Example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastprogress.fastprogress import master_bar, progress_bar\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Finished loop 0.<p>Finished loop 1.<p>Finished loop 2.<p>Finished loop 3.<p>Finished loop 4.<p>Finished loop 5.<p>Finished loop 6.<p>Finished loop 7.<p>Finished loop 8.<p>Finished loop 9."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mb = master_bar(range(10))\n",
    "for i in mb:\n",
    "    for j in progress_bar(range(100), parent=mb):\n",
    "        time.sleep(0.01)\n",
    "        mb.child.comment = f'second bar stat'\n",
    "    mb.main_bar.comment = f'first bar stat'\n",
    "    mb.write(f'Finished loop {i}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Finished loop 0.<p>Finished loop 1.<p>Finished loop 2.<p>Finished loop 3.<p>Finished loop 4.<p>Finished loop 5.<p>Finished loop 6.<p>Finished loop 7.<p>Finished loop 8.<p>Finished loop 9."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mb = master_bar(range(10))\n",
    "#Name of the curves to put in the legend box.\n",
    "mb.names = ['cos', 'sin']\n",
    "for i in mb:\n",
    "    for j in progress_bar(range(100), parent=mb):\n",
    "        time.sleep(0.01)\n",
    "        mb.child.comment = f'second bar stat'\n",
    "    mb.main_bar.comment = f'first bar stat'\n",
    "    mb.write(f'Finished loop {i}.')\n",
    "    x = np.arange(0, 2*(i+1)*np.pi/10, 0.01)\n",
    "    y1, y2 = np.cos(x), np.sin(x)\n",
    "    graphs = [[x,y1], [x,y2]]\n",
    "    #Specifying the boundaries is better to have the box at the right size.\n",
    "    x_bounds = [0, 2*np.pi]\n",
    "    y_bounds = [-1,1]\n",
    "    mb.update_graph(graphs, x_bounds, y_bounds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Updating every iteration in the inner loop is possible but slower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Finished loop 0.<p>Finished loop 1.<p>Finished loop 2.<p>Finished loop 3.<p>Finished loop 4.<p>Finished loop 5.<p>Finished loop 6.<p>Finished loop 7.<p>Finished loop 8.<p>Finished loop 9."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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j0HyQ0SHaliY9of/zcORH+OO/RkejKNdUKiVFJSZcnAUuNzBNtF27dqxfv56ZM2eybds2/Pz+OqPQzc2NW265BYAuXbpw9uxZPcL+kx4VwT4gXAgRClxAe7O/58qDhBAtgdrArnL31QbypJSFQoggoBcwR4eYDNGxkT8zh7XirZUnWL5tH6N2Pg71O8OAl40OzTb1eQbitsKqmdC0NwSEGh2R4giu8cm9IiWlJmJTckBAeB0fxA1sKduiRQv279/PypUref755xkyZMhfHnd1df1zvMHZ2ZmSkpIqn+NaLK4IpJQlwGPAGuAEsERKeUwI8boQovwsoPHA9/KvXe5aA1FCiEPAJmC2lLJmhsmryeTeodwU6k/QhqcxlRZrlzhcbnzmgENzcobbPgMnF1j6sLaxjaJYmaTMAopLJY0DvHC+wX3FExMT8fLy4r777uPZZ5/lwIEDOkd5bbpMcJVSrgRWXnHfy1fcfrWC5+0E2ukRg7VwchJ80vIQtZOO8LnXE0z1D1Wr9izh1xBGzIFfH4Jd87TeRIpiJTLzi7mcV0RILQ+83G787fTIkSPMmDEDJycnXF1d+fTTT3n22Wd1jPTaVBtqvaWdhs96c9G/Ez3iH+HFkRFM6RNmdFS2TUpYcj+cWgPTtmgb3CiKjm6kDXVJqYlTyTm4Ogua1fHBycomNKg21EYxlcLSR8DZlZD7FjCodV3mrIkmJllNgbSIEHDLB1pLjl+nQUmR0REpDk5KyYWMfEqlpFGAl9UlgapSiUBPuz6G+N0w/F2EXwP+dXs7fNxdeHrJIUosaVmtgHcQ3PqRNq1v21yjo1EcXGZ+MZn5xYTUcsfD1fbXBqlEoJdLMbDxTWh1C7S/C4BgX3deH92GIxcy+XrHWWPjswetRkD7cbDtPUi26TkFihWq7GXy4lITFzLy8XJzIdgKN5mByv8uZVQi0IPJBMv/Aa4eMPK9vyx+GtmuHoNah/DvddGcT8szMEg7MfRt8KgFyx5Xs4gU3Xh4eJCWllapN9CkjHxMEhrWtqyFRHWRUpKWloaHR+U3wbH+tni24I/FcG6HduniitXDQgjeGNOGwe9t5Z+/HmHx5G5W+cdjM7wDYfgc+Hmy1piux3SjI1LsQMOGDUlISCA1NfWaxxUUl3Ipp4hani7EZble81gjeXh40LBhw0ofrxKBpbKTYd1L0KQ3dH6gwkPq+Xkyc3grXlp6lJ/2J3BnZKMKj1Mqqe0dcPgH2PC6drnI37pajii2x9XVldDQay9YzC0sYfB7W/B2d2HFE31wc7GfCyr285sYZdVzUFwAt354zX4493ZrTGST2ry54gSp2ZXYu0C5OiG0S3AAy59UexcoNWLu2miSsgqYfUc7u0oCoBKBZaJXab3z+86AoObXPNTJSTD7jnbkF5Xy6vJjNRSgHfNvBINegdMb4PASo6NR7NzB+AwW7jzLfd2b0KVJgNHh6E4lghtVmA0rnoE6EZVe7dq8ji+PDWjOisNJbI5OqeYAHUDXKdCwq9bhNf/y9Y9XlBtQXGpi1s+HqePrzoxhLY0Op1qoRHCjNs+GrETtklAVegk91C+M0CBvXl12jMISNevFIk7O2iWi/HTY+JbR0Sh2auGOs5y8mM1ro9pSy8N6B4gtoRLBjUg5CXs+g873Q6NuVXqqu4szr41qw9m0POZvucqOZkrl1WsPXadC1JeQeNDoaBQ7k5xVwAfrTzGgVR2Gta1rdDjVRiWCqpISVj4Lbj4w8NUbeom+LYIZ0a4u8zbFEp+u1hZY7OZ/ajuarXhGW9OhKDp5e+UJik2SV2617/5WKhFU1bFf4Ow2GPiSNqf9Br04MgJnJ8Hrv6sVshbz9Ichb8CFKG1Nh6LoYPeZNH47mMj0vmE0CfQ2OpxqpRJBVRTmwJoXoV4H6DLJopeq7+/JEwPDWXc8mY0n9d1/1CG1vxsa3wTrX4W8dKOjUWxcSamJV347RgN/Tx7uf+0ZgfZAJYKq2DoHshNhxFxdNqF/sFcozYK9eXXZcQqK1cCxRYTQ/rsUZMKG14yORrFx3+w6R3RyNi/dEoGnm+03lbselQgqK/WU1l20031VHiC+GjcXJ94Y3Zbz6Xks2KoGji1Wty10fwj2L4KkQ0ZHo9iolOwC3l93ir4tghnaJuT6T7ADKhFU1prnwc0bBun7abNn8yCGtanLp1tOk5xVoOtrO6R+M8GzNqx+Xq04Vm7Iu6ujKSgp5dVbIxymL5hKBJURsw5i12tvMt5Bur/88yNaUVIqeXdNtO6v7XA8/WHAC1oTwBPLjI5GsTFHL2Ty04EEJvUKJSzYx+hwaowuiUAIMUwIES2EiBVCzKrg8YlCiFQhxEHz15Ryj00QQsSYvyboEY+uSothzQsQ0Eybr14NmgR6M6l3U37an8CRhMxqOYdD6TxRW/G99iWtD5SiVIKUkjd+P05tLzceG2D/A8TlWZwIhBDOwMfAcCACGC+EqGjS7Q9Syo7mry/Mzw0AXgG6A92AV4QQtS2NSVf7F8KlaG16YhVWEFfVYzc3J8jHjdd/P1blTSWUKzi7wNC3IOMc7PnU6GgUG7HmWDJ74tJ5anALu11BfDV6VATdgFgp5RkpZRHwPTC6ks8dCqyTUqZLKS8D64BhOsSkj/zLsOltaNoHWo6o1lP5erjyzJCW7Dt7mZVHLlbruRxCswHQYjhs/bfWKlxRrqGwpJR/rTpBixAfxnd1vDbxeiSCBkB8udsJ5vuudIcQ4rAQ4ichRNm/dGWfixBimhAiSggRdb3NI3Szda6WDIa+fc0W03q5K7IRrer68vbKE2o6qR6GvAkl+bDpTaMjUazcNzvPcS4tjxdGRuDi7HhDp3r8xhW9Q155bWM50FRK2R5YDyyqwnO1O6WcL6WMlFJGBgcH33CwlZZ2GvZ8rk0Xrde++s8HODsJXr41ggsZ+Xy5Pa5GzmnXgppDt4fgwGJIOmx0NIqVSssp5KONMfRvGUy/FjXw3mKF9EgECUD5WqohkFj+ACllmpSybDeWBUCXyj7XMOteBhd3GPBSjZ62Z7MghkSE8MmmWFKy1UCnxfrN0KaTrvmnmk6qVOiD9THkFZXy4sjWRodiGD0SwT4gXAgRKoRwA8YBf5m3J4SoV+7mKOCE+ec1wBAhRG3zIPEQ833GitsGJ3+H3k/9bQ/imvD8iNYUlpj4aENMjZ/b7njWhv6ztP5QseuNjkaxMjHJ2fxv73nu7d6Y5nV8jQ7HMBYnAillCfAY2hv4CWCJlPKYEOJ1IcQo82FPCCGOCSEOAU8AE83PTQfeQEsm+4DXzfcZR0qtGqjVAG561JAQQoO8Gd+tMd/tjedMao4hMdiVLpOgdiisewVMauxF+X/vrI7Gy9WZJwe1MDoUQ+kyKiKlXCmlbCGlbCalfMt838tSymXmn5+XUraRUnaQUt4spTxZ7rlfSSmbm7++1iMeixz/DRIPaK2NXT0NC+OJgeF4uDgxd61aZGYxFzetW2zKMW3Te0UB9p1NZ/2JZKb3b0aAd/VNDbcFjjc8fi2lxbDhdQhuDR3GGxpKsK87U/uGsfLIRf44r7ZhtFjEbVC/k7aTmVpk5vCklMxedZI6vu482CvU6HAMpxJBeX8shvTT2qboOnQXtdTUPmEE+bjxr1Un1SIzSzk5aX2ishJg73yjo1EMtu54MvvPXeapwS0corvo9ahEUKYoV9uHuPFN0MI61rR5u7vwj4Hh7I1LZ+NJtdm9xcL6QfNBsO3farN7B1ZSamLOmmjCgr25s0tDo8OxCioRlNn9CeQka58arajj4LhujQkN8uad1ScpNamqwGKDXtP2LNj2ntGRKAb5+UACsSk5PDe0pUMuHquI+lcAyE2D7R9Cq1ugcXejo/kLV2cnZgxtyankHH4+kGB0OLavblvoME5bLJip/j0dTX5RKe+vi6FjI3+GtrHfzeirSiUCgG1zoTgXBr5sdCQVGt62Lh0b+fP+ulOq9YQebn5B+77pbWPjUGrcwp1nuZhVwKzhrRxmr4HKUIng8lnYuwA63gvBLY2OpkJCCGYNb0VSZgELd541Ohzb598Iuk2Fg/+DlBPXP16xCxl5RXyyOZYBrerQIyzQ6HCsikoEm97WZgj1f97oSK6pR1gg/VoE89mW02QVFBsdju3r8wy4+8Kmt4yORKkhn24+TU5hCc8Ns84PfEZy7ERw8SgcXgLdp4NfhU1PrcqzQ1qSkVfMl9tUQzqLeQVoK8dPLIfEP4yORqlmKVlaNX1bxwa0qlvL6HCsjmMngk1vg3st6P2k0ZFUSruGfgxrU5cvt8dxObfI6HBsX49HwDMANqo21fbu402xlJok/xgUbnQoVslxE8GFAxC9Ano+pjUmsxFPD2lBblEJn205bXQots/D/CEgdj2c22V0NEo1Sbicx//2nufOyEY0CfQ2Ohyr5LiJYNNb2qfB7tONjqRKWoT4MqZjAxbtOktKlmqVYLGuU8EnBDa+odpU26l5G2MRCB53sH2Iq8IxE8H53dqnwF7/0D4V2pgnB4VTUiqZtynW6FBsn5sX9J0B53bA6Y1GR6Po7OylXH7cn8A93RtT39+4JpLWzjETwcY3wbuONoXQBjUJ9ObOyEZ8t/c88el5Rodj+zpPAL/GqiqwQx9uiMHVWfDIzc2MDsWqOV4iOLNF26Skz9PgZrvXC58Y2BwhhNq8Rg8ubtB/pjZ76OQKo6NRdBKTnM3SgxeY0LMpdXw9jA7HqjlWIpBSGxvwra9tVmLD6vl5cl/3Jvx8IIHTavMay7UfB4Hh2t+H2rzGLry//hTebi5M76uqgetxrEQQux7i92j72Lra/ieER25uhoerMx+sV1WBxZxd4ObnIeU4HP3F6GgUCx29kMnKIxd5sHcotR1805nKcJxEIKU2NuDfGDreZ3Q0ugjycWdSr6YsP5TIiaQso8OxfRG3QUhb2Pw2lJYYHY1igffXncLP05XJvdWmM5WhSyIQQgwTQkQLIWKFELMqePxpIcRxIcRhIcQGIUSTco+VCiEOmr+WXflc3ZxcAUkHod8s7ZqwnZjWpxm+7i58qKoCyzk5aVuUpp9RW1rasAPnL7PhZArT+obh5+lqdDg2weJEIIRwBj4GhgMRwHghRMQVh/0BREop2wM/AXPKPZYvpexo/hpFdTCZtFXEgc2h/d3Vcgqj+Hm5Mql3KKuPXeRYYqbR4di+liOgbnvY+q6qCmzUe2tPEejtxsSeTY0OxWboURF0A2KllGeklEXA98Do8gdIKTdJKcvmOe4GanZboOO/ahuX939euxZsZyb3DsXXw0XNINKDENrfyeU4OPy90dEoVbTrdBrbYy/xcP9meLvb3//r1UWPRNAAiC93O8F839VMBlaVu+0hhIgSQuwWQoy52pOEENPMx0WlpqZWPjpTqbYFZZ0IaHN75Z9nQ/w8XXmwVyhrjiWrqkAPLYdDvQ7mqkB1erUVUkreX3+KOr7u3NejyfWfoPxJj0RQ0e4OFa7KEULcB0QC75a7u7GUMhK4B/hACFHhXC8p5XwpZaSUMjI4OLjy0R39GS6dgv6ztGvAdupBc1Wgxgp08GdVcBYOqarAVuw6k8beuHQe7q/NplMqT493xgSgUbnbDYHEKw8SQgwCXgBGSSkLy+6XUiaav58BNgOddIhJYyqFLXOgThtodatuL2uNymZIrD2ezNELqiqwWIthUK+jqgpsyIfrYwj2dWd8t8ZGh2Jz9EgE+4BwIUSoEMINGAf8ZfaPEKIT8DlaEkgpd39tIYS7+ecgoBdwXIeYNMd+hbQY6PecXVcDZSb1MlcFaqzAcmVVQcY5OPSd0dEo17H7TBp74tJ5uJ+qBm6Exe+OUsoS4DFgDXACWCKlPCaEeF0IUTYL6F3AB/jximmirYEoIcQhYBMwW0qpTyIwmbRPc8GtoXX1TEayNn6erkzpHcY6VRXoo8VQqN9JVQU2oKwauKe7qgZuhC4fk6WUK6WULaSUzaSUb5nve1lKucz88yApZciV00SllDullO2klB3M37/UIx4Aji+F1JPaKmIHqAbKTOrdlFoeLmq1sR7+rArOq6rAiu2NS2fXmTQe6humqoEbZJ/vkCaTNjYQ1BIirjoRyS7V8nBlSp8w1p9QVYEuwmtnRS4AACAASURBVIdA/c5aVVCidoWzRh9uOEWQjzv3dlczhW6UfSaCk8sh9YR5bMDxPiFM7KWqAt2oqsCqRZ1NZ0esVg14ujne/+t6sb9EUFYNBIZDm9uMjsYQtTxcmWquCo4kqKrAYuGDoUEX2DpXVQVW5sMNMQR6u3FvDzU2YAn7SwTRKyD5qLbrlANWA2Um9mqKn6crH244ZXQotq+sKsg8D4f+Z3Q0itn+c5fZFnOJaX3D8HJTq4gtYV+JQErY8g4ENIO2dxgdjaF8PVyZ2ieU9SdSOJyQYXQ4tq/5IGgQqaoCK/LhhhgCvN24/yY1NmAp+0oE0avg4hHo+6xd9hSqqgk9tapAjRXo4M+qIB4O/tfoaBzeH+cvs/VUKlP7qGpAD/aTCKSELbOhdii0u8voaKxCWVWw8aSqCnTRfKBWFWx7T1UFBvtwQwy1vVx5QFUDurCfRBCzFpIOqWrgCmVVgepMqgMhoN9MrSpQYwWGORifweboVKb0CVMdRnViH4lASq3DqH8Tu9tvwFK+HloPovUnUtS6Aj2ED9bWFWz7t1ptbJCPNsTg7+XKBLXfgG7sIxHErofEA9DnGXBWOxJdqWxdgepBpAMhtE62al2BIQ4nZLDxZApT+4Tho6oB3dh+IiirBvwaQ4fxRkdjlWp5uDJZ9SDST/gQcw+iuaoqqGEfbYjBz1ONDejN9hPB6Y1wIQr6PGVXexHrbWKvpmoXM72UjRVknFN7G9egoxcyWX8ihSm9Q/H1UJW/nmw7EZStG6jVEDreZ3Q0Vq1sF7O1x5M5nphldDi2r8UwtYtZDftwQwy1PFyY0Kup0aHYHdtOBGc2Q/weVQ1U0oO9QvF1V1WBLoSAfrO0XcwOLzE6Grt39EIm644nM7l3GLVUNaA7200EZdWAb33odL/R0dgEPy9XJvVqyupjFzmRpKoCi7UcDnXbm6uCEqOjsWsfbYjB18OFiaoaqBa2mwjOboPzu6D3U+DibnQ0NuPB3qH4uLvwn42qKrBY2VjB5Tg48qPR0dit44lZrD2ezIO9QvHzVNVAdbDdRLD5HfCpC50fMDoSm+Lv5cakXk1ZeeQi0RezjQ7H9rUaCSHtVFVQjeZtisHX3YUHe4UaHYrdss1EUJQD57ZD7yfB1cPoaGzOZHNVoMYKdCAE9J8J6afh6M9GR2N3oi9ms/LIRa2brpeqBqqLLolACDFMCBEthIgVQsyq4HF3IcQP5sf3CCGalnvsefP90UKIoZU6YfZF8AmBLhP1CN/h+Hu5MaFnE1YeTeJUsqoKLNZyJIS0ha1zwFRqdDR2Zd6mWLzdnFU1UM0sTgRCCGfgY2A4EAGMF0JEXHHYZOCylLI58D7wjvm5EcA4oA0wDPjE/HrXVpgNvf4Brp6Whu+wpvQOw8vVWVUFenBy0nbDS4tVVYGOYlNy+P1wIg/0bEptbzUrsDrpURF0A2KllGeklEXA98DoK44ZDSwy//wTMFAIIcz3fy+lLJRSxgGx5te7TtSu0GWSDqE7rtrebjzQsykrjiQRo6oCy7W6FepEaLvjqapAFx9visXDxZkpvVU1UN30SAQNgPhytxPM91V4jJSyBMgEAiv5XACEENOEEFFCiKhc4Q1uXjqE7tim9gnD09WZ/2yMNToU2/dnVRADx341OhqbF3cpl98OXuD+m5oQ6KNmBVY3PRKBqOA+WcljKvNc7U4p50spI6WUkd4h6hOCHgK83XjgpqYsP5xIbIqqCizWejQEt1ZVgQ4+3hSLq7MTU/uEGR2KQ9AjESQAjcrdbggkXu0YIYQL4AekV/K5SjWa2icUDxdVFeiirCq4FK2qAgucT8vj1z8ucG/3JgT7qmqgJuiRCPYB4UKIUCGEG9rg77IrjlkGTDD