Fixed apmtools Cheatsheet, download links, additional download links for APAV

41eea5c9 · Markus Kuehbach · Florian Dobener · b3a1d31f · 41eea5c9 · 41eea5c9
Commit 41eea5c9 authored 2 years ago by Markus Kuehbach Committed by Florian Dobener 2 years ago
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -32,6 +32,8 @@ workflow:
      - IMAGE_NAME: mpes-jupyter
        DIR: docker/mpes
        DOCKERFILE: Dockerfile.jupyter
+      - IMAGE_NAME: apmtools-webtop
+        DIR: docker/apmtools

 stages:
  - build

--- a/docker/apmtools/APAVRunTestSuite.ipynb
+++ b/docker/apmtools/APAVRunTestSuite.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "85a3a200-f5e7-4e42-89db-19869f60279a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m============================= test session starts ==============================\u001b[0m\n",
+      "platform linux -- Python 3.8.16, pytest-7.3.1, pluggy-1.0.0\n",
+      "PyQt5 5.15.9 -- Qt runtime 5.15.2 -- Qt compiled 5.15.2\n",
+      "rootdir: /home/mkuehbach/Sprint14/finishing_in_migrated_places/test-apav/apav\n",
+      "plugins: qt-4.2.0, anyio-3.6.2\n",
+      "collected 218 items                                                            \u001b[0m\n",
+      "\n",
+      "test_background.py \u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m                            [ 11%]\u001b[0m\n",
+      "test_helpers.py \u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m                                          [ 18%]\u001b[0m\n",
+      "test_histogram.py \u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m                                                     [ 19%]\u001b[0m\n",
+      "test_isotopic.py \u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m                [ 38%]\u001b[0m\n",
+      "test_massspectrum.py \u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m                                    [ 45%]\u001b[0m\n",
+      "test_multiplevent.py \u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m                                              [ 48%]\u001b[0m\n",
+      "test_plotting.py \u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m                                                 [ 51%]\u001b[0m\n",
+      "test_range.py \u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m                                   [ 62%]\u001b[0m\n",
+      "test_roi.py \u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m                                [ 75%]\u001b[0m\n",
+      "test_spatial.py \u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m                      [ 91%]\u001b[0m\n",
+      "test_validate.py \u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m.\u001b[0m\u001b[32m                                      [100%]\u001b[0m\n",
+      "\n",
+      "\u001b[32m============================= \u001b[32m\u001b[1m218 passed\u001b[0m\u001b[32m in 5.06s\u001b[0m\u001b[32m ==============================\u001b[0m\n",
+      "QXcbClipboard: Unable to receive an event from the clipboard manager in a reasonable time\n"
+     ]
+    }
+   ],
+   "source": [
+    "! python -c 'from apav.tests import run_tests; run_tests()'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4184ea94-78ce-45c3-81a2-375861756231",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
+%% Cell type:code id:85a3a200-f5e7-4e42-89db-19869f60279a tags:
+
+``` python
+! python -c 'from apav.tests import run_tests; run_tests()'
+```
+
+%% Output
+
+    [1m============================= test session starts ==============================[0m
+    platform linux -- Python 3.8.16, pytest-7.3.1, pluggy-1.0.0
+    PyQt5 5.15.9 -- Qt runtime 5.15.2 -- Qt compiled 5.15.2
+    rootdir: /home/mkuehbach/Sprint14/finishing_in_migrated_places/test-apav/apav
+    plugins: qt-4.2.0, anyio-3.6.2
+    collected 218 items                                                            [0m
+    
+    test_background.py [32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m                            [ 11%][0m
+    test_helpers.py [32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m                                          [ 18%][0m
+    test_histogram.py [32m.[0m[32m.[0m[32m                                                     [ 19%][0m
+    test_isotopic.py [32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m                [ 38%][0m
+    test_massspectrum.py [32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m                                    [ 45%][0m
+    test_multiplevent.py [32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m                                              [ 48%][0m
+    test_plotting.py [32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m                                                 [ 51%][0m
+    test_range.py [32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m                                   [ 62%][0m
+    test_roi.py [32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m                                [ 75%][0m
+    test_spatial.py [32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m                      [ 91%][0m
+    test_validate.py [32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m.[0m[32m                                      [100%][0m
+    
+    [32m============================= [32m[1m218 passed[0m[32m in 5.06s[0m[32m ==============================[0m
+    QXcbClipboard: Unable to receive an event from the clipboard manager in a reasonable time
+
+%% Cell type:code id:4184ea94-78ce-45c3-81a2-375861756231 tags:
+
+``` python
+```
--- a/docker/apmtools/Cheatsheet.ipynb
+++ b/docker/apmtools/Cheatsheet.ipynb
@@ -33,6 +33,24 @@
    "***"
   ]
  },
+  {
+   "cell_type": "markdown",
+   "id": "e29f2d4c-199c-4db3-8bab-6a341dc7ea83",
+   "metadata": {},
+   "source": [
+    "Load a Python library for unpacking ZIP files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "731941ed-e3b9-4ae1-8372-8211ff900544",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import zipfile as zp"
