Repository: mwouts/jupytext
Pull request #233 (2019-05-21 22:04:55)

1.1.3
**Improvements**

- Support for IDL notebooks and .pro scripts (#232)

codecov[bot]: # [Codecov](https://codecov.io/gh/mwouts/jupytext/pull/233?src=pr&el=h1) Report
> Merging [#233](https://codecov.io/gh/mwouts/jupytext/pull/233?src=pr&el=desc) into [master](https://codecov.io/gh/mwouts/jupytext/commit/6a870b2376cb61fe957b39e0bd6ca15da9ce30e5?src=pr&el=desc) will **decrease** coverage by `10.79%`.
> The diff coverage is `60%`.

[![Impacted file tree graph](https://codecov.io/gh/mwouts/jupytext/pull/233/graphs/tree.svg?width=650&token=a1pNjsiILi&height=150&src=pr)](https://codecov.io/gh/mwouts/jupytext/pull/233?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master     #233      +/-   ##
==========================================
- Coverage   99.23%   88.43%   -10.8%     
==========================================
  Files          65       65              
  Lines        6172     6176       +4     
==========================================
- Hits         6125     5462     -663     
- Misses         47      714     +667
```


| [Impacted Files](https://codecov.io/gh/mwouts/jupytext/pull/233?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [jupytext/languages.py](https://codecov.io/gh/mwouts/jupytext/pull/233/diff?src=pr&el=tree#diff-anVweXRleHQvbGFuZ3VhZ2VzLnB5) | `100% <ø> (ø)` | :arrow_up: |
| [jupytext/version.py](https://codecov.io/gh/mwouts/jupytext/pull/233/diff?src=pr&el=tree#diff-anVweXRleHQvdmVyc2lvbi5weQ==) | `100% <100%> (ø)` | :arrow_up: |
| [tests/test\_mirror.py](https://codecov.io/gh/mwouts/jupytext/pull/233/diff?src=pr&el=tree#diff-dGVzdHMvdGVzdF9taXJyb3IucHk=) | `40.14% <50%> (-59.86%)` | :arrow_down: |
| [tests/test\_black.py](https://codecov.io/gh/mwouts/jupytext/pull/233/diff?src=pr&el=tree#diff-dGVzdHMvdGVzdF9ibGFjay5weQ==) | `29.47% <0%> (-70.53%)` | :arrow_down: |
| [tests/test\_read\_simple\_pandoc.py](https://codecov.io/gh/mwouts/jupytext/pull/233/diff?src=pr&el=tree#diff-dGVzdHMvdGVzdF9yZWFkX3NpbXBsZV9wYW5kb2MucHk=) | `44.82% <0%> (-55.18%)` | :arrow_down: |
| [tests/test\_knitr\_spin.py](https://codecov.io/gh/mwouts/jupytext/pull/233/diff?src=pr&el=tree#diff-dGVzdHMvdGVzdF9rbml0cl9zcGluLnB5) | `45.45% <0%> (-54.55%)` | :arrow_down: |
| [jupytext/pandoc.py](https://codecov.io/gh/mwouts/jupytext/pull/233/diff?src=pr&el=tree#diff-anVweXRleHQvcGFuZG9jLnB5) | `43.75% <0%> (-52.09%)` | :arrow_down: |
| [tests/test\_active\_cells.py](https://codecov.io/gh/mwouts/jupytext/pull/233/diff?src=pr&el=tree#diff-dGVzdHMvdGVzdF9hY3RpdmVfY2VsbHMucHk=) | `48.52% <0%> (-51.48%)` | :arrow_down: |
| [tests/test\_read\_simple\_rmd.py](https://codecov.io/gh/mwouts/jupytext/pull/233/diff?src=pr&el=tree#diff-dGVzdHMvdGVzdF9yZWFkX3NpbXBsZV9ybWQucHk=) | `50% <0%> (-50%)` | :arrow_down: |
| ... and [14 more](https://codecov.io/gh/mwouts/jupytext/pull/233/diff?src=pr&el=tree-more) | |

------

[Continue to review full report at Codecov](https://codecov.io/gh/mwouts/jupytext/pull/233?src=pr&el=continue).
> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)
> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/mwouts/jupytext/pull/233?src=pr&el=footer). Last update [6a870b2...b0bba64](https://codecov.io/gh/mwouts/jupytext/pull/233?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).


Patch:
diff --git a/.gitignore b/.gitignore
index c2614c85..4c8dd3c0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
 .idea
+.vscode
 .build
 .cache
 .coverage
diff --git a/HISTORY.rst b/HISTORY.rst
index dc96657a..ecf1064a 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -3,6 +3,14 @@
 Release History
 ---------------
 
+1.1.3 (2019-05-22)
+++++++++++++++++++++++
+
+**Improvements**
+
+- Support for IDL notebooks and .pro scripts (#232)
+
+
 1.1.2 (2019-05-16)
 ++++++++++++++++++++++
 
diff --git a/README.md b/README.md
index fa5ffce2..13b55be0 100755
--- a/README.md
+++ b/README.md
@@ -385,7 +385,7 @@ The `light` format has:
 - Code cells are exported verbatim (except for Jupyter magics, which are commented), and separated with blank lines. Code cells are reconstructed from consistent Python paragraphs (no function, class or multiline comment will be broken).
 - Cells that contain more than one Python paragraphs need an explicit start-of-cell delimiter that is, by default, `# +` (`// +` in C++, etc). Cells that have explicit metadata have a cell header `# + {JSON}` where the metadata is represented, in JSON format. The default end of cell delimiter is `# -`, and is omitted when followed by another explicit start of cell marker.
 
-The `light` format is currently available for Python, Julia, R, Bash, Scheme, Clojure, Matlab, Octave, C++ and q/kdb+. Open our sample notebook in the `light` format [here](https://github.com/mwouts/jupytext/blob/master/demo/World%20population.lgt.py).
+The `light` format is currently available for Python, Julia, R, Bash, Scheme, Clojure, Matlab, Octave, C++, q/kdb+ and IDL. Open our sample notebook in the `light` format [here](https://github.com/mwouts/jupytext/blob/master/demo/World%20population.lgt.py).
 
 A variation of the `light` format is the `bare` format, with no cell marker at all. Please note that this format will split your code cells on code paragraphs. By default, this format still includes a YAML header - if you prefer to also remove the header, set `"notebook_metadata_filter": "-all"` in the jupytext section of your notebook metadata.  
 
@@ -417,7 +417,7 @@ where cell type is either omitted (code cells), or `[markdown]` or  `[raw]`. The
 
 Percent scripts created by Jupytext have a header with an explicit format information. The format of scripts with no header is inferred automatically: scripts with at least one `# %%` cell are identified as `percent` scripts. Scripts with at least one double percent cell, and an uncommented Jupyter magic command, are identified as `hydrogen` scripts.
 
-The `percent` format is currently available for Python, Julia, R, Bash, Scheme, Clojure, Matlab, Octave, C++ and q/kdb+. Open our sample notebook in the `percent` format [here](https://github.com/mwouts/jupytext/blob/master/demo/World%20population.pct.py).
+The `percent` format is currently available for Python, Julia, R, Bash, Scheme, Clojure, Matlab, Octave, C++, q/kdb+ and IDL. Open our sample notebook in the `percent` format [here](https://github.com/mwouts/jupytext/blob/master/demo/World%20population.pct.py).
