diff --git "a/figures/fig4-5/figure4-sbi_with_rule_variants.ipynb" "b/figures/fig4-5/figure4-sbi_with_rule_variants.ipynb"
deleted file mode 100644--- "a/figures/fig4-5/figure4-sbi_with_rule_variants.ipynb"
+++ /dev/null
@@ -1,919 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Figure 4 and 5: Inference over distance-based rules\n",
-    "\n",
-    "This notebook contains the Python code for generating Figure 4 and 5 in the paper. \n",
-    "The figure shows the results for performing SBI on the distance-based wiring rules.\n",
-    "\n",
-    "The training data and inference objects are pre computed and only loaded for visualization in this notebook.\n",
-    "The following results are loaded: \n",
-    "- SBI results for five different posteriors on the neuron-level rule, one for each subvolume edge length\n",
-    "- SBI results for the synapse-level rule.\n",
-    "\n",
-    "For details on how to perform inference, see `/tutorials` or `/scripts`\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import pickle\n",
-    "import torch\n",
-    "import sys\n",
-    "\n",
-    "sys.path.append(\"../\")\n",
-    "from plotting_utils import color_boxplot, custom_marginal_plot\n",
-    "\n",
-    "from sbi.inference import SNPE, prepare_for_sbi\n",
-    "from sbi.simulators.simutils import simulate_in_batches\n",
-    "from sbi.analysis import pairplot\n",
-    "from consbi.simulators import RuleSimulator, DistanceRuleSimulator, peters_rule_subcellular\n",
-    "from consbi.simulators.utils import collect_synapse_counts_over_depth\n",
-    "\n",
-    "from consbi import BASE_PATH, DATA_PATH, RESULTS_PATH\n",
-    "\n",
-    "import matplotlib as mpl\n",
-    "plt.rcParams.update(mpl.rcParamsDefault)\n",
-    "plt.style.use(BASE_PATH.joinpath(\"figures/plotting_settings.mplstyle\"))\n",
-    "%matplotlib inline\n",
-    "\n",
-    "\n",
-    "# Colorblind color palette\n",
-    "colors = ['#377eb8', '#ff7f00', '#4daf4a',\n",
-    "                  '#f781bf', '#a65628', '#984ea3',\n",
-    "                  '#999999', '#e41a1c', '#dede00']\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load inferred posteriors from file\n",
-    "we performed inference for each subvolumes size for the neuron-level rule (5 sizes), and once for the synapse-level rule given subvolume size 1µm.\n",
-    "\n",
-    "So, six files in total."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Select posterior files.\n",
-    "files = [\n",
-    "    \"amortized_posterior_N1000000_PetersSubCellular_ss50_ep288.p\",\n",
-    "    \"amortized_posterior_N1000000_PetersCubesCutoff_ss50_cs50_ep253.p\",\n",
-    "    \"amortized_posterior_N1000000_PetersCubesCutoff_ss50_cs25_ep261.p\",\n",
-    "    \"amortized_posterior_N1000000_PetersCubesCutoff_ss50_cs10_ep130.p\",\n",
-    "    \"amortized_posterior_N1000000_PetersCubesCutoff_ss50_cs5_ep242.p\",\n",
-    "    \"amortized_posterior_N1000000_PetersCubesCutoff_ss50_cs1_ep211.p\",\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "xo_labels = [r\"L4\", r\"L4SEP\", r\"L4SP\", r\"L4SS\", r\"L5IT\", r\"L5PT\", r\"L6\"]\n",
-    "# number of predictive samples to simulate.\n",
-    "N = 1000\n",
-    "\n",
-    "# subvolume edge length sizes for each file defined above.\n",
-    "css = [1, 50, 25, 10, 5, 1]\n",
-    "rule_labels = [rf\"voxel size {i}$\\mu$m\" for i in css]\n",
-    "xo = torch.tensor([[0.43, 0.43, 0.42, 0.64, 0.17, 0.44, 0.09]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load results or re-run? \n",
