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Notebook-Tests: nbmake + Subprocess-Zellen + inkrementeller Test

Browse source 
Alle 25 Notebooks sind nun fehlerfrei testbar (25 passed). Dafür drei
Änderungen im Build und eine neue Test-Infrastruktur:

1. pyproject.toml: nbmake>=1.5 in der dev-Gruppe.

2. build_version_04.py: Notebooks bekommen zwei Setup-Zellen und
   Subprocess-Zellen für Programme, die nicht direkt im Kernel laufen:
   - NOTEBOOK_SETUP_2: __file__ definieren, sys.path für or_kern,
     multiprocessing auf fork (Spawn findet Kernel-Funktionen nicht).
   - _braucht_subprocess: erkennt highspy (Solver-Konflikt), pytest
     (SystemExit), get_context('spawn'), subprocess+__file__ (sucht
     .py-Dateien), sys.exit (SystemExit). Diese Programme erscheinen
     als Markdown-Codeblock (sichtbar, nicht ausführbar) plus einer
     Subprocess-Zelle mit NO_COLOR=1 (verhindert ANSI-Codes, die der
     Parser von Mutationstest.py nicht verarbeitet).
   - Solver-Konflikt-Erkennung: wenn ein Kapitel sowohl ortools als
     auch highspy importiert, laufen auch reine ortools-Programme als
     Subprocess (highspy schon im Kernel).

3. teste_code_04.py: inkrementeller Test — nur geänderte/neue Programme
   und Notebooks (via git status). Netzabhängige Dateien (yfinance)
   werden automatisch erkannt und nur getestet, wenn Internet verfügbar.

4. PLAN.md Abschnitt 10: neuer Verifikationsschritt 4 (pytest --nbmake).
   PROGRESS.md und CLAUDE.md aktualisiert.

Gefundene und behobene Probleme:
- or_kern-Import: sys.path im Notebook erweitert.
- __file__ nicht definiert: zweite Setup-Zelle setzt es.
- multiprocessing spawn: fork erzwungen (Ein_System_Vier_Ansaetze.py).
- ortools/highspy-Konflikt: Subprocess für beide Solver.
- pytest SystemExit: Subprocess für test_or_kern.py und Mutationstest.py.
- sys.exit(0): Subprocess für Installationstest.py.
- ANSI-Codes in pytest-Ausgabe: NO_COLOR=1 in Subprocess-Zelle.
This commit is contained in:
dschlueter 2026-09-11 22:38:59 +02:00
commit a4fc247116
70 changed files with 4073 additions and 305 deletions
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36
OR_HTML_04/Notebooks_04/anhang-fehlerdiagnose.ipynb generated
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@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Wenn die fünf Schritte nicht reichen: den Konflikt einkreisen\n",
"\n",
"`Konfliktsuche.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},

36
OR_HTML_04/Notebooks_04/bruecke.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Das Programm\n",
"\n",
"`Strukturbruecke.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},

81
OR_HTML_04/Notebooks_04/cpsat.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Was Propagation leistet\n",
"\n",
"`Propagation_Demo.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -116,6 +150,15 @@
"`CP_SAT_Vertretungssystem.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Praxisfall: Dynamisches Vertretungssystem\n",
"\n",
"`CP_SAT_Vertretungssystem.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -375,6 +418,15 @@
"`JobShop_Intervalle.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Intervallvariablen: Job-Shop-Scheduling\n",
"\n",
"`JobShop_Intervalle.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -505,6 +557,15 @@
"`Parallele_Suche.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Parallele Suche: was `num_workers` wirklich bewirkt\n",
"\n",
"`Parallele_Suche.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -717,6 +778,15 @@
"`CP_SAT_Statusfaelle.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Die fünf Antworten von CP-SAT\n",
"\n",
"`CP_SAT_Statusfaelle.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -957,6 +1027,15 @@
"`Strafgewichte.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Finde den Denkfehler\n",
"\n",
"`Strafgewichte.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,

45
OR_HTML_04/Notebooks_04/cvar.ipynb generated
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@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Die zwei Schwächen des Markowitz-Modells\n",
"\n",
"`VaR_CVaR_Demo.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -152,6 +186,15 @@
"`CVaR_Portfolio.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implementierung: CVaR-Portfolio mit Reibung\n",
"\n",
"`CVaR_Portfolio.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,

36
OR_HTML_04/Notebooks_04/dekomposition.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Das Programm\n",
"\n",
"`Spaltengenerierung.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},

45
OR_HTML_04/Notebooks_04/dynamische-programmierung.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Die Bellman-Gleichung\n",
"\n",
"`Bellman_Minimalbeispiel.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -132,6 +166,15 @@
"`Mehrperiodige_Order_Execution.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Das Almgren-Chriss-Problem: optimale Orderausführung\n",
"\n",
"`Mehrperiodige_Order_Execution.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,

63
OR_HTML_04/Notebooks_04/einfuehrung.ipynb generated
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@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Warum Ausprobieren scheitert — mit eigener Rechnung\n",
"\n",
"`Brute_Force_Vergleich.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -150,6 +184,15 @@
"`Bausteine_Vorlage.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Die Bausteine in der Praxis: eine Vorlage\n",
"\n",
"`Bausteine_Vorlage.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -250,6 +293,15 @@
"`Bot_Allokation.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Umsetzung mit Google OR-Tools\n",
"\n",
"`Bot_Allokation.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -363,6 +415,15 @@
"`Excel_Bruecke.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Von Excel zu Python: Ihre Daten liegen schon da\n",
"\n",
"`Excel_Bruecke.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,

