Images in Jupyter Notebook to Markdown conversion

["Sébastien Boisgérault" <sebastien.boisgerault-Re5JQEeQqe8AvxtiuMwx3w@public.gmane.org>]

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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)
:::
:::