-    "Generating the results will take some time, consider re-loading them from `results_folder/distance-based-rules-comparison.p`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# NOTE: to re-generate results set to True\n",
-    "regenerate_data = False\n",
-    "\n",
-    "if not regenerate_data:\n",
-    "    with open(RESULTS_PATH.joinpath(\"distance-based-rules-comparison.p\"), \"rb\") as fh:\n",
-    "        xs, xos, xos_fixed, maps, features, ths, thos = pickle.load(fh).values()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Cells for re-generating results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "if regenerate_data:\n",
-    "    # Calculate posterior MAP for each file\n",
-    "    maps = []\n",
-    "    for idx, file in enumerate(files):\n",
-    "        with open(RESULTS_PATH.joinpath(file), \"rb\") as fh:\n",
-    "            prior, posterior_v14, seed, de = pickle.load(fh).values()\n",
-    "        # update posterior to sbi version 19.2\n",
-    "        posterior = SNPE(prior).build_posterior(density_estimator=posterior_v14.net)\n",
-    "        posterior.set_default_x(xo)\n",
-    "        maps.append(posterior.map(show_progress_bars=False))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Generate data predicted from distance assumption (Peters' rule) /  without inference \n",
-    "\n",
-    "To compare inference vs. fixed rules (Peters' rule), we first get the predictive distributions for these two rules: for the synapse-level and for the neuron-level."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "if regenerate_data:\n",
-    "    # Simulate subcellular rule with fixed p=1 (Peters)\n",
-    "    with open(files[0], \"rb\") as fh:\n",
-    "        prior, posterior, seed, de = pickle.load(fh).values()\n",
-    "    simulator = RuleSimulator(\n",
-    "                DATA_PATH.joinpath(\"subcellular_features\"),\n",
-    "                peters_rule_subcellular,\n",
-    "                verbose=False,\n",
-    "                num_subsampling_pairs=50,\n",
-    "                experiment_name=\"peters-subcellular\",\n",
-    "            )\n",
-    "    batch_simulator, prior = prepare_for_sbi(simulator, prior)\n",
-    "    xos_fixed = []\n",
-    "    xos_fixed.append(simulate_in_batches(\n",
-    "        batch_simulator, torch.ones(1000, 1), sim_batch_size=10, num_workers=20\n",
-    "    ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "if regenerate_data:\n",
-    "    # Simulate common cubes rule with fixed th=1 (Peters)\n",
-    "    with open(files[1], \"rb\") as fh:\n",
-    "        prior, posterior, seed, de = pickle.load(fh).values()\n",
-    "\n",
-    "\n",
-    "    model = DistanceRuleSimulator(\n",
-    "        path_to_model=DATA_PATH.joinpath(\"cube_model\"),\n",
-    "        num_subsampling_pairs=50,\n",
-    "        cube_size=1, \n",
-    "        feature_set_name=\"set-6\",\n",
-    "    )\n",
-    "\n",
-    "    def simulator(th):\n",
-    "        return model.rule(th, feature=model.common_cubes, connection_fun=model.cutoff_rule)\n",
-    "    batch_simulator, prior = prepare_for_sbi(simulator, prior)\n",
-    "    xos_fixed.append(simulate_in_batches(\n",
-    "        batch_simulator, torch.ones(1000, 1), sim_batch_size=10, num_workers=20\n",
-    "    ))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Prior and posterior predictive for each posterior.\n",