63
OR_HTML_04/Notebooks_04/finanzdaten.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Logarithmische Rendite{idx:Logarithmische Rendite}\n",
"\n",
"`Renditen_Vergleich.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -115,6 +149,15 @@
"`Schaetzrauschen_Demo.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Das Schätzfehler{idx:Schätzfehler}-Problem\n",
"\n",
"`Schaetzrauschen_Demo.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -228,6 +271,15 @@
"`Finanzdaten_Ledoit_Wolf.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Praxis: Datenpipeline mit korrekter Spaltenreihenfolge\n",
"\n",
"`Finanzdaten_Ledoit_Wolf.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -392,6 +444,15 @@
"`Kovarianz_Falle.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Finde den Denkfehler\n",
"\n",
"`Kovarianz_Falle.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,

63
OR_HTML_04/Notebooks_04/fundament.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dasselbe in Python\n",
"\n",
"`Matrixform.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -101,6 +135,15 @@
"`Konvexitaet_Demo.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Konvexität sichtbar machen\n",
"\n",
"`Konvexitaet_Demo.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -191,6 +234,15 @@
"`Visualisierung_Loesungsraum.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Geometrische Visualisierung des Lösungsraums\n",
"\n",
"`Visualisierung_Loesungsraum.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -348,6 +400,15 @@
"`Skalierung_Kondition.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Das Experiment\n",
"\n",
"`Skalierung_Kondition.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,

63
OR_HTML_04/Notebooks_04/graphen.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Das Minimum-Cost-Flow-Problem (MCNFP)\n",
"\n",
"`Min_Cost_Flow.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -144,6 +178,15 @@
"`Zuordnung_Ungarisch.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Der Satz von Birkhoff und von Neumann{idx:Satz von Birkhoff und von Neumann}\n",
"\n",
"`Zuordnung_Ungarisch.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -266,6 +309,15 @@
"`VRP_Flotten_Routing.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Praxisbeispiel: Flotten-Routing\n",
"\n",
"`VRP_Flotten_Routing.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -448,6 +500,15 @@
"`VRP_Kapazitaetsfalle.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Finde den Denkfehler\n",
"\n",
"`VRP_Kapazitaetsfalle.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,

54
OR_HTML_04/Notebooks_04/handelsmaschine.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Die Engine\n",
"\n",
"`QuantitativeTradingEngine.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -352,6 +386,15 @@
"`Backtest_Fallen.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Die fünf Selbsttäuschungen des Backtestens\n",
"\n",
"`Backtest_Fallen.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -465,6 +508,15 @@
"`Data_Snooping.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Finde den Denkfehler\n",
"\n",
"`Data_Snooping.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,

63
OR_HTML_04/Notebooks_04/lp.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Das Simplex-Tableau in Python\n",
"\n",
"`Simplex_Tableau_LP.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -207,6 +241,15 @@
"`Dualitaet_Nachweis.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Der starke Dualitätssatz\n",
"\n",
"`Dualitaet_Nachweis.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -302,6 +345,15 @@
"`Sensitivitaetsanalyse.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Praxisfall: Sensitivitätsanalyse mit korrekten Schattenpreisen\n",
"\n",
"`Sensitivitaetsanalyse.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -429,6 +481,15 @@
"`Toleranzen_und_Entartung.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Die ehrliche Auskunft: Schattenpreis-Spannen\n",
"\n",
"`Toleranzen_und_Entartung.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,

54
OR_HTML_04/Notebooks_04/markowitz.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Warum Diversifikation funktioniert\n",
"\n",
"`Diversifikation_Demo.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -94,6 +128,15 @@
"`Markowitz_CVXPY.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Vollimplementierung mit CVXPY\n",
"\n",
"`Markowitz_CVXPY.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -374,6 +417,15 @@
"`Renditeschaetzung_Falle.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Finde den Denkfehler\n",
"\n",
"`Renditeschaetzung_Falle.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,

36
OR_HTML_04/Notebooks_04/mehrziel.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Das Programm\n",
"\n",
"`Mehrziel_Pareto.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},

54
OR_HTML_04/Notebooks_04/metaheuristiken.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulated Annealing\n",
"\n",
"`Simulated_Annealing.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -346,6 +380,15 @@
"`Metaheuristik_vs_Exakt.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Ab wann lohnt es sich?\n",
"\n",
"`Metaheuristik_vs_Exakt.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -595,6 +638,15 @@
"`Large_Neighborhood_Search.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Large Neighborhood Search\n",
"\n",
"`Large_Neighborhood_Search.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,

174
OR_HTML_04/Notebooks_04/milp.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Warum Runden fundamental scheitert\n",
"\n",
"`Runden_Gegenbeispiel.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -138,6 +172,15 @@
"`Rucksack.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Beispiel: Das Rucksackproblem{idx:Rucksackproblem}\n",
"\n",
"`Rucksack.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -213,6 +256,15 @@
"`MILP_Portfolio_Fixgebuehren.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Praxisfall: Portfolio mit Ordergebühren und Kardinalitätsgrenze\n",
"\n",
"`MILP_Portfolio_Fixgebuehren.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -434,11 +486,14 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"## Alle Statusfälle behandeln\n",
"\n",
"`Solverstatus_und_Gap.py`\n",
"\n",
"```python\n",
"#!/usr/bin/env python3\n",
"\n",
"# Solverstatus_und_Gap.py\n",
@ -654,7 +709,36 @@
" print(\" Warmstart_Effekt.py - dort halbiert ein Heuristik-Hinweis die Zeit).\")\n",
" print(\" * Sie planen laufend neu und der gestrige Plan ist fast noch gueltig.\")\n",
" print(\"In beiden Faellen gilt: MESSEN, nicht glauben.\")\n",
" print(\"=\" * 78)"
" print(\"=\" * 78)\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# In einem Notebook kann 'Solverstatus_und_Gap.py' nicht direkt ausgefuehrt werden\n",
"# (ortools/highspy-Konflikt im selben Kernel, pytest oder multiprocessing).\n",
"# Deshalb als Subprocess — so laeuft es genauso wie im Terminal.\n",
"import subprocess, sys, os\n",
"# NO_COLOR/TERM=dumb verhindern ANSI-Codes in der Ausgabe (pytest\n",
"# im Jupyter-Kernel wuerde sonst farbige Escape-Sequenzen ausgeben).\n",
"_env = dict(os.environ, NO_COLOR='1', TERM='dumb')\n",
"for _d in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" _p = os.path.join(_d, 'Solverstatus_und_Gap.py')\n",
" if os.path.isfile(_p):\n",
" _r = subprocess.run([sys.executable, _p], cwd=_d, env=_env,\n",
" capture_output=True, text=True)\n",
" if _r.stdout: print(_r.stdout)\n",
" if _r.stderr: print(_r.stderr)\n",
" _r.check_returncode()\n",
" break\n",
"else:\n",
" print('Solverstatus_und_Gap.py nicht gefunden')"
]
},
{
@ -667,11 +751,14 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"## Warm-Starts: was sie können und was nicht\n",
"\n",
"`Warmstart_Effekt.py`\n",
"\n",
"```python\n",
"#!/usr/bin/env python3\n",
"\n",
"# Warmstart_Effekt.py\n",
@ -819,7 +906,36 @@
" print(\" davon ab, wie schnell der Solver von allein eine vergleichbar gute\")\n",
" print(\" Loesung findet. Das ist die eigentliche Lehre: Ein Warm-Start ist\")\n",
" print(\" eine Messung wert, keine Glaubensfrage.\")\n",
" print(\"=\" * 78)"
" print(\"=\" * 78)\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# In einem Notebook kann 'Warmstart_Effekt.py' nicht direkt ausgefuehrt werden\n",
"# (ortools/highspy-Konflikt im selben Kernel, pytest oder multiprocessing).\n",
"# Deshalb als Subprocess — so laeuft es genauso wie im Terminal.\n",
"import subprocess, sys, os\n",
"# NO_COLOR/TERM=dumb verhindern ANSI-Codes in der Ausgabe (pytest\n",
"# im Jupyter-Kernel wuerde sonst farbige Escape-Sequenzen ausgeben).\n",
"_env = dict(os.environ, NO_COLOR='1', TERM='dumb')\n",
"for _d in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" _p = os.path.join(_d, 'Warmstart_Effekt.py')\n",
" if os.path.isfile(_p):\n",
" _r = subprocess.run([sys.executable, _p], cwd=_d, env=_env,\n",
" capture_output=True, text=True)\n",
" if _r.stdout: print(_r.stdout)\n",
" if _r.stderr: print(_r.stderr)\n",
" _r.check_returncode()\n",
" break\n",
"else:\n",
" print('Warmstart_Effekt.py nicht gefunden')"
]
},
{
@ -832,11 +948,14 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"## Finde den Denkfehler\n",
"\n",
"`Big_M_Falle.py`\n",
"\n",
"```python\n",
"#!/usr/bin/env python3\n",
"\n",
"# Big_M_Falle.py\n",
@ -1030,7 +1149,36 @@
" print(\"nicht darauf: Presolve kann das nur, wenn eine implizite Schranke\")\n",
" print(\"herleitbar ist. Die Pruefung aus pruefe() kostet Millisekunden und\")\n",
" print(\"funktioniert immer.\")\n",
" print(\"=\" * 78)"
" print(\"=\" * 78)\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# In einem Notebook kann 'Big_M_Falle.py' nicht direkt ausgefuehrt werden\n",
"# (ortools/highspy-Konflikt im selben Kernel, pytest oder multiprocessing).\n",
"# Deshalb als Subprocess — so laeuft es genauso wie im Terminal.\n",
"import subprocess, sys, os\n",
"# NO_COLOR/TERM=dumb verhindern ANSI-Codes in der Ausgabe (pytest\n",
"# im Jupyter-Kernel wuerde sonst farbige Escape-Sequenzen ausgeben).\n",
"_env = dict(os.environ, NO_COLOR='1', TERM='dumb')\n",
"for _d in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" _p = os.path.join(_d, 'Big_M_Falle.py')\n",
" if os.path.isfile(_p):\n",
" _r = subprocess.run([sys.executable, _p], cwd=_d, env=_env,\n",
" capture_output=True, text=True)\n",
" if _r.stdout: print(_r.stdout)\n",
" if _r.stderr: print(_r.stderr)\n",
" _r.check_returncode()\n",
" break\n",
"else:\n",
" print('Big_M_Falle.py nicht gefunden')"
]
}
],

36
OR_HTML_04/Notebooks_04/modellierung.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Derselbe Datensatz, fünf Modelle\n",
"\n",
"`Vom_Wunsch_zum_Modell.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},

125
OR_HTML_04/Notebooks_04/oekosystem.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Die Entscheidung in drei Fragen\n",
"\n",
"`Solver_Wahl.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -121,11 +155,14 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"## Wie die Isolation aussieht, wenn sie tragen soll\n",
"\n",
"`Ein_System_Vier_Ansaetze.py`\n",
"\n",
"```python\n",
"#!/usr/bin/env python3\n",
"\n",
"# Ein_System_Vier_Ansaetze.py\n",
@ -270,7 +307,45 @@
" print(\"Alle Wege fuehren zum selben, von Hand bestaetigten Optimum.\")\n",
" print(\"(Die Zeiten enthalten Prozessstart und Import - sie messen NICHT die\")\n",
" print(\" reine Solverleistung. Die Uebungsaufgabe 'Laufzeitvergleich' trennt beides.)\")\n",
" print(\"=\" * 78)"
" print(\"=\" * 78)\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# In einem Notebook kann 'Ein_System_Vier_Ansaetze.py' nicht direkt ausgefuehrt werden\n",
"# (ortools/highspy-Konflikt im selben Kernel, pytest oder multiprocessing).\n",
"# Deshalb als Subprocess — so laeuft es genauso wie im Terminal.\n",
"import subprocess, sys, os\n",
"# NO_COLOR/TERM=dumb verhindern ANSI-Codes in der Ausgabe (pytest\n",
"# im Jupyter-Kernel wuerde sonst farbige Escape-Sequenzen ausgeben).\n",
"_env = dict(os.environ, NO_COLOR='1', TERM='dumb')\n",
"for _d in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" _p = os.path.join(_d, 'Ein_System_Vier_Ansaetze.py')\n",
" if os.path.isfile(_p):\n",
" _r = subprocess.run([sys.executable, _p], cwd=_d, env=_env,\n",
" capture_output=True, text=True)\n",
" if _r.stdout: print(_r.stdout)\n",
" if _r.stderr: print(_r.stderr)\n",
" _r.check_returncode()\n",
" break\n",
"else:\n",
" print('Ein_System_Vier_Ansaetze.py nicht gefunden')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Die Schichten im Überblick\n",
"\n",
"`Modellierungsschichten.py`\n"
]
},
{
@ -470,11 +545,14 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"## Vier Stufen an einem Transportproblem\n",
"\n",
"`Vektorisierte_Modellgenerierung.py`\n",
"\n",
"```python\n",
"#!/usr/bin/env python3\n",
"\n",
"# Vektorisierte_Modellgenerierung.py\n",
@ -714,7 +792,36 @@
" print(\" Schreibweise derselben Matrix - Schleife gegen Kronecker-Produkt.\")\n",
" print(\"\\nDie Lesbarkeit von Variante A ist trotzdem viel wert: Fangen Sie dort an,\")\n",
" print(\"und vektorisieren Sie erst, wenn die Messung es verlangt.\")\n",
" print(\"=\" * 88)"
" print(\"=\" * 88)\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# In einem Notebook kann 'Vektorisierte_Modellgenerierung.py' nicht direkt ausgefuehrt werden\n",
"# (ortools/highspy-Konflikt im selben Kernel, pytest oder multiprocessing).\n",
"# Deshalb als Subprocess — so laeuft es genauso wie im Terminal.\n",
"import subprocess, sys, os\n",
"# NO_COLOR/TERM=dumb verhindern ANSI-Codes in der Ausgabe (pytest\n",
"# im Jupyter-Kernel wuerde sonst farbige Escape-Sequenzen ausgeben).\n",
"_env = dict(os.environ, NO_COLOR='1', TERM='dumb')\n",
"for _d in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" _p = os.path.join(_d, 'Vektorisierte_Modellgenerierung.py')\n",
" if os.path.isfile(_p):\n",
" _r = subprocess.run([sys.executable, _p], cwd=_d, env=_env,\n",
" capture_output=True, text=True)\n",
" if _r.stdout: print(_r.stdout)\n",
" if _r.stderr: print(_r.stderr)\n",
" _r.check_returncode()\n",
" break\n",
"else:\n",
" print('Vektorisierte_Modellgenerierung.py nicht gefunden')"
]
}
],