 
 If the `percent` format is your favorite one, add the following to your `.jupyter/jupyter_notebook_config.py` file:
 ```python
diff --git a/jupytext/languages.py b/jupytext/languages.py
index 8d665821..2d47df87 100644
--- a/jupytext/languages.py
+++ b/jupytext/languages.py
@@ -1,7 +1,8 @@
 """Determine notebook or cell language"""
 
 _JUPYTER_LANGUAGES = ['R', 'bash', 'sh', 'python', 'python2', 'python3', 'javascript', 'js', 'perl',
-                      'html', 'latex', 'markdown', 'pypy', 'ruby', 'script', 'svg', 'writefile', 'matlab', 'octave']
+                      'html', 'latex', 'markdown', 'pypy', 'ruby', 'script', 'svg', 'writefile', 
+                      'matlab', 'octave', 'idl']
 
 _SCRIPT_EXTENSIONS = {'.py': {'language': 'python', 'comment': '#'},
                       '.R': {'language': 'R', 'comment': '#'},
@@ -13,7 +14,8 @@ _SCRIPT_EXTENSIONS = {'.py': {'language': 'python', 'comment': '#'},
                       '.scm': {'language': 'scheme', 'comment': ';;'},
                       '.sh': {'language': 'bash', 'comment': '#'},
                       '.q': {'language': 'q', 'comment': '/'},
-                      '.m': {'language': 'matlab', 'comment': '%'}}
+                      '.m': {'language': 'matlab', 'comment': '%'},
+                      '.pro': {'language': 'idl', 'comment': ';'}}
 
 _COMMENT_CHARS = [_SCRIPT_EXTENSIONS[ext]['comment'] for ext in _SCRIPT_EXTENSIONS if
                   _SCRIPT_EXTENSIONS[ext]['comment'] != '#']
diff --git a/jupytext/version.py b/jupytext/version.py
index 205f6829..fc953519 100644
--- a/jupytext/version.py
+++ b/jupytext/version.py
@@ -1,3 +1,3 @@
 """Jupytext's version number"""
 
-__version__ = '1.1.2'
+__version__ = '1.1.3'


Test patch:
diff --git a/tests/notebooks/ipynb_idl/demo_gdl_fbp.ipynb b/tests/notebooks/ipynb_idl/demo_gdl_fbp.ipynb
new file mode 100644
index 00000000..059ee38f
--- /dev/null
+++ b/tests/notebooks/ipynb_idl/demo_gdl_fbp.ipynb
@@ -0,0 +1,313 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## GDL demo notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Demonstration of GDL [(gnudatalanguage)](https://github.com/gnudatalanguage/gdl)\n",
+    "\n",
+    "This notebook creates a Shepp-Logan phantom, projects it and then performs an FBP reconstruction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": []
+    }
+   ],
+   "source": [
+    ";; L.A. Shepp and B.F. Logan, “The Fourier reconstruction of a head section,”\n",
+    ";; IEEE Trans. Nucl. Sci. 21(3), 21–43 (1974).\n",
+    "function shepplogan, size = size\n",
+    "\n",
+    "  if NOT keyword_set(size) then size = 256\n",
+    "\n",
+    "  phantom = fltarr(size,size)\n",
+    "\n",
+    "  tmp  = (findgen(size)-((size-1)/2.0)) / (size/2.0)\n",
+    "  xcor = rebin(tmp,size,size)\n",
+    "  ycor = rebin(transpose(tmp),size,size)\n",
+    "  tmp  = fltarr(size,size)\n",
+    "  \n",
+    "  aa={cx: 0.0,  cy: 0.0,    maj:0.69,   min:0.92,  theta:  0.0, val: 2.0 }\n",