-    "Next we sample the posterior and simulate the posterior (and prior) predictives for each of the six posteriors (one for synapse-level, and five for neuron-level cube sizes)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "if regenerate_data:\n",
-    "    ths = []\n",
-    "    thos = []\n",
-    "    xs = []\n",
-    "    xos = []\n",
-    "    features = []\n",
-    "\n",
-    "    for idx, file in enumerate(files):\n",
-    "        with open(file, \"rb\") as fh:\n",
-    "            prior, posterior_v14, seed, de = pickle.load(fh).values()\n",
-    "        posterior = SNPE(prior).build_posterior(density_estimator=posterior_v14.net)\n",
-    "\n",
-    "        # construct simulator appropriate for current idx.\n",
-    "        if idx == 0: # synapse-level posterior, subcellular features.\n",
-    "            simulator = RuleSimulator(\n",
-    "                DATA_PATH.joinpath(\"subcellular_features\"),\n",
-    "                peters_rule_subcellular,\n",
-    "                verbose=False,\n",
-    "                num_subsampling_pairs=50,\n",
-    "                experiment_name=\"peters-subcellular\",\n",
-    "            )\n",
-    "        else:  # neuron-level, all other indices.     \n",
-    "            model = DistanceRuleSimulator(\n",
-    "                path_to_model=DATA_PATH.joinpath(\"cube_model\"),\n",
-    "                num_subsampling_pairs=50,\n",
-    "                cube_size=css[idx], \n",
-    "                feature_set_name=\"set-6\",  # feature set for neuron-level cubes.\n",
-    "            )\n",
-    "            features.append(model.common_cubes)\n",
-    "\n",
-    "            def simulator(th):\n",
-    "                return model.rule(th, feature=model.common_cubes, connection_fun=model.cutoff_rule)       \n",
-    "\n",
-    "        # generate params.\n",
-    "        ths.append(prior.sample((N,)))\n",
-    "        thos.append(posterior.sample((N,), x=xo, show_progress_bars=False))\n",
-    "\n",
-    "        # run simulations.\n",
-    "        simulator, prior = prepare_for_sbi(simulator, prior)\n",
-    "        xs.append(simulate_in_batches(simulator, ths[-1], sim_batch_size=10, num_workers=20))\n",
-    "        xos.append(simulate_in_batches(simulator, thos[-1], sim_batch_size=10, num_workers=20))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Save results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "if regenerate_data:\n",
-    "    with open(RESULTS_PATH.joinpath(\"distance-based-rules-comparison.p\"), \"wb\") as fh:\n",
-    "        pickle.dump(dict(\n",
-    "                        xs=xs,  # prior predictives\n",
-    "                        xos=xos,  # posterior predictives\n",
-    "                        xos_fixed=xos_fixed,  # predictives null-model\n",
-    "                        maps=maps,  # MAP estimates\n",
-    "                        features=features,  # common cube features\n",
-    "                        ths=ths,  # prior samples\n",
-    "                        thos=thos,  # posterior samples\n",
-    "                    ),\n",
-    "                    fh)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Prepare plotting\n",
-    "# The features are the number of shared subvolumes needed for figure panel A.\n",
-    "features = np.array(features).squeeze().T\n",
-    "\n",
-    "# from list to tensor\n",
-    "thos = torch.stack(thos).squeeze().T.numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# load data for number of synpases per cortical depth\n",
