Display a blank page after opening a ipynb file

[Photonico]

Hello,

If I open a modified ipynb file, the editor displays a blank page.
If I use >Python: Import Jupyter Notebook, it will be imported in the cell mode.
OS: Linux Python:3.7
how to fix/set it to normal jupyter notebook?

[jmew]

Thanks for the issue. Could you attach to this issue the notebook that is causing you to see the blank page to see if we can reproduce it?

[Photonico]

Thanks for the issue. Could you attach to this issue the notebook that is causing you to see the blank page to see if we can reproduce it?

My file:

{
 "nbformat": 4,
 "nbformat_minor": 2,
 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "version": "3.7.6-final"
  },
  "orig_nbformat": 2,
  "file_extension": ".py",
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3,
  "kernelspec": {
   "name": "python37664bitadd2d29322fc4c69b2fa8d1ba2d3991d",
   "display_name": "Python 3.7.6 64-bit"
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Dynamical Systems in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Linear Dynamical System\n",
    "Let's simulate a time-invariant linear dynamic system  \n",
    "$x_{t+1}=Ax_t, t=1,...,T$  \n",
    "with dynamics matrix  \n",
    "$A= \\begin{bmatrix}\n",
    "    0.97 &  0.10 & -0.05 \\\\\n",
    "   -0.30 &  0.99 &  0.05 \\\\\n",
    "    0.01 & -0.04 &  0.96\n",
    "    \\end{bmatrix}$  \n",
    "and initial state $x_1=(1,0,-1)$. We store the state trajectory in the $n \\times T$ matrix `state_traj`, with the $i$th column $x_t$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7f5492314150>]"
     },
     "metadata": {},
     "execution_count": 63
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x1 = np.matrix([ 1, 0, -1])     # Initinal state\n",
    "n  = x1.size\n",
    "T  = 50 \n",
    "A  = np.matrix([[ 0.97, 0.10,-0.05],\n",
    "                [-0.30, 0.99, 0.05],\n",
    "                [ 0.04,-0.04, 0.96]])\n",
    "\n",
    "state_traj = np.hstack((np.transpose(x1), np.zeros((n,T-1))))\n",
    "\n",
    "for t in np.arange(1,T):\n",
    "    state_traj[:,t] = np.dot(A,state_traj[:,t-1])\n",
    "\n",
    "print()\n",
    "\n",
    "plt.plot(np.arange(0,T), np.transpose(state_traj)[:,0], label = \"xt_1\",)\n",
    "plt.plot(np.arange(0,T), np.transpose(state_traj)[:,1], label = \"xt_2\",)\n",
    "plt.plot(np.arange(0,T), np.transpose(state_traj)[:,2], label = \"xt_3\",)"
   ]
  }
 ]
}

I found that there is a probability that the context is displayed.
It needs to reload the VSCode window some times.

[jmew]

I was able to reproduce with this notebook, but only on the first launch of VS Code. Are you on the insiders version of the extension by the way?

[Photonico]

I was able to reproduce with this notebook, but only on the first launch of VS Code. Are you on the insiders version of the extension by the way?

My VSCode version is 1.43.1
My Python version is 2020.3.71659

[DavidKutu]

hey @jmew, where you able to reproduce this on Windows or on Linux?

[jmew]

@DavidKutu sorry for the late reply, it was able to repro this on Windows

[hromanoc]

I have the same problem: microsoft/vscode-python#11225. My case was closed, all my screenshot are there (#11225)

[ghost]

I also have the same problem for at least 30 days. In fact, I feel it's getting worse but it's not consistent or measurable.

My VSCode version is 1.44.2
My Python version is 2020.4.74986

[oleggoryunov]

I have the same problem and in fact it started to happen too often so it became impossible to work.

Sadly I am forced to shift away from VSC+this plugin back to original JupyterLab until this issue is resolved.

My bug is closed as a duplicate, but for reference here is its number https://github.com/microsoft/vscode-python/issues/11379

[lepennec]

I have the same issue with Linux (vscode 1.44.2 + vscode-python 2020.4.76186).
Sometimes when I open a notebook (often the first notebook I open in the session), I have a blank page. If I close the file and reopen it, I see the notebook everytime.

[greazer]

This is likely a dupe of #1062, which is currently being tracked as VS Code bug: microsoft/vscode#98335.