Python demo: research

README.md

@@ -4,11 +4,13 @@
 
 - [ ] python
     - [x] get jupyter lab running
-    - [ ] drawing utility
-    - [ ] interface for pathfinding
+    - [x] drawing utility
+    - [x] interface for pathfinding
     - [ ] research methods
     - [ ] implement methods
+    - [ ] performance measurement: time/visited nodes
     - [ ] finish text on the page
+    - [ ] create a dedicated python script
 - [ ] C++
     - [ ] re-use 2D game engine
     - [ ] add mouse-click movement (direct, no pathfinding, player always selected)

python/pathfinding_demo.ipynb

@@ -24,7 +24,7 @@
     {
       "id": "16f8fedb-ac10-450c-b5c7-f820a985902d",
       "cell_type": "markdown",
-      "source": "# Pathfinding demo",
+      "source": "# Pathfinding demo\n\n## Optimal path finding\n\nNon-exhaustive list of methods follows. \n\n$V$ is the set of all vertices; $|V|$ is the size of the set\n\n1. [Bellman-Ford algorithm](https://en.wikipedia.org/wiki/Bellman%E2%80%93Ford_algorithm)\n    - exhaustive method\n    - $O(|V|*|E|)$\n2. Dijkstra\n3. A*",
       "metadata": {
         "tags": [],
         "slideshow": {
@@ -36,7 +36,7 @@
     {
       "id": "fbdf9d2c-d050-4744-b559-abc71e550725",
       "cell_type": "code",
-      "source": "#\n# Imports\n#\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom typing import Protocol\nfrom dataclasses import dataclass",
+      "source": "#\n# Imports\n#\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom typing import Protocol",
       "metadata": {
         "trusted": true,
         "tags": [],