Exercise 3: separate context map from walker #14
4 changed files with 294 additions and 25 deletions
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@ -35,51 +35,65 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1.6800000000000002\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"class Particle:\n",
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" def __init__(self, mass, velocity):\n",
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" def __init__(self, mass=1, velocity=0., position=0.):\n",
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" self.mass = mass\n",
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" self.velocity = velocity\n",
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" self.position = position\n",
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"\n",
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" def momentum(self):\n",
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" return self.mass * self.velocity\n",
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"\n",
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"particle = Particle(mass=2.1, velocity=0.8)\n",
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"print(particle.momentum())"
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" def update_position(self, dt):\n",
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" new_position = self.position + dt * self.velocity\n",
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" self.position = new_position\n",
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" return"
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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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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"8.28\n"
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"1.6800000000000002\n",
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"0.0\n"
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]
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}
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],
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"source": [
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"position = 8.2\n",
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"new_position = update_position(particle.velocity, position, dt=0.1)\n",
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"print(new_position)"
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"particle = Particle(mass=2.1, velocity=0.8, position=0.)\n",
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"print(particle.momentum())\n",
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"print(particle.position)"
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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": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.08000000000000002\n"
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]
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}
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],
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"source": [
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"particle.update_position(dt=0.1)\n",
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"print(particle.position)"
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]
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},
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{
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@ -118,7 +132,7 @@
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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"
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"version": "3.13.6"
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},
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"toc": {
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"nav_menu": {
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@ -142,5 +156,5 @@
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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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"nbformat_minor": 4
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}
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@ -0,0 +1,204 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"nteract": {
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"transient": {
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"deleting": false
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}
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}
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},
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"source": [
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"# 1. Take a look at this (working) code\n",
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"\n",
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"... and run it. We discussed that the `context_map` varies independently of the walker. Identify the part of the code that will be affected by this change."
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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": 1,
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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 plotting import plot_trajectory, plot_trajectory_hexbin\n",
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"from walker import Walker\n"
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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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"jupyter": {
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"outputs_hidden": true,
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"source_hidden": false
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},
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"nteract": {
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"transient": {
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"deleting": false
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}
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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))\n",
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"\n",
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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": "markdown",
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"metadata": {
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||||
"nteract": {
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||||
"transient": {
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"deleting": false
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}
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}
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},
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"source": [
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"# 2. Propose modifications to the code that calls the walker (above) to reflect the idea of a separate context_map module\n",
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"\n",
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"1. how would the import statement change as a result of needing a separate context_map module?\n",
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"2. what input arguments do the context_map functions need to take?\n",
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"3. how does the initialization of the walker change?\n",
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" - i.e. instead of \"map_type\"\n",
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"\n"
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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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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"\n",
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"from plotting import plot_trajectory, plot_trajectory_hexbin\n",
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"from walker import Walker\n",
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"from context_map import create_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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"context_map = create_context_map(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": null,
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"metadata": {},
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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, context_map)\n",
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"\n",
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"# Sample a next step 1000 times\n",
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"initial_position = (100, 50)\n",
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"N_steps = 1000\n",
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"trajectory = np.array((N_steps, 2), dtype=np.int8)\n",
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"\n",
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"for step in range(N_steps):\n",
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" i,j = walker.sample_next_step(i, j)\n",
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" trajectory[step] = np.array([i,j], dtype = np.int8)\n",
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"\n",
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"plot_trajectory(trajectory, context_map)"
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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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||||
"nteract": {
|
||||
"transient": {
|
||||
"deleting": false
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||||
}
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}
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},
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"source": [
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"# 3. (optional) Actually break out the context map initialization\n",
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"1. Move context map initialization to three functions in a separate `context_map.py` module which all return a `context_map` array\n",
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"2. Modify the constructor of Walker to take a `context_map` array instead of a `map_type`\n",
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"3. Modify this notebook to use the new code and see if the code you wrote works!\n",
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"4. Try to run all the types:\n",
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" - Run one simulation with a flat context map\n",
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" - Run one simulation with a hill context map\n",
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" - Run one simulation with a labyrinth context map\n",
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"\n",
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"When you have a working implementation: PR! You can link the issue in the commit message (by writing \"issue #3\")."
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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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"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 3 (ipykernel)",
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"language": "python",
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||||
"name": "python3"
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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": "3.13.6"
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||||
},
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||||
"nteract": {
|
||||
"version": "0.28.0"
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},
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||||
"toc": {
|
||||
"nav_menu": {
|
||||
"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": 4
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}
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@ -0,0 +1,22 @@
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import matplotlib.pyplot as plt
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import numpy as np
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def plot_trajectory(trajectory, context_map):
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""" Plot a trajectory over a context map. """
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trajectory = np.asarray(trajectory)
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plt.matshow(context_map)
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plt.plot(trajectory[:, 1], trajectory[:, 0], color='r')
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plt.show()
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def plot_trajectory_hexbin(trajectory):
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""" Plot an hexagonal density map of a trajectory. """
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trajectory = np.asarray(trajectory)
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with plt.rc_context({'figure.figsize': (4, 4), 'axes.labelsize': 16,
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'xtick.labelsize': 14, 'ytick.labelsize': 14}):
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plt.hexbin(trajectory[:, 1], trajectory[:, 0], gridsize=30,
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extent=(0, 200, 0, 200), edgecolors='none', cmap='Reds')
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plt.gca().invert_yaxis()
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plt.xlabel('X')
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plt.ylabel('Y')
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@ -47,7 +47,7 @@
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"metadata": {
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||||
"collapsed": true,
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||||
"jupyter": {
|
||||
"outputs_hidden": false,
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"outputs_hidden": true,
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"source_hidden": false
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},
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"nteract": {
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@ -95,14 +95,43 @@
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"%matplotlib inline\n",
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"\n",
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"from plotting import plot_trajectory, plot_trajectory_hexbin\n",
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"from walker import Walker\n",
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"from context_map import create_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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"context_map = create_context_map(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": null,
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"metadata": {},
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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, context_map)\n",
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"\n",
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"# Sample a next step 1000 times\n",
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"initial_position = (100, 50)\n",
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"N_steps = 1000\n",
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"trajectory = np.array((N_steps, 2), dtype=np.int8)\n",
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"\n",
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"for step in range(N_steps):\n",
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" i,j = walker.sample_next_step(i, j)\n",
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" trajectory[step] = np.array([i,j], dtype = np.int8)\n",
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"\n",
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"plot_trajectory(trajectory, context_map)"
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]
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},
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{
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"cell_type": "markdown",
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@ -151,7 +180,7 @@
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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": "3.11.3"
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"version": "3.13.6"
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},
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||||
"nteract": {
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||||
"version": "0.28.0"
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@ -171,5 +200,5 @@
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}
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||||
},
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"nbformat": 4,
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"nbformat_minor": 1
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"nbformat_minor": 4
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||||
}
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||||
|
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