201 lines
118 KiB
Plaintext
201 lines
118 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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"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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"# 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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"\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": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-08-18T09:45:09.308173Z",
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"start_time": "2022-08-18T11:45:08.852431+02:00"
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},
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"execution": {
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"iopub.execute_input": "2022-08-20T06:21:31.332Z",
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"iopub.status.busy": "2022-08-20T06:21:31.327Z",
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"iopub.status.idle": "2022-08-20T06:21:31.339Z",
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"shell.execute_reply": "2022-08-20T06:21:31.343Z"
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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, plot_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": 2,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2022-08-20T06:21:31.938Z",
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"iopub.status.busy": "2022-08-20T06:21:31.932Z",
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"iopub.status.idle": "2022-08-20T06:21:32.867Z",
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"shell.execute_reply": "2022-08-20T06:21:32.871Z"
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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": 3,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2022-08-20T06:21:32.883Z",
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"iopub.status.busy": "2022-08-20T06:21:32.879Z",
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"iopub.status.idle": "2022-08-20T06:21:33.026Z",
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"shell.execute_reply": "2022-08-20T06:21:33.033Z"
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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)\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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"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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{
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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": false,
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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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"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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],
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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": "3.11.3"
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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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"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": 1
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}
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