Material for ASPP 2024
This commit is contained in:
Pietro Berkes
commit
849682b13b
30
.gitignore
vendored
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30
.gitignore
vendored
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# OS specific
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.DS_Store
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# Editors
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.vscode
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.idea
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*.swp
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# Python
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.venv/
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build/
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_build/
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dist/
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eggs/
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.eggs/
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sdist/
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*.egg-info/
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*.egg
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*.pyc
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__pycache__/
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tests-reports/
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.mypy_cache/
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.pytest_cache/
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.env
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# Jupyter
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.ipynb_checkpoints/
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# Project
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_archive/
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6
LICENSE.txt
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6
LICENSE.txt
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@ -0,0 +1,6 @@
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The material in this repository is released under the
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CC Attribution-Share Alike 4.0 International
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license.
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Full license text available at
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https://creativecommons.org/licenses/by-sa/4.0/
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10
README.md
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10
README.md
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@ -0,0 +1,10 @@
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# Scientific programming patterns
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Material for the class "Scientific programming patterns", first given as ASPP 2022 in Bilbao
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# From this:
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||||
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# To this:
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||||
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||||
182
code_snippets/factory_methods.ipynb
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182
code_snippets/factory_methods.ipynb
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|
@ -0,0 +1,182 @@
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{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-08-12T12:14:28.926189Z",
|
||||
"start_time": "2022-08-12T12:14:28.923089Z"
|
||||
},
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
"import numpy as np"
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||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-08-12T12:21:00.003395Z",
|
||||
"start_time": "2022-08-12T12:20:59.997851Z"
|
||||
},
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dict_ = {'a': 3.1, 'b': 4.2}\n",
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||||
"with open('my_class.json', 'w') as f:\n",
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" json.dump(dict_, f)"
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||||
]
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||||
},
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||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-08-12T12:23:03.889266Z",
|
||||
"start_time": "2022-08-12T12:23:03.883050Z"
|
||||
},
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class MyClass:\n",
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||||
" \n",
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||||
" def __init__(self, a, b):\n",
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||||
" \"\"\"The basic constructor takes 'raw' values.\"\"\"\n",
|
||||
" self.a = a\n",
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||||
" self.b = b\n",
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||||
" \n",
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||||
" @classmethod\n",
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||||
" def from_random_values(cls, random_state=np.random):\n",
|
||||
" \"\"\"Create a MyClass instance with random parameters.\"\"\"\n",
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||||
" a = random_state.rand()\n",
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||||
" b = random_state.randn()\n",
|
||||
" return cls(a, b)\n",
|
||||
" \n",
|
||||
" @classmethod\n",
|
||||
" def from_json(cls, json_fname):\n",
|
||||
" \"\"\"Create a MyClass instance with parameters read form a json file.\"\"\"\n",
|
||||
" with open(json_fname, 'r') as f:\n",
|
||||
" dict_ = json.load(f)\n",
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||||
" a = dict_['a']\n",
|
||||
" b = dict_['b']\n",
|
||||
" return cls(a, b)\n",
|
||||
"\n",
|
||||
"my_class = MyClass.from_random_values()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-08-12T12:23:12.242599Z",
|
||||
"start_time": "2022-08-12T12:23:12.237477Z"
|
||||
},
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'a': 0.842940228048758, 'b': 0.2797222990193814}"
|
||||
]
|
||||
},
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"my_class = MyClass.from_random_values()\n",
|
||||
"my_class.__dict__"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-08-12T12:23:44.439726Z",
|
||||
"start_time": "2022-08-12T12:23:44.432540Z"
|
||||
},
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'a': 3.1, 'b': 4.2}"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"my_class = MyClass.from_json('my_class.json')\n",
|
||||
"my_class.__dict__"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"hide_input": false,
|
||||
"kernelspec": {
|
||||
"display_name": "Python [conda env:bog]",
|
||||
"language": "python",
|
||||
"name": "conda-env-bog-py"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.5"
|
||||
},
|
||||
"toc": {
|
||||
"nav_menu": {
|
||||
"height": "12px",
|
||||
"width": "252px"
|
||||
},
|
||||
"navigate_menu": true,
|
||||
"number_sections": true,
|
||||
"sideBar": true,
|
||||
"threshold": 4,
|
||||
"toc_cell": false,
|
||||
"toc_section_display": "block",
|
||||
"toc_window_display": false
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
287
code_snippets/walker_initializers.ipynb
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287
code_snippets/walker_initializers.ipynb
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@ -0,0 +1,287 @@
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|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-09-02T11:13:12.648377Z",
|
||||
"start_time": "2022-09-02T11:13:12.165387Z"
|
||||
},
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-09-02T11:14:10.246402Z",
|
||||
"start_time": "2022-09-02T11:14:10.235135Z"
|
||||
},
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class Walker:\n",
|
||||
" \"\"\" The Walker knows how to walk at random on a context map. \"\"\"\n",
|
||||
"\n",
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||||
" def __init__(self, sigma_i, sigma_j, size, map_type='flat'):\n",
|
||||
" self.sigma_i = sigma_i\n",
|
||||
" self.sigma_j = sigma_j\n",
|
||||
" self.size = size\n",
|
||||
"\n",
|
||||
" if map_type == 'flat':\n",
|
||||
" context_map = np.ones((size, size))\n",
|
||||
" elif map_type == 'hills':\n",
|
||||
" grid_ii, grid_jj = np.mgrid[0:size, 0:size]\n",
|
||||
" i_waves = np.sin(grid_ii / 130) + np.sin(grid_ii / 10)\n",
|
||||
" i_waves /= i_waves.max()\n",
|
||||
" j_waves = np.sin(grid_jj / 100) + np.sin(grid_jj / 50) + \\\n",
|
||||
" np.sin(grid_jj / 10)\n",
|
||||
" j_waves /= j_waves.max()\n",
|
||||
" context_map = j_waves + i_waves\n",
|
||||
" elif map_type == 'labyrinth':\n",
|
||||
" context_map = np.ones((size, size))\n",
|
||||
" context_map[50:100, 50:60] = 0\n",
|
||||
" context_map[20:89, 80:90] = 0\n",
|
||||
" context_map[90:120, 0:10] = 0\n",
|
||||
" context_map[120:size, 30:40] = 0\n",
|
||||
" context_map[180:190, 50:60] = 0\n",
|
||||
"\n",
|
||||
" context_map[50:60, 50:200] = 0\n",
|
||||
" context_map[179:189, 80:130] = 0\n",
|
||||
" context_map[110:120, 0:190] = 0\n",
|
||||
" context_map[120:size, 30:40] = 0\n",
|
||||
" context_map[180:190, 50:60] = 0\n",
|
||||
" context_map /= context_map.sum()\n",
|
||||
" self.context_map = context_map\n",
|
||||
"\n",
|
||||
" # Pre-compute a 2D grid of coordinates for efficiency\n",
|
||||
" self._grid_ii, self._grid_jj = np.mgrid[0:size, 0:size]\n",
|
||||
"\n",
|
||||
"walker = Walker(sigma_i=3, sigma_j=4, size=200, map_type='hills')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-09-02T11:15:44.357965Z",
|
||||
"start_time": "2022-09-02T11:15:44.351558Z"
|
||||
},
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class Walker:\n",
|
||||
" \"\"\" The Walker knows how to walk at random on a context map. \"\"\"\n",
|
||||
"\n",
|
||||
" def __init__(self, sigma_i, sigma_j, size, context_map):\n",
|
||||
" self.sigma_i = sigma_i\n",
|
||||
" self.sigma_j = sigma_j\n",
|
||||
" self.size = size\n",
|
||||
" self.context_map = context_map\n",
|
||||
" # Pre-compute a 2D grid of coordinates for efficiency\n",
|
||||
" self._grid_ii, self._grid_jj = np.mgrid[0:size, 0:size]\n",
|
||||
"\n",
|
||||
" @classmethod\n",
|
||||
" def from_context_map_type(cls, sigma_i, sigma_j, size, map_type):\n",
|
||||
" \"\"\" Create an instance of Walker with a context map defined by type.\"\"\"\n",
|
||||
" if map_type == 'flat':\n",
|
||||
" context_map = np.ones((size, size))\n",
|
||||
" elif map_type == 'hills':\n",
|
||||
" grid_ii, grid_jj = np.mgrid[0:size, 0:size]\n",
|
||||
" i_waves = np.sin(grid_ii / 130) + np.sin(grid_ii / 10)\n",
|
||||
" i_waves /= i_waves.max()\n",
|
||||
" j_waves = np.sin(grid_jj / 100) + np.sin(grid_jj / 50) +\\\n",
|
||||
" np.sin(grid_jj / 10)\n",
|
||||
" j_waves /= j_waves.max()\n",
|
||||
" context_map = j_waves + i_waves\n",
|
||||
" elif map_type == 'labyrinth':\n",
|
||||
" context_map = np.ones((size, size))\n",
|
||||
" context_map[50:100, 50:60] = 0\n",
|
||||
" context_map[20:89, 80:90] = 0\n",
|
||||
" context_map[90:120, 0:10] = 0\n",
|
||||
" context_map[120:size, 30:40] = 0\n",
|
||||
" context_map[180:190, 50:60] = 0\n",
|
||||
"\n",
|
||||
" context_map[50:60, 50:200] = 0\n",
|
||||
" context_map[179:189, 80:130] = 0\n",
|
||||
" context_map[110:120, 0:190] = 0\n",
|
||||
" context_map[120:size, 30:40] = 0\n",
|
||||
" context_map[180:190, 50:60] = 0\n",
|
||||
"\n",
|
||||
" context_map /= context_map.sum()\n",
|
||||
" return cls(sigma_i, sigma_j, size, context_map)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-09-02T11:15:49.092194Z",
|
||||
"start_time": "2022-09-02T11:15:49.086575Z"
|
||||
},
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"walker = Walker.from_context_map_type(sigma_i=3, sigma_j=4, size=200, map_type='hills')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-09-02T11:19:37.723607Z",
|
||||
"start_time": "2022-09-02T11:19:37.717518Z"
|
||||
},
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def flat_context_map_builder(size):\n",
|
||||
" \"\"\" A context map where all positions are equally likely. \"\"\"\n",
|
||||
" return np.ones((size, size))\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def hills_context_map_builder(size):\n",
|
||||
" \"\"\" A context map with bumps and valleys. \"\"\"\n",
|
||||
" grid_ii, grid_jj = np.mgrid[0:size, 0:size]\n",
|
||||
" i_waves = np.sin(grid_ii / 130) + np.sin(grid_ii / 10)\n",
|
||||
" i_waves /= i_waves.max()\n",
|
||||
" j_waves = np.sin(grid_jj / 100) + np.sin(grid_jj / 50) + \\\n",
|
||||
" np.sin(grid_jj / 10)\n",
|
||||
" j_waves /= j_waves.max()\n",
|
||||
" context_map = j_waves + i_waves\n",
|
||||
" return context_map\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def labyrinth_context_map_builder(size):\n",
|
||||
" \"\"\" A context map that looks like a labyrinth. \"\"\"\n",
|
||||
" context_map = np.ones((size, size))\n",
|
||||
" context_map[50:100, 50:60] = 0\n",
|
||||
" context_map[20:89, 80:90] = 0\n",
