demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dit is een demonstratie van enkele van de mogelijkheden van Jupyter Notebooks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactie: widgets\n",
    "\n",
    "Het is soms omslachtig om de programmatekst te veranderen voor het uitproberen van de effecten van verschillende waarden voor een parameter. Met behulp van Widgets kun je dit soort experimenten interactief maken.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enabling the `widget` backend.\n",
    "# This requires jupyter-matplotlib a.k.a. ipympl.\n",
    "# ipympl can be install via pip or conda.\n",
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "825644e3491e48e7946287003062f342",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntSlider(value=0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "w = widgets.IntSlider()\n",
    "display(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cfa123e55464417da98b88267a2c3986",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output(layout=Layout(border='1px solid black'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "out = widgets.Output(layout={'border': '1px solid black'})\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "with out:\n",
    "    for i in range(10):\n",
    "        print(i, 'Hello world!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "with out: print(\"Hi there\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NB: we kunnen dit ook gebruiken om in verschillende stappen de uitvoer van bijv. een Turtle gfx programma op te bouwen?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def f(m, b): # with the interactive widget parameters\n",
    "    # plt.figure(2)\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    plt.ylim(-10, 11)\n",
    "    plt.xlim(-10, 11)\n",
    "    plt.axis(\"equal\") # for square grid (correct angles)\n",
    "    plt.xlabel(\"x\")\n",
    "    plt.ylabel(\"y\")\n",
    "    \n",
    "    # Major ticks every 2 minor ticks every 1\n",
    "    major_ticks = np.arange(-10, 11, 2)\n",
    "    minor_ticks = np.arange(-10, 11, 1)\n",
    "\n",
    "    ax.set_xticks(major_ticks)\n",
    "    ax.set_xticks(minor_ticks, minor=True)\n",
    "    ax.set_yticks(major_ticks)\n",
    "    ax.set_yticks(minor_ticks, minor=True)\n",
    "\n",
    "    # And a corresponding grid\n",
    "    plt.grid(which='minor', color=\"b\", alpha=0.2)\n",
    "    plt.grid(which='major', color='b', alpha=0.6)\n",
    "    \n",
    "    x = np.linspace(-10, 11, num=1000)\n",
    "    plt.plot(x, m * x + b) # the function...\n",
    "    plt.suptitle(\"linear function: y=m*x + b\")\n",
    "\n",
    "    # plt.grid(True)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1c236d3e4454f6ba12945e1b31c6ee8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.0, description='m', max=2.0, min=-2.0), FloatSlider(value=0.0, descr…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interactive_plot = widgets.interactive(f, m=(-2.0, 2.0), b=(-3, 3, 0.5))\n",
    "interactive_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-6, -5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5,  6])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(-6, 7, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-6, -4, -2,  0,  2,  4,  6])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(-6, 7, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enabling the `widget` backend.\n",
    "# This requires jupyter-matplotlib a.k.a. ipympl.\n",
    "# ipympl can be install via pip or conda.\n",
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Testing matplotlib interactions with a simple plot\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot(np.sin(np.linspace(0, 20, 100)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-b8a25ff5f983>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmpl_toolkits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmplot3d\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maxes3d\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m111\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprojection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'3d'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import axes3d\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# Grab some test data.\n",
    "X, Y, Z = axes3d.get_test_data(0.05)\n",
    "\n",
    "# Plot a basic wireframe.\n",
    "ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)\n",
    "\n",
    "fig.canvas.layout.max_width = '800px'\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipycanvas import Canvas\n",
    "\n",
    "canvas = Canvas(size=(200, 200))\n",
    "\n",
    "canvas.fill_rect(25, 25, 100, 100)\n",
    "canvas.clear_rect(45, 45, 60, 60)\n",
    "canvas.stroke_rect(50, 50, 50, 50)\n",
    "\n",
    "display(canvas)\n",
    "3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "canvas.clear()\n",
    "display(canvas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geïnstalleerde extensies\n",
    "\n",
    "Welke extensies hebben we geïnstalleerd (met conda)?\n",
    "\n",
    "* https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20Basics.html\n",
    "    * Jupyter widgets\n",
    "* Jupyter nb_conda\n",
    "    * om in een notebook met conda-omgevingen te werken (nog verder uitzoeken)\n",
    "* matplotlib\n",
    "* widget-versie van matplotlib\n",
    "* mongodb interface? pymongo\n",
    "* ipysheets?\n",
    "* nbgrader\n",
    "* spreadsheat-extensie: ipysheet\n",
    "* rise (voor slides; werkt alleen met notebook, niet met lab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e4d7287bc9ca4f1b90844751b926a354",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=9, column_start=0, row_end=5, row_start=0, squeeze_column=False, squeeze_row=Fals…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from ipysheet import from_array, to_array\n",
    "\n",
    "arr = np.random.randn(6, 10)\n",
    "\n",
    "sheet = from_array(arr)\n",
    "sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5359da413e0048348294cc699136d23c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, numeric_format=None, row_end=2, row_start=0, squeeze_row=False…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr = np.array([True, False, True])\n",
    "\n",
    "sheet = from_array(arr)\n",
    "sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True],\n",
       "       [False],\n",
       "       [ True]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_array(sheet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c2b0ec2ef1d548e5b05ea168e25f8b6b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='', description='Search:'), Sheet(cells=(Cell(choice=[], column_end=0, column_start=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from ipysheet import from_dataframe\n",
    "from ipywidgets import Text, VBox, link\n",
    "\n",
    "df = pd.DataFrame({'A': 1.,\n",
    "                   'B': pd.Timestamp('20130102'),\n",
    "                   'C': pd.Series(1, index=list(range(4)), dtype='float32'),\n",
    "                   'D': np.array([False, True, False, False], dtype='bool'),\n",
