基于rssi室内定位报告rssi分布情况标识位置

Posted 2020-10-04 rsapaper

 

 

 

 

 

 

 

 

import matplotlib

matplotlib.use(\'Agg\')
import numpy as np
from numpy import array
from matplotlib import pyplot
from scipy import integrate
import math
import time

from sys import path

path.append(\'D:\\pymine\\clean\\Gauss_rssi_model\\import_function\')
from draw import *

import matplotlib.mlab as mlab

zhfont1 = matplotlib.font_manager.FontProperties(fname=\'C:\\Windows\\Fonts\\STKAITI.TTF\')

import sqlite3

from openpyxl import Workbook

print(\'ing\')


def gen_rssi_model(rssi_list):
    x_list = sorted(list(set(rssi_list)))
    frequency_first_count, frequency_first_value, frequency_first_index, frequency_first_value_frequency, frequency_second_count, frequency_second_value, frequency_second_index, frequency_second_value_frequency = 0, 0, 0, 0, 0, 0, 0, 0

    rssi_list_len = len(rssi_list)
    for i in x_list:
        i_value, i_count, i_index = i, rssi_list.count(i), x_list.index(i)
        if i_count > frequency_first_count:
            frequency_first_value, frequency_first_count, frequency_first_index, frequency_first_value_frequency = i_value, i_count, i_index, i_count / rssi_list_len
        if i_count < frequency_first_count and i_count > frequency_second_count:
            frequency_second_value, frequency_second_count, frequency_second_index, frequency_second_value_frequency = i_value, i_count, i_index, i_count / rssi_list_len

    res_dic, gauss_rssi_model_type = {}, 1
    tmp_max, tmp_min = max(frequency_first_index, frequency_second_index), min(frequency_first_index,
                                                                               frequency_second_index)
    frequency_first_second_middle_value, frequency_first_second_middle_count, frequency_first_second_middle_index = \\
        x_list[tmp_min + 1], rssi_list.count((x_list[tmp_min + 1])), tmp_min + 1
    len_ = len(x_list)
    for i in range(0, len_, 1):
        if i <= tmp_min or i >= tmp_max:
            continue
        i_value = x_list[i]
        i_count = rssi_list.count(i_value)
        i_index = i
        if i_count < frequency_first_second_middle_count:
            frequency_first_second_middle_value, frequency_first_second_middle_count, frequency_first_second_middle_index = i_value, i_count, i_index

    if frequency_first_second_middle_value > (frequency_first_value + frequency_second_value) * 0.5:
        gauss_rssi_model_type = 2

    res_dic[\'gauss_rssi_model_type\'], res_dic[\'frequency_first_value\'], res_dic[\'frequency_first_count\'], res_dic[
        \'frequency_first_index\'], res_dic[\'frequency_second_value\'], res_dic[\'frequency_second_count\'], res_dic[
        \'frequency_second_index\'], res_dic[\'frequency_first_second_middle_value\'], res_dic[
        \'frequency_first_second_middle_count\'], res_dic[
        \'frequency_first_second_middle_index\'] = gauss_rssi_model_type, frequency_first_value, frequency_first_count, frequency_first_index, frequency_second_value, frequency_second_count, frequency_second_index, frequency_first_second_middle_value, frequency_first_second_middle_count, frequency_first_second_middle_index

    return res_dic


def pdf_Normal_distribution_integrate(average_, standard_deviation, x1, x2):
    f = lambda x: (np.exp(-(x - average_) ** 2 / (2 * standard_deviation ** 2))) / (
        np.sqrt(2 * np.pi)) / standard_deviation
    # return integrate.quad(f, x1, x2)
    # TODO MODIFY  try to be more precise
    # return integrate.quad(f, -np.inf, x1)[0] - integrate.quad(f, -np.inf, x2)[0]
    return integrate.quad(f, -np.inf, x2)[0] - integrate.quad(f, -np.inf, x1)[0]


# TODO DEL
# mlab.normpdf(len_, np_average, np_std)
def pdf_Normal_distribution_integrate_2_linear_combination(average0, standard_deviation0, average1, standard_deviation1,
                                                           x1, x2, weight0=0.5, weight1=0.5):
    p0, p1 = pdf_Normal_distribution_integrate(average0, standard_deviation0, x1,
                                               x2), pdf_Normal_distribution_integrate(average1, standard_deviation1, x1,
                                                                                      x2)
    return weight0 * p0 + weight1 * p1


def get_list_quartern_1_3(l):
    quartern_index_1 = math.ceil(len(l) / 4)
    quartern_value_1 = l[quartern_index_1]
    quartern_index_3 = quartern_index_1 * 3
    quartern_value_3 = l[quartern_index_3]
    dic_ = {}
    dic_[\'quartern_index_1\'] = quartern_index_1
    dic_[\'quartern_value_1\'] = quartern_value_1

    dic_[\'quartern_index_3\'] = quartern_index_3
    dic_[\'quartern_value_3\'] = quartern_value_3

    return dic_


def draw_frequency_hist(l_, title_, xlabel=\'rssi\', dir_=\'./savefig/\'):
    rssi_list = l_
    np_std = np.std(rssi_list)
    np_average = np.average(rssi_list)
    data_ = array(l_)
    pyplot.hist(data_, 300)
    xlabel = \'%s--std=%s,average=%s,sample_number=%s\' % (xlabel, np_std, np_average, len(rssi_list))
    pyplot.xlabel(xlabel)
    pyplot.ylabel(\'Frequency\')
    localtime_ = time.strftime("%y%m%d%H%M%S", time.localtime())
    title_ = \'%s%s\' % (title_, localtime_)
    pyplot.title(title_, fontproperties=zhfont1)
    dir_ = \'%s%s\' % (dir_, title_)
    pyplot.savefig(dir_)
    pyplot.close()


def draw_probability_density(rssi_list, title_, xlabel=\'rssi\', dir_=\'./savefig/\'):
    np_std = np.std(rssi_list)
    np_average = np.average(rssi_list)
    x_list = sorted(list(set(rssi_list)))
    len_ = len(rssi_list)
    loop_ = len(x_list)
    x, y = [], []
    for i in range(0, loop_, 1):
        val = x_list[i]
        probability_density = rssi_list.count(val) / len_
        x.append(val)
        y.append(probability_density)
    pyplot.plot(x, y)
    xlabel = \'%s--std=%s,average=%s,sample_number=%s\' % (xlabel, np_std, np_average, len(rssi_list))
    pyplot.xlabel(xlabel)
    pyplot.ylabel(\'ProbabilityDensity\')
    localtime_ = time.strftime("%y%m%d%H%M%S", time.localtime())
    title_ = \'%s%s\' % (title_, localtime_)
    pyplot.title(title_, fontproperties=zhfont1)
    dir_ = \'%s%s\' % (dir_, title_)
    pyplot.savefig(dir_)
    pyplot.close()



def from_db_to_res(db, sql, odd_even=0):
    conn = sqlite3.connect(db)
    cursor = conn.execute(sql)
    res_dic, counter_ = {}, 0
    for row in cursor:
        counter_ += 1
        if counter_ % 2 == odd_even:
            continue
        db_id, gather_point, mac, rssi, timestamp = row
        gather_point = gather_point.replace(\'\\n\', \'\')
        if gather_point not in res_dic:
            res_dic[gather_point] = {}
            res_dic[gather_point][\'rssi_list\'] = []
        res_dic[gather_point][\'rssi_list\'].append(rssi)

    for gather_point in res_dic:
        rssi_list = sorted(res_dic[gather_point][\'rssi_list\'])
        np_std = np.std(rssi_list)
        np_average = np.average(rssi_list)
        res_dic[gather_point][\'rssi_list_np_std\'] = np_std
        res_dic[gather_point][\'rssi_list_np_average\'] = np_average

        rssi_model = gen_rssi_model(rssi_list)

        res_dic[gather_point][\'rssi_model\'] = rssi_model
        list_quartern_1_3_dic = get_list_quartern_1_3(rssi_list)

        res_dic[gather_point][\'quartern_index_1\'], res_dic[gather_point][\'quartern_value_1\'], res_dic[gather_point][
            \'quartern_index_3\'], res_dic[gather_point][\'quartern_value_3\'] = \\
            list_quartern_1_3_dic[\'quartern_index_1\'], list_quartern_1_3_dic[\'quartern_value_1\'], list_quartern_1_3_dic[
                \'quartern_index_3\'], list_quartern_1_3_dic[\'quartern_value_3\']

    return res_dic


db, sql = \'wifi_Tom_0814.db\', \'SELECT * FROM wifi\'
Tom_home_dic_even = from_db_to_res(db, sql)
Tom_home_dic_odd = from_db_to_res(db, sql, 1)
db, sql = \'wifi_beta_office_0812am.db\', \'SELECT * FROM wifi WHERE belongpoint IN ("sw_office_Bata_table") \'
Beta_table_dic_even = from_db_to_res(db, sql)
Beta_table_dic_odd = from_db_to_res(db, sql, 1)

