Added starter files

Author: LuckyQXW

Playground.ipynb

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+{
+    "metadata": {
+        "kernelspec": {
+            "name": "python3",
+            "display_name": "Python 3",
+            "language": "python"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 1,
+    "cells": [
+        {
+            "cell_type": "code",
+            "execution_count": 0,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import geopandas as gpd\n",
+                "import pandas as pd\n",
+                "import matplotlib.pyplot as plt\n",
+                "\n",
+                "SHAPE_FILE = '/course/food_access/tl_2010_53_tract00/tl_2010_53_tract00.shp'\n",
+                "FOOD_ACCESS_FILE = '/course/food_access/food_access.csv'"
+            ]
+        }
+    ]
+}

README.md

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-# hw5
-
+This repo has starter code for a CSE 163 assignment run in GitHub classroom.

cse163_imgd.py

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+"""
+Ryan Siu
+Runs imgd with student output against expected output
+and produces images showing the pixel differences.
+"""
+import subprocess
+import os
+
+import hw5
+
+PLOTS = [
+    "map.png",
+    "population_map.png",
+    "county_population_map.png",
+    "county_food_access.png",
+    "low_access.png"
+]
+IMGD_ARGS = [
+    "--pixel-correct-threshold", "0.985",
+    "--diff-mode", "always",
+    "--correct-colour", "ffffff",
+]
+
+
+def run_imgd(expected, actual, args=IMGD_ARGS):
+    """
+    Runs imgd of student output against expected.
+    Produces diff image only if both student and expected output exist.
+    """
+    if not os.path.exists(actual):
+        print(f"Could not find the file: {actual} after running hw5.py\n"
+              "Make sure to call all plotting functions in your main method!")
+    elif not os.path.exists(expected):
+        print(f"Could not find the file: {expected}\n")
+    else:
+        print(f"Running image comparison tool on {actual}...")
+        output = subprocess.run(["/opt/ed/bin/imgd", expected, actual]
+                                + args, capture_output=True)
+        output = output.stdout.decode("utf-8")
+        print(output)
+        os.rename("diff.png", f"{os.path.splitext(actual)[0]}_diff.png")
+
+
+def main():
+    print("Running hw5.main(). This may take a few seconds")
+    hw5.main()
+    print()
+    for plot_name in PLOTS:
+        run_imgd(f"expected/{plot_name}", plot_name)
+
+
+if __name__ == "__main__":
+    main()

expected/county_food_access.png

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+ISO-8859-1

expected/county_population_map.png

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+GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]

expected/low_access.png

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+# Food Desert Analysis
+
+## What is one way that government officials could use `plot_food_access_by_county` or `plot_low_access_tracts` to shape public policy on how to improve food access?
+
+
+
+## What is a limitation or concern with using the plots in this way?
+
+

expected/map.png

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+def main():
+    state_data = load_in_data(
+        'food_access/tl_2010_53_tract00/tl_2010_53_tract00.shp',
+        'food_access/food_access.csv'
+    )
+    print(percentage_food_data(state_data))
+    plot_map(state_data)
+    plot_population_map(state_data)
+    plot_population_county_map(state_data)
+    plot_food_access_by_county(state_data)
+    plot_low_access_tracts(state_data)
+
+
+if __name__ == '__main__':
+    main()

expected/population_map.png

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+[tool.poetry]
+name = "cse163-hw5"
+version = "0.1.0"
+description = "Test starter code for CSE 163 - HW5"
+authors = ["Hunter Schafer <[email protected]>"]
+
+[tool.poetry.dependencies]
+python = "^3.8"
+flake8 = "3.8.4"
+geopandas = "0.9.0"
+matplotlib = "3.4.2"
+
+[tool.poetry.dev-dependencies]
+pytest = "^5.2"
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"