{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "edfc2fc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function subplot in module matplotlib.pyplot:\n",
      "\n",
      "subplot(*args, **kwargs) -> 'Axes'\n",
      "    Add an Axes to the current figure or retrieve an existing Axes.\n",
      "    \n",
      "    This is a wrapper of `.Figure.add_subplot` which provides additional\n",
      "    behavior when working with the implicit API (see the notes section).\n",
      "    \n",
      "    Call signatures::\n",
      "    \n",
      "       subplot(nrows, ncols, index, **kwargs)\n",
      "       subplot(pos, **kwargs)\n",
      "       subplot(**kwargs)\n",
      "       subplot(ax)\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    *args : int, (int, int, *index*), or `.SubplotSpec`, default: (1, 1, 1)\n",
      "        The position of the subplot described by one of\n",
      "    \n",
      "        - Three integers (*nrows*, *ncols*, *index*). The subplot will take the\n",
      "          *index* position on a grid with *nrows* rows and *ncols* columns.\n",
      "          *index* starts at 1 in the upper left corner and increases to the\n",
      "          right. *index* can also be a two-tuple specifying the (*first*,\n",
      "          *last*) indices (1-based, and including *last*) of the subplot, e.g.,\n",
      "          ``fig.add_subplot(3, 1, (1, 2))`` makes a subplot that spans the\n",
      "          upper 2/3 of the figure.\n",
      "        - A 3-digit integer. The digits are interpreted as if given separately\n",
      "          as three single-digit integers, i.e. ``fig.add_subplot(235)`` is the\n",
      "          same as ``fig.add_subplot(2, 3, 5)``. Note that this can only be used\n",
      "          if there are no more than 9 subplots.\n",
      "        - A `.SubplotSpec`.\n",
      "    \n",
      "    projection : {None, 'aitoff', 'hammer', 'lambert', 'mollweide', 'polar', 'rectilinear', str}, optional\n",
      "        The projection type of the subplot (`~.axes.Axes`). *str* is the name\n",
      "        of a custom projection, see `~matplotlib.projections`. The default\n",
      "        None results in a 'rectilinear' projection.\n",
      "    \n",
      "    polar : bool, default: False\n",
      "        If True, equivalent to projection='polar'.\n",
      "    \n",
      "    sharex, sharey : `~matplotlib.axes.Axes`, optional\n",
      "        Share the x or y `~matplotlib.axis` with sharex and/or sharey. The\n",
      "        axis will have the same limits, ticks, and scale as the axis of the\n",
      "        shared Axes.\n",
      "    \n",
      "    label : str\n",
      "        A label for the returned Axes.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    `~.axes.Axes`\n",
      "    \n",
      "        The Axes of the subplot. The returned Axes can actually be an instance\n",
      "        of a subclass, such as `.projections.polar.PolarAxes` for polar\n",
      "        projections.\n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    **kwargs\n",
      "        This method also takes the keyword arguments for the returned Axes\n",
      "        base class; except for the *figure* argument. The keyword arguments\n",
      "        for the rectilinear base class `~.axes.Axes` can be found in\n",
      "        the following table but there might also be other keyword\n",
      "        arguments if another projection is used.\n",
      "    \n",
      "        Properties:\n",
      "        adjustable: {'box', 'datalim'}\n",
      "        agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image\n",
      "        alpha: float or None\n",
      "        anchor: (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', ...}\n",
      "        animated: bool\n",
      "        aspect: {'auto', 'equal'} or float\n",
      "        autoscale_on: bool\n",
      "        autoscalex_on: unknown\n",
      "        autoscaley_on: unknown\n",
      "        axes_locator: Callable[[Axes, Renderer], Bbox]\n",
      "        axisbelow: bool or 'line'\n",
      "        box_aspect: float or None\n",
      "        clip_box: `~matplotlib.transforms.BboxBase` or None\n",
      "        clip_on: bool\n",
      "        clip_path: Patch or (Path, Transform) or None\n",
      "        facecolor or fc: :mpltype:`color`\n",
      "        figure: `~matplotlib.figure.Figure` or `~matplotlib.figure.SubFigure`\n",
      "        forward_navigation_events: bool or \"auto\"\n",
      "        frame_on: bool\n",
      "        gid: str\n",
      "        in_layout: bool\n",
      "        label: object\n",
      "        mouseover: bool\n",
      "        navigate: bool\n",
      "        navigate_mode: unknown\n",
      "        path_effects: list of `.AbstractPathEffect`\n",
      "        picker: None or bool or float or callable\n",
      "        position: [left, bottom, width, height] or `~matplotlib.transforms.Bbox`\n",
      "        prop_cycle: `~cycler.Cycler`\n",
      "        rasterization_zorder: float or None\n",
      "        rasterized: bool\n",
      "        sketch_params: (scale: float, length: float, randomness: float)\n",
      "        snap: bool or None\n",
      "        subplotspec: unknown\n",
      "        title: str\n",
      "        transform: `~matplotlib.transforms.Transform`\n",
      "        url: str\n",
      "        visible: bool\n",
      "        xbound: (lower: float, upper: float)\n",
      "        xlabel: str\n",
      "        xlim: (left: float, right: float)\n",
      "        xmargin: float greater than -0.5\n",
      "        xscale: unknown\n",
      "        xticklabels: unknown\n",
      "        xticks: unknown\n",
      "        ybound: (lower: float, upper: float)\n",
      "        ylabel: str\n",
      "        ylim: (bottom: float, top: float)\n",
      "        ymargin: float greater than -0.5\n",
      "        yscale: unknown\n",
      "        yticklabels: unknown\n",
      "        yticks: unknown\n",
      "        zorder: float\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    .. versionchanged:: 3.8\n",
      "        In versions prior to 3.8, any preexisting Axes that overlap with the new Axes\n",
      "        beyond sharing a boundary was deleted. Deletion does not happen in more\n",
      "        recent versions anymore. Use `.Axes.remove` explicitly if needed.\n",