k = \'sw_office_Bata_table\'
rssi_list = Beta_table_dic_odd[k][\'rssi_list\']
title_ = \'%s%s\' % (\'o-\', k)
draw_frequency_hist_probability_density(rssi_list, title_, xlabel=\'rssi\', dir_=\'./savefig/\')
k = \'sw_office_Bata_table\'
rssi_list = Beta_table_dic_even[k][\'rssi_list\']
title_ = \'%s%s\' % (\'e-\', k)
draw_frequency_hist_probability_density(rssi_list, title_, xlabel=\'rssi\', dir_=\'./savefig/\')

k = \'下沙88栋\'
rssi_list = Tom_home_dic_odd[k][\'rssi_list\']
title_ = \'%s%s\' % (\'o-\', k)
draw_frequency_hist_probability_density(rssi_list, title_, xlabel=\'rssi\', dir_=\'./savefig/\')
k = \'下沙88栋\'
rssi_list = Tom_home_dic_even[k][\'rssi_list\']
title_ = \'%s%s\' % (\'e-\', k)
draw_frequency_hist_probability_density(rssi_list, title_, xlabel=\'rssi\', dir_=\'./savefig/\')

report_dic = {}


def compute_relative_integrate(dic_, dic_x, type_, direction_=\'Tom_Beta\'):
    global report_dic
    if direction_ not in report_dic:
        report_dic[direction_] = {}
    report_dic[direction_][type_] = {}
    report_dic[direction_][type_][\'simple_dic\'] = {}
    if direction_ == \'Tom_Beta\':
        dic_, dic_x = dic_[\'下沙88栋\'], dic_x[\'sw_office_Bata_table\']
    elif direction_ == \'Tom_Tom\':
        dic_, dic_x = dic_[\'下沙88栋\'], dic_x[\'下沙88栋\']
    elif direction_ == \'Beta_Tom\':
        dic_, dic_x = dic_[\'sw_office_Bata_table\'], dic_x[\'下沙88栋\']
    elif direction_ == \'Beta_Beta\':
        dic_, dic_x = dic_[\'sw_office_Bata_table\'], dic_x[\'sw_office_Bata_table\']

    average_, standard_deviation, x1, x2 = dic_[\'rssi_list_np_average\'], dic_[\'rssi_list_np_std\'], dic_x[
        \'quartern_value_1\'], dic_x[\'quartern_value_3\']
    res = pdf_Normal_distribution_integrate(average_, standard_deviation, x1, x2)
    simple_dic = {}
    simple_dic[\'integrand\'], simple_dic[\'to\'], simple_dic[\'res\'] = dic_, dic_x, res
    report_dic[direction_][type_][\'simple_dic\'] = simple_dic
    return res


# TODO MODIFY
dic_, dic_x = Tom_home_dic_even, Beta_table_dic_even
Tom_e_Beta_e_pdf_integrate = compute_relative_integrate(dic_, dic_x, \'e_e\')

dic_, dic_x = Tom_home_dic_even, Beta_table_dic_odd
Tom_e_Beta_o_pdf_integrate = compute_relative_integrate(dic_, dic_x, \'e_o\')

dic_, dic_x = Tom_home_dic_odd, Beta_table_dic_even
Tom_o_Beta_e_pdf_integrate = compute_relative_integrate(dic_, dic_x, \'o_e\')

dic_, dic_x = Tom_home_dic_odd, Beta_table_dic_odd
Tom_o_Beta_o_pdf_integrate = compute_relative_integrate(dic_, dic_x, \'o_o\')

dic_, dic_x = Tom_home_dic_odd, Tom_home_dic_odd
Tom_o_Tom_o_pdf_integrate = compute_relative_integrate(dic_, dic_x, \'o_o\', \'Tom_Tom\')

dic_, dic_x = Tom_home_dic_odd, Tom_home_dic_even
Tom_o_Tom_e_pdf_integrate = compute_relative_integrate(dic_, dic_x, \'o_e\', \'Tom_Tom\')

dic_, dic_x = Tom_home_dic_even, Tom_home_dic_even
Tom_e_Tom_e_pdf_integrate = compute_relative_integrate(dic_, dic_x, \'e_e\', \'Tom_Tom\')

dic_, dic_x = Tom_home_dic_even, Tom_home_dic_odd
Tom_e_Tom_o_pdf_integrate = compute_relative_integrate(dic_, dic_x, \'e_o\', \'Tom_Tom\')

dic_, dic_x = Beta_table_dic_even, Tom_home_dic_even
Beta_e_Tom_e_pdf_integrate = compute_relative_integrate(dic_, dic_x, \'e_e\', \'Beta_Tom\')

dic_, dic_x = Beta_table_dic_even, Tom_home_dic_odd
Beta_e_Tom_o_pdf_integrate = compute_relative_integrate(dic_, dic_x, \'e_o\', \'Beta_Tom\')

dic_, dic_x = Beta_table_dic_odd, Tom_home_dic_even
Beta_o_Tom_e_pdf_integrate = compute_relative_integrate(dic_, dic_x, \'o_e\', \'Beta_Tom\')

dic_, dic_x = Beta_table_dic_odd, Tom_home_dic_odd
Beta_o_Tom_o_pdf_integrate = compute_relative_integrate(dic_, dic_x, \'o_o\', \'Beta_Tom\')

dic_, dic_x = Beta_table_dic_odd, Beta_table_dic_odd
Beta_o_Beta_o_pdf_integrate = compute_relative_integrate(dic_, dic_x, \'o_o\', \'Beta_Beta\')

dic_, dic_x = Beta_table_dic_odd, Beta_table_dic_even
Beta_o_Beta_e_pdf_integrate = compute_relative_integrate(dic_, dic_x, \'o_e\', \'Beta_Beta\')

dic_, dic_x = Beta_table_dic_even, Beta_table_dic_even
Beta_e_Beta_e_pdf_integrate = compute_relative_integrate(dic_, dic_x, \'e_e\', \'Beta_Beta\')

dic_, dic_x = Beta_table_dic_even, Beta_table_dic_odd
Beta_e_Beta_o_pdf_integrate = compute_relative_integrate(dic_, dic_x, \'e_o\', \'Beta_Beta\')

for direction_ in report_dic:
    for type_ in report_dic[direction_]:
        ll = []
        simple_dic = report_dic[direction_][type_][\'simple_dic\']
        to_dic, integrand_dic = simple_dic[\'to\'], simple_dic[\'integrand\']
        to_quartern_index_1, to_quartern_index_3, to_quartern_value_1, to_quartern_value_3, to_rssi_list_np_average, to_rssi_list_np_std = \\
            to_dic[\'quartern_index_1\'], to_dic[\'quartern_index_3\'], to_dic[\'quartern_value_1\'], to_dic[
                \'quartern_value_3\'], \\
            to_dic[\'rssi_list_np_average\'], to_dic[\'rssi_list_np_std\']
        integrand_quartern_index_1, integrand_quartern_index_3, integrand_quartern_value_1, integrand_quartern_value_3, integrand_rssi_list_np_average, integrand_rssi_list_np_std = \\
            integrand_dic[\'quartern_index_1\'], integrand_dic[\'quartern_index_3\'], integrand_dic[\'quartern_value_1\'], \\
            integrand_dic[\'quartern_value_3\'], integrand_dic[\'rssi_list_np_average\'], integrand_dic[\'rssi_list_np_std\']
        to_rssi_model, integrand_rssi_model = to_dic[\'rssi_model\'], integrand_dic[\'rssi_model\']

        to_gauss_rssi_model_type, to_frequency_first_value, to_frequency_first_count, to_frequency_first_index, to_frequency_second_value, to_frequency_second_count, to_frequency_second_index, to_frequency_first_second_middle_value, to_frequency_first_second_middle_count, to_frequency_first_second_middle_index = \\
            to_rssi_model[\'gauss_rssi_model_type\'], to_rssi_model[\'frequency_first_value\'], \\
            to_rssi_model[\'frequency_first_count\'], to_rssi_model[\'frequency_first_index\'], \\
            to_rssi_model[\'frequency_second_value\'], to_rssi_model[\'frequency_second_count\'], \\
            to_rssi_model[\'frequency_second_index\'], to_rssi_model[\'frequency_first_second_middle_value\'], \\
            to_rssi_model[\'frequency_first_second_middle_count\'], to_rssi_model[
                \'frequency_first_second_middle_index\']

        integrand_gauss_rssi_model_type, integrand_frequency_first_value, integrand_frequency_first_count, integrand_frequency_first_index, integrand_frequency_second_value, integrand_frequency_second_count, integrand_frequency_second_index, integrand_frequency_first_second_middle_value, integrand_frequency_first_second_middle_count, integrand_frequency_first_second_middle_index = \\
            integrand_rssi_model[\'gauss_rssi_model_type\'], integrand_rssi_model[\'frequency_first_value\'], \\
            integrand_rssi_model[\'frequency_first_count\'], integrand_rssi_model[\'frequency_first_index\'], \\
            integrand_rssi_model[\'frequency_second_value\'], integrand_rssi_model[\'frequency_second_count\'], \\
            integrand_rssi_model[\'frequency_second_index\'], integrand_rssi_model[\'frequency_first_second_middle_value\'], \\
            integrand_rssi_model[\'frequency_first_second_middle_count\'], integrand_rssi_model[
                \'frequency_first_second_middle_index\']

        res_single = pdf_Normal_distribution_integrate(integrand_rssi_list_np_average, integrand_rssi_list_np_std,
                                                       to_quartern_value_1, to_quartern_value_3)

