{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This file is part of the [test suite](./tests) and will be moved there when [nbval#116](https://github.com/computationalmodelling/nbval/issues/116#issuecomment-793148404) is fixed.\n",
    "\n",
    "See [DEMO.ipynb](DEMO.ipynb) instead for notebook examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "\n",
    "from tqdm.notebook import tqdm_notebook\n",
    "from tqdm.notebook import tnrange\n",
    "\n",
    "# avoid displaying widgets by default (pollutes output cells)\n",
    "tqdm = partial(tqdm_notebook, display=False)\n",
    "trange = partial(tnrange, display=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function display in module tqdm.notebook:\n",
      "\n",
      "display(self, msg=None, pos=None, close=False, bar_style=None)\n",
      "    Use `self.sp` to display `msg` in the specified `pos`.\n",
      "    \n",
      "    Consider overloading this function when inheriting to use e.g.:\n",
      "    `self.some_frontend(**self.format_dict)` instead of `self.sp`.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    msg  : str, optional. What to display (default: `repr(self)`).\n",
      "    pos  : int, optional. Position to `moveto`\n",
      "      (default: `abs(self.pos)`).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(tqdm_notebook.display)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# basic use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c18c038bf964b55941e228503292506",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e29668be41ca4e40b16fb98572b333a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with tqdm_notebook(range(10)) as t:\n",
    "    for i in t:\n",
    "        print(i)\n",
    "\n",
    "t = tqdm_notebook(total=10)\n",
    "t.update()\n",
    "t.refresh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# reset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 10%|█         | 1/10 [00:00<00:00, 30.04it/s]\n",
      " 20%|██        | 1/5 [00:00<00:00, 495.37it/s]\n"
     ]
    }
   ],
   "source": [
    "print(t)\n",
    "t.reset(total=5)\n",
    "t.update(1)\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# repr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/9 [00:00<?, ?it/s]\n",
      "  0%|          | 0/9 [00:00<?, ?it/s]\n",
      "1\n",
      "  0%|          | 0/9 [00:00<?, ?it/s]\n",
      "  0%|          | 0/9 [00:00<?, ?it/s]\n"
     ]
    }
   ],
   "source": [
    "with trange(1, 10) as pbar:\n",
    "    print(pbar)\n",
    "    print(pbar.container)\n",
    "    it = iter(pbar)\n",
    "    print(next(it))\n",
    "    print(pbar)\n",
    "    print(pbar.container)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = trange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/10 [00:00<?, ?it/s]\n",
      "  0%|          | 0/10 [00:00<?, ?it/s]\n"
     ]
    }
   ],
   "source": [
    "print(t)\n",
    "print(t.container)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in t:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:00<00:00, 169.05it/s]\n",
      "100%|##########| 10/10 [00:00<00:00, 168.12it/s]\n"
     ]
    }
   ],
   "source": [
    "print(t)\n",
    "print(t.container)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# no total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no total: 0it [00:00, ?it/s]\n",
      "no total: 1it [00:00, 33.56it/s]\n"
     ]
    }
   ],
   "source": [
    "with tqdm(desc=\"no total\") as t:\n",
    "    print(t)\n",
    "    t.update()\n",
    "    print(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ncols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0%|                                      | 0/10 [00:00<?, ?it/s]\n",
      " 10%|███                           | 1/10 [00:00<00:00, 32.29it/s]\n"
     ]
    }
   ],
   "source": [
    "with trange(10, ncols=66) as t:\n",
    "    print(t)\n",
    "    for i in t:\n",
    "        if i == 1:\n",
    "            break\n",
    "    print(t)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:tqdm]",
   "language": "python",
   "name": "conda-env-tqdm-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython"
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}