NMAD                  0.105 mag\n",
      "    Fit std                   0.118 mag\n",
      "Calculating SALT2 magnitude residuals...\n",
      "SALT2 magnitude residuals fit: \n",
      "    alpha:   0.148\n",
      "    beta:    2.706\n",
      "    σ_int:   0.118\n",
      "    RMS:     0.140\n",
      "    NMAD:    0.106\n",
      "    WRMS:    0.140\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "a = TwinsEmbeddingAnalysis()\n",
    "a.settings['figure_directory'] = './output_generation/figures/'\n",
    "a.settings['latex_directory'] = './output_generation/latex/'\n",
    "a.run_analysis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recovering other indicators of intrinsic diversity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choose the indicators to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "isomap_indicators = [\n",
    "    'isomap_c1',\n",
    "    'isomap_c2',\n",
    "    'isomap_c3',\n",
    "]\n",
    "\n",
    "sugar_indicators = [\n",
    "    'sugar_q1',\n",
    "    'sugar_q2',\n",
    "    'sugar_q3',\n",
    "]\n",
    "\n",
    "snemo_indicators = [\n",
    "    'snemo7_c1',\n",
    "    'snemo7_c2',\n",
    "    'snemo7_c3',\n",
    "    'snemo7_c4',\n",
    "    'snemo7_c5',\n",
    "    'snemo7_c6',\n",
    "]\n",
    "\n",
    "salt_indicators = [\n",
    "    'salt_x1',\n",
    "]\n",
    "\n",
    "target_indicators = {\n",
    "    'spectrum_EWCaIIHK' : 'pEW CaII HK',\n",
    "    'spectrum_EWSiII4000': 'pEW SiII 4000 $\\AA$',\n",
    "    'spectrum_EWSiII5972': 'pEW SiII 5972 $\\AA$',\n",
    "    'spectrum_EWSiII6355': 'pEW SiII 6355 $\\AA$',\n",
    "    'spectrum_vCaIIHK': 'Velocity Ca II HK',\n",
    "    'spectrum_vSiII6355': 'Velocity Si II 6355 $\\AA$',\n",
    "\n",
    "    'salt_x1': 'SALT2 $x_1$ (B14)',\n",
    "    \n",
    "    # 'isomap_c1': 'Twins Embedding $\\\\xi_1$',\n",
    "    # 'isomap_c2': 'Twins Embedding $\\\\xi_2$',\n",
    "    # 'isomap_c3': 'Twins Embedding $\\\\xi_3$',\n",
    "\n",
    "    # 'nordin_uNi_pre': 'uNi pre-peak',\n",
    "    # 'nordin_uTi_pre': 'uTi pre-peak',\n",
    "    # 'nordin_uSi_pre': 'uSi pre-peak',\n",
    "    'nordin_uCa_pre': 'uCa pre-peak',\n",
    "    'nordin_uCa_max': 'uCa near-peak',\n",
    "    'nordin_uNi_max': 'uNi near-peak',\n",
    "    'nordin_uSi_max': 'uSi near-peak',\n",
    "    'nordin_uTi_max': 'uTi near-peak',\n",
    "    # 'nordin_uNi_post': 'uNi post-peak',\n",
    "    'nordin_uTi_post': 'uTi post-peak',\n",
    "    # 'nordin_uSi_post': 'uSi post-peak',\n",
    "    # 'nordin_uCa_post': 'uCa post-peak',\n",
    "    \n",
    "    'sugar_q1': 'SUGAR $q_1$',\n",
    "    'sugar_q2': 'SUGAR $q_2$',\n",
    "    'sugar_q3': 'SUGAR $q_3$',\n",
    "\n",
    "    'snemo7_c1': 'SNEMO7 $c_1$',\n",
    "    'snemo7_c2': 'SNEMO7 $c_2$',\n",
    "    'snemo7_c3': 'SNEMO7 $c_3$',\n",
    "    'snemo7_c4': 'SNEMO7 $c_4$',\n",
    "    'snemo7_c5': 'SNEMO7 $c_5$',\n",
    "    'snemo7_c6': 'SNEMO7 $c_6$',\n",
    "    \n",
    "    # 'snemo15_c1': 'SNEMO15 Component 1',\n",
    "    # 'snemo15_c2': 'SNEMO15 Component 2',\n",
    "    # 'snemo15_c3': 'SNEMO15 Component 3',\n",
    "    # 'snemo15_c4': 'SNEMO15 Component 4',\n",
    "    # 'snemo15_c5': 'SNEMO15 Component 5',\n",
    "    # 'snemo15_c6': 'SNEMO15 Component 6',\n",
    "    # 'snemo15_c7': 'SNEMO15 Component 7',\n",
    "    # 'snemo15_c8': 'SNEMO15 Component 8',\n",
    "    # 'snemo15_c9': 'SNEMO15 Component 9',\n",
    "    # 'snemo15_c10': 'SNEMO15 Component 10',\n",
    "    # 'snemo15_c11': 'SNEMO15 Component 11',\n",
    "    # 'snemo15_c12': 'SNEMO15 Component 12',\n",
    "    # 'snemo15_c13': 'SNEMO15 Component 13',\n",
    "    # 'snemo15_c14': 'SNEMO15 Component 14',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from manifold_gp import ManifoldGaussianProcess\n",
    "\n",
    "def find_gp_transformation(target_indicator, reference_indicators, target_uncertainty=None, mask=True, verbosity=1):\n",
