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89 lines
2.8 KiB
Python
Executable File
89 lines
2.8 KiB
Python
Executable File
#!/usr/bin/env python3
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# This is a simple script that takes in an scurve file produced by
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# csvcolumn_to_scurve and produces a png graph of the scurve.
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import argparse
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import csv
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import matplotlib.pyplot as plt
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import numpy as np
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FIELDS = ['N/total', 'New/Old']
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def get_data(input_file):
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global FIELDS
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for row in csv.DictReader(input_file):
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yield (float(row[FIELDS[0]]), float(row[FIELDS[1]]))
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def main():
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p = argparse.ArgumentParser()
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p.add_argument('input_csv_file', type=argparse.FileType('r'))
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p.add_argument('output_file', type=str)
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p.add_argument('-y-axis-num-tick-marks', type=int,
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help='The number of y tick marks to use above/below zero.')
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p.add_argument('-y-axis-min', type=float,
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help='Override the min y axis that we use')
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p.add_argument('-y-axis-max', type=float,
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help='Override the min y axis that we use')
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p.add_argument('-title', type=str,
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help='Title of the graph')
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p.add_argument('-x-axis-title', type=str,
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help='The title to use on the x-axis of the graph')
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p.add_argument('-y-axis-title', type=str,
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help='The title to use on the x-axis of the graph')
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args = p.parse_args()
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data = np.array(list(get_data(args.input_csv_file)))
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assert np.all(data >= 0)
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x = data[:, 0]
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y = data[:, 1]
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x_axis_title = args.x_axis_title or FIELDS[0]
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y_axis_title = args.y_axis_title or FIELDS[1]
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title = args.title or "{} vs {}".format(x_axis_title, y_axis_title)
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fig, ax = plt.subplots()
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fig.set_size_inches(18.5, 18.5)
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fig.suptitle(title, fontsize=20)
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ax.set_xlabel(x_axis_title, fontsize=20)
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ax.set_ylabel(y_axis_title, fontsize=20)
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ax.plot(x, y)
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ax.scatter(x, y)
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# To get good bounds, we:
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#
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# 1. Re-center our data at 0 by subtracting 1. This will give us the %
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# difference in between new and old (i.e. (new - old)/old)
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#
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# 2. Then we take the maximum absolute delta from zero and round to a
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# multiple of 5 away from zero. Lets call this value limit.
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#
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# 3. We set [min_y, max_y] = [1.0 - limit, 1.0 + limit]
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recentered_data = y - 1.0
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max_magnitude = int(np.max(np.abs(recentered_data)) * 100.0)
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y_limit = float(((max_magnitude // 5) + 1) * 5) * 0.01
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ax.set_xlim(0.0, 1.0)
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y_min = args.y_axis_min or 1.0 - y_limit
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y_max = args.y_axis_max or 1.0 + y_limit
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assert y_min <= y_max
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ax.set_ylim(y_min, y_max)
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ax.grid(True)
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ax.xaxis.set_ticks(np.arange(0.0, 1.0, 0.05))
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if args.y_axis_num_tick_marks:
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y_delta = y_max - y_min
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y_tickmark_frequency = y_delta / float(args.y_axis_num_tick_marks)
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ax.yaxis.set_ticks(np.arange(y_min, y_max, y_tickmark_frequency))
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plt.savefig(args.output_file)
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if __name__ == "__main__":
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main()
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