-    "depth_data_folder = DATA_PATH.joinpath(\"cube_model\", \"depthplot\")\n",
-    "\n",
-    "# from the structural model we know the average number of VPM boutons for 40 cortial depths\n",
-    "num_boutons_per_depth = np.loadtxt(os.path.join(depth_data_folder, \"boutons.csv\"))\n",
-    "# cortical depths \n",
-    "depths = np.loadtxt(os.path.join(depth_data_folder, \"depths.csv\"))\n",
-    "num_bins = depths.size\n",
-    "# the structural model has 14 pre-synaptic VPM neurons in total\n",
-    "num_vpm_neurons = 14\n",
-    "\n",
-    "# we know which neuron-pair-subvolume combination belongs to which depth index \n",
-    "# (at which depth the neuron pair meets)\n",
-    "neuron_pair_depth_idxs = np.loadtxt(os.path.join(depth_data_folder, \"bin_indices.csv\"), dtype=int)\n",
-    "\n",
-    "# we simulated synapse counts from 100 posterior samples and want to relate them to number of available VPM boutons\n",
-    "with open(os.path.join(depth_data_folder, \"simulated_synapse_counts.p\"), 'rb') as f: \n",
-    "    dataDict = pickle.load(f)\n",
-    "xs = dataDict[\"xs\"]  # 100 posterior predictive samples\n",
-    "# we also simulated given the fixed rule theta=1, i.e., connection whenever there is structural overlap.\n",
-    "xs_fixed = dataDict[\"xp1\"]  # 1 simulation (fixed rule is deterministic)\n",
-    "\n",
-    "# normalize by the number of VPM neurons\n",
-    "# num_boutons_per_depth /= num_vpm_neurons\n",
-    "# xs /= num_vpm_neurons\n",
-    "# xs_fixed /= num_vpm_neurons\n",
-    "\n",
-    "# for each ijk index, check which depth index it belongs to and then collect synpase counts\n",
-    "x_per_depth = np.array([collect_synapse_counts_over_depth(xi, neuron_pair_depth_idxs, num_bins) \n",
-    "                        for xi in xs])  # for each posterior sample\n",
-    "xp1_per_depth = collect_synapse_counts_over_depth(xs_fixed[0,:], neuron_pair_depth_idxs, num_bins)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Neuron-level rule, Figure 4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1800x380 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(18, 3.8))\n",
-    "outer_grid = fig.add_gridspec(1, 2, wspace=.14, hspace=0, width_ratios=[.4, .6])\n",
-    "alpha=0.9\n",
-    "handlelength = 0.8\n",
-    "hspace_rows = 0.6\n",
-    "\n",
-    "# First column\n",
-    "grid1 = outer_grid[0, 0].subgridspec(1, 2, wspace=0.4, hspace=hspace_rows)\n",
-    "ax1 = grid1.subplots()\n",
-    "\n",
-    "# second column\n",
-    "grid2 = outer_grid[0, 1].subgridspec(1, 1, wspace=0.0, hspace=hspace_rows)\n",
-    "grid21 = grid2[0, 0].subgridspec(1, 7, wspace=0.15, hspace=0.)\n",
-    "ax2 = grid21.subplots(subplot_kw=dict(sharey=True))\n",
-    "\n",
-    "# grid22 = grid2[1, 0].subgridspec(1, 7, wspace=0.15, hspace=0.)\n",
-    "# ax3 = grid22.subplots(subplot_kw=dict(sharey=True))\n",
-    "\n",
-    "# Common cubes features\n",
-    "plt.sca(ax1[0])\n",
-    "bp1 = plt.boxplot(features[:, ::-1], notch=False, showfliers=False, \n",
-    "#                   showmeans=True,\n",
-    "                  flierprops=dict(markeredgecolor=\"grey\", marker=\"+\", markersize=5),\n",
-    "                  labels=css[1:][::-1],\n",
-    "                  patch_artist=True, \n",
-    "                 )\n",
-    "# plt.yscale(\"log\")\n",
-    "plt.ylabel(fr\"subvolumes $v$\")\n",
-    "plt.xlabel(r\"subvolume size [µm]\")\n",
-    "color_boxplot(thos[:, 1:], bp1, colors[1:])\n",
-    "plt.ylim(0, 100)\n",
-    "plt.yticks(np.linspace(0, 100, 5))\n",
-    "\n",
-    "# Common cubes posteriors\n",
-    "plt.sca(ax1[1])\n",
-    "# plt\n",
-    "for ii in range(1, 6):\n",
-    "    plt.hist(thos[:, -ii], bins=15, histtype=\"stepfilled\", \n",
-    "             alpha=0.8, \n",
-    "             color=colors[:7][ii],\n",
-    "             density=True, \n",
-    "             label=f\"{css[-ii]}µm\"\n",
-    "            )\n",
-    "plt.xlabel(r\"connection threshold $\\theta_{thres}$\")\n",
-    "plt.ylabel(r\"$p(\\theta_{thres} | x_{o})$\")\n",
-    "plt.xticks(np.linspace(0, 40, 5))\n",
-    "plt.ylim(0, 1)\n",
-    "plt.yticks([0, 1], [\"\", \"\"])\n",
-    "# plt.axvline(x=1, color=colors[8], label=\"1µm \\nfixed \"+r\"$\\theta=1$\", lw=2)\n",
-    "plt.axvline(x=1, \n",
-    "            color=\"gray\", #colors[8], \n",
-    "            label=\"Peters' \\nrule\", lw=3)\n",
-    "plt.legend(bbox_to_anchor=(1.3, 1.1), \n",
-    "           handlelength=handlelength,\n",
-    "           loc=\"upper right\",\n",
-    "          )\n",
-    "\n",
-    "## Posterior predictive\n",
-    "bins = np.linspace(0, 1, 15)\n",
-    "bbox_to_anchor = (.35, .45)\n",
-    "ii = 1  # plot only 1mu subvolume posterior.\n",
-    "custom_marginal_plot(ax2, xos[-ii].numpy(), xo, \n",
-    "                     x_label=f\"inferred, {css[-ii]}µm\", \n",
-    "                     points_label=\"measured\", \n",
-    "                     show_xlabels=True,\n",
-    "                     labels=xo_labels,\n",
-    "#                      show_tick_labels=False,  # not implemented\n",
-    "                     num_bins=bins,\n",
-    "                     color=colors[:7][ii], \n",
-    "                     plot_legend=False,\n",
-    "                     handlelength=handlelength,\n",
-    "                     histtype=\"stepfilled\",\n",
-    "                     alpha=0.8,\n",
-    "#                      bbox_to_anchor=(0.28, .9)\n",
-    "                    )\n",
-    "\n",
-    "custom_marginal_plot(ax2, xos_fixed[1].numpy(), \n",
-    "                     points=None, \n",
-    "                     x_label=f\"Peters' rule\",\n",
-    "                     points_label=\"measured\", \n",
-    "                     show_xlabels=False,\n",
-    "#                      show_tick_labels=False,  # not implemented\n",
-    "                     num_bins=bins,\n",
-    "                     color=\"gray\", #colors[8], \n",
-    "                     alpha=0.7,\n",
-    "                     plot_legend=False,\n",
-    "                     histtype=\"stepfilled\",)\n",
-    "\n",
-    "plt.sca(ax2[-1])\n",
-    "plt.legend(handlelength=handlelength,\n",
-    "           bbox_to_anchor=bbox_to_anchor, \n",
-    "           loc=\"lower left\",\n",
-    "          );\n",
-    "\n",
-    "# Add Letters.\n",
-    "weight = \"bold\"\n",
-    "fontsize = 18\n",
-    "x = 0.075\n",
-    "y = 0.9\n",
-    "dy = .45\n",
-    "dx = 0.26\n",
-    "dx2 = 0.45\n",
-    "fig.text(x, y, \"A\", fontsize=fontsize, fontweight=weight)\n",
-    "fig.text(dx, y, \"B\", fontsize=fontsize, fontweight=weight)\n",
-    "fig.text(dx2, y, \"C\", fontsize=fontsize, fontweight=weight);\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
-     ]
-    }
-   ],
-   "source": [
-    "dpi = 600\n",
-    "fig.savefig(\"figure4-distance_rule_neuron_level.png\", dpi=dpi, bbox_inches='tight')\n",
-    "fig.savefig(\"figure4-distance_rule_neuron_level.pdf\", dpi=dpi, bbox_inches='tight')\n",
-    "fig.savefig(\"figure4-distance_rule_neuron_level.eps\", dpi=dpi, bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Synpase-level rule, Figure 5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1800x380 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(18, 3.8))\n",
-    "outer_grid = fig.add_gridspec(1, 2, wspace=.14, hspace=0, width_ratios=[.4, .6])\n",
-    "alpha=0.9\n",
-    "handlelength = 0.8\n",
-    "hspace_rows = 0.6\n",
-    "\n",
-    "# First column\n",
-    "grid1 = outer_grid[0, 0].subgridspec(1, 2, wspace=0.4, hspace=hspace_rows)\n",
-    "ax1 = grid1.subplots()\n",
-    "\n",
-    "# second column\n",
-    "grid2 = outer_grid[0, 1].subgridspec(1, 1, wspace=0.0, hspace=hspace_rows)\n",
-    "grid21 = grid2[0, 0].subgridspec(1, 7, wspace=0.15, hspace=0.)\n",
-    "ax3 = grid21.subplots(subplot_kw=dict(sharey=True))\n",
-    "\n",
-    "\n",
-    "plt.sca(ax1[0])\n",
-    "plt.hist(thos[:, 0], bins=np.arange(0, 1, 0.025), histtype=\"stepfilled\", \n",
-    "         alpha=alpha, \n",
-    "         color=colors[0],\n",
-    "         density=True, \n",
-    "         label=\"inferred\"\n",
-    "        )\n",
-    "plt.ylabel(r\"$p(\\theta_{prob} | x_{o})$\")\n",
-    "plt.xlabel(r\"synapse probability $\\theta_{prob}$\")\n",
-    "# plt.axvline(x=.99, color=colors[6], label=r\"fixed $\\theta$\", lw=2)\n",
-    "plt.axvline(x=1.0, color=colors[6], label=\"Peters' \\nrule\", lw=3)\n",
-    "plt.xlim(0, 1.1)\n",
-    "plt.ylim(0, 9)\n",
-    "plt.yticks([0, 9], [\"\", \"\"])\n",
-    "plt.xticks(np.linspace(0, 1, 3))\n",
-    "plt.legend(loc=\"upper center\", \n",
-    "           handlelength=handlelength,\n",
-    "           bbox_to_anchor=(.6, 1.0), \n",
-    "          );\n",
-    "\n",
-    "# subcellular level\n",
-    "custom_marginal_plot(ax3, x=xos[0].numpy(), points=xo, \n",
-    "                     x_label=f\"inferred\", \n",
-    "                     points_label=\"measured\", \n",
-    "                     show_xlabels=False,\n",
-    "#                      show_tick_labels=False,  # not implemented\n",
-    "                     num_bins=bins,\n",
-    "                     color=colors[0], \n",
-    "                     plot_legend=False,\n",
-    "                     alpha=0.8,\n",
-    "#                      bbox_to_anchor=(0.28, .9)\n",
-    "                    )\n",
-    "\n",
-    "custom_marginal_plot(ax3, xos_fixed[0].numpy(), \n",
-    "                     points=None, \n",
-    "                     x_label=f\"Peters' rule\",\n",
-    "                     points_label=\"measured\", \n",
-    "                     show_xlabels=True,\n",
-    "                     labels=xo_labels,\n",
-    "#                      show_tick_labels=False,  # not implemented\n",
-    "                     num_bins=bins,\n",
-    "                     color=colors[6], \n",
-    "                     alpha=0.7,\n",
-    "                     plot_legend=False,\n",
-    "                     bbox_to_anchor=(.7, .7))\n",
-    "plt.sca(ax3[-1])\n",
-    "plt.legend(handlelength=handlelength,\n",
-    "           bbox_to_anchor=bbox_to_anchor, \n",
-    "           loc=\"lower left\",\n",
-    "          );\n",
-    "\n",
-    "ax = ax1[1]\n",
-    "plt.sca(ax)\n",
-    "markersize = 4\n",
-    "# for better displaying we divide the total number of boutons by the number of VPM neurons.\n",
-    "# i.e. we plot the average number of boutons per VPM neuron.\n",
-    "ax.plot(depths, num_boutons_per_depth / num_vpm_neurons, linestyle='--', \n",
