{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "The processes involved in decision-making, such as deliberation on sensory evidence and the preparation and execution of motor actions, are thought to emerge from the coordinated dynamics within and between cortical layers [@bib25; @bib54], cell types [@bib130; @bib51; @bib95; @bib88], and brain areas [@bib58; @bib29]. A large body of research has described differences in decision-related dynamics across brain areas [@bib46; @bib166; @bib140; @bib63] and a smaller set of studies has provided insight into layer-dependent dynamics during decision-making [@bib25; @bib54; @bib26; @bib14]. However, we currently do not understand how decision-related dynamics emerge across putative cell types. Here, we address this open question by developing a new method, _WaveMAP_, that combines non-linear dimensionality reduction and graph-based clustering. We apply _WaveMAP_ to extracellular waveforms to identify putative cell classes and examine their physiological, functional, and laminar distribution properties.\n",
    "\n",
    "In mice, and to some extent in rats, transgenic tools allow the in vivo detection of particular cell types [@bib130; @bib95], whereas in vivo studies in primates are largely restricted to using features of the extracellular action potential (EAP) such as trough to peak duration, spike width, and cell firing rate (FR). Early in vivo monkey work [@bib119] introduced the importance of EAP features, such as spike duration and action potential (AP) width, in identifying cell types. These experiments introduced the concept of broad- and narrow-spiking neurons. Later experiments in the guinea pig [@bib105], cat [@bib7], and the rat [@bib152; @bib13] then helped establish the idea that these broad- and narrow-spiking extracellular waveform shapes mostly corresponded to excitatory and inhibitory cells, respectively. These results have been used as the basis for identifying cell types in primate recordings [@bib72; @bib111; @bib112]. This method of identifying cell types in mammalian cortex in vivo is widely used in neuroscience but it is insufficient to capture the known structural and transcriptomic diversity of cell types in the monkey and the mouse [@bib66; @bib86]. Furthermore, recent observations in the monkey defy this simple classification of broad- and narrow-spiking cells as corresponding to excitatory and inhibitory cells, respectively. Three such examples in the primate that have resisted this principle are narrow-spiking pyramidal tract neurons in deep layers of M1 (Betz cells, [@bib174]; [@bib154]), narrow and broad spike widths among excitatory pyramidal tract neurons of premotor cortex [@bib93], and narrow-spiking excitatory cells in layer III of V1, V2, and MT [@bib33; @bib3; @bib127; @bib79].\n",
    "\n",
    "To capture a more representative diversity of cell types in vivo, more recent studies have incorporated additional features of EAPs (beyond AP width) such as trough to peak duration [@bib4], repolarization time [@bib170; @bib10], and triphasic waveform shape [@bib12; @bib139]. Although these user-specified methods are amenable to human intuition, they are insufficient to distinguish between previously identified cell types [@bib87; @bib174; @bib112]. It is also unclear how to choose these user-specified features in a principled manner (i.e. one set that maximizes explanatory power) as they are often highly correlated with one another. This results in different studies choosing between different sets of specified features each yielding different inferred cell classes [@bib170; @bib175; @bib75; @bib161]. Thus, it is difficult to compare putative cell types across literature. Some studies even conclude that there is no single set of specified features that is a reliable differentiator of type [@bib178].\n",
    "\n",
    "These issues led us to investigate techniques that do not require feature specification but are designed to find patterns in complex datasets through non-linear dimensionality reduction. Such methods have seen usage in diverse neuroscientific contexts such as single-cell transcriptomics [@bib162; @bib16], in analyzing models of biological neural networks [@bib101; @bib82], the identification of behavior [@bib9; @bib67; @bib47], and in electrophysiology [@bib71; @bib60; @bib83; @bib103; @bib43].\n",
    "\n",
    "Here, in a novel technique that we term _WaveMAP_, we combine a non-linear dimensionality reduction method (Universal Manifold Approximation and Projection \\[UMAP], [@bib106]) with graph community detection (Louvain community detection, [@bib22]; we colloquially call ‘clustering’) to understand the physiological properties, decision-related dynamics, and laminar distribution of candidate cell types during decision-making. We applied _WaveMAP_ to extracellular waveforms collected from neurons in macaque dorsal premotor cortex (PMd) in a decision-making task using laminar multi-channel probes (16 electrode ‘U-probes’). We found that _WaveMAP_ significantly outperformed current approaches without need for user-specification of waveform features like trough to peak duration. This data-driven approach exposed more diversity in extracellular waveform shape than any constructed spike features in isolation or in combination. Using interpretable machine learning, we also show that _WaveMAP_ picks up on nuanced and meaningful biological variability in waveform shape.\n",
    "\n",
    "_WaveMAP_ revealed three broad-spiking and five narrow-spiking waveform types that differed significantly in shape, physiological, functional, and laminar distribution properties. Although most narrow-spiking cells had the high maximum firing rates typically associated with inhibitory neurons, some had firing rates similar to broad-spiking neurons which are typically considered to be excitatory. The time at which choice selectivity (‘discrimination time’) emerged for many narrow-spiking cell classes was earlier than broad-spiking neuron classes—except for the narrow-spiking cells that had broad-spiking like maximum firing rates. Finally, many clusters had distinct laminar distributions that appear layer-dependent in a manner matching certain anatomical cell types. This clustering explains variability in discrimination time over and above previously reported laminar differences [@bib25]. Together, this constellation of results reveals previously undocumented relationships between waveform shape, physiological, functional, and laminar distribution properties that are missed by traditional approaches. Our results provide powerful new insights into how candidate cell classes can be better identified and how these types coordinate with specific timing, across layers, to shape decision-related dynamics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results\n",
    "\n",
    "## Task and behavior\n",
    "\n",
    "Two male rhesus macaques (T and O) were trained to perform a red-green reaction time decision-making task ([Figure 1A](#fig1)). The task was to discriminate the dominant color of a central static red-green checkerboard cue and to report their decision with an arm movement towards one of two targets (red or green) on the left or right ([Figure 1A](#fig1))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 1.\n",
    ":::\n",
    "![Figure 1](elife-67490.ipynb.media/fig1.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recording locations, waveform shapes, techniques, task, and discrimination behavior.\n",
    "\n",
    "(**A**) An illustration of the behavioral setup in the discrimination task. The monkey was seated with one arm free and one arm gently restrained in a plastic tube via a cloth sling. An infrared-reflecting (IR) bead was taped to the forefinger of the free hand and was used in tracking arm movements. This gave us a readout of the hand’s position and allowed us to mimic a touch screen. (**B**) A timeline of the decision-making task (top). At bottom is defined the parametrization of difficulty in the task in terms of color coherence and signed color coherence (SC). (**C**) Average discrimination performance and (**D**) Reaction time (RT) over sessions of the two monkeys as a function of the SC of the checkerboard cue. RT plotted here includes both correct and incorrect trials for each session and then averaged across sessions. Gray markers show measured data points along with 2 × S.E.M. estimated over sessions. For many data points in (**C**), the error bars lie within the marker. X-axes in both (**C**), (**D**) depict the SC in %. Y-axes depict the percent responded red in (**C**) and RT in (**D**). Also shown in the inset of (**C**) are discrimination thresholds (mean ± S.D. over sessions) estimated from a Weibull fit to the overall percent correct as a function of coherence. The discrimination threshold is the color coherence at which the monkey made 81.6% correct choices. Seventy-five sessions for monkey T (128,989 trials) and 66 sessions for monkey O (108,344 trials) went into the averages. (**E**) The recording location in caudal PMd (top); normalized and aligned isolated single-unit waveforms (n = 625, 1.6 ms each, bottom); and schematic of the 16-channel Plexon U-probe (right) used during the behavioral experiment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The timeline of the task is as follows: a trial began when the monkey touched the center target and fixated on a cross above it. After a short randomized period, two targets red and green appeared on the either side of the center target (see [Figure 1B](#fig1), top). The target configuration was randomized: sometimes the left target was red and the right target was green or vice versa. After another short randomized target viewing period, a red-green checkerboard appeared in the center of the screen with a variable mixture of red and green squares.\n",
    "\n",
    "We parameterized the variability of the checkerboard by its signed color coherence and color coherence. The signed color coherence (SC) provides an estimate of whether there are more red or green squares in the checkerboard. Positive SC indicates the presence of more red squares, whereas negative SC indicates more green squares. SC close to zero (positive or negative) indicates an almost even number of red or green squares ([Figure 1B](#fig1), bottom). The coherence (C) provides an estimate of the difficulty of a stimulus. Higher coherence indicates that there is more of one color than the other (an easy trial) whereas a lower coherence indicates that the two colors are more equal in number (a difficult trial).\n",
    "\n",
    "Our monkeys demonstrated the range of behaviors typically observed in decision-making tasks: monkeys made more errors and were slower for lower coherence checkerboards compared to higher coherence checkerboards ([Figure 1C,D](#fig1)). We used coherence, choice, and reaction times (RT) to analyze the structure of decision-related neural activity.\n",
    "\n",
    "## Recordings and single neuron identification\n",
    "\n",
    "While monkeys performed this task, we recorded single neurons from the caudal aspect of dorsal premotor cortex (PMd; [Figure 1E](#fig1), top) using single tungsten (FHC electrodes) or linear multi-contact electrodes (Plexon U-Probes, 625 neurons, 490 U-probe waveforms; [Figure 1E](#fig1), right) and a Cerebus Acquisition System (Blackrock Microsystems). In this study, we analyzed the average EAP waveforms of these neurons. All waveforms were analyzed after being filtered by a fourth-order high-pass Butterworth filter (250 Hz). A 1.6 ms snippet of the waveform was recorded for each spike and used in these analyses, a duration longer than many studies of waveform shape [@bib112].\n",
    "\n",
    "We restricted our analysis to well-isolated single neurons identified through a combination of careful online isolation combined with offline spike sorting (see Methods section: _Identification of single neurons during recordings_). Extracellular waveforms were isolated as single neurons by only accepting waveforms with minimal ISI violations (1.5% < 1.5 ms). This combination of online vigilance, combined with offline analysis, provides us the confidence to label these waveforms as single neurons.\n",
    "\n",
    "We used previously reported approaches to align, average, and normalize spikes [@bib78; @bib153]. Spikes were aligned in time via their depolarization trough and normalized between −1 and 1. ‘Positive spiking’ units with large positive amplitude pre-hyperpolarization spikes were dropped from the analysis due to their association with dendrites and axons [@bib57; @bib12; @bib161]. Recordings were pooled across monkeys to increase statistical power for _WaveMAP_.\n",
    "\n",
    "## Non-linear dimensionality reduction with graph clustering reveals robust low-dimensional structure in extracellular waveform shape\n",
    "\n",
    "In _WaveMAP_ ([Figure 2](#fig2)), we use a three-step strategy for the analysis of extracellular waveforms: We first passed the normalized and trough-aligned waveforms ([Figure 2A–i](#fig2)) into UMAP to obtain a high-dimensional graph ([Figure 2A–ii](#fig2); [@bib106]). Second, we used this graph ([Figure 2B–iii](#fig2)) and passed it into Louvain clustering ([Figure 2B-iv](#fig2), [@bib22]), to delineate high-dimensional clusters. Third, we used UMAP to project the high-dimensional graph into two dimensions ([Figure 2B–v](#fig2)). We colored the data points in this projected space according to their Louvain cluster membership found in step two to arrive at our final _WaveMAP_ clusters ([Figure 2B–vi](#fig2)). We also analyzed the _WaveMAP_ clusters using interpretable machine learning ([Figure 2B–vii](#fig2)) and also an inverse transform of UMAP ([Figure 2B–viii](#fig2)). A detailed explanation of the steps associated with _WaveMAP_ is available in the methods, and further mathematical details of _WaveMAP_ are available in the Supplementary Information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 2.\n",
    ":::\n",
    "![Figure 2](elife-67490.ipynb.media/fig2.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Schematic of _WaveMAP._\n",
    "\n",
    "(**A**) _WaveMAP_ begins with UMAP which projects high-dimensional data into lower dimension while preserving local and global relationships (see [Figure 2—figure supplement 1A](#fig2s1) for an intuitive diagram). Normalized average waveforms from single units (**i**) are passed to UMAP [@bib106] which begins with the construction of a high-dimensional graph (ii). In the high-dimensional space (ii.a), UMAP constructs a distance metric local to each data point (ii.b). The unit ball (ball with radius of one) of each local metric stretches to the 1st-nearest neighbor. Beyond this unit ball, local distances decrease (ii.c) according to an exponential distribution that is scaled by the local density. This local metric is used to construct a weighted graph with asymmetric edges (ii.d). The 1-nearest neighbors are connected by en edge of weight 1.0. For the next $k\\mathrm{-}\\mathrm{1}$-nearest neighbors, this weight then falls off according to the exponential local distance metric (in this diagram $\\mathrm{\\text{k}}\\mathrm{=}\\mathrm{4}$ with some low weight connections omitted for clarity). These edges, $a$ and $b$, are made symmetric according to $a\\mathrm{+}b\\mathrm{-}a\\mathrm{\\cdot }b$ (ii.e). (**B**) The high-dimensional graph (iii) captures latent structure in the high-dimensional space. We can use this graph in Louvain community detection (Louvain, iv) [@bib22] to find clusters (see [Figure 2—figure supplement 1B](#fig2s1) for an intuitive diagram). In Louvain, each data point is first initialized as belonging to its own ‘community’ (iv.a, analogous to a cluster in a metric space). Then, in an iterative procedure, each data point joins neighboring communities until a measure called ‘modularity’ is maximized (iv.b, see Supplemental Information for a definition of modularity). Next, data points in the same final community are aggregated to a single node and the process repeats until the maximal modularity is found on this newly aggregated graph. This process then keeps repeating until the maximal modularity graph is found and the final community memberships are passed back to the original data points. We can also use this graph to find a low-dimensional representation through a graph layout procedure (**v**). The graph layout proceeds by finding a ‘low energy’ configuration that balances attractive (shown as springs in v.a) and repulsive (not shown) forces between pairs of points as a function of edge weight or lack thereof. This procedure iteratively minimizes the cross-entropy between the low-dimensional and high-dimensional graphs (v.b). The communities found through Louvain are then combined with the graph layout procedure to arrive at a set of clusters in a low-dimensional embedded space (vi). These clusters (vi, top) can be used to classify the original waveforms (vi, bottom). To investigate ‘why’ these data points became clusters, each cluster is examined for locally (within-cluster) important features (SHAP [@bib98]), (vii) and globally important trends (UMAP inverse transform, viii). Not shown is the classifier SHAP values are calculated from. The diagrams for the graph construction and layout are based on UMAP documentation and the diagram for Louvain community detection is based on [@bib22]. [Figure 2—figure supplement 1](#fig2s1): An intuitive diagram of local and global distance preservation in UMAP and a schematic of the Louvain clustering process.\n",
    ":::\n",
    "{#fig2}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 2—figure supplement 1.\n",
    ":::\n",
    "![Figure 2-figure supplement 1](elife-67490.ipynb.media/fig2-figsupp1.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diagrams of UMAP and Louvain community detection.\n",
    "\n",
    "(**A**) A demonstration of UMAP projection on a 3D point cloud skeleton of a wooly mammoth. Local and global structures are incorporated and projected into lower dimension. This preservation of information is evident in the maintained structure of individual bone shapes and sensible spatial relationships between the body parts. Idea from M. Noichl (<https://github.com/MNoichl/UMAP-examples-mammoth->; [@bib123]) and mammoth skeleton from the Smithsonian Institute’s Smithsonian 3D (<https://3d.si.edu/>). (**B**) The Louvain community detection algorithm is applied to weighted symmetric graphs and proceeds in three steps which are said to be one ‘pass’ of the algorithm: (1) each node is assigned to its own cluster; (2) each node is randomly moved into a neighboring cluster and if modularity increases, it becomes a member of that cluster; (3) once modularity no longer increases, each cluster is collapsed into one node. This process repeats for multiple passes until modularity no longer increases. The final cluster memberships are then passed back to the data points on the original graph.\n",
    ":::\n",
    "{#fig2s1}\n",
    "\n",
    "[Figure 3A](#fig3) shows how _WaveMAP_ provides a clear organization without the need for prior specification of important features. For expository reasons, and to link to prior literature [@bib105; @bib31], we use the trough to peak duration to loosely subdivide these eight clusters into ‘narrow-spiking’ and ‘broad-spiking’ cluster sets. The broad-spiking clusters had a trough to peak duration of 0.74 ± 0.24 ms (mean ± S.D.) and the narrow-spiking clusters had a trough to peak duration of 0.36 ± 0.07 ms (mean ± S.D.). The narrow-spiking neurons are shown in warm colors (including green) at right in [Figure 3A](#fig3) and the broad-spiking neurons are shown in cool colors at left in the same figure. The narrow-spiking set was composed of five clusters with ‘narrow-spiking’ waveforms (clusters ①, ②, ③, ④, ⑤) and comprised ∼12%, ∼12%, ∼18%, ∼7%, and ∼19% (n = 72, 78, 113, 43, and 116) respectively of the total waveforms, for ∼68% of total waveforms. The broad-spiking set was composed of three ‘broad-spiking’ waveform clusters (⑥, ⑦, and ⑧) comprising ∼13%, ∼5%, and ∼15% (n = 80, 29, and 94) respectively and collectively ∼32% of total waveforms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kenjilee/Library/Caches/pypoetry/virtualenvs/wavemap-paper-era-91rYGfW6-py3.8/lib/python3.8/site-packages/umap/__init__.py:9: UserWarning: Tensorflow not installed; ParametricUMAP will be unavailable\n",
      "  warn(\"Tensorflow not installed; ParametricUMAP will be unavailable\")\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import random\n",
    "\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib.gridspec import GridSpec\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy\n",
    "from scipy import io\n",
    "import pickle as pkl\n",
    "import h5py\n",
    "import xml.etree.ElementTree as ET\n",
    "\n",
    "import networkx as nx\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import xgboost as xgb\n",
    "from umap import umap_ as umap\n",
    "import cylouvain\n",
    "import shap\n",
    "import igraph as ig\n",
    "import community"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#These are colors used in the paper\n",
    "CUSTOM_PAL_SORT_3 = ['#5e60ce','#00c49a','#ffca3a','#D81159','#fe7f2d','#7bdff2','#0496ff','#efa6c9','#ced4da']\n",
    "GMM_PAL = ['#d62424','#12db41','#f0c905','#248cd6']\n",
    "\n",
    "# In RGB form\n",
    "coherence_colors = [[0.609, 0.283,\t0.724],\n",
    "[0.259,\t0.314, 0.635],\n",
    "[0.251,\t0.412, 0.698],\n",
    "[0.176,\t0.631, 0.859],\n",
    "[0.369,\t0.749, 0.549],\n",
    "[0.898,\t0.654, 0.169],\n",
    "[0.898,\t0.41, 0.165]]\n",
    "\n",
    "#These are the depths that the V-probe channels are located at\n",
    "DEPTHS = [0.15,0.3,0.45,0.60,0.75,0.9,1.05,1.20,1.35,1.50,1.65,1.80,1.95,2.1,2.25,2.4]\n",
    "\n",
    "#This converts time points to real time. There are 48 samples per waveform colleted at 30 kilosamples\n",
    "SAMP_RATE_TO_TIME = 1/(48/30000) \n",
    "\n",
    "#Setting of random seed across Python kernel and packages to ensure reproducibility \n",
    "RAND_STATE=42\n",
    "np.random.seed(RAND_STATE)\n",
    "os.environ['PYTHONHASHSEED'] = str(RAND_STATE)\n",
    "random.seed(RAND_STATE)\n",
    "\n",
    "#UMAP Parameters\n",
    "#The number of neighbors considered when constructing the high-d graph. \n",
    "#Made more global-information preserving by increasing it from 15 to 20.\n",
    "N_NEIGHBORS = 20 \n",
    "\n",
    "#The minimum distance between points in the projected space.\n",
    "#Used for visualization but doesn't affect clustering.\n",
    "MIN_DIST = 0.1\n",
    "\n",
    "#Louvain Clustering Parameters\n",
    "RESOLUTION = 1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_group(label_ix, labels, groups_df, colors, f, arr, mean_only=False, detailed=False):\n",
    "    group_ixs = [i for i,x in enumerate(labels) if x == label_ix-1]\n",
    "    group_waveforms = groups_df.iloc[group_ixs]['waveform'].tolist()\n",
    "    \n",
    "    if not mean_only:\n",
    "        for i,_ in enumerate(group_waveforms):\n",
    "            plt.plot(group_waveforms[i],c=colors[label_ix-1],alpha=0.3,linewidth=1.5)\n",
    "    \n",
    "    if not mean_only:\n",
    "        plt.plot(np.mean(group_waveforms,axis=0),c='k',linestyle='-')\n",
    "    else:\n",
    "        plt.plot(np.mean(group_waveforms,axis=0),c=colors[label_ix-1],linestyle='-')\n",
    "\n",
    "    arr.spines['right'].set_visible(False)\n",
    "    arr.spines['top'].set_visible(False)\n",
    "    arr.spines['bottom'].set_visible(False)\n",
    "    arr.spines['left'].set_visible(False)\n",
    "    \n",
    "    # arr.figure.subplots_adjust(left=0.5,right=0.75,top=0.75,bottom=0.5)\n",
    "\n",
    "    if detailed:\n",
    "        \n",
    "        avg_peak = np.mean([np.argmax(x) for x in group_waveforms[14:]])\n",
    "        arr.axvline(avg_peak,color='k',zorder=0)\n",
    "        \n",
    "        arr.set_ylim([-1.3,1.3])\n",
    "        arr.set_yticks([])\n",
    "        arr.set_xticks([0,7,14,21,28,35,42,48])\n",
    "        arr.tick_params(axis='both', which='major', labelsize=12)\n",
    "        arr.set_xticklabels([0,'',0.5,'',1.0,'',1.5,''])\n",
    "        arr.spines['left'].set_visible(False)\n",
    "        arr.grid(False)\n",
    "        arr.set_xlim([0,48])\n",
    "\n",
    "    if not detailed:\n",
    "        arr.set(xticks=[],yticks=[])\n",
    "\n",
    "        if not mean_only:\n",
    "            # x,y = 2.1,0.7\n",
    "            # ellipse = mpl.patches.Ellipse((x,y), width=13.0, height=0.92, facecolor='w',\n",
    "            #                      edgecolor='k',linewidth=1.5)\n",
    "            # label = arr.annotate(str(label_ix), xy=(x-0.25, y-0.15),fontsize=12, color = 'k', ha=\"center\")\n",
    "            # arr.add_patch(ellipse)\n",
    "\n",
    "            if i != -1:\n",
    "                x, y = 23,-0.7\n",
    "                n_waveforms = plt.text(x, y, \n",
    "                                       'n = '+str(len(group_waveforms))+\n",
    "                                       ' ('+str(int(len(group_waveforms)/len(groups_df)*100))+'%)'\n",
    "                                       , fontsize=10)\n",
    "    \n",
    "    return f, arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 630x405.36 with 9 Axes>,\n",
       " <matplotlib.axes._axes.Axes at 0x7fd59a177c40>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 630x405.36 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rel_path = os.getcwd()\n",
    "fullDataPath = os.path.join(rel_path,'data/full_data.npy');\n",
    "full_data = np.load(fullDataPath)\n",
    "\n",
    "reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n",
    "                    random_state=RAND_STATE)\n",
    "mapper = reducer.fit(full_data)\n",
    "embedding = reducer.transform(full_data)\n",
    "\n",
    "umap_df = pd.DataFrame(embedding, columns=('x', 'y'))\n",
    "umap_df['waveform'] = list(full_data)\n",
    "\n",
    "G = nx.from_scipy_sparse_matrix(mapper.graph_)\n",
    "clustering = cylouvain.best_partition(G, resolution = RESOLUTION)\n",
    "clustering_solution = list(clustering.values())\n",
    "umap_df['color'] = clustering_solution\n",
    "\n",
    "cluster_colors = [CUSTOM_PAL_SORT_3[i] for i in clustering_solution]\n",
    "\n",
    "#Plot the scatter of waveforms in UMAP-space\n",
    "f,arr = plt.subplots(1,figsize=[8.75,5.63]);\n",
    "f.tight_layout()\n",
    "arr.scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n",
    "            marker='o', c=cluster_colors, s=32, edgecolor='w',\n",
    "            linewidth=0.5)\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['bottom'].set_visible(False)\n",
    "arr.spines['left'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.set_xticks([]);\n",
    "arr.set_yticks([]);\n",
    "arr.set_xlim(-5,12)\n",
    "arr.set_ylim(-1,10)\n",
    "\n",
    "arr.arrow(-4.1,0.7,0,1.5, width=0.05, shape=\"full\", ec=\"none\", fc=\"black\")\n",
    "arr.arrow(-4.1,0.7,1.2,0, width=0.05, shape=\"full\", ec=\"none\", fc=\"black\")\n",
    "\n",
    "arr.text(-4.1,0.2,\"UMAP 1\", va=\"center\")\n",
    "arr.text(-4.6,0.9,\"UMAP 2\",rotation=90, ha=\"left\", va=\"bottom\");\n",
    "\n",
    "#Add the individual waveforms for each Louvain cluster\n",
    "ax1 = f.add_axes([0.175,0.215,0.11,0.11])\n",
    "plot_group(1,clustering_solution,umap_df,CUSTOM_PAL_SORT_3,f,ax1)\n",
    "\n",
    "ax2 = f.add_axes([0.35,0.7,0.12,0.12])\n",
    "plot_group(2,clustering_solution,umap_df,CUSTOM_PAL_SORT_3,f,ax2)\n",
    "\n",
    "ax3 = f.add_axes([0.55,0.7,0.12,0.12])\n",
    "plot_group(3,clustering_solution,umap_df,CUSTOM_PAL_SORT_3,f,ax3)\n",
    "\n",
    "ax4 = f.add_axes([0.55,0.075,0.12,0.12])\n",
    "plot_group(4,clustering_solution,umap_df,CUSTOM_PAL_SORT_3,f,ax4)\n",
    "\n",
    "ax5 = f.add_axes([0.8,0.7,0.12,0.12])\n",
    "plot_group(5,clustering_solution,umap_df,CUSTOM_PAL_SORT_3,f,ax5)\n",
    "\n",
    "ax6 = f.add_axes([0.175,0.575,0.12,0.12])\n",
    "plot_group(6,clustering_solution,umap_df,CUSTOM_PAL_SORT_3,f,ax6)\n",
    "\n",
    "ax7 = f.add_axes([0.375,0.15,0.12,0.12])\n",
    "plot_group(7,clustering_solution,umap_df,CUSTOM_PAL_SORT_3,f,ax7)\n",
    "\n",
    "ax8 = f.add_axes([0.775,0.25,0.12,0.12])\n",
    "plot_group(8,clustering_solution,umap_df,CUSTOM_PAL_SORT_3,f,ax8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This cell takes very long to run! \n",
    "# Skip it and use the next one to use cached variables (the ones in the publication figure).\n",
    "# Uncomment the code below to run it.\n",
    "\n",
    "# resolution_list = np.linspace(0,10,21)\n",
    "# modularity_dict = {}\n",
    "# n_clusts_dict = {}\n",
    "\n",
    "# subsets=[80]\n",
    "\n",
    "# for res in resolution_list:\n",
    "#     print(\"\\n\" + BlueCol + str(res))\n",
    "#     for frac in subsets:\n",
    "#         rand_list = []\n",
    "#         n_clusts = []\n",
    "#         for i in list(range(1,25)):\n",
    "#             reducer_rand_test = umap.UMAP(n_neighbors = N_NEIGHBORS, \n",
    "#                                      min_dist=MIN_DIST, \n",
    "#                                      random_state=random.randint(1,100000))\n",
    "#             rand_data = np.random.permutation(full_data)[0:(int(len(full_data)*frac)),:]\n",
    "#             mapper = reducer_rand_test.fit(rand_data)\n",
    "#             embedding_rand_test = reducer_rand_test.transform(rand_data)\n",
    "\n",
    "#             umap_df_rand_test = pd.DataFrame(embedding_rand_test, columns=('x', 'y'))\n",
    "#             G = nx.from_scipy_sparse_matrix(mapper.graph_)\n",
    "#             clustering = cylouvain.best_partition(G, resolution = res)\n",
    "#             modularity = cylouvain.modularity(clustering, G)\n",
    "#             clustering_solution = list(clustering.values())\n",
    "#             rand_list.append(modularity)\n",
    "#             n_clusts.append(len(set(clustering_solution)))\n",
    "#         modularity_dict.update({str(res): rand_list})\n",
    "#         n_clusts_dict.update({str(res): n_clusts})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x180 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Run this cell to generate the plot 3B using cached values.\n",
    "# Use the command `del` to delete the empty n_clusts_dict and modularity dict if created when running previous cell.\n",
    "\n",
    "resolution_list = np.linspace(0,10,21)\n",
    "\n",
    "if 'n_clusts_dict' not in list(locals().keys()):\n",
    "    n_clusts_dict = pkl.load(open('data/n_clusts_dict.pkl','rb'))\n",
    "\n",
    "if 'modularity_dict' not in list(locals().keys()):\n",
    "    modularity_dict = pkl.load(open('data/modularity_dict.pkl','rb'))\n",
    "\n",
    "avg_n_clusts = []\n",
    "for k in list(n_clusts_dict.keys()):\n",
    "    avg_n_clusts.append(np.mean(n_clusts_dict[k]))\n",
    "    \n",
    "std_n_clusts = []\n",
    "for k in list(n_clusts_dict.keys()):\n",
    "    std_n_clusts.append(np.std(n_clusts_dict[k]))\n",
    "    \n",
    "std_modularity = []\n",
    "for k in list(modularity_dict.keys()):\n",
    "    std_modularity.append(np.std(modularity_dict[k]))\n",
    "    \n",
    "avg_modularity = []\n",
    "for k in list(modularity_dict.keys()):\n",
    "    avg_modularity.append(np.mean(modularity_dict[k]))\n",
    "\n",
    "f, ax1 = plt.subplots(figsize=[3,2.5])\n",
    "\n",
    "ax1.errorbar(resolution_list,avg_modularity,yerr=std_modularity,\n",
    "             c = '#5c95ff', marker='o', fillstyle='full', markerfacecolor='w', \n",
    "             linewidth=1, markeredgewidth=1)\n",
    "ax1.set_ylabel('Modularity Score',fontsize=12)\n",
    "ax1.set_xlabel('Resolution Parameter',fontsize=12)\n",
    "ax1.set_xlim([0,8])\n",
    "ax1.set_xticks([0,2,4,6,8])\n",
    "ax1.yaxis.label.set_color('#5c95ff')\n",
    "ax1.tick_params(axis='y',colors='#5c95ff')\n",
    "ax1.set_ylim(0,1.0)\n",
    "ax1.set_yticks([0,0.2,0.4,0.6,0.8,1.0])\n",
    "# ax1.set_yticklabels([0.0,'',0.2,'',0.4,'',0.6,'',0.8,'',1.0],fontsize=12)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax1.spines['right'].set_color('gray')\n",
    "ax1.spines['left'].set_color('#5c95ff')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.errorbar(resolution_list[1:],avg_n_clusts[1:],yerr=std_n_clusts[1:],\n",
    "            c = 'gray', marker='.', fillstyle='full', markerfacecolor='w', \n",
    "             linewidth=1, markeredgewidth=1, linestyle='dashed')\n",
    "ax2.set_ylabel('Number of Clusters',fontsize=12,c='gray')\n",
    "# ax2.spines['left'].set_color('b')\n",
    "ax2.tick_params(axis='y',colors='gray')\n",
    "ax2.set_ylim([0,18])\n",
    "ax2.set_yticks([0,4,8,12,16]);\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax2.spines['right'].set_color('gray')\n",
    "ax2.spines['left'].set_color('#5c95ff')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22,  0,  0,  0,  0,  1,  0,  0],\n",
       "       [ 0, 26,  3,  0,  1,  0,  0,  0],\n",
       "       [ 0,  0, 28,  1,  0,  0,  0,  0],\n",
       "       [ 0,  0,  1, 18,  0,  0,  0,  0],\n",
       "       [ 0,  0,  1,  0, 28,  0,  0,  0],\n",
       "       [ 1,  0,  0,  0,  2, 24,  1,  0],\n",
       "       [ 0,  0,  1,  2,  0,  0,  7,  0],\n",
       "       [ 0,  0,  0,  0,  1,  0,  0, 19]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testSize = 0.3;\n",
    "\n",
    "UMAP_X = np.stack(umap_df['waveform'].to_numpy().tolist(), axis=0)\n",
    "UMAP_y = umap_df['color'].to_numpy()\n",
    "\n",
    "unclassified_ixs = [ix for ix,clust in enumerate(UMAP_y) if clust == -1]\n",
    "\n",
    "UMAP_X = np.delete(UMAP_X,unclassified_ixs,axis=0)\n",
    "UMAP_y = np.delete(UMAP_y,unclassified_ixs,axis=0)\n",
    "\n",
    "UMAP_X_train, UMAP_X_test, UMAP_y_train, UMAP_y_test = train_test_split(UMAP_X, UMAP_y, test_size=testSize, random_state=RAND_STATE)\n",
    "\n",
    "numCV = 5\n",
    "\n",
    "UMAP_model = xgb.XGBClassifier(objective='multi:softmax')\n",
    "\n",
    "#Final params were obtained through a grid search; more values to explore can be specified\n",
    "UMAP_param_dist = {\"max_depth\": [4],\n",
    "              \"min_child_weight\" : [2.5],\n",
    "              \"n_estimators\": [100],\n",
    "              \"learning_rate\": [0.3],\n",
    "              \"seed\": [RAND_STATE]}\n",
    "UMAP_grid_search = GridSearchCV(UMAP_model, param_grid=UMAP_param_dist, \n",
    "                           cv = numCV,\n",
    "                           verbose=0, n_jobs=-1)\n",
    "UMAP_grid_search.fit(UMAP_X_train, UMAP_y_train)\n",
    "\n",
    "confusion_matrix(UMAP_y_test,UMAP_grid_search.predict(UMAP_X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clustering_solution = list(clustering.values())\n",
    "N_CLUST = len(set(clustering_solution))\n",
    "confusion_mat_counts = confusion_matrix(UMAP_y_test,UMAP_grid_search.predict(UMAP_X_test))\n",
    "\n",
    "conf_mat_row_list = []\n",
    "\n",
    "for row in confusion_mat_counts:\n",
    "    row_sum = np.sum(row)\n",
    "    \n",
    "    row_percent = []\n",
    "    \n",
    "    for val in row:\n",
    "        row_percent.append(val/row_sum)\n",
    "    \n",
    "    conf_mat_row_list.append(row_percent)\n",
    "\n",
    "conf_mat = np.array(conf_mat_row_list)\n",
    "\n",
    "colormap = mpl.cm.YlGnBu\n",
    "colormap.set_under('white')\n",
    "\n",
    "eps = np.spacing(0.0)\n",
    "f, arr = plt.subplots(1,figsize=[4,3])\n",
    "mappable = arr.imshow(conf_mat,cmap=colormap,vmin=eps,vmax=1.)\n",
    "color_bar = f.colorbar(mappable, ax=arr, extend='min')\n",
    "color_bar.set_label('P (Predicted | True)',fontsize=12,labelpad=15,fontname=\"Arial\")\n",
    "color_bar.ax.tick_params(size=3,labelsize=12)\n",
    "\n",
    "#Specify label behavior of the main diagonal\n",
    "for i in range(0,N_CLUST):\n",
    "    if int(conf_mat[i,i]*100) == 100:\n",
    "        arr.text(i-0.38,i+0.17,int(round(conf_mat[i,i]*100)),fontsize=10,c='white',fontname=\"Arial\")\n",
    "    else:\n",
    "        arr.text(i-0.34,i+0.16,int(round(conf_mat[i,i]*100)),fontsize=10,c='white',fontname=\"Arial\")\n",
    "        \n",
    "#Specify label behavior of the off-diagonals\n",
    "for i in range(0,N_CLUST):\n",
    "    for j in range(0,N_CLUST):\n",
    "        if conf_mat[i,j] < 0.1 and conf_mat[i,j] != 0:\n",
    "            arr.text(j-0.2,i+0.15,int(round(conf_mat[i,j]*100)),fontsize=10,c='k',fontname=\"Arial\")\n",
    "        elif conf_mat[i,j] >= 0.1 and conf_mat[i,j] < 0.5 and conf_mat[i,j] != 0:\n",
    "            arr.text(j-0.4, i+0.15,int(round(conf_mat[i,j]*100)),fontsize=10,c='k',fontname=\"Arial\")\n",
    "\n",
    "arr.set_xticks(range(0,N_CLUST))\n",
    "arr.set_xticklabels(range(1,N_CLUST+1),fontsize=12);\n",
    "arr.set_yticks(range(0,N_CLUST))\n",
    "arr.set_yticklabels(range(1,N_CLUST+1),fontsize=12);\n",
    "arr.set_xlabel('Predicted Class',fontsize=12);\n",
    "arr.set_ylabel('True Class',fontsize=12);\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UMAP and Louvain clustering reveal a robust diversity of averaged single-unit waveform shapes.\n",
    "\n",
    "(**A**) Scatter plot of normalized EAP waveforms in UMAP space colored by Louvain cluster membership. Adjacent to each numbered cluster (① through ⑧) is shown all member waveforms and the average waveform shape (in black). Each waveform is 1.6 ms. Percentages do not add to 100% due to rounding. (**B**) Louvain clustering resolution parameter versus modularity score (in blue, axis at left) and the number of clusters (communities) found (in gray, axis at right). This was averaged over 25 runs for _WaveMAP_ using 25 random samples and seeds of 80% of the full dataset at each resolution parameter from 0 to 8 in 0.5 unit increments (a subset of the data was used to obtain error bars). Each data point is the mean ± S.D. with many S.D. bars smaller than the marker size. Green chevrons indicate the resolution parameter of 1.5 chosen and its position along both curves. (**C**) The confusion matrix of a gradient boosted decision tree classifier with 5-fold cross-validation and hyperparameter optimization. The main diagonal shows the held-out classification accuracy for each cluster and the off-diagonals show the misclassification rates for each cluster to each other cluster. The average accuracy across all clusters was 91%. [Figure 3—figure supplement 1](#fig3s1): A stability analysis of _WaveMAP_ clustering showing solutions are stable with respect to random seed, random data subset, and in an ensembled version of Louvain. [Figure 3—figure supplement 2](#fig3s2): Different amplitude normalizations have similar effect but this processing is essential to _WaveMAP_ extracting meaningful structure. [Figure 3—figure supplement 3](#fig3s3): Pre-processing waveform data with principal component analysis does not alter _WaveMAP_ results.\n",
    ":::\n",
    "{#fig3}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 3—figure supplement 1.\n",
    ":::\n",
    "![](elife-67490.ipynb.media/fig3-figsupp1.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_plots = 15\n",
    "n_rows = 3\n",
    "n_cols = 5\n",
    "resolutions = [1.0, 1.5, 2.0, 5.0, 10.0]\n",
    "f,arr = plt.subplots(nrows=n_rows, ncols=n_cols,figsize=[15,8])\n",
    "\n",
    "for i,res in enumerate(resolutions):\n",
    "    for j in range(n_rows):\n",
    "        resolution = res\n",
    "        my_umap = umap.UMAP(n_neighbors=N_NEIGHBORS\n",
    "                              ,min_dist=MIN_DIST,random_state=random.randint(0,10000), metric='euclidean')\n",
    "        my_umap.fit(full_data)\n",