    "                   'E': pd.Categorical([\"test\", \"train\", \"test\", \"train\"]),\n",
    "                   'F': 'foo'})\n",
    "\n",
    "df.loc[[0, 2], ['B']] = np.nan\n",
    "\n",
    "\n",
    "s = from_dataframe(df)\n",
    "\n",
    "search_box = Text(description='Search:')\n",
    "link((search_box, 'value'), (s, 'search_token'))\n",
    "\n",
    "VBox((search_box, s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "520f23c0b1034ae38f1715d5a6b123a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatSlider(value=0.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import FloatSlider, IntSlider, Image\n",
    "import ipysheet\n",
    "\n",
    "slider = FloatSlider()\n",
    "slider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "882e7aa25c7f4e6082f00850d5e5a943",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, row_end=0, row_start=0, style={'min-width': '150px'}, type='wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sheet = ipysheet.sheet()\n",
    "\n",
    "cell1 = ipysheet.cell(0, 0, slider, style={'min-width': '150px'})\n",
    "cell3 = ipysheet.cell(2, 2, 42.)\n",
    "cell_sum = ipysheet.cell(3, 2, 42.)\n",
    "\n",
    "@ipysheet.calculation(inputs=[(cell1, 'value'), cell3], output=cell_sum)\n",
    "def calculate(a, b):\n",
    "    return a + b\n",
    "\n",
    "sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c0c22d00a3ce4c38b61f11972a7ca199",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntSlider(value=0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slider2 = IntSlider()\n",
    "cell1.value = slider2\n",
    "slider2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "575b142c580c4e07afbab67ab6756691",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, row_end=4, row_start=0, squeeze_row=False, type='widget', valu…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sheet = ipysheet.sheet()\n",
    "\n",
    "column1 = ipysheet.column(0, [FloatSlider() for _ in range(5)])\n",
    "column2 = ipysheet.column(1, [1.] * 5)\n",
    "\n",
    "sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell = ipysheet.cell(3, 4)\n",
    "cell.value = \"Hi\"\n",
    "cell00 = ipysheet.cell(0,0)\n",
    "cell00.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "575b142c580c4e07afbab67ab6756691",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, row_end=4, row_start=0, squeeze_row=False, type='widget', valu…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "column1.value = column1.value[:-1] + [IntSlider()]\n",
    "sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>FloatSlider(value=0.0)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>FloatSlider(value=0.0)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>FloatSlider(value=0.0)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Hi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>IntSlider(value=0)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        A    B     C     D     E\n",
       "0                       0  1.0  None  None  None\n",
       "1  FloatSlider(value=0.0)  1.0  None  None  None\n",
       "2  FloatSlider(value=0.0)  1.0  None  None  None\n",
       "3  FloatSlider(value=0.0)  1.0  None  None    Hi\n",
       "4      IntSlider(value=0)  1.0  None  None  None"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipysheet.to_dataframe(sheet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: './image.png'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-245417b52464>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0msheet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mipysheet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msheet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mcolumn1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mipysheet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./image.png'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'min-width'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'60px'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'min-height'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'50px'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mcolumn2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mipysheet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumn\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;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-32-245417b52464>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0msheet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mipysheet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msheet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mcolumn1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mipysheet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./image.png'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'min-width'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'60px'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'min-height'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'50px'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mcolumn2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mipysheet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumn\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;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/eelco/anaconda3/lib/python3.7/site-packages/ipywidgets/widgets/widget_media.py\u001b[0m in \u001b[0;36mfrom_file\u001b[0;34m(cls, filename, **kwargs)\u001b[0m\n\u001b[1;32m    161\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfrom_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\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;32m--> 163\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'image'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\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;32m/home/eelco/anaconda3/lib/python3.7/site-packages/ipywidgets/widgets/widget_media.py\u001b[0m in \u001b[0;36m_from_file\u001b[0;34m(cls, tag, filename, **kwargs)\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0mReturns\u001b[0m \u001b[0man\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mMedia\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0mset\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \"\"\"\n\u001b[0;32m---> 44\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load_file_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m'format'\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/eelco/anaconda3/lib/python3.7/site-packages/ipywidgets/widgets/widget_media.py\u001b[0m in \u001b[0;36m_load_file_value\u001b[0;34m(cls, filename)\u001b[0m\n\u001b[1;32m     93\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m             \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './image.png'"
     ]
    }
   ],
   "source": [
    "sheet = ipysheet.sheet()\n",
    "\n",
    "column1 = ipysheet.column(0, [Image.from_file('./image.png') for _ in range(5)], style={'min-width': '60px', 'min-height': '50px'})\n",
    "column2 = ipysheet.column(1, [1.] * 5)\n",
    "\n",
    "sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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