+   ]
+  },
  {
   "cell_type": "markdown",
   "id": "e7afeb96-16a6-4d5a-9986-7aacfc9980f4",
@@ -42,7 +60,8 @@
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "Having APTyzer, APAV, and paraprobe-toolbox configured in one container comes with the benefit<br>\n",
-    "that one can switch between using these tools or couple them together via an e.g. jupyter notebook.\n",
+    "that one can switch between these tools or create an analysis which couples them together in a<br>\n",
+    "jupyter notebook.\n",
    "</div>"
   ]
  },
@@ -75,7 +94,8 @@
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
-    "Computational requirements: Examples with dataset with a few million ions like most used below should be processable even on a computer with a single core and say at least 4GB main memory.\n",
+    "Computational requirements: Examples with dataset with a few million ions like most used below should be<br>\n",
+    "processable even on a computer with a single core and say at least four GB main memory for docker tasks.\n",
    "</div>"
   ]
  },
@@ -126,7 +146,9 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "! cd /home/atom_probe_tools/aptyzer && wget https://www.zenodo.org/record/6794809/files/aut_leoben_leitner.tar.gz && tar -xvf aut_leoben_leitner.tar.gz"
+    "! cd /home/atom_probe_tools/aptyzer && wget https://www.zenodo.org/record/7908429/files/aut_leoben_leitner.zip\n",
+    "zp.ZipFile(\"/home/atom_probe_tools/aptyzer/aut_leoben_leitner.zip\").extractall(\n",
+    "    path=\"/home/atom_probe_tools/aptyzer\", members=None, pwd=None)"
   ]
  },
  {
@@ -186,12 +208,13 @@
   },
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
-    "Computational requirements: Examples with dataset with a few million ions like most used below should be processable even on a computer with a single core<br>\n",
-    "and four GB main memory. Having multiple CPU cores can be useful as the tools of the paraprobe-toolbox use multi-threading for most of the<br>\n",
-    "numerical and geometrical analyses.<br>\n",
-    "Making guarantees about the maximum data set sizes (in terms of number of ions) is difficult as it strongly depends on which type of analyses are performed<br>\n",
-    "and how these are parameterized. Noteworthy to mention is that even the largest examples at the time of writing this cheatsheet which are available in the<br>\n",
-    "paraprobe-toolbox were processable with a laptop with 32GB main memory in total. Examples with a few million ions consumed not more than one to eight GB.<br>\n",
+    "Computational requirements: Examples with dataset with a few million ions like most used below should be processable even on a computer with a<br>\n",
+    "single core and four GB main memory for docker tasks. Having multiple CPU cores can be useful as the tools of the paraprobe-toolbox use<br>\n",
+    "multi-threading for most of the numerical and geometrical analyses.<br>\n",
+    "Making guarantees about the maximum data set sizes (in terms of number of ions) is difficult as it strongly depends on which type of analyses<br>\n",
+    "are performed and how these are parameterized. Noteworthy to mention is that even the largest examples at the time of writing this cheatsheet<br>\n",
+    "which are available in the paraprobe-toolbox were processable with a laptop with 32GB main memory in total. Examples with a few million ions<br>\n",
+    "consumed not more than one to eight GB.<br>\n",
    "</div>"
   ]
  },
@@ -202,9 +225,10 @@
   "source": [
    "The paraprobe-toolbox is a collection of software tools for applying computational geometry tasks on point cloud data<br>\n",
    "such as tomographic reconstructions of atom probe data to extract and characterize microstructural features.<br>\n",
-    "The tool is developed by <a href=\"https://arxiv.org/abs/2205.13510\">Markus Kühbach et al.</a>. The tool is instructed via a jupyter notebook which documents<br>\n",
-    "how to chain via scripting different analysis steps. Apart from this, it is possible to use the tools via<br>\n",
-    "Python scripting to perform e.g. batch processing on computer clusters.<br>\n",
+    "The tool is developed by <a href=\"https://arxiv.org/abs/2205.13510\">Markus Kühbach et al.</a><br>\n",
+    "The tool is instructed via a jupyter notebook which documents how to chain and script different<br>\n",
+    "analysis steps into a workflow using Python. This can be useful especially for batch processing<br>\n",
+    "on computer clusters.<br>\n",
    "\n",
    "Tools of the paraprobe-toolbox are chained into computational workflows. Each step uses a different scientific<br>\n",
    "speciality tool. All these tools have *paraprobe* as a prefix in their executable name.<br>\n",
@@ -248,8 +272,9 @@
   "id": "bce1299f-24fb-4ffc-99d3-99aac8921136",
   "metadata": {},
   "source": [
-    "Reconstruction and ranging data (i.e. POS, ePOS, APT, RNG, RRNG, NXS) to be consumed by paraprobe-toolbox should be stored in *teaching/example_data*.<br>\n",
-    "Analyses with relevant jupyter notebooks should be stored in *teaching/example_analyses*.\n",
+    "Reconstruction and ranging data (i.e. POS, ePOS, APT, RNG, RRNG, NXS) to be consumed by paraprobe-toolbox<br>\n",
+    "should be stored in *teaching/example_data*. Analyses with relevant jupyter notebooks should be stored<br>\n",
+    "in *teaching/example_analyses*.\n",
    "\n",
    "The tools use NeXus data schemas and HDF5 files. The specification of these files is documented in the so-called<br>\n",
    "<a href=\"https://fairmat-experimental.github.io/nexus-fairmat-proposal\">nexus-fairmat-proposal</a> (see specifically the NORTH/apmtools pages)."
@@ -274,6 +299,32 @@
    "! cd /home/atom_probe_tools/paraprobe-toolbox/teaching/example_data/aut_leoben_leitner && tar -xvf aut_leoben_leitner.tar.gz"
   ]