    "    def parse_column(col):\n",
    "        if isinstance(col, str):\n",
    "            return a.indicators[col]\n",
    "        else:\n",
    "            return col\n",
    "\n",
    "    ref_columns = [parse_column(i) for i in reference_indicators]\n",
    "    target_column = parse_column(target_indicator)\n",
    "    \n",
    "    for column in ref_columns + [target_column]:\n",
    "        mask = mask & ~column.mask\n",
    "\n",
    "    ref_values = np.array(ref_columns)\n",
    "    target_values = np.asarray(target_column)\n",
    "\n",
    "    # Normalize the indicator. This should not affect the result in principle,\n",
    "    # but some of the indicators (e.g. velocities) have very large values which can\n",
    "    # cause floating point errors. This operation is reversible, so it shouldn't affect\n",
    "    # the result if the fit is good.\n",
    "    norm_mean = np.mean(target_values[mask])\n",
    "    norm_std = np.std(target_values[mask])\n",
    "    target_values = (target_values - norm_mean) / norm_std\n",
    "    \n",
    "    # See if there is an uncertainty column for our indicator if one wasn't specified.\n",
    "    if target_uncertainty is not False and isinstance(target_indicator, str):\n",
    "        err_col = target_indicator + '_err'\n",
    "        if err_col in a.indicators.colnames:\n",
    "            target_uncertainty = err_col\n",
    "    else:\n",
    "        target_uncertainty = False\n",
    "\n",
    "    if target_uncertainty:\n",
    "        uncertainty_column = parse_column(target_uncertainty)\n",
    "        mask = mask & ~uncertainty_column.mask\n",
    "        uncertainty_values = np.array(uncertainty_column) / norm_std\n",
    "    else:\n",
    "        uncertainty_values = np.zeros(len(target_values))\n",
    "\n",
    "    gp = ManifoldGaussianProcess(a, ref_values.T, target_values, uncertainty_values, mask=mask)\n",
    "    gp.fit(verbosity=verbosity)\n",
    "\n",
    "    pred = gp.predict_out_of_sample()\n",
    "    \n",
    "    # diff = target_values - pred[0]\n",
    "    # explained_variance = 1 - np.mean(diff[mask]**2) / np.var(target_values[mask])\n",
    "    \n",
    "    # Calculate the explained variance. Since we normalized our indicator, its\n",
    "    # variance is 1.\n",
    "    int_disp = gp.parameters[0]\n",
    "    int_disp_err = gp.parameter_uncertainties[0]\n",
    "\n",
    "    explained_variance = 1 - int_disp**2\n",
    "    explained_variance_uncertainty = 2. * int_disp * int_disp_err\n",
    "\n",
    "    transformation = pred[0] * norm_std + norm_mean\n",
    "\n",
    "    return explained_variance, explained_variance_uncertainty, transformation, mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d4f0258553a847cda010136469c0c20a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='indicator', options=('spectrum_EWCaIIHK', 'spectrum_EWSiII4000', '…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_indicator(indicator, reference_indicators)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ipywidgets import interact\n",
    "\n",
    "reference_indicators = {\n",
    "    'twins_embedding': isomap_indicators,\n",
    "    'sugar': sugar_indicators,\n",
    "    'snemo': snemo_indicators,\n",
    "    'salt': salt_indicators,\n",
    "}\n",
    "\n",
    "def plot_indicator(indicator, reference_indicators):\n",
    "    explained_variance, explained_variance_err, transformation, mask = find_gp_transformation(indicator, reference_indicators)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.scatter(transformation[mask], a.indicators[indicator][mask])\n",
    "\n",
    "    plt.xlabel('Transformation of the Isomap latent space')\n",
    "    plt.ylabel(indicator)\n",
    "    plt.title(f'Explained variance: {explained_variance:.2f} +/- {explained_variance_err:.2f}')\n",
    "\n",
    "interact(plot_indicator, indicator=target_indicators.keys(), reference_indicators=reference_indicators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "spectrum_EWCaIIHK    0.907 0.828\n",
      "spectrum_EWSiII4000  0.896 0.894\n",
      "spectrum_EWSiII5972  0.652 0.774\n",
      "spectrum_EWSiII6355  0.935 0.885\n",
      "spectrum_vCaIIHK     0.714 0.575\n",
      "spectrum_vSiII6355   0.832 0.728\n",
      "\n",
      "salt_x1              0.869 0.947\n",
      "\n",
      "nordin_uCa_pre       0.877 0.733\n",
      "nordin_uCa_max       0.837 0.668\n",
      "nordin_uNi_max       0.707 0.423\n",
      "nordin_uSi_max       0.929 0.844\n",
      "nordin_uTi_max       0.744 0.767\n",
      "nordin_uTi_post      0.835 0.778\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kyle/supernova/snfactory/manifold_twins/analysis/manifold_gp.py:181: RuntimeWarning: invalid value encountered in sqrt\n",
      "  self.parameter_uncertainties = np.sqrt(np.diag(param_cov))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sugar_q1             0.838 1.000\n",
      "sugar_q2             0.903 1.000\n",
      "sugar_q3             0.711 1.000\n",
      "\n",
      "snemo7_c1            0.937 0.906\n",
      "snemo7_c2            0.834 0.952\n",
      "snemo7_c3            0.873 0.914\n",
      "snemo7_c4            0.703 0.835\n",
      "snemo7_c5            0.728 0.821\n",
      "snemo7_c6            0.780 0.752\n"
     ]
    }
   ],
   "source": [
    "# Compare how indicators are recovered with different latent spaces\n",
    "mask = (~a.indicators['sugar_q1'].mask) & (~a.indicators['isomap_c1'].mask)\n",
    "\n",
    "last_kind = ''\n",
    "for target_indicator in target_indicators:\n",
    "    kind = target_indicator.split('_')[0]\n",
    "    if kind != last_kind:\n",
    "        print(\"\")\n",
    "        last_kind = kind\n",
    "    isomap_trans = find_gp_transformation(target_indicator, isomap_indicators, mask=mask, verbosity=-1)\n",
    "    sugar_trans = find_gp_transformation(target_indicator, sugar_indicators, mask=mask, verbosity=-1)\n",
    "    \n",
    "    print(f'{target_indicator:20s} {isomap_trans[0]:.3f} {sugar_trans[0]:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2dfd1515b0284a29ab44e20b0a8565f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ref_indicators = isomap_indicators\n",
    "\n",
    "labels = []\n",
    "all_correlations = []\n",
    "all_explained_variances = []\n",
    "all_explained_variance_uncertainties = []\n",
    "\n",
    "for target_indicator, target_label in tqdm(target_indicators.items()):\n",
    "    if target_indicator in ref_indicators:\n",
    "        continue\n",
    "    explained_variance, explained_variance_uncertainty, transformation, mask = \\\n",
    "        find_gp_transformation(target_indicator, ref_indicators, verbosity=-1)\n",
    "\n",
    "    indicator_values = a.indicators[target_indicator]\n",
    "\n",
    "    labels.append(target_label)\n",
    "    all_explained_variances.append(explained_variance)\n",
    "    all_explained_variance_uncertainties.append(explained_variance_uncertainty)\n",
    "\n",
    "    correlations = [np.corrcoef(indicator_values[mask], a.embedding[mask, i])[0, 1] for i in range(a.embedding.shape[1])]\n",
    "    all_correlations.append(correlations)\n",
    "\n",
    "all_correlations = np.array(all_correlations)\n",
    "all_explained_variances = np.array(all_explained_variances)\n",
    "all_explained_variance_uncertainties = np.array(all_explained_variance_uncertainties)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 352.8x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import colors\n",
    "fig = plt.figure(figsize=(4.9, 12.), constrained_layout=False)\n",
    "\n",
    "# left bottom width height\n",
    "ax1 = fig.add_axes([0.26, 0.01, 0.45, 0.8])\n",
    "ax2 = fig.add_axes([0.62, 0.01, 0.3, 0.8], sharey=ax1)\n",