+    "  bb={cx: 0.0,  cy:-0.0184, maj:0.6624, min:0.874, theta:  0.0, val:-0.98}\n",
+    "  cc={cx: 0.22, cy: 0.0,    maj:0.11,   min:0.31,  theta:-18.0, val:-0.02}\n",
+    "  dd={cx:-0.22, cy: 0.0,    maj:0.16,   min:0.41,  theta: 18.0, val:-0.02}\n",
+    "  ee={cx: 0.0,  cy: 0.35,   maj:0.21,   min:0.25,  theta:  0.0, val:-0.01}\n",
+    "  ff={cx: 0.0,  cy: 0.1,    maj:0.046,  min:0.046, theta:  0.0, val:-0.01}\n",
+    "  gg={cx: 0.0,  cy:-0.1,    maj:0.046,  min:0.046, theta:  0.0, val:-0.01}\n",
+    "  hh={cx:-0.08, cy:-0.605,  maj:0.046,  min:0.023, theta:  0.0, val:-0.01}\n",
+    "  ii={cx: 0.0,  cy:-0.605,  maj:0.023,  min:0.023, theta:  0.0, val:-0.01}\n",
+    "  jj={cx: 0.06, cy:-0.605,  maj:0.023,  min:0.046, theta:  0.0, val:-0.01}\n",
+    "\n",
+    "  list = [aa,bb,cc,dd,ee,ff,gg,hh,ii,jj]\n",
+    "  for n = 0, n_elements(list)-1 do begin\n",
+    "\n",
+    "     tmp = ((xcor-list[n].cx) / list[n].maj)^2 $\n",
+    "           + ((ycor-list[n].cy) / list[n].min)^2\n",
+    "     \n",
+    "     if list[n].theta NE 0 then begin\n",
+    "        nx = (size-1) * (list[n].cx + 1) / 2\n",
+    "        ny = (size-1) * (list[n].cy + 1) / 2\n",
+    "        tmp = rot(tmp, -list[n].theta, 1, nx, ny, /interp, /pivot)\n",
+    "     endif\n",
+    "     \n",
+    "     phantom[where(tmp LE 1.0)] += list[n].val\n",
+    "     \n",
+    "  endfor\n",
+    "  \n",
+    "  return, phantom < 1.1\n",
+    "  \n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": []
+    }
+   ],
+   "source": [
+    ";; Enable inline plotting\n",
+    "!inline=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": []
+    }
+   ],
+   "source": [
+    "phantom = shepplogan()\n",
+    "window, 0, xsize=256, ysize=256\n",
+    "tvscl, phantom > 0.95"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we define simple forward and backprojection functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": []
+    }
+   ],
+   "source": [
+    "function proj, image, angle\n",
+    "\n",
+    "    sino_size_x = max(size(image,/dim))\n",
+    "    sino_size_y = n_elements(angle)\n",
+    "\n",
+    "    sino = fltarr(sino_size_x, sino_size_y)\n",
+    "    \n",
+    "    for aa=0, n_elements(angle)-1 do begin\n",
+    "    \n",
+    "        sino[*,aa] = total(rot(image, angle[aa], /interp), 2)\n",
+    "    \n",
+    "    endfor\n",
+    "\n",
+    "    return, sino\n",
+    "    \n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": []
+    }
+   ],
+   "source": [
+    "function back, sino, angle\n",
+    "\n",
+    "    image_size = n_elements(sino[*,0])\n",
+    "    image = fltarr(image_size,image_size)\n",
+    "\n",
+    "    for aa=0, n_elements(angle)-1 do begin\n",
+    "    \n",
+    "        image += rot(rebin(sino[*,aa],[image_size,image_size]), -angle[aa], /interp)\n",
+    "    \n",
+    "    endfor\n",
+    "\n",
+    "    return, image / n_elements(angle)\n",
+    "\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This allows us to create a sinogram:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": []
+    }
+   ],
+   "source": [
+    "angles = findgen(360)\n",
+    "sino = proj(phantom, angles)\n",
+    "window, 0, xsize=256, ysize=360\n",
+    "tvscl, sino"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On this we can apply a simple FBP reconstruction:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": []
+    }
+   ],
+   "source": [
+    ";; G.L. Zeng, “Revisit of the Ramp Filter,”\n",
+    ";; IEEE Trans. Nucl. Sci. 62(1), 131–136 (2015).\n",
+    "function fbp, sino, angles\n",
+    "  \n",
+    "  ntot = n_elements(sino[*,0])\n",
+    "  nang = n_elements(sino[0,*])\n",
+    "  npos = ntot / 2 + 1           ; integer division needed !\n",
+    "  nneg = ntot - npos\n",
+    "\n",
+    "  freq = findgen(ntot)\n",
+    "  freq[npos:ntot-1] = REVERSE(freq[1:nneg])\n",
+    "  freq[0] = 1\n",
+    "\n",
+    "  filter = -1 / (!pi * freq)^2\n",
+    "  filter[where(freq mod 2 EQ 0)] *= -0.0\n",
+    "  filter[0] = 0.25\n",
+    "\n",
+    "  filter = abs(fft(filter)) * ntot\n",
+    "  filter[ntot/4:ntot/4+ntot/2-1] *= 0.0\n",
+    "  filter = rebin(filter,ntot,nang)\n",
+    "\n",
+    "  ;; apply filter to the sinogram\n",
+    "  fsino  = fft(sino,  dim=1)\n",
+    "  fsino *= filter\n",
+    "  fsino  = fft(fsino, dim=1, /overwrite, /inverse)\n",
+    "  fsino  = !pi * real_part(fsino)\n",
+    "\n",
+    "  ;; backproject the filtered sinogram\n",
+    "  return, back(fsino, angles)\n",
+    "\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": []
+    }
+   ],
+   "source": [
+    "reconstruction = fbp(sino, angles)\n",
+    "window, 0, xsize=512, ysize=256\n",
+    "\n",
+    "tvscl, [phantom, reconstruction] > 0.8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The end"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "IDL [conda env:gdl] *",
+   "language": "IDL",
+   "name": "conda-env-gdl-idl"
+  },
+  "language_info": {
+   "codemirror_mode": "idl",
+   "file_extension": ".pro",
+   "mimetype": "text/x-idl",
+   "name": "idl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tests/notebooks/mirror/ipynb_to_percent/demo_gdl_fbp.pro b/tests/notebooks/mirror/ipynb_to_percent/demo_gdl_fbp.pro
new file mode 100644
index 00000000..a8838ba5
--- /dev/null
+++ b/tests/notebooks/mirror/ipynb_to_percent/demo_gdl_fbp.pro
@@ -0,0 +1,161 @@
+; -*- coding: utf-8 -*-
+; ---
+; jupyter:
+;   kernelspec:
+;     display_name: IDL [conda env:gdl] *
+;     language: IDL
+;     name: conda-env-gdl-idl
+; ---
+
+; %% [markdown]
+; ## GDL demo notebook
+
+; %% [markdown]
+; Demonstration of GDL [(gnudatalanguage)](https://github.com/gnudatalanguage/gdl)
+;
+; This notebook creates a Shepp-Logan phantom, projects it and then performs an FBP reconstruction.
+
+; %%
+;; L.A. Shepp and B.F. Logan, “The Fourier reconstruction of a head section,”
+;; IEEE Trans. Nucl. Sci. 21(3), 21–43 (1974).
+function shepplogan, size = size
+
+  if NOT keyword_set(size) then size = 256
+
+  phantom = fltarr(size,size)
+
+  tmp  = (findgen(size)-((size-1)/2.0)) / (size/2.0)
+  xcor = rebin(tmp,size,size)
+  ycor = rebin(transpose(tmp),size,size)
+  tmp  = fltarr(size,size)
+  
+  aa={cx: 0.0,  cy: 0.0,    maj:0.69,   min:0.92,  theta:  0.0, val: 2.0 }
+  bb={cx: 0.0,  cy:-0.0184, maj:0.6624, min:0.874, theta:  0.0, val:-0.98}
+  cc={cx: 0.22, cy: 0.0,    maj:0.11,   min:0.31,  theta:-18.0, val:-0.02}
+  dd={cx:-0.22, cy: 0.0,    maj:0.16,   min:0.41,  theta: 18.0, val:-0.02}
+  ee={cx: 0.0,  cy: 0.35,   maj:0.21,   min:0.25,  theta:  0.0, val:-0.01}
+  ff={cx: 0.0,  cy: 0.1,    maj:0.046,  min:0.046, theta:  0.0, val:-0.01}
+  gg={cx: 0.0,  cy:-0.1,    maj:0.046,  min:0.046, theta:  0.0, val:-0.01}
+  hh={cx:-0.08, cy:-0.605,  maj:0.046,  min:0.023, theta:  0.0, val:-0.01}
+  ii={cx: 0.0,  cy:-0.605,  maj:0.023,  min:0.023, theta:  0.0, val:-0.01}
+  jj={cx: 0.06, cy:-0.605,  maj:0.023,  min:0.046, theta:  0.0, val:-0.01}
+
+  list = [aa,bb,cc,dd,ee,ff,gg,hh,ii,jj]
+  for n = 0, n_elements(list)-1 do begin
+
+     tmp = ((xcor-list[n].cx) / list[n].maj)^2 $
+           + ((ycor-list[n].cy) / list[n].min)^2
+     
+     if list[n].theta NE 0 then begin
+        nx = (size-1) * (list[n].cx + 1) / 2
+        ny = (size-1) * (list[n].cy + 1) / 2
+        tmp = rot(tmp, -list[n].theta, 1, nx, ny, /interp, /pivot)
+     endif
+     
+     phantom[where(tmp LE 1.0)] += list[n].val
+     
+  endfor
+  
+  return, phantom < 1.1
+  
+end
+
+; %%
+;; Enable inline plotting
+!inline=1
+
+; %%
+phantom = shepplogan()
+window, 0, xsize=256, ysize=256
+tvscl, phantom > 0.95
+
+; %% [markdown]
+; Now we define simple forward and backprojection functions:
+
+; %%
+function proj, image, angle
+
+    sino_size_x = max(size(image,/dim))
+    sino_size_y = n_elements(angle)
+
+    sino = fltarr(sino_size_x, sino_size_y)
+    
+    for aa=0, n_elements(angle)-1 do begin
+    
+        sino[*,aa] = total(rot(image, angle[aa], /interp), 2)
+    
+    endfor
+
+    return, sino
+    
+end
+
+; %%
+function back, sino, angle
+
+    image_size = n_elements(sino[*,0])
+    image = fltarr(image_size,image_size)
+
+    for aa=0, n_elements(angle)-1 do begin
+    
+        image += rot(rebin(sino[*,aa],[image_size,image_size]), -angle[aa], /interp)
+    
+    endfor
+
+    return, image / n_elements(angle)
+
+end
+
+; %% [markdown]
+; This allows us to create a sinogram:
+
+; %%
+angles = findgen(360)
+sino = proj(phantom, angles)
+window, 0, xsize=256, ysize=360
+tvscl, sino
+
+; %% [markdown]
+; On this we can apply a simple FBP reconstruction:
+
+; %%
+;; G.L. Zeng, “Revisit of the Ramp Filter,”
+;; IEEE Trans. Nucl. Sci. 62(1), 131–136 (2015).
+function fbp, sino, angles
+  
+  ntot = n_elements(sino[*,0])
+  nang = n_elements(sino[0,*])
+  npos = ntot / 2 + 1           ; integer division needed !
+  nneg = ntot - npos
+
+  freq = findgen(ntot)
+  freq[npos:ntot-1] = REVERSE(freq[1:nneg])
+  freq[0] = 1
+
+  filter = -1 / (!pi * freq)^2
+  filter[where(freq mod 2 EQ 0)] *= -0.0
+  filter[0] = 0.25
+
+  filter = abs(fft(filter)) * ntot
+  filter[ntot/4:ntot/4+ntot/2-1] *= 0.0
+  filter = rebin(filter,ntot,nang)
+
+  ;; apply filter to the sinogram
+  fsino  = fft(sino,  dim=1)
+  fsino *= filter
+  fsino  = fft(fsino, dim=1, /overwrite, /inverse)
+  fsino  = !pi * real_part(fsino)
+
+  ;; backproject the filtered sinogram
+  return, back(fsino, angles)
+
+end
+
+; %%
+reconstruction = fbp(sino, angles)
+window, 0, xsize=512, ysize=256
+
+tvscl, [phantom, reconstruction] > 0.8
+
+; %% [markdown]
+; ### The end
diff --git a/tests/notebooks/mirror/ipynb_to_script/demo_gdl_fbp.pro b/tests/notebooks/mirror/ipynb_to_script/demo_gdl_fbp.pro
new file mode 100644
index 00000000..a3dca408
--- /dev/null
+++ b/tests/notebooks/mirror/ipynb_to_script/demo_gdl_fbp.pro
@@ -0,0 +1,155 @@
+; -*- coding: utf-8 -*-
+; ---
+; jupyter:
+;   kernelspec:
+;     display_name: IDL [conda env:gdl] *
+;     language: IDL
+;     name: conda-env-gdl-idl
+; ---
+
+; ## GDL demo notebook
+
+; Demonstration of GDL [(gnudatalanguage)](https://github.com/gnudatalanguage/gdl)
+;
+; This notebook creates a Shepp-Logan phantom, projects it and then performs an FBP reconstruction.
+
+; +
+;; L.A. Shepp and B.F. Logan, “The Fourier reconstruction of a head section,”
+;; IEEE Trans. Nucl. Sci. 21(3), 21–43 (1974).
+function shepplogan, size = size
+
+  if NOT keyword_set(size) then size = 256
+
+  phantom = fltarr(size,size)
+
+  tmp  = (findgen(size)-((size-1)/2.0)) / (size/2.0)
+  xcor = rebin(tmp,size,size)
+  ycor = rebin(transpose(tmp),size,size)
+  tmp  = fltarr(size,size)
+  
+  aa={cx: 0.0,  cy: 0.0,    maj:0.69,   min:0.92,  theta:  0.0, val: 2.0 }
+  bb={cx: 0.0,  cy:-0.0184, maj:0.6624, min:0.874, theta:  0.0, val:-0.98}
+  cc={cx: 0.22, cy: 0.0,    maj:0.11,   min:0.31,  theta:-18.0, val:-0.02}
+  dd={cx:-0.22, cy: 0.0,    maj:0.16,   min:0.41,  theta: 18.0, val:-0.02}
+  ee={cx: 0.0,  cy: 0.35,   maj:0.21,   min:0.25,  theta:  0.0, val:-0.01}
+  ff={cx: 0.0,  cy: 0.1,    maj:0.046,  min:0.046, theta:  0.0, val:-0.01}
+  gg={cx: 0.0,  cy:-0.1,    maj:0.046,  min:0.046, theta:  0.0, val:-0.01}
+  hh={cx:-0.08, cy:-0.605,  maj:0.046,  min:0.023, theta:  0.0, val:-0.01}
+  ii={cx: 0.0,  cy:-0.605,  maj:0.023,  min:0.023, theta:  0.0, val:-0.01}
+  jj={cx: 0.06, cy:-0.605,  maj:0.023,  min:0.046, theta:  0.0, val:-0.01}
+
+  list = [aa,bb,cc,dd,ee,ff,gg,hh,ii,jj]
+  for n = 0, n_elements(list)-1 do begin
+
+     tmp = ((xcor-list[n].cx) / list[n].maj)^2 $
+           + ((ycor-list[n].cy) / list[n].min)^2
+     
+     if list[n].theta NE 0 then begin
+        nx = (size-1) * (list[n].cx + 1) / 2
+        ny = (size-1) * (list[n].cy + 1) / 2
+        tmp = rot(tmp, -list[n].theta, 1, nx, ny, /interp, /pivot)
