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