    "# cax = fig.add_subplot(gs[0, 2])\n",
    "\n",
    "# fig, axes = plt.subplots(1, 3, sharey=True, figsize=(4.0, 10), gridspec_kw={'width_ratios': (3, 1, 1)}, constrained_layout=False)\n",
    "# fig.set_constrained_layout_pads(w_pad=0.01, h_pad=0.01, hspace=0.01, wspace=0.01)\n",
    "\n",
    "# cmap1 = a.settings['colormap']\n",
    "# cmap1 = a.settings['colormap']\n",
    "# sm1 = plt.cm.ScalarMappable(cmap=cmap1, norm=plt.Normalize(vmin=-1, vmax=1))\n",
    "# sm1._A = []\n",
    "\n",
    "# Make a truncated colormap\n",
    "# cmap2 = plt.cm.Purples\n",
    "cmap2 = plt.cm.Blues\n",
    "# cmap2 = colors.LinearSegmentedColormap.from_list('unexp_var_cmap', cmap2(np.linspace(0., 0.8, 100)))\n",
    "sm2 = plt.cm.ScalarMappable(cmap=cmap2, norm=plt.Normalize(vmin=0., vmax=1.))\n",
    "sm2._A = []\n",
    "\n",
    "def do_plot(ax, ax_data, sm, xlabels, ylabels=None, data_errs=None, threshold=0.5, aspect=1.):\n",
    "    ax_data = np.atleast_2d(ax_data)\n",
    "    im = ax.imshow(sm.to_rgba(np.abs(ax_data)), interpolation='nearest', aspect=aspect, vmin=-1, vmax=1)\n",
    "    ax.set(\n",
    "        xticks=np.arange(ax_data.shape[1]),\n",
    "        yticks=np.arange(ax_data.shape[0]),\n",
    "        xticklabels=xlabels,\n",
    "    )\n",
    "\n",
    "    if ylabels is not None:\n",
    "        ax.set_yticklabels(ylabels)\n",
    "    else:\n",
    "        ax.tick_params(axis='y', which='both', left=False, right=False)\n",
    "\n",
    "    # Ticks on top\n",
    "    ax.get_xaxis().set_ticks_position('top')\n",
    "\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=70, ha=\"left\", va='center',\n",
    "             rotation_mode=\"anchor\")\n",
    "\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    fmt = '.2f'\n",
    "    for i in range(ax_data.shape[0]):\n",
    "        for j in range(ax_data.shape[1]):\n",
    "            if data_errs is not None:\n",
    "                label = f'{ax_data[i, j]:.2f} ± {data_errs[i, j]:.2f}'\n",
    "            else:\n",
    "                label = f'{ax_data[i, j]:.2f}'\n",
    "            ax.text(j, i, label, ha=\"center\", va=\"center\",\n",
    "                    color=\"white\" if np.sign(threshold) * np.abs(ax_data[i, j]) > threshold else \"black\")\n",
    "\n",
    "# do_plot(ax1, norm_coefficients, sm1, ['Component 1 Amplitude', 'Component 2 Amplitude', 'Component 3 Amplitude'], labels)\n",
    "do_plot(ax1, all_correlations, sm2, ['Correlation with $\\\\xi_1$', 'Correlation with $\\\\xi_2$', 'Correlation with $\\\\xi_3$'], labels)\n",
    "# do_plot(ax2, all_explained_variances[:, None], sm2, ['Fraction of Variance\\nExplained'], threshold=0.5)\n",
    "# do_plot(ax2, np.array([all_explained_variances, all_transformed_correlations]).T, sm2, ['Fraction of Variance\\nExplained', 'Transformed Correlation'])\n",
    "do_plot(ax2, np.array([all_explained_variances]).T, sm2, ['Fraction of Variance\\nExplained'], aspect=0.45, data_errs=np.array([all_explained_variance_uncertainties]).T)\n",
    "\n",
    "ax2.tick_params(labelleft=False)\n",
    "\n",
    "# cbar1 = fig.colorbar(sm1, ax=ax1, orientation='vertical', aspect=60, pad=0.05, ticks=[-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "# cbar1.ax.set_yticklabels(['-1', '', '', '', '', '0', '', '', '', '', '1'])\n",
    "# cbar2 = fig.colorbar(sm2, ax=ax2, orientation='vertical', aspect=60, pad=0.05, ticks=[0, 0.25, 0.5, 0.75, 1])\n",
    "# cbar2 = fig.colorbar(sm2, ax=ax2, orientation='vertical', aspect=60, pad=0.05, ticks=[0.5, 0.6, 0.7, 0.8, 0.9, 1])\n",
    "cbar2 = fig.colorbar(sm2, ax=ax2, orientation='vertical', aspect=60, pad=0.1, ticks=[0., 0.2, 0.4, 0.6, 0.8, 1])\n",
    "# cbar2.ax.set_yticklabels(['0', '', '0.5', '', '1'])\n",
    "\n",
    "a.savefig('indicators_recovery.pdf', bbox_inches='tight', pad_inches=0)"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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