+     endif
+     
+     phantom[where(tmp LE 1.0)] += list[n].val
+     
+  endfor
+  
+  return, phantom < 1.1
+  
+end
+; -
+
+;; Enable inline plotting
+!inline=1
+
+phantom = shepplogan()
+window, 0, xsize=256, ysize=256
+tvscl, phantom > 0.95
+
+; Now we define simple forward and backprojection functions:
+
+; +
+function proj, image, angle
+
+    sino_size_x = max(size(image,/dim))
+    sino_size_y = n_elements(angle)
+
+    sino = fltarr(sino_size_x, sino_size_y)
+    
+    for aa=0, n_elements(angle)-1 do begin
+    
+        sino[*,aa] = total(rot(image, angle[aa], /interp), 2)
+    
+    endfor
+
+    return, sino
+    
+end
+
+; +
+function back, sino, angle
+
+    image_size = n_elements(sino[*,0])
+    image = fltarr(image_size,image_size)
+
+    for aa=0, n_elements(angle)-1 do begin
+    
+        image += rot(rebin(sino[*,aa],[image_size,image_size]), -angle[aa], /interp)
+    
+    endfor
+
+    return, image / n_elements(angle)
+
+end
+; -
+
+; This allows us to create a sinogram:
+
+angles = findgen(360)
+sino = proj(phantom, angles)
+window, 0, xsize=256, ysize=360
+tvscl, sino
+
+; On this we can apply a simple FBP reconstruction:
+
+; +
+;; G.L. Zeng, “Revisit of the Ramp Filter,”
+;; IEEE Trans. Nucl. Sci. 62(1), 131–136 (2015).
+function fbp, sino, angles
+  
+  ntot = n_elements(sino[*,0])
+  nang = n_elements(sino[0,*])
+  npos = ntot / 2 + 1           ; integer division needed !
+  nneg = ntot - npos
+
+  freq = findgen(ntot)
+  freq[npos:ntot-1] = REVERSE(freq[1:nneg])
+  freq[0] = 1
+
+  filter = -1 / (!pi * freq)^2
+  filter[where(freq mod 2 EQ 0)] *= -0.0
+  filter[0] = 0.25
+
+  filter = abs(fft(filter)) * ntot
+  filter[ntot/4:ntot/4+ntot/2-1] *= 0.0
+  filter = rebin(filter,ntot,nang)
+
+  ;; apply filter to the sinogram
+  fsino  = fft(sino,  dim=1)
+  fsino *= filter
+  fsino  = fft(fsino, dim=1, /overwrite, /inverse)
+  fsino  = !pi * real_part(fsino)
+
+  ;; backproject the filtered sinogram
+  return, back(fsino, angles)
+
+end
+
+; +
+reconstruction = fbp(sino, angles)
+window, 0, xsize=512, ysize=256
+
+tvscl, [phantom, reconstruction] > 0.8
+; -
+
+; ### The end
diff --git a/tests/test_mirror.py b/tests/test_mirror.py
index 0d53fb72..198cc2c6 100644
--- a/tests/test_mirror.py
+++ b/tests/test_mirror.py
@@ -276,6 +276,16 @@ def test_ipynb_to_md(nb_file):
     assert_conversion_same_as_mirror(nb_file, 'md', 'ipynb_to_md')
 
 
+@pytest.mark.parametrize('nb_file', list_notebooks('ipynb_idl'))
+def test_ipynb_to_pro(nb_file):
+    assert_conversion_same_as_mirror(nb_file, 'pro', 'ipynb_to_script')
+
+
+@pytest.mark.parametrize('nb_file', list_notebooks('ipynb_idl'))
+def test_ipynb_to_pro_percent(nb_file):
+    assert_conversion_same_as_mirror(nb_file, 'pro:percent', 'ipynb_to_percent')
+
+
 @requires_pandoc
 @pytest.mark.parametrize('nb_file', list_notebooks('ipynb', skip='(functional|Notebook with|flavors)'))
 def test_ipynb_to_pandoc(nb_file):