  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c6e219d-1a5d-4417-b157-3745558f6660",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# additional optional examples are available for testing transcoding of Matlab figure files\n",
+    "! cd /home/atom_probe_tools/paraprobe-toolbox/teaching/example_data/ger_erlangen_felfer && wget https://www.zenodo.org/record/7908429/files/ger_erlangen_felfer_ck10.zip\n",
+    "zp.ZipFile(\"/home/atom_probe_tools/paraprobe-toolbox/teaching/example_data/ger_erlangen_felfer/ger_erlangen_felfer_ck10.zip\").extractall(\n",
+    "    path=\"/home/atom_probe_tools/paraprobe-toolbox/teaching/example_data/ger_erlangen_felfer\", members=None, pwd=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "893e0ef4-90af-4946-8b6c-454f0db2fd89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# additional optional examples are available for testing with a very small dataset for efficient debugging\n",
+    "! cd /home/atom_probe_tools/paraprobe-toolbox/teaching/example_data/usa_denton_smith && wget https://www.zenodo.org/record/7908429/files/usa_denton_smith_apav_si.zip\n",
+    "zp.ZipFile(\"/home/atom_probe_tools/paraprobe-toolbox/teaching/example_data/usa_denton_smith/usa_denton_smith_apav_si.zip\").extractall(\n",
+    "    path=\"/home/atom_probe_tools/paraprobe-toolbox/teaching/example_data/usa_denton_smith\", members=None, pwd=None)"
+   ]
+  },
  {
   "cell_type": "markdown",
   "id": "c409e217-e14a-4bba-a07e-e16753288bd8",
@@ -333,8 +384,9 @@
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
-    "Computational requirements: Examples with dataset with a few million ions like most used below should be processable even on a computer with a single core and say at least 4GB main memory.<br>\n",
-    "Having multiple CPU cores can be useful as APAV uses multi-threading for some costly numerical analyses.\n",
+    "Computational requirements: Examples with dataset with a few million ions like most used below should be processable even<br>\n",
+    "on a computer with a single core and say at least four GB main memory for docker tasks. Having multiple CPU cores can be<br>\n",
+    "useful because APAV uses multi-threading for some costly numerical analyses.\n",
    "</div>"
   ]
  },
@@ -359,9 +411,12 @@
   },
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
-    "Jesse Smith has also made available example data which can be used for learning the multi-hit event analyses capabilities of APAV. If these files are not available <a href=\"https://doi.org/10.5281/zenodo.6794809\">via this Zenodo repository (version >9)</a>,<br>\n",
-    "users are requested to inspect the respective file share location from the revision of the APAV JOSS paper <a href=\"https://github.com/openjournals/joss-reviews/issues/4862\">see the comment from jdasm from November 30, 2022 here</a><br>\n",
-    "The respective GBCO dataset relevant here is especially this one R5038_00333-v02.epos.  <a href=\"https://doi.org/10.1017/S1431927621012794\">Scientific details can be found here</a>.\n",
+    "Jesse Smith has also made available example data which can be used for learning the multi-hit event analyses capabilities of APAV.<br>\n",
+    "If these files are not available <a href=\"https://doi.org/10.5281/zenodo.6794809\">via this Zenodo repository (version >9)</a>,<br>\n",
+    "users are requested to inspect the respective file share location from the revision of the APAV JOSS paper<br>\n",
+    "<a href=\"https://github.com/openjournals/joss-reviews/issues/4862\">see the comment from jdasm from November 30, 2022 here</a>.<br>\n",
+    "The respective GBCO dataset relevant here is especially this one R5038_00333-v02.epos.<br>\n",
+    "<a href=\"https://doi.org/10.1017/S1431927621012794\">Scientific details can be found here</a>.\n",
    "</div>"
   ]
  },
@@ -392,17 +447,19 @@
   "id": "ba59fb6e-2b63-4e95-99f7-75248c2a4f79",
   "metadata": {},
   "source": [
-    "### Step 2: Use available files in community-specific formats from your uploads section or download the above-mentioned examples from APAV."
+    "### Step 2: Use available files in community-specific formats from your uploads section/download examples from APAV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
-   "id": "f9bf1d28-abff-4628-aaa8-cb61f796e412",
+   "id": "7c256126-3704-4947-9598-d01707a97323",
   "metadata": {},
   "outputs": [],
   "source": [
-    "import shutil"
+    "! cd /home/atom_probe_tools/apav && wget https://www.zenodo.org/record/7908429/files/usa_denton_smith_apav_si.zip\n",
+    "zp.ZipFile(\"/home/atom_probe_tools/apav/usa_denton_smith_apav_si.zip\").extractall(\n",
+    "    path=\"/home/atom_probe_tools/apav\", members=None, pwd=None)"
   ]
  },
  {
@@ -412,7 +469,9 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "! cd /home/atom_probe_tools/apav && wget https://www.zenodo.org/record/77885531/files/apm_sprint14_apav_usa_denton_smith.zip && shutil.unpack_archive(\"apm_sprint14_apav_usa_denton_smith.zip\")"
+    "! cd /home/atom_probe_tools/apav && wget https://www.zenodo.org/record/7908429/files/usa_denton_smith_apav_gbco.zip\n",
+    "zp.ZipFile(\"/home/atom_probe_tools/apav/usa_denton_smith_apav_gbco.zip\").extractall(\n",
+    "    path=\"/home/atom_probe_tools/apav\", members=None, pwd=None)"
   ]
  },
  {
@@ -455,7 +514,7 @@
    "* **APAV** https://pypi.org/project/APAV v1.4.0\n",
    "* **NeXus** https://fairmat-experimental.github.io/nexus-fairmat-proposal latest\n",
    "\n",
-    "Last revision: Markus Kühbach, 2023/04/17\n",
+    "Last revision: Markus Kühbach, 2023/05/08\n",
    "\n",
    "<a href=\"https://www.fairmat-nfdi.eu/fairmat/\">FAIRmat</a> is a consortium on research data management which is part of the German NFDI.<br>\n",
    "The project is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project 460197019.\n",
@@ -489,7 +548,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.9.12"
  }
 },
 "nbformat": 4,

 %% Cell type:markdown id:88054eb2 tags:

 <img src="NOMADOasisLogo.png" alt="Drawing" style="height: 149px;"/>&emsp;&emsp;<img src="FAIRmatNewLogo.png" alt="Drawing" style="height: 149px;"/>

 %% Cell type:markdown id:9ecf3020 tags:

 # apmtools container cheatsheet

 %% Cell type:markdown id:4091c1e2 tags:

 The apmtools container offers functionalities to work and perform data analyses<br>
 with reconstructed and ranged datasets from atom probe microscopy experiments.<br>

 This container includes three tools:
 * **APTyzer** by Alexander Reichmann et al. https://github.com/areichm/APTyzer
 * **paraprobe-toolbox** by Markus Kühbach et al. https://gitlab.com/paraprobe/paraprobe-toolbox
 * **APAV** by Jesse Smith et al. https://gitlab.com/jesseds/apav

 Each tool comes shipped with tutorial-style examples.
 ***

+%% Cell type:markdown id:e29f2d4c-199c-4db3-8bab-6a341dc7ea83 tags:
+
+Load a Python library for unpacking ZIP files.
+
+%% Cell type:code id:731941ed-e3b9-4ae1-8372-8211ff900544 tags:
+
+``` python
+import zipfile as zp
+```
+
 %% Cell type:markdown id:e7afeb96-16a6-4d5a-9986-7aacfc9980f4 tags:

 <div class="alert alert-block alert-info">
 Having APTyzer, APAV, and paraprobe-toolbox configured in one container comes with the benefit<br>
-that one can switch between using these tools or couple them together via an e.g. jupyter notebook.
+that one can switch between these tools or create an analysis which couples them together in a<br>
+jupyter notebook.
 </div>

 %% Cell type:markdown id:fd044275-1ce9-4fea-bbe8-9903c3620a18 tags:

 <div class="alert alert-block alert-info">
 Containers of the Nomad Remote Tools Hub (NORTH) are configured such that they have access to the data in the uploads section of the Oasis instance.<br>
 Individual examples which exemplify how to use the tools in the apmtools container may have to be unpacked/decompressed before their first use.
 </div>

 %% Cell type:markdown id:69238a64 tags:

 # Getting started with APTyzer

 %% Cell type:markdown id:4aa12615-4734-4802-b6b9-407d6f1797bd tags:

 <div class="alert alert-block alert-info">
-Computational requirements: Examples with dataset with a few million ions like most used below should be processable even on a computer with a single core and say at least 4GB main memory.
+Computational requirements: Examples with dataset with a few million ions like most used below should be<br>
+processable even on a computer with a single core and say at least four GB main memory for docker tasks.
 </div>

 %% Cell type:markdown id:6e324fa1 tags:

 APTyzer is a Python-based tool with a graphical user interface (GUI) which can be used for displaying of a tomographically<br>
 reconstructed atom probe dataset thus enabling composition and interfacial excesses analyses. APTyzer can also be used for<br>
 preparing the meshing of single interface patches (grain or phase boundaries) via setting control points manually.<br>
 These points can be exported and loaded with the paraprobe-toolbox. APTyzer is maintained by Alexander Reichmann,<br>
 who is a PhD student with Lorenz Romaner at the Montanuniversität Leoben, Austria.<br>

 To use the tool you should navigate into the respective sub-directory.<br>

 %% Cell type:markdown id:1aa22278 tags:

 ### Step 1: Navigate into the respective sub-directory via the explorer panel on the left side

 %% Cell type:code id:5fd98af7 tags:

 ``` python
 ! cd /home/atom_probe_tools/aptyzer
 ```

 %% Cell type:markdown id:cc1408cc-45ec-40c6-90cb-dcec5f900619 tags:

 ### Step 2: Download and unpack an example dataset to get started

 %% Cell type:code id:ffa2006f tags:

 ``` python
-! cd /home/atom_probe_tools/aptyzer && wget https://www.zenodo.org/record/6794809/files/aut_leoben_leitner.tar.gz && tar -xvf aut_leoben_leitner.tar.gz
+! cd /home/atom_probe_tools/aptyzer && wget https://www.zenodo.org/record/7908429/files/aut_leoben_leitner.zip
+zp.ZipFile("/home/atom_probe_tools/aptyzer/aut_leoben_leitner.zip").extractall(
+    path="/home/atom_probe_tools/aptyzer", members=None, pwd=None)
 ```

 %% Cell type:markdown id:1d7b6c36-5967-46be-835d-3678c4ef8865 tags:

 ### Step 3: Start APTyzer by executing the APT_analyzer.ipynb in a new browser tab

 %% Cell type:markdown id:65cc8b4d-3f83-49fd-94f8-4309852499de tags:

 Open the aptyzer.ipynb via the JupyterLab side bar (on the left side). Clicking through this notebook will start the graphical user interface.

 %% Cell type:markdown id:8cea07f1-ccc5-40f4-881a-f9fec8a3f206 tags:

 ### Step 4: Run the tool using the POS and RRNG with the aut_leoben_leitner example dataset.

 %% Cell type:markdown id:051121d5-f318-42a7-81d8-a11349401e4c tags:

 There is a detailed tutorial (tutorial.pdf) how to use APTyzer for arbitary datasets. The tool offers export functionalities<br>
 for manually selected control points to support building computational geometry models of interfaces.<br>
 These control points can be exported to an HDF5 file and this file can be used for example as input for the<br>
 paraprobe-nanochem tool from the paraprobe-toolbox.<br>
 A respective tutorial how to achieve this is available in the paraprobe-toolbox teaching material<br>
 and here specifically the aut_leoben_leitner example. This tutorial will show how to use the<br>
 control points and create an automatically meshed model for an interface in the dataset.<br>
 ***

 %% Cell type:markdown id:7bda5aa4 tags:

 # Getting started with paraprobe-toolbox

 %% Cell type:markdown id:144832aa-b357-406c-84ad-d410d27bd8c0 tags:

 <div class="alert alert-block alert-info">
-Computational requirements: Examples with dataset with a few million ions like most used below should be processable even on a computer with a single core<br>
-and four GB main memory. Having multiple CPU cores can be useful as the tools of the paraprobe-toolbox use multi-threading for most of the<br>
-numerical and geometrical analyses.<br>
-Making guarantees about the maximum data set sizes (in terms of number of ions) is difficult as it strongly depends on which type of analyses are performed<br>
-and how these are parameterized. Noteworthy to mention is that even the largest examples at the time of writing this cheatsheet which are available in the<br>
-paraprobe-toolbox were processable with a laptop with 32GB main memory in total. Examples with a few million ions consumed not more than one to eight GB.<br>
+Computational requirements: Examples with dataset with a few million ions like most used below should be processable even on a computer with a<br>
+single core and four GB main memory for docker tasks. Having multiple CPU cores can be useful as the tools of the paraprobe-toolbox use<br>
+multi-threading for most of the numerical and geometrical analyses.<br>
+Making guarantees about the maximum data set sizes (in terms of number of ions) is difficult as it strongly depends on which type of analyses<br>
+are performed and how these are parameterized. Noteworthy to mention is that even the largest examples at the time of writing this cheatsheet<br>
+which are available in the paraprobe-toolbox were processable with a laptop with 32GB main memory in total. Examples with a few million ions<br>
+consumed not more than one to eight GB.<br>
 </div>

 %% Cell type:markdown id:ec70f493 tags:

 The paraprobe-toolbox is a collection of software tools for applying computational geometry tasks on point cloud data<br>
 such as tomographic reconstructions of atom probe data to extract and characterize microstructural features.<br>
-The tool is developed by <a href="https://arxiv.org/abs/2205.13510">Markus Kühbach et al.</a>. The tool is instructed via a jupyter notebook which documents<br>
-how to chain via scripting different analysis steps. Apart from this, it is possible to use the tools via<br>
-Python scripting to perform e.g. batch processing on computer clusters.<br>
+The tool is developed by <a href="https://arxiv.org/abs/2205.13510">Markus Kühbach et al.</a><br>
+The tool is instructed via a jupyter notebook which documents how to chain and script different<br>
+analysis steps into a workflow using Python. This can be useful especially for batch processing<br>
+on computer clusters.<br>

 Tools of the paraprobe-toolbox are chained into computational workflows. Each step uses a different scientific<br>
 speciality tool. All these tools have *paraprobe* as a prefix in their executable name.<br>
 The tools use CPU parallelization and specific libraries of the computational geometry or other specialists'<br>
 communities. The jupyter notebooks enable users to achieve a complete automation of their<br>
 data analyses (if this is desired). Internally, each tools keeps track of input and output files via<br>
 hashes and time stamps to enable provenance tracking and support repeatable and reproducible research.<br>
 All results are openly accessible and documented via so-called <a href="https://fairmat-experimental.github.io/nexus-fairmat-proposal">NeXus application definitions (see the NORTH/apmtools pages).</a><br>
 Such workflows can include parameter studies, mesh processing, writing of reports, and creation of plots.<br>

 %% Cell type:markdown id:57dd96fe tags:

 ### Step 1: Enter the paraprobe-toolbox/teaching/example_analyses sub-directory via the explorer panel to the left.

 %% Cell type:code id:f6461c64-cbcd-4d0a-8be3-850205382a71 tags:

 ``` python
 # analysis results are stored here:
 ! ls /home/atom_probe_tools/paraprobe-toolbox/teaching/example_analyses
 # measurements are stored here:
 ! ls /home/atom_probe_tools/paraprobe-toolbox/teaching/example_data
 # enter the root/prefix/home directory of paraprobe-toolbox:
 ! cd /home/atom_probe_tools/paraprobe-toolbox
 ```

 %% Cell type:markdown id:bce1299f-24fb-4ffc-99d3-99aac8921136 tags:

-Reconstruction and ranging data (i.e. POS, ePOS, APT, RNG, RRNG, NXS) to be consumed by paraprobe-toolbox should be stored in *teaching/example_data*.<br>
-Analyses with relevant jupyter notebooks should be stored in *teaching/example_analyses*.
+Reconstruction and ranging data (i.e. POS, ePOS, APT, RNG, RRNG, NXS) to be consumed by paraprobe-toolbox<br>
+should be stored in *teaching/example_data*. Analyses with relevant jupyter notebooks should be stored<br>
+in *teaching/example_analyses*.

 The tools use NeXus data schemas and HDF5 files. The specification of these files is documented in the so-called<br>
 <a href="https://fairmat-experimental.github.io/nexus-fairmat-proposal">nexus-fairmat-proposal</a> (see specifically the NORTH/apmtools pages).

 %% Cell type:markdown id:670cc82a-2ea2-4a48-bded-793b15e2f58f tags:

 ### Step 2: Unpack the example datasets or use a NeXus/HDF5 file from your uploads section.

 %% Cell type:code id:5d4dc19a-6d1d-4e04-ab61-0b40038e8577 tags:

 ``` python
 ! cd /home/atom_probe_tools/paraprobe-toolbox/teaching/example_data/usa_portland_wang && tar -xvf usa_portland_wang.tar.gz
 ! cd /home/atom_probe_tools/paraprobe-toolbox/teaching/example_data/aut_leoben_leitner && tar -xvf aut_leoben_leitner.tar.gz
 ```

+%% Cell type:code id:5c6e219d-1a5d-4417-b157-3745558f6660 tags:
+
+``` python
+# additional optional examples are available for testing transcoding of Matlab figure files
+! cd /home/atom_probe_tools/paraprobe-toolbox/teaching/example_data/ger_erlangen_felfer && wget https://www.zenodo.org/record/7908429/files/ger_erlangen_felfer_ck10.zip
+zp.ZipFile("/home/atom_probe_tools/paraprobe-toolbox/teaching/example_data/ger_erlangen_felfer/ger_erlangen_felfer_ck10.zip").extractall(
+    path="/home/atom_probe_tools/paraprobe-toolbox/teaching/example_data/ger_erlangen_felfer", members=None, pwd=None)
+```
+
+%% Cell type:code id:893e0ef4-90af-4946-8b6c-454f0db2fd89 tags:
+
+``` python
+# additional optional examples are available for testing with a very small dataset for efficient debugging
+! cd /home/atom_probe_tools/paraprobe-toolbox/teaching/example_data/usa_denton_smith && wget https://www.zenodo.org/record/7908429/files/usa_denton_smith_apav_si.zip
+zp.ZipFile("/home/atom_probe_tools/paraprobe-toolbox/teaching/example_data/usa_denton_smith/usa_denton_smith_apav_si.zip").extractall(
+    path="/home/atom_probe_tools/paraprobe-toolbox/teaching/example_data/usa_denton_smith", members=None, pwd=None)
+```
+
 %% Cell type:markdown id:c409e217-e14a-4bba-a07e-e16753288bd8 tags:

 Alternatively, NeXus/HDF5 files in your uploads section can also be used. In order to do so, you can move<br>
 the respective file(s) into one of the example_data analysis sections inside the container.<br>
 This works because the container is configured such that the directory which represents<br>
 the uploads section is mounted into the apmtools container and is accessible via config directory.<br>

 %% Cell type:code id:22c54c6b-fe0d-4e47-9247-4d5f42761134 tags:

 ``` python
 # ! mv  # move the file from the config directory to your desired directory in the teaching/example_data area
 ```

 %% Cell type:markdown id:4fa357bd-7e4a-4c78-afeb-cc4debe34f48 tags:

 ### Step 3: Start with the unpacked example usa_portland_wang.

 %% Cell type:markdown id:8c8865e7-70e9-4309-93c3-5eaf73e2d2e0 tags:

 Each example of the paraprobe-toolbox comes with a detailed jupyter notebook which guides through the analysis.<br>
 Especially the usa_portland_wang and the aut_leoben_leitner (see connection to APTyzer tool) examples<br>
 show the multi-faceted analysis features of the tools in the paraprobe-toolbox.<br>
 These examples show how Python and C/C++ applications work together with NeXus, HDF5, and H5Web<br>
 to provide tools which can be flexibly be coupled to other open-source atom probe software and<br>
 used to complement analyses which have been achieved with commercial software like APSuite/IVAS.<br>
 More complex examples with customizable jupyter notebooks are available in the documentation<br>
 of the tool https://paraprobe-toolbox.readthedocs.io/en/latest/.<br>
 ***

 %% Cell type:markdown id:80d09ac1-e56d-4923-9ba3-9e869d368a51 tags:

 # Getting started with APAV

 %% Cell type:markdown id:bfec8daa-59c3-47b9-a0af-8f03afd5c9a6 tags:

 <div class="alert alert-block alert-info">
-Computational requirements: Examples with dataset with a few million ions like most used below should be processable even on a computer with a single core and say at least 4GB main memory.<br>
-Having multiple CPU cores can be useful as APAV uses multi-threading for some costly numerical analyses.
+Computational requirements: Examples with dataset with a few million ions like most used below should be processable even<br>
+on a computer with a single core and say at least four GB main memory for docker tasks. Having multiple CPU cores can be<br>
+useful because APAV uses multi-threading for some costly numerical analyses.
 </div>

 %% Cell type:markdown id:c10fcef1-e82f-4400-af22-f6df70687faf tags:

 APAV (Atom Probe Analysis and Visualization) is a Python package for analysis and visualization of atom probe tomography datasets.<br>
 The tool is developed by <a href="https://joss.theoj.org/papers/10.21105/joss.04862">Jesse Smith et al.</a>. Complementary to the design of the paraprobe-toolbox functionalities,<br>
 APAV can be chained into workflows via e.g. a jupyter notebook. A particular functional strength and focus of APAV<br>
 has been ranging and handling of so-called multi-hit events via a graphical user interface via Python.<br>

 APAV has a detailed documentation https://apav.readthedocs.io/en/latest/index.html.

 %% Cell type:markdown id:b75f60d5-3151-4986-9222-ddaeb1fa7e5f tags:

 <div class="alert alert-block alert-info">
-Jesse Smith has also made available example data which can be used for learning the multi-hit event analyses capabilities of APAV. If these files are not available <a href="https://doi.org/10.5281/zenodo.6794809">via this Zenodo repository (version >9)</a>,<br>
-users are requested to inspect the respective file share location from the revision of the APAV JOSS paper <a href="https://github.com/openjournals/joss-reviews/issues/4862">see the comment from jdasm from November 30, 2022 here</a><br>
-The respective GBCO dataset relevant here is especially this one R5038_00333-v02.epos.  <a href="https://doi.org/10.1017/S1431927621012794">Scientific details can be found here</a>.
+Jesse Smith has also made available example data which can be used for learning the multi-hit event analyses capabilities of APAV.<br>
+If these files are not available <a href="https://doi.org/10.5281/zenodo.6794809">via this Zenodo repository (version >9)</a>,<br>
+users are requested to inspect the respective file share location from the revision of the APAV JOSS paper<br>
+<a href="https://github.com/openjournals/joss-reviews/issues/4862">see the comment from jdasm from November 30, 2022 here</a>.<br>
+The respective GBCO dataset relevant here is especially this one R5038_00333-v02.epos.<br>
+<a href="https://doi.org/10.1017/S1431927621012794">Scientific details can be found here</a>.
 </div>

 %% Cell type:markdown id:f88501b0-a4bd-427d-b303-a45a3556ab35 tags:

 ### Step 1: Enter the apav sub-directory via the explorer panel to the left.

 %% Cell type:code id:474644bb-75fd-4514-bab5-8d9d30e46610 tags:

 ``` python
 ! ls /home/atom_probe_tools/apav
 ```

 %% Cell type:markdown id:ba59fb6e-2b63-4e95-99f7-75248c2a4f79 tags:

-### Step 2: Use available files in community-specific formats from your uploads section or download the above-mentioned examples from APAV.
+### Step 2: Use available files in community-specific formats from your uploads section/download examples from APAV.

-%% Cell type:code id:f9bf1d28-abff-4628-aaa8-cb61f796e412 tags:
+%% Cell type:code id:7c256126-3704-4947-9598-d01707a97323 tags:

 ``` python
-import shutil
+! cd /home/atom_probe_tools/apav && wget https://www.zenodo.org/record/7908429/files/usa_denton_smith_apav_si.zip
+zp.ZipFile("/home/atom_probe_tools/apav/usa_denton_smith_apav_si.zip").extractall(
+    path="/home/atom_probe_tools/apav", members=None, pwd=None)
 ```

 %% Cell type:code id:5d092aaf-663d-4ef3-9ac7-5fc6762d785f tags:

 ``` python
-! cd /home/atom_probe_tools/apav && wget https://www.zenodo.org/record/77885531/files/apm_sprint14_apav_usa_denton_smith.zip && shutil.unpack_archive("apm_sprint14_apav_usa_denton_smith.zip")
+! cd /home/atom_probe_tools/apav && wget https://www.zenodo.org/record/7908429/files/usa_denton_smith_apav_gbco.zip
+zp.ZipFile("/home/atom_probe_tools/apav/usa_denton_smith_apav_gbco.zip").extractall(
+    path="/home/atom_probe_tools/apav", members=None, pwd=None)
 ```

 %% Cell type:markdown id:f65c621e-05f5-45ca-9a01-4cbd3206bc55 tags:

 The APAV documentation details which formats are supported.

 %% Cell type:markdown id:9e3fb02c-1706-4864-a27c-06aaadc5c11b tags:

 ### Step 3: Start a new jupyter-notebook and use it to compose your own analysis workflow.

 %% Cell type:markdown id:57c641c2-748c-4c8a-a2f1-4d9428f141a5 tags:

 The APAV documentation can support you with starting your own analyses.
 ***

 %% Cell type:markdown id:a27bbf75-8a2a-4df3-8096-f942afc5e656 tags:

 # Version, funding, feedback
 * **APTyzer** https://github.com/areichm/APTyzer 887b82f
 * **paraprobe-toolbox** https://gitlab.com/paraprobe/paraprobe-toolbox e349fd3 (v0.4)
 * **APAV** https://pypi.org/project/APAV v1.4.0
 * **NeXus** https://fairmat-experimental.github.io/nexus-fairmat-proposal latest

-Last revision: Markus Kühbach, 2023/04/17
+Last revision: Markus Kühbach, 2023/05/08

 <a href="https://www.fairmat-nfdi.eu/fairmat/">FAIRmat</a> is a consortium on research data management which is part of the German NFDI.<br>
 The project is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project 460197019.

 If you spot issues with this container or you would like to suggest how we can improve the apmtools container:<br>
 Please contact the respective maintainer of this container.

 %% Cell type:code id:ff8b1235-d4c8-4ce0-9013-d1d87b7191df tags:

 ``` python
 ```

--- a/docker/apmtools/Dockerfile
+++ b/docker/apmtools/Dockerfile
@@ -22,6 +22,7 @@ RUN mkdir -p /home \
  && mkdir -p /home/atom_probe_tools/aptyzer

 COPY Cheatsheet.ipynb FAIRmatNewLogo.png NOMADOasisLogo.png /home/atom_probe_tools/
+COPY APAVRunTestSuite.ipynb /home/atom_probe_tools/apav/
 ENV PATH=/usr/local/miniconda3/bin:/home/atom_probe_tools/paraprobe-toolbox/code/thirdparty/mandatory/cmake/cmake-3.26.3/localinstallation/bin:$PATH

 # query version afterwards like so dpkg -s <packagename> | grep '^Version:'
@@ -35,7 +36,7 @@ RUN cd ~ \
  && chmod +x nodesource_setup.sh \
  && ./nodesource_setup.sh \
  && apt install -y nodejs=18.16.0-deb-1nodesource1 \
-  && apt install -y m4=1.4.18-4 file=1:5.38-4 git=1:2.25.1-1ubuntu3.11 wget=1.20.3-1ubuntu2 mesa-common-dev=21.2.6-0ubuntu0.1~20.04.2 libglu1-mesa-dev=9.0.1-1build1 build-essential=12.8ubuntu1.1 mpich=3.3.2-2build1 libgmp-dev=2:6.2.0+dfsg-4ubuntu0.1 libmpfr-dev=4.0.2-1 libssl-dev=1.1.1f-1ubuntu2.18 hwloc=2.1.0+dfsg-4 \
+  && apt install -y m4=1.4.18-4 file=1:5.38-4 git=1:2.25.1-1ubuntu3.11 wget=1.20.3-1ubuntu2 libgl1-mesa-dev=21.2.6-0ubuntu0.1~20.04.2 libglu1-mesa-dev=9.0.1-1build1 build-essential=12.8ubuntu1.1 mpich=3.3.2-2build1 libgmp-dev=2:6.2.0+dfsg-4ubuntu0.1 libmpfr-dev=4.0.2-1 libssl-dev=1.1.1f-1ubuntu2.18 hwloc=2.1.0+dfsg-4 \
  && wget https://repo.anaconda.com/miniconda/Miniconda3-py38_23.1.0-1-Linux-x86_64.sh \
  && mv Miniconda3-py38_23.1.0-1-Linux-x86_64.sh miniconda3-py38_23.1.0-1-Linux-x86_64.sh \
  && chmod +x miniconda3-py38_23.1.0-1-Linux-x86_64.sh \
@@ -51,7 +52,7 @@ RUN cd ~ \
  && python3 -m pip install ecdf==0.7.0 pytictoc==1.5.2 ifes-apt-tc-data-modeling==0.0.8 python-docs-theme==2023.3.1 \
  && cd /home \
  && cd /home/atom_probe_tools/apav \
-  && python3 -m pip install apav==1.4.0 \
+  && python3 -m pip install apav[dev]==1.4.0 \
  && cd /home/atom_probe_tools \
  && git clone https://github.com/areichm/APTyzer.git \
  && cd APTyzer \
@@ -111,6 +112,9 @@ RUN cd ~ \
  && cd voro \
  && git checkout 56d619faf3479313399516ad71c32773c29be859 \
  && cd /home/atom_probe_tools/paraprobe-toolbox \
+  && mkdir -p /home/atom_probe_tools/paraprobe-toolbox/teaching/example_data/usa_denton_smith \
+  && mkdir -p /home/atom_probe_tools/paraprobe-toolbox/teaching/example_data/ger_erlangen_felfer \
+  && cd /home/atom_probe_tools/paraprobe-toolbox \
  && sed -i 's|set(MYPROJECTPATH "<<YOURPATH>>/paraprobe-toolbox")|set(MYPROJECTPATH \"'"$PWD"'\/\")|g' code/PARAPROBE.Dependencies.cmake \
  && cd /home/atom_probe_tools/paraprobe-toolbox \
  && chmod +x PARAPROBE.Step05.Build.Tools.sh \