-    "#         marker='o', \n",
-    "        color='k', label='available \\nboutons')\n",
-    "ax.errorbar(depths, \n",
-    "            y=x_per_depth.mean(0) / num_vpm_neurons, \n",
-    "            yerr=(x_per_depth / num_vpm_neurons).std(0), \n",
-    "            linestyle='-', \n",
-    "            marker='o', ms=markersize,\n",
-    "            color=colors[0], \n",
-    "            label='inferred')\n",
-    "ax.plot(depths, xp1_per_depth / num_vpm_neurons, \n",
-    "            linestyle='-', \n",
-    "            marker='o', ms=markersize,\n",
-    "            color=colors[6],\n",
-    "            label=\"Peters' rule\")\n",
-    "ax.set_ylabel(r'synapses')\n",
-    "ax.set_xlabel(r'cortical depth [µm]')\n",
-    "plt.legend(handlelength=handlelength, \n",
-    "           bbox_to_anchor=(.5, 1.05), \n",
-    "#            markerfirst=False,\n",
-    "           frameon=False,\n",
-    "          )\n",
-    "num_ticks = 5\n",
-    "\n",
-    "from matplotlib import ticker\n",
-    "formatter = ticker.ScalarFormatter(useMathText=True)\n",
-    "formatter.set_scientific(True) \n",
-    "formatter.set_powerlimits((-1,1)) \n",
-    "ax.yaxis.set_major_formatter(formatter)\n",
-    "\n",
-    "plt.ylim(-100, 4500)\n",
-    "plt.yticks(np.linspace(0, 4000, num_ticks))\n",
-    "# plt.yscale(\"log\")\n",
-    "plt.xlim(-50, 2000)\n",
-    "plt.xticks(np.linspace(0, 2000, num_ticks))\n",
-    "\n",
-    "# Add Letters.\n",
-    "weight = \"bold\"\n",
-    "fontsize = 18\n",
-    "x = 0.09\n",
-    "y = 0.96\n",
-    "dy = .45\n",
-    "dx = 0.255\n",
-    "dx2 = 0.455\n",
-    "fig.text(x, y, \"A\", fontsize=fontsize, fontweight=weight)\n",
-    "fig.text(dx, y, \"B\", fontsize=fontsize, fontweight=weight)\n",
-    "fig.text(dx2, y, \"C\", fontsize=fontsize, fontweight=weight);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
-     ]
-    }
-   ],
-   "source": [
-    "fig.savefig(\"figure5-distance_rule_synapse_level.png\", dpi=dpi, bbox_inches='tight')\n",
-    "fig.savefig(\"figure5-distance_rule_synapse_level.pdf\", dpi=dpi, bbox_inches='tight')\n",
-    "fig.savefig(\"figure5-distance_rule_synapse_level.eps\", dpi=dpi, bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.4300000071525574"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(xo[0, 0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def check_whether_within_one_std(x, xo):\n",
-    "    \n",
-    "    for ix, ixo in zip(x.T, xo[0]):\n",
-    "        lower = round(float(ix.mean()-ix.std()), 2)\n",
-    "        upper = round(float(ix.mean()+ix.std()), 2)\n",
-    "        print(f\"{ixo:.2f} in [{lower}, {upper}] ? {lower <= ixo <= upper}, mean error={abs(ixo-ix.mean()):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Neuron-level rule 1mum resolution: \n",
-      "0.43 in [0.36, 0.53] ? True, mean error=0.02\n",
-      "0.43 in [0.27, 0.43] ? True, mean error=0.08\n",
-      "0.42 in [0.42, 0.59] ? True, mean error=0.08\n",
-      "0.64 in [0.39, 0.56] ? False, mean error=0.17\n",
-      "0.17 in [0.17, 0.32] ? True, mean error=0.08\n",
-      "0.44 in [0.28, 0.45] ? True, mean error=0.07\n",
-      "0.09 in [0.1, 0.23] ? False, mean error=0.08\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Neuron-level rule 1mum resolution: \")\n",
-    "check_whether_within_one_std(xos[-1], xo)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Peters' rule at neuron level:\n",
-      "0.43 in [0.58, 0.71] ? False, mean error=0.22\n",
-      "0.43 in [0.49, 0.63] ? False, mean error=0.13\n",
-      "0.42 in [0.63, 0.76] ? False, mean error=0.27\n",
-      "0.64 in [0.6, 0.73] ? True, mean error=0.02\n",
-      "0.17 in [0.39, 0.53] ? False, mean error=0.29\n",
-      "0.44 in [0.54, 0.67] ? False, mean error=0.16\n",
-      "0.09 in [0.27, 0.4] ? False, mean error=0.25\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Peters' rule at neuron level:\")\n",
-    "check_whether_within_one_std(xos_fixed[1], xo)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Synapse-level rule 1mum resolution: \n",
-      "0.43 in [0.37, 0.53] ? True, mean error=0.02\n",
-      "0.43 in [0.3, 0.44] ? True, mean error=0.06\n",
-      "0.42 in [0.42, 0.57] ? True, mean error=0.08\n",
-      "0.64 in [0.4, 0.56] ? False, mean error=0.16\n",
-      "0.17 in [0.21, 0.35] ? False, mean error=0.11\n",
-      "0.44 in [0.32, 0.47] ? True, mean error=0.05\n",
-      "0.09 in [0.14, 0.25] ? False, mean error=0.11\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Synapse-level rule 1mum resolution: \")\n",
-    "check_whether_within_one_std(xos[0], xo)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Peters' rule at synapse level:\n",
-      "0.43 in [0.58, 0.72] ? False, mean error=0.22\n",
-      "0.43 in [0.49, 0.63] ? False, mean error=0.13\n",
-      "0.42 in [0.63, 0.76] ? False, mean error=0.27\n",
-      "0.64 in [0.6, 0.73] ? True, mean error=0.02\n",
-      "0.17 in [0.4, 0.53] ? False, mean error=0.29\n",
-      "0.44 in [0.53, 0.67] ? False, mean error=0.16\n",
-      "0.09 in [0.27, 0.41] ? False, mean error=0.25\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Peters' rule at synapse level:\")\n",
-    "check_whether_within_one_std(xos_fixed[0], xo)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import binom_test\n",
-    "\n",
-    "def check_diff(x1, x2, xo):\n",
-    "\n",
-    "    for ix1, ix2, ixo in zip(x1.T, x2.T, xo[0]):\n",
-    "        num_greater, pval = num_a_greater_b_and_binom_test(abs(ix1 - ixo), abs(ix2 - ixo), n=ix1.shape[0])\n",
-    "        print(f\"binom test pval = {pval}\")\n",
-    "        \n",
-    "def num_a_greater_b_and_binom_test(a, b, n):\n",
-    "    num_a_greater_b = (a > b).sum()\n",
-    "    return num_a_greater_b, binom_test(num_a_greater_b, n, 0.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Difference in errors between inferred rule and Peters' rule\n",
-      "binom test pval = 9.787189025791391e-218\n",
-      "binom test pval = 7.821676804592202e-31\n",
-      "binom test pval = 1.3048562890778308e-239\n",
-      "binom test pval = 5.03379261836412e-140\n",
-      "binom test pval = 5.729994990541079e-255\n",
-      "binom test pval = 8.595940665568503e-90\n",
-      "binom test pval = 1.0265540152973663e-236\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Difference in errors between inferred rule and Peters' rule\")\n",
-    "N = 1000\n",
-    "check_diff(xos[-1][:N], xos_fixed[1][:N], xo)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
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-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
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-  },
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-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.16"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
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- },
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