"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "venn2([eu, subUnion], ('EU', 'Benelux, Nordics and Baltics'))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ad46e130-fa09-45b4-b153-f2ce9a844009",
+ "metadata": {},
+ "source": [
+ "Exercise 3 \n",
+ "Gebruik de methode difference om de landen weer te geven die (nog) geen lid zijn van de EU
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "38cc4967-92d4-46de-80c7-835296449c29",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'Iceland', 'Norway'}"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nordics.difference(eu)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.7 64-bit",
+ "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": "3.10.7"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/variables/strings_with_solutions.ipynb b/notebooks/opdrachten/notebooks/variables/strings_with_solutions.ipynb
similarity index 79%
rename from notebooks/variables/strings_with_solutions.ipynb
rename to notebooks/opdrachten/notebooks/variables/strings_with_solutions.ipynb
index f7bb6299..860c36ca 100644
--- a/notebooks/variables/strings_with_solutions.ipynb
+++ b/notebooks/opdrachten/notebooks/variables/strings_with_solutions.ipynb
@@ -58,7 +58,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"id": "3aa4bb80-8b55-4979-bee5-0fab4eca1067",
"metadata": {},
"outputs": [],
@@ -76,10 +76,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"id": "58b57719-afe6-4dcf-a153-184dc168b280",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "str"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"type(planet)"
]
@@ -94,7 +105,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"id": "9b672530",
"metadata": {},
"outputs": [],
@@ -112,10 +123,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"id": "da2b4860-f028-4716-907d-60efc875d649",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mercury\n"
+ ]
+ }
+ ],
"source": [
"print(planet)"
]
@@ -130,10 +149,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"id": "89fa18e5-b595-4c00-a925-9928393ed6bb",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "str"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"star = 'Sun'\n",
"type(star)"
@@ -149,10 +179,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"id": "6ac50411-8461-4587-acd3-72fb10040b3e",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Merury doesn't have an atmosphere because it is so close to the Sun\n",
+ "\"We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard\" - JFK\n"
+ ]
+ }
+ ],
"source": [
"print(\"Merury doesn't have an atmosphere because it is so close to the Sun\")\n",
"print('\"We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard\" - JFK')"
@@ -177,7 +216,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"id": "7ad87022-4b2e-4067-9f8f-1a43fad63029",
"metadata": {},
"outputs": [],
@@ -195,7 +234,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"id": "c1aed156-b5aa-4ed2-a5f6-98c28828314d",
"metadata": {},
"outputs": [],
@@ -214,7 +253,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"id": "b5006e39-e666-4c9f-8d0f-3e21b4e810c6",
"metadata": {},
"outputs": [],
@@ -224,10 +263,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"id": "c82af862-5226-472d-821a-5ddd1da812bf",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Being the planet closest to the Sun, Mercury guards its secrets very carefully\n"
+ ]
+ }
+ ],
"source": [
"print(formatStr)"
]
@@ -242,10 +289,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"id": "a261aff8-ae79-4d7d-9cc3-45ceb2e93878",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"formatStr == fStr == formatOperatorStr"
]
@@ -262,10 +320,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"id": "b031bd79-349e-490a-a546-994b098b7a36",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Since Mercury is so much closer to the Sun than we are, in the Mercurian sky the Sun appears three times as big as from here on Earth\n",
+ "Since Mercury is so much closer to the Sun than we are, in the Mercurian sky the Sun appears three times as big as from here on Earth\n",
+ "Since Mercury is so much closer to the Sun than we are, in the Mercurian sky the Sun appears three times as big as from here on Earth\n"
+ ]
+ }
+ ],
"source": [
"# Oplossing\n",
"print(\"Since {0} is so much closer to the {1} than we are, in the Mercurian sky the {1} appears three times as big as from here on {2}\".format(planet, star, myHomePlanet))\n",
@@ -291,10 +359,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"id": "03dfb7ba-ecd3-4a0c-9b8e-1df72ec0eed2",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Venus\n",
+ "Being the planet closest to the Sun, Mercury guards its secrets very carefully\n"
+ ]
+ }
+ ],
"source": [
"planet = \"Venus\"\n",
"print(planet)\n",
@@ -311,11 +388,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"id": "093046a0-ac71-410d-b7f2-fdf9b5f12b08",
"metadata": {},
- "outputs": [],
- "source": [
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Being the planet closest to the Sun, Venus guards its secrets very carefully\n",
+ "Being the planet closest to the Sun, Mercury guards its secrets very carefully\n"
+ ]
+ }
+ ],
+ "source": [
+ "sentence = formatStr\n",
"print(sentence.replace(\"Mercury\",\"Venus\"))\n",
"print(sentence)"
]
@@ -355,10 +442,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "daefaefb-1aee-4cda-b04a-3536eeafab8f",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "So far, KBO's have been seen from distances ranging from 30AU to 50AU from the Sun (for reference, Pluto's average orbit is at about 39AU).\n"
+ ]
+ }
+ ],
"source": [
"object = \"kbo\"\n",
"distanceUnit = \"au\"\n",
@@ -467,7 +562,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3.10.4 64-bit",
+ "display_name": "Python 3.10.7 64-bit",
"language": "python",
"name": "python3"
},
@@ -481,11 +576,11 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.4"
+ "version": "3.10.7"
},
"vscode": {
"interpreter": {
- "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+ "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
}
}
},
diff --git a/notebooks/opdrachten/notebooks/variables/tuples.ipynb b/notebooks/opdrachten/notebooks/variables/tuples.ipynb
new file mode 100644
index 00000000..726c62a6
--- /dev/null
+++ b/notebooks/opdrachten/notebooks/variables/tuples.ipynb
@@ -0,0 +1,254 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5a5999c2-eb9d-4bf9-8373-b9964e2c152d",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
Author: Jeroen Boogaard \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "88e57004-2f85-4311-84c0-c4e148f421a7",
+ "metadata": {},
+ "source": [
+ "Samengestelde variabelen - tuples "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9cdb9135-2650-4c30-a810-0685e5b4b50b",
+ "metadata": {},
+ "source": [
+ "**Een tuple is een immutable list**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6837aea3-45db-4940-8b9d-a406fdc01db1",
+ "metadata": {},
+ "source": [
+ "Tuple variabelen aanmaken en afdrukken "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "99e472c0-ba9b-4e25-ae03-75fd62f31e8f",
+ "metadata": {},
+ "source": [
+ "**Gebruik haakjes () voor het aanmaken van een tuple variabele**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eac2c3b2-52a0-4847-bffc-ad8d2eff0507",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "soho = (\"SOlar\", \"Heliospheric\", \"Observatory\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c680333b-23c3-42fc-8c04-d6e8a281fa40",
+ "metadata": {},
+ "source": [
+ "Om een tuple om te zetten naar printable string , kun je de methode join() gebruiken, een methode van type str , niet van tuple \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "73948594-ecc9-4e70-90a1-75fd5f48deb0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f\"The SOHO {', '.join(soho) } telescope is studying and staring at the Sun\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cb5cde87-1945-4329-8b7c-89eec6b1bc16",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "discovery = (\"Triton\", \"William Lassell\", 1846)\n",
+ "print( \"%s discovered %s in %s\" %(discovery) )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e9d5d50b-750c-4b59-ae28-ea58ba287b70",
+ "metadata": {},
+ "source": [
+ "**Gebruik methodes van list om een bepaald element uit een tuple op te vragen**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "edad1885-0775-4769-986e-f230ce22c35f",
+ "metadata": {},
+ "source": [
+ "Vraag het eerste element van de tuple op
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "16176f6e-c3ba-4128-93e9-787a13e3135e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "soho[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "da99a925-ffe6-4676-a8ed-77674478b269",
+ "metadata": {},
+ "source": [
+ "Vraag het tweede element van de tuple op
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8bcbfbaf-a21e-4177-88b7-99abc41e1520",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "soho.index('Heliospheric')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f85217d7-6d16-4c05-8829-efa5228873d3",
+ "metadata": {},
+ "source": [
+ "Vraag het laatste element van de tuple op
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a2363a8f-843f-46c2-9a42-2498ac88715c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "soho[-1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7ff7c206-0d29-448d-bf78-3f48fad9d4dd",
+ "metadata": {},
+ "source": [
+ "Exercise 1 \n",
+ "Bij een tuple kun je de list methode sort() niet gebruiken, laat zien hoe je met de methode sorted() de volgorde van de elementen in soho toch blijvend kunt veranderen."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "582540fc-8c6d-4335-ae09-bac02e4a61b7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Oplossing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5090e646-ff02-408e-ade7-4df431df5eda",
+ "metadata": {},
+ "source": [
+ "**Nadat een tuple variabele is aangemaakt kun je de waarde niet meer wijzigen..**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "435fed63-70fb-4ec7-bf0f-cd28822327d6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nickEnSimon = (\"Nick\", \"Simon\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "945eec7d-3174-430b-b6a2-01834e2cbb0c",
+ "metadata": {},
+ "source": [
+ "**..de variabele zelf kan wel wijzigen
**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7986f368-9d3e-4455-832f-ebe2505ff49b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "duet = (\"Nick\", \"Simon\")\n",
+ "print(duet)\n",
+ "duet = (\"Suzan\", \"Freek\" )\n",
+ "print(duet)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e1a00c3-05ee-413d-9fb5-25ae398455a7",
+ "metadata": {},
+ "source": [
+ "Exercise 2 \n",
+ "Pas een cast toe zodat Simon zich kan ontpop pen tot solo-artiest
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "30d5121b-c454-4e7c-b9ec-7cee761e34a6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Oplossing"
+ ]
+ }
+ ],
+ "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": "3.10.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/opdrachten/notebooks/variables/tuples_with _solutions.ipynb b/notebooks/opdrachten/notebooks/variables/tuples_with _solutions.ipynb
new file mode 100644
index 00000000..14e88386
--- /dev/null
+++ b/notebooks/opdrachten/notebooks/variables/tuples_with _solutions.ipynb
@@ -0,0 +1,359 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5a5999c2-eb9d-4bf9-8373-b9964e2c152d",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
Author: Jeroen Boogaard \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "88e57004-2f85-4311-84c0-c4e148f421a7",
+ "metadata": {},
+ "source": [
+ "Samengestelde variabelen - tuples "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9cdb9135-2650-4c30-a810-0685e5b4b50b",
+ "metadata": {},
+ "source": [
+ "**Een tuple is een immutable list**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6837aea3-45db-4940-8b9d-a406fdc01db1",
+ "metadata": {},
+ "source": [
+ "Tuple variabelen aanmaken en afdrukken "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "99e472c0-ba9b-4e25-ae03-75fd62f31e8f",
+ "metadata": {},
+ "source": [
+ "**Gebruik haakjes () voor het aanmaken van een tuple variabele**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "eac2c3b2-52a0-4847-bffc-ad8d2eff0507",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "soho = (\"SOlar\", \"Heliospheric\", \"Observatory\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c680333b-23c3-42fc-8c04-d6e8a281fa40",
+ "metadata": {},
+ "source": [
+ "Om een tuple om te zetten naar printable string , kun je de methode join() gebruiken, een methode van type str , niet van tuple \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "73948594-ecc9-4e70-90a1-75fd5f48deb0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The SOHO SOlar, Heliospheric, Observatory telescope is studying and staring at the Sun\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f\"The SOHO {', '.join(soho) } telescope is studying and staring at the Sun\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "cb5cde87-1945-4329-8b7c-89eec6b1bc16",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Triton discovered William Lassell in 1846\n"
+ ]
+ }
+ ],
+ "source": [
+ "discovery = (\"Triton\", \"William Lassell\", 1846)\n",
+ "print( \"%s discovered %s in %s\" %(discovery) )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e9d5d50b-750c-4b59-ae28-ea58ba287b70",
+ "metadata": {},
+ "source": [
+ "**Gebruik methodes van list om een bepaald element uit een tuple op te vragen**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "edad1885-0775-4769-986e-f230ce22c35f",
+ "metadata": {},
+ "source": [
+ "Vraag het eerste element van de tuple op
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "16176f6e-c3ba-4128-93e9-787a13e3135e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'SOlar'"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "soho[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "da99a925-ffe6-4676-a8ed-77674478b269",
+ "metadata": {},
+ "source": [
+ "Vraag het tweede element van de tuple op
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "8bcbfbaf-a21e-4177-88b7-99abc41e1520",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "soho.index('Heliospheric')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f85217d7-6d16-4c05-8829-efa5228873d3",
+ "metadata": {},
+ "source": [
+ "Vraag het laatste element van de tuple op
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "a2363a8f-843f-46c2-9a42-2498ac88715c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Observatory'"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "soho[-1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7ff7c206-0d29-448d-bf78-3f48fad9d4dd",
+ "metadata": {},
+ "source": [
+ "Exercise 1 \n",
+ "Bij een tuple kun je de list methode sort() niet gebruiken, laat zien hoe je met de methode sorted() de volgorde van de elementen in soho toch blijvend kunt veranderen."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "582540fc-8c6d-4335-ae09-bac02e4a61b7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['Heliospheric', 'Observatory', 'SOlar']\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Oplossing\n",
+ "\n",
+ "soho = sorted(soho)\n",
+ "print(soho)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5090e646-ff02-408e-ade7-4df431df5eda",
+ "metadata": {},
+ "source": [
+ "**Nadat een tuple variabele is aangemaakt kun je de waarde niet meer wijzigen..**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "435fed63-70fb-4ec7-bf0f-cd28822327d6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nickEnSimon = (\"Nick\", \"Simon\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "945eec7d-3174-430b-b6a2-01834e2cbb0c",
+ "metadata": {},
+ "source": [
+ "**..de variabele zelf kan wel wijzigen
**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "7986f368-9d3e-4455-832f-ebe2505ff49b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "('Nick', 'Simon')\n",
+ "('Suzan', 'Freek')\n"
+ ]
+ }
+ ],
+ "source": [
+ "duet = (\"Nick\", \"Simon\")\n",
+ "print(duet)\n",
+ "duet = (\"Suzan\", \"Freek\" )\n",
+ "print(duet)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e1a00c3-05ee-413d-9fb5-25ae398455a7",
+ "metadata": {},
+ "source": [
+ "Exercise 2 \n",
+ "Pas een cast toe zodat Simon zich kan ontpop pen tot solo-artiest
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "30d5121b-c454-4e7c-b9ec-7cee761e34a6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['Nick']\n",
+ "Simon\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Oplossing\n",
+ "\n",
+ "duet = (\"Nick\", \"Simon\")\n",
+ "duet = list(duet)\n",
+ "soloArtiest = duet.pop()\n",
+ "print(duet)\n",
+ "print(soloArtiest)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b92d3ed3",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "173a75c5",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.7 64-bit",
+ "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": "3.10.7"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/variables/wk_1_solutions/booleans_with_solutions.ipynb b/notebooks/variables/wk_1_solutions/booleans_with_solutions.ipynb
new file mode 100644
index 00000000..0c68690e
--- /dev/null
+++ b/notebooks/variables/wk_1_solutions/booleans_with_solutions.ipynb
@@ -0,0 +1,978 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "e05f8d41-8ce4-46f2-88fc-03112bb8f0ef",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
Author: Jeroen Boogaard \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c4ded465-714d-47b6-a0bf-fa659d77aa51",
+ "metadata": {},
+ "source": [
+ "Comparators "
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "59318472-e9cb-4808-85b0-3b2e8c3a5910",
+ "metadata": {},
+ "source": [
+ "Een boolean variabele kan maar twee waarden aannemen (denk aan binaire getallen)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1bce0cbf",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "9cb8158d-2dd1-4fc4-a35c-cb28d325b9a0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "A boolean is either True or False\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f\"A boolean is either %s or %s\" %(bool(1),bool(0)) )"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "db3175c5-a2b8-4dc1-b4ff-6923914983a4",
+ "metadata": {},
+ "source": [
+ "Je kunt een boolean gebruiken als vlag"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "531fd143-9083-41ce-9909-3ee6ba146a43",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "writePermission = False"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "94e47202-88bb-4519-8226-4aae44756da9",
+ "metadata": {},
+ "source": [
+ "Een boolean waarde volgt vaak uit een vergelijking"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "45238ba5-ee56-4413-afa0-1d8e25d400f2",
+ "metadata": {},
+ "source": [
+ "Gelijke of ongelijke waarde "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "0586fabd-a532-4495-b896-ef99c7f14c86",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "20 == 20"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "bb75917f-1fe7-4983-a4aa-5fdaf4431212",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\"apples\" == \"apples\" "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "585c9edf-a617-4a51-a282-7a69b26e4f2d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "20 != 30"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "5d4e0a15-a4c7-4d62-b269-df67a3d93bd2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\"apples\" != \"peers\""
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "952de0d5-bf6f-441a-903c-1a5629cf5d02",
+ "metadata": {},
+ "source": [
+ "Dit geldt uiteraard ook voor de booleans zelf"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "2fc52ff2-907d-40f7-93e7-e392a7cd34d8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "True == True"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "17db77bf-a33c-4c5e-b685-0a1e15bde0e4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "True != False"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "376ca78d-d14b-4728-a5bf-7586861c680c",
+ "metadata": {},
+ "source": [
+ "**Wees bij het vergelijken van Strings alert op verschillen in syntax**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "822a45ce-1b79-4bef-b62c-4724d3d19f0e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\"grey\" != \"gray\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "c993c184-04fb-4227-acdb-661d880b8e65",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\"Apples\".lower() == \"apples\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "214eaa09-c975-4c89-9931-e343d28e6c29",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\"peers \".rstrip() == \"peers\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d60ba2ad-7883-4fea-bf6f-c0480eecc453",
+ "metadata": {},
+ "source": [
+ "Groter of kleiner "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "bc9b8b19-95c4-4743-b160-751ba666a827",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "20 < 30"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "13637d3c-4a79-4437-9477-98250d6c513f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "30 > 20"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "4dd4383a-ed75-458f-8815-7cb6dcfebc35",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(\"apples\") > len(\"peers\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "c413fbae-c009-4f80-845a-a0955f855ce6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "20 <= 20"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "c95351b2-7509-48ae-884d-e8636d702ffb",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(\"apples\") >= len( \"apples \".rstrip() )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "64c259ec-6e19-4484-8ef1-17a714a8f92e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "2 * 20 > 20"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4f568d66-ab6f-4cc7-a582-d115809a340e",
+ "metadata": {},
+ "source": [
+ "**Gebruik haakjes als dit de leesbaarheid van de code verbetert**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "8d1ebfee-d109-4b39-85f3-94ab7d864bec",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(2 * 20) > 20"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "01bcf858-ba88-4a20-bc7f-2c8f33331a25",
+ "metadata": {},
+ "source": [
+ "Exercise 5 \n",
+ "Gebruik conversie naar numerieke waarden om aan te tonen dat True > False
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "78b5cb38-db60-42f6-8986-0fef1136fe2c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Oplossing\n",
+ "bin(True) > bin(False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c92180af-5a9d-4d15-9d67-0bb1a265eab8",
+ "metadata": {},
+ "source": [
+ "Logische operatoren "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9cda642c-510a-4d9f-a377-300c79da82df",
+ "metadata": {},
+ "source": [
+ "**De operator and genereert alleen True als beide booleans True zijn**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "8966c9cf-4efd-480e-aab0-b5ca45554116",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "True and (\"apples\" == \"apples\" )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2cbc3861-28ed-4a16-92f1-2d3ee899c9b1",
+ "metadata": {},
+ "source": [
+ "omdat bovenstaande expressie opnieuw een boolean oplevert, kun je ketting verder uitbreiden"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "c6130898-56d9-456f-b4d5-0bb43061adc1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "True and (\"apples\" == \"apples\" ) and ( (2 * 20) > 20 )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "24d487e7-a90f-4733-8e3c-00213070a543",
+ "metadata": {},
+ "source": [
+ "Een conjunctie is een and-chain van booleaanse expressies "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "07ed36d0-0e39-4268-a405-948d3c66dd4c",
+ "metadata": {},
+ "source": [
+ "Exercise 6 \n",
+ "Maak een and-chain met variabelen p , q en r in de volgorde die het snelst resultaat geeft.\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "50fb137d-d26c-4db6-a30b-b770deee5111",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "p = True\n",
+ "q = True\n",
+ "r = False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "afaaf4a2-debe-4551-b846-86e0fd5d6276",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Oplossing\n",
+ "\n",
+ "r and p and q"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "02bc3b3a-3f77-458b-91ea-147dffb8eacf",
+ "metadata": {},
+ "source": [
+ "**De operator or genereert True zodra een van de booleans True is**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "6a110005-7fc4-45ae-99ae-deca019d7c9e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "True or (\"apples\" == \"pears\" )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "48d93caf-e0d4-4ce9-931e-b3de1a6cb6cf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "True or ( \"apples\" != \"peers\" ) or ( 20 >= 30 )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6df18484-019a-4974-9d91-b3d5b9d52917",
+ "metadata": {},
+ "source": [
+ "Een disjunctie is een or-chain van booleaanse expressies "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "51806f08-18b0-47bc-afc8-c3675ac68b78",
+ "metadata": {},
+ "source": [
+ "Exercise 7 \n",
+ "Maak een or-chain met variabelen p , q en r in de volgorde die het snelst resultaat geeft.\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "ce679e04-c27c-4da9-8bf9-b07bc65367e4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "p = False\n",
+ "q = True\n",
+ "r = (p and q)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "45f3678c-3a0f-4f1a-a97c-16220e43bead",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Oplossing\n",
+ "\n",
+ "q or p or r"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6b1ca4a9-2441-4d13-a3b7-2ca1f677d73b",
+ "metadata": {},
+ "source": [
+ "**De operator not genereert ontkracht een booleaanse expressie**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "4c58444d-f362-4767-9158-777b679dfc5b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "True or False, that's the question..\n"
+ ]
+ }
+ ],
+ "source": [
+ "toBe = True\n",
+ "print(f\"{toBe} or {not toBe}, that's the question..\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "3511fcde-46d4-4482-aeb6-2d7f7cdd47d9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "False or True, that's the question..\n"
+ ]
+ }
+ ],
+ "source": [
+ "notToBe = False\n",
+ "print(f\"{notToBe} or {not notToBe}, that's the question..\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b1302985-b583-4e24-8c59-a60d0e58afc1",
+ "metadata": {},
+ "source": [
+ "Een negatie is de ontkenning van een booleaanse expressies "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8e32af01-e81a-47db-bec1-37fe477c52d2",
+ "metadata": {},
+ "source": [
+ "**Vereenvoudig waar mogelijk**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "7f6a3733-6abb-453f-b2db-84f5e1d86fe7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\"apples\" == \"peers\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aeebc931-e0d2-4416-9d1c-a680bc15aaa4",
+ "metadata": {},
+ "source": [
+ "in plaats van"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "9ee4ce6d-5eb1-49de-b2d4-757e7eb616b2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "not (\"apples\" != \"peers\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f9bd3ad6-1ff3-484e-a05a-62a44f2169ca",
+ "metadata": {},
+ "source": [
+ "Functies "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "66251148-42b6-481b-8a30-756d7a672059",
+ "metadata": {},
+ "source": [
+ "Volgens de eerste stelling van DeMorgan kun is de expressie not (p and q ) equivalent aan (not p) or (not q) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "6e1e58b9-be3b-47dc-9401-69eac4e2ca83",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def notBothTrue(p, q):\n",
+ " return (not p) or (not q)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "d639e193-9a8f-4314-b57c-0c2258ac93ad",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "notBothTrue(True,True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "2246e4f4-d8fe-4fff-b732-032b219813e6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "notBothTrue(False,True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "3b03ee47-654c-4626-aeae-7ea24f0836ae",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "notBothTrue(False,False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6dc62829-c21f-4842-bf2b-ff16b26d24bb",
+ "metadata": {},
+ "source": [
+ "Volgens de tweede stelling van DeMorgan kun is de expressie not (p or q ) equivalent aan (not p) and (not q) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3edf9fe9-2d3f-4cdf-9bee-40312e708254",
+ "metadata": {},
+ "source": [
+ "Exercise 8 \n",
+ "Implementeer de tweede stelling van DeMorgan in een functie bothFalse \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "beb7d797-db96-4619-96a9-8cebc6047317",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Oplossing\n",
+ "\n",
+ "def bothFalse(p, q):\n",
+ " return (not p) and (not q)\n",
+ "\n",
+ "# print((not p) and (not q))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.7 64-bit",
+ "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": "3.10.7"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "5b93afe9c2e217d44e354b055ed180e932b72842f9b6422dadc1ad80da9caf67"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/functions_with_solutions.ipynb b/notebooks/variables/wk_1_solutions/functions_with_solutions.ipynb
similarity index 74%
rename from notebooks/functions_with_solutions.ipynb
rename to notebooks/variables/wk_1_solutions/functions_with_solutions.ipynb
index d9722634..be00ba81 100644
--- a/notebooks/functions_with_solutions.ipynb
+++ b/notebooks/variables/wk_1_solutions/functions_with_solutions.ipynb
@@ -42,10 +42,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"id": "610cb44d-3ec8-413e-9e83-8f7602ab1481",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Because of Mercury's elliptical – egg-shaped – orbit, and sluggish rotation, the Sun appears to rise briefly, set, and rise again from some parts of the planet's surface. The same thing happens in reverse at sunset.\n"
+ ]
+ }
+ ],
"source": [
"def printExampleString():\n",
" print(\"Because of Mercury's elliptical – egg-shaped – orbit, and sluggish rotation, the Sun appears to rise briefly, set, and rise again from some parts of the planet's surface. The same thing happens in reverse at sunset.\")\n",
@@ -63,10 +71,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"id": "10ef19a8-1b77-4c99-a3fd-fe4554daa771",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " De docstring is een beschrijving van een functie, die bedoeld is voor de programmeur. \n",
+ " De Python interpreter negeert deze String\n",
+ " \n"
+ ]
+ }
+ ],
"source": [
"def showDocString():\n",
" \"\"\"\n",
@@ -95,10 +114,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"id": "6c24ec9f-d20e-497c-a3d7-3e28be4ee28f",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The meteoroid entered the atmosphere at over 63720 kilometers per hour.\n"
+ ]
+ }
+ ],
"source": [
"def milesToKilometers(distance):\n",
" return round(distance * 1.609344, 2)\n",
@@ -117,10 +144,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"id": "9aa67dcd-cf00-43d0-a9f7-94b03f6daf9d",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "A mile is longer than a kilometer and is equivalent to approximately 1.6 kilometers\n"
+ ]
+ }
+ ],
"source": [
"def milesToKilometers(distance=1):\n",
" return distance * 1.609344\n",
@@ -139,7 +174,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"id": "62ae6724-341c-4d15-a3ac-7072746d70e8",
"metadata": {},
"outputs": [],
@@ -165,7 +200,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"id": "d62120a0-45df-49f9-9998-41e09c0acb54",
"metadata": {},
"outputs": [],
@@ -184,10 +219,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"id": "cf2642fa-be01-43bc-ac74-3f50c74add66",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "p\tq\tnot(p and q)\t\u001b[1mdeMorgan1(p, q)\u001b[0m\n",
+ "False\tFalse\tTrue\t\t\u001b[1mTrue\u001b[0m\n",
+ "False\tTrue\tTrue\t\t\u001b[1mTrue\u001b[0m\n",
+ "True\tFalse\tTrue\t\t\u001b[1mTrue\u001b[0m\n",
+ "True\tTrue\tFalse\t\t\u001b[1mFalse\u001b[0m\n"
+ ]
+ }
+ ],
"source": [
"logTable = [f\"p\\tq\\tnot(p and q)\\t\\033[1mdeMorgan1(p, q)\\033[0m\"]\n",
"for p in [False, True]:\n",
@@ -217,10 +264,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"id": "06311702-5a8f-4840-bb3f-5704d14912f9",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "p\tq\tnot(p or q)\t\u001b[1mdeMorgan2(p, q)\u001b[0m\n",
+ "False\tFalse\tTrue\t\t\u001b[1mTrue\u001b[0m\n",
+ "False\tTrue\tFalse\t\t\u001b[1mFalse\u001b[0m\n",
+ "True\tFalse\tFalse\t\t\u001b[1mFalse\u001b[0m\n",
+ "True\tTrue\tFalse\t\t\u001b[1mFalse\u001b[0m\n"
+ ]
+ }
+ ],
"source": [
"# Oplossing\n",
"\n",
@@ -245,10 +304,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"id": "6ea79e2d-704a-413d-a393-81e4298eb11e",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The lowest value of digits is 0 and 99 is the highest value\n"
+ ]
+ }
+ ],
"source": [
"digits = list( range(0,100,3) )\n",
"print(f\"The lowest value of digits is { min(digits) } and { max(digits) } is the highest value\")"
@@ -256,10 +323,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"id": "84dfab1b-d3cb-45c2-a6bd-0738b3d10de6",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1683"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"sum(digits)"
]
@@ -278,7 +356,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"id": "c6bf6571-2329-4ef8-b21a-c1f330395f1c",
"metadata": {},
"outputs": [],
@@ -295,7 +373,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3.10.7 64-bit",
"language": "python",
"name": "python3"
},
@@ -309,7 +387,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.6"
+ "version": "3.10.7"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "5b93afe9c2e217d44e354b055ed180e932b72842f9b6422dadc1ad80da9caf67"
+ }
}
},
"nbformat": 4,
diff --git a/notebooks/variables/wk_1_solutions/numbers_with_solutions.ipynb b/notebooks/variables/wk_1_solutions/numbers_with_solutions.ipynb
new file mode 100644
index 00000000..64240b55
--- /dev/null
+++ b/notebooks/variables/wk_1_solutions/numbers_with_solutions.ipynb
@@ -0,0 +1,666 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "e9707e7f-ea3f-4ca0-b763-48d6b9de1b41",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
Author: Jeroen Boogaard \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "657cd66d-31d9-4efd-a6d7-ca2fa44899c4",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "74d6a93a",
+ "metadata": {},
+ "source": [
+ "Numerieke variabelen aanmaken en afdrukken "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "7dcb3c82",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Today, we know of about 190 impact craters on Earth.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# integer\n",
+ "craters = 190\n",
+ "# Numbers can be injected into a String\n",
+ "print(f\"Today, we know of about {craters} impact craters on Earth.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "3aa4bb80-8b55-4979-bee5-0fab4eca1067",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Voeg underscores toe om de leesbaarheid te vergroten\n",
+ "meteoritesFound = 50_000\n",
+ "# float\n",
+ "ateroidsPercentage = 99.8"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "58b57719-afe6-4dcf-a153-184dc168b280",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "More than 50000 meteorites have been found on Earth. Of these, 99.8 percent come from asteroids\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f\"More than {meteoritesFound} meteorites have been found on Earth. Of these, {ateroidsPercentage} percent come from asteroids\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0776fbd2",
+ "metadata": {},
+ "source": [
+ "Rekenkundige bewerkingen "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "9b672530",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "100_000\n"
+ ]
+ }
+ ],
+ "source": [
+ "meteorWeight = 100 * 10**3\n",
+ "print(format(meteorWeight, '_d'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "83a9b161-6b4d-4426-9a07-c578e9013e39",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "120"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a = 70\n",
+ "b = 50\n",
+ "a + b"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "52a8711e-a579-443a-8258-b0db17ce459f",
+ "metadata": {},
+ "source": [
+ "Blijf positief.."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "ffbf37d4-4e77-4238-94dc-f43656c3fb19",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The distance between a and b is 20 km\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"The distance between a and b is %s km\" %abs(b-a) )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "bf3a78d1-d953-47f4-9089-af79e285225d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3500"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a * b"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "570f5967-317d-413d-82e4-96e0f2b38bbb",
+ "metadata": {},
+ "source": [
+ "Variabelen kunnen op verschillende manieren door elkaar gedeeld worden "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fd24becd-420d-4e1a-b04a-c9ad23d5d87d",
+ "metadata": {},
+ "source": [
+ "division: / "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "3e7d9698-d034-4427-8c82-599597027f68",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1.4"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a / b"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf2e0f0a-5e6d-4ce6-949f-8fa8d709bcc8",
+ "metadata": {},
+ "source": [
+ "floor division: // "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "475803e1-5cb0-4e01-8148-c9ca26f8f0c2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a // b"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f029cba1-cd5a-42e0-9150-f4f168a880fd",
+ "metadata": {},
+ "source": [
+ "modulo: % "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "a90e787f-42ea-41d1-85e3-55844e7bfefe",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.4"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(a % b) / b"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "183e72f2-62b0-4a13-b1bf-336393e0edab",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(a // b) + ((a % b) / b) == a /b"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "85e55613-e7fb-452d-a670-1221eb5f1d93",
+ "metadata": {},
+ "source": [
+ "Een positieve draairichting is tegen de klok in"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "c47e6012-2a51-4719-9f5d-c25b26375860",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "After 2 clockwise turnarounds, the angle is 80 degrees to the right\n"
+ ]
+ }
+ ],
+ "source": [
+ "rotation = -800\n",
+ "spins = abs(rotation) // 360\n",
+ "degrees = abs(rotation) % 360\n",
+ "print(f\"After {spins} clockwise turnarounds, the angle is {degrees} degrees to the right\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "25154a61-d3f0-46c1-b512-0a632706d61d",
+ "metadata": {},
+ "source": [
+ "Exercise 3 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "f2197985-95c6-4864-929f-f8f8e8f97995",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "durationInMinutes = 140"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aa8a2605-0a88-49df-935d-d996c59b414a",
+ "metadata": {},
+ "source": [
+ "Print de volgende String met gebruik van de variabelen durationInMinutes , durationInHours en minutesLeft zonder de variabelen te veranderen :
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "260f3b99-3b4c-4d08-852a-09f268b1d335",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "If there are 2 hours passed from 140 minutes, there are 20 minutes left\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Oplossing\n",
+ "durationInHours = durationInMinutes // 60\n",
+ "minutesLeft = durationInMinutes % 60\n",
+ "print(f\"If there are {durationInHours} hours passed from {durationInMinutes} minutes, there are {minutesLeft} minutes left\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d1623e47-4b05-40af-a27a-d2daad15946b",
+ "metadata": {},
+ "source": [
+ "**Built-in Python module math**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "da2b4860-f028-4716-907d-60efc875d649",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "100000.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "import math\n",
+ "\n",
+ "meteorWeight = 100 * math.pow(10,3)\n",
+ "print(meteorWeight)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3e7c9c3b-e4d2-4458-afb7-6c0db5b83415",
+ "metadata": {},
+ "source": [
+ "De output is een float. Je kunt dit weer omzetten in een integer. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "30b1a7bb",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "100000"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(meteorWeight)\n",
+ "int(meteorWeight)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "303b29ff-129b-485b-9656-2166f656f70a",
+ "metadata": {},
+ "source": [
+ "Functies "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1ce7212f-35f4-4c55-a7ff-179439d73799",
+ "metadata": {},
+ "source": [
+ "**Functies kunnen worden aangeroepen met een argument**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "89fa18e5-b595-4c00-a925-9928393ed6bb",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The meteoroid entered the atmosphere at over 63720 kilometers per hour.\n"
+ ]
+ }
+ ],
+ "source": [
+ "def milesToKilometers(distance):\n",
+ " return round(distance * 1.609344, 2)\n",
+ "\n",
+ "distanceInMiles = 11 \n",
+ "print( f\"The meteoroid entered the atmosphere at over {int(milesToKilometers(distanceInMiles)*3600)} kilometers per hour.\" )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ce403ddc-278c-475a-baab-525e9122103d",
+ "metadata": {},
+ "source": [
+ "**Functies kunnen worden aangeroepen zonder argument d.m.v. een default value **"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "44762233",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "A mile is longer than a kilometer and is equivalent to approximately 1.6 kilometers\n"
+ ]
+ }
+ ],
+ "source": [
+ "def milesToKilometers(distance=1):\n",
+ " return distance * 1.609344\n",
+ "\n",
+ "print(f\"A mile is longer than a kilometer and is equivalent to approximately { round(milesToKilometers(),1) } kilometers\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b17d362a-d109-4311-bdc7-2b9fe24657cd",
+ "metadata": {},
+ "source": [
+ "Exercise 4 \n",
+ "Schrijf een functie voor het omzetten van een gewicht in pounds naar een geheel aantal kilo's
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "082009a5-fd6d-4436-b68c-95dab923ad94",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Oplossing\n",
+ "def poundsToKilos(pounds=1):\n",
+ " return 0.4535924 * pounds "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "d9e1b5b4-45eb-43ec-ba20-82bfe5f1bb66",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The rotation in degrees is : 270\n"
+ ]
+ }
+ ],
+ "source": [
+ "def radiansToDegrees(radians):\n",
+ " return (radians*180) / math.pi\n",
+ "\n",
+ "rotationInRadians = 1.5 * math.pi\n",
+ "rotationInDegrees = radiansToDegrees(rotationInRadians)\n",
+ "\n",
+ "print(\"The rotation in degrees is : {}\".format( round(rotationInDegrees), 0) )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ead8af10-0e7d-466b-97e2-402df06449c2",
+ "metadata": {},
+ "source": [
+ "Andertallige stelsels "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6bd99486-92d8-421a-8875-ca5f7dca85ab",
+ "metadata": {},
+ "source": [
+ "Een binary digit heeft de waarde 0 of 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "bcc232e8-0aa9-48e6-a9b6-f2f04f28f4b3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0b1\n",
+ "1\n"
+ ]
+ }
+ ],
+ "source": [
+ "a = bin(1)\n",
+ "print(a)\n",
+ "print(a[2:])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b7d54104-305d-4784-b8f1-4fb9c5fbc954",
+ "metadata": {},
+ "source": [
+ "Rekenen met binaire getallen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "70e70e99-4b26-4891-956a-ba029e9eb62c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 + 1 = 10\n"
+ ]
+ }
+ ],
+ "source": [
+ "sum = bin(int(a, 2) + int(a, 2))\n",
+ "c = int(sum[2:])\n",
+ "print(f\"{a[2:]} + {a[2:]} = {c}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "fee282af-d4cd-47f3-a756-4cc6a179c0a7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3.0"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "base = 2\n",
+ "d = '11'\n",
+ "d1 = int(d[:1])\n",
+ "d0 = int(d[1:])\n",
+ "d1 * math.pow(base,1) + d0 * math.pow(base,0)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.7 64-bit",
+ "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": "3.10.7"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "5b93afe9c2e217d44e354b055ed180e932b72842f9b6422dadc1ad80da9caf67"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/variables/wk_1_solutions/strings_with_solutions.ipynb b/notebooks/variables/wk_1_solutions/strings_with_solutions.ipynb
new file mode 100644
index 00000000..044fb97d
--- /dev/null
+++ b/notebooks/variables/wk_1_solutions/strings_with_solutions.ipynb
@@ -0,0 +1,614 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "e9707e7f-ea3f-4ca0-b763-48d6b9de1b41",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
Author: Jeroen Boogaard \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "657cd66d-31d9-4efd-a6d7-ca2fa44899c4",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "34793cd9-f1cd-4165-a297-6518c0b63ac5",
+ "metadata": {},
+ "source": [
+ "Enkelvoudige variabelen - Strings "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2998c5f8-ab4f-44f2-a50d-81d9295938e9",
+ "metadata": {},
+ "source": [
+ "Een String is een enkelvoudig datatype voor textuele content."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "74d6a93a",
+ "metadata": {},
+ "source": [
+ "String variabelen aanmaken en afdrukken "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4e19d1e7-9226-4e7b-a8b9-1dd1d31e69fd",
+ "metadata": {},
+ "source": [
+ "Om een String (tijdelijk) in het gegeugen op te slaan, kun je een variabele aanmaken. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "3aa4bb80-8b55-4979-bee5-0fab4eca1067",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "planet = \"Mercury\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f622a120-6626-4352-98e9-16a12675cb3a",
+ "metadata": {},
+ "source": [
+ "De variabele planet heeft nu de waarde \"Mercury\" en is krijgt daardoor het type String"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "58b57719-afe6-4dcf-a153-184dc168b280",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "str"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(planet)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0776fbd2",
+ "metadata": {},
+ "source": [
+ "**Gebruik camelCase voor de naam van een variabele**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "9b672530",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "myHomePlanet = \"Earth\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cee89732-4d32-4e88-835f-bb3d311b436e",
+ "metadata": {},
+ "source": [
+ "**Gebruik haakjes voor het afdrukken van een string**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "da2b4860-f028-4716-907d-60efc875d649",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mercury\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(planet)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3e7c9c3b-e4d2-4458-afb7-6c0db5b83415",
+ "metadata": {},
+ "source": [
+ "Zowel enkele als dubbele quotes zijn toegestaan."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "89fa18e5-b595-4c00-a925-9928393ed6bb",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "str"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "star = 'Sun'\n",
+ "type(star)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8191edd1-e5ef-432d-b2d2-0045f325d21f",
+ "metadata": {},
+ "source": [
+ "Wanneer gebruik je enkele en wanneer dubbele quotes? Wees vooral praktisch.."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "6ac50411-8461-4587-acd3-72fb10040b3e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Merury doesn't have an atmosphere because it is so close to the Sun\n",
+ "\"We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard\" - JFK\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"Merury doesn't have an atmosphere because it is so close to the Sun\")\n",
+ "print('\"We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard\" - JFK')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ac51e674-660e-49d3-a8da-92de6b4732b3",
+ "metadata": {},
+ "source": [
+ "Variabelen kunnen als onderdeel van een omvattende String worden afgedrukt \n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4bb3ec2d-62fb-41f1-9c30-05a2ee5f39b1",
+ "metadata": {},
+ "source": [
+ "met de methode format() "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "7ad87022-4b2e-4067-9f8f-1a43fad63029",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "formatStr = \"Being the planet closest to the {0}, {1} guards its secrets very carefully\".format(star, planet)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c56cf4ff-bf8d-4898-a10f-08a06a7911cb",
+ "metadata": {},
+ "source": [
+ "door gebruik van een f-string "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "c1aed156-b5aa-4ed2-a5f6-98c28828314d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fStr = f\"Being the planet closest to the {star}, {planet} guards its secrets very carefully\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "324aa354-4066-4490-a8ce-4d7c5e26649d",
+ "metadata": {},
+ "source": [
+ "met de String Formatting Operator \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "b5006e39-e666-4c9f-8d0f-3e21b4e810c6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "formatOperatorStr = \"Being the planet closest to the %s, %s guards its secrets very carefully\" %(star,planet)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "c82af862-5226-472d-821a-5ddd1da812bf",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Being the planet closest to the Sun, Mercury guards its secrets very carefully\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(formatStr)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f5e43af5-f38a-45af-930e-ff36fc1e9a19",
+ "metadata": {},
+ "source": [
+ "**Gebruik comparators om de waarden van variabelen met elkaar te vergelijken**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "a261aff8-ae79-4d7d-9cc3-45ceb2e93878",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "formatStr == fStr == formatOperatorStr"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b17d362a-d109-4311-bdc7-2b9fe24657cd",
+ "metadata": {},
+ "source": [
+ "Exercise 1 \n",
+ "Print de volgende String op drie verschillende manieren en met gebruik van de variabelen planet , star en myHomePlanet
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "b031bd79-349e-490a-a546-994b098b7a36",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Since Mercury is so much closer to the Sun than we are, in the Mercurian sky the Sun appears three times as big as from here on Earth\n",
+ "Since Mercury is so much closer to the Sun than we are, in the Mercurian sky the Sun appears three times as big as from here on Earth\n",
+ "Since Mercury is so much closer to the Sun than we are, in the Mercurian sky the Sun appears three times as big as from here on Earth\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Oplossing\n",
+ "print(\"Since {0} is so much closer to the {1} than we are, in the Mercurian sky the {1} appears three times as big as from here on {2}\".format(planet, star, myHomePlanet))\n",
+ "print(f\"Since {planet} is so much closer to the {star} than we are, in the Mercurian sky the {star} appears three times as big as from here on {myHomePlanet}\")\n",
+ "print(\"Since %s is so much closer to the %s than we are, in the Mercurian sky the %s appears three times as big as from here on %s\" %(planet, star, star, myHomePlanet))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7769085e-e1a5-4c0a-893c-e45e4fd8066b",
+ "metadata": {},
+ "source": [
+ "String operaties "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1788a47e-6d0f-4d24-b04d-6108b7e58001",
+ "metadata": {},
+ "source": [
+ "Ookal verander je de waarde van de geinjecteerde variabele , de samengestelde variabele behoudt zijn oorspronkelijke waarde"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "03dfb7ba-ecd3-4a0c-9b8e-1df72ec0eed2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Venus\n",
+ "Being the planet closest to the Sun, Mercury guards its secrets very carefully\n"
+ ]
+ }
+ ],
+ "source": [
+ "planet = \"Venus\"\n",
+ "print(planet)\n",
+ "print(formatStr)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5f4d3cc2",
+ "metadata": {},
+ "source": [
+ "Ook na de operatie replace() variabele sentence nog zijn initiele waarde"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "093046a0-ac71-410d-b7f2-fdf9b5f12b08",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Being the planet closest to the Sun, Venus guards its secrets very carefully\n",
+ "Being the planet closest to the Sun, Mercury guards its secrets very carefully\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(formatStr.replace(\"Mercury\",\"Venus\"))\n",
+ "print(formatStr)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aa6e1a4b-a372-462c-9068-595b5cb10147",
+ "metadata": {},
+ "source": [
+ "**Gebruik functies voor het omzetten van en naar hoofdletters in Strings**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "9a62d1d6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "THE NEAR MISSION\n",
+ "dwarf planets\n",
+ "Cosmonaut Alexi Leonov\n",
+ "Cosmonaut Alexi Leonov\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"The NEAR Mission\".upper())\n",
+ "print(\"Dwarf Planets\".lower())\n",
+ "\n",
+ "cosmonaut = \"alexi leonov\"\n",
+ "print(\"Cosmonaut {}\".format(cosmonaut.title()))\n",
+ "print(f\"Cosmonaut {cosmonaut.title()}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bb04b748-ae6c-4c6c-ac5f-582f74f1db66",
+ "metadata": {},
+ "source": [
+ "Exercise 2 \n",
+ "Print de volgende String met gebruik van de gegeven variabelen, een String Formatting Operator en String operaties, zonder de variabelen zelf te veranderen
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "daefaefb-1aee-4cda-b04a-3536eeafab8f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "object = \"kbo\"\n",
+ "distanceUnit = \"au\"\n",
+ "star = \"SUN\"\n",
+ "\n",
+ "# Oplossing\n",
+ "print(\"So far, {0}'s have been seen from distances ranging from 30{1} to 50{1} from the {2} (for reference, Pluto's average orbit is at about 39{1}).\"\n",
+ " .format(object.upper(), distanceUnit.upper(), star.title()))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "55e59489-924e-4f13-8c49-feb48c990e91",
+ "metadata": {},
+ "source": [
+ "**Gebruik split() om een zin op te splitsen in woorden**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4e38c568-2327-48f2-8925-652179a7c856",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rockyPlanets = \"Mercury, Venus, Earth and Mars\".replace(',','').split()\n",
+ "rockyPlanets.remove(\"and\")\n",
+ "print(rockyPlanets)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "02e8e2ce-2256-478c-8899-376a779fbd72",
+ "metadata": {},
+ "source": [
+ "Inhoud van een String over meerdere regels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "fd14febc-fe4b-435b-ab40-5fdfa2c8a92e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " Standing on Mercury's surface at its closest approach to the Sun, \n",
+ " our star would appear more than three times larger than it does on Earth.\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "multiLineStr = \"\"\"\n",
+ " Standing on Mercury's surface at its closest approach to the Sun, \n",
+ " our star would appear more than three times larger than it does on Earth.\n",
+ " \"\"\"\n",
+ "print(multiLineStr)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "44a4224e-125f-41fa-abc6-8efc225c3478",
+ "metadata": {},
+ "source": [
+ "Functies "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b5f9f663-aac7-49a1-9147-1636b9f73039",
+ "metadata": {},
+ "source": [
+ "**Functies kunnen worden aangeroepen zonder argument**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dca1eb76-de0d-4d67-ab27-b862226a3583",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def printExampleString():\n",
+ " print(\"Because of Mercury's elliptical – egg-shaped – orbit, and sluggish rotation, the Sun appears to rise briefly, set, and rise again from some parts of the planet's surface. The same thing happens in reverse at sunset.\")\n",
+ " \n",
+ "printExampleString() "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "62470c21-2eae-4508-87cc-eae3c5e611fe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def showDocString():\n",
+ " \"\"\"\n",
+ " De docstring is een beschrijving van een functie, die bedoeld is voor de programmeur. \n",
+ " De Python interpreter negeert deze String\n",
+ " \"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "e38bc85a-0ec8-48d8-9891-b627881a9294",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'showDocString' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn [20], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mshowDocString\u001b[49m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__doc__\u001b[39m)\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'showDocString' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "print(showDocString.__doc__)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.7 64-bit",
+ "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": "3.10.7"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "5b93afe9c2e217d44e354b055ed180e932b72842f9b6422dadc1ad80da9caf67"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/variables/wk_2/A-Venn-diagram-of-unions-and-intersections-for-two-sets-A-and-B-and-their-complements.png b/notebooks/variables/wk_2/A-Venn-diagram-of-unions-and-intersections-for-two-sets-A-and-B-and-their-complements.png
new file mode 100644
index 00000000..8cd4ac49
Binary files /dev/null and b/notebooks/variables/wk_2/A-Venn-diagram-of-unions-and-intersections-for-two-sets-A-and-B-and-their-complements.png differ
diff --git a/notebooks/variables/wk_2/wk_2_sets.ipynb b/notebooks/variables/wk_2/wk_2_sets.ipynb
new file mode 100644
index 00000000..04f24847
--- /dev/null
+++ b/notebooks/variables/wk_2/wk_2_sets.ipynb
@@ -0,0 +1,568 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "39ad5385-bd3b-4733-8583-aaee47dfd023",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
Author: Jeroen Boogaard \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2e3ab9dd-f769-4b88-87d0-59bb6af381f6",
+ "metadata": {},
+ "source": [
+ "Samengestelde variabelen - Sets "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1dd30cb7-4573-48b9-9b50-4e64b414213e",
+ "metadata": {},
+ "source": [
+ "**Een set is een mutable dataverzameling van unieke elementen.**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "443e2275-c790-4153-82c2-f08782f7856c",
+ "metadata": {},
+ "source": [
+ "Set variabelen aanmaken en afdrukken "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "b5ca0430-4eb8-4eeb-a899-151ebbbf7f13",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'Voyager', 'Pioneer'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "spaceCrafts = set()\n",
+ "counter = 0\n",
+ "spaceCrafts = {\"Pioneer\", \"Voyager\"}\n",
+ "print(spaceCrafts)"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "15eaabd3-5697-474e-9a46-a384a242c8a4",
+ "metadata": {},
+ "source": [
+ "Elementen kunnen aan een bestaande set worden toegevoegd.."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "e8e00fbb-0a04-4754-9fdc-0ea3bc54f43e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "spaceCrafts.add(\"Voyager\")"
+ ]
+ },
+ {
+ "cell_type": "raw",
+ "id": "93db2ef6-ce73-4ce3-8cff-7aa0babea2f2",
+ "metadata": {},
+ "source": [
+ "maar elk element komt slechts 1 keer voor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "e51fcac8-8062-4c48-bc39-d191f7742c65",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'Voyager', 'Pioneer'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(spaceCrafts)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7e55fb56-f386-4785-8841-90d1844db4d7",
+ "metadata": {},
+ "source": [
+ "Exercise 1 \n",
+ "Gegeven "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "065d6065-9d59-47f3-ba49-0c6cd4dd15f3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377]\n"
+ ]
+ }
+ ],
+ "source": [
+ "fibonacciList = [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377]\n",
+ "print(fibonacciList)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "def776af-43e4-4bd7-a17b-8778b318dcfd",
+ "metadata": {},
+ "source": [
+ "Gevraagd \n",
+ "Maak gebruik van casting om variabele fibonacciList te ontdubbelen
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4eabe9bd-dd7f-4f83-872f-e63e25146ca4",
+ "metadata": {},
+ "source": [
+ "Oplossing "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "65a28c92-758e-49b9-b2b6-bd78e79e7642",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{0, 1, 2, 3, 34, 5, 8, 233, 377, 13, 144, 21, 55, 89}\n"
+ ]
+ }
+ ],
+ "source": [
+ "fibonacciList = set(fibonacciList)\n",
+ "\n",
+ "print(fibonacciList)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3c57550b-cd11-4a6d-a806-946830cbb1cd",
+ "metadata": {},
+ "source": [
+ "Operaties "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "46e0e7e4-1cc9-47f3-bc53-d3888252e583",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nordics = {\"Denmark\", \"Finland\", \"Iceland\", \"Norway\"}\n",
+ "baltics = {\"Estonia\", \"Latvia\", \"Lithuania\"}\n",
+ "eu = {\"Austria\", \"Belgium\", \"Bulgaria\", \"Croatia\", \"Cyprus\", \"Czechia\", \"Denmark\", \"Estonia\", \"Finland\", \"France\", \"Germany\", \"Greece\", \"Hungary\", \"Ireland\", \"Italy\", \"Latvia\", \"Lithuania\", \"Luxembourg\", \"Malta\", \"The Netherlands\", \"Poland\", \"Portugal\", \"Romania\", \"Slovakia\", \"Slovenia\", \"Spain\", \"Sweden\"}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b2c79908-ca34-4657-8481-7a959aa046a5",
+ "metadata": {},
+ "source": [
+ "Exercise 2 \n",
+ "Gegeven "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "96daa91c-96a8-41a7-b202-6e4a77876ef0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "benelux = {\"Belgium\", \"The Netherlands\", \"Luxembourg\"}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "80d32381-df51-4185-a4ba-1ed9cf9c6ba2",
+ "metadata": {},
+ "source": [
+ "Gevraagd \n",
+ "Maak een String met als value \"BeNeLux\" die is opgebouwd uit de letters van corresponderende items uit de set benelux zonder de set variabele zelf aan te passen
\n",
+ "Hints:
\n",
+ " Maak gebruik (tijdelijke) variable van het type list \n",
+ " Pas daarin het item \"The Netherlands zodat het consistent is met de andere items \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "871adbea-f738-4d20-8fe9-50ef3e6cbd15",
+ "metadata": {},
+ "source": [
+ "Oplossing "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "53417987-3497-4658-a6e1-02d9370b966a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "BeNeLux\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "# Oplossing - Netherlands gelijk aan rest\n",
+ "\n",
+ "benelux = {\"Belgium\", \"The Netherlands\", \"Luxembourg\"}\n",
+ "\n",
+ "beneluxList = (list(benelux))\n",
+ "\n",
+ "beneluxNoThe = [sub.replace(\"The Netherlands\", \"Netherlands\") for sub in beneluxList]\n",
+ "\n",
+ "# print(beneluxNoThe)\n",
+ "\n",
+ "# Oplossing - BeNeLux\n",
+ "\n",
+ "benelux = {\"Belgium\", \"The Netherlands\", \"Luxembourg\"}\n",
+ "\n",
+ "# beneluxList = sorted(list(benelux))\n",
+ "\n",
+ "# print (\"Benelux LIST: \", (beneluxList))\n",
+ "\n",
+ "beneluxAcronym1 = [sub.replace(\"The Netherlands\", \"Ne\") for sub in beneluxList]\n",
+ "beneluxAcronym2 = [sub.replace(\"Belgium\", \"Be\") for sub in beneluxAcronym1]\n",
+ "beneluxAcronym3 = [sub.replace(\"Luxembourg\", \"Lux\") for sub in beneluxAcronym2]\n",
+ "\n",
+ "# print(type(beneluxAcronym3))\n",
+ "# print(beneluxAcronym3)\n",
+ "\n",
+ "beneluxAcronym4 = str(beneluxAcronym3[0] + beneluxAcronym3[2] + beneluxAcronym3[1])\n",
+ "\n",
+ "print(beneluxAcronym4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "978e6a57-c504-4a92-8926-c7d1bd6d5027",
+ "metadata": {},
+ "source": [
+ "Visualisatie "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3b6e45d3-f358-4ffc-87a0-ae0bb5aa424b",
+ "metadata": {},
+ "source": [
+ "Open een (git)bash terminal en runpip install matplotlib-venn \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1a0ef2e6-bd8b-4bc0-a6b3-cdaadd2b2942",
+ "metadata": {},
+ "source": [
+ "Vervolgens importeren we de modules venn2 , venn3 en pylot "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "a0d796dc-7580-4937-a2cc-09d5b5c4e432",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from matplotlib_venn import venn2, venn3\n",
+ "from matplotlib import pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e376b04f-106c-4e3b-aeb5-79af2221a522",
+ "metadata": {},
+ "source": [
+ "**Gebruik een Venn diagram om Sets en hun relaties te visualiseren**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ad3f1cb6-7c43-4636-bf27-f1cd811cd140",
+ "metadata": {},
+ "source": [
+ "
De Sets benelux, nordics en baltics zijn disjunct ..
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "1a90d4ce-5d4b-45eb-becb-9a041ee81510",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "venn3([benelux, nordics, baltics], ('Benelux', 'Nordics', 'Baltics'))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7e2160a3-a90e-4d16-9f11-5f39458c5f9d",
+ "metadata": {},
+ "source": [
+ "dat wil zeggen dat ze geen enkel item met elkaar gemeen hebben
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "7e84e822-09ec-4b96-9268-6208c53535c1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'Belgium',\n",
+ " 'Denmark',\n",
+ " 'Estonia',\n",
+ " 'Finland',\n",
+ " 'Iceland',\n",
+ " 'Latvia',\n",
+ " 'Lithuania',\n",
+ " 'Luxembourg',\n",
+ " 'Norway',\n",
+ " 'The Netherlands'}"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "benelux.symmetric_difference(nordics).symmetric_difference(baltics)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ea751320-05f1-41d1-97ba-3f8de4240166",
+ "metadata": {},
+ "source": [
+ "**Gebruik union om verzamelingen verenigen**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "4f803d2e-f0cc-43bf-a5b9-f282adb27811",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "subUnion = benelux.union(nordics).union(baltics)\n",
+ "venn2([benelux, subUnion], ('Benelux', 'Benelux, Nordics and Baltics'))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b67a0424-1e9a-4b4d-952e-aa1d351c14cb",
+ "metadata": {},
+ "source": [
+ "De Sets benelux, nordics en baltics zijn allen een eigen subset van eu
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3229dad1-3579-41bd-b4e6-8d49f5c04ed4",
+ "metadata": {},
+ "source": [
+ "Niet alle landen uit subUnion zijn lid van de Europese Unie
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "7678cfc6-6e6a-4132-b221-1e303c28ef4f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "baltics.issubset(eu) and benelux.issubset(eu) and nordics.issubset(eu)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e6dc0310-0784-434a-acc1-5a21b4a4b2c5",
+ "metadata": {},
+ "source": [
+ "**Gebruik de methode intersection voor de doorsnede van twee Sets**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "e79a1f39-afc1-43f1-a060-3e6a7976b5fd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'Denmark', 'Finland'}"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "eu.intersection(nordics)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "789bb115-e680-4d25-8646-709ba8e8e346",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "venn2([eu, subUnion], ('EU', 'Benelux, Nordics and Baltics'))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ad46e130-fa09-45b4-b153-f2ce9a844009",
+ "metadata": {},
+ "source": [
+ "Exercise 3 \n",
+ "Gebruik de methode difference om de landen weer te geven die (nog) geen lid zijn van de EU
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "38cc4967-92d4-46de-80c7-835296449c29",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'Iceland', 'Norway'}"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nordics.difference(eu)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.7 64-bit",
+ "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": "3.10.7"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "5b93afe9c2e217d44e354b055ed180e932b72842f9b6422dadc1ad80da9caf67"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/variables/wk_2/wk_2_tuples.ipynb b/notebooks/variables/wk_2/wk_2_tuples.ipynb
new file mode 100644
index 00000000..ee18214e
--- /dev/null
+++ b/notebooks/variables/wk_2/wk_2_tuples.ipynb
@@ -0,0 +1,390 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5a5999c2-eb9d-4bf9-8373-b9964e2c152d",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
Author: Jeroen Boogaard \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "88e57004-2f85-4311-84c0-c4e148f421a7",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "Samengestelde variabelen - tuples "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9cdb9135-2650-4c30-a810-0685e5b4b50b",
+ "metadata": {},
+ "source": [
+ "**Een tuple is een immutable list**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6837aea3-45db-4940-8b9d-a406fdc01db1",
+ "metadata": {},
+ "source": [
+ "Tuple variabelen aanmaken en afdrukken "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "99e472c0-ba9b-4e25-ae03-75fd62f31e8f",
+ "metadata": {},
+ "source": [
+ "**Gebruik haakjes () voor het aanmaken van een tuple variabele**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "eac2c3b2-52a0-4847-bffc-ad8d2eff0507",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "soho = (\"Solar\", \"Heliospheric\", \"Observatory\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c680333b-23c3-42fc-8c04-d6e8a281fa40",
+ "metadata": {},
+ "source": [
+ "Om een tuple om te zetten naar printable string , kun je de methode join() gebruiken, een methode van type str , niet van tuple \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "73948594-ecc9-4e70-90a1-75fd5f48deb0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The SOHO Solar, Heliospheric, Observatory telescope is studying and staring at the Sun\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f\"The SOHO {', '.join(soho) } telescope is studying and staring at the Sun\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "cb5cde87-1945-4329-8b7c-89eec6b1bc16",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Triton discovered William Lassell in 1846\n"
+ ]
+ }
+ ],
+ "source": [
+ "discovery = (\"Triton\", \"William Lassell\", 1846)\n",
+ "print( \"%s discovered %s in %s\" %(discovery) )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e9d5d50b-750c-4b59-ae28-ea58ba287b70",
+ "metadata": {},
+ "source": [
+ "**Gebruik methodes van list om een bepaald element uit een tuple op te vragen**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "edad1885-0775-4769-986e-f230ce22c35f",
+ "metadata": {},
+ "source": [
+ "Vraag het eerste element van de tuple op
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "16176f6e-c3ba-4128-93e9-787a13e3135e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Solar'"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "soho[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "da99a925-ffe6-4676-a8ed-77674478b269",
+ "metadata": {},
+ "source": [
+ "Vraag het tweede element van de tuple op
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "8bcbfbaf-a21e-4177-88b7-99abc41e1520",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "soho.index('Heliospheric')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f85217d7-6d16-4c05-8829-efa5228873d3",
+ "metadata": {},
+ "source": [
+ "Vraag het laatste element van de tuple op
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "a2363a8f-843f-46c2-9a42-2498ac88715c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Observatory'"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "soho[-1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7ff7c206-0d29-448d-bf78-3f48fad9d4dd",
+ "metadata": {},
+ "source": [
+ "Exercise 1 \n",
+ "Bij een tuple kun je de list methode sort() niet gebruiken, laat zien hoe je met de methode sorted() de volgorde van de elementen in soho toch blijvend kunt veranderen."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "582540fc-8c6d-4335-ae09-bac02e4a61b7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "('Heliospheric', 'Observatory', 'Solar')\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "tuple"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Oplossing\n",
+ " \n",
+ "# Gebruik sorted() om alfabetisch te sorteren\n",
+ "# Gebruik tuple() om de list die de methode sorted() creëert weer om te zetten naar een tuple\n",
+ "\n",
+ "sortedSoho = tuple(sorted(soho))\n",
+ "print (sortedSoho)\n",
+ "type (sortedSoho)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5090e646-ff02-408e-ade7-4df431df5eda",
+ "metadata": {},
+ "source": [
+ "**Nadat een tuple variabele is aangemaakt kun je de waarde niet meer wijzigen..**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "435fed63-70fb-4ec7-bf0f-cd28822327d6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nickEnSimon = (\"Nick\", \"Simon\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "945eec7d-3174-430b-b6a2-01834e2cbb0c",
+ "metadata": {},
+ "source": [
+ "**..de variabele zelf kan wel wijzigen
**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "7986f368-9d3e-4455-832f-ebe2505ff49b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "('Nick', 'Simon')\n",
+ "('Suzan', 'Freek')\n"
+ ]
+ }
+ ],
+ "source": [
+ "duet = (\"Nick\", \"Simon\")\n",
+ "print(duet)\n",
+ "duet = (\"Suzan\", \"Freek\" )\n",
+ "print(duet)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e1a00c3-05ee-413d-9fb5-25ae398455a7",
+ "metadata": {},
+ "source": [
+ "Exercise 2 \n",
+ "Pas een cast toe zodat Simon zich kan ontpop pen tot solo-artiest
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "30d5121b-c454-4e7c-b9ec-7cee761e34a6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Solo-artiest: Simon\n",
+ "Solo-artiest: Simon\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Oplossing\n",
+ "\n",
+ "# Cast is omzetten naar ander data-type\n",
+ "# Functie .pop() om element uit de (gecaste) list te halen. \n",
+ "\n",
+ "nickEnSimon = (\"Nick\", \"Simon\")\n",
+ "\n",
+ "popSimon = list(nickEnSimon).pop()\n",
+ "# popperDePopSimon = popSimon.pop(1)\n",
+ "\n",
+ "print(\"Solo-artiest:\", popSimon)\n",
+ "\n",
+ "# Poging 2\n",
+ "\n",
+ "nickEnSimon = (\"Nick\", \"Simon\")\n",
+ "\n",
+ "nickEnSimon = list(nickEnSimon).pop()\n",
+ "\n",
+ "print(\"Solo-artiest:\", nickEnSimon)\n",
+ "print(type(nickEnSimon))\n",
+ "\n",
+ "\n",
+ "# fruits = ['apple', 'banana', 'cherry']\n",
+ "\n",
+ "# x = fruits.pop(1)\n",
+ "\n",
+ "# print(x)\n",
+ "\n",
+ "# print(fruits)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0dfddbcf-151f-4cff-bc1b-d8a5a24f562c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.7 64-bit",
+ "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": "3.10.7"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "5b93afe9c2e217d44e354b055ed180e932b72842f9b6422dadc1ad80da9caf67"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/variables/wk_2/wk_2_venn_diagrams (and, or, not).pdf b/notebooks/variables/wk_2/wk_2_venn_diagrams (and, or, not).pdf
new file mode 100644
index 00000000..6aff9c02
Binary files /dev/null and b/notebooks/variables/wk_2/wk_2_venn_diagrams (and, or, not).pdf differ
diff --git a/notebooks/variables/wk_2/wk_2_venn_diagrams.pdf b/notebooks/variables/wk_2/wk_2_venn_diagrams.pdf
new file mode 100644
index 00000000..70ed7ac6
Binary files /dev/null and b/notebooks/variables/wk_2/wk_2_venn_diagrams.pdf differ
diff --git a/notebooks/variables/wk_2_solutions/lists_with_solutions.ipynb b/notebooks/variables/wk_2_solutions/lists_with_solutions.ipynb
new file mode 100644
index 00000000..b622148a
--- /dev/null
+++ b/notebooks/variables/wk_2_solutions/lists_with_solutions.ipynb
@@ -0,0 +1,323 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5a5999c2-eb9d-4bf9-8373-b9964e2c152d",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
Author: Jeroen Boogaard \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "88e57004-2f85-4311-84c0-c4e148f421a7",
+ "metadata": {},
+ "source": [
+ "Samengestelde variabelen - lists "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6837aea3-45db-4940-8b9d-a406fdc01db1",
+ "metadata": {},
+ "source": [
+ "List variabelen aanmaken en afdrukken "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "99e472c0-ba9b-4e25-ae03-75fd62f31e8f",
+ "metadata": {},
+ "source": [
+ "**Gebruik teksthaken [] voor het aanmaken van een list variabele**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "eac2c3b2-52a0-4847-bffc-ad8d2eff0507",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "neptuneInnerMoons = [\"Naiad\", \"Thalassa\", \"Despina\", \"Galatea\", \"Proteus\", \"Hippocamp\", \"Larissa\"]\n",
+ "neptuneOutermoons = [\"Triton\", \"Halimede\", \"Sao\", \"Psamathe\", \"Laomedeia\", \"Neso\", \"Nereid\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d1354bbe-99d3-4653-a787-dde02513ac0a",
+ "metadata": {},
+ "source": [
+ "Exercise 9 \n",
+ "Schrijf een statement om te bewijzen dat Neptunus evenveel binnen- als buitenmanen heeft
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "b4aec7a1-ca64-4cdd-b498-5d88ea756b2d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Oplossing\n",
+ "\n",
+ "len(neptuneInnerMoons) == len(neptuneOutermoons)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7c74a678-a81b-4b7a-8bd6-57c4e1d41a04",
+ "metadata": {},
+ "source": [
+ "**Gebruik index 0 voor het verkrijgen van het eerste element**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "aeadd5cd-2193-4581-8d52-9d085211630a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Triton is the largest moon of Neptune.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"%s is the largest moon of Neptune.\" %(neptuneOutermoons[0]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cf9045c4-8007-4292-ab0f-7c54bc0df848",
+ "metadata": {},
+ "source": [
+ "**Met sort() wordt de volgorde van de elementen in een list blijvend veranderd .**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "27578e90-465b-4105-ac45-3e91bf2166f8",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Halimede is the largest moon of Neptune.\n"
+ ]
+ }
+ ],
+ "source": [
+ "neptuneOutermoons.sort()\n",
+ "print(\"%s is the largest moon of Neptune.\" %(neptuneOutermoons[0]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "340d4087-83ca-4686-afd3-6a8cb168843a",
+ "metadata": {},
+ "source": [
+ "**Gebruik de functie len() voor het verkrijgen van het aantal elementen in een list**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "45ccf585-96e6-4174-8ed6-06b3f4778310",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Neptune is the farthest planet of our solar system and it has 14 known moons.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f\"Neptune is the farthest planet of our solar system and it has { len(neptuneInnerMoons) + len(neptuneOutermoons) } known moons.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "9249ccbd-fc21-469d-a8b9-769f1fbb9551",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "('Naiad', 'Halimede')\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "list"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "neptuneMoons = list(zip(neptuneInnerMoons, neptuneOutermoons))\n",
+ "print(neptuneMoons[0])\n",
+ "type(neptuneMoons)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "b49facab-981d-4fd6-b982-e8400828fb23",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "myMatrix = [\n",
+ " 1, 2, 3,\n",
+ " 4, 5, 6,\n",
+ " ]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "be781ec3-3076-4fe6-950f-804390d048ac",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['naiad', 'thalassa', 'despina', 'galatea', 'proteus', 'hippocamp', 'larissa', 'triton', 'halimede', 'sao', 'psamathe', 'laomedeia', 'neso', 'nereid']\n",
+ "['naiad', 'thalassa', 'despina', 'galatea', 'proteus', 'hippocamp', 'larissa', 'triton', 'halimede', 'sao', 'psamathe', 'laomedeia', 'neso', 'nereid']\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Oplossing 1\n",
+ "\n",
+ "neptuneInnerMoons = [\"Naiad\", \"Thalassa\", \"Despina\", \"Galatea\", \"Proteus\", \"Hippocamp\", \"Larissa\"]\n",
+ "neptuneOutermoons = [\"Triton\", \"Halimede\", \"Sao\", \"Psamathe\", \"Laomedeia\", \"Neso\", \"Nereid\"]\n",
+ "\n",
+ "lowerMoons = neptuneInnerMoons + neptuneOutermoons\n",
+ "\n",
+ "a = [x.lower() for x in lowerMoons]\n",
+ "print(a)\n",
+ "\n",
+ "# Oplossing 2\n",
+ "\n",
+ "c = neptuneInnerMoons + neptuneOutermoons\n",
+ "a = (map(lambda x: x.lower(), c))\n",
+ "b = list(a)\n",
+ "print (b)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f0dc9127-ed86-4dfc-aef4-91bdd18669e2",
+ "metadata": {},
+ "source": [
+ "Map en join "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "330c963e-e9d1-4bfe-bcfe-2feac875a5de",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['NAIAD', 'THALASSA', 'DESPINA', 'GALATEA', 'PROTEUS', 'HIPPOCAMP', 'LARISSA']\n"
+ ]
+ }
+ ],
+ "source": [
+ "iterator = map(lambda moon: moon.upper(), neptuneInnerMoons)\n",
+ "print(list(iterator))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "f7e1bd63-eae4-4755-9300-4d58673e4a6f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "In order of increasing distance, the list of 7 regular Neptune moons is Despina, Galatea, Hippocamp, Larissa, Naiad, Proteus, Thalassa.\n"
+ ]
+ }
+ ],
+ "source": [
+ "neptuneInnerMoonsStr = \", \".join(map(str,sorted(neptuneInnerMoons))) \n",
+ "print( f\"In order of increasing distance, the list of { len(neptuneInnerMoons) } regular Neptune moons is {neptuneInnerMoonsStr}.\" )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ff5e9179-cb99-4c2b-bd3e-68f6a4205aae",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.7 64-bit",
+ "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": "3.10.7"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "5b93afe9c2e217d44e354b055ed180e932b72842f9b6422dadc1ad80da9caf67"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/variables/wk_3/marslander.csv b/notebooks/variables/wk_3/marslander.csv
new file mode 100644
index 00000000..d75d6851
--- /dev/null
+++ b/notebooks/variables/wk_3/marslander.csv
@@ -0,0 +1,2 @@
+length,width,weight,deckHeight,robotArmLength,numberOfSolarPanels,scienceInstruments,image
+6,1.56,360,"(83, 108)",1.8,2,"('seismometer', 'heat probe', 'radio science experiment')",C:\MakeAIWork\pics\mars.nasa.jpg
diff --git a/notebooks/variables/wk_3/matrix_vermenigvuldigen_test_nn.py b/notebooks/variables/wk_3/matrix_vermenigvuldigen_test_nn.py
new file mode 100644
index 00000000..ce75b057
--- /dev/null
+++ b/notebooks/variables/wk_3/matrix_vermenigvuldigen_test_nn.py
@@ -0,0 +1,114 @@
+# ts1 = [[1, 1, 1], [1, 0, 1], [1, 1, 1]]
+# ts2 = [[0, 1, 0], [1, 0, 1], [0, 1, 0]]
+# ts3 = [[0, 1, 0], [1, 1, 1], [0, 1, 0]]
+# ts4 = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
+
+# weights = [[1, 1, 1], [1, 1, 1], [1, 1, 1]]
+# print(type(weights))
+
+# ms4 = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
+
+# for rowIndex in range(len(ts4)):
+# for columnIndex in range(len(weights[0])):
+# for termIndex in range(len([weights])):
+# ms4[rowIndex][columnIndex] += ts4[rowIndex][termIndex] * weights[termIndex][columnIndex]
+
+# for result in ms4:
+# print(result)
+
+
+# x4
+
+# 2
+
+# Program to multiply two matrices using nested loops
+# print()
+
+# # # 3x3 matrix
+# X = [[12,7,3],
+# [4 ,5,6],
+# [7 ,8,9]]
+# # # 3x4 matrix
+# Y = [[5,8,1,2],
+# [6,7,3,0],
+# [4,5,9,1]]
+# # # result is 3x4
+# result = [[0,0,0,0],
+# [0,0,0,0],
+# [0,0,0,0]]
+
+# # iterate through rows of X
+# for i in range(len(X)):
+# # iterate through columns of Y
+# for j in range(len(Y[0])):
+# # iterate through rows of Y
+# for k in range(len(Y)):
+# result[i][j] += X[i][k] * Y[k][j]
+
+# for r in result:
+
+# print(r)
+
+
+
+# # 3
+# print()
+# Program to multiply two matrices using list comprehension
+
+# 3x3 matrix
+# X = [(12,7,3),
+# (4 ,5,6),
+# (7 ,8,9)]
+
+
+
+X = (
+ (1, 1, 1),
+ (1, 0, 1),
+ (1, 1, 1)
+ )
+
+# 3x4 matrix
+# Y = [(5,8,1,2),
+# (6,7,3,0),
+# (4,5,9,1)]
+
+Y = [(1,1,1),
+ (1,1,1),
+ (1,1,1)]
+
+# result is 3x4
+result = [[sum(a*b for a,b in zip(X_row,Y_col)) for Y_col in zip(*Y)] for X_row in X]
+
+print(result)
+
+for r in result:
+ print(r)
+
+# for rowIndex in range(len(ts4)):
+# for columnIndex in range(len(weights[0])):
+# for termIndex in range(len(ts4[rowIndex])):
+# ms1[rowIndex][columnIndex] += ts4[rowIndex][termIndex] * weights[termIndex][columnIndex]
+
+# for result in ms1:
+# print(result)
+
+
+# tuple >(,)NOPE
+
+# ts1 = [(1, 1, 1), (1, 0, 1), (1, 1, 1)]
+# ts2 = [[0, 1, 0], [1, 0, 1], [0, 1, 0]]
+# ts3 = [[0, 1, 0], [1, 1, 1], [0, 1, 0]]
+# ts4 = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
+
+# weights = [(1, 1, 1), (1, 1, 1), (1, 1, 1)]
+
+# ms1 = [(0, 0, 0), (0, 0, 0), (0, 0, 0)]
+
+# for rowIndex in range(len(ts1)):
+# for columnIndex in range(len(weights[0])):
+# for termIndex in range(len(ts1[rowIndex])):
+# ms1[rowIndex][columnIndex] += ts1[rowIndex][termIndex] * weights[termIndex][columnIndex]
+
+# for result in ms1:
+# print(result)
diff --git a/notebooks/variables/wk_3/perceptron.py b/notebooks/variables/wk_3/perceptron.py
new file mode 100644
index 00000000..23fce550
--- /dev/null
+++ b/notebooks/variables/wk_3/perceptron.py
@@ -0,0 +1,23 @@
+x_input = [0.1, 0.5, 0.2]
+w_weights = [0.4, 0.3, 0.6]
+threshold = 0.5
+
+def step(weighted_sum):
+ if weighted_sum > threshold:
+ return 1 # ('x' > (1,0))
+ else:
+ return 0 # ('0' > (0,1))
+
+def perceptron():
+ weighted_sum = 0
+ for x,w in zip (x_input, w_weights):
+ weighted_sum += x*w
+ print(weighted_sum)
+ return step(weighted_sum)
+
+output = perceptron()
+print('output: '+ str(output))
+
+# https://www.youtube.com/watch?v=-KLnurhX-Pg&ab_channel=PythonSimplified
+
+# input x weights = weighted sum < threshold
\ No newline at end of file
diff --git a/notebooks/variables/wk_3/symbool_herkenning.py b/notebooks/variables/wk_3/symbool_herkenning.py
new file mode 100644
index 00000000..171e1910
--- /dev/null
+++ b/notebooks/variables/wk_3/symbool_herkenning.py
@@ -0,0 +1,351 @@
+import math as mt
+from math import exp
+
+# Define symbols
+
+symbolVecs = {'o': (1, 0), 'x': (0 ,1)} # voor cost functie
+symbolChars = dict((value, key) for key, value in symbolVecs.items ())
+
+# Define datasets (training should normally be much larger than test set for best results)
+
+# trainingSet = (
+# ((
+# (1, 1, 1),
+# (1, 0, 1),
+# (1, 1, 1)
+# ), 'o'),
+# ((
+# (0, 1, 0),
+# (1, 0, 1),
+# (0, 1, 0)
+# ), 'o'),
+# ((
+# (0, 1, 0),
+# (1, 1, 1),
+# (0, 1, 0)
+# ), 'x'),
+# ((
+# (1, 0, 1),
+# (0, 1, 0),
+# (1, 0, 1)
+# ), 'x'),
+# )
+
+X = [
+ [
+ [1, 1, 1],
+ [1, 0, 1],
+ [1, 1, 1]
+ ],
+ [
+ [0, 1, 0],
+ [1, 0, 1],
+ [0, 1, 0]
+ ],
+ [
+ [0, 1, 0],
+ [1, 1, 1],
+ [0, 1, 0]
+ ],
+ [
+ [1, 0, 1],
+ [1, 1, 1],
+ [1, 0, 1]
+ ],
+ ]
+
+# mList = [[1, 1, 1],
+# [1, 0, 1],
+# [1, 1,1]]
+
+
+# eList = mList[0][0]
+
+mTup = ((1, 1, 1),
+ (1, 0, 1),
+ (1, 1, 1),
+ )
+
+# 'o'
+
+# eTup = mTup[2][0]
+
+# print('mTup: ', mTup)
+# print('eTup: ', eTup)
+
+# def mat2vec(mat):
+
+# # Convert 3 x 3 to 9 x 1
+# vec = []
+
+# rows = len(mat)
+
+# for row in range(0, rows):
+
+# cols = len(mat[row])
+
+# for col in range(0, cols):
+
+# vec.append(mat[row][col])
+
+# return vec
+
+# print(mTup)
+
+# # v = mat2vec(mTup)
+
+# # print(v)
+
+# # def in2out(input, weights):
+# # pass
+
+# s =((
+# (1, 1, 1),
+# (1, 0, 1),
+# (1, 1, 1)
+# ), 'O')
+
+# print('s[0]: ', s[0])
+# print('s[1]: ', s[1])
+
+
+# def initWeights(vec):
+# n = len(vec)
+# weights = []
+# for i in range(0,n):
+# weights.append(0.5)
+# return weights
+
+# vec = mat2vec(s[0])
+# weights = initWeights(vec)
+
+# print ('weights: ', weights)
+
+
+# def in2out(vec, weights):
+
+# # Compute vec[i] * weights[i]
+
+# n = len(vec)
+
+# Sum = 0.0
+
+# for i in range(0, n):
+
+# Sum += vec[i] * weights[i]
+
+# return mt.sqrt(Sum)
+
+# # # TODO:
+# # # Return softmax
+
+# vec = mat2vec(s[0])
+# weights = initWeights(vec)
+# outTO1 = in2out(vec, weights)
+
+# print('output Tuple1: ', outTO1)
+
+
+
+# Two possible Outputs thus, matrix (1,2) 2-nodes, therefore we need matrix Input (trainingSet) (3,3) or (9,1) 9-nodes * matrix Weight (2,9) 18-links
+
+# input nodes do not get input from the links, output nodes get input from the links
+
+
+# Epoch
+
+#================================================#
+
+# class Node:
+# def __init__(self):
+value = [] # (is X or 0?)
+# self.inLinks = [self.inNode?] * self.weight # 9 links = input links > is voor de Output dus moet 2 waardes worden?
+Y = [
+ [1, 1, 1],
+ [1, 1, 1],
+ [1, 1, 1]
+]
+# inNode = trainingSet
+
+# for rowIndex in range(len(inNode)): # 9x doorlopen
+# for columnIndex in range(len(weight[0])): # 2x doorlopen
+# for termIndex in range(len(inNode[1])): # 1x doorlopen
+# value[rowIndex][columnIndex] += inNode[rowIndex][termIndex] * weight[termIndex][columnIndex]
+
+result = [[sum(a*b for a,b in zip(X_row,Y_col)) for Y_col in zip(*Y)] for X_row in X]
+
+print(result)
+
+for r in result:
+ print(r)
+
+# Node()
+
+#================================================#
+
+# class Link:
+# def __init__(self, inNode, outNode):
+# self.weight = 0.5
+# self.newWeight = 0.4
+# self.inNode = # trainingSet > 9x
+# self.outNode = sum.float(self.inLinks.items * self.weight.items) - for all elements self.inNode if is X or 0 > 2x
+#
+# ? self.weightMatrix = (
+# (0.5, 0.5, 0.5),
+# (0.5, 0.5, 0.5),
+# (0.5, 0.5, 0.5)
+# )
+
+
+
+# Link()
+
+#================================================#
+
+# Add to output - https://machinelearningmastery.com/softmax-activation-function-with-python/
+
+#---------------E X P L A N A T I O N-----------------#
+
+def softmax(outputs):
+
+ exponentials = []
+
+ for item in outputs:
+ exponentials.append(mt.exp(item))
+ # >>> exponentials = [exp(7), exp(9)]
+
+ sumExponentials = sum(exponentials) # <<< met 'sum' kan de lijst direct opgeteld worden
+
+ probabilities = []
+
+ for item in exponentials:
+ probabilities.append(item/sumExponentials)
+ return probabilities
+
+
+probabilities = softmax([7, 9])
+
+print(probabilities)
+
+
+# softmax([7, 9]) # < "7, 9" zijn voorbeelden, bij weight = 1.
+# prob1 = exp(7) / exp(7) + exp(9) = ~
+# prob2 = exp(9) / exp(7) + exp(9) = ~
+
+#---------------E X P L A N A T I O N-----------------#
+
+
+## transform values into probabilities
+## from math import exp, done
+## calculate each probability
+
+# outTX1 = 1 - outTO1
+
+# print('outTX1: ', outTX1)
+
+
+# prob1 = mt.exp(outTO1) / (mt.exp(outTO1) + mt.exp(outTX1))
+# prob2 = mt.exp(outTX1) / (mt.exp(outTO1) + mt.exp(outTX1))
+#
+## report probabilities
+
+# print('Probability 1: ', prob1,
+# '\nProbability 2: ', prob2)
+
+## report sum of probabilities
+
+# print('Sum of probabilities: ', prob1 + prob2)
+
+#------------------------------------------------#
+
+# import math
+#
+# def soft_max(x):
+# exponents = []
+# for element in x:
+# exponents.append(mt.exp(element))
+# summ = sum(exponents)
+# for i in range(len(exponents)):
+# exponents[i] = exponents[i] / summ
+# return exponents
+#
+# if __name__=="__main__":
+# arr = [0.844521, 0.147048]
+# output = soft_max(arr)
+# print(output)
+#
+
+#================================================#
+
+# Mean Squared Error
+
+#---------------E X P L A N A T I O N-----------------#
+
+def lossFunction(probabilities, labels): # < rondje of kruisje, label < om te checken hoever je van de waarheid af zit voor 1 datapunt
+
+ loss = 0
+ # - if statemenent als safety-check om de lengtes te vergelijken -
+ # if len(probabilities) != len(labels):
+ # raise IndexError ("Labels zijn niet even lang!")
+
+ for i in range(len(probabilities)):
+
+ error = labels[i]-probabilities[i]
+ squaredError = error**2
+ loss += squaredError
+
+ return loss
+
+outputs = [9, 7]
+probabilities = [0.8 , 0.2]
+labels = [1, 0]
+
+print('Loss: ', lossFunction(softmax(outputs), labels))
+
+#---------------E X P L A N A T I O N-----------------#
+
+# OF
+
+#---------------E X P L A N A T I O N-----------------#
+
+
+
+
+# probabilities = [0.11 , 0.88]
+# labels = [0, 1]
+
+# probabilities - labels = [0.11 , 0.88] - [0, 1] = [0.11, -0.12]
+
+
+# subtracted = list()
+
+# for item1, item2 in zip(probabilities, labels):
+# subtracted.append(item1 - item2)
+# return subtracted
+
+# print('Subtracted:', subtracted)
+# print(sum(subtracted))
+# print(mt.exp(sum(subtracted)))
+
+# loss = mt.exp(sum(subtracted))
+
+# print('Loss:', loss)
+
+
+# symbolVecs = {'o': (1, 0), 'x': (0 ,1)} # voor cost functie
+# symbolChars = dict((value, key) for key, value in symbolVecs.items ())
+
+# return loss
+
+# Cost function is mean squared error: (4 x)
+# Compute differences between the observed values and the predictions. answerX = (y - y^), answer0 = (y - y^)
+# Square each of these differences > use math.exp(y - y^)
+# Add all these squared differences together > 4x .add? + + voor alle input matrices
+# Divide this sum by the sample length > len() /4
+#
+# mse = mt.exp(prob1 - 1)
+# mse = (1/len(trainingSet)) * mt.exp(Xy_observed - Xy_predicted) ???
+#
+# print('MSE:', mse)
+
+#================================================#
diff --git a/notebooks/variables/wk_3/symbool_herkenning_werkbestand.py b/notebooks/variables/wk_3/symbool_herkenning_werkbestand.py
new file mode 100644
index 00000000..81fd623f
--- /dev/null
+++ b/notebooks/variables/wk_3/symbool_herkenning_werkbestand.py
@@ -0,0 +1,373 @@
+import math as mt
+from math import exp
+from re import I
+
+# Define symbols
+
+symbolVecs = {'o': (1, 0), 'x': (0 ,1)} # voor cost functie
+symbolChars = dict((value, key) for key, value in symbolVecs.items ())
+
+# Define datasets (training should normally be much larger than test set for best results)
+
+trainingSet = (
+ ((
+ (1, 1, 1),
+ (1, 0, 1),
+ (1, 1, 1)
+ ), 'o'),
+ ((
+ (0, 1, 0),
+ (1, 0, 1),
+ (0, 1, 0)
+ ), 'o'),
+ ((
+ (0, 1, 0),
+ (1, 1, 1),
+ (0, 1, 0)
+ ), 'x'),
+ ((
+ (1, 0, 1),
+ (0, 1, 0),
+ (1, 0, 1)
+ ), 'x'),
+)
+
+
+# result list initialization
+# result = []
+
+# for tuple in trainingSet:
+# for elements in tuple:
+# result.append(elements)
+
+# printing output
+# print(result[0])
+# print(type(result))
+
+# tSet1 = []
+
+# for numbers in result[0]:
+# for singleNumber in numbers:
+# tSet1.append(singleNumber)
+
+# print(tSet1)
+# print(type(tSet1))
+
+
+# print(type(trainingSet))
+
+# myit = iter(trainingSet)
+
+# print(next(myit))
+# print(next(myit))
+# print(next(myit))
+# print(next(myit))
+
+
+# trainingSet1 = list(trainingSet)
+# print((trainingSet1))
+# tSetOne = list(trainingSet1)
+# print(tSetOne)
+# print(type(tSetOne))
+
+
+# result list initialization
+# result = []
+
+# for tuple in trainingSet:
+# for elements in tuple:
+# result.append(elements)
+
+# # return result
+
+# # printing output
+# print(result[0])
+# print(type(result))
+
+# trainingSetList = result
+
+# tSet = []
+
+# for tuple in trainingSetList:
+# tSet += list(trainingSetList)
+
+# print(tSet)
+
+# tSet = []
+
+# for numbers in trainingSet[3][0]: # met [][] kan de eerste stap overgeslagen worden en het alleen de matrix teruggegeven worden
+# for singleNumber in numbers:
+# tSet.append(singleNumber)
+
+
+# print('Trainingset 01: ', tSet)
+
+# # print(type(tSet1))
+
+# trainingLabel = []
+
+# for label in trainingSet[3][1]:
+# for signifier in label:
+# trainingLabel.append(signifier)
+
+
+# print('Label set 01: ', trainingLabel)
+# tSet = []
+
+# for tuple in trainingSet:
+# tSet += list(trainingSet)
+
+mList = [[0, 0, 1],
+ [1, 1, 1],
+ [1, 0, 0]]
+
+
+eList = mList[0][0]
+
+mTup = ((0, 0, 1),
+ (1, 1, 1),
+ (1, 0, 0))
+
+eTup = mTup[0][0]
+
+print('mTup: ', mTup)
+
+def mat2vec(mat):
+
+ # Convert 3 x 3 to 9 x 1
+ vec = []
+
+ rows = len(mat)
+
+ for row in range(0, rows):
+
+ cols = len(mat[row])
+
+ for col in range(0, cols):
+
+ vec.append(mat[row][col])
+
+ return vec
+
+print(mTup)
+
+# v = mat2vec(mTup)
+
+# print(v)
+
+# def in2out(input, weights):
+# pass
+
+s =((
+ (0, 0, 1),
+ (1, 1, 1),
+ (1, 0, 0)
+ ), 'X')
+
+# print(s[0])
+# print(s[1])
+
+
+def initWeights(vec):
+ n = len(vec)
+ weights = []
+ for i in range(0,n):
+ weights.append(0.5)
+ return weights
+
+vec = mat2vec(s[0])
+weights = initWeights(vec)
+
+print ('weights: ', weights)
+
+
+def in2out(vec, weights):
+
+ # Compute vec[i] * weights[i]
+
+ n = len(vec)
+
+ Sum = 0.0
+
+ for i in range(0, n):
+
+ Sum += vec[i] * weights[i]
+
+ return mt.sqrt(Sum)
+
+# # TODO:
+# # Return softmax
+
+vec = mat2vec(s[0])
+weights = initWeights(vec)
+out = in2out(vec, weights)
+
+print('output: ', out)
+
+
+# print(tSet)
+
+# res = list(map(list, trainingSet))
+
+# # print(res)
+# # print(type(res))
+
+# res2 = list(map(list, res[0]))
+
+# print(res2[0])
+# print(type(res2))
+
+# trainingSet1Tuple1 = trainingSet1[0]
+# # print(trainingSet1Tuple1[0:3])
+# # print(trainingSet1Tuple1[2])
+
+# trainingSet1Tuple1Elements = list(trainingSet1Tuple1[0:3])
+# # print(trainingSet1Tuple1Elements)
+# print('===========')
+# for i0_1 in trainingSet1Tuple1Elements:
+# print ('i0_1: ', i0_1)
+# print('===========')
+
+# lineInMatrix1_0 = i0_1
+# print('===========')
+# print(lineInMatrix1_0)
+# print('lineInMatrix1_0: ', lineInMatrix1_0)
+
+# (1, 1, 1),
+# (1, 0, 1),
+# (1, 1, 1)
+
+# trainingSet4 = trainingSet[3]
+# # print((trainingSet4))
+# # print(len(trainingSet))
+
+# trainingSet4Tuple = trainingSet4[0]
+# # print(trainingSet4Tuple[0:3])
+# # print(trainingSet4Tuple[2])
+
+# trainingSet4TupleElements = trainingSet4Tuple[0:3]
+# # print(trainingSet4TupleElements)
+# print('===========')
+# for ix_2 in trainingSet4TupleElements:
+# print ('ix_2: ',ix_2)
+
+# print('===========')
+
+# setFour = list(ix_2)
+# print(setFour) # [1, 0, 1]
+# print(type(setFour))
+
+
+# trainingSet bestaat uit tuples: trainingSet1_0 (incl. label) +
+# trainingSet2_0 (incl. label) +
+# trainingSet3_x (incl. label) +
+# trainingSet4_x (incl. label)
+#
+# trainingSet1_0 = (((1, 1, 1), (1, 0, 1), (1, 1, 1)), '0')
+
+# lineInMatrix1_0 = (1, 1, 1) - no label
+
+
+
+
+
+
+
+# Two possible Outputs thus, matrix (1,2) 2-nodes, therefore we need matrix Input (trainingSet) (3,3) or (9,1) 9-nodes * matrix Weight (2,9) 18-links
+
+# input nodes do not get input from the links, output nodes get input from the links
+
+
+# Epoch
+
+#================================================#
+
+# class Node:
+# def __init__(self):
+# self.value = None # (is X or 0?)
+# self.inLinks = [self.inNode?] * self.weight # 9 links = input links > is voor de Output dus moet 2 waardes worden?
+
+# for rowIndex in range(len(self.inNode1_0)): # 9x doorlopen
+# for columnIndex in range(len(self.weight[0])): # 2x doorlopen
+# for termIndex in range(len(self.inNode[1])): # 1x doorlopen
+# 'matrixC'[rowIndex][columnIndex] += self.inNode[rowIndex][termIndex] * self.weight[termIndex][columnIndex]
+
+# Node()
+
+#================================================#
+
+# class Link:
+# def __init__(self, inNode, outNode):
+# self.weight = 0.5
+# self.newWeight = 0.4
+# self.inNode = # trainingSet > 9x
+# self.outNode = sum.float(self.inLinks.items * self.weight.items) - for all elements self.inNode if is X or 0 > 2x
+#
+# ? self.weightMatrix = (
+# (0.5, 0.5, 0.5),
+# (0.5, 0.5, 0.5),
+# (0.5, 0.5, 0.5)
+# )
+# Link()
+
+#================================================#
+
+# Add to output - https://machinelearningmastery.com/softmax-activation-function-with-python/
+# def Softmax():
+#
+# transform values into probabilities
+# from math import exp, done
+# calculate each probability
+#
+# probability1 = exponential(1) / (exponential(1) + exponential(2))
+# probability2 = exponential(2) / (exponential(1) + exponential(2))
+#
+# report probabilities
+
+# print(probability1, probability2)
+
+# report sum of probabilities
+
+# print(probability1 + probability2)
+
+
+# import math
+
+# def soft_max(x):
+# exponents = []
+# for element in x:
+# exponents.append(math.exp(element))
+# summ = sum(exponents)
+# for i in range(len(exponents)):
+# exponents[i] = exponents[i] / summ
+# return exponents
+
+# if __name__=="__main__":
+# arr = [0.844521, 0.147048]
+# output = soft_max(arr)
+# print(output)
+#================================================#
+
+# def Loss()
+
+# Cost function is mean squared error: (4 x)
+# Compute differences between the observed values and the predictions. answerX = (y - y^), answer0 = (y - y^)
+# Square each of these differences > use math.exp(y - y^)
+# Add all these squared differences together > 4x .add? + + voor alle input matrices
+# Divide this sum by the sample length > len() /4
+#
+# mse = (1/len(trainingSet)) * math.exp(Oy_predicted - Oy_actual_observed)
+# mse = (1/len(trainingSet)) * math.exp(Xy_predicted - Xy_actual_observed) ???
+#
+#
+
+
+#================================================#
+
+# for rowIndex in range(len(trainingSet)):
+# for columnIndex in range(len(matrixB[0])):
+# for termIndex in range(len(trainingSet[rowIndex])):
+# matrixC[rowIndex][columnIndex] += trainingSet[rowIndex][termIndex] * matrixB[termIndex][columnIndex]
+#
+# for result in matrixC:
+# print(result)
\ No newline at end of file
diff --git a/notebooks/variables/wk_3/wk_3_dictionaries.ipynb b/notebooks/variables/wk_3/wk_3_dictionaries.ipynb
new file mode 100644
index 00000000..ade9b82e
--- /dev/null
+++ b/notebooks/variables/wk_3/wk_3_dictionaries.ipynb
@@ -0,0 +1,478 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5713f5a7-348e-4905-a2dc-b4b750d3eb7b",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
Author: Jeroen Boogaard \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8c60c67e-7b39-45ac-acd5-54e1013ec115",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d697ae0a-1505-43f5-817d-efeaed6009a4",
+ "metadata": {},
+ "source": [
+ "Samengestelde variabelen - Dictionaries "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5968c213-ab8e-41d5-b245-a23956f3af6f",
+ "metadata": {},
+ "source": [
+ "**Een dict is een mutable dataverzameling van key-value pairs .**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5812a326-b1a7-409e-9b2d-7b2bb5102532",
+ "metadata": {},
+ "source": [
+ "Dictionary variabelen aanmaken en afdrukken "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "0dabdb25-6ff5-4edc-96fd-7019734055e0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_items([('length', 6), ('width', 1.56), ('weight', 360), ('deckHeight', (83, 108)), ('robotArmLength', 1.8), ('numberOfSolarPanels', 2)])"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "marslanderSpecs = { 'length': 6, 'width': 1.56, 'weight': 360, 'deckHeight': (83, 108), 'robotArmLength': 1.8, 'numberOfSolarPanels': 2}\n",
+ "marslanderSpecs.items()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "839821d9-8d2c-4c18-a55e-c7645cbb3160",
+ "metadata": {},
+ "source": [
+ "De String Formatting Operator maakt gebruik van een tuple "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "9fc3a9ee-6b10-41e0-9d45-6d4b85940f7f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "The Deck Height of the Marslander has a range from 83 to 108\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(type(marslanderSpecs.get('deckHeight')))\n",
+ "print( 'The Deck Height of the Marslander has a range from %s to %s' %marslanderSpecs.get('deckHeight'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "db849391-b9b9-4900-95ba-24d21c65138f",
+ "metadata": {},
+ "source": [
+ "Elementen toevoegen "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "321b9ef8-559f-4f09-8110-0d07f6091807",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'length': 6, 'width': 1.56, 'weight': 360, 'deckHeight': (83, 108), 'robotArmLength': 1.8, 'numberOfSolarPanels': 2, 'scienceInstruments': ('seismometer', 'heat probe', 'radio science experiment'), 'image': 'C:\\\\MakeAIWork\\\\pics\\\\mars.nasa.jpg'}\n"
+ ]
+ },
+ {
+ "data": {
+ "image/jpeg": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "marslanderSpecs.update( {'scienceInstruments' : (\"seismometer\", \"heat probe\", \"radio science experiment\")} )\n",
+ "marslanderSpecs.update( {'image': \"C:\\MakeAIWork\\pics\\mars.nasa.jpg\"} )\n",
+ "print(marslanderSpecs)\n",
+ "\n",
+ "from IPython.display import Image\n",
+ "Image(filename='C:\\MakeAIWork\\pics\\mars.nasa.jpg')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# d=marslanderSpecs\n",
+ "# sum=0\n",
+ "# for i in range(0,len(d),1):\n",
+ "# sum=sum+1\n",
+ "# i=i+1\n",
+ "# print (i)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ef481205-753b-42ef-858d-84696e760708",
+ "metadata": {},
+ "source": [
+ "Excercise 1 \n",
+ "\n",
+ "
\n",
+ " Maak in de huidige directory (notebooks) de directory csv aan \n",
+ " Importeer de library csv of pandas \n",
+ " Exporteer de dictionary marslanderSpecs naar het bestand csv/marslander.csv \n",
+ " \n",
+ " TIP : Zoek op https://stackoverflow.com/en naar geschikte voorbeelden\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a0df37c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Oplossing\n",
+ "\n",
+ "import csv\n",
+ "\n",
+ "with open(\"marslander.csv\", 'w',newline='')as csvfile:\n",
+ " header = ['length', 'width', 'weight', 'deckHeight', 'robotArmLength', 'numberOfSolarPanels', 'scienceInstruments', 'image']\n",
+ " writer = csv.DictWriter(csvfile, fieldnames=header)\n",
+ " writer.writeheader()\n",
+ " writer.writerow(marslanderSpecs)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "09a77d13-56a2-4d84-9554-266bac12c8bb",
+ "metadata": {},
+ "source": [
+ "Mutaties "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5c443d17-4f1c-444d-988a-28753319ebe5",
+ "metadata": {},
+ "source": [
+ "**Voorkom information loss door de inhoud van de dictionary eerst naar een ander plaats in het geheugen te kopiëren**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3756229c-0b16-43b6-a495-c8bce87ff708",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "marslanderSpecsCopy = marslanderSpecs.copy()\n",
+ "print(marslanderSpecsCopy)\n",
+ "\n",
+ "print(id(marslanderSpecs))\n",
+ "print(id(marslanderSpecsCopy))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0e990d4a-a8e3-4230-8749-a72347ffdb84",
+ "metadata": {},
+ "source": [
+ "**De volgorde van waarmee items uit een data colection worden gepopt is Last In First Out (LIFO)
**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7fead617-0c2b-47f0-b535-084cde28ef54",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lastItem = marslanderSpecsCopy.popitem()\n",
+ "print(lastItem) \n",
+ "print(marslanderSpecsCopy.popitem())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6e5c60aa-5693-4098-bcca-7d9b735c439e",
+ "metadata": {},
+ "source": [
+ "Exercise 2 \n",
+ "Gegeven "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b78f2633-5fcd-46ab-a3a0-bcefb541d7e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "moonlanderSpecs = { 'name': \"Apollo Lunar Module\", 'length': 7.04, 'width': 9.4 }\n",
+ "moonlanderSpecsCopy = moonlanderSpecs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "28b30616-b186-4b6e-9d4e-1c307d4e1b0f",
+ "metadata": {},
+ "source": [
+ "Gevraagd \n",
+ "\n",
+ "Toon m.b.v. mutaties aan dat moonlanderSpecs en moonlanderSpecsCopy naar dezelfde plaats in het geheugen refereren. \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8fefac94-8935-4a0b-94ea-f9ac86e6c1fa",
+ "metadata": {},
+ "source": [
+ "Oplossing "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "76955594-1a07-4e1a-abff-e77b717a8cf0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Original:\n",
+ "moonlanderSpecs: {'name': 'Apollo Lunar Module', 'length': 7.04, 'width': 9.4}\n",
+ "\n",
+ "When we .pop - moonlanderSpecs.popitem() - the last item, both orignal and copy mutate:\n",
+ "\n",
+ "moonlanderSpecs: {'name': 'Apollo Lunar Module', 'length': 7.04}\n",
+ "moonlanderSpecsCopy: {'name': 'Apollo Lunar Module', 'length': 7.04}\n",
+ "\n",
+ "Check:\n",
+ "\n",
+ "moonlanderSpecsCopy == moonlanderSpecs: True \n",
+ "\n",
+ "id(moonlanderSpecs): 1456494341056\n",
+ "id(moonlanderSpecsCopy2): 1456494341056\n"
+ ]
+ }
+ ],
+ "source": [
+ "moonlanderSpecs = { 'name': \"Apollo Lunar Module\", 'length': 7.04, 'width': 9.4 }\n",
+ "moonlanderSpecsCopy2 = moonlanderSpecs\n",
+ "\n",
+ "print ('Original:')\n",
+ "print ('moonlanderSpecs: ', moonlanderSpecs)\n",
+ "\n",
+ "moonlanderSpecs.popitem()\n",
+ "\n",
+ "print ('\\nWhen we .pop - moonlanderSpecs.popitem() - the last item, both orignal and copy mutate:')\n",
+ "\n",
+ "print ('\\nmoonlanderSpecs: ', moonlanderSpecs)\n",
+ "print ('moonlanderSpecsCopy: ', moonlanderSpecsCopy2)\n",
+ "\n",
+ "print ('\\nCheck:')\n",
+ "print ('\\nmoonlanderSpecsCopy == moonlanderSpecs:', moonlanderSpecsCopy2 == moonlanderSpecs, '\\n')\n",
+ "\n",
+ "print('id(moonlanderSpecs): ', id(moonlanderSpecs))\n",
+ "print('id(moonlanderSpecsCopy2): ', id(moonlanderSpecsCopy2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ab2b77bd-ce14-4b32-91ab-ee81aded45df",
+ "metadata": {},
+ "source": [
+ "Visualisatie "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5cfb1537-4bb1-453e-8e09-06b52a742814",
+ "metadata": {},
+ "source": [
+ "**Importeer de Python Imaging Library (PIL) voor het renderen van een Image** "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "c1c7a3e1-2814-4d53-ae6f-51b6e9ab34fd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from PIL import Image"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "913b1435-3083-4d25-8d26-5ae6b4c10d10",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "img = Image.open( marslanderSpecs.get('image') )\n",
+ "percentage = 1.5\n",
+ "width, height = img.size\n",
+ "resizedDimensions = (int(width * percentage), int(height * percentage))\n",
+ "resizedImg = img.resize(resizedDimensions)\n",
+ "resizedImg.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "141254b3-9a26-4a73-9477-dc5701d8b72c",
+ "metadata": {},
+ "source": [
+ "Iteratie "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "65af11f0-5de0-40c7-b075-8f930bc27801",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "length 6\n",
+ "width 1.56\n",
+ "weight 360\n",
+ "deckHeight (83, 108)\n",
+ "robotArmLength 1.8\n",
+ "numberOfSolarPanels 2\n",
+ "scienceInstruments ('seismometer', 'heat probe', 'radio science experiment')\n",
+ "image C:\\MakeAIWork\\pics\\mars.nasa.jpg\n"
+ ]
+ }
+ ],
+ "source": [
+ "for (key, value) in marslanderSpecs.items():\n",
+ " print(key, value)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8d0e34dc-3d69-47c1-9e78-e9b7124c63b7",
+ "metadata": {},
+ "source": [
+ "Exercise 3 \n",
+ "Laat m.b.v. iteratie zien dat alle elementen van een dictionary 2-tuples zijn
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "68dc008a-32f0-4b7f-8f9e-fb179dee9e5f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "('length', 6)\n",
+ "('width', 1.56)\n",
+ "('weight', 360)\n",
+ "('deckHeight', (83, 108))\n",
+ "('robotArmLength', 1.8)\n",
+ "('numberOfSolarPanels', 2)\n",
+ "('scienceInstruments', ('seismometer', 'heat probe', 'radio science experiment'))\n",
+ "('image', 'C:\\\\MakeAIWork\\\\pics\\\\mars.nasa.jpg')\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Oplossing\n",
+ "\n",
+ "# Doorloop alle elementen van de dictionary en druk ze af\n",
+ "# Resultaat per element een tuple met 2 waarden (key & value, immers)\n",
+ "\n",
+ "for tuple in marslanderSpecs.items():\n",
+ " print (tuple)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a5514288-5623-4f8c-a41e-f4782fddf522",
+ "metadata": {},
+ "source": [
+ "NB : Zorg ervoor dat je zowel dit notebook als het bij Execercise 1 aangemaakte csv-bestand naar je remote git repository pusht
"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.7 64-bit",
+ "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": "3.10.7"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "5b93afe9c2e217d44e354b055ed180e932b72842f9b6422dadc1ad80da9caf67"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/variables/wk_3/wk_3_logic_gates_tec.pdf b/notebooks/variables/wk_3/wk_3_logic_gates_tec.pdf
new file mode 100644
index 00000000..dfe5e619
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diff --git a/notebooks/variables/wk_3/wk_3_neural_network_sketch.pdf b/notebooks/variables/wk_3/wk_3_neural_network_sketch.pdf
new file mode 100644
index 00000000..dee4c774
Binary files /dev/null and b/notebooks/variables/wk_3/wk_3_neural_network_sketch.pdf differ
diff --git a/notebooks/variables/wk_4/__pycache__/employee.cpython-310.pyc b/notebooks/variables/wk_4/__pycache__/employee.cpython-310.pyc
new file mode 100644
index 00000000..d9e25d01
Binary files /dev/null and b/notebooks/variables/wk_4/__pycache__/employee.cpython-310.pyc differ
diff --git a/notebooks/variables/wk_4/matrix.py b/notebooks/variables/wk_4/matrix.py
new file mode 100644
index 00000000..e06e2efb
--- /dev/null
+++ b/notebooks/variables/wk_4/matrix.py
@@ -0,0 +1,31 @@
+#!/usr/bin/env python
+
+class Matrix:
+
+ def __init__(self, vectorList):
+ self.vectorList = vectorList
+
+ def __add__(self, matrix2):
+ vectorList2 = matrix2.vectorList
+ vectorList3 = [self.addVectors(v1, v2) for (v1, v2) in zip(vectorList1, vectorList2)]
+ return Matrix(vectorList3)
+
+ def addVectors(self, v1, v2):
+ if len(v1) != len(v2):
+ return None
+ else:
+ v3 = [sum(tup) for tup in zip(v1, v2)]
+ # print(v3)
+ return v3
+
+ def __str__(self):
+ return f"{self.vectorList}"
+
+vectorList1 = ([1, 2, 3], [4, 5, 6], [4, 5, 6])
+vectorList2 = ([1, 1, 1], [1, 1, 1], [4, 5, 6])
+
+matrix1 = Matrix(vectorList1)
+matrix2 = Matrix(vectorList2)
+matrix3 = matrix1 + matrix2
+
+print(f"Matrix1 {matrix1} + Matrix2 {matrix2} = Matrix3 {matrix3}")
diff --git a/pics/mars.nasa.jpg b/pics/mars.nasa.jpg
new file mode 100644
index 00000000..0f2ba7d7
Binary files /dev/null and b/pics/mars.nasa.jpg differ
diff --git a/scripts/Activate.ps1 b/scripts/Activate.ps1
new file mode 100644
index 00000000..07424284
--- /dev/null
+++ b/scripts/Activate.ps1
@@ -0,0 +1,473 @@
+<#
+.Synopsis
+Activate a Python virtual environment for the current PowerShell session.
+
+.Description
+Pushes the python executable for a virtual environment to the front of the
+$Env:PATH environment variable and sets the prompt to signify that you are
+in a Python virtual environment. Makes use of the command line switches as
+well as the `pyvenv.cfg` file values present in the virtual environment.
+
+.Parameter VenvDir
+Path to the directory that contains the virtual environment to activate. The
+default value for this is the parent of the directory that the Activate.ps1
+script is located within.
+
+.Parameter Prompt
+The prompt prefix to display when this virtual environment is activated. By
+default, this prompt is the name of the virtual environment folder (VenvDir)
+surrounded by parentheses and followed by a single space (ie. '(.venv) ').
+
+.Example
+Activate.ps1
+Activates the Python virtual environment that contains the Activate.ps1 script.
+
+.Example
+Activate.ps1 -Verbose
+Activates the Python virtual environment that contains the Activate.ps1 script,
+and shows extra information about the activation as it executes.
+
+.Example
+Activate.ps1 -VenvDir C:\Users\MyUser\Common\.venv
+Activates the Python virtual environment located in the specified location.
+
+.Example
+Activate.ps1 -Prompt "MyPython"
+Activates the Python virtual environment that contains the Activate.ps1 script,
+and prefixes the current prompt with the specified string (surrounded in
+parentheses) while the virtual environment is active.
+
+.Notes
+On Windows, it may be required to enable this Activate.ps1 script by setting the
+execution policy for the user. You can do this by issuing the following PowerShell
+command:
+
+PS C:\> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
+
+For more information on Execution Policies:
+https://go.microsoft.com/fwlink/?LinkID=135170
+
+#>
+Param(
+ [Parameter(Mandatory = $false)]
+ [String]
+ $VenvDir,
+ [Parameter(Mandatory = $false)]
+ [String]
+ $Prompt
+)
+
+<# Function declarations --------------------------------------------------- #>
+
+<#
+.Synopsis
+Remove all shell session elements added by the Activate script, including the
+addition of the virtual environment's Python executable from the beginning of
+the PATH variable.
+
+.Parameter NonDestructive
+If present, do not remove this function from the global namespace for the
+session.
+
+#>
+function global:deactivate ([switch]$NonDestructive) {
+ # Revert to original values
+
+ # The prior prompt:
+ if (Test-Path -Path Function:_OLD_VIRTUAL_PROMPT) {
+ Copy-Item -Path Function:_OLD_VIRTUAL_PROMPT -Destination Function:prompt
+ Remove-Item -Path Function:_OLD_VIRTUAL_PROMPT
+ }
+
+ # The prior PYTHONHOME:
+ if (Test-Path -Path Env:_OLD_VIRTUAL_PYTHONHOME) {
+ Copy-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME -Destination Env:PYTHONHOME
+ Remove-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME
+ }
+
+ # The prior PATH:
+ if (Test-Path -Path Env:_OLD_VIRTUAL_PATH) {
+ Copy-Item -Path Env:_OLD_VIRTUAL_PATH -Destination Env:PATH
+ Remove-Item -Path Env:_OLD_VIRTUAL_PATH
+ }
+
+ # Just remove the VIRTUAL_ENV altogether:
+ if (Test-Path -Path Env:VIRTUAL_ENV) {
+ Remove-Item -Path env:VIRTUAL_ENV
+ }
+
+ # Just remove VIRTUAL_ENV_PROMPT altogether.
+ if (Test-Path -Path Env:VIRTUAL_ENV_PROMPT) {
+ Remove-Item -Path env:VIRTUAL_ENV_PROMPT
+ }
+
+ # Just remove the _PYTHON_VENV_PROMPT_PREFIX altogether:
+ if (Get-Variable -Name "_PYTHON_VENV_PROMPT_PREFIX" -ErrorAction SilentlyContinue) {
+ Remove-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Scope Global -Force
+ }
+
+ # Leave deactivate function in the global namespace if requested:
+ if (-not $NonDestructive) {
+ Remove-Item -Path function:deactivate
+ }
+}
+
+<#
+.Description
+Get-PyVenvConfig parses the values from the pyvenv.cfg file located in the
+given folder, and returns them in a map.
+
+For each line in the pyvenv.cfg file, if that line can be parsed into exactly
+two strings separated by `=` (with any amount of whitespace surrounding the =)
+then it is considered a `key = value` line. The left hand string is the key,
+the right hand is the value.
+
+If the value starts with a `'` or a `"` then the first and last character is
+stripped from the value before being captured.
+
+.Parameter ConfigDir
+Path to the directory that contains the `pyvenv.cfg` file.
+#>
+function Get-PyVenvConfig(
+ [String]
+ $ConfigDir
+) {
+ Write-Verbose "Given ConfigDir=$ConfigDir, obtain values in pyvenv.cfg"
+
+ # Ensure the file exists, and issue a warning if it doesn't (but still allow the function to continue).
+ $pyvenvConfigPath = Join-Path -Resolve -Path $ConfigDir -ChildPath 'pyvenv.cfg' -ErrorAction Continue
+
+ # An empty map will be returned if no config file is found.
+ $pyvenvConfig = @{ }
+
+ if ($pyvenvConfigPath) {
+
+ Write-Verbose "File exists, parse `key = value` lines"
+ $pyvenvConfigContent = Get-Content -Path $pyvenvConfigPath
+
+ $pyvenvConfigContent | ForEach-Object {
+ $keyval = $PSItem -split "\s*=\s*", 2
+ if ($keyval[0] -and $keyval[1]) {
+ $val = $keyval[1]
+
+ # Remove extraneous quotations around a string value.
+ if ("'""".Contains($val.Substring(0, 1))) {
+ $val = $val.Substring(1, $val.Length - 2)
+ }
+
+ $pyvenvConfig[$keyval[0]] = $val
+ Write-Verbose "Adding Key: '$($keyval[0])'='$val'"
+ }
+ }
+ }
+ return $pyvenvConfig
+}
+
+
+<# Begin Activate script --------------------------------------------------- #>
+
+# Determine the containing directory of this script
+$VenvExecPath = Split-Path -Parent $MyInvocation.MyCommand.Definition
+$VenvExecDir = Get-Item -Path $VenvExecPath
+
+Write-Verbose "Activation script is located in path: '$VenvExecPath'"
+Write-Verbose "VenvExecDir Fullname: '$($VenvExecDir.FullName)"
+Write-Verbose "VenvExecDir Name: '$($VenvExecDir.Name)"
+
+# Set values required in priority: CmdLine, ConfigFile, Default
+# First, get the location of the virtual environment, it might not be
+# VenvExecDir if specified on the command line.
+if ($VenvDir) {
+ Write-Verbose "VenvDir given as parameter, using '$VenvDir' to determine values"
+}
+else {
+ Write-Verbose "VenvDir not given as a parameter, using parent directory name as VenvDir."
+ $VenvDir = $VenvExecDir.Parent.FullName.TrimEnd("\\/")
+ Write-Verbose "VenvDir=$VenvDir"
+}
+
+# Next, read the `pyvenv.cfg` file to determine any required value such
+# as `prompt`.
+$pyvenvCfg = Get-PyVenvConfig -ConfigDir $VenvDir
+
+# Next, set the prompt from the command line, or the config file, or
+# just use the name of the virtual environment folder.
+if ($Prompt) {
+ Write-Verbose "Prompt specified as argument, using '$Prompt'"
+}
+else {
+ Write-Verbose "Prompt not specified as argument to script, checking pyvenv.cfg value"
+ if ($pyvenvCfg -and $pyvenvCfg['prompt']) {
+ Write-Verbose " Setting based on value in pyvenv.cfg='$($pyvenvCfg['prompt'])'"
+ $Prompt = $pyvenvCfg['prompt'];
+ }
+ else {
+ Write-Verbose " Setting prompt based on parent's directory's name. (Is the directory name passed to venv module when creating the virtual environment)"
+ Write-Verbose " Got leaf-name of $VenvDir='$(Split-Path -Path $venvDir -Leaf)'"
+ $Prompt = Split-Path -Path $venvDir -Leaf
+ }
+}
+
+Write-Verbose "Prompt = '$Prompt'"
+Write-Verbose "VenvDir='$VenvDir'"
+
+# Deactivate any currently active virtual environment, but leave the
+# deactivate function in place.
+deactivate -nondestructive
+
+# Now set the environment variable VIRTUAL_ENV, used by many tools to determine
+# that there is an activated venv.
+$env:VIRTUAL_ENV = $VenvDir
+
+if (-not $Env:VIRTUAL_ENV_DISABLE_PROMPT) {
+
+ Write-Verbose "Setting prompt to '$Prompt'"
+
+ # Set the prompt to include the env name
+ # Make sure _OLD_VIRTUAL_PROMPT is global
+ function global:_OLD_VIRTUAL_PROMPT { "" }
+ Copy-Item -Path function:prompt -Destination function:_OLD_VIRTUAL_PROMPT
+ New-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Description "Python virtual environment prompt prefix" -Scope Global -Option ReadOnly -Visibility Public -Value $Prompt
+
+ function global:prompt {
+ Write-Host -NoNewline -ForegroundColor Green "($_PYTHON_VENV_PROMPT_PREFIX) "
+ _OLD_VIRTUAL_PROMPT
+ }
+ $env:VIRTUAL_ENV_PROMPT = $Prompt
+}
+
+# Clear PYTHONHOME
+if (Test-Path -Path Env:PYTHONHOME) {
+ Copy-Item -Path Env:PYTHONHOME -Destination Env:_OLD_VIRTUAL_PYTHONHOME
+ Remove-Item -Path Env:PYTHONHOME
+}
+
+# Add the venv to the PATH
+Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH
+$Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH"
+
+# SIG # Begin signature block
+# MIIptAYJKoZIhvcNAQcCoIIppTCCKaECAQExDzANBglghkgBZQMEAgEFADB5Bgor
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+# bmMuMTswOQYDVQQDEzJEaWdpQ2VydCBUcnVzdGVkIEc0IFJTQTQwOTYgU0hBMjU2
+# IFRpbWVTdGFtcGluZyBDQQIQCnpKiJ7JmUKQBmM4TYaXnTANBglghkgBZQMEAgEF
+# AKCB0TAaBgkqhkiG9w0BCQMxDQYLKoZIhvcNAQkQAQQwHAYJKoZIhvcNAQkFMQ8X
+# DTIyMDgwMTIxNTg1OFowKwYLKoZIhvcNAQkQAgwxHDAaMBgwFgQUhQjzhlFcs9MH
+# fba0t8B/G0peQd4wLwYJKoZIhvcNAQkEMSIEIHyNQTONtnHz/YAa8LGmlY6N6PQ3
+# 7DylBqoN5FA0dRnUMDcGCyqGSIb3DQEJEAIvMSgwJjAkMCIEIJ2mkBXDScbBiXhF
+# ujWCrXDIj6QpO9tqvpwr0lOSeeY7MA0GCSqGSIb3DQEBAQUABIICAKGdksbLZZtB
+# 9oAx3iJixYbXCrrGmVL7UVI0KcTVluymicFHhMgcl5eDkRuO/MMdB9LYL8ACGuaH
+# z1H5GNEqSN4POjOo4zLqlo7azaSIH7mW+0xdkhzH2sv8g/LyFDq9c2+6LI1QcrTf
+# tK7YCzH4lr3yRdpaHzIMoPK+kg73bytEBhxBRBlyILYt6SCtmn/kozPBiNEndk3+
+# SwA1j7Dx4NlxUpAQKpPlsuBjkOZxgmAwk4nDaSVWDsd3RWlxhj7BkD49X2s4jmuR
+# LWxDb18ZCRdzELY3DnssjW/dr7o5dR4LTAkTWiX68ydG1qXbcmQEabAGHxcOE0gn
+# 5KSHlIAmX3SHS4yeWEI9hTxCVVW+i65rBCAE2F0elIRTZx88sqZFueNBhfPOBjec
+# LbMoJUh42iG9eWS7d7P/1POq6Zj9ZJsOG7Iq3vqwpq4pboLTbzzKPX0cxDj1Y4Db
+# dwinJaHYjckJjvHjspNX9qSIo48Jn66vDSsA/zysE6QUVsJ9Gmenc5H/5WeoULPu
+# YJMfdOijcTxI4NS2pqJno7zL4i/8VWnCiieqXT2JBmkULHId6jZ0PX/nyaQmybF4
+# HL4lzFCGwdnC3V8ADBAqI9lzP9bnj0i/vblknG5hikERqNpWe9YrtXhCEyI8yYip
+# qe0PCJ71XjHWxpBw1Yt6yTommRnxHj5M
+# SIG # End signature block
diff --git a/scripts/activate b/scripts/activate
new file mode 100644
index 00000000..ce167e55
--- /dev/null
+++ b/scripts/activate
@@ -0,0 +1,69 @@
+# This file must be used with "source bin/activate" *from bash*
+# you cannot run it directly
+
+deactivate () {
+ # reset old environment variables
+ if [ -n "${_OLD_VIRTUAL_PATH:-}" ] ; then
+ PATH="${_OLD_VIRTUAL_PATH:-}"
+ export PATH
+ unset _OLD_VIRTUAL_PATH
+ fi
+ if [ -n "${_OLD_VIRTUAL_PYTHONHOME:-}" ] ; then
+ PYTHONHOME="${_OLD_VIRTUAL_PYTHONHOME:-}"
+ export PYTHONHOME
+ unset _OLD_VIRTUAL_PYTHONHOME
+ fi
+
+ # This should detect bash and zsh, which have a hash command that must
+ # be called to get it to forget past commands. Without forgetting
+ # past commands the $PATH changes we made may not be respected
+ if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then
+ hash -r 2> /dev/null
+ fi
+
+ if [ -n "${_OLD_VIRTUAL_PS1:-}" ] ; then
+ PS1="${_OLD_VIRTUAL_PS1:-}"
+ export PS1
+ unset _OLD_VIRTUAL_PS1
+ fi
+
+ unset VIRTUAL_ENV
+ unset VIRTUAL_ENV_PROMPT
+ if [ ! "${1:-}" = "nondestructive" ] ; then
+ # Self destruct!
+ unset -f deactivate
+ fi
+}
+
+# unset irrelevant variables
+deactivate nondestructive
+
+VIRTUAL_ENV="C:\Users\El Director\MakeAIWork"
+export VIRTUAL_ENV
+
+_OLD_VIRTUAL_PATH="$PATH"
+PATH="$VIRTUAL_ENV/Scripts:$PATH"
+export PATH
+
+# unset PYTHONHOME if set
+# this will fail if PYTHONHOME is set to the empty string (which is bad anyway)
+# could use `if (set -u; : $PYTHONHOME) ;` in bash
+if [ -n "${PYTHONHOME:-}" ] ; then
+ _OLD_VIRTUAL_PYTHONHOME="${PYTHONHOME:-}"
+ unset PYTHONHOME
+fi
+
+if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT:-}" ] ; then
+ _OLD_VIRTUAL_PS1="${PS1:-}"
+ PS1="(MakeAIWork) ${PS1:-}"
+ export PS1
+ VIRTUAL_ENV_PROMPT="(MakeAIWork) "
+ export VIRTUAL_ENV_PROMPT
+fi
+
+# This should detect bash and zsh, which have a hash command that must
+# be called to get it to forget past commands. Without forgetting
+# past commands the $PATH changes we made may not be respected
+if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then
+ hash -r 2> /dev/null
+fi
diff --git a/scripts/activate.bat b/scripts/activate.bat
new file mode 100644
index 00000000..9fc0be48
--- /dev/null
+++ b/scripts/activate.bat
@@ -0,0 +1,34 @@
+@echo off
+
+rem This file is UTF-8 encoded, so we need to update the current code page while executing it
+for /f "tokens=2 delims=:." %%a in ('"%SystemRoot%\System32\chcp.com"') do (
+ set _OLD_CODEPAGE=%%a
+)
+if defined _OLD_CODEPAGE (
+ "%SystemRoot%\System32\chcp.com" 65001 > nul
+)
+
+set VIRTUAL_ENV=C:\Users\El Director\MakeAIWork
+
+if not defined PROMPT set PROMPT=$P$G
+
+if defined _OLD_VIRTUAL_PROMPT set PROMPT=%_OLD_VIRTUAL_PROMPT%
+if defined _OLD_VIRTUAL_PYTHONHOME set PYTHONHOME=%_OLD_VIRTUAL_PYTHONHOME%
+
+set _OLD_VIRTUAL_PROMPT=%PROMPT%
+set PROMPT=(MakeAIWork) %PROMPT%
+
+if defined PYTHONHOME set _OLD_VIRTUAL_PYTHONHOME=%PYTHONHOME%
+set PYTHONHOME=
+
+if defined _OLD_VIRTUAL_PATH set PATH=%_OLD_VIRTUAL_PATH%
+if not defined _OLD_VIRTUAL_PATH set _OLD_VIRTUAL_PATH=%PATH%
+
+set PATH=%VIRTUAL_ENV%\Scripts;%PATH%
+set VIRTUAL_ENV_PROMPT=(MakeAIWork)
+
+:END
+if defined _OLD_CODEPAGE (
+ "%SystemRoot%\System32\chcp.com" %_OLD_CODEPAGE% > nul
+ set _OLD_CODEPAGE=
+)
diff --git a/scripts/deactivate.bat b/scripts/deactivate.bat
new file mode 100644
index 00000000..62a39a75
--- /dev/null
+++ b/scripts/deactivate.bat
@@ -0,0 +1,22 @@
+@echo off
+
+if defined _OLD_VIRTUAL_PROMPT (
+ set "PROMPT=%_OLD_VIRTUAL_PROMPT%"
+)
+set _OLD_VIRTUAL_PROMPT=
+
+if defined _OLD_VIRTUAL_PYTHONHOME (
+ set "PYTHONHOME=%_OLD_VIRTUAL_PYTHONHOME%"
+ set _OLD_VIRTUAL_PYTHONHOME=
+)
+
+if defined _OLD_VIRTUAL_PATH (
+ set "PATH=%_OLD_VIRTUAL_PATH%"
+)
+
+set _OLD_VIRTUAL_PATH=
+
+set VIRTUAL_ENV=
+set VIRTUAL_ENV_PROMPT=
+
+:END
diff --git a/scripts/moonlander.py b/scripts/moonlander.py
new file mode 100644
index 00000000..d784c8e0
--- /dev/null
+++ b/scripts/moonlander.py
@@ -0,0 +1,157 @@
+# Open source, license: Apache 2, (C) GEATEC engineering
+
+'''
+Requirements:
+
+- Game should simulate landing on the moon with limited amount of fuel
+- Should be realtime, obeying laws of physics
+- No attitude or lateral control, just vertical thrust
+- Show moonscape, lander and motor exhaust in graphical window
+- Show velocity, acceleration, motor on/of and fuel in text window
+- Toggle motor on/of with mouse click
+
+Testspecs:
+
+- Make a smooth landing
+- Make a crash landing
+- Relaunch and gain altitude until fuel runs out
+
+Design:
+
+- Moon and Lander are Things in a World
+- Picture of moonscape as background
+- Lander is a sprite
+- Only use standard Python distro >= 3.8, so only turtle graphics
+- Single threaded, state machine with varying delta time
+
+'''
+
+import time as tm
+import turtle as tr
+import random as rd
+
+class Thing:
+ def __init__ (self, world):
+ self.world = world
+
+ def compute (self):
+ pass
+
+ def render (self):
+ pass
+
+class Moon (Thing):
+ image = 'moon.gif'
+
+ def render (self):
+ if self.world.lander.damage > 0:
+ tr.bgpic ('nopic')
+ for i in range (int (self.world.lander.damage)):
+ tr.bgcolor (rd.choice (('black', 'white', 'red', 'cyan', 'yellow')))
+ tm.sleep (0.01)
+ tr.bgpic (self.image)
+
+class Lander (Thing):
+ normalImage = 'lander_normal.gif'
+ firingImage1 = 'lander_firing_1.gif'
+ firingImage2 = 'lander_firing_2.gif'
+
+ def __init__ (self, world):
+ super () .__init__ (world)
+
+ self.y = 90
+ self.v = 0
+ self.fuel = 50
+
+ self.firing = False
+ self.touched = False
+
+ tr.addshape (self.normalImage)
+ tr.addshape (self.firingImage1)
+ tr.addshape (self.firingImage2)
+
+ tr.onscreenclick (self._toggle)
+
+ def compute (self):
+ self.v += self.world.period * (8 if self.firing else -4)
+ self.y += self.world.period * self.v
+
+ if self.firing:
+ self.fuel -= self.world.period * 10
+
+ if self.fuel <= 0:
+ self.fuel = 0
+ self.firing = False
+
+ self.damage = 0.1 * self.v * self.v if self.y <= 0 else 0
+
+ if self.y < 0:
+ if not self.touched:
+ tr.title ('The Eagle has ' + (f'CRASHED with velocity {self.v:6.2f} m/s, sorry...' if self.v < -7 else f'landed with velocity {self.v:6.2f} m/s!'))
+ self.touched = True
+
+ self.firing = False
+ self.y = 0
+ self.v = 0
+
+ def render (self):
+ tr.shape (rd.choice ((self.firingImage1, self.firingImage2)) if self.firing else self.normalImage)
+ self.world.goto (0, self.y)
+ tr.update ()
+ print (f'Height: {self.y:5.2f} Velocity: {self.v:5.2f} Firing: {self.firing} Fuel: {self.fuel:5.2f}')
+
+ def _toggle (self, *args):
+ self.firing = not self.firing and self.fuel > 0
+
+class World:
+ wScreen, hScreen = 1920, 1080
+ hImage = 200
+ resolution = 0.1 # Meter (in the scene) per pixel
+ wScene, hScene = wScreen * resolution , hScreen * resolution
+ xTarget, yTarget = -0.035 * wScene, -0.16 * hScene
+
+ def __init__ (self):
+ self.moon = Moon (self)
+ self.lander = Lander (self)
+ self.things = self.moon, self.lander
+
+ self._prepare ()
+ input ('Press [Enter] to start, click to toggle engine, keep an eye on fuel...')
+ self.run ()
+
+ def run (self):
+ time = tm.time ()
+ while True:
+ oldTime = time
+ time = tm.time ()
+ self.period = time - oldTime
+
+ for thing in self.things:
+ thing.compute ()
+
+ for thing in self.things:
+ thing.render ()
+
+ tm.sleep (0.05)
+
+ def goto (self, xScene, yScene):
+ tr.goto ((xScene + self.xTarget) / self.resolution, (yScene + self.yTarget) / self.resolution - self.hImage)
+
+ def _prepare (self):
+ tr.setup (self.wScreen, self.hScreen)
+ tr.bgpic (self.moon.image)
+ tr.title ('Tranquility Base here...')
+ tr.speed (0)
+ tr.delay (0)
+ tr.up ()
+
+# win = tr.Screen()
+
+# while True:
+# win.update()
+
+# tr.done()
+
+World ()
+
+
diff --git a/scripts/pip.exe b/scripts/pip.exe
new file mode 100644
index 00000000..62e339c6
Binary files /dev/null and b/scripts/pip.exe differ
diff --git a/scripts/pip3.10.exe b/scripts/pip3.10.exe
new file mode 100644
index 00000000..62e339c6
Binary files /dev/null and b/scripts/pip3.10.exe differ
diff --git a/scripts/pip3.exe b/scripts/pip3.exe
new file mode 100644
index 00000000..62e339c6
Binary files /dev/null and b/scripts/pip3.exe differ
diff --git a/scripts/python.exe b/scripts/python.exe
new file mode 100644
index 00000000..5f0701a4
Binary files /dev/null and b/scripts/python.exe differ
diff --git a/scripts/pythonw.exe b/scripts/pythonw.exe
new file mode 100644
index 00000000..bdada72d
Binary files /dev/null and b/scripts/pythonw.exe differ
diff --git a/scripts/tesla_bmw_need_for_speed.py b/scripts/tesla_bmw_need_for_speed.py
new file mode 100644
index 00000000..39e30fc0
--- /dev/null
+++ b/scripts/tesla_bmw_need_for_speed.py
@@ -0,0 +1,161 @@
+# Open source, license: Apache 2, (C) GEATEC engineering
+
+'''
+Requirements:
+
+- Game should simulate landing on the moon with limited amount of fuel
+- Should be realtime, obeying laws of physics
+- No attitude or lateral control, just vertical thrust
+- Show moonscape, lander and motor exhaust in graphical window
+- Show velocity, acceleration, motor on/of and fuel in text window
+- Toggle motor on/of with mouse click
+
+Testspecs:
+
+- Make a smooth landing
+- Make a crash landing
+- Relaunch and gain altitude until fuel runs out
+
+Design:
+
+- Moon and Lander are Things in a World
+- Picture of moonscape as background
+- Lander is a sprite
+- Only use standard Python distro >= 3.8, so only turtle graphics
+- Single threaded, state machine with varying delta time
+
+'''
+
+import time as tm
+import turtle as tr
+import random as rd
+
+class Thing:
+ def __init__ (self, world):
+ self.world = world
+
+ def compute (self):
+ pass
+
+ def render (self):
+ pass
+
+class Road (Thing):
+ image = 'moon.gif'
+
+ def render (self):
+ if self.world.lander.damage > 0:
+ tr.bgpic ('nopic')
+ for i in range (int (self.world.lander.damage)):
+ tr.bgcolor (rd.choice (('black', 'white', 'red', 'cyan', 'yellow')))
+ tm.sleep (0.01)
+ tr.bgpic (self.image)
+
+class Car (Thing):
+ normalImage = 'lander_normal.gif'
+ firingImage1 = 'lander_firing_1.gif'
+ firingImage2 = 'lander_firing_2.gif'
+
+ def __init__ (self, world):
+ super () .__init__ (world)
+
+ self.y = 90
+ self.v = 0
+ self.fuel = 50
+
+ self.firing = False
+ self.touched = False
+
+ tr.addshape (self.normalImage)
+ tr.addshape (self.firingImage1)
+ tr.addshape (self.firingImage2)
+
+ tr.onscreenclick (self._toggle)
+
+ def compute (self):
+ self.v += self.world.period * (8 if self.firing else -4)
+ self.y += self.world.period * self.v
+
+ if self.firing:
+ self.fuel -= self.world.period * 10
+
+ if self.fuel <= 0:
+ self.fuel = 0
+ self.firing = False
+
+ self.damage = 0.1 * self.v * self.v if self.y <= 0 else 0
+
+ if self.y < 0:
+ if not self.touched:
+ tr.title ('The Eagle has ' + (f'CRASHED with velocity {self.v:6.2f} m/s, sorry...' if self.v < -7 else f'landed with velocity {self.v:6.2f} m/s!'))
+ self.touched = True
+
+ self.firing = False
+ self.y = 0
+ self.v = 0
+
+ def render (self):
+ tr.shape (rd.choice ((self.firingImage1, self.firingImage2)) if self.firing else self.normalImage)
+ self.world.goto (0, self.y)
+ tr.update ()
+ print (f'Height: {self.y:5.2f} Velocity: {self.v:5.2f} Firing: {self.firing} Fuel: {self.fuel:5.2f}')
+
+ def _toggle (self, *args):
+ self.firing = not self.firing and self.fuel > 0
+
+class World:
+ wScreen, hScreen = 1920, 1080
+ hImage = 200
+ resolution = 0.1 # Meter (in the scene) per pixel
+ wScene, hScene = wScreen * resolution , hScreen * resolution
+ xTarget, yTarget = -0.035 * wScene, -0.16 * hScene
+
+ def __init__ (self):
+ self.moon = Moon (self)
+ self.lander = Lander (self)
+ self.things = self.moon, self.lander
+
+ self._prepare ()
+ input ('Press [Enter] to start et laisez les bons temps rouler...')
+ self.run ()
+
+ def run (self):
+ time = tm.time ()
+ while True:
+ oldTime = time
+ time = tm.time ()
+ self.period = time - oldTime
+
+ for thing in self.things:
+ thing.compute ()
+
+ for thing in self.things:
+ thing.render ()
+
+ tm.sleep (0.05)
+
+ def goto (self, xScene, yScene):
+ tr.goto ((xScene + self.xTarget) / self.resolution, (yScene + self.yTarget) / self.resolution - self.hImage)
+
+ def _prepare (self):
+ tr.setup (self.wScreen, self.hScreen)
+ tr.bgpic (self.moon.image)
+ tr.title ('The not so fast, but curious...')
+ tr.speed (0)
+ tr.delay (0)
+ tr.up ()
+
+
+carTesla = Car ('Tesla', 10, 150)
+carBMW = Car ('BMW', 5, 220)
+
+# win = tr.Screen()
+
+# while True:
+# win.update()
+
+# tr.done()
+
+World ()
+
+
diff --git a/scripts/wk_2_les_numerieke_simulatie/Lesplan-integreren-en-differentieren.docx b/scripts/wk_2_les_numerieke_simulatie/Lesplan-integreren-en-differentieren.docx
new file mode 100644
index 00000000..ed5bc6e8
Binary files /dev/null and b/scripts/wk_2_les_numerieke_simulatie/Lesplan-integreren-en-differentieren.docx differ
diff --git a/scripts/wk_2_les_numerieke_simulatie/Slides-integreren-en-differentieren.pptx b/scripts/wk_2_les_numerieke_simulatie/Slides-integreren-en-differentieren.pptx
new file mode 100644
index 00000000..1ab2a7c5
Binary files /dev/null and b/scripts/wk_2_les_numerieke_simulatie/Slides-integreren-en-differentieren.pptx differ
diff --git a/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car.py b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car.py
new file mode 100644
index 00000000..59c8b6d8
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car.py
@@ -0,0 +1,43 @@
+#-------------------------------------------
+
+class Car:
+
+ # Car's mass in kilogram
+ m = 600.0
+
+ # Car's thrust in Newton
+ F = 800.0
+
+ # Car's start speed in m/s
+ v = 0.0
+
+ # Car's displacement in meters
+ x = 0.0
+
+ #-------------------------------------------
+
+ def step(self, dt):
+
+ a = self.F / self.m
+
+ dv = a * dt
+ self.v = self.v + dv
+
+ print("v: ", self.v)
+
+ dx = self.v * dt
+ self.x = self.x + dx
+
+ print("x: ", self.x)
+
+ if self.x < 100.0:
+
+ return True
+
+ else:
+
+ return False
+
+ #-------------------------------------------
+
+#-------------------------------------------
\ No newline at end of file
diff --git a/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_djg/__pycache__/car.cpython-310.pyc b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_djg/__pycache__/car.cpython-310.pyc
new file mode 100644
index 00000000..bbee3d44
Binary files /dev/null and b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_djg/__pycache__/car.cpython-310.pyc differ
diff --git a/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_djg/__pycache__/world.cpython-310.pyc b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_djg/__pycache__/world.cpython-310.pyc
new file mode 100644
index 00000000..6b20414e
Binary files /dev/null and b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_djg/__pycache__/world.cpython-310.pyc differ
diff --git a/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_djg/car.py b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_djg/car.py
new file mode 100644
index 00000000..5333ae4a
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_djg/car.py
@@ -0,0 +1,27 @@
+
+class Car:
+ def __init__(self, name, m, F):
+
+ self.name = name
+ self.m = m
+ self.F = F
+ self.v = 0.0
+ self.x = 0.0
+
+
+ def move(self, dt):
+
+ a = self.F / self.m
+
+ dv = a * dt
+ self.v = self.v + dv
+
+ print("Velocity " + self.name + ": " + str(self.v))
+
+ dx = self.v * dt
+ self.x = self.x + dx
+
+ print("Position " + self.name + " " + str(self.x))
+
+
+
\ No newline at end of file
diff --git a/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_djg/sim.py b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_djg/sim.py
new file mode 100644
index 00000000..4a893a33
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_djg/sim.py
@@ -0,0 +1,8 @@
+# from file import class
+from world import World
+
+# Create instance of World class
+world = World()
+
+world.bang()
+# world.stop()
\ No newline at end of file
diff --git a/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_djg/world.py b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_djg/world.py
new file mode 100644
index 00000000..3cc6fdeb
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_djg/world.py
@@ -0,0 +1,44 @@
+import time as tm
+from car import Car
+
+
+class World:
+
+ def __init__(self):
+
+ self.t = 0.0
+ self.dt = 0.1
+
+ self.running = True
+
+ self.carT = Car("Tesla", 1200.0, 300.0)
+ self.carB = Car("BMW", 1600.0, 500.0)
+
+
+
+ def bang(self):
+
+ while self.running:
+
+ self.t += self.dt
+
+ self.carT.move(self.dt)
+ self.carB.move(self.dt)
+
+ print("\nTime: ", self.t)
+
+ if self.carT.x > 100.0:
+
+ print("\nTesla is victorious!\n")
+
+ self.running = False
+
+ if self.carB.x > 100.0:
+
+ print("\nBMW is victorious!\n")
+
+ self.running = False
+
+
+ tm.sleep(self.dt)
+
\ No newline at end of file
diff --git a/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_jaqcues_djg/__pycache__/car.cpython-310.pyc b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_jaqcues_djg/__pycache__/car.cpython-310.pyc
new file mode 100644
index 00000000..bbee3d44
Binary files /dev/null and b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_jaqcues_djg/__pycache__/car.cpython-310.pyc differ
diff --git a/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_jaqcues_djg/__pycache__/world.cpython-310.pyc b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_jaqcues_djg/__pycache__/world.cpython-310.pyc
new file mode 100644
index 00000000..bc67999a
Binary files /dev/null and b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_jaqcues_djg/__pycache__/world.cpython-310.pyc differ
diff --git a/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_jaqcues_djg/car.py b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_jaqcues_djg/car.py
new file mode 100644
index 00000000..ccd0e147
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_jaqcues_djg/car.py
@@ -0,0 +1,33 @@
+
+from re import S
+
+
+class Car:
+ def __init__(self, name, aMax, vMax):
+
+ # v in m/s (km/h to m/s is: /3.6)
+ # a in m/s/s
+
+
+ self.name = name
+ self.v = 0.0
+ self.x = 0.0
+ self.vMax = 0.0
+ self.aMax = 0.0
+
+
+ def move(self, dt):
+
+ pass
+
+ # a = self.F / self.m
+
+ # dv = a * dt
+ # self.v = self.v + dv
+
+ # print("Velocity " + self.name + ": " + str(self.v))
+
+ # dx = self.v * dt
+ # self.x = self.x + dx
+
+ # print("Position " + self.name + " " + str(self.x))
\ No newline at end of file
diff --git a/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_jaqcues_djg/sim.py b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_jaqcues_djg/sim.py
new file mode 100644
index 00000000..4a893a33
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_jaqcues_djg/sim.py
@@ -0,0 +1,8 @@
+# from file import class
+from world import World
+
+# Create instance of World class
+world = World()
+
+world.bang()
+# world.stop()
\ No newline at end of file
diff --git a/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_jaqcues_djg/world.py b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_jaqcues_djg/world.py
new file mode 100644
index 00000000..9e9a4742
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/car_simulation_race_jaqcues_djg/world.py
@@ -0,0 +1,47 @@
+import time as tm
+from car import Car
+
+
+class World:
+
+ def __init__(self):
+
+ self.t = 0.0
+ self.dt = 0.1
+
+ self.running = True
+
+ # 150 km/h = 41.66667 m/s
+ # 220 km/h = 61.11111 m/s
+
+ self.carT = Car("Tesla", 10, 150.0)
+ self.carB = Car("BMW", 5, 220.0)
+
+
+
+ def bang(self):
+
+ while self.running:
+
+ self.t += self.dt
+
+ self.carT.move(self.dt)
+ self.carB.move(self.dt)
+
+ print("\nTime: ", self.t)
+
+ if self.carT.x > 100.0:
+
+ print("\nTesla is victorious!\n")
+
+ self.running = False
+
+ if self.carB.x > 100.0:
+
+ print("\nBMW is victorious!\n")
+
+ self.running = False
+
+
+ tm.sleep(self.dt)
+
\ No newline at end of file
diff --git a/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/sim.py b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/sim.py
new file mode 100644
index 00000000..78f8c7a8
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/sim.py
@@ -0,0 +1,13 @@
+#-------------------------------------------
+
+from world import World
+
+#-------------------------------------------
+
+# Create new world instance
+world = World()
+
+# Start the simulation
+world.bang()
+
+#-------------------------------------------
\ No newline at end of file
diff --git a/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/world.py b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/world.py
new file mode 100644
index 00000000..c3654da1
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/car-sim-oop/world.py
@@ -0,0 +1,54 @@
+#-------------------------------------------
+
+import time
+
+from car import Car
+
+#-------------------------------------------
+
+class World:
+
+ #-------------------------------------------
+
+ def __init__(self):
+
+ # Global time in seconds
+ self.t = 0.0
+
+ # Time step in seconds
+ self.dt = 0.1
+
+ # Simulation flag
+ self.running = True
+
+ # Create a car instance
+ self.car = Car()
+
+ #-------------------------------------------
+
+ def bang(self):
+
+ self.running = True
+
+ while self.running:
+
+ self.t = self.t + self.dt
+
+ self.__tick__()
+
+ # Prevent burning the cpu
+ time.sleep(0.1)
+
+ #-------------------------------------------
+
+ # Mark internal (aka 'private') functions with '__'
+ def __tick__(self):
+
+ # Exit loop when the car has stopped
+ if not self.car.step(self.dt):
+
+ self.running = False
+
+ #-------------------------------------------
+
+#-------------------------------------------
\ No newline at end of file
diff --git a/scripts/wk_2_les_numerieke_simulatie/plot.ipynb b/scripts/wk_2_les_numerieke_simulatie/plot.ipynb
new file mode 100644
index 00000000..8cb6e06b
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/plot.ipynb
@@ -0,0 +1,143 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "id": "4645c9b0-ae06-436a-a1bc-60921be2975f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 89,
+ "id": "da6a93ee-1cb4-435b-8c7f-befb773e12a6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " time (s) position (m) speed (m/s)\n",
+ "0 0.1 0.013333 0.133333\n",
+ "1 0.2 0.040000 0.266667\n",
+ "2 0.3 0.080000 0.400000\n",
+ "3 0.4 0.133333 0.533333\n",
+ "4 0.5 0.200000 0.666667\n",
+ ".. ... ... ...\n",
+ "117 11.8 93.613333 15.733333\n",
+ "118 11.9 95.200000 15.866667\n",
+ "119 12.0 96.800000 16.000000\n",
+ "120 12.1 98.413333 16.133333\n",
+ "121 12.2 100.040000 16.266667\n",
+ "\n",
+ "[122 rows x 3 columns]\n"
+ ]
+ }
+ ],
+ "source": [
+ "tesla = pd.read_csv('./race-sim-oop-csv/Tesla.csv')\n",
+ "\n",
+ "print(tesla)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "id": "c1fa38aa-37e6-434d-817b-288e884ed255",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " time (s) position (m) speed (m/s)\n",
+ "0 0.1 0.0075 0.075\n",
+ "1 0.2 0.0225 0.150\n",
+ "2 0.3 0.0450 0.225\n",
+ "3 0.4 0.0750 0.300\n",
+ "4 0.5 0.1125 0.375\n",
+ ".. ... ... ...\n",
+ "117 11.8 52.6575 8.850\n",
+ "118 11.9 53.5500 8.925\n",
+ "119 12.0 54.4500 9.000\n",
+ "120 12.1 55.3575 9.075\n",
+ "121 12.2 56.2725 9.150\n",
+ "\n",
+ "[122 rows x 3 columns]\n"
+ ]
+ }
+ ],
+ "source": [
+ "bmw = pd.read_csv('./race-sim-oop-csv/BMW.csv')\n",
+ "\n",
+ "print(bmw)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "id": "784da3f3-dbd7-482a-8d07-e1e2af8c01d6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "t = tesla['time (s)']\n",
+ "\n",
+ "x_tesla = tesla['position (m)']\n",
+ "v_tesla = tesla['speed (m/s)']\n",
+ "\n",
+ "x_bmw = bmw['position (m)']\n",
+ "v_bmw = bmw['speed (m/s)']\n",
+ "\n",
+ "fig, ax = plt.subplots()\n",
+ "\n",
+ "ax.set_title(\"Racing results\")\n",
+ "\n",
+ "l1, = ax.plot(t, x_tesla, linewidth=1, color='red', linestyle='dashed', label='Tesla (x)')\n",
+ "l2, = ax.plot(t, v_tesla, 'r-', linewidth=1, label='Tesla (v)')\n",
+ "\n",
+ "l3, = ax.plot(t, x_bmw, linewidth=1, color='blue', linestyle='dashed', label='BMW (x)')\n",
+ "l4, = ax.plot(t, v_bmw, 'b-', linewidth=1, label='BMW (v)')\n",
+ "\n",
+ "ax.legend(handles=[l1, l2, l3, l4])\n",
+ "\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "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": "3.8.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/BMW.csv b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/BMW.csv
new file mode 100644
index 00000000..49d1b317
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/BMW.csv
@@ -0,0 +1,123 @@
+time (s),position (m),speed (m/s)
+0.1,0.0075000000000000015,0.07500000000000001
+0.2,0.022500000000000006,0.15000000000000002
+0.30000000000000004,0.04500000000000001,0.22500000000000003
+0.4,0.07500000000000001,0.30000000000000004
+0.5,0.11250000000000002,0.37500000000000006
+0.6,0.15750000000000003,0.45000000000000007
+0.7,0.21000000000000005,0.5250000000000001
+0.7999999999999999,0.2700000000000001,0.6000000000000001
+0.8999999999999999,0.3375000000000001,0.675
+0.9999999999999999,0.4125000000000001,0.75
+1.0999999999999999,0.4950000000000001,0.825
+1.2,0.5850000000000001,0.8999999999999999
+1.3,0.6825000000000001,0.9749999999999999
+1.4000000000000001,0.7875000000000001,1.0499999999999998
+1.5000000000000002,0.9000000000000001,1.1249999999999998
+1.6000000000000003,1.02,1.1999999999999997
+1.7000000000000004,1.1475,1.2749999999999997
+1.8000000000000005,1.2825,1.3499999999999996
+1.9000000000000006,1.4249999999999998,1.4249999999999996
+2.0000000000000004,1.5749999999999997,1.4999999999999996
+2.1000000000000005,1.7324999999999997,1.5749999999999995
+2.2000000000000006,1.8974999999999997,1.6499999999999995
+2.3000000000000007,2.07,1.7249999999999994
+2.400000000000001,2.25,1.7999999999999994
+2.500000000000001,2.4375,1.8749999999999993
+2.600000000000001,2.6325,1.9499999999999993
+2.700000000000001,2.835,2.0249999999999995
+2.800000000000001,3.045,2.0999999999999996
+2.9000000000000012,3.2624999999999997,2.175
+3.0000000000000013,3.4875,2.25
+3.1000000000000014,3.7199999999999998,2.325
+3.2000000000000015,3.96,2.4000000000000004
+3.3000000000000016,4.2075,2.4750000000000005
+3.4000000000000017,4.4624999999999995,2.5500000000000007
+3.5000000000000018,4.725,2.625000000000001
+3.600000000000002,4.995,2.700000000000001
+3.700000000000002,5.2725,2.7750000000000012
+3.800000000000002,5.5575,2.8500000000000014
+3.900000000000002,5.8500000000000005,2.9250000000000016
+4.000000000000002,6.15,3.0000000000000018
+4.100000000000001,6.4575000000000005,3.075000000000002
+4.200000000000001,6.772500000000001,3.150000000000002
+4.300000000000001,7.095000000000001,3.2250000000000023
+4.4,7.425000000000001,3.3000000000000025
+4.5,7.762500000000001,3.3750000000000027
+4.6,8.107500000000002,3.450000000000003
+4.699999999999999,8.460000000000003,3.525000000000003
+4.799999999999999,8.820000000000004,3.600000000000003
+4.899999999999999,9.187500000000004,3.6750000000000034
+4.999999999999998,9.562500000000004,3.7500000000000036
+5.099999999999998,9.945000000000004,3.8250000000000037
+5.1999999999999975,10.335000000000004,3.900000000000004
+5.299999999999997,10.732500000000005,3.975000000000004
+5.399999999999997,11.137500000000006,4.050000000000004
+5.4999999999999964,11.550000000000006,4.125000000000004
+5.599999999999996,11.970000000000006,4.200000000000005
+5.699999999999996,12.397500000000006,4.275000000000005
+5.799999999999995,12.832500000000007,4.350000000000005
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+11.599999999999975,50.895000000000046,8.700000000000006
+11.699999999999974,51.77250000000004,8.775000000000006
+11.799999999999974,52.65750000000004,8.850000000000005
+11.899999999999974,53.55000000000004,8.925000000000004
+11.999999999999973,54.45000000000004,9.000000000000004
+12.099999999999973,55.35750000000004,9.075000000000003
+12.199999999999973,56.272500000000036,9.150000000000002
diff --git a/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/Tesla.csv b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/Tesla.csv
new file mode 100644
index 00000000..22e4fdb5
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/Tesla.csv
@@ -0,0 +1,123 @@
+time (s),position (m),speed (m/s)
+0.1,0.013333333333333334,0.13333333333333333
+0.2,0.04,0.26666666666666666
+0.30000000000000004,0.08000000000000002,0.4
+0.4,0.13333333333333336,0.5333333333333333
+0.5,0.2,0.6666666666666666
+0.6,0.28,0.7999999999999999
+0.7,0.37333333333333335,0.9333333333333332
+0.7999999999999999,0.48000000000000004,1.0666666666666667
+0.8999999999999999,0.6000000000000001,1.2
+0.9999999999999999,0.7333333333333334,1.3333333333333333
+1.0999999999999999,0.8800000000000001,1.4666666666666666
+1.2,1.04,1.5999999999999999
+1.3,1.2133333333333334,1.7333333333333332
+1.4000000000000001,1.4,1.8666666666666665
+1.5000000000000002,1.5999999999999999,1.9999999999999998
+1.6000000000000003,1.8133333333333332,2.1333333333333333
+1.7000000000000004,2.04,2.2666666666666666
+1.8000000000000005,2.2800000000000002,2.4
+1.9000000000000006,2.5333333333333337,2.533333333333333
+2.0000000000000004,2.8000000000000003,2.6666666666666665
+2.1000000000000005,3.08,2.8
+2.2000000000000006,3.3733333333333335,2.933333333333333
+2.3000000000000007,3.68,3.0666666666666664
+2.400000000000001,4.0,3.1999999999999997
+2.500000000000001,4.333333333333333,3.333333333333333
+2.600000000000001,4.68,3.4666666666666663
+2.700000000000001,5.04,3.5999999999999996
+2.800000000000001,5.413333333333333,3.733333333333333
+2.9000000000000012,5.8,3.8666666666666663
+3.0000000000000013,6.2,3.9999999999999996
+3.1000000000000014,6.613333333333333,4.133333333333333
+3.2000000000000015,7.04,4.266666666666667
+3.3000000000000016,7.48,4.4
+3.4000000000000017,7.933333333333334,4.533333333333334
+3.5000000000000018,8.4,4.666666666666668
+3.600000000000002,8.88,4.800000000000002
+3.700000000000002,9.373333333333335,4.933333333333335
+3.800000000000002,9.880000000000003,5.066666666666669
+3.900000000000002,10.400000000000002,5.200000000000003
+4.000000000000002,10.933333333333335,5.333333333333337
+4.100000000000001,11.480000000000002,5.46666666666667
+4.200000000000001,12.040000000000003,5.600000000000004
+4.300000000000001,12.613333333333337,5.733333333333338
+4.4,13.200000000000005,5.866666666666672
+4.5,13.800000000000004,6.000000000000005
+4.6,14.413333333333338,6.133333333333339
+4.699999999999999,15.040000000000004,6.266666666666673
+4.799999999999999,15.680000000000005,6.400000000000007
+4.899999999999999,16.33333333333334,6.53333333333334
+4.999999999999998,17.000000000000007,6.666666666666674
+5.099999999999998,17.680000000000007,6.800000000000008
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diff --git a/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/__pycache__/car.cpython-39.pyc b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/__pycache__/car.cpython-39.pyc
new file mode 100644
index 00000000..cd56864b
Binary files /dev/null and b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/__pycache__/car.cpython-39.pyc differ
diff --git a/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/__pycache__/track.cpython-39.pyc b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/__pycache__/track.cpython-39.pyc
new file mode 100644
index 00000000..8893de1e
Binary files /dev/null and b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/__pycache__/track.cpython-39.pyc differ
diff --git a/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/__pycache__/world.cpython-39.pyc b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/__pycache__/world.cpython-39.pyc
new file mode 100644
index 00000000..971c5b9b
Binary files /dev/null and b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/__pycache__/world.cpython-39.pyc differ
diff --git a/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/car.py b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/car.py
new file mode 100644
index 00000000..894d4452
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/car.py
@@ -0,0 +1,65 @@
+#-------------------------------------------
+
+import csv
+
+#-------------------------------------------
+
+class Car:
+
+ #-------------------------------------------
+
+ def __init__(self, name, mass, thrust):
+
+ # Car's start speed in m/s
+ self.v = 0.0
+
+ # Car's displacement in meters
+ self.x = 0.0
+
+ # List of recordings
+ self.recording = []
+
+ # Cars identifier
+ self.name = name
+
+ # Car's mass in kilogram
+ self.m = mass
+
+ # Car's thrust in Newton
+ self.F = thrust
+
+ #-------------------------------------------
+
+ def step(self, t, dt):
+
+ a = self.F / self.m
+
+ dv = a * dt
+ self.v = self.v + dv
+
+ dx = self.v * dt
+ self.x = self.x + dx
+
+ self.recording.append([t, self.x, self.v])
+
+ return self.x, self.v
+
+ #-------------------------------------------
+
+ def saveResults(self):
+
+ # Colomn names
+ fields = ["time (s)", "position (m)", "speed (m/s)"]
+
+ # Save the recording to disk
+ with open(self.name + ".csv", 'w') as f:
+
+ # Using csv.writer method from CSV package
+ write = csv.writer(f)
+
+ write.writerow(fields)
+ write.writerows(self.recording)
+
+ #-------------------------------------------
+
+#-------------------------------------------
\ No newline at end of file
diff --git a/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/sim.py b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/sim.py
new file mode 100644
index 00000000..e9d34c0e
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/sim.py
@@ -0,0 +1,32 @@
+#-------------------------------------------
+
+from world import World
+
+from track import Track
+
+from car import Car
+
+#-------------------------------------------
+
+# Create new world instance
+world = World()
+
+# Create new track instance
+track = Track(100)
+
+# Create a Tesla
+tesla = Car("Tesla", 600, 800)
+
+# Creat a BMW
+bmw = Car("BMW", 1200, 900)
+
+track.addCar(tesla)
+track.addCar(bmw)
+
+# Add track to world
+world.addTrack(track)
+
+# Start the simulation
+world.bang()
+
+#-------------------------------------------
\ No newline at end of file
diff --git a/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/track.py b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/track.py
new file mode 100644
index 00000000..238c9393
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/track.py
@@ -0,0 +1,49 @@
+#-------------------------------------------
+
+class Track:
+
+ #-------------------------------------------
+
+ def __init__(self, distance):
+
+ self.distance = distance
+
+ # List of cars
+ self.cars = []
+
+ #-------------------------------------------
+
+ def addCar(self, car):
+
+ self.cars.append(car)
+
+ #-------------------------------------------
+
+ def step(self, t, dt):
+
+ proceed = True
+
+ for car in self.cars:
+
+ x, v = car.step(t, dt)
+
+ # Stop simulation when the car has reached end of track
+ if x > self.distance:
+
+ print("We have a winner: ", car.name)
+
+ proceed = False
+
+ return proceed
+
+ #-------------------------------------------
+
+ def saveResults(self):
+
+ for car in self.cars:
+
+ car.saveResults()
+
+ #-------------------------------------------
+
+#-------------------------------------------
\ No newline at end of file
diff --git a/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/world.py b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/world.py
new file mode 100644
index 00000000..1e6c53d3
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop-csv/world.py
@@ -0,0 +1,60 @@
+#-------------------------------------------
+
+import time
+
+#-------------------------------------------
+
+class World:
+
+ #-------------------------------------------
+
+ def __init__(self):
+
+ # Global time in seconds
+ self.t = 0.0
+
+ # Time step in seconds
+ self.dt = 0.1
+
+ # Simulation flag
+ self.running = True
+
+ # Reserve a track spot
+ self.track = None
+
+ #-------------------------------------------
+
+ def addTrack(self, track):
+
+ self.track = track
+
+
+ #-------------------------------------------
+
+ def bang(self):
+
+ self.running = True
+
+ while self.running:
+
+ self.t = self.t + self.dt
+
+ self.__tick__()
+
+ # Prevent burning the cpu
+ time.sleep(0.1)
+
+ #-------------------------------------------
+
+ # Mark internal (aka 'private') functions with '__'
+ def __tick__(self):
+
+ if not self.track.step(self.t, self.dt):
+
+ self.track.saveResults()
+
+ self.running = False
+
+ #-------------------------------------------
+
+#-------------------------------------------
\ No newline at end of file
diff --git a/scripts/wk_2_les_numerieke_simulatie/race-sim-oop/car.py b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop/car.py
new file mode 100644
index 00000000..5fca3f9f
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop/car.py
@@ -0,0 +1,40 @@
+#-------------------------------------------
+
+class Car:
+
+ #-------------------------------------------
+
+ def __init__(self, name, mass, thrust):
+
+ # Car's start speed in m/s
+ self.v = 0.0
+
+ # Car's displacement in meters
+ self.x = 0.0
+
+ # Cars identifier
+ self.name = name
+
+ # Car's mass in kilogram
+ self.m = mass
+
+ # Car's thrust in Newton
+ self.F = thrust
+
+ #-------------------------------------------
+
+ def step(self, dt):
+
+ a = self.F / self.m
+
+ dv = a * dt
+ self.v = self.v + dv
+
+ dx = self.v * dt
+ self.x = self.x + dx
+
+ return self.x, self.v
+
+ #-------------------------------------------
+
+#-------------------------------------------
\ No newline at end of file
diff --git a/scripts/wk_2_les_numerieke_simulatie/race-sim-oop/sim.py b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop/sim.py
new file mode 100644
index 00000000..e9d34c0e
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop/sim.py
@@ -0,0 +1,32 @@
+#-------------------------------------------
+
+from world import World
+
+from track import Track
+
+from car import Car
+
+#-------------------------------------------
+
+# Create new world instance
+world = World()
+
+# Create new track instance
+track = Track(100)
+
+# Create a Tesla
+tesla = Car("Tesla", 600, 800)
+
+# Creat a BMW
+bmw = Car("BMW", 1200, 900)
+
+track.addCar(tesla)
+track.addCar(bmw)
+
+# Add track to world
+world.addTrack(track)
+
+# Start the simulation
+world.bang()
+
+#-------------------------------------------
\ No newline at end of file
diff --git a/scripts/wk_2_les_numerieke_simulatie/race-sim-oop/track.py b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop/track.py
new file mode 100644
index 00000000..27e23da0
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop/track.py
@@ -0,0 +1,39 @@
+#-------------------------------------------
+
+class Track:
+
+ #-------------------------------------------
+
+ def __init__(self, distance):
+
+ self.distance = distance
+
+ # List of cars
+ self.cars = []
+
+ #-------------------------------------------
+
+ def addCar(self, car):
+
+ self.cars.append(car)
+
+ #-------------------------------------------
+
+ def step(self, dt):
+
+ for car in self.cars:
+
+ x, v = car.step(dt)
+
+ # Stop simulation when the car has reached end of track
+ if x > self.distance:
+
+ print("We have a winner: ", car.name)
+
+ return False
+
+ return True
+
+ #-------------------------------------------
+
+#-------------------------------------------
\ No newline at end of file
diff --git a/scripts/wk_2_les_numerieke_simulatie/race-sim-oop/world.py b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop/world.py
new file mode 100644
index 00000000..066ab8c2
--- /dev/null
+++ b/scripts/wk_2_les_numerieke_simulatie/race-sim-oop/world.py
@@ -0,0 +1,58 @@
+#-------------------------------------------
+
+import time
+
+#-------------------------------------------
+
+class World:
+
+ #-------------------------------------------
+
+ def __init__(self):
+
+ # Global time in seconds
+ self.t = 0.0
+
+ # Time step in seconds
+ self.dt = 0.1
+
+ # Simulation flag
+ self.running = True
+
+ # Reserve a track spot
+ self.track = None
+
+ #-------------------------------------------
+
+ def addTrack(self, track):
+
+ self.track = track
+
+
+ #-------------------------------------------
+
+ def bang(self):
+
+ self.running = True
+
+ while self.running:
+
+ self.t = self.t + self.dt
+
+ self.__tick__()
+
+ # Prevent burning the cpu
+ time.sleep(0.1)
+
+ #-------------------------------------------
+
+ # Mark internal (aka 'private') functions with '__'
+ def __tick__(self):
+
+ if not self.track.step(self.dt):
+
+ self.running = False
+
+ #-------------------------------------------
+
+#-------------------------------------------
\ No newline at end of file
diff --git a/scripts/practicum_lin_algebra/add_vectors.py b/scripts/wk_2_practicum_lin_algebra/add_vectors.py
similarity index 100%
rename from scripts/practicum_lin_algebra/add_vectors.py
rename to scripts/wk_2_practicum_lin_algebra/add_vectors.py
diff --git a/scripts/wk_2_praticum_lineare_algebra/vectoren_optellen.py b/scripts/wk_2_praticum_lineare_algebra/vectoren_optellen.py
new file mode 100644
index 00000000..8cab866e
--- /dev/null
+++ b/scripts/wk_2_praticum_lineare_algebra/vectoren_optellen.py
@@ -0,0 +1,41 @@
+#!/user/bin/#!/usr/bin/env python3
+
+# Gebruik twee lijsten [] - want onveranderlijk
+# Tel van beiden index [0] bij elkaar op, zo ook voor indices [1] en [2] etc.
+
+a = [0, 3, 4]
+b = [1, 4, 7]
+
+# Tel van beiden index [0] bij elkaar op, zo ook voor indices [1] en [2] etc.
+
+c = [a[0] + b[0], a[1] + b[1], a[2] + b[2]]
+
+print (c)
+type (c)
+print ()
+
+# Opgezocht! - Vectors in Linear Algebra
+
+a = [0, 3, 4]
+b = [1, 4, 7]
+
+print("Vector a = ", a)
+print("Vector b = ", b)
+
+# Twee onveranderlijke lijsten []
+
+# Variabele die de nieuw berekende vector gaat weergeven 'sum'
+#
+# Indeces in lijst a doorlopen en bij corresponderende indeces in lijst b optellen
+# Beperk het aantal keer dat de lijst doorlopen wordt door de length van
+# lijst a te gebruiken
+#
+# Druk het resultaat af
+
+
+sum = []
+for i in range(len(a)):
+ sum.append(a[i] + b[i])
+
+print("Vector Addition = ", sum)
+
diff --git a/scripts/wk_2_praticum_lineare_algebra/wk2_matrix_optellen_test.py b/scripts/wk_2_praticum_lineare_algebra/wk2_matrix_optellen_test.py
new file mode 100644
index 00000000..bc188988
--- /dev/null
+++ b/scripts/wk_2_praticum_lineare_algebra/wk2_matrix_optellen_test.py
@@ -0,0 +1,12 @@
+
+matrixA = [[4, 0, 3], [1, -1, 7], [-3, 3, 2]]
+matrixB = [[-2, 3, 1], [2, -3, -5], [4, 0, 7]]
+
+matrixC = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
+
+for rowIndex in range(len(matrixA)):
+
+ matrixC[rowIndex] += matrixA[rowIndex] + matrixB
+
+for result in matrixC:
+ print(result)
diff --git a/scripts/wk_2_praticum_lineare_algebra/wk2_matrix_vermenigvuldigen_1.py b/scripts/wk_2_praticum_lineare_algebra/wk2_matrix_vermenigvuldigen_1.py
new file mode 100644
index 00000000..cd8c9dd4
--- /dev/null
+++ b/scripts/wk_2_praticum_lineare_algebra/wk2_matrix_vermenigvuldigen_1.py
@@ -0,0 +1,99 @@
+# matrixA = [[0, 3], [1, 4], [2, 5]]
+# matrixB = [[9, 7], [8, 6]]
+# matrixC = [] # final result
+
+# for rowA in range(len(matrixA)):
+# # print ('\nlen(matrixA):', len(matrixA))
+# # print ('rowA:', rowA)
+# rowC = [] # the new row in new matrix
+# for rowB in range(len(matrixB[0])):
+# # print ('\nlen(matrixB):', len(matrixB))
+# # print ('rowB:', rowB)
+# productC = 0 # the new element in the new row
+# for column in range(len(matrixA[rowA])):
+# productC += matrixA[rowA][column] * matrixB[column][rowB]
+# # print ('\nlen(matrixA[rowA]):', len(matrixA[rowA]))
+# # print ('rowC:', rowC)
+# # print ('productC:', productC)
+
+# rowC.append(productC) # append sum of product into the new row
+
+# matrixC.append(rowC) # append the new row into the final result
+
+
+# print('\nmatrixC:', matrixC)
+
+# #-------------------------------------------------#
+
+# class Matrix():
+
+# def __init__(self, list):
+
+# self.mat = list
+# self.dim = (len(list), len(list[0]))
+# self.rows=[lst[i][:] for i in range(self.dim[0])]
+# self.columns=[[lst[i][j] for i in range(self.dim[0])] for j in range(self.dim[1])]
+
+
+# def matrixMul(self):
+
+# matrixA = [[0, 3], [1, 4], [2, 5]]
+# matrixB = [[9, 7], [8, 6]]
+# matrixC = []
+
+# for rowA in range(len(matrixA)):
+# rowC = []
+
+# for rowB in range(len(matrixB[0])):
+# productC = 0
+
+# for column in range(len(matrixA[rowA])):
+# productC += matrixA[rowA][column] * matrixB[column][rowB]
+# rowC.append(productC)
+# matrixC.append(rowC)
+
+
+# matrixA =
+# matrixB =
+
+# object = matrixMul()
+
+
+# print('\nmatrixC:', matrixC)
+
+
+class Matrix:
+
+ def __init__(self, matList):
+ self.mat = matList
+ self.lenght = (len(matList), len(matList[0]))
+ self.rows = [matList[a][:] for a in range(self.lenght[0])]
+ self.columns = [[matList[a][b] for a in range(self.lenght[0])] for b in range(self.lenght[1])]
+
+ # def get(self, a, b):
+ # if (a) <= self.lenght[0] and (b) <= self.lenght[1]:
+ # return self.mat[a-1][b-1]
+ # else:
+ # print("index not in matrix!")
+ # return None
+
+ def __multList(self, list1, list2):
+ if len(list1) == len(list2):
+ return sum([list1[a]*list2[a] for a in range(len(list1))])
+
+ def mult(self, other):
+ matrixC = []
+ for a in range(len(self.rows)):
+ rows = []
+ for b in range(len(other.columns)):
+ rows.append(self.__multList(other.columns[b],self.rows[a]))
+ matrixC.append(rows)
+ return Matrix(matrixC)
+
+
+A = Matrix([[0, 3], [1, 4], [2, 5]])
+B = Matrix([[9, 7], [8, 6]])
+
+C = A.mult(B)
+
+print('\nMatrixC: ', C.mat)
\ No newline at end of file
diff --git a/scripts/wk_2_praticum_lineare_algebra/wk2_matrix_vermenigvuldigen_2.py b/scripts/wk_2_praticum_lineare_algebra/wk2_matrix_vermenigvuldigen_2.py
new file mode 100644
index 00000000..2f9ecfed
--- /dev/null
+++ b/scripts/wk_2_praticum_lineare_algebra/wk2_matrix_vermenigvuldigen_2.py
@@ -0,0 +1,13 @@
+
+matrixA = [[4, 0, 3], [1, -1, 7], [-3, 3, 2]]
+matrixB = [[-2, 3, 1], [2, -3, -5], [4, 0, 7]]
+
+matrixC = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
+
+for rowIndex in range(len(matrixA)):
+ for columnIndex in range(len(matrixB[0])):
+ for termIndex in range(len(matrixA[rowIndex])):
+ matrixC[rowIndex][columnIndex] += matrixA[rowIndex][termIndex] * matrixB[termIndex][columnIndex]
+
+for result in matrixC:
+ print(result)
diff --git a/scripts/wk_2_praticum_lineare_algebra/wk4_matrix_vermenigvuldigen_klasse_1.py b/scripts/wk_2_praticum_lineare_algebra/wk4_matrix_vermenigvuldigen_klasse_1.py
new file mode 100644
index 00000000..8fa5ff49
--- /dev/null
+++ b/scripts/wk_2_praticum_lineare_algebra/wk4_matrix_vermenigvuldigen_klasse_1.py
@@ -0,0 +1,45 @@
+
+# Werkt, maar kan vast makkelijker.
+
+class Matrix:
+
+ def __init__(self, matList):
+
+ self.mat = matList
+ self.lenght = (len(matList), len(matList[0]))
+ self.rows = [matList[a][:] for a in range(self.lenght[0])]
+ self.columns = [[matList[a][b] for a in range(self.lenght[0])] for b in range(self.lenght[1])]
+
+ def get(self, a, b):
+ if (a) <= self.lenght[0] and (b) <= self.lenght[1]:
+ return self.mat[a-1][b-1]
+ else:
+ print("index not in matrix!")
+ return None
+
+ def __multList(self, matList1, matList2):
+
+ if len(matList1) == len(matList2):
+ return sum([matList1[a] * matList2[a] for a in range(len(matList1))])
+
+ def mult(self, other):
+ matrixC = []
+
+ for a in range(len(self.rows)):
+ rows = []
+
+ for b in range(len(other.columns)):
+ rows.append(self.__multList(other.columns[b],self.rows[a]))
+
+ matrixC.append(rows)
+
+ return Matrix(matrixC)
+
+
+A = Matrix([[0, 3], [1, 4], [2, 5]])
+B = Matrix([[9, 7], [8, 6]])
+E = Matrix([[1, 2], [5, 6]])
+
+C = A.mult(B)
+
+print('\nMultiplication Matrix C: ', C.mat)
diff --git a/scripts/wk_2_praticum_lineare_algebra/wk4_matrix_vermenigvuldigen_klasse_2.py b/scripts/wk_2_praticum_lineare_algebra/wk4_matrix_vermenigvuldigen_klasse_2.py
new file mode 100644
index 00000000..255776de
--- /dev/null
+++ b/scripts/wk_2_praticum_lineare_algebra/wk4_matrix_vermenigvuldigen_klasse_2.py
@@ -0,0 +1,98 @@
+class Matrix:
+
+ def __init__(self, matList):
+
+ self.mat = matList
+ # self.lenght = (len(matList), len(matList[0]))
+ self.rows = [matList[a][:] for a in range(self.lenght[0])]
+ self.columns = [[matList[a][b] for a in range(self.lenght[0])] for b in range(self.lenght[1])]
+
+ # def get(self, a, b):
+ # if (a) <= self.lenght[0] and (b) <= self.lenght[1]:
+ # return self.mat[a-1][b-1]
+ # else:
+ # print("index not in matrix!")
+ # return None
+
+ def __multList(self, matList1, matList2):
+
+ if len(matList1) == len(matList2):
+ return sum([matList1[a] * matList2[a] for a in range(len(matList1))])
+
+ def mult(self, other):
+
+ for i in range(len(matList1)):
+ for j in range(len(other[0])):
+ for k in range(len(matList1[i])):
+ matrixC[i][j] += matList1[i][k] * other[k][j]
+
+ return Matrix(matrixC)
+ # result = Matrix(matrixC)
+
+
+matrixA = [[4, 0, 3], [1, -1, 7], [-3, 3, 2]]
+matrixB = [[-2, 3, 1], [2, -3, -5], [4, 0, 7]]
+
+matrixC = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
+
+print(matrixC)
+
+
+#-----------------------------------------------------------------#
+
+
+
+
+matrixC = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
+
+for i in range(len(matrixA)):
+ for j in range(len(matrixB[0])):
+ for k in range(len(matrixA[i])):
+ matrixC[i][j] += matrixA[i][k] * matrixB[k][j]
+
+
+
+
+#-------------------------------------------------#
+
+# class MatrixAdd:
+
+# def __init__(self, matList):
+
+# self.mat = matList
+# self.lenght = (len(matList))
+# # self.rows = [matList[a][:] for a in range(self.lenght[0])]
+
+# def __addList(self, matList1, matList2):
+
+# if len(matList1) == len(matList2):
+# for a in range(len(matList1)):
+# return append([matList1[a] + matList2[a]])
+
+# def add(self, other):
+
+# matrix_G = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
+
+# for a in range(0, len(matList1)):
+
+# return MatrixAdd(matList1.append(matList2))
+
+
+
+# # print(matrixG)
+
+# E = MatrixAdd([[4, 0, 3], [1, -1, 7], [-3, 3, 2]])
+# F = MatrixAdd([[-2, 3, 1], [2, -3, -5], [4, 0, 7]])
+
+# G = MatrixAdd([[0, 0, 0], [0, 0, 0], [0, 0, 0]])
+
+# # G = E + (F)
+
+# # G = matrixG
+
+# # print('\nAddition Matrix G: ', matrix_G)
+
+# # print(result)
+
+
+
diff --git a/scripts/wk_3_les_machine_learning/Slide-ML.pptx b/scripts/wk_3_les_machine_learning/Slide-ML.pptx
new file mode 100644
index 00000000..f6faacee
Binary files /dev/null and b/scripts/wk_3_les_machine_learning/Slide-ML.pptx differ
diff --git a/scripts/wk_3_les_machine_learning/brain.jpeg b/scripts/wk_3_les_machine_learning/brain.jpeg
new file mode 100644
index 00000000..79f7ef71
Binary files /dev/null and b/scripts/wk_3_les_machine_learning/brain.jpeg differ
diff --git a/scripts/wk_3_les_machine_learning/huisprijs-docent.ipynb b/scripts/wk_3_les_machine_learning/huisprijs-docent.ipynb
new file mode 100644
index 00000000..f6640b0d
--- /dev/null
+++ b/scripts/wk_3_les_machine_learning/huisprijs-docent.ipynb
@@ -0,0 +1,953 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "783305be-8517-4058-a458-23956ad0be80",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 207,
+ "id": "7ad7ed7f-ad3f-46cd-9b05-b424251cf191",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!pip install ipywidgets"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 261,
+ "id": "392d5d78-a879-492f-ab43-d12b79c0af6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from ipywidgets import interact\n",
+ "import matplotlib.pyplot as plt\n",
+ "import random as rnd\n",
+ "import math\n",
+ "import time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bb47051d-0426-4e47-b9ef-9e610fd51c18",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# Machine Learning"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e2eb9983-c570-4454-8195-6c64977c659c",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### Bronnen"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "45f3a8dc-4d1a-46aa-b4e9-89c24cfb16f0",
+ "metadata": {},
+ "source": [
+ "**Fast.ai**\n",
+ "\n",
+ "+ [Practical Deep Learning for Coders](https://course.fast.ai/)\n",
+ "+ [Neural net foundations](https://course.fast.ai/Lessons/lesson3.html)\n",
+ "\n",
+ "**CodingTrain**:\n",
+ "\n",
+ "+ [Linear Regression with Gradient Descent](https://www.youtube.com/watch?v=L-Lsfu4ab74)\n",
+ "+ [Mathematics of Gradient Descent](https://www.youtube.com/watch?v=jc2IthslyzM)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "079a9765-fa1d-4225-b339-c52cc87727ba",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Huisprijs model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b32c6aad-ed24-476d-8289-c7fe41db0959",
+ "metadata": {},
+ "source": [
+ "+ **prijs** wordt bepaald door **prijs per vierkante meter** en **vaste grondprijs**\n",
+ "+ p = m*x + b"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f33e24d6-c4dd-448c-bf41-f4187be22b5b",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### Preparation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 209,
+ "id": "8afb3625-afa5-4325-a24f-f8f89ba90f49",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Prices in euro\n",
+ "m = 0.7\n",
+ "b = 300\n",
+ "\n",
+ "m2 = [x for x in range(100, 1000, 10)]\n",
+ "price = [m * x + b for x in m2]\n",
+ "\n",
+ "# print(m2)\n",
+ "# print(price)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 210,
+ "id": "5ce87853-41a7-4029-be48-ffbaf566bb06",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots()\n",
+ "\n",
+ "ax.set(xlim=[0, 1100], ylim=[0, 1100], xlabel='Oppervlakte in m2', ylabel='Prijs in duizend euro', title='Huisprijs')\n",
+ "\n",
+ "plt.scatter(m2, price)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2cfe702b-b479-4282-a7a6-e882663f4cfb",
+ "metadata": {},
+ "source": [
+ "Let's add some randomness"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 211,
+ "id": "7a48ca5b-4a6c-4f1d-9857-3190d9cdd804",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The answer to everything\n",
+ "rnd.seed(42)\n",
+ "\n",
+ "# Mean\n",
+ "mu = 25\n",
+ "\n",
+ "# Define standard deviation (spread)\n",
+ "sigma = 70\n",
+ "\n",
+ "# Generate data\n",
+ "noisy_price = [m * x + b + rnd.gauss(mu, sigma) for x in m2]\n",
+ "\n",
+ "# print(noisy_price)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3f33de09-ccaf-4618-8984-bc6636aa6995",
+ "metadata": {},
+ "source": [
+ "And plot the results..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 212,
+ "id": "5dab5ecc-df21-4931-8689-19764d929f71",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots()\n",
+ "\n",
+ "ax.set(xlim=[0, 1100], ylim=[0, 1100], xlabel='Oppervlakte in m2', ylabel='Prijs in duizend euro', title='Huisprijs')\n",
+ "\n",
+ "plt.scatter(m2, noisy_price)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "76412025-3e68-4c79-90a1-98a8f02ba192",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 213,
+ "id": "689fee69-062e-4dd9-b642-1a15b22a84ed",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "m2 = [100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780, 790, 800, 810, 820, 830, 840, 850, 860, 870, 880, 890, 900, 910, 920, 930, 940, 950, 960, 970, 980, 990]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 214,
+ "id": "de8dba48-750c-4ecb-8740-856c0c40e6cb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "prices = [384.91367692954503, 389.8967479767937, 401.20788969026364, 465.1388607569204, 414.0688201351979, 325.18526099613297, 460.2622840847401, 425.28637650519823, 435.81289210983635, 466.1119350690599, 481.26084158347044, 553.44910806194, 524.9645554759068, 493.73550242106825, 441.31748783586255, 428.9736342758598, 524.2439536647842, 605.7756579042834, 523.9159804732369, 520.5573694360451, 572.2243354280608, 440.25182913939256, 527.1405877998808, 590.3253772815467, 624.1383069765612, 553.1559229141405, 603.3619901081537, 601.374941452989, 645.7628766092549, 520.074445002628, 644.777548130977, 505.9835725792402, 435.60382044468037, 583.5176490260349, 568.8932967557804, 701.3208570139484, 693.4986129729026, 568.6647681093013, 720.31529963436, 597.8458023030355, 668.9629307564601, 661.4270155430428, 697.0093900222645, 753.3045418080637, 747.688965355471, 734.4919571308446, 762.4963668464409, 757.4944426355083, 687.111017434628, 687.7840234328396, 712.102220810479, 786.9528480333747, 741.4919100301831, 929.5027955736383, 715.6495220469454, 703.0787780068489, 840.7931460828787, 893.5294986940426, 836.3984883587482, 866.5072142177371, 914.8441484209513, 815.4180766788587, 729.3928746802716, 798.7546416176901, 909.7027030121603, 748.9425115745198, 859.3471924551083, 881.7266151539443, 848.9085484630431, 928.6542390457668, 925.6545819068597, 1054.4985961830512, 942.3977437441195, 863.3417578949857, 873.6741685366178, 861.7893229130596, 993.6591491871075, 894.321701563041, 936.0817458409582, 1000.4511833502772, 904.3575906544015, 941.4439425467597, 840.1099614077259, 900.2264966672901, 943.2584511465706, 1019.103269196167, 1080.5445290710309, 1002.7073230785402, 1029.2954995439861, 1029.7578557983063]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c46adc44-592b-47bd-8496-4846784a3a9d",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### Handmatig matchen\n",
+ "uitdaging: wie matcht het best?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 260,
+ "id": "192806ff-291c-4c27-83ac-22b14753bbb7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "fef29fdb41e3444298a734a9003d54ae",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(FloatSlider(value=0.0, description='m', max=2.0, min=-2.0, step=0.01), IntSlider(value=0…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 260,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def manualFit(m, b):\n",
+ " fit = [m * x + b for x in m2]\n",
+ " \n",
+ " fig, ax = plt.subplots()\n",
+ "\n",
+ " ax.set(xlim=[0, 1100], ylim=[0, 1100], xlabel='Oppervlakte in m2', ylabel='Prijs in duizend euro', title='Huisprijs')\n",
+ " \n",
+ " plt.scatter(m2, prices)\n",
+ " plt.plot(m2, fit, 'red')\n",
+ " \n",
+ " plt.show()\n",
+ " \n",
+ "interact(manualFit, m=(-2,2,0.01), b=(-1000,1000))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e8a06ddd-344a-42a6-8fc5-3bccc20376a4",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### Toevoegen error & loss"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 216,
+ "id": "e7f3c443-70cb-422c-98bf-d25de4f048f5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "cf28b1a5ced242fa8b361034a11332ca",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(FloatSlider(value=0.0, description='m', max=2.0, min=-2.0, step=0.01), IntSlider(value=0…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 216,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def error(actual, predicted):\n",
+ " return actual - predicted\n",
+ "\n",
+ "def loss(fit):\n",
+ " l = 0\n",
+ " # Sum of squared errors\n",
+ " for i in range(0, len(m2)):\n",
+ " err = error(prices[i], fit[i])\n",
+ " l += (err**2)\n",
+ " return math.sqrt(l / len(m2))\n",
+ "\n",
+ "def manualFit(m, b):\n",
+ " fit = [m * x + b for x in m2]\n",
+ " \n",
+ " fig, ax = plt.subplots()\n",
+ "\n",
+ " ax.set(xlim=[0, 1100], ylim=[0, 1100], xlabel='Oppervlakte in m2', ylabel='Prijs in duizend euro', title='Huisprijs')\n",
+ " \n",
+ " plt.scatter(m2, prices)\n",
+ " plt.plot(m2, fit, 'red')\n",
+ " \n",
+ " plt.show()\n",
+ " \n",
+ " print(loss(fit))\n",
+ " \n",
+ "interact(manualFit, m=(-2,2,0.01), b=(-1000,1000))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3774bb48-10bb-41b5-a312-3c370eb6c9fe",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### Automatiseren (machine learning)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 312,
+ "id": "1a4b8b32-0998-4ba8-bde9-2d92b6677b4d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "initial loss: 732.9188985681157\n",
+ "epoch: 0\n",
+ "loss: 145.45988597423664\n",
+ "epoch: 1\n",
+ "loss: 136.99645973532003\n",
+ "epoch: 2\n",
+ "loss: 129.24391672487062\n",
+ "epoch: 3\n",
+ "loss: 122.15533638953781\n",
+ "epoch: 4\n",
+ "loss: 115.68654006219647\n",
+ "mFit: 0.9120270795378896 bFit: 126.93364980134625\n",
+ "final loss: 115.68654006219647\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Learning iterations\n",
+ "epochs = 5\n",
+ "\n",
+ "# Learning rate\n",
+ "learningRateM = 0.000001\n",
+ "learningRateB = 0.01\n",
+ "\n",
+ "#------------------------------------------\n",
+ "\n",
+ "# Initial values (\"guess\")\n",
+ "mFit = 0.0\n",
+ "bFit = 0.0\n",
+ "\n",
+ "#------------------------------------------\n",
+ "\n",
+ "def loss(m, b):\n",
+ " \n",
+ " l = 0\n",
+ " \n",
+ " # Sum of squared errors\n",
+ " for i in range(0, len(m2)):\n",
+ " \n",
+ " guess = m * m2[i] + b\n",
+ " err = prices[i] - guess\n",
+ " \n",
+ " l += (err**2)\n",
+ " \n",
+ " return math.sqrt(l / len(m2))\n",
+ "\n",
+ "#------------------------------------------\n",
+ "\n",
+ "def gradientDescent(m, b, i):\n",
+ " \n",
+ " guess = m * m2[i] + b\n",
+ " err = prices[i] - guess\n",
+ "\n",
+ " # print(\"error: \", err)\n",
+ "\n",
+ " # Stochastic gradient descent\n",
+ " m += err * learningRateM * m2[i]\n",
+ " b += err * learningRateB\n",
+ "\n",
+ " # print(\"m: \", m, \"b: \", b)\n",
+ " \n",
+ " return m, b\n",
+ " \n",
+ "#------------------------------------------\n",
+ "\n",
+ "def learn(m, b):\n",
+ " \n",
+ " for i in range(0, len(m2)):\n",
+ "\n",
+ " # Adjust m and b, given input price\n",
+ " m, b = gradientDescent(m, b, i)\n",
+ " \n",
+ " return m, b\n",
+ "\n",
+ "#------------------------------------------\n",
+ "\n",
+ "def plotFit():\n",
+ " \n",
+ " fig, ax = plt.subplots()\n",
+ "\n",
+ " ax.set(xlim=[0, 1100], ylim=[0, 1100], xlabel='Oppervlakte in m2', ylabel='Prijs in duizend euro', title='Huisprijs')\n",
+ "\n",
+ "\n",
+ " fit = [mFit * x + bFit for x in m2]\n",
+ "\n",
+ " plt.scatter(m2, prices)\n",
+ " plt.plot(m2, fit, 'red')\n",
+ "\n",
+ " plt.show()\n",
+ "\n",
+ "#------------------------------------------\n",
+ "\n",
+ "epoch = 0\n",
+ "\n",
+ "# Inital loss\n",
+ "l = loss(mFit, bFit)\n",
+ "\n",
+ "print(\"initial loss: \", loss(mFit, bFit))\n",
+ "\n",
+ "#------------------------------------------\n",
+ "\n",
+ "while epoch < epochs:\n",
+ " \n",
+ " print(\"epoch: \", epoch)\n",
+ " \n",
+ " mFit, bFit = learn(mFit, bFit)\n",
+ " \n",
+ " print(\"loss: \", loss(mFit, bFit))\n",
+ " \n",
+ " epoch += 1\n",
+ " \n",
+ " time.sleep(1)\n",
+ " \n",
+ " # plotFit()\n",
+ " \n",
+ "#------------------------------------------\n",
+ "\n",
+ "print(\"mFit: \", mFit, \"bFit: \", bFit)\n",
+ "print(\"final loss: \", loss(mFit, bFit))\n",
+ "\n",
+ "plotFit()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d2eebb08-e24b-4f99-a22b-2ae89d0ce43f",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "# Deep Learning"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7336d81e-3fbe-405b-8924-5fb9d995dbc1",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### Preparation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 252,
+ "id": "276b49e5-4e09-47c1-bc79-5819674625ab",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def preparePlot(t):\n",
+ " \n",
+ " fig, ax = plt.subplots()\n",
+ " ax.set(xlim=[-10, 10], ylim=[-50, 400], xlabel='x', ylabel='y', title=t)\n",
+ "\n",
+ "def plotCurve(x, y, scatter):\n",
+ "\n",
+ " if scatter:\n",
+ " plt.scatter(x, y)\n",
+ " else:\n",
+ " plt.plot(x, y, 'red')\n",
+ "\n",
+ "def showPlot():\n",
+ " \n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 183,
+ "id": "5aa5ca0e-99ce-4a7a-a222-6e2277b280af",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n"
+ ]
+ }
+ ],
+ "source": [
+ "x = [x for x in range(-10, 11, 1)]\n",
+ "\n",
+ "print(x)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 255,
+ "id": "2f91d22a-57cf-463f-b6e6-9a35e655f0d2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[160, 125, 94, 67, 44, 25, 10, -1, -8, -11, -10, -5, 4, 17, 34, 55, 80, 109, 142, 179, 220]\n"
+ ]
+ }
+ ],
+ "source": [
+ "a = 2\n",
+ "b = 3\n",
+ "c = -10\n",
+ "\n",
+ "# Prepare random data\n",
+ "y = [a * x**2 + b * x + c for x in x]\n",
+ "\n",
+ "# Mean\n",
+ "mu = 5\n",
+ "\n",
+ "# Define standard deviation (spread)\n",
+ "sigma = 20\n",
+ "\n",
+ "# Prepare random data\n",
+ "yNoise = [a * x**2 + b * x + c + rnd.gauss(mu, sigma) for x in x]\n",
+ "\n",
+ "print(y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 256,
+ "id": "4647d61c-33c8-4edc-b356-f57dc689aa76",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "preparePlot(\"Parabool\")\n",
+ "plotCurve(x,y, False)\n",
+ "plotCurve(x,yNoise, True)\n",
+ "showPlot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1421b00c-3903-426c-803e-e8c4e34b48d3",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Doel: best fit maken"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4100e5ee-acbe-4f07-9ba1-307a8d64f379",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### ReLU: Rectified Linear Unit"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 278,
+ "id": "1dcf2502-27de-4664-866c-42ac0b2e4344",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def relu(m, b, x):\n",
+ " \n",
+ " y = m * x + b\n",
+ " \n",
+ " if y < 0:\n",
+ " return 0\n",
+ " else:\n",
+ " return y"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 279,
+ "id": "93dbeb1e-71f8-405a-8d80-6c3b65abee19",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5fff91fa07304a0d9d5342ab573162b7",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(FloatSlider(value=0.0, description='m', max=10.0, min=-10.0), FloatSlider(value=0.0, des…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 279,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def manualRelu(m, b):\n",
+ " \n",
+ " rlu = [relu(m, b, x) for x in x]\n",
+ " \n",
+ " preparePlot(\"Relu\")\n",
+ " plotCurve(x, rlu, False)\n",
+ " \n",
+ "interact(manualRelu, m=(-10,10,0.1), b=(-10,10,0.1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e875413a-ee27-4ef3-b317-f3bf1df27441",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### Handmatige parabool fit met ReLU"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 286,
+ "id": "e42ed231-5941-48ad-8e64-67a9a22ab1f7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5497900c8c0b4e268e7b8b78b7b9808a",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(FloatSlider(value=0.0, description='m1', max=50.0, min=-50.0, step=0.01), FloatSlider(va…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 286,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def parabolaFit(m1, b1, m2, b2):\n",
+ " \n",
+ " fit = [relu(m1, b1, x) + relu(m2, b2, x) for x in x]\n",
+ " \n",
+ " preparePlot(\"Twee relu's\")\n",
+ " \n",
+ " plotCurve(x, fit, False)\n",
+ " plotCurve(x, yNoise, True)\n",
+ " \n",
+ " showPlot()\n",
+ "\n",
+ "interact(parabolaFit, m1=(-50,50,0.01), b1=(-50,50,0.01), m2=(-50,50,0.01), b2=(-50,50,0.01))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1e292586-9762-4918-a02a-2c8bf1112451",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### Automatische parabool fit"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 311,
+ "id": "aaf91953-511e-43a6-b997-7a3a6464d4bc",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "initial loss: 92.81163720137685\n",
+ "epoch: 0\n",
+ "loss: 59.73026134865199\n",
+ "m1Fit: 8.31648309729432 b1Fit: 1.4696764533432922 m2Fit: 13.561127636076101 b2Fit: -0.24799465680500446\n",
+ "final loss: 59.73026134865199\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Learning iterations\n",
+ "epochs = 1\n",
+ "\n",
+ "# Learning rate\n",
+ "learningRate = 0.01\n",
+ "\n",
+ "#------------------------------------------\n",
+ "\n",
+ "# Initial values (\"guess\")\n",
+ "m1Fit = 0.0\n",
+ "b1Fit = 0.0\n",
+ "m2Fit = 0.0\n",
+ "b2Fit = 0.0\n",
+ "\n",
+ "#------------------------------------------\n",
+ "\n",
+ "def loss(m1, b1, m2, b2):\n",
+ " \n",
+ " l = 0\n",
+ " \n",
+ " # Sum of squared errors\n",
+ " for i in range(0, len(x)):\n",
+ " \n",
+ " guess = relu(m1, b1, x[i]) + relu(m2, b2, x[i])\n",
+ " err = y[i] - guess\n",
+ " \n",
+ " l += (err**2)\n",
+ " \n",
+ " return math.sqrt(l / len(x))\n",
+ "\n",
+ "#------------------------------------------\n",
+ "\n",
+ "def gradientDescent(m1, b1, m2, b2, i):\n",
+ " \n",
+ " # First guess\n",
+ " guess1 = relu(m1, b1, x[i]) + relu(m2, b2, x[i])\n",
+ " err1 = y[i] - guess1\n",
+ "\n",
+ " # print(\"error: \", err)\n",
+ "\n",
+ " # Stochastic gradient descent\n",
+ " m1 += err1 * learningRate * x[i]\n",
+ " b1 += err1 * learningRate\n",
+ " \n",
+ " # Second guess\n",
+ " guess2 = relu(m1, b1, x[i]) + relu(m2, b2, x[i])\n",
+ " err2 = y[i] - guess2\n",
+ "\n",
+ " # print(\"error: \", err)\n",
+ "\n",
+ " # Stochastic gradient descent\n",
+ " m2 += err2 * learningRate * x[i]\n",
+ " b2 += err2 * learningRate\n",
+ "\n",
+ " # print(\"m: \", m, \"b: \", b)\n",
+ " \n",
+ " return m1, b1, m2, b2\n",
+ " \n",
+ "#------------------------------------------\n",
+ "\n",
+ "def learn(m1, b1, m2, b2):\n",
+ " \n",
+ " for i in range(0, len(x)):\n",
+ "\n",
+ " # Adjust m and b, given input\n",
+ " m1, b1, m2, b2 = gradientDescent(m1, b1, m2, b2, i)\n",
+ " \n",
+ " return m1, b1, m2, b2\n",
+ "\n",
+ "#------------------------------------------\n",
+ "\n",
+ "epoch = 0\n",
+ "\n",
+ "# Inital loss\n",
+ "l = loss(m1Fit, b1Fit, m2Fit, b2Fit)\n",
+ "\n",
+ "print(\"initial loss: \", loss(m1Fit, b1Fit, m2Fit, b2Fit))\n",
+ "\n",
+ "#------------------------------------------\n",
+ "\n",
+ "while epoch < epochs:\n",
+ " \n",
+ " print(\"epoch: \", epoch)\n",
+ " \n",
+ " m1Fit, b1Fit, m2Fit, b2Fit = learn(m1Fit, b1Fit, m2Fit, b2Fit)\n",
+ " \n",
+ " print(\"loss: \", loss(m1Fit, b1Fit, m2Fit, b2Fit))\n",
+ " \n",
+ " epoch += 1\n",
+ " \n",
+ " time.sleep(1)\n",
+ " \n",
+ " # plotFit()\n",
+ " \n",
+ "#------------------------------------------\n",
+ "\n",
+ "print(\"m1Fit: \", m1Fit, \"b1Fit: \", b1Fit, \"m2Fit: \", m2Fit, \"b2Fit: \", b2Fit)\n",
+ "print(\"final loss: \", loss(m1Fit, b1Fit, m2Fit, b2Fit))\n",
+ "\n",
+ "parabolaFit(m1Fit, b1Fit, m2Fit, b2Fit)"
+ ]
+ }
+ ],
+ "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": "3.8.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/wk_3_les_machine_learning/huisprijs-student.ipynb b/scripts/wk_3_les_machine_learning/huisprijs-student.ipynb
new file mode 100644
index 00000000..3a8c5136
--- /dev/null
+++ b/scripts/wk_3_les_machine_learning/huisprijs-student.ipynb
@@ -0,0 +1,688 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "783305be-8517-4058-a458-23956ad0be80",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "7ad7ed7f-ad3f-46cd-9b05-b424251cf191",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!pip install ipywidgets"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "id": "392d5d78-a879-492f-ab43-d12b79c0af6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from ipywidgets import interact\n",
+ "import matplotlib.pyplot as plt\n",
+ "import random as rnd\n",
+ "import math\n",
+ "import time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bb47051d-0426-4e47-b9ef-9e610fd51c18",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# Machine Learning"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e2eb9983-c570-4454-8195-6c64977c659c",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### Bronnen"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "45f3a8dc-4d1a-46aa-b4e9-89c24cfb16f0",
+ "metadata": {},
+ "source": [
+ "**Fast.ai**\n",
+ "\n",
+ "+ [Practical Deep Learning for Coders](https://course.fast.ai/)\n",
+ "+ [Neural net foundations](https://course.fast.ai/Lessons/lesson3.html)\n",
+ "\n",
+ "**CodingTrain**:\n",
+ "\n",
+ "+ [Linear Regression with Gradient Descent](https://www.youtube.com/watch?v=L-Lsfu4ab74)\n",
+ "+ [Mathematics of Gradient Descent](https://www.youtube.com/watch?v=jc2IthslyzM)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "079a9765-fa1d-4225-b339-c52cc87727ba",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Huisprijs model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b32c6aad-ed24-476d-8289-c7fe41db0959",
+ "metadata": {},
+ "source": [
+ "+ **prijs** wordt bepaald door **prijs per vierkante meter** en **vaste grondprijs**\n",
+ "+ p = m*x + b"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "76412025-3e68-4c79-90a1-98a8f02ba192",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "id": "689fee69-062e-4dd9-b642-1a15b22a84ed",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "m2 = [100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780, 790, 800, 810, 820, 830, 840, 850, 860, 870, 880, 890, 900, 910, 920, 930, 940, 950, 960, 970, 980, 990]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "id": "de8dba48-750c-4ecb-8740-856c0c40e6cb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "prices = [384.91367692954503, 389.8967479767937, 401.20788969026364, 465.1388607569204, 414.0688201351979, 325.18526099613297, 460.2622840847401, 425.28637650519823, 435.81289210983635, 466.1119350690599, 481.26084158347044, 553.44910806194, 524.9645554759068, 493.73550242106825, 441.31748783586255, 428.9736342758598, 524.2439536647842, 605.7756579042834, 523.9159804732369, 520.5573694360451, 572.2243354280608, 440.25182913939256, 527.1405877998808, 590.3253772815467, 624.1383069765612, 553.1559229141405, 603.3619901081537, 601.374941452989, 645.7628766092549, 520.074445002628, 644.777548130977, 505.9835725792402, 435.60382044468037, 583.5176490260349, 568.8932967557804, 701.3208570139484, 693.4986129729026, 568.6647681093013, 720.31529963436, 597.8458023030355, 668.9629307564601, 661.4270155430428, 697.0093900222645, 753.3045418080637, 747.688965355471, 734.4919571308446, 762.4963668464409, 757.4944426355083, 687.111017434628, 687.7840234328396, 712.102220810479, 786.9528480333747, 741.4919100301831, 929.5027955736383, 715.6495220469454, 703.0787780068489, 840.7931460828787, 893.5294986940426, 836.3984883587482, 866.5072142177371, 914.8441484209513, 815.4180766788587, 729.3928746802716, 798.7546416176901, 909.7027030121603, 748.9425115745198, 859.3471924551083, 881.7266151539443, 848.9085484630431, 928.6542390457668, 925.6545819068597, 1054.4985961830512, 942.3977437441195, 863.3417578949857, 873.6741685366178, 861.7893229130596, 993.6591491871075, 894.321701563041, 936.0817458409582, 1000.4511833502772, 904.3575906544015, 941.4439425467597, 840.1099614077259, 900.2264966672901, 943.2584511465706, 1019.103269196167, 1080.5445290710309, 1002.7073230785402, 1029.2954995439861, 1029.7578557983063]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "id": "ea244261",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780, 790, 800, 810, 820, 830, 840, 850, 860, 870, 880, 890, 900, 910, 920, 930, 940, 950, 960, 970, 980, 990]\n",
+ "[384.91367692954503, 389.8967479767937, 401.20788969026364, 465.1388607569204, 414.0688201351979, 325.18526099613297, 460.2622840847401, 425.28637650519823, 435.81289210983635, 466.1119350690599, 481.26084158347044, 553.44910806194, 524.9645554759068, 493.73550242106825, 441.31748783586255, 428.9736342758598, 524.2439536647842, 605.7756579042834, 523.9159804732369, 520.5573694360451, 572.2243354280608, 440.25182913939256, 527.1405877998808, 590.3253772815467, 624.1383069765612, 553.1559229141405, 603.3619901081537, 601.374941452989, 645.7628766092549, 520.074445002628, 644.777548130977, 505.9835725792402, 435.60382044468037, 583.5176490260349, 568.8932967557804, 701.3208570139484, 693.4986129729026, 568.6647681093013, 720.31529963436, 597.8458023030355, 668.9629307564601, 661.4270155430428, 697.0093900222645, 753.3045418080637, 747.688965355471, 734.4919571308446, 762.4963668464409, 757.4944426355083, 687.111017434628, 687.7840234328396, 712.102220810479, 786.9528480333747, 741.4919100301831, 929.5027955736383, 715.6495220469454, 703.0787780068489, 840.7931460828787, 893.5294986940426, 836.3984883587482, 866.5072142177371, 914.8441484209513, 815.4180766788587, 729.3928746802716, 798.7546416176901, 909.7027030121603, 748.9425115745198, 859.3471924551083, 881.7266151539443, 848.9085484630431, 928.6542390457668, 925.6545819068597, 1054.4985961830512, 942.3977437441195, 863.3417578949857, 873.6741685366178, 861.7893229130596, 993.6591491871075, 894.321701563041, 936.0817458409582, 1000.4511833502772, 904.3575906544015, 941.4439425467597, 840.1099614077259, 900.2264966672901, 943.2584511465706, 1019.103269196167, 1080.5445290710309, 1002.7073230785402, 1029.2954995439861, 1029.7578557983063]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(m2)\n",
+ "print(prices)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c46adc44-592b-47bd-8496-4846784a3a9d",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### Handmatig matchen\n",
+ "uitdaging: wie matcht het best?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "id": "192806ff-291c-4c27-83ac-22b14753bbb7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "0b8e3ee11872479fb7ee6aa28f1ce5e7",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(FloatSlider(value=0.0, description='m', max=2.0, min=-2.0, step=0.01), IntSlider(value=0…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def manualFit(m, b):\n",
+ " fit = [m * x + b for x in m2]\n",
+ " \n",
+ " fig, ax = plt.subplots()\n",
+ "\n",
+ " ax.set(xlim=[0, 1100], ylim=[0, 1100], xlabel='Oppervlakte in m2', ylabel='Prijs in duizend euro', title='Huisprijs')\n",
+ " \n",
+ " plt.scatter(m2, prices)\n",
+ " plt.plot(m2, fit, 'red')\n",
+ " \n",
+ " plt.show()\n",
+ "\n",
+ " print(loss(fit))\n",
+ " \n",
+ "interact(manualFit, m=(-2,2,0.01), b=(-1000,1000))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e8a06ddd-344a-42a6-8fc5-3bccc20376a4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Toevoegen error & loss"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "id": "e7f3c443-70cb-422c-98bf-d25de4f048f5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# TODO: implement\n",
+ "\n",
+ "def error(actual, predicted):\n",
+ " return actual - predicted\n",
+ "\n",
+ "def loss(fit):\n",
+ " l = 0 # l = loss\n",
+ " # Sum of errors squared\n",
+ " for i in range(0, len(m2)):\n",
+ " err = error(prices[i], fit[i])\n",
+ " l += (err**2)\n",
+ " return math.sqrt(l / len(prices)) \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3774bb48-10bb-41b5-a312-3c370eb6c9fe",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Automatiseren (machine learning)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1a4b8b32-0998-4ba8-bde9-2d92b6677b4d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# TODO: implement\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d2eebb08-e24b-4f99-a22b-2ae89d0ce43f",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "# Deep Learning"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7336d81e-3fbe-405b-8924-5fb9d995dbc1",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### Preparation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "276b49e5-4e09-47c1-bc79-5819674625ab",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def preparePlot(t):\n",
+ " \n",
+ " fig, ax = plt.subplots()\n",
+ " ax.set(xlim=[-10, 10], ylim=[-50, 400], xlabel='x', ylabel='y', title=t)\n",
+ "\n",
+ "def plotCurve(x, y, scatter):\n",
+ "\n",
+ " if scatter:\n",
+ " plt.scatter(x, y)\n",
+ " else:\n",
+ " plt.plot(x, y, 'red')\n",
+ "\n",
+ "def showPlot():\n",
+ " \n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5aa5ca0e-99ce-4a7a-a222-6e2277b280af",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n"
+ ]
+ }
+ ],
+ "source": [
+ "x = [x for x in range(-10, 11, 1)]\n",
+ "\n",
+ "print(x)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2f91d22a-57cf-463f-b6e6-9a35e655f0d2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[160, 125, 94, 67, 44, 25, 10, -1, -8, -11, -10, -5, 4, 17, 34, 55, 80, 109, 142, 179, 220]\n"
+ ]
+ }
+ ],
+ "source": [
+ "a = 2\n",
+ "b = 3\n",
+ "c = -10\n",
+ "\n",
+ "# Prepare random data\n",
+ "y = [a * x**2 + b * x + c for x in x]\n",
+ "\n",
+ "# Mean\n",
+ "mu = 5\n",
+ "\n",
+ "# Define standard deviation (spread)\n",
+ "sigma = 20\n",
+ "\n",
+ "# Prepare random data\n",
+ "yNoise = [a * x**2 + b * x + c + rnd.gauss(mu, sigma) for x in x]\n",
+ "\n",
+ "print(y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4647d61c-33c8-4edc-b356-f57dc689aa76",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "preparePlot(\"Parabool\")\n",
+ "plotCurve(x,y, False)\n",
+ "plotCurve(x,yNoise, True)\n",
+ "showPlot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1421b00c-3903-426c-803e-e8c4e34b48d3",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Doel: best fit maken"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4100e5ee-acbe-4f07-9ba1-307a8d64f379",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### ReLU: Rectified Linear Unit"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1dcf2502-27de-4664-866c-42ac0b2e4344",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def relu(m, b, x):\n",
+ " \n",
+ " y = m * x + b\n",
+ " \n",
+ " if y < 0:\n",
+ " return 0\n",
+ " else:\n",
+ " return y"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "93dbeb1e-71f8-405a-8d80-6c3b65abee19",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "f1013d0290164640a12ad7f5d4058d5b",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(FloatSlider(value=0.0, description='m', max=10.0, min=-10.0), FloatSlider(value=0.0, des…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def manualRelu(m, b):\n",
+ " \n",
+ " rlu = [relu(m, b, x) for x in x]\n",
+ " \n",
+ " preparePlot(\"Relu\")\n",
+ " plotCurve(x, rlu, False)\n",
+ " \n",
+ "interact(manualRelu, m=(-10,10,0.1), b=(-10,10,0.1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e875413a-ee27-4ef3-b317-f3bf1df27441",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### Handmatige parabool fit met ReLU"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e42ed231-5941-48ad-8e64-67a9a22ab1f7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "3f8b6a53fb164369938f630b328edb01",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(FloatSlider(value=0.0, description='m1', max=50.0, min=-50.0, step=0.01), FloatSlider(va…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def parabolaFit(m1, b1, m2, b2):\n",
+ " \n",
+ " fit = [relu(m1, b1, x) + relu(m2, b2, x) for x in x]\n",
+ " \n",
+ " preparePlot(\"Twee relu's\")\n",
+ " \n",
+ " plotCurve(x, fit, False)\n",
+ " plotCurve(x, yNoise, True)\n",
+ " \n",
+ " showPlot()\n",
+ "\n",
+ "interact(parabolaFit, m1=(-50,50,0.01), b1=(-50,50,0.01), m2=(-50,50,0.01), b2=(-50,50,0.01))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1e292586-9762-4918-a02a-2c8bf1112451",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "### Automatische parabool fit"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "aaf91953-511e-43a6-b997-7a3a6464d4bc",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "initial loss: 92.81163720137685\n",
+ "epoch: 0\n",