    "        embedding = my_umap.transform(full_data)\n",
    "\n",
    "\n",
    "        G = nx.from_scipy_sparse_matrix(my_umap.graph_)\n",
    "        clustering = cylouvain.best_partition(G, resolution = resolution)\n",
    "        clustering_solution = list(clustering.values())\n",
    "\n",
    "        umap_df = pd.DataFrame(embedding, columns=('x', 'y'))\n",
    "\n",
    "        umap_df['dbscan_color'] = clustering_solution\n",
    "        husl_colors = [sns.color_palette('husl',len(set(clustering_solution)))[i] for i in clustering_solution]\n",
    "\n",
    "        arr[j,i].scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n",
    "                      marker='o',c=husl_colors, s=30, edgecolor='w',\n",
    "                      linewidth=0.25)\n",
    "\n",
    "        arr[j,i].spines['top'].set_visible(False)\n",
    "        arr[j,i].spines['left'].set_visible(False)\n",
    "        arr[j,i].spines['right'].set_visible(False)\n",
    "        arr[j,i].spines['bottom'].set_visible(False)\n",
    "\n",
    "        arr[j,i].set_xticks([])\n",
    "        arr[j,i].set_yticks([])\n",
    "\n",
    "plt.subplots_adjust(wspace=0, hspace=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_fractions = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]\n",
    "ITERATIONS = 100\n",
    "\n",
    "reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n",
    "                  random_state=RAND_STATE)\n",
    "mapper = reducer.fit(full_data)\n",
    "embedding = reducer.transform(full_data)\n",
    "\n",
    "CV_df = pd.DataFrame(embedding, columns=('x', 'y'))\n",
    "CV_df['waveform'] = list(full_data)\n",
    "G = nx.from_scipy_sparse_matrix(mapper.graph_)\n",
    "clustering = community.best_partition(G, resolution = RESOLUTION, \n",
    "                                      random_state = RAND_STATE)\n",
    "clustering_solution = list(clustering.values())\n",
    "CV_df['truth_ix'] = clustering_solution\n",
    "\n",
    "def cluster_scoring(CV_df,scoring_func,sample_frac = 0.8):\n",
    "\n",
    "    sample_df = CV_df.sample(frac=sample_frac).sort_index()\n",
    "    sample_data = np.stack(sample_df['waveform'].to_numpy())\n",
    "\n",
    "    samp_reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n",
    "                    random_state=RAND_STATE) #n_epochs = 5000, learning_rate = 0.25, negative_sampling_rate = 15\n",
    "    samp_mapper = samp_reducer.fit(sample_data)\n",
    "    samp_embedding = samp_reducer.transform(sample_data)\n",
    "\n",
    "    samp_G = nx.from_scipy_sparse_matrix(samp_mapper.graph_)\n",
    "    samp_clustering = community.best_partition(samp_G, resolution = RESOLUTION, \n",
    "                                        random_state=RAND_STATE)\n",
    "    samp_clustering_solution = list(samp_clustering.values())\n",
    "    sample_df['sample_ix'] = samp_clustering_solution\n",
    "\n",
    "    sample_ixs = sample_df['sample_ix'].tolist()\n",
    "    truth_ixs = sample_df['truth_ix'].tolist()\n",
    "\n",
    "    info = scoring_func(sample_ixs,truth_ixs)\n",
    "\n",
    "    return info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This code takes very long to run (~30 min) on a Macbook Pro. Running the subsequent cell will load cached variables.\n",
    "# If you'd still like to run it, uncomment the below code and run.\n",
    "\n",
    "# clust_rand_dict = {}\n",
    "# mutual_info_stability = []\n",
    "\n",
    "# for frac in subsets:\n",
    "#     rand_list = []\n",
    "#     for i in list(range(1,100)):\n",
    "#         reducer_rand_test = umap.UMAP(n_neighbors = N_NEIGHBORS, \n",
    "#                                  min_dist=MIN_DIST, \n",
    "#                                  random_state=random.randint(1,100000))\n",
    "#         rand_data = np.random.permutation(full_data)[0:(int(len(full_data)*frac)),:]\n",
    "#         mapper = reducer_rand_test.fit(rand_data)\n",
    "#         embedding_rand_test = reducer_rand_test.transform(rand_data)\n",
    "\n",
    "#         umap_df_rand_test = pd.DataFrame(embedding_rand_test, columns=('x', 'y'))\n",
    "#         G = nx.from_scipy_sparse_matrix(mapper.graph_)\n",
    "#         clustering = cylouvain.best_partition(G, resolution = RESOLUTION)\n",
    "#         clustering_solution = list(clustering.values())\n",
    "#         rand_list.append(len(set(clustering_solution)))\n",
    "\n",
    "#     clust_rand_dict.update({str(frac): rand_list})\n",
    "\n",
    "# subset_avg_rand_list = []\n",
    "# subset_std_rand_list = []\n",
    "\n",
    "# for k,v in clust_rand_dict.items():\n",
    "#     subset_avg_rand_list.append(np.average(v))\n",
    "#     subset_std_rand_list.append(np.std(v))\n",
    "\n",
    "# for frac in data_fractions:\n",
    "#     ami_scores = []\n",
    "#     i = 0\n",
    "#     while i < ITERATIONS:\n",
    "#     ami = cluster_scoring(CV_df,adjusted_mutual_info_score, sample_frac=frac)\n",
    "#     ami_scores.append(ami)\n",
    "#     i+=1\n",
    "#     mutual_info_stability.append(ami_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ej4JQj7uAW/1YvBewKfz7WyAeYewVwKlGwl4KdDcS9kLgDCCb7tmBKCZqD/TGYcM7y3WpNpPuRs/SOs1Muss9y4lk3grRqV69noonXqXytfegpATVs0eDp6ANMXnlHK6b8xSfbdnA4b2HcvnMR7m3maFSsvoS0tgbuLte2x+BT4HbGxpkJOwSYATwTWAUsAfaknzPj8VrchGgUQVnJt0Lc3kgsMGznNGEdrNnOWejNzCFFqB69XoqnnyVyle1Yutw8uF0+uG3qJqzSBcq/umZdQoXdzr/pB2esWzzeq6f8zSTV81hRI/dePnIn3FYn6E8s3yGJHwUWhIfvUDygFVGwt4XWI8O/Wp4UCxeYyTsSX4s3h14L3w1iQYVnGc5T5lJ9wzPctJPT2sxk+7+DQx1gKeBIZ7lrETHsEmCymZSvXo9Ff98jcpX3v1asZ1xXG3YVCqx5LJbJ9B7QwWlg/rR6fyT6tQ0qKyp4p5P/sOtCyZTQgm/HXkqPxl6LOVhZah8CJUSiooEcBLwOPAgev9sO3ovrjGmGgl7nB+Lv9McAbKt4P4U/h2fywPNpDvfs5wD0MvTUmChmXS3NzJMaIC6ik3toNjS6XDMGC5f+gRQxr8vrLs/O3XtQq6e/SQfb/qC7/YfzR/3P43du0jNTqH18GNxJ+36diNhv4tevUUpurYMeNlI2JPQ5mntIsuPxW+IKkODCs5MujPDv1M8y+mFjkZQaKW1w36cZznnmEn30QwHEMd7lpPNMVgIqePHdvHvUb0Nqucu1ortpMPodMa3KNllR8WWjS+2+fzqwwRPLZ/Bnl378PRhNsf3G9lKn0AQGsaPxf+bQ/fOwPPhdXrO+UajJNKJEslwM/CL8LYK6AB8hN78Sye1u50pO2FOQu2MNOTHVrr/MLr+7EeRFNszy2fwcc8avqzZxiGv/pYxPffgX6s+YGv1dq7d50R+NvwEOpd2aPQ5gtASGAn7vzTw2/dj8SOzjfVj8Vz3/jMS5RT1PGAQcCfwc+Bo4Dv1O5lJ9zfh5fNm0q0T2+ZZzrnNETKsjfoXdH6qauDS1AqzWNj25Ku1fmxArR/b1vizkZXbzfNeYOLhl9aegl703oMM6NyTRw75MUO7N3yaKgitRH1/t37A/6GTaTaKkbCHAT9CJwRYATyRlscuElEU3Boz6a7yLGc+cEBohu7g1+ZZznfRnsa3eZZTwtdey+XooPtHchEs7bld0AHC/2cm3cme5XwfeAwdYVE01Cz/IrMf2/IvIo2/fWFyh4y6Dx58EdfOfkqUm9Au+LH4xPptRsJ+Fh0IcHO2sUbC/i76d/4v9H7ccOB9I2Gf68fiL0SVIYqC2+5ZzhBgIfBNz3L+DfTM0G80cCzQF+3sm6IKuC2qQBk4HlhsJt1U7NwLaD+aoiGoqkZ175KzH1s6DUUiLNy0ukVlFYRmsgJoyAMjnd8D3/dj8drIBSNhH43OMdmiCu4PwN+A76G9kM9Ha9U6mEn3t8BvPcuxzaQbxVM5KnsDqz3LeQA4AO1Tc239Tp7lvBlejq7/Xj4TBAFb//xPAn8zW25/jC7XnN2oH1umZ+zaqUeDGXVzQdxDhJbCSNgX1WvqAsSAKK4fuwP1DyWmUffAoVGiKLj/mkn3XwChA+8wdCWeOqROUYHOmUK5mnGKWo72pTnGTLrvhibqZM9y9jCTbkUTn5k3VDzxKpWvvEfHs0+gdPe+bLnlHwSbtlAyaNcd/Nga4k8LJrNqm8+lMyZy/9jzC7poi1BU1N973wxMR4dwNcZs4Gq+dlcDHaY1OxcBoii4Tz3L+RcwwUy6U2k4t1vKtmppH4SVwAIz6b4LYCbdSZ7lTEDnm5uf6mQm3aOhdiV3VAvL0CpU/ud9tj08mfLjDqLTORZKKSomTweiZxO5c+G/+cP8lzhnj0M5apfhEokg5A1+LH5MM4b/BHjRSNhXof3gBgJbgO/m8pAoCm4v9EnGbZ7lGOiTkYlm0l2b3il1itqE0K7GeBm4w7OcMWbSnelZzpHoo+eC3oermrOILXc9Qdn+Q+ninNmkjB3xRa9z07xJnD5wLH8+8GxKVQkTl74FiKkptD9Gwt4QBtjXb1/jx+J9M41J4cfiC4yEPQIYh85HuRJ414/FcwoaiBKL6gP3AfeFEQr3A78DOmXq71nOUcCN7JgBOMrGYqb5V3uWcwoQ9yynK1ABxMykm5dlzKLkc6v+bDWbb36Akv596PLri1DluWeOn7BkKr+Y8wzf6/8N7htzHqWqxesHCUJzKa/fYCTscnSwQFaMhD0aWO/H4tPS2gYaCbuXH4tHzhAe6ZflWc6BwAXodCXvoVOYN8S96LizWbSQg29oGh/SaMcCoObLTWz+9d+grJSuN19CSfcuOT/jkaXTuXr2k5zYbxQPHHwhZSWN/nsRhFoiVrbfwffUj8Uj+Z6mOfh2MhL21Hpv747eh2uMR9EHm+l0QLubRV4sRYlkmIMO03oQONBMuisbGVIpYVmZCbZVsvk3f6fG20S3W6+gtF/vHfqctu8yoOFgvaeXz+CKWY9xXN99mXjIxXQokdrdQnQiVrav9T31Y/HJRsLO1fd0Atp6G0vdkqEB8AUQpWjMID8WX5Le4Mfii42EvWdEGYBoK7irzaT7ag7PnOtZzigz6X6YiyDFTlBdw5ZbH6H6k+V0veEiyoYPytgvW9GW51fM4tL3J3LELsN4dNwldCzdwQIQBMhe+DlKZfvjgcVh7QbI0fc05eBrJOx3/Fh8QdM+Ap8bCftAPxaflWowEvaB6L24yERRcKPCUKk6ZFmlDQZmepazDF0FJ9W/SXtwxcK2CZPYPv1DOl92KuWHZgrXzc7Lq+bwf+89yNhee/HkoZfRpSxzTKkcLgiN0Ghle0LfUyNhZ/U9jcBhRsLOWAHLj8UfbGTsXcAkI2Hfiq78NQS4Bl2EOjKRFFzadQd0ls1sedF/mYsAOwMVz0+h4rkpdDjlSDqekrsHy2tffMR5707gAHMQTx9m060s4/mOIKTIVvi50cr2pPme+rH4u6GJOtlI2Hv4sXguvqf1/eD6oRXVW+gtrwbxY/G/GwnbQ8euppTy1X4sHiWXXC05Z/T1LKcP2eNKxTRNY/vbH7L1/ucpP2wUnX98Ss7jp65dyNlv38/w7v149vDx9Cjv3ApSCjsRUSrbrwQW+LH4uwB+LD7JSNg7+J42RiY/uDC6YUTE8U+jk+c2mZx9C8ykuw7YM0uXdcDatL9ryRD5sDNQtfAzNv/xYUqHDaTLteeiSnP7ut9et4gfTv8re3Xrw6QjrqRnh9xPXAWhHq8A48JMHZC5sv3LwJ5Gwh4DYCTslvQ9/Qd6VZYRI2GPMRL2yLT7XYyE/ZiRsD8wEvZ9RsLOmu68PlFOUdPDrhRwENBgqSYz6db+ij3LKQd+gLbjdyqqV69n82/+RonZna43/RjVKbc8bDM3LOX06XEGdO7JC0dcRe+OOf13FYSMRKxsv9pI2KcAcSNh1/qe+rF4Tr6nYfGYdLoA56D39BrCBW4C5ob3E9COvn9DBxzcStqJb2PkugcXoJe4P4/y8DBV+ZOe5VzD10kzi5b0jLxfjb+doKqabreOp6Rn90jjn1k+g/kbV7Khcgvfn/ZnOpWW88I3r6Jvpx6tLLmwMxGxsn1L+J5WsaMv7AogW53kEYRB9kbCNoETgZF+LP6xkbBfQPvQtZyCS+3BeZazB1BuJt1F2fqH6c1TpFZ8mdIrFRUZM/Le8TjVi1dQOqjxjB6phJX/OOTi2mD5y2c+yvR1n0g8qVCo7FXvfrMfi69rZEwZX1evHwes9mPxjwH8WHx5qPQiE8VEHYq20fsDJZ7lrAO+YybdhjYb16G1dipMaw1wVS5CFSIZM/JefRZb489GygiSKWHlvWPO4doPnhIFJxQkfiy+rAnD5qEjpZ4CzgReS71hJOwB6FKEkYliot4D3Gom3YkAnuVciA7HOjZT5/Q9uJ2J5mbkbTBh5UZJWCkUFkbCbjRSwY/FM+oP4Dp0FpH70K4rR6S990O0i0lkoii4XVPKDcBMug9lyveWwrOczug6qHWyCLRwEsy8o2Tgrs3KyDuwS68WSVgpCHnA0egM4I8Bq3IZ6Mfi04yEPQjtbPyxH4tvSnv7JeDJXJ4XRcGVeZbTy0y6G6DWDy5bEP2/0AG66UfKAVDUCq7Tmd+OXFm+Psu3bGBtxSZ+POMh/j72QklYKRQ6o9GZvy9D+8U+DDwX9RQ2VGo7BPb7sfjCXAWJouD+ArzjWc4/w/sfkj0jZ38z6UZy5CsmUvtsuWbkrQ5quOz9iZQoxVV7Hy8JK4WCx4/F5wBXGwn758AJ6Mp8dxoJ+yXgV34s3mb7Lo3ul5lJ929oTdwBnQPONpPuX7MM+dCznJ3SrupwzBhK9uhH6cjB9Lj/+kiHC3/55DWmrfuEWw84A3vosYzo0Z/D+wzlnW/9WpSbUND4sXiNH4u/DPwGHZFwAToaos2IlGvHTLqvk5bixLOcP5hJtyG/tqeBBZ7lfAjUZt80k25Dm4o7LR94y/ndvBf5Xv9vcNagce0tjiC0GEbC7ol2zD0P7Sb2CDDUj8WXtqUcTU0mdjkNO+7eii75tbiJz94p2FJVyY9nPESfjt24+xs/alLKckHIR8Lap4cCzwOOH4tHqaLVKjRVwWX7NW4yk+6tTXzuTsNv5j7Hwk2ree7wK+glYVhCcXEqOgb9u8B3jMSOgQd+LJ45IWIjGAn7Qz8Wj5xvrKkKLuspqmc5lwMJdAwbAKlTWAFeXT2Pvy2Zgj30WI7ddac7jxGKn+ZU02qMP+TSuUEF51lOrIG3FNmLRvwUnev9L2ltQSNjGiUsPPOwmXQLOjBzXcUm7JmPsG+P/vxmv+/v8L4krBQKHT8Wn9KKz348l/7ZVnDZCnO+19AbZtJt8YRlnuUMA26nCemd8okgCLhi1mN427fw3BHj6SQpxwUhI2H1rV+hDyl2Q+eoewS4xY/FK7ONTadBBWcm3SYtM1s6ksGznC7oCjs/A3LS3vnGxKVvMXnVHH4/6geMNHZvb3EEIZ+5FTgYuBRYBuwB/BqdXj2ymdMaJZlaOpLh/vCVNWlmWNEe0lK+tAcN1UNd/NUafjHnGY7uuw8/GdqaWxSCUBScDhzgx+Lrw/uFRsKeBXxAOyu4Fotk8CzHBqrMpPugZzl7tsQz24PtNdVcPOMhOpSU8dcx51EiRZoFoTEa8tTIyZ+qwV+aZzlNTXbXkpEMFwBjPcuZjU7Q19mznNme5fSv39FMukebSfdoYHYLzd1i3LpgMrO+XMbdB55F/845pbMShKLDSNjXR+j2NDqryAlGwh5hJGwL7Vf3VC5zZVvB3Qd8w7Oc/5hJ97gcntlikQxm0j04dR2u4OaaSbddTdDGOHmqDtNNnYa+s34xty9IctagcZwy4MD2FE0QgGiV7dP6ngI87MfiLem9cCS62HQ2rkUfMtyLzkW5Ap1J5He5TJRNwZV5lvMKcKBnOS/Uf9NMug2luZBIhpCN27dyyYx/MLBLL/50wOntLY4gRKpsn9a3VbwX/Fg8Y4odI2Hf5sfiqXIIR/ix+A3ADc2ZK5uCOxGd1HI48GwOz2yVSAYz6S4FCsrl/7oPnmb5lg28fNTPpNyf0JY0t7I9RsJuD++FS/i63svz6BPTZpHNTeRz4GHPcj4zk+6bUWsyIJEMADz3+Swe/+wdrt3nRMb1HtLe4ghCiiiV7SGi90J9jIQ934/FR4TXy2k46qkS7Wlxox+Lp7L0fmAk7GeAj4CORsK+OdPAcGUXiSinqCs8y5lH3ZoMJ5tJd0ED/VslkqEQSK+K9YG3nL269uHafRpPeCkILUyzKtsbCdsGqvxY/EEjYe+Z49zpFbPOydKvBF2x72F0tXuA09CruD3Qp6UDc5x7B6ImvKxfkyFOwzUZdkpbLHNVrEeYtGKW5HUT8okole0vALoYCXs2Og9k5/D6JD8WX5nt4X4sPi3turGQrTeMhF2bH86PxdcQHiIYCbvMj8UvjPB5stIaNRlKgGvQe3jl6EravzeTblVzhc1nMlfFOleqYgn5xivAHUbCHhbuw+1Q2d6PxWu9F8IV3Fw/Fo/kvdCQWVmflJnpx+JOA+9fGD6vL/X23v1YfGywoNUAAA41SURBVEmUOSDa6UhZeq3TCDUZ/oBe3d0N3Akchj6JKWqkKpZQCISrpFRl+/loM/FqI2EfFK7SmsvAtNcw4HrgOGAoWi9cH7ZnJfR/WwGsBhalvT7JOrAerVGTwQIOCqva41nOS+jwiqJmeI9+UhVLKAiiVLZP67uUHLwX0s1KI2E/CfzIj8WfTWuLocOwGiMO/BaY6MfiW6POX5/WqMlQklJu4fgK0hx+i5VrhluMn/koU9cuZHtNNVPXLmT8zEe5ZrjV3qIJQntxItrdI50XgCgnbz2B+5uj3KCJNRkaYbZnOXehC0YDjCfHo+ZCJLXPJlWxBKGWRejyBn9Oa/sJ0YIAHkCb0g82R4DWCLZPfaDp6BVikuy55YqG0waO5aFP9SGSJK4UBC4GnjMS9rXoUKsBQBXwgwhjxwFXhnGrdTay/Vj8yKgCtJiC8yynA/B34Dkz6V4Qtr2E9q/JGOcmCELx4sfi/wvDvcah/WhXAW+T5nOXhQnhq1k0quA8y/lJas8t/ToDN6M9oqentf0YvVl4IzpwVhCEnQg/Ft8O/BfASNij0HHqZ6MVXrZxE7O9H5VsNRkWAu8Ch3mW8xYwH62wGlJw3wHGmkm3dlPQTLorPcs5D621RcEJwk5GGNx/FnA+cAAwDbgqS/9Gsw75sXjU84CsK7iRaB82C12Zej9ggGc5LjDFTLrP1etfma7cUphJd6NnORX124sV2XsTdnbCegrfQ0dEnIA+bHgCHYJ1euiL1xAPNPL4ABjcSJ9asim4vcykO8WznBVm0v0BQJjj7Q3gm0B9BVftWU53M+luSm/0LKc7OqJBEISdgy+AGuAfwG/8WHwW1Ma4ZsWPxfdqSUGy+cHd7VnOx+hV23We5RwPBGbSnWQm3Wsy9H8CmOBZTtdUQ3g9gdzSLQmCUNjMAUx0zOtYI2H3bC9BGlRwZtI9EdgX2AT46GrVQzzLmetZzv0Zhrhhv9We5bzjWc576OPdL9EHEIIg7ASEmUyGoONerwFWGwn7RaArbWzNZT1FNZNulWc5C8ykex+AZzmDgDOAQzP0rQEu8Szn98CB6CXqe2bSzZp9QBCE4sOPxZehQ61+ayTsI9D1TWvQOd8e9GPxa9tCjkbdRMyke3KG69ey9F8KLG2uYIIgFAdhCqVpRsK+Em0JntdWc7dGJIMgCMIO+LH4NvRe/RNtNacU6BQEoWgpiBWcZznnoItRBMAW4Eoz6b7fvlIJgpDv5P0KzrOc4cBtgBXWRP0duqCNIAhCVgphBVcBXGwm3VXh/ftAP89yOphJtzLVybOcN8PLvC4MLQjtTZTCz0bC3sFq8mPxgrOa8n4FZybdpWbSfQnAsxyFToP+QrpyEwQhGmmFn3/gx+LDgSXUqzJvJOxaqymsxVCwVlPeK7gUYVTEU+jc7hfXf99MukebSfdooCXyygtCsZKp8PPZRsJWaX0qgIv9WLyO1WQk7A5tKGeLUAgmasrB+EV0RpNjMgX1C4JQS7bK9o0Wfg7rMCwFCBXfncALfixecFZT3iu4sKLXFOAfZtK9qb3lEYQCp9HCzymMhN0VHTA/EJ1VqODIewWHzuE+CDjVs5xT09qPM5Pu+naSSRDymWyV7aMUfsZI2HWspuYWf2kv8l7BmUn3FuCW9pZDEIqERgs/Gwm71mryY/GCtpryXsEJgtBy+LH4GiNhpwo/d0BXuDrPSNgHARPCU9Naq8lI2HWsJj8WLyirSRScIOxkNFb42Y/Fi8ZqKhg3EUEQhFwRBScIQtEiCk4QhKJFFJwgCEWLKDhBEIoWUXCCIBQtouAEQShaRMEJglC0iIITBKFoEQUnCELRIgpOEISiRRScIAhFiyg4QRCKFlFwgiAULaLgBEEoWkTBCYJQtIiCEwShaCmIjL6e5exQidtMuhuzjxIEIRMRK9s32qcQyPsVnGc5tZW4zaSbsRK3IAjRiFjZvtE+hULeKzjCStxm0q1TiduzHJVljCAImYlS2T5Kn4KgEEzURitxA3iW82Z4Obo5kzmOw+zZs5vzCEFoFqNHj8Z13WY9ojmV7SP2KQgKQcFFrsQd8lX1h4v/AThNmezuu+/+HNgV2NaU8S1A5/BvexXalfnbef4pU6ZUua7bqSmDp637xAVOy9Ilyu8p199c3lIICi5jJW4z6dapxG0m3aNbaL5FwKIgCFrqeTmhlHoTQObfuedvKn4s7pD9f+5RKttH6VMQFMIe3CvAOM9yhoX3O1TiFgQhMq8A44yEne33FKVPQZD3Cs5MumuAC4FnPMuZD4wCrm5fqQShMPFj8drfk5Gwa39PRsI+yEjYs7P1aS+Zm4MKgqC9ZRAEQWgV8n4FJwiC0FREwQmCULSIgmtnlFLnKKU+UErNVkpNV0odFLb/P6XUAqXUIqXUjUqpVnOyVEqdopTamHbflnOPUkq9qZT6n1LqfaXUmLaUQSl1qlJqTvj9v6GUGqKUKlVK3Z02/2WtMbfQBgRBIK92egHDgVXAbuH9Segj+pOA/wFdgU7AFOCMVpJhGNo15qs0Gdpq7i7h5z8pvP8+sKCtZED7vG0Ghob3PwVeAmxgMtqNqmco08Ht/e9FXrm/ZAXXvlQAFwdBsCq8fx/oB5wOPB4EweYgCLah4wLPaenJlVJdgEeBn6U1n9oWc4ccDywOgmByeP8CcEYbylAKKMAI77uhHbxPBR4KgqAqCIIvgSdbaX6hlREF144EQbA0CIKXAEIT7E70j3w3dgyV2b0VRLg/fM1Ja8sUptMacwPsDaxWSj2glHofeBW9amoTGYIg+Art4zVdKbUSGA9c11bzC62PKLg8QCnVFXgKGApcTOb/Li0aJqOUsoGqIAgerPdWq8+dRjnaHP1bEAQHAX9Bm4Yd20IGpdQo4AZg3yAI+gO3AM+iV3atPr/Q+oiCa2eUUoOA6egf0DFBEHjofbjd0roNQK8iWpILgLFKqdlopdI5vP68DeZOsRJYEATBuwBBEExCK5eaNpLhBOCtIAgWh/f3AiOBZW00v9DKiIJrR5RSvdAb6IkgCM4MgiAV4D0JOFsp1VUp1RGtjJ5vybmDIDg4CIKRQRCMRq+itobXz7X23Gm8DOyZdnJ6JBAAbhvJMAs4Sim1a3h/CvAp+vu/SClVppQygTNbaX6hlSmEYPti5ifAIOBUpdSpae3HAQngPaAD+gf3cFsIFATBi6Hp1upzB0GwWil1ChAPzfQKIBYEwbS2kCEIgteVUrcBbyqlKoEN6JPchcAQ4INw/vuDIJjS0vMLrY+EagmCULSIiSoIQtEiCk4QhKJFFJwgCEWLKDhBEIoWUXCCIBQtouB2EpRSv1NKrVNKrW7BZ96olHo0vN5TKRUopcT1SMgbRMHlEUopVyn1pVLqbaXU7mntZyml/tyM5w5Cp5zeNwiCfhneP1opVaOU+irt9WJT52tAhqVKqa1KqU1KKS9MDXWZUirSv8G2UqCiqIsLUXB5glLqYGAMOpvINOD6sN0Afg78qhmPHwSsD4JgTZY+K4Mg6Jb2+m4z5muI7wZB0B3YA10p/TrggVaYRxAAUXD5xF7AtCAIKoD/AIPD9luA24IgyFpwVyllKKUeVkqtVUotU0r9SilVopT6FjpLR/9wZfaPqAKFK7vP67UtDZ/ZZIIg8IMgeAH4IXC+Umpk+OyTw8SXG5VSy5VSN6YNmxr+9cLPcWiYnPJ1pdT60Px+LAytSsl6nVJqRbhqXKiUOi5sL1FKXa+UWhyOfSoMm8s4T3M+q9C+iILLH+YB31RKdUaHas1TOrvv8CAIHo8w/i/ovGaDgaOA84ALgyB4DTiRr1doF7SK9E0gCIL30EHs3wybNqPlNoGTgZ+EoVwAR4Z/zfBzvI3O5fYHoD8wAp3m6EYApdRwdPqjseGq8QRgafiMK9Bxp0eFY79EB9o3NI9QoIiCyxOCIJiLTtXzDtqkvBX4M3ClUupKpdTU+iuUFEqpUnRA+C+CINgUBMFS4A7g3BxE6B/ujaVeZzT3M0VkJdALIAiCN4Mg+DAIgpogCOYAT6CVUEaCIFgUBMGrQRBUBEGwFp1PL9W/Gp12aV+lVHmYey+VNeQy4JdBEHwerphvBE6TfbfiQxRcHhEEwV1BEBwQBMEP0Zltp6L/G12CXtXNJ9ybq0cfdG61ZWlty9BpfqKyMggCM+31VJM+RO4MQAe5o5Q6ROm6CGuVUj5aEfVpaKBSalel1JOhGboRnZ24D2jlh67wfiOwJuzXPxy6B/BcSpmjv9dqYNcdJhEKGlFweUiYvucS4GZ0frI5QRBsB2YA+2cYsg7Yjv7hphgErGimKJvRdRNScpUCuzTzmbUopcaiFdy0sOlxdEbjgUEQGMB9aDMUdBql+vw+bB8VBEEPdFrx2uI0QRA8HgTBEejvJQD+FL61HDixnkLvFATBigbmEQoUUXD5yZ3AjUEQbEHnJxurlOoGHA0sqd85CIJqdEbgW5RS3ZVSe6DrLDzaTDk+BjqFm//l6JPcTNl2c0Ip1UMp9R10rYNHgyD4MHyrO7AhCIJt4anyWWnD1qITYQ5Oa+sOfAX4SqkB6NPm1BzDlVLHKp1PbhuwNRwPWnHeEn5PKKV2UUp9P8s8QoEiCi7PUEodi97gfg5qN+JfQq86jkG7V2TiCvSKawl6RfQ4UD8deU4EQeCjK0xNQK8GN9O8zLYvKqU2oT/LL9GK/MK0923g5rDPDWilnZJlC/pE+a3QtBwH3AQcCPjo7yiR9qyO6O9qHbAa6Av8InzvbvRK8ZVwrneAQ7LMIxQokg9OEISiRVZwgiAULaLgBEEoWkTBCYJQtIiCEwShaBEFJwhC0SIKThCEokUUnCAIRYsoOEEQihZRcIIgFC3/H0AZOmJc/nH1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 273.6x201.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "subsets = [0.1,0.2,0.3,0.4,\n",
    "           0.5,0.6,0.7,0.8,\n",
    "           0.9]\n",
    "\n",
    "if 'subset_avg_rand_list' not in list(locals().keys()):\n",
    "    subset_avg_rand_list = pkl.load(open('data/subset_avg_rand_list.pkl','rb'))\n",
    "\n",
    "if 'subset_std_rand_list' not in list(locals().keys()):\n",
    "    subset_std_rand_list = pkl.load(open('data/subset_std_rand_list.pkl','rb'))\n",
    "\n",
    "if 'mutual_info_stability' not in list(locals().keys()):\n",
    "    mutual_info_stability = pkl.load(open('data/mutual_info_stability.pkl','rb'))\n",
    "    \n",
    "f,arr = plt.subplots(1,figsize=[3.8,2.8])\n",
    "arr.errorbar(np.array(subsets,dtype=np.float),[np.mean(i)*10 for i in mutual_info_stability[:-1]],\n",
    "             yerr=[np.std(i)*10 for i in mutual_info_stability[:-1]],\n",
    "             marker='o',markerfacecolor='w',color='#ef476f',linewidth=1.5,\n",
    "             markeredgewidth=1)\n",
    "\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.set_xlabel('% of Full Dataset',fontsize=12)\n",
    "arr.set_ylabel('# of Louvain \\nCommunities', fontsize=12,fontname=\"Arial\",color='#ef476f')\n",
    "arr.set_ylim([0,10])\n",
    "arr.set_yticks([0,2,4,6,8,10])\n",
    "arr.set_yticklabels([0,2,4,6,8,10],fontsize=12,fontname=\"Arial\",color='#ef476f')\n",
    "arr.spines['left'].set_bounds(0,10)\n",
    "arr.spines['bottom'].set_bounds(0.0,1)\n",
    "arr.spines['right'].set_color('#0ead69')\n",
    "arr.spines['left'].set_color('#ef476f')\n",
    "arr.tick_params(axis='y',colors='#ef476f',width=1.5)\n",
    "arr.tick_params(axis='x',width=1.5)\n",
    "arr.set_xticklabels(['','20','40','60','80',''],fontsize=12,\n",
    "                     fontname='Arial')\n",
    "\n",
    "arr.spines['right'].set_linewidth(1.5)\n",
    "arr.spines['left'].set_linewidth(1.5)\n",
    "arr.spines['bottom'].set_linewidth(1.5)\n",
    "\n",
    "\n",
    "# arr.axhline(np.max(subset_avg_rand_list),color='k',linestyle='dashed')\n",
    "\n",
    "plt.tight_layout()\n",
    "arr.errorbar(np.array(subsets,dtype=np.float),\n",
    "                  subset_avg_rand_list[:-1],yerr=subset_std_rand_list[:-1],\n",
    "                  c = '#0ead69', marker='o', fillstyle='full', \n",
    "                  markerfacecolor='w', linewidth=1.5, markeredgewidth=1)\n",
    "\n",
    "arr2 = arr.twinx()\n",
    "arr2.spines['top'].set_visible(False)\n",
    "arr2.spines['right'].set_visible(False)\n",
    "arr2.set_xlim([-0.1,1.1])\n",
    "arr2.set_xticks([0.,0.2,0.4,0.6,0.8,1.0])\n",
    "arr2.set_xlabel('% of Full Dataset',fontsize=12,fontname='Arial')\n",
    "arr2.set_ylim([0.0,1.1])\n",
    "arr2.set_yticks([0.0,0.2,0.4,0.6,0.8,1.0,1.1])\n",
    "arr2.set_yticklabels(['0.0','0.2','0.4','0.6','0.8','1.0',''],fontsize=12,\n",
    "                     fontname='Arial',color='#0ead69')\n",
    "arr2.yaxis.set_label_coords(1.17,0.5)\n",
    "arr2.set_ylabel('Adj. Mutual \\nInfo. Score',fontsize=12,color='#0ead69')\n",
    "arr2.spines['bottom'].set_bounds([0.0,1.0])\n",
    "arr2.spines['right'].set_bounds([0.0,1.0])\n",
    "arr2.spines['left'].set_visible(False)\n",
    "arr2.tick_params(axis='y',colors='#0ead69',width=1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "[]"
      ],
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add ECG to the choice of community algorithms\n",
    "def community_ecg(self, weights=None, ens_size=16, min_weight=0.05):\n",
    "    W = [0]*self.ecount()\n",
    "    ## Ensemble of level-1 Louvain \n",
    "    for i in range(ens_size):\n",
    "        p = np.random.permutation(self.vcount()).tolist()\n",
    "        g = self.permute_vertices(p)\n",
    "        l = g.community_multilevel(weights=weights, return_levels=True)[0].membership\n",
    "        b = [l[p[x.tuple[0]]]==l[p[x.tuple[1]]] for x in self.es]\n",
    "        W = [W[i]+b[i] for i in range(len(W))]\n",
    "    W = [min_weight + (1-min_weight)*W[i]/ens_size for i in range(len(W))]\n",
    "    ## Force min_weight outside 2-core\n",
    "    core = self.shell_index()\n",
    "    ecore = [min(core[x.tuple[0]],core[x.tuple[1]]) for x in self.es]\n",
    "    w = [W[i] if ecore[i]>1 else min_weight for i in range(len(ecore))]\n",
    "    part = self.community_multilevel(weights=w)\n",
    "    part.W = w\n",
    "    part.CSI = 1-2*np.sum([min(1-i,i) for i in w])/len(w)\n",
    "    return part\n",
    "\n",
    "ig.Graph.community_ecg = community_ecg\n",
    "\n",
    "def readGraph(fn, directed=False):\n",
    "    g = ig.Graph.Read_Ncol(fn+'.edgelist',directed=directed)\n",
    "    c = np.loadtxt(fn+'.community',dtype='uint8')\n",
    "    node_base = min([int(x['name']) for x in g.vs]) ## graphs have 1-based or 0-based nodes \n",
    "    comm_base = min(c) ## same for communities\n",
    "    comm = [c[int(x['name'])-node_base]-comm_base for x in g.vs]\n",
    "    g.vs['community'] = comm\n",
    "    g.vs['shape'] = 'circle'\n",
    "    pal = ig.RainbowPalette(n=max(comm)+1)\n",
    "    g.vs['color'] = [pal.get(int(i)) for i in comm]\n",
    "    g.vs['size'] = 10\n",
    "    g.es['width'] = 1\n",
    "    return g\n",
    "\n",
    "\n",
    "rand_data = full_data[0:(int(len(full_data))),:]\n",
    "my_umap = umap.UMAP(n_neighbors=20\n",
    "                    ,min_dist=0.1, metric='euclidean',random_state=RAND_STATE)\n",
    "my_umap.fit(rand_data)\n",
    "my_umap_embedding = my_umap.transform(rand_data)\n",
    "\n",
    "G = nx.from_scipy_sparse_matrix(my_umap.graph_)\n",
    "umap_igraph = ig.Graph(len(G), list(zip(*list(zip(*nx.to_edgelist(G)))[:2])))\n",
    "\n",
    "umap_ECG = umap_igraph.community_ecg(ens_size=10,min_weight=0.5)\n",
    "\n",
    "umap_df = pd.DataFrame(my_umap_embedding, columns=('x', 'y'))\n",
    "\n",
    "umap_df['dbscan_color'] = umap_ECG.membership\n",
    "ecg_colormap = [sns.color_palette(\"husl\", len(set(umap_ECG.membership)))[i] for i in umap_ECG.membership]\n",
    "\n",
    "f, arr = plt.subplots(1,figsize=[5,4])\n",
    "\n",
    "arr.scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n",
    "            marker='o',c=ecg_colormap, s=30, edgecolor='w',\n",
    "            linewidth=0.25)\n",
    "\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['left'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.spines['bottom'].set_visible(False)\n",
    "\n",
    "arr.set_xticks([])\n",
    "arr.set_yticks([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stability analysis of _WaveMAP_: (**A**) _WaveMAP_ instantiated with three different UMAP random seeds (each row is a different seed) and Louvain resolution parameters.\n",
    "\n",
    "(**B**) The mean ± S.D. number of clusters (Louvain communities; in red) produced by _WaveMAP_ and adjusted mutual information score (in green) across 100 random samples at various proportions of the full dataset. Number of clusters and adjusted mutual information (AMI) are omitted for 100% of the data because the number of clusters is equal to our result and thus AMI is 1.0 by definition of it being itself. (**C**) Ensemble clustering on graphs (ECG) is also applied to the UMAP graph. This also produced eight clusters.\n",
    ":::\n",
    "{#fig3s1}\n",
    "\n",
    "figure: Figure 3—figure supplement 2.\n",
    ":::\n",
    "![](elife-67490.ipynb.media/fig3-figsupp2.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This code takes a very long time (30 minutes on my Macbook Pro 2019) to run. If you'd like to use it, uncomment it and run.\n",
    "#If you'd like to use a cached version of the output variables, skip it and run the next cell.\n",
    "\n",
    "# subsets = [0.1,0.2,0.3,0.4,\n",
    "#            0.5,0.6,0.7,0.8,\n",
    "#            0.9,1.0]\n",
    "\n",
    "# clust_rand_dict = {}\n",
    "# for frac in subsets:\n",
    "#     rand_list = []\n",
    "#     for i in list(range(1,100)):\n",
    "#         reducer_rand_test = umap.UMAP(n_neighbors = N_NEIGHBORS, \n",
    "#                                  min_dist=MIN_DIST, \n",
    "#                                  random_state=random.randint(1,100000))\n",
    "#         rand_data = np.random.permutation(full_data)[0:(int(len(full_data)*frac)),:]\n",
    "#         mapper = reducer_rand_test.fit(rand_data)\n",
    "#         embedding_rand_test = reducer_rand_test.transform(rand_data)\n",
    "\n",
    "#         umap_df_rand_test = pd.DataFrame(embedding_rand_test, columns=('x', 'y'))\n",
    "#         G = nx.from_scipy_sparse_matrix(mapper.graph_)\n",
    "#         clustering = cylouvain.best_partition(G, resolution = RESOLUTION)\n",
    "#         clustering_solution = list(clustering.values())\n",
    "#         rand_list.append(len(set(clustering_solution)))\n",
    "\n",
    "#     clust_rand_dict.update({str(frac): rand_list})\n",
    "\n",
    "# subset_avg_rand_list = []\n",
    "# subset_std_rand_list = []\n",
    "\n",
    "# for k,v in clust_rand_dict.items():\n",
    "#     subset_avg_rand_list.append(np.average(v))\n",
    "#     subset_std_rand_list.append(np.std(v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aEI2Afg+8IyKlrcgNwNQI8ZcBJ1PW2FOBjBPQhAkTyMrK4sEHH7RtpQxforJEEJGeuMmAWsCrqvp6hLibVTWl/ZtEjIFeeuklRo4cyXvvvccZZ5wBmC1bDSd+SwRVLcCdE+RyEnlQVcNNS28QkTNU9avY65gebN68mSFDhrB8+fJj4jEMP+LdhOx2wq/rLAK2eJvSHym9GW7WLt3Yv38/vXv35qGHHuLCCy9MdXWMNCdeAUWyXXkItw/cjjjzThklJSUMGDCAyy67jFtuuSXV1TEygHgFFGkg8K2qPhRnvmERkd7AHFU9KdF5lzJu3Di+/fZbpkzx30DI3BGM8kQ6H+i6cI9wpy+EY5mI3A4sAQ6V3iydBo8HEWkHPEx061ZxsWTJEubMmcPq1avLLJqGYrviGOWJ1AKNjPDsvQjP7gR+gJv+LkWJLLqweBsrPoM7m3VePHlUxkcffcSwYcN4+eWXw3qTGoYfYQWkqj3CPYuEqibaynKGFz6MFElEhgJDAc4+++yoM//666/p3bs3kydPpmvXrkHqadRAYvZIrTTDBFoiiMgI4AJVvcVz4tuoqidWli7adaCjR49y9dVXk52dzbRp02KtnlGziNsWLlaWAfcAPULCJXHmNQi4QETWAytw2wavF5Fm8WRWfgyTl5fHwYMHmTRpUpzVM2o6YVsgEfmRqr4bc4ZJskRIRAsUakmwaNEiRo8ezerVq2naNKxtrGGUEnML9CcAEVkZY0EbRCStl+83bNjAiBEjWLJkiYnHCESkWbgTROQvwPki8ufyD1X1v8KkS4olgqoWAZW2Pn6EumR36NCBffv2MW3aNM4///wgVTKMiAK6EugJZAPPx5BnWlki+LlkDxw40HbRMRJDOFfV0gBc4n22ANpGET/ujeQTFUJdus0l20gQcbt0twNeBJrhxkx7gZ+r6pYw8ScCX5BAS4RYCZ1EMJdsI0HEPY39e+AhVW2kqicDDxDZOe5OL80XOLHtxR1HkhLat29vLtlG0kjGnghZenw/hNIQlxlPIhg7diyDBw9OyAlxhlGeZOyJUAu3kHol7oj7vwATVbU4AfWNmVLXa3PJNpJBNGOgoThBlNkTQVUfCxP/d7idSP+Ea+GGAltUdVSiKl0Z0SykGkaMxOfSraozRWQ7x/dEGKER9kQArgC6qtuVFBFZTpiTvQ0j04lrT4RKqFUqHi/tIRE5EimBYWQqyVhNXC8iU0WkjRemUokrQlVhHqVGokmGO8NJuBO9r8QJ9BXc1r4pWQcyjAQR3zqQiAz3++4Tr66IzAZ6quogVT0d57l6FPhX7PU1jPQnrIBEZKt3YPDdInKed9bPbRHymgCcBLwVcu824BRgfALqahhpR6QWKBd4AieKcbijG9uJyDQRudYn/tXAjap67PQ6Vf0SuAnwi28YGU8kAbVSdzDwF6raR1XPBYpwJ3T/p0/8w6r6ffmbqvovQmziDKM6EWka+xERaQOcIiL3AusAVdWXgJd84h8VkYaq+m3oTRFpiLNIMIxqR9gWSN0x9h2Ab3En010LtBGRjSIywyfJfGCWiDQoveF9n0Vs/kQVEJEBIvKBtx/CWyJi2+cYaUHEhVRVLRaRLapa6t59NtAX6OYTfRrOfOcrEfkIJ872wLO4CYa4EJFsYBJwvqruEpGrcK4S0e9dZRhJIhnrQC2B84ES4D1vIiFofjmquty7Pg34HDhRVQ/7pbF1ICMJxH+8SSyo27ugKBn5iYgAU4A/lxdPvBsrGkYQMmZjAG88tRB3aviQ8s9VdaaqdlXVrrbTjlFVZISAvLHXWzirhh6quj/FVTIMIAMEJCKNgTeBJar6S7+1JsNIFQkfAyWB4bgZt2vLWUD8VFX3pahOhgFkgIBUNR+wM+WNtCTtu3CGkc6YgAwjACYgwwiACcgwAmACMowAmIAMIwAmIMMIgAnIMAJgAjKMAJiADCMAJiDDCIAJyDACYAIyjACYgAwjACYgwwiACcgwApARAhKRn4vIh96G94u8I1QMI+WkvYBEpCnwFNBHVbOBj4H/TW2tDMOR9gICLgNWq+rfvevHgP7eHnGGkVLSfk8E4Czgs5Drz3FHrjQk5OCu0I0VgY9wx7OUQUQ2AgcD1KUJsDdA+qCksvya/NsBNqpqhXcqEwQUrpU8GnqhqjOBmZXkdVBV496YXkTWBEkflFSWX5N/e2n5fvczoQv3KfDDkOszga9V9d8pqo9hHCMTBPQX4D9EpJ13/Sv8zycyjCon7btwqrpbRG4BFotIXWAH7tjIeKisi5fs9EFJZfk1+beHLT/hx5sYRk0iE7pwhpG2mIAMIwAmoDCEO5dVRMaIyBYR2S4i45O5oCsivUUkdK2rSsoWkY4i8oaIrBORNSLSpYrLv9Yz3VovIoUi0kZEaovIIyHl/yoZZceMqlooF4BsYBfwQ+/6Ktx0+lW408obAPVwx670TVId2gHbgQMhdUh62UB977df5V33ArZUYflZwL+Btt71ncByYASwAjfx1cir04WpflesBfLnEDBEVXd512uAM4DrgXmq+m9VPYiz0RuQ6MJFpD7wDHBXyO1rq6JsnOnUDlVd4V3/GXewdFWVXxt3HunJ3vWJOOuRa4GnVLVYVb8GnktS+TFhAvJBVYv0+KHGx85lxS3oljcrap6EKszwwoch9/xMmpJR9jm4k9af8FbfX8P91a+S8lX1AG6t7y0R+RK4A7i3qsqPFRNQBHzOZfX79zrqcy9ImSOAYlV9styjpJftUQfXXZupznTm97iu0w+qonwR6Qj8Fuigqs1wZ0M9j2uZkl5+rJiAwhDmXFY/s6LPE1z0IOACEVmPe3GzvO+fV0HZAF8CW1T1XQBVfQn38pZUUfmXA39T1R3e9R9xhsGfVFH5sZHqQVg6BqAxsBMYV+7+NbjxUAPcX+RC4OYk1qMlxycRqqRs3Fjvn0AX7/onwO4qLL8nUASc7l33wU2mjASW4rqTpwCbgYtT/a6kvSlPigh7LiuwBHgPqIuzyZtTFRVS1aVe9yapZavqVyLSG5judWEPAdep6qoqKr9ARCYBb4jIYZyYewFbgTbAB175M1T1zUSXHytmymMYAbAxkGEEwARkGAEwARlGAExAhhEAE5BhBMAElEBE5CnPgvtFEanj3WskIm+LiN9KfmX5nSQifxORj0SkT7ln40Vkj2exHBoibrzhWVn/QkRaisiBMHFURDZ4+X3ghXuirPMQz5oi4YjI46WW4emCrQMlCBE5D2e93UlE/oRbUV8G3A/kq+qhOLLtjFtQbBvm+QJVvSO+GldKD1XdCyAiTYBlIqKqOrmSdN2BjUmq089wNoJpg7VAieMQUM8zPm0AHBaRTsBZqrosUkLP72ed5wOzSkQuFJFs4EngTK8lyIq2Il7r9Idw17HiCeku4B5xnO61sm+LyE6vVTvNW3T+L+BOEbk9XDyvTsO9lm21iPyfiHTw7p8pIi+IyPvev8cY734+0Ax4VkR+FO9vSTQmoAShqluBVTifme+A14GHgbsjpRORc4E/4bYuPg9nSPkSzidnCM61oLOqfu+T/IZy3bffJu4XVeADnJlPE+CXwNuq2g1ojfu9A1X1BZzV+lRV/WO4eCJSG5gGXKGqF+A27OjulTMXeFJVuwAXApeKSF9VHYuz0+uvnp1eOmBduASiqvcB94HzaAVWA/8Skedwjmq/V9XXyiXrCaxU1Y+9PApEZDfQBajMTCSZXbjylNble1V9REQuEpG7cI5/uUCFlzpcPFU9KiKLcC4Ly3Fbl83zTIcuBhqLyP1eNifiurILk/rr4sQElAREpCHO+LEnMBbnUbkYZ4yZUy66Xy+gFs6t4HCcVVCcU1opdePMJ5QLgJ2qekBEfodrHZ7EGZXWKVceAJHiqeoAEckFLsX5+wwGBnrPf6yq33l5NCHYdsxJxbpwyWEcMEXd7qk/AI7g3AHq+8QtAC4TkdYAItIT5zwWpJuyB+jijVca4LxM40ZEmgG/w3VJwU2QTFPVuThL7Z9x3F+nGCeUsPFEpImIfAbsU9VpuFa7k6r+C3gHzxNXRE4B/oYzJi2fd1pgLVCCEZH2QEdVLZ32nQHMw70kD5SPr6qbvGnfJSJyAm6ccI2qfiPx79nxLHAl8HfgC+BtfFqISigUkaM4fyjFjUume88mAA97Y65i3NivdKbwZeAPXt1946nqXhF5AFgpIt97z4Z46W/00m/AtZzzVfVZ79mLwAIRGaKqf4nx9yQFs8Y2jABYF84wAmACMowAmIAMIwAmIMMIgAnIMAJgAjKMAJiADCMA/w91NT9/SlawzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 216x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if 'subset_avg_rand_list' not in list(locals().keys()):\n",
    "    subset_avg_rand_list = pkl.load(open('data/subset_avg_rand_list.pkl','rb'))\n",
    "\n",
    "if 'subset_std_rand_list' not in list(locals().keys()):\n",
    "    subset_std_rand_list = pkl.load(open('data/subset_std_rand_list.pkl','rb'))\n",
    "\n",
    "f, arr = plt.subplots(1,figsize=[3,2.5])\n",
    "arr.errorbar(np.array(subsets,dtype=np.float),subset_avg_rand_list[:-1],yerr=subset_std_rand_list[:-1],\n",
    "             c = 'k', marker='o', fillstyle='full', markerfacecolor='w', linewidth=1, markeredgewidth=1)\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.set_xlabel('% of Full Dataset', fontsize=12,fontname=\"Arial\")\n",
    "arr.set_xticks([0.1,0.2,0.4,0.6,0.8,1.0])\n",
    "arr.set_xticklabels(['','20','40','60','80',''],fontsize=12,fontname=\"Arial\")\n",
    "arr.set_ylabel('# of Louvain \\nCommunities', fontsize=12,fontname=\"Arial\")\n",
    "arr.set_yticks([0,2,4,6,8,10])\n",
    "arr.set_yticklabels([0,2,4,6,8,10],fontsize=12,fontname=\"Arial\")\n",
    "arr.spines['left'].set_bounds(0,10)\n",
    "arr.spines['bottom'].set_bounds(0.1,1)\n",
    "arr.axhline(np.max(subset_avg_rand_list),color='k',linestyle='dashed')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,arr = plt.subplots(1,figsize=[7,5]);\n",
    "f.tight_layout()\n",
    "arr.scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), \n",
    "            marker='o', c=cluster_colors, s=32, edgecolor='w',\n",
    "            linewidth=0.5)\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['bottom'].set_visible(False)\n",
    "arr.spines['left'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.set_xticks([]);\n",
    "arr.set_yticks([]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if 'renorm_subset_avg_rand_list' not in list(locals().keys()):\n",
    "    renorm_subset_avg_rand_list = pkl.load(open('data/renorm_subset_avg_rand_list.pkl','rb'))\n",
    "\n",
    "if 'renorm_subset_std_rand_list' not in list(locals().keys()):\n",
    "    renorm_subset_std_rand_list = pkl.load(open('data/renorm_subset_std_rand_list.pkl','rb'))\n",
    "    \n",
    "f, arr = plt.subplots(1,figsize=[3,2.5])\n",
    "arr.errorbar(np.array(subsets,dtype=np.float),renorm_subset_avg_rand_list[:-1],yerr=renorm_subset_std_rand_list[:-1],c = 'k', marker='o', fillstyle='full', markerfacecolor='w', linewidth=1, markeredgewidth=1)\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.set_xlabel('% of Full Dataset', fontsize=12,fontname=\"Arial\")\n",
    "arr.set_xticks([0.1,0.2,0.4,0.6,0.8,1.0])\n",
    "arr.set_xticklabels(['','20','40','60','80',''],fontsize=12,fontname=\"Arial\")\n",
    "arr.set_ylabel('# of Louvain \\nCommunities', fontsize=12,fontname=\"Arial\")\n",
    "arr.set_yticks([0,2,4,6,8,10])\n",
    "arr.set_yticklabels([0,2,4,6,8,10],fontsize=12,fontname=\"Arial\")\n",
    "arr.spines['left'].set_bounds(0,10)\n",
    "arr.spines['bottom'].set_bounds(0.1,1)\n",
    "arr.axhline(np.max(renorm_subset_avg_rand_list),color='k',linestyle='dashed')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "renorm_fullDataPath = os.path.join(os.getcwd(),'data/trough_normalizedWaveforms.npy');\n",
    "renorm_full_data = np.load(renorm_fullDataPath)\n",
    "\n",
    "renorm_reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n",
    "                    random_state=RAND_STATE)\n",
    "renorm_mapper = renorm_reducer.fit(renorm_full_data)\n",
    "renorm_embedding = renorm_reducer.transform(renorm_full_data)\n",
    "\n",
    "renorm_umap_df = pd.DataFrame(renorm_embedding, columns=('x', 'y'))\n",
    "renorm_umap_df['waveform'] = list(renorm_full_data)\n",
    "\n",
    "renorm_G = nx.from_scipy_sparse_matrix(renorm_mapper.graph_)\n",
    "renorm_clustering = cylouvain.best_partition(renorm_G, resolution = RESOLUTION)\n",
    "renorm_clustering_solution = list(renorm_clustering.values())\n",
    "renorm_umap_df['color'] = renorm_clustering_solution\n",
    "\n",
    "renorm_cluster_colors = [CUSTOM_PAL_SORT_3[i] for i in renorm_clustering_solution]\n",
    "\n",
    "f,arr = plt.subplots(1,figsize=[7,5]);\n",
    "f.tight_layout()\n",
    "arr.scatter(renorm_umap_df['x'].tolist(), renorm_umap_df['y'].tolist(), \n",
    "            marker='o', c=renorm_cluster_colors, s=32, edgecolor='w',\n",
    "            linewidth=0.5)\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['bottom'].set_visible(False)\n",
    "arr.spines['left'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.set_xticks([]);\n",
    "arr.set_yticks([]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x324 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unnormalized_waveforms = scipy.io.loadmat(os.path.join(rel_path,'data/cleanedWaveforms.mat'))['cleanedWaveforms']\n",
    "\n",
    "unnorm_reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n",
    "                    random_state=RAND_STATE)\n",
    "unnorm_mapper = unnorm_reducer.fit(unnormalized_waveforms)\n",
    "unnorm_embedding = unnorm_reducer.transform(unnormalized_waveforms)\n",
    "\n",
    "umap_df_unnormalized = pd.DataFrame(unnorm_embedding, columns=('x', 'y'))\n",
    "umap_df_unnormalized['cleaned_waveform'] = list(unnormalized_waveforms)\n",
    "\n",
    "unnorm_spike_amplitudes = [np.log(max(x)-min(x)) for x in umap_df_unnormalized.cleaned_waveform.tolist()]\n",
    "umap_df_unnormalized['log_amp'] = unnorm_spike_amplitudes\n",
    "\n",
    "unnorm_G = nx.from_scipy_sparse_matrix(unnorm_mapper.graph_)\n",
    "unnorm_clustering = cylouvain.best_partition(unnorm_G, resolution = RESOLUTION)\n",
    "unnorm_clustering_solution = list(unnorm_clustering.values())\n",
    "umap_df_unnormalized['color'] = unnorm_clustering_solution\n",
    "\n",
    "unnorm_cluster_colors = [sns.color_palette(\"husl\", len(set(unnorm_clustering_solution)))[i] for i in unnorm_clustering_solution]\n",
    "\n",
    "f,arr = plt.subplots(1,figsize=[7,4.5],tight_layout = {'pad': 0});\n",
    "f.tight_layout()\n",
    "arr.scatter(umap_df_unnormalized['x'].tolist(), umap_df_unnormalized['y'].tolist(), \n",
    "            marker='o', c=unnorm_cluster_colors, s=32, edgecolor='w',\n",
    "            linewidth=0.5)\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['bottom'].set_visible(False)\n",
    "arr.spines['left'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.set_xticks([]);\n",
    "arr.set_yticks([]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'umap_df_unnormalized' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/_7/5t5ls51s3nb01xmzyc1fv9780000gn/T/ipykernel_88377/2274923538.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtight_layout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'pad'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m scat = arr.scatter(umap_df_unnormalized['x'].tolist(), umap_df_unnormalized['y'].tolist(), \n\u001b[0m\u001b[1;32m      4\u001b[0m             \u001b[0mmarker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'o'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mumap_df_unnormalized\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'log_amp'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medgecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m             linewidth=0.5)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'umap_df_unnormalized' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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tqm9k+32/oz6AuBLL/NyfTHK0u3/c3V9P8rVshwJnb5l9vzHJ7UnS3Z9P8sxs/xsK7K2l/i443V6Hgcspb8au+15VL0vyz9mOAu+zrs6T7n13P9rdF3f35d19ebY/33Gou8/6uuaMZf68+WS2zxakqi7O9lsLD65zyPPQMvv+zSSvTpKqenG2w+DUWqd8ejqa5A2L3054RZJHu/vbu33Tnr6V0C6nvBFL7vt7kzw7yccXn/X8Zncf2tjQ54kl9549sOTe35Hkj6rq/iT/m+Tt3e0M5TlYct/fluRfquqvsv1BxDf6H8BzV1UfzXboXrz4/Ma7kvxSknT3B7L9eY5rk5xI8oMkb1rqef23AQB+wpUPAYAhDACAIQwAgCEMAIAhDACAIQwAgCEMAIAhDACA8f8VXQVfTH1P2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,arr = plt.subplots(1,figsize=[7,4.5],tight_layout = {'pad': 0});\n",
    "f.tight_layout()\n",
    "scat = arr.scatter(umap_df_unnormalized['x'].tolist(), umap_df_unnormalized['y'].tolist(), \n",
    "            marker='o', c=umap_df_unnormalized['log_amp'], s=32, edgecolor='w',\n",
    "            linewidth=0.5)\n",
    "\n",
    "cax = f.add_axes([0.97,0.18,0.015,0.5])\n",
    "cbar = f.colorbar(scat, cax=cax, label='Log(Amplitude) (A.U.)',orientation='vertical')\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['bottom'].set_visible(False)\n",
    "arr.spines['left'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.set_xticks([]);\n",
    "arr.set_yticks([]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different amplitude normalizations have similar effect but are essential for meaningful _WaveMAP_ structure.\n",
    "\n",
    "(**A**) As in [Figure 3—figure supplement 1B](#fig3s1), the number of Louvain communities found across various random subsets and random seeds. The mean number of clusters shown on the full dataset with a dashed line. (**B**) The _WaveMAP_ clusters on waveforms with ±one trough to peak normalization (used in the paper). (**C**) The same random subsetting and random seed strategy in (**A**) applied to waveform data normalized to trough depth. (**D**) _WaveMAP_ clusters applied to waveform data normalized to trough depth. (**E**) Un-normalized waveforms were passed through _WaveMAP_ with the same parameters used previously. (**F**) Each waveform in the projected UMAP space found in (**E**) is colored according to the amplitude (log of the difference between maximum and minimum values).\n",
    ":::\n",
    "{#fig3s2}\n",
    "\n",
    "figure: Figure 3—figure supplement 3.\n",
    ":::\n",
    "![](elife-67490.ipynb.media/fig3-figsupp3.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA()\n",
    "pca.fit(full_data)\n",
    "\n",
    "f, arr = plt.subplots(1)\n",
    "arr.bar(np.arange(full_data.shape[1]),pca.explained_variance_ratio_,color='#2a9d8f')\n",
    "arr.set_xlabel('PCA Components',fontsize=14)\n",
    "arr.set_ylabel('Variance Explained',fontsize=14)\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.set_xlim([-0.5,4.5])\n",
    "arr.set_xticklabels(['','1','2','3','4','5'],fontsize=12)\n",
    "arr.set_ylim([0,0.7])\n",
    "arr.set_yticks([0.0,0.2,0.4,0.6,0.7])\n",
    "arr.set_yticklabels(['0.0','0.2','0.4','0.6',''],fontsize=12);\n",
    "xlocs = [0,1,2,3,4]\n",
    "for i, v in enumerate(pca.explained_variance_ratio_[:5]):\n",
    "    num = np.round(v,2)\n",
    "    arr.text(xlocs[i] - 0.18, v + 0.01, str(num),fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PC_reduce = PCA(n_components=3)\n",
    "PCs = PC_reduce.fit_transform(full_data)\n",
    "data_approx = PC_reduce.inverse_transform(PCs)\n",
    "\n",
    "approx_reducer = umap.UMAP(n_neighbors = N_NEIGHBORS, min_dist=MIN_DIST, \n",
    "                    random_state=RAND_STATE)\n",
    "approx_mapper = approx_reducer.fit(data_approx)\n",
    "approx_embedding = approx_reducer.transform(data_approx)\n",
    "\n",
    "umap_approx_df = pd.DataFrame(approx_embedding, columns=('x', 'y'))\n",
    "\n",
    "approx_G = nx.from_scipy_sparse_matrix(approx_mapper.graph_)\n",
    "approx_clustering = cylouvain.best_partition(approx_G, resolution = RESOLUTION)\n",
    "approx_clustering_solution = list(approx_clustering.values())\n",
    "umap_approx_df['color'] = approx_clustering_solution\n",
    "\n",
    "approx_cluster_colors = [sns.color_palette(\"husl\", len(set(approx_clustering_solution)))[i] for i in approx_clustering_solution]\n",
    "\n",
    "f,arr = plt.subplots(1,figsize=[7,4.5],tight_layout = {'pad': 0});\n",
    "f.tight_layout()\n",
    "\n",
    "arr.scatter(umap_approx_df['x'].tolist(), umap_approx_df['y'].tolist(), \n",
    "            marker='o', c=approx_cluster_colors, s=32, edgecolor='w',\n",
    "            linewidth=0.5)\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['bottom'].set_visible(False)\n",
    "arr.spines['left'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.set_xticks([]);\n",
    "arr.set_yticks([]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-processing with PCA does not alter _WaveMAP_ structure: the full dataset was pre-processed with principal component analysis (PCA) and projected into the space of the first three principal components.\n",
    "\n",
    "The scree plot, with explained variance above each bar, shows that the dataset is low-dimensional with 94% of the variance explained in three components. The clustering on the right was produced by applying _WaveMAP_ to the embedded dataset and is very similar to those produced by _WaveMAP_ on data without pre-processing.\n",
    ":::\n",
    "{#fig3s3}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of clusters identified by _WaveMAP_ is dependent on the resolution parameter for Louvain clustering. A principled way to choose this resolution parameter is to use the modularity score (a measure of how tightly interconnected the members of a cluster are) as the objective function to maximize. We chose a resolution parameter of 1.5 that maximized modularity score while ensuring that we did not overly fractionate the dataset (n &lt; 20 within a cluster; [Figure 3A,B](#fig3), and columns of [Figure 3—figure supplement 1A](#fig3s1)). Additional details are available in the ‘Parameter Choice’ section of the Supplementary Information.\n",
    "\n",
    "Louvain clustering with this resolution parameter of 1.5 identified eight clusters in total ([Figure 3A](#fig3)). Note, using a slightly higher resolution parameter (2.0), a suboptimal solution in terms of modularity, led to seven clusters ([Figure 3—figure supplement 1A](#fig3s1)). The advantage of Louvain clustering is that it is hierarchical and choosing a slightly larger resolution parameter will only merge clusters rather than generating entirely new cluster solutions. Here, we found that the higher resolution parameter merged two of the broad-spiking clusters ⑥ and ⑦ while keeping the rest of the clusters largely intact and more importantly, did not lead to material changes in the conclusions of analyses of physiology, decision-related dynamics, or laminar distribution described below. Finally, an alternative ensembled version of the Louvain clustering algorithm (ensemble clustering for graphs \\[ECG] [@bib132]), which requires setting no resolution parameter, produced a clustering almost exactly the same as our results ([Figure 3—figure supplement 1C](#fig3s1)).\n",
    "\n",
    "To validate that _WaveMAP_ finds a ‘real’ representation of the data, we examined if a very different method could learn the same representation. We trained a gradient boosted decision tree classifier (with a softmax multi-class objective) on the exact same waveform data (vectors of 48 time points, 1.6 ms time length) passed to _WaveMAP_ and used a test-train split with k-fold cross-validation applied to the training data. Hyperparameters were tuned with a 5-fold cross-validated grid search on the training data and final parameters shown in [Table 1](#table1). After training, the classification was evaluated against the held-out test set (which was never seen in model training/tuning) and the accuracy, averaged over clusters, was 91%. [Figure 3C](#fig3) shows the associated confusion matrix which contains accuracies for each class along the main diagonal and misclassification rates on the off-diagonals. Such successful classification at high levels of accuracy was only possible because there were ‘generalizable’ clusterings of similar waveform shapes in the high-dimensional space revealed by UMAP.\n",
    "\n",
    "table: Table 1.\n",
    ":::\n",
    "### Non-default model hyperparameters used.\n",
    "\n",
    "| Function                                            | Function name            | Parameters                                                                 | Value                              |\n",
    "| --------------------------------------------------- | ------------------------ | -------------------------------------------------------------------------- | ---------------------------------- |\n",
    "| UMAP Algorithm (Python)                             | umap.UMAP                | n_neighbors min_dist random_state metric                                   | 20 0.1 42 ’euclidean’              |\n",
    "| Louvain Clustering (Python)                         | cylouvain.best_partition | resolution                                                                 | 1.5                                |\n",
    "| UMAP Gradient Boosted Decision Tree (Python)        | xgboost.XGBClassifier    | max_depth min_child_weight n_estimators learning_rate objective rand_state | 4 2.5 100 0.3 ’multi:softmax’ 42   |\n",
    "| GMM Gradient Boosted Decision Tree (Python)         | xgboost.XGBClassifier    | max_depth min_child_weight n_estimators learning_rate objective seed       | 10 2.5 110 0.05 ’multi:softmax’ 42 |\n",
    "| 8-Class GMM Gradient Boosted Decision Tree (Python) | xgboost.XGBClassifier    | max_depth min_child_weight n_estimators learning_rate objective seed       | 2 1.5 100 0.3 ’multi:softmax’ 42   |\n",
    "| Gaussian Mixture Model (MATLAB)                     | fitgmdist                | k start replicates statset(’MaxIter’)                                      | 4 ’randsample’ 50 200              |\n",
    "| DBSCAN (Python)                                     | sklearn.cluster.DBSCAN   | eps min_samples                                                            | 3 15                               |\n",
    ":::\n",
    "{#table1}\n",
    "\n",
    "We find that cluster memberships found by _WaveMAP_ are stable with respect to random seed when resolution parameter and n_neighbors parameter are fixed. This stability of _WaveMAP_ clusters with respect to random seed is because much of the variability in UMAP layout is the result of the projection process ([Figure 2B–v](#fig2).a). Louvain clustering operates before this step on the high-dimensional graph generated by UMAP which is far less sensitive to the random seed. Thus, the actual layout of the projected clusters might differ subtly according to random seed, but the cluster memberships largely do not (see Supplementary Information and columns of [Figure 3—figure supplement 1A](#fig3s1)). Here, we fix the random seed purely for visual reproducibility purposes in the figure. Thus, across different random seeds and constant resolution, the clusters found by _WaveMAP_ did not change because the graph construction was consistent across random seed at least on our dataset ([Figure 3—figure supplement 1A](#fig3s1)).\n",
    "\n",
    "We also found that _WaveMAP_ was robust to data subsetting (randomly sampled subsets of the full dataset, see Supplementary Information [@bib167]), unlike other clustering approaches ([Figure 3—figure supplement 1B](#fig3s1), green, [Figure 4—figure supplement 1](#fig4s1)). We applied _WaveMAP_ to 100 random subsets each from 10% to 90% of the full dataset and compared this to a ‘reference’ clustering produced by the procedure on the full dataset. WaveMAP was consistent in both cluster number ([Figure 3—figure supplement 1B](#fig3s1), red) and cluster membership (which waveforms were frequently ‘co-members’ of the same cluster; [Figure 3—figure supplement 1B](#fig3s1), green).\n",
    "\n",
    "Finally, our results were also robust to another standard approach to normalizing spike waveforms: normalization to trough depth. This method exhibited the same stability in cluster number ([Figure 3—figure supplement 2C](#fig3s2)), and also showed no differences in downstream analyses ([Figure 3—figure supplement 2D](#fig3s2)). Without amplitude normalization, interesting structure was lost ([Figure 3—figure supplement 2E](#fig3s2)) because UMAP likely attempts to explain both waveform amplitude and shape (shown as a smooth gradient in the trough to peak height difference [Figure 3—figure supplement 2F](#fig3s2)). In addition, common recommendations to apply PCA before non-linear dimensionality reduction were not as important for our waveform dataset, which was fairly low-dimensional (first three PC’s explained 94% variance). Projecting waveforms into a three-dimensional PC-space before _WaveMAP_ produced a clustering very similar to data without this step ([Figure 3—figure supplement 3](#fig3s3)).\n",
    "\n",
    "## Traditional clustering methods with specified features sub-optimally capture waveform diversity\n",
    "\n",
    "Our unsupervised approach ([Figure 3](#fig3)) generates a stable clustering of waveforms. However, is our method better than the traditional approach of using specified features [@bib153; @bib170; @bib76; @bib78; @bib113; @bib13; @bib111; @bib112; @bib155; @bib152; @bib72]? To compare how _WaveMAP_ performs relative to traditional clustering methods built on specified features, we applied a Gaussian mixture model (GMM) to the three-dimensional space produced by commonly used waveform features. In accordance with previous work, the features we chose ([Figure 4A](#fig4)) were action potential (AP) width of the spike (width in milliseconds of the full-width half minimum of the depolarization trough [@bib174]); the peak ratio the ratio of pre-hyperpolarization peak (A1) to the post-hyperpolarization peak (A2) [@bib12]; and the trough to peak duration (time in ms from the depolarization trough to post-hyperpolarization peak) which is the most common feature used in analyses of extracellular recordings [@bib153; @bib78; @bib112]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "GMMclasslabelpath = os.path.join(rel_path,\n",
    "'data/waveformsClassified_250hz_refiltered.mat')\n",
    "GMMfeaturepath = os.path.join(rel_path,\n",
    "'data/gmm_features.mat')\n",
    "BICpath = os.path.join(rel_path,\n",
    "'data/BIC_list.mat')\n",
    "eightclassGMMpath = os.path.join(rel_path,'data/8_class_GMM.mat');\n",
    "filtfulldfPath = os.path.join(rel_path,'data/filt_full_df.pkl');\n",
    "\n",
    "BIC_list = scipy.io.loadmat(BICpath)['BIC_list'][0]\n",
    "\n",
    "GMM_class_labels = scipy.io.loadmat(GMMclasslabelpath)['classifies'].T\n",
    "gmm_features_data = scipy.io.loadmat(GMMfeaturepath)['features']\n",
    "\n",
    "GMM_class_labels = GMM_class_labels[~np.isnan(GMM_class_labels)]\n",
    "GMM_class_df = pd.DataFrame(GMM_class_labels,columns=['Class'])\n",
    "gmm_feat_data_nonan = gmm_features_data[~np.isnan(gmm_features_data)].reshape(len(GMM_class_df),3)\n",
    "\n",
    "UMAP_and_GMM = pd.concat([umap_df,pd.DataFrame(gmm_feat_data_nonan,columns=['troughToPeak','prePostHyper','FWHM1'])],axis=1)\n",
    "UMAP_and_GMM['dbscan_hex'] = cluster_colors\n",
    "UMAP_and_GMM['gmm_labels'] = GMM_class_labels\n",
    "\n",
    "UMAP_and_GMM['troughToPeak_abs'] = UMAP_and_GMM['troughToPeak'].divide(SAMP_RATE_TO_TIME)\n",
    "UMAP_and_GMM['FWHM1_abs'] = UMAP_and_GMM['FWHM1'].divide(SAMP_RATE_TO_TIME)\n",
    "UMAP_and_GMM['color'] = umap_df['dbscan_color']\n",
    "UMAP_and_GMM['waveform'] = list(full_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.6)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8j1a2nqlAdtKzThBORox4tK55MTbBsix83+fAgQNHtGhJkhS4McC40+EajUZg2cYKAyzLwjTN4GEIj/OLx+NHjAuZzf2IrcJ4rqnpW2yxdgX/v8veT50mdZr88/A3OM84jUebz/Ifpbs4T1vDO1LXgk9gIYEg2y8WP3EPFEXBNM2WXnMZptA1eaI+/CRYxMnKFoqiEI1GAwKOLfiLbJqwkrIsjyKjsF6xWGxUDTLcQeH7PpFIJBAByLIcjJ9vNBo0Go2gPBKudb4Y40MY3yLGiHBZ7BykQ7thXxhdS4oYru9S8xrM0/J8t/IzDrpDPGE/zzP+juD6iySMuNcirDDNEcWOYRgBWVvZfXGwJZ80BmGXrpWYate8cEtFcTcajRKJRILXi7pes9kkk8lgGAb1ej0gZzgmVBRllFUT8WG4aKxp2qgHQVjPcLPwbCFiq/N7EyVrLtXPZEX7AmzfZZ6cJ0oESZK4MfUKZBQ0SSWvZJAlCV3WUJXR5YlarYbnecFz6Ps+hUJh2jNwZwIqcA9Aq+PDaDRKs9lsORGn0jUv3FdBIBH/NZtNUqlUsGuRKDv4vk8qlcIwDBqNBs1mk1qtRqPRIJPJjIrxXNfFMAwsy6LRaARZvfCqL0gnapNjSXuy1TWtnvI9UbLGQGeZNB+k0ceuNdZToMrHc7fwu/qTrNAXclZkFYqvBO1OlmVRr9cBRnlF9Xo9ON5KqL7vnxSLGI1GsSzrpNQRJ0t+iDhNJF+AgHSi7icEAGExQK1Ww7ZtTNMMMm+WZQUzV8V23rZtj8qqChc9nU6Psp7ie5qmSbVapVarsXnzZiRJQtM0UqkU6XSadDodbKHdCrSaiNP9vAg6XbRxvXEpVxjnEUVHssF0zCADLlz/8WSEJ0PFdFIK+nDYIp7s1X08CP2o67rBnnxh0o1nVcVvCRN8rExKJGpEnCrOEePdBWkjkQimaXLw4EGGh4eDODSRSNDd3U0qlULTNMrlMqVSie3btwdulq7rRKNR0un0CZ39Ot33LVOj4FdQJSVo2NUZXaQfoMgep5e0nGCx3EWJGmWvhj4/HixQY2uCAr7kI3PYoxDnpdTYETNpxGjORCJBvV4/Ylcpkck+lt95rDgpBX04rDedjRBE27VrF7lcDjiyOD/WqoZlcSJOVFV1VLwRTgCE/1uQsNls0t/fz/DwMIqikMvl6O7uptlsoigK1Wo1eGBUVaWtrY22tsPdCHv37qVUKlGtVtm/fz+NRoNoNDrKck4U/xztIR+L6T4yJap8qXw3D5obMb0mH2y7iZpj8qrYRcQY2RqhQIV/LXyLHfY+ZCQ+1n4LXyz8N73OIKlmjL/vugWjaB5RE7Qll8ftLdxX+z1L9HksVDpJezEWm534jhe4ouHulvDiahgG8XicoaGhQFze6owpnMRJ30JvOlsSEGMhFDWivDCVOl4kMpIsSCaTo5I1Qr4mivTCCorEQbVaDa6DpmmsXLmSeDxOuVwO9vwYGyONRxwh0Vq6dCkwQhjTNIOhzbt378a2bWKxWEBM0RUzXuF7IkzXVRz0SjxqbqbkVehzhnm48Qym1+Ss6CqWSSPNP1W/zg57ZLaRIUV4rrmLXmcQgP1WH7vdXlaxYJTgQVVVDngD/NvwnRT9KndWfspfZt/G7ytP85e5t5EuHt6izbZtarUasixjmiapVCq4L5Y10ukvVE2tbgqGk0xE0ZPYKkxnroxQu4jO+8nODbdSiQ1wxMNaLBap1+tBZs73R3au0jQNwzAwDCMgmyCPKK9EIpEgWSN6Hx3HCXSuRyOOcMOi0WiwlbbIFpbLZfr7+9m2bVuweBiGga7rgZpoIkyXiIasE5Uigeu4QO3gGXM7SmhARFyKsljrZo/dS9O3WaLNQ0YmqyS5KH06C/TOkebgMdlu07ewfYemN5JhHnQLyJJE2a2S8vUgHBAIb3AqBluL+yJ+myBmK6FKkiT7vt9yeYuwiK2aaTq2kC9kZ8JNDJNTJFUMw2B4eJhsNntEEiWMsft3CJKJzwm7lDASgyxatCiwauGEga7rASHD7lM0Gg3iRZGpHWsdpgIRGyUSCebNG7FGlmUxMDCAaZqUy2X27dsXZILDyaDwojkVItYxqVAnTYK/zr2Tn9cfIaMkiRLh+uRL6JZywbltpPhw9m1st/eRUzLMl/N8LP8ehrwi/1/xZ3ypdDfvzrya+XZ+1BSELjnHhcZanmw+z/+TvZFONcfZxqkskjvxsKnX61iWRSwWC7Kl4hqK8lDY1RYkb7lrejJICKOJ2IosXHhWpW3bwegPIbgGghEVQisqanvDw8PA4SBelCSAoFtDEFFAlB2GhoaOeIh1XQ9amgSxDcMIVm4xMGpsS5SAWBRmajdiXdfp7Ow8YoqZSAbt3LkzELOnUini8XiwGEx034pU+Ublh2wwN7NcW8j7M2/g/ck3YuNi+k3iGEExXiBPmqZmM+gWOeAPkpETfKn4PbaX9xKLx/ly+R4+krmZSMiRSxPnXclXM5ws86nCHeyz+8jISf4+dyspSQ8svG3bwTWXJIlqtTpuslDE/uJ3tbKgf1IQdk1bQUQhzobD6pjwAyyymSLBEv5OogAsEitCwiZJI/t2iMZdQQ5N05BlmWg0yuLFi3Fdl6GhoaBwHO7+D1vjaDQ6blIoDJF0GDv+73gx9n0URSGbzZLNHlZAigVscHCQWq3GQw89hK7ro+JN4Trv1/p5oPEkAM9aO9hobeNy/RxU5Amnq73gD/D18r38or4BGYmP52/hoDOEZChIgOOPjA8ei7ScYL87wIBdIIKO6Vs8Z+/iInUNjuMECRkY6a4QyTGRVBPPhaZpwXVtdS3xpCdrhJtwomNFEW/JshzUCYW7KPZiFDfFMAw0TQu+HxxeGX3fD25SvV4Pkilh8ok6nyCUrut0dHQEblI0Gg3ILR5cmJ64+2S4T5qmkcvliMVi1Go1zjrrLJrNJqVSiVKpxL59+4LfLi2PYls2iqbiSR6GPHmTbdmr8pi5BRcXD5n7qr/nTzOv5qvFe+hQ23hn+jqSjIQyY5NVhhyhIVkU3Qp5OcNirZukmhx1jmmagYhECEnEMxDWG4uumFbipNURY7FYS8dliNqgePjDMaIYFiQSIsLqiAFCgrwi2VMqlZAkiVgsFmRKxQ0Vv0WobwTxxDwbkQkVGbqjbdvmSxJDTRnXg2zk+LyG6ZQnjoamCyVHJZkfGVMRiUTo6Oigo6MD0zSpVCojrWB1m7/O30wfQ7i+xwuNPp5Vd9BOFifqsdXaS8Nvcl5kDW2H5s4klCi6pDHoFmn4Fov1LrJyindlXktWypJzukEf+S3Dw8NBgqst38YjjU3ckn4t2+w9nKItoYu2cX+riKthdBlGhBiipa3lRDyZdcRCodAy4ffRpG0ibhRkEqUGkUETCiBZlslkMqTT6cASimA/XBuEkenoqqpSr9cDEo79Dkfbtk2SJLYM+3zh4T6ars81q1KsTR1bo8x4m6wcCxnrDvzw+Sq/2VEmq7t8oN2nPXp4cRC1VAAlqfNLcwMPmhtZG+nhdH0F++UhvlK9l/3lAV6TuIzfFB9lo7GV98ReTSqSxMHltuyN9DvD1H2Tc43V/PXg/0X1Va4w1mMZDo7XSdaOBO6k53nUHZNN1naKdpmrU5fi4FCXj8x8hmu9YtEdD+HyxYsqRmyV3lSQITyMSbipsViMSqWCaZo0Go3AGgrpmSg1CNIKyyoyb2I1FRClA9HhMZEQYCIBuovET7eWaboj5P7Z1gprz0/iT2FTlbEYj/DHQsT+msdPto6UaQZKDTbsa3DNyniwAInR9a7r8qj0HBuam9nv9LPD3sdLO87mR7XfscXZRcWr89/1X/LG7Mv5RekR+t0hntu4hc7TF7BX6qXsVrkgto6mb/H65MvB9/hl/THuqNzDmUYP78++kUK2xFK/G4YtNFfh1cmXMuAWuX346wCsi/Twt23vovNQl5/jODSbTSKRyKgMdxjiPrYiVBqLWUHEVupNRWkinOUUGU4haRPZ0TCBhJY0bAFN0wwIKYr+rusGdcfJXMGjWmkJupMqW/pH/j8XV5D9kQdnuqv0ZB0nhaaP7ULGkNCP8vwpMsgSiLsVVQ9/jxomVerEVIOkGsNwdFQU5qntHHAHSMgxNEllj92LjQP4LNK7eFX2EpYZi4lGl7CD53mysY03pV/OVmsPhqSzsfk8NyQu5+PDX6ZHW8wWeyc/rv2OXY399OgLeUv2lWiaxtnqKn7q/Z60kkBD5aAzRL87RKeSDVzZRqMRhB3jXUNJkiiVSjiO0/L9OtVDX0Dxfb+l6utWjFQUtSLhaiiKQqPRoF6vB/GeuDFiYtfYmpIgXniqt7AumqYFg7DC2kXRuS90o0dLwEyUoPE9j2tOSZCKKJSbLpctS1DZ9zzk89OWmAlLNd7C8ELF4/88PEih4fKqU1K8sieOMcHX7YrLvPPsHD/fVqI7pnP2/BGLX6TK1yr38rj5HCu1RdyWuZEeeSHXxV7CM9Y2bk5fzxptGXJCYpe1n5pv8ifpq8nLWdZGe+ivynxpwwALVg/xivQl/Mvwt9jQ3MxCtYO/yP4JGTnJCm0hmqRQdCp0yFmetrayyduBlz+8sCxV5xH1I7i+iy6rpORE8Bwoh4ZA1+v1UVrfUdc8pDltdcSmHvrQlrdAnMiRioKA1Wo1cEGEy2FZVhCzhR/I8MiE8PuEh/2GLbc4T3x3IV0zDGPGRuYnFJdreoxDn+NSOIY9NQTGs8ye5/GzrWX6qiOW8t4tJc5bEGNe4khL4DgOruNwwTyNNW1pCgMHSR9KhL7g9vFQ42kAnrG286y1k9Oqi7lGuZArI+eR07OovkKHnOPa2Eup+yZ7m328wjifqB9hj+lwsOJwCcuwpD0ccAdRkRlwR0TgCSnGbZkb2Wrt5abkKxm0CpTlOldGLmD4hQF29D5HLBajrTvPh1JvYZvzAmsjPeTNFLZus0XZwz3er1kc6eKl0bOw+xsTXqdWNwQLCIv4j8A/+L4/8TecYYjui5l2TUUhutlsYprmqAyloigkk8lRw4OAIFaUZTmQeem6Tm9v77gkFAjHW6o6spuQEAeESxLHgxPlLQiZXEI/7EJHNRllHNd0bLJHURSqlcN7oUSk0RLAmDQi2VNlFVt1+ZX12Ii77+ks0rtok9Ik5QRRJw0q5GIK+ZjKj5/wePvl85mn5EnKURzf5eLoOoabZdZFe7hIX4sEbFZ3cmZ8Faeoi+loy+IvHSkpDQ8Pk90f4XypBymns1c9iKZo/GP/V6m7DTb4m0hkYpxvnHLUhgOhBW4lxBKZBjSgpUScSYvoeR6lUomhoaGA4KJsEHYzBOFEUV9YS0FWkboWyRzx8IXrfWGI14VboI5WkhCQZRkfCYnpTSmYKZdJyL0uW5KmbnkM1D2uOTVDR2x8axhO9oiWMIGFcic3p6/nd42NnBFZSY+yEBsTU7P5j+rdPOlsZbu9n9cnrqDXGeTy6Pn8qG8z70q8gTXJTtqjEn9xSTuFhkdKM7kl81oeM5/lgujpdMptxI0oXyj9Ny/YfbwxdSV5OcN3Sz/lXOM0rk1cQh2TRDxKd6SbWq1GNd7kK8V7eKz6LO9vv4m6ZAbXeNAtTGrtxLPRSggi/oXv+y3tSZqJGNGyLIaGhhgcHKTRaJBOpwNdaJhEjcbI+iLcUxi52MK9FMeEpRQ9amGrCeNvXiqKw2GyClKK0odoDBawfZmNvQ4P761zRrfBefN1ItLk12Am3SWRwJHNEq/tiRCNJdDV8a3A2GTP2EHKUSJcFbmIy4xzMPyRB9jJGAxIRXZVD+Di4uOx2drOCm0RO5oHOWgXOeD30jyQ5JUr4rRHoT2qsNev8u3CT3hb+lq+WryHt6Sv4knzee6p/oamb7PV3ss/5N/LlYmLiMsxPjn8NXY097HaWMb7Mm/gF9oGHilvYm20B1+CLeYuXpG6kJ8PP0xeyfBS4xyahRH5op2XsCSbmKnjDB8eCCYW45Yna1pNQjg2i+j7PpVKhcHBwUAAncvlWLp0KfF4PLhw4dYWoYIRBVs4fKHFyifS1WKlF+6oeD8R74lBUuHvK6RyouYoYkVBZlHAF9I5RVHoa8CXNwzhA88cbNCV7KAnTUtxZJg0L9gAACAASURBVAJHnrDoP/ZcMcdnLAQJxWuSXozzImv4ufkIhqRzWfwcnqpv4yxjLd93nyKjJIkooxe3mBQhLhsMeyUeb27hbVxD07dx8fDxcfFwfJe9di89+iK2NfZiyhY/qz/MRbF16JLGc/ZuflnfwKfa38c3B/6Hf+3+INdrl6KioBQ8GnIDe77M8+5eHFzkKKzNLaM5MPKsSJJEb28vkiQFXSsnGiJGbLm6RoicRWvLRLBtm+HhYYaGhqhUKiSTSfL5PAsXLhy3PUm0twjXCxhl1QTxxb/iZ4ctpXBfXdcNEjzhDCqM3CxBQl3XR1yiQ3MxRa1KTAYT+2dEIhEymQw1C8IXu2S6SBml9Zk69cjp1xMV/Y9FkaNaMjfqV3B57FxkRSYmRbgseg6/rjzDX3fdhFZp58x5h70F1wepmeZDmZspMMwp+mKeMJ/lyvgF7HYO8ILdx1tSr6LpNTk3spqIr2HLDnucg1jYNPwmDUzOiazG8z1ScoK/6XoXCgr7pQHatSz5VBIlovI96zf82/B3APiT1DWsjfcgD408J0KyOF4ocqKghv49MVuvToDwrsHhOaGiX25oaCgQSre1tbFgwQKSyeSk7kJ4buhYPSEcTn6IB04U3kVZQrwu7JqMbWESrmdYNCyIGVZliBsZjj8dx2FeQmd1p8GzfSZL2nRW5HR8//huuiRJQSllLMa2WgmMJeJUi/5TFemrqopahE4/NYrcp2eWYzo+WlZClg71ASLxfGOQ3UWTff06r1i6jH/Ov5/HzGfxPZ+PtP0pFa/KQ/Wn8WSP9V4nsiTx3uyNfL/2a86MnMIva4+SluNcGD2dlBInLcfJykk+3v+f9DnD6JLKx9rfQ9aP82B9IwAyMpvs7TSSlyPP1zCGZcxaI+hIaRWEa9pSEsLhvSOEjnNwcJChoSFKpRKxWIxcLsdpp502paA53PAbbvCc7DVAED8K4qmqGljK8R62sHvrOA7JZBLXdQMBeJig4vXi/YLxGYrLu8/OUHfBUCChTJ2E4/2u/obPT7eWcWy4fEn7qL8JSyfURKItSLjJghxHK/qPdVnD1+ZoGlZVVclms7i+iyIrKLIS/IaIAmG/4FlnNx/o/SoDzRrv7HoVv9t3Jtev7uAsw6PpWeyy9/OvhW8RlQxUX+bc3Gp+XP4dp0SX8LLoudxb/S29zgAfzr2dJeo8dlsH+PjB/+SW/Os46A4hI2H5DputnVzuncU6fSX7nQFUWaFTyfHD0m95qPIUH8nfTNrUW7oBDYyIvq8BuoCf+L6/v1UfvHPnTnzf54477uCCCy4AoKOjg56enmmljsdr+BUkn46rJwgmLAtwVNdEPHQiiyhiQyEml2WZZDIZyKpE8V9Yy5jiEptmZWO8B6PuwNceH+b5gSZW0+JgxeZDL0mgyCPqHGHphCUHRv1/mERji/5CFiZ2xBJWTVzrWq1G3TGJWA08s4mUakNJH9bDDpsevf4QW/zn0GWZsyOriEo6Bnowq6Zuw96KxR32ffhaA8n2+PrwT/jyotX80nyGgl/hB9X7uTl9PfucfuJSlHlqngp1nrCe4wn7Oc6JreFDbW9lp70fyZf4Xf1JflV5FM8fIXGbmmbYKqEis0pbjFU0uTFxOaclluHqPjWnwZ2DP8bB5dHms7xKP6/ls01V4E+AHLAJaAkRN2zYwN///d9jmiZXXHEF0WiUFStWHNN7hRt+hWWEY0vzizhQ07RJE0jh3aLEiMWwBlV0ZGiaRiKRGLeE0fRlarZERIG4MvVaavi3Wa5PoX74tT3tMX6yvcbzAyaXLomzrkMLvku42VVoaSeKA4UlFYOOxURs0TQNUDVr+OYQT//rx2n0HeCUtS9j0S3vp5TRMD2b4aZCn97Ld8o/w5Qa3Jy+lrgUo88d4orY+VT8Gj4aG/uiyMkEfVWHrqRKzsuRi0vcW3iWqtzgZfHzOOAMcHF0Hc8393ChcToZOYkje8hIPFJ/hmvil3CuvoZ20vzKeSzIZhftCh9rezd77F7apQyp4ZHZs17N41RtPnKHzt8Wv4RzSLi3Sls6KlHXKqjAvwMO8FyrPvS8887jhz/8IWeddRbLly9n3759x/xeouFXPJyiBngsEKLf8DCn8SA65+PxeDAsOOzSTWWIccNTuOvZCg/vrTM/rXHreW1ktekLG1K6xDWrUnzjiWHaYiq5uMa3nhrZBPq5AZO/u7yLeYcsnUggJZPJgIQTxYHhmFHshCwSa2LchJeW8J98nvhQgyYyezc9SL7/tfzOKKHKCtuVfXRLeTRJY6O1hV/XH+e1ycs5aA3xnL0bTVLY1TzAmhU9rLYvJ6cmOK9tAQ4OFa/KCn0hvgIuHv9WuJOr4+t5e/paolIEBZk1kWXstQ9ybewS2ipxqLu4cZvzpVPp6Xg/lmvTSZa0kyBakqnVaphePXhWbNtG64e/6biZzfZO5qntdFZTNO3GtLyymYDq+/4D03nB8PAwN998M/fddx/5fJ7bb7+dm2666YjzPvvZz/Lv//7vDA4OkkgkeMMb3sC//Mu/HHHjj7egLx4WEauIfrjjQbhxNIyxxftYLDYh6SZr8h1o+Px2Vw2A3cMWT/aavGyxPul3H/t3WYILFxj05Lpx7Cab+g8nazwfmq4fXHOx959woY+WBRWxYDiGFrpd3/dR2w2+Uf0RL08n6HMHWah14sjQjChElQi39t3OArWTslflL7JvY9PQc1wRP49/Hv4mz9m7kEsyn2n/c3J6hnf0fYzTpbV8pP1t/Eflu1TsChktyTXJS3F8m5yc5h/y72XALdAmp1gTWcY/DX2DFfpCLoicxqnKUpr99SDRJkkSWr+Lhoyp1ohm9VFJujBs20Y+IHGmunSkEcBrBvNhWwlZkqTbJUm6YqovuO2229B1nb6+Pr797W9z6623snnz5iPOu+6663jiiScol8ts2rSJjRs38vnPf/6I847WFzYVCDcw3Bt4PBBWcSyEtctkMqRSKfL5fHCzxIyZ6cjZNFlCDhndhD75ayey0qoMXXEJuT7MqpzC4oyOLME582N0xkfvPCwmh5fLZYrFYrCAiVEhYxFOOoWTTyW1zkPVjWxY2KDrne8mdsH5rPrA3zDQbfCstYt2tY0hr0haSYLk8YrYhUQkHR+PmGTg49PvDrPbPkCXmqUkD7DL38VqbTEXp9ahqxo2Nmcaq1hUznNO5FS2Wy/w35Wf84v6Bs6JruYx81l+Xn2EYbs4Kv4V0xLEyBPRcT/R9RPnjL3vrS7onwo8OpWTa7Uad911F5s2bSKRSLB+/Xquu+46vvWtb/GpT31q1LnLly8P/lskB7Zv3z7qHDEq8Hi0psIiFQqFGav7jKegERpLoUU9XnREfW4+N8dvd1VZnotwWofGTGjvY26ND1/SScX2iKsSce1wHC1EB0BQ62w2m0F7ULjEEO7TFPcn3OoVkTQiis5P3SfZcsY8brnsRn5Q385i2eAUbTEZKc5u9wDdSjtLlIWsSvZQ8AtIkjzSTaEspE1J4fsQlSLgenQoGWpanQ8Nfg4bhyfN5/l47j3EM1F+VX2U++oPM0/t4Ge1h/lg9s082tjMamMpy+UFNNwKQLCLc71eD0KUsFWfqgfW6pGKqu/7r5YkaUq6jq1bt6KqKitXrgyOrVu3jvvvv3/c8++8805uueUWKpUK+XyeT3/606P+Lma3HK/WVNd1stlsoDM9HoRjgzAZxQamlUpl1BS3Y4WCx/ndKmd1ZtFkH887fhJ60Sz3bqth7S5w3akpOqLSqEI9jIzwCKuI4LAVCTfLCuKKYViRSGSU2x8tKHwo/yfc33ic06M9JOUY/1X7Oee7p7E+dgYfz9+C6Vtoksp2cy9PNJ/jgNnPm9OvJKHGWKrNo+aaVLwap+pLuTx1Dj0sZA+9XJu4hBfsg1S8Bg4unyn8v5xprKLuNel1BlmqzmeB2sEn224j6ulQ95BibkAyMfhKbBArVE5TJWGrEzVwuI5YOvQFjqqwqVarwY63Aul0mkqlMu75N910EzfddBPbtm3jm9/85hEF0pncESqcMT1WiPpfeJ+FcKpf6GLDDcPHA8/zUICZaLBwfYl7nqvx4K4yRtRgX8niw5d2YPijC/W6rgczPlVVxfZlKk0VXEhpIzGjIK6IlUV7mNiWrlKp4Jg266QVnJM+Fd/36XWHiEoRyn6NpBzn65Uf8OPagxjo/GfXR9jh7mWTXOI7xZ/Sred5VWw9/c4QWS3Nm1NXcaq/mKFmAUOJEJE03pi6kt1WLxoq26wXOC+6hlcnLuOA08dbk9cwz8tRK1Rp+o3gHol5M2IbbkkaGZsYvt5TwUkRfUuS9BFgIfBb3/e/fbSTE4lEMA9UoFwuk0wefa/Tnp4e1qxZw3vf+17uvvvu4LgQfh9rXCfI53keAwMDx0VoWZaDzV2kQ2MSBekEEcNF+ZOFia6V48Fw/fBCVG562J5PQjuyUB/MLvXh4T0m33pyCN/zecO6LC9dqATEFVYzEomM8jSE8Ftkj8vlMlkjwT923MZ/V3+BhUPOjvJp960UVJOH60/zstj5DLtlFGRuTlxPRI2wxd7FM+YOkCGtx9ktH+A/Br+Hq/lssXbz0bZ3sbO5nw+3vY0vle7i6tjF3JK4gUw1iuk3AjdULBLhntPh4eEjLNtUn7OwWqpVUIHTAR2YdF+vlStX4jgO27Zto6enB4CNGzeyZs2aST/IcRx27Ngx6tjx7AgVLuSLGTPHA8/zqFQq5HK5QAMrCJjP54Na5Uw0/B4rjlpSUXyuPTXFnoKJJMGrV6fJRCRUeeLu/JoNP99eBiQkWeJXO2qcPz8aEHfsfo3RaBRN09BjEWqeiYWN5EO8v5/Sz3+K3JFi2SVZuuUYr/pNkxd+8mm62+dx6fv+il0Rm+sTLyFKhLJU59uFu1iqz2dtZAVPm9tYpHax3+/HB6punV53iL3OQT4x/GX+b/uH+WTuNjrsDEZVDVxOsQhrmhYs6mLhnCjzPVW0eoMk1ff9N0iSlGIKvYjxeJwbbriBj370o3zlK1/hqaee4t577+Whhx464tyvfOUrXHfddXR0dPDss89y++23c+WVV44653h2hBJuo3hoZgpiYRC6Td/3j2hjOhpcZIaaEp4HbQbo0vRiP0mSMD0JWZLQmN5r52s1PvKSPNF4nDZDQjsU7k5UL4woMD+lc7AyYkm7UxpRTUYLERdGrkm9XifabCIP9GNtfIJYOom9YAFaw2Ho3/8Ptm0yQJn57noSV5xJ4UcPsNJpR9/nEf/tRh69VuF7lV/yjvS19GgLWa4v4Bvl/2G+2sGn2v+MXnuQs6JreCS+meetvVyin0HdabBE6SavZFjc7KDZaNKwG4F3IrLliUQiKLcIvXBYnH8sC/1MPlNTgYgRy5OdKPCFL3yBd77znXR0dJDL5fjiF7/ImjVreOCBB7jqqqsCn/zBBx/kb/7mb6hWq7S3t/P617+eT3ziE6PeSxDxWFw9UbY43uRMuG9RuFuisx+mp9CRJIknDzp89bEhXA9esybNK5YZKONMpx6LqqswWPfxJYnf76lxoGrzlnUZOo2pk9GxbWhW6WpPTOn8iAKvPy3D/JSOLPmsXxwjqgIcJm7Vhv0Nn0VWhfLXvkxz2zbiL38ZerqN/g//NZ0f+HMkWUY34uRs8AYdZFUnTQx73258x0H1JN6ov5QF2U6+WPxvPtf5Ib46cC8WNrvtXr5b/hklr8pBZ5i/yr0d27dJEOPrg/fwxtgrSBR0LMUKNtQRExjEvRNCBRG7hwUex4qWbykPIEmS6k9R/t/W1sY999xzxPFLLrlkVGD89a9/fdL3Eh0Yk8WY40GULWq12jG7t4qikE6ng5VVdPSLzn1g1G+aDKYrc9+2Iu6he/iL7RUuXBglc/TNpKh7Cnc+U2Jv0eG5gSZvP6uNoYMNvr2xyJ+dn0ENWcaZbJXqr/t8+dEhJAnOWxAfNZVt5PfAb3bXUBWX9H0/xH70Mez9L1B/bgsLP/tZ4qethUYDY9Uq9GXLUJ98kuRZl1Jy6iRuegPlu+4mMm8+xbjPwQNbMObpvDF1JTHJYJ7aTsWrkVISWL5NnzPMdnsvJbfCHqeXFcpCPpx5G41CHbNcpxrxqNVq5HK5oIVOkK9SqYxqPRPXSJRajgXT8YJmAjLAVEk40wi7psf6gB1rsicc5yWTSdLp9KgCvdiBScRlTV9mV0Vi87BPxR0/RtQUmJ86zLr2hMYU6vSYrsTSrMH8lM7bz86xv2zRndKwXR/vBPUoSpLEQ3vr7Bi22D5kcefGAoP10Vag4jUxc8+TXXCAqj+MH1FR23JInoecSpO46GL2//X/pvzjH1P83vdIv+4Gtv/w2+z/wr9hticpnb6QekJl4/98jRJ1dtr7OddYw4BT4PXJK0jJCRarXbwpdSXPNLeRV9JYvs1/Fr4/Mj/I0rBrIx6PSB6NnZ4gVFVi1GXYkh0rCX3fD7KurcLJS/8xukt/qj1uYViWNa3NQoS6Iix3GqsptH2FgiWxdcjihSKckV+IJMs8vMfizkMazrXdBjeflSYqj35wZd/lhjUpupIqDcfn0iVxYvLR1zgXBduTuOOJIfaVHBRZ4rNXz+eh3VXeuDZDRPYRXJzpByOmHf7tigyyPPr9C8ogDa3A/+r7Mv/wimtp691LrtBN+3XXo3S009yzG23hQmRk7P37aA4PYXTMoxmxMfMJLMnFrdaZ965b+Wz0Ea5VLyMlxfhM5R62WXs5x1iN4zvo6Hw0/27Oiqxig7mJ92RvoF3JMqAUkdo0lOGRorzeGWOH3keNBosyXSTLEcxiPUgszZQ+9KTVEU8Wxk77nu6FnG58KIbGhuVeYRfEl2QeO2BxoOry+YcGyMUUfhPx+IdXJnh8/2HCb+lr0nAkouOUmpKKw1XLxQjEo5BQkuhrKHz3mQJF0+OdZ+e5e3OJgjkyfPetZ6RIqt6MuqJh+L7PeQuiHKza7C3aXLEiQdeYwVGOb5HUNA4ywMei/8PX3vMeogfqOL6ObjkYS5aid3RS3fociVWrMeYvIPbjEulFCxlWfarvuJZ2v4NfuvdzqXIOa7UV6JLOTckr+dO+T/BcZTevS7wMz3dZb5zBF0vf467KL7kpeSV9zjAbyk/TKeX4QNebyDRi7Ij08eHBz9PrDtKl5Ph8x4eQNUgPRoJsqYhtj2c+abgVrlU4qUSMxWKUSqVjEn67rjut+E24orIsk81mg3alsItad+CZgybmIY+maLrEVRnT8ThzXoznB0aIf0pHhKg68Q0e77eEa5EOMjsKPl97fJAtAyZtMZXdhWGuPiXFE/vrLEgqJBSXsc+Q53kUi0WKxSL5fJ5sNntchedMBN6yNo3l+aOGCg+ZPo4HndF2dkkHeEPqchbXU3DH96hv2UXnn30Aa9Mz+LZN/r230a5pSNkM+77yBRr7dqL395JRZOQ/vY7n6aNeM1mizePH9QfZYu3iqvjF/GDeZ2n4TSKSTsMxkV3otQdZoS/k9EgPHx34EovkTnZZ+/mdtZHXRS/jBaePg84QzUNJnr1OH7+ubOB9ydfhD4/UPrPZbDDN/ViTNmKxbiVOukXs6+s7pqB6IpHyeEgkEqRSqXHJF4ahQEKX6Unr/GqnguX6nN4ZIWtIXLxQZ3Gmg7rtsSSjEpWn/n0jch9O+dfIahopfgkDjQxb+muUmy4l0yWqySxIaVywMMbLlsVo072AhGJ6wcDAAKVSiXQ6TT6fp1wus2fPHhzHIZVKjWhENQ09NrWMqYAsjSbhnrLH5x4aoGK5vHx5klecejZnR04l4TRwu0zU1efQ3L6d/ttvByD+kkvJ3vRmlI48uB5ePIqmJVCWLCZfU7ASeR5pbqYnspgvlr7HfLWDbcXv8i/tH2BLcycPNjZycWQtC/R2LjBO477671EkhZySRvZkXDwySgJDN1ilLMGQRvY/nK92kJLilJwqXoRgdKXYB1HXdfr7+zFN85hKGOJatwonbVs2OPaNaCzLmlBWF4amabS1tU05A6bgcd2pCbYO2/zTlfMwNPArgyQONe0uTwFIMKa+Z/oKB2sjl7ArLmGEaoeaXKZ54FN4zZ04PtSj+/nF8Js5JW/w7vNy/MeGIVRZ4sa1GWKqRJs+ogwR5Gs2m+RyORYvXkxfXx8RI4qc6iChaixashTJH+mfrHo6391SZqDS4Po1OvO1Gol4nGQyOWWXX5IkfrWzwkuWJvB9iZguYzYMuuIGrr2fwvad+POb2Pv2IUej+I6DuflZlGSSyr3/Q9crr8M168id7bi7X6D5Xz9k2fqL+djyP2UfgyxQO1GQqfkNGp7JXdVf4eBwd/3XnGGcwhsTr+DC6OkYUoS/z93CTyoPslDu5FxlNZqmcSqL+Wb3J3jG2sY8pZ0HSo9zc+Z69IKEcmjCuoBhGORyuUBxNXYfk8nQyv1Y4NC2bNKh6LTVhDQMY9ojFcV+75MVXFVVJZ1OTzsNnVBczu5QAB/TNHmu7wXoapvwfBeZ+3bUufvZMpWmz2vXpLn6lAQKHnHVQ6KJZ+1HAgoND8vZxsKUx6P7TAZrNh+8uJ2tg01+vrXIN4sN/teFGYZ3PxuMiTQMI0hkLVq8mM1D8J/37adhO7z7/E6W6hXSqSS/3e/y8N46EhJffbLC/74ky969OymXy4HLls1mJ92S7bTOGF9/fJgnDzR4+UKVc20bP2VQ+9FPaDz5JFHDIHb22TQ3b8arVkm87GVI0Sile76P1tWJMziIVqlx8PZ/HNFs/vDHLP/321EWdLFaX8r9jSe4PvES4nKUzdZONFSWafOwcUgR53RlZFLDMnUeZ6VPodg/jGx6OJERgcE6ZQVrtCUMOMMsi3Zj1BR81SebzR7xu+LxeLDfSaPRCCYo6LoedGRMhGMpqR0PVEmSzvV9f0ptUDONY9kRaiouqa7rQcvOZCg7CgN1j7gm0xn1kTicIJmKS9Nw4aG9dfYWbaqWx/efLbEyH+HXO8u8dGmCC+Zl0DJX4hR/gIeKnL4G3Y1x58YDnN5l8OSBOv/1dIGo30RWZKq+zrp16wLyCeGCJElIlJkX2cxHLo3y6EA3P9phcuvaGPv37+fgcHpkOJSmUWvaqKrOypUrg3inVCoxMDDAtm3bAMhkMgExDWMkueT7Pt0JlT1FizUdBu9M7sfeuBvv9NV4jQZSJIL52GNkbriB3Lvfjec44LpUfvEL2t55M0oqjVso0ty2Dcn1wPNw9u0n0ZR4pLGJs4xTeEn0bJZq8/l942leHjufDeYmLo6ewWKte9SMSUVRMHydWrVGNBqlWCyOGnTVrXaMCPPVo++4ZRgGhmEE84NErVFMdhD/LZ4V0W0ytrnhREMF3nfIIP4P8L1WWsXwSMWpWsTwEKSJ3lOksidLZFRchf/zyDC7hy1UGT64voOuhMKBiktCl2mPaJMS0VCgJ2fw2911JOCUfIRdwxZNB77+2BDLXt5FZ+ZNRFIvY56vcefmBF0pl1xUplyt0xlPkdAVImqMmCbRkdCRZRtN9dC8nXjN7cjR0/DlLuz+/0vzwAPULJdzut5BMfNycB26urq4cVGWAVtnsGJy9SlJSr272TPUz/KlHbSlGnRmdbK5hQxbK9FlkBpFCoUCvlMgoe1E9kuoyQuIygs4f0GU09tkMjv2Ubjja7gd7eRvvRXPdYmuXIlvWQx/85u4lQpqWxv5970P64UXKN11F6lrriG2bh3ln/wEv9Egtn49XjxKt9pOzW/w7fJP+Lv8u/nP0ve5NHYmb0lexeWx89A5UvUQzm6HB10JTGfWqjhPTLQL92gKiLlDrc6YwggRvw2cBVwG/JAW738x3RjxaOTKZDLBJjNHS8oIVJo+u4dHGkAdDx7Z10DG59F9dUzb49YLcpOSWcXjtWtStCdUyk2PiCIRUSQGaodd51pDYWBApVAY5rw2nUQ6zq3n57h/V5XBusMnr+xmoGozL6UxLw4qTSRnO42d7wJJRtIXEFnwz9SGfk/eGJnFGbUf5U1nvJa4NvLQ5GSZT1wmga8jKQlk0rBiPl7hu9R2fQtfkrHa3sWXN13AUEPmg5d0smhRErX+fayBO0bGRFR/hZT6S25cniXrNqnefRey6+A1GhR/8IMRwm3dSu/f/i3tt90GgBSJYO3ciTZ/Pl6zyfAdd9DxoQ8x75/+Cd/zqMkWj7OTx8zneVf61ZyqLaFNSvLpjg/ypPk8C7UuGp55aM/EMdf2kH40vKXB8UAQW+iIw5Pdw4Rs9bwaGIkRfwb8TJKkFZyEIcPTJeJEtUPhfkw2KyaMmCaRj6kMHmofOqU9gu+Bpip0xFW2DTZZN87qONZlzag2ly+J0lf3UGSJB3bXSOoSr1qdZGjvVvaUS+TzeZYvX35oOrnPSxaqXDA/j6FKGCosTo5YBM3fh2duxq0/jW/vx0MFt4lql4lmzoXGBmIRCb39Unw9gu8fakuyn4Xef8Z36+idt+IZ61GkKs3ar4lGDZouFAfv47TuC/neMw737yhy1UKLNs2H6MX41YdRvH20ZaMk/DRSqcRwex6pXsM3TXBd3P5+PNMkfc01yMkk9Q0bqD30ENHTT0ebP5/sG9+IpGkobW0c/OQ/knv724kuXUZbrkmt/BSfLn6b1yeuwPJsuuU87bE2kGCXtZ/T/SOn+Kmqiuu6RKPRY95ufOz7iSYBodQRVlHUlCfaTuBEQ5UkyQCavu9vn/TsGUZYWTPVTFatVjviuEhdH42ADU+h0PTRZIn2iAf4ZHWPP1+f57nBJp0JjVRE4d337KNojrimn7t2AeYeM/xBDFkqe4sW+ZjC/DjIhwTdCcVBURsUi0UuSvpcmFZJaBXS+XmoixcFbxEeQDw2jSRJEubgvchaO4q+AJQ0sldFjnSiGZ0o896H19yBR5SGshQDiZEZ2Rb24DdBThFpuwHf2oMWWYyndKFEFuJaO4nIOonESgYHI0QMl7MXxemMHlAPLAAAIABJREFUP4k9+H1UScHofht29Wlc16ZQPEgykyf39rcxfMc3AEhecQWebeM3GtQ3bKC5ezeJSy7B3LyZyKpVDHzuc3i1GmouR+dHPkL3R/6W4T1b+U7sIeKWgYTEvdX7OWAP8Fdtb2ePc5D5ap52uY1lxjySxBgPIrkyEz2gY2e3Oo4TJG3EyJbwOMxWQgXafN8/IEmS7Pt+SyXn090RSqgnxiIWix3Vrzd9hf/aVOHBPTUiqsSfXdTOysyIVctHXC5ZMGKNHuuzaIspI61AikTDcrEOWWBZllHcLcRK9zNf7uHBF06DhZ3k5Qb9/f0MDAygqir5fJ557blRD06YfGPh+z6FQoG+vj5c12X1opUoigWSjLHg4+BbyNFTcSoP4CVfzb271vCzbSWSRp3/v73zjpOqPPv+95zpfXdm+7KwgDQXEaVIEysGQRGsKIqKvuZJjI/GEjUxiiWSNxLb46PmBQlgRMUkYkkQFogsJRakBGFBlg7bZ9uUnXru94/Zc5xtsOCyiya/z2c+Oztzyj33ua/76td172grOTYAHZLOjdExktChB4EYMdtIzD3moE+5HJ1lAEh6etjGEasyMu1MIwWpXiKHXkBEy5BkEyJ6GHP2/yHq/QsZ0ngatx8l2hDA8aMfEfr6axoKC/HccQe+4mIyHnqI6NGjyE4nnh//GP+nnyIiEZT6esLV1QljjcmIpaAfHwc+5HCsnCc8d/EX/xocOiu7Ivux62w86Z1Hpt7NL1JvxSW1nQ57MhFXx0KyXqn2tIRvK5ZXVFS02VPlVEMP+AG6mgihOUfsiHNencDkKBlbk6/sWNzQF4ENBxOcNBwTrNnnZ+BwZ6swqFynAacxkcdnN8hk2XXsV7PUxR78+2aihGtxGHMZk/Y4JdUWfIFDuFwuBg8enCA4WcKo8yITR5HTEFKC7yWsniEEJhTlW+Krq6vD5XKRmZlJZpqeuHc10ZpV6F0T0DnGoYT3Ey79HUrkMCbLmZR4swnHIRyIUbjXz+1DnSgKGNJnEq/9K5IkIxl6osR8CCVA3L+RaM2HgIIxrZq7R/4XAiNyvJpYU6cFoQQQcT+x2g+J+3YS+PtR/AfDuC+fhjE3F2N+PrHKSuK1tbinT6fqpZeI7NmDZLWS9tOf4rrySgIbNqBPT0d2OFACAXRmE43xEHVxH9n6dPSSnn6GHvwf1zT2RY7wRt0H7Ise4Uisknn17/PrlDuwtZIROt5n47tCq1rQVL+1q6E/kVzEzobZbD7hrsFq1L16/vGIEMCoA7dFR01j4h75KW3XD82yKPzywsxExWm7ngyzwgFJQicFiTfuRUQrEYogHj6Cg30MzL4UB2doC0Wv12MSXxM+8iwojRjSbkF2TAVJgcCnhGtWIYxnUh0ZTV2DIDMzkwEDBiRiJKlGBNYTrXkbSeciVvd3dNYCwqW/Q0TL0bsuJVL/KT/qPYWdVYld3GNJWBP1VBGpWoTBcR6SoQdCMiDpXEiyDSXwFRIRhBIm1rAeY8qNxPEg6zIx5f6KcNnzyHoXBs904o3FGFLOIVq5BJMzFYRC484d0BjCt2IFKTffjD49ncjBg5jOOgvbyJGES0rQp6eT8fDDhHfvxjx4MNGyMrBYOGoL4YzbGGE6k6Gm/izIeAKjZKBO9qEQRwJSdPaEsaSdZ9fZHPF46Or7qejWEDf1B3fUWKNGR9jtdiKRiFbg93hIMSjcNzadjYcDpFv1DMsxtV01TQgyzXEyzRJCxPD5AoRCIQ4cOEyeJ4zePgpz4HMkfRpmzwUY42FkWa+JnbIUI1a+FJREgHi0+h1k02jikVoaSp5Fr9djdZvolXMxvfJsxIWFUCSG0RAmWv0aOlNPRKQUdD5AB+gw5z2LEj4IOivx6vfpm3MdBZlmchwGxufbAEG84RPivrWIaDnGrP9G0lmRjAMQkgSGdBRfEZI+A9kyECHZ0OND1H9EzP85pqx7iIcOEK37G0Lo0FksuC6/AaUxSs38BdjGjiX41Vco4TCmvDwaN29Gn5uLbcQIKubMQe/x4Fu+nMxf/xr/ypXEystJvesuagkQTQ1yV+RqyuNeZlf+gWc8P8FQLRhq6ccTqXexoP4DDIqOm82XIwcFwtKa+3U1YaglUbpcR5QkaagQYmt3hrp1VEdM7imh9iXsCIQQZFniXDvQ2mSqbpv7CiHw+/1UVlbi9Xo1n2RGVj56gxV0OowZtyOb+hKR+mFqkUiLpEcyZBONbiYajaDIDmR/GKdVSRSmSr0SveUM4oH16KxnIUXKMRsHEI0YEI3foDP1wuCeRsy/GUPqFcQbd0M8BKY+oNRj6fEYRkMmPx+tR0aQ0CYk1MBUJbSbcGgv5vz/Ja7LI141F8U8Cn3WGUgiiCHlSkRkF0rkCNGqBSihPcTqV2Du8RQiGkZvHYjQOdD3SEX4BEooROO2bVgKCggVFxPcvJmG5ctJueYalHAYY14e8fp6IocPo/h8hDNcWC8ey89ZhBJXSG1w8XlwO/FgolFrgyOAy5SG02AiS/LQ392TaDRKtKqR3dW7CQQC2Gw2LQrI4XB0mWiqojs5Ytd7L1ugoxyxIz0ljoW27iFEogtxZWUlNTU1WK1WrREqwNatWymv8RE3Z5NmzyDFBIpkRUras4QQ1NXVUVFRQaZnAhanDpvsx5R2HXFdH3TUIaVehWzKIXz0KYTSAEocc94zSNG9yPphyCmTiXiXoLePxNxjNrHGb4jX/Bmd+yZ0pp6Ey14mHvgXpqx7UaRcBKAjACKKlDKZeGgPSrgUo+caFCnRmi2uy2H/vj+S67IglAgm/RmJROVoGSAhGTIRsWpkYy6Y+hAu+z166/0ECtdiu3AC5kGDaPjoI0wDBuC5+25iFRVIskzNggWk3XsvktlM/MABjH36ILvd9PjJvdRlWjgS/TsmycAk2zj2xY8Q0DVylr4v1pCRXd/sIhqNYrfbcblcOJ1OXGlpZGZmJqzGoRB1dXUcOnQIn89HMBhk//79eDyeTnFhHA/J/S27ElLTjtOexfSUc8hhw4bx4YcfcvToUQYNGnSqbwckCKehoYHKykpqa2ux2WxaWlFyeXlZlomi470dPlburkWJxZg1Io0+uhrM5kSNz9raWmprazWDS3LMYzLh6+UwivcPRGveRQntBxHG3Ot/kM19AAlkO0gyQrIiKfWES59DMuZgTL8T/4FHUWK+RBU513jkjEeQ42VEK15EidVg8Mwgph9KLNZIJGbGbHGg1+sJxeqoq/wIt1SC2TMVWWdGhEuQdS5Cpc+C4kefeiU68yBCZXMxOC9CCt9IxdPPkf2739P45SaEoiA7nUS9Xuznnotv9WrCe/bguPxydHY70bIydE4n4bKj1H/8MfYLL+Lw9aP4lzjAhZZhyMiERJgUyY5TsWnz4vP5qKtLRPc0NjZisVgSROlyad2zZFnmiy++oHfv3lqLACFEm+F5nYWNGzcyevTok+qQ3AG0y9r1kiTZhRAdT+w7BfiujWg6AiGEFm9ZU1OjtQDv1atXK+JLdjU0hCQ+PxrSHvgXpTH69jFRUlKilaQ/a1AaFpOMZNC3+1ukWAWyMQeQQYTROUYhmXoSrVpMpO5jhGTH2nMOsnUY4eolSOYBiHiQeHAXsmzElHELOmMqkmwDWSZWtZR44w6QbaDUoY9+iU6XTlzO1UR3WU7lINPJSC8nVvksIrQPg/sqFKMOU/aDSDo7QgkSPjoHSZeC4rOhs9uxX3Qp0YpKjGeeSWT3bkLbt2MdOZLA5s2Ejxwh5aabUHw+ZJcLgywjwmGqF7xBpUcmveRrBsWupsDWH6Ek7eOieQs9l8uFy+WiV69eCCEIBoPU1dVRVlam1c5VHe1ms5mUlBT69OmTeCZNRHn48GEikQgOh0MTZ20223cWZbsrQ38KsKQ7/IjQvLFJZ0MVGauqqqitrdWaxxyP+JJh0kGOTWZHmY9YLEbPnmmkpTrJ73F+wtoZLyFw6Nc0hqqQXZOpjv4Inz+RDeB2uzUCVmJlROs/xZTzSyTZgohVg5CIBT5PyB3CR8y3DpNtGGAk2rARWZ8KKZdi7fU0kaPPEA5uRWcbiskyEIlwQu9LvYZo5TwQYRQhY868H2G4FCIHEOEAg2xpiIblCYONJBOpXICl1/PEfOuJ1n2EIWUSoIfa2/C+9Sdck86l/u/LsV18CSIYpP6DD3BccgmV//f/YurbF+uwYXhffRVDXh728ePxrVqF6+qribgs+OI12M4bRJ01TmoLc4NaiFjV+ZxOp8Zx1A0tGo3S0NCAJEmkpqZqLe8OHjwIJMIbnU4nTqeTHj160LNnT61han19PSUlJQQCAS0SJzU1FZfLdVI6X3c49McBSzgG2zyVsFgsWqn0zoCaxV5VVaX56NLS0sjPz+8w8SXrfA0NDdxccCa7cq04rUaGZJqw6xNjlWWZmPdv6PChM5shvJo+va7B63NRW1vLzp07CYfDOJ1OBvbNhHgdoUMPonNcjCn7PoSIIRl6Q7QGRBy97WziOJFS70JnPAOTrR9R71+Q7OeihHYglCCx+jUY3FuRbecgBTYniDVegxKrBySkcDFx4cR3+CmMekFK9o8R6IjqXIjIUWRjT2RTDxTvQSTZic51KcTOoerVhcipvZEdLjJ/+SixigoamzIt0OtJmTaN8MGD+Favxnz22ZgGDQK9Hr3bTaCHm/jj95Alw8aUcibLMVpWkFSjVtTS/yrXDgaDlJWVUVVVhd1uJzs7m4EDB2qlMv1+PwaDQWvN4Pf7KS0tJRaLaQnfLpcLj8eD2+3WiknV1dVRWlpKcXGx1gtDFWe7w2F/POhJVPnuNqiE+F0SMRVFoba2VstiT0lJIS0tTRNloGPEV19fT3l5ucY9MzMz6d+/P7Is0ztDbdiiNDtHMuZ8exHZDrJJE7vy8/M1fais2kde5hMojZsBqN/3OJb0aZh6PofiW4tsykM29yPs/xKMfZCc1xOuegbRuBODayxK+AiSMRehhJBkMxHvR+icFyMZeyFbR6D4PwMRwZAyATm4HdnVGyVymIj3Taz5L6OED0PkAAb31QgpBWPGj1FCu4k1bEBvuRJDr6GYew3E+4fXSb35FmreeAPZbCZaUYH9wgupnjcP58SJWIYPxzFhAqHiYiIHD+L40Y+ocpp4R3xDTbyB6x0TSBcpreZXdQmojW7Ky8upqqpClmWysrIYPHiwljGjbpjJ56jEpPZPUee1traWAwcOAGjNclTidLlcGtHV19dTU1PDvn37iMfjuFwujThV4u3OVgp6YH3Te1mSJKWrXRgn24hGJb7KyspE74XUVDIyMpq1g+so8VVUVFBTU6MRX9++fbXmK+p56rQk6zl6vR69YwKICCJ8CL17KjGaN9qRZVlbFJIURRc7SKjqHWRTL/zKEIq/qiYj4yJ6ZtTTuPvHgIKkS8Xa+0XiKIi4l5jvn5h6JnQ8yXQG6NyEQvVY7W7iwW2YMu/AmHY9kiGTWMM/iAe+RO8YTazRjYhWomBDl/kokggRlxxIogIlcpBY7cfoU64k2jAX1+Q7qHpuPqaCAmSzGclgIOWGG2hYvpzAunWk33sv1a++So9XXiG4eTPmgQMpf+MNJJuN9D5T+YVzJhFiOLAii9bCldpPoqqqCkiEJfbs2VNrYlRXVwckxE+73a65plTRvmW8qTqvNptNa5MAidaBlZWVms/ZZDJht9ux2Wzk5eXRu3dvAHw+H/X19RQXFycamzaV7W+ZidFV0APvAwiRiHfqan/iiYim8XhcIz6fz4fb7SYrK4szzvg2cl8tmXiixNevXz8t1E7VZYQQWK1WbRG0peegT0FOuQWEINYOsapQhAHJdjEW6wiEZMIkzAwbpiSyxwOfEo2GUBSBTldJLOLFnHEb4fAu4r716BwXoE+fSCDYSKBOxp37M+INHyAkCcWQhYjVQKyOSPlLSHoP8WAxlp5zwZBLFHeT/dvUpI9GId6IiFWhhHZC3IdkiWMeOhwJicjBg9gvugjvokVE9+5Fl5FB7OhRnFdeSdzvp3bBAtIfeADJZEJ2J6oXWIQJSwtPmGqdLisro7a2Fo/HQ25ubjMRVX32ap6pWqdWdSGoz8HlcrXiWOrziMfjhEIhDAYDdrudjIwMLZjb7/fj8/moqqrSCE6t3OByuUhJSdEy99UW5Rs3buSss84iPT39uGuys6AX37ZkuwZIF0K83mV35/j9L+LxODU1NVRWVhIIBHC73eTk5GCz2ZoVmv0uxNfyfmqemtrcRq/X43Q629VzkjeRYxklEmORiePSHENqvKzONJJ47V9BhMCQQzRuRR+tA9MAJHMBjUeew5T9M4RpCilpRmRsCGMPJElHPLiZaO1H6B0XYEy7jWjdMuI4iOEiGs+mpcQldKnIpnyMmT8jUvcJxvTbCO+J4px4OUd+9jMkIch88knkoiIM+fnEa2pQQiFs551HvKaGtHvvJVJainPaNEznnNOKgzQ2NlJWVkZlZSU2m42cnBwGDBigVQtoaGjQGtsYjUZNIlLnTO0+ldzLor6+Hrfb3Wwu1eehWqrVeGVV/JVlGYvFoiUFqPdJ5sBqCUaz2ay1XDj33HNPqF5uZ0AvSdIDQAGJqt8fd+nd+TbwOxnxeByv10tlZSXBYBC3201ubq5WoQu+O+dLRjIHAzSdVRVP1cWQrLO0V4qjPWI91r0AzOYzsPd5iXjUS4w0vtkXoG+eHtn/JSj1SLKFmJTDtm3bsFqtZGamk+k6Dyl+lFDFy4jwIeLEkTP+CyFnIFtGEY67ESLeipPEhRMp7kfWp2NKv5VYKAJhE5GDBzH26EG8tpb6ZctwTp5M/XvvgSThuuoq/OvX47z8cuTUVEweDyS5aqLRKBUVFZSXlyNJEtnZ2QwfPrzVvfV6PTabjUgkgtFo1FqeRyIRotGo1ndEreyndi5WFEVbJ4qiYDabtWcTCAS0zlCRSERrqab2gmxsbCQcDmt6plrGU6/X09jYqBmAvF4vb731Flu3buX666/nhRdeOMbK7VzogWHAfuB9IcSmLrtzE9Q+BiaTiYqKCiorK2lsbMTj8ZCXl9esMpdaAqO9qs4dETvVh6wukJYcLLk1t9pfQY3iSeaMLcXOZCQTcTKxthR7Y7FYsz58gUCIaNSI2RwhJycHs8OKzvQ0sfpV6OwjwTqUYcPMmpHC69UxKN+DpEsBk4QSDyAbe6Hv8TK+oI5Gn4Tb3XqzEEKgswwhWvk6RMuQIz+m+tUXkQxmXJMnE967F9lqxTJyJJbhwxNJwT4f1pQUsFgQKSkIQInF8Hq9lJWVEQwGyczMZPDgwc2c7C3F9OTy+C2fRXKSuBpHXF9fr4mvtbW1mrSi1+tJT0/HZDLh9/vbrOAej8dpbGzUErltNht6vR6DwUAkEiEQCKAoCmVlZSxYsICqqiouu+wyJkyY0OVNaCQS4mhVO9+fUl2xoaGB2267jdraWu677z7y8/NJS0vDbDZri/NkiE/tVJV8fHsiY7KJPBqNYrFYCAaD2gNXDQftcbVkgm5sbCQQCGjFnlwuV7M8yeR7BQIBrRCW+tCtVqsWwme327GaowjfSpTIESS9B53zCmI0r7Up4yNW8y6R+vXoLIMJma+jtCKxsal+tLagI4iMDyQ9oS2HqH7xpUTrYlkm/f778X3yCaGSEkQ8Tsbs2YStViSTCclgwO/3a75Zt9tNdnY2Doejle+trTlXC0Or82232zWx0e/3I8sygUAAg8GgdSpWK/ap4qvKCW02G7Is4/f7tc1PhUqsavEoVYpRo6lkWaa4uJgPP/yQQYMGMWvWLAYPHnyq/YftR9YIIaq6q5ziz3/+c+rr65k4cSI2m42MjAxMJtNJE19bYqeK9kTG9sRNVfdsiwhbGWxIFCUKBoOaTtlW0WRVBKutrdWMC+rLaDRqlkWVk0rRfYSrFmjny6Z8MI1tsRE40HtuxeC+BiHZICKRltZAQ0MD+/btIxQK4XQ6NVO9xWJBJzUQr5pH2PcPMPbBmP4LZJOJ8L59mAsKkC0WGnfv/nbgwSBRl4uqigqNSOx2e1Lpj7bR1py3N9/JnwMacarHqFW71Ro2QLNNL7ltg6IoGI1GrRyG1+vF7/cTiUTYtWsXCxcuJCUlhdtvv50PP/ywy9t0twW1P2K3ZF288cYbzJgxg6VLlxKNRvF6vVrMp9vtJjU1tZn7IJn4HA5HuzpfW2hvAbQUN1VOqO6iyTqeyvWSW0arxKaKooqiEI1GURSFYDCoVQYrKyujpqYGo9GoWQ9V3Ubd5VuKUK3rKUnNOiV/y9kNQCoIMBjA4/Hg8Xi0cami7K5duwiFQpxbYCVWvQK9XofeMJzGr7bjnDQpEbKWnY3scGAbPZrApk0YBg/Gq9dzaM8eMjIycDqd2m9XCaI9KzE0F9PV/202W5t+Y7PZrH2uGmvUOVGNOKpOrV5X/Vyn02mEqX7v8/nw+/2EQiGtoe51113H0qVLyc3NPe6a6UpIx6HBEyLQmpoa7rjjDlauXElaWhpz5szhpptuOuY58XicHTt2sGbNGlatWsWhQ4c4++yzGTp0KH379sXn89GjRw+i0SgpKSlkZma2Ejs7imMtmORj2hNh6+vrNQOCyWTSiBgSHDEUCmmL02w2EwgEqK1NdJByu91awVv1mup4VF2mpbimk/wodX8h5luPzjoEvWcmDQGDJtqp3XJV8bcjv09RFOT4Qfx77yYWDeK0zKb8geeJVVSiz8zEOXkycZ8Py+WXEzSbESYT5qZq6e1JA23NV7JrQYhEmzPVNaGuOZWb2Ww2TW9UjS3wbayp+n9bkKRES3G3243X6yUYDGqb5WeffcaiRYsYOnQos2bN4vzzz++WFKfk4bb7RWcS4o033oiiKLzxxhts3bqVyZMns3HjRgoKCjp8jWg0ymeffcajjz7Kv/71LwYOHMjEiRPJzs6md+/e2O123G43Ho/nlPWwa8vAUFNTo5m/VX+U2+1uRgTBYJDa2lqtOY7FYiErKwudTtemXpR8v/YWs0QEvRxC0tmJRGXq6upobEy0rzabzZpJ/3huk+R7IWKYxA5idX8D/yQqZ7+E4vMTq6zEdf31hEpLiel0ZDz0EPrU1Gbnh8PhZhbPljp2S51P/dxoNBIMBjUXhtrbULWAqvryibgNVJFUlmUtUMDr9bJkyRJKS0u59dZbueGGG7q8WPAxcOoJMRAIkJqaytdff03//v0BuOWWW8jNzeW3v/1th0eq4rPPPuOcc84hHA6zdu1aCgsL2bBhAy6Xi+HDh1NQUEBOTg4Oh0OLMzxVXV7D4bDWLjoWi2k6nbrY1eJRoVCItLQ0LaFYp9ORkpII9zoekSQTv+qgVsUzRVFwuVzodDrNBxeLxTAajaSlpWkcu6GhQdORVBEy+dqAxnn0ej1Wi4VQXR2RlSuJbdiAzmjEOXkyVe++i5yVBbfdxoHqak3HdDqdmq6mcrLk5qGq2GmxWDTfH3zL2dRShcnWTUDjtifSt14l6FAoRDAY5G9/+xsrVqxg4sSJzJo1i0GDBnVLFsVxcOoJccuWLYwdO7bZjjZ37lzWrl3LRx991NHLHBdHjx5l1apVrFq1iq1bt9KnTx+GDRvGwIEDSUtLw+Vy4Xa7tQDgzoDKbWKxWKKsvdFIJBKhvr4ev99PWloaNpsNk8mk9fNoySFUV0Wydc9sNreqPhcOh6murm4WWAAJHdfj8WhGh2R3isqBqqurNSJJS0vT7qtuAioxhkIhfD4fkUiE9PR0ctLS0DU2QjBIzeLFiEiE1Jkzkfv00XSt2tpa6uvrNf+c6uZRW2arhJSss6mRM8nz2Bkpbyrh79+/nwULFmCxWJg1axaTJ08+LQO6k9C+1bSz7uD3+1uJAGocYWciNzeXW2+9lVtvvRUhBDt37qSwsJDFixdz+PBhzjrrLIYOHUr//v21ndzj8Wgc5WSg1+txOBzU1dUlOi/5/TgcDvLy8jCZTJolUd3R23P6B4NBzUCjQt08AM3Hplpe1eupVj01WFkliGQ/ZbJFUlEUjehUglZFxWg0qlUh8Hg8mlgbtVrRO524//u/EwYnkwk5GtWa+bhcid4adXV1GidSexBaLJZmKU2KomgWy5bV+U6WCFWiDgQCvPvuu2zatIkbbriBxYsXk52dfVLXPJ3QaYRot9u1hE4VDQ0Np7SrjiRJFBQUUFBQwH333Uc0GuXzzz+nsLCQ5557jnA4zLBhwxgyZAi9e/fWAoTdbnebfq+WUMtolJeX4/V6cTqdZGVlaWk6gBb5kRyyZTQaWxlNkvstJMPr9WpirOoPU2tsqhZDVS9VrY9ut7uVDhsMBlulF6nRLmpmgdlsJi8vr1mAQkvdUjOcNAUaqJur2iZdDQmzWq0YDAaN06rjVMfaGW3N1A1FURQ2btzI/PnzGTFiBLfffjuvv/56pxteXnnlFRYuXMj27du58cYbWbhw4XHPueSSS1izZg3Rpk3rZNFphNi/f39isRh79uyhX79+AGzbtu2EDDXfFQaDgXHjxjFu3DiefPJJGhoaNP1y/vz5OJ1Ohg8fzllnnaU5oVXDT7J+GQgEtDQdq9WqBZa39eDbi7Zp+VDa48aqW0YlZoPBoBGg0WjUFra6KajW42SRVhVhLRYLgUCAcDhMTU0NkiTh8Xg0S6UaFtjy3JZB2Or/4XBYE2HVSCA1SF/NblAJXyW85DlK5n4nQjTxeKJHpN/vp6SkhDVr1nDZZZexYcOGU7qx5+Tk8Nhjj7FixYrjtm0DeOutt05Irz0WOtVqOn36dCRJYv78+WzdupVJkyadsNX0VKK0tJTCwsJW+uWgQYOIRqOkpaVpPsScnBzS09M7NUctHA7j9XpbPWSV+FTrohpBoubJwbemfrXMhNVq1TisoihUVlZqkSlms1lLmq2trdV0KkCrz6P+Lp/Ppxl5TCaTxhHVuE/1vOTg9pZdmoBWf9vCsQgzGo1q4WiZjTQ2AAAVGUlEQVRr167l448/5sorr2Tq1KlawHhX4bHHHuPIkSPH5Ij19fWMGDGCxYsXM3r06I5yxFOvIwK8+uqrzJo1Swsxe+21104bIoTEjpesXxYXF/PCCy/w8ssv43A4mDJlCvn5+fTt21cLxXK73aSkpHRKZS+TyaSl6KiLTqfTaWZ99aXGaqpcRl34kCAIv9+PEIKamhqNA9rtdrKyshKRMy2C15MNJMkGlEAggNfr1cZntVo1zq66SNT7qtdRv0+OclE59vHQFrdUFIX6+no+/fRT/vrXv3LGGWcwa9YsHnrooW5N1D0efvnLX/KTn/yErKysTrlep3LE7yO2b9+O2+0mIyODL774gsLCQlavXk04HGb48OEMGTKEXr16af5Lt9uN0+nstB1aLeugLszkpNjGxkZ8Pl8zzqRaI1XrrRpYkJwS5nK5tIB1NZsh2YmuiuFq404Vqs6XrOudiqAr1YAUCARYs2YNX375pabX3nPPPUyZMqXT73kiOB5H3LRpE3feeSebNm3iyJEj9O7d+ztzxH97QmwPDQ0NFBUVUVhYyPr163E6nYwYMULzXyYHFlgslu9EmO1FxMRiMS0bBdAIRC0JAbSZVK0ajNTqB4B2DTULJTlmsyugituKorBt2zbmz5/P4MGDmTVrFiNGjOjuiJdmOBYhKorCqFGjeO6557jgggs4cOBApxBil/H+jlqkFi5cyB133NHMePLxxx9z4YUXds1Am+B0Orniiiu44oorgIR+uWrVKgoLC1vplyqXVC2yJxpE3FYNzXg8TnV1NdXV1VoAs9puQM3BjEajmgEmmbOpeqVqjVVLDCaH4HVVeLF6v2AwSFFREVu3bmXixIkUFhY2S3H7vqChoUFzncC36kOPHj147733OP/880/qul1GiCdikRo9ejTr168/5jFdjZycHGbOnMnMmTM1/bKwsJBFixZx+PBhhgwZovkv7XZ7s8D1juqXQiS6RKm1PdPT0+nbty96vb5ZVkayDuj3+1uFhen1ei24QPXxqTGbqkjYVp/JzoLK/YQQHDlyhBUrVpCRkcFNN93EL37xi9Mx4gX4tjWbKjqHQqFWm6TL5aK0tFT7//Dhw4wcOZKvvvrqO5XW6HLR9Hjy98KFC5k/f/5pR4jHQjQa1fTLNWvWEAqFNP0yPz8fq9WqibEOh6OVGBYIBLSSgk6nk5ycHFJSUjq0YNWMEJ/Pp/ndVOJrL940GAxqsbOdhWTDTlVVFf/4xz+ora1lypQpXHrppae14UXF7NmzefLJJ5t99sQTTzBr1izOPPNMdu7cSc+ePZt931mi6WlJiHfffbcWUX/LLbfw6KOPfi8epAqfz6f5L1X9cuTIkRQUFJCVlaW5FhRFoaamBoPBQHZ2Nunp6SdtnW0ZLHC8DJOqqqpOqcuSbP2sra1l2bJl9OnTh5tvvpm0tLTvfP0fGLpfR+woxo8fz9dff02vXr3YsWMHN9xwA3q9nkcffbS7h9ZhOByONvXLFStWsG7dOkwmE2PGjOGqq67C4XBohbBUJ/nJ4ER6Nej1eux2e7Og8hNB8vHhcJjPPvuMUCjExRdfzEsvvXTaip6nM047jtgS77zzDs899xxfffVVZw+ly1FSUsL8+fMZM2YM+/fvb5V/2a9fP+x2OykpKZp+eaokATW1S3VzdIQY1WPi8TgHDx5k+/btDBgwgClTppyyzJcfGL4/HLElTpUvqztwxhlnNEsJu/fee5vpl88991wr/VIV0T0eD06ns9PM/Hq9HrfbTSQSobGxkfr6+jbnWXWZCCG0zJe8vDyuvvpqpk6d+h/u10noMueNGrGfbJFqGZkPsHz5cioqKgDYtWsXTz/9NFdddVWH7/PKK68wfPhwTCYTt9122zGPfeGFF8jKysLpdDJr1qxONV50FAaDgbFjxzJ79myKiopYvXo1kyZNori4mF/96lfMnj2bN998k9WrV7Nx40a2bNnCoUOHtOia7wI1eFtthaZG5ailC8PhMLt27eLJJ5/kf//3fzGbzTz//PM8+OCD9OnTp1OJsKamhmnTpmGz2ejVqxdLlixp87jLL78cu92uvYxGI2eddVanjaMrIEnSmZIkXdz0Xpaaqnof65xOY0UdtUg9+OCDvPnmm/j9fjIzM7n55pv59a9/3eE8s7/+9a/Isqy5SdoTgVesWMHMmTNZs2YNOTk5TJs2jVGjRp1UEvOphKpfrlq1ii1bttCnTx+GDx/OmWeeSUpKiha4ntx56kQRi8U0q60sy3i9Xt555x327t3LjBkzmDFjBu6mit6nCidb3eHCCy/k4osv5vHHHz+l4+skSJIk6YB3gdeEEKu1L36okTXH00Vvuukm8vPzefbZZwFYvXo1M2bMoLy8vAtHeWIQQrBr1y5Wrlyp6ZdDhgzhnHPOoX///litVlJSUvB4PMfVL1WfZWlpKX6/H51Ox/Lly/n444+56KKLmDVrFkOHDu0S0fNkqzscOHCAvn37snfvXvLz80/5OL8rJEnSCSEUSZJWAvOFEEslSbIBN532OuKpwo4dO5qJvGeffTYVFRV4vV6tAtrpBkmSGDRoEIMGDdL0yy+//JKVK1fyu9/9rpl+2bt3b62mjdvt1voEhkIhSktLqaysxGw2U1xczMKFC7Fardx+++3Mnj2707vwHg/ffPMNer1eI0JIPI+1a9ce87zFixdz/vnnfy+IEKCJCNNIFPRWRbwocN6/LSH6/f5mxXfV9z6f77QlxJYwGAyMGTOGMWPGMHv2bHw+H0VFRaxcuZJ58+Zp/ssBAwZQUVHB2WefTSAQoLGxkTVr1rBlyxauueYa3nrrLfLy8rrN8HKy1R0WL17MY489diqH1mH4fD6tOrnqEmrLFSWEqJYkKR/4vIlDRiRJ8v7bEmLLigLq+1OZeHqq4XA4mDx5MpMnTwagrKyMu+++m3nz5tGvXz+Kioqoq6ujpqaGaDTKyy+/rPk6uxMnU91h/fr1lJeXc+21157q4R0XRUVF3HXXXXzwwQda7mRbRNhEeHFgHzBZCKFWj157+oS8dzEKCgrYtm2b9v+2bdu0mqk/FGRnZzNnzhwqKyv57LPPuPfee3n33XfZvXs3JSUlTJgwobuHCDSv7qDieNUdFi1axNVXX43dbu+KIR4T48ePRwjBpk2bNF/rRx99xD333ENlZWXyoarN5f8Bl0mS1Lvp/1U/OELsqJtk5syZvPHGG+zcuZO6ujqeeeaZ47o7vo8YMGAAer0eSZKYOHGiFiupZuSfDrDZbFx99dU8/vjjBAIBNmzYwAcffMAtt9zS5vGNjY0sXbq0W59XcnEugIkTJ/L+++9rBaWXLl3KkCFDyMjI0M5p0hElIcQWYBswU5KkFEBowbrtvL53eOKJJwRN7TjV1xNPPCEOHjwobDabOHjwoHbs73//e5GRkSEcDoe47bbbRCgUOuH7eb1eMXXqVGG1WkXPnj3FW2+91e649Hq9sNls2mvv3r0n/Tt/aPB6veKqq64SVqtV5OXlafNYVFQkbDZbs2OXLFkievbsKRRF6fJxxuPxNv/ftm2b6N27t9iyZYvYsWOHGDp0qKiqqmp5OoCu6e8VwNvAXUKIH677oqvQUf/X7NmzKSkp4U9/+lM3jfQ/6EzMmzePTz/9lMsuu4ypU6ficrkYM2YMl19+ORs3bmTChAncf//9WomRJkOYZg2TJEkGLgAWAFO6VTRVK4h9XxEIBPjLX/7C008/jd1uZ9y4cUyZMoU333yzu4f2H3xHtBd/u3btWoYNG8a8efPo06cPv/nNb3jggQcAuOuuu3jllVcoLi6msrKSXbt2abVm24AQQvwDmAtc2uWEuHfvXvbt2wdwrEF+L9Ce/2vHjh1tHv/RRx/hdrspKCjgtdde66ph/gcnAbUKQssGOEIIHnzwQb744guefvpp3n77bT7//HNWrVrFjTfeiNVq5c4776SkpIQZM2bw05/+lKKiImpqaoBEaE2LW/0/IcQLXU6I99xzD2eccQaZmZlMmTKFFStWdHl31s7Cifi/rr/+eoqLi6mqqmLevHk89dRTvP3221011P/gGEhef6qEVlhYyMUXX8y0adP4/e9/rz3TUaNGccMNN7B//37uuusupkyZwp49e/jkk08wmUyMHz+e6upq/vznP/Pmm2+SkpLCgw8+SHFxsXp90eJvVFIzG47x6nQMHDhQLF++XOzatUv85je/EWeffbbYsWPHqbjVKcfmzZuFxWJp9tncuXPFFVdccdxz58yZI66++upTNbT/4DhQFKWV4UXFv/71LzFy5Egxd+5csW7dOnHBBReIRx99VPh8PiGEEPv37xeXXXaZmDFjhqiqqhL/8z//I8455xxx9OhRsXbtWmG328X27dvbunS7tNalHPGLL75ACEGvXr0YMGAADz/8MDabrU3O0Bkl2081Tsb/peKHlN71fURyl+FFixbxxz/+kVAoBMBvf/tbpk2bxgMPPMC4ceOYOnUqixcvprCwEIBVq1Zx4MAB/vSnP5GWlkZpaSm7du1i2bJljB8/nldffVWrdp+cw3ksdCkhrlu3TitDCPDVV19htVq1HLsjR46wdetWoO0S9aebCHsi/q8PPviA2tpahBB88cUXvPzyyyeU3nU8dDSN6D9I4Ouvv2bdunWMHTuWP/zhD8yfP5/rrruOHTt2YLVa8Xq9vPjii/Tp04fnn3+e++67j4kTJwIwdOhQDhw4wFNPPcWoUaNoaGjg+eef58orrwQSAeuqj1Zd28etvHAsdtl5gkACkyZNEmlpaSI1NVWMGDFCnHfeeWL69OmipKREvPbaa2Ly5MmiX79+wuPxiFdeeaXd6yiK0syHtGHDBvHII490i1+po/6v6dOnC7fbLWw2mxgwYIB46aWXOnUc06dPF9dff73w+Xxi3bp1wul0iq+//rpT7/FDwhVXXCE8Ho949tlnhRAJcfPee+8V5513nli4cKGQJEncfPPNYtWqVdo533zzjSaeLl26VNx6663i+eefb1PEbWcttktrXUaIjY2NIj8/X2zatEmEQiGxfPly8fbbb2s/6pJLLhFr164VQgixZcsWMWbMGLF582YhRGICXnzxRbFo0aJWTtJYLCaefPJJMXjwYCFEa4frvwP8fr8wGAxi9+7d2mc333yzePjhh7txVKcW7W26sVjsmBtyLBYTQiQ2ytTUVLF48WLtert27RJOp1O899574rzzzhOffPKJds7jjz8u7rvvPnHkyJF2x9OBtdf9OuK6detwuVxaY82JEycyffp0gsEgf//739m/fz+7d+/m0KFDWh6c2o55z549VFZW8tprrzFw4EDmzp2rha0FAgE2bdrEddddp3F4tbzDvwtO1I3yQ0B7bi/VJVZTU9NmYxxVRDz//PPJycnROjBLkkRGRgbDhw/HYDBw//33M3fuXC677DIyMzP55z//ybXXXktubm6z+6m6n6pzniy6LPvi7bffJjc3V2vaofbsU7vcjho1imXLlvGrX/1KU6KPHj0KwKWXXsqkSZMAtErR06ZNo2/fvpSXl7Nz504eeeQRgOQohn8bdFWT2NMJ+/bto6ioiGuvvbZZ4PeSJUuYM2cOFouFgoICnn322VaNTNX2BlOmTGHZsmWMHj2ac889l7q6OrxeL71792bIkCFMnTqVoqIihg8frrVgb4nOaE4EdJ1ounLlSrFixYpm7FsVIa666irx6quvap9v2bJFvPnmm+Kbb74RkUhELFu2TDz22GNi/fr14sCBA2Ls2LFiw4YNQgghli1bJnr37i2EEMLn84m33npLXHfddaK8vLzNcagixA9JhP0ubpTvK1asWCGys7PF4cOHRVFRkXj//ffF3r17xQUXXCAWLFggKioqxLhx48TMmTM1FUd95uq627dvn/B4PGLEiBHi9ddfFyNGjBDXXHNNmzHHsVhME2u/A9qltePFmnYJJEmaBNxGopbHMsAERIQQMUmS3gNsgBcYCgwEtgIPCCGKJEl6GhgP/AJ4AjACrwDLhRDhFvcxCCFadZZsivtLzFTr7yT41gF7OqKp3EItUCCE2NP02WKgVAjxSLcO7hRBkiQzsAewkoiJvgW4BBgihLis6ZhBwKPAN0KIZ1qcLwkhhCRJHwF1wEHgEIkSFl2v1xyLSrvqBeiA/yaRGlIJfExiUQH4gGuSjp0LLAXyADvwAaAAfwNmJh0ntbiHE3ga+ANwI4nod2MbY5G7ez5Ocg7fIRHNbwPGAvXqHH5fXk3PyKk+v2M9C+AG4J/AZ4Cp6bO7gY0tjnsGeAmwtFxzTX9vBb4EsrpzDXT75LcxwSZgMAn9VQfMAb4CHgLuJLHzz2n6rgA4ALwBPK9OdlsTCfQA/gF8A/wWWA1MBoaT4KajWj6IpoXxRyAv6Ttb0wPXd/dctfh9bhLSRIDEzn5Td4/pBMcvk+BKtwOGNr6Xkv82vXeQyHYf37QeLmtaKwOTjnkDeLvluUnfG4BdwPS27ttVr9MuMVgIERZCfC2EiIlEWYGnSWQ0DwLOBDaTELniwAigAfgv4EfA6KZrtCVa9AbygV8LIR4RQlwCbAIGAGOAJZIkbZUkaVTS+WOaXnVJ10kjUeagdbZxN0IIUSOEmCqEsAkhegohvjce/SaVQQFWABcBMUmS9JIk3SlJ0g3QOkZTkiRZCOED9pLYUAG2kBBXX5ckKU+SpFy+3aC0c5PuqxMJVWU7CbG2++ihu3fCk9g5PUBO0/tXgI+a3t8CbGvnHAm4A1ifvAO3cdy7wEtN728CjpDYpe8BHMcYU6ud9j+vDj1LmeYcbiRQAkwFvgbWk5BgfgO4k+eab0XLGSS4YG7T/7kkxPSNQDXwKmBv7/5Nf1O7ey5OC2PNd4EkSW4hRE1T4daPgZ1CiAdaHJMKPEZiwmclfZ4F3Az4SeiYQ4CHgUlCCL8kSTuB3UBPEpz4IeDHwJ+EEEfbGIssukPR/55DkiQ7MEII8Q9JktaSEDl/KxJ1P6cD1wIfCCHebDnHTYa2L0kQXk8SG+fjQJoQouQExyGJbiKI7z0hQjMLmJEEtzzQ4vOzSBh5/iyEmNf03QXAz0nUlXQB55CwuH4hhJggSVIvEjrlpUKIfU3n5AKHSeiMR5s+uwjwCSE2deFP/t6hrUUuSdItwCggg4Tx7RrgfBJcbIoQYr0kSR7gVySMOHe2OF8WiToww0hIMEYSBHw06RgdCan0tN4g/z9JaKtt/puoOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 273.6x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[3.8,3])\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "for i in [int(x) for x in np.unique(UMAP_and_GMM['gmm_labels'])]:\n",
    "    to_plot_df = UMAP_and_GMM[UMAP_and_GMM['gmm_labels'] == i]\n",
    "    x = to_plot_df['troughToPeak_abs']\n",
    "    y = to_plot_df['prePostHyper']\n",
    "    z = to_plot_df['FWHM1_abs']\n",
    "    ax.scatter(x,y,z,c=GMM_PAL[i-1],marker='o',alpha=0.75,s=20,linewidth=0.75,edgecolor='w',depthshade=True)\n",
    "    \n",
    "    ax.plot(x, z, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='y', zs=1.6)\n",
    "    ax.plot(y, z, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='x', zs=1.4)\n",
    "    ax.plot(x, y, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='z', zs=0.)\n",
    "\n",
    "ax.tick_params(pad=-1)\n",
    "\n",
    "ax.set_xlabel('Trough to peak ($\\mu$s)',fontsize=12,labelpad=5)\n",
    "ax.set_ylabel('Peak ratio',fontsize=12,labelpad=5)\n",
    "ax.set_zlabel('AP width ($\\mu$s)',fontsize=12,labelpad=0)\n",
    "ax.view_init(elev=20, azim=220)\n",
    "\n",
    "ax.set_xticks([0,0.7,1.4])\n",
    "ax.set_xticklabels(['',0.7,1.4],fontsize=12)\n",
    "ax.set_yticks([0,0.5,1,1.5])\n",
    "ax.set_yticklabels([0,0.5,1.0,1.5],fontsize=12)\n",
    "ax.set_zticks([0,0.3,0.6])\n",
    "ax.set_zticklabels([0,0.3,0.6],fontsize=12)\n",
    "\n",
    "ax.set_xlim([0,1.4])\n",
    "ax.set_ylim([0,1.6])\n",
    "ax.set_zlim([0.,0.6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, arr = plt.subplots()\n",
    "f.set_size_inches(3., 2.)\n",
    "\n",
    "arr.plot(BIC_list,c='k',\n",
    "            marker='o',fillstyle='full',markerfacecolor='w',linewidth=1,markeredgewidth=1)\n",
    "\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.set_xlabel('# of Clusters', fontsize=12)\n",
    "arr.set_xticks([0,2,4,6,8,9])\n",
    "arr.set_xticklabels([1,3,5,7,9,''],fontsize=12)\n",
    "arr.set_ylabel('BIC', fontsize=12)\n",
    "arr.set_yticks([12500,13000,13500,14000,14500])\n",
    "arr.set_yticklabels(['1.25','1.3','1.35','1.4','1.45'],fontsize=12)\n",
    "arr.spines['left'].set_bounds(12500,14500)\n",
    "arr.spines['bottom'].set_bounds(0,9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   5 | elapsed:    1.0s remaining:    1.5s\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of   5 | elapsed:    1.0s remaining:    0.7s\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:    1.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:    1.4s finished\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.stack(UMAP_and_GMM['waveform'].to_numpy().tolist(), axis=0)\n",
    "y = UMAP_and_GMM['gmm_labels'].to_numpy()\n",
    "\n",
    "unclassified_ixs = [ix for ix,clust in enumerate(y) if clust == -1]\n",
    "\n",
    "X = np.delete(X,unclassified_ixs,axis=0)\n",
    "y = np.delete(y,unclassified_ixs,axis=0)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=RAND_STATE)\n",
    "\n",
    "data_dmatrix = xgb.DMatrix(data=X,label=y)\n",
    "\n",
    "model = xgb.XGBClassifier(objective='multi:softmax')\n",
    "param_dist = {\"max_depth\": [10],\n",
    "              \"min_child_weight\" : [2.5],\n",
    "              \"n_estimators\": [110],\n",
    "              \"learning_rate\": [0.05],\n",
    "              \"seed\": [RAND_STATE]}\n",
    "grid_search = GridSearchCV(model, param_grid=param_dist, \n",
    "                           cv = 5, \n",
    "                           verbose=10, n_jobs=-1)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "confusion_matrix(y_test,grid_search.predict(X_test))\n",
    "\n",
    "confusion_mat_counts = confusion_matrix(y_test,grid_search.predict(X_test))\n",
    "\n",
    "conf_mat_row_list = []\n",
    "\n",
    "for row in confusion_mat_counts:\n",
    "    row_sum = np.sum(row)\n",
    "    row_percent = []\n",
    "    \n",
    "    for val in row:\n",
    "        row_percent.append(val/row_sum)\n",
    "    \n",
    "    conf_mat_row_list.append(row_percent)\n",
    "\n",
    "conf_mat = np.array(conf_mat_row_list)\n",
    "colormap = mpl.cm.YlGnBu\n",
    "colormap.set_under('white')\n",
    "\n",
    "f, arr = plt.subplots()\n",
    "f.set_size_inches(3, 3)\n",
    "plt.tight_layout()\n",
    "mappable = arr.imshow(conf_mat,cmap=colormap,vmin=0.,vmax=1.)\n",
    "color_bar = f.colorbar(mappable, ax=arr)\n",
    "color_bar.set_label('P (Predicted | True)',fontsize=12,labelpad=15)\n",
    "color_bar.ax.tick_params(size=3,labelsize=12)\n",
    "arr.set_xticks([0,1,2,3])\n",
    "arr.set_xticklabels([1,2,3,4],fontsize=12);\n",
    "arr.set_yticks([0,1,2,3])\n",
    "arr.set_yticklabels([1,2,3,4],fontsize=12);\n",
    "arr.set_xlabel('Predicted Class',fontsize=12)\n",
    "arr.set_ylabel('True Class',fontsize=12)\n",
    "\n",
    "for i in range(0,4):\n",
    "    if int(conf_mat[i,i]*100) == 100:\n",
    "        arr.text(i-0.35,i+0.15,int(conf_mat[i,i]*100),fontsize=12,c='white')\n",
    "    else:\n",
    "        arr.text(i-0.25,i+0.15,int(conf_mat[i,i]*100),fontsize=12,c='white')\n",
    "        \n",
    "for i in range(0,4):\n",
    "    for j in range(0,4):\n",
    "        if conf_mat[i,j] < 0.1 and conf_mat[i,j] != 0:\n",
    "            arr.text(j-0.15,i+0.15,int(conf_mat[i,j]*100),fontsize=12,c='k')\n",
    "        elif conf_mat[i,j] >= 0.1 and conf_mat[i,j] <= 0.5:\n",
    "            arr.text(j-0.2,i+0.15,int(conf_mat[i,j]*100),fontsize=12,c='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_data_df = pd.DataFrame({'waveform': full_data.tolist()})\n",
    "data_classified_df = pd.concat([umap_df,full_data_df,GMM_class_df],axis=1)\n",
    "\n",
    "group_ixs = [i for i,x in enumerate(data_classified_df.Class.tolist()) if x == 1]\n",
    "group_waveforms = data_classified_df.iloc[group_ixs]['waveform'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_gmm_group(label_ix, labels, groups_df, colors, f, arr):\n",
    "    group_ixs = [i for i,x in enumerate(labels) if x == label_ix]\n",
    "    group_waveforms = groups_df.iloc[group_ixs]['waveform'].tolist()\n",
    "    \n",
    "    for i,_ in enumerate(group_waveforms):\n",
    "        plt.plot(group_waveforms[i],c=colors[label_ix-1],alpha=0.2,linewidth=1.)\n",
    "        \n",
    "    plt.plot(np.mean(group_waveforms,axis=0),c='k',linewidth=1.)\n",
    "\n",
    "    arr.spines['right'].set_visible(False)\n",
    "    arr.spines['top'].set_visible(False)\n",
    "    arr.spines['bottom'].set_visible(False)\n",
    "    arr.spines['left'].set_visible(False)\n",
    "    \n",
    "    arr.set(xticks=[],yticks=[])\n",
    "\n",
    "        # if not mean_only:\n",
    "            # x,y = 2.1,0.7\n",
    "            # ellipse = mpl.patches.Ellipse((x,y), width=13.0, height=0.92, facecolor='w',\n",
    "            #                      edgecolor='k',linewidth=1.5)\n",
    "            # label = arr.annotate(str(label_ix), xy=(x-0.25, y-0.15),fontsize=12, color = 'k', ha=\"center\")\n",
    "            # arr.add_patch(ellipse)\n",
    "\n",
    "    if i != -1:\n",
    "        x, y = 20,-1.\n",
    "        n_waveforms = plt.text(x, y, \n",
    "                               'n = '+str(len(group_waveforms))+\n",
    "                               ' ('+str(int(len(group_waveforms)/len(groups_df)*100))+'%)'\n",
    "                               , fontsize=10)\n",
    "    \n",
    "    return f, arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "GMM_class_labels = GMM_class_labels[~np.isnan(GMM_class_labels)]\n",
    "GMM_class_df = pd.DataFrame(GMM_class_labels,columns=['Class'])\n",
    "\n",
    "f, arr = plt.subplots(figsize=[7,7])\n",
    "\n",
    "class_labels = [x for x in np.unique(data_classified_df['Class']) if str(x) != 'nan']\n",
    "\n",
    "for ix in np.unique(class_labels):\n",
    "    filt_df = data_classified_df[data_classified_df['Class']==ix]\n",
    "    arr.scatter(filt_df['x'],filt_df['y'], s=30,marker='o', linewidth=0.25, \n",
    "                edgecolors='white', alpha=1, c=GMM_PAL[int(ix-1)])\n",
    "\n",
    "ns = Line2D(xdata=[], ydata=[], marker='o', markerfacecolor=GMM_PAL[0], \n",
    "                color='w', label='Narrow-Spiking (NS)')\n",
    "bs = Line2D(xdata=[], ydata=[], marker='o', markerfacecolor=GMM_PAL[1], \n",
    "                      color='w', label='Broad-Spiking (BS)')\n",
    "nst = Line2D(xdata=[], ydata=[], marker='o', markerfacecolor=GMM_PAL[2], \n",
    "                      color='w', label='Narrow-Spiking Triphasic (NST)')\n",
    "bst = Line2D(xdata=[], ydata=[], marker='o', markerfacecolor=GMM_PAL[3], \n",
    "                      color='w', label='Broad-Spiking Triphasic (BST)')\n",
    "\n",
    "\n",
    "arr.legend(handles=[ns,bs,nst,bst],fontsize=12,frameon=False)\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['bottom'].set_visible(False)\n",
    "arr.spines['left'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.set_xticks([]);\n",
    "arr.set_yticks([]);\n",
    "\n",
    "ax1 = f.add_axes([0.57,0.67,0.17,0.13])\n",
    "plot_gmm_group(1,data_classified_df.Class.tolist(),data_classified_df,GMM_PAL,f,ax1)\n",
    "\n",
    "ax2 = f.add_axes([0.04,0.4,0.17,0.13])\n",
    "plot_gmm_group(2,data_classified_df.Class.tolist(),data_classified_df,GMM_PAL,f,ax2)\n",
    "\n",
    "ax3 = f.add_axes([0.4,0.07,0.17,0.13])\n",
    "plot_gmm_group(3,data_classified_df.Class.tolist(),data_classified_df,GMM_PAL,f,ax3)\n",
    "\n",
    "ax4 = f.add_axes([0.12,0.07,0.17,0.13])\n",
    "plot_gmm_group(4,data_classified_df.Class.tolist(),data_classified_df,GMM_PAL,f,ax4)\n",
    "\n",
    "plt.tight_layout();\n",
    "f.set_size_inches(7, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 4.\n",
    ":::\n",
    "![](elife-67490.ipynb.media/fig4.jpg)\n",
    "\n",
    "### Gaussian mixture model clustering on specified features fails to capture the breadth of waveform diversity.\n",
    "\n",
    "(**A**) The three EAP waveform landmarks used to generate the specified features passed to the GMM on a sample waveform. ![](elife-67490.ipynb.media/elife-67490-inf1-v4.tif)is the pre-hyperpolarization peak (**A1**); ![](elife-67490.ipynb.media/elife-67490-inf2-v4.tif)is the depolarization trough; and ![](elife-67490.ipynb.media/elife-67490-inf3-v4.tif)is the post-hyperpolarization peak (**A2**). (**B**) A three-dimensional scatter plot with marginal distributions of waveforms and GMM classes on the three specified features in (**A**). Narrow-spiking (NS) are in red; broad-spiking (BS) in green; narrow-spiking triphasic (NST) in yellow; and broad-spiking triphasic (BST) types are in blue. Trough to peak was calculated as the time between ![](elife-67490.ipynb.media/elife-67490-inf4-v4.tif)and ![](elife-67490.ipynb.media/elife-67490-inf5-v4.tif); peak ratio was determined as the ratio between the heights of ![](elife-67490.ipynb.media/elife-67490-inf6-v4.tif)and ![](elife-67490.ipynb.media/elife-67490-inf7-v4.tif)(A1/A2); and AP width was determined as the width of the depolarization trough ![](elife-67490.ipynb.media/elife-67490-inf8-v4.tif)using the MLIB toolbox [@bib160]. (**C**) The optimal cluster number in the three-dimensional feature space in (**B**) was determined to be four clusters using the Bayesian information criterion (BIC) [@bib170]. The number of clusters was chosen to be at the ‘elbow’ of the BIC curve (green chevron). (**D**) A confusion matrix for a gradient boosted decision tree classifier with 5-fold cross-validation with hyperparameter optimization. The main diagonal contains the classification accuracy percentages across the four GMM clusters and the off-diagonal contains the misclassification rates. The average accuracy across classes was 78%. (**E**) The same scatter plot of normalized EAP waveforms in UMAP space as in [Figure 3A](#fig3) but now colored by GMM category. [Figure 4—figure supplement 1](#fig4s1): We show that _WaveMAP_ clusterings are more consistent across random data subsets than either DBSCAN on t-SNE or a GMM on PCA. [Figure 4—figure supplement 2](#fig4s2): GMMs fail to full capture the latent structure in the waveforms.\n",
    ":::\n",
    "{#fig4}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x324 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from openTSNE import TSNE\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.mixture import GaussianMixture\n",
    "import cycler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import adjusted_mutual_info_score\n",
    "from scipy.stats import sem\n",
    "\n",
    "tsne = TSNE(\n",
    "    perplexity=30,\n",
    "    metric=\"euclidean\",\n",
    "    n_jobs=-1,\n",
    ")\n",
    "\n",
    "tsne_embed = tsne.fit(full_data)\n",
    "tsne_df = pd.DataFrame(tsne_embed, columns=('x', 'y'))\n",
    "clustering = DBSCAN(eps=3, min_samples=15).fit(tsne_embed)\n",
    "tsne_df['cluster'] = clustering.labels_\n",
    "\n",
    "tsne_colormap = [sns.color_palette(\"husl\", len(set(clustering.labels_)))[i] for i in clustering.labels_]\n",
    "\n",
    "f,arr = plt.subplots(1,figsize=[7,4.5],tight_layout = {'pad': 0});\n",
    "f.tight_layout()\n",
    "\n",
    "arr.scatter(tsne_df['x'].tolist(), tsne_df['y'].tolist(), \n",
    "            marker='o', c=tsne_colormap, s=32, edgecolor='w',\n",
    "            linewidth=0.5)\n",
    "\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['bottom'].set_visible(False)\n",
    "arr.spines['left'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.set_xticks([]);\n",
    "arr.set_yticks([]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_fracs = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]\n",
    "PC_reduce = PCA(n_components=3)#3\n",
    "PCs = PC_reduce.fit_transform(full_data)\n",
    "ground_truth_gmm = GaussianMixture(n_components=4)\n",
    "ground_truth_PCA_classes = ground_truth_gmm.fit_predict(PCs)\n",
    "GMM_on_PCA_df = pd.DataFrame(PCs, columns=('x', 'y', 'z'))\n",
    "GMM_on_PCA_df['waveform'] = list(full_data)\n",
    "GMM_on_PCA_df['truth_ix'] = ground_truth_PCA_classes\n",
    "\n",
    "n = len(data_fracs)\n",
    "for k,frac in enumerate(data_fracs):\n",
    "    \n",
    "    BIC_aggregates = {}\n",
    "    for n_clusts in range(1,11): #11\n",
    "\n",
    "        BIC_scores = []\n",
    "\n",
    "        for _ in range(1,100): #100\n",
    "            PC_reduce = PCA(n_components=3)#3\n",
    "\n",
    "            sample_df = GMM_on_PCA_df.sample(frac=frac).sort_index()\n",
    "            random_rows = np.vstack(sample_df['waveform'].to_numpy())\n",
    "\n",
    "            PCs = PC_reduce.fit_transform(random_rows)\n",
    "\n",
    "            gmm_PCA = GaussianMixture(n_components=n_clusts)\n",
    "            gmm_PCA.fit(PCs)\n",
    "            BIC_scores.append(gmm_PCA.bic(PCs))\n",
    "\n",
    "        BIC_aggregates[n_clusts] = BIC_scores\n",
    "\n",
    "        BIC_means = [np.mean(BIC_aggregates[i]) for i in list(BIC_aggregates.keys())]\n",
    "        BIC_stds = [np.std(BIC_aggregates[i]) for i in list(BIC_aggregates.keys())]\n",
    "\n",
    "AMI_aggregates = {}\n",
    "\n",
    "for k,frac in enumerate(data_fracs):\n",
    "\n",
    "    AMI_scores = []\n",
    "\n",
    "    for _ in range(1,100): #25\n",
    "        PC_reduce = PCA(n_components=3)#3\n",
    "\n",
    "        sample_df = GMM_on_PCA_df.sample(frac=frac).sort_index()\n",
    "        random_rows = np.vstack(sample_df['waveform'].to_numpy())\n",
    "\n",
    "        PCs = PC_reduce.fit_transform(random_rows)\n",
    "\n",
    "        gmm_PCA = GaussianMixture(n_components=4)\n",
    "        gmm_PCA.fit(PCs)\n",
    "        PCA_classes = gmm_PCA.fit_predict(PCs)\n",
    "        truth_classes = sample_df['truth_ix'].tolist()\n",
    "        AMI_scores.append(adjusted_mutual_info_score(PCA_classes,truth_classes))\n",
    "\n",
    "    AMI_aggregates[k] = AMI_scores\n",
    "\n",
    "AMI_means = [np.mean(AMI_aggregates[i]) for i in list(AMI_aggregates.keys())]\n",
    "AMI_sems = [sem(AMI_aggregates[i]) for i in list(AMI_aggregates.keys())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,arr = plt.subplots(1,figsize=[1.5,1.5])\n",
    "arr.errorbar(range(1,11),BIC_means,yerr=BIC_stds,marker='.')\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.spines['left'].set_visible(False)\n",
    "arr.spines['bottom'].set_bounds(1,10)\n",
    "arr.set_xticks([1,2,3,4,5,6,7,8,9,10])\n",
    "arr.set_xticklabels(['','2','','4','','6','','8','','10'])\n",
    "arr.set_xlabel('# of Clusters')\n",
    "arr.set_yticks([])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PCs = PC_reduce.fit_transform(full_data)\n",
    "\n",
    "gmm_PCA = GaussianMixture(n_components=4)\n",
    "gmm_PCA.fit(PCs)\n",
    "\n",
    "PCA_classes = gmm_PCA.fit_predict(PCs)\n",
    "cluster_colors_PCA = [sns.color_palette(\"husl\", len(set(PCA_classes)))[i] for i in PCA_classes]\n",
    "\n",
    "if PCs.shape[1] == 2:\n",
    "    f, ax = plt.subplots(1);\n",
    "    ax.scatter(PCs[:,0], PCs[:,1], cmap=plt.cm.nipy_spectral,\n",
    "            edgecolor='w',c=cluster_colors_PCA,s=25);\n",
    "elif PCs.shape[1] == 3:\n",
    "    fig = plt.figure(1, figsize=(4, 3));\n",
    "    ax = Axes3D(fig, elev=18, azim=54);\n",
    "    ax.scatter(PCs[:, 0], PCs[:, 1], PCs[:, 2], cmap=plt.cm.nipy_spectral,\n",
    "            edgecolor='w',c=cluster_colors_PCA,s=25);\n",
    "\n",
    "    ax.w_xaxis.set_ticklabels([])\n",
    "    ax.w_yaxis.set_ticklabels([])\n",
    "    ax.w_zaxis.set_ticklabels([])\n",
    "\n",
    "    ax.w_xaxis.set_label_text('PC 1', fontsize=12)\n",
    "    ax.w_yaxis.set_label_text('PC 2', fontsize=12)\n",
    "    ax.w_zaxis.set_label_text('PC 3', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#As with the above warnings about simulation code, this next cell takes very long to run and cached versions will be\n",
    "# loaded in the next cell. Uncomment the code below to \n",
    "\n",
    "# tsne = TSNE(\n",
    "#         perplexity=30,\n",
    "#         metric=\"euclidean\",\n",
    "#         n_jobs=-1)\n",
    "\n",
    "# tsne_embed = tsne.fit(full_data)\n",
    "# tsne_df = pd.DataFrame(tsne_embed, columns=('x', 'y'))\n",
    "# clustering = DBSCAN(eps=3, min_samples=15).fit(tsne_embed)\n",
    "# tsne_df['truth_ix'] = clustering.labels_\n",
    "# tsne_df['waveform'] = list(full_data)\n",
    "\n",
    "# tSNE_AMI_aggregates = {}\n",
    "\n",
    "# for k,frac in enumerate(data_fracs):\n",
    "#     tSNE_AMI_scores = []\n",
    "\n",
    "#     for _ in range(1,100):\n",
    "#         tsne = TSNE(\n",
    "#               perplexity=30,\n",
    "#               metric=\"euclidean\",\n",
    "#               n_jobs=-1)\n",
    "\n",
    "#         sample_df = tsne_df.sample(frac=frac).sort_index()\n",
    "#         random_rows = np.vstack(sample_df['waveform'].to_numpy())\n",
    "#         tsne_embed = tsne.fit(random_rows)\n",
    "#         samp_clustering = DBSCAN(eps=3, min_samples=15).fit(tsne_embed)\n",
    "#         truth_classes = sample_df['truth_ix'].tolist()\n",
    "#         tSNE_AMI_scores.append(adjusted_mutual_info_score(samp_clustering.labels_,truth_classes))\n",
    "\n",
    "#     tSNE_AMI_aggregates[k] = tSNE_AMI_scores\n",
    "\n",
    "# tSNE_AMI_means = [np.mean(tSNE_AMI_aggregates[i]) for i in list(tSNE_AMI_aggregates.keys())]\n",
    "# tSNE_AMI_stds = [np.std(tSNE_AMI_aggregates[i]) for i in list(tSNE_AMI_aggregates.keys())]\n",
    "# tSNE_AMI_sems = [sem(tSNE_AMI_aggregates[i]) for i in list(tSNE_AMI_aggregates.keys())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd57d3056d0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if 'AMI_aggregates' not in list(locals().keys()):\n",
    "    AMI_aggregates = pkl.load(open('data/AMI_aggregates.pkl','rb'))\n",
    "\n",
    "if 'AMI_means' not in list(locals().keys()):\n",
    "    AMI_means = pkl.load(open('data/AMI_means.pkl','rb'))\n",
    "\n",
    "if 'AMI_sems' not in list(locals().keys()):\n",
    "    AMI_sems = pkl.load(open('data/AMI_sems.pkl','rb'))\n",
    "\n",
    "\n",
    "if 'tSNE_AMI_aggregates' not in list(locals().keys()):\n",
    "    tSNE_AMI_aggregates = pkl.load(open('data/tSNE_AMI_aggregates.pkl','rb'))\n",
    "\n",
    "if 'tSNE_AMI_means' not in list(locals().keys()):\n",
    "    tSNE_AMI_means = pkl.load(open('data/tSNE_AMI_means.pkl','rb'))\n",
    "\n",
    "if 'tSNE_AMI_sems' not in list(locals().keys()):\n",
    "    tSNE_AMI_sems = pkl.load(open('data/tSNE_AMI_sems.pkl','rb'))\n",
    "    \n",
    "f,arr = plt.subplots(1,figsize=[4,3])\n",
    "wavemap = arr.errorbar(data_fractions[:-1],[np.mean(i) for i in mutual_info_stability[:-1]],\n",
    "             [sem(i) for i in mutual_info_stability[:-1]],\n",
    "             marker='o',markerfacecolor='w',color='dodgerblue',label='WaveMAP')\n",
    "\n",
    "tsne = arr.errorbar(data_fractions[:-1],tSNE_AMI_means[:-1],tSNE_AMI_sems[:-1],\n",
    "             marker='o',markerfacecolor='w',color='green',label='DBSCAN on t-SNE')\n",
    "\n",
    "gmm = arr.errorbar(data_fractions[:-1],AMI_means[:-1],\n",
    "             AMI_sems[:-1],\n",
    "             marker='o',markerfacecolor='w',color='red',label='GMM on PCA')\n",
    "\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.set_xlim([0.0,1.1])\n",
    "arr.set_xticks([0.1,0.2,0.4,0.6,0.8,1.0])\n",
    "arr.set_xticklabels(['','20','40','60','80','100'])\n",
    "arr.set_xlabel('% of Full Dataset',fontsize=12)\n",
    "arr.set_ylim([-.1,1.1])\n",
    "arr.set_yticks([0.0,0.2,0.4,0.6,0.8,1.0])\n",
    "arr.set_ylabel('Adj. Mutual \\nInfo. Score',fontsize=12)\n",
    "arr.spines['bottom'].set_bounds([0.1,1.0])\n",
    "arr.spines['left'].set_bounds([0.0,1.0])\n",
    "plt.tight_layout()\n",
    "arr.legend(loc=4, frameon=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 90x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "AMI_colors = ['#5F9AD3','#4EA34C','#EF403A']\n",
    "AMIPalette = sns.set_palette(sns.color_palette(AMI_colors))\n",
    "\n",
    "data1 = pd.DataFrame(mutual_info_stability[3],columns=['AMI'])\n",
    "data1['DimRed'] = 'WaveMAP'\n",
    "\n",
    "data2 = pd.DataFrame(AMI_aggregates[3],columns=['AMI'])\n",
    "data2['DimRed'] = 'GMMOnPCA'\n",
    "\n",
    "data3 = pd.DataFrame(tSNE_AMI_aggregates[3],columns=['AMI'])\n",
    "data3['DimRed'] = 'DBSCANontSNE'\n",
    "\n",
    "data = pd.concat([data1,data3,data2])\n",
    "\n",
    "f, arr = plt.subplots(1,figsize=[1.25,3])\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['bottom'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.spines['left'].set_bounds([0.25,1.0])\n",
    "plot = sns.stripplot(x='DimRed',y='AMI',hue='DimRed',data=data,ax=arr,alpha=0.2,palette=AMIPalette)\n",
    "plot.set(xticklabels='',xlabel='',ylabel='Adj. Mutual \\nInfo. Score');\n",
    "arr.tick_params(bottom=False)\n",
    "arr.set_ylim([0.25,1.1])\n",
    "arr.set_yticks([0.25,0.5,0.75,1.0])\n",
    "plot.get_legend().remove()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 90x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1 = pd.DataFrame(mutual_info_stability[8],columns=['AMI'])\n",
    "data1['DimRed'] = 'WaveMAP'\n",
    "\n",
    "data2 = pd.DataFrame(AMI_aggregates[8],columns=['AMI'])\n",
    "data2['DimRed'] = 'GMMOnPCA'\n",
    "\n",
    "data3 = pd.DataFrame(tSNE_AMI_aggregates[8],columns=['AMI'])\n",
    "data3['DimRed'] = 'DBSCANontSNE'\n",
    "\n",
    "data = pd.concat([data1,data3,data2])\n",
    "\n",
    "f, arr = plt.subplots(1,figsize=[1.25,3])\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['bottom'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.spines['left'].set_bounds([0.25,1.0])\n",
    "plot = sns.stripplot(x='DimRed',y='AMI',hue='DimRed',data=data,ax=arr,alpha=0.2,palette=AMIPalette)\n",
    "plot.set(xticklabels='',xlabel='',ylabel='Adj. Mutual \\nInfo. Score');\n",
    "arr.tick_params(bottom=False)\n",
    "arr.set_ylim([0.25,1.1])\n",
    "arr.set_yticks([0.25,0.5,0.75,1.0])\n",
    "plot.get_legend().remove()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 4—figure supplement 1.\n",
    ":::\n",
    "![](elife-67490.ipynb.media/fig4-figsupp1.jpg)\n",
    "\n",
    "### WaveMAP clusterings are more consistent than either DBSCAN on t-SNE or a GMM on PCA.\n",
    "\n",
    "(**A**) At top, DBSCAN (eps = 3, n_neighbors = 15) was applied to t-SNE’s projected space (perplexity = 30) over the full dataset producing 10 clusters. Parameters were chosen to produce similar structure to _WaveMAP_ to facilitate comparison. At bottom, a Gaussian mixture model (n_components = 4, replicates = 25) is applied to the three-dimensional projected space (94% variance explained) produced by the first three principal components of the full dataset (GMM on PCA). The number of clusters chosen was by selecting the value at the elbow (green arrow) of a BIC ± S.D. vs. number of clusters plot shown in the inset. (**B**) Adjusted mutual information scores (AMI; mean ± S.E.M.) for 100 random sample clusterings of both _WaveMAP_, DBSCAN on t-SNE, and GMM on PCA. Standard error of the mean bars are smaller than marker size. The AMI was calculated by constructing a ‘reference’ clustering by applying the respective method to the full dataset and then compared to the clusterings produced by random subsamples. (**C**) A jittered strip plot showing the AMI for 100 random subsets using each method. Subsets were randomly sampled from 40% (left) and 90% (right) of the full dataset. Gray boxes correspond to the data points surrounded by gray boxes in (**B**).\n",
    ":::\n",
    "{#fig4s1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[12,3])\n",
    "\n",
    "def plot_view(fig, pos, elev, azim):\n",
    "    ax = fig.add_subplot(pos, projection='3d')\n",
    "    ax.set_xlim([0,1.4])\n",
    "    ax.set_ylim([0,1.6])\n",
    "    ax.set_zlim([0.,0.6])\n",
    "    ax.view_init(elev=20, azim=220)\n",
    "    ax.set_xticks([0,0.7,1.4])\n",
    "    ax.set_xticklabels(['',0.7,1.4],fontsize=12)\n",
    "    ax.set_yticks([0,0.5,1,1.5])\n",
    "    ax.set_yticklabels([0,0.5,1.0,1.5],fontsize=12)\n",
    "    ax.set_zticks([0,0.3,0.6])\n",
    "    ax.set_zticklabels([0,0.3,0.6],fontsize=12)\n",
    "    ax.tick_params(pad=-1)\n",
    "\n",
    "    for i in [int(x) for x in np.unique(UMAP_and_GMM['gmm_labels'])]:\n",
    "        to_plot_df = UMAP_and_GMM[UMAP_and_GMM['gmm_labels'] == i]\n",
    "        x = to_plot_df['troughToPeak_abs']\n",
    "        y = to_plot_df['prePostHyper']\n",
    "        z = to_plot_df['FWHM1_abs']\n",
    "        ax.scatter(x,y,z,c=GMM_PAL[i-1],marker='o',alpha=0.75,s=20,linewidth=0.75,edgecolor='w',depthshade=True)\n",
    "\n",
    "        ax.plot(x, z, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='y', zs=1.5)\n",
    "        ax.plot(y, z, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='x', zs=1.4)\n",
    "        ax.plot(x, y, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='z', zs=0)\n",
    "\n",
    "    ax.tick_params(pad=-1)\n",
    "\n",
    "    ax.set_xlabel('Trough to peak ($\\mu$s)',fontsize=12,labelpad=5)\n",
    "    ax.set_ylabel('Peak ratio',fontsize=12,labelpad=5)\n",
    "    ax.set_zlabel('AP width ($\\mu$s)',fontsize=12,labelpad=0)\n",
    "    ax.view_init(elev=elev, azim=azim)\n",
    "\n",
    "    ax.scatter(UMAP_and_GMM['troughToPeak_abs'],UMAP_and_GMM['prePostHyper'],UMAP_and_GMM['FWHM1_abs'],\n",
    "               c=UMAP_and_GMM['dbscan_hex'],marker='o',alpha=0.75,s=20,linewidth=0.25,edgecolor='w',depthshade=True)\n",
    "    \n",
    "plot_view(fig,131,20,200)\n",
    "plot_view(fig,132,20,250)\n",
    "plot_view(fig,133,20,220)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 273.6x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "eightclassGMMpath = os.path.join(rel_path,'data/8_class_GMM.mat');\n",
    "eight_GMM_classes = scipy.io.loadmat(eightclassGMMpath)['classifies']\n",
    "\n",
    "eight_GMM_classes = eight_GMM_classes[0,:].tolist()\n",
    "eight_GMM_classes = [x for x in eight_GMM_classes if not np.isnan(x)]\n",
    "\n",
    "classifies_pal = sns.color_palette(\"husl\", 8)\n",
    "\n",
    "fig = plt.figure(figsize=[3.8,3])\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.set_xlim([0,1.4])\n",
    "ax.set_ylim([0,1.6])\n",
    "ax.set_zlim([0.,0.6])\n",
    "ax.view_init(elev=20, azim=220)\n",
    "ax.set_xticks([0,0.7,1.4])\n",
    "ax.set_xticklabels(['',0.7,1.4],fontsize=12)\n",
    "ax.set_yticks([0,0.5,1,1.5])\n",
    "ax.set_yticklabels([0,0.5,1.0,1.5],fontsize=12)\n",
    "ax.set_zticks([0,0.3,0.6])\n",
    "ax.set_zticklabels([0,0.3,0.6],fontsize=12)\n",
    "ax.tick_params(pad=-1)\n",
    "\n",
    "classifies_df = UMAP_and_GMM\n",
    "classifies_df['eight_gmm_classes'] = eight_GMM_classes\n",
    "\n",
    "for i in range(1,9):\n",
    "    to_plot_df = classifies_df[classifies_df['eight_gmm_classes'] == i]\n",
    "    x = to_plot_df['troughToPeak_abs']\n",
    "    y = to_plot_df['prePostHyper']\n",
    "    z = to_plot_df['FWHM1_abs']\n",
    "    ax.scatter(x,y,z,color=classifies_pal[i-1],marker='o',alpha=0.75,s=20,linewidth=0.75,edgecolor='w',depthshade=True)\n",
    "    \n",
    "    ax.plot(x, z, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='y', zs=1.5)\n",
    "    ax.plot(y, z, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='x', zs=1.4)\n",
    "    ax.plot(x, y, '.', c=[0.825,0.825,0.825], alpha=0.4, zdir='z', zs=0)\n",
    "\n",
    "ax.tick_params(pad=-1)\n",
    "\n",
    "ax.set_xlabel('Trough to peak ($\\mu$s)',fontsize=12,labelpad=5)\n",
    "ax.set_ylabel('Peak ratio',fontsize=12,labelpad=5)\n",
    "ax.set_zlabel('AP width ($\\mu$s)',fontsize=12,labelpad=0)\n",
    "ax.view_init(elev=20, azim=220)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   5 | elapsed:    1.1s remaining:    1.7s\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of   5 | elapsed:    1.2s remaining:    0.8s\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:    1.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:    1.2s finished\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "eight_classifies_nonan = [x for x in eight_GMM_classes if ~np.isnan(x)]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(full_data[~np.isnan(full_data).any(axis=1)], \n",
    "                                                    eight_classifies_nonan, test_size=.3, random_state=RAND_STATE)\n",
    "\n",
    "model = xgb.XGBClassifier(objective='multi:softmax')\n",
    "param_dist = {\"max_depth\": [4],\n",
    "              \"min_child_weight\" : [2.5],\n",
    "              \"n_estimators\": [100],\n",
    "              \"learning_rate\": [0.3],\n",
    "              \"seed\": [RAND_STATE]}\n",
    "UMAP_grid_search = GridSearchCV(model, param_grid=param_dist, \n",
    "                           cv = 5, \n",
    "                           verbose=10, n_jobs=-1)\n",
    "UMAP_grid_search.fit(X_train, y_train)\n",
    "\n",
    "confusion_matrix(y_test,UMAP_grid_search.predict(X_test))\n",
    "\n",
    "confusion_mat_counts_eight_GMM = confusion_matrix(y_test,UMAP_grid_search.predict(X_test))\n",
    "\n",
    "conf_mat_row_list = []\n",
    "\n",
    "for row in confusion_mat_counts_eight_GMM:\n",
    "    row_sum = np.sum(row)\n",
    "    \n",
    "    row_percent = []\n",
    "    \n",
    "    for val in row:\n",
    "        row_percent.append(val/row_sum)\n",
    "    \n",
    "    conf_mat_row_list.append(row_percent)\n",
    "\n",
    "conf_mat = np.array(conf_mat_row_list)\n",
    "\n",
    "colormap = mpl.cm.YlGnBu\n",
    "colormap.set_under('white')\n",
    "\n",
    "eps = np.spacing(0.0)\n",
    "f, arr = plt.subplots(1,figsize=[4,3])\n",
    "mappable = arr.imshow(conf_mat,cmap=colormap,vmin=eps,vmax=1.)\n",
    "color_bar = f.colorbar(mappable, ax=arr, extend='min')\n",
    "color_bar.set_label('P (Predicted | True)',fontsize=12,labelpad=15,fontname=\"Arial\")\n",
    "color_bar.ax.tick_params(size=3,labelsize=12)\n",
    "\n",
    "n_classes = len(set(eight_classifies_nonan))\n",
    "\n",
    "#Specify label behavior of the main diagonal\n",
    "for i in range(0,n_classes):\n",
    "    if int(conf_mat[i,i]*100) == 100:\n",
    "        arr.text(i-0.38,i+0.17,int(round(conf_mat[i,i]*100)),fontsize=10,c='white',fontname=\"Arial\")\n",
    "    else:\n",
    "        arr.text(i-0.34,i+0.16,int(round(conf_mat[i,i]*100)),fontsize=10,c='white',fontname=\"Arial\")\n",
    "        \n",
    "#Specify label behavior of the off-diagonals\n",
    "for i in range(0,n_classes):\n",
    "    for j in range(0,n_classes):\n",
    "        if conf_mat[i,j] < 0.1 and conf_mat[i,j] != 0:\n",
    "            arr.text(j-0.2,i+0.15,int(round(conf_mat[i,j]*100)),fontsize=10,c='k',fontname=\"Arial\")\n",
    "        elif conf_mat[i,j] >= 0.1 and conf_mat[i,j] < 0.4 and conf_mat[i,j] != 0:\n",
    "            arr.text(j-0.4, i+0.15,int(round(conf_mat[i,j]*100)),fontsize=10,c='k',fontname=\"Arial\")\n",
    "\n",
    "\n",
    "arr.set_xticks(range(0,n_classes))\n",
    "arr.set_xticklabels(range(1,n_classes+1),fontsize=12);\n",
    "arr.set_yticks(range(0,n_classes))\n",
    "arr.set_yticklabels(range(1,n_classes+1),fontsize=12);\n",
    "arr.set_xlabel('Predicted Class',fontsize=12);\n",
    "arr.set_ylabel('True Class',fontsize=12);\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x126 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x126 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+7SR//T8P8MC/uY5UYgjXL+N5DoaRJPA9VC10MmmqQTazhm0bP08s0oXtlIlGctTqMwTSJxHrOzXAkzKgWpvC9RqkkiOnfBO+77BQOszM3CsEgaBcOcbUwssIqSIQ1BrTeK4Pik8Q+IBP6FQzUEWCfa+Uee7pSfb8aIaRVXGuvbGLGz8wyqrRYUw9juO38LwGrlMHRWCqcRRdRxEmlpEMZ1zCwPMbuJ6N61WRUqCiUGvNI1BJxnvJZtazbuQuerq2YZnpk9V59Qmk1phFCIVdL/w7+rt38PLe/49UYhDbruK4dWbn6nztXzzLLz9wLWvW9OHjoIkIrl/FMJIIBKpqEYvmSSWG2b7lC2iKztTMCyRi/RhGYsnEHM+3KVfGUZRwanoyOOZ6TeYWXmOu8BqmkWZ2/lUK5TfQ1ChNu0i9PgsE+NLBdwMCGu07amiqhu9bvLanxo9emmb3iyewIgqbr8mw9doetmwdJJfPhuUHTaTrgqogw0EMoKJpBqaZRFdNWm41DD+0arhuFR+JogiyqQ3c/cFfJxk/FTi8+gRSLB8BqfHcj/4DsWgfR8b/hlisD8+1qdan+e1ff52t1+a55+8NAAJdj+H7LqYWQQgVw0xh6nGSyT7WDN+NrlnYToVkYojEIhHXs+G4dSrV42iaSSLWfyr24XktTsztZW7hVYTQKFcnKFaOtge3DYSiIAJQVJ16cx7XbaDrCXzpEvg2qqpCoDN5vMGru2fYv3eeI4eqxBMGa9amWbU2y+o1XQyPJoklBJ7fIggCQOJ5LTTVAKEghIKm6LhuM0yWwmtbnuAff/7vTn6NRQWy3NX9F4TtVHG9BkiJrsfOGWg6X/zA48Tcy0SieU7M70ZTY7hOHd93eOHZOexWwEc+0Y/rNjDNJIrQUTQVoaj4vouuamTSI2zd8Dli0S48v0WlenxZ4gAw9Bj57AZct0GpMoYQgli0B9NIMNh3A/0972N2YR9B4KBrJr4MmJl9BcerE8gAQ9XJZdcTeA7FyhEUBZLR1UhcHKfG2vVJNmwYQf4kOHadsWMzHDtSYexohRefO87x8RqGqTI0kmRoJMbgUJLu3iSZnEMqrSHxsWWDIHDDevPgxHST82kcltWClCrjMpxeOSiELmhN0VEUHVU12qHotiOoPRUrVsbCDG0jSaU6ydjULiJWGiEU1o1+nFg0v6wf43Tmiwd5Zf/D5NLreXn/HxEze2i5JRr1Av/6n+3hH/5f1zC8VkHTDNLxtfhBA9drIIF8ej2DfTeycc2PoethSHy+cIBMavVZg1/ni++71BoncNwaphGuuTnZqpQqE7wx/jc0m/O07DqV6hil6gRSSrLJERLxPirNGarVKQw9RjzSB0gazjxB4KOrForQkdJrtxgSicf8XIuJY2XGjhaZnKgyO9tgbqZOreoST+hE4xqCcLpdWHDIZHVuv2uYRx4+eNLsi29BDh19lFjb89dsFijXxttZ3RKQCKGBkChCoGlRfN8DoeC4Ver1KTzXJhLJ4wc20UiO+eIBdmz/eZLtdLzl/QgOAC27FFaU76IoAteu89ijJxgeTTG6XodAYhoZVFXgeDaBH5BJr2Jo4ANsWPXJU+JotooYeuyixQGgqjqpxBAQtp7l6gRB4GEYceLRLq6/5qepNWY4Pv08hWiW3q5rmS3sY6F4iLnCwXDZRbQLz2tRqY6hG2Gmmmkk8fwWtl0HYWKZWQLh4jo2A/0OfX05rtvhEviN9lIKH88PqJSa1GtOmFwtVJJpiWG2gHNnri1LIC/t/a8XVGFvFiUo1SYxjQTV+jTRaDcv7X2Ia9Z/lq7seYWkT+G4der1ORShMT3zEkKoNO0i5UqdH3xvin/+r7cRBGFSbyLahZQBntsgEsmzavh21o9+7JQ4pJTU6tPksxsv4vstjmkkMI0EUkoct0a1Ph16PhWNob6b6OnaxsLCa3TlN+J7Dm+MP8588TVadhlVNQkIcJsFmnYJVYlgGhamkUJVTaT0EVIhEsmgKTqSdtJBuwVXhIrve9jdJQJC72yztUC9OQ9AX/e2c9q/LIHse6WCCGNKKKpAUQSRiEI8oRFLaOj66dlOon3I9hEg0FBE+J7rOVQqE0SMFPsO/Cm3f+CBZS0RcNwq84XXicd6OTLxOKowsZ0y3//eJNfvyNPdpyIwiEe6EUKl0ZgHVbB+1SdZO/IRDCN26l61+jTxWO9KZIafFSHEKbFA2A21nBIy8EgmhqjUJvC8Bjdu+xKOU+ON8b9lrrAPz7eRAbh+A9et4Xk1Gs1Ce+GVelqdSVAUFASK0JAC/LYrXxUqUlHbSzQMwCNq9fCJD/32Oe1elkD+9tFZkBBISeCD70vsVphIU6/5pLM6vX0Wq9fH2HptkoHhcPmiIMwCF6qG69YQvoNQVFy3Qbk2SaO5QLk6Tjo5ct62uF6LQuUIyeggrlNHUU1qNZunn5zjX/2b7ShCIxrLh+JoFmm5FdaM3M3qkQ9hmalT9/EDF9upkIj3L6cqLhpV1YlFuohFugDIZzfQaM5zfPo5bKfK6NDt9Pdcy3zxEI3mPFJKbKdKvTmPY1fwpQ2Bi6JqqEJH1SxQNJASGfg4bg1FqKiKgaJqeG6LYvEwXtDA0LP8/Y/+EZp27nU1yxLIl//F2rN+5nuSuVmbE5MtDrxW5/d/5xhSwofu7uKWO/JYEYuYmQijlYFN4DYAQa02g6ZqHJ9+cVkCAWg2iyC10HvoOux8/ARbtqfIdRlIGUEg8Hwb12+QSPSzZd29b5uhVKrHSSYGl1XupUBRNOKxXjauvZeWXebE/CvoeozRwdupN+Yolg8TBB49XVtxnSpNuxAmMrktPBku2wxzWHxk4GPqCYSiEPgenmvj+g6mlSGu9vL+a/9vEvHzm6ktSyC5zOYwlKwYQIDnu7huHaEoyMAjk1JYtarFjpscpBSMH2uw87EGX/nFfdzzY4Pce98AqWSOruwWjk08RcOeQwKu32Rq9iVWDd1+XrOaIPDb8Q6PYukgQeDheQGPf3+Wn/vlDejqmwuvPa+FrllsXP33yGXODFJ5vh0OHld4YdPFYpkpRgc+SKO5EIbpIxli0ZuoN05QrU9hxvtZNXwXlpGmUD5MqTJGszmP6zcJAj8M5PkuvnTQFINABmjtdTiDvTsYGbz5vG1ZlkCu3fQFmq15vMAlnRgmGunCdVscm3yCcnmMpl1gsPcW5hb240uXVatPMPqlGD/xhWv57d/837z8/GP8o5+/AWWDTlduM1OzL+B5oeoXCoc5MfcKa0buOqcdrlunXJnC822qjROAzwvPFOkbiDA0EgOhhk4iKdB1g6jVw8Y1n3rbGKdSnTg127gSiUZyRKxse2A5Rya9hlx2A9XqJNOzL9FohstGe/NbyWc3oSgqjlvD81toqoWUkkZzDs+zmZz5IYnYAKNDd6Ao6nnbsGxPqu+7LJQOMjO/D0OLkEoOk89uYr7wGsenX2Ry5oekk6NUa1PEo/1Mzb6IrsVw3RqP/eAof/6t/Tz4m59h9eoctcZ0GJ/wG+halvWrPsLN1//iObOwq7Up9rz+bSZO/JBy+RiBdPjGr7zOj39+gK3bh1A1DVXRMLUouexmuvOb2L7p/jPu4bh16o1ZMqlVy/n+l5VwDDJH4DsYehxdj1FrzDAzt4dK7ThSBlhmmli0G1WxsJ0iLbuMpoUpDauH71wq7XBlPKmqqtOd20IyPsDUzG4q1ePUGjP0d9/AUL+KqqrMFw7Snd3E5MxuurIbmSvsJ51cx90f0zFNm1/7l9/hd/7Ll9CjkkRsiGLlAK5XaQffZs85JnC9BoXSG7huC4nLwdcaIGHTNRl0TUcooCoGycQIqqqybvRjb7tHpXqcTHrJFLwrjtOnzK5Xp9kqIoRCb/e19PdcjxAKtcYc1eoEzeYCup6gp2sb8WgPsWjXuXJSF+WCXe2WmWZk8BamZl4mCDwmT7xAOjlCT3475fJxfOkSjYVBsGp9jkLlAF3Zbey4eYFGXfLPf+m/8eBvfoj+/kGKlQOAR60+x1zx4DkF4vte6Km0ywDsfHyOW+/MoWsJvMDFVCJomk5f97VIfKKRzBnXt+zy5dyo5aIRQmDo8TPGTn7ghslC0S76urYtZ8HUklzUxF9VdAZ7d2AaCVRVx3YqKIpKKjWE4zRJx4YolA6zcc0nMLQEpcphImaem2/PcP2NQ/zpw6+2w96hTsvVCQqlw0uWGQQ+jfa6ENevUSm77N9T4f235kG6oYNI0RjuuxU3aNDX/b633aNan1osYeZdjaroZ2xQs1Jc/J4BQtDbtY1MajW1+glAMtB7I4YZpVKbQFF0WnaVbHqUqJkO0+cI+MR9Q/zwmUnGjs5g6Ekg3L6hVptasjzHrXH8xAt4ngMEPLezwPbrUyTiSXwpURSVeKyfZGIAS0+Sy5w5NW8054mYmUvqFDsf9u7dy0c/+lHy+fyiDsLf/d3f5YYbbsA0Tb74xS++7fM/+IM/YO3atcTjce655x6mppautwtlxWopkxplaOBm5osHcb0GqwfvJB7vo1afpFIdI5teh6KYRKPZMOVfLfKpe9fxJ3/4IuappBWXRquI59tnLSf0EexpT1Elux6f57a78kjpoqkG0UiefC50mSfi/Wc0w1JK6o1ZYhe2+n1F0XWdz372szz00EOLft7f388DDzzAT//0T7/tsyeffJJf/dVf5bvf/S6FQoFVq1bxuc997pLYuaL/RrFInvWjH6dYOsJC6RBb1v843V1bmVnYQ4AHiiQRGSQVHwACbvlQmqNH5jhy4GTCTEClNkWjsXDWMhrNOSrVScDhwP4qhqWwak301HYLyXgvyVgPqqqRSZ45Q6nWp4nH+s7p0h8dHeW3fuu32LZtG6lUip/4iZ+g1WpdXOW8hQ0bNvAzP/MzbNmyZdHPP/3pT3PvvfeSy719Rdz3vvc9PvOZz7BlyxYMw+ArX/kKTz31FG+88caK2giXYJ9Uw4ixcc29OE6Vmbk9jPTfRjI+zPHJ50hEh6jUxsll1yNQUXSPT//kBr79Jy8C4dy8Vp+lWD77Fy2Uj+A6FQCefmKBWz+URwgNIRXSiRFSiWF0PUYs2kP8NG+hH7g4ToWIlTnbrc/gkUce4dFHH+Xo0aPs2bOHb37zm4uet2vXLtLp9FmPXbt2nV/FLZPT3RMnX+/du3fFy7kkHbGmGWxa++kwcildsuk1JOJ94bzcLSOkwIpkgYBrb4wwebxKYS4UiB80WCgtLhDXazI1vZsAl1rVY/+rVXbcnAGUcD1rJB0G3VCJtlfSn6RSnSC5DKfYl7/8Zfr7+8lms3zqU59i9+7di5536623UiqVznrceuut513m+XLPPffwyCOPsGfPHprNJl//+tcRQtBoNM598TK5ZCM100zQnd+CoUZIJQZxvRaaZtKV2cSJud30d10HSAQBH7xziF1PFtpX+pRKRxa9Z7NVYKbwKgA/3FVg23VJojENBZNUaphkvB9djZJKDJ2Rse16zfbKt9ii912M3t43W59oNEqtVlt2HVwqPvzhD/O1r32N++67j9HRUUZHR0kkEgwOrnxM6ZIO5TPJVSAU+rquIxHrw/PccLslVcNuFdBUi4CAWz6YZdcTY7hOuEV2ob2k8K3Mzu+n0ZhBSsmuJxa45Y48oBCNZYhHwq2fLCuNphlErDcFUq6MkUoMX5LvuHPnTuLx+FmPnTt3XpJyf+7nfo5Dhw4xMzPDfffdh+d5XHPNNSteziUVSMTKYOhRHLfM5vX3oaoaQeCRjA1Qro1jmBnAp7svwdBInN0vVgHawafi2+43OfM8Epcjh+oEgWTthhhgkYj2YVlpktF+0skRVNU8NRCtNWaxrMyKZIotxm233UatVjvrcdttty16nZSSVquF47Qz41otbPvN2ZvnebRaLXzfx/d9Wq0WnuedOnfv3r1IKRkfH+dLX/oSv/ALv0Amc37jq+VwSQWiKBoIhXRyFM9rMNj7/tAvISRSKCjt4iWC2+7s5ul2N+P5VWYW9p9xL993mJkLu5enn1zgljty7eTgLMnEIJpmkEoN43qNU7kdvu/SahUudFO3S8rY2BiRSOTULCYSibBhw5vR5gcffJBIJMJv/MZv8PDDDxOJRHjwwQeBUCD33/aKKEMAAAmhSURBVH8/8XicHTt2cNNNN/GNb3zjkth5yZc91BqzqIqGouihW166NFtljow/xnzhAI7bAlwUEeOX/8kz/NIDa+ntt7hmw/1sWXcfqcQwiqIyPvUcjz7xi1Rrdb7yS/v56m9uIplK0J1bTz6ziQ1rPo6Uknis55QgFoqHSCYGL+XuxFcTl+eZdVErS6O5gGkkyKRXoQqdeCTHUN/NxKO9hJrzEQJuuj3P00+GPpBC6Qi6FqFQOsTs/D5eO/wdAmx2PT7PtuuSJFM6kUiKeKw3zGeVMky6aYujWpvCMOIdcVwkl1wgihLu8SVlQDoxjKpZGGaKTGqYrtxGNFUHTPygwS23d/HcrgKuG7BQeoPp2d3ksxsJpKBWn8PzAp78m3nuuqcb0IlHu0nG+unOX0OzVSCXDt3qJzPdE7HlZ8t3OJN3JCARj/VQqU0CMNBzA45dxjIz4eazWgxNsQCP7t44A4MRdr9YxrYLvP7G/+SV/d/itUOPMF84wMs/LNHdZzI0GsXUkqQTI+RyG1koHaS/5wZU1cD1mlTr06ST7548jyuZd0QglpnG81q4XhNFUenrvg7XrZPLrGNo4BYUcTJhH267s4tdT8wDPqXqOIaZYHruVQLZ4rG/nuXDH+tGETHi8S5y2fWoik5PfjuGEQ8fFFQZI5te+15/ONCK8Y6FNNPJUcqVMQBMM0kqOUKtfoLhgZuIxwcAA0mT7Td0MX28xcx0i3pjiuNTL1CpHeXA/hp2K2DL9iSqopHPbiJqZklEw0FpqXIM26mRy2x41+Z5XIm8YwJRVR3LTFOpTSGlJBnvZ3jgFiJWhk3r70VXw5wQTXP4wG1ZHv2rGYIg4I3x/0Wp4PDHvz/Gpz/Xj6IYDPTtIB7tJp/dgO87FMtHiJhZ0snhTsuxwryjSRHxWC+KojJffJ1SZZx6c454tAdTi7J5/edQlTA0f/cne5g9YfPQ7x6jWnH5L//+CLd/OM/269NkEqNErRSpxBClyhjZzFry2Q2YZvKd/CrvGS7b9g+OWz9jfe3s/D527/9jSpVJJE1cJ+Dhh8Z55cUy170/zRf+4TCKEmX10B105zfTm99GV27TBeVZdliUK3t/kHpzjmMTOxk7/hTj008BAikD9u6usGlrAtNI05PfzOrhD9HTtZVkrL/Taqwsl29/kPMhFuliqP8Dp5ZCzhcP4gcttr4vBRiMDN1CKjFILrOOWCTfEcc7xBXTgpwkCAImJndxbPpZarVpKtUpVg/fSTLZSzzSS2/39o539NJwZXcxi9GyyxRKh4lGcsQi3WiaddmTja9i3n0C6fCOcnmCdR3e3XQE0mFJltXFCCH2Aiub/3/h5IH5y20EV44dcHG2WFLKt+UsLnea25JS3nCBBqwoQogXrwRbrhQ74OJsEUK8uNj7nS6mw5J0BNJhSZYrkP/3klhxYVwptlwpdsDF2bLotcv1g3R4j9HpYjosSUcgHZakI5AOS3JeAhFCZIUQfymEqAshxoQQ95/7qovjfMsUQnxVCOEKIWqnHSu2O50Q4ueFEC8KIWwhxDfPce4vCiFOCCEqQog/FEKceyvjS2CLEOKLQgj/LXVyx4WUeb4tyH8mfDJfD/B54PeEEIvvfLJyLKfMP5VSxk87Ft8e4MKYAh4E/nCpk4QQHwX+JXAXMAKsBr62gnacty1tnn1LnTx5IQWeUyBCiBhwH/AVKWVNSrkL+CvgCxdS4PlwOco8G1LKv5BS/g/g7NsehfwD4CEp5T4pZRH4BvDFy2TLinE+Lch6wJNSHjztvVeAS9mCLLfMTwkhCkKIfUKIf3IJ7VqKLYQ2nuQVoEcI8fY9pN4Z3ieEmBdCHBRCfEUIcUHZg+dzURyovOW9MrAyzxW7+DIfIXTyzADvB74jhChJKf/7JbRvMeKENp7k5OsE7+B/fJungGuAMULh/inhg+p+fbk3Op8WpAa8NQE0CVSXW9gyOO8ypZT7pZRTUkpfSvkM8DvAj19C287GW20++fpS1tOiSCmPSCmPSikDKeWrwNe5wDo5H4EcBDQhxLrT3tsO7LuQAs+TiykzXMf5zrOP0MaTbAdmpJTvdOuxGBdcJ+cUiJSyDvwF8HUhREwIcQvwY8CfXEiB58NyyhRC/JgQIiNCdgBfBr67UrYIITQhhEW4DaMqhLDO0p//MfAzQojNQog08ADwzZWyYzm2CCE+JoToab/eCHyFC60TKeU5DyAL/A+gDowD95/PdRdznK1M4Dagdtp5/52wj68BrwNfXmE7vsqbz1U7eXwVGG6XOXzaub9EOBaqAH8EmJfDFuC32nbUgSOEXYx+IWV2gnUdlqTjau+wJB2BdFiSjkA6LElHIB2WpCOQDkvSEUiHJXnXC6QdoLvjHSprczsfY0U9tUKI7wgh3v7kxSuAK94PIoQ4/TELUcAG/Pbf/0hK+a130JbvAH8mpfz2Ct93B/B7UsrrV/K+K8EVL5DTEUIcA35WSvnYZSi7jzDe0i+lXPHlp0KIQ8DnpJSLrnC7XFwNXcwxIcSH26+/KoT4MyHEw0KIqhDiVSHEeiHErwghZoUQE0KIu0+7NiWEeEgIMS2EmBRCPCiEONtjqT8CvHy6ONpl/zMhxJ52auRDQogeIcRft8t/TAiRaZ9rte1aEEKUhBAvnIyXtHkS+MSKV9BF8q4XyCJ8ijColwF+BHyf8HsOEMYkfv+0c79JmCexFngfcDfws2e571bgwCLv30convXtsv8a+FWgq13ul9vn/QMgBQwBOeAfA6c/FOc1zowGXxFcjQLZKaX8vpTSA/6M8If6DSmlC3wbGBVCpNv/vR8H/qmUsi6lnAX+I/CTZ7lvmsVzO/6TlHJGSjkJ7AR+KKX8Ubul+UtC4QG4hMJY285deUlKeXpSVLVdxhXFFbOJ3Qoyc9rrJjAvpfRP+xvC7K9+QAemT5uUKMDEWe5bZPGMtreW99a/Tz6X9U8IW49vt9MBHgb+VVu4tO9dOvvXujxcjS3I+TJBOCPKSynT7SMppTxb3usewm7kgpBSulLKr0kpNwM3A58Efuq0UzZxZk7rFcF7ViBSymngB8C/F0IkhRCKEGKNEOL2s1zyN8B17YSdZSOE+JAQYmt7EFwh7HKC0065nXD8ckXxnhVIm58CDGA/YRfy58CiD5mRUs4AjxNmtl0Ive37VwgHpH9HO0NOCHEjYRLU8xd470vGu8oPcrkRQmwG/huwQ65gxbUdcA9JKf/3St1zpegIpMOSvNe7mA7noCOQDkvSEUiHJekIpMOSdATSYUk6AumwJB2BdFiS/x+BSuquYZ940AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 144x126 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x126 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pe0nv2kNw8UKnsLqLlnCU5nBkIsU5YdyajlvTCbmhyufnnNo6wAlDOJhO0Z5O0WcYBF1uGsJhbFuRLRTYm0xwIB0nVSxi4XS5g+MyN04ro6OBgFvXEQW6riO2QgE2CluBhY2tFKJsLOVclIPjPAGUgKkUugi2UhRKx3cDj13zMbRxjHUmJJDvrL0Qw7Qo2DZFyyRjmqSKBfqMHHsSAySLBXKmRSKfRxNFdzZHxizQa+TJ2iYKqFi5nJV33cG+H/6E1vsepOUrtwDQaxXZE+8/pkDe6GwHILN7D7VX/AkAdf4gHreL6hkyaRbz+oh5fayqqsFWikyxSKpYIFEwSOSLnF1boGjb2CgSBYMdfX1sTwzQnc1QUHbpRFuIArEtXGhYZoEhF5nIUJOjieMP0cVxH2iioSHooqFrIMp5RLWvkKfS5cHrdvFXy1cTGuc0wIQE4tJ0Qp6RPaiXNs6jNZ3EtGyypsnuxABF2yZrFtFFeGLfLhI5gyQ2msdD8xc/x5a//gbxNzcTPXsVAM+1tfK++sZRuxlL2bzR24VSivSuvbT8pSOuRdEojcHQjHRXayKEPR7CHg8NwdDQ+rxlYZgmhmXx/vpGsmaRgmVjWBadmRRb+/toTSXoNgxyZhFT2VhKgVII4pw4cc6JU12CpgmCoEp/lgKjUEQJxFweVtbU8qfNi1lTf+yx3iCTNhejidAcjpAo5OnKZol5fYTcLtozGdyaxgeamtna38c7vT1ksNH9flq+fDP7/t+/suIf78AVDLCpq5N0sTDqXUjBsjmQy5Dv6kb3evDEYgQQlkZjNA6r/NmAt+R1HrlTnMNHFji/LOV0I0XLoj9v0JFOszeVoD2TosfIkSwUyFlF8qZdSlsay4jT0CyOxlhbM4ezampZGqsaMbexmPTJuojHS8TjZX44zBs9XcwNhXm1qx1dNKr8PhbHYmwf6MdAEVm9gsjZqzj0i1/RfPNn6SoYtKVSowokbznezMyuPQQXOeOPSr+fBZHKaZ0bmkp00dDFGe8E3B7mhipYU99w0vKfMo+MV3exorKGKr+fTy9ZTn0gSNTtPEkf8XgYHC003nA9fetfxi69MmpTT8eox8yXIrjSu/cSXOxcYjWBAC0VJ3dwejoxpS67sMfj9IkCa+rqWVM7h/pgiLpgGL30CIEnFiPQPJ/4m28D8HJ726jHS5dElNm1h1CpBWkOR2fM4PRUZMrDn5pCYdozaap8Aar8fi6Z08iCcAUht5vB4MCqC8+nf8MfANiZ6B96Id2R7BrowzZNsvtbCS5sxgtcWFc/IwenpwpTLhBNhIUVUQ6kkoTcbsJex2V9dk0NHnFakdgFa0i8tRkrZ5AFMsWRJtnh+YP7yR1sw1NdhR4IEPN4WV1dP9VFOK05KQGUHl1nSTSGX3eTN20aQ2HCHh/1gSBewB0OE1q6hIFNbwDQk8uOeJw3e7vJ7NlLcJEz/mgMRajwzqqwviHuv/9+zj33XCoqKpg7dy5f+9rXMM33nPJ333035513Hl6vl5tuuumo/bPZLF/5yleorq4mEolw8cUXT4mdJzXCttrv55yaWuoCQWr8fuoCfgKltw1WXXgB/etfAeDN7s4R90+iHBd7SSArqmpOjuFTQDab5Qc/+AG9vb28+uqrPPfcc9x5551D2xsaGrjtttv4/Oc/P+L+X/ziF+nv7+fdd9+lv7+fu+66a0rsPOkh2CLCGdFKLmmYR20wTNjjCCS29hxS727HTKV55uCBo/YbHJcMzsFowAfmzpsSG5ubm7nzzjtZtWoVkUiEG264AcMwJjWPL3/5y6xbtw6Px0NjYyM33ngjGzduHNp+/fXXc91111FVdbTvYvv27TzxxBP8+Mc/pqamBl3XOffcc49KNxlMW4z+/HCEq5taiHkDaIAeCBBetpTE5q1sS/QdNVDNmiZ2oYDR1kGweT41bg8LI9Eps+/hhx/m6aefZt++fWzevJn77rtvxHQbNmwgGo2OumzYsGFc+b300kssX758XGlfe+015s+fz7e+9S2qq6tZuXIljzzyyHiLNiGmNap9VXUNF9Q30JpKMGAWqThrJYm3tlB14flkzCKRYfMFbekE2f2t+Brq0bweWqIx9CkMrLn11ltpaHAcUtdeey1vvfXWiOkuuugi4vETe7fZvffey6ZNm/jJT34yrvSHDh1i69atfPSjH6W9vZ1XXnmFD3/4w5x55pksW7bshGw5kml9ykdEuGHxGcwNOTEckdUrSL69BaUU/cbh8Vm/27eXzJ59Q+OPtXVT602sr3/v7igQCJBOn1io32g89thjfOMb3+Cpp56iunp8U/x+vx+3281tt92Gx+Phkksu4bLLLuOZZ56ZdPum/TGwgMvDB5ta8AC+hjmgaRiH2nnlUOth6f7jwJ7S+GMBOrDuJLqbx2L9+vWEQqFRl/Xr14+679NPP80tt9zCr3/9a1auXDnuPFetWnXUuqnyBU27QACumtfCvGCF87DS6pUk3t7CE3t3D41DssUicVSpBWlhXiDMnNDkRI6dKOvWrSOdTo+6rFu3bsT9nn/+eW688UYeeeQR1q5de9R20zQxDAPLsrAsC8Mwhm6DL774YubNm8cdd9yBaZps3LiRF154gSuvvHLSyzcjBFLh9XJNi+M6j5TGIfsKOQbyzp3Da11tWDmDfHcP/nlNnFdbN+vf9vO9732PRCLB1VdfPdTaXHXVVUPbb7/9dvx+P9///vd54IEH8Pv93H777QC43W4ef/xxnnzySSKRCLfccgs/+9nPWLp06eQbqpSayDJltKdS6kOPP6TOuf9flOb3qfN++VP1bzvfVUopdeN/PKqWfvc2FVy8UK195AG1uadrKk05XRnxnM+IFgSgNhjgyqZmXKEg/qa5pN7dwYPbt7Ar3s8uI0ty81ZCZyzGDywuv57ypDFjBKKLxuVNzQDOOOTNzbQVC+yI92EXCvQ8+wK1H7yMdXOa8Llm7TPns44ZIxCAZZVVnBetovqSC+l94SUKfX38fMvb9K1/hUDLfPxzG/n4ksm9zy8zNjNKIG5N5/pFS/HNqaf2yg/Q+tMH2Vcw6PzN09Rf4wzglh1H2FyZ42dGCQTg4rlNaEDD9f+JzJ59HHrgIVCKitUruLS+YTa/wWdWMuNq263pfHrRMjSvh3lf+Awdj/2GumuuJCTCV89aM93mnXbMyFdQpfN5vvn6Bl7u7qT3pY1Unr+G/33h5VzaNP/YO5c5XmbXO8qKtsXXNr7Iyz2drI1V88+XTb6XsMxhzC6BgPOYw92b3+CWM1dRcQJP7JcZF7NPIGVOKqfeRw3LTD1lgZQZk7JAyoxJWSBlxqQskDJjMqG7GBHZivNV0ZlANdA73UYwc+yAE7PFp5RaceTKic6bG0qp847TgElFRDbNBFtmih1wYraIyKaR1pe7mDJjUhZImTGZqEB+PCVWHB8zxZaZYgecmC0j7jtRV3uZ04xyF1NmTMoCKTMmZYGUGZNxCUREKkXk30UkIyIHRORTU23YePMUkW+LSFFE0sOWBZNox38WkU0ikheR+46R9q9FpFNEkiJyr4hM6uuPxmuLiNwkItYRdXLp8eQ53hbk/+J836YOuBH4oYiM72UWx89E8nxIKRUatuydRDvagduBe8dKJCJXAl8H/gSYDywAvjOJdozblhKvHFEnLx5PhscUiIgEgY8C31RKpZVSG4AngD8/ngzHw3TkORpKqUeVUo8BfcdI+lngHqXUNqXUAPA94KZpsmXSGE8LsgQwlVI7h617G5jKFmSieV4rIv0isk1EvjyFdo3FchwbB3kbqBOR6XqQ52wR6RWRnSLyTRE5rscRx7NTCEgesS6B88mRqWIieT6M4+TpAt4HPCIicaXUL6bQvpEI4dg4yODvMCfxii/xErACOIAj3IdwPstzx0QPNJ4WJA1UHLGuAkiNkHayGHeeSql3lFLtSilLKfUy8E/Ax6bQttE40ubB31NZTyOilNqrlNqnlLKVUluA73KcdTIegewEXCIy/HNTq4Ftx5PhODmRPAe/p3Oy2YZj4yCrgS6l1MluPUbiuOvkmAJRSmWAR4HvikhQRC4EPgL8/HgyHA8TyVNEPiIiMXFYC9wKPD5ZtoiIS0R8OF8U1UXEN0p//jPgCyJypohEgduA+ybLjonYIiJXiUhd6fdS4Jscb52M9uKQ4QtQCTwGZIBW4FPj2e9EltHyBNYB6WHpfoHTx6eB7cCtk2zHt3nvi66Dy7eBeaU85w1L+1WcsVAS+CngnQ5bgDtLdmSAvThdjPt48ixP1pUZk7KrvcyYlAVSZkzKAikzJmWBlBmTskDKjElZIGXGZNYLpDRBd+lJyuvMUjzGpHpqReQREbnq2ClPPjPeDyIiwz+zEADygFX6/y+UUg+eRFseAX6llPrlJB93LfBDpdTUfBXoBJjxAhmOiOwHblZKPTsNec/BmW9pUEpN+uOnIrIL+KRSasQn3KaLU6GL2S8iHyj9/raI/EpEHhCRlIhsEZElIvINEekWkYMicsWwfSMico+IdIhIm4jcLiKjfSXgg8Abw8VRyvu/i8jmUmjkPSJSJyJPlfJ/VkRipbS+kl19IhIXkdcH50tKvAh8eNIr6ASZ9QIZgWtxJvViwJvAb3HK2YgzJ/Evw9LehxMnsQg4G7gCuHmU464Edoyw/qM44llSyvsp4G+AmlK+t5bSfRaIAE1AFfAlYPhXk97l8NngGcGpKJD1SqnfKqVM4Fc4J+r7Sqki8EugWUSipav3auC/KKUySqlu4C7gE6McN8rIsR3/rJTqUkq1AeuBV5VSb5Zamn/HER5AEUcYi0qxK39USg0PikqV8phRnIpvxe8a9jsH9CqlrGH/gxP91QC4gY5hNyUacHCU4w4wckTbkfkd+X+o9PvnOK3HL0vhAA8Af1sSLqVjn9jH76aAU7EFGS8Hce6IqpVS0dJSoZQaLe51M043clwopYpKqe8opc4E3g9cA3xmWJJlHB7TOiM4bQWilOoAngH+j4hUiIgmIgtF5JJRdvkdcE4pYGfCiMhlIrKyNAhO4nQ5w7/9egnO+GVGcdoKpMRnAA/wDk4X8m/AnJESKqW6gOdxItuOh/rS8ZM4A9LfU4qQE5E1OEFQrx3nsaeMWeUHmW5E5EzgfmCtmsSKKzng7lFKPTlZx5wsygIpMyanexdT5hiUBVJmTMoCKTMmZYGUGZOyQMqMSVkgZcakLJAyY/L/AVgW1RYdRF5xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 144x126 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x126 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x126 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x126 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(1,9):\n",
    "    f, arr = plt.subplots()\n",
    "    f.set_size_inches(2, 1.75)\n",
    "    GMM_cluster = classifies_df[classifies_df['eight_gmm_classes']==i]\n",
    "    \n",
    "    for _,row in GMM_cluster.iterrows():\n",
    "        plt.plot(row['waveform'],alpha=.3,linewidth=.6,c=classifies_pal[int(i-1)])\n",
    "        \n",
    "    plt.plot(np.nanmean(GMM_cluster['waveform'].tolist(),axis=0),c='k',linewidth=1.)\n",
    "\n",
    "    arr.spines['right'].set_visible(False)\n",
    "    arr.spines['top'].set_visible(False)\n",
    "    arr.set_ylim([-1.4,1.1])\n",
    "    arr.set_xticks([0,14,28,42,48])\n",
    "    arr.set_xticklabels(['0','0.5','1.0','1.5',''])\n",
    "    arr.set_xlabel('Time (ms)',fontsize=12)\n",
    "    arr.set_xlim([0,48])\n",
    "    arr.set_yticks([])\n",
    "    arr.tick_params(axis='both', which='major', labelsize=12)\n",
    "    \n",
    "    arr.spines['left'].set_visible(False)\n",
    "    \n",
    "    x, y = 23,-0.8\n",
    "\n",
    "    n_waveforms = plt.text(x, y, 'n = '+str(len(GMM_cluster))\n",
    "                       , fontsize=12)\n",
    "    plt.tight_layout()\n",
    "    plt.margins(0,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "if 'wavemap_scores' not in list(locals().keys()):\n",
    "    wavemap_scores = pkl.load(open('data/wavemap_scores.pkl','rb'))\n",
    "\n",
    "if 'wavemap_n_neigbors_scores' not in list(locals().keys()):\n",
    "    wavemap_n_neigbors_scores = pkl.load(open('data/wavemap_n_neigbors_scores.pkl','rb'))\n",
    "\n",
    "if 'gmm_scores' not in list(locals().keys()):\n",
    "    gmm_scores = pkl.load(open('data/gmm_scores.pkl','rb'))\n",
    "\n",
    "resolutions_to_use = {0.5: 17, 1.0: 13, 1.5: 8, 2.5: 6, 3.0: 5, 5.5: 4, 6.5: 3, 9.0: 2}\n",
    "n_neighbors_to_use = {5: 15, 10: 13, 20: 9, 50: 7, 250: 6, 625: 5}\n",
    "\n",
    "joint_clusts_to_use = [2,3,4,5,6,7,8,9,13,15,17]\n",
    "\n",
    "gmm_data = np.array(list(zip(UMAP_and_GMM['troughToPeak_abs'].tolist(),\n",
    "                  UMAP_and_GMM['FWHM1_abs'].tolist(),\n",
    "                  UMAP_and_GMM['prePostHyper'].tolist())))\n",
    "\n",
    "\n",
    "wavemap_means = [np.mean(x) for x in wavemap_scores]\n",
    "wavemap_stds = [np.std(x) for x in wavemap_scores]\n",
    "gmm_means = [np.mean(x) for x in gmm_scores]\n",
    "gmm_stds = [np.std(x) for x in gmm_scores]\n",
    "\n",
    "n_neighbors_means = [np.mean(x) for x in wavemap_n_neigbors_scores]\n",
    "n_neighbors_stds = [np.std(x) for x in wavemap_n_neigbors_scores]\n",
    "\n",
    "f, arr = plt.subplots(1)\n",
    "wavemap_n_neighbors = arr.errorbar(list(n_neighbors_to_use.values()),\n",
    "                                   n_neighbors_means,yerr=n_neighbors_stds,markerfacecolor='white',\n",
    "                                   marker='o',label='WaveMAP ($\\Delta$ n_neighbors)',color='#1A71A3')\n",
    "wavemap = arr.errorbar(list(resolutions_to_use.values()),wavemap_means,markerfacecolor='white',\n",
    "                       yerr=wavemap_stds,marker='o',label='WaveMAP ($\\Delta$ resolution)',color='#95D7E3')\n",
    "gmm_on_feat = arr.errorbar(joint_clusts_to_use,gmm_means,yerr=gmm_stds,marker='o',markerfacecolor='white',\n",
    "                           label='GMM on Features ($\\Delta$ n_components)',color='#DC4549')\n",
    "\n",
    "arr.legend(loc=3,frameon=False)\n",
    "arr.set_ylim([0.5,1.])\n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.spines['bottom'].set_bounds(2,17)\n",
    "arr.set_xticklabels(['',2,4,6,8,10,12,14,16],fontsize=12)\n",
    "arr.set_yticklabels([0.5,0.6,0.7,0.8,0.9,1.0],fontsize=12)\n",
    "arr.set_xlabel('Number of Clusters',fontsize=16)\n",
    "arr.set_ylabel('Classifier Accuracy',fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 4—figure supplement 2.\n",
    ":::\n",
    "![](elife-67490.ipynb.media/fig4-figsupp2.jpg)\n",
    "\n",
    "### Comparison of GMM and UMAP in the constructed feature space.\n",
    "\n",
    "(**A**) Three views of the eight _WaveMAP_ clusters shown in the constructed feature space. The clusters maintain some structure but are largely mixed and linearly inseparable. (**B**) A GMM instantiated with eight clusters in the constructed feature space of [Figure 4B](#fig4). (**C**) Confusion matrix for a gradient boosted decision tree classifier with the same hyperparameters as the one trained on four GMM classes (see hyperparameters in [Table 1](#table1)). Numbers listed are in percent accuracy on the main diagonal and misclassification rate percentage on the off-diagonals against held-out data. (**D**) Each cluster of waveforms in the eight class GMM with average waveforms in black. (**E**) Both _WaveMAP_ and a GMM on features were used on the full dataset to generate results of various cluster number and a gradient boosted decision tree (hyperparameters optimized to the four-class GMM) was trained on each. Shown is the classifier accuracy (mean ± S.D.) across stratified k-folds (k = 5) and various cluster number from 2 to 16. In dark blue, we generated _WaveMAP_ mappings of different cluster number by changing the n_neighbors parameter associated with UMAP; in light blue, we generated _WaveMAP_ mappings of various cluster number by changing resolution associated with Louvain. In red, we changed the n_components for the Gaussian mixture model.\n",
    ":::\n",
    "{#fig4s2}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result of the GMM applied to these three measures is shown in [Figure 4B](#fig4). This method identified four waveform clusters that roughly separated into broad-spiking (BS, ∼33%, n = 208), narrow-spiking (NS, ∼43%, n = 269), broad-spiking triphasic (BST, ∼9%, n = 55), and narrow-spiking triphasic (NST, ∼15%, n = 93) ([Figure 4B](#fig4)). Triphasic waveforms, thought to be neurons with myelinated axons or neurites [@bib12; @bib139; @bib40; @bib8; @bib161], contain an initial positive spike before the trough and can be identified by large peak ratios ([Figure 4A](#fig4)). These GMM clusters are similar to those obtained from other clusterings of EAP’s in macaque cortex [@bib61; @bib170]. We selected four clusters by examining the Bayesian information citerion (BIC) statistic as a function of the number of clusters and identified the cluster number at the elbow (green chevron in [Figure 4C](#fig4)).\n",
    "\n",
    "To compare the generalizability of this representation with the representation provided by UMAP, we trained the same decision tree classifier on the waveform data (after separate hyperparameter tuning, [Table 1](#table1)) but this time using the four GMM classes as target labels. After training, the accuracy across all four classes averaged ∼78% with no classification accuracy over 95% and misclassifications between every class ([Figure 4D](#fig4)). The classifier trained on specified features under-performed the classifier trained on the whole waveform found by _WaveMAP_. In _WaveMAP_, the individual classification accuracy of most classes exceeded 95% with few misclassifications between groups even though there were double the number of clusters. This result suggests that the clusters based on specified features are less differentiable than _WaveMAP_ clusters even when a much lower cluster number is considered.\n",
    "\n",
    "This deficit can be understood as an inability of the GMM to fully capture the latent structure of the data. If we examine the gray data point shadows ([Figure 4B](#fig4)), no features contain clear clusters and neither do they contain Gaussian distributions which is an assumption of the GMM model. Examining the marginal distributions in [Figure 4B](#fig4), none of the features induce a clear separability between the clusters alone or in conjunction. Furthermore, the reproducible clusters found by _WaveMAP_ are linearly inseparable in the feature space of the three GMM features ([Figure 4—figure supplement 2A](#fig4s2)). Note, this is not an artifact of using a lower cluster number in the GMM as opposed to the eight found by _WaveMAP_. Even if the GMM is instantiated with eight clusters ([Figure 4—figure supplement 2B](#fig4s2)), a classifier is still unable to generalize this clustering with even modest accuracy (average of 56% across clusters; [Figure 4—figure supplement 2C](#fig4s2)) even if the waveforms shapes found by the GMM with eight clusters seem somewhat sensible ([Figure 4—figure supplement 2D](#fig4s2)). In fact, across all cluster numbers (n_components from 2 to 16), a classifier tuned _for the GMM_ performed more poorly on the GMM labels than a _WaveMAP_ projection with the same number of clusters ([Figure 4—figure supplement 2E](#fig4s2), in red). Tuning _WaveMAP_ parameters that induce different cluster numbers, whether n_neighbors (in dark blue) or resolution (in light blue), had little effect on classifier performance ([Figure 4—figure supplement 2E](#fig4s2), in blues). _WaveMAP_ yielded mappings that were more generalizable than a GMM on features across every number of clusters and both parameters investigated. Thus, it is a deficit of the GMM on constructed feature-based approach to capture the full diversity of waveforms, especially at high cluster number, and not a peculiarity of the model parameters chosen or number of clusters induced.\n",
    "\n",
    "We also investigated the representation of specified features in the projected UMAP space. We color coded the waveforms in UMAP, in [Figure 5—figure supplement 1](#fig5s1), according to each point’s feature values using the same features as in [Figure 4](#fig4) ([Figure 5—figure supplement 1A](#fig5s1)): AP width ([Figure 5—figure supplement 1B](#fig5s1)), trough to peak duration ([Figure 5—figure supplement 1C](#fig5s1)), and peak ratio ([Figure 5—figure supplement 1D](#fig5s1)). We find that _WaveMAP_ implicitly captures each of these specified features shown as a smooth gradient of values. Our method also exposes the correlation between certain specified features: the gradient between trough to peak duration and AP width points point roughly in the same direction so thus both features are highly correlated. This correlation between features exposes their redundancy and is another reason why traditional approaches fail to capture the full diversity of waveform shapes.\n",
    "\n",
    "To obtain a clearer picture of how _WaveMAP_ captures latent structure missed by specified features, we color the points in UMAP space by their GMM cluster identity in [Figure 4E](#fig4). Here, _WaveMAP_ is able to recapitulate the same structure observed by specified features as a gradient from broad- to narrow-spiking along the UMAP-1 direction. Our technique also captures the transition from triphasic to biphasic along the UMAP-2 direction. _WaveMAP_ is also able to find clusters that occupy an intermediate identity between GMM classes. For instance, _WaveMAP_ cluster ② ([Figure 3A](#fig3)) is nearly equal parts broad- and narrow-spiking in the GMM clustering ([Figure 4E](#fig4)). If a GMM were used, ② would be split between two classes despite it having a distinct waveform shape characterized by a small pre-hyperpolarization peak, a moderate post-hyperpolarization peak, and relatively constant repolarization slope.\n",
    "\n",
    "## _WaveMAP_ interpretably recapitulates and expands upon known waveform features\n",
    "\n",
    "We have established that _WaveMAP_ has the ability to discover extracellular waveform clusters, but a common contention with such non-linear methods is that they are uninterpretable. Here, using an interpretable machine learning approach, we show that _WaveMAP_ produces sensible results [@bib114; @bib6]. To identify the features our algorithm is paying attention to, we first computed the inverse mapping of the UMAP transform to probe the projected space in a systematic way. Second, we leverage the gradient boosted decision tree classifier in [Figure 3C](#fig3) and used a decision tree implementation (path-dependent TreeSHAP [@bib96]) of SHapley Additive exPlanations (SHAP values [@bib98]; [@bib97]) to reveal what waveform features are implicitly used to differentiate clusters.\n",
    "\n",
    "To quantify the differences between Louvain clusters, we applied a grid of ‘test points’ to the UMAP projected space ([Figure 5A](#fig5), top) and inverted the transform at each location; each of these test points is a coordinate on a grid (black x’s) and shows the waveform associated with every point in the projected space ([Figure 5A](#fig5), bottom). On the bottom of [Figure 5A](#fig5) is shown the waveform that corresponds to each point in UMAP space color-coded to the nearest cluster or to gray if there were no nearby clusters. As UMAP-1 increases, there is a smooth transition in the sign of the inflection of the repolarization slope (the second derivative) from negative to positive (slow to fast repolarization rate). That is, the post-hyperpolarization peak becomes more sharp as we increase in the UMAP-1 direction. As UMAP-2 increases, we see a widening of the post-hyperpolarization slope distinct from the change in its inflection (UMAP-1). These two UMAP dimensions recapitulate the known importance of hyperpolarization properties in clustering waveforms. Both hyperpolarization rate (proportional to trough to peak width) and hyperpolarization slope inflection (proportional to repolarization time) are separate but highly informative properties [@bib170; @bib4]. Furthermore, since repolarization rate and post-hyperpolarization width associate with different UMAP dimensions, this implies that these two processes are somewhat independent factors shaping the waveform. Repolarization rates are goverened by potassium channel dynamics and may play an important in waveform shape [@bib154]. Thus, _WaveMAP_ not only finds an interpretable and smoothly varying low-dimensional space it also offers biological insights; in this case, how cell types might differ according to channel protein expression and dynamics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 324x504 with 101 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Note that the coordinates may or may not align to those found in the original manuscript \n",
    "# due to OS-level differences in the UMAP projection process. The overall structure, however, will be preserved.\n",
    "\n",
    "def find_nearest_color(embedding, test_coord, threshold_dist=0.5):\n",
    "    x_array, y_array = embedding[:,0], embedding[:,1]\n",
    "    \n",
    "    # Take coordinates of test point to calculate an array of each point's distance to test then return index\n",
    "    # where the minimum value is found\n",
    "    dist_array = np.array(np.abs(x_array-test_coord[0])+np.abs(y_array-test_coord[1]))\n",
    "    idx = dist_array.argmin()\n",
    "    \n",
    "    if dist_array[idx] <= threshold_dist:\n",
    "        return cluster_colors[idx]\n",
    "    \n",
    "    else:\n",
    "        return (0.8,0.8,0.8)\n",
    "\n",
    "\n",
    "corners = np.array([\n",
    "    [6.7, 8.5],  # top-left\n",
    "    [8.9, 8.5],  # top-right\n",
    "    [-3.3, 2.8],  # bottom-left\n",
    "    [7.5, 1.6],  # bottom-right\n",
    "])\n",
    "\n",
    "test_pts = np.array([\n",
    "    (corners[0]*(1-x) + corners[1]*x)*(1-y) +\n",
    "    (corners[2]*(1-x) + corners[3]*x)*y\n",
    "    for y in np.linspace(0, 1, 10)\n",
    "    for x in np.linspace(0, 1, 10)\n",
    "])\n",
    "\n",
    "inv_transformed_points = reducer.inverse_transform(test_pts)\n",
    "\n",
    "# Set up the grid\n",
    "fig = plt.figure(figsize=(4.5,7))\n",
    "gs = GridSpec(20, 10, fig)\n",
    "gs.update(wspace=0.05, hspace=0.05)\n",
    "scatter_ax = fig.add_subplot(gs[:10, :10])\n",
    "waveform_axes = np.zeros((10, 10), dtype=object)\n",
    "for i in range(10):\n",
    "    for j in range(10):\n",
    "        waveform_axes[i, j] = fig.add_subplot(gs[10+ i,j])\n",
    "\n",
    "scatter_ax.scatter(reducer.embedding_[:, 0], reducer.embedding_[:, 1],\n",
    "                c=cluster_colors, s=30,linewidth=0.25,edgecolor='white',zorder=1)\n",
    "scatter_ax.scatter(test_pts[:, 0], test_pts[:, 1], marker='x', \n",
    "                   c='k',\n",
    "                   s=30, zorder=2, alpha=1)\n",
    "\n",
    "# Plot each of the generated waveforms\n",
    "for i in range(10):\n",
    "    for j in range(10):\n",
    "        waveform_axes[i, j].plot(inv_transformed_points[i*10 + j], \n",
    "                                     c = find_nearest_color(reducer.embedding_,\n",
    "                                                            test_pts[i*10 + j]),\n",
    "                                linewidth=1.0)\n",
    "            \n",
    "        waveform_axes[i, j].set(xticks=[], yticks=[])\n",
    "        waveform_axes[i, j].spines['right'].set_visible(False)\n",
    "        waveform_axes[i, j].spines['top'].set_visible(False)\n",
    "        waveform_axes[i, j].spines['left'].set_visible(False)\n",
    "        waveform_axes[i, j].spines['bottom'].set_visible(False)\n",
    "        \n",
    "scatter_ax.set(xticks=[], yticks=[])\n",
    "scatter_ax.spines['right'].set_visible(False)\n",
    "scatter_ax.spines['top'].set_visible(False)\n",
    "scatter_ax.spines['left'].set_visible(False)\n",
    "scatter_ax.spines['bottom'].set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_bars = 10\n",
    "X = np.stack(UMAP_and_GMM['waveform'].to_numpy().tolist(), axis=0)\n",
    "y = UMAP_and_GMM['gmm_labels'].to_numpy()\n",
    "\n",
    "unclassified_ixs = [ix for ix,clust in enumerate(y) if clust == -1]\n",
    "\n",
    "X = np.delete(X,unclassified_ixs,axis=0)\n",
    "y = np.delete(y,unclassified_ixs,axis=0)\n",
    "\n",
    "UMAP_model = xgb.XGBClassifier(UMAP_grid_search.best_params_)\n",
    "UMAP_model.fit(UMAP_X_train,UMAP_y_train)\n",
    "explainer = shap.TreeExplainer(UMAP_model)\n",
    " \n",
    "shap_values = explainer.shap_values(X)\n",
    "\n",
    "clust_colors = []\n",
    "\n",
    "\n",
    "SHAP_CUSTOM_PAL_SORT_3 = ['#ffca3a','#7bdff2','#efa6c9','#fe7f2d','#00c49a','#5e60ce','#D81159','#0496ff','#ced4da'] #Need to do this so the UMAP colormap aligns with the SHAP color order\n",
    "umap_cmap = mpl.colors.ListedColormap(SHAP_CUSTOM_PAL_SORT_3, name='umap_cmap')\n",
    "\n",
    "fig = plt.figure();\n",
    "\n",
    "shap.summary_plot(shap_values[:], X, [str(np.round(x*(1/30000)*1000,2)) for x in pd.DataFrame(X).columns.tolist()],\n",
    "                  plot_type='bar',show=False,color=umap_cmap,\n",
    "                  max_display = n_bars)\n",
    "\n",
    "ax = fig.gca();\n",
    "ax.set_xlabel('Mean Abs. SHAP Value',size=12,fontname='Arial')\n",
    "ax.set_ylabel('Time Point (ms)',size=12,fontname='Arial')\n",
    "ax.get_legend().remove()\n",
    "ax.set_xlim([0,3.5])\n",
    "ax.set_xticks([0.0,3.5])\n",
    "ax.set_xticklabels([0,3.5],fontsize=12,fontname='Arial')\n",
    "fig.set_size_inches(4,3.5);\n",
    "\n",
    "ytick_labels = [round(np.float(i.get_text())/(1000/30000)) for i in ax.get_yticklabels()][::-1]\n",
    "bar_heights = []\n",
    "\n",
    "for j in range(n_bars):\n",
    "\n",
    "    bar_height = ax.patches[j].get_width()\n",
    "    bar_heights.append(bar_height)\n",
    "\n",
    "bar_heights = bar_heights[::-1]\n",
    "percent_total_height = [x/sum(bar_heights) for x in bar_heights]\n",
    "for k,label in enumerate(ytick_labels):\n",
    "    arr.axvline(label,color='k',alpha=percent_total_height[k])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, arr = plt.subplots(1,figsize=[2,1.5])\n",
    "\n",
    "for i in range(0,full_data.shape[0]):\n",
    "    arr.plot(full_data[i].T, c = 'k', alpha = 0.025,linewidth=1.5);\n",
    "    \n",
    "arr.tick_params(direction='out',colors='k', axis='both')\n",
    "    \n",
    "arr.spines['top'].set_visible(False)\n",
    "arr.spines['right'].set_visible(False)\n",
    "arr.spines['left'].set_visible(False)\n",
    "\n",
    "arr.set_xlabel('Time (ms)', fontsize=12,fontname='Arial');\n",
    "arr.set_xticks([0,14,28,42])\n",
    "arr.set_xticklabels(['0','0.5','1.0','1.5'],fontsize=12,fontname='Arial')\n",
    "\n",
    "arr.set_yticks([]);\n",
    "\n",
    "for i,t in enumerate(ytick_labels):\n",
    "    arr.axvline(t,alpha=percent_total_height[i]*20,\n",
    "                color='goldenrod',lw=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 43.2x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 43.2x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 43.2x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 43.2x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 43.2x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 43.2x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGIAAABECAYAAAB6UOAlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAGqElEQVR4nO2c228cVx3HP+cys7Oza++u7dipCUlb95IgAoE2ipQKEEIEXhCVQH1ASCh/QB54578AIeAFXpDIA0ggtU88JBVQNaVAbxKqsFqpdWwntrNje3e9czmHh10nTurLxvbOztrzebHsGa3PnO/5Xc+ZFdZacgaPHPQAcjrkQmSEXIiMkAuREXIhMoLe4/qhpVSzs7MAzMzM7Hrf9evXAajVagBcuXLlsIaQBcROF3KLyAh7WcRAiY3ln2sh7zRiylJw1td8saRRYseFNbRkVojEWv5ab+MvtznhSO5GhveaMX9blXx/3ONUQQ16iIdKZoX493rEQmj46XiBF8sOFvigGfPaSpvfzDf5esXlm1X3yFhHJoVYigzvt2Oe9zUXR1ygE+XOlxyeLWpeXdngRhDyYSvmhxMek+7wW0cmg/X/WjGOgBfKzmeueVLwg4kiPzrhUY8tv5xv8o/VEDPkPbPMCREayyfthKeLGlfu7Ha+UHK4Nu0z4yleW2nz28UWK5FJcaSHS+aE+GgjwQDPeXu7mxEt+fFkkZfHC9xuJ/zidoNbqyHD2FHOnBAfb8TUlGC8R78vhODFEZdrnytxqqD4y0qb3y22uBcPl3VkSojQWOqJ5Yl9BN+qllydKvK9sQKftBN+PtfgjSGKHZkSYjHsrOIxZ38pqRCCS6Mu16ZLnC4oXl1p8+v5Jp+2k8McZl/IlBC3w86EVfXBhlVzJD+ZKvLKhEeQWH413+SPd1vUM+yuMlVHzIUGVwj8XbKlXhFC8KWyw3O+5ka9zRurEe81Yr5cdrg86jCVsdojU0LMhwk1LRCHWC17UvDdMY9Loy436yH/aUS8vR7xlKe4NOJwzs9G7yozQkTGshgaagd0SztR05KXJzy+XXP513rEm2sRf7i7wYgSfLXs8JWyw4QzOE+dGSHuRAYDjOn+rs6SknytUuClUZcPWwm31kJeD0JuBiFTjuScr3m+qJkuyANbirEW2eNn9CxEYm1fTXgzUPfLIh5Fik5b/ayvWY0N7zdjPmjE3AxCbgQhBQGnC4onCoopRzLpSKpa4kl2dJ3WWlqm8yy31iLaxnL1pN/TeHoW4vd3WsyHhklHMuFIalpS04KKllSUoKREz+pvx3JkUICv0vfXo1pyedTl8qhLM7HMbsTMthLmwoS/ByFbk18BOAK0EDgCXCkw1hJZaBpL3C1bfCl4oez0bBU9C3HO1/gy4U5keGc9YuOROkkCo1owpiUXyg4XSvqxhKnHlqo+mJiHga8E50sO50udhmNsLUuRYSkyBLGlZSxRd+JD0/kpRUccXwpGlGTMkTzjKZzHyP56FuLiiMvFkQe/txLLvdiwmliC2BAklnpsmA8Nf1ra4K01ydUpf9fG3VaCxFBJyS09DloITrqKk31Od/cdrItKUFSK6Uf+bqzl7fWIPy+3eWst4qWK29Pn1WPLs0VJtN8BDTmHvgSlEFwccZnxFK8HIaHZu9cTW8t6Yqmo7FlEWvTtyb9VLdAwljfXwj3vXY0tloO3NoaZvj35aU9xpqB4txHvee9mD6ja5xoiy/R1CT5TVCyEhlayu3sKutezGKzToq9P/qSnsMDHe7ShNy2iMoAaIiv0VYhTrkLT2XXbjXpsKUvxWHn3UaOvQjhScKqg+Ghjd4sIYkPlGMcHSGFj6ClPMR8a2ruksUFij3XGBCkJYYG5HeKEtZ2K/DhnTJCCENPdM6p3o+2FaHb7Ncc5Y4IUhPCkoKIEy9H2rinotiurxzhjgpQOD0y5kqUdTuEFSTd1zS2i/0w5ipXYkGxzxqi+aRF5jOg/k67EAPVtrCKIDbrbyz/OpGQRnX+zvM25onpsqajDPbkxjKQixImuENvFiaxuCKVNKjPgSEFNCZbjz8aIoLtFetxJbSmOu4ql8GGLSKxl7ZhvCG2S2gxMaMG9xBBtaXXkG0IPSE+Ibpy4syVO1O/XELlrSlGITqtjcYt72qyq82CdohBVLdACFrb0nIJ8Q+g+qQkhhWBcy4ctIrH4UvR89ukok6pPmHAkC1uEWIry9vcmqQvRMJb1pNN3+rSd8Pkj9lUO+yXVY/mbmdNCaChKQWjhTC4EMCAhFkPDZmvpTA/vUx8HUhWipCRjWvBuI6KiJdXusf6cAbxV+o1KgbnQ8N9mzJO5W7pP6kJcKGtOOJ39idwtPSB1IZQQfKdWwBEw42XmFb6BM5CZOOtrfna6nInXarPCwCJlLsLDiGH8Sp2jSJ47ZoRciIyQC5ERciEyQi5ERsiFyAj/B3J0Vo7wOXkaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 108x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFcAAABVCAYAAAA8AHhnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAEtElEQVR4nO2bS2hdRRzGf2nSpGitqe8+tFU0VjRUyh984YOioGAQWzehKi60kpUupD6WotCqGwtFK6iIQnclcEWoBbtQRPHzFSpatcEaKsUaTBWxNzWJizm3hJjkniQzuTNxfhCSTGbO/fKd4X/mfGdO09jYGJkwLGq0gIVMNjcg2dyAZHMDks0NSFLmViqVMSCGr1IkZW5qZHMDks0NSDY3INncgGRzA5LNDUg2NyBNKUWOi18pv4APyakemsr0yzM3IC0+DmJmzcB24CFgCfA+8Kik3ybpezPwMrAWaAYOA89J2utDS0z4mrlPAfcA1wGri7a3p+h7CLgXOBdoBx4H3jGzqzxpiQYvMxfYCjwrqR/AzLYBP5rZGklHxneU9GvtZzNbBIziTvLlwLee9ETBnGeumbUDlwCf19okHQb+ANZPM24IqAIfAp/iSsmCwkdZOKv4fmJC+xCwbKpBktqBpbgS8R7wjwctUeHD3D+L72dPaG/Hzd4pkVSV1AvcCjzsQUtUzNlcSUPAz8CGWpuZXYabtX0lD9MCXDFXLbHh64L2GvCkmR0ABoEdwD5JP03saGabge9xF68W4AFgI/CiJy3R4Mvc7cBy4DOgDdgP3A9gZluA3ZKWFn1XFP1XAMO4pVm3pP31PmTv6gpdXV2eJIcnqdvfSqUyFom5+fa30WRzA5JUWZhNKnaqJ4SScmWh1AXNZzBjZucAvcC64ljHgTeB5yWlc6ZLULYs+Axm/gJ6gFWSlgG3A1uAR2asPnLKLsW8BTOSqsA3E44/Clw5u38hXurO3FDBjJm9a2Z/A/24fGL3LPRHTZmyECSYkXR38fcbcCXmP/U7dcqYGyyYkTQi6RPcidtVQktS1DV3noKZ/3Vw4y2YMbPrgTOBj3HZwk3AY8VnLCjKmuszmGkFXgA6cHtdjwI7izHTkoObgOTgJnOabG5AkioLMw1uAoU2kMtC42nEdqbbgAO4AKdGn6QbfWiJCV/P0ManZoPAG7hb2rum6D8ybum2YPFVFrYCOyT1SzoBbAPuNLM1no6fJHOeuVOlZmZWS82OTDKs2cwGgMXFuGckfT1XLbHRiO1M3wHXApfinkb0AR+Y2UoPWqLCR82dUWom6RhwrPh1CHjazO7D1efXPeiJhli2M41Scu2YEo3YzrQRdzL6gTOAJ4ALgX2etERDI7Yzrcc97T0Pt9b9ArhD0kC9D8mpWEByKpY5TTY3INncgGRzA5LNDUg2NyDZ3IAktc5ta2s7ODw8fLLROlpbW5dUq9Vr6vXzdYc2L3R2dp6UZI3WYWYq0y+XhYBkcwOSmrmx7CcrpSOpC1pqpDZzkyKbG5AklmJm1gG8hXtDaBB4UNIP86zhJWAz7hWwTkkH641JZea+CuyS1IHb3t+Il1N6gVuYfKvApERvrpldgHv4uado2gNsMLPz51OHpI/KPIoaT/TmAhcDRyWNgHtJBfilaI+aFMxNlhTMHQBWFTspazsqVxbtURO9ucXrrl8B3UVTN/ClpOONU1WOJO7QzGwdbim2HPgdtxQ7NM8adgKbgItwb3sOSrp6ujFJmJsq0ZeFlMnmBiSbG5BsbkCyuQHJ5gYkmxuQfwFdwOHVp/MLUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 43.2x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 43.2x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_group_no_axis(label_ix, labels, groups_df, colors, mean_only=False, detailed=False):\n",
    "    group_ixs = [i for i,x in enumerate(labels) if x == label_ix-1]\n",
    "    group_waveforms = groups_df.iloc[group_ixs]['waveform'].tolist()\n",
    "    \n",
    "    f, arr = plt.subplots()\n",
    "    f.set_figheight(1.8*0.65)\n",
    "    f.set_figwidth(3.0*0.65)\n",
    "    if not mean_only:\n",
    "        for i,_ in enumerate(group_waveforms):\n",
    "            plt.plot(group_waveforms[i],c=colors[label_ix-1],alpha=0.3,linewidth=1.5)\n",
    "    \n",
    "    if not mean_only:\n",
    "        plt.plot(np.mean(group_waveforms,axis=0),c='k',linestyle='-')\n",
    "    else:\n",
    "        plt.plot(np.mean(group_waveforms,axis=0),c=colors[label_ix-1],linestyle='-')\n",
    "    arr.spines['right'].set_visible(False)\n",
    "    arr.spines['top'].set_visible(False)\n",
    "\n",
    "    if detailed:\n",
    "        \n",
    "        avg_peak = np.mean([np.argmax(x) for x in group_waveforms[14:]])\n",
    "        arr.axvline(avg_peak,color='k',zorder=0)\n",
    "        \n",
    "        arr.set_ylim([-1.3,1.3])\n",
    "        arr.set_yticks([])\n",
    "        arr.set_xticks([0,7,14,21,28,35,42,48])\n",
    "        arr.tick_params(axis='both', which='major', labelsize=12)\n",
    "        arr.set_xticklabels([0,'',0.5,'',1.0,'',1.5,''])\n",
    "        arr.spines['left'].set_visible(False)\n",
    "        arr.grid(False)\n",
    "        arr.set_xlim([0,48])\n",
    "\n",
    "    if not detailed:\n",
    "        arr.set(xticks=[],yticks=[])\n",
    "\n",
    "        if not mean_only:\n",
    "            x,y = 2.1,0.7\n",
    "            ellipse = mpl.patches.Ellipse((x,y), width=9.0, height=0.72, facecolor='w',\n",
    "                                 edgecolor='k',linewidth=1.5)\n",
    "            label = arr.annotate(str(label_ix), xy=(x-0.25, y-0.15),fontsize=12, color = 'k', ha=\"center\")\n",
    "            arr.add_patch(ellipse)\n",
    "\n",
    "            if i != -1:\n",
    "                x, y = 23,-0.7\n",
    "                n_waveforms = plt.text(x, y, \n",
    "                                       'n = '+str(len(group_waveforms))+\n",
    "                                       ' ('+str(round(len(group_waveforms)/len(groups_df)*100,2))+'%)'\n",
    "                                       , fontsize=10)\n",
    "    \n",
    "    return f, arr\n",
    "\n",
    "n_bars = 3\n",
    "for i in range(len(shap_values)):\n",
    "    fig = plt.figure()\n",
    "    \n",
    "    clust_color = CUSTOM_PAL_SORT_3[i]\n",
    "    shap.summary_plot(shap_values[i], X, \n",
    "                      [str(np.round(x*(1000/30000),2)) for x in pd.DataFrame(X).columns.tolist()],\n",
    "                      plot_type=\"bar\", \n",
    "                      max_display = n_bars, color = clust_color,show=False)\n",
    "    ytick_labels = [np.float(i.get_text())*0.05 for i in ax.get_yticklabels()][::-1]\n",
    "    \n",
    "    ax = fig.gca()\n",
    "    fig.set_size_inches(0.6,1.0)\n",
    "    ax.set_xlabel('',fontsize=12)\n",
    "    ax.set_ylabel('',fontsize=12)\n",
    "    ax.set_xlim(0,1)\n",
    "    SHAP_RESORTED_ORDER = (6, 5, 1, 7, 4, 2, 8, 3)\n",
    "    \n",
    "    f, arr = plot_group_no_axis(SHAP_RESORTED_ORDER[i],clustering_solution,UMAP_and_GMM,SHAP_CUSTOM_PAL_SORT_3,mean_only=True)\n",
    "    ytick_labels = [round(np.float(i.get_text())/(1000/30000)) for i in ax.get_yticklabels()][::-1]\n",
    "    bar_heights = []\n",
    "    arr.spines['left'].set_visible(False)\n",
    "    arr.spines['right'].set_visible(False)\n",
    "    arr.spines['bottom'].set_visible(False)\n",
    "\n",
    "    for j in range(n_bars):\n",
    "\n",
    "        bar_height = ax.patches[j].get_width()\n",
    "        bar_heights.append(bar_height)\n",
    "\n",
    "    bar_heights = bar_heights[::-1]\n",
    "    percent_total_height = [x/sum(bar_heights) for x in bar_heights]\n",
    "    for k,label in enumerate(ytick_labels):\n",
    "        arr.axvline(label,color='k',alpha=percent_total_height[k])\n",
    "    f.set_size_inches([1.5,1.0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 5.\n",
    ":::\n",
    "![](elife-67490.ipynb.media/fig5.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WaveMAP provides interpretable representations that both validate and extend known and unknown features importances.\n",
    "\n",
    "(**A**) _WaveMAP_ applied to the EAP’s as in [Figure 3A](#fig3) but overlaid with a grid of test points (black x’s, top) spanning the embedded space. At bottom, the inverse UMAP transform is used to show the predicted waveform at each test point. For each x above, the predicted waveform is shown, plotted, and assigned the color of the nearest cluster or in gray if no cluster is nearby. Note that there exists instability in the waveform shape (see waveforms at corners) as test points leave the learned embedded space. (**B**) The mean absolute SHAP values for 10 time points along all waveforms subdivided according to the SHAP values contributed by each _WaveMAP_ cluster. These SHAP values were informed by applying path-dependent TreeSHAP to a gradient boosted decision tree classifier trained on the waveforms with the _WaveMAP_ clusters as labels. In the inset, all waveforms are shown and in gold are shown the time points for which the SHAP values are shown on the left. Each vertical line is such that the most opaque line contains the greatest SHAP value across _WaveMAP_ clusters; the least opaque, the smallest SHAP value. (**C**) Each averaged _WaveMAP_ waveform cluster is shown with the three time points containing the greatest SHAP values for each cluster individually. As before, the SHAP value at each time point is proportional to the opacity of the gray vertical line also shown as a bar graph at left. [Figure 5—figure supplement 1](#fig5s1): _WaveMAP_ implicitly captures waveform features (such as trough to peak or AP width) without the need for prior specification.\n",
    ":::\n",
    "{#fig5}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gmm_feat_df = pd.DataFrame(gmm_feat_data_nonan,\n",
    "                          columns=['trough_to_peak','peak_ratio','trough_fwhm'])\n",
    "\n",
    "GMM_class_df = pd.DataFrame(GMM_class_labels,columns=['Class'])\n",
    "full_data_df = pd.DataFrame({'Waveform': full_data.tolist()})\n",
    "data_classified_df = pd.concat([umap_df,full_data_df,GMM_class_df,gmm_feat_df],axis=1)\n",
    "data_classified_df.loc[data_classified_df['Class']==1,'color'] = GMM_PAL[0]\n",
    "data_classified_df.loc[data_classified_df['Class']==2,'color'] = GMM_PAL[1]\n",
    "data_classified_df.loc[data_classified_df['Class']==3,'color'] = GMM_PAL[2]\n",
    "data_classified_df.loc[data_classified_df['Class']==4,'color'] = GMM_PAL[3]\n",
    "\n",
    "data_classified_df['trough_to_peak_abs'] = data_classified_df['trough_to_peak'].divide(SAMP_RATE_TO_TIME)\n",
    "data_classified_df['trough_fwhm_abs'] = data_classified_df['trough_fwhm'].divide(SAMP_RATE_TO_TIME)\n",
    "\n",
    "def feature_scatter(feature_name,cmap='mako',save=False):\n",
    "    cmap = sns.color_palette(cmap, as_cmap=True)\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    fig.set_size_inches(5, 4)\n",
    "    scat = ax.scatter(data_classified_df['x'],data_classified_df['y'],c=data_classified_df[feature_name],cmap=cmap)\n",
    "    cax = fig.add_axes([0.1, 0.05, 0.8, 0.03])\n",
    "    cbar = fig.colorbar(scat, cax=cax, orientation='horizontal')\n",
    "    \n",
    "    \n",
    "    if feature_name == 'trough_to_peak_abs':\n",
    "        feature_label = 'Absolute Trough to Peak'\n",
    "        \n",
    "    elif feature_name == 'trough_fwhm_abs':\n",
    "        feature_label = 'Absolute Trough FWHM'\n",
    "        \n",
    "    elif feature_name == 'peak_ratio':\n",
    "        feature_label = 'Peak Ratio'\n",
    "    \n",
    "    cbar.set_label(feature_label,labelpad=10,fontsize=16)\n",
    "    cbar.ax.tick_params(labelsize=16)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['bottom'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.set_xticks([]);\n",
    "    ax.set_yticks([]);\n",
    "    \n",
    "    if save:\n",
    "        plt.savefig('Feature_'+feature_name+'.pdf',format='pdf')    \n",
    "    \n",
    "    return None\n",
    "\n",
    "feature_scatter('trough_fwhm_abs',cmap='crest',save=True)\n",
    "feature_scatter('trough_to_peak_abs',cmap='flare',save=True)\n",
    "feature_scatter('peak_ratio',cmap=\"ch:start=.2,rot=.5\",save=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 5—figure supplement 1.\n",
    ":::\n",
    "![](elife-67490.ipynb.media/fig5-figsupp1.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WaveMAP implicitly captures waveform features without the need for specification.\n",
    "\n",
    "(**A**) Three waveform shape features used in traditional clustering approaches. The three EAP waveform landmarks used to generate the specified features passed to the GMM on a sample waveform.\n",
    "\n",
    "![](elife-67490.ipynb.media/elife-67490-inf9-v4.tif)is the pre-hyperpolarization peak (**A1**); ![](elife-67490.ipynb.media/elife-67490-inf10-v4.tif)is the depolarization trough; and ![](elife-67490.ipynb.media/elife-67490-inf11-v4.tif)is the post-hyperpolarization peak (**A2**). AP width is the distance in time between the falling and rising phase of the depolarization trough at its full-width half minimum. The trough to peak duration is the distance between the minimum of the depolarization trough and the peak of the post-hyperpolarization peak. The peak ratio is the height (above zero) of the pre-hyperpolarization peak over the height (again, above zero) of the post-hyperpolarization peak. The same diagram as in [Figure 4A](#fig4) but repeated here. (**B, C, D**) The waveform data points in the projected UMAP space and color coded according to their AP width, trough to peak duration, and peak ratio, respectively.\n",
    ":::\n",
    "{#fig5s1}\n",
    "\n",
    "In [Figure 5B](#fig5), we made use of SHAP values to identify which aspects of waveform shape the gradient boosted decision tree classifier utilizes in assigning what waveform to which cluster [@bib98; @bib97]. SHAP values build off of the game theoretic quantity of Shapley values [@bib150; @bib159], which poses that each feature (point in time along the waveform) is of variable importance in influencing the classifier to decide whether the data point belongs to a specific class or not. Operationally, SHAP values are calculated by examining the change in classifier performance as each feature is obscured (the waveform’s amplitude at each time point in this case), one-by-one [@bib98]. [Figure 5B](#fig5) shows the top-10 time points in terms of mean absolute SHAP value (colloquially called ‘SHAP value’) and their location. It is important to note that not every time point is equally informative for distinguishing every cluster individually and thus each bar is subdivided into the mean absolute SHAP value contribution of the eight constituent waveform classes. For instance, the 0.7 ms location is highly informative for cluster ⑤ and the 0.3 ms point is highly informative for cluster ⑦ ([Figure 5C](#fig5)).\n",
    "\n",
    "In the inset is shown all waveforms along with each of the top ten time points (in gold) with higher SHAP value shown with more opacity. The time points with highest SHAP value tend to cluster around two different locations giving us an intuition for which locations are most informative for telling apart the Louvain clusters. For instance, the 0.5 to 0.65 ms region contains high variability amongst waveforms and is important in separating out broad- from narrow-spiking clusters. This region roughly contains the post-hyperpolarization peak which is a feature of known importance and incorporated into nearly every study of EAP waveform shape (see [Table 1](#table1) in [@bib174]). Similarly, SHAP values implicate the region around 0.3 ms to 0.4 ms as time points that are also of importance and these correspond to the pre-hyperpolarization peak which is notably able to partition out triphasic waveforms [@bib12]. Importance is also placed on the location at 0.6 ms corresponding to the inflection point which is similarly noted as being informative [@bib170; @bib10]. These methods also implicate other regions of interest that have not been previously noted in the literature to the best of our knowledge: two other locations are highlighted farther along the waveform at 1.1 and 1.27 ms and are important for differentiating ⑧ and ① from the other waveforms. This result suggests that using only up to 1.0 ms or less of the waveform may obscure diversity.\n",
    "\n",
    "In [Figure 5C](#fig5), we show the three locations that are most informative for delineating a specific cluster; these appear as gray lines with their opacity proportional to their SHAP importance. These individually informative features often do align with those identified as globally-informative but do so with cluster-specific weights. Put another way, not every time point is equally informative for identifying waveforms individually and these ‘most informative’ parts of each waveform do not always perfectly align with globally informative features. In summary, _WaveMAP_ independently and sensibly arrived at a more nuanced incorporation of the very same features identified in previous work—and several novel ones—using a completely unsupervised framework which obviated the need to specify waveform features.\n",
    "\n",
    "In the second half of the paper, we investigate whether these clusters have distinct physiological (in terms of firing rate), functional, and laminar distribution properties which could give credence that _WaveMAP_ clusters connect to cell types."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## _WaveMAP_ clusters have distinct physiological properties\n",
    "\n",
    "A defining aspect of cell types is that they vary in their physiology and especially firing rate properties [@bib119; @bib105; @bib32; @bib126; @bib31; @bib34]. However, these neuronal characterizations via waveform ex vivo are not always conserved when the same waveform types are observed in vivo during behavior [@bib158; @bib157]. To connect our waveform clusters to physiological cell types in vivo, we identified each cluster’s firing rate properties. We performed several analyses using the firing rate (FR) in spikes per second (spikes/s) for each cluster during the decision-making task described in [Figure 1](#fig1).\n",
    "\n",
    "The trial-averaged FRs are aligned to stimulus onset (stim-aligned) and separated into preferred (PREF, solid trace) or non-preferred (NONPREF, dashed trace) reach direction trials. This is shown for both broad- ([Figure 6A](#fig6)) and narrow-spiking ([Figure 6B](#fig6)) clusters. A neuron’s preferred direction (right or left) was determined as the reach direction in which it had a higher FR on average in the 100 ms time period before movement onset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'WaveMAP_Paper/data/FR_traces'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/_7/5t5ls51s3nb01xmzyc1fv9780000gn/T/ipykernel_88377/412515482.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mFR_trace_loc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'WaveMAP_Paper/data/FR_traces'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mFR_traces\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFR_trace_loc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mUMAP_FR_traces\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mFR_traces\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'GMM'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'WaveMAP_Paper/data/FR_traces'"
     ]
    }
   ],
   "source": [
    "def read_pkl(pkl_file_loc):\n",
    "    return pkl.load(open(pkl_file_loc,'rb'))\n",
    "\n",
    "FR_trace_loc = 'WaveMAP_Paper/data/FR_traces'\n",
    "FR_traces = os.listdir(FR_trace_loc)\n",
    "\n",
    "UMAP_FR_traces = [x for x in FR_traces if not x.startswith('GMM')]\n",
    "GMM_FR_traces = [x for x in FR_traces if x.startswith('GMM')]\n",
    "\n",
    "UMAP_traces_df = pd.DataFrame(columns = ['clust','PREF','NONPREF','PREF_UPPER_BOUND','PREF_LOWER_BOUND',\n",
    "                        'NONPREF_UPPER_BOUND','NONPREF_LOWER_BOUND'])\n",
    "GMM_traces_df = pd.DataFrame(columns = ['clust','PREF','NONPREF','PREF_UPPER_BOUND','PREF_LOWER_BOUND',\n",
    "                        'NONPREF_UPPER_BOUND','NONPREF_LOWER_BOUND'])\n",
    "\n",
    "for i in range(0,8):\n",
    "    traces = sorted([x for x in UMAP_FR_traces if str(i) in x],key=len)\n",
    "    trace_arr = []\n",
    "  \n",
    "    trace_arr.append(i)\n",
    "    for trace in traces:\n",
    "        if 'pref_'+str(i)+'.pkl' == trace:\n",
    "            trace_arr.append(read_pkl(os.path.join(FR_trace_loc,trace)))\n",
    "        elif 'nonpref_'+str(i)+'.pkl' == trace:\n",
    "            trace_arr.append(read_pkl(os.path.join(FR_trace_loc,trace)))\n",
    "        elif 'pref_bounds_'+str(i)+'.pkl' == trace:\n",
    "            upper_bound, lower_bound = read_pkl(os.path.join(FR_trace_loc,trace))\n",
    "            trace_arr.append(upper_bound)\n",
    "            trace_arr.append(lower_bound)\n",
    "        elif 'nonpref_bounds_'+str(i)+'.pkl' == trace:\n",
    "            upper_bound, lower_bound = read_pkl(os.path.join(FR_trace_loc,trace))\n",
    "            trace_arr.append(upper_bound)\n",
    "            trace_arr.append(lower_bound)\n",
    "  \n",
    "    trace_series = pd.Series(trace_arr,index=UMAP_traces_df.columns)\n",
    "    UMAP_traces_df = UMAP_traces_df.append(trace_series,ignore_index=True)\n",
    "\n",
    "for i in range(1,5):\n",
    "    traces = sorted([x for x in GMM_FR_traces if str(i) in x],key=len)\n",
    "    trace_arr = []\n",
    "\n",
    "    trace_arr.append(i)\n",
    "    for trace in traces:\n",
    "        if 'GMM_pref_'+str(i)+'.pkl' == trace:\n",
    "            trace_arr.append(read_pkl(os.path.join(FR_trace_loc,trace)))\n",
    "        elif 'GMM_nonpref_'+str(i)+'.pkl' == trace:\n",
    "            trace_arr.append(read_pkl(os.path.join(FR_trace_loc,trace)))\n",
    "        elif 'GMM_pref_bounds_'+str(i)+'.pkl' == trace:\n",
    "            upper_bound, lower_bound = read_pkl(os.path.join(FR_trace_loc,trace))\n",
    "            trace_arr.append(upper_bound)\n",
    "            trace_arr.append(lower_bound)\n",
    "        elif 'GMM_nonpref_bounds_'+str(i)+'.pkl' == trace:\n",
    "            upper_bound, lower_bound = read_pkl(os.path.join(FR_trace_loc,trace))\n",
    "            trace_arr.append(upper_bound)\n",
    "            trace_arr.append(lower_bound)\n",
    "  \n",
    "    trace_series = pd.Series(trace_arr,index=GMM_traces_df.columns)\n",
    "    GMM_traces_df = GMM_traces_df.append(trace_series,ignore_index=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'UMAP_traces_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/_7/5t5ls51s3nb01xmzyc1fv9780000gn/T/ipykernel_88377/2501769193.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mix\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBS_ORDERING\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mPREF\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUMAP_traces_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'PREF'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mNONPREF\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUMAP_traces_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'NONPREF'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mPREF_UPPER\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUMAP_traces_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'PREF_UPPER_BOUND'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'UMAP_traces_df' is not defined"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x239.76 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "BS_ORDERING = [5,6,0]\n",
    "NS_ORDERING = [7,1,2,3,4]\n",
    "\n",
    "f, arr = plt.subplots(3,figsize=[2,3.33])\n",
    "\n",
    "time = np.arange(-0.1,0.5,0.001)\n",
    "\n",
    "for i,ix in enumerate(BS_ORDERING):\n",
    "    PREF = UMAP_traces_df.iloc[ix]['PREF']\n",
    "    NONPREF = UMAP_traces_df.iloc[ix]['NONPREF']\n",
    "    PREF_UPPER = UMAP_traces_df.iloc[ix]['PREF_UPPER_BOUND']\n",
    "    PREF_LOWER = UMAP_traces_df.iloc[ix]['PREF_LOWER_BOUND']\n",
    "    NONPREF_UPPER = UMAP_traces_df.iloc[ix]['NONPREF_UPPER_BOUND']\n",
    "    NONPREF_LOWER = UMAP_traces_df.iloc[ix]['NONPREF_LOWER_BOUND']\n",
    "    arr[i].plot(time,PREF,color=CUSTOM_PAL_SORT_3[ix])\n",
    "    arr[i].plot(time,NONPREF,'--',color=CUSTOM_PAL_SORT_3[ix])\n",
    "    arr[i].fill_between(time,PREF_UPPER,PREF_LOWER,\n",
    "            color='gray',alpha=0.2)\n",
    "    arr[i].fill_between(time,NONPREF_UPPER,NONPREF_LOWER,\n",
    "            color='gray',alpha=0.2)\n",
    "    arr[i].set_ylim(5,30)\n",
    "    arr[i].set_xticks([-0.1,0.1,0.3,0.5])\n",
    "    arr[i].set_xlim(-0.1,0.5)\n",
    "    arr[i].set_yticks([5,30])\n",
    "    arr[i].spines['left'].set_position(('axes', -0.05))\n",
    "    arr[i].spines['top'].set_visible(False)\n",
    "    arr[i].spines['right'].set_visible(False)\n",
    "    arr[i].axvline(0,ymin=0.,ymax=30,linestyle='dashed',color='k')\n",
    "    f.tight_layout()\n",
    "\n",
    "f, arr = plt.subplots(5,figsize=[2,6])\n",
    "\n",
    "time = np.arange(-0.1,0.5,0.001)\n",
    "\n",
    "for i,ix in enumerate(NS_ORDERING):\n",
    "    PREF = UMAP_traces_df.iloc[ix]['PREF']\n",
    "    NONPREF = UMAP_traces_df.iloc[ix]['NONPREF']\n",
    "    PREF_UPPER = UMAP_traces_df.iloc[ix]['PREF_UPPER_BOUND']\n",
    "    PREF_LOWER = UMAP_traces_df.iloc[ix]['PREF_LOWER_BOUND']\n",
    "    NONPREF_UPPER = UMAP_traces_df.iloc[ix]['NONPREF_UPPER_BOUND']\n",
    "    NONPREF_LOWER = UMAP_traces_df.iloc[ix]['NONPREF_LOWER_BOUND']\n",
    "    arr[i].plot(time,PREF,color=CUSTOM_PAL_SORT_3[ix])\n",
    "    arr[i].plot(time,NONPREF,'--',color=CUSTOM_PAL_SORT_3[ix])\n",
    "    arr[i].fill_between(time,PREF_UPPER,PREF_LOWER,\n",
    "            color='gray',alpha=0.2)\n",
    "    arr[i].fill_between(time,NONPREF_UPPER,NONPREF_LOWER,\n",
    "            color='gray',alpha=0.2)\n",
    "    arr[i].set_ylim(5,30)\n",
    "    arr[i].set_xticks([-0.1,0.1,0.3,0.5])\n",
    "    arr[i].set_xlim(-0.1,0.5)\n",
    "    arr[i].set_yticks([5,30])\n",
    "    arr[i].spines['left'].set_position(('axes', -0.05))\n",
    "    arr[i].spines['top'].set_visible(False)\n",
    "    arr[i].spines['right'].set_visible(False)\n",
    "    arr[i].axvline(0,ymin=0.,ymax=30,linestyle='dashed',color='k')\n",
    "    f.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 6.\n",
    ":::\n",
    "![](elife-67490.ipynb.media/fig6.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UMAP clusters exhibit distinct physiological properties.\n",
    "\n",
    "(**A**) Stimulus-aligned trial-averaged firing rate (FR; spikes/s) activity in PMd for broad-spiking _WaveMAP_ clusters. The traces shown are separated into trials for PREF direction reaches (solid traces) and NONPREF direction reaches (dashed traces) and across the corresponding _WaveMAP_ clusters. Shaded regions correspond to bootstrapped standard error of the mean. Dashed vertical line is stimulus-onset time. (**B**) The same plots as in (**A**) but for narrow-spiking _WaveMAP_ clusters. (**C**) Baseline median FR ± S.E.M. for the neurons in the eight different classes. Baselines were taken as the average FR from 200 ms of recording before checkerboard stimulus onset. (**D**) Median maximum FR ± S.E.M. for the neurons in the eight different clusters. This was calculated by taking the median of the maximum FR for each neuron across the entire trial. (**E**) Median FR range ± S.E.M. calculated as the median difference, per neuron, between its baseline and max FR. ---- p &lt; 0.05; ---- p &lt; 0.01; ---- p &lt; 0.005; Mann-Whitney _U_ test, FDR adjusted. [Figure 6—figure supplement 1](#fig6s1): GMM clusters are less physiologically distinguishable than _WaveMAP_ clusters.\n",
    ":::\n",
    "{#fig6}\n",
    "\n",
    "figure: Figure 6—figure supplement 1.\n",
    ":::\n",
    "![](elife-67490.ipynb.media/fig6-figsupp1.jpg)\n",
    "\n",
    "### GMM clusters are less physiologically distinguishable than _WaveMAP_ clusters.\n",
    "\n",
    "(**A**) Stimulus-aligned trial-averaged firing rate activity in PMd for GMM clusters. As in [Figure 6](#fig6), the traces are separated into PREF and NONPREF trials with solid and dashed lines respectively. Shaded regions correspond to bootstrapped standard error of the mean (S.E.M.). The dashed vertical line denotes the stimulus-onset time. (**B**) Baseline median firing rates (FR) ± S.E.M. for the four GMM clusters. Baselines were calculated as the average firing rate during the first 200 ms of the trial. (**C**) Median maximum FRs ± S.E.M. for the neurons in the four GMM clusters. This was caculated by taking the median of the maximum FR for each neuron across the entire trial. (**D**) Median FR range ± S.E.M. calculated as the median difference, per neuron, between its baseline and max FR. ---- p &lt; 0.05; ---- p &lt; 0.01;—— p &lt; 0.005; Mann-Whitney _U_ test, FDR adjusted.\n",
    ":::\n",
    "{#fig6s1}\n",
    "\n",
    "To further quantify the FR differences between clusters, we calculated three properties of the FR response to stimulus: baseline FR, max FR, and FR range.\n",
    "\n",
    "### Baseline FR\n",
    "\n",
    "Cell types are thought to demonstrate different baseline FRs. We estimated baseline FR ([Figure 6C](#fig6)) as the median FR across the 200 ms time period before the appearance of the red-green checkerboard and during the hold period after targets appeared for the broad ([Figure 6A](#fig6)), and narrow-spiking clusters ([Figure 6B](#fig6)). The broad-spiking clusters showed significant differences in baseline FR when compared against the narrow-spiking clusters (p = 0.0028, Mann-Whitney _U_ test). Similar patterns were observed in another study of narrow- vs. broad-spiking neurons in PMd during an instructed delay task [@bib76]. We also found that not all broad-spiking neurons had low baseline FR and not all narrow-spiking neurons had high baseline FR. The broad-spiking clusters ⑥ and ⑦ were not significantly different but both differed significantly from ⑧ in that their baseline FR was much higher (10.3 ± 0.7 and 13.2 ± 1.9 spikes/s vs. 7.6 ± 0.75 spikes/s \\[median ± bootstrap S.E.]; p = 0.0052, p = 0.0029 respectively, Mann-Whitney _U_ test, FDR adjusted). The narrow-spiking clusters ([Figure 6B](#fig6), right) ②, ③, and ④ had relatively low median baseline FRs (7.5 ± 1.1, 7.4 ± 0.4, 6.5 ± 0.7 spikes/s, median ± bootstrap S.E.) and were not significantly different from one another but all were significantly different from ① and ⑤ (p = 0.04, p = 2.8e-4, p = 2.8e-7, p = 4.9e-5, respectively, Mann-Whitney _U_ test, FDR adjusted; see [Figure 6C](#fig6)).\n",
    "\n",
    "### Maximum FR\n",
    "\n",
    "A second important property of cell types is their maximum FR [@bib119; @bib105; @bib32]. We estimated the maximum FR for a cluster as the median of the maximum FR of neurons in the cluster in a 1200 ms period aligned to movement onset (800 ms before and 400 ms after movement onset; [Figure 6D](#fig6)). In addition to significant differences in baseline FR, broad- vs. narrow-spiking clusters showed a significant difference in max FR (p = 1.60e-5, Mann-Whitney _U_ test). Broad-spiking clusters were fairly homogeneous with low median max FR (24.3 ± 1.0, median ± bootstrap S.E.) and no significant differences between distributions. In contrast, there was significant heterogeneity in the FR’s of narrow-spiking neurons: three clusters (①, ③, and ⑤) had uniformly higher max FR (33.1 ± 1.1, median ± bootstrap S.E.) while two others (② and ④) were uniformly lower in max FR (23.0 ± 1.4, median ± bootstrap S.E.) and were comparable to the broad-spiking clusters. Nearly each of the higher max FR narrow-spiking clusters were significantly different than each of the lower max FR clusters (all pairwise relationships p &lt; 0.001 except ③ to ④ which was p = 0.007, Mann-Whitney _U_ test, FDR adjusted).\n",
    "\n",
    "### FR range\n",
    "\n",
    "Many neurons, especially inhibitory types, display a sharp increase in FR and also span a wide range during behavior [@bib76; @bib78; @bib25; @bib72; @bib69]. To examine this change over the course of a trial, we took the median difference across trials between the max FR and baseline FR per neuron to calculate the FR range. We again found the group difference between broad- and narrow-spiking clusters to be significant (p = 0.0002, Mann-Whitney _U_ test). Each broad-spiking cluster (⑥, ⑦, and ⑧) had a median increase of around 10.8 spikes/s (10.8 ± 0.8, 10.7 ± 2.3, and 10.9 ± 1.9 spikes/s respectively, median ± bootstrap S.E.) and each was nearly identical in FR range differing by less than 0.2 spikes/s. In contrast, the narrow-spiking clusters showed more variation in their FR range—similar to the pattern observed for max FR. ①, ③, and ⑤ had a large FR range (20.3 ± 1.1 spikes/s, median ± bootstrap S.E.) and the clusters ③ and ④ had a relatively smaller FR range (13.4 ± 1.3 spikes/s, median ± bootstrap S.E.). These results demonstrate that some narrow-spiking clusters, in addition to having high baseline FR, highly modulated their FR over the course of a behavioral trial.\n",
    "\n",
    "Such physiological heterogeneity in narrow-spiking cells has been noted before [@bib4; @bib10; @bib135] and in some cases, attributed to different subclasses of a single inhibitory cell type [@bib134; @bib182]. Other work also strongly suggests that narrow-spiking cells contain excitatory neurons with distinct FR properties contributing to this diversity [@bib174; @bib127].\n",
    "\n",
    "Furthermore, if _WaveMAP_ has truly arrived at a closer delineation of underlying cell types compared to previous methods, it should produce a ‘better’ clustering of physiological properties beyond just a better clustering of waveform shape. To address this issue, we calculate the same firing rate traces and physiological properties as in [Figure 6](#fig6) but with the GMM clusters ([Figure 6—figure supplement 1](#fig6s1)). While the FR traces maintain the same trends (BS does not increase its FR prior to the split into PREF and NONPREF while NS does; compare to _WaveMAP_ broad-spiking vs. narrow-spiking clusters respectively), much of the significant differences between clusters is lost across all physiological measures even though fewer groups are compared ([Figure 6—figure supplement 1B,C and D](#fig6s1)). We also quantitatively estimate these differences by calculating the effect sizes (Cohen’s _f^2^_) across the _WaveMAP_ and GMM clusterings with a one-way ANOVA. The effect size was larger for _WaveMAP_ vs. GMM clustering respectively for every physiological property: baseline FR (0.070 vs. 0.013), maximum FR (0.035 vs. 0.011), and FR range (0.055 vs. 0.034).\n",
    "\n",
    "## _WaveMAP_ clusters have distinct decision-related dynamics\n",
    "\n",
    "Our analysis in the previous section showed that there is considerable heterogeneity in their physiological properties. Are these putative cell types also functionally different? Prior literature argues that neuronal cell types have distinct functional roles during cortical computation with precise timing. For instance, studies of macaque premotor [@bib155], inferotemporal (IT) [@bib120], and frontal eye field (FEF) [@bib46] areas show differences in decision-related functional properties: between broad- and narrow-spiking neurons, narrow-spiking neurons exhibit choice-selectivity earlier than broad-spiking neurons. In the mouse, specific aspects of behavior are directly linked with inhibitory cell types [@bib130; @bib51]. Here, we examine the functional properties of each cluster based on two inferred statistics: choice-related dynamics and discrimination time.\n",
    "\n",
    "### Choice-related dynamics\n",
    "\n",
    "The first property we assessed for these _WaveMAP_ clusters was the dynamics of the choice-selective signal. The neural prediction made by computational models of decision-making (for neurons that covary with an evolving decision) is the build-up of average neural activity in favor of a choice is faster for easier compared to harder color coherences [@bib25; @bib46; @bib140]. Build-up activity is measured by analyzing the rate of change of choice-selective activity vs. time. We therefore examined the differences in averaged stimulus-aligned choice-selectivity signals (defined as |left - right|) for different checkerboard color coherences for each cluster.\n",
    "\n",
    "In [Figure 7A and B](#fig7), we show average choice-selectivity signals across the seven color coherence levels ([Figure 7A](#fig7), legend) for an example broad- (⑥) and narrow-spiking cluster (①). For ⑥ ([Figure 7A](#fig7)), easier stimuli (higher coherence) only led to modest increases in the rate at which the choice selectivity signal increases. In contrast, ① ([Figure 7B](#fig7)) shows faster rates for the choice-selective signal as a function of coherence. We summarized these effects by measuring the rate of change for the choice-selective signal between 175 and 325 ms for stimulus-aligned trials in each coherence condition (dashed lines in [Figure 7A,B](#fig7)). This rate of rise for the choice-selective signal (spikes/s/s) vs. coherence is shown for broad- ([Figure 7C](#fig7)) and narrow-spiking ([Figure 7D](#fig7)) clusters. The broad-spiking clusters demonstrate fairly similar coherence-dependent changes with each cluster being somewhat indistinguishable and only demonstrating a modest increase with respect to coherence. In contrast, the narrow-spiking clusters show a diversity of responses with ① and ⑤ demonstrating a stronger dependence of choice-related dynamics on coherence compared to the other three narrow-spiking clusters which were more similar in response to broad-spiking neurons.\n",
    "\n",
    "figure: Figure 7.\n",
    ":::\n",
    "![](elife-67490.ipynb.media/fig7.jpg)\n",
    "\n",
    "#### UMAP clusters exhibit distinct functional properties.\n",
    "\n",
    "(**A**) Average firing rate (FR) over time for ⑥ (used as a sample broad-spiking cluster) across trials of different color coherences. The gray-dashed lines indicate the linear regression lines used to calculate the FR rate of rise. (**B**) Average FR over time for ① (used as a sample narrow-spiking cluster) across different color coherences. (**C**) FR rate of rise vs. color coherence for broad- and (**D**) narrow-spiking clusters. Error bars correspond to standard error of the mean across trials. (**E**) Bootstrapped median color coherence slope is shown with the bootstrapped standard error of the median for each cluster on a per-neuron basis. Coherence slope is a linear regression of the cluster-specific lines in the previous plots **C** and **D**. (**F**) Median bootstrapped discrimination time for each cluster with error bars as the bootstrapped standard error of the median. Discrimination time was calculated as the the amount of time after checkerboard appearance at which the choice-selective signal could be differentiated from the baseline FR [@bib25]. dotted line p &lt; 0.05; dashed line p &lt; 0.01; solid line p &lt; 0.005; Mann-Whitney _U_ test, FDR adjusted.\n",
    ":::\n",
    "{#fig7}\n",
    "\n",
    "We further summarized these plots by measuring the dependence of the rate of rise of the choice-selective signal as a function of coherence measured as the slope of a linear regression performed on the rate of rise vs. color coherence for each cluster ([Figure 7E](#fig7)). The coherence slope for broad-spiking clusters was moderate and similar to ②, ③, and ④ while the coherence slope for ① and ⑤ was steeper. Consistent with [Figure 7C and D](#fig7), the choice selective signal for ① and ⑤ showed the strongest dependence on stimulus coherence.\n",
    "\n",
    "### Discrimination time\n",
    "\n",
    "The second property that we calculated was the discrimination time for clusters which is defined as the first time in which the choice-selective signal (again defined as |left - right|) departed from the FR of the hold period. We calculated the discrimination time on a neuron-by-neuron basis by computing the first time point in which the difference in FR for the two choices was significantly different from baseline using a bootstrap test (at least 25 successive time points significantly different from baseline FR corrected for multiple comparisons [@bib25]). Discrimination time for broad-spiking clusters (255 ± 94 ms, median ± bootstrap S.E.) was significantly later than narrow-spiking clusters (224 ± 89 ms, p &lt; 0.005, median ± bootstrap S.E., Mann-Whitney _U_ test). Clusters ① and ⑤, with the highest max FRs (34.0 ± 1.4 and 33.0 ± 1.8 spikes/s, median ± S.E.) and most strongly modulated by coherence, had the fastest discrimination times as well (200.0 ± 4.9 and 198.5 ± 4.9 ms, median ± S.E.).\n",
    "\n",
