Material for ASPP 2024
This commit is contained in:
Pietro Berkes
commit
849682b13b
97 changed files with 8170 additions and 0 deletions
182
code_snippets/factory_methods.ipynb
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182
code_snippets/factory_methods.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-08-12T12:14:28.926189Z",
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"start_time": "2022-08-12T12:14:28.923089Z"
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||||
},
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import json\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-08-12T12:21:00.003395Z",
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"start_time": "2022-08-12T12:20:59.997851Z"
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},
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"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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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-08-12T12:23:03.889266Z",
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"start_time": "2022-08-12T12:23:03.883050Z"
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},
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"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",
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" 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",
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" \"\"\"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",
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" return cls(a, b)\n",
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" \n",
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" @classmethod\n",
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" def from_json(cls, json_fname):\n",
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" \"\"\"Create a MyClass instance with parameters read form a json file.\"\"\"\n",
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" with open(json_fname, 'r') as f:\n",
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" dict_ = json.load(f)\n",
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" a = dict_['a']\n",
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" b = dict_['b']\n",
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" return cls(a, b)\n",
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"\n",
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"my_class = MyClass.from_random_values()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-08-12T12:23:12.242599Z",
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"start_time": "2022-08-12T12:23:12.237477Z"
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},
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'a': 0.842940228048758, 'b': 0.2797222990193814}"
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]
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},
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"my_class = MyClass.from_random_values()\n",
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"my_class.__dict__"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-08-12T12:23:44.439726Z",
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"start_time": "2022-08-12T12:23:44.432540Z"
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},
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'a': 3.1, 'b': 4.2}"
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]
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},
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"execution_count": 24,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"my_class = MyClass.from_json('my_class.json')\n",
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"my_class.__dict__"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"hide_input": false,
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"kernelspec": {
|
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"display_name": "Python [conda env:bog]",
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"language": "python",
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"name": "conda-env-bog-py"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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},
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"toc": {
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"nav_menu": {
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"height": "12px",
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"width": "252px"
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},
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"navigate_menu": true,
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"number_sections": true,
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"sideBar": true,
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"threshold": 4,
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"toc_cell": false,
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"toc_section_display": "block",
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"toc_window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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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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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-09-02T11:13:12.648377Z",
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"start_time": "2022-09-02T11:13:12.165387Z"
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},
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-09-02T11:14:10.246402Z",
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"start_time": "2022-09-02T11:14:10.235135Z"
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||||
},
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||||
"collapsed": false
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},
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"outputs": [],
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"source": [
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"class Walker:\n",
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" \"\"\" The Walker knows how to walk at random on a context map. \"\"\"\n",
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"\n",
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" def __init__(self, sigma_i, sigma_j, size, map_type='flat'):\n",
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" self.sigma_i = sigma_i\n",
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" self.sigma_j = sigma_j\n",
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" self.size = size\n",
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"\n",
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" if map_type == 'flat':\n",
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" context_map = np.ones((size, size))\n",
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" elif map_type == 'hills':\n",
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" grid_ii, grid_jj = np.mgrid[0:size, 0:size]\n",
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" i_waves = np.sin(grid_ii / 130) + np.sin(grid_ii / 10)\n",
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" i_waves /= i_waves.max()\n",
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" j_waves = np.sin(grid_jj / 100) + np.sin(grid_jj / 50) + \\\n",
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" np.sin(grid_jj / 10)\n",
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" j_waves /= j_waves.max()\n",
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" context_map = j_waves + i_waves\n",
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" elif map_type == 'labyrinth':\n",
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" context_map = np.ones((size, size))\n",
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" context_map[50:100, 50:60] = 0\n",
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" context_map[20:89, 80:90] = 0\n",
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" context_map[90:120, 0:10] = 0\n",
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" context_map[120:size, 30:40] = 0\n",
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" context_map[180:190, 50:60] = 0\n",
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"\n",
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" context_map[50:60, 50:200] = 0\n",
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" context_map[179:189, 80:130] = 0\n",
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" context_map[110:120, 0:190] = 0\n",
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" context_map[120:size, 30:40] = 0\n",
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" context_map[180:190, 50:60] = 0\n",
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" context_map /= context_map.sum()\n",
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" self.context_map = context_map\n",
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"\n",
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" # Pre-compute a 2D grid of coordinates for efficiency\n",
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" self._grid_ii, self._grid_jj = np.mgrid[0:size, 0:size]\n",
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"\n",
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"walker = Walker(sigma_i=3, sigma_j=4, size=200, map_type='hills')"
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]
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},
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{
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||||
"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"ExecuteTime": {
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||||
"end_time": "2022-09-02T11:15:44.357965Z",
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||||
"start_time": "2022-09-02T11:15:44.351558Z"
|
||||
},
|
||||
"collapsed": false
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||||
},
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||||
"outputs": [],
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||||
"source": [
|
||||
"class Walker:\n",
|
||||
" \"\"\" The Walker knows how to walk at random on a context map. \"\"\"\n",
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||||
"\n",
|
||||
" def __init__(self, sigma_i, sigma_j, size, context_map):\n",
|
||||
" self.sigma_i = sigma_i\n",
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||||
" self.sigma_j = sigma_j\n",
|
||||
" self.size = size\n",
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||||
" self.context_map = context_map\n",
|
||||
" # Pre-compute a 2D grid of coordinates for efficiency\n",
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||||
" self._grid_ii, self._grid_jj = np.mgrid[0:size, 0:size]\n",
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"\n",
|
||||
" @classmethod\n",
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||||
" def from_context_map_type(cls, sigma_i, sigma_j, size, map_type):\n",
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||||
" \"\"\" Create an instance of Walker with a context map defined by type.\"\"\"\n",
|
||||
" if map_type == 'flat':\n",
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" 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",
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||||
" j_waves /= j_waves.max()\n",
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||||
" context_map = j_waves + i_waves\n",
|
||||
" elif map_type == 'labyrinth':\n",
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||||
" context_map = np.ones((size, size))\n",
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||||
" context_map[50:100, 50:60] = 0\n",
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||||
" context_map[20:89, 80:90] = 0\n",
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||||
" context_map[90:120, 0:10] = 0\n",
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||||
" context_map[120:size, 30:40] = 0\n",
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||||
" context_map[180:190, 50:60] = 0\n",
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"\n",
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||||
" context_map[50:60, 50:200] = 0\n",
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||||
" 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",
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"\n",
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||||
" context_map /= context_map.sum()\n",
|
||||
" return cls(sigma_i, sigma_j, size, context_map)\n"
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||||
]
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||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
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||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-09-02T11:15:49.092194Z",
|
||||
"start_time": "2022-09-02T11:15:49.086575Z"
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||||
},
|
||||
"collapsed": true
|
||||
},
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||||
"outputs": [],
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||||
"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",
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||||
"\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
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue