How to convert output cells to text within Jupyter?

Question

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.

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:

How can I copy to the clipboard the output of a cell in a Jupyter notebook?

Erwann · Accepted Answer · 2024-06-16 01:29:28Z

0

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.

Source:

https://reference.wolfram.com/language/guide/MathematicalTypesetting.html

edited Jun 16, 2024 at 1:29

answered Jun 16, 2024 at 0:40

Erwann

1151 silver badge8 bronze badges

Sign up to request clarification or add additional context in comments.

Comments

Wayne · Accepted Answer · 2025-03-06 18:10:54Z

The post title says 'within Jupyter'; however, those ending up here looking to at least recover some text useful without running the notebook in Jupyter may be interested in recovering the text output of already-executed notebooks regardless of the kernel used.

You can use nbformat to parse output from any .ipynb file. It is one of the utility resources underlying Jupyter and so if you have Jupyter, you can can use it in Python code to parse notebooks or make new ones. It has many of the abstractions around notebooks already baked in and so is more useful than a general json parser for trying to parse the json in .ipynb.
(PLUS, THE WAY ILLUSTRATED BELOW GETS OUT ALL THE MATH FORMULA TEXT WITHOUT NEEDING TO COPY IT BY HAND. So the OP may want to combine what OP has worked out for in the notebook run with this to not have to copy the text of each cell by hand later.)

How to recover the example output as text:

import os
notebook_example = "debug.ipynb"
import nbformat as nbf
ntbk = nbf.read(notebook_example, nbf.NO_CONVERT)
text_collected = ""
for cell in ntbk.cells:
    if cell.cell_type == 'code':
        # parse out the `text/plain` part of any real output
        if cell.outputs:
            text_collected += cell.outputs[0]['data']['text/plain']+"\n"
            print(cell.outputs[0]['data']['text/plain'])
# Write collected text to a file
with open('suplied_nb_output_text.txt', 'w') as output_handler:
   output_handler.write(text_collected)

(If you notebook is more basic, you may need to replace the two lines below if cell.outputs: to the following:

text_collected += cell.outputs[0]['text']+"\n"
print(cell.outputs[0]['text'])

Use print(cell.outputs) on the line prior to consider your needs.)

Produces the following text from the provided debug.ipynb example code:

b + a x
-------
d + c x
                                           2                    2
                                          a  + 4 b c - 2 a d + d
                                     Sqrt[-----------------------]
                                                     2
                             a - d                  c
{{x -> ConditionalExpression[----- - -----------------------------, 
                              2 c                  2
 
              2            2                     2            2
            -a  + 2 a d - d                    -a  + 2 a d - d
>      (b > ---------------- && c > 0) || (b < ---------------- && c < 0)]}, 
                  4 c                                4 c
 
                                              2                    2
                                             a  + 4 b c - 2 a d + d
                                        Sqrt[-----------------------]
                                                        2
                                a - d                  c
>   {x -> ConditionalExpression[----- + -----------------------------, 
                                 2 c                  2
 
              2            2                     2            2
            -a  + 2 a d - d                    -a  + 2 a d - d
>      (b > ---------------- && c > 0) || (b < ---------------- && c < 0)]}}
                  4 c                                4 c

That code also saves the collected text and makes a text file with that as the content.
It isn't the best because it isn't in full formula form, i.e., a squared should be a^2. But it's on the way to having the text and since OP had answer, I stopped looking for the next step. Plus, as I mentioned parenthetically above, this could get those nice math formulas by combining with the approach posted by the OP so OP can collect all the formula text automatically after executing the notebooks. For non-Wolram, Python kernels, the formulas are based on MathJax/or LaTex and so already accessible if you recover the text IIRC. You may need the input code though and not the output in that case, and so see here.)

Details:

See notebook working it out & illustrating process in more here.

Collectives™ on Stack Overflow

How to convert output cells to text within Jupyter?

2 Answers 2

Comments

Details:

Comments

Your Answer

Linked

Hot Network Questions

Collectives™ on Stack Overflow

2 Answers 2

Comments

Details:

Comments

Your Answer

Sign up or log in

Post as a guest

Linked

Related