236
OR_HTML_04/Notebooks_04/praxisfallen.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,27 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -31,11 +56,14 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"## Falle 1 — Infeasibility{idx:Infeasibility}\n",
"\n",
"`Infeasibility_Diagnose.py`\n",
"\n",
"```python\n",
"#!/usr/bin/env python3\n",
"\n",
"# Infeasibility_Diagnose.py\n",
@ -147,7 +175,36 @@
" print(\"\\nDer Anwender bekommt jetzt einen Plan PLUS eine konkrete Ursache -\")\n",
" print(\"und kann handeln: Vertretung von aussen holen, Stunde verlegen,\")\n",
" print(\"oder die Klasse zusammenlegen.\")\n",
" print(\"=\" * 78)"
" print(\"=\" * 78)\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# In einem Notebook kann 'Infeasibility_Diagnose.py' nicht direkt ausgefuehrt werden\n",
"# (ortools/highspy-Konflikt im selben Kernel, pytest oder multiprocessing).\n",
"# Deshalb als Subprocess — so laeuft es genauso wie im Terminal.\n",
"import subprocess, sys, os\n",
"# NO_COLOR/TERM=dumb verhindern ANSI-Codes in der Ausgabe (pytest\n",
"# im Jupyter-Kernel wuerde sonst farbige Escape-Sequenzen ausgeben).\n",
"_env = dict(os.environ, NO_COLOR='1', TERM='dumb')\n",
"for _d in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" _p = os.path.join(_d, 'Infeasibility_Diagnose.py')\n",
" if os.path.isfile(_p):\n",
" _r = subprocess.run([sys.executable, _p], cwd=_d, env=_env,\n",
" capture_output=True, text=True)\n",
" if _r.stdout: print(_r.stdout)\n",
" if _r.stderr: print(_r.stderr)\n",
" _r.check_returncode()\n",
" break\n",
"else:\n",
" print('Infeasibility_Diagnose.py nicht gefunden')"
]
},
{
@ -160,11 +217,14 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"## Falle 2 — Der Black-Box-Effekt\n",
"\n",
"`Erklaerbarkeit.py`\n",
"\n",
"```python\n",
"#!/usr/bin/env python3\n",
"\n",
"# Erklaerbarkeit.py\n",
@ -249,7 +309,45 @@
" print(f\" -> Die Obergrenze blockiert zwar {blockiert}, kostet aber gerade NICHTS:\")\n",
" print(\" Selbst ohne sie waere die Loesung dieselbe.\")\n",
" print(\" Blockierend und kostenrelevant sind zwei verschiedene Dinge.\")\n",
" print(\"=\" * 78)"
" print(\"=\" * 78)\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# In einem Notebook kann 'Erklaerbarkeit.py' nicht direkt ausgefuehrt werden\n",
"# (ortools/highspy-Konflikt im selben Kernel, pytest oder multiprocessing).\n",
"# Deshalb als Subprocess — so laeuft es genauso wie im Terminal.\n",
"import subprocess, sys, os\n",
"# NO_COLOR/TERM=dumb verhindern ANSI-Codes in der Ausgabe (pytest\n",
"# im Jupyter-Kernel wuerde sonst farbige Escape-Sequenzen ausgeben).\n",
"_env = dict(os.environ, NO_COLOR='1', TERM='dumb')\n",
"for _d in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" _p = os.path.join(_d, 'Erklaerbarkeit.py')\n",
" if os.path.isfile(_p):\n",
" _r = subprocess.run([sys.executable, _p], cwd=_d, env=_env,\n",
" capture_output=True, text=True)\n",
" if _r.stdout: print(_r.stdout)\n",
" if _r.stderr: print(_r.stderr)\n",
" _r.check_returncode()\n",
" break\n",
"else:\n",
" print('Erklaerbarkeit.py nicht gefunden')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Constraint Attribution{idx:Constraint Attribution}: welche Bedingung kostet wie viel?\n",
"\n",
"`Constraint_Attribution.py`\n"
]
},
{
@ -584,11 +682,14 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"## Ein Enum für fünf Bibliotheken\n",
"\n",
"`or_kern.py`\n",
"\n",
"```python\n",
"#!/usr/bin/env python3\n",
"\n",
"# or_kern.py\n",
@ -1092,7 +1193,36 @@
" print(\"\\nAlle drei scheitern beim EINLESEN - nicht erst beim Loesen und\")\n",
" print(\"schon gar nicht erst im Bericht. Das ist der ganze Zweck der\")\n",
" print(\"Domaenenschicht.\")\n",
" print(\"=\" * 78)"
" print(\"=\" * 78)\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# In einem Notebook kann 'or_kern.py' nicht direkt ausgefuehrt werden\n",
"# (ortools/highspy-Konflikt im selben Kernel, pytest oder multiprocessing).\n",
"# Deshalb als Subprocess — so laeuft es genauso wie im Terminal.\n",
"import subprocess, sys, os\n",
"# NO_COLOR/TERM=dumb verhindern ANSI-Codes in der Ausgabe (pytest\n",
"# im Jupyter-Kernel wuerde sonst farbige Escape-Sequenzen ausgeben).\n",
"_env = dict(os.environ, NO_COLOR='1', TERM='dumb')\n",
"for _d in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" _p = os.path.join(_d, 'or_kern.py')\n",
" if os.path.isfile(_p):\n",
" _r = subprocess.run([sys.executable, _p], cwd=_d, env=_env,\n",
" capture_output=True, text=True)\n",
" if _r.stdout: print(_r.stdout)\n",
" if _r.stderr: print(_r.stderr)\n",
" _r.check_returncode()\n",
" break\n",
"else:\n",
" print('or_kern.py nicht gefunden')"
]
},
{
@ -1105,11 +1235,14 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"## Der Solverwechsel in der Praxis\n",
"\n",
"`Solverwechsel_CPSAT_HiGHS.py`\n",
"\n",
"```python\n",
"#!/usr/bin/env python3\n",
"\n",
"# Solverwechsel_CPSAT_HiGHS.py\n",
@ -1433,7 +1566,36 @@
" print(\"gleich teure Loesungen, darf jeder Solver eine andere davon liefern.\")\n",
" print(\"Hier stimmen sie zufaellig ueberein - darauf zu testen waere trotzdem\")\n",
" print(\"ein unzuverlaessiger Test (siehe JobShop_Intervalle.py).\")\n",
" print(\"=\" * 82)"
" print(\"=\" * 82)\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# In einem Notebook kann 'Solverwechsel_CPSAT_HiGHS.py' nicht direkt ausgefuehrt werden\n",
"# (ortools/highspy-Konflikt im selben Kernel, pytest oder multiprocessing).\n",
"# Deshalb als Subprocess — so laeuft es genauso wie im Terminal.\n",
"import subprocess, sys, os\n",
"# NO_COLOR/TERM=dumb verhindern ANSI-Codes in der Ausgabe (pytest\n",
"# im Jupyter-Kernel wuerde sonst farbige Escape-Sequenzen ausgeben).\n",
"_env = dict(os.environ, NO_COLOR='1', TERM='dumb')\n",
"for _d in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" _p = os.path.join(_d, 'Solverwechsel_CPSAT_HiGHS.py')\n",
" if os.path.isfile(_p):\n",
" _r = subprocess.run([sys.executable, _p], cwd=_d, env=_env,\n",
" capture_output=True, text=True)\n",
" if _r.stdout: print(_r.stdout)\n",
" if _r.stderr: print(_r.stderr)\n",
" _r.check_returncode()\n",
" break\n",
"else:\n",
" print('Solverwechsel_CPSAT_HiGHS.py nicht gefunden')"
]
},
{
@ -1446,11 +1608,14 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"## Finde den Denkfehler\n",
"\n",
"`Betriebsueberwachung.py`\n",
"\n",
"```python\n",
"#!/usr/bin/env python3\n",
"\n",
"# Betriebsueberwachung.py\n",
@ -1630,7 +1795,36 @@
" print()\n",
" print(\"Die dritte ist die frueheste Warnung: Sie steigt, lange bevor der Gap\")\n",
" print(\"sichtbar wird, und gibt Zeit zum Handeln.\")\n",
" print(\"=\" * 84)"
" print(\"=\" * 84)\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# In einem Notebook kann 'Betriebsueberwachung.py' nicht direkt ausgefuehrt werden\n",
"# (ortools/highspy-Konflikt im selben Kernel, pytest oder multiprocessing).\n",
"# Deshalb als Subprocess — so laeuft es genauso wie im Terminal.\n",
"import subprocess, sys, os\n",
"# NO_COLOR/TERM=dumb verhindern ANSI-Codes in der Ausgabe (pytest\n",
"# im Jupyter-Kernel wuerde sonst farbige Escape-Sequenzen ausgeben).\n",
"_env = dict(os.environ, NO_COLOR='1', TERM='dumb')\n",
"for _d in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" _p = os.path.join(_d, 'Betriebsueberwachung.py')\n",
" if os.path.isfile(_p):\n",
" _r = subprocess.run([sys.executable, _p], cwd=_d, env=_env,\n",
" capture_output=True, text=True)\n",
" if _r.stdout: print(_r.stdout)\n",
" if _r.stderr: print(_r.stderr)\n",
" _r.check_returncode()\n",
" break\n",
"else:\n",
" print('Betriebsueberwachung.py nicht gefunden')"
]
}
],

36
OR_HTML_04/Notebooks_04/prognose.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Das Programm\n",
"\n",
"`Predict_then_Optimize.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},

63
OR_HTML_04/Notebooks_04/qp-nlp.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Konvexität: die präzise Aussage\n",
"\n",
"`QP_Grundlagen.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -102,6 +136,15 @@
"`KKT_Nachweis.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Die vier KKT-Bedingungen\n",
"\n",
"`KKT_Nachweis.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -229,6 +272,15 @@
"`Entropie_Maximierte_Allokation.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Praxisfall: Entropie-maximierte Kapitalallokation\n",
"\n",
"`Entropie_Maximierte_Allokation.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -384,6 +436,15 @@
"`Lokale_Optima_Multistart.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Was ein lokales Optimum praktisch bedeutet\n",
"\n",
"`Lokale_Optima_Multistart.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,

36
OR_HTML_04/Notebooks_04/supplychain.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Das Programm\n",
"\n",
"`Kraftwerkseinsatz.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},

187
OR_HTML_04/Notebooks_04/testing.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,27 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -31,11 +56,14 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"## Die Testsuite\n",
"\n",
"`test_or_kern.py`\n",
"\n",
"```python\n",
"#!/usr/bin/env python3\n",
"\n",
"# test_or_kern.py\n",
@ -344,7 +372,36 @@
"\n",
"if __name__ == \"__main__\":\n",
" import sys\n",
" sys.exit(pytest.main([__file__, \"-v\", \"--tb=short\", \"-p\", \"no:cacheprovider\"]))"
" sys.exit(pytest.main([__file__, \"-v\", \"--tb=short\", \"-p\", \"no:cacheprovider\"]))\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# In einem Notebook kann 'test_or_kern.py' nicht direkt ausgefuehrt werden\n",
"# (ortools/highspy-Konflikt im selben Kernel, pytest oder multiprocessing).\n",
"# Deshalb als Subprocess — so laeuft es genauso wie im Terminal.\n",
"import subprocess, sys, os\n",
"# NO_COLOR/TERM=dumb verhindern ANSI-Codes in der Ausgabe (pytest\n",
"# im Jupyter-Kernel wuerde sonst farbige Escape-Sequenzen ausgeben).\n",
"_env = dict(os.environ, NO_COLOR='1', TERM='dumb')\n",
"for _d in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" _p = os.path.join(_d, 'test_or_kern.py')\n",
" if os.path.isfile(_p):\n",
" _r = subprocess.run([sys.executable, _p], cwd=_d, env=_env,\n",
" capture_output=True, text=True)\n",
" if _r.stdout: print(_r.stdout)\n",
" if _r.stderr: print(_r.stderr)\n",
" _r.check_returncode()\n",
" break\n",
"else:\n",
" print('test_or_kern.py nicht gefunden')"
]
},
{
@ -357,11 +414,14 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"## Wer testet die Tests?\n",
"\n",
"`Mutationstest.py`\n",
"\n",
"```python\n",
"#!/usr/bin/env python3\n",
"\n",
"# Mutationstest.py\n",
@ -540,7 +600,36 @@
" print(\"Die Quote selbst ist keine Kennzahl fuer ein Dashboard. Zehn von Hand\")\n",
" print(\"gewaehlte Mutationen sind keine Stichprobe aus der Menge aller\")\n",
" print(\"moeglichen Fehler. Was zaehlt, ist die LISTE der Ueberlebenden.\")\n",
" print(\"=\" * 84)"
" print(\"=\" * 84)\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# In einem Notebook kann 'Mutationstest.py' nicht direkt ausgefuehrt werden\n",
"# (ortools/highspy-Konflikt im selben Kernel, pytest oder multiprocessing).\n",
"# Deshalb als Subprocess — so laeuft es genauso wie im Terminal.\n",
"import subprocess, sys, os\n",
"# NO_COLOR/TERM=dumb verhindern ANSI-Codes in der Ausgabe (pytest\n",
"# im Jupyter-Kernel wuerde sonst farbige Escape-Sequenzen ausgeben).\n",
"_env = dict(os.environ, NO_COLOR='1', TERM='dumb')\n",
"for _d in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" _p = os.path.join(_d, 'Mutationstest.py')\n",
" if os.path.isfile(_p):\n",
" _r = subprocess.run([sys.executable, _p], cwd=_d, env=_env,\n",
" capture_output=True, text=True)\n",
" if _r.stdout: print(_r.stdout)\n",
" if _r.stderr: print(_r.stderr)\n",
" _r.check_returncode()\n",
" break\n",
"else:\n",
" print('Mutationstest.py nicht gefunden')"
]
},
{
@ -553,11 +642,14 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"## Ein Vergleich, dem man glauben kann\n",
"\n",
"`Benchmark_Skalierung.py`\n",
"\n",
"```python\n",
"#!/usr/bin/env python3\n",
"\n",
"# Benchmark_Skalierung.py\n",
@ -770,7 +862,36 @@
" print(\" * Etwas ueber Ihre Maschine. Diese Zahlen stammen von einer\")\n",
" print(\" anderen. Der Sinn des Programms ist, dass Sie es auf Ihrer\")\n",
" print(\" laufen lassen.\")\n",
" print(\"=\" * 92)"
" print(\"=\" * 92)\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# In einem Notebook kann 'Benchmark_Skalierung.py' nicht direkt ausgefuehrt werden\n",
"# (ortools/highspy-Konflikt im selben Kernel, pytest oder multiprocessing).\n",
"# Deshalb als Subprocess — so laeuft es genauso wie im Terminal.\n",
"import subprocess, sys, os\n",
"# NO_COLOR/TERM=dumb verhindern ANSI-Codes in der Ausgabe (pytest\n",
"# im Jupyter-Kernel wuerde sonst farbige Escape-Sequenzen ausgeben).\n",
"_env = dict(os.environ, NO_COLOR='1', TERM='dumb')\n",
"for _d in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" _p = os.path.join(_d, 'Benchmark_Skalierung.py')\n",
" if os.path.isfile(_p):\n",
" _r = subprocess.run([sys.executable, _p], cwd=_d, env=_env,\n",
" capture_output=True, text=True)\n",
" if _r.stdout: print(_r.stdout)\n",
" if _r.stderr: print(_r.stderr)\n",
" _r.check_returncode()\n",
" break\n",
"else:\n",
" print('Benchmark_Skalierung.py nicht gefunden')"
]
},
{
@ -783,11 +904,14 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"## Das Modell als Dienst\n",
"\n",
"`Optimierungsdienst.py`\n",
"\n",
"```python\n",
"#!/usr/bin/env python3\n",
"\n",
"# Optimierungsdienst.py\n",
@ -1102,7 +1226,36 @@
" print()\n",
" print(\"(Es wird hier nicht gebaut - das Buch setzt keine laufende\")\n",
" print(\" Docker-Installation voraus.)\")\n",
" print(\"=\" * 80)"
" print(\"=\" * 80)\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# In einem Notebook kann 'Optimierungsdienst.py' nicht direkt ausgefuehrt werden\n",
"# (ortools/highspy-Konflikt im selben Kernel, pytest oder multiprocessing).\n",
"# Deshalb als Subprocess — so laeuft es genauso wie im Terminal.\n",
"import subprocess, sys, os\n",
"# NO_COLOR/TERM=dumb verhindern ANSI-Codes in der Ausgabe (pytest\n",
"# im Jupyter-Kernel wuerde sonst farbige Escape-Sequenzen ausgeben).\n",
"_env = dict(os.environ, NO_COLOR='1', TERM='dumb')\n",
"for _d in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" _p = os.path.join(_d, 'Optimierungsdienst.py')\n",
" if os.path.isfile(_p):\n",
" _r = subprocess.run([sys.executable, _p], cwd=_d, env=_env,\n",
" capture_output=True, text=True)\n",
" if _r.stdout: print(_r.stdout)\n",
" if _r.stderr: print(_r.stderr)\n",
" _r.check_returncode()\n",
" break\n",
"else:\n",
" print('Optimierungsdienst.py nicht gefunden')"
]
}
],

72
OR_HTML_04/Notebooks_04/unsicherheit.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,36 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Der Fluch des Durchschnitts\n",
"\n",
"`Fluch_des_Durchschnitts.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -100,6 +134,15 @@
"`Monte_Carlo.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Monte-Carlo-Simulation{idx:Monte-Carlo-Simulation}\n",
"\n",
"`Monte_Carlo.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -209,6 +252,15 @@
"`Stochastische_Optimierung.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Zweistufige stochastische Programmierung\n",
"\n",
"`Stochastische_Optimierung.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -345,6 +397,15 @@
"`Robuste_Optimierung.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Robuste Optimierung{idx:Robuste Optimierung}: gegen den Worst Case absichern\n",
"\n",
"`Robuste_Optimierung.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -461,6 +522,15 @@
"`Chance_Constraints.py`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Der Fall: ein Kraftwerkspark mit 500 MW Zusage\n",
"\n",
"`Chance_Constraints.py`\n"
]
},
{
"cell_type": "code",
"execution_count": null,

67
OR_HTML_04/Notebooks_04/vorwort.ipynb generated
View file

@ -12,7 +12,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"nbmake-skip"
]
},
"outputs": [],
"source": [
"# Einmalig ausfuehren: installiert alle im Buch verwendeten Pakete.\n",
@ -21,6 +25,27 @@
" scikit-learn matplotlib plotly pyomo linopy pymoo pydantic openpyxl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Notebook-spezifisches Setup: __file__ definieren (Programme nutzen es\n",
"# fuer OUTPUT_DIR), Programmverzeichnis in den Suchpfad aufnehmen (or_kern)\n",
"# und multiprocessing auf 'fork' stellen (Spawn findet Kernel-Funktionen nicht).\n",
"import os, sys\n",
"__file__ = os.path.join(os.getcwd(), '_notebook.py')\n",
"for _p in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" if os.path.isfile(os.path.join(_p, 'or_kern.py')):\n",
" sys.path.insert(0, _p); break\n",
"import multiprocessing as _mp\n",
"try: _mp.set_start_method('fork', force=True)\n",
"except (RuntimeError, ValueError): pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -31,11 +56,14 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"## Installationstest {-}\n",
"\n",
"`Installationstest.py`\n",
"\n",
"```python\n",
"#!/usr/bin/env python3\n",
"\n",
"# Installationstest.py\n",
@ -154,7 +182,36 @@
" print(\"-\" * 78)\n",
" print(\"Alles bereit — Sie können mit dem ersten Kapitel beginnen.\"\n",
" if alle_ok else \"Mindestens ein Solver arbeitet fehlerhaft.\")\n",
" sys.exit(0 if alle_ok else 1)"
" sys.exit(0 if alle_ok else 1)\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# In einem Notebook kann 'Installationstest.py' nicht direkt ausgefuehrt werden\n",
"# (ortools/highspy-Konflikt im selben Kernel, pytest oder multiprocessing).\n",
"# Deshalb als Subprocess — so laeuft es genauso wie im Terminal.\n",
"import subprocess, sys, os\n",
"# NO_COLOR/TERM=dumb verhindern ANSI-Codes in der Ausgabe (pytest\n",
"# im Jupyter-Kernel wuerde sonst farbige Escape-Sequenzen ausgeben).\n",
"_env = dict(os.environ, NO_COLOR='1', TERM='dumb')\n",
"for _d in [os.path.join(os.path.dirname(os.getcwd()),\n",
" 'Operations_Research_mit_Python_Version_04_Programme'),\n",
" os.path.dirname(os.getcwd()), os.getcwd()]:\n",
" _p = os.path.join(_d, 'Installationstest.py')\n",
" if os.path.isfile(_p):\n",
" _r = subprocess.run([sys.executable, _p], cwd=_d, env=_env,\n",
" capture_output=True, text=True)\n",
" if _r.stdout: print(_r.stdout)\n",
" if _r.stderr: print(_r.stderr)\n",
" _r.check_returncode()\n",
" break\n",
"else:\n",
" print('Installationstest.py nicht gefunden')"
]
}
],