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jupyter-notebookwolfram-mathematica

How to convert output cells to text within Jupyter?


The screenshot shown is a Jupyter notebook running a Wolfram Engine kernel. There are two problems: a) The output cells are images, preventing copying to the clipboard. b) Some expressions within them appear hidden. For example, in cell [9], the condition is enclosed in a box with a "+" button, but it is not actionable. Is there a way to retrieve the full output in text format? The only workaround I've found is to peek in the source file.

enter image description here

debug.ipynb:

    {
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "48f1dc25-9bc7-4a33-a9dd-b47480856414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><img alt=\"Output\" src=\"data:image/png;base64,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\"></div>"
      ],
      "text/plain": [
       "b + a x\n",
       "-------\n",
       "d + c x"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f[x]=(a*x+b)/(c*x+d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8ce9a0e1-f0ca-413f-8c9c-b2186e3bee40",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><img alt=\"Output\" src=\"data:image/png;base64,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\"></div>"
      ],
      "text/plain": [
       "                                           2                    2\n",
       "                                          a  + 4 b c - 2 a d + d\n",
       "                                     Sqrt[-----------------------]\n",
       "                                                     2\n",
       "                             a - d                  c\n",
       "{{x -> ConditionalExpression[----- - -----------------------------, \n",
       "                              2 c                  2\n",
       " \n",
       "              2            2                     2            2\n",
       "            -a  + 2 a d - d                    -a  + 2 a d - d\n",
       ">      (b > ---------------- && c > 0) || (b < ---------------- && c < 0)]}, \n",
       "                  4 c                                4 c\n",
       " \n",
       "                                              2                    2\n",
       "                                             a  + 4 b c - 2 a d + d\n",
       "                                        Sqrt[-----------------------]\n",
       "                                                        2\n",
       "                                a - d                  c\n",
       ">   {x -> ConditionalExpression[----- + -----------------------------, \n",
       "                                 2 c                  2\n",
       " \n",
       "              2            2                     2            2\n",
       "            -a  + 2 a d - d                    -a  + 2 a d - d\n",
       ">      (b > ---------------- && c > 0) || (b < ---------------- && c < 0)]}}\n",
       "                  4 c                                4 c"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "text/html": [],
      "text/plain": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sol=Solve[f[x]==x,x,Reals]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4166ccbd-3e65-4817-babc-0fd220712ba7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Wolfram Language 14",
   "language": "Wolfram Language",
   "name": "wolframlanguage14"
  },
  "language_info": {
   "codemirror_mode": "mathematica",
   "file_extension": ".m",
   "mimetype": "application/vnd.wolfram.m",
   "name": "Wolfram Language",
   "pygments_lexer": "mathematica",
   "version": "12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

terminal:

$ jupyter --version
Selected Jupyter core packages...
IPython          : 8.22.2
ipykernel        : 6.29.4
ipywidgets       : 8.1.2
jupyter_client   : 8.6.1
jupyter_core     : 5.7.2
jupyter_server   : 2.13.0
jupyterlab       : 4.1.6
nbclient         : 0.10.0
nbconvert        : 7.16.3
nbformat         : 5.10.4
notebook         : 7.1.2
qtconsole        : not installed
traitlets        : 5.14.2
$ uname -a
Linux elitebook 6.8.4-arch1-1 #1 SMP PREEMPT_DYNAMIC Fri, 05 Apr 2024 00:14:23 +0000 x86_64 GNU/Linux

Also see:


Solution

  • To obtain copy-compatible output cells within Jupyter using the Wolfram Engine as the kernel, wrap expressions with either of ToString, OutputForm, or InputForm. This can be done globally using $PrePrint = InputForm.

    enter image description here

    Source: