Pandasを使ってグラフを作成 #code

pandas-plot.py

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import glob
plt.rcParams['font.family'] = 'sans-serif'  # 使用するフォント
# x軸の目盛線が内向き('in')か外向き('out')か双方向か('inout')
plt.rcParams['xtick.direction'] = 'in'
# y軸の目盛線が内向き('in')か外向き('out')か双方向か('inout')
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['xtick.major.width'] = 1.0  # x軸主目盛り線の線幅
plt.rcParams['ytick.major.width'] = 1.0  # y軸主目盛り線の線幅
plt.rcParams['font.size'] = 8  # フォントの大きさ
plt.rcParams['axes.linewidth'] = 1.0  # 軸の線幅edge linewidth。囲みの太さ


def render_result(filename):
    df = pd.read_csv(filename)
    # print(df['score'])
    # df.plot(x=df.columns[0], legend=False, figsize=[10, 8])
    ax = df.plot(x=df.columns[0], y=df.columns[1], label='swarm-1', figsize=[10, 8])
    df.plot(x=df.columns[0], y=df.columns[2], label='swarm-2', ax=ax)
    plt.legend(loc='best', fancybox=True, framealpha=1, fontsize=18)
    plt.ylim(0.0, 1.0)
    plt.grid(which='major', c='grey', linestyle='-')
    plt.xlabel('Learning step [-]', fontsize=20)
    # plt.ylabel('Accuracy rate[%]', fontsize=20)
    plt.ylabel('Score [-]', fontsize=20)
    plt.tick_params(labelsize=18)
    plt.savefig('learning1-score.png', bbox_inches='tight', transparent=False)


def render_output(out_filename):
    df = pd.read_csv(out_filename)
    df.objsize *= 10
    df.plot(x='objsize', figsize=[10, 8], lw=3)
    plt.grid(which='major', color='grey', linestyle='-')
    plt.legend(loc='best', fancybox=True, framealpha=1, fontsize=18)
    plt.tick_params(labelsize=18)
    plt.xlabel('Object size [mm]', fontsize=20)
    plt.ylabel('Output [-]', fontsize=20)
    plt.savefig('learning2-output.png',
                bbox_inches='tight', transparent=False)
    plt.savefig('learning2-output.pdf',
                bbox_inches='tight', transparent=False)


def make_label_from_file_output_growth(str):
    conf = re.findall("[0-9]+", str)
    return 'learning step:' + conf[0]
    # return 'input:'+conf[1]+',step:'+conf[0]


def sort_rule_output_growth(x):
    conf = re.findall("[0-9]+", x)
    return int(conf[0])


def render_output_growth():
    n = 1
    files = []
    files += glob.glob('./data/outputlogs/output_func-*.csv')
    files = sorted(files, key=sort_rule_output_growth)
    label = make_label_from_file_output_growth(files[0])
    print(label)
    df = pd.read_csv(files[0])
    ax = df.plot(x='objsize', y='goal-'+str(n), lw=3,
                 label=label, figsize=[10, 8])
    plt.grid(which='major', color='grey', linestyle='-')
    for i in range(1, len(files)):
        data = pd.read_csv(files[i])
        label = make_label_from_file_output_growth(files[i])
        data.plot(x='objsize', y='goal-'+str(n), lw=3,
                  label=label, ax=ax)
    ax.tick_params(labelsize=18, direction='in')
    plt.xlabel('Object size', fontsize=20)
    plt.ylabel('Output', fontsize=20)
    plt.legend(loc='best', fancybox=True, fontsize=18)
    # plt.legend(loc='best', fancybox=True, framealpha=1, fontsize=20)
    plt.grid(which='major', color='black', linestyle='-')
    plt.savefig('learning1-output'+str(n)+'-growth.pdf',
                bbox_inches='tight', transparent=False)
    plt.savefig('learning1-output'+str(n)+'-growth.png',
                bbox_inches='tight', transparent=False)


if __name__ == "__main__":
    timestamps=[""]
    for timestamp in timestamps:
        result_filename = "data/result" + timestamp + ".csv"
        render_result(result_filename)
        out_last_filename = "data/output_last" + timestamp + ".csv"
        render_output(out_last_filename)'
    render_output_growth()
    plt.show()
    # plt.savefig('plotname.png', bbox_inches='tight', transparent=False)
    # plt.savefig('plotname.pdf', bbox_inches='tight', transparent=False)