        # 双峰模型：假设两个“子分布”均为正太分布且离散程度相同均等于全量数据的方差
        res_double = pdf_Normal_distribution_integrate_2_linear_combination(integrand_frequency_first_value,
                                                                            integrand_rssi_list_np_std,
                                                                            integrand_frequency_second_value,
                                                                            integrand_rssi_list_np_std,
                                                                            to_quartern_value_1, to_quartern_value_3)
        report_dic[direction_][type_][\'simple_dic\'][\'res_single\'], report_dic[direction_][type_][\'simple_dic\'][
            \'res_double\'] = res_single, res_double

dd = 8




# wb = Workbook()
# worksheet = wb.active
# title_ = \'direction_, type_, res_single, res_double, to_rssi_list_np_average, to_rssi_list_np_std, to_quartern_index_1, to_quartern_value_1, to_quartern_index_3, to_quartern_value_3, to_gauss_rssi_model_type, to_frequency_first_value, to_frequency_first_count, to_frequency_first_index, to_frequency_second_value, to_frequency_second_count, to_frequency_second_index, to_frequency_first_second_middle_value, to_frequency_first_second_middle_count, to_frequency_first_second_middle_index,  integrand_rssi_list_np_average, integrand_rssi_list_np_std,integrand_quartern_index_1, integrand_quartern_value_1, integrand_quartern_index_3,integrand_quartern_value_3,integrand_gauss_rssi_model_type, integrand_frequency_first_value, integrand_frequency_first_count, integrand_frequency_first_index, integrand_frequency_second_value, integrand_frequency_second_count, integrand_frequency_second_index, integrand_frequency_first_second_middle_value, integrand_frequency_first_second_middle_count, integrand_frequency_first_second_middle_index\'
# title_l = title_.replace(\' \', \'\').split(\',\')
# worksheet.append(title_l)
# for direction_ in report_dic:
#     for type_ in report_dic[direction_]:
#         ll = []
#         simple_dic = report_dic[direction_][type_][\'simple_dic\']
#         to_dic, integrand_dic = simple_dic[\'to\'], simple_dic[\'integrand\']
#         to_quartern_index_1, to_quartern_index_3, to_quartern_value_1, to_quartern_value_3, to_rssi_list_np_average, to_rssi_list_np_std = \\
#             to_dic[\'quartern_index_1\'], to_dic[\'quartern_index_3\'], to_dic[\'quartern_value_1\'], to_dic[
#                 \'quartern_value_3\'], \\
#             to_dic[\'rssi_list_np_average\'], to_dic[\'rssi_list_np_std\']
#         integrand_quartern_index_1, integrand_quartern_index_3, integrand_quartern_value_1, integrand_quartern_value_3, integrand_rssi_list_np_average, integrand_rssi_list_np_std = \\
#             integrand_dic[\'quartern_index_1\'], integrand_dic[\'quartern_index_3\'], integrand_dic[\'quartern_value_1\'], \\
#             integrand_dic[\'quartern_value_3\'], integrand_dic[\'rssi_list_np_average\'], integrand_dic[\'rssi_list_np_std\']
#         to_rssi_model, integrand_rssi_model = to_dic[\'rssi_model\'], integrand_dic[\'rssi_model\']
#
#         to_gauss_rssi_model_type, to_frequency_first_value, to_frequency_first_count, to_frequency_first_index, to_frequency_second_value, to_frequency_second_count, to_frequency_second_index, to_frequency_first_second_middle_value, to_frequency_first_second_middle_count, to_frequency_first_second_middle_index = \\
#             to_rssi_model[\'gauss_rssi_model_type\'], to_rssi_model[\'frequency_first_value\'], \\
#             to_rssi_model[\'frequency_first_count\'], to_rssi_model[\'frequency_first_index\'], \\
#             to_rssi_model[\'frequency_second_value\'], to_rssi_model[\'frequency_second_count\'], \\
#             to_rssi_model[\'frequency_second_index\'], to_rssi_model[\'frequency_first_second_middle_value\'], \\
#             to_rssi_model[\'frequency_first_second_middle_count\'], to_rssi_model[
#                 \'frequency_first_second_middle_index\']
#
#         integrand_gauss_rssi_model_type, integrand_frequency_first_value, integrand_frequency_