    "Together the analysis of choice-related dynamics and discrimination time showed that there is considerable heterogeneity in the properties of narrow-spiking neuron types. Not all narrow-spiking neurons are faster than broad-spiking neurons and choice-selectivity signals have similar dynamics for many broad-spiking and narrow-spiking neurons. ① and ⑤ have the fastest discrimination times and strongest choice dynamics. In contrast, the broad-spiking neurons have uniformly slower discrimination times and weaker choice-related dynamics.\n",
    "\n",
    "## _WaveMAP_ clusters contain distinct laminar distributions\n",
    "\n",
    "In addition to having certain physiological properties and functional roles, numerous studies have shown that cell types across phylogeny, verified by single-cell transcriptomics, are defined by distinct patterns of laminar distribution in cortex [@bib66; @bib169]. Here, we examined the laminar distributions of _WaveMAP_ clusters and compared them to laminar distributions of GMM clusters. The number of waveforms from each cluster was counted at each of sixteen U-probe channels separately. These channels were equidistantly spaced every 0.15 mm between 0.0 and 2.4 mm. This spanned the entirety of PMd which is approximately 2.5 mm in depth from the pial surface to white matter [@bib5]. However, making absolute statements about layers is difficult with these measurements because of errors in aligning superficial electrodes with layer I across different days. This could lead to shifts in estimates of absolute depth; up to 0.15 mm (the distance between the first and second electrode) of variability is induced in the alignment process (see Materials and methods). However, relative comparisons are likely better preserved. Thus, we use relative comparisons to describe laminar differences between distributions and in comparison to anatomical counts in fixed tissue in later sections.\n",
    "\n",
    "Above each column of [Figure 8A and B](#fig8) are the laminar distributions for all waveforms in the associated set of clusters (in gray); below these are the laminar distributions for each cluster set’s constituent clusters. On the right ([Figure 8C](#fig8)), we show the distribution of all waveforms collected at top in gray with each GMM cluster’s distribution shown individually below.\n",
    "\n",
    "figure: Figure 8.\n",
    ":::\n",
    "![](elife-67490.ipynb.media/fig8.jpg)\n",
    "\n",
    "### Laminar distribution of _WaveMAP_ waveform clusters.\n",
    "\n",
    "(**A, B**) The overall histogram for the broad- and narrow-spiking waveform clusters are shown at top across cortical depths on the left and right respectively (in gray); below are shown histograms for their constituent _WaveMAP_ clusters. These waveforms are shown sorted by the cortical depth at which they were recorded from the (0.0 mm \\[presumptive pial surface] to 2.4 mm in 0.15 mm increments). Broad-spiking clusters were generally centered around middle layers and were less distinct in their differences in laminar distribution. Narrow-spiking clusters are shown on the right and were varied in their distribution with almost every cluster significantly varying in laminar distribution from every other. (**C**) Depth histograms for all waveforms collected (top, in gray) and every GMM cluster (below). dotted line p &lt; 0.05; dashed line p &lt; 0.01; solid line p &lt; 0.005; two-sample Kolmogorov-Smirnov Test, FDR adjusted. [Figure 8—figure supplement 1](#fig8s1): Composite figure showing each _WaveMAP_ cluster with waveform, physiological, functional, and laminar distribution properties.\n",
    ":::\n",
    "{#fig8}\n",
    "\n",
    "figure: Figure 8—figure supplement 1.\n",
    ":::\n",
    "![](elife-67490.ipynb.media/fig8-figsupp1.jpg)\n",
    "\n",
    "### Detailed summary of each UMAP cluster and features.\n",
    "\n",
    "(**A, B**) A detailed summary of broad- (**A**) and narrow-spiking (**B**) cluster waveform shapes, physiological measures, and laminar distribution. Each waveform shape is shown at left with the average waveform shown as a black trace. The average post-hyperpolarization peak position is shown with a black line. The three waveform features used in the GMM classification ([Figure 4A](#fig4)) are shown in the middle as the mean ± S.E. The baseline and max FR for each cluster are subsequently shown in spikes/s (median ± bootstrap S.E.). Functional properties, discrimination time and coherence slope, are shown in milliseconds and spikes/s/s/% coherence (both shown in median ± bootstrap S.E.). Laminar distributions are also shown with each column in the histogram being the number of each waveform found at each channel location. Channels are spaced every 0.15 mm apart from 0.0 to 2.4 mm.\n",
    ":::\n",
    "{#fig8s1}\n",
    "\n",
    "The overall narrow- and broad-spiking populations did not differ significantly according to their distribution (p = 0.24, Kolmogorov-Smirnov test). The broad-spiking cluster set of neurons (⑥ , ⑦ , and ⑧) are generally thought to contain cortical excitatory pyramidal neurons enriched in middle to deep layers [@bib121; @bib105]. Consistent with this view, we found these broad-spiking clusters ([Figure 8A](#fig8)) were generally centered around middle to deep layers with broad distributions and were not significantly distinguishable in laminarity (all comparisons p > 0.05, two-sample Kolmogorov-Smirnov test, FDR adjusted).\n",
    "\n",
    "In contrast, narrow-spiking clusters ([Figure 8B](#fig8)) were distinctly varied in their distribution such that almost every cluster had a unique laminar distribution. Cluster ① contained a broad distribution. It was significantly different in laminar distribution from clusters ② and ④ (p = 0.002 and p = 0.013, respectively, two-sample Kolmogorov-Smirnov, FDR adjusted).\n",
    "\n",
    "Cluster ② showed a strongly localized concentration of neurons at a depth of 1.1 ± 0.33 mm (mean ± S.D.). It was significantly different from almost all other narrow-spiking clusters (p = 0.002, p = 1e-5, p = 0.010 for ①, ④, and ⑤ respectively; two-sample Kolmogorov-Smirnov test, FDR adjusted). Similarly, cluster ③ also showed a strongly localized laminar distribution but was situated more superficially than ② with a heavier tail (1.0 ± 0.6 mm, mean ± S.D.).\n",
    "\n",
    "Cluster ④ was uniquely deep in its cortical distribution (1.70 ± 0.44, mean ± S.D.). These neurons had a strongly triphasic waveform shape characterized by a large pre-hyperpolarization peak. These waveforms have been implicated as arising from myelinated excitatory pyramidal cells [@bib12], which are especially dense in this caudal region of PMd [@bib11].\n",
    "\n",
    "The last cluster, ⑤, like ① was characterized by a broad distribution across cortical depths unique among narrow-spiking neurons and was centered around a depth of 1.3 ± 0.65 mm (mean ± S.D.) and present in all layers [@bib5].\n",
    "\n",
    "Such laminar differences were not observed when we used GMM clustering. Laminar distributions for BS, BST, NS, and NST did not significantly differ from each other ([Figure 8C](#fig8); BS vs. BST had p = 0.067, all other relationships p > 0.2; two-sample Kolmogorov-Smirnov test, FDR adjusted). Each GMM cluster also exhibited broad distributions across cortex which is at odds with our understanding of cell types using histology (discussed in the next section).\n",
    "\n",
    "## Some narrow-spiking _WaveMAP_ cluster laminar distributions align with inhibitory subtypes\n",
    "\n",
    "We have shown that _WaveMAP_ clusters have more distinct laminarity than GMM clusters. If _WaveMAP_ clusters are consistent with cell type, we should expect their distributions to be relatively consistent with distributions from certain anatomical types visualized via immunohistochemistry (IHC). An especially well-studied set of non-overlapping anatomical inhibitory neuron types in the monkey are parvalbumin-, calretinin-, and calbindin-positive GABAergic interneurons (PV^+^, CR^+^, and CB^+^ respectively) [@bib39]. Using IHC, we examined tissue from macaque rostral PMd stained for each of these three interneuron types. We then conducted stereological counting of each type averaged across six exemplars to quantify cell type distribution across cortical layers (see [Figure 9A and B](#fig9), [@bib146]) and compared it to the distributions in [Figure 8](#fig8).\n",
    "\n",
    "figure: Figure 9.\n",
    ":::\n",
    "![](elife-67490.ipynb.media/fig9.jpg)\n",
    "\n",
    "### Anatomical labeling of three inhibitory interneuron types in PMd.\n",
    "\n",
    "(**A**) Sample maximum intensity projection of immunohistological (IHC) staining of rostral PMd calbindin-positive (CB^+^) interneurons in blue. Note the many weakly-positive excitatory pyramidal neurons (arrows) in contrast to the strongly-positive interneurons (arrowheads). Only the interneurons were considered in stereological counting. In addition, only around first 1.5 mm of tissue is shown (top of layer V) but the full tissue area was counted down to the 2.4 mm (approximately the top of white matter). Layer IV exists as a thin layer in this area. Layer divisions were estimated based on depth and referencing [@bib5] [@bib5]. (**B**) Sample maximum intensity projection of IHC staining of PMd calretinin-positive (CR^+^) and parvalbumin-positive (PV^+^) interneurons in yellow and fuschia respectively. The same depth of tissue and layer delineations were used as in (**A**). (**C, D, E**) Stereological manual counts [@bib146] (mean ± S.D.) of CB^+^, CR^+^, PV^+^ cells in PMd, respectively. Counts were collected from six specimens, each with all three IHC stains, and with counts normalized to each sample. Source files for this figure are available on Dryad (<https://doi.org/10.5061/dryad.z612jm6cf>).\n",
    ":::\n",
    "{#fig9}\n",
    "\n",
    "Both CB^+^ and CR^+^ cells ([Figure 9C and D](#fig9), respectively) exhibited a similarly restricted superficial distribution most closely resembling ③. In addition, CR^+^ and CB^+^ cells are known to have very similar physiological properties and spike shape [@bib181]. An alternative possibility is that one of CR^+^ or CB^+^ might correspond to ② and the other to ③ but this is less likely given their nearly identical histological distributions ([Figure 9C and D](#fig9)) and similar physiology [@bib181].\n",
    "\n",
    "In contrast, _WaveMAP_ cluster ①, had laminar properties consistent with PV^+^ neurons ([Figure 9B](#fig9)): both were concentrated superficially but proliferated into middle layers ([Figure 9E](#fig9)). In addition, there were striking physiological and functional similarities between ① and PV^+^ cells. In particular, both ① and PV^+^ cells have low baseline FR, early responses to stimuli and robust modulation of FR similar to PV^+^ cells in mouse M1 [@bib51]. Cluster ⑤ also had similar properties to ① and could also correspond to PV^+^ cells.\n",
    "\n",
    "Together, these results from IHC suggest that the narrow-spiking clusters identified from _WaveMAP_ potentially map on to different inhibitory types.\n",
    "\n",
    "## Heterogeneity in decision-related activity emerges from both cell type and layer\n",
    "\n",
    "Our final analysis examines whether these _WaveMAP_ clusters can explain some of the heterogeneity observed in decision-making responses in PMd over and above previous methods [@bib25]. Heterogeneity in decision-related activity can emerge from cortical depth, different cell types within each layer, or both. To quantify the relative contributions of _WaveMAP_ clusters and cortical depth, we regressed discrimination time on both separately and together and examined the change in variance explained (adjusted ${R}^{2}$). We then compared this against the GMM clusters with cortical depth to show that _WaveMAP_ better explains the heterogeneity of decision-related responses.\n",
    "\n",
    "We previously showed that some of the variability in decision-related responses is explained by the layer from which the neurons are recorded [@bib25]. Consistent with previous work, we found that cortical depth explains some variability in discrimination time (1.7%). We next examined if the _WaveMAP_ clusters identified also explained variability in discrimination time: a categorical regression between _WaveMAP_ clusters and discrimination time, explained a much larger 6.6% of variance. Including both cortical depth and cluster identity in the regression explained 7.3% of variance in discrimination time.\n",
    "\n",
    "In contrast, we found that GMM clusters regressed against discrimination time only explained 3.3% of variance and the inclusion of both GMM cluster and cortical depth only explained 4.6% of variance.\n",
    "\n",
    "Thus, we find that _WaveMAP_ clustering explains a much larger variance relative to cortical depth alone. This demonstrates that _WaveMAP_ clusters come closer to cell types than previous efforts and are not artifacts of layer-dependent decision-related inputs. That is, both the cortical layer in which a cell type is found as well _WaveMAP_ cluster membership contributes to the variability in decision-related responses. Furthermore, _WaveMAP_ clusters outperform GMM clusters as regressors of a functional property associated with cell types. These results further highlight the power of _WaveMAP_ to separate out putative cell types and help us better understand decision-making circuits.\n",
    "\n",
    "# Discussion\n",
    "\n",
    "Our goal in this study was to further understand the relationship between waveform shape and the physiology, function , and laminar distribution of cell populations in dorsal premotor cortex during perceptual decision-making. Our approach was to develop a new method, _WaveMAP_, that combines a recently developed non-linear dimensionality reduction technique (UMAP) with graph clustering (Louvain community detection) to uncover hidden diversity in extracellular waveforms. We found this approach not only replicated previous studies by distinguishing between narrow- and broad-spiking neurons, but did so in a way that (1) revealed additional diversity, and (2) obviated the need to examine particular waveform features. In this way, our results demonstrate how traditional feature-based methods obscure biological detail that is more faithfully revealed by our _WaveMAP_ method. Furthermore, through interpretable machine learning, we show our approach not only leverages many of the features already established as important in the literature but expands upon them in a more nuanced manner—all with minimal supervision or stipulation of priors. Finally, we show that the candidate cell classes identified by _WaveMAP_ have distinct physiological properties, decision-related dynamics, and laminar distribution. The properties of each _WaveMAP_ cluster are summarized in [Figure 8—figure supplement 1A and B](#fig8s1) for broad- and narrow-spiking clusters, respectively.\n",
    "\n",
    "_WaveMAP_ combines UMAP with high-dimensional graph clustering and interpretable machine learning to better identify candidate cell classes. Our approach might also be useful in other domains that employ non-linear dimensionality reduction such as computational ethology [@bib2; @bib67; @bib9], analysis of multi-scale population structure [@bib42], and metascientific analyses of the literature [@bib124]. We also note that while traditional uses of non-linear dimensionality reduction and UMAP has been to data lacking autoregressive properties, such as transcriptomic expression [@bib16], this does not seem to be an issue for _WaveMAP_. Even though our waveforms have temporal autocorrelation, our method still is able to pick out interesting structure. Other work has found similar success in analyzing time series data with non-linear dimensionality reduction [@bib149; @bib43; @bib71; @bib60; @bib2].\n",
    "\n",
    "## Advantages of _WaveMAP_ over traditional methods\n",
    "\n",
    "At the core of _WaveMAP_ is UMAP which has some advantages over other non-linear dimensionality reduction methods that have been applied in this context. Although most algorithms offer fast implementations that scale well to large input dimensionalities and volumes of data [@bib94; @bib125], UMAP also projects efficiently into arbitrary _output_ dimensionalities while also returning an invertible transform. That is, we can efficiently project new data into any arbitrary dimensional projected space without having to recompute the mapping.\n",
    "\n",
    "These properties provide three advantages over other non-linear dimensionality reduction approaches: First, our method is stable in the sense that it produces a consistent number of clusters and each cluster has the same members across random subsamples ([Figure 3—figure supplement 1B](#fig3s1)). Clustering in the high-dimensional space rather than the projected space lends stability to our approach. Second, it allows exploration of any region of the projected space no matter the intuited latent dimensionality—this yields an intuitive understanding of how UMAP non-linearly transforms the data, which might be related to underlying biological phenomena. Thus, UMAP allows _WaveMAP_ to go beyond a ‘discriminative model’ typical of other clustering techniques and function as a ‘generative model’ with which to make predictions. Third, it enables cross-validation of a classifier trained on cluster labels, impossible with methods that don’t return an invertible transform. To cross-validate unsupervised methods, unprocessed test data must be passed into a transform computed _only_ on training data and evaluated with some loss function [@bib117]. This is only possible if an invertible transform is admitted by the method of dimensionality reduction as in UMAP.\n",
    "\n",
    "A final advantage of UMAP is that it inherently allows for not just unsupervised but supervised and semi-supervised learning whereas some other methods do not [@bib143]. This key difference enables ‘transductive inference’ which is making predictions on unlabeled test points based upon information gleaned from labeled training points. This opens up a diverse number of novel applications in neuroscience through informing the manifold learning process with biological ground truths (in what is called ‘metric learning’) [@bib18; @bib180]. Experimentalists could theoretically pass biological ground truths to _WaveMAP_ as training labels and ‘teach’ _WaveMAP_ to produce a manifold that more closely hews to true underlying diversity. For instance, if experimentalists ‘opto-tag’ neurons of a particular cell type [@bib142; @bib41; @bib71; @bib30; @bib62], this information can be passed along with the extracellular waveform to _WaveMAP_ which would, in a semi-supervised manner, learn manifolds better aligned to biological truth.\n",
    "\n",
    "A learned manifold could also be useful in future experiments to identify cell types in real-time without opto-tagging. This could be done by projecting the averaged waveforms found within an experiment into the learned _WaveMAP_ manifold. This method would be especially useful in a scenario in which the number of electrodes exceeds the number of channels available to record from simultaneously and not all cell types are of equal interest to record (e.g. Neuropixels probes which have 960 electrodes but simultaneously record from only 384; [@bib171]; [@bib73]). We believe this is a rich area that can be explored in future work.\n",
    "\n",
    "_WaveMAP_ uses a fully unsupervised method for separating and clustering waveform classes associated with distinct laminar distributions and functional properties in a decision-making task. One concern with fully unsupervised methods is that the features used for separation are unclear. However, by applying interpretable machine learning [@bib150; @bib98], we showed that our unsupervised methods utilized many of the same waveform features derived by hand in previous work but did so in a single unifying framework. Our interpretable machine learning approach shows how each waveform feature delineates certain waveform clusters at the expense of others and—more importantly—shows how they can be optimally recombined to reveal the full diversity of waveform shapes.\n",
    "\n",
    "Our novel approach of using non-linear dimensionality reduction with graph clustering on the population of extracellular action potentials compared to specified waveform features has parallels with the evolution of new approaches for the analysis of neuronal firing rates in relevant brain areas [@bib151; @bib28; @bib102; @bib137; @bib177]. Classically, the approach to analyzing firing rates involved in cognition was to develop simple metrics that separated neurons recorded in relevant brain areas. For instance, tuning is used to separate neurons in the motor [@bib55] and visual cortex [@bib68]. Similarly, visuomotor indices that categorize neurons along a visual to motor continuum are used to understand firing rates during various tasks in the frontal eye fields [@bib23] and premotor cortex [@bib25]. However, these specified features quash other aspects of a firing rate profile in favor of focusing on only a few other aspects. New approaches to analyze firing rates use dimensionality reduction techniques such as principal component analysis [@bib151; @bib28; @bib37], tensor component analysis [@bib179], demixed principal component analysis [@bib84], targeted dimensionality reduction [@bib102], and autoencoder neural networks [@bib128]. These methods have provided insight into heterogeneous neural activity patterns in many brain areas without the need for specified features like tuning or a visuomotor index. Our study strongly suggests that non-linear dimensionality reduction methods applied to the entire extracellular waveform are better than using hand-derived waveform features such as trough to peak duration, repolarization time, spike width and other metrics. This progression from user-defined features to data-driven methods follows similar trends in the field of machine learning.\n",
    "\n",
    "## Waveform cluster shapes are unlikely to arise from electrode placement\n",
    "\n",
    "It is a possibility that the diversity of waveforms we observe is just an artifact of electrode placement relative to the site of discharge. This supposes that waveform shape changes with respect to the distance between the neuron and the electrode. This is unlikely because both in vitro studies [@bib40] and computational simulations [@bib56] show distance from the soma mostly induces changes in amplitude. There is a small widening in waveform width but this occurs at distances in which the amplitude has attenuated below even very low spike thresholds [@bib56]. We controlled for this cell-type-irrelevant variation in amplitude by normalizing spike troughs/peaks during preprocessing to be between −1 and +1. It should also be noted that without any normalization, all structure was lost in the UMAP projection which instead yielded one large point cloud ([Figure 3—figure supplement 2E](#fig3s2)). Intuitively, this can be understood as UMAP allocating most of the projected space to explaining amplitude differences rather than shape variation. This can be visualized by coloring each point by the log of the amplitude of each spike (log of difference in maximum vs. minimum values) and observing that it forms a smooth gradient in the projected space ([Figure 3—figure supplement 2F](#fig3s2)).\n",
    "\n",
    "It is possible that differences that we observe in waveform shape could be due to recording from different morphological structures (dendrites, soma, or axons) rather than different cell types. However, we believe that most of our waveforms are from the soma. While it is true that there are some cell structures associated with different waveform shapes (such as triphasic waveforms near neurites, especially axons [@bib12]; [@bib40]; [@bib139]; [@bib161]), highly controlled in vitro studies show that a large majority of EAP’s are from somata (86%) [@bib40]. In concordance with these results, we only observed one cluster (④, 6% of all EAP’s) with a triphasic shape and these waveforms were only found in deep layers where myelination is prevalent. Thus, we believe that almost all of our waveforms come from somata, with the possible exclusion of ④. Finally, we observed distinct physiological properties ([Figure 6](#fig6)), decision-related dynamics ([Figure 7](#fig7)), and laminar distribution ([Figure 8](#fig8)) for each _WaveMAP_ cluster. This would not be the case if the waveforms were just obtained from different compartments of the same neurons.\n",
    "\n",
    "Given that electrode location has little effect on waveform shape, we might then ask what about a neuron’s waveform shape, in terms of cellular physiology, is captured by _WaveMAP_? We propose that the space found by UMAP-1 and UMAP-2 sensibly covaries according to documented properties of K^+^ ion channel dynamics. As UMAP-1 increases, we observe a smooth transition of the inflection of the repolarization slope from negative to positive (slow to fast repolarization rate; [Figure 5A](#fig5)). Said differently, the post-hyperpolarization peak becomes sharper as we increase in the UMAP-1 direction. These observations are consistent with the same gradual change in intracellular AP repolarization slope facilitated by the kinetics of the fast voltage-gated Kv3 potassium-channel in an activity-dependent manner [@bib74]. These channels are necessary for sustained high-frequency firing [@bib45]. In the UMAP-2 direction, there is a smooth decrease in the width of the post-hyperpolarization peak and this direction roughly traverses from broad- to narrow-spiking to triphasic waveforms. This gradual change too has been noted as being associated with the kinetics of the Kv3 potassium-channel: blocking this channel in a dose-dependent manner with tetraethylammonium induces a gradual widening of post-hyperpolarization peak width [@bib50; @bib15]. Both of these changes in intracellular waveform shape likely have a strong effect on the shape of extracellular waveforms [@bib65].\n",
    "\n",
    "## Reliance on waveform features might obscure cell type diversity\n",
    "\n",
    "Our results show a greater proportion of narrow- (putatively inhibitory) vs. broad-spiking (putatively excitatory) neurons (69% vs. 31%, respectively); this is inconsistent with anatomical studies [@bib48; @bib182; @bib134]. These studies demonstrate, through direct labeling of cell type, that in the macaque cortex, 65–80% of neurons are excitatory while 20–35% are inhibitory. We are not the only study to report this puzzling result: Onorato and colleagues [@bib127] also report greater numbers of narrow-spiking compared to broad-spiking neurons in monkey V1. Thus, care must be taken when attempting links between spike waveform shape and cell type [@bib93]. A resolution to this discrepancy is to rethink equating narrow-spiking to inhibitory cells and broad-spiking to excitatory cells. Anatomical studies show that a substantial number of excitatory neurons in the monkey motor and visual cortices express the Kv3.1b potassium channel which is known to confer neurons with the ability to produce action potentials of narrow spike width and high firing rate [@bib33; @bib79; @bib80; @bib70; @bib93]. Furthermore, researchers have used antidromic stimulation to show that narrow-spiking neurons can be excitatory in motor and premotor cortex [@bib174; @bib93].\n",
    "\n",
    "We therefore believe prior studies have underexplored the diversity of classes accessed by their physiological recordings. Evidence of this is that histograms of peak width (and other specified features) across literature are often not cleanly bimodal [@bib87; @bib183] especially in premotor cortices [@bib112]. In addition, the relative proportions of narrow vs. broad is often dependent on the cutoff chosen which widely varies across studies [@bib174; @bib112]. Analyses like ours which look at entire waveforms—rather than a few specified features—extract this diversity from extracellular recordings whereas specified features mix waveform classes.\n",
    "\n",
    "## Better parcellation of waveform variability leads to biological insight\n",
    "\n",
    "We find that many narrow-spiking subtypes in PMd signal choice earlier than broad-spiking neurons in our decision-making task ([Figure 7F](#fig7)). These observations are consistent with another study of PMd in monkeys in reach target selection and movement production [@bib155]. In this study, narrow-spiking neurons signaled the selected target 25 ms earlier than broad-spiking neurons. Our results are also consistent with other studies of narrow- vs. broad-spiking neurons in the frontal eye fields (FEF) [@bib46] and inferior temporal area (IT) [@bib120] during decision-making. In these studies, narrow-spiking neurons had higher firing rates before movement onset compared to broad-spiking neurons—a result consistent with our observations for some ‘narrow-spiking’ PMd neurons. Our analyses recapitulate these results and provide additional insights into how different narrow-spiking cell types correlate with decisions. We reproduce the result that narrow-spiking cells, as a whole, have a faster discrimination time than broad-spiking cells but in addition we show that certain narrow-spiking cells respond as slowly as broad-spiking cells (② and ④; [Figure 7F](#fig7)). This lends further evidence to our theory that ② and ④ are likely narrow-spiking excitatory cells. In contrast, ③ and ① while both narrow-spiking, had distributions that more aligned with histologically-verified inhibitory types. In addition, ③ and ① had physiological properties more in line with inhibitory cell types.\n",
    "\n",
    "_WaveMAP_ suggests that narrow-spiking waveforms encompass many cell classes with distinct shape and laminar distribution. One of our narrow-spiking clusters (cluster ②) was restricted to more superficial layers ([Figure 8B](#fig8)) and had certain functional properties—low baseline firing rate and longer discrimination times—which are thought to be more closely aligned to properties of excitatory neurons [@bib155]. Another narrow-spiking cluster, ④, exhibited physiological and functional properties similar to ② (all comparisons not significant in [Figure 6C,D and E](#fig6) or [Figure 7E and F](#fig7)) but with a distinct laminar distribution ([Figure 8B](#fig8)) and highly triphasic waveform shape ([Figure 8—figure supplement 1B](#fig8s1)). In contrast to ②, which was concentrated in layer III, ④ was restricted to deep layers. These tri-phasic neurons could either be large corticospinal excitatory pyramidal cells [@bib70; @bib154; @bib174], or axons [@bib12; @bib139; @bib161].\n",
    "\n",
    "## High-density probes and optogenetics can provide better insight into cell classes in the primate\n",
    "\n",
    "Our recordings here were performed with 16 channel U-probes which provided reasonable estimates of laminar organization for these different putative cell classes. Use of high-density electrophysiological methods providing higher electrode counts perpendicular to the cortical surface would provide further insight into the laminar organization of different cell types [@bib73; @bib44]. High-density recordings would allow us to perform _WaveMAP_ in an additional dimension (across multiple electrodes) to increase confidence in identified cell classes [@bib118] and localization of signal to somata [@bib71]. Sensitive electrodes providing spatial access to neural activity [@bib73] can also improve our understanding of how these cell classes are organized both parallel and perpendicular to cortical surface [@bib144; @bib118] and across areas [@bib44]. Access to cell types with high-density recordings would also allow for the identification of ‘me-types’ through electromorphology [@bib60; @bib162]. This information could also help inform detailed models of cortical circuits that incorporate cell type information [@bib59; @bib21; @bib136].\n",
    "\n",
    "Another powerful tool that has been leveraged in the study of cell types during behavior is optogenetics [@bib130; @bib95; @bib88]. Although in its infancy relative to its use in the mouse, optogenetics in monkeys offers direct interrogation of cell types. Future studies will allow us to more precisely link putative cell classes in vivo to function [@bib35]. NHP optogenetics is slowly advancing and efforts in many research groups around the world are producing new methods for in vivo optogenetics [@bib172]. We expect future experiments using the promising new mDlx [@bib38] and h56d [@bib109] promoter sequences to selectively opto-tag inhibitory neurons or PV^+^ neurons directly [@bib176] will greatly benefit validation of these derived cell classes. Finally, _WaveMAP_’s ability to find clusters of putative biological relevance using waveform shape alone encourages its application in settings where ground truth evaluation is particularly difficult to obtain such as in the human brain [@bib129].\n",
    "\n",
    "# Materials and methods\n",
    "\n",
    "table: Key resources table\n",
    ":::\n",
    "| Reagent type (species) or resource | Designation                               | Source or reference | Identifiers                                                                    | Additional information |\n",
    "| ---------------------------------- | ----------------------------------------- | ------------------- | ------------------------------------------------------------------------------ | ---------------------- |\n",
    "| Primary antibody                   | Rabbit anti-calbindin D-28k (polyclonal)  | Swant               | Cat#: CB38 RRID:[AB_10000340](https://scicrunch.org/resolver/AB_10000340)      | 1:2000 dilution        |\n",
    "| Primary antibody                   | Rabbit anti-calretinin D-28k (polyclonal) | Swant               | Cat#: 7697 RRID:[AB_2619710](https://scicrunch.org/resolver/AB_2619710)        | 1:2000 dilution        |\n",
    "| Primary antibody                   | Guinea pig anti-parvalbumin (polyclonal)  | Swant               | Cat#: GP72 RRID:[AB_2665495](https://scicrunch.org/resolver/AB_2665495)        | 1:2000 dilution        |\n",
    "| Secondary antibody                 | Donkey anti-rabbit Alexa 546              | ThermoFisher        | Cat#: A10040 RRID:[AB_2534016](https://scicrunch.org/resolver/AB_2534016)      | 1:200 dilution         |\n",
    "| Secondary antibody                 | Donkey anti-guinea pig Alexa 546          | Jackson             | Cat#: 706-545-148 RRID:[AB_2340472](https://scicrunch.org/resolver/AB_2340472) | 1:200 dilution         |\n",
    ":::\n",
    "{#keyresource}\n",
    "\n",
    "## Code and data availability\n",
    "\n",
    "All figures and figure supplements can be generated from the code and data included with the manuscript and uploaded to Dryad/Zenodo (RRID:[SCR_005910](https://scicrunch.org/resolver/SCR_005910)/RRID:[SCR_004129](https://scicrunch.org/resolver/SCR_004129)) (<https://doi.org/10.5061/dryad.z612jm6cf>; [@bib91]) and on Github (<https://github.com/EricKenjiLee/WaveMAP_Paper>). Pre-processing of raw averaged data was conducted in MATLAB (RRID:[SCR_001622](https://scicrunch.org/resolver/SCR_001622)) using the files located in Preprocessing.zip (see contained README.md). [Figure 1](#fig1) was generated using MATLAB whereas all other figures were generated in Python (RRID:[SCR_008394](https://scicrunch.org/resolver/SCR_008394)) using the Jupyter/Google CoLab (RRID:[SCR_018315](https://scicrunch.org/resolver/SCR_018315)/RRID:[SCR_018009](https://scicrunch.org/resolver/SCR_018009)) notebook available with this manuscript. Please see the Readme.md file included in the zip file WaveMAP_Paper.zip for instructions on how to generate all manuscript figures and supplementary figures. Raw confocal fluorescence images with associated CellCounter annotations are also available [@bib91]. Further information about _WaveMAP_ and updated notebooks can also be obtained from the Chandrasekaran lab website at Boston University (<http://www.chandlab.org>).\n",
    "\n",
    "## Subjects and surgery\n",
    "\n",
    "Our experiments were conducted using two adult male macaque monkeys (_Macaca mulatta_; monkey T, 7 years, 14 kg; O, 11 years, 15.5 kg) that were trained to reach to visual targets for a juice reward. Our monkeys were housed in a social vivarium with a normal day/night cycle. This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All the procedures were approved by the Stanford Administrative Panel on Laboratory Animal Care (APLAC, Protocol Number 8856 entitled ‘Cortical Processing of Arm Movements’). Surgical procedures were performed under anesthesia, and every effort was made to minimize suffering. Appropriate analgesia, pain relief, and antibiotics were administered to the animals when needed after surgical approval.\n",
    "\n",
    "After initial training to come out of the cage and sit comfortably in a chair, monkeys underwent sterile surgery for implantation of head restraint holders (Crist Instruments, cylindrical head holder) and standard recording cylinders (Crist Instruments, Hagerstown, MD). We placed our cylinders over caudal PMd (+16, 15 stereotaxic coordinates) and surface normal to the cortex. We covered the skull within the cylinder with a thin layer of dental acrylic/PALACOS bone cement.\n",
    "\n",
    "## Apparatus\n",
    "\n",
    "Monkeys sat in a customized chair (Crist Instruments, Snyder Chair) with their head restrained via the surgical implant. The arm not used for reaching was loosely restrained using a tube and a cloth sling. Experiments were controlled and data were collected under a custom computer control system (xPC target and Psychtoolbox-3 \\[RRID:[SCR_002881](https://scicrunch.org/resolver/SCR_002881)] [@bib81]). Visual stimuli were displayed on an Acer HN2741 computer screen placed approximately 30 cm from the monkey and a photodetector (Thorlabs PD360A) was used to record the onset of the visual stimulus at a 1 ms resolution. Every session, we taped a small infrared reflective bead (11.5 mm, NDI Digital passive spheres) 1 cm from the tip of the middle digit of the right hand (left hand, monkey O). The position of this bead was tracked optically in the infrared (60 Hz, 0.35 mm root mean square accuracy; Polaris system; Northern Digital).\n",
    "\n",
    "Eye position was tracked with an overhead infrared camera made by ISCAN along with associated software (estimated accuracy of 1°, ISCAN, Burlington, MA). To get a stable eye image for the overhead infrared camera, an infrared dichroic mirror was positioned at a 45° angle (facing upward) immediately in front of the nose. This mirror reflected the image of the eye in the infrared range while letting visible light pass through. A visor placed around the chair prevented the monkey from touching the infrared mirror, the juice tube, or bringing the bead to their mouth.\n",
    "\n",
    "## Behavioral training\n",
    "\n",
