TwoChannelNIRS/visualize.py at master · micchu/TwoChannelNIRS · GitHub

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#coding:utf-8

'''
Created on 2016/02/25
@author: misato

結果の視覚化
'''

import os
import numpy as np
import pandas as pd

from visualization.con_csv import read_data, read_table, generate_pivot
from visualization.box_plot import box_plot

def visualize():
    # データ置き場
    target_directory = "analysis/result/Comparison_20160222"

    # 視覚化対象
    target_method = "LDA"
    target_data = "accuracy"
    plot_type = "box"

    # 描画
    parameter_list, filename_list = read_table(os.path.join(target_directory, target_method, "table.csv"))
    parametername_list = ["_".join(p) for p in parameter_list]
    data_list = []
    for i in range(len(parameter_list)):
        target_filename = os.path.join(target_directory, target_method, filename_list[i])
        subject, data = read_data(target_filename, target_data)
        data_list.append(data)

    data_size = len(data_list)
    box_plot(data_list[:data_size/3], parametername_list[:data_size/3], target_data, 0.0, _title = "HBX")
    box_plot(data_list[data_size/3:data_size/3*2], parametername_list[data_size/3:data_size/3*2], target_data, 0.0, _title = "HBX3")
    box_plot(data_list[data_size/3*2:], parametername_list[data_size/3*2:], target_data, 0.0, _title = "HBX1")


def convert_to_pivot():
    """
    統一ピボット形式へデータを変換
    """
    # データ置き場
    target_directory = "analysis/result/Comparison_20160222"

    # 対象
    target_method = "LDA"
    target_data = "all"
    plot_type = "box"

    # pivot作成対象となるデータのパラメータ、結果ファイル名を格納したtable.csvを読込む
    parameter_list, filename_list = read_table(os.path.join(target_directory, target_method, "table.csv"))

    # パラメータの読み込み
    parametername_list = ["_".join(str(p)) for p in parameter_list]
    # 対応するデータの読み込み
    data_list = []
    for i in range(len(filename_list)):
        target_filename = os.path.join(target_directory, target_method, filename_list[i])
        # データの読み込み
        _subject_list, _data_list = read_data(target_filename, target_data)
        data_list.append(_data_list)

    #
    header_list = ["signal", "re_method", "re_size", "feature", "nfold",
                   "subject_id","accuracy","loss","average_precition",
                   "average_recall","average_Fmeasure","average_distance","corr","p_value"]
    output_filename = os.path.join(target_directory, target_method + ".csv")
    generate_pivot(header_list, parameter_list, data_list, output_filename)

#    parameter_array = np.array(parameter_list)
#    data_array = np.array(data_list)
#    marge_array = np.hstack([parameter_array, data_array.reshape(len(data_list),1)])
#    print marge_array

if __name__=="__main__":
    #convert_to_pivot()
    visualize()