|
||||
" context_map[90:120, 0:10] = 0\n",
|
||||
" context_map[120:size, 30:40] = 0\n",
|
||||
" context_map[180:190, 50:60] = 0\n",
|
||||
"\n",
|
||||
" context_map[50:60, 50:200] = 0\n",
|
||||
" context_map[179:189, 80:130] = 0\n",
|
||||
" context_map[110:120, 0:190] = 0\n",
|
||||
" context_map[120:size, 30:40] = 0\n",
|
||||
" context_map[180:190, 50:60] = 0\n",
|
||||
"\n",
|
||||
" return context_map"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-09-02T11:20:25.815725Z",
|
||||
"start_time": "2022-09-02T11:20:25.811489Z"
|
||||
},
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class Walker:\n",
|
||||
"\n",
|
||||
" def __init__(self, sigma_i, sigma_j, size, context_map):\n",
|
||||
" self.sigma_i = sigma_i\n",
|
||||
" self.sigma_j = sigma_j\n",
|
||||
" self.size = size\n",
|
||||
" self.context_map = context_map\n",
|
||||
" # Pre-compute a 2D grid of coordinates for efficiency\n",
|
||||
" self._grid_ii, self._grid_jj = np.mgrid[0:size, 0:size]\n",
|
||||
"\n",
|
||||
" @classmethod\n",
|
||||
" def from_context_map_builder(cls, sigma_i, sigma_j, size, context_map_builder):\n",
|
||||
" \"\"\"Initialize the context map from an external builder.\n",
|
||||
"\n",
|
||||
" `builder` is a callable that takes a `size` as input parameter\n",
|
||||
" and outputs a `size` x `size` numpy array of positive values.\n",
|
||||
" \"\"\"\n",
|
||||
" context_map = context_map_builder(size)\n",
|
||||
" context_map /= context_map.sum()\n",
|
||||
" return cls(sigma_i, sigma_j, size, context_map)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-09-02T11:20:26.367914Z",
|
||||
"start_time": "2022-09-02T11:20:26.362287Z"
|
||||
},
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"walker = Walker.from_context_map_builder(\n",
|
||||
" sigma_i=3, \n",
|
||||
" sigma_j=4, \n",
|
||||
" size=200, \n",
|
||||
" context_map_builder=hills_context_map_builder,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"a"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"hide_input": false,
|
||||
"kernelspec": {
|
||||
"display_name": "Python [conda env:bog]",
|
||||
"language": "python",
|
||||
"name": "conda-env-bog-py"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.5"
|
||||
},
|
||||
"toc": {
|
||||
"nav_menu": {
|
||||
"height": "12px",
|
||||
"width": "252px"
|
||||
},
|
||||
"navigate_menu": true,
|
||||
"number_sections": true,
|
||||
"sideBar": true,
|
||||
"threshold": 4,
|
||||
"toc_cell": false,
|
||||
"toc_section_display": "block",
|
||||
"toc_window_display": false
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
223
code_snippets/walker_next_step.ipynb
Normal file
223
code_snippets/walker_next_step.ipynb
Normal file
|
|
@ -0,0 +1,223 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8ad9fe94",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class Walker:\n",
|
||||
" # ...\n",
|
||||
" \n",
|
||||
" def sample_next_step(self, current_i, current_j, random_state=np.random):\n",
|
||||
" \"\"\" Sample a new position for the walker. \"\"\"\n",
|
||||
" # Combine the next-step proposal with the context map to get a\n",
|
||||
" # next-step probability map\n",
|
||||
" next_step_map = self._next_step_proposal(current_i, current_j)\n",
|
||||
" selection_map = self._compute_next_step_probability(next_step_map)\n",
|
||||
"\n",
|
||||
" # Draw a new position from the next-step probability map\n",
|
||||
" r = random_state.rand()\n",
|
||||
" cumulative_map = np.cumsum(selection_map)\n",
|
||||
" cumulative_map = cumulative_map.reshape(selection_map.shape)\n",
|
||||
" i_next, j_next = np.argwhere(cumulative_map >= r)[0]\n",
|
||||
"\n",
|
||||
" return i_next, j_next\n",
|
||||
"\n",
|
||||
" def _next_step_proposal(self, current_i, current_j):\n",
|
||||
" \"\"\" Create the 2D proposal map for the next step of the walker. \"\"\"\n",
|
||||
" # 2D Gaussian distribution , centered at current position,\n",
|
||||
" # and with different standard deviations for i and j\n",
|
||||
" grid_ii, grid_jj = self._grid_ii, self._grid_jj\n",
|
||||
" sigma_i, sigma_j = self.sigma_i, self.sigma_j\n",
|
||||
"\n",
|
||||
" rad = (\n",
|
||||
" (((grid_ii - current_i) ** 2) / (sigma_i ** 2))\n",
|
||||
" + (((grid_jj - current_j) ** 2) / (sigma_j ** 2))\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" p_next_step = np.exp(-(rad / 2.0)) / (2.0 * np.pi * sigma_i * sigma_j)\n",
|
||||
" return p_next_step / p_next_step.sum()\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "95148e45",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class Walker:\n",
|
||||
" # ...\n",
|
||||
"\n",
|
||||
" def _next_step_proposal(self, current_i, current_j):\n",
|
||||
" \"\"\" Create the 2D proposal map for the next step of the walker. \"\"\"\n",
|
||||
" raise NotImplementedError(\"`_next_step_proposal` not implemented\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "da34226f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class GaussianWalker(Walker):\n",
|
||||
" # ...\n",
|
||||
"\n",
|
||||
" def _next_step_proposal(self, current_i, current_j):\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
"class RectangularWalker(Walker):\n",
|
||||
" # ...\n",
|
||||
"\n",
|
||||
" def _next_step_proposal(self, current_i, current_j):\n",
|
||||
"\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
"class JumpingWalker(Walker):\n",
|
||||
" # ...\n",
|
||||
"\n",
|
||||
" def _next_step_proposal(self, current_i, current_j):\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cfd90d16",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class Walker:\n",
|
||||
" # ...\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" def _compute_next_step_probability(self, next_step_map):\n",
|
||||
" \"\"\" Compute the next step probability map from next step proposal and\n",
|
||||
" context map. \"\"\"\n",
|
||||
" next_step_probability = next_step_map * self.context_map\n",
|
||||
" next_step_probability /= next_step_probability.sum()\n",
|
||||
" return next_step_probability\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "72041675",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class GaussianWalkerWithProductInteraction(Walker):\n",
|
||||
" def _next_step_proposal(self, current_i, current_j):\n",
|
||||
" # ...\n",
|
||||
" def _compute_next_step_probability(self, next_step_map):\n",
|
||||
" # ...\n",
|
||||
"\n",
|
||||
" \n",
|
||||
"class GaussianWalkerWithSumInteraction(Walker):\n",
|
||||
" def _next_step_proposal(self, current_i, current_j):\n",
|
||||
" # ...\n",
|
||||
" def _compute_next_step_probability(self, next_step_map):\n",
|
||||
" # ...\n",
|
||||
"\n",
|
||||
" \n",
|
||||
"class RectangularWalkerWithProductInteraction(Walker):\n",
|
||||
" def _next_step_proposal(self, current_i, current_j):\n",
|
||||
" # ...\n",
|
||||
" def _compute_next_step_probability(self, next_step_map):\n",
|
||||
" # ...\n",
|
||||
"\n",
|
||||
" \n",
|
||||
"class RectangularWalkerWithSumInteraction(Walker):\n",
|
||||
" def _next_step_proposal(self, current_i, current_j):\n",
|
||||
" # ...\n",
|
||||
" def _compute_next_step_probability(self, next_step_map):\n",
|
||||
" # ...\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "5ee2e200",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class Walker:\n",
|
||||
" def __init__(self, size, context_map, next_step_proposal, next_step_proposal_arguments):\n",
|
||||
" self.next_step_proposal = next_step_proposal\n",
|
||||
" # ...\n",
|
||||
"\n",
|
||||
" \n",
|
||||
" def sample_next_step(self, current_i, current_j, random_state=np.random):\n",
|
||||
" \"\"\" Sample a new position for the walker. \"\"\"\n",
|
||||
" # Combine the next-step proposal with the context map to get a\n",
|
||||
" # next-step probability map\n",
|
||||
" next_step_map = self.next_step_proposal(current_i, current_j, **next_step_proposal_arguments)\n",
|
||||
" selection_map = self._compute_next_step_probability(next_step_map)\n",
|
||||
"\n",
|
||||
" # Draw a new position from the next-step probability map\n",
|
||||
" r = random_state.rand()\n",
|
||||
" cumulative_map = np.cumsum(selection_map)\n",
|
||||
" cumulative_map = cumulative_map.reshape(selection_map.shape)\n",
|
||||
" i_next, j_next = np.argwhere(cumulative_map >= r)[0]\n",
|
||||
"\n",
|
||||
" return i_next, j_next\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
105
code_snippets/walker_with_plotting.ipynb
Normal file
105
code_snippets/walker_with_plotting.ipynb
Normal file
|
|
@ -0,0 +1,105 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "2120045b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class Walker:\n",
|
||||
" \"\"\" The Walker knows how to walk at random on a context map. \"\"\"\n",
|
||||
"\n",
|
||||
" def __init__(self, sigma_i, sigma_j, size, map_type='flat'):\n",
|
||||
" # ...\n",
|
||||
"\n",
|
||||
" def plot_trajectory(self, trajectory):\n",
|
||||
" \"\"\" Plot a trajectory over a context map. \"\"\"\n",
|
||||
" trajectory = np.asarray(trajectory)\n",
|
||||
" plt.matshow(self.context_map)\n",
|
||||
" plt.plot(trajectory[:, 1], trajectory[:, 0], color='r')\n",
|
||||
" plt.show()\n",
|
||||
"\n",
|
||||
" def plot_trajectory_hexbin(self, trajectory):\n",
|
||||
" \"\"\" Plot an hexagonal density map of a trajectory. \"\"\"\n",
|
||||
" trajectory = np.asarray(trajectory)\n",
|
||||
" with plt.rc_context({'figure.figsize': (4, 4), \n",
|
||||
" 'axes.labelsize': 16, \n",
|
||||
" 'xtick.labelsize': 14, \n",
|
||||
" 'ytick.labelsize': 14}):\n",
|
||||
" plt.hexbin(\n",
|
||||
" trajectory[:, 1], trajectory[:, 0], \n",
|
||||
" gridsize=30, extent=(0, 200, 0, 200), \n",
|
||||
" edgecolors='none', cmap='Reds'\n",
|
||||
" )\n",
|
||||
" plt.gca().invert_yaxis()\n",
|
||||
" plt.xlabel('X')\n",
|
||||
" plt.ylabel('Y')\n",
|
||||
" \n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "e8b1035c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class Walker:\n",
|
||||
" \"\"\" The Walker knows how to walk at random on a context map. \"\"\"\n",
|
||||
"\n",
|
||||
" def __init__(self, sigma_i, sigma_j, size, map_type='flat'):\n",
|
||||
" # ...\n",
|
||||
"\n",
|
||||
" def plot_trajectory(self, trajectory):\n",
|
||||
" \"\"\" Plot a trajectory over a context map. \"\"\"\n",
|
||||
" trajectory = np.asarray(trajectory)\n",
|
||||
" plt.matshow(self.context_map)\n",
|
||||
" plt.plot(trajectory[:, 1], trajectory[:, 0], color='r')\n",
|
||||
" plt.show()\n",
|
||||
"\n",
|
||||
" def plot_trajectory_hexbin(self, trajectory):\n",
|
||||
" \"\"\" Plot an hexagonal density map of a trajectory. \"\"\"\n",
|
||||
" trajectory = np.asarray(trajectory)\n",
|
||||
" plt.hexbin(\n",
|
||||
" trajectory[:, 1], trajectory[:, 0], \n",
|
||||
" gridsize=30, extent=(0, 200, 0, 200), \n",
|
||||
" edgecolors='none', cmap='Reds'\n",
|
||||
" )\n",
|
||||
" plt.gca().invert_yaxis()\n",
|
||||
" plt.xlabel('X')\n",
|
||||
" plt.ylabel('Y')\n",
|
||||
" \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
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generate_assets/explain_the_walker.ipynb
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1511
generate_assets/explain_the_walker.ipynb
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|
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|
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|
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generate_assets/walk7.png
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generate_assets/walk7.png
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590
notebooks/01a_Classes.ipynb
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590
notebooks/01a_Classes.ipynb
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|
|
@ -0,0 +1,590 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%% md\n"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"# The smell of classes"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%% md\n"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"## The \"data bundle\" smell"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2018-07-27T15:05:51.531289Z",
|
||||
"start_time": "2018-07-27T17:05:51.526519+02:00"
|
||||
},
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def momentum(mass, velocity):\n",
|
||||
" return mass * velocity\n",
|
||||
"\n",
|
||||
"def energy(mass, velocity):\n",
|
||||
" return 0.5 * mass * velocity ** 2\n",
|
||||
"\n",
|
||||
"def update_position(velocity, position, dt):\n",
|
||||
" return position + velocity * dt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2018-07-27T15:05:51.905235Z",
|
||||
"start_time": "2018-07-27T17:05:51.900153+02:00"
|
||||
},
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"9.0\n",
|
||||
"1.2000000000000002\n",
|
||||
"1.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Naive\n",
|
||||
"mass1 = 10.0\n",
|
||||
"velocity1 = 0.9\n",
|
||||
"\n",
|
||||
"mass2 = 12.0\n",
|
||||
"velocity2 = 0.1\n",
|
||||
"\n",
|
||||
"print(momentum(mass1, velocity1))\n",
|
||||
"print(momentum(mass2, velocity2))\n",
|
||||
"print(momentum(mass1, velocity2)) # ??"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%% md\n"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"We have two parameters that will be sent to these functions over and over again: `mass` and `velocity`.\n",
|
||||
"\n",
|
||||
"Moreover, the parameters cannot be mixed up (e.g. the velocity of one particle with the mass of another)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2018-07-27T15:05:52.116795Z",
|
||||
"start_time": "2018-07-27T17:05:52.112569+02:00"
|
||||
},
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"9.0\n",
|
||||
"1.2000000000000002\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"masses = [10.0, 12.0]\n",
|
||||
"velocities = [0.9, 0.1]\n",
|
||||
"\n",
|
||||
"print(momentum(masses[0], velocities[0]))\n",
|
||||
"print(momentum(masses[1], velocities[1]))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2018-07-27T15:05:52.548364Z",
|
||||
"start_time": "2018-07-27T17:05:52.544726+02:00"
|
||||
},
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"9.0\n",
|
||||
"1.2000000000000002\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"particle1 = {'mass': 10.0, 'velocity': 0.9}\n",
|
||||
"particle2 = {'mass': 12.0, 'velocity': 0.1}\n",
|
||||
"\n",
|
||||
"print(momentum(particle1['mass'], particle1['velocity']))\n",
|
||||
"print(momentum(particle2['mass'], particle2['velocity']))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%% md\n"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"All of the functions above can be rewritten as a function of this particle \"instance\", eliminating the bookkeeping for the individual parameters."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2018-07-27T15:05:53.571400Z",
|
||||
"start_time": "2018-07-27T17:05:53.567192+02:00"
|
||||
},
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"9.0\n",
|
||||
"1.2000000000000002\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def momentum(particle):\n",
|
||||
" return particle['mass'] * particle['velocity']\n",
|
||||
"\n",
|
||||
"print(momentum(particle1))\n",
|
||||
"print(momentum(particle2))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%% md\n"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"An annoying thing of this solution is that we have to remember the name of the keys in the dictionary, and the solution is sensitive to typos.\n",
|
||||
"\n",
|
||||
"To solve this, we could write a function to build a particle, a.k.a a \"constructor\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2018-07-27T15:06:20.004037Z",
|
||||
"start_time": "2018-07-27T17:06:19.998500+02:00"
|
||||
},
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"9.0\n",
|
||||
"1.2000000000000002\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def init_particle(mass, velocity):\n",
|
||||
" self = {\n",
|
||||
" 'mass': mass,\n",
|
||||
" 'velocity': velocity\n",
|
||||
" }\n",
|
||||
" return self\n",
|
||||
"\n",
|
||||
"particle1 = init_particle(10.0, 0.9)\n",
|
||||
"particle2 = init_particle(12.0, 0.1)\n",
|
||||
"print(momentum(particle1))\n",
|
||||
"print(momentum(particle2))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%% md\n"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"`particle1` and `particle2` are called \"instances\" of the particle \"class\".\n",
|
||||
"\n",
|
||||
"Python classes are a way to formalize this pattern: creating a bundle of data that belongs together. E.g. the parameters of an experiment, the results of a simulation, etc."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%% md\n"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"## Introducing classes as a data bundle template"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2018-08-04T13:04:49.208543Z",
|
||||
"start_time": "2018-08-04T15:04:49.203180+02:00"
|
||||
},
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class Particle:\n",
|
||||
" def __init__(self, mass, velocity):\n",
|
||||
" self.mass = mass\n",
|
||||
" self.velocity = velocity\n",
|
||||
"\n",
|
||||
"particle1 = Particle(10.0, 0.9)\n",
|
||||
"particle2 = Particle(12.0, 0.1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2018-07-27T15:07:09.818544Z",
|
||||
"start_time": "2018-07-27T17:07:09.814535+02:00"
|
||||
},
