209 lines
140 KiB
Plaintext
209 lines
140 KiB
Plaintext
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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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"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."
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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": "2023-08-15T08:17:58.966008Z",
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"start_time": "2023-08-15T08:17:58.961545Z"
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},
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"execution": {
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"iopub.execute_input": "2024-08-05T14:06:56.784Z",
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"iopub.status.busy": "2024-08-05T14:06:56.776Z",
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"iopub.status.idle": "2024-08-05T14:06:56.789Z",
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"shell.execute_reply": "2024-08-05T14:06:56.793Z"
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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 create_context_map, plot_trajectory, sample_next_step"
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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": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-08-15T08:18:00.805824Z",
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"start_time": "2023-08-15T08:18:00.167515Z"
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},
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"execution": {
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"iopub.execute_input": "2024-08-05T14:12:32.248Z",
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"iopub.status.busy": "2024-08-05T14:12:32.244Z",
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"iopub.status.idle": "2024-08-05T14:12:32.938Z",
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"shell.execute_reply": "2024-08-05T14:12:32.947Z"
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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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"i, j = 100, 50 # initial position\n",
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"sigma_i, sigma_j = 3, 4 # parameters of the next step map\n",
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"size = 200 # size of the image\n",
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"context_map = create_context_map(size, 'hills') # fixed context map\n",
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"\n",
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"# Sample a next step 1000 times\n",
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"trajectory = []\n",
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"for _ in range(1000):\n",
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" i, j = sample_next_step(i, j, sigma_i, sigma_j, context_map)\n",
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" trajectory.append((i, j))\n",
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"\n",
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"plot_trajectory(trajectory, context_map)\n"
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]
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},
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{
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"attachments": {},
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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. Rewrite the code to look like it would in your wildest dreams!\n",
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"\n",
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"How could you restructure the interface of the `walker` library so that the functionality stays the same, but the code is less smelly?\n",
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"\n",
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"1. could this code work better/more efficiently as a class?\n",
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" - e.g. `walker = Walker(...)`\n",
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"2. how should the setup/initialization work?\n",
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" - e.g. which parameters would you pass to instantiate the Walker class? How do you pass them to the class?\n",
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"3. which functions should be part of the class? \n",
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" - e.g. is `sample_next_step` a method of `Walker`? Is `create_context_map` a method of Walker? How about `plot_trajectory`? If so, call them with `walker.sample_next_step()`\n",
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"4. what would the import statement look like?\n",
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"\n",
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"**Important:** We are *not* asking you to implement the changes in `walker.py`! Imagine the new interface, and write the code that would use it in the next cell. Of course it won't run (yet)\n",
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"\n",
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"Here are some things to keep in mind:\n",
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"- Which things will you do a lot? They should be easy to do. The interface for doing those things should be simple.\n",
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"- Which things should be modifiable from external code?\n",
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"- As a rule of thumb, think about how it sounds when you read it aloud\n",
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" - “Giving orders”-style\n",
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" - “Telling a story“-style\n",
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"\n",
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" \n",
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"For inspiration think about how you call code from other libraries. Here is an example of instantiating an object and calling one of its methods:\n",
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"````python\n",
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"import scipy as sp\n",
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"\n",
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"# Sampling from a gaussian with specific mean and sd:\n",
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"# Create a Gaussian (normal) distribution object\n",
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"gaussian_dist = sp.stats.norm(loc=mean, scale=sd)\n",
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"# Sample 10 values from the Gaussian distribution\n",
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"samples = gaussian_dist.rvs(size=10)\n",
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"````\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": 4,
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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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"source": [
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"# Write the code that uses your new `walker` interface here!\n",
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"# walker = Walker(sigma_i=3, sigma_j=4, ...)\n",
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"# ..."
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]
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},
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{
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"cell_type": "code",
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"jupyter": {
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"outputs_hidden": false,
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"source_hidden": false
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},
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"nteract": {
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"deleting": false
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}
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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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],
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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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"codemirror_mode": {
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"name": "ipython",
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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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},
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"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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"height": "12px",
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"navigate_menu": true,
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