logos2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notebooks: \n",
    "[[1](IntroDS001.ipynb)]\n",
    "[[2](IntroDS002.ipynb)]\n",
    "[[3](IntroDS003.ipynb)]\n",
    "[[4](IntroDS004.ipynb)]\n",
    "[[5](IntroDS005.ipynb)]\n",
    "[[6](IntroDS006.ipynb)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making the matplotlib logo, in matplotlib\n",
    "\n",
    "Downloaded from the [matplotlib website](https://matplotlib.org/gallery/misc/logos2.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Matplotlib logo\n",
    "\n",
    "\n",
    "This example generates the current matplotlib logo.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.font_manager\n",
    "from matplotlib.patches import Circle, Rectangle, PathPatch\n",
    "from matplotlib.textpath import TextPath\n",
    "import matplotlib.transforms as mtrans\n",
    "\n",
    "MPL_BLUE = '#11557c'\n",
    "\n",
    "\n",
    "def get_font_properties():\n",
    "    # The original font is Calibri, if that is not installed, we fall back\n",
    "    # to Carlito, which is metrically equivalent.\n",
    "    if 'Calibri' in matplotlib.font_manager.findfont('Calibri:bold'):\n",
    "        return matplotlib.font_manager.FontProperties(family='Calibri',\n",
    "                                                      weight='bold')\n",
    "    if 'Carlito' in matplotlib.font_manager.findfont('Carlito:bold'):\n",
    "        print('Original font not found. Falling back to Carlito. '\n",
    "              'The logo text will not be in the correct font.')\n",
    "        return matplotlib.font_manager.FontProperties(family='Carlito',\n",
    "                                                      weight='bold')\n",
    "    print('Original font not found. '\n",
    "          'The logo text will not be in the correct font.')\n",
    "    return None\n",
    "\n",
    "\n",
    "def create_icon_axes(fig, ax_position, lw_bars, lw_grid, lw_border, rgrid):\n",
    "    \"\"\"\n",
    "    Create a polar axes containing the matplotlib radar plot.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    fig : matplotlib.figure.Figure\n",
    "        The figure to draw into.\n",
    "    ax_position : (float, float, float, float)\n",
    "        The position of the created Axes in figure coordinates as\n",
    "        (x, y, width, height).\n",
    "    lw_bars : float\n",
    "        The linewidth of the bars.\n",
    "    lw_grid : float\n",
    "        The linewidth of the grid.\n",
    "    lw_border : float\n",
    "        The linewidth of the Axes border.\n",
    "    rgrid : array-like\n",
    "        Positions of the radial grid.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    ax : matplotlib.axes.Axes\n",
    "        The created Axes.\n",
    "    \"\"\"\n",
    "    with plt.rc_context({'axes.edgecolor': MPL_BLUE,\n",
    "                         'axes.linewidth': lw_border}):\n",
    "        ax = fig.add_axes(ax_position, projection='polar')\n",
    "        ax.set_axisbelow(True)\n",
    "\n",
    "        N = 7\n",
    "        arc = 2. * np.pi\n",
    "        theta = np.arange(0.0, arc, arc / N)\n",
    "        radii = np.array([2, 6, 8, 7, 4, 5, 8])\n",
    "        width = np.pi / 4 * np.array([0.4, 0.4, 0.6, 0.8, 0.2, 0.5, 0.3])\n",
    "        bars = ax.bar(theta, radii, width=width, bottom=0.0, align='edge',\n",
    "                      edgecolor='0.3', lw=lw_bars)\n",
    "        for r, bar in zip(radii, bars):\n",
    "            color = *cm.jet(r / 10.)[:3], 0.6  # color from jet with alpha=0.6\n",
    "            bar.set_facecolor(color)\n",
    "\n",
    "        ax.tick_params(labelbottom=False, labeltop=False,\n",
    "                       labelleft=False, labelright=False)\n",
    "\n",
    "        ax.grid(lw=lw_grid, color='0.9')\n",
    "        ax.set_rmax(9)\n",
    "        ax.set_yticks(rgrid)\n",
    "\n",
    "        # the actual visible background - extends a bit beyond the axis\n",
    "        ax.add_patch(Rectangle((0, 0), arc, 9.58,\n",
    "                               facecolor='white', zorder=0,\n",
