Re: Images in Jupyter Notebook to Markdown conversion

[John MacFarlane <jgm-TVLZxgkOlNX2fBVCVOL8/A@public.gmane.org>]

See the --extract-media option in the manual.


Sébastien Boisgérault <sebastien.boisgerault-Re5JQEeQqe8AvxtiuMwx3w@public.gmane.org> writes:

> Hello everyone,
>
> When i convert the images.ipynb notebook to markdown, the resuting document 
> contains
>
> ::: {.output .execute_result execution_count="4"}
> ![](fcbe61351f965c3664d65d5e93991915402e0483.png)
> :::
>
> but AFAICT, the png file is not generated. Using the standalone option does 
> not change anything. And of course, the round-tripping (md -> ipynb) fails 
> with:
>
> File fcbe61351f965c3664d65d5e93991915402e0483.png not found in resource path
>
> The original notebook can be visualized here: 
> https://github.com/boisgera/python-advanced-eval/blob/master/samples/images.ipynb. 
> The (Grace Hopper) image it contains is an image/png cell output 
> (Matplotlib is used to display the image) :
>
>   {
>    "cell_type": "code",
>    "execution_count": 4,
>    "metadata": {},
>    "outputs": [
>     {
>      "data": {
>       "image/png": 
> "iVBORw0KGgoAAAANSUhEUgAAAgAAAAJYCAIAAACbx8k3AAEAAElEQVR4nGT92dMsWZIfhrn72SIil2+/S9Xtqq7qbboxPSswwGAAGQwkRYEyQgYZARIvMiNIo0wveseDTGaCXkTqX+CLKJO4CRIIgaSGAIfAAAPMYLqnZ+nprurat7t+Wy4RcTZ3PXhGVPYgH67lzS8z4sRZfPn5z93x6tG/7X3jXUA0tWApAgAGbd/vlst 
> ...
>
> I am using pandoc 2.16.2 (Linux). Is it considered a bug or am I missing 
> something? Shall I file an issue? 
>
> Regards,
>
> Sébastien
>
>
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> ---
> jupyter:
>   nbformat: 4
>   nbformat_minor: 2
> ---
>
> ::: {.cell .code execution_count="1"}
> ``` python
> import matplotlib.pyplot as plt
> from matplotlib.cbook import get_sample_data
> ```
> :::
>
> ::: {.cell .code execution_count="2"}
> ``` python
> with get_sample_data("grace_hopper.jpg") as file:
>     image_array = plt.imread(file)
> ```
> :::
>
> ::: {.cell .code execution_count="3"}
> ``` python
> image_array
> ```
>
> ::: {.output .execute_result execution_count="3"}
>     array([[[ 21,  24,  77],
>             [ 27,  30,  85],
>             [ 33,  35,  92],
>             ...,
>             [ 76, 114, 189],
>             [ 71, 109, 184],
>             [ 76, 114, 189]],
>
>            [[ 26,  29,  82],
>             [ 25,  28,  83],
>             [ 27,  29,  86],
>             ...,
>             [ 60,  98, 173],
>             [ 56,  94, 169],
>             [ 62, 100, 175]],
>
>            [[ 21,  24,  77],
>             [ 20,  23,  78],
>             [ 20,  22,  79],
>             ...,
>             [ 79, 117, 190],
>             [ 80, 118, 191],
>             [ 88, 126, 199]],
>
>            ...,
>
>            [[153,  13,  24],
>             [163,  18,  31],
>             [164,  18,  29],
>             ...,
>             [ 14,  13,  19],
>             [ 13,  12,  18],
>             [ 11,  10,  16]],
>
>            [[149,  12,  22],
>             [156,  14,  26],
>             [160,  16,  26],
>             ...,
>             [ 13,  12,  18],
>             [ 13,  12,  18],
>             [ 12,  11,  17]],
>
>            [[150,  12,  25],
>             [154,  14,  25],
>             [157,  16,  25],
>             ...,
>             [ 14,  13,  19],
>             [ 14,  13,  19],
>             [ 14,  13,  19]]], dtype=uint8)
> :::
> :::
>
> ::: {.cell .code execution_count="4"}
> ``` python
> plt.imshow(image_array)
> plt.show()
> ```
>
> ::: {.output .execute_result execution_count="4"}
> ![](fcbe61351f965c3664d65d5e93991915402e0483.png)
> :::
> :::

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