ifs.ipynb

{
  "metadata": {
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    },
    "kernelspec": {
      "name": "python",
      "display_name": "Python (Pyodide)",
      "language": "python"
    }
  },
  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "code",
      "source": "%matplotlib inline\nimport pylab as pl\nimport numpy as np",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "class Hist:\n    def __init__(self, width=300):\n        self.xmin = None\n        self.xmax = None\n        self.ymax = None\n        self.ymin = None\n        self.width = width\n        self.height = None\n        self.count = None\n        \n    def update_range(self, x, y):\n        if self.xmin is None:\n            self.xmin = x.min()\n            self.xmax = x.max()\n            self.ymin = y.min()\n            self.ymax = y.max()\n        else:\n            self.xmin = min(self.xmin, x.min())\n            self.xmax = max(self.xmax, x.max())\n            self.ymin = min(self.ymin, y.min())\n            self.ymax = max(self.ymax, y.max())\n            \n        if self.xmax > self.xmin:\n            self.height = int(self.width / (self.xmax - self.xmin) * (self.ymax - self.ymin))\n            \n    def add(self, x, y):\n        x2 = np.clip((x - self.xmin) / (self.xmax - self.xmin), 0, 1)\n        y2 = np.clip((y - self.ymin) / (self.ymax - self.ymin), 0, 1)\n        x3 = (x2 * self.width).astype(int)\n        y3 = (y2 * self.height).astype(int)\n        data = y3 * (self.width + 1) + x3\n        c = np.bincount(data, minlength=(self.width + 1) * (self.height + 1))\n        c = c.reshape((self.height + 1, -1))\n        if self.count is None:\n            self.count = c\n        else:\n            self.count += c",
      "metadata": {
        "trusted": true
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "eq1 = np.array([[0,0,0],[0,0.16,0]])\np1 = 0.01\n\neq2 = np.array([[0.2,-0.26,0],[0.23,0.22,1.6]])\np2 = 0.07\n\neq3 = np.array([[-0.15, 0.28, 0],[0.26,0.24,0.44]])\np3 = 0.07\n\neq4 = np.array([[0.85, 0.04, 0],[-0.04, 0.85, 1.6]])\np4 = 0.85\n\np = np.cumsum([p1, p2, p3, p4])\neq = np.asarray([eq1, eq2, eq3, eq4])\n\nn = 1000\npos = np.ones((n, 3), dtype=np.float64)\npos[:, :2] = np.random.uniform(-0.3, 0.3, size=(n, 2))\n\ncounts = None\nhist = Hist(width=400)\n\nfor i in range(2000):\n    rands = np.random.rand(n)\n    select = np.searchsorted(p, rands)\n    tmp = (eq[select] @ pos[:, :, None])\n    pos[:, :2] = tmp[:, :, 0]\n    if i > 50:\n        hist.add(pos[:, 0], pos[:, 1])\n    else:\n        hist.update_range(pos[:, 0], pos[:, 1])",
      "metadata": {
        "trusted": true
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "img = np.log(hist.count + 1)\npl.imshow(img, origin='lower');",
      "metadata": {
        "trusted": true
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "%timeit np.add.at(a, ([1, 1, 2], [0, 0, 1]), 1)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 12,
      "outputs": [
        {
          "name": "stdout",
          "text": "11.5 µs ± 276 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "np.histogram2d([1, 1, 2], [0, 0, 1], ([0, 3], [0, 3]))",
      "metadata": {
        "trusted": true
      },
      "execution_count": 14,
      "outputs": [
        {
          "execution_count": 14,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(array([[3.]]), array([0, 3]), array([0, 3]))"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "np.histogramdd??",
      "metadata": {
        "trusted": true
      },
      "execution_count": 16,
      "outputs": [
        {
          "name": "stdout",
          "text": "\u001b[0;31mSignature:\u001b[0m\n\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistogramdd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0msample\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0mrange\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0mnormed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0mdensity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n\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;31mSource:\u001b[0m   \n\u001b[0;34m@\u001b[0m\u001b[0marray_function_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_histogramdd_dispatcher\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m\u001b[0;32mdef\u001b[0m \u001b[0mhistogramdd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m                \u001b[0mdensity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;34m\"\"\"\u001b[0m\n\u001b[0;34m    Compute the multidimensional histogram of some data.\u001b[0m\n\u001b[0;34m\u001b[0m\n\u001b[0;34m    Parameters\u001b[0m\n\u001b[0;34m    ----------\u001b[0m\n\u001b[0;34m    sample : (N, D) array, or (D, N) array_like\u001b[0m\n\u001b[0;34m        The data to be histogrammed.\u001b[0m\n\u001b[0;34m\u001b[0m\n\u001b[0;34m        Note the unusual interpretation of sample when an array_like:\u001b[0m\n\u001b[0;34m\u001b[0m\n\u001b[0;34m        * When an array, each row is a coordinate in a D-dimensional space -\u001b[0m\n\u001b[0;34m          such as ``histogramdd(np.array([p1, p2, p3]))``.\u001b[0m\n\u001b[0;34m        * When an array_like, each element is the list of values for single\u001b[0m\n\u001b[0;34m          coordinate - such as ``histogramdd((X, Y, Z))``.\u001b[0m\n\u001b[0;34m\u001b[0m\n\u001b[0;34m        The first form should be preferred.\u001b[0m\n\u001b[0;34m\u001b[0m\n\u001b[0;34m    bins : sequence or int, optional\u001b[0m\n\u001b[0;34m        The bin specification:\u001b[0m\n\u001b[0;34m\u001b[0m\n\u001b[0;34m        * A sequence of arrays describing the monotonically increasing bin\u001b[0m\n\u001b[0;34m          edges along each dimension.\u001b[0m\n\u001b[0;34m        * The number of bins for each dimension (nx, ny, ... =bins)\u001b[0m\n\u001b[0;34m        * The number of bins for all dimensions (nx=ny=...=bins).\u001b[0m\n\u001b[0;34m\u001b[0m\n\u001b[0;34m    range : sequence, optional\u001b[0m\n\u001b[0;34m        A sequence of length D, each an optional (lower, upper) tuple giving\u001b[0m\n\u001b[0;34m        the outer bin edges to be used if the edges are not given explicitly in\u001b[0m\n\u001b[0;34m        `bins`.\u001b[0m\n\u001b[0;34m        An entry of None in the sequence results in the minimum and maximum\u001b[0m\n\u001b[0;34m        values being used for the corresponding dimension.\u001b[0m\n\u001b[0;34m        The default, None, is equivalent to passing a tuple of D None values.\u001b[0m\n\u001b[0;34m    density : bool, optional\u001b[0m\n\u001b[0;34m        If False, the default, returns the number of samples in each bin.\u001b[0m\n\u001b[0;34m        If True, returns the probability *density* function at the bin,\u001b[0m\n\u001b[0;34m        ``bin_count / sample_count / bin_volume``.\u001b[0m\n\u001b[0;34m    normed : bool, optional\u001b[0m\n\u001b[0;34m        An alias for the density argument that behaves identically. To avoid\u001b[0m\n\u001b[0;34m        confusion with the broken normed argument to `histogram`, `density`\u001b[0m\n\u001b[0;34m        should be preferred.