/PBbYKKWU5vvHmWcVhQLhwF4dYlIqKdDHnQduasLyQ4mcTs0xOhzbFzEGgltp6wpMJqOjsUmfbI7F2UnwUD9VDdQUixOB+Zr/Y8Aa4ASwREp5TAjxuhBilPmwL4FAIUQs8DQwy/zcY8AS4DiwGnhUSqlq6ho2tW8Y7i7OzFNVgeWcnKDvDEg9CceXGh2NzUm4nMdP+xMY37URIbXUGqGaIrQP5rYlMjJSRkVFGR2GXXl75Qm+2HaG9U/3IyzYx+hwbJupFD7pAcIZHt6pJQelUl749Qg/RiWw5bn+1PNT08MtYjJpCx7F/w/FCiH2SykjrzxU/YUqgDaDyM3FiXmbVFVgMSdnrQdR6gk4ceVVUuVqEjPyWRIVz52RDVUS0MORH+HLIZCTet1DVSJQAAj2dee+7k1Y+scF4i7lGh2O7WtzGwS1MM8gUmMFlfH5ltNICQ/3b2Z0KLbPVKqNUxXngVfgdQ9XiUD507R+Ybg6O6mxAj04OWtjBSnH4ORyo6OxeslZBXy3L56xXRrSsLZaI2SxY79qa1r6PVepS5MqESh/quPrwb3dm7D04AXOpamqwGJt74DA5qoqqITPt5yh1CR5pH9zo0OxfaZS7W+uToS24r0SVCJQ/mJ6vzBcnISqCvRQVhUkH4XoFUZHY7VSsgv4ds85buvUgMaBqhqw2PHftLUsfWdUeqKCSgTKX9Sp5cE93Rvzyx8XOJ+WZ3Q4tq/tWAhopu2mZ4Mz9GrCF9viKC418ejNqhqwmMmkjQ0EtYSIK1u+XZ1KBMrfTO/XDGcnwbxNqjOpxZxdtE9mF49A9Eqjo7E6aTmFLN51jtEdGxAa5G10OLbv5HJIOW4eG7h+I+cyKhEofxNSy4N7ujXmlwMXiE9XVYHF2t0JAWGwebaqCq7wxfY4CkpKVTWgB5NJGxsIDNdmrVWBSgRKhab3a4aTEHys1hVYztkF+jwLFw9D9Cqjo7Eal3OL+GbnWW5pX5/mddQiRotFr9TGo/rOqFI1ACoRKFdR18+Dcd0a8dP+BFUV6KH93VC7KWxRVUGZr3bEkVtUyuMDVDVgMSm1caiAMG22WhWpRKBc1cP9targk82njQ7F9pWNFSQdglNrjI7GcJl5xSzccZYR7erSIsTX6HBs36k1WsXZ51ntb62KVCJQrqqenyd3d23Ej1HxJFxWVYHF2t8N/k1UVQB8vTOO7MISHrs53OhQbJ+U2t9U7abQ/q4begmVCJRrerh/M4RAVQV6cHaFvs9C4h8Qs87oaAyTVVDMV9vjGBIRQkT9WkaHY/ti1ml/U32e0f7GboBKBMo11ff35K5IrSq4kJFvdDi2r8N48G8Mm//lsFXBV9vjyCoo4YmBqhqwmJSw+W3tb6rD+Bt+GZUIlOt6xDy179PNagaRxZxdtU9uiQcgdr3R0dS4zPxivjRXA20b+Bkdju07tUarBvo+d8PVAKhEoFRCA39P7oxsxJJ9CSRlqqrAYh3uAb/GDrmu4MvtcWQXlPDkoBZGh2L7pNQqy9pNocM4i15KJQKlUh7p3wyJ5FM1VmA5Fzfo8zRciILTG4yOpsZk5hXz9fY4hrWpq8YG9BC9CpIOWlwNgEoESiU1rO3F2C4N+X5vvKoK9NDxXvBrBJsdpwfRF9vPkF1Ywj8GqbEBi5WNDQSEabPRLGRRIhBCBAgh1gkhYszfa1dwTEchxC4hxDEhxGEhxN3lHlsohIgTQhw0f3W0JB6lej3SvzkmKflMVQWWc3GD3k9Bwl44s8noaKrd5dwivtoex4h2dWldT1UDFjv5u9a/qu9zN7Ru4EqWVgSzgA1SynBgg/n2lfKAB6SUbYBhwAdCCP9yj8+QUnY0fx20MB6lGjUK8OKOzg35bl88yVkFRodj+zrdB7UaOsRYwYJtZ8grLuUfA9XYgMVMJu1vJqCZ1sdKB5YmgtHAIvPPi4AxVx4gpTwlpYwx/5wIpADBFp5XMcijNzen1KTGCnTh4g59noL4PXBms9HRVJv03CIW7TzLiHb1aFlXrSK22IllWk+h/rN0qQbA8kQQIqVMAjB/r3Otg4UQ3QA3oPy7yFvmS0bvCyHcr/HcaUKIKCFEVGrq9TdjVqpH40Av7ujcgO/2nidFVQWW63Q/1Gpg1/sVzN+qVQNPqnUDljOZtL+VoBY31FPoaq6bCIQQ64UQRyv4qvyuB9rr1AMWA5OklGX79j0PtAK6AgHAzKs9X0o5X0oZKaWMDA5WBYWRHrs5nBKT5NMtqiqwmIu7NlZwfhfEbTU6Gt2l5RTyza6z3Nq+PuGqp5Dlji817zcws8odRq/luolASjlIStm2gq/fgGTzG3zZG31KRa8hhKgFrABelFLuLvfaSVJTCHwNdNPjl1KqV+NAL27r1ID/7VFVgS463Q++9bRPenZm/tYzFBSXqlXEejCVmquBllXeb+B6LL00tAyYYP55AvDblQcIIdyAX4FvpJQ/XvFYWRIRaOMLRy2MR6khj93cnBKT5POtZ4wOxfa5ekDvp+HcDojbZnQ0uknNLmTRrrOM6qD2G9DFsV8h9ST017caAMsTwWxgsBAiBhhsvo0QIlII8YX5mLuAvsDECqaJfiuEOAIcAYKANy2MR6khTYO8Gd2xPt/uOUdqdqHR4di+zg9oVcHm2UZHopvPt5ymqMSkqgE9lFUDwa0hQt9qACxMBFLKNCnlQClluPl7uvn+KCnlFPPP/5VSupabIvrnNFEp5QApZTvzpab7pJQ5lv9KSk15fEA4RSUm5m9VYwUWc/WAXk/Cue1wdrvR0VgsOauA/+45x5iODQgLVtWAxY78CJdOmasB/dcBq5XFyg0LDfJmTMcGLN6tqgJddJkAPiF2URXM2xhLSalUq4j1UFKk9RSq2w5aV2mOTqWpRKBY5LEBzSkqMbFgmxorsJirp1YVnN0GZ3cYHc0NO5+Wx3d7z3N310Y0CfQ2Ohzb98diuHwWBrxcLdUAqESgWCgs2IfRHRvwza6zagaRHiIngU9d2Pimza4r+GDDKZydBI8PUNWAxYrzYcscaNQDwgdX22lUIlAs9uSgcEpKJR9tjDE6FNvn6gn9ZsD5nTa5X8Gp5Gx+/eMCE3o2pa6fh9Hh2L69CyDnIgx8GYSottOoRKBYrEmgN+O7Neb7vfGcS8s1Ohzb1+kBrcf8+te0laQ25L21p/B2c2F6v2ZGh2L7CjJh+3vQbCA07VWtp1KJQNHF4wOa4+IseG/dKaNDsX0ubnDzi5B8BI79YnQ0lXYoPoPVxy4ypU8oAd5uRodj+3Z9DPmXYeBL1X4qlQgUXdSp5cGkXqEsO5TI8cQso8OxfW3vgJC22lhBabHR0VTK3LXR1PZyZXLvUKNDsX25l7RE0HoU1O9U7adTiUDRzfS+zfB1d2Hu2mijQ7F9Tk4w4CW4HKfNGrFyu06nsS3mEo/0b46vh2W7ZSnA9vehOA9ufqFGTqcSgaIbPy9XpvdvxsaTKUSdTTc6HNvXYqg2W2TLHCjKMzqaq5JSMndtNCG13Ln/piZGh2P7Mi9og8Ttx0GdVjVySpUIFF1N6hlKHV933ll9Emmj0x+thhAw6BXIToK9842O5qo2Raew/9xlnhgYjoervj1wHNLWOSBN2iriGqISgaIrTzdnHh8Yzr6zl9kcrfaNsFiTntB8sHapID/D6Gj+ptQkmbM6msYBXtwV2cjocGxf2mk4sBi6TNRmjtUQlQgU3Y3r2ogmgV7MWRONyaSqAosNfAkKMmDnR0ZH8je//nGBkxezeXZoS1yd1duJxda/Ci4e0HdGjZ5W/ZdTdOfq7MTTg1twIimL5YcTjQ7H9tXroM0i2v0pZCcbHc2fCopL+ffaaDo09OOWdvWMDsf2xe/VtqHs9QT4htToqVUiUKrFre3r07peLf699hSFJaVGh2P7bn4BSotgi/U0pPt6x1mSMguYNbw1Tk7Vt+rVIUgJa1/Smg7e9FiNn14lAqVaODkJ/jmiFefT81i865zR4di+wGYQORn2L4SUk0ZHQ3puEZ9simVgqzrc1CzQ6HBs38nfIX439H8e3Gu+bbdKBEq16RMeTL8WwXy0IYaMvCKjw7F9/WaCmw+se9noSPjPxhhyi0qYObxmpjfatdJibWwgqKW2bakBVCJQqtU/R7Qmp7CE/2yMNToU2+cdCH2egZg1cGazYWGcS8vlv7vPcVdkI1qoDektd2ARpMXC4NfA2cWQEFQiUKpVy7q+3BXZiG92nVUN6fTQfTr4NYK1LxrWkO7dNdG4ODnx1OAWhpzfrhRmaxsRNekFLYYZFoZFiUAIESCEWCeEiDF/r32V40rL7Ve8rNz9oUKIPebn/2De6F6xM08PboGrsxNzVqvWExZz9YCBr8DFI3D4hxo//cH4DH4/nMTUPqGE1FJtpi224yPITYXBb1Rrm+nrsbQimAVskFKGAxvMtyuSX26/4lHl7n8HeN/8/MvAZAvjUaxQnVoePNS3GSuOJLH/nGo9YbG2d0D9zrDxjRptPSGl5O2VJwj0dmOaajNtuawk2DUP2twODbsYGoqliWA0sMj88yJgTGWfKIQQwADgpxt5vmJbpvbVWk+8ueKEaj1hKScnGPImZF2A3Z/U2GnXHEtmb1w6Tw4Kx8fdmGvZdmWTubNsDbSZvh5LE0GIlDIJwPy9zlWO8xBCRAkhdgshyt7sA4EMKWWJ+XYC0OBqJxJCTDO/RlRqqmpdYGu83Fx4dkhL/jifwcojF40Ox/Y17QWtbtFaT+SkVPvpCopLeWvlcVqE+DC+W+NqP5/du3AA/vgWekyHgDCjo7l+IhBCrBdCHK3ga3QVztNYShkJ3AN8IIRoBlR0QeyqHxWllPOllJFSysjg4OAqnFqxFnd0aUirur7MXn2CgmK1yMxig16DkgLY9Fa1n+qrHXHEp+fz8i1tcFGtJCwjJayeBd5BNd5K4mqu+19USjlIStm2gq/fgGQhRD0A8/cKP5pIKRPN388Am4FOwCXAXwhRVmM2BFQ/Ajvm7CR46ZYI4tPz+XJ7nNHh2L6g5tB1KuxfBEmHqu00yVkFzNsYy+CIEHqHB1XbeRzG0Z8hfo+2D7GHn9HRAJZfGloGTDD/PAH47coDhBC1hRDu5p+DgF7AcaldKN4EjL3W8xX70qt5EMPa1GXexliSMvONDsf29Z8FXgGw8jntk2Y1mLM6mpJSyQsjWlfL6zuUolxtQWC9DtDxXqOj+ZOliWA2MFgIEQMMNt9GCBEphPjCfExrIEoIcQjtjX+2lPK4+bGZwNNCiFi0MYMvLYxHsQEvjGyNSUreXml8qwSb5+mvTSeN3w1Hfrr+8VV0MD6Dnw8k8GDvUJoGeev++g5nx4faIP+wd8DJevZuELY4gyMyMlJGRUUZHYZigffXneLDDTF8P60HPcJUrxqLmEzwxQDIvgiPRenWq8Zkktz+6U4uZOSz6dn+aqaQpTLOw7yu0GokjP3KkBCEEPvN47V/oUZ9FENM79eMBv6evLrsGCWlxqyQtRtOTjB8jraT2ba5ur3sb4cucDA+g+eGtlRJQA/rXgaENshvZVQiUAzh6ebMiyNbc/JiNv/be97ocGxfo27QYTzsnKftcmWhnMISZq86SfuGftzRuaEOATq4s9vh2K/Q+0nwt76d3FQiUAwzrG1dejYL5N9rT5Geq7qTWmzQq9ruVquft/il3lt7ipTsQl4b1UbtNWCpkiL4/Wnwbww9nzA6mgqpRKAYRgjBq6PakFNYwrtrVB8ii/nWhX7Pad1Jo1ff8MscS8xk4c44xndrTKfGFbYPU6pi1zy4FA0j5oKbl9HRVEglAsVQLUJ8mXBTU77fd5795y4bHY7t6z5d62u/coY2VbGKTCbJS0uPUtvLjZlD1V4DFrt8DrbM0VaBtxhqdDRXpRKBYrinh7Sgbi0PXvj1CMVq4NgyLm5wy/uQeR62vFPlp/8QFc+B8xn8c0Rr/LxcqyFAB7NqptZVdJj1bDFaEZUIFMP5uLvw6qg2nLyYrVYc66FpL22nq53z4OLRSj8tLaeQ2atO0i00gNs7X7Xtl1JZJ1fAqVXaoj8rHCAuTyUCxSoMbVOXwREhfLD+FPHpNdda2W4Nfh08a8PvT1Z6A5t/rTpJbmEJb45pizCwN75dKMrVqoE6EdDjEaOjuS6VCBSr8dqoNjgJwUu/HVWtqi3lFQBD34aEfbD/6+sevvtMGj/tT2BKnzC1/aQeNs+GzHgY+R44W/8lNpUIFKtR39+TZ4a0ZHN0qmpVrYf2d0FoP1j/mrbq+Cryi0qZ9fNhGgd48cTA5jUYoJ26sF+bKdT5AWhyk9HRVIpKBIpVmXBTE9o2qMWry4+RmVdsdDi2TQht4LikQLtMcRXvrz/F2bQ8Zt/RDi83tYLYIiVF8Nvj4BOibR5kI1QiUKyKi7MTs29vT3puEa/9fszocGxfYDPoPxOOL4VjS//28KH4DL7Ydobx3RrRs5lqMW2x7e9DyjEtAVtJi+nKUIlAsTptG/jxSP9m/HLgAhtOJBsdju3r+Q+o1xFWPAO5l/68u6jExMyfDxPs687zqsW05VJOwNZ3oe1YaDnc6GiqRCUCxSo9NqA5LUN8ef6XI+oSkaWcXWDMp1CYBSuf/fPuTzef5uTFbN4a045aHtY/oGnVTKXw22PgUQuGV339htFUIlCskruLM3Pv7ECaukSkj5AI6DdTa3x2bClHL2Qyb1MMozrUZ1BEiNHR2b6d/4ELUVoXWG/bu8SmEoFitdo19OPhfuoSkW56PQn1OiJXPMOr322htpcbr49uY3RUti/pMGx8EyJGQ9s7jI7mhqhEoFi1xwdql4hm/XKEtJxCo8OxbeZLRKX5mUzN/JB3x7bH38vN6KhsW3EB/DINvALhlg+0mVo2SCUCxaq5uzjzwbiOZOYVM/Pnw2qhmYV2ZNdhdtFdDHWOol/OSqPDsX0b34DUEzD6Y20Rn42yKBEIIQKEEOuEEDHm73/rWSuEuFkIcbDcV4EQYoz5sYVCiLhyj3W0JB7FPrWuV4uZw1ux/kQK/92jNrG5UZn5xTz74yE2+Y+ltGk/bd+CSzFGh2W7zmzRFo51nQLhg4yOxiKWVgSzgA1SynBgg/n2X0gpN0kpO0opOwIDgDxgbblDZpQ9LqU8aGE8ip2a1LMpfVsE8+bvx4lNyTY6HJsjpeTFpUdJyS7kvXGdcb79c20Tm58na4uglKrJS4elj0Bgc62vk42zNBGMBhaZf14EjLnO8WOBVVJK1VVMqRInJ8Hcse3xdnfh8e8OUlhSanRINuW7vfEsP5TI04Nb0KGRP9SqB6PnQdIh2GQ7K2CtgpSw9GHISYbb54Obt9ERWczSRBAipUwCMH+vc53jxwHfXXHfW0KIw0KI94UQ7hbGo9ixOrU8eHdse04kZfGvlSeNDsdmHE/M4tXlx+gTHsTD/Zr9/wOtRkKXSbDjI4hdb1yAtmbXPDi1Wmsh0aCL0dHo4rqJQAixXghxtIKv0VU5kRCiHtAOWFPu7ueBVkBXIAC4akMUIcQ0IUSUECIqNTW1KqdW7MjA1iE82CuUhTvPsvxQotHhWL2cwhIe/d8Banu58v7dHf++//DQtyGkDfw8BTLU+Mt1xe+D9a9C61uh+0NGR6Ob6yYCKeUgKWXbCr5+A5LNb/Blb/Qp13ipu4BfpZR/LhOVUiZJTSHwNdDtGnHMl1JGSikjg4ODK/v7KXbo+RGtiGxSm5k/H1bjBdcgpeSfvxzhXFouH43rRJBPBQW3mxfc9Y22MnbJBChRU3SvKi8dfpoEtRrAqHk2O1W0IpZeGloGTDD/PAH47RrHjueKy0LlkohAG1+o/HZKisNydXZi3j2d8XJz5qHF+8kpLDE6JKv0393nWHYokWeGtKR7WODVDwxsBmM+gcQDsOafNRegLTGVwq/TtXbedy4ET3+jI9KVpYlgNjBYCBEDDDbfRggRKYT4ouwgIURToBGw5YrnfyuEOAIcAYIANWqlVEpdPw8+Gt+JuEu5an1BBXafSeO15ccZ0KrOX8cFrqb1rdDzCdj3BRz6ofoDtDWb3oKYNTB8NjTobHQ0urOo+biUMg0YWMH9UcCUcrfPAn/bBFVKOcCS8yuOrWezIGYMbcU7q0/Str4fD/evxBueA0i4nMcj3x6gcaAXH4yrYFzgaga+om2qsvwfEBRul294N+Toz7Dt39BlIkRONjqaaqFWFis2bXq/MG7tUJ85a06y+qja1Sy/qJRp3+ynuNTEggciq9ZV1NkF7lwEPsHw3XjIvFB9gdqKpEOw9FFo1AOGv2tX4wLlqUSg2DQhBO+ObU+Hhv489cNBjl7INDokw5SaJE8vOciJi1l8NL4TzYJ9qv4iPsEw/gdt8/XvxmnfHVVWEnx3j9Y64u7F4GK/fZlUIlBsnoerM/Mf6EJtL1emLIoiOavA6JBqnJSSN34/zqqjF3lxZAQ3t7zekp5rCImAsV9B8lGtoZrJARfvFWTBt3dCQQaM/w58LPj3tAEqESh2oY6vB19O7Ep2QTETvtrrcJvZfLk9joU7zzK5dyiTe4da/oIthsDQf8HJ32HVc9pqWkdRUgRL7teayd31DdTrYHRE1U4lAsVutK5Xi8/vj+RMai6TF+0jv8gxPskuP5TImytOMLJdPV7Qc8vJHtOh1z+0mUSb/6Xf61ozkwmWPQ5nNsOtH0Hzv82FsUsqESh2pXd4EB+M68j+85d55Ftt0NSerT+ezFM/HKRr09r8+64OlZ8hVFmDXoNO98GWd2DP5/q+trWRUqt+Dn8PN78Ane41OqIaoxKBYndGtKvHW2PasSk6lSd/OEiJnSaDzdEpPPLtAdrUr8WXE7vi4eqs/0mEgFs+hFa3aG+SBxbrfw5rICWsfRH2LYCbHoO+M4yOqEZZtI5AUazVPd0bk11QzL9WncRkknw0vhOuzvbzuWdH7CUeWryf8BAfvnmwe/VuPu/sAnd8Cd+Ph2WPgakEIidV3/lqmpSw4XWtmVy3h7RmcnY6TfRq7Of/DEW5wkP9mvHiyNasOnqRR789QFGJfVQGm06mMHnRPkKDvFk8uTt+XtWYBMq4esC476D5YPj9SW3c5e9yZQAACdVJREFUwB5ICetegu3vaZ1Yh7/jcEkAVCJQ7NyUPmG8emsEa48n89DiKPKKbLsv0dI/LjD1myjC6/jy7ZTuBHjX4Nx2Vw8Y9y20GA4rntHaV9vybKLSEvjtUdj5H+g6FUa+55BJAFQiUBzAxF6hvH1bO7acSuXuz3eTkm2b6wwW7ojjyR8O0rVpAP+b2p3AirqJVjcXd21KZcQY7ZP0qudsc51Bcb42RfTgt9D/eRjxLjg57tuh4/7mikO5p3tjFjwQSWxKDrd9vNOm2leXlJp4bfkxXl1+nCERIXw9qSu+1TkmcD0ubjD2a+j5OOydDz/cZ1srkLOSYOEtEL0KRsyF/rMcthIooxKB4jAGtg7hh4d6UFhi4rZPdrLueLLRIV1XRl4RE7/ex9c7zvJgr1A+ubdz9cwOqionJ21QdcRcbbeur4ZCepzRUV3fhf2w4GZIOaG1jeg21eiIrIJKBIpDad/Qn6WP9qRJoBdTv4nindUnrXZ66YHzl7l13nb2xqUzZ2x7Xr41Ahdrm/nUbSrcswQy4uHzfnBypdERVUxK2LsAvhoOzq4wZZ3WelsBVCJQHFDD2l78NL0n47s15tPNpxk3fzdnL1nPpY1Sk+TjTbHc+dkuTCb4/qEe3BXZyOiwri58MDy0BQKaalNMVz5nXZeKctPg+3tg5bMQ2hembta251T+JGxxQ4/IyEgZFRVldBiKHVj6xwVe+u0oJaWSWcNbcX+PJvqvzq2C6IvZPP/LYQ6cz+CW9vV467Z2+HkaOB5QFcUFsP4V2PMZBITBmE+hcQ/j4pESjv0Cq2ZB/mUY/Dp0n+7Qg8JCiP1Sysi/3a8SgeLokjLzmfnzEbaeSqVDQz9evrUNXZrUrtEYcgpL+GRTLPO3nsHXw4WXb41gTMcGCFscxIzbBr89AhnnoeO9MPBl8K1bszGkndZmNMWuh3odYdR/oF77mo3BCqlEoCjXIKXk1z8u8M7qkyRnFTKqQ32eGNic5nV8q/W8hSWlfLv7PB9viiUtt4g7OjfkhZGta3Z9QHUozIatc2H3J+Dspm2D2X0aeFZzgs1Kgq1z4P/au//Yqso7juPvj7d0lgqWUi0VEOkkpDKjRVbHurHFMWWbcVvispmMbEsm/6jRGOPmZtT9t7+W7S8mAaduDMIUNjLJHHPgZgzjRxUrP0aAFXut2EqpUBjFS7/+cR6ShtyWyj23p+fe7ytpes/pzTnfJze933Oe8zzfp+15qLgcbns8miOQ8SIK4InAuVE5NZBj+dZDrHrtf5zJneOOG6Zx76LZzL92SqxX58f6B1iz/R1+v+0I758YYGHjVB5dMpfma8f2TqTojh2CzU9E5awrJ0HLj6PlHmtifuZx9G3Y/jS8tS6a13DLD6N6QZPq4z1PyhUlEUj6DvAU0AS0hLWK871vCfAbIAOsNLPzi9zPBtYCtUAbsNTMzl7svJ4IXLEd6x/g2dc7ePb1Dk6eyfHpq6q5+5aZ3D6vnsa66ktKCqfP5tiyv4dN7e+xed/7nM0N8sU5dSxb1MgXrq9LZzfQaB1tj9b93fPnaLvxS3DTPVHJiuqpl3bME12wdyPs2QCd26CiCm76LrQ+BLUxrMlQgoqVCJqAQeBp4JF8iUBSBjgAfBXIAjuAe8xsr6R1wHozWyvpt8BuM1t+sfN6InBjpX8gx0tvdfGnnVl2HjkOwPSaKlqvn8oNDZOZO20yM2urmDKxkomVGSRxbtA4eeYjOnv/T+fx07S/+yG7Oo6zO9vHQG6Quisq+fqNDSz93Czm1Be362ncOX4Edq+BN1bDh+8AgmuaYdbno5E8VzfBpAaoqo0mrpnBuY/gVA/0HYHew5DdAZ3boXtvdMyr50UJoHlptKykG1ZRu4YkbWX4RLAQeMrM7gjbj4U//RLoAaaZWe7C943EE4FLQmfvaV490MOrB3rY0dFL3wWroE3IiEGLhn8OVXGZmDf9ShbMmsLipnpaZteSSXBk0rgwOAhdbXDwFTj0SrRIfO6C0h+ZyigJcMF31KeuhJmfjZJH011QN2fMwk674RLBWDxBmQ50DtnOArcCU4E+M8sN2T99uINIWgYsC5sDkt4uQqxJqgM+SDqImJVim+AS2nUI2Ag8UZRwYpGiz+oE0VfKeuCRkd6YojZ9IoW0a1a+nRdNBJL+AeQb+/VzM/vLKE6c79LHRtifl5mtAFaEmHbmy2pp5m1Kj1Jsl7cpPYrRrosmAjNbXOA5ssDQIQIzgC6ijFYjqSLcFZzf75xzbgyNxRS7HcAcSbMlVQLfAzZa9HBiC3B3eN8PgNHcYTjnnItRQYlA0rclZYGFwEuSXg77r5G0CSBc7d8PvAzsA9aZ2Z5wiJ8AD0s6SPTMYNUoT72ikLjHKW9TepRiu7xN6RF7u1I5ocw551x8yrf6knPOOcATgXPOlb1UJQJJSyT9V9JBST9NOp44SHpGUncpzYuQNFPSFkn7JO2R9GDSMRVK0uWStkvaHdr0i6RjioukjKQ3JP016VjiIqlDUrukNyWVxOxTSTWSXpC0P/xvLYzt2Gl5RjBSqYpEAyuQpEVAP/C8mX0m6XjiIKkBaDCzNkmTgF3At9L8WSkqBFRtZv2SJgCvAQ+a2baEQyuYpIeBBcBkM7sz6XjiIKkDWGBmJTOhTNJzwL/NbGUYgTnRzPriOHaa7ghagINmdjgUplsLfDPhmApmZv8CepOOI05m9p6ZtYXXJ4lGiw07azwNLNIfNieEn3RcRY1A0gzgG8DKpGNxw5M0GVhEGFlpZmfjSgKQrkSQr1RFqr9cyoGk64Bm4D/JRlK40IXyJtANbDaz1LcJ+DXwKFHxyFJiwN8l7QrladKukag22+9CN95KSdVxHTxNieATlaRwyZN0BfAi8JCZnUg6nkKZ2Tkzu5loFnyLpFR35Um6E+g2s11Jx1IErWY2H/gacF/ogk2zCmA+sNzMmoFTQGzPSdOUCIYrVeHGodCP/iKw2szWJx1PnMIt+VZgScKhFKoVuCv0p68FbpP0h2RDioeZdYXf3cAGoq7lNMsC2SF3oS8QJYZYpCkR5C1VkXBMLo/wYHUVsM/MfpV0PHGQdJWkmvC6ClgM7E82qsKY2WNmNsPMriP6f/qnmX0/4bAKJqk6DFIgdJ/cDqR6VJ6ZHQU6Jc0Nu74CxDb4IjULeYY1C86XqsgAzwwpVZFaktYAXwbqQrmOJ81stKU2xqtWYCnQHvrUAX5mZpsSjKlQDcBzYfTaZUSlUkpmuGWJqQc2hBXfKoA/mtnfkg0pFg8Aq8OF8GHgR3EdODXDR51zzhVHmrqGnHPOFYEnAuecK3OeCJxzrsx5InDOuTLnicA558qcJwLnnCtzngicc67MfQyA7doBoQFMBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mb = master_bar(range(10))\n",
    "mb.names = ['cos', 'sin']\n",
    "for i in mb:\n",
    "    for j in progress_bar(range(100), parent=mb):\n",
    "        if j%10 == 0:\n",
    "            k = 100 * i + j\n",
    "            x = np.arange(0, 2*k*np.pi/1000, 0.01)\n",
    "            y1, y2 = np.cos(x), np.sin(x)\n",
    "            graphs = [[x,y1], [x,y2]]\n",
    "            x_bounds = [0, 2*np.pi]\n",
    "            y_bounds = [-1,1]\n",
    "            mb.update_graph(graphs, x_bounds, y_bounds)\n",
    "            mb.child.comment = f'second bar stat'\n",
    "    mb.main_bar.comment = f'first bar stat'\n",
    "    mb.write(f'Finished loop {i}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Console rendering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastprogress.fastprogress import ConsoleMasterBar, ConsoleProgressBar "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished loop 0.                                                                                                \n",
      "Finished loop 1.                                                                                                \n",
