168 lines
160 KiB
Plaintext
168 lines
160 KiB
Plaintext
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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": "2022-08-18T09:50:40.616906Z",
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"start_time": "2022-08-18T11:50:40.181358+02:00"
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},
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"execution": {
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"iopub.execute_input": "2022-08-20T06:27:54.689Z",
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"iopub.status.busy": "2022-08-20T06:27:54.685Z",
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"iopub.status.idle": "2022-08-20T06:27:55.297Z",
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"shell.execute_reply": "2022-08-20T06:27:55.319Z"
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"\n",
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"from walker import Walker\n",
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"from plotting import plot_trajectory, plot_trajectory_hexbin"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"# Simulate a trajectory"
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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": 3,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2022-08-20T06:27:55.307Z",
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"iopub.status.busy": "2022-08-20T06:27:55.303Z",
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"iopub.status.idle": "2022-08-20T06:27:56.162Z",
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"shell.execute_reply": "2022-08-20T06:27:56.165Z"
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Create a Walker instance\n",
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"walker = Walker(sigma_i=3, sigma_j=4, size=200, map_type='hills')\n",
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"\n",
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"# Sample a next step 1000 times\n",
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"i, j = 100, 50\n",
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"trajectory = []\n",
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"for _ in range(1000):\n",
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" i, j = walker.sample_next_step(i, j)\n",
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" trajectory.append((i, j))"
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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": 5,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2022-08-20T06:27:56.177Z",
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"iopub.status.busy": "2022-08-20T06:27:56.172Z",
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"iopub.status.idle": "2022-08-20T06:27:56.321Z",
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"shell.execute_reply": "2022-08-20T06:27:56.344Z"
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 480x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"plot_trajectory(trajectory, walker.context_map)"
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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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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
||
|
|
"<Figure size 400x400 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot_trajectory_hexbin(trajectory)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"hide_input": false,
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||
|
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"kernelspec": {
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|
"display_name": "Python 3 (ipykernel)",
|
||
|
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"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
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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",
|
||
|
|
"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": "3.10.11"
|
||
|
|
},
|
||
|
|
"nteract": {
|
||
|
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"version": "0.28.0"
|
||
|
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},
|
||
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"toc": {
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||
|
|
"nav_menu": {
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|
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"height": "12px",
|
||
|
|
"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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||
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"nbformat_minor": 1
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|
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}
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