+ "loss: 59.73026134865199\n",
+ "m1Fit: 8.31648309729432 b1Fit: 1.4696764533432922 m2Fit: 13.561127636076101 b2Fit: -0.24799465680500446\n",
+ "final loss: 59.73026134865199\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Learning iterations\n",
+ "epochs = 1\n",
+ "\n",
+ "# Learning rate\n",
+ "learningRate = 0.01\n",
+ "\n",
+ "#------------------------------------------\n",
+ "\n",
+ "# Initial values (\"guess\")\n",
+ "m1Fit = 0.0\n",
+ "b1Fit = 0.0\n",
+ "m2Fit = 0.0\n",
+ "b2Fit = 0.0\n",
+ "\n",
+ "#------------------------------------------\n",
+ "\n",
+ "def loss(m1, b1, m2, b2):\n",
+ " \n",
+ " l = 0\n",
+ " \n",
+ " # Sum of squared errors\n",
+ " for i in range(0, len(x)):\n",
+ " \n",
+ " guess = relu(m1, b1, x[i]) + relu(m2, b2, x[i])\n",
+ " err = y[i] - guess\n",
+ " \n",
+ " l += (err**2)\n",
+ " \n",
+ " return math.sqrt(l / len(x))\n",
+ "\n",
+ "#------------------------------------------\n",
+ "\n",
+ "def gradientDescent(m1, b1, m2, b2, i):\n",
+ " \n",
+ " # First guess\n",
+ " guess1 = relu(m1, b1, x[i]) + relu(m2, b2, x[i])\n",
+ " err1 = y[i] - guess1\n",
+ "\n",
+ " # print(\"error: \", err)\n",
+ "\n",
+ " # Stochastic gradient descent\n",
+ " m1 += err1 * learningRate * x[i]\n",
+ " b1 += err1 * learningRate\n",
+ " \n",
+ " # Second guess\n",
+ " guess2 = relu(m1, b1, x[i]) + relu(m2, b2, x[i])\n",
+ " err2 = y[i] - guess2\n",
+ "\n",
+ " # print(\"error: \", err)\n",
+ "\n",
+ " # Stochastic gradient descent\n",
+ " m2 += err2 * learningRate * x[i]\n",
+ " b2 += err2 * learningRate\n",
+ "\n",
+ " # print(\"m: \", m, \"b: \", b)\n",
+ " \n",
+ " return m1, b1, m2, b2\n",
+ " \n",
+ "#------------------------------------------\n",
+ "\n",
+ "def train(m1, b1, m2, b2):\n",
+ " \n",
+ " for i in range(0, len(x)):\n",
+ "\n",
+ " # Adjust m and b, given input\n",
+ " m1, b1, m2, b2 = gradientDescent(m1, b1, m2, b2, i)\n",
+ " \n",
+ " return m1, b1, m2, b2\n",
+ "\n",
+ "#------------------------------------------\n",
+ "\n",
+ "epoch = 0\n",
+ "\n",
+ "# Inital loss\n",
+ "l = loss(m1Fit, b1Fit, m2Fit, b2Fit)\n",
+ "\n",
+ "print(\"initial loss: \", loss(m1Fit, b1Fit, m2Fit, b2Fit))\n",
+ "\n",
+ "#------------------------------------------\n",
+ "\n",
+ "while epoch < epochs:\n",
+ " \n",
+ " print(\"epoch: \", epoch)\n",
+ " \n",
+ " m1Fit, b1Fit, m2Fit, b2Fit = train(m1Fit, b1Fit, m2Fit, b2Fit)\n",
+ " \n",
+ " print(\"loss: \", loss(m1Fit, b1Fit, m2Fit, b2Fit))\n",
+ " \n",
+ " epoch += 1\n",
+ " \n",
+ " time.sleep(1)\n",
+ " \n",
+ " # plotFit()\n",
+ " \n",
+ "#------------------------------------------\n",
+ "\n",
+ "print(\"m1Fit: \", m1Fit, \"b1Fit: \", b1Fit, \"m2Fit: \", m2Fit, \"b2Fit: \", b2Fit)\n",
+ "print(\"final loss: \", loss(m1Fit, b1Fit, m2Fit, b2Fit))\n",
+ "\n",
+ "parabolaFit(m1Fit, b1Fit, m2Fit, b2Fit)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.7 64-bit",
+ "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": "3.10.7"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "5b93afe9c2e217d44e354b055ed180e932b72842f9b6422dadc1ad80da9caf67"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/wk_3_les_machine_learning/kruisjesrondjes.py b/scripts/wk_3_les_machine_learning/kruisjesrondjes.py
new file mode 100644
index 00000000..171b5799
--- /dev/null
+++ b/scripts/wk_3_les_machine_learning/kruisjesrondjes.py
@@ -0,0 +1,133 @@
+# Define datasets (training set should normally be much larger than test set for best results)
+
+import math
+
+
+trainingSet = (
+ ((
+ (1, 1, 1),
+ (1, 0, 1),
+ (1, 1, 1)
+ ), 'O'),
+ ((
+ (0, 1, 0),
+ (1, 0, 1),
+ (0, 1, 0)
+ ), 'O'),
+ ((
+ (0, 1, 0),
+ (1, 1, 1),
+ (0, 1, 0)
+ ), 'X'),
+ ((
+ (1, 0, 1),
+ (0, 1, 0),
+ (1, 0, 1)
+ ), 'X')
+)
+
+testSet = (
+ ((
+ (0, 1, 1),
+ (1, 0, 1),
+ (1, 1, 0)
+ ), 'O'),
+ ((
+ (1, 0, 1),
+ (1, 0, 1),
+ (1, 1, 0)
+ ), 'O'),
+ ((
+ (1, 0, 0),
+ (1, 1, 1),
+ (0, 0, 1)
+ ), 'X'),
+ ((
+ (0, 0, 1),
+ (1, 1, 1),
+ (1, 0, 0)
+ ), 'X')
+)
+
+# mList = [[0, 0, 1],
+# [1, 1, 1],
+# [1, 0, 0]]
+
+# eList = mList[0][0]
+
+# mTup = ((0, 1, 1),
+# (1, 1, 1),
+# (0, 0, 1))
+
+# eTup = mTup[0][0]
+
+# print(eTup)
+
+def mat2vec(mat):
+
+ # Convert 3 x 3 to 9 x 1
+ vec = []
+
+ rows = len(mat)
+
+ for row in range(0, rows):
+
+ cols = len(mat[row])
+
+ for col in range(0, cols):
+
+ vec.append(mat[row][col])
+
+ return vec
+
+# print(mTup)
+
+# v = mat2vec(mTup)
+
+# print(v)
+
+# def in2out(input, weights):
+# pass
+
+s =((
+ (1, 1, 0),
+ (0, 0, 1),
+ (1, 1, 0)
+ ), 'X')
+
+# print(s[0])
+# print(s[1])
+
+def initWeights(vec):
+
+ n = len(vec)
+
+ weights = []
+
+ for i in range(0, n):
+
+ weights.append(1)
+
+ return weights
+
+
+def in2out(vec, weights):
+
+ n = len(vec)
+
+ Sum = 0.0
+
+ # Compute vec[i] * weights[i]
+ for i in range(0, n):
+
+ Sum += vec[i] * weights[i]
+
+ # TODO: softmax
+ return math.sqrt(Sum)
+
+
+v = mat2vec(s[0])
+w = initWeights(v)
+out = in2out(v, w)
+
+print(out)
\ No newline at end of file
diff --git a/scripts/wk_3_les_machine_learning/learning.jpg b/scripts/wk_3_les_machine_learning/learning.jpg
new file mode 100644
index 00000000..919094e3
Binary files /dev/null and b/scripts/wk_3_les_machine_learning/learning.jpg differ
diff --git a/scripts/wk_3_les_machine_learning/lightbulb-brain.jpg b/scripts/wk_3_les_machine_learning/lightbulb-brain.jpg
new file mode 100644
index 00000000..d5e5c584
Binary files /dev/null and b/scripts/wk_3_les_machine_learning/lightbulb-brain.jpg differ
diff --git a/scripts/wk_3_les_modules_inkapseling_overerving_en_veelvormigheid/employee_salaray.py b/scripts/wk_3_les_modules_inkapseling_overerving_en_veelvormigheid/employee_salaray.py
new file mode 100644
index 00000000..74382da5
--- /dev/null
+++ b/scripts/wk_3_les_modules_inkapseling_overerving_en_veelvormigheid/employee_salaray.py
@@ -0,0 +1,33 @@
+# Encapsulation / Information hiding
+class Salary:
+ def __init__(self, pay):
+ self.pay = pay
+
+ def get_total(self):
+ return (self.pay * 12)
+
+
+class Employee:
+ # Dunder method with Arguments
+ def __init__(self, pay, bonus):
+ self.pay = pay
+ self.bonus = bonus
+ # Composition
+ self.obj_salary = Salary(self.pay)
+
+ # Docstring
+ """
+ This function calculates and
+ returns the annual salary
+ """
+ def annual_salary(self):
+ return "Total: " + str(self.obj_salary.get_total() + self.bonus)
+
+# Object
+employee1 = Employee(600, 500)
+employee2 = Employee(800, 0)
+print(f"Annual salary of employee1 : {employee1.annual_salary()}")
+print(f"Annual salary of employee2 : {employee2.annual_salary()}")
+
+# # Dunder method
+# print(employee1.annual_salary().__doc__)
diff --git a/scripts/wk_3_les_modules_inkapseling_overerving_en_veelvormigheid/shape/__pycache__/shape.cpython-310.pyc b/scripts/wk_3_les_modules_inkapseling_overerving_en_veelvormigheid/shape/__pycache__/shape.cpython-310.pyc
new file mode 100644
index 00000000..f6afcb58
Binary files /dev/null and b/scripts/wk_3_les_modules_inkapseling_overerving_en_veelvormigheid/shape/__pycache__/shape.cpython-310.pyc differ
diff --git a/scripts/wk_3_les_modules_inkapseling_overerving_en_veelvormigheid/shape/shape.py b/scripts/wk_3_les_modules_inkapseling_overerving_en_veelvormigheid/shape/shape.py
new file mode 100644
index 00000000..05889625
--- /dev/null
+++ b/scripts/wk_3_les_modules_inkapseling_overerving_en_veelvormigheid/shape/shape.py
@@ -0,0 +1,41 @@
+import math
+from abc import ABC, abstractmethod
+
+# Abstraction: Abstract Superclass
+class Shape(ABC):
+ # Dunder method with Arguments/Parameters
+ def __init__(self, width, height):
+ # Self
+ # Attributes
+ self.width = width
+ self.height = height
+
+ # Interface
+ @abstractmethod
+ # Self
+ def area(self, width, height):
+ pass
+
+ # Static variable
+ pi = math.pi
+
+# Inheritance : Subclass
+class Rectangle(Shape):
+ def __init__(self, width, height):
+ super().__init__(width, height)
+ self.area(width, height)
+
+ # Operator overloading
+ def area(self, width, height):
+ self.area = width * height
+
+# Inheritance : Subclass
+class Triangle(Shape):
+ # Dunder method with Arguments
+ def __init__(self, width, height):
+ super().__init__(width, height)
+ self.area(width, height)
+
+ # Operator overloading
+ def area(self, width, height):
+ self.area = 0.5 * self.width * self.width * Shape.pi
diff --git a/scripts/wk_3_les_modules_inkapseling_overerving_en_veelvormigheid/shape/testShape.py b/scripts/wk_3_les_modules_inkapseling_overerving_en_veelvormigheid/shape/testShape.py
new file mode 100644
index 00000000..2a156404
--- /dev/null
+++ b/scripts/wk_3_les_modules_inkapseling_overerving_en_veelvormigheid/shape/testShape.py
@@ -0,0 +1,9 @@
+from shape import Rectangle
+from shape import Triangle
+
+rectangle = Rectangle(4, 5)
+print(f"The area of the rectangle is : {rectangle.area}")
+
+triangle = Triangle(4, 5)
+print(f"The area of the triangle is : {triangle.area}")
+
diff --git a/scripts/wk_3_les_modules_inkapseling_overerving_en_veelvormigheid/uml/class_diagram.md b/scripts/wk_3_les_modules_inkapseling_overerving_en_veelvormigheid/uml/class_diagram.md
new file mode 100644
index 00000000..55841789
--- /dev/null
+++ b/scripts/wk_3_les_modules_inkapseling_overerving_en_veelvormigheid/uml/class_diagram.md
@@ -0,0 +1,22 @@
+```mermaid
+classDiagram
+ Shape <|-- Rectangle
+ Shape <|-- Triangle
+ Shape <|-- Circle
+ Shape: +init(width, height)
+ Shape : +int width
+ Shape : +int height
+ Shape: +area(width, height)
+ class Rectangle {
+ Shape: +init(width, height)
+ Rectangle: +area(width, height)
+ }
+ class Triangle {
+ Shape: +init(width, height)
+ Rectangle: +area(width, height)
+ }
+ class Circle {
+ Shape: +init(width)
+ Circle: +area(width)
+ }
+```
\ No newline at end of file
diff --git a/scripts/wk_3_practicum_numpy/numpy/.ipynb_checkpoints/numpy_opdracht1-checkpoint.ipynb b/scripts/wk_3_practicum_numpy/numpy/.ipynb_checkpoints/numpy_opdracht1-checkpoint.ipynb
new file mode 100644
index 00000000..79f6db2b
--- /dev/null
+++ b/scripts/wk_3_practicum_numpy/numpy/.ipynb_checkpoints/numpy_opdracht1-checkpoint.ipynb
@@ -0,0 +1,289 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "b45dd50e-d75c-41d2-a549-110cc111e32b",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
Author: Jeroen Boogaard \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "63f0720b-4a62-479d-873b-c5f6cad9a89d",
+ "metadata": {},
+ "source": [
+ "Numpy "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3619d610-30b3-41a2-b5f0-ae37b9d5b105",
+ "metadata": {},
+ "source": [
+ "**Imports**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "42257a05-c794-4121-a9cf-fe53fa3a1dc1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from PIL import Image\n",
+ "from scipy import ndimage"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8e48110a-ff83-4fba-aab9-7cb3678fe668",
+ "metadata": {},
+ "source": [
+ "Opdracht 1 "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "47ac5b87-14b1-437e-91cb-93dadadef8f9",
+ "metadata": {},
+ "source": [
+ "Gegeven "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8605ba6e-3f4e-4fe1-b76f-c784f2f44735",
+ "metadata": {},
+ "source": [
+ "Planeet | Grootte to.v. de omvang van de Aarde\n",
+ "---|---\n",
+ "Jupiter | 1120%\n",
+ "Saturnus | 945%\n",
+ "Uranus (25,362 km / 15,759 km) | 400%\n",
+ "Neptunus (24,622 km / 15,299 km) | 388%\n",
+ "Aarde (6,371 km / 3,959 km) | 100%\n",
+ "Venus (6,052 km / 3,761 km) | 95%\n",
+ "Mars (3,390 km / 2,460 km) | 53%"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2b471d98-c5b0-4db0-a1c6-155094548c23",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "filename = \"csv/planets.csv\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e5fa129a-d9a8-4899-ba69-87537e1819f5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ls pics"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ad98fa30-0e0a-4ec4-9930-b63db74c11f2",
+ "metadata": {},
+ "source": [
+ "Gevraagd \n",
+ "\n",
+ "Schaal voor elke (erkende) planeet uit ons zonnestelsel de bijbehorende image t.o.v. van de aarde. Het geschaalde plaatje moet groter zijn dan het plaatje van de aarde als de bijbehorende planeet groter is dan de aarde. Is de planeet kleiner dan de aarde dan moet het nieuwe plaatje kleiner zijn. Gebruik voor de schaalfactor het percentage. \n",
+ "Tip: Indien nodig kun je de images normaliseren door eerst het plaatje van de aarde te schalen\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c0c854fb-54db-4baa-b149-2e7d0c1b6391",
+ "metadata": {},
+ "source": [
+ "Oplossing \n",
+ "\n",
+ " \n",
+ " Open het bestand csv/planets.csv en voeg daar de kolom image \n",
+ " \n",
+ " \n",
+ " Importeer het csv-bestand en sla de data op in een dictionary \n",
+ " \n",
+ " \n",
+ " Open een image uit van een item uit de dictionary \n",
+ " \n",
+ " \n",
+ " Schaal de image m.b.v. een numpy array \n",
+ " \n",
+ " \n",
+ " Sla de geschaalde image op \n",
+ " \n",
+ " \n",
+ " Schrijf een functie voor het schalen van een image \n",
+ " \n",
+ " \n",
+ " Maak een loop waarbij voor elke planeet een geschaalde image wordt gemaakt en opgelagen \n",
+ " \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b2a7b7fb-56b3-43f7-a6b5-34adc057c3d1",
+ "metadata": {},
+ "source": [
+ "**Stap 2: Importeer het csv-bestand en sla de data op in een dictionary**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cbcb0e9b-46f8-490d-9a85-eb29037a93cb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "planetDataFrame = pd.read_csv(filename, header = 0, sep = ',')\n",
+ "# print(planetDataFrame)\n",
+ "# print(planetDataFrame.columns)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9d9d8c73-c8fa-41b0-848c-04741aa06bbf",
+ "metadata": {},
+ "source": [
+ "**Stap 3: Open een image uit van een item uit de dictionary**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "83d43bb9-694e-4390-a8a3-30f6535f6591",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "img = Image.open('pics/earth.jpg')\n",
+ "# img.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "645b438b-324d-427c-9efd-d621e37445f7",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ },
+ "tags": []
+ },
+ "source": [
+ "**Stap 4: Schaal de image m.b.v. een numpy array**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8894e6bd-6237-4cf6-8d95-222d6a3be56e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "array = np.array(img)\n",
+ "scaleFactor = 1\n",
+ "scaleArray = ndimage.zoom(array, (scaleFactor, scaleFactor, 1))\n",
+ "imgScaled = Image.fromarray(scaleArray)\n",
+ "# imgScaled.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8611929d-d109-4f62-881f-b8a64db1e648",
+ "metadata": {},
+ "source": [
+ "**Stap 5: Sla de geschaalde image op**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2e34fa25-3cc3-4e5f-b29a-7793e505db61",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# imgScaled.save(img.filename.replace(\".jpg\",\"_scaled.jpg\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f142ebe1-f561-447b-8e2e-e8855b20cd50",
+ "metadata": {},
+ "source": [
+ "**Stap 6: Schrijf een functie voor het schalen van een image**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5a550077-c83c-4af2-96fa-7a32158d5198",
+ "metadata": {},
+ "source": [
+ "**Stap 7: Maak een loop waarbij voor elke planeet een geschaalde image wordt gemaakt en opgelagen**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11d181d1-c906-4ad1-9d8f-69836a4e8cc7",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "03ac855f-3986-4b4a-823c-10c6d8b33024",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0be19a7e-a00f-4773-a19a-50b07618f856",
+ "metadata": {},
+ "source": [
+ "\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.9.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/wk_3_practicum_numpy/numpy/csv/.ipynb_checkpoints/planets-checkpoint.csv b/scripts/wk_3_practicum_numpy/numpy/csv/.ipynb_checkpoints/planets-checkpoint.csv
new file mode 100644
index 00000000..dd92f4ee
--- /dev/null
+++ b/scripts/wk_3_practicum_numpy/numpy/csv/.ipynb_checkpoints/planets-checkpoint.csv
@@ -0,0 +1,10 @@
+Name, Diameter, Mass, Inclination, Eccentricity, Semi_majorAxis, SurfaceGravity, OrbitalPeriod, SiderealRotation, Satellites
+Mercury, 4879.4, 3.302×10^23, 7.004, 0.20563593, 0.38709927, 3.7, 0.241, 58.65, 0
+Venus, 12103.6, 4.869×10^24, 3.39471, 0.00677672, 0.72333566, 8.87, 0.615, 243.0187, 0
+Earth, 12756.3, 5.974×10^24, 0.00005, 0.01671123, 1.00000261, 9.78, 1, 0.997271, 1
+Mars, 6794.4, 6.419×10^23, 1.85061, 0.0933941, 1.52371034, 3.71, 1.881, 1.02595, 2
+Jupiter, 142984, 1.899×10^27, 1.3053, 0.04838624, 5.202887, 24.79, 11.86, 0.4135, 63
+Saturn, 120536, 5.688×10^26, 2.48446, 0.05386179, 9.53667594, 8.96, 29.46, 0.4264, 64
+Uranus, 51118, 8.683×10^25, 0.774, 0.04725744, 19.18916464, 7.77, 84.01, 0.7181, 27
+Neptune, 49572, 1.024×10^26, 1.76917, 0.00859048, 30.06992276, 11, 164.79, 0.6712, 14
+Pluto, 2370.0, 1.3×10^22, 17.08900092, 0.250248713, 39.44506973, 0.62, 247.7406624, 6.387230, 5
diff --git a/scripts/wk_3_practicum_numpy/numpy/csv/planets.csv b/scripts/wk_3_practicum_numpy/numpy/csv/planets.csv
new file mode 100644
index 00000000..dd92f4ee
--- /dev/null
+++ b/scripts/wk_3_practicum_numpy/numpy/csv/planets.csv
@@ -0,0 +1,10 @@
+Name, Diameter, Mass, Inclination, Eccentricity, Semi_majorAxis, SurfaceGravity, OrbitalPeriod, SiderealRotation, Satellites
+Mercury, 4879.4, 3.302×10^23, 7.004, 0.20563593, 0.38709927, 3.7, 0.241, 58.65, 0
+Venus, 12103.6, 4.869×10^24, 3.39471, 0.00677672, 0.72333566, 8.87, 0.615, 243.0187, 0
+Earth, 12756.3, 5.974×10^24, 0.00005, 0.01671123, 1.00000261, 9.78, 1, 0.997271, 1
+Mars, 6794.4, 6.419×10^23, 1.85061, 0.0933941, 1.52371034, 3.71, 1.881, 1.02595, 2
+Jupiter, 142984, 1.899×10^27, 1.3053, 0.04838624, 5.202887, 24.79, 11.86, 0.4135, 63
+Saturn, 120536, 5.688×10^26, 2.48446, 0.05386179, 9.53667594, 8.96, 29.46, 0.4264, 64
+Uranus, 51118, 8.683×10^25, 0.774, 0.04725744, 19.18916464, 7.77, 84.01, 0.7181, 27
+Neptune, 49572, 1.024×10^26, 1.76917, 0.00859048, 30.06992276, 11, 164.79, 0.6712, 14
+Pluto, 2370.0, 1.3×10^22, 17.08900092, 0.250248713, 39.44506973, 0.62, 247.7406624, 6.387230, 5
diff --git a/scripts/wk_3_practicum_numpy/numpy/pics/.ipynb_checkpoints/mars.nasa-checkpoint.jpg b/scripts/wk_3_practicum_numpy/numpy/pics/.ipynb_checkpoints/mars.nasa-checkpoint.jpg
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diff --git a/scripts/wk_3_practicum_numpy/numpy/planets_without_image.csv b/scripts/wk_3_practicum_numpy/numpy/planets_without_image.csv
new file mode 100644
index 00000000..cff964c3
--- /dev/null
+++ b/scripts/wk_3_practicum_numpy/numpy/planets_without_image.csv
@@ -0,0 +1,10 @@
+Name, Diameter, Mass, Inclination, Eccentricity, Semi_majorAxis, SurfaceGravity, OrbitalPeriod, SiderealRotation, Satellites
+Mercury,4879.4, 3.302×10^23,7.004,0.20563593,0.38709927,3.7,0.241,58.65,0
+Venus,12103.6, 4.869×10^24,3.39471,0.00677672,0.72333566,8.87,0.615,243.0187,0
+Earth,12756.3, 5.974×10^24,5e-05,0.01671123,1.00000261,9.78,1.0,0.997271,1
+Mars,6794.4, 6.419×10^23,1.85061,0.0933941,1.52371034,3.71,1.881,1.02595,2
+Jupiter,142984.0, 1.899×10^27,1.3053,0.04838624,5.202887,24.79,11.86,0.4135,63
+Saturn,120536.0, 5.688×10^26,2.48446,0.05386179,9.53667594,8.96,29.46,0.4264,64
+Uranus,51118.0, 8.683×10^25,0.774,0.04725744,19.18916464,7.77,84.01,0.7181,27
+Neptune,49572.0, 1.024×10^26,1.76917,0.00859048,30.06992276,11.0,164.79,0.6712,14
+Pluto,2370.0, 1.3×10^22,17.08900092,0.250248713,39.44506973,0.62,247.7406624,6.38723,5
diff --git a/scripts/wk_3_practicum_numpy/numpy/wk3_numpy_opdracht_1.ipynb b/scripts/wk_3_practicum_numpy/numpy/wk3_numpy_opdracht_1.ipynb
new file mode 100644
index 00000000..eefd0972
--- /dev/null
+++ b/scripts/wk_3_practicum_numpy/numpy/wk3_numpy_opdracht_1.ipynb
@@ -0,0 +1,493 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "b45dd50e-d75c-41d2-a549-110cc111e32b",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
Author: Jeroen Boogaard \n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "63f0720b-4a62-479d-873b-c5f6cad9a89d",
+ "metadata": {},
+ "source": [
+ "Numpy "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3619d610-30b3-41a2-b5f0-ae37b9d5b105",
+ "metadata": {},
+ "source": [
+ "**Imports**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "42257a05-c794-4121-a9cf-fe53fa3a1dc1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from PIL import Image\n",
+ "from scipy import ndimage"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8e48110a-ff83-4fba-aab9-7cb3678fe668",
+ "metadata": {},
+ "source": [
+ "Opdracht 1 "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "47ac5b87-14b1-437e-91cb-93dadadef8f9",
+ "metadata": {},
+ "source": [
+ "Gegeven "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8605ba6e-3f4e-4fe1-b76f-c784f2f44735",
+ "metadata": {},
+ "source": [
+ "Planeet | Grootte to.v. de omvang van de Aarde\n",
+ "---|---\n",
+ "Jupiter | 1120%\n",
+ "Saturnus | 945%\n",
+ "Uranus | 400%\n",
+ "Neptunus | 388%\n",
+ "Aarde| 100%\n",
+ "Venus | 95%\n",
+ "Mars | 53%"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "2b471d98-c5b0-4db0-a1c6-155094548c23",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "filename = \"csv/planets.csv\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "e5fa129a-d9a8-4899-ba69-87537e1819f5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Volume in drive C is OS\n",
+ " Volume Serial Number is BE76-42DF\n",
+ "\n",
+ " Directory of c:\\MakeAIWork\\scripts\\wk_3_practicum_numpy\\numpy\\pics\n",
+ "\n",
+ "21/09/2022 22:48 .\n",
+ "21/09/2022 22:52 ..\n",
+ "16/09/2022 13:07 .ipynb_checkpoints\n",
+ "16/09/2022 10:28 29,680 earth.jpg\n",
+ "16/09/2022 10:28 22,232 jupiter.jpg\n",
+ "16/09/2022 10:28 27,847 mars.jpg\n",
+ "16/09/2022 10:28 40,854 mercury.jpg\n",
+ "16/09/2022 10:28 14,150 neptune.jpg\n",
+ "18/09/2022 19:05 86,704 pluto.jpg\n",
+ "16/09/2022 10:28 40,218 saturn.jpg\n",
+ "16/09/2022 10:28 9,969 uranus.jpg\n",
+ "21/09/2022 12:27 15,308 venus.jpg\n",
+ " 9 File(s) 286,962 bytes\n",
+ " 3 Dir(s) 258,314,379,264 bytes free\n"
+ ]
+ }
+ ],
+ "source": [
+ "ls pics"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ad98fa30-0e0a-4ec4-9930-b63db74c11f2",
+ "metadata": {},
+ "source": [
+ "Gevraagd \n",
+ "\n",
+ "Schaal voor elke (erkende) planeet uit ons zonnestelsel de bijbehorende image t.o.v. van de aarde. Het geschaalde plaatje moet groter zijn dan het plaatje van de aarde als de bijbehorende planeet groter is dan de aarde. Is de planeet kleiner dan de aarde dan moet het nieuwe plaatje kleiner zijn. Gebruik voor de schaalfactor het percentage. \n",
+ "Tip: Indien nodig kun je de images normaliseren door eerst het plaatje van de aarde te schalen\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c0c854fb-54db-4baa-b149-2e7d0c1b6391",
+ "metadata": {},
+ "source": [
+ "Oplossing \n",
+ "\n",
+ " \n",
+ " Open het bestand csv/planets.csv en voeg daar de kolom image \n",
+ " \n",
+ " \n",
+ " Importeer het csv-bestand en sla de data op in een dictionary \n",
+ " \n",
+ " \n",
+ " Open een image uit van een item uit de dictionary \n",
+ " \n",
+ " \n",
+ " Schaal de image m.b.v. een numpy array \n",
+ " \n",
+ " \n",
+ " Sla de geschaalde image op \n",
+ " \n",
+ " \n",
+ " Schrijf een functie voor het schalen van een image \n",
+ " \n",
+ " \n",
+ " Maak een loop waarbij voor elke planeet een geschaalde image wordt gemaakt en opgelagen \n",
+ " \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5d58da59",
+ "metadata": {},
+ "source": [
+ "**Stap 1+ : Open het bestand csv/planets.csv en voeg daar de kolom 'Image' aan toe.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "caf1b5fc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Oplossing\n",
+ "\n",
+ "# De informatie kan domweg in de csv geplakt worden via de csv-preview, \n",
+ "# maar het is ook mogelijk de paden van de afbeeldingen direct in het DataFrame te zetten. \n",
+ "\n",
+ "# Voeg de paden van de afbeeldingen en een nieuwe kolom header 'Image 'aan het Dataframe toe:\n",
+ "\n",
+ "pDF = pd.read_csv(filename, header = 0, sep = ',')\n",
+ "\n",
+ "# columns = [\n",
+ "# 'Name', ' Diameter', ' Mass', ' Inclination', \n",
+ "# ' Eccentricity',' Semi_majorAxis', ' SurfaceGravity', \n",
+ "# ' OrbitalPeriod', ' SiderealRotation', ' Satellites'\n",
+ "# ]\n",
+ "\n",
+ "imagesPlanets = [\n",
+ " \"pics/mercury.jpg\", \"pics/venus.jpg\", \"pics/earth.jpg\",\n",
+ " \"pics/mars.jpg\", \"pics/jupiter.jpg\", \"pics/saturn.jpg\", \n",
+ " \"pics/uranus.jpg\", \"pics/neptune.jpg\", \"pics/pluto.jpg\",\n",
+ " ] \n",
+ "\n",
+ "pDF[\"Image\"] = imagesPlanets\n",
+ "\n",
+ "# print(pDF)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b2a7b7fb-56b3-43f7-a6b5-34adc057c3d1",
+ "metadata": {},
+ "source": [
+ "**Stap 2: Importeer het csv-bestand en sla de data op in een dictionary**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "cbcb0e9b-46f8-490d-9a85-eb29037a93cb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pDF = pd.read_csv(filename, header = 0, sep = ',')\n",
+ "# print(pDF)\n",
+ "# type(pDF)\n",
+ "# # print(pDF.columns)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9d9d8c73-c8fa-41b0-848c-04741aa06bbf",
+ "metadata": {},
+ "source": [
+ "**Stap 3: Open een image uit van een item uit de dictionary**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "83d43bb9-694e-4390-a8a3-30f6535f6591",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "img = Image.open('pics/jupiter.jpg')\n",
+ "# type(img)\n",
+ "# img.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "645b438b-324d-427c-9efd-d621e37445f7",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "**Stap 4: Schaal de image m.b.v. een numpy array**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "8894e6bd-6237-4cf6-8d95-222d6a3be56e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "array = np.array(img)\n",
+ "type(array)\n",
+ "scaleFactor = 3\n",
+ "scaleArray = ndimage.zoom(array, (scaleFactor, scaleFactor, 1))\n",
+ "imgScaled = Image.fromarray(scaleArray)\n",
+ "# imgScaled.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8611929d-d109-4f62-881f-b8a64db1e648",
+ "metadata": {},
+ "source": [
+ "**Stap 5: Sla de geschaalde image op**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "2e34fa25-3cc3-4e5f-b29a-7793e505db61",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "imgScaled.save(img.filename.replace(\".jpg\",\"_scaled.jpg\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f142ebe1-f561-447b-8e2e-e8855b20cd50",
+ "metadata": {},
+ "source": [
+ "**Stap 6: Schrijf een functie voor het schalen van een image**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "18a7f670",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pDF = pd.read_csv(filename, header = 0, sep = ',')\n",
+ "\n",
+ "def scaleImages():\n",
+ "\n",
+ " scaleImage = Image.open('pics/jupiter.jpg')\n",
+ " array = np.array(scaleImage)\n",
+ " \n",
+ " scaleFactor = (49572.0 / 12756.3) # = (diameterPlanet / diameterEarth)\n",
+ " scaleArray = ndimage.zoom(array, (scaleFactor, scaleFactor, 1))\n",
+ " imageNewScaled = Image.fromarray(scaleArray)\n",
+ " # imageNewScaled.show()\n",
+ " imageNewScaled.save(scaleImage.filename.replace(\".jpg\",\"_scaled.jpg\"))\n",
+ " \n",
+ " \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5a550077-c83c-4af2-96fa-7a32158d5198",
+ "metadata": {},
+ "source": [
+ "**Stap 7: Maak een loop waarbij voor elke planeet een geschaalde image wordt gemaakt en opgelagen**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "1f784d0f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Name Diameter Mass Inclination Eccentricity \\\n",
+ "0 Mercury 4879.4 3.302×10^23 7.004000 0.205636 \n",
+ "1 Venus 12103.6 4.869×10^24 3.394710 0.006777 \n",
+ "2 Earth 12756.3 5.974×10^24 0.000050 0.016711 \n",
+ "3 Mars 6794.4 6.419×10^23 1.850610 0.093394 \n",
+ "4 Jupiter 142984.0 1.899×10^27 1.305300 0.048386 \n",
+ "5 Saturn 120536.0 5.688×10^26 2.484460 0.053862 \n",
+ "6 Uranus 51118.0 8.683×10^25 0.774000 0.047257 \n",
+ "7 Neptune 49572.0 1.024×10^26 1.769170 0.008590 \n",
+ "8 Pluto 2370.0 1.3×10^22 17.089001 0.250249 \n",
+ "\n",
+ " Semi_majorAxis SurfaceGravity OrbitalPeriod SiderealRotation \\\n",
+ "0 0.387099 3.70 0.241000 58.650000 \n",
+ "1 0.723336 8.87 0.615000 243.018700 \n",
+ "2 1.000003 9.78 1.000000 0.997271 \n",
+ "3 1.523710 3.71 1.881000 1.025950 \n",
+ "4 5.202887 24.79 11.860000 0.413500 \n",
+ "5 9.536676 8.96 29.460000 0.426400 \n",
+ "6 19.189165 7.77 84.010000 0.718100 \n",
+ "7 30.069923 11.00 164.790000 0.671200 \n",
+ "8 39.445070 0.62 247.740662 6.387230 \n",
+ "\n",
+ " Satellites ScaleToEarth \n",
+ "0 0 0.38 \n",
+ "1 0 0.95 \n",
+ "2 1 1.00 \n",
+ "3 2 0.53 \n",
+ "4 63 11.20 \n",
+ "5 64 9.45 \n",
+ "6 27 4.00 \n",
+ "7 14 3.88 \n",
+ "8 5 0.19 \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Oplossing \n",
+ "\n",
+ "def batchScaleImages():\n",
+ " \n",
+ " earthDiameter = 1\n",
+ "\n",
+ " openedImages = []\n",
+ " scaledToEarth = [] \n",
+ " scaledImages = []\n",
+ " \n",
+ " # 1. Voeg een kolom aan het Dataframe toe met schaalfactor voor de planeten om te kunnen schalen:\n",
+ "\n",
+ " # trueSize = [] # ScaleFactor = (diameterPlanet / diameterEarth)\n",
+ "\n",
+ " # for i in range(len(pDF)):\n",
+ " # trueSizePlanets = pDF[' Diameter'][i]/pDF[' Diameter'][2]\n",
+ " # trueSize.append(trueSizePlanets)\n",
+ "\n",
+ " # pDF[\"ScaleFactor\"] = trueSize\n",
+ "\n",
+ " # 2. Of, korter, voeg berekende afgeleiden van de percentages als lijst toe:\n",
+ "\n",
+ " scaleToEarth = [0.38, 0.95, 1, 0.53, 11.20, 9.45, 4, 3.88, 0.19] \n",
+ "\n",
+ " pDF[\"ScaleToEarth\"] = scaleToEarth \n",
+ " \n",
+ " \n",
+ " for imagePlanet in imagesPlanets:\n",
+ " img = Image.open(imagePlanet)\n",
+ " openedImages.append(img) \n",
+ " \n",
+ " for planetDiameter in scaleToEarth:\n",
+ " scaleFactor = planetDiameter / earthDiameter\n",
+ " scaledToEarth.append(scaleFactor) \n",
+ " \n",
+ " for planetScaled, openedImage in zip(scaledToEarth, openedImages):\n",
+ " array = np.array(openedImage)\n",
+ " scaleFactor = planetScaled\n",
+ " scaleArray = ndimage.zoom(array, (scaleFactor, scaleFactor, 1))\n",
+ " imageNewScaled = Image.fromarray(scaleArray)\n",
+ " scaledImages.append(imageNewScaled)\n",
+ " \n",
+ " imageNewScaled.show() \n",
+ " \n",
+ " for scaledImage, openedImage in zip (scaledImages, openedImages):\n",
+ " scaledImage.save(openedImage.filename.replace(\".jpg\",\"_scaled.jpg\"))\n",
+ " \n",
+ " \n",
+ "batchScaleImages()\n",
+ "\n",
+ "print(pDF)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a668a8e8-4d7e-4c6d-adbc-61935887fabe",
+ "metadata": {},
+ "source": [
+ "**Bonus: Voeg Mercurius aan de tabel toe en schaal ook daarvan het plaatje**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "78212296",
+ "metadata": {},
+ "source": [
+ "**✓**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11d181d1-c906-4ad1-9d8f-69836a4e8cc7",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "03ac855f-3986-4b4a-823c-10c6d8b33024",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.7 64-bit",
+ "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": "3.10.7"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "5b93afe9c2e217d44e354b055ed180e932b72842f9b6422dadc1ad80da9caf67"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/sh/venv.sh b/sh/venv.sh
old mode 100755
new mode 100644
diff --git a/shape/__pycache__/rectangle.cpython-310.pyc b/shape/__pycache__/rectangle.cpython-310.pyc
new file mode 100644
index 00000000..7b575205
Binary files /dev/null and b/shape/__pycache__/rectangle.cpython-310.pyc differ
diff --git a/shape/rectangle.py b/shape/rectangle.py
new file mode 100644
index 00000000..a641b0d7
--- /dev/null
+++ b/shape/rectangle.py
@@ -0,0 +1,9 @@
+class Rectangle:
+
+ def __init__(self, width, height):
+ self.width = width
+ self.height = height
+
+ def area(self):
+ return self.area = (width * height)
+
\ No newline at end of file
diff --git a/shape/shape_shape/employee.py b/shape/shape_shape/employee.py
new file mode 100644
index 00000000..b68e56e0
--- /dev/null
+++ b/shape/shape_shape/employee.py
@@ -0,0 +1,47 @@
+# Encapsulation / Information hiding
+class Salary:
+ def __init__(self, pay):
+ self.pay = pay
+
+ def get_total(self):
+ return (self.pay * 12)
+
+class Employee:
+ # Constructor method with Arguments
+ # def __init__(self, firstName, lastName, pay, bonus):
+ def __init__(self, **kwargs):
+ self.firstname = kwargs['firstName']
+ self.lastname = kwargs['lastName']
+ self.pay = kwargs['pay']
+ self.bonus = kwargs['bonus']
+ # Composition
+ self.obj_salary = Salary(self.pay)
+
+ def getFullname(self):
+ return '{} {}'.format(self.firstname, self.lastname)
+
+ @property
+ def fullname(self):
+ return '{} {}'.format(self.firstname, self.lastname)
+
+ # Docstring
+ """
+ This function calculates and
+ returns the annual salary
+ """
+ @property
+ def annualSalary(self):
+ return self.obj_salary.get_total() + self.bonus
+
+ def main():
+ # Object
+ employee1 = Employee(firstName="Marc", lastName="Rotsaert", pay=6000, bonus=500)
+ employee2 = Employee(firstName="Anton", lastName="Diepenhorst", pay=4500, bonus=1000)
+ print(f"Annual salary of {employee1.getFullname()} : {employee1.annualSalary}")
+ print(f"Annual salary of {employee2.getFullname()} : {employee2.annualSalary}")
+ print(f"__name__: {__name__}")
+
+ if __name__ == "main":
+ main()
+
+Employee.main()
diff --git a/shape/shape_shape/shape.py b/shape/shape_shape/shape.py
new file mode 100644
index 00000000..d089868d
--- /dev/null
+++ b/shape/shape_shape/shape.py
@@ -0,0 +1,42 @@
+import math
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+
+# Abstraction: Abstract Superclass
+class Shape(ABC):
+ # Constructor method with Arguments/Parameters
+ def __init__(self, width, height):
+ # Self
+ # Attributes
+ self.width = width
+ self.height = height
+
+ # Interface
+ @abstractmethod
+ # Self
+ def area(self, width, height):
+ pass
+
+ # Static variable
+ pi = math.pi
+
+# Inheritance : Subclass
+class Rectangle(Shape):
+ def __init__(self, width, height):
+ super().__init__(width, height)
+ self.area(width, height)
+
+ # Operator overloading
+ def area(self, width, height):
+ self.area = width * height
+
+# Inheritance : Subclass
+class Triangle(Shape):
+ # Dunder method with Arguments
+ def __init__(self, width, height):
+ super().__init__(width, height)
+ self.area(width, height)
+
+ # Operator overloading
+ def area(self, width, height):
+ self.area = 0.5 * self.width * self.width * Shape.pi
diff --git a/shape/shape_shape/testShape.py b/shape/shape_shape/testShape.py
new file mode 100644
index 00000000..2a156404
--- /dev/null
+++ b/shape/shape_shape/testShape.py
@@ -0,0 +1,9 @@
+from shape import Rectangle
+from shape import Triangle
+
+rectangle = Rectangle(4, 5)
+print(f"The area of the rectangle is : {rectangle.area}")
+
+triangle = Triangle(4, 5)
+print(f"The area of the triangle is : {triangle.area}")
+
diff --git a/shape/test_rectangle.py b/shape/test_rectangle.py
new file mode 100644
index 00000000..e5953cfc
--- /dev/null
+++ b/shape/test_rectangle.py
@@ -0,0 +1,25 @@
+'''
+Rectangle heeft:
+ - init(self, width, height)
+ - area(self)
+
+Stap 1: teken het UML ClassDiagram
+
+Stap 2: Defineeer class Rectangle in rectangle.py
+
+Stap 3: Maak een Rectangle object aan in testRectangle.py
+
+Stap 4: Pas class Rectangle aan totdat testRectangle werkt
+
+Stap 5: Maak methode area() aan
+
+Stap 6: Roep vanuit testRectangle.py methode area() van je object aan
+
+'''
+
+#!/user/bin/env python
+
+from rectangle import Rectangle
+
+rectangle = Rectangle(4, 5)
+print(f'The area of the rectangle is: {rectangle.area()}')
\ No newline at end of file
diff --git a/shape/uml/class_rectangle.md b/shape/uml/class_rectangle.md
new file mode 100644
index 00000000..d62c4259
--- /dev/null
+++ b/shape/uml/class_rectangle.md
@@ -0,0 +1,8 @@
+'''mermaid
+classDiagram
+ class Rectangle {
+ - width
+ - height
+ + init(width, height)
+ + area()
+ }
\ No newline at end of file
diff --git a/simpylc/simpylc_howto.pdf b/simpylc/simpylc_howto.pdf
deleted file mode 100644
index 9d360b8a..00000000
Binary files a/simpylc/simpylc_howto.pdf and /dev/null differ
diff --git a/simpylc/test_simpylc.sh b/simpylc/test_simpylc.sh
deleted file mode 100755
index 2e12d6ff..00000000
--- a/simpylc/test_simpylc.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/usr/bin/env bash
-
-export LIBGL_ALLOW_SOFTWARE=1
-python -m simpylc -s
-python simulations/car/world.py
\ No newline at end of file
diff --git a/simulations/arduino_led_timer/__pycache__/led_timer.cpython-310.pyc b/simulations/arduino_led_timer/__pycache__/led_timer.cpython-310.pyc
new file mode 100644
index 00000000..c34c6f2b
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diff --git a/simulations/arduino_led_timer/__pycache__/timing.cpython-310.pyc b/simulations/arduino_led_timer/__pycache__/timing.cpython-310.pyc
new file mode 100644
index 00000000..5a090a37
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diff --git a/simulations/arduino_led_timer/__pycache__/world.cpython-310.pyc b/simulations/arduino_led_timer/__pycache__/world.cpython-310.pyc
new file mode 100644
index 00000000..0107a618
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diff --git a/simulations/arduino_led_timer/led_timer.py b/simulations/arduino_led_timer/led_timer.py
new file mode 100644
index 00000000..596f7ce2
--- /dev/null
+++ b/simulations/arduino_led_timer/led_timer.py
@@ -0,0 +1,48 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+class LedTimer (sp.Module):
+ def __init__ (self):
+ sp.Module.__init__ (self)
+
+ self.rampTimer = sp.Timer ()
+ self.pulse = sp.Oneshot ()
+ self.direction = sp.Marker ()
+ self.blinkTime = sp.Register ()
+ self.blinkTimer = sp.Timer ()
+ self.led = sp.Marker ()
+ self.runner = sp.Runner ()
+
+ def sweep (self):
+ self.rampTimer.reset (self.rampTimer > 9)
+ self.pulse.trigger (self.rampTimer < 0.1)
+ self.direction.mark (not self.direction, self.pulse)
+ self.blinkTime.set (3 - self.rampTimer / 3, self.direction, self.rampTimer / 3)
+ self.blinkTimer.reset (self.blinkTimer > self.blinkTime)
+ self.led.mark (self.blinkTimer < 0.05)
diff --git a/simulations/arduino_led_timer/native.cpp b/simulations/arduino_led_timer/native.cpp
new file mode 100644
index 00000000..aba0c6ce
--- /dev/null
+++ b/simulations/arduino_led_timer/native.cpp
@@ -0,0 +1,34 @@
+/*
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicence.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicence for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your licence.
+*/
+
+void setup () {
+ pinMode (13, OUTPUT);
+}
+
+void loop () {
+ cycle ();
+ digitalWrite (13, led);
+}
+
diff --git a/simulations/arduino_led_timer/timing.py b/simulations/arduino_led_timer/timing.py
new file mode 100644
index 00000000..762fa271
--- /dev/null
+++ b/simulations/arduino_led_timer/timing.py
@@ -0,0 +1,41 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+class Timing (sp.Chart):
+ def __init__ (self):
+ sp.Chart.__init__ (self)
+
+ def define (self):
+ self.channel (sp.world.ledTimer.rampTimer, sp.red, 0, 9, 180)
+ self.channel (sp.world.ledTimer.direction, sp.white, 0, 1, 20)
+ self.channel (sp.world.ledTimer.pulse, sp.aqua, 0, 1, 20)
+ self.channel (sp.world.ledTimer.blinkTime, sp.blue, 0, 3, 60)
+ self.channel (sp.world.ledTimer.blinkTimer, sp.yellow, 0, 3, 60)
+ self.channel (sp.world.ledTimer.led, sp.green, 0, 1, 20)
+
diff --git a/simulations/arduino_led_timer/world.py b/simulations/arduino_led_timer/world.py
new file mode 100644
index 00000000..1cff35d6
--- /dev/null
+++ b/simulations/arduino_led_timer/world.py
@@ -0,0 +1,37 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import os
+import sys as ss
+
+ss.path.append (os.path.abspath ('../..')) # If you want to store your simulations somewhere else, put SimPyLC in your PYTHONPATH environment variable
+
+import simpylc as sp
+import led_timer as lt
+import timing as tm
+
+sp.World (lt.LedTimer, tm.Timing)
diff --git a/simulations/arduino_traffic_lights/__pycache__/timing.cpython-310.pyc b/simulations/arduino_traffic_lights/__pycache__/timing.cpython-310.pyc
new file mode 100644
index 00000000..11b383e9
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diff --git a/simulations/arduino_traffic_lights/__pycache__/traffic_lights.cpython-310.pyc b/simulations/arduino_traffic_lights/__pycache__/traffic_lights.cpython-310.pyc
new file mode 100644
index 00000000..fd92a0c6
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diff --git a/simulations/arduino_traffic_lights/__pycache__/visualisation.cpython-310.pyc b/simulations/arduino_traffic_lights/__pycache__/visualisation.cpython-310.pyc
new file mode 100644
index 00000000..abcc5cc0
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diff --git a/simulations/arduino_traffic_lights/__pycache__/world.cpython-310.pyc b/simulations/arduino_traffic_lights/__pycache__/world.cpython-310.pyc
new file mode 100644
index 00000000..498416d4
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diff --git a/simulations/arduino_traffic_lights/native.cpp b/simulations/arduino_traffic_lights/native.cpp
new file mode 100644
index 00000000..75eddfa2
--- /dev/null
+++ b/simulations/arduino_traffic_lights/native.cpp
@@ -0,0 +1,44 @@
+/*
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicence.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicence for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+__________________________________________________________________________
+
+
+THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your licence.
+*/
+
+void setup () {
+ analogWriteResolution (12);
+
+ pinMode (33, OUTPUT); pinMode (35, OUTPUT); pinMode (37, OUTPUT); pinMode (39, OUTPUT);
+ pinMode (41, OUTPUT); pinMode (43, OUTPUT); pinMode (45, OUTPUT); pinMode (47, OUTPUT);
+ pinMode (49, INPUT); pinMode (51, INPUT);
+}
+
+void loop () {
+ modeButton = !digitalRead (49); brightButton = !digitalRead (51);
+
+ cycle ();
+
+ digitalWrite (39, northGreenLamp); digitalWrite (41, northRedLamp);
+ digitalWrite (35, eastGreenLamp); digitalWrite (37, eastRedLamp);
+ digitalWrite (47, southGreenLamp); digitalWrite (33, southRedLamp);
+ digitalWrite (43, westGreenLamp); digitalWrite (45, westRedLamp);
+
+ analogWrite (DAC0, streetLamp);
+}
diff --git a/simulations/arduino_traffic_lights/timing.py b/simulations/arduino_traffic_lights/timing.py
new file mode 100644
index 00000000..4ac83de3
--- /dev/null
+++ b/simulations/arduino_traffic_lights/timing.py
@@ -0,0 +1,53 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+class Timing (sp.Chart):
+ def __init__ (self):
+ sp.Chart.__init__ (self)
+
+ def define (self):
+ self.channel (sp.world.trafficLights.northGreenLamp, sp.green)
+ self.channel (sp.world.trafficLights.northRedLamp, sp.red)
+ self.channel (sp.world.trafficLights.eastGreenLamp, sp.green)
+ self.channel (sp.world.trafficLights.eastRedLamp, sp.red)
+ self.channel (sp.world.trafficLights.southGreenLamp, sp.green)
+ self.channel (sp.world.trafficLights.southRedLamp, sp.red)
+ self.channel (sp.world.trafficLights.westGreenLamp, sp.green)
+ self.channel (sp.world.trafficLights.westRedLamp, sp.red)
+ self.channel (sp.world.trafficLights.regularPhaseTimer, sp.aqua, 0, 20, 60)
+ self.channel (sp.world.trafficLights.cyclePhaseTimer, sp.aqua, 0, 40, 60)
+ self.channel (sp.world.trafficLights.blinkTimer, sp.aqua, 0, 0.3, 60)
+ self.channel (sp.world.trafficLights.modeButton, sp.white)
+ self.channel (sp.world.trafficLights.modePulse, sp.white)
+ self.channel (sp.world.trafficLights.modeStep, sp.white, 0, 3, 60)
+ self.channel (sp.world.trafficLights.brightButton, sp.yellow)
+ self.channel (sp.world.trafficLights.brightPulse, sp.yellow)
+ self.channel (sp.world.trafficLights.brightDirection, sp.yellow)
+ self.channel (sp.world.trafficLights.streetLamp, sp.yellow, 0, 5000, 60)
+
diff --git a/simulations/arduino_traffic_lights/traffic_lights.py b/simulations/arduino_traffic_lights/traffic_lights.py
new file mode 100644
index 00000000..74c306ea
--- /dev/null
+++ b/simulations/arduino_traffic_lights/traffic_lights.py
@@ -0,0 +1,161 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+class TrafficLights (sp.Module):
+ def __init__ (self):
+ sp.Module.__init__ (self)
+
+ self.page ('Trafic lights')
+
+ self.group ('Timers', True)
+ self.regularPhaseTimer = sp.Timer ()
+ self.cyclePhaseTimer = sp.Timer ()
+ self.tBlink = sp.Register (0.3)
+ self.blinkTimer = sp.Timer ()
+ self.blinkPulse = sp.Oneshot ()
+ self.blink = sp.Marker ()
+
+ self.group ('Mode switching')
+ self.modeButton = sp.Marker ()
+ self.modePulse = sp.Oneshot ()
+ self.modeStep = sp.Register ()
+ self.regularMode = sp.Marker (True)
+ self.cycleMode = sp.Marker ()
+ self.nightMode = sp.Marker ()
+ self.offMode = sp.Marker ()
+
+ self.group ('Night blinking')
+ self.allowRed = sp.Marker ()
+
+ self.group ('Regular mode phases', True)
+ self.northSouthGreen = sp.Marker (True)
+ self.northSouthBlink = sp.Marker ()
+ self.eastWestGreen = sp.Marker ()
+ self.eastWestBlink = sp.Marker ()
+
+ self.group ('Cycle mode phases')
+ self.northGreen = sp.Marker ()
+ self.northBlink = sp.Marker ()
+ self.eastGreen = sp.Marker ()
+ self.eastBlink = sp.Marker ()
+ self.southGreen = sp.Marker ()
+ self.southBlink = sp.Marker ()
+ self.westGreen = sp.Marker ()
+ self.westBlink = sp.Marker ()
+
+ self.group ('Lamps')
+ self.northGreenLamp = sp.Marker ()
+ self.northRedLamp = sp.Marker ()
+ self.eastGreenLamp = sp.Marker ()
+ self.eastRedLamp = sp.Marker ()
+ self.southGreenLamp = sp.Marker ()
+ self.southRedLamp = sp.Marker ()
+ self.westGreenLamp = sp.Marker ()
+ self.westRedLamp = sp.Marker ()
+
+ self.group ('Regular phase end times', True)
+ self.tNorthSouthGreen = sp.Register (5)
+ self.tNorthSouthBlink = sp.Register (7)
+ self.tEastWestGreen = sp.Register (12)
+ self.tEastWestBlink = sp.Register (14)
+
+ self.group ('Cycle phase end times')
+ self.tNorthGreen = sp.Register (5)
+ self.tNorthBlink = sp.Register (7)
+ self.tEastGreen = sp.Register (12)
+ self.tEastBlink = sp.Register (14)
+ self.tSouthGreen = sp.Register (19)
+ self.tSouthBlink = sp.Register (21)
+ self.tWestGreen = sp.Register (26)
+ self.tWestBlink = sp.Register (28)
+
+ self.group ('Street illumination')
+ self.brightButton = sp.Marker ()
+ self.brightPulse = sp.Oneshot ()
+ self.brightDirection = sp.Marker (True)
+ self.brightMin = sp.Register (2047)
+ self.brightMax = sp.Register (4095)
+ self.brightFluxus = sp.Register (200)
+ self.brightDelta = sp.Register ()
+ self.streetLamp = sp.Register (2047)
+
+ self.group ('System')
+ self.runner = sp.Runner ()
+
+ def sweep (self):
+ self.part ('Timers')
+ self.regularPhaseTimer.reset (self.regularPhaseTimer > self.tEastWestBlink or self.cycleMode or self.nightMode or self.offMode)
+ self.cyclePhaseTimer.reset (self.cyclePhaseTimer > self.tWestBlink or self.regularMode or self.nightMode or self.offMode)
+ self.blinkTimer.reset (self.blinkTimer > self.tBlink)
+ self.blinkPulse.trigger (self.blinkTimer == 0)
+ self.blink.mark (not self.blink, self.blinkPulse)
+
+ self.part ('Mode switching')
+ self.modePulse.trigger (self.modeButton)
+ self.modeStep.set ((self.modeStep + 1) % 4, self.modePulse)
+ self.regularMode.mark (self.modeStep == 0)
+ self.cycleMode.mark (self.modeStep == 1)
+ self.nightMode.mark (self.modeStep == 2)
+ self.offMode.mark (self.modeStep == 3)
+
+ self.part ('Regular mode phases')
+ self.northSouthGreen.mark (0 < self.regularPhaseTimer < self.tNorthSouthGreen)
+ self.northSouthBlink.mark (self.tNorthSouthGreen < self.regularPhaseTimer < self.tNorthSouthBlink)
+ self.eastWestGreen.mark (self.tNorthSouthBlink < self.regularPhaseTimer < self.tEastWestGreen)
+ self.eastWestBlink.mark (self.tEastWestGreen < self.regularPhaseTimer)
+
+ self.part ('Cycle mode phases')
+ self.northGreen.mark (0 < self.cyclePhaseTimer < self.tNorthGreen)
+ self.northBlink.mark (self.tNorthGreen < self.cyclePhaseTimer < self.tNorthBlink)
+ self.eastGreen.mark (self.tNorthBlink < self.cyclePhaseTimer < self.tEastGreen)
+ self.eastBlink.mark (self.tEastGreen < self.cyclePhaseTimer < self.tEastBlink)
+ self.southGreen.mark (self.tEastBlink < self.cyclePhaseTimer < self.tSouthGreen)
+ self.southBlink.mark (self.tSouthGreen < self.cyclePhaseTimer < self.tSouthBlink)
+ self.westGreen.mark (self.tSouthBlink < self.cyclePhaseTimer < self.tWestGreen)
+ self.westBlink.mark (self.tWestGreen < self.cyclePhaseTimer)
+
+ self.part ('Night blinking')
+ self.allowRed.mark (self.regularMode or self.cycleMode or (self.nightMode and self.blink))
+
+ self.part ('Traffic lamps')
+ self.northGreenLamp.mark (self.northSouthGreen or self.northGreen or ((self.northSouthBlink or self.northBlink) and self.blink))
+ self.northRedLamp.mark (not (self.northSouthGreen or self.northGreen or self.northSouthBlink or self.northBlink) and self.allowRed)
+ self.eastGreenLamp.mark (self.eastWestGreen or self.eastGreen or ((self.eastWestBlink or self.eastBlink) and self.blink))
+ self.eastRedLamp.mark (not (self.eastWestGreen or self.eastGreen or self.eastWestBlink or self.eastBlink) and self.allowRed)
+ self.southGreenLamp.mark (self.northSouthGreen or self.southGreen or ((self.northSouthBlink or self.southBlink) and self.blink))
+ self.southRedLamp.mark (not (self.northSouthGreen or self.southGreen or self.northSouthBlink or self.southBlink) and self.allowRed)
+ self.westGreenLamp.mark (self.eastWestGreen or self.westGreen or ((self.eastWestBlink or self.westBlink) and self.blink))
+ self.westRedLamp.mark (not (self.eastWestGreen or self.westGreen or self.eastWestBlink or self.westBlink) and self.allowRed)
+
+ self.part ('Street illumination')
+ self.brightPulse.trigger (self.brightButton)
+ self.brightDirection.mark (not self.brightDirection, self.brightPulse)
+ self.brightDelta.set (-self.brightFluxus * sp.world.period, self.brightDirection, self.brightFluxus * sp.world.period)
+ self.streetLamp.set (sp.limit (self.streetLamp + self.brightDelta, self.brightMin, self.brightMax), self.brightButton)
+
diff --git a/simulations/arduino_traffic_lights/visualisation.py b/simulations/arduino_traffic_lights/visualisation.py
new file mode 100644
index 00000000..c832f61f
--- /dev/null
+++ b/simulations/arduino_traffic_lights/visualisation.py
@@ -0,0 +1,91 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+class TrafficLamp (sp.Cylinder):
+ def __init__ (self, green = False):
+ sp.Cylinder.__init__ (self, size = (0.1, 0.1, 0.2), center = (0, 0, 0.5) if green else (0, 0, 0.7), color = (0, 1, 0) if green else (1, 0, 0))
+ self.originalColor = self.color
+
+ def __call__ (self, on):
+ self.color = self.originalColor if on else sp.tsMul (self.originalColor, 0.2)
+ return sp.Cylinder.__call__ (self)
+
+class StreetLamp (sp.Cylinder):
+ def __init__ (self, green = False):
+ sp.Cylinder.__init__ (self, size = (0.4, 0.4, 0.4), center = (0, 0, 2), color = (1, 1, 0.2))
+ self.originalColor = self.color
+
+ def __call__ (self, brightness):
+ self.color = sp.tsMul (self.originalColor, 0.2 + 0.8 * brightness)
+ return sp.Cylinder.__call__ (self)
+
+class Visualisation (sp.Scene):
+ def __init__ (self):
+ sp.Scene.__init__ (self)
+
+ self.crossing = sp.Beam (size = (3, 3, 0.1), pivot = (0, 1, 0), color = (0.1, 0.1, 0.1))
+ self.sidewalk = sp.Beam (size = (1, 1, 0.1), center = (-1, -1, 0.1), joint = (1, 1, 0), color = (0, 0.3, 0))
+ self.pole = sp.Cylinder (size = (0.05, 0.05, 1), center = (0, 0.45, 0.45), color = (1, 1, 1))
+
+ self.redLamp = TrafficLamp ()
+ self.greenLamp = TrafficLamp (True)
+ self.streetLamp = StreetLamp ()
+
+ def display (self):
+ control = sp.world.trafficLights
+
+ self.crossing (rotation = 30, parts = lambda:
+ self.sidewalk (rotation = 0, parts = lambda:
+ self.pole (parts = lambda:
+ self.redLamp (control.northRedLamp) +
+ self.greenLamp (control.northGreenLamp)
+ )
+ ) +
+ self.sidewalk (rotation = -90, parts = lambda:
+ self.pole (parts = lambda:
+ self.redLamp (control.eastRedLamp) +
+ self.greenLamp (control.eastGreenLamp)
+ )
+
+ ) +
+ self.sidewalk (rotation = -180, parts = lambda:
+ self.pole (parts = lambda:
+ self.redLamp (control.southRedLamp) +
+ self.greenLamp (control.southGreenLamp)
+ )
+ ) +
+ self.sidewalk (rotation = -270, parts = lambda:
+ self.pole (parts = lambda:
+ self.redLamp (control.westRedLamp) +
+ self.greenLamp (control.westGreenLamp)
+ )
+ ) +
+ self.streetLamp ((control.streetLamp - control.brightMin) / (control.brightMax - control.brightMin))
+ )
+
diff --git a/simulations/arduino_traffic_lights/world.py b/simulations/arduino_traffic_lights/world.py
new file mode 100644
index 00000000..b0424ec2
--- /dev/null
+++ b/simulations/arduino_traffic_lights/world.py
@@ -0,0 +1,38 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import os
+import sys as ss
+
+ss.path.append (os.path.abspath ('../..')) # If you want to store your simulations somewhere else, put SimPyLC in your PYTHONPATH environment variable
+
+import simpylc as sp
+import traffic_lights as tl
+import timing as tm
+import visualisation as vs
+
+sp.World (tl.TrafficLights, tm.Timing, vs.Visualisation)
diff --git a/simulations/blinking_light/__pycache__/blinking_light.cpython-310.pyc b/simulations/blinking_light/__pycache__/blinking_light.cpython-310.pyc
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index 00000000..efc145e1
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diff --git a/simulations/blinking_light/__pycache__/timing.cpython-310.pyc b/simulations/blinking_light/__pycache__/timing.cpython-310.pyc
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diff --git a/simulations/blinking_light/__pycache__/world.cpython-310.pyc b/simulations/blinking_light/__pycache__/world.cpython-310.pyc
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diff --git a/simulations/blinking_light/blinking_light.py b/simulations/blinking_light/blinking_light.py
new file mode 100644
index 00000000..68dcd390
--- /dev/null
+++ b/simulations/blinking_light/blinking_light.py
@@ -0,0 +1,45 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+class BlinkingLight (sp.Module):
+ def __init__ (self):
+ sp.Module.__init__ (self)
+
+ self.blinkTimer = sp.Timer ()
+ self.pulse = sp.Oneshot ()
+ self.counter = sp.Register ()
+ self.led = sp.Marker ()
+ self.run = sp.Runner ()
+
+ def sweep (self):
+ self.blinkTimer.reset (self.blinkTimer > 8)
+ self.pulse.trigger (self.blinkTimer > 3)
+ self.counter.set (self.counter + 1, self.pulse)
+ self.led.mark (not self.led, self.pulse)
+
diff --git a/simulations/blinking_light/timing.py b/simulations/blinking_light/timing.py
new file mode 100644
index 00000000..12f46198
--- /dev/null
+++ b/simulations/blinking_light/timing.py
@@ -0,0 +1,39 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+class Timing (sp.Chart):
+ def __init__ (self):
+ sp.Chart.__init__ (self)
+
+ def define (self):
+ self.channel (sp.world.blinkingLight.blinkTimer, sp.red, 0, 12, 50)
+ self.channel (sp.world.blinkingLight.pulse, sp.blue, 0, 1, 50)
+ self.channel (sp.world.blinkingLight.counter, sp.yellow, 0, 20, 100)
+ self.channel (sp.world.blinkingLight.led, sp.lime, 0, 1, 50)
+
diff --git a/simulations/blinking_light/world.py b/simulations/blinking_light/world.py
new file mode 100644
index 00000000..5f3aef7a
--- /dev/null
+++ b/simulations/blinking_light/world.py
@@ -0,0 +1,37 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import os
+import sys as ss
+
+ss.path.append (os.path.abspath ('../..')) # If you want to store your simulations somewhere else, put SimPyLC in your PYTHONPATH environment variable
+
+import simpylc as sp
+import blinking_light as bl
+import timing as tm
+
+sp.World (bl.BlinkingLight, tm.Timing)
diff --git a/simulations/car/__pycache__/control_server.cpython-310.pyc b/simulations/car/__pycache__/control_server.cpython-310.pyc
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index 00000000..23e27bc9
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diff --git a/simulations/car/__pycache__/physics.cpython-310.pyc b/simulations/car/__pycache__/physics.cpython-310.pyc
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diff --git a/simulations/car/__pycache__/socket_wrapper.cpython-310.pyc b/simulations/car/__pycache__/socket_wrapper.cpython-310.pyc
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diff --git a/simulations/car/__pycache__/visualisation.cpython-310.pyc b/simulations/car/__pycache__/visualisation.cpython-310.pyc
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index 00000000..f0a5309c
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diff --git a/simulations/car/__pycache__/world.cpython-310.pyc b/simulations/car/__pycache__/world.cpython-310.pyc
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diff --git a/simulations/car/control_client/Sonardata_trainingmodel_kevin.ipynb b/simulations/car/control_client/Sonardata_trainingmodel_kevin.ipynb
new file mode 100644
index 00000000..6945dc32
--- /dev/null
+++ b/simulations/car/control_client/Sonardata_trainingmodel_kevin.ipynb
@@ -0,0 +1,305 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "fb0edb38-4579-4461-92ff-df0685d94638",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!/usr/bin/env python\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "3a801bb8-d56c-4afd-9202-8789a22b446b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "# print(pd.__version__)\n",
+ "import numpy as np\n",
+ "\n",
+ "# ke numpy values easier to read.\n",
+ "np.set_printoptions(precision=3, suppress=True)\n",
+ "\n",
+ "import tensorflow as tf\n",
+ "keras = tf.keras\n",
+ "\n",
+ "from tensorflow.keras.models import Model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "0eb55e30-3900-4d93-9794-0aa61aead802",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sonar_data = pd.read_csv(\n",
+ " \"C:/Users/kcvan/MakeAIWork/KEVINPROJECT1/CSVfiles/01_S_280922 - 01_S_280922.csv\",\n",
+ " names=[\"Sonar1\", \"Sonar2\", \"Sonar3\", \"SteeringAngle\"])\n",
+ "\n",
+ "#sonar_data.head() "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "1d2b4e85-fbdf-44de-b40f-99a247d39759",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print(sonar_data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "40fadfff-5c9a-45ec-b87e-ae68b612d421",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sonar_sensors = sonar_data.copy()\n",
+ "sonar_angle = sonar_sensors.pop('SteeringAngle')\n",
+ "#sonar_sensors.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "2a327ef0-6fb5-43ca-a3e1-e8f70d8d2d11",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#print(sonar_angle)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "05f2fa53-767f-4428-8118-316e29522113",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#sonar_angle.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "dbc9c2a5-fc4a-4295-a691-bd44f4c1261b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[1.451, 1.271, 1.766],\n",
+ " [1.452, 1.272, 1.767],\n",
+ " [1.451, 1.27 , 1.766],\n",
+ " ...,\n",
+ " [1.983, 1.74 , 1.353],\n",
+ " [1.983, 1.74 , 1.353],\n",
+ " [1.983, 1.74 , 1.353]])"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sonar_sensors = np.array(sonar_sensors)\n",
+ "sonar_sensors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "ef72fd36-1a3b-478d-9823-151066a24669",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "normalize = keras.layers.Normalization()\n",
+ "normalize.adapt(sonar_sensors)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "839a7df2-c8dc-4705-9d71-5fc41e7c8e27",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "norm_sonar_model_01 = tf.keras.Sequential([\n",
+ " normalize,\n",
+ " keras.Input(shape=(3,)),\n",
+ " keras.layers.Dense(256, activation='relu', name=\"layer1\"),\n",
+ " keras.layers.Dense(128, activation='relu', name=\"layer2\"),\n",
+ " keras.layers.Dense(64, activation='relu', name=\"layer3\"),\n",
+ " keras.layers.Dense(1)\n",
+ "])\n",
+ "\n",
+ "norm_sonar_model_01.compile(loss = tf.keras.losses.MeanSquaredError(), metrics = ['accuracy'],\n",
+ " optimizer = tf.keras.optimizers.Adam(learning_rate=0.01))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "eebed64f-bed2-4d0c-b34d-406e226f2894",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/15\n",
+ "157/157 [==============================] - 1s 2ms/step - loss: 34.5629 - accuracy: 0.6012\n",
+ "Epoch 2/15\n",
+ "157/157 [==============================] - 0s 2ms/step - loss: 8.9713 - accuracy: 0.7311\n",
+ "Epoch 3/15\n",
+ "157/157 [==============================] - 0s 2ms/step - loss: 7.6705 - accuracy: 0.7333\n",
+ "Epoch 4/15\n",
+ "157/157 [==============================] - 0s 2ms/step - loss: 5.7696 - accuracy: 0.7576\n",
+ "Epoch 5/15\n",
+ "157/157 [==============================] - 0s 2ms/step - loss: 4.0488 - accuracy: 0.7570\n",
+ "Epoch 6/15\n",
+ "157/157 [==============================] - 0s 2ms/step - loss: 6.6283 - accuracy: 0.7489\n",
+ "Epoch 7/15\n",
+ "157/157 [==============================] - 0s 2ms/step - loss: 4.6597 - accuracy: 0.7560\n",
+ "Epoch 8/15\n",
+ "157/157 [==============================] - 0s 2ms/step - loss: 3.9064 - accuracy: 0.7590\n",
+ "Epoch 9/15\n",
+ "157/157 [==============================] - 0s 2ms/step - loss: 3.7362 - accuracy: 0.7572\n",
+ "Epoch 10/15\n",
+ "157/157 [==============================] - 0s 2ms/step - loss: 5.2944 - accuracy: 0.7407\n",
+ "Epoch 11/15\n",
+ "157/157 [==============================] - 0s 2ms/step - loss: 4.1378 - accuracy: 0.7546\n",
+ "Epoch 12/15\n",
+ "157/157 [==============================] - 0s 2ms/step - loss: 4.9081 - accuracy: 0.7548\n",
+ "Epoch 13/15\n",
+ "157/157 [==============================] - 0s 2ms/step - loss: 3.8064 - accuracy: 0.7582\n",
+ "Epoch 14/15\n",
+ "157/157 [==============================] - 0s 2ms/step - loss: 3.2076 - accuracy: 0.7602\n",
+ "Epoch 15/15\n",
+ "157/157 [==============================] - 0s 2ms/step - loss: 3.6547 - accuracy: 0.7610\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "norm_sonar_model_01.fit(sonar_sensors, sonar_angle, epochs=15)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "5a517549-a966-4094-9c7a-467c498d2d27",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#norm_sonar_model_01.save('norm_sonar_model_01')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "c9099563-c5d2-4abf-ab56-0b789eea11f4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[1.458, 1.277, 1.77 ],\n",
+ " [1.458, 1.277, 1.77 ],\n",
+ " [1.458, 1.277, 1.77 ],\n",
+ " ...,\n",
+ " [1.782, 1.706, 1.16 ],\n",
+ " [1.782, 1.706, 1.16 ],\n",
+ " [1.782, 1.706, 1.16 ]])"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "val_sonar_data = pd.read_csv(\n",
+ " \"C:/Users/kcvan/MakeAIWork/KEVINPROJECT1/CSVfiles/validation_sonar_01.csv\",\n",
+ " names=[\"Sonar1\", \"Sonar2\", \"Sonar3\", \"SteeringAngle\"])\n",
+ "\n",
+ "val_sonar_sensors = val_sonar_data.copy()\n",
+ "val_sonar_angle = val_sonar_sensors.pop('SteeringAngle')\n",
+ "\n",
+ "val_sonar_sensors = np.array(val_sonar_sensors)\n",
+ "val_sonar_sensors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "ba07a1cb-f186-40b1-a05f-47b02f305f79",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "35/35 - 0s - loss: 2.1288 - accuracy: 0.7649 - 247ms/epoch - 7ms/step\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[2.128823757171631, 0.7649219632148743]"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "norm_sonar_model_01.evaluate(val_sonar_sensors, val_sonar_angle, verbose=2)"
+ ]
+ }
+ ],
+ "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",
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diff --git a/simulations/car/control_client/data_files/sonar.csv b/simulations/car/control_client/data_files/sonar.csv
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diff --git a/simulations/car/control_client/data_files/sonar_kevin.csv b/simulations/car/control_client/data_files/sonar_kevin.csv
new file mode 100644
index 00000000..0a111371
--- /dev/null
+++ b/simulations/car/control_client/data_files/sonar_kevin.csv
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diff --git a/simulations/car/control_client/data_files/sonar_no_headers.csv b/simulations/car/control_client/data_files/sonar_no_headers.csv
new file mode 100644
index 00000000..c9942604
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\ No newline at end of file
diff --git a/simulations/car/control_client/default.samples b/simulations/car/control_client/default.samples
new file mode 100644
index 00000000..e69de29b
diff --git a/simulations/car/control_client/driving_agent.png b/simulations/car/control_client/driving_agent.png
new file mode 100644
index 00000000..9dfe284f
Binary files /dev/null and b/simulations/car/control_client/driving_agent.png differ
diff --git a/simulations/car/control_client/driving_agent.py b/simulations/car/control_client/driving_agent.py
new file mode 100644
index 00000000..333286fe
--- /dev/null
+++ b/simulations/car/control_client/driving_agent.py
@@ -0,0 +1,15 @@
+#!/usr/bin/env python
+
+class DrivingAgent:
+
+ # def __init__(self, **kwargs):
+ # self.name = kwargs['name']
+
+ def main():
+ agent = DrivingAgent(name = "Toyota")
+ print("The agent is driving")
+
+ if __name__ == "__main__":
+ main()
+
+DrivingAgent.main()
\ No newline at end of file
diff --git a/simulations/car/control_client/driving_agent_jeroenA.py b/simulations/car/control_client/driving_agent_jeroenA.py
new file mode 100644
index 00000000..51eb4700
--- /dev/null
+++ b/simulations/car/control_client/driving_agent_jeroenA.py
@@ -0,0 +1,127 @@
+#!/usr/bin/env python
+
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import numpy as np
+import tensorflow as tf
+import socket as sc
+import parameters as pm
+import socket_wrapper as sw
+import time as tm
+import traceback as tb
+import math as mt
+import sys as ss
+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
+
+loadSonarModel = r'/Users/JeroenArians/MakeAIWork/simpylc/simulations/car/control_client/learningModelSonar'
+
+
+class DrivingAgent:
+ def __init__(self):
+ self.model = None
+ self.steeringAngle = 0
+
+ with open(pm.sampleFileName, 'w') as self.sampleFile:
+ with sc.socket(*sw.socketType) as self.clientSocket:
+ self.clientSocket.connect(sw.address)
+ self.socketWrapper = sw.SocketWrapper(self.clientSocket)
+ self.halfApertureAngle = False
+
+ while True:
+ self.input()
+ self.sweep()
+ self.output()
+ tm.sleep(0.02)
+
+ def input(self):
+ sensors = self.socketWrapper.recv()
+
+ if not self.halfApertureAngle:
+ self.halfApertureAngle = sensors['halfApertureAngle']
+ self.sectorAngle = 2 * self.halfApertureAngle / pm.lidarInputDim
+ self.halfMiddleApertureAngle = sensors['halfMiddleApertureAngle']
+
+ if 'lidarDistances' in sensors:
+ self.lidarDistances = sensors['lidarDistances']
+ else:
+ self.sonarDistances = sensors['sonarDistances']
+ if self.model == None:
+ self.model = tf.keras.models.load_model(loadSonarModel)
+
+ def lidarSweep(self):
+ nearestObstacleDistance = pm.finity
+
+ nearestObstacleAngle = 0
+
+ nextObstacleDistance = pm.finity
+ nextObstacleAngle = 0
+
+ for lidarAngle in range(-self.halfApertureAngle, self.halfApertureAngle):
+ lidarDistance = self.lidarDistances[lidarAngle]
+
+ if lidarDistance < nearestObstacleDistance:
+ nextObstacleDistance = nearestObstacleDistance
+ nextObstacleAngle = nearestObstacleAngle
+
+ nearestObstacleDistance = lidarDistance
+ nearestObstacleAngle = lidarAngle
+
+ elif lidarDistance < nextObstacleDistance:
+ nextObstacleDistance = lidarDistance
+ nextObstacleAngle = lidarAngle
+
+ targetObstacleDistance = (
+ nearestObstacleDistance + nextObstacleDistance) / 2
+
+ self.steeringAngle = (nearestObstacleAngle + nextObstacleAngle) / 2
+ self.targetVelocity = pm.getTargetVelocity(self.steeringAngle)
+
+ def sonarSweep(self):
+ steering_angle_model = self.model.predict(
+ np.array([self.sonarDistances]))
+ self.steeringAngle = float(steering_angle_model[0][0])
+ self.targetVelocity = pm.getTargetVelocity(self.steeringAngle)
+
+ def sweep(self):
+ if hasattr(self, 'lidarDistances'):
+ self.lidarSweep()
+ else:
+ self.sonarSweep()
+
+ def output(self):
+ actuators = {
+ 'steeringAngle': self.steeringAngle,
+ 'targetVelocity': self.targetVelocity
+ }
+
+ self.socketWrapper.send(actuators)
+
+
+DrivingAgent()
diff --git a/simulations/car/control_client/driving_agent_sonar_scikit_under_construction.py b/simulations/car/control_client/driving_agent_sonar_scikit_under_construction.py
new file mode 100644
index 00000000..4bd0c72b
--- /dev/null
+++ b/simulations/car/control_client/driving_agent_sonar_scikit_under_construction.py
@@ -0,0 +1,128 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import tensorflow as tf
+keras = tf.keras
+from keras import Model
+import numpy as np
+
+import time as tm
+import sys as ss
+import os
+import socket as sc
+
+ss.path += [os.path.abspath (relPath) for relPath in ('..',)]
+
+import socket_wrapper as sw
+import parameters as pm
+
+import pickle
+import sklearn as sk
+from sklearn.neural_network import MLPClassifier
+
+
+mpl = pickle.load(open('../finalized_sonar_model.pkl', 'rb'))
+
+
+class DrivingAgent:
+ def __init__ (self):
+ self.steeringAngle = 0
+ self.model = None
+
+ with sc.socket (*sw.socketType) as self.clientSocket:
+ self.clientSocket.connect (sw.address)
+ self.socketWrapper = sw.SocketWrapper (self.clientSocket)
+ self.halfApertureAngle = False
+
+ while True:
+ self.input ()
+ self.sweep ()
+ self.output ()
+ tm.sleep (0.02)
+
+ def input (self):
+ sensors = self.socketWrapper.recv ()
+
+ if not self.halfApertureAngle:
+ self.halfApertureAngle = sensors ['halfApertureAngle']
+ self.sectorAngle = 2 * self.halfApertureAngle / pm.lidarInputDim
+ self.halfMiddleApertureAngle = sensors ['halfMiddleApertureAngle']
+
+ if 'lidarDistances' in sensors:
+ self.lidarDistances = sensors ['lidarDistances']
+ else:
+ self.sonarDistances = sensors ['sonarDistances']
+ if self.model == None:
+ self.model = mpl
+
+ def lidarSweep (self):
+ nearestObstacleDistance = pm.finity
+ nearestObstacleAngle = 0
+
+ nextObstacleDistance = pm.finity
+ nextObstacleAngle = 0
+
+ for lidarAngle in range (-self.halfApertureAngle, self.halfApertureAngle):
+ lidarDistance = self.lidarDistances [lidarAngle]
+
+ if lidarDistance < nearestObstacleDistance:
+ nextObstacleDistance = nearestObstacleDistance
+ nextObstacleAngle = nearestObstacleAngle
+
+ nearestObstacleDistance = lidarDistance
+ nearestObstacleAngle = lidarAngle
+
+ elif lidarDistance < nextObstacleDistance:
+ nextObstacleDistance = lidarDistance
+ nextObstacleAngle = lidarAngle
+
+ targetObstacleDistance = (nearestObstacleDistance + nextObstacleDistance) / 2
+
+ self.steeringAngle = (nearestObstacleAngle + nextObstacleAngle) / 2
+ self.targetVelocity = pm.getTargetVelocity (self.steeringAngle)
+
+ def sonarSweep (self):
+ steeringAngleModel = self.model.predict(np.array(self.sonarDistances))
+ self.steeringAngle = float(steeringAngleModel (0))
+ self.targetVelocity = pm.getTargetVelocity (self.steeringAngle)
+
+ def sweep (self):
+ if hasattr (self, 'lidarDistances'):
+ self.lidarSweep ()
+ else:
+ self.sonarSweep ()
+
+ def output (self):
+ actuators = {
+ 'steeringAngle': self.steeringAngle,
+ 'targetVelocity': self.targetVelocity
+ }
+
+ self.socketWrapper.send (actuators)
+
+
+DrivingAgent ()
diff --git a/simulations/car/control_client/driving_agent_sonar_tensorflow_working.py b/simulations/car/control_client/driving_agent_sonar_tensorflow_working.py
new file mode 100644
index 00000000..e22ff180
--- /dev/null
+++ b/simulations/car/control_client/driving_agent_sonar_tensorflow_working.py
@@ -0,0 +1,126 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import tensorflow as tf
+keras = tf.keras
+from keras import Model
+import numpy as np
+
+import time as tm
+import sys as ss
+import os
+import socket as sc
+
+ss.path += [os.path.abspath (relPath) for relPath in ('..',)]
+
+import socket_wrapper as sw
+import parameters as pm
+
+
+loadSonarModel = r'../sonar_model_a'
+# loadSonarModel = r'../sonar_model_b'
+# loadSonarModel = r'../sonar_model_c'
+
+
+class DrivingAgent:
+ def __init__ (self):
+ self.steeringAngle = 0
+ self.model = None
+
+ with sc.socket (*sw.socketType) as self.clientSocket:
+ self.clientSocket.connect (sw.address)
+ self.socketWrapper = sw.SocketWrapper (self.clientSocket)
+ self.halfApertureAngle = False
+
+ while True:
+ self.input ()
+ self.sweep ()
+ self.output ()
+ tm.sleep (0.02)
+
+ def input (self):
+ sensors = self.socketWrapper.recv ()
+
+ if not self.halfApertureAngle:
+ self.halfApertureAngle = sensors ['halfApertureAngle']
+ self.sectorAngle = 2 * self.halfApertureAngle / pm.lidarInputDim
+ self.halfMiddleApertureAngle = sensors ['halfMiddleApertureAngle']
+
+ if 'lidarDistances' in sensors:
+ self.lidarDistances = sensors ['lidarDistances']
+ else:
+ self.sonarDistances = sensors ['sonarDistances']
+ if self.model == None:
+ self.model = tf.keras.models.load_model(loadSonarModel)
+
+ def lidarSweep (self):
+ nearestObstacleDistance = pm.finity
+ nearestObstacleAngle = 0
+
+ nextObstacleDistance = pm.finity
+ nextObstacleAngle = 0
+
+ for lidarAngle in range (-self.halfApertureAngle, self.halfApertureAngle):
+ lidarDistance = self.lidarDistances [lidarAngle]
+
+ if lidarDistance < nearestObstacleDistance:
+ nextObstacleDistance = nearestObstacleDistance
+ nextObstacleAngle = nearestObstacleAngle
+
+ nearestObstacleDistance = lidarDistance
+ nearestObstacleAngle = lidarAngle
+
+ elif lidarDistance < nextObstacleDistance:
+ nextObstacleDistance = lidarDistance
+ nextObstacleAngle = lidarAngle
+
+ targetObstacleDistance = (nearestObstacleDistance + nextObstacleDistance) / 2
+
+ self.steeringAngle = (nearestObstacleAngle + nextObstacleAngle) / 2
+ self.targetVelocity = pm.getTargetVelocity (self.steeringAngle)
+
+ def sonarSweep (self):
+ steeringAngleModel = self.model.predict(np.array(self.sonarDistances))
+ self.steeringAngle = float(steeringAngleModel [0][0])
+ self.targetVelocity = pm.getTargetVelocity (self.steeringAngle)
+
+ def sweep (self):
+ if hasattr (self, 'lidarDistances'):
+ self.lidarSweep ()
+ else:
+ self.sonarSweep ()
+
+ def output (self):
+ actuators = {
+ 'steeringAngle': self.steeringAngle,
+ 'targetVelocity': self.targetVelocity
+ }
+
+ self.socketWrapper.send (actuators)
+
+
+DrivingAgent ()
diff --git a/simulations/car/control_client/driving_neural_agent.py b/simulations/car/control_client/driving_neural_agent.py
new file mode 100644
index 00000000..79bf8567
--- /dev/null
+++ b/simulations/car/control_client/driving_neural_agent.py
@@ -0,0 +1,128 @@
+#!/usr/bin/env python
+
+import tensorflow as tf
+keras = tf.keras
+from keras import Model
+import numpy as np
+
+import time as tm
+import sys as ss
+import os
+import socket as sc
+
+ss.path += [os.path.abspath (relPath) for relPath in ('..',)]
+
+import socket_wrapper as sw
+import parameters as pm
+
+loadSonarmodel = r'../control_client/tensorflow/norm_sonar_01_model'
+
+class DrivingAgent:
+
+ def __init__(self): # + **kwargs
+ # self.name = kwargs['name']
+ # print(self.name )
+ self.model = None
+ self.steeringAngle = 0
+
+ with sc.socket (*sw.socketType) as self.clientSocket:
+ self.clientSocket.connect (sw.address)
+ self.socketWrapper = sw.SocketWrapper (self.clientSocket)
+ self.halfApertureAngle = False
+
+ while True:
+ self.input ()
+ self.sweep ()
+ self.output()
+ tm.sleep (0.02)
+
+ def input (self):
+ sensors = self.socketWrapper.recv()
+
+ if not self.halfApertureAngle:
+ self.halfApertureAngle = sensors ['halfApertureAngle']
+ self.sectorAngle = 2 * self.halfApertureAngle / pm.lidarInputDim
+ self.halfMiddleApertureAngle = sensors ['halfMiddleApertureAngle']
+
+ if 'lidarDistances' in sensors:
+ self.lidarDistances = sensors ['lidarDistances']
+ else:
+ self.sonarDistances = sensors ['sonarDistances']
+
+ def lidarSweep (self):
+ nearestObstacleDistance = pm.finity
+ nearestObstacleAngle = 0
+
+ nextObstacleDistance = pm.finity
+ nextObstacleAngle = 0
+
+# for lidarAngle in range (-self.halfApertureAngle, self.halfApertureAngle):
+# lidarDistance = self.lidarDistances [lidarAngle]
+
+# if lidarDistance < nearestObstacleDistance:
+# nextObstacleDistance = nearestObstacleDistance
+# nextObstacleAngle = nearestObstacleAngle
+
+# nearestObstacleDistance = lidarDistance
+# nearestObstacleAngle = lidarAngle
+
+# elif lidarDistance < nextObstacleDistance:
+# nextObstacleDistance = lidarDistance
+# nextObstacleAngle = lidarAngle
+
+# targetObstacleDistance = (nearestObstacleDistance + nextObstacleDistance) / 2
+
+# self.steeringAngle = (nearestObstacleAngle + nextObstacleAngle) / 2
+# self.targetVelocity = pm.getTargetVelocity (self.steeringAngle)
+
+ def sonarSweep (self):
+ steeringAngleModel = self.model.predict(np.array(self.sonar_01_sensors))
+ self.steeringAngle = float(steeringAngleModel [0][0])
+
+ # obstacleDistances = [pm.finity for sectorIndex in range (3)]
+ # obstacleAngles = [0 for sectorIndex in range (3)]
+
+# for sectorIndex in (-1, 0, 1):
+# sonarDistance = self.sonarDistances [sectorIndex]
+# sonarAngle = 2 * self.halfMiddleApertureAngle * sectorIndex
+
+# if sonarDistance < obstacleDistances [sectorIndex]:
+# obstacleDistances [sectorIndex] = sonarDistance
+# obstacleAngles [sectorIndex] = sonarAngle
+
+# if obstacleDistances [-1] > obstacleDistances [0]:
+# leftIndex = -1
+# else:
+# leftIndex = 0
+
+# if obstacleDistances [1] > obstacleDistances [0]:
+# rightIndex = 1
+# else:
+# rightIndex = 0
+
+# self.steeringAngle = (obstacleAngles [leftIndex] + obstacleAngles [rightIndex]) / 2
+ self.targetVelocity = pm.getTargetVelocity (self.steeringAngle)
+
+ def sweep (self):
+ if hasattr (self, 'lidarDistances'):
+ self.lidarSweep ()
+ else:
+ self.sonarSweep ()
+
+ def output (self):
+ actuators = {
+ 'steeringAngle': self.steeringAngle,
+ 'targetVelocity': self.targetVelocity
+ }
+
+ self.socketWrapper.send (actuators)
+
+# def main():
+# agent = DrivingAgent(name="Ashok Leyland Limited")
+# print("The agent is driving")
+
+# if __name__ == "__main__":
+# main()
+
+
+DrivingAgent() # .main
\ No newline at end of file
diff --git a/simulations/car/control_client/hardcoded_client.py b/simulations/car/control_client/hardcoded_client.py
new file mode 100644
index 00000000..cf63b442
--- /dev/null
+++ b/simulations/car/control_client/hardcoded_client.py
@@ -0,0 +1,160 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import time as tm
+import traceback as tb
+import math as mt
+import sys as ss
+import os
+import socket as sc
+
+ss.path += [os.path.abspath (relPath) for relPath in ('..',)]
+
+import socket_wrapper as sw
+import parameters as pm
+
+class HardcodedClient:
+ def __init__ (self):
+ self.steeringAngle = 0
+
+ with open (pm.sampleFileName, 'w') as self.sampleFile:
+ with sc.socket (*sw.socketType) as self.clientSocket:
+ self.clientSocket.connect (sw.address)
+ self.socketWrapper = sw.SocketWrapper (self.clientSocket)
+ self.halfApertureAngle = False
+
+ while True:
+ self.input ()
+ self.sweep ()
+ self.output ()
+ self.logTraining ()
+ tm.sleep (0.02)
+
+ def input (self):
+ sensors = self.socketWrapper.recv ()
+
+ if not self.halfApertureAngle:
+ self.halfApertureAngle = sensors ['halfApertureAngle']
+ self.sectorAngle = 2 * self.halfApertureAngle / pm.lidarInputDim
+ self.halfMiddleApertureAngle = sensors ['halfMiddleApertureAngle']
+
+ if 'lidarDistances' in sensors:
+ self.lidarDistances = sensors ['lidarDistances']
+ else:
+ self.sonarDistances = sensors ['sonarDistances']
+
+ def lidarSweep (self):
+ nearestObstacleDistance = pm.finity
+ nearestObstacleAngle = 0
+
+ nextObstacleDistance = pm.finity
+ nextObstacleAngle = 0
+
+ for lidarAngle in range (-self.halfApertureAngle, self.halfApertureAngle):
+ lidarDistance = self.lidarDistances [lidarAngle]
+
+ if lidarDistance < nearestObstacleDistance:
+ nextObstacleDistance = nearestObstacleDistance
+ nextObstacleAngle = nearestObstacleAngle
+
+ nearestObstacleDistance = lidarDistance
+ nearestObstacleAngle = lidarAngle
+
+ elif lidarDistance < nextObstacleDistance:
+ nextObstacleDistance = lidarDistance
+ nextObstacleAngle = lidarAngle
+
+ targetObstacleDistance = (nearestObstacleDistance + nextObstacleDistance) / 2
+
+ self.steeringAngle = (nearestObstacleAngle + nextObstacleAngle) / 2
+ self.targetVelocity = pm.getTargetVelocity (self.steeringAngle)
+
+ def sonarSweep (self):
+ obstacleDistances = [pm.finity for sectorIndex in range (3)]
+ obstacleAngles = [0 for sectorIndex in range (3)]
+
+ for sectorIndex in (-1, 0, 1):
+ sonarDistance = self.sonarDistances [sectorIndex]
+ sonarAngle = 2 * self.halfMiddleApertureAngle * sectorIndex
+
+ if sonarDistance < obstacleDistances [sectorIndex]:
+ obstacleDistances [sectorIndex] = sonarDistance
+ obstacleAngles [sectorIndex] = sonarAngle
+
+ if obstacleDistances [-1] > obstacleDistances [0]:
+ leftIndex = -1
+ else:
+ leftIndex = 0
+
+ if obstacleDistances [1] > obstacleDistances [0]:
+ rightIndex = 1
+ else:
+ rightIndex = 0
+
+ self.steeringAngle = (obstacleAngles [leftIndex] + obstacleAngles [rightIndex]) / 2
+ self.targetVelocity = pm.getTargetVelocity (self.steeringAngle)
+
+ def sweep (self):
+ if hasattr (self, 'lidarDistances'):
+ self.lidarSweep ()
+ else:
+ self.sonarSweep ()
+
+ def output (self):
+ actuators = {
+ 'steeringAngle': self.steeringAngle,
+ 'targetVelocity': self.targetVelocity
+ }
+
+ self.socketWrapper.send (actuators)
+
+ def logLidarTraining (self):
+ sample = [pm.finity for entryIndex in range (pm.lidarInputDim + 1)]
+
+ for lidarAngle in range (-self.halfApertureAngle, self.halfApertureAngle):
+ sectorIndex = round (lidarAngle / self.sectorAngle)
+ sample [sectorIndex] = min (sample [sectorIndex], self.lidarDistances [lidarAngle])
+
+ sample [-1] = self.steeringAngle
+ print (*sample, file = self.sampleFile)
+
+ def logSonarTraining (self):
+ sample = [pm.finity for entryIndex in range (pm.sonarInputDim + 1)]
+
+ for entryIndex, sectorIndex in ((2, -1), (0, 0), (1, 1)):
+ sample [entryIndex] = self.sonarDistances [sectorIndex]
+
+ sample [-1] = self.steeringAngle
+ print (*sample, file = self.sampleFile)
+
+ def logTraining (self):
+ if hasattr (self, 'lidarDistances'):
+ self.logLidarTraining ()
+ else:
+ self.logSonarTraining ()
+
+HardcodedClient ()
diff --git a/simulations/car/control_client/hardcoded_client_w_annotations.py b/simulations/car/control_client/hardcoded_client_w_annotations.py
new file mode 100644
index 00000000..dd16f988
--- /dev/null
+++ b/simulations/car/control_client/hardcoded_client_w_annotations.py
@@ -0,0 +1,160 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import time as tm # Voor 'time out', niet elke milliseconde rekenen
+import traceback as tb # Wordt niet gebruikt
+import math as mt # Wordt niet gebruikt
+import sys as ss # Systeem
+import os # Operating system
+import socket as sc # Om tussen World en Car(= hardcoded_client)) te kunnen communiceren.
+
+ss.path += [os.path.abspath (relPath) for relPath in ('..',)] # Voegt toe aan directory waar info weggeschreven wordt
+
+import socket_wrapper as sw # Zie 'socket'
+import parameters as pm # Module parameters.py
+
+class HardcodedClient:
+ def __init__ (self):
+ self.steeringAngle = 0
+
+ with open (pm.sampleFileName, 'w') as self.sampleFile: # Om data te loggen in default.samples-bestand
+ with sc.socket (*sw.socketType) as self.clientSocket: # Socket/Socketwrapper
+ self.clientSocket.connect (sw.address)
+ self.socketWrapper = sw.SocketWrapper (self.clientSocket) # van World naar Car en vice versa, ontvangen en sturen van data, connection
+ self.halfApertureAngle = False # halfApertureAngle wordt hier aangemaakt, lege plaats in het geheugen
+
+ while True: # Zolang het True is wordt onderstaande uitgevoerd, in dit geval alleen handmatig te stoppen
+ self.input ()
+ self.sweep ()
+ self.output ()
+ self.logTraining ()
+ tm.sleep (0.02) # neem even pauze, nieuwe stuurhoek wordt hier/nu steeds meegegeven
+
+ def input (self): # Voeg hier Breakpoint toe en ga met debugging er door heen om te zien wat er gebeurt
+ sensors = self.socketWrapper.recv () # info uit 'World', geeft aan waar de Car zich bevindt en wat er om hem heen gebeurt
+
+ if not self.halfApertureAngle: # Met deze 'if not (dus; False) zie voorgaand blok (self.halfApertureAngle = False) wordt onderstaand loopje geactiveerd
+ self.halfApertureAngle = sensors ['halfApertureAngle'] # kijkhoek is totaal (120 graden), gedeeld door aantal sensoren. Helft geeft aan: wat is links, wat is rechts
+ self.sectorAngle = 2 * self.halfApertureAngle / pm.lidarInputDim
+ self.halfMiddleApertureAngle = sensors ['halfMiddleApertureAngle']
+
+ if 'lidarDistances' in sensors:
+ self.lidarDistances = sensors ['lidarDistances'] # lidarDistances aangemaakt
+ else:
+ self.sonarDistances = sensors ['sonarDistances'] # idem voor sonarDistances
+
+ def lidarSweep (self): # sweep is per graden > 120 ### Sweep doet bijna alles!!! Hiermee wordt de Car bestuurd
+ nearestObstacleDistance = pm.finity # Max. afstand, om het zicht enigzins te beperken. Met ook nog informatie van ver weg staande objecten wordt het te complex.
+ nearestObstacleAngle = 0
+
+ nextObstacleDistance = pm.finity ## we kijken in deze functie naar de 2 dichtsbijstaande pylonen
+ nextObstacleAngle = 0
+
+ for lidarAngle in range (-self.halfApertureAngle, self.halfApertureAngle): # 120 graden/2 van -60 naar +60 sweepen, 0 is rechtdoor. Welke info vindt je in het bereik
+ lidarDistance = self.lidarDistances [lidarAngle] # doorloopt alle hoeken van -60 t/m +60 (dus -60, -59, - 58 etc.) en geeft de afstand tot de pylonen terug
+
+ if lidarDistance < nearestObstacleDistance: # dus afstand kleiner dan 20
+ nextObstacleDistance = nearestObstacleDistance # Pylon die eerst het dichtbijzijnde was, wordt vervangen door pylon die nog dichterbij is.
+ nextObstacleAngle = nearestObstacleAngle # Maar, ex-dicthtbijzijnde pylon is nog steeds interessant, want als volgende aan de beurt
+
+ nearestObstacleDistance = lidarDistance # dichtbijzijnde krijgt de afstand mee
+ nearestObstacleAngle = lidarAngle
+
+ elif lidarDistance < nextObstacleDistance: # check anders de pylon die op één na dichtbijzijnde is
+ nextObstacleDistance = lidarDistance
+ nextObstacleAngle = lidarAngle
+
+ targetObstacleDistance = (nearestObstacleDistance + nextObstacleDistance) / 2 # hiermee rijd je precies tussen 2 pylonen door
+ # gemiddelde hoek links en rechts
+ self.steeringAngle = (nearestObstacleAngle + nextObstacleAngle) / 2
+ self.targetVelocity = pm.getTargetVelocity (self.steeringAngle) # Snelheid is afhankelijk van stuurhoek, zie parameters
+
+ def sonarSweep (self):
+ obstacleDistances = [pm.finity for sectorIndex in range (3)] # niet doorgenomen met Frank #
+ obstacleAngles = [0 for sectorIndex in range (3)]
+
+ for sectorIndex in (-1, 0, 1):
+ sonarDistance = self.sonarDistances [sectorIndex]
+ sonarAngle = 2 * self.halfMiddleApertureAngle * sectorIndex
+
+ if sonarDistance < obstacleDistances [sectorIndex]:
+ obstacleDistances [sectorIndex] = sonarDistance
+ obstacleAngles [sectorIndex] = sonarAngle
+
+ if obstacleDistances [-1] > obstacleDistances [0]:
+ leftIndex = -1
+ else:
+ leftIndex = 0
+
+ if obstacleDistances [1] > obstacleDistances [0]:
+ rightIndex = 1
+ else:
+ rightIndex = 0
+
+ self.steeringAngle = (obstacleAngles [leftIndex] + obstacleAngles [rightIndex]) / 2
+ self.targetVelocity = pm.getTargetVelocity (self.steeringAngle)
+
+ def sweep (self): # Wordt meegegeven in World bij keuze 'l/s'
+ if hasattr (self, 'lidarDistances'): # hasattr (has attribute) is een standaardfunctie, hier is attribute 'lidarDistances'
+ self.lidarSweep ()
+ else:
+ self.sonarSweep ()
+
+ def output (self): # Copy/pasten naar eigen code
+ actuators = {
+ 'steeringAngle': self.steeringAngle,
+ 'targetVelocity': self.targetVelocity
+ }
+
+ self.socketWrapper.send (actuators) # Hier worden stuurhoek en snelheid doorgegeven aan Car
+
+ def logLidarTraining (self): # Alleen om trainingsdata op te slaan, straks niet meer nodig.
+ sample = [pm.finity for entryIndex in range (pm.lidarInputDim + 1)]
+
+ for lidarAngle in range (-self.halfApertureAngle, self.halfApertureAngle):
+ sectorIndex = round (lidarAngle / self.sectorAngle)
+ sample [sectorIndex] = min (sample [sectorIndex], self.lidarDistances [lidarAngle])
+
+ sample [-1] = self.steeringAngle
+ print (*sample, file = self.sampleFile)
+
+ def logSonarTraining (self): # Alleen om trainingsdata op te slaan, straks niet meer nodig.
+ sample = [pm.finity for entryIndex in range (pm.sonarInputDim + 1)]
+
+ for entryIndex, sectorIndex in ((2, -1), (0, 0), (1, 1)):
+ sample [entryIndex] = self.sonarDistances [sectorIndex]
+
+ sample [-1] = self.steeringAngle
+ print (*sample, file = self.sampleFile)
+
+ def logTraining (self): # Om keuze uit bovenstaande functies te maken
+ if hasattr (self, 'lidarDistances'):
+ self.logLidarTraining ()
+ else:
+ self.logSonarTraining ()
+
+HardcodedClient ()
diff --git a/simulations/car/control_client/not_so_hardcoded_client.py b/simulations/car/control_client/not_so_hardcoded_client.py
new file mode 100644
index 00000000..9f81c50d
--- /dev/null
+++ b/simulations/car/control_client/not_so_hardcoded_client.py
@@ -0,0 +1,147 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import tensorflow as tf
+keras = tf.keras
+from keras import Model
+import numpy as np
+
+import time as tm
+import sys as ss
+import os
+import socket as sc
+
+ss.path += [os.path.abspath (relPath) for relPath in ('..',)]
+
+import socket_wrapper as sw
+import parameters as pm
+
+# loadSonarModel = tf.keras.models.load_model('../control_client/tensorflow/norm_sonar_01_model',
+# custom_objects=None, compile=True, options=None
+# )
+
+loadSonarmodel = r'../control_client/tensorflow/norm_sonar_01_model'
+
+
+class DrivingAgentTemp:
+ def __init__ (self):
+ self.model = None
+ self.steeringAngle = 0
+
+ with sc.socket (*sw.socketType) as self.clientSocket:
+ self.clientSocket.connect (sw.address)
+ self.socketWrapper = sw.SocketWrapper (self.clientSocket)
+ self.halfApertureAngle = False
+
+ while True:
+ self.input ()
+ self.sweep ()
+ tm.sleep (0.02)
+
+ def input (self):
+ sensors = self.socketWrapper.recv ()
+
+ if not self.halfApertureAngle:
+ self.halfApertureAngle = sensors ['halfApertureAngle']
+ self.sectorAngle = 2 * self.halfApertureAngle / pm.lidarInputDim
+ self.halfMiddleApertureAngle = sensors ['halfMiddleApertureAngle']
+
+ if 'lidarDistances' in sensors:
+ self.lidarDistances = sensors ['lidarDistances']
+ else:
+ self.sonarDistances = sensors ['sonarDistances']
+
+ def lidarSweep (self):
+ nearestObstacleDistance = pm.finity
+ nearestObstacleAngle = 0
+
+ nextObstacleDistance = pm.finity
+ nextObstacleAngle = 0
+
+ for lidarAngle in range (-self.halfApertureAngle, self.halfApertureAngle):
+ lidarDistance = self.lidarDistances [lidarAngle]
+
+ if lidarDistance < nearestObstacleDistance:
+ nextObstacleDistance = nearestObstacleDistance
+ nextObstacleAngle = nearestObstacleAngle
+
+ nearestObstacleDistance = lidarDistance
+ nearestObstacleAngle = lidarAngle
+
+ elif lidarDistance < nextObstacleDistance:
+ nextObstacleDistance = lidarDistance
+ nextObstacleAngle = lidarAngle
+
+ targetObstacleDistance = (nearestObstacleDistance + nextObstacleDistance) / 2
+
+ self.steeringAngle = (nearestObstacleAngle + nextObstacleAngle) / 2
+ self.targetVelocity = pm.getTargetVelocity (self.steeringAngle)
+
+ def sonarSweep (self):
+ steeringAngleModel = self.model.predict(np.array(self.sonar_01_sensors))
+ self.steeringAngle = float(steeringAngleModel [0][0])
+
+ # obstacleDistances = [pm.finity for sectorIndex in range (3)]
+ # obstacleAngles = [0 for sectorIndex in range (3)]
+
+ # for sectorIndex in (-1, 0, 1):
+ # sonarDistance = self.sonarDistances [sectorIndex]
+ # sonarAngle = 2 * self.halfMiddleApertureAngle * sectorIndex
+
+ # if sonarDistance < obstacleDistances [sectorIndex]:
+ # obstacleDistances [sectorIndex] = sonarDistance
+ # obstacleAngles [sectorIndex] = sonarAngle
+
+ # if obstacleDistances [-1] > obstacleDistances [0]:
+ # leftIndex = -1
+ # else:
+ # leftIndex = 0
+
+ # if obstacleDistances [1] > obstacleDistances [0]:
+ # rightIndex = 1
+ # else:
+ # rightIndex = 0
+
+ # self.steeringAngle = (obstacleAngles [leftIndex] + obstacleAngles [rightIndex]) / 2
+ # self.targetVelocity = pm.getTargetVelocity (self.steeringAngle)
+
+ def sweep (self):
+ if hasattr (self, 'lidarDistances'):
+ self.lidarSweep ()
+ else:
+ self.sonarSweep ()
+
+ def output (self):
+ actuators = {
+ 'steeringAngle': self.steeringAngle,
+ 'targetVelocity': self.targetVelocity
+ }
+
+ self.socketWrapper.send (actuators)
+
+
+DrivingAgentTemp()
diff --git a/simulations/car/control_client/parameters.py b/simulations/car/control_client/parameters.py
new file mode 100644
index 00000000..d90466c1
--- /dev/null
+++ b/simulations/car/control_client/parameters.py
@@ -0,0 +1,36 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+finity = 20.0 # Needs to be float to obtain ditto numpy array
+
+lidarInputDim = 16
+sonarInputDim = 3
+
+sampleFileName = 'default.samples'
+
+def getTargetVelocity (steeringAngle):
+ return (90 - abs (steeringAngle)) / 60
diff --git a/simulations/car/control_client/scikitlearn/finalized_sonar_model.pkl b/simulations/car/control_client/scikitlearn/finalized_sonar_model.pkl
new file mode 100644
index 00000000..029462d9
Binary files /dev/null and b/simulations/car/control_client/scikitlearn/finalized_sonar_model.pkl differ
diff --git a/simulations/car/control_client/scikitlearn/finalized_sonar_model.sav b/simulations/car/control_client/scikitlearn/finalized_sonar_model.sav
new file mode 100644
index 00000000..e9e8ae6e
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diff --git a/simulations/car/control_client/scikitlearn/pickle_model.pkl b/simulations/car/control_client/scikitlearn/pickle_model.pkl
new file mode 100644
index 00000000..b9346114
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diff --git a/simulations/car/control_client/scikitlearn/scikit_learn_iris_copy.ipynb b/simulations/car/control_client/scikitlearn/scikit_learn_iris_copy.ipynb
new file mode 100644
index 00000000..a8ae1d21
--- /dev/null
+++ b/simulations/car/control_client/scikitlearn/scikit_learn_iris_copy.ipynb
@@ -0,0 +1,995 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "# Location of dataset\n",
+ "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
+ "\n",
+ "# Assign colum names to the dataset\n",
+ "names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']\n",
+ "\n",
+ "# Read dataset to pandas dataframe\n",
+ "irisdata = pd.read_csv(url, names=names)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
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+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from sklearn import preprocessing\n",
+ "le = preprocessing.LabelEncoder()\n",
+ "\n",
+ "y = y.apply(le.fit_transform)\n",
+ "\n",
+ "y.style\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import train_test_split\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.preprocessing import StandardScaler\n",
+ "scaler = StandardScaler()\n",
+ "scaler.fit(X_train)\n",
+ "\n",
+ "X_train = scaler.transform(X_train)\n",
+ "X_test = scaler.transform(X_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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MLPClassifier(hidden_layer_sizes=(16, 16, 16), max_iter=1000) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. \n",
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+ ]
+ },
+ "execution_count": 107,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "norm_lidar_01_model.fit(lidar_01_sensors, lidar_01_labels, epochs=500)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 108,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "10/10 [==============================] - 0s 3ms/step - loss: 45.4270 - accuracy: 0.0702 - mae: 4.1588\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[45.42701721191406, 0.07023411244153976, 4.15880012512207]"
+ ]
+ },
+ "execution_count": 108,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "norm_lidar_01_model.evaluate(val_lidar_01_sensors, val_lidar_01_labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 109,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "10/10 [==============================] - 0s 2ms/step\n"
+ ]
+ },
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+ "execution_count": 110,
+ "metadata": {},
+ "outputs": [
+ {
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+ "text": [
+ "INFO:tensorflow:Assets written to: norm_lidar_01_model\\assets\n"
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+ "source": [
+ "norm_lidar_01_model.save('norm_lidar_01_model')"
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diff --git a/simulations/car/control_client/tensorflow/tensorflow_plus_scikitlearn_sonar_1_test_book.ipynb b/simulations/car/control_client/tensorflow/tensorflow_plus_scikitlearn_sonar_1_test_book.ipynb
new file mode 100644
index 00000000..dfe19c7b
--- /dev/null
+++ b/simulations/car/control_client/tensorflow/tensorflow_plus_scikitlearn_sonar_1_test_book.ipynb
@@ -0,0 +1,2707 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "import tensorflow as tf\n",
+ "keras = tf.keras\n",
+ "from keras import Model\n",
+ "\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "# Make numpy values easier to read.\n",
+ "np.set_printoptions(precision=4, suppress=True)\n",
+ "\n",
+ "from sklearn.model_selection import train_test_split"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "filename = \"../data_files/sonar_kevin.csv\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sonar_01_train = pd.read_csv(filename, sep = ',')"
+ ]
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+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
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+ ]
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+ {
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+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ }
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+ "source": [
+ "norm_sonar_01_model.evaluate(sonar_01_sensors_test, sonar_01_labels_test)"
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+ " [ 21.7976],\n",
+ " [ 0.3824],\n",
+ " [ 0.3042]], dtype=float32)"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "norm_sonar_01_model.predict(sonar_01_sensors_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: C:/MakeAIWork/simulations/car/control_client/tensorflow/modelT\\assets\n"
+ ]
+ }
+ ],
+ "source": [
+ "norm_sonar_01_model.save('C:/MakeAIWork/simulations/car/control_client/tensorflow/modelT')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Training en Test aanmaken?\n",
+ "- Validatie. Done!\n",
+ "- Hoe data opslaan en exporteren? Check\n",
+ "- Accuracy? - Done\n",
+ "- Model prediction! model.predict inputs + real values (array?) Done!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "https://www.tensorflow.org/guide/keras/save_and_serialize"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "tf.random.set_seed(1) Not working correctly/at all?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "https://www.askpython.com/python-modules/saving-loading-models-tensorflow"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.7 64-bit",
+ "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": "3.10.7"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "5b93afe9c2e217d44e354b055ed180e932b72842f9b6422dadc1ad80da9caf67"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/car/control_client/tensorflow/tensorflow_sonar_1_test_book.ipynb b/simulations/car/control_client/tensorflow/tensorflow_sonar_1_test_book.ipynb
new file mode 100644
index 00000000..62b5723a
--- /dev/null
+++ b/simulations/car/control_client/tensorflow/tensorflow_sonar_1_test_book.ipynb
@@ -0,0 +1,909 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "import tensorflow as tf\n",
+ "keras = tf.keras\n",
+ "from keras import Model\n",
+ "\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "# Make numpy values easier to read.\n",
+ "np.set_printoptions(precision=4, suppress=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "filename = \"../data_files/sonar_kevin.csv\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sonar_01_train = pd.read_csv(filename, sep = ',')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " 1.7597 \n",
+ " -22 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "execution_count": 81,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "norm_sonar_01_model.fit(sonar_01_sensors, sonar_01_labels, epochs=80)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "10/10 [==============================] - 0s 3ms/step - loss: 0.9443 - accuracy: 0.7124 - mae: 0.3319\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
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+ "execution_count": 82,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "norm_sonar_01_model.evaluate(val_sonar_01_sensors, val_sonar_01_labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "10/10 [==============================] - 0s 2ms/step\n"
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+ " [ 0.0546]], dtype=float32)"
+ ]
+ },
+ "execution_count": 83,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "norm_sonar_01_model.predict(val_sonar_01_sensors)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: C:/MakeAIWork/simulations/car/control_client/tensorflow/sonar_model_a\\assets\n"
+ ]
+ }
+ ],
+ "source": [
+ "norm_sonar_01_model.save('C:/MakeAIWork/simulations/car/control_client/tensorflow/sonar_model_a')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Training en Test aanmaken?\n",
+ "- Validatie. Done!\n",
+ "- Hoe data opslaan en exporteren? Check\n",
+ "- Accuracy? - Done\n",
+ "- Model prediction! model.predict inputs + real values (array?) Done!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "https://www.tensorflow.org/guide/keras/save_and_serialize"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "tf.random.set_seed(1) Not working correctly/at all?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "https://www.askpython.com/python-modules/saving-loading-models-tensorflow"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.7 64-bit",
+ "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": "3.10.7"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "5b93afe9c2e217d44e354b055ed180e932b72842f9b6422dadc1ad80da9caf67"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/car/control_client/tensorflow/tensorflow_test_01.py b/simulations/car/control_client/tensorflow/tensorflow_test_01.py
new file mode 100644
index 00000000..eddb07e8
--- /dev/null
+++ b/simulations/car/control_client/tensorflow/tensorflow_test_01.py
@@ -0,0 +1,41 @@
+import tensorflow as tf
+keras = tf.keras
+
+import pandas as pd
+import numpy as np
+
+# Make numpy values easier to read.
+np.set_printoptions(precision=4, suppress=True)
+
+filename = "C:/MakeAIWork/simulations/car/control_client/sonar.csv"
+
+sonar_01_train = pd.read_csv(filename, header = 0, sep = ',')
+# print(sonar_01_train)
+# type(sonar_01_train)
+
+# sonar_01_train.head()
+
+sonar_01_sensors = sonar_01_train.copy()
+sonar_01__labels = sonar_01_sensors.pop('Steering Angle')
+
+# print(sonar_01__labels)
+
+sonar_01_sensors = np.array(sonar_01_sensors)
+
+# print(sonar_01_sensors)
+
+normalize = keras.layers.Normalization()
+normalize.adapt(sonar_01_sensors)
+
+norm_sonar_01_model = tf.keras.Sequential([
+ normalize,
+ keras.layers.Dense(64, activation = 'relu'),
+ keras.layers.Dense(64, activation = 'relu'),
+ keras.layers.Dense(64),
+ keras.layers.Dense(1)
+])
+
+norm_sonar_01_model.compile(loss = tf.keras.losses.MeanSquaredError(),
+ optimizer = tf.keras.optimizers.Adam(learning_rate = 0.0001))
+
+norm_sonar_01_model.fit(sonar_01_sensors, sonar_01__labels, epochs = 1000)
\ No newline at end of file
diff --git a/simulations/car/control_client/tensorflow/tensorflow_test_lidar_01.py b/simulations/car/control_client/tensorflow/tensorflow_test_lidar_01.py
new file mode 100644
index 00000000..2003d557
--- /dev/null
+++ b/simulations/car/control_client/tensorflow/tensorflow_test_lidar_01.py
@@ -0,0 +1,49 @@
+import tensorflow as tf
+keras = tf.keras
+
+import pandas as pd
+import numpy as np
+
+# Make numpy values easier to read.
+np.set_printoptions(precision=4, suppress=True)
+
+filename = "C:/MakeAIWork/simulations/car/control_client/tensorflow_lidar _01_test.csv"
+
+lidar_01_train = pd.read_csv(filename, header = 0, sep = ',')
+# print(lidar_01_train)
+# type(lidar_01_train)
+
+# lidar_01_train.style
+
+lidar_01_sensors = lidar_01_train.copy()
+lidar_01__labels = lidar_01_sensors.pop('Steering Angle')
+
+
+
+lidar_01_sensors = np.array(lidar_01_sensors)
+
+# print(lidar_01_sensors)
+
+normalize = keras.layers.Normalization()
+normalize.adapt(lidar_01_sensors)
+
+# Kijken of het mogelijk is om het startpunt vast te zetten, nu variabel.
+
+norm_lidar_01_model = tf.keras.Sequential([
+ normalize,
+ keras.layers.Dense(64, activation = 'elu'),
+ keras.layers.Dense(64, activation = 'elu'),
+ keras.layers.Dense(64)
+])
+
+norm_lidar_01_model.compile(loss = tf.keras.losses.MeanSquaredError(),
+ optimizer = tf.keras.optimizers.Adam(learning_rate=0.00001))
+
+history = norm_lidar_01_model.fit(lidar_01_sensors, lidar_01__labels, epochs = 200)
+
+# norm_lidar_01_model.summary()
+
+# # history.history
+
+# print("Evaluate")
+# result = norm_lidar_01_model.evaluate(norm_lidar_01_model)
\ No newline at end of file
diff --git a/simulations/car/control_client/tensorflow/trainingmodel_lidar_tensorflow_book.ipynb b/simulations/car/control_client/tensorflow/trainingmodel_lidar_tensorflow_book.ipynb
new file mode 100644
index 00000000..f3aa403b
--- /dev/null
+++ b/simulations/car/control_client/tensorflow/trainingmodel_lidar_tensorflow_book.ipynb
@@ -0,0 +1,556 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "import tensorflow as tf\n",
+ "keras = tf.keras\n",
+ "from keras import Model\n",
+ "\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "# Make numpy values easier to read.\n",
+ "np.set_printoptions(precision=1, suppress=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "filename = \"../lidar_second_try.csv\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lidar_01_train = pd.read_csv(filename, sep = ',')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 63\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m 64\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m---> 65\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 66\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m 67\u001b[0m filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
+ "File \u001b[1;32mc:\\Users\\El Director\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\engine\\training.py:1564\u001b[0m, in \u001b[0;36mModel.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m 1556\u001b[0m \u001b[39mwith\u001b[39;00m tf\u001b[39m.\u001b[39mprofiler\u001b[39m.\u001b[39mexperimental\u001b[39m.\u001b[39mTrace(\n\u001b[0;32m 1557\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m 1558\u001b[0m epoch_num\u001b[39m=\u001b[39mepoch,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1561\u001b[0m _r\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m,\n\u001b[0;32m 1562\u001b[0m ):\n\u001b[0;32m 1563\u001b[0m callbacks\u001b[39m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m-> 1564\u001b[0m tmp_logs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrain_function(iterator)\n\u001b[0;32m 1565\u001b[0m \u001b[39mif\u001b[39;00m data_handler\u001b[39m.\u001b[39mshould_sync:\n\u001b[0;32m 1566\u001b[0m context\u001b[39m.\u001b[39masync_wait()\n",
+ "File \u001b[1;32mc:\\Users\\El Director\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 148\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m 149\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 151\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m 152\u001b[0m filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
+ "File \u001b[1;32mc:\\Users\\El Director\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 912\u001b[0m compiler \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mxla\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mnonXla\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 914\u001b[0m \u001b[39mwith\u001b[39;00m OptionalXlaContext(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 915\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_call(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)\n\u001b[0;32m 917\u001b[0m new_tracing_count \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m 918\u001b[0m without_tracing \u001b[39m=\u001b[39m (tracing_count \u001b[39m==\u001b[39m new_tracing_count)\n",
+ "File \u001b[1;32mc:\\Users\\El Director\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:947\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 944\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n\u001b[0;32m 945\u001b[0m \u001b[39m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[0;32m 946\u001b[0m \u001b[39m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[1;32m--> 947\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_stateless_fn(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds) \u001b[39m# pylint: disable=not-callable\u001b[39;00m\n\u001b[0;32m 948\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_stateful_fn \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 949\u001b[0m \u001b[39m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[0;32m 950\u001b[0m \u001b[39m# in parallel.\u001b[39;00m\n\u001b[0;32m 951\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n",
+ "File \u001b[1;32mc:\\Users\\El Director\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:2496\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 2493\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n\u001b[0;32m 2494\u001b[0m (graph_function,\n\u001b[0;32m 2495\u001b[0m filtered_flat_args) \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[1;32m-> 2496\u001b[0m \u001b[39mreturn\u001b[39;00m graph_function\u001b[39m.\u001b[39;49m_call_flat(\n\u001b[0;32m 2497\u001b[0m filtered_flat_args, captured_inputs\u001b[39m=\u001b[39;49mgraph_function\u001b[39m.\u001b[39;49mcaptured_inputs)\n",
+ "File \u001b[1;32mc:\\Users\\El Director\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:1862\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m 1858\u001b[0m possible_gradient_type \u001b[39m=\u001b[39m gradients_util\u001b[39m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m 1859\u001b[0m \u001b[39mif\u001b[39;00m (possible_gradient_type \u001b[39m==\u001b[39m gradients_util\u001b[39m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m 1860\u001b[0m \u001b[39mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m 1861\u001b[0m \u001b[39m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1862\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_build_call_outputs(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_inference_function\u001b[39m.\u001b[39;49mcall(\n\u001b[0;32m 1863\u001b[0m ctx, args, cancellation_manager\u001b[39m=\u001b[39;49mcancellation_manager))\n\u001b[0;32m 1864\u001b[0m forward_backward \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m 1865\u001b[0m args,\n\u001b[0;32m 1866\u001b[0m possible_gradient_type,\n\u001b[0;32m 1867\u001b[0m executing_eagerly)\n\u001b[0;32m 1868\u001b[0m forward_function, args_with_tangents \u001b[39m=\u001b[39m forward_backward\u001b[39m.\u001b[39mforward()\n",
+ "File \u001b[1;32mc:\\Users\\El Director\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:499\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m 497\u001b[0m \u001b[39mwith\u001b[39;00m _InterpolateFunctionError(\u001b[39mself\u001b[39m):\n\u001b[0;32m 498\u001b[0m \u001b[39mif\u001b[39;00m cancellation_manager \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 499\u001b[0m outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39;49mexecute(\n\u001b[0;32m 500\u001b[0m \u001b[39mstr\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msignature\u001b[39m.\u001b[39;49mname),\n\u001b[0;32m 501\u001b[0m num_outputs\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_num_outputs,\n\u001b[0;32m 502\u001b[0m inputs\u001b[39m=\u001b[39;49margs,\n\u001b[0;32m 503\u001b[0m attrs\u001b[39m=\u001b[39;49mattrs,\n\u001b[0;32m 504\u001b[0m ctx\u001b[39m=\u001b[39;49mctx)\n\u001b[0;32m 505\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 506\u001b[0m outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m 507\u001b[0m \u001b[39mstr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msignature\u001b[39m.\u001b[39mname),\n\u001b[0;32m 508\u001b[0m num_outputs\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_outputs,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 511\u001b[0m ctx\u001b[39m=\u001b[39mctx,\n\u001b[0;32m 512\u001b[0m cancellation_manager\u001b[39m=\u001b[39mcancellation_manager)\n",
+ "File \u001b[1;32mc:\\Users\\El Director\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m 52\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m 53\u001b[0m ctx\u001b[39m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 54\u001b[0m tensors \u001b[39m=\u001b[39m pywrap_tfe\u001b[39m.\u001b[39;49mTFE_Py_Execute(ctx\u001b[39m.\u001b[39;49m_handle, device_name, op_name,\n\u001b[0;32m 55\u001b[0m inputs, attrs, num_outputs)\n\u001b[0;32m 56\u001b[0m \u001b[39mexcept\u001b[39;00m core\u001b[39m.\u001b[39m_NotOkStatusException \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m 57\u001b[0m \u001b[39mif\u001b[39;00m name \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+ "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "norm_lidar_01_model.fit(lidar_01_sensors, lidar_01_labels, epochs=500)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "norm_lidar_01_model.evaluate(val_lidar_01_sensors, val_lidar_01_labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "norm_lidar_01_model.predict(val_lidar_01_sensors)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "norm_lidar_01_model.save('norm_lidar_01_model')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.7 64-bit",
+ "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": "3.10.7"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "5b93afe9c2e217d44e354b055ed180e932b72842f9b6422dadc1ad80da9caf67"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/car/control_client/tensorflow/trainingmodel_sonar_ tensorflow_book.ipynb b/simulations/car/control_client/tensorflow/trainingmodel_sonar_ tensorflow_book.ipynb
new file mode 100644
index 00000000..8fa7eb33
--- /dev/null
+++ b/simulations/car/control_client/tensorflow/trainingmodel_sonar_ tensorflow_book.ipynb
@@ -0,0 +1,875 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "import tensorflow as tf\n",
+ "keras = tf.keras\n",
+ "from keras import Model\n",
+ "\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "# Make numpy values easier to read.\n",
+ "np.set_printoptions(precision=4, suppress=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "filename = \"../sonar_kevin.csv\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sonar_01_train = pd.read_csv(filename, sep = ',')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "metadata": {},
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+ }
+ ],
+ "source": [
+ "norm_sonar_01_model.fit(sonar_01_sensors, sonar_01_labels, epochs=80)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "10/10 [==============================] - 0s 3ms/step - loss: 0.9443 - accuracy: 0.7124 - mae: 0.3319\n"
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+ }
+ ],
+ "source": [
+ "norm_sonar_01_model.evaluate(val_sonar_01_sensors, val_sonar_01_labels)"
+ ]
+ },
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+ "execution_count": 83,
+ "metadata": {},
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+ ]
+ },
+ "execution_count": 83,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "norm_sonar_01_model.predict(val_sonar_01_sensors)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: C:/MakeAIWork/simulations/car/control_client/tensorflow/sonar_model_a\\assets\n"
+ ]
+ }
+ ],
+ "source": [
+ "norm_sonar_01_model.save('C:/MakeAIWork/simulations/car/control_client/tensorflow/sonar_model_a')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.7 64-bit",
+ "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": "3.10.7"
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+ "vscode": {
+ "interpreter": {
+ "hash": "5b93afe9c2e217d44e354b055ed180e932b72842f9b6422dadc1ad80da9caf67"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/car/control_client/tensorflow/trainingmodel_sonar_ tensorflow_python_clean.py b/simulations/car/control_client/tensorflow/trainingmodel_sonar_ tensorflow_python_clean.py
new file mode 100644
index 00000000..2d6407ab
--- /dev/null
+++ b/simulations/car/control_client/tensorflow/trainingmodel_sonar_ tensorflow_python_clean.py
@@ -0,0 +1,47 @@
+import tensorflow as tf
+keras = tf.keras
+from keras import Model
+
+import pandas as pd
+import numpy as np
+
+np.set_printoptions(precision=4, suppress=True)
+
+filename = "../sonar_kevin.csv"
+
+sonar_01_train = pd.read_csv(filename, sep = ',')
+
+sonar_01_labels = sonar_01_train.iloc[:, [3]]
+sonar_01_sensors = sonar_01_train.iloc[:,[0, 1, 2]]
+
+sonar_01_sensors = np.array(sonar_01_sensors)
+
+val_sonar_01_sensors = np.array(sonar_01_sensors [500:799])
+val_sonar_01_labels = np.array(sonar_01_labels [500:799])
+
+normalize = keras.layers.Normalization()
+normalize.adapt(sonar_01_sensors)
+
+norm_sonar_01_model = tf.keras.Sequential([
+ normalize,
+ keras.Input(shape = (3,)),
+ keras.layers.Dense(16, activation = 'relu'),
+ keras.layers.Dense(16, activation = 'relu'),
+ keras.layers.Dense(1)
+])
+
+norm_sonar_01_model.compile(
+ loss = tf.keras.losses.MeanSquaredError(),
+ metrics = ['accuracy', 'mae'],
+ optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)
+ )
+
+norm_sonar_01_model.summary()
+
+norm_sonar_01_model.fit(sonar_01_sensors, sonar_01_labels, epochs=500)
+
+norm_sonar_01_model.evaluate(val_sonar_01_sensors, val_sonar_01_labels)
+
+norm_sonar_01_model.predict(val_sonar_01_sensors)
+
+norm_sonar_01_model.save('norm_sonar_01_model')
\ No newline at end of file
diff --git a/simulations/car/control_client/tensorflow/trainingmodel_tensorflow_sonar_w_scikitlearn(test_train_sep)_book.ipynb b/simulations/car/control_client/tensorflow/trainingmodel_tensorflow_sonar_w_scikitlearn(test_train_sep)_book.ipynb
new file mode 100644
index 00000000..6c2e6f2c
--- /dev/null
+++ b/simulations/car/control_client/tensorflow/trainingmodel_tensorflow_sonar_w_scikitlearn(test_train_sep)_book.ipynb
@@ -0,0 +1,2707 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "import tensorflow as tf\n",
+ "keras = tf.keras\n",
+ "from keras import Model\n",
+ "\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "# Make numpy values easier to read.\n",
+ "np.set_printoptions(precision=4, suppress=True)\n",
+ "\n",
+ "from sklearn.model_selection import train_test_split"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "filename = \"../sonar_kevin.csv\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sonar_01_train = pd.read_csv(filename, sep = ',')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "norm_sonar_01_model.fit(sonar_01_sensors_train, sonar_01_labels_train, epochs=100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
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+ {
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+ "output_type": "stream",
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+ "32/32 [==============================] - 1s 9ms/step - loss: 5.8487 - accuracy: 0.6006 - mae: 1.0236\n"
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+ "metadata": {},
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+ }
+ ],
+ "source": [
+ "norm_sonar_01_model.evaluate(sonar_01_sensors_test, sonar_01_labels_test)"
+ ]
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+ " [ -0.951 ],\n",
+ " [ 21.9679],\n",
+ " [ 0.2384],\n",
+ " [ 20.7914],\n",
+ " [ 21.7976],\n",
+ " [ 0.3824],\n",
+ " [ 0.3042]], dtype=float32)"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "norm_sonar_01_model.predict(sonar_01_sensors_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: C:/MakeAIWork/simulations/car/control_client/tensorflow/modelT\\assets\n"
+ ]
+ }
+ ],
+ "source": [
+ "norm_sonar_01_model.save('C:/MakeAIWork/simulations/car/control_client/tensorflow/modelT')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- Training en Test aanmaken?\n",
+ "- Validatie. Done!\n",
+ "- Hoe data opslaan en exporteren? Check\n",
+ "- Accuracy? - Done\n",
+ "- Model prediction! model.predict inputs + real values (array?) Done!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "https://www.tensorflow.org/guide/keras/save_and_serialize"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "tf.random.set_seed(1) Not working correctly/at all?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "https://www.askpython.com/python-modules/saving-loading-models-tensorflow"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.10.7 64-bit",
+ "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": "3.10.7"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "5b93afe9c2e217d44e354b055ed180e932b72842f9b6422dadc1ad80da9caf67"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/car/control_client/test.py b/simulations/car/control_client/test.py
new file mode 100644
index 00000000..a307777d
--- /dev/null
+++ b/simulations/car/control_client/test.py
@@ -0,0 +1,25 @@
+import tensorflow as tf
+keras = tf.keras
+from keras import Model
+import numpy as np
+
+import time as tm
+import sys as ss
+import os
+import socket as sc
+
+ss.path += [os.path.abspath (relPath) for relPath in ('..',)]
+
+import socket_wrapper as sw
+import parameters as pm
+
+import pickle
+import sklearn as sk
+from sklearn.neural_network import MLPClassifier
+
+mpl = pickle.load('C:/MakeAIWork/simulations/car/control_client/scikitlearn/pickle_model.pkl')
+
+sample = [pm.finity for entryindex in range (pm.sonarInputDim)]
+steeringAngle = mpl.predict(np.array(sample))
+
+print(mpl)
\ No newline at end of file
diff --git a/simulations/car/control_client/test_files/abalone_test.py b/simulations/car/control_client/test_files/abalone_test.py
new file mode 100644
index 00000000..d5fd3823
--- /dev/null
+++ b/simulations/car/control_client/test_files/abalone_test.py
@@ -0,0 +1,46 @@
+import pandas as pd
+import numpy as np
+
+# Make numpy values easier to read.
+np.set_printoptions(precision=3, suppress=True)
+
+import tensorflow as tf
+keras = tf.keras
+
+
+abalone_train = pd.read_csv(
+ "https://storage.googleapis.com/download.tensorflow.org/data/abalone_train.csv",
+ names=["Length", "Diameter", "Height", "Whole weight", "Shucked weight",
+ "Viscera weight", "Shell weight", "Age"])
+
+abalone_train.head()
+
+# print(abalone_train)
+# type(abalone_train)
+
+abalone_features = abalone_train.copy()
+abalone_labels = abalone_features.pop('Age')
+
+# print(abalone_features)
+
+abalone_features = np.array(abalone_features)
+abalone_features
+
+# print(abalone_features)
+
+normalize = keras.layers.Normalization()
+normalize.adapt(abalone_features)
+
+norm_abalone_model = tf.keras.Sequential([
+ normalize,
+ keras.layers.Dense(64),
+ keras.layers.Dense(1)
+])
+
+norm_abalone_model.compile(loss = tf.keras.losses.MeanSquaredError(),
+ optimizer = tf.keras.optimizers.Adam())
+
+norm_abalone_model.fit(abalone_features, abalone_labels, epochs=10)
+
+
+
diff --git a/simulations/car/control_client/test_files/abalone_train.csv b/simulations/car/control_client/test_files/abalone_train.csv
new file mode 100644
index 00000000..0e995da7
--- /dev/null
+++ b/simulations/car/control_client/test_files/abalone_train.csv
@@ -0,0 +1,3320 @@
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+0.485,0.395,0.14,0.6295,0.2285,0.127,0.225,14
+0.51,0.395,0.12,0.6175,0.262,0.122,0.193,12
+0.645,0.525,0.19,1.4635,0.6615,0.3435,0.435,19
+0.465,0.355,0.12,0.4975,0.2375,0.099,0.14,8
+0.53,0.44,0.135,0.7835,0.313,0.1715,0.2185,9
+0.605,0.475,0.145,1.0185,0.4695,0.225,0.27,15
+0.66,0.515,0.155,1.4415,0.7055,0.3555,0.335,10
+0.455,0.375,0.125,0.484,0.2155,0.102,0.165,7
+0.34,0.25,0.09,0.179,0.0775,0.033,0.055,6
+0.725,0.565,0.21,2.1425,1.03,0.487,0.503,14
+0.5,0.375,0.145,0.5795,0.239,0.1375,0.185,9
+0.46,0.355,0.13,0.517,0.2205,0.114,0.165,9
+0.63,0.505,0.155,1.105,0.492,0.226,0.325,11
+0.65,0.515,0.125,1.1805,0.5235,0.283,0.3275,9
+0.575,0.42,0.19,1.764,0.914,0.377,0.4095,10
+0.43,0.34,0.125,0.384,0.1375,0.061,0.146,14
+0.415,0.325,0.115,0.3285,0.1405,0.051,0.106,12
+0.44,0.345,0.17,0.4085,0.15,0.0825,0.1515,12
+0.66,0.5,0.165,1.3195,0.667,0.269,0.341,9
+0.6,0.455,0.17,1.1915,0.696,0.2395,0.24,8
+0.61,0.48,0.165,1.244,0.6345,0.257,0.305,12
+0.675,0.57,0.225,1.587,0.739,0.2995,0.435,10
+0.27,0.195,0.06,0.073,0.0285,0.0235,0.03,5
+0.575,0.475,0.16,0.895,0.3605,0.221,0.271,9
+0.41,0.32,0.095,0.2905,0.141,0.063,0.073,5
+0.535,0.415,0.135,0.78,0.3165,0.169,0.2365,8
+0.465,0.37,0.12,0.4365,0.188,0.0815,0.147,9
+0.45,0.36,0.13,0.478,0.191,0.127,0.137,7
+0.66,0.525,0.16,1.277,0.4975,0.319,0.394,13
+0.555,0.435,0.145,0.6975,0.262,0.1575,0.24,11
+0.585,0.475,0.16,1.0505,0.48,0.234,0.285,10
+0.595,0.48,0.15,1.11,0.498,0.228,0.33,10
+0.595,0.475,0.155,0.984,0.4865,0.184,0.2755,10
+0.68,0.52,0.195,1.4535,0.592,0.391,0.4125,10
+0.62,0.51,0.175,1.615,0.5105,0.192,0.675,12
+0.295,0.22,0.07,0.1365,0.0575,0.0295,0.035,6
+0.445,0.32,0.12,0.378,0.152,0.0825,0.12,8
+0.695,0.55,0.215,1.9565,0.7125,0.541,0.59,14
+0.55,0.425,0.15,0.639,0.269,0.1345,0.217,9
+0.525,0.43,0.165,0.717,0.289,0.1745,0.195,10
+0.605,0.475,0.18,0.9365,0.394,0.219,0.295,15
+0.7,0.525,0.19,1.6015,0.707,0.365,0.43,10
+0.53,0.42,0.13,0.8365,0.3745,0.167,0.249,11
+0.395,0.315,0.105,0.3515,0.1185,0.091,0.1195,16
+0.45,0.355,0.12,0.412,0.1145,0.0665,0.16,19
diff --git a/simulations/car/control_server.py b/simulations/car/control_server.py
new file mode 100644
index 00000000..d302e1c5
--- /dev/null
+++ b/simulations/car/control_server.py
@@ -0,0 +1,62 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import socket as sc
+import time as tm
+
+import simpylc as sp
+import socket_wrapper as sw
+
+class ControlServer:
+ def __init__ (self):
+ with sc.socket (*sw.socketType) as serverSocket:
+ serverSocket.bind (sw.address)
+ serverSocket.listen (sw.maxNrOfConnectionRequests)
+
+ while True:
+ self.clientSocket = serverSocket.accept ()[0]
+ self.socketWrapper = sw.SocketWrapper (self.clientSocket)
+
+ with self.clientSocket:
+ while True:
+ sensors = {
+ 'halfApertureAngle': sp.world.visualisation.scanner.halfApertureAngle,
+ 'halfMiddleApertureAngle': sp.world.visualisation.scanner.halfMiddleApertureAngle
+ } | (
+ {'lidarDistances': sp.world.visualisation.scanner.lidarDistances}
+ if hasattr (sp.world.visualisation.scanner, 'lidarDistances') else
+ {'sonarDistances': sp.world.visualisation.scanner.sonarDistances}
+ )
+
+ self.socketWrapper.send (sensors)
+ tm.sleep (0.02)
+ actuators = self.socketWrapper.recv ()
+
+ sp.world.physics.steeringAngle.set (actuators ['steeringAngle'])
+ sp.world.physics.targetVelocity.set (actuators ['targetVelocity'])
+
+
\ No newline at end of file
diff --git a/simulations/car/dimensions.py b/simulations/car/dimensions.py
new file mode 100644
index 00000000..c1cf58c1
--- /dev/null
+++ b/simulations/car/dimensions.py
@@ -0,0 +1,39 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+wheelDiameter = 0.10
+wheelRadius = wheelDiameter / 2
+displacementPerWheelAngle = sp.radiansPerDegree * wheelRadius
+
+wheelBase = 0.40
+wheelShift = wheelBase / 2
+
+apertureAngle = 120
+middleApertureAngle = 45
\ No newline at end of file
diff --git a/simulations/car/lidar.track b/simulations/car/lidar.track
new file mode 100644
index 00000000..f93f3745
--- /dev/null
+++ b/simulations/car/lidar.track
@@ -0,0 +1,32 @@
+- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+- - - - - - - * - - - * - - - - - - - - - - - - - - - - - - - -
+- - - - - - - - - - - - - - * - - - - - - - - - - - * - - - - -
+- - - * - - - * - - * - - - - - - - * - - - * - - - - - - - * -
+- - - - - * - - - - - - - * - - - - - - @ - - - - * - - - - - -
+- - - - - - - - - - - - - - - - - * - - - - * - - - - - * - - -
+- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+- * - * - - - - - - - - - - - - - - - - - - - - - - - - - - * -
+- - - - - - - - - - - - - - - - - - - - - - - - - - - - * - - -
+- - - - - - - - - - * - - * - - - - - - - - - - - - - - - - - -
+- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+- - * - * - - * - - - - * - - - - * - - - - - - - - - - * - * -
+- - - - - - - - - * - - - - - * - - - - - - - - - - - - - - - -
+- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+- * - * - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+- - - - - - - * - * - - - - - - - * - * - - - - - - - * - * - -
+- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+- - - - - - - - - - - - - - - - - - - - - - - - - - - @ - - - -
+- * - * - - - - * - * - - - - - - - - - - - - - - - - - - - - -
+- - - - - - - - - - - - - - - - - * - * - - - - - - * - * - - -
+- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+- - - - - - - - - * - * - - - - - - - - - - - - - - - - - - - -
+- - * - * - - - - - - - - - - - - - - - - - - - - - - - - - - -
+- - - - - - - - - - - - - - - - - * - * - - - - - - * - * - - -
+- - - - - - - - - * - * - - - - - - - - - - - - - - - - - - - -
+- - - * - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+- * - - - - - - - - - - - - - - - - - - * - - - - - - - - - - -
+- - - - * - - - - * - - - - - - - - * - - - - - - - - * - * - -
+- - - - - - * - * - - * - - - - - - - - - * - - - - - - - - - -
+- - * - - - - - - - - - - - - - - - - - - - - - * - * - - - - -
+- - - - - - * - - * - - - - - - - - - - * - - - - - - - * - - -
+- - - - - - - - - - - - - - - - - - - - - - - - * - - - - - - -
diff --git a/simulations/car/physics.py b/simulations/car/physics.py
new file mode 100644
index 00000000..86194d1b
--- /dev/null
+++ b/simulations/car/physics.py
@@ -0,0 +1,141 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+import dimensions as dm
+
+class Physics (sp.Module):
+ def __init__ (self):
+ sp.Module.__init__ (self)
+
+ self.page ('car physics dashboard')
+
+ self.group ('wheels', True)
+
+ self.acceleration = sp.Register (2)
+
+ self.targetVelocity= sp.Register ()
+ self.velocity = sp.Register ()
+
+ self.steeringAngle = sp.Register ()
+
+ self.group ('position', True)
+
+ self.attitudeAngle = sp.Register (50)
+ self.courseAngle = sp.Register ()
+
+ self.positionX = sp.Register ()
+ self.positionY = sp.Register ()
+
+ self.group('slip', True)
+
+ self.radialAcceleration = sp.Register ()
+ self.slipping = sp.Marker ()
+
+ self.group ('camera')
+
+ self.soccerView = sp.Latch (True)
+ self.heliView = sp.Latch ()
+ self.driverView = sp.Latch ()
+
+ self.driverFocusDist = sp.Register (2)
+
+ self.page ('car physics internals')
+
+ self.group ('wheels', True)
+
+ self.midWheelAngularVelocity = sp.Register ()
+ self.midWheelAngle = sp.Register (30)
+
+ self.midSteeringAngle = sp.Register ()
+ self.inverseMidCurveRadius = sp.Register (20)
+ self.midAngularVelocity = sp.Register ()
+
+ self.group ('position', True)
+
+ self.tangentialVelocity = sp.Register ()
+ self.velocityX = sp.Register ()
+ self.velocityY = sp.Register ()
+
+ self.group('slip', True)
+ self.radialVelocity = sp.Register ()
+
+ self.group ('camera')
+
+ self.soccerViewOneshot = sp.Oneshot ()
+ self.heliViewOneshot = sp.Oneshot ()
+ self.driverViewOneshot = sp.Oneshot ()
+
+ self.driverFocusX = sp.Register ()
+ self.driverFocusY = sp.Register ()
+
+ def sweep (self):
+ self.page ('car physics')
+
+ self.group ('speed')
+
+ self.velocity.set (self.velocity + self.acceleration * sp.world.period, self.velocity < self.targetVelocity, self.velocity - self.acceleration * sp.world.period)
+ self.midWheelAngularVelocity.set (self.velocity / dm.displacementPerWheelAngle)
+ self.midWheelAngle.set (self.midWheelAngle + self.midWheelAngularVelocity * sp.world.period)
+ self.tangentialVelocity.set (self.midWheelAngularVelocity * dm.displacementPerWheelAngle)
+
+ self.group ('direction')
+
+ self.midSteeringAngle.set (sp.atan (0.5 * sp.tan (self.steeringAngle)))
+
+ self.inverseMidCurveRadius.set (sp.sin (self.midSteeringAngle) / dm.wheelShift)
+ self.midAngularVelocity.set (sp.degreesPerRadian * self.tangentialVelocity * self.inverseMidCurveRadius)
+
+ self.attitudeAngle.set (self.attitudeAngle + self.midAngularVelocity * sp.world.period)
+ self.courseAngle.set (self.attitudeAngle + self.midSteeringAngle)
+
+ self.radialAcceleration.set (sp.max (abs (self.tangentialVelocity * self.tangentialVelocity * self.inverseMidCurveRadius) - 0.5, 0))
+ self.slipping.mark (sp.abs (self.radialAcceleration) > 0.55)
+ self.radialVelocity.set (self.radialVelocity + self.radialAcceleration * sp.world.period, self.slipping, 0)
+
+ self.velocityX.set (self.tangentialVelocity * sp.cos (self.courseAngle) + self.radialVelocity * sp.sin (self.courseAngle))
+ self.velocityY.set (self.tangentialVelocity * sp.sin (self.courseAngle) + self.radialVelocity * sp.cos (self.courseAngle))
+
+ self.group ('position')
+
+ self.positionX.set (self.positionX + self.velocityX * sp.world.period)
+ self.positionY.set (self.positionY + self.velocityY * sp.world.period)
+
+ self.group ('camera')
+
+ self.soccerView.unlatch (self.heliViewOneshot or self.driverViewOneshot)
+ self.heliView.unlatch (self.soccerViewOneshot or self.driverViewOneshot)
+ self.driverView.unlatch (self.soccerViewOneshot or self.heliViewOneshot)
+
+ self.soccerViewOneshot.trigger (self.soccerView)
+ self.heliViewOneshot.trigger (self.heliView)
+ self.driverViewOneshot.trigger (self.driverView)
+
+ self.driverFocusX.set (self.positionX + self.driverFocusDist * sp.cos (self.attitudeAngle))
+ self.driverFocusY.set (self.positionY + self.driverFocusDist * sp.sin (self.attitudeAngle))
+
\ No newline at end of file
diff --git a/simulations/car/socket_wrapper.py b/simulations/car/socket_wrapper.py
new file mode 100644
index 00000000..be0049a4
--- /dev/null
+++ b/simulations/car/socket_wrapper.py
@@ -0,0 +1,69 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import socket as sc
+import json as js
+
+address = 'localhost', 50012
+socketType = sc.AF_INET, sc.SOCK_STREAM
+maxNrOfConnectionRequests = 5
+maxMessageLength = 2048
+
+class SocketWrapper:
+ def __init__ (self, clientSocket):
+ self.clientSocket = clientSocket
+
+ def send (self, anObject):
+ buffer = bytes (f'{js.dumps (anObject):<{maxMessageLength}}', 'ascii')
+
+ totalNrOfSentBytes = 0
+
+ while totalNrOfSentBytes < maxMessageLength:
+ nrOfSentBytes = self.clientSocket.send (buffer [totalNrOfSentBytes:])
+
+ if not nrOfSentBytes:
+ self.raiseConnectionError ()
+
+ totalNrOfSentBytes += nrOfSentBytes
+
+ def recv (self):
+ totalNrOfReceivedBytes = 0
+ receivedChunks = []
+
+ while totalNrOfReceivedBytes < maxMessageLength:
+ receivedChunk = self.clientSocket.recv (maxMessageLength - totalNrOfReceivedBytes)
+
+ if not receivedChunk:
+ self.raiseConnectionError ()
+
+ receivedChunks.append (receivedChunk)
+ totalNrOfReceivedBytes += len (receivedChunk)
+
+ return js.loads (b''.join (receivedChunks) .decode ('ascii'))
+
+ def raiseConnectionError (self):
+ raise RuntimeError ('Socket connection broken')
diff --git a/simulations/car/sonar.track b/simulations/car/sonar.track
new file mode 100644
index 00000000..951880c7
--- /dev/null
+++ b/simulations/car/sonar.track
@@ -0,0 +1,32 @@
+- - - - - - - - - - - - - * * * - - - - - - - - - - - - - - - -
+- - - - - - - * * * * * * - - - * * * - - - - - - - - - - - - -
+- - - - - - * - - - - - - - - - - - - * * * * - - - - - - - - -
+- - - - - * - - - - - - - - - - - - - - - - - * - - - - - - - -
+- - - - * - - - - - - - - * * * - - - - @ - - - * - - - - - - -
+- - - * - - - - * * * * * - - - * * * - - - - - - * - - - - - -
+- - * - - - - * - - - - - - - - - - - * * * - - - - * - - - - -
+- * - - - - * - - - - - - - - - - - - - - - * - - - - * - - - -
+* - - - - * - - - - - - - - - - - - - - - - - * - - - - * - - -
+* - - - * - - - - - - - - - - - - - - - - - - - * - - - - * - -
+* - - - * - - - - - - - - - - - - - - - - - - - - * - - - - * -
+* - - - * - - - - - - - - - - - - - - - - - - - - - * - - - * -
+- * - - - * - - - - - - - - - - - - - - - - - - - - * - - - * -
+- * - - - * - - - - - - - - - - - - - - - - - - - - - * - - - *
+- * - - - * - - - - - - - - - - - - - - - - - - - - - * - - - *
+- - * - - - * - - - - - - - - - - - - - - - - - - - - * - - - *
+- - * - - - * - - - - - - - - - - - - - - - - - - - - * - - - *
+- - * - - - * - - - - - - - - - - - - - - - - - - - - * - - - *
+- * - - - * - - - - - - - - - - - - - - - - - - - - * - - - * -
+- * - - - * - - - - - - - - - - - - - - - - - - - * - - - * - -
+- * - - - * - - - - - - - - - - - - - - - - - - * - - - -*- - -
+* - - - * - - - - - - - - - - - - - - - - - - * - - - - * - - -
+* - - - * - - - - - - - - - - - - - - - - - * - - - - * - - - -
+* - - - * - - - - - - - - - - * * * * * * * - - - - * - - - - -
+- * - - - * - - - - - - - - * - - - - - - - - - - * - - - - - -
+- * - - - * - - - - - - - * - - - - - - - - - - * - - - - - - -
+- * - - - - * - - - - - * - - - - - - - - - - * - - - - - - - -
+- - * - - - - * - - - * - - - - * * * * * * * - - - - - - - - -
+- - - * - - - - * * * - - - - * - - - - - - - - - - - - - - - -
+- - - - * - - - - - - - - - * - - - - - - - - - - - - - - - - -
+- - - - - * - - - - - - - * - - - - - - - - - - - - - - - - - -
+- - - - - - * * * * * * * - - - - - - - - - - - - - - - - - - -
diff --git a/simulations/car/visualisation.py b/simulations/car/visualisation.py
new file mode 100644
index 00000000..691c3d7a
--- /dev/null
+++ b/simulations/car/visualisation.py
@@ -0,0 +1,238 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+'''
+
+ z
+ |
+ o -- y
+ /
+ x
+
+'''
+
+import random as rd
+import os
+
+import simpylc as sp
+
+import dimensions as dm
+
+normalFloorColor = (0, 0.001, 0)
+collisionFloorColor = (1, 0, 0.3)
+normalTireColor = (0.03, 0.03, 0.03)
+
+class Scanner (sp.Cylinder):
+ def __init__ (self, apertureAngle, middleApertureAngle, obstacles, **arguments):
+ super () .__init__ (color = (0, 0, 0), **arguments)
+
+ self.apertureAngle = apertureAngle
+ self.halfApertureAngle = self.apertureAngle // 2
+
+ self.middleApertureAngle = middleApertureAngle
+ self.halfMiddleApertureAngle = self.middleApertureAngle // 2
+
+ self.obstacles = obstacles
+
+ if scannerType == 'lidar':
+ self.lidarDistances = [sp.finity for angle in range (self.apertureAngle)] # 0, ..., halfApertureAngle - 1, -halfApertureAngle, ..., -1
+ else:
+ self.sonarDistances = [sp.finity for sectorIndex in range (3)]
+
+ def scan (self, mountPosition, mountAngle):
+ if scannerType == 'lidar':
+ self.lidarDistances = [sp.finity for angle in range (self.apertureAngle)]
+ else:
+ self.sonarDistances = [sp.finity for sectorIndex in range (3)]
+
+ for obstacle in self.obstacles:
+ relativePosition = sp.tSub (obstacle.center, mountPosition)
+ distance = sp.tNor (relativePosition)
+ absoluteAngle = sp.atan2 (relativePosition [1], relativePosition [0])
+ relativeAngle = (round (absoluteAngle - mountAngle) + 180) % 360 - 180
+
+ if -self.halfApertureAngle <= relativeAngle < self.halfApertureAngle - 1: # In case of coincidence, favor nearby obstacle
+ if scannerType == 'lidar':
+ self.lidarDistances [relativeAngle] = round (min (distance, self.lidarDistances [relativeAngle]), 4)
+ else:
+ sectorIndex = (
+ -1
+ if relativeAngle < -self.halfMiddleApertureAngle else
+ 0
+ if relativeAngle < self.halfMiddleApertureAngle else
+ 1
+ )
+
+ self.sonarDistances [sectorIndex] = round (min (distance, self.sonarDistances [sectorIndex]), 4)
+
+class Line (sp.Cylinder):
+ def __init__ (self, **arguments):
+ super () .__init__ (size = (0.01, 0.01, 0), axis = (1, 0, 0), angle = 90, color = (0, 1, 1), **arguments)
+
+class BodyPart (sp.Beam):
+ def __init__ (self, **arguments):
+ super () .__init__ (color = (0.7, 0, 0), **arguments)
+
+class Wheel:
+ def __init__ (self, **arguments):
+ self.suspension = sp.Cylinder (size = (0.01, 0.01, 0.001), axis = (1, 0, 0), angle = 90, pivot = (0, 0, 1), **arguments)
+ self.rim = sp.Beam (size = (0.08, 0.06, 0.02), pivot = (0, 1, 0), color = (0.2, 0, 0))
+ self.tire = sp.Cylinder (size = (dm.wheelDiameter, dm.wheelDiameter, 0.04), axis = (1, 0, 0), angle = 90, color = normalTireColor)
+ self.line = Line ()
+
+ def __call__ (self, wheelAngle, slipping, steeringAngle = 0):
+ return self.suspension (rotation = steeringAngle, parts = lambda:
+ self.rim (rotation = wheelAngle, parts = lambda:
+ self.tire (color = (rd.random (), 0.5 * rd.random (), 0.5 * rd.random ()) if slipping else normalTireColor) +
+ self.line ()
+ ) )
+
+class Window (sp.Beam):
+ def __init__ (self, **arguments):
+ super () .__init__ (axis = (0, 1, 0), color = (0, 0, 0.2), **arguments)
+
+class Floor (sp.Beam):
+ side = 16
+ spacing = 0.5
+ halfSteps = round (0.5 * side / spacing)
+
+ class Stripe (sp.Beam):
+ def __init__ (self, **arguments):
+ super () .__init__ (size = (0.004, Floor.side, 0.001), **arguments)
+
+ def __init__ (self, **arguments):
+ super () .__init__ (size = (self.side, self.side, 0.0005), color = normalFloorColor)
+ self.xStripes = [self.Stripe (center = (0, nr * self.spacing, 0.0001), angle = 90, color = (0, 0.004, 0)) for nr in range (-self.halfSteps, self.halfSteps)]
+ self.yStripes = [self.Stripe (center = (nr * self.spacing, 0, 0), color = (0, 0.004, 0)) for nr in range (-self.halfSteps, self.halfSteps)]
+
+ def __call__ (self, parts):
+ return super () .__call__ (color = collisionFloorColor if self.scene.collided else normalFloorColor, parts = lambda:
+ parts () +
+ sum (xStripe () for xStripe in self.xStripes) +
+ sum (yStripe () for yStripe in self.yStripes)
+ )
+
+class Visualisation (sp.Scene):
+ def __init__ (self):
+ super () .__init__ ()
+ self.roadCones = []
+ trackFileName = 'lidar.track' if scannerType == 'lidar' else 'sonar.track'
+
+ with open (f'{os.path.dirname (os.path.abspath (__file__))}/{trackFileName}') as trackFile:
+ track = trackFile.readlines ()
+
+ for rowIndex, row in enumerate (track):
+ for columnIndex, column in enumerate (row):
+ if column == '*':
+ self.roadCones.append (sp.Cone (
+ size = (0.07, 0.07, 0.15),
+ center = (columnIndex / 4 - 7.75, rowIndex / 2 - 7.75, 0.15),
+ color = (1, 0.3, 0),
+ group = 1
+ ))
+ elif column == "@":
+ self.startX = columnIndex / 4 - 8
+ self.startY = rowIndex / 2 - 8
+ self.init = True
+
+ self.camera = sp.Camera ()
+
+ self.floor = Floor (scene = self)
+
+ self.fuselage = BodyPart (size = (0.65, 0.165, 0.09), center = (0, 0, 0.07), pivot = (0, 0, 1), group = 0)
+ self.fuselageLine = Line ()
+
+ self.wheelFrontLeft = Wheel (center = (dm.wheelShift, 0.08, -0.02))
+ self.wheelFrontRight = Wheel (center = (dm.wheelShift, -0.08, -0.02))
+
+ self.wheelRearLeft = Wheel (center = (-dm.wheelShift, 0.08, -0.02))
+ self.wheelRearRight = Wheel (center = (-dm.wheelShift, -0.08, -0.02))
+
+ self.cabin = BodyPart (size = (0.20, 0.16, 0.06), center = (-0.06, 0, 0.07))
+ self.windowFront = Window (size = (0.045, 0.158, 0.14), center = (0.15, 0, -0.025), angle = -56)
+ self.windowRear = Window (size = (0.042, 0.158, 0.18), center = (-0.18, 0, -0.025),angle = 72)
+
+ self.scanner = Scanner (dm.apertureAngle, dm.middleApertureAngle, self.roadCones, size = (0.02, 0.02, 0.03), center = (0.05, 0, 0.03))
+
+ def display (self):
+ if self.init:
+ self.init = False
+ sp.world.physics.positionX.set (self.startX)
+ sp.world.physics.positionY.set (self.startY)
+
+ if sp.world.physics.soccerView:
+ self.camera (
+ position = sp.tEva ((sp.world.physics.positionX + 2, sp.world.physics.positionY, 2)),
+ focus = sp.tEva ((sp.world.physics.positionX + 0.001, sp.world.physics.positionY, 0))
+ )
+ elif sp.world.physics.heliView:
+ self.camera (
+ position = sp.tEva ((0.0000001, 0, 20)),
+ focus = sp.tEva ((0, 0, 0))
+ )
+ elif sp.world.physics.driverView:
+ self.camera (
+ position = sp.tEva ((sp.world.physics.positionX, sp.world.physics.positionY, 1)),
+ focus = sp.tEva ((sp.world.physics.driverFocusX, sp.world.physics.driverFocusY, 0))
+ )
+
+ self.floor (parts = lambda:
+ self.fuselage (position = (sp.world.physics.positionX, sp.world.physics.positionY, 0), rotation = sp.world.physics.attitudeAngle, parts = lambda:
+ self.cabin (parts = lambda:
+ self.windowFront () +
+ self.windowRear () +
+ self.scanner ()
+ ) +
+
+ self.wheelFrontLeft (
+ wheelAngle = sp.world.physics.midWheelAngle,
+ slipping = sp.world.physics.slipping,
+ steeringAngle = sp.world.physics.steeringAngle
+ ) +
+ self.wheelFrontRight (
+ wheelAngle = sp.world.physics.midWheelAngle,
+ slipping = sp.world.physics.slipping,
+ steeringAngle = sp.world.physics.steeringAngle
+ ) +
+
+ self.wheelRearLeft (
+ wheelAngle = sp.world.physics.midWheelAngle,
+ slipping = sp.world.physics.slipping
+ ) +
+ self.wheelRearRight (
+ wheelAngle = sp.world.physics.midWheelAngle,
+ slipping = sp.world.physics.slipping
+ ) +
+
+ self.fuselageLine ()
+ ) +
+
+ sum (roadCone () for roadCone in self.roadCones)
+ )
+
+ if hasattr (self.fuselage, 'position'):
+ self.scanner.scan (self.fuselage.position, self.fuselage.rotation)
diff --git a/simulations/car/world.py b/simulations/car/world.py
new file mode 100644
index 00000000..d9942b2b
--- /dev/null
+++ b/simulations/car/world.py
@@ -0,0 +1,47 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import os
+import sys as ss
+import time as tm
+
+ss.path += [os.path.abspath (relPath) for relPath in ('../../..', '..')] # If you want to store your simulations somewhere else, put SimPyLC in your PYTHONPATH environment variable
+scannerType = 'lidar' if input ('Lidar or sonar : ') == 'l' else 'sonar' # Should be done prior to any SimPyLC related imports due to concurrency
+
+import simpylc as sp
+
+import control_server as cs
+import physics as ps
+import visualisation as vs
+
+vs.scannerType = scannerType
+
+sp.World (
+ cs.ControlServer,
+ ps.Physics,
+ vs.Visualisation
+)
diff --git a/simulations/one_armed_robot/__pycache__/control.cpython-310.pyc b/simulations/one_armed_robot/__pycache__/control.cpython-310.pyc
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index 00000000..d25d08e5
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diff --git a/simulations/one_armed_robot/__pycache__/timing.cpython-310.pyc b/simulations/one_armed_robot/__pycache__/timing.cpython-310.pyc
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index 00000000..d57ab746
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diff --git a/simulations/one_armed_robot/__pycache__/visualisation.cpython-310.pyc b/simulations/one_armed_robot/__pycache__/visualisation.cpython-310.pyc
new file mode 100644
index 00000000..0ccc3e1f
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diff --git a/simulations/one_armed_robot/__pycache__/world.cpython-310.pyc b/simulations/one_armed_robot/__pycache__/world.cpython-310.pyc
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index 00000000..84fe3992
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diff --git a/simulations/one_armed_robot/control.py b/simulations/one_armed_robot/control.py
new file mode 100644
index 00000000..81d6574b
--- /dev/null
+++ b/simulations/one_armed_robot/control.py
@@ -0,0 +1,190 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+class Control (sp.Module):
+ def __init__ (self):
+ sp.Module.__init__ (self)
+
+ self.page ('movement control')
+
+ self.group ('torso drive control', True)
+ self.torVoltFac = sp.Register (0.8)
+ self.torVoltMax = sp.Register (10)
+ self.torVolt = sp.Register ()
+ self.torEnab = sp.Marker ()
+
+ self.group ('torso angle')
+ self.torAngSet = sp.Register ()
+ self.torAng = sp.Register ()
+ self.torAngOld = sp.Register ()
+ self.torAngDif = sp.Register ()
+ self.torMarg = sp.Register (15)
+ self.torRound = sp.Marker ()
+ self.torSpeedFac = sp.Register (0.5)
+ self.torSpeedMax = sp.Register (20)
+ self.torSpeedSet = sp.Register()
+ self.torSpeed = sp.Register ()
+ self.torSpeedDif = sp.Register ()
+
+ self.group ('general')
+ self.go = sp.Marker ()
+
+ self.group ('upper arm drive control', True)
+ self.uppVoltFac = sp.Register (0.25)
+ self.uppVoltMax = sp.Register (10)
+ self.uppVolt = sp.Register ()
+ self.uppEnab = sp.Marker ()
+
+ self.group ('upper arm angle')
+ self.uppAngSet = sp.Register ()
+ self.uppAng = sp.Register ()
+ self.uppAngOld = sp.Register ()
+ self.uppAngDif = sp.Register ()
+ self.uppMarg = sp.Register (15)
+ self.uppRound = sp.Marker ()
+ self.uppSpeedFac = sp.Register (0.5)
+ self.uppSpeedMax = sp.Register (20)
+ self.uppSpeedSet = sp.Register ()
+ self.uppSpeed = sp.Register ()
+ self.uppSpeedDif = sp.Register ()
+
+ self.group ('fore arm drive control', True)
+ self.forVoltFac = sp.Register (0.25)
+ self.forVoltMax = sp.Register (10)
+ self.forVolt = sp.Register ()
+ self.forEnab = sp.Marker ()
+
+ self.group ('fore arm angle')
+ self.forAngSet = sp.Register ()
+ self.forAng = sp.Register ()
+ self.forAngOld = sp.Register ()
+ self.forAngDif = sp.Register ()
+ self.forMarg = sp.Register (15)
+ self.forRound = sp.Marker ()
+ self.forSpeedFac = sp.Register (0.5)
+ self.forSpeedMax = sp.Register (20)
+ self.forSpeedSet = sp.Register ()
+ self.forSpeed = sp.Register ()
+ self.forSpeedDif = sp.Register ()
+
+ self.group ('wrist drive control', True)
+ self.wriVoltFac = sp.Register (0.25)
+ self.wriVoltMax = sp.Register (10)
+ self.wriVolt = sp.Register ()
+ self.wriEnab = sp.Marker ()
+
+ self.group ('wrist angle')
+ self.wriAngSet = sp.Register ()
+ self.wriAng = sp.Register ()
+ self.wriAngOld = sp.Register ()
+ self.wriAngDif = sp.Register ()
+ self.wriMarg = sp.Register (3)
+ self.wriRound = sp.Marker ()
+ self.wriSpeedFac = sp.Register (0.5)
+ self.wriSpeedMax = sp.Register (20)
+ self.wriSpeedSet = sp.Register ()
+ self.wriSpeed = sp.Register ()
+ self.wriSpeedDif = sp.Register ()
+
+ self.group ('hand and fingers setpoints', True)
+ self.hanAngSet = sp.Register ()
+ self.hanEnab = sp.Marker ()
+ self.finAngSet = sp.Register ()
+ self.finEnab = sp.Marker ()
+ self.finDelay = sp.Register (1)
+ self.finTimer = sp.Timer ()
+ self.finLatch = sp.Latch ()
+
+ self.group ('sweep time measurement')
+ self.sweepMin = sp.Register (1000)
+ self.sweepMax = sp.Register ()
+ self.sweepWatch = sp.Timer ()
+ self.run = sp.Runner ()
+
+ def input (self):
+ self.part ('true angles')
+ self.torAng.set (sp.world.robot.torAng)
+ self.uppAng.set (sp.world.robot.uppAng)
+ self.forAng.set (sp.world.robot.forAng)
+ self.wriAng.set (sp.world.robot.wriAng)
+
+ def sweep (self):
+ self.part ('torso')
+ self.torAngDif.set (self.torAngSet - self.torAng)
+ self.torRound.mark (sp.abs (self.torAngDif) < self.torMarg)
+ self.torSpeedSet.set (sp.limit (self.torSpeedFac * self.torAngDif, self.torSpeedMax))
+ self.torSpeed.set ((self.torAng - self.torAngOld) / sp.world.period)
+ self.torSpeedDif.set (self.torSpeedSet - self.torSpeed)
+ self.torVolt.set (sp.limit (self.torVoltFac * self.torSpeedDif, self.torVoltMax))
+ self.torEnab.mark (self.go)
+ self.torAngOld.set (self.torAng)
+
+ self.part ('upper arm')
+ self.uppAngDif.set (self.uppAngSet - self.uppAng)
+ self.uppRound.mark (sp.abs (self.uppAngDif) < self.uppMarg)
+ self.uppSpeedSet.set (sp.limit (self.uppSpeedFac * self.uppAngDif, self.uppSpeedMax))
+ self.uppSpeed.set ((self.uppAng - self.uppAngOld) / sp.world.period)
+ self.uppSpeedDif.set (self.uppSpeedSet - self.uppSpeed)
+ self.uppVolt.set (sp.limit (self.uppVoltFac * self.uppSpeedDif, self.uppVoltMax))
+ self.uppEnab.mark (self.go and self.torRound)
+ self.uppAngOld.set (self.uppAng)
+
+ self.part ('fore arm')
+ self.forAngDif.set (self.forAngSet - self.forAng)
+ self.forRound.mark (sp.abs (self.forAngDif) < self.forMarg)
+ self.forSpeedSet.set (sp.limit (self.forSpeedFac * self.forAngDif, self.forSpeedMax))
+ self.forSpeed.set ((self.forAng - self.forAngOld) / sp.world.period)
+ self.forSpeedDif.set (self.forSpeedSet - self.forSpeed)
+ self.forVolt.set (sp.limit (self.forVoltFac * self.forSpeedDif, self.forVoltMax))
+ self.forEnab.mark (self.go and self.torRound and self.uppRound)
+ self.forAngOld.set (self.forAng)
+
+ self.part ('wrist')
+ self.wriAngDif.set (self.wriAngSet - self.wriAng)
+ self.wriRound.mark (sp.abs (self.wriAngDif) < self.wriMarg)
+ self.wriSpeedSet.set (sp.limit (self.wriSpeedFac * self.wriAngDif, self.wriSpeedMax))
+ self.wriSpeed.set ((self.wriAng - self.wriAngOld) / sp.world.period)
+ self.wriSpeedDif.set (self.wriSpeedSet - self.wriSpeed)
+ self.wriVolt.set (sp.limit (self.wriVoltFac * self.wriSpeedDif, self.wriVoltMax))
+ self.wriEnab.mark (self.go and self.torRound and self.uppRound and self.forRound)
+ self.wriAngOld.set (self.wriAng)
+
+ self.part ('hand and fingers')
+ self.hanEnab.mark (self.go and self.torRound and self.uppRound and self.forRound and self.wriRound)
+ self.finTimer.reset (not self.hanEnab)
+ self.finEnab.mark (self.finTimer > self.finDelay)
+ self.finLatch.latch (self.finTimer > 0.01)
+
+ self.part ('sweep time measurement')
+ self.sweepMin.set (sp.world.period, sp.world.period < self.sweepMin)
+ self.sweepMax.set (sp.world.period, sp.world.period > self.sweepMax)
+ self.sweepWatch.reset (self.sweepWatch > 2)
+ self.sweepMin.set (1000, not self.sweepWatch)
+ self.sweepMax.set (0, not self.sweepWatch)
+
diff --git a/simulations/one_armed_robot/robot.py b/simulations/one_armed_robot/robot.py
new file mode 100644
index 00000000..b6ef8394
--- /dev/null
+++ b/simulations/one_armed_robot/robot.py
@@ -0,0 +1,156 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+class Robot (sp.Module):
+ def __init__ (self):
+ sp.Module.__init__ (self)
+
+ self.page ('robot physics')
+
+ self.group ('torso electronics', True)
+ self.torVolt = sp.Register ()
+ self.torEnab = sp.Marker ()
+ self.torGain = sp.Register (2)
+ self.torMax = sp.Register (20)
+
+ self.group ('torso mechanics')
+ self.torInert = sp.Register (8)
+ self.torTorq = sp.Register ()
+ self.torBrake = sp.Marker ()
+ self.torAccel = sp.Register ()
+ self.torSpeed = sp.Register ()
+ self.torAng = sp.Register ()
+
+ self.group ('upper arm electronics', True)
+ self.uppVolt = sp.Register ()
+ self.uppEnab = sp.Marker ()
+ self.uppGain = sp.Register (2)
+ self.uppMax = sp.Register (20)
+
+ self.group ('upper arm mechanics')
+ self.uppInert = sp.Register (4)
+ self.uppTorq = sp.Register ()
+ self.uppBrake = sp.Marker ()
+ self.uppAccel = sp.Register ()
+ self.uppSpeed = sp.Register ()
+ self.uppAng = sp.Register ()
+
+ self.group ('fore arm electronics', True)
+ self.forVolt = sp.Register ()
+ self.forEnab = sp.Marker ()
+ self.forGain = sp.Register (2)
+ self.forMax = sp.Register (20)
+
+ self.group ('fore arm mechanics')
+ self.forInert = sp.Register (2)
+ self.forTorq = sp.Register ()
+ self.forBrake = sp.Marker ()
+ self.forAccel = sp.Register ()
+ self.forSpeed = sp.Register ()
+ self.forAng = sp.Register ()
+ self.forShift = sp.Register ()
+
+ self.group ('wrist electronics', True)
+ self.wriVolt = sp.Register ()
+ self.wriEnab = sp.Marker ()
+ self.wriGain = sp.Register (2)
+ self.wriMax = sp.Register (20)
+
+ self.group ('wrist mechanics')
+ self.wriInert = sp.Register (1)
+ self.wriTorq = sp.Register ()
+ self.wriBrake = sp.Marker ()
+ self.wriAccel = sp.Register ()
+ self.wriSpeed = sp.Register ()
+ self.wriAng = sp.Register ()
+
+ self.group ('hand and finger servos', True)
+ self.hanAngSet = sp.Register ()
+ self.hanAng = sp.Register ()
+ self.hanEnab = sp.Marker ()
+ self.finAngSet = sp.Register ()
+ self.finAng = sp.Register ()
+ self.finEnab = sp.Marker ()
+
+ def input (self):
+ self.part ('torso')
+ self.torVolt.set (sp.world.control.torVolt)
+ self.torEnab.mark (sp.world.control.torEnab)
+
+ self.part ('upper arm')
+ self.uppVolt.set (sp.world.control.uppVolt)
+ self.uppEnab.mark (sp.world.control.uppEnab)
+
+ self.part ('fore arm')
+ self.forVolt.set (sp.world.control.forVolt)
+ self.forEnab.mark (sp.world.control.forEnab)
+
+ self.part ('wrist')
+ self.wriVolt.set (sp.world.control.wriVolt)
+ self.wriEnab.mark (sp.world.control.wriEnab)
+
+ self.part ('hand and fingers')
+ self.hanAngSet.set (sp.world.control.hanAngSet)
+ self.hanEnab.mark (sp.world.control.hanEnab)
+ self.finAngSet.set (sp.world.control.finAngSet)
+ self.finEnab.mark (sp.world.control.finEnab)
+
+ def sweep (self):
+ self.part ('Torso')
+ self.torTorq.set (sp.limit (self.torGain * self.torVolt, self.torMax), self.torEnab, 0)
+ self.torBrake.mark (not self.torEnab)
+ self.torAccel.set (self.torSpeed / -sp.world.period, self.torBrake, self.torTorq / self.torInert)
+ self.torSpeed.set (self.torSpeed + self.torAccel * sp.world.period)
+ self.torAng.set (self.torAng + self.torSpeed * sp.world.period)
+
+ self.part ('upper arm')
+ self.uppTorq.set (sp.limit (self.uppGain * self.uppVolt, self.uppMax), self.uppEnab, 0)
+ self.uppBrake.mark (not self.uppEnab)
+ self.uppAccel.set (self.uppSpeed / -sp.world.period, self.uppBrake, self.uppTorq / self.uppInert)
+ self.uppSpeed.set (self.uppSpeed + self.uppAccel * sp.world.period)
+ self.uppAng.set (self.uppAng + self.uppSpeed * sp.world.period)
+
+ self.part ('fore arm')
+ self.forTorq.set (sp.limit (self.forGain * self.forVolt, self.forMax), self.forEnab, 0)
+ self.forBrake.mark (not self.forEnab)
+ self.forAccel.set (self.forSpeed / -sp.world.period, self.forBrake, self.forTorq / self.forInert)
+ self.forSpeed.set (self.forSpeed + self.forAccel * sp.world.period)
+ self.forAng.set (self.forAng + self.forSpeed * sp.world.period)
+
+ self.part ('wrist')
+ self.wriTorq.set (sp.limit (self.wriGain * self.wriVolt, self.wriMax), self.wriEnab, 0)
+ self.wriBrake.mark (not self.wriEnab)
+ self.wriAccel.set (self.wriSpeed / -sp.world.period, self.wriBrake, self.wriTorq / self.wriInert)
+ self.wriSpeed.set (self.wriSpeed + self.wriAccel * sp.world.period)
+ self.wriAng.set (self.wriAng + self.wriSpeed * sp.world.period)
+
+ self.part ('hand and fingers')
+ self.hanAng.set (self.hanAngSet, self.hanEnab)
+ self.finAng.set (self.finAngSet, self.finEnab)
+
diff --git a/simulations/one_armed_robot/robot_plan.jpg b/simulations/one_armed_robot/robot_plan.jpg
new file mode 100644
index 00000000..696058a1
Binary files /dev/null and b/simulations/one_armed_robot/robot_plan.jpg differ
diff --git a/simulations/one_armed_robot/timing.py b/simulations/one_armed_robot/timing.py
new file mode 100644
index 00000000..581485ad
--- /dev/null
+++ b/simulations/one_armed_robot/timing.py
@@ -0,0 +1,61 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+class Timing (sp.Chart):
+ def __init__ (self):
+ sp.Chart.__init__ (self)
+
+ def define (self):
+ self.channel (sp.world.robot.torEnab, sp.white)
+ self.channel (sp.world.robot.torVolt, sp.red, -10, 10, 20)
+ self.channel (sp.world.robot.torAng, sp.red, -220, 220, 70)
+ self.channel (sp.world.robot.torBrake, sp.red)
+
+ self.channel (sp.world.robot.uppEnab, sp.white)
+ self.channel (sp.world.robot.uppVolt, sp.lime, -10, 10, 20)
+ self.channel (sp.world.robot.uppAng, sp.lime, -110, 110, 35)
+ self.channel (sp.world.robot.uppBrake, sp.lime)
+
+ self.channel (sp.world.robot.forEnab, sp.white)
+ self.channel (sp.world.robot.forVolt, sp.blue, -10, 10, 20)
+ self.channel (sp.world.robot.forAng, sp.blue, -110, 110, 35)
+ self.channel (sp.world.robot.forBrake, sp.blue)
+
+ self.channel (sp.world.robot.wriEnab, sp.white)
+ self.channel (sp.world.robot.wriVolt, sp.yellow, -10, 10, 20)
+ self.channel (sp.world.robot.wriAng, sp.yellow, -110, 110, 35)
+ self.channel (sp.world.robot.wriBrake, sp.yellow)
+
+ self.channel (sp.world.robot.hanEnab, sp.white)
+ self.channel (sp.world.robot.hanAng, sp.aqua, -110, 110, 35)
+
+ self.channel (sp.world.robot.finEnab, sp.white)
+ self.channel (sp.world.robot.finAng, sp.fuchsia, -55, 55, 35)
+
+
diff --git a/simulations/one_armed_robot/visualisation.py b/simulations/one_armed_robot/visualisation.py
new file mode 100644
index 00000000..7dcaa2e3
--- /dev/null
+++ b/simulations/one_armed_robot/visualisation.py
@@ -0,0 +1,73 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+class Visualisation (sp.Scene):
+ def __init__ (self):
+ sp.Scene.__init__ (self)
+ self.base = sp.Cylinder (size = (0.3, 0.3, 0.4), center = (0, 0, 0.2), pivot = (0, 0, 1), color = (1, 1, 0.2))
+ self.torso = sp.Beam (size = (0.4, 0.4, 0.6), center = (0, 0, 0.5), pivot = (0, 0, 1), color = (0.5, 0.5, 0.5))
+
+ armColor = (0.7, 0.7, 0.7)
+ self.upperArm = sp.Beam (size = (1, 0.2, 0.2), center = (0.4, -0.3, 0.1), joint = (-0.4, 0, 0), pivot = (0, 1, 0), color = armColor)
+ self.foreArm = sp.Beam (size = (0.7, 0.15, 0.15), center = (0.65, 0.175, 0), joint = (-0.25, 0, 0), pivot = (0, 1, 0), color = armColor)
+ self.wrist = sp.Beam (size = (0.3, 0.1, 0.1), center = (0.40, -0.125, 0), joint = (-0.05, 0, 0), pivot = (0, 1, 0), color = armColor)
+
+ handColor = (1, 0.01, 0.01)
+ handSideSize = (0.1, 0.1, 0.1)
+ self.handCenter = sp.Beam (size = (0.1, 0.09, 0.09), center = (0.15, 0, 0), pivot = (1, 0, 0), color = handColor)
+ self.handSide0 = sp.Beam (size = handSideSize, center = (0, -0.075, -0.075), color = handColor)
+ self.handSide1 = sp.Beam (size = handSideSize, center = (0, 0.075, -0.075), color = handColor)
+ self.handSide2 = sp.Beam (size = handSideSize, center = (0, 0.075, 0.075), color = handColor)
+ self.handSide3 = sp.Beam (size = handSideSize, center = (0, -0.075, 0.075), color = handColor)
+
+ fingerColor = (0.01, 1, 0.01)
+ fingerSize = (0.3, 0.05, 0.05)
+ fingerJoint = (-0.125, 0, 0)
+ self.finger0 = sp.Beam (size = fingerSize, center = (0.15, 0, -0.1), joint = fingerJoint, pivot = (0, -1, 0), color = fingerColor)
+ self.finger1 = sp.Beam (size = fingerSize, center = (0.15, 0, 0.1), joint = fingerJoint, pivot = (0, 1, 0), color = fingerColor)
+ self.finger2 = sp.Beam (size = fingerSize, center = (0.15, -0.1, 0), joint = fingerJoint, pivot = (0, 0, 1), color = fingerColor)
+ self.finger3 = sp.Beam (size = fingerSize, center = (0.15, 0.1, 0), joint = fingerJoint, pivot = (0, 0, -1), color = fingerColor)
+
+ def display (self):
+ self.base (parts = lambda:
+ self.torso (rotation = sp.world.robot.torAng, parts = lambda:
+ self.upperArm (rotation = sp.world.robot.uppAng, parts = lambda:
+ self.foreArm (rotation = sp.world.robot.forAng, shift = (sp.world.robot.forShift, 0, 0), parts = lambda:
+ self.wrist (rotation = sp.world.robot.wriAng, parts = lambda:
+ self.handCenter (rotation = sp.world.robot.hanAng, parts = lambda:
+ self.handSide0 () +
+ self.handSide1 () +
+ self.handSide2 () +
+ self.handSide3 () +
+ self.finger0 (rotation = sp.world.robot.finAng) +
+ self.finger1 (rotation = sp.world.robot.finAng) +
+ self.finger2 (rotation = sp.world.robot.finAng) +
+ self.finger3 (rotation = sp.world.robot.finAng)
+ ) ) ) ) ) )
+
diff --git a/simulations/one_armed_robot/world.py b/simulations/one_armed_robot/world.py
new file mode 100644
index 00000000..d5dc7930
--- /dev/null
+++ b/simulations/one_armed_robot/world.py
@@ -0,0 +1,39 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import os
+import sys as ss
+
+ss.path.append (os.path.abspath ('../..')) # If you want to store your simulations somewhere else, put SimPyLC in your PYTHONPATH environment variable
+
+import simpylc as sp
+import robot as rb
+import control as ct
+import visualisation as vs
+import timing as tm
+
+sp.World (ct.Control, rb.Robot, vs.Visualisation, tm.Timing)
diff --git a/simulations/pid_controller/__pycache__/pid_controller.cpython-310.pyc b/simulations/pid_controller/__pycache__/pid_controller.cpython-310.pyc
new file mode 100644
index 00000000..ce4bc144
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diff --git a/simulations/pid_controller/__pycache__/timing.cpython-310.pyc b/simulations/pid_controller/__pycache__/timing.cpython-310.pyc
new file mode 100644
index 00000000..a4515692
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diff --git a/simulations/pid_controller/__pycache__/world.cpython-310.pyc b/simulations/pid_controller/__pycache__/world.cpython-310.pyc
new file mode 100644
index 00000000..07f9998f
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diff --git a/simulations/pid_controller/native.cpp b/simulations/pid_controller/native.cpp
new file mode 100644
index 00000000..a96c2380
--- /dev/null
+++ b/simulations/pid_controller/native.cpp
@@ -0,0 +1,43 @@
+/*
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicence.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicence for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your licence.
+*/
+
+int const analogInPin = A3;
+int const analogOutPin = 2;
+
+void setup () {
+ // pinMode (analogInPin, INPUT);
+ pinMode (analogOutPin, OUTPUT);
+ Serial.begin(9600);
+}
+
+void loop () {
+ nActIn = analogRead (analogInPin);
+ cycle ();
+ analogWrite (analogOutPin, nOut);
+ Serial.println (nActIn);
+}
+
diff --git a/simulations/pid_controller/pid_controller.py b/simulations/pid_controller/pid_controller.py
new file mode 100644
index 00000000..8f216074
--- /dev/null
+++ b/simulations/pid_controller/pid_controller.py
@@ -0,0 +1,84 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+class PidController (sp.Module):
+ def __init__ (self):
+ sp.Module.__init__ (self)
+
+ self.page ('p i d controller')
+
+ self.group ('inputs', True)
+ self.uRefIn = sp.Register ()
+ self.uActIn = sp.Register ()
+
+ self.group ('outputs')
+ self.uOut = sp.Register ()
+
+ self.group ('p i d settings')
+ self.cProp = sp.Register ()
+ self.cInt = sp.Register (1)
+ self.cDif = sp.Register ()
+
+ self.group ('raw correction terms', True)
+ self.uCorIn = sp.Register ()
+ self.uCorIntIn = sp.Register ()
+ self.uCorDifIn = sp.Register ()
+
+ self.group ('scaled correction terms')
+ self.uCorOut = sp.Register ()
+ self.uCorIntOut = sp.Register ()
+ self.uCorDifOut = sp.Register ()
+
+ self.group ('auxiliary', True)
+ self.uMax = sp.Register (3.5)
+ self.nMax = sp.Register (1024)
+ self.uCorOldIn = sp.Register ()
+ self.nActIn = sp.Register ()
+ self.nOut = sp.Register ()
+
+ self.group ('physics')
+ self.simulatePhysics = sp.Marker ()
+ self.transferFactor = sp.Register (1)
+
+ def sweep (self):
+ self.uRefIn.set (self.uMax / 2)
+ self.nActIn.set (self.transferFactor * self.nOut, self.simulatePhysics)
+ self.uActIn.set (self.nActIn * self.uMax / self.nMax)
+
+ self.uCorOldIn.set (self.uCorIn)
+ self.uCorIn.set (self.uRefIn - self.uActIn)
+ self.uCorIntIn.set (sp.limit (self.uCorIntIn + self.uCorIn * sp.world.period, self.uMax))
+ self.uCorDifIn.set ((self.uCorIn - self.uCorOldIn) / sp.world.period)
+
+ self.uCorOut.set (self.cProp * self.uCorIn)
+ self.uCorIntOut.set (self.cInt * self.uCorIntIn)
+ self.uCorDifOut.set (self.cDif * self.uCorDifIn)
+
+ self.uOut.set (self.uCorOut + self.uCorIntOut + self.uCorDifOut)
+ self.nOut.set (self.uOut * self.nMax / self.uMax)
\ No newline at end of file
diff --git a/simulations/pid_controller/timing.py b/simulations/pid_controller/timing.py
new file mode 100644
index 00000000..1ccf3dff
--- /dev/null
+++ b/simulations/pid_controller/timing.py
@@ -0,0 +1,45 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+absRange = 5
+absHeight = 75
+
+class Timing (sp.Chart):
+ def __init__ (self):
+ sp.Chart.__init__ (self)
+
+ def define (self):
+ self.channel (sp.world.pidController.uRefIn, sp.lime, 0, absRange, absHeight)
+ self.channel (sp.world.pidController.uActIn, sp.lime, 0, absRange, absHeight)
+
+ self.channel (sp.world.pidController.uCorOut, sp.white, -absRange, absRange, 2 * absHeight)
+ self.channel (sp.world.pidController.uCorIntOut, sp.white, -absRange, absRange, 2 * absHeight)
+ self.channel (sp.world.pidController.uCorDifOut, sp.white, -absRange, absRange, 2 *absHeight)
+
+ self.channel (sp.world.pidController.uOut, sp.lime, 0, absRange, absHeight)
diff --git a/simulations/pid_controller/world.py b/simulations/pid_controller/world.py
new file mode 100644
index 00000000..4954bedc
--- /dev/null
+++ b/simulations/pid_controller/world.py
@@ -0,0 +1,37 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import os
+import sys as ss
+
+ss.path.append (os.path.abspath ('../..')) # If you want to store your simulations somewhere else, put SimPyLC in your PYTHONPATH environment variable
+
+import simpylc as sp
+import pid_controller as pc
+import timing as tm
+
+sp.World (pc.PidController, tm.Timing)
diff --git a/simulations/rocket/__pycache__/common.cpython-310.pyc b/simulations/rocket/__pycache__/common.cpython-310.pyc
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index 00000000..abcc06f7
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diff --git a/simulations/rocket/__pycache__/control.cpython-310.pyc b/simulations/rocket/__pycache__/control.cpython-310.pyc
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index 00000000..47b1a048
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diff --git a/simulations/rocket/__pycache__/rocket.cpython-310.pyc b/simulations/rocket/__pycache__/rocket.cpython-310.pyc
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index 00000000..fdbfa8d5
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diff --git a/simulations/rocket/__pycache__/timing.cpython-310.pyc b/simulations/rocket/__pycache__/timing.cpython-310.pyc
new file mode 100644
index 00000000..9cd19a8f
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diff --git a/simulations/rocket/__pycache__/transforms.cpython-310.pyc b/simulations/rocket/__pycache__/transforms.cpython-310.pyc
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index 00000000..68af3896
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diff --git a/simulations/rocket/__pycache__/visualisation.cpython-310.pyc b/simulations/rocket/__pycache__/visualisation.cpython-310.pyc
new file mode 100644
index 00000000..233783c2
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diff --git a/simulations/rocket/__pycache__/world.cpython-310.pyc b/simulations/rocket/__pycache__/world.cpython-310.pyc
new file mode 100644
index 00000000..d13a289e
Binary files /dev/null and b/simulations/rocket/__pycache__/world.cpython-310.pyc differ
diff --git a/simulations/rocket/common.py b/simulations/rocket/common.py
new file mode 100644
index 00000000..b3a16000
--- /dev/null
+++ b/simulations/rocket/common.py
@@ -0,0 +1,55 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+useQuaternions = True
+useGramSchmidt = True # Only matters if useQuaternions == False
+
+g = 10
+
+earthMoonDist = 500
+
+earthDiam = 50
+earthMass = 8e7
+
+moonDiam = 15
+moonMass = 1e6
+
+# Gravity made proportional to r^-0.5 instead of r^-2 to get a more "telling" simulation
+
+gamma = g * (earthDiam / 2) * (earthDiam / 2) / earthMass
+
+def getGravVec (mass0, mass1, diam, relPos):
+ relPos = sp.tEva (relPos)
+ dist = sp.tNor (relPos)
+ factor = -1 if dist > diam / 2 else 0.1
+ return sp.tsMul (sp.tUni (relPos), factor * gamma * mass0 * mass1 / (dist * dist))
+
+
+
+
diff --git a/simulations/rocket/control.py b/simulations/rocket/control.py
new file mode 100644
index 00000000..4cc1cad3
--- /dev/null
+++ b/simulations/rocket/control.py
@@ -0,0 +1,81 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+class Control (sp.Module):
+ def __init__ (self):
+ sp.Module.__init__ (self)
+
+ self.page ('rocket control')
+
+ self.group ('gimbal angle controls blue/yellow', True)
+ self.toYellow = sp.Marker ()
+ self.toBlue = sp.Marker ()
+
+ self.group ('gimbal angle state blue/yellow')
+ self.blueYellowDelta = sp.Register ()
+ self.blueYellowAngle = sp.Register ()
+
+ self.group ('thruster angle controls green/red', True)
+ self.toRed = sp.Marker ()
+ self.toGreen = sp.Marker ()
+
+ self.group ('thruster angle state green/red')
+ self.greenRedDelta = sp.Register ()
+ self.greenRedAngle = sp.Register ()
+
+ self.group ('fuel throttle controls', True)
+ self.throttleOpen = sp.Marker ()
+ self.throttleClose = sp.Marker ()
+
+ self.group ('fuel throttle state')
+ self.throttleDelta = sp.Register ()
+ self.throttlePercent = sp.Register ()
+
+ def input (self):
+ self.part ('gimbal angle blue/yellow')
+ self.blueYellowAngle.set (sp.world.rocket.blueYellowAngle)
+
+ self.part ('thruster angle green/red')
+ self.greenRedAngle.set (sp.world.rocket.greenRedAngle)
+
+ self.part ('fuel throttle')
+ self.throttlePercent.set (sp.world.rocket.throttlePercent)
+
+ def sweep (self):
+ self.part ('gimbal angle blue/yellow')
+ self.blueYellowDelta.set (-1 if self.toBlue else 1 if self.toYellow else 0)
+
+ self.part ('thruster angle green/red')
+ self.greenRedDelta.set (-1 if self.toGreen else 1 if self.toRed else 0)
+
+ self.part ('fuel throttle')
+ self.throttleDelta.set (-1 if self.throttleClose else 1 if self.throttleOpen else 0)
+
+
+
diff --git a/simulations/rocket/rocket.py b/simulations/rocket/rocket.py
new file mode 100644
index 00000000..e6ac5c35
--- /dev/null
+++ b/simulations/rocket/rocket.py
@@ -0,0 +1,325 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import numpy as np
+
+import simpylc as sp
+
+import common as cm
+
+import transforms as tf
+
+# General remarks:
+# - The physical reality of precession indicates that rotation matrix cannot be
+# found by applying angular acceleration in x, y and z direction successively.
+# - To avoid gimbal lock, non-unique Euler angles and numerical instability of
+# (modified) Gram Schmidt, the use of quaternions seems the simplest way to go.
+
+class Rocket (sp.Module):
+ def __init__ (self):
+ sp.Module.__init__ (self)
+
+ self.page ('rocket physics')
+
+ self.group ('gimbal angle blue/yellow', True)
+ self.blueYellowDelta = sp.Register ()
+ self.blueYellowRoughAngle = sp.Register ()
+ self.blueYellowAngle = sp.Register ()
+
+ self.group ('thruster angle green/red')
+ self.greenRedDelta = sp.Register ()
+ self.greenRedRoughAngle = sp.Register ()
+ self.greenRedAngle = sp.Register ()
+
+ self.group ('fuel throttle')
+ self.throttleDelta = sp.Register ()
+ self.throttlePercent = sp.Register ()
+ self.thrust = sp.Register ()
+
+ self.group ('ship')
+ self.shipMass = sp.Register (5000)
+ self.effectiveRadius = sp.Register (0.15)
+ self.effectiveHeight = sp.Register (1.5)
+ self.thrusterTiltSpeed = sp.Register (30)
+ self.thrusterMaxAngle = sp.Register (90)
+ self.throttleSpeed = sp.Register (20)
+ self.thrusterMaxForce = sp.Register (100000)
+
+ self.group ('sweep time measurement')
+ self.sweepMin = sp.Register (1000)
+ self.sweepMax = sp.Register ()
+ self.sweepWatch = sp.Timer ()
+ self.run = sp.Runner ()
+
+ self.group ('linear accelleration', True)
+ self.linAccelX = sp.Register ()
+ self.linAccelY = sp.Register ()
+ self.linAccelZ = sp.Register ()
+
+ self.group ('linear velocity')
+ self.linVelocX = sp.Register ()
+ self.linVelocY = sp.Register ()
+ self.linVelocZ = sp.Register ()
+
+ self.group ('position')
+ self.positionX = sp.Register ()
+ self.positionY = sp.Register ()
+ self.positionZ = sp.Register (cm.earthDiam / 2)
+
+ self.group ('thrust in ship frame')
+ self.forwardThrust = sp.Register ()
+ self.blueYellowThrust = sp.Register ()
+ self.greenRedThrust = sp.Register ()
+
+ self.group ('thrust in world frame')
+ self.thrustX = sp.Register ()
+ self.thrustY = sp.Register ()
+ self.thrustZ = sp.Register ()
+
+ self.group ('angular acceleration', True)
+ self.angAccelX = sp.Register ()
+ self.angAccelY = sp.Register ()
+ self.angAccelZ = sp.Register ()
+
+ self.group ('angular velocity')
+ self.angVelocX = sp.Register ()
+ self.angVelocY = sp.Register ()
+ self.angVelocZ = sp.Register ()
+
+ self.group ('torques in ship frame')
+ self.blueYellowTorque = sp.Register ()
+ self.greenRedTorque = sp.Register ()
+
+ self.group ('torques in world frame')
+ self.torqueX = sp.Register ()
+ self.torqueY = sp.Register ()
+ self.torqueZ = sp.Register ()
+
+ if cm.useQuaternions:
+ self._shipRotQuat = sp.quatFromAxAng (np.array ((1, 0, 0)), 0)
+
+ self.group ('ship rotation quaternion')
+ self.shipRotQuat0 = sp.Register ()
+ self.shipRotQuat1 = sp.Register ()
+ self.shipRotQuat2 = sp.Register ()
+ self.shipRotQuat3 = sp.Register ()
+ self._shipRotMat = sp.rotMatFromQuat (self._shipRotQuat)
+ else:
+ self._shipRotMat = np.array ([ # Columns are tangent (front), normal (up) and binormal (starboard) of ship
+ [1, 0, 0],
+ [0, 1, 0],
+ [0, 0, 1]
+ ])
+
+ self.group ('attitude')
+ self.attitudeX = sp.Register ()
+ self.attitudeY = sp.Register ()
+ self.attitudeZ = sp.Register ()
+
+ self.group ('earth gravity', True)
+ self.distEarthSurf = sp.Register ()
+ self.earthGravX = sp.Register ()
+ self.earthGravY = sp.Register ()
+ self.earthGravZ = sp.Register ()
+
+ self.group ('moon gravity')
+ self.distMoonSurf = sp.Register ()
+ self.moonGravX = sp.Register ()
+ self.moonGravY = sp.Register ()
+ self.moonGravZ = sp.Register ()
+
+ self.group ('total force')
+ self.totalForceX = sp.Register ()
+ self.totalForceY = sp.Register ()
+ self.totalForceZ = sp.Register ()
+
+ def input (self):
+ self.part ('gimbal angle blue/yellow')
+ self.blueYellowDelta.set (sp.world.control.blueYellowDelta)
+
+ self.part ('thruster angle green/red')
+ self.greenRedDelta.set (sp.world.control.greenRedDelta)
+
+ self.part ('fuel throttle')
+ self.throttleDelta.set (sp.world.control.throttleDelta)
+
+ def sweep (self):
+ self.part ('gimbal angle blue/yellow')
+ self.blueYellowRoughAngle.set (
+ sp.limit (
+ self.blueYellowRoughAngle + self.blueYellowDelta * self.thrusterTiltSpeed * sp.world.period,
+ self.thrusterMaxAngle
+ )
+ )
+ self.blueYellowAngle.set (sp.snap (self.blueYellowRoughAngle, 0, 3))
+
+ self.part ('thruster angle green/red')
+ self.greenRedRoughAngle.set (
+ sp.limit (
+ self.greenRedRoughAngle + self.greenRedDelta * self.thrusterTiltSpeed * sp.world.period,
+ self.thrusterMaxAngle
+ )
+ )
+ self.greenRedAngle.set (sp.snap (self.greenRedRoughAngle, 0, 3))
+
+ self.part ('fuel throttle')
+ self.throttlePercent.set (
+ sp.limit (
+ self.throttlePercent + self.throttleDelta * self.throttleSpeed * sp.world.period,
+ 0,
+ 100
+ )
+ )
+ self.thrust.set (self.throttlePercent * self.thrusterMaxForce / 100)
+
+ self.part ('linear movement')
+
+ thrusterForceVec = np.array ((0, 0, self.thrust ()))
+
+ if cm.useQuaternions:
+ thrusterRotQuat = sp.quatMul (
+ sp.quatFromAxAng (np.array ((1, 0, 0)), self.blueYellowAngle),
+ sp.quatFromAxAng (np.array ((0, 1, 0)), -self.greenRedAngle)
+ )
+ shipForceVec = sp.quatVecRot (thrusterRotQuat, thrusterForceVec)
+ else:
+ # Local coord sys, so "forward" order
+ thrusterRotMat = tf.getRotXMat (self.blueYellowAngle) @ tf.getRotYMat (-self.greenRedAngle)
+ shipForceVec = thrusterRotMat @ thrusterForceVec
+
+ self.forwardThrust.set (shipForceVec [2])
+ self.blueYellowThrust.set (shipForceVec [1])
+ self.greenRedThrust.set (shipForceVec [0])
+
+ if cm.useQuaternions:
+ worldForceVec = sp.quatVecRot (self._shipRotQuat, shipForceVec)
+ else:
+ worldForceVec = self._shipRotMat @ shipForceVec
+
+ self.thrustX.set (worldForceVec [0])
+ self.thrustY.set (worldForceVec [1])
+ self.thrustZ.set (worldForceVec [2])
+
+ earthGravVec = cm.getGravVec (self.shipMass, cm.earthMass, cm.earthDiam, sp.tEva ((self.positionX, self.positionY, self.positionZ)))
+ self.earthGravX.set (earthGravVec [0])
+ self.earthGravY.set (earthGravVec [1])
+ self.earthGravZ.set (earthGravVec [2])
+
+ moonGravVec = cm.getGravVec (self.shipMass, cm.moonMass, cm.moonDiam, sp.tSub (sp.tEva ((self.positionX, self.positionY, self.positionZ)), (0, 0, cm.earthMoonDist)))
+ self.moonGravX.set (moonGravVec [0])
+ self.moonGravY.set (moonGravVec [1])
+ self.moonGravZ.set (moonGravVec [2])
+
+ self.totalForceX.set (self.thrustX + self.earthGravX + self.moonGravX)
+ self.totalForceY.set (self.thrustY + self.earthGravY + self.moonGravY)
+ self.totalForceZ.set (self.thrustZ + self.earthGravZ + self.moonGravZ)
+
+ self.linAccelX.set (self.totalForceX / self.shipMass)
+ self.linAccelY.set (self.totalForceY / self.shipMass)
+ self.linAccelZ.set (self.totalForceZ / self.shipMass)
+
+ self.linVelocX.set (self.linVelocX + self.linAccelX * sp.world.period)
+ self.linVelocY.set (self.linVelocY + self.linAccelY * sp.world.period)
+ self.linVelocZ.set (self.linVelocZ + self.linAccelZ * sp.world.period)
+
+ self.positionX.set (self.positionX + self.linVelocX * sp.world.period)
+ self.positionY.set (self.positionY + self.linVelocY * sp.world.period)
+ self.positionZ.set (self.positionZ + self.linVelocZ * sp.world.period)
+
+ self.part ('angular movement')
+
+ rSq = self.effectiveRadius * self.effectiveRadius
+ hSq = self.effectiveHeight * self.effectiveHeight
+
+ # Source: https://en.wikipedia.org/wiki/List_of_moments_of_inertia#List_of_3D_inertia_tensors
+ shipInertMat = self.shipMass () / 12 * np.array (
+ (
+ ((3 * rSq + hSq) / 12 , 0 , 0 ),
+ (0 , (3 * rSq + hSq) / 12 , 0 ),
+ (0 , 0 , rSq / 6)
+ )
+ )
+ invInertMat = np.linalg.inv (self._shipRotMat @ shipInertMat @ self._shipRotMat.T)
+
+ self.blueYellowTorque.set (self.blueYellowThrust * self.effectiveHeight / 2)
+ self.greenRedTorque.set (-self.greenRedThrust * self.effectiveHeight / 2)
+ shipTorqueVec = np.array ((self.blueYellowTorque (), self.greenRedTorque (), 0))
+
+ if cm.useQuaternions:
+ rawTorqueVec = sp.quatVecRot (self._shipRotQuat, shipTorqueVec)
+ else:
+ rawTorqueVec = self._shipRotMat @ shipTorqueVec
+ self.torqueX.set (rawTorqueVec [0])
+ self.torqueY.set (rawTorqueVec [1])
+ self.torqueZ.set (rawTorqueVec [2])
+ torqueVec = np.array ((self.torqueX (), self.torqueY (), self.torqueZ ()))
+
+ rawAngAccelVec = sp.degreesPerRadian * invInertMat @ torqueVec
+
+ self.angAccelX.set (rawAngAccelVec [0])
+ self.angAccelY.set (rawAngAccelVec [1])
+ self.angAccelZ.set (rawAngAccelVec [2])
+
+ self.angVelocX.set (self.angVelocX + self.angAccelX * sp.world.period)
+ self.angVelocY.set (self.angVelocY + self.angAccelY * sp.world.period)
+ self.angVelocZ.set (self.angVelocZ + self.angAccelZ * sp.world.period)
+ angVelocVec = sp.radiansPerDegree * np.array ((self.angVelocX (), self.angVelocY (), self.angVelocZ ()))
+
+ # Actual integration over one timestep
+ # Source: Friendly F# and C++ (fun with game physics), by Dr Giuseppe Maggiore and Dino Dini, May 22, 2014
+ if cm.useQuaternions:
+ # Quaternions are much more numerically stable
+ self._shipRotQuat = sp.normized (self._shipRotQuat + sp.quatMul (sp.quatFromVec (angVelocVec), self._shipRotQuat) / 2 * sp.world.period ())
+
+ self.shipRotQuat0.set (self._shipRotQuat [0])
+ self.shipRotQuat1.set (self._shipRotQuat [1])
+ self.shipRotQuat2.set (self._shipRotQuat [2])
+ self.shipRotQuat3.set (self._shipRotQuat [3])
+
+ self._shipRotQuat [0] = self.shipRotQuat0 ()
+ self._shipRotQuat [1] = self.shipRotQuat1 ()
+ self._shipRotQuat [2] = self.shipRotQuat2 ()
+ self._shipRotQuat [3] = self.shipRotQuat3 ()
+
+ self._shipRotMat = sp.rotMatFromQuat (self._shipRotQuat)
+ else:
+ # N.B. The rotation matrix cannot be found by applying angular velocity in x, y and z direction successively
+ self._shipRotMat = self._shipRotMat + np.cross (angVelocVec, self._shipRotMat, axisb = 0, axisc = 0) * sp.world.period ()
+ if cm.useGramSchmidt:
+ tf.modifiedGramSchmidt (self._shipRotMat)
+
+ rawAttitudeVec = tf.getXyzAngles (self._shipRotMat)
+ self.attitudeX.set (rawAttitudeVec [0])
+ self.attitudeY.set (rawAttitudeVec [1])
+ self.attitudeZ.set (rawAttitudeVec [2])
+
+ self.part ('sweep time measurement')
+ self.sweepMin.set (sp.world.period, sp.world.period < self.sweepMin)
+ self.sweepMax.set (sp.world.period, sp.world.period > self.sweepMax)
+ self.sweepWatch.reset (self.sweepWatch > 2)
+ self.sweepMin.set (1000, not self.sweepWatch)
+
diff --git a/simulations/rocket/thruster_rotation.jpg b/simulations/rocket/thruster_rotation.jpg
new file mode 100644
index 00000000..dd75fdc6
Binary files /dev/null and b/simulations/rocket/thruster_rotation.jpg differ
diff --git a/simulations/rocket/timing.py b/simulations/rocket/timing.py
new file mode 100644
index 00000000..91a063c9
--- /dev/null
+++ b/simulations/rocket/timing.py
@@ -0,0 +1,48 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+import common as cm
+
+class Timing (sp.Chart):
+ def __init__ (self):
+ sp.Chart.__init__ (self)
+
+ def define (self):
+ '''
+ if useQuaternions:
+ self.channel (sp.world.rocket.shipRotQuat0, sp.white, -1, 1, 100)
+ self.channel (sp.world.rocket.shipRotQuat1, sp.white, -1, 1, 100)
+ self.channel (sp.world.rocket.shipRotQuat2, sp.white, -1, 1, 100)
+ self.channel (sp.world.rocket.shipRotQuat3, sp.white, -1, 1, 100)
+ '''
+
+ self.channel (sp.world.rocket.attitudeX, sp.red, -180, 180, 100)
+ self.channel (sp.world.rocket.attitudeY, sp.green, -180, 180, 100)
+ self.channel (sp.world.rocket.attitudeZ, sp.blue, -180, 180, 100)
+
diff --git a/simulations/rocket/transforms.py b/simulations/rocket/transforms.py
new file mode 100644
index 00000000..010e35a8
--- /dev/null
+++ b/simulations/rocket/transforms.py
@@ -0,0 +1,100 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import numpy as np
+
+import simpylc as sp
+
+# Angles are in degrees, gonio functions are defined accordingly in SimPyLC,
+# and will also call evaluate
+
+def getRotXMat (angleX):
+ c = sp.cos (angleX)
+ s = sp.sin (angleX)
+ return np.array ([
+ [1, 0, 0],
+ [0, c, -s],
+ [0, s, c]
+ ])
+
+def getRotYMat (angleY):
+ c = sp.cos (angleY)
+ s = sp.sin (angleY)
+ return np.array ([
+ [c, 0, s],
+ [0, 1, 0],
+ [-s, 0, c]
+ ])
+
+def getRotZMat (angleZ):
+ c = sp.cos (angleZ)
+ s = sp.sin (angleZ)
+ return np.array ([
+ [c, -s, 0],
+ [s, c, 0],
+ [0, 0, 1]
+ ])
+
+def isClose(x, y):
+ return abs (x - y) <= 1e-8 + 1e-5 * abs (y)
+
+def getXyzAngles (rotMat): # rotMat == rotMatZ @ rotMatY @ rotMatX
+ # Source: Computing Euler angles from a rotation matrix, by Gregory G. Slabaugh
+ # http://thomasbeatty.com/MATH%20PAGES/ARCHIVES%20-%20NOTES/Applied%20Math/euler%20angles.pdf
+ angleZ = 0
+ if isClose (rotMat [2, 0], -1):
+ angleY = sp.pi / 2.0
+ angleX = sp.atan2 (rotMat [0, 1], rotMat [0, 2])
+ elif isClose (rotMat [2, 0], 1):
+ angleY = -sp.pi / 2
+ angleX = sp.atan2 (-rotMat [0, 1], -rotMat [0, 2])
+ else:
+ angleY = -sp.asin (rotMat [2, 0])
+ cosAngleY = sp.cos (angleY)
+ angleX = sp.atan2 (rotMat [2, 1] / cosAngleY, rotMat [2, 2] / cosAngleY)
+ angleZ = sp.atan2 (rotMat [1, 0] / cosAngleY, rotMat [0, 0] / cosAngleY)
+ return np.array ([angleX, angleY, angleZ])
+
+def modifiedGramSchmidt (rotMat): # Numpy QR factorization can't be used, since it gives a Q with a changed orientation
+
+ # Create references to column vectors
+ t = rotMat [ : , 0]
+ n = rotMat [ : , 1]
+ b = rotMat [ : , 2]
+
+ # Normalize T
+ t /= np.linalg.norm (t)
+
+ # Remove projection of T from N and normalize
+ n -= t * np.dot (n, t)
+ n /= np.linalg.norm (n)
+
+ # Compute B perpendicular to T and N with right orientation
+ # Remove projection of
+ b -= t * np.dot (b, t)
+ b -= n * np.dot (b, n)
+ b /= np.linalg.norm (b)
diff --git a/simulations/rocket/visualisation.py b/simulations/rocket/visualisation.py
new file mode 100644
index 00000000..3ddee90b
--- /dev/null
+++ b/simulations/rocket/visualisation.py
@@ -0,0 +1,99 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import random as rd
+
+import simpylc as sp
+
+import common as cm
+
+rd.seed ()
+
+'''
+
+ z
+ |
+ o -- y
+ /
+ x
+
+'''
+
+
+class Visualisation (sp.Scene):
+ def __init__ (self):
+ sp.Scene.__init__ (self)
+
+ self.camera = sp.Camera ()
+
+ self.earth = sp.Ellipsoid (size = 3 * (cm.earthDiam,), center = (0, 0, 0), color = (0, 0, 0.9))
+ self.moon = sp.Ellipsoid (size = 3 * (cm.moonDiam,), center = (0, 0, cm.earthMoonDist), color = (0.6, 0.6, 0.6))
+
+ self.body = sp.Cylinder (size = (0.3, 0.3, 1), center = (0, 0, 0.85 + 0.4), pivot = (0, 0, 1), color = (1, 1, 0.2))
+ self.nose = sp.Cone (size = (0.3, 0.3, 0.5), center = (0, 0, 0.75), color = (1, 1, 0.2))
+ self.bracket = sp.Cylinder (size = (0.1, 0.1, 0.1), center = (0, 0, -0.55), color = (1, 1, 0.2))
+ self.gimbal = sp.Ellipsoid (size = 3 * (0.12,), center = (0, 0, -0.05), pivot = (1, 0, 0), color = (1, 1, 0.2))
+ self.thruster = sp.Cone (size = (0.2, 0.2, 0.3), pivot = (0, -1, 0), center = (0, 0, -0.09), joint = (0, 0, 0.09), color = (1, 1, 0.2)) # See thruster_rotation.jpg for pivot
+ # Center at -(0.3/2 - 0.12/2)
+ self.flame = sp.Cone (size = (0.1, 0.1, 1), center = (0, 0, -0.65), joint = (0, 0, 0.5), axis = (0, 1, 0), angle = 180, color = (1, 0.7, 0))
+ self.tankRed = sp.Ellipsoid (size = 3 * (0.1,), center = (0.16, 0, 0), color = (1, 0, 0))
+ self.tankGreen = sp.Ellipsoid (size = 3 * (0.1,), center = (-0.16, 0, 0), color = (0, 1, 0))
+ self.tankYellow = sp.Ellipsoid (size = 3 * (0.1,), center = (0, 0.16, 0), color = (1, 1, 0))
+ self.tankBlue = sp.Ellipsoid (size = 3 * (0.1,), center = (0, -0.16, 0), color = (0, 0, 1))
+
+ def display (self):
+ self.camera (
+ position = sp.tEva ((sp.world.rocket.positionX + 4, sp.world.rocket.positionY, sp.world.rocket.positionZ)),
+ focus = sp.tEva ((sp.world.rocket.positionX, sp.world.rocket.positionY, sp.world.rocket.positionZ + 1.5))
+ )
+
+ self.earth ()
+ self.moon ()
+
+ self.body (
+ position = sp.tEva ((sp.world.rocket.positionX, sp.world.rocket.positionY, sp.world.rocket.positionZ)),
+ attitude = sp.world.rocket._shipRotMat,
+ parts = lambda:
+ self.nose () +
+ self.bracket (
+ parts = lambda:
+ self.tankGreen () +
+ self.tankRed () +
+ self.tankBlue () +
+ self.tankYellow () +
+ self.gimbal (
+ rotation = sp.world.rocket.blueYellowAngle,
+ parts = lambda:
+ self.thruster (
+ rotation = sp.world.rocket.greenRedAngle,
+ parts = lambda:
+ self.flame (
+ scale = sp.tsMul ((1, 1, 1),
+ sp.world.rocket.thrust / sp.world.rocket.thrusterMaxForce * (0.9 + 0.1 * rd.random ())),
+ color = (1, 0.3 + 0.7 * rd.random (), 0))
+ ) ) ) )
+
diff --git a/simulations/rocket/world.py b/simulations/rocket/world.py
new file mode 100644
index 00000000..40410046
--- /dev/null
+++ b/simulations/rocket/world.py
@@ -0,0 +1,39 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import os
+import sys as ss
+
+ss.path.append (os.path.abspath ('../..')) # If you want to store your simulations somewhere else, put SimPyLC in your PYTHONPATH environment variable
+
+import simpylc as sp
+import rocket as rk
+import control as ct
+import visualisation as vs
+import timing as tm
+
+sp.World (rk.Rocket, ct.Control, vs.Visualisation, tm.Timing)
diff --git a/simulations/train/__pycache__/control.cpython-310.pyc b/simulations/train/__pycache__/control.cpython-310.pyc
new file mode 100644
index 00000000..64ba1057
Binary files /dev/null and b/simulations/train/__pycache__/control.cpython-310.pyc differ
diff --git a/simulations/train/__pycache__/physics.cpython-310.pyc b/simulations/train/__pycache__/physics.cpython-310.pyc
new file mode 100644
index 00000000..8e16e264
Binary files /dev/null and b/simulations/train/__pycache__/physics.cpython-310.pyc differ
diff --git a/simulations/train/__pycache__/timing.cpython-310.pyc b/simulations/train/__pycache__/timing.cpython-310.pyc
new file mode 100644
index 00000000..28072506
Binary files /dev/null and b/simulations/train/__pycache__/timing.cpython-310.pyc differ
diff --git a/simulations/train/__pycache__/world.cpython-310.pyc b/simulations/train/__pycache__/world.cpython-310.pyc
new file mode 100644
index 00000000..66c27eb9
Binary files /dev/null and b/simulations/train/__pycache__/world.cpython-310.pyc differ
diff --git a/simulations/train/control.py b/simulations/train/control.py
new file mode 100644
index 00000000..bf88487c
--- /dev/null
+++ b/simulations/train/control.py
@@ -0,0 +1,96 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+class Control (sp.Module):
+ def __init__ (self):
+ sp.Module.__init__ (self)
+
+ self.page ('train control')
+
+ self.group ('control buttons', True)
+ self.brakeLiftButton = sp.Marker ()
+ self.driveEnableButton = sp.Marker ()
+
+ self.group ('warning lamps')
+ self.brakeWarnLamp = sp.Marker ()
+ self.speedWarnLamp = sp.Marker ()
+
+ self.group ('state')
+ self.accel = sp.Register ()
+ self.speed = sp.Register ()
+ self.position = sp.Register ()
+
+ self.group ('auxiliary', True)
+ self.maxSpeed = sp.Register ()
+ self.speedWarnFraction = sp.Register (0.8)
+ self.oldSpeed = sp.Register ()
+ self.blinkTime = sp.Register (0.5)
+ self.blinkTimer = sp.Timer ()
+ self.blinkEdge = sp.Oneshot ()
+ self.blinkOn = sp.Marker ()
+
+ self.group ('sweep time measurement')
+ self.sweepMin = sp.Register (sp.finity)
+ self.sweepMax = sp.Register ()
+ self.watchTime = sp.Register (2)
+ self.watchTimer = sp.Timer ()
+ self.run = sp.Runner ()
+
+ def input (self):
+ self.part ('speed')
+ self.speed.set (sp.world.physics.speed)
+ self.maxSpeed.set (sp.world.physics.maxSpeed)
+
+ self.part ('position')
+ self.position.set (sp.world.physics.position)
+
+ def sweep (self):
+ self.part ('dynamics')
+ self.accel.set ((self.speed - self.oldSpeed) / sp.world.period)
+ self.oldSpeed.set (self.speed)
+
+ self.part ('warnings')
+ self.blinkTimer.reset (self.blinkTimer > self.blinkTime)
+ self.blinkEdge.trigger (not self.blinkTimer)
+ self.blinkOn.mark (not self.blinkOn, self.blinkEdge)
+ self.brakeWarnLamp.mark (not self.brakeLiftButton and self.driveEnableButton and self.blinkOn)
+ self.speedWarnLamp.mark (self.speed > self.speedWarnFraction * self.maxSpeed and self.blinkOn)
+
+ self.part ('sweep time masurement')
+ self.sweepMin.set (sp.world.period, sp.world.period < self.sweepMin)
+ self.sweepMax.set (sp.world.period, sp.world.period > self.sweepMax)
+ self.watchTimer.reset (self.watchTimer > self.watchTime)
+ self.sweepMin.set (sp.finity, not self.watchTimer)
+ self.sweepMax.set (0, not self.watchTimer)
+
+ def output (self):
+ self.part ('control signals')
+ sp.world.physics.brakeLift.mark (self.brakeLiftButton)
+ sp.world.physics.driveEnable.mark (self.driveEnableButton)
+
diff --git a/simulations/train/physics.py b/simulations/train/physics.py
new file mode 100644
index 00000000..b52eaa8d
--- /dev/null
+++ b/simulations/train/physics.py
@@ -0,0 +1,65 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+class Physics (sp.Module):
+ def __init__ (self):
+ sp.Module.__init__ (self)
+
+ self.page ('train physics')
+
+ self.group ('control signals', True)
+ self.brakeLift = sp.Marker ()
+ self.driveEnable = sp.Marker ()
+
+ self.group ('state')
+ self.targetAccel = sp.Register ()
+ self.speed = sp.Register ()
+ self.position = sp.Register ()
+
+ self.group ('limits', True)
+ self.maxBrakeDecel = sp.Register (5)
+ self.maxDriveAccel= sp.Register (2)
+ self.maxDriveDecel = sp.Register (3)
+ self.maxSpeed = sp.Register (30)
+ self.maxPosition = sp.Register (20_000)
+
+ self.group ('auxiliary')
+ self.brakeAccel = sp.Register ()
+ self.driveAccel = sp.Register ()
+
+ def sweep (self):
+ self.part ('acceleration')
+ self.brakeAccel.set (0, self.brakeLift, -self.maxBrakeDecel)
+ self.driveAccel.set (self.maxDriveAccel, self.driveEnable, -self.maxDriveDecel)
+ self.targetAccel.set (self.brakeAccel + self.driveAccel)
+
+ self.part ('integration')
+ self.speed.set (sp.limit (self.speed + self.targetAccel * sp.world.period, 0, self.maxSpeed))
+ self.position.set (self.position + self.speed * sp.world.period)
+
\ No newline at end of file
diff --git a/simulations/train/timing.py b/simulations/train/timing.py
new file mode 100644
index 00000000..2d9c4c93
--- /dev/null
+++ b/simulations/train/timing.py
@@ -0,0 +1,44 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import simpylc as sp
+
+class Timing (sp.Chart):
+ def __init__ (self):
+ sp.Chart.__init__ (self)
+
+ def define (self):
+ self.channel (sp.world.control.brakeLiftButton, sp.lime)
+ self.channel (sp.world.control.driveEnableButton, sp.lime)
+
+ self.channel (sp.world.control.brakeWarnLamp, sp.red)
+ self.channel (sp.world.control.speedWarnLamp, sp.red)
+
+ self.channel (sp.world.physics.targetAccel, sp.yellow, -10, 5, 100)
+
+ self.channel (sp.world.control.speed, sp.white, 0, 50, 200)
+ self.channel (sp.world.control.accel, sp.white, -10, 5, 100)
diff --git a/simulations/train/world.py b/simulations/train/world.py
new file mode 100644
index 00000000..f6dbe040
--- /dev/null
+++ b/simulations/train/world.py
@@ -0,0 +1,38 @@
+'''
+====== Legal notices
+
+Copyright (C) 2013 - 2021 GEATEC engineering
+
+This program is free software.
+You can use, redistribute and/or modify it, but only under the terms stated in the QQuickLicense.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY, without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the QQuickLicense for details.
+
+The QQuickLicense can be accessed at: http://www.qquick.org/license.html
+
+__________________________________________________________________________
+
+
+ THIS PROGRAM IS FUNDAMENTALLY UNSUITABLE FOR CONTROLLING REAL SYSTEMS !!
+
+__________________________________________________________________________
+
+It is meant for training purposes only.
+
+Removing this header ends your license.
+'''
+
+import os
+import sys as ss
+
+ss.path.append (os.path.abspath ('../..')) # If you want to store your simulations somewhere else, put SimPyLC in your PYTHONPATH environment variable
+
+import simpylc as sp
+import physics as ph
+import control as ct
+import timing as tm
+
+sp.World (ph.Physics, ct.Control, tm.Timing)
\n",
+ "
\n",
+ "Author: Jeroen Boogaard\n",
+ "
\n",
+ "
\n",
+ "Author: Jeroen Boogaard\n",
+ "