      "Finished loop 2.                                                                                                \n",
      "Finished loop 3.                                                                                                \n",
      "Finished loop 4.                                                                                                \n",
      "Finished loop 5.                                                                                                \n",
      "Finished loop 6.                                                                                                \n",
      "Finished loop 7.                                                                                                \n",
      "Finished loop 8.                                                                                                \n",
      "Finished loop 9.                                                                                                \n"
     ]
    }
   ],
   "source": [
    "mb = ConsoleMasterBar(range(10))\n",
    "for i in mb:\n",
    "    for j in ConsoleProgressBar(range(100), parent=mb):\n",
    "        time.sleep(0.01)\n",
    "        mb.child.comment = f'second bar stat'\n",
    "    mb.write(f'Finished loop {i}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can force the console rendering without changing your code by using the following command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastprogress import force_console_behavior\n",
    "master_bar,progress_bar = force_console_behavior()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished loop 0.                                                                                                \n",
      "Finished loop 1.                                                                                                \n",
      "Finished loop 2.                                                                                                \n",
      "Finished loop 3.                                                                                                \n",
      "Finished loop 4.                                                                                                \n",
      "Finished loop 5.                                                                                                \n",
      "Finished loop 6.                                                                                                \n",
      "Finished loop 7.                                                                                                \n",
      "Finished loop 8.                                                                                                \n",
      "Finished loop 9.                                                                                                \n"
     ]
    }
   ],
   "source": [
    "mb = master_bar(range(10))\n",
    "for i in mb:\n",
    "    for j in progress_bar(range(100), parent=mb):\n",
    "        time.sleep(0.01)\n",
    "        mb.child.comment = f'second bar stat'\n",
    "    mb.main_bar.comment = f'first bar stat'\n",
    "    mb.write(f'Finished loop {i}.')\n",
    "    #mb.update_graph(graphs, x_bounds, y_bounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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