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.9"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"particle1.velocity"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2018-07-27T15:07:09.997264Z",
|
||||
"start_time": "2018-07-27T17:07:09.994114+02:00"
|
||||
},
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"12.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"particle2.mass"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2018-07-27T15:07:11.559629Z",
|
||||
"start_time": "2018-07-27T17:07:11.555632+02:00"
|
||||
},
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
},
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'mass': 10.0, 'velocity': 0.9}"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"particle1.__dict__"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%% md\n"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"## Class methods"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2018-07-27T12:13:56.972938Z",
|
||||
"start_time": "2018-07-27T14:13:56.969323+02:00"
|
||||
},
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"9.0\n",
|
||||
"1.2000000000000002\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def momentum(particle):\n",
|
||||
" return particle.mass * particle.velocity\n",
|
||||
"\n",
|
||||
"print(momentum(particle1))\n",
|
||||
"print(momentum(particle2))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"9.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"class Particle:\n",
|
||||
" def __init__(self, mass, velocity):\n",
|
||||
" self.mass = mass\n",
|
||||
" self.velocity = velocity\n",
|
||||
"\n",
|
||||
" def momentum(self):\n",
|
||||
" return self.mass * self.velocity\n",
|
||||
"\n",
|
||||
"particle1 = Particle(10.0, 0.9)\n",
|
||||
"print(particle1.momentum())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%% md\n"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"We have been using class instances and methods all along..."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'A scanner darkly'"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"s = 'A scanner Darkly'\n",
|
||||
"s.capitalize()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'apple', 'banana', 'pineapple'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"x = set(['apple', 'banana', 'apple', 'pineapple'])\n",
|
||||
"x"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'apple', 'banana', 'kiwi', 'pineapple'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"x.union(['banana', 'kiwi'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"hide_input": false,
|
||||
"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": "3.11.3"
|
||||
},
|
||||
"toc": {
|
||||
"nav_menu": {
|
||||
"height": "174px",
|
||||
"width": "252px"
|
||||
},
|
||||
"navigate_menu": true,
|
||||
"number_sections": true,
|
||||
"sideBar": true,
|
||||
"threshold": 4,
|
||||
"toc_cell": false,
|
||||
"toc_position": {
|
||||
"height": "953px",
|
||||
"left": "0px",
|
||||
"right": "1253px",
|
||||
"top": "127px",
|
||||
"width": "320px"
|
||||
},
|
||||
"toc_section_display": "block",
|
||||
"toc_window_display": false
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
279
notebooks/01b_Classes_teacher_edition.ipynb
Normal file
279
notebooks/01b_Classes_teacher_edition.ipynb
Normal file
|
|
@ -0,0 +1,279 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# The smell of classes"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## The \"data bundle\" smell"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2021-08-11T12:50:34.342224Z",
|
||||
"start_time": "2021-08-11T14:50:34.336560+02:00"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def momentum(mass, velocity):\n",
|
||||
" return mass * velocity\n",
|
||||
"\n",
|
||||
"def energy(mass, velocity):\n",
|
||||
" return 0.5 * mass * velocity ** 2\n",
|
||||
"\n",
|
||||
"def update_position(velocity, position, dt):\n",
|
||||
" return position + velocity * dt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2021-08-11T12:50:34.776689Z",
|
||||
"start_time": "2021-08-11T14:50:34.769617+02:00"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Naive\n",
|
||||
"mass1 = 10.0\n",
|
||||
"velocity1 = 0.9\n",
|
||||
"\n",
|
||||
"mass2 = 12.0\n",
|
||||
"velocity2 = 0.1\n",
|
||||
"\n",
|
||||
"print(momentum(mass1, velocity1))\n",
|
||||
"print(momentum(mass2, velocity2))"
|
||||
]
|
||||
},
|
||||