    "Our animals were trained using the following operant conditioning protocol. First, the animal was rewarded for arm movements toward the screen and learnt to take pieces of fruit on the screen. Once the animal acquired the association between reaching and reward, the animal was conditioned to reach and touch a target for a juice reward. The position, as well as the color of this target, was then randomized as the monkey learned to touch targets of various colors at different locations on the screen. We then used a design in which the monkey first held the central hold for a brief period, and then a checkerboard cue, which was nearly 100% red or 100% green, appeared for 400–600 ms and finally the two targets appeared. The monkey received a reward for making a reach to the color of the target that matched the predominant color of the checkerboard cue. Two-target ‘Decision’ blocks were interleaved with single target blocks to reinforce the association between checkerboard color and the correct target. After two weeks of training with this interleaved paradigm, the animal reliably reached to the target matching the color of the central checkerboard cue. We switched the paradigm around by adopting a design in which the targets appeared before the checkerboard cue onset. We initially trained on holding periods (where the monkeys view targets) from 300 to 1800 ms. We trained the animal to maintain the hold on the center until the checkerboard cue appeared by providing small amounts of juice at rough time intervals. When the animal reliably avoided breaking central hold during the hold period, we stopped providing the small amounts of juice for holding but maintained the juice reward for correct reaches. After the animal learned to stay still during the target viewing period, we introduced more difficult checkerboard cues (decreased color coherences) to the animal while reducing the maximal holding period to 900 ms. We then trained the animal to discriminate the checkerboard as accurately and as fast as possible while discouraging impulsivity by adopting timeouts.\n",
    "\n",
    "## Electrophysiological recordings\n",
    "\n",
    "To guide the stereotaxic coordinates for our eletrophysiological recordings we used known response-to-muscle palpation properties of PMd and M1. Our chambers were placed normal to the surface of cortex and aligned with the skull of the monkey. Recordings were performed perpendicular to the surface of the brain. Recordings were made anterior to the central sulcus, lateral to the spur of the arcuate sulcus, and lateral to the precentral dimple. For both monkeys, we were able to identify the upper and lower arm representation by repeated palpation at a large number of sites to identify muscle groups associated with the sites. Recordings were performed in the PMd and M1 contralateral to the arm used by the monkey. Monkey T used his right arm (O used his left arm) to perform tasks.\n",
    "\n",
    "A subset of the electrophysiological recordings were performed using traditional single electrode recording techniques. Briefly, we made small burr holes through the PALACOS/acrylic using handheld drills. We then used a Narishige drive with a blunt guide tube placed in firm contact with the dura. Recordings were obtained using FHC electrodes to penetrate the overlying dura (UEWLGCSEEN1E, 110 mm long and 250 µm thick electrodes with a standard blunt tip and profile, epoxylite insulation, and an impedance of 5–7 MΩ) . Every effort was made to isolate single units during the recordings with FHC electrodes by online monitoring and seeking out well-isolated signals (see next section below).\n",
    "\n",
    "We performed linear multi-contact electrode (U-probe) recordings in the same manner as single electrode recordings with some minor modifications. We used 180 µm thick 16-electrode U-probes (15 µm Pt/Ir electrode site diameter, 150 μm spacing, circular shape, polyimide insulation, and secured in medical-grade epoxy. Electrode contacts were ∼100 KΩ in impedance). We used a slightly sharpened guide tube to provide more purchase on dura. We also periodically scraped away, under ketamine-dexmetotomidine anesthesia, any overlying tissue on the dura. Both these modifications greatly facilitated penetration of the U-probe. We typically penetrated the brain at very slow rates (~2–5 μm/s). Once we felt we had a reasonable sample population of neurons, potentially spanning different cortical layers, we stopped and waited for 45–60 min for the neuronal responses to stabilize. The experiments then progressed as usual.\n",
    "\n",
    "We attempted to minimize the variability in U-probe placement on a session-by-session basis. Our approach was to place the U-probe so that the most superficial electrodes (electrodes 1, 2 on the 16 channel probe) were in layer I and able to record multi-unit spiking activity. Any further movement of the electrode upwards resulted in the disappearance of spiking activity and a change in the overall activity pattern of the electrode (suppression of overall LFP amplitudes). Similarly, driving the electrodes deeper resulted in multiphasic extracellular waveforms and also a change in auditory markers which were characterized by decreases in overall signal intensity and frequency content. Both markers suggested that the electrode entered white matter. Recording yields and electrode placement were in general much better in monkey T (average of ~16 units per session) than monkey O (average of ~nine units per session). We utilized these physiological markers as a guide to place electrodes and thus minimize variability in electrode placement on a session-by-session basis. Importantly, the variability in placement would act against our findings of depth-related differences shown in [Figure 8](#fig8).\n",
    "\n",
    "## Identification of single neurons during recordings\n",
    "\n",
    "Our procedure for identifying well-isolated single neurons was as follows: In the case of the single FHC tungsten electrode recordings, we moved the electrode and conservatively adjusted the threshold until we identified a well-demarcated set of waveforms. We took extra care to separate these waveforms from the noise and other smaller neurons. Our ability to isolate neurons was helped by the fact that these electrodes have a very small exposed area (hence high impedance) allowing for excellent isolation. Once a stable set of waveforms was identified, hoops from the Central software (Blackrock Microsystems) were used to demarcate the waveforms from noise and other potential single neurons. The electrode was allowed to settle for at least 15 min to ensure that the recording was stable. Once stability was confirmed, we began data collection. If we found that the recording was unstable, we discarded the neuron and moved the electrode to a newly isolated neuron and repeated the procedure. For a stable recording, we stored the waveform snippet and the time of the spike. Offline, we first visualized the waveforms in MATLAB by performing PCA. If we found that our online identification of the waveforms was inadequate, we either discarded the recording, identified it as a multi-unit (not used in this paper), or exported the data to Plexon Offline Sorter and redrew the cluster boundaries. We also took advantage of Plexon Offline Sorter’s ability to visualize how PCA features changed with time to ensure the quality and stability of our isolation. Finally, after redrawing cluster boundaries, we exported the data back to our analysis pipeline.\n",
    "\n",
    "For our 16-channel Plexon U-Probe recordings, we again lowered the electrode until we found a stable set of waveforms. The small contact area of these electrode sites again ensured excellent identification and low levels of background noise (∼10–20 µV). We then waited at least 45 min until the recordings were very stable. Such an approach ensured that we minimized electrode drift. In our experience, the U-probes also have less drift than Neuropixel or other recording methods. We then again repeated the conservative thresholding and identification procedure outlined for the FHC electrodes. For U-probes, we did not move the electrodes once they had settled. Instead, we constantly monitored the recordings and any changes in a particular electrode over time led to the units from that electrode being discarded and not included in further analysis. Finally, the same offline procedures used for FHC electrodes were repeated for the U-probe recordings.\n",
    "\n",
    "## Preprocessing of single-unit recordings\n",
    "\n",
    "We obtained 996 extracellularly recorded single units (778 units recorded with the U-probe) from PMd across two monkeys (450 from Monkey O and 546 from Monkey T). Of these, we identified 801 units whose ISI violations (refractory period ≤ 1.5 ms) ≤ 1.5% [@bib25]. Our waveforms were filtered with a 4th-order 250 Hz high-pass Butterworth filter. The waveforms for each of the units were extracted for a duration of 1.6 ms with a pre-trough period of 0.4 ms, sampled at 30 kHz.\n",
    "\n",
    "## Alignment and normalization of waveforms\n",
    "\n",
    "In order to calculate the mean waveform for each single unit, we upsampled individual waveforms calculated over different trials by a factor of 10 and aligned them based on the method proposed in [@bib78]. For each waveform, we calculated its upswing slope (slope between trough to peak) and the downswing slope (slope to the trough) and re-aligned to the midpoint of the slope that exceeded the other by a factor of 1.5. Following this alignment, we chose the best set of waveforms for calculating the mean as those that satisfied the criteria (1) less the two standard deviations (S.D.) from the mean at each point and (2) average deviation from the mean across time was less than 0.4 [@bib78]. The final set of waveforms for each unit was averaged and downsampled to 48 time points. Upon visual inspection, we then identified 761 units (625 single units with 490 U-probe recorded units) whose average waveforms qualified the criteria of exhibiting a minimum of two phases with trough occurring first. The remaining waveforms, unless stated otherwise here, were removed from the analysis. We excluded positive-spiking waveforms because of their association with axons [@bib161]. Finally, we normalized the waveforms by dividing the extreme value of the amplitude such that the maximum deviation is ±1 unit [@bib153].\n",
    "\n",
    "It is important to note that the preprocessing we use, individual mean subtraction and ±1 unit normalization, operates independently of the data. Using another commonly used preprocessing normalization, normalization to trough depth [@bib76], we obtained extremely similar results. We found ±1 unit trough to peak normalization had virtually the same number of clusters as normalization to trough ($8.29\\pm 0.84$ vs. $8.16\\pm 0.65$ clusters, mean ± S.D.; [Figure 3—figure supplement 2A and C](#fig3s2)). Furthermore, both normalizations picked out the same structure ([Figure 3—figure supplement 2B and D](#fig3s2)); the normalization to trough did have a 9th cluster splitting off of ⑤ but this was something also seen with ±one unit trough to peak normalization in certain data subsets as well.\n",
    "\n",
    "## A step-by-step guide to UMAP and Louvain clustering in _WaveMAP_\n",
    "\n",
    "To provide the reader with an intuitive overview of _WaveMAP_, we provide a step-by-step exposition of the different stages in the workflow shown in [Figure 2](#fig2) beginning with UMAP followed by Louvain community detection. UMAP is a non-linear method that enables the capture of latent structures in high-dimensional data as a graph. This graph can then be used to visualize the latent structure in a low-dimensional embedding [@bib106]. For a detailed description of the methods, please refer to the Supplemental Information or respective references for UMAP [@bib106] and Louvain community detection [@bib22].\n",
    "\n",
    "### [Figure 2A–i](#fig2)\n",
    "\n",
    "We pass 625 normalized single-unit waveforms into UMAP. This normalization is crucial for exposing interesting structure in downstream analysis, although the particular normalization is less important ([Figure 3—figure supplement 2](#fig3s2)). UMAP uses a five-step (ii.a to ii.e in [Figure 2A](#fig2)) procedure to construct a weighted high-dimensional graph.\n",
    "\n",
    "### [Figure 2A–ii.a](#fig2)\n",
    "\n",
    "In the first step, the data for each waveform is viewed in its original (sometimes called ‘ambient’) 48-dimensional space with each dimension corresponding to one of 48 time points along the waveform recording.\n",
    "\n",
    "### [Figure 2A–ii.b](#fig2)\n",
    "\n",
    "A local metric is then assigned to each data point such that a unit ball (distance of one) surrounding it extends to the 1st-nearest neighbor. This ensures that every point is connected to at least one other point.\n",
    "\n",
    "### [Figure 2A–ii.c](#fig2)\n",
    "\n",
    "Beyond this first connection, the distances to the next $\\mathrm{(}k\\mathrm{-}\\mathrm{1}\\mathrm{)}$-nearest neighbors increases according to an exponential distribution scaled by the local density. This is shown as a ‘glow’ around each of the unit balls in [Figure 2A–ii](#fig2).c.\n",
    "\n",
    "### [Figure 2A–ii.d](#fig2)\n",
    "\n",
    "The distances from the local point to the $k\\mathrm{-}\\mathrm{1}$ data points beyond the unit ball are made to be probabilistic (‘fuzzy’) according to their distance (k = four in [Figure 2A–ii](#fig2).d with some low weight connections omitted for clarity). This also means that the metric around each data point has a different notion of ‘how far’ their neighbors are. Distances are shorter in dense regions (with respect to the ambient space) than are distances in sparser regions leading to a graph with asymmetric edge weights. If the notion of a probabilistic connection is confusing, this construction can just be understood as an asymmetric directed graph with edge weights between zero and one. One way to understand this is through the following real life example: to someone living in a dense city, traveling several miles may seem very far, while for a rural resident, this distance might be trivial even if the absolute distance is the same.\n",
    "\n",
    "### [Figure 2A–ii.e](#fig2)\n",
    "\n",
    "The edge weights between any two data points, $a$ and $b$, are ‘averaged together’ according to the formula $a\\mathrm{+}b\\mathrm{-}a\\mathrm{\\cdot }b$ known as probabilistic sum. This results in a graph that now has symmetric edge weights.\n",
    "\n",
    "### [Figure 2Biv](#fig2)\n",
    "\n",
    "However, before we project this graph into lower dimension, we first apply clustering to the high-dimensional graph with a method known as Louvain community detection. This method proceeds in two steps per pass: modularity optimization and community aggregation ([Figure 2—figure supplement 1B](#fig2s1)).\n",
    "\n",
    "### [Figure 2B-iv.a](#fig2):\n",
    "\n",
    "In the first step of Louvain, each node in the graph is assigned to its own ‘community’ which can be interpreted as its own cluster. Next, each node will join a neighboring node’s community such as to maximize an objective function known as ‘modularity score’ (see Supplemental Information for the exact equation). This score is maximized when the sum of the weighted edges within a community is maximal relative to the sum of the weighted edges incident on the community from nodes outside the community. This procedure operates on all nodes until modularity can no longer be increased across the network; this concludes the modularity optimization step. In the next step, community aggregation, all nodes in a community are collapsed into a single node to create a new network. This completes the first pass of Louvain which then repeats modularity optimization and aggregation on this new graph until modularity is once again maximized. This continues until modularity no longer increases across hierarchies of graphs. Note that the resolution parameter is set to one in the work of [@bib22] but we use an implementation of Louvain that allows for changing of this parameter according to the definition of modularity given in [@bib89]. The resolution parameter gives the user some ability to specify how large of a community they expect as might be related to a phenomenon of interest and should be chosen empirically.\n",
    "\n",
    "### [Figure 2B-iv.b](#fig2)\n",
    "\n",
    "The final Louvain community memberships are propagated back to the original nodes and assigned as labels for the associated data points which completes the classification step.\n",
    "\n",
    "## [Figure 2B–v](#fig2)\n",
    "\n",
    "In the second step of UMAP, graph layout, the high-dimensional graph with symmetric edge weights from the previous step is projected down into some lower dimension (here it is two dimensions).\n",
    "\n",
    "To initialize this process, the graph is first passed through a Laplacian eigenmap [@bib17] which helps regularize the initial embedding of the graph in low dimension [@bib85].\n",
    "\n",
    "### [Figure 2B–v.a](#fig2)\n",
    "\n",
    "We know the graph in high dimension but we have not yet found this graph in low dimension. From here a force directed graph layout procedure is used to align the initialized low-dimensional graph with the one found in high dimension. The force directed graph layout procedure minimizes the difference in distances in the instantiated graph compared to the high-dimensional one by alternatingly applying an attractive and a repulsive force according to the edge weights. These forces are chosen to minimize the cross-entropy between the graphs. This process ensures that points close in high dimension but far in low dimension are brought together (attraction) and those that are far in high dimension but close in low dimension are pushed apart (repulsion).\n",
    "\n",
    "### [Figure 2B–v.b](#fig2)\n",
    "\n",
    "A final embedding of the data is found using stochastic gradient descent but by fixing the seed in our procedure, we enforce standard gradient descent. Although this requires more memory and is less performant, it guarantees that embeddings will look the same (even if this doesn’t affect clustering).\n",
    "\n",
    "### [Figure 2B–vi](#fig2)\n",
    "\n",
    "In the final step of our method, we combine the labels found through Louvain clustering with a low-dimensional embedding to arrive at our _WaveMAP_ solution.\n",
    "\n",
    "## WaveMAP parameter selection and validation\n",
    "\n",
    "The normalized extracellular waveforms were passed into the Python package umap 0.4.0rc3 (RRID:[SCR_018217](https://scicrunch.org/resolver/SCR_018217)) [@bib106] with the parameters shown in [Table 1](#table1). The n_neighbors value was increased to 20 to induce more emphasis on global structure. UMAP utilizes a stochastic k-nearest neighbor search to establish the graph and stochastic gradient descent to arrive at the embedding thus it produces similar but different embeddings in the projected space. For reproducibility reasons, the random_state was fixed in the algorithm and in numpy. The choice of random seed only impacted the projection and not the clustering ([Figure 3—figure supplement 1A](#fig3s1)). From here, the graph provided by umap.graph\\_ was passed into the Louvain community detection algorithm to generate the clustering seen in [Figure 3A](#fig3). For details of the UMAP algorithm, see Supplementary Information.\n",
    "\n",
    "Graph networks are often hierarchical and it has been recommended that the Louvain resolution parameter be chosen to elicit the phenomenon of interest [@bib131; @bib90]. To select the resolution parameter $t$, we chose a value that best maximized modularity score (a measure of the ratio between connections within a cluster vs. incoming from outside of it; see Supplementary Information) while still returning an statistically analyzable number of clusters (n > 20). We selected a resolution parameter (green marker on [Figure 3B](#fig3)) that maximized modularity score of Louvain clustering while still returning clusters of $n>20$ to allow for downstream statistical analyses. We note that this was also very close to the ‘elbow’ in terms of number of clusters; this verifies that we have reached near-optimality in a second sense of obtaining stable cluster number. These scores were calculated over 25 random UMAP instantiations of 80% of the full dataset in 5% intervals. For algorithmic details of Louvain clustering, see Supplementary Information.\n",
    "\n",
    "To validate that our parameter selection was stable and produced the same number of clusters reliably, we used a bootstrap and applied the _WaveMAP_ procedure to random subsets of the full dataset ([Figure 3—figure supplement 1B](#fig3s1), [@bib167]). We obtained 100 random samples from 10% to 90% of the full data set in 10% increments while simultaneously choosing a different random seed for the UMAP algorithm each time. We calculated both the number of Louvain clusters and the adjusted mutual information score (AMI) across these random samples and plot it on the same graph [Figure 3—figure supplement 1B](#fig3s1), red and green. The AMI is a measure of how much ‘information’ is shared between a pair of clusterings with information specifically as Shannon entropy. The Shannon entropy of a given clustering (often called a ‘partitioning’), ${\\displaystyle H(X)}$, is defined as,\n",
    "\n",
    "$$\n",
    "{\\displaystyle H(X)=-\\sum _{i=1}^{n}P({x}_{i})\\mathrm{log}P({x}_{i})}\n",
    "$$\n",
    "\n",
    "with the probability ${\\displaystyle P({x}_{i})}$ pertaining to the probability that a certain data point ${\\displaystyle {x}_{i}}$ will belong to a certain cluster. The clustering entropy can be understood as how ‘unpredictable’ the results of a random variable are (in this case the random variable is the particular clustering solution). For instance, a fair coin is less predictable (greater entropy) than a loaded coin (lower entropy).\n",
    "\n",
    "Intuitively, AMI is how much information we receive about one variable given an observation of another and vice versa [@bib168] (or how much knowing one clustering allows us to predict another) corrected for random chance. Thus, it’s bounded between 0 and 1 with 0 for two completely independent variables and one for completely identical variables. Formally, un-adjusted mutual information, $I(X,Y)$, is defined as,\n",
    "\n",
    "$$\n",
    "{\\displaystyle I(X,Y)=H(X)-H(X|Y){\\displaystyle ={D}_{KL}({P}_{X,Y}(x,y)\\phantom{\\rule{0.2778em}{0ex}}\\Vert \\phantom{\\rule{0.2778em}{0ex}}{P}_{X}(x)\\cdot {P}_{Y}(y))}}\n",
    "$$\n",
    "\n",
    "where $H(X|Y)$ is the conditional Shannon entropy, ${P}_{X,Y}(x,y)$ is the joint distribution of $X$ and $Y$, and ${P}_{X}(x)$ is a marginal distribution.\n",
    "\n",
    "The value at 100% is omitted because it has the same cluster number as our dataset and zero variance since _WaveMAP_ is invariant to random seed selection. Thus the variation in cluster number due to sampling and random seed are compounded and shown together in [Figure 3—figure supplement 1B](#fig3s1).\n",
    "\n",
    "Ensemble clustering for graphs (ECG) [@bib132; @bib133] was used to validate the clusters found in [Figure 3A](#fig3) (see [Figure 3—figure supplement 1C](#fig3s1)). We added the algorithm (<https://github.com/ftheberge/Ensemble-Clustering-for-Graphs>; [@bib165]) into the python-igraph package [@bib36] and passed UMAP graphs into it directly. We set the number of partitions $k$ to be 10 to produce the plot in [Figure 3—figure supplement 1C](#fig3s1). This algorithm uses $k$ different randomized instantiations of the clusters in the graph followed by one round of Louvain clustering ([Figure 2—figure supplement 1B](#fig2s1)). Each of these $k$ level-1 graphs (called level-1 partitions since one round of Louvain was performed) are then combined as a single graph such that when edges co-occur between nodes in one of the $k$ graphs, it is more heavily weighted. This ensembling of several graphs via the weight function ${W}_{𝒫}$ (see Supplemental Materials and methods section _Ensemble clustering for graphs (ECG)_) yields the final ECG graph.\n",
    "\n",
    "## Gradient boosted decision tree classifier\n",
    "\n",
    "We then trained a gradient boosted decision tree classifier in xgboost 1.0.2 (RRID:[SCR_021361](https://scicrunch.org/resolver/SCR_021361)) [@bib27]. A 30–70% test-train split was used with the test set never seen by model training or hyperparameter tuning. A 5-fold cross-validation was applied to the training data and optimal hyperparameters were obtained after a grid search on the folds using scikit-learn’s (RRID:[SCR_002577](https://scicrunch.org/resolver/SCR_002577)) GridSearchCV function with final parameters in [Table 1](#table1). The default multi-class objective function multi:softmax was also used. The percent accuracy for each cluster against all others is plotted as a confusion matrix in [Figure 3C](#fig3) by applying the final classifier model to the unseen test set.\n",
    "\n",
    "The same procedure was used when training on the GMM labels found in [Figure 4D](#fig4) and for the eight cluster GMM labels in [Figure 4—figure supplement 2B](#fig4s2). Each of these classifiers also separately underwent hyperparameter tuning using scikit-learn’s GridSearchCV function as well with final hyperparameters shown in 2.\n",
    "\n",
    "It is important to note that cross-validation was done after the cluster labels were generated by looking at the entire dataset (both via the algorithm itself and our tuning of parameters). This results in data leakage [@bib117; @bib77] which potentially hurts out-of-dataset performance. Thus, classifier performance is only used here to demonstrate UMAP’s ability to sensibly separate waveforms within-dataset relative to traditional GMM methods ([Figure 4D](#fig4)). [Figure 3C](#fig3) is not leveraged to provide firm insight into how such a classifier would perform out-of-dataset. It is also important to note that none of the parameters for _WaveMAP_ (n_neighbors or resolution) were tuned to optimize for classifier performance and thus the direction of bias is not necessarily deleterious.\n",
    "\n",
    "## Specified waveform shape features\n",
    "\n",
    "To compute specified features for each normalized waveforms ([Figure 4A](#fig4)), we first up-sampled the waveforms from 48 to 480 time points using a cubic spline interpolation method. We then used this up-sampled waveform to compute three separate features: trough to peak duration, AP width, and peak ratio. Trough to peak is the time from the bottom of the depolarization trough (global minimum) to the post-hyperpolarization peak (subsequent local maximum). AP width was calculated as the width of the depolarization trough at the full-width half-minimum point. Both these measures were found using the mwave function from the MLIB 1.7.0.0 toolbox [@bib160]. Peak ratio was the ratio of heights (above baseline) between the pre-hyperpolarization (maximum before trough) and the post-hyperpolarization peak (maximum after trough).\n",
    "\n",
    "## Gaussian mixture model clustering\n",
    "\n",
    "Using the specified feature values (trough to peak, AP width, and peak ratio), the normalized waveforms were clustered in the three-dimensional feature space using a Gaussian mixture model (GMM) with hard-assignment (each data point belongs to one cluster) through MATLAB’s fitgmdist function across 50 replicates ([Figure 4B](#fig4)). Each replicate is a different random instantiation of the GMM algorithm and the model with the largest log likelihood is chosen.\n",
    "\n",
    "The Bayesian information criterion (BIC) was used to determine the optimal cluster number and is defined as\n",
    "\n",
    "$$\n",
    "\\mathrm{BIC}=-2\\mathrm{ln}P(X|\\theta )+K\\mathrm{ln}(n)\n",
    "$$\n",
    "\n",
    "where the first term $-2\\mathrm{ln}P(X|\\theta )$ is a ‘goodness of fit’ term obtained from the negative log likelihood function, that is, the conditional probability of observing the sample $X$ given a vector of parameters $\\theta$. In the particular case of GMM, the function $P(X|\\theta )$ is the probability of observing the clusters given an underlying particular sum of multivariate Gaussians (the likelihood). The second term $K\\mathrm{ln}(n)$ is a penalty on the number of parameters $n$ which approximates model complexity. Penalizing the number of model parameters (number of clusters $K$) scaled by the number of data points $n$ captures the idea that ‘simplicity is better’. This criterion ultimately constrains the number of Gaussians used to fit the data.\n",
    "\n",
    "Assuming we have ${N}_{f}$ features and ${N}_{c}$ clusters we can calculate $K$ using the following framework: For each Gaussian mixture model, the total number of parameters is ${N}_{f}$ means and ${N}_{f}({N}_{f}+1)/2$ covariance parameters. Another free parameter that is learned is the weight for each Gaussian that sums up to 1, leaving us with ${N}_{c}-1$ unique weights. Thus the $K$ which is the effective number of parameters for a GMM is,\n",
    "\n",
    "$$\n",
    "K={N}_{c}\\left({N}_{f}+\\frac{{N}_{f}({N}_{f}+1)}{2}\\right)+{N}_{c}-1\n",
    "$$\n",
    "\n",
    "The ‘best’ model in a BIC-sense will have the set of parameters $\\theta$ maximizing the likelihood function (thus minimizing the negative log likelihood) for a given model or model family—a number of multivariate Gaussians in a three-dimensional feature space in this case. To arrive at the parameters best approximating the Gaussian distribution giving rise to the data (Maximum Likelihood Estimation or MLE), the Expectation-Maximization (EM) algorithm was used. The optimal cluster number was selected as the lowest number of clusters between 1 and 10 at which the change in BIC was minimized (at the ‘elbow’ in [Figure 4C](#fig4)).\n",
    "\n",
    "## Interpretable machine learning: UMAP inverse transform and SHAP\n",
    "\n",
    "To facilitate interpretability, we used the invertibility of the UMAP transform (which itself is based on Delauney triangulation) to generate test waveforms tiling the projected space [Figure 5A](#fig5). 100 evenly-spaced test coordinates were generated spanning a portion of the embedded space and passed backwards through the UMAP transform using umap’s built-in inverse_transform function. The waveform generated at each test point is shown color-coded to the nearest cluster color or in gray if the distance exceeds 0.5 units in UMAP space.\n",
    "\n",
    "Using the package shap (RRID:[SCR_021362](https://scicrunch.org/resolver/SCR_021362); [@bib97]), SHAP values were calculated for the classifier trained on _WaveMAP_ identified clusters. The trained XGBoost model was passed directly into the tree model-specific shap.TreeExplainer [@bib96] which then calculated the mean absolute SHAP values (the average impact on model performance, postive or negative) for all waveform time points (features). TreeExplainer assigned SHAP values for every time point class-by-class and these were used to generate the class-specific SHAP plots ([Figure 5C](#fig5)). The SHAP values for each time point, across classes, was summed to generate the overall SHAP values for each time point ([Figure 5B](#fig5)).\n",
    "\n",
    "## Choice-selective signal\n",
    "\n",
    "We use an approach developed in [@bib110] to estimate the choice-selective signal. We chose such an approach because decision-related activity of PMd neurons does not simply increase or decrease in firing rate and often shows considerable temporal modulation. We estimated for each neuron a choice-selective signal on a time point-by-time point basis as absolute value of the firing rate difference between left and right choice trials (|left - right|) or equivalently PREF-NONPREF. We use this choice-selective signal to understand choice-related dynamics and estimate discrimination time.\n",
    "\n",
    "## Choice-related dynamics\n",
    "\n",
    "To understand the dynamics of the choice-selectivity signal as a function of the unsigned checkerboard coherence, we performed the following analysis. As described above, we first estimated the choice-selectivity signal in spikes/s for each neuron and each checkerboard coherence as shown for example in [Figure 7A,B](#fig7). We then estimated the slope of this choice-selectivity signal in the 175–325 ms period after checkerboard onset. Repeating this analysis for each color coherence provided us with an estimate of the rate of change of the choice selectivity signal ($\\eta$) for all the coherences in spikes/s/s. Averaging over neurons for each cluster provided us with the graphs in [Figure 7C,D](#fig7). We then estimated the dependence of $\\eta$ on color coherence by regressing $\\eta$ and color coherence to estimate how strongly choice-selectivity signals in a particular cluster were modulated by the stimulus input. This modulation is summarized in [Figure 7E](#fig7) and measured as ‘coherence slope’ in units of spikes/s/s/% color coherence.\n",
    "\n",
    "## Discrimination time\n",
    "\n",
    "We identified the discrimination time, that is the time at which the neuron demonstrated significant choice selectivity, on a neuron-by-neuron basis. We compared the choice-selective signal at each point to the 95th percentile of the bootstrap estimates of baseline choice-selective signal (i.e. before checkerboard stimulus onset). We enforced the condition that the choice-selective signal should be significantly different from the baseline for at least 25 ms after this first identified time to be included as an estimate of a time of significant choice selectivity for that neuron. Using longer windows provided very similar results.\n",
    "\n",
    "## Experimental subjects (anatomical data)\n",
    "\n",
    "Archived tissues were harvested from six young rhesus macaques of both sexes (9 ± 1.13 years, _Macaca mulatta_). These subjects were close in age to the macaques used in the main study and were part of part of a larger program of studies on aging and cognition led by Dr. Douglas Rosene. These monkeys were obtained from the Yerkes National Primate Center (RRID:[SCR_001914](https://scicrunch.org/resolver/SCR_001914)) and housed individually in the Laboratory Animal Science Center at the Boston University School of Medicine; these facilities are fully accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC). Research was conducted in strict accordance with the guidlines of the National Institutes of Health’s Guide for the Care and Use of Laboratory Animals and Public Health Service Policy on the Humane Care and Use of Laboratory Animals.\n",
    "\n",
    "## Perfusion and fixation\n",
    "\n",
    "All brain tissue for histological studies was fixed and harvested using our well-established two-stage perfusion protocol as described [@bib108]. After sedation with ketamine hydrochloride (10 mg/ml) and deep anesthetization with sodium pentobarbital (to effect, 15 mg/kg i.v.), monkeys were perfused through the ascending aorta with ice-cold Krebs–Henseleit buffer containing (in mM): 6.4 ${\\mathrm{Na}}_{2}{\\mathrm{HPO}}_{4}$, 1.4 ${\\mathrm{Na}}_{2}{\\mathrm{PO}}_{4}$, 137.0 NaCl, 2.7 KCl, and 1.0 _MgCl_~2~ at pH 7.4 (Sigma-Aldrich) followed by fixation with 4% paraformaldehyde in 0.1M phosphate buffer (PB, pH 7.4, 37°C). The fixed brain sample was blocked, in situ, in the coronal plane, removed from the skull and cryoprotected in a series of glycerol solutions, and flash frozen in 70°C isopentane [@bib141]. The brain was cut on a freezing microtome in the coronal plate at 30 µm and were systematically collected into 10 matched series and stored in cryoprotectant (15% glycerol, in 0.1M PB, pH 7.4) at −80°C [@bib53].\n",
    "\n",
    "## Immunohistochemistry\n",
    "\n",
    "To assess the laminar distribution of interneurons, we batch processed 30 µm coronal sections through the rostral dorsal premotor cortex area (PMdr) from six specimens. Sections were immunolabelled for inhibitory neuronal subtypes based on their expression of calcium binding proteins, calbindin (CB), calretinin (CR), and parvalbumin (PV), which label non-overlapping populations in primates [@bib39]. Free floating sections were first rinsed (3 x 10 min, 4°C) in 0.01M phosphate-buffered saline (PBS) and incubated in 50 mM glycine for 1 hr at 4°C. Sections were then rinsed in 0.01M PBS (3 x 10 min, 4°C), and antigen retrieval was performed with 10 mM sodium citrate (pH 8.5) in a 60–70°C water bath for 20 min. Sections were then rinsed in 0.01M PBS (3 x 10 min, 4°C) and incubated in pre-block (0.01M PBS, 5% bovine serum albumin \\[BSA], 5% normal donkey serum \\[NDS], 0.2% Triton X-100) to reduce any non-specific binding of secondary antibodies. Primary antibodies ([Figure 1](#fig1)) were diluted in 0.1 M PB, 0.2% acetylated BSA (BSA-c), 1% NDS, 0.1% Triton X-100. To increase the penetration of the antibody, two microwave incubation sessions (2 × 10 min at 150 watts) using the Pelco Biowave Pro (Ted Pella), followed by a 48 hr incubation at 4°C with gentle agitation. After rinsing (3 x 10 min) in 0.01M PBS at 4°C, sections were co-incubated with secondary antibodies diluted in incubation buffer (see 1), microwaved 2 × 10 min at 150 W, and placed at 4°C for 24 hr with gentle agitation. Sections were then rinsed (3 x 10 min) in 0.1M PB, mounted onto slides and coverslipped with prolong anti-fade gold mounting medium (ThermoFisher) and cured at room temperature in the dark.\n",
    "\n",
    "## Confocal microscopy and visualization of immunofluorescent labeling\n",
    "\n",
    "Immunofluorescent labeling was imaged using a laser-scanning confocal microscope (Leica SPE) using 488 and 561 nm diode lasers. For each coronal section, two sets of tile scan images of a cortical column, ~200 µm wide and spanning, from pia to the white matter boundary, were obtained in the PMdr. This corresponded to the area 6FR in cytoarchitectural maps [@bib11; @bib115; @bib116] and area F7 in several functional maps [@bib104; @bib138]. The two columns were spaced 200 µm apart. All images were acquired using a plain apochromat 40x/1.3 NA oil-immersion objective at a resolution of 0.268 x 0.268 x 0.5 µm voxel size. The resulting image stacks were deconvolved and converted to 8-bit images using AutoQuant (Media Cybernetics; RRID:[SCR_002465](https://scicrunch.org/resolver/SCR_002465)) to improve the signal to noise ratio [@bib108].\n",
    "\n",
    "## Stereological cell counting\n",
    "\n",
    "Due to its demonstrated ability in producing minimally-biased results, 3D stereologic cell counting [@bib146] was utilized to count parvalbumin- (PV^+^), calretinin- (CR^+^) and calbindin-positive (CB^+^) cells. Using the CellCounter plugin in Fiji (RRID:[SCR_002285](https://scicrunch.org/resolver/SCR_002285)) [@bib145] on each image stack after maximum intensity projection, the inhibitory cells were counted slice by slice, recognized by their round shape (as opposed to pyramids), lack of apical dendrite, and relatively high uniform intensity. Cells at the bottom slice of each image stack and touching the left image border were excluded to avoid double-counting.\n",
    "\n",
    "## Statistics\n",
    "\n",
    "All statistical tests (Kolmogorov-Smirnov, Kruskal-Wallis, and Mann-Whitney _U_) were conducted using the package scipy.stats (RRID:[SCR_008058](https://scicrunch.org/resolver/SCR_008058)) [@bib147]. Multiple comparisons were corrected for using false detection-rate adjusted p-values (Benjamini-Hochberg); this was done using scipy.stats.multitest and scikit-posthocs (RRID:[SCR_021363](https://scicrunch.org/resolver/SCR_021363)) [@bib164]. Ordinary least squares regressions were conducted in the package statsmodels (RRID:[SCR_016074](https://scicrunch.org/resolver/SCR_016074)) [@bib148]. Bootstrapped standard errors of the median were calculated by taking 5000 random samples with replacement (a bootstrap) of a dataset and then the standard deviation of each bootstrap was taken. Effect sizes were given as adjusted ${R}^{2}$ values or Cohen’s ${f}^{2}$ (of a one-way ANOVA) using statsmodels.formula.api.ols and statsmodels.stats.oneway respectively."
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