HW8 Complete

matPlotLib/graph2.py

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+import numpy as np
+import argparse
+from matplotlib import pyplot as plt
+from os import sys
+
+
+def graph_from_csv(filename, multiline=False, clean_fn=lambda x: x.strip(),
+                   independent_convert_fn=lambda x: int(x), dependent_convert_fn=lambda x: int(x),
+                   xlabel="", ylabel="", title="", xscale="linear", yscale="linear", approximate=False,
+                   approximate_size=1, xbase=10, ybase=10):
+    """
+    Generates a graph from a CSV file. Supports multiple sets of the same independent variable.
+
+    Created by Hunter Henrichsen, 2020.
+    Params:
+    filename -- The CSV file to read.
+    multiline -- Whether to average (using np.mean()) the datasets that share an independent variable.
+    clean_fn -- The function run on each line to clean it before splitting on commas.
+    independent_convert_fn -- The function to convert the independent variable to the desired type.
+    dependent_convert_fn -- The function to convert the dependent variable to the desired type.
+    """
+    data = {}
+
+    # Read data.
+    with open(filename, 'r') as file:
+        header = file.readline()
+        columns = clean_fn(header).split(',')
+        for i in range(1, len(columns)):
+            data[i] = {
+                'name': columns[i],
+                'data': {},
+            }
+        while line := file.readline():
+            split_line = clean_fn(line).split(',')
+            for i in range(1, len(split_line)):
+                iv = independent_convert_fn(split_line[0])
+                if iv not in data[i]['data']:
+                    data[i]['data'][iv] = []
+                data[i]['data'][iv].append(dependent_convert_fn(split_line[i]))
+        file.close()
+
+    # Process data to averaged.
+    if not multiline:
+        for key in data:
+            current_set = data[key]
+            for n in data[key]['data']:
+                dataset = data[key]['data'][n]
+                data[key]['data'][n] = np.mean(dataset)
+
+    # Begin plotting.
+    plt.title(title)
+    plt.xscale(xscale, basex=xbase)
+    plt.yscale(yscale, basey=ybase)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    for key in data:
+        current_set = data[key]
+        lists = sorted(current_set['data'].items())
+        x, y = zip(*lists)
+        if approximate:
+            approx = np.polyfit(np.log(x) if xscale == "log" else x, np.log(y) if yscale == "log" else y,
+                                approximate_size)
+            print(current_set['name'], "Approximation: (" + str(ybase ** approx[1]) + ")x^" + str(approx[0]))
+        plt.plot(x, y, label=current_set['name'])
+    plt.legend()
+    plt.show()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Transforms a CSV file into a matplotlib graph.')
+    parser.add_argument('--xscale', action='store', nargs='?', default='linear')
+    parser.add_argument('--yscale', action='store', nargs='?', default='linear')
+    parser.add_argument('--xbase', action='store', nargs='?', default=10, type=int)
+    parser.add_argument('--ybase', action='store', nargs='?', default=10, type=int)
+    parser.add_argument('--xlabel', action='store', nargs='?', default='')
+    parser.add_argument('--ylabel', action='store', nargs='?', default='')
+    parser.add_argument('--title', action='store', nargs='?', default='')
+    parser.add_argument('--approximate', action='store_true', default=False)
+    parser.add_argument('--approximateSize', action='store', default=1, type=int)
+    parser.add_argument('filename', nargs=1, type=str)
+    args = parser.parse_args()
+    print(args)
+    graph_from_csv(args.filename[0], multiline=False, xscale=args.xscale, xbase=args.xbase, yscale=args.yscale,
+                   ybase=args.ybase, xlabel=args.xlabel, ylabel=args.ylabel, title=args.title,
+                   approximate=args.approximate, approximate_size=args.approximateSize)