\u001b[0m\n\u001b[0;34m    weights : (N,) array_like, optional\u001b[0m\n\u001b[0;34m        An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`.\u001b[0m\n\u001b[0;34m        Weights are normalized to 1 if normed is True. If normed is False,\u001b[0m\n\u001b[0;34m        the values of the returned histogram are equal to the sum of the\u001b[0m\n\u001b[0;34m        weights belonging to the samples falling into each bin.\u001b[0m\n\u001b[0;34m\u001b[0m\n\u001b[0;34m    Returns\u001b[0m\n\u001b[0;34m    -------\u001b[0m\n\u001b[0;34m    H : ndarray\u001b[0m\n\u001b[0;34m        The multidimensional histogram of sample x. See normed and weights\u001b[0m\n\u001b[0;34m        for the different possible semantics.\u001b[0m\n\u001b[0;34m    edges : list\u001b[0m\n\u001b[0;34m        A list of D arrays describing the bin edges for each dimension.\u001b[0m\n\u001b[0;34m\u001b[0m\n\u001b[0;34m    See Also\u001b[0m\n\u001b[0;34m    --------\u001b[0m\n\u001b[0;34m    histogram: 1-D histogram\u001b[0m\n\u001b[0;34m    histogram2d: 2-D histogram\u001b[0m\n\u001b[0;34m\u001b[0m\n\u001b[0;34m    Examples\u001b[0m\n\u001b[0;34m    --------\u001b[0m\n\u001b[0;34m    >>> r = np.random.randn(100,3)\u001b[0m\n\u001b[0;34m    >>> H, edges = np.histogramdd(r, bins = (5, 8, 4))\u001b[0m\n\u001b[0;34m    >>> H.shape, edges[0].size, edges[1].size, edges[2].size\u001b[0m\n\u001b[0;34m    ((5, 8, 4), 6, 9, 5)\u001b[0m\n\u001b[0;34m\u001b[0m\n\u001b[0;34m    \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m        \u001b[0;31m# Sample is an ND-array.\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m        \u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mD\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mAttributeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m        \u001b[0;31m# Sample is a sequence of 1D arrays.\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m        \u001b[0msample\u001b[0m \u001b[0;34m=\u001b[0m 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argument\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;32mif\u001b[0m \u001b[0mrange\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m        \u001b[0mrange\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;32melif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m        \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'range argument must have one entry per dimension'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;31m# Create edge arrays\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_range\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m            \u001b[0;32mif\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m                \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m                    \u001b[0;34m'`bins[{}]` must be positive, when an integer'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m            \u001b[0msmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_outer_edges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m            \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m                \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0;34m'`bins[{}]` must be monotonically increasing, when an array'\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m                    \u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m        \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m            \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m                \u001b[0;34m'`bins[{}]` must be a scalar or 1d array'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m        \u001b[0mnbin\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m 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work around gh-11022\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m        \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearchsorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0medges\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mside\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'right'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m        \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_range\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;31m# Using digitize, values that fall on an edge are put in the right bin.\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;31m# For the rightmost bin, we want values equal to the right edge to be\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;31m# counted in the last bin, and not as an outlier.\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_range\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m        \u001b[0;31m# Find which points are on the rightmost edge.\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m        \u001b[0mon_edge\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0medges\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m        \u001b[0;31m# Shift these points one bin to the left.\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m        \u001b[0mNcount\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mon_edge\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;31m# Compute the sample indices in the flattened histogram matrix.\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;31m# This raises an error if the array is too large.\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0mxy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel_multi_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNcount\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;31m# Compute the number of repetitions in xy and assign it to the\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;31m# flattened histmat.\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0mhist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbincount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminlength\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnbin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;31m# Shape into a proper matrix\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0mhist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;31m# This preserves the (bad) behavior observed in gh-7845, for now.\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0mhist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'safe'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;31m# Remove outliers (indices 0 and -1 for each dimension).\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0mcore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0mhist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;31m# handle the aliasing normed argument\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m    \u001b[0;32mif\u001b[0m \u001b[0mnormed\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mdensity\u001b[0m \u001b[0;32mis\u001b[0m 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          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    }
  ]
}