      "    \n",
      "    If you do not want this behavior, use the `.Figure.add_subplot` method\n",
      "    or the `.pyplot.axes` function instead.\n",
      "    \n",
      "    If no *kwargs* are passed and there exists an Axes in the location\n",
      "    specified by *args* then that Axes will be returned rather than a new\n",
      "    Axes being created.\n",
      "    \n",
      "    If *kwargs* are passed and there exists an Axes in the location\n",
      "    specified by *args*, the projection type is the same, and the\n",
      "    *kwargs* match with the existing Axes, then the existing Axes is\n",
      "    returned.  Otherwise a new Axes is created with the specified\n",
      "    parameters.  We save a reference to the *kwargs* which we use\n",
      "    for this comparison.  If any of the values in *kwargs* are\n",
      "    mutable we will not detect the case where they are mutated.\n",
      "    In these cases we suggest using `.Figure.add_subplot` and the\n",
      "    explicit Axes API rather than the implicit pyplot API.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    .Figure.add_subplot\n",
      "    .pyplot.subplots\n",
      "    .pyplot.axes\n",
      "    .Figure.subplots\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    ::\n",
      "    \n",
      "        plt.subplot(221)\n",
      "    \n",
      "        # equivalent but more general\n",
      "        ax1 = plt.subplot(2, 2, 1)\n",
      "    \n",
      "        # add a subplot with no frame\n",
      "        ax2 = plt.subplot(222, frameon=False)\n",
      "    \n",
      "        # add a polar subplot\n",
      "        plt.subplot(223, projection='polar')\n",
      "    \n",
      "        # add a red subplot that shares the x-axis with ax1\n",
      "        plt.subplot(224, sharex=ax1, facecolor='red')\n",
      "    \n",
      "        # delete ax2 from the figure\n",
      "        plt.delaxes(ax2)\n",
      "    \n",
      "        # add ax2 to the figure again\n",
      "        plt.subplot(ax2)\n",
      "    \n",
      "        # make the first Axes \"current\" again\n",
      "        plt.subplot(221)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "help(plt.subplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7e1cd0d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([12.,  9.,  8.,  8., 14.,  7., 14., 11.,  6., 11.]),\n",
       " array([5.69544311e-04, 1.00098850e-01, 1.99628155e-01, 2.99157460e-01,\n",
       "        3.98686766e-01, 4.98216071e-01, 5.97745376e-01, 6.97274682e-01,\n",
       "        7.96803987e-01, 8.96333292e-01, 9.95862598e-01]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.random.rand(100)\n",
    "a = np.array([1,2,3])\n",
    "ax1 = plt.subplot(2,3,1)\n",
    "ax1.hist(x)\n",
    "ax2 = plt.subplot(2,3,2)\n",
    "ax2.plot(x)\n",
    "ax3 = plt.subplot(2,3,3)\n",
    "ax3.bar(range(len(a)),a)\n",
    "        \n",
    "ax4 = plt.subplot(2,3,4)\n",
    "ax4.plot(x,'o')\n",
    "ax5 = plt.subplot(2,3,5)\n",
    "ax5.bar(range(len(a)), 4- a)\n",
    "ax6 = plt.subplot(2,3,6)\n",
    "ax6.hist(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8775936e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.random.rand(100)\n",
    "a = np.array([1,2,3])\n",
    "fig, axes = plt.subplots(2,3, figsize=(15,10))\n",
    "for l in axes:\n",
    "    for dessin in l:\n",
    "        dessin.hist(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9b1ca013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([inf, inf, inf, inf, inf])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(5)*np.inf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "499df5e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8206de87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros_like(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7fd772d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(3.0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean([1,5,2,4,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ca56200a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7a2a8f05ba30>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x  = np.linspace(0,100,1000)\n",
    "fig, axes = plt.subplots(2,1)\n",
    "axes[0].plot(x,x)\n",
    "axes[1].plot(x,x**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1851ea16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7a2a8d4a2950>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x  = np.linspace(0,100,1000)\n",
    "fig, axes = plt.subplots()\n",
    "axes.plot(x,x)\n",
    "axes.plot(x,x**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d32aacb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(19.166666666666668)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean([1,2,3,4,5,100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d3b17674",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(inf)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.array([[1,2,3,4,5,np.inf]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "362b0a6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bgauzere/Projets/Fil_Rouge_M8/.venv/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: invalid value encountered in reduce\n",
      "  ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.float64(nan)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.array([[-np.inf,np.inf]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8d8c1bca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(-0.5213064208371526)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.random.randn(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "eab01227",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(-0.08044438297335342)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.random.randn(100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ca135f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(-0.03777869169636438)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.random.randn(1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1e24064c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.00040519932726394323)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.random.randn(10000000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc40439b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fil_rouge_m8",
   "language": "python",
   "name": "fil_rouge_m8"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}