    "                               clip_on=False, in_layout=False))\n",
    "        return ax\n",
    "\n",
    "\n",
    "def create_text_axes(fig, height_px):\n",
    "    \"\"\"Create an axes in *fig* that contains 'matplotlib' as Text.\"\"\"\n",
    "    ax = fig.add_axes((0, 0, 1, 1))\n",
    "    ax.set_aspect(\"equal\")\n",
    "    ax.set_axis_off()\n",
    "\n",
    "    path = TextPath((0, 0), \"matplotlib\", size=height_px * 0.8,\n",
    "                    prop=get_font_properties())\n",
    "\n",
    "    angle = 4.25  # degrees\n",
    "    trans = mtrans.Affine2D().skew_deg(angle, 0)\n",
    "\n",
    "    patch = PathPatch(path, transform=trans + ax.transData, color=MPL_BLUE,\n",
    "                      lw=0)\n",
    "    ax.add_patch(patch)\n",
    "    ax.autoscale()\n",
    "\n",
    "\n",
    "def make_logo(height_px, lw_bars, lw_grid, lw_border, rgrid, with_text=False):\n",
    "    \"\"\"\n",
    "    Create a full figure with the Matplotlib logo.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    height_px : int\n",
    "        Height of the figure in pixel.\n",
    "    lw_bars : float\n",
    "        The linewidth of the bar border.\n",
    "    lw_grid : float\n",
    "        The linewidth of the grid.\n",
    "    lw_border : float\n",
    "        The linewidth of icon border.\n",
    "    rgrid : sequence of float\n",
    "        The radial grid positions.\n",
    "    with_text : bool\n",
    "        Whether to draw only the icon or to include 'matplotlib' as text.\n",
    "    \"\"\"\n",
    "    dpi = 100\n",
    "    height = height_px / dpi\n",
    "    figsize = (5 * height, height) if with_text else (height, height)\n",
    "    fig = plt.figure(figsize=figsize, dpi=dpi)\n",
    "    fig.patch.set_alpha(0)\n",
    "\n",
    "    if with_text:\n",
    "        create_text_axes(fig, height_px)\n",
    "    ax_pos = (0.535, 0.12, .17, 0.75) if with_text else (0.03, 0.03, .94, .94)\n",
    "    ax = create_icon_axes(fig, ax_pos, lw_bars, lw_grid, lw_border, rgrid)\n",
    "\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A large logo:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 110x110 with 1 Axes>,\n",
       " <matplotlib.projections.polar.PolarAxes at 0x1162924d0>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 110x110 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_logo(height_px=110, lw_bars=0.7, lw_grid=0.5, lw_border=1,\n",
    "          rgrid=[1, 3, 5, 7])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A small 32px logo:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 32x32 with 1 Axes>,\n",
       " <matplotlib.projections.polar.PolarAxes at 0x116cb9b50>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 32x32 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_logo(height_px=32, lw_bars=0.3, lw_grid=0.3, lw_border=0.3, rgrid=[5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A large logo including text, as used on the matplotlib website.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Calibri'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Carlito'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original font not found. The logo text will not be in the correct font.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjkAAAB9CAYAAABAvXI+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOydd3gU1d7HP9uTTd0hEHoTpEiRIiJFRSzotYBl9Yr9Kip6sXfWtvaO13K99o6rIlbUFwRUpCu9EzD0NunJ9n3/mN2FkOzObLZkE+bzPLl4k7NzzpmdOfOd3/kVTSAQQEVFRUVFRUWluaFt7AGoqKioqKioqCQDVeSoqKioqKioNEtUkaOioqKioqLSLFFFjoqKioqKikqzRBU5KioqKioqKs0SVeSoqKioqKioNEtUkaOioqKioqLSLFFFjoqKioqKikqzRBU5KioqKioqKs0SVeSoqKioqKioNEtUkaOioqKioqLSLFFFjoqKioqKikqzRBU5KioqKioqKs0SfWMPQEVFRSVRCFbbZUBGlCY+0WF/N1XjUUkNgtXWAThDptla0WGfl6D+LgTyZZq9Jzrs3gT0dRbQVqbZV6LDfiDevpojqshRUVFpFghWmwZ4CzBFabYPUEVO82M48KZMm2eAhIgcYArRhYcXeDtBfT0MHCfTZjagipx6ULerVFRUmgtmogscUB8EzZUWCtok5LsPimm5/kTRYQ8koj8FfYF6XUdEFTkqKirNBfVhcOSSyu8+1WJabm4+oCyB/TUrVJGjoqLSXFBFzpFLKr/7VFqN9ECeTLNEWo2aHapPjoqKSnMhnUROe6Av0BPogeS/0QYoQHKMDjlHO4M/+4FdwE5gPbAOWAHsSNF4mzrNUuQAQgr7apaoIkdFpQEIVtsnQHaUJn5gnPqGlVIaU+R0AM4ETgVOQBI5sdA5wu+3A/OBmcAMYFsDxxc3gtV2E/IRTE+JDvsfqRjPYTRXkZNOwr1JooocFZUYEaw2LXAx0bd7S1SBk3JS/UBoD4wH/gn0P/QP+8urWFa0kxVFO9myR2RPaQV7SirYU1JJpdOF1+cHQK/Tkp1hotCSTaElh8L8HLoUCvTr2pZju7alIDerPXBR8AdgOfAp8DGSAEolJwHnyLSZkoqB1ENzFR6qyIkTVeSoqMROPvL+bOrCk3pS8UDQIlkzJgX/1QCUVdUw86+N/Lh0HQvWFbPjgOQHmmvOoFubFrS25DC4ewcKLTnkZJow6HTUOGvQG4xU1LiCAqiCtcV7+HbhGsqrnQC0a5HH0J4dGTOoJ6cO6E5eVmZ/JEH1JPAT8HLwX3+c81JCOj9wlYxNTGFfqshJE1SRo6ISO+rCk54k83vRIVlsJiP52FBe7WTavJVMn7+KP9Zuxevz069LG84f3pf+QUtMl0IBjUZT7wFFUUQQ6rpc+P1+tu4tYVnRTpYX7WTuys18Oe9z9Dotw3p1ZuwJfTh/eF9NrjljDDAGyYfnMSQLj6+B81NCOl/3cmMrFx12T4r6AlXkpA2qyFFRiR114UlPkvG9aIBzkSwnvQDWbtvD/2Ys4PPfluN0ezmp31E8dfU/OH1gD9oXyAXCyKPVaunaugVdW7fg/GF9Adi+v4yf/1zP94vXcudb3zL5gxlcNLI/E84cSq8OhT2AD4H7gfuAb4BkbJWm5XWvMAIplSHdiewvLc95U0IVOSoqsaMuPOlJor+XHkg+JmcAbNixj8c+ncl3i9ZQmJ/NpPNGMn7UQNq1iF/YyNG+II9rTh/CNacPYceBMj6e/Sfv/ryI92cu4ewhvZn8z1M5ul3LXsB0pO2rW5AsPIlE7vw6RYe9OsF9KiHVEUgeYLNMm50J6ktda+JEFTkqKrGjLjzpSaK+Fx1wG9IWkGmnWM7Tjl/4ePaftG2Ry39uHMdFI/th1Ne/fPp8PrZv306HDh3QahOfiqxdizzuvnAUt44dyee/reDpz39h+B2vcOnJA7jHegpthdwzkByUHwBeIgFbWILVlglkyjRL160qSODYRIf9LuCuRB1PBnWtiRNV5KioxI668KQnifheOgCfACP8fj/v/t9iHv7oZ0xGPfYrxnDN6UMwGeoum1VVVSxauJAFv89l5eo1VNa4mDhxImPGjGnANJRh1OsZP2ogFwzvyzs/L+aFr+Yybd5KHrn8DK46dbBJq9U+B5wHXEr8kVjpfM2n89jipTnPLSWoIkdFJXbUhSc9kfteKkWH3R3l76OBqUBB8d4S/v36V/y2egtXnXocD192Ornm2sXNA4EAq1ev5sdvv+avZcvo1zqD4a013HxGPot3Olm+4q+kipwQGUYDE88exmWnDOShj37izre+5ZsFq3n5hrF0bGUZCfwFXALMiqObdL7m03ls8dKc55YSVJGjohI76sKTZgSdT/NlmkX7Tm5CCsfWTp+/ikmvf0V+dibTJl/Fyf2OqtXQ7/ezYP58Pv/0I3zVZZzVWcfEM/MxGw5uTfUsMDJ14doGzqZh5JozeHHCeZw79BgmvT6dEXe+wss3jmPsCX0KgJ+Rwt5fbeDh0/maT+exxUtznltKUEWOikrsqAtP+mFR0Ka+70QLPAXc5ff7eerz2Tz35RzOH9aXFyacW8d6s3z5ct793+sYPRVc0cPAsYXZ9YaIt8nW4XRWUFJSQn5+Pj6fD5/Ph9/vx+/3EwgE8Hq9VFVVodVq0Wq16HQ6dDpdxJBzpYzq1415z9/M7f/7hmte/Iy12/Zwz4WjtFqt9hWgI1IEVqx5ddL5mk/nscVLc55bSlBFjopK7KgLT/rRkO9EC7wOTKh0urjxP1/yw5J1PHTpaUw6b2QtsVFSUsKbr79C0brVXNPHxHFtsyKKkUAggM/no7tgYMmSJQwaNAidToder0er1aIPOiyH/tvv9+P1enE6nfh8ko+wTqfDaDRiMpka5Lyca87gzVsuok/n1tg/ncna4j28dvMFZGeY7kayeN1IbEInna/5dB5bgxGsNg3xb8Ee8agiR0UldprlotrEifU7CQuc8monFz3xAWuL9/DxXZcyZnDPWh/6/bffePO/r3JmZwO3nZqLQVdX3AQI4PP68Pp8aACtTktvC2zbWsRpp51W72CqqqowmUx1jxUUSS6Xi7KyMgKBAJmZmWRkZMRk5dFoNNw69kR6tm/FhJc/54LH3ufz+68g15wxIdgkFqGTztd8Oo8tHrIBg0ybpjivlJL4+EYVleZPc11UmzKxfidPEhQ4Yx99l4079jH9watrCRy3280rU17k07df4+FhZi7pba4jcPyBAG6PB5fTRQAwBa0vBr2B3i2NrF21MuaJaDQa9Ho9WVlZWCwW8vPzCQQCiKJIRUVF2NqjlDGDe/LVg1ezYcc+xtnfC5WMmAA8EcNh0vmaT+exxUNznVdKUS059SBYbTnA8cAQoADIATKAMqAE2ADMEB32/SkYixkYDhyHlPQqFylfRTXSBb4MWAoUNYWCkILVZgCOQkq01glpPqGfLMALOIEKYBNSQrMNwO40ml+6JkVLOEGH3j7AMKRK2dnBHy/Sd7QNWAAsFR32mkYaJsT2QLgJuLvK6cb65Ids3SPy3i3n069z61qNJ997N/nuvbwwKgeTvra4CQQCeLxe/H4/Br0eg0GPhtptulkMbNu5C7fbjdFobPDEtFotZrOZzMxM3G43ZWVlGAwGsrKyFG9l9e/cmvdvOZ8rp0zD+uSHfPnAlWRlGO8BioHXFBwinR+46Ty2eEj4vIJbYF2Q7ufeSM+2HKTM3hXAPmAxsEB02BNV66tRadIiR7DadEjVoKNRIzrsX8kcx4hUXfdkJEHRH3krl1+w2uYhpVD/RnTYNygatAyC1WYChgKnAKOC/y1nsgQoEay26cAzosO+LhFjiZfg99MPqXrxSKSHZVcadt1VCFbbKuBzYKrosO9K2EDrQbDaBgNH1/MnPfJJ0VyC1XZpgobyreiwVyhpKFht/YFjZJotFh32jVGOoQFOQKqJNAxJ7Gcr6N4rWG3LgDnAFNFhT3WFbKUPhFOAl/1+Pze+8gVr/t7N9Aevpn+XNpSWlpKXlxf2mTmqWzf2rdmH/pCVILwt5fWiNxjqFTchDDoNHYVMNm3aRO/eveOeoEajwWQyYTQacblclJSUYDabZbexvF4vZWVlDO97NF8+cCVjH32Xia9+ybu3XYxWq/0PsBaYLVhtrYBTIxymj4IhHidYbbmxz6wOy0WHfXUM7VMmcgSrrTvSC2c0Yh1/JBIyL8Fq6waMRbqfhwGFSjoXrLb1wDzgNdFhX6rkM+lIkxY5SJaNj2XarAfqFTlBK8m/kUIr28bYtxbpwT0SeFaw2n4AJooO+98xHufQsUwA7gFayzSvDwtwNXBVUOzc3xhiR7Da2gAXIKXCH4l8TRml5CA9fE8AnhOstl+Q6vV8LDrsyShKeDNwZQM/m4f8damUNkhvWEq4Fmnc0RgP1BE5QXFzIVImV7lFvD70wODgzyTBansDeEp02BOV3l4O2QfC8N6d/Uh5cLRPfzGb7xev4+O7LmVgt/YA5Ofn1xI6115/I/aHdvDB6u1c3ddMIBDA7Xaj0WoxZZgiiptD6ZUPa9esabDIEUUxLGRCaDQaMjIyMJlMVFZWhsdcn1UnJHDy8/PR6XQM7NaeNyZdyGXPfsozX8zhXusp2uA5GYj0chfPdftgHJ89lElAIkWOB6hs+HBqMRZ4RqZNrOOPRFwiR7DajkeKpDsXFFysdekR/LlGsNq+Bh4WHfZlDThOo9LUfXIafBEIVtsYpAvxKWIXOPVxFrCmIW/wgtV2HlItlBdpmMA5FA0wDlgoWG2nx3ksRQhWW2vBartJsNrmADuA/wBnkziBczhapDfO94E/BKtNznrREJRcW6kgFpNxg+4HwWrriZQozkHDBM7hGJFeHjYLVttDgtWWinUm6tw1Gg3v3HbxBKDl9PmrePaLOUy+ZHQtHxydTkd+fj5lZWV4vV50Oh133TeZJaKJn4uqcLlc6A16jAaDIoED0EvQsW5lw54Lbrebq666iquuvpy33n6DAwdqf3UajYacnByysrIoKSnB46ldZPtwgRPizMG9eODi0TzzxWy+XrAKoBXwsUGnLWjQQBOPYquLwgikAwnc6k774pyC1SYIVtubSNvI59EwgXM45wF/CVbbp4LVpsSymzYccSJHsNo0gtVmB2Yg+RgkEjPwsWC1/VtJY8FqyxastneQiurFK24OJxf4QbDark/wcWshWG0CUjG6V5C2pRJxQ8XCEOBPwWp7IOjvkyjSQeSUxxge2pD74SykjLijYhmYQjKAh4EPglvCySTq3K8/cygt87L7bttXyqTXv2LcsD7cOvbEOu0OFzpZWVncdf9kPlrrZVO5Bp1WV8/RI9OzwMC6DRsJBGJ/xi5YuIC+g1vywKvdcRn+YtKtE3nr7TeorKxtlDAajVgsFioqKnA6nUBkgRPitnEnMm5YH/79+nS27y8FOOnlG8eNjXmQySEWkZDqCKS0FjnB7bS/kKy6yeASYK5gtSX6eZU0jiiRE3yjfBmYnLQRSbwctBRFRLDasoDvkbaYkoUO+K9gtV2UxD4KSL2wORwjUjHFrwWrLUOusULSQeTEuljGej9cDHyNJEaSyXgkwZ3Mct0R596lUMB26WkEAgEm/Xc6uVlSZuBIfiyHCp3Kykry8/O56977eW5xNbsrvTENKj9Dh9moZdeuXeEEgG63G5fLhd/vx+124/V66xVBM2fNYMjoLPIEA+dcUcgDr3aj3PcnN918Pb/9/luttlqtFovFgtPppLKyMqrAAckK9OKE88g1m5j03+kEAgHOH9b3nC6FSgp6J51Yrnsl13wiA0TSVuQIVls/4DekhI/JZCAwX7DaeiS5n4RwxIicYJTIe8j7LCSK14J+NnUI/v4boO6rZHJ4V7DaOifp2OkgBkKcCXwedHiOl3SYV9JEjmC1XQt8Sur88kYDc5Jo6o641fLk1WeRaTTw/qwlzF25mSnXj62TyfhwdDodJpOJqqoqsrKy6NuvH5deeQ2PLaim2qMstUxI1HTP17JkyRJKSkqorq6uJWzcbjfV1dWUlJQgiiKlpaXU1NSwf/9+Nm/eTJ8hB3Vhdq6eCye0ZoKtLZ863uC555+ipuZgQJtGoyErKyucfyeSwAmRa85gyvVjmbNiMx/MWopBr9M/cdVZiuaWZGK57pVssTVVS47iuQlW21BgLgqdihNAZyRXgZ5yDRubI0Hk7A9uY3wBXJ7k8RxKF+pxxAtaGr5CivJIFVlIFqxkEI8Y8CCFLG5H8uVJhHPg2cAD8RwgaPFTUiYg2SRa5HiRotRuAt5EmQUuAGxBMoH/iRTW31BH72ORBHcyLH/1zv20AUdz+sAe7C6p4MEPfuTyUwYx+tjusgdzOp14vV5atGhBeXk5Xq+X088Yw4ChJ/Hc4mp8/vq3nwIcTOTndrsJAMcIGoqLNiEIArm5uWRnZ5OVlYVOpyM7O5vc3FwEQUAQBHJycggEAsz48Xv6D81Bq0X6Bg6hw1Fm7ni+C38tW8x3338b/r3X66W8vJwWLVqEMyjLMfrY7lx2yiBsH/7I7pIKzhjUg9MG1BdUmFISbclpqiJHUV+C1TYCmIl87bYQO4EVwBKkyLqGpn4QgOkJiqhLGk09ukrpBfcEkuNUJALAbGARUt6ZVUj5cLxIe769kBboc5BCapVyp2C1fSI67CsgHB7+JRDNITiAFIa7CFgeHIsIuA8Zy4DgfGJxEj1HsNr6ig577NnJoqPkOyhGcoL7C9gK/B38d4/osNd6LQ5a3AqQoqhGBX+UhK8eykOC1faZ6LCvj/FzIYxITuD1MRrpWojGD0iLR7wsV9ow6POSI9PsANJ5nSLTbjOS1XM+Ush5+WF9ZSFde8OR9v47Kx0nUgTXDUjZhhNCJOdTjUbDQ+OlW+3ZL2aj1+l49PIzZI/n8Xiorq7GYrGg0WhqRV1dde11PP7wNt5bVcy/+tU21Pp8PjxeL1qtFoPRiDa4Hda7IMB3y5UF2+h0OjIzM5n3x6+Mv9WC3+fD6/GgNxjQ6Q6+k65dWk5mZg5nnC7tih/ug5OXl0dJSQk6nQ6DIbrLyqOXncF3C9fw3JdzeO7ac3jp+vP295v43PuHCLn2yKfq2IzkW5gIlEYTQvqJnFD+qFT0BZKhwoH0IhuJUuAdpK2shYen3wgaAfohhZf/E2mNUEoPpKKvqTQgxMSRIHJGAFdE+FsJ8D/gDdFh3xKhzV6gCPhesNqeQFrYXwIGKehbBzwPhPK6v44UhVUfB5Dert8UHfaiCG32Ib1V/xAcy0lIFpq+CsYCcD2J366r7zsIIL1ZfAX8FGU+dRAddi+wO/jZrwCC+Tv+DdyO5Nwthxa4nwaGgIsOuxO4s76/CVbb+8iLnNdFh/27hvQdB0qcKbxIW1SR9jHWAo8DnwW/h3oRHfYqJCE+R7DankUSOjaUO88/JVht34gO+w6F7eWo1/l07NBj6N2xkM27DvDBrKXYLj2NvKzoKY78fj/l5eVhgQMHfXRCQufO+x7grtsm0aGohtO7ZkrbTh43oMFkNNbx9emQp6ekVKSyspLsbPndug0bNqDRuenY3YxGoyEQAI/bjc8LBqORPTucfPbaHh595Elyc3PrdTIOibOSkhIsFkvUpIH52ZncOnYkj02dycSzh9G1dYuCfVMfXYT08ESw2s5GXuTMEx32eu+ZJJPKHDnpGMn1PFKaifrYF/z764e/qByK6LB7kBLKLhWstleQnlGPI6UUUMJlgtX2keiw/6SwfUo5ErarIgmcb4CeosN+bxSBUwvRYQ+IDvvvSOIiaoLBQxgtWG1tBavtAiI7GU8DeokO+31KBUFwLHOQRJzSi+uKJPhEHPodbAUeAjqLDvvposP+eiwCJxKiw75XdNhtQDekbUcljA8mwUo06ZpdVcm42hHZKfE1oL/osH8cTeAcjuiwu0WH/TWkBfEvhR/LBexK+1BAvXO/84KTAXh86kwKLdlcN0beCFteXk5OTk4dUXCoM7LRaMT2yGN8st7D8j01Umi5Xl+vwAHQajQc3crM+vXKDIv/N+tHhpySEz6WRgNGkxG9Xk9ZSTVvPb6df/3rerp06RI1ikqr1ZKTk0N5ecTnW5jrpOgzHp86M/QrGwe3M9P1mofUji0dI7mGR/j9r8AxosP+dDSBczjB58r3SJbaT5R+DpiSolQRMZOWg4qBhviDeJGS7o0VHfa9Dek0+CZ7MVI2SDk0wC1IFqPDcQNXAReKDvu+Bo6lHClBlZJkHDlE37ZrCC2QHN7OAI4SHfZHRYe9OMF9ABA0s1pRVnNHh2TNSTTpuuA31DfKC9woOuw3Bd/oGkTwXhoF/K7wI+ODFrpEUGfuJ/XtSq+OhWzcuY/p81dx1wWjyDRGfz45nU60Wm3EEgyHCp2CggL+fdudPL+4hhKPTja0vGeuj7WrV8lOxOVyMf+PPzju5HrcKzQaHK8doH/f4xly3PGyYeIghZdrtVpZ/5xMo4G7LjyZr/5Yxaad+0HaIg75DabrNQ9p6COTiI4Ubj9H4g3gtIY+UyBs3bkcZSU/QNq2kt8LbgSONJHjBcaJDvub8ZoUgxfBBJRV8b2butsJLuBs0WF/PwFjcSJtRSlhSDx91cMjosN+suiw/3y4f00yCJ6ryUjbLnJckKBIq0NJ1wW/ISInAPxTdNj/m4gBiA57GZKpe5OC5kYk35xEUGfuE86U3Are+nEhBblZXHxidMt7IBCgqqqKnJzoz5WQ0BFFka5du3L5Vdfy2IIaqtzRL/1eBQbWrpJ3sVqwcAGde2STa6kryH5y7MPvLOD6CRNxuVyIohhV4ITIycmhqqpKNlfPJSceS0FuFm/+uCD0q1uC/6brNQ/NVOSgbPu5Pl4QHfYbYsyvVS/B9fxmJD9SJdwab5/J4EgTORMS6SshOuxrkHxPGsJVosP+fwkcyyLgDwVNlfgSxdJvUmtIRegzgOSjs1umaS6SQ10ikbvmAkiOfqmmIdlqHxAddqXbf4oI1tkaT52YoHqZGHTGj5da30mrvGxOH3g0VU43n85dxpWnDiZDxopTWVlJVlZW1BpQIVwuF0ajEZ/Px6jRoxk8YhTPLYkccQVwtGCgaGuxbAXxWbNmMGR03R3llYvKWDSzmnvvllJ8+Xy+cA0rOUKh5YcnETycDKOBK08dzNS5y6hyugH+gRSSrIoc5X0lKidPQ15avkUqy5IwgmvtdUi+qXKcLlht8RdpSzBNVuQEncBiWdg/Fx32d5MwlI8a8JkPRId9asJHomwsA5Jg3Ug5osMeipqTI2G5iILnTS60vCRJtbTkiHVR/AV4OhkDCQruDxQ0LQQSkaiy1tzPH94XnVbL9PmrqHK6uWL04KgfFkWRZcuWKaoUHkrkl5eXh8VioaysjEsvvxJNi868szJyJG6mQUthroktW7bg9/vx+XwEAoFa1pUDBw6wuaiIPsfVjsjds8PJ1Fd2c/99D2I2mykrK8NisZCXlxcOVZfDZDLh8Xjw+6NbnC4/ZRCVTjfT568C6flwCarISde+DmUXcE0yrOmiw16C5KOlhFTloVNMkxU5KHMCC1HGQdNrovkxxvYHgDuSMRCk0GU5zEgOvM0BB/LbhSMT2F8+8rll0nmxD+EH/p3k7cVIIfiHk4h9/Fpzv2C4FGw4ff4qRvTuTIeW0dOHvP7qSzz77LPcOukGfvv114jWFr/fT0VFBXl5eWg0mvDWVWVlJbfeeQ8ryjP5qaiu0AkEAni8Xo7O9bN06VLKysqoqKjA6/VSWlqKKIqUlJTw088zGDgiH4Ph4LLsrPbx1hPbufrq6+jUqVMtHxyNRkNeXh4VFRWy4kWj0WA2m6mqqorarmMrC8N7dw7VtAK4lKYvckpS2FdjiZwHRIc9kZmdD+d9lNXRSzu/nKYscmK5CGxJ3FbZD0RfOWpzbxIvxu1ICfbkSIdEd3EjOux7kHd0HZrALpv6Yh/ig+BWa9IQHfblSGGpcgxLQHfhubfMy2JQ9w5U1Lj4bVURZx7XK+oH9+3bx9q1a3A8Vci/znLz4zf/4+aJ1zJr5ky83tpBZvVFXoWEjsfj4V7bQ3y63svKvdIWUiijsStoaTmmQE/x5g1YLBby8/MxGAxYLJZwksA5c2cxYGQmHreHQAD8/gAfvbSLQceO4MSRJ9XrZByKoKqokE/NYjKZpASFMr45Zw7uya8ri6h0ugCGtBFylWTRTdfrvjSWaME4+4LGETlrgQ8T1G+9iA67C2UV6rumW12rI0HkVALJ2KYCwnuWWxU2L6Fh21tKx+JDSrwnR5OqIiuDnJNrIisrNweR40YqmpkK3lHQpqtgtcWbij4891A24zkrNuH2+hgzKHp5nZ9+msGJA0xkGLX0P9rE4zdkMelCmDfrPW6YcDU/fP8dbrcbt9uNRqOpd0srJHQyMjK44577eH5xDdvLXLhcrmB5CCMGvZ7eBSbWrltX7zg2btyIzuDlqF55aHU63C4XPzn24q60cMXlV0eNogqNSW7bSqPRkJGRIRtpNWZQT9xeH3NWbAbg1AHdI+VhOZSUX/cxJMBMFOkqciYnUMhFQ8n9DLElE0w6R4LI+UR02BNRLiAaWxW2+zAYCZVMtipoEy07ZlNju8zfTQlyboXmIXIcosP+d1JHcpBvFLaLd1EMz/3EPkcB8OPS9fTs0IrOUQpOBgIBfps7i9GDawuXXl2MPPivLO67wsCKhZ9x/XVX8fnnn0WNYgoJnbZt23LRpZfz2IIaPBqDtK0U3OEsMGsJ+Lzs21c3snfmLz+Fc+PodFo2rXSy4OdKbp54K1VVVbJRVNnZ2bKOxQCZmZmyIqdLa4Ee7VsyY4kkyIb16iyXtr9GdNgbWhogHpREIDV3kVOE8pxtcSE67MuAbQqaJsI6mzCOBJHzdlJHIaE0L4xSJRwPWxW0aU4iR8l+e6KqXzcHkfNZUkdRmx0oizaJd1EMz/24ozsAsGh9MSf26Rr1Q8XFxQR8Trq2r9+176j2Bu69wsxD1xrZtWkGE2+8lqmffhxRTOh0OoxGI4OPO44hI0/hucU1tSKuNBoNvVqaWHeYNefw3Dh7dzr55JXd3H3XAxgMBoxGoxYTfccAACAASURBVGyYuE6nQ6/Xy1pztFotgUBA1ofnpD5HsXi9tKwN6tZeLlAhna/55i5yHAnMsKwEJQk/VUtOglByEVSgzC8gXpQ4cB5AKoqWbJRE9kQvwdy0UDIXpYXr5GjqIqcMSFjaAjmCi6+SJJWRsjArpQWAkGPmqDYtKK92snnXAY7t2jbqhxYtXMigXnrZsPE2LeD2S7N55uYsDvz9Izde/y8+eP8dysrKarVzuVz4fD5atGjBOWPPR9eyC2+vqG3g6JnnZ+2q2svAoblxXDU+3npiB5eNv5pWrVrRokWLcMFPOcxmM9XV1bLtQr450ejftS2bdh2gvNpJt7YFCDlRq6mk8zXf7EVOgvpTipL7uVPSRxEDTbl2lZKLYFGKwnmVbIcsSpHiVjKWWBylE04w/F9AKvzXHum7NAZ/Yg1vH6WgTbO25BxyPuWYHnQgTCXLgFNl2sT7/bQA6N1Rcu1ZsUWKMegvI3JWrljMWYOiX25SmLdUmqF1Cz03XajnklNNfDV3NjdN/JGTTx7FuPOtWCwWKisrEQQBjUaDIAhcM+EGnn78UX7cXMOYo6SaWb0KDMxZXbtG7qxZMzj+jGwCgQAfT9lFv2OOZ/CgweEtqtzcXEpKSjAYDFFrUOn1evx+P36/P2o7o9FITU0NGRmR3w9C527l1l0M792FXh1aMW/N1kjNVZFzECURSInqaxPKREciUdJfotbbhNDcRc7CpI9CQomwSKexJNtHKUywwu1gpHpfJwHdkWoopdKalMqCeY2x4OehTBz+kuyB1IOczxTEsSgGr69cgKPbtQRg+ZadmE2G8P+vj0AgwOaiv+l1YXR3E5/fV2erqEW+jmvPM3PRaBNf//oHt06azaDBgxl3/sW0aCFdIjqdjjZt2nDzrXfw5GOP0CbbRf9CE13yDezcsy/sF7N//36KirZw1XFHM/PL/VSX5nLB1dZaPjharZasrCxFGZlDVppoAkav19eJHDuco9sVkGk0sKxoJ8N7d+Hodi1VkSPfX1mKI7l+SfFWFSi7n3MEq03XSPnC6tDcRY58HvXEEL20sUQqtqpA2VgUF2xrCMGoh4uBy5AKyDW2D1CiRF0qM57GglLTttICmolEfv8kvu3EsAWrWxvpNPy9p4TOhQK6KNaM/fv3Y9RDXnZ0bejz+TEY6l8m87J1XHGWmXEnm/hm7l/YHljCsccO4KKLL6NDhw7odDq6devGDTdN4oUpL/DkSTra5ujpWmBmw4YNtG/fntmzZzFwZD4bVlby+w8V2CbfSUFBQR1hZTKZwqUZom2vhdpFEzmhz0c7ll6no0trgeK9kstbt7ZRgxQb45qH1NaSCovpZPcVRMnc0vV+BinqrTEyv9ehufvkKFGdiUDJWJR4pSeCRrM2CFZbC8Fqux/J+fkD4HQaX+BA4rbnlISjp2vdKhdQf/xyclFy7uMxb4fnXmiRrBy7S8ppbYlu8di+fTvtCuUzHAcCAbSa6MtkpimA9TQzb9yXR5f81Tw4+U6eeuIRNm/ejE6n47jjjuPCS8Zjn19FWY2H7tle/ly6BLfbzc8zZ9Cll5GPp+xi4o2T6NSpU71OxkrDv/V6vWzpCJAsTXLOx4WWHHaJUv6dwvyo5/NIsOSkLJIrhu3nVG9VgfK1NG22rJq7JUeutlGiaGpjSeiiFHzLuR14CGWWpFSTqDeKdN2uUjKuVfFUGY8DJW9+Da22DIeKnPyQyKmke7voenT37t0UWqJb+gMKdzl9Ph8GgxGDXsO4k7P4x/AAMxdt4snHHqBjx65cdMnlnHX22WzfXswzi/7gnKMM/Lx5A8XFffD7Xfz0mZvzzr2AgQMHhgWOy+Vi8+bN9OjRI/y7zMxMysrKyMyUv8XkLD46nQ6fr+5W3KG0zs9h0y7JSFNoiZpa60gQOansKxf5Z7Of1O0OHIpSkRPPPZ1QmrvISdXN19TGkijnOASrrSNSldroBYIaDw+p3a5KV5GzOumjqB8lwiqeisnhuYcigPaUVjDymC5RP1Qiilhyolsy5ITCwXaSY3IIo0HDWcPNnD40wNyl23j5hUfIF9pw1tnns3vnDn7fUcz6vUWYf/uFfXuqOGnUCEafcioVFRWsWrWKX+b8wcoVy3A5K7n77rsZMWIEcDD8W46QgNHrIy/vWq1W1uJTaMlm3potAAjZR3x0Vbr1tUV02JVuHSUSpS9KcVdBTxTNWeR4SbLvySHIjSUlybIUmjnLE/VGL1ht/ZFCkiN7eDY++xPhnBc8t3Lfc3UKkj3Wh5JFcUfSR1E/SrYr4xGh4bmbgr4zVU43uebo/vdVVeUUZEQXMIEAsiInmrVHr9MweoiZkwcHmLt0N59/8jp6Uz7bynRUVFfy69w/aNlSoE/vY3n+xVdZt3Y1mQXHgDCc/GETqd7xf6xbvzEscg6OK7r4UiKGtFqt7HZVrjkjVI0ckzHqo0IVOanvK1WuGIej1P2gUSN4D6VJihyFTmBiKjzPBavNjHykUKoWASVmzkTtG7dHKggar8DZiZS1czdQAzgP+ZGLVMgEbpVpk6hzb0Y+ci2dF/tk1W6TQ4nZOp4FMTz3jOCD2OP1RQ2hBvC43RjklutAQLYaKwGQM/botBqG9TMxarCRWYsreHlqGQFAQ4B9+0ReffUV9ObW6AuGE8jIhept1Py9Dee+v9iVXzsM3uv14vf7o24z+Xw+vF4vBkPk+sV+vx+PJ/q7zveL13KgQjIWZBij1kJO5+u+uYqcdL6fQRU5cZPqdN7RSKctjJSMRbDacoHvgeiJSGrjBH4G5iDldygiTpOrYLV1IXUiJ52+58NRMrbGinRopaDNnjiOH5576I1Go9HgdDoRxci7sh6fB5/PjzNKkr1AIACBAL6oFo8Afn+gznG8vgA79/ko3u1j6+4AW3fr+HtnJU6XdCxN8H87d+zAkKEnoNFo6vGRGUyfPn1qzcPv91NaWhrVkuPxePB4PNTURDYe+3w+AoFA1HNUS0ZFtwyl63XvQnk0ULx9QWrXmnS+n11IiUfTgqYqctLpgXMkjuV/QD+FbecALwM/iw57otV9KqOd0ul7PhwlY0tZbqTDUCKE49lKC8/d5ZYMf3qdFr3BgCBEfhcym7Px+bVkmCIb53x+Pz6fD2MUi0iAAPsOONl5ALbu9LB1t44tu3zs3uekVUsLnTt3oUOn7rQ07GbF+pkAtGnfjl3bd2AyGdhavI2cLDN33z8Zv9+PxWKJOllRFLFYLFFFTkVFBRkZGVEtOTU1NQQCAczmyL42ZwzuyebdkghyeqIaVdM1AeaBBFrz003kpPX93Aj5eyKiipz4OaLGIlhtA5Fy4MhRBlwjOuzT4ulPBiU3nCpyJJSUHkkGRylokxCR43RL2y85mSbKq6Mnds7KyqVyf/R1WKPR1PJt8foCbN/jZesuL1t2+dm6W8vWHU58fujapSOduxxN/+HdOa9zZ9q3b4/X6+WHH3/ki2lfUlUmuQdOumUSAwcM5Jp/XYPWoGf00KHMmvsbd9xyM3fdN1lW5ITGFQ25jMegzKm6rMpJTtC3KSQg6+uOxnlrT9nWfJB0EzmNdT93U9Cmsfz/6kUVOfFzpI3lMQVtSoHTRId9SZx9yaGk5pEqciSihsckkWMVtFkp3yQi4bmH/EcKLTnsKamI+iFBEFi/JbJfS2mFjy27PGwqdrJ9v4+tO33s3u+ksJVAl87d6dy1B2NP6kLnzp3RaDTk5OSEt5oqKyv5Yto0vvn+OzQFeThrnAwcOoTLL/4nFouFdevW0WVQZ/Zs3MtF/xzP1q1bMFbu5vGHbVx17fWcMnp0vWPy+eR9jZS28/l8mKJYsUCKUguF5e8vj2iEFUWHvTEeuOmW7TiR/R3J93PCUUVO/BwxYxGsthOAMxU0PTcFAgdgoII2qsiRSPmiKFhthUAbBU0Xx9FNeO57SyULfmF+NrtLo4uc1q1b86sYwOMNsH2vV9pq2hWQrDM7nQTQ0qVTBwrbduTY4X0YG7TO1LcFVFNTg9PpxOv1Mu2br/np55/JOb4vWSMHUPnbX9x4/fUMHDAAi8WC3+9n+crl5HbLxV3jpqioiHsnP8S9d97GRd38TPvwTdavXc21199Ypy+n0xk1kzEQtjzJWWnkcuSAlG8olGBxb1nE3ZF0vuZVkZN4BihoE8/9nHBUkRM/R9JYLlLQ5hPRYf8tjj5iYaiCNqrIkWiMzNNK3vpKkRzRG0p47rtLpC2h1pYcNu6MXmmgU6dOrNpYwWUPOmld2ILOnbvTuWtPxp7cmS5dupCfL1WaKCsrIysrK2rOmaqqKj6a+inzFywg/6TBtH/gOvZ//D0Ffh2PPvU0JpMpXItKp9OxsWgjPa84GleZiwMHDjBixAjuum8yzzz2MJOPM/LVxoXcd+cG7pn8EC1bSsGLgUAAp9MZ1c8IlIkXULaltae0gh7B+l+7xYiiMZ2v+eYsclJ+PwtWWx7QVUHTRckeSyyoIid+jqSxyFWTBpgcx/EVI1ht+UBPBU2btcgRrDYTyha8eOpDNZTjFbRZ0tDtjsNzF4WETZfWLfhw1lI8Xh8Gff0PfIvFwssvv0y7du2iOuiaTCZcLle9Imf37t18Nu0L/pg/n9xTjqfLS/dQvWoj2554C+v5F3DOWWdRUVFRq9imx+Nh29ZtjOg2jP1/H2BLkZRsr3fv3ljHX8Er0z7m0eMNzNlRyl23TeLWO+7m2AEDcDqdmEwmWQuNy+WS3YZSYu3xeH1s2S1yxWgpx+emyKJRFTkSbhIXNq1kbo1xPx+noE0lsD7ZA4kFVeTET1MbS4OK6QW3HvrKNFslOuxbGnL8BjBcYbtmLXJQXpyzT1JHUT/nK2gTj2k7h0PWsA079gHQv0sbnB4v67fvpU/nyLtlclYRAKPRSGlpKVlZtXXklNde5Y/58xH+cSLdXreBz8+O16aSubuUZ+yP0a5dO8rKymoJHIDNmzfTokML9CY9+R3y2TB7Y/hvZ571DzZtWMdba/7ixj46ull0PPf8U4w5eyyjRp9KQYF8MKHL5QpboSLh9XqjWqYA1m3fi8vj5diukm//+uC5rYdmL3IUJgJNdSRXut7PS9Ol+niIplqgM50eOEfKWEYpaPNTA4/dEK5U2E4VORJKto4ShmC19QD6K2g6P45uas19TbGUbqdvlzZoNBqWFe2M+uGQlSYaWq0WrVaL11s7umjNmjW0vPIcWl0yhur1W9l829MMb92ZRyfbaNu2bb0CB2DN2jUU9JKGndc+j53bd9ayrNxw0yR2BCz83/YAXXPg2ZOy+HPW1/z3lSlUV0dP+eL1esPjjYYSa8+yop1oNBr6dG4NwNptEVMZNXuRw2FiOsl9gbK5tROstpRlmhesNj1wgYKm8dzPSUEVOfFzpIxFydbQXw08dkwIVlsrYKzC5ok690qq6kb3dk0OSkVON8Fqi1plMcEo8d8SkRJENpRacy+rcrJh+z6yM0x0b1vAchmRYzQacbvlS+yYzWaqqmrvRNx6083sff9btr/4IaVvfMmDd97DdVdfQ2ZmJqIokpeXV69vzKq1q2jZS3o2GTINZGSZ2L//oHHVaDRyn+1hvtrsY0MZZGq8PHxCJu2df3PHLTezdevWiOOsqqqKmvcmhNvtxmiMXoF9edFOjm5XQHaGifXb91JWFbFaSWNc85DadTdl+bgEq80IKL1PlbxEJIoTUZYI8NNkDyRWVJETP0fKWJSEa6eq0vqVQNQ884dQkqA+laQzb4yCeUpFjgblW3xxEXzrU2Jp+1R02KObUqJTZ+6LNxYDMLRnJ+au3Bz1wxqNJpxpOBpGoxGfz1erXa9evThrzBkco8/htSkv07t3b7xeLy6Xi+zsbCoqKurUhgoEAqxft57CngefFZaOAsXFxbXaFRQUcPvd9/H84mrKPBoI+LmybwaXd/Px4P33MGf27Dpj9Pl8+P1+WfHi8/nC845EIBBgzopNDO3ZCYDFG7ZFO2TSa/JFIN0cgVPZV4iRCepTCf9S0OYv0WFvjMroUWnOIidhlbZlkBuLn9Sl4E6mc1wsN1/SCDoc36GweanosMvVv1KKXN0qaJwFP5bvRekWX7xcjbKkYe/F2U+duc9ZUQTAmEE92LTrABt3RvQlASAzMzNq+YMQOTk5VFTUNlpcNf5ybv/3JDIzM/F6veEtqqysLLKysigpKQlnFgbYs2cPOpMOs3DQ2pLbMYetf28N//9AIEBNTQ1t27blfOs/eXapB/RG3C43J7Q38cTIbD5//w3+++p/atWeqqioIDtb3gBQU1NDZmZm1DYbd+6naLfImEGS8XZOdLGoipzU9xXiCsFqS/ozXLDa+gL/VND0vSQPpUE0OZGj0AmsQnTYk17qXbDadMh7uZekIlmWQjNnPM5xckVIITW5G54EChW2TaQFTYmVRklNtUQTy6J4vmC1yafUjQPBassEHlLQdDWwNM7u6sz9l+Ub8fn9nNi3K5lGAzOWRA/0CPnlyFXtNhgMaDSaen14DhU4oS0qo9GIIAj4fD5EUaSyspLVq1dT2Kv2pZvfKZ8txVvwer1UVlYiiiI+nw9BEDh37Dg69jyWN5bVYDBKQqdtjpbnTs6hfP1CHrj7Dg4cOIDL5UKr1UaNEgNJQCnxx/lxyToyjQZO7NsVn9/PL8uiRvg3xjUP6Sc8GkPkdAZOTlC/0XgcZGvVeoBPUjCWmGlyIofUO4FFw4L8l98ctqpA2d77GXEcX5ZgMsLrY/hIqkWOkhIGiSaWRdEEXJ6sgQS5BWinoN27CYhGqTP3ksoaFq0vxmwycnK/o5ixeG3UA2g0GkUOyCBZcyorK2ttW9UncA49dnZ2NoIgoNfrWbZyGXndcnE6XTidLvx+P+bCLIq2FFFVVYVer0cQBLKzs8NbSjf++xb+9ubyY5ELo8mE2+XGpIe7hpgZnlvKHbfczOJFixRZcZSGof+wZB2j+h1FptHAovXFlFZFNdY0xjUP8td9gMRtVaeryAGYkKB+60Ww2k4EzlHQ9FvRYW9Q5G6yaYoi50jxgYmVZI8luhenxEVBf4yEI1htLYAPkReVh5JqkaNkiybRxLooPhCsIp9wBKutP/CIgqbFwOsJ6LLeuX/xu+QWMG5YHxauL46W4wU46FgsZ83RarXk5uZSVlZGIBCIKnAORaPRkJGRwabNm2jXty0ZGSZMJiNarYbCo1qyb/c+cnJyyMjIqCNATCYT9z34CJ9v9LJ2vycsdAIEOPfoDG7pp+Xt//6H6dO+jDr+QCBAdXW1rGPyxp37WLS+mHHDpGwRn/8u62KRriKnNIGhzOkscqyC1TYoQX3XIpj8730FTb2kKD9aQ1BFTnwcSWPZpaBNK+CkOPqol2BU0HRiX1ATee7LFbRJd0sOSN/RC4keRDDibRoQ3fNV4k7RYU+Ek3a9c58+fxUer4+zh/TGkp3JB7OiVxjRarWYTCaczogRRGEMBgNms5mSkhJKS0tlBU6IqqoqDuw/gKWjtFsoiRkNepOerPws9uyJGKJNy5YtuePue3lucTVijR+jyYTL5cblctO3dSbPj8pl0c9f8dRjj0YMMw9ZceTCyz+YuRRLdib/GNILv9/v+3r+KrmppVzYK92aT2CX6SxyNMBbgtWmxJ1AMcGX1U+QtsTkeEV02KObTBsRVeTEx5E0lo3yTQC4NZHOcILVVgDMAEY04OOJPPdKis6NCS7AqaQhDuH/Eqy2GxM1AMFqa4P0HSlJ+T4H+CJBXdc795LKGr5fvJYMo4Hxowby4S9LqXJGd9HLysqiurq6TkRUfej1esU5aUJs2LCBwu6FaHV121s6WepEWB1O3379GHvRJTy1sBqvX6ogHvD70Wo1CJk6HhuZTcuKTdxxy811juX3+6murq6T0PBwqpxuPpq9lPGjBpJhNFBSWTOzpFLWr7iVYLUNkWuUYNIt23Ei+2vI/Xws8EbQRzRuglnU3wPOUtB8H8qst42GKnLi40gay89I0VlynI0yx1NZBKvtVGAFDRM4kNhzryQzbzfg+WRt2UWgoVFvrwlW293xdi5YbYORzo2SYql+4JZUZIZ94wcpJ9m1ZxxPRbWLD3+J7uMc8p85PILqcEJbVC1atAhbdORC0AFWr10dTgJ4OLkdc/i7+G/ZY5x73ljadO/Lq0sr0el0mDIycLvc+AN+9FoN1/Y3c2lXL5PvvYtf584Nfy4UeSXni/PhL0upqHZx7RlSNQ6zyfg4yqIx3xSsNiV+WIlCFTl1uQL4NN6XLMFqaw3MBsYr/Mj9osOequjhBqGKnPg4YsYiOuzlwP8pbP6gYLW9GYy0iRnBaisUrLYXg/1Fyssf2b5/kESee6WRQDcDywSr7WnBajtdsNqSHXGm5HuPVEL6acFqe1Ww2mKOkBGstkzBarsF+A1ljsYATyc4j0bEuS9cX8xfm3fQsZWFi086lhemzaWiJrpzcSjqKJIT8uE+OBkZGeTk5FBaWiq71bVq7Spa9qw/QW1+hzyKiouifj40rn9efiVbvXnM/NuDVqMJ++j4A5IFamTHTB4bkcXUd17nzf++Fk5iKBdRVVHj4oVpc7nkpAF0bGUBWJppMvwO/Ck7MOgHrBCstncFq+0ywWprq+Az8XCki5xIW+cXAT8IVtvRsXYqWG1awWobi/TCcoLCj80G3o21r1TTFGtXHTHCIkZSMZaPgX8obHstcKZgtTmAz4GF0ULpg1tcxyGJBCvRfTtmIll4bpcZQ8LOveiw7xastvkoWwCOCf7cDXgEq20LktCoBuRe++eIDvvDSsYUPGdyAqUEKez+mQh/nwhcJlhtzwGvy0VIBB3ArwbuRHkoP0jfmS2G9kqIes2/+NXcqg/uvDTrPuspTJu3kte+m8c9F50S9YA5OTmUlJSg1+tr+dpEcjI2GAwIgkBFRQVOp5Ps7Ow6daF8Ph9Fm4oY3KN+Y5elk4VF0yL7DYXCy7VaLW3atOH+Bx/hnjtupXOejl4FxqDQcWE0GdFqtHTMM/DcqBymLJ3Hg/ev4T7bw1HnDPDqt/OoqHFxrzV8fh5BilD6CmVJ5wTgquAPgtVWjJQfrAp5C7AfGB2DhS9pNfri6C+VkVy3A29F+NtoYI1gtb0LPA1sjnZegy9h5wIPEFs9rO3AJelWp6o+mqvISVUoW1MbS7wP/c+AScBQhe3bAbcFf7YJVtu3SDdHCVJehSwkZ92BSPvKStKZFwOXAk8paJtogfkU8HWMnzEAsbxZxVLBNx95a+wBYApwDZFLc+QCjwKPClbbeqT6MyuR3hg1SCUt+iFVFY/5LRFYB/wzkQti0CwfNQv194vW7gZK2hfkD772jON59dt5XHP6EFrmRb7MtFoteXl5lJaWIggCGo1GNopKo9GQm5uLx+OhoqICrVaL2WwO5635+++/yWuVi9Fcv27Pa5fH3l178fl8tY7v8XjCfkLZ2dnh4xUWFnLbnffw7LNP8uwoHS0ydXWETqZBw639dcwolqqZ33HP/fTpU/8zbF9ZJa99N4/rxhxP+4I8kN7mvwv++U0kcRprfqWOKMuSDlIusVi2MFNWZiGI3NpaksCko0rm9g1SpGmkdBA6pJfMa4GdwZezpUgJcj1I983RSOt4P2LXARXAWNFh3xvj5xqFprhdleoLPBpH1FiClpibkN7wYqUDktXgCaTw4beQHr6TkHxulAic1cAI0WHfR+Oc++8A2XCTOIllzIqEbTAx5rlIToJy9EB6G38e6QH3P+BZpAW1IQJnC3BqEnJoyM49IJ3LuwFuG3ciBr2Oe9/9XvbAer2e7OxsSktL8Xg8isLEQbLqWCwWzGYz1dXV4SSAK1etJLdj5NJnOoOO3Ja57Ny5E4/HE04KGAr5tlgsdRL99T/2WM65wMrTC6vx+AK1tq58fj9ul1Sf6vweWdw+0MjzT9ojhpnf8873GPQ6bh17YvhXBO9x0WGvBF6WPWnxEet9msoK5OkYyVWCtJYuUtC2LVJhzSeA/wJvAy8FPz+Q2AVODfAP0WGPN5FnymiKIudI2yJSSkrGIjrsf9I43vS/AiNFhz1USCflVrSgyLuM5NaoSrjIARAd9o3AmUT2z0kGi4ATRYd9RxKOrXTus4HPhRwzT1/zD776YxXfLFgt+0GTyYTRaIxabDMSBoOBvLy8sDgxmUxsnLuRz67+nBn3/sTsJ+fy24vzWPTaImY/OZcZ9/7E/m372bt3LzU1NWGxlJeXFzWL8dhx59PqqD68uUKKgJKEjlHKgKzThsfct5WJZ07O4Z333mf16tpz/3rBKqbPX8Uz15yNkGMGcATP2aE8CSxUfAJiJ21FTir7Urj9XCo67N6g+PwHsVl+42Un0gvLbynsM25UkRMfR+pYHgXeSNCxlPAlcIbosB+6790o51502Jcj7XsrSY7YEJIicgCCb1/noSxKLl5eRxI425N0/FjmfgdQecHwfvzjuF7c9fa3HCiPHjTk9XpxOp3hmlVyiQLrI5RNecwZY/j666+Z8uwUJo6fyAUjL+D0PqdzUveTuWDkBUwcP5F33nmHQYMGkZubqygrcej4N996O+uqzfxcJNXI8rg9GAwGfF5f2BkZ4IciF/1796RXr17h3+0vr+Kut77j7CG9OH94X5C2Ie48vJ9gEdUxwE8xnwRlqCJHIg9l288ABK2jpwPJeIk4nLnAQNFh/yMFfSUUVeTER1MbS0Kc44L75zciZblM1F50fbiQrEYXiw774eErcvN1JijhXB1Eh30B0tbNZJSVu4iFpIkcANFh/wXJfJ2sArZO4ErRYZ8YZ4VxOWKZ+zbgNo1Gw/PXnYvX52fiq9PwRciJc6gPjtlsxmw2h2tKNRSNRkNBQQF9+/ZlxIgRnHbaaQwfPpwRI0bQt29fCgqU7L7WJSMjg/sffISP17lZuUsqDWHQ62tFXX2/qYZFool7Jj8Ytu74/H5uCp6D5649NySqbkM6V3UIhgmfVKFCwQAACkJJREFUibTtuaZBg42MKnIa2JfosBcDpwHJTMb3HJIFR0lEa9rRHEWOhxSY5BUWCq1K8kIfGotiM2ei+hQd9oDosD8ODAAWJOq4QQJI1pveosP+8OEOq8FzLzffpIpL0WGvCs6/K1K00dQE9ZlUkQMgOuzfIUVSTI+hLzlqgP8APUWH/YMEHjcSsc79beCHVvnZ/G/SRcxatpHHPp1Z5wP1ORmbTKawM7KSrMipJj8/n2uuu4GXlnkoD9roQj46P26sZPpWePixJ2vVt7J/8n/MWraRN2+x0io/G+AH4J1o/QTv+W+B/kiJ4l4iMQ9XVeTE0Vcw2/AgJDHiqfOJhvMVcLzosN+VyGdHqmlS0VXBh/nPMs3KE5hsLBoGDkYgRCJZb8uHY0L+gZUUT3jRYV8lWG0jkHI0XIlkPm2oeN6IJG7eEB32rVHaZSAf5VTvG2miCZqM3wPeC16fPZCcrNsHf9ohVWc3oux+i2XcDV6ARYd9FzBOsNoGIDnnnolkLo+VUuBVYErQITxV7EUqIxGNQ50jA0jX55+jj+3e4dHLxzD5gxn07ljIRSP7A9GLbYaKZ5aXl+NyucjJyVGc7ThZ+P3+cPLCE086if379/HUz1/x2IhsDDoNMzY7mV5s4I577sdiORgc5fh1GS9/8zuPX3kmp/TvBtI1dyUKAwqCD7wZwR+CeXG6cvC674B0LRlRVuZjvrIZh1kM7JZpkyjhUYr8dRbr+CPhUtDX7/X9UnTYa4C7BKttClKI+cVITsex4gU+Ap5J51INsaBpyF6zikokghkzxwF9gV7Bn8PzqbiQIn2WIC1Yi4Clh/ncqMggWG2vAzfINLtYdNgdCo6lRcrtMyL4MxwpEeOhDykfsAnJCTX0s0J02BP59phshgC/BwIBw82vf8W0eSv57L7LGdazo+IoKpfLRWVlJZmZmWRmZiryn6kPURQRhJjzMBIIBKipqaGmpobs7Oxwor9AIMAzTzxGTsl6sgwaFokmHnn8KSwWC6WlpeTl5fHHumIufvJDLhjej//cOBaNRuNB+r6VROqoNBGC1u5OHLyfRwJdgEMTtAaQfAsXIVnjFyKtw6kMTkg6qshRSTqC1WZAurm0SFt4TemhmLYEEy1eJNPsVNFhnxVHH0akfEY1gCtFVtJkMx74yOXxcukzH7Nw3d+8ddNYTjvuGMVRVKGq3k6nE7PZXG8FcTliFTmBQACn00l1dTUZGRmYzeY6fdbU1HDTDRNo1aolkx96NLxF5fP5+HnxKq579WuG9uzEx3ePx2TQg3QuPolp4CpNlmDJmWykba3qZnI/R0UVOSoqTRTBapsFRE/hCwNEh31ZKsbTxLgXeLLa5eaSpz7iz03b+eSeyzixj5Iaowfx+/3U1NSEq3xnZmYqFkpKRY7P56OmpgaXy0VGRgaZmZmyW2WBQKCWAJq7cjPjn/mYgd3aM/XeyzCbjCCdg6cVDVZFpYnSFB2PVVRUJNIpuq+p8TTwtNlkZOq9l3F8z05Yn/iAqXP/iukgWq2WrKwsBEHAYDBQUVGBKIpUVFTgdrsbFHoeCARwu921jhUqH5GVlaXIF+hQgfPpnL+4+MkPGdqzE5/dd3lI4DxF5FIfKirNhibleKyiolILVeQ0nABwH4DZZLznk7vHc8eb3zLx1WmsKd7DQ+NPRxeDY3EoJ47JZAqLlJDvTujver0erVYbFikhC43f78fv9+P1esOiyGAwYDQaFVUPj4TP7+fhj37m1e/mMX7UQJ679pzQFtXTwP00LHO5ikqTQhU5KipNl0bLFdRMCAmdUpNB/+R/bhxLn86tmfz+DNZu28trN50ftc5VJA4VPCH8fj8+ny8saA618ITEj06nS1jE1r6ySia+Oo05Kzbz5FVnMeHMoSGxdC+SBUcVOCpHBKpPjopKE0Sw2jKRLy+xQ3TY26diPM2AS4F3AePsFZuYMOVzAJ7519mMG9Y3KR02NLoqGoFAgK/mr+Lut79DA7x5i5WT+x0FUpbrq1GdjFWOMFSfHBWVpom6VZVYPkEKtS0e1a8b81+cxIhjuvCvlxxc9cJU9pQmOrF14tldUsFVL0zl2pccjDymC/NfnBQSOMVIKQFUgaNyxKGKHBWVpokqchLPYqTMsd8W5Gbx7u2X8PatVuat3sKgf7/I41NnUl6dfhmPy6udPD51JoMnvcgfa7byzm0X8+7tl1CQmwXwLVK16SWNO0oVlcZB9clRUWmaqCInOexHKmJ6JTBl3LC+uaP6dePlb37jte/+4N3/W8ytY0/kylMHk5NpkjlUcimvdvLBrKW8NP1Xalwerj9rKLecN5K8rEyAcmAS8AGq/43KEYzqk6Oi0gQRrLaLALlMxm+IDrtcRmSVyLRFctIdD7BLLOeZL2bz8ew/yTAauPTkAVx56mB6dTg8obcyGuqTs3bbHt6fuYRP5vyF0+1h/KiB3H3hKNoIuaEmHyOV6tjZoIGpqDQjVEuOikrTRLXkJJ+dwGXAG8DTbYTcE16ccB53XTiKd35ayAezlvK/GQsYcFQ7zhzckzMH96R3x8IGh3xHIhAIsKZ4DzOWrGPGknX8tXkHBblZTBhzPNeccTxtD4qb+cA9wG8JHYCKShNGteSoqDRBBKvtAeAxmWZ3iA77C6kYzxGABjgDeBA4AcDt9TJjyTqm/7GKWcs3UVnjon1BHif1PYpju7alf9e2HNOpNZlGQ70HjGTJqXF7WP33bpYX7WRZ0U7mrtzM9v1lZGeaGN2/G2OH9eHMwT0x6sPvqPOBR4GfULemVFRqoVpyVFSaJqolJ7UEgB+DP0OAW4x6/QXnDe1jOm9oH9xeL/PWbOXHJetYuL6Yz35dhtfnR6fV0qFlHoX5ObS25FBoySHXnIFep6WmpgaD0UR5tZM9JRXsLqlgT2kF2/aV4fP70eu09O5YyJmDezJmcE+G9+58qLBxAV8CU1CLa6qoREQVOSoqTRNV5DQei5D8dG4CLgAuNer1J47q100/ql83AJxuD2uK97C8aCdb95bw/+3cMUocURzH8R9ZVhRMYIKokLAGImywENlCiLVnyG1S5Ry5gGBOYOURLMKmT6EiKrKgiBtTzGy9hbC78/x84F/NFG+6L7w376IJmOHfy4zuH/M0Huc5SbfTyerKUjart9lar7Lf7+XTepW9zx+y09uY3FA88ZTkNPWv4MdJbmf50dBGIgfaSeTM322Sn828S3LYzNflpe7uYPvjm8H2i+5i/JfkLPV21Ekzdy9aMbwyIgfaSeQslrskv5pJktUkO0m+JOmn/lNrM8lakuVmkuShmask56kPO/9JMkzyO8loNsuHMokcaCeRs9hGqbe1nJeBOXLjMbTTtMh5jjMbwCsncqBl3n/73klSTXnt5vrox3gW6wFYVLaroJ0Opjx/nMkqABaYywABgCLZrgIAiiRyAIAiiRwAoEgiBwAoksgBAIokcgCAIokcAKBIIgcAKJLIAQCKJHIAgCKJHACgSCIHACiSyAEAivQfb1uC5c7D1f4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 550x110 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_logo(height_px=110, lw_bars=0.7, lw_grid=0.5, lw_border=1,\n",
    "          rgrid=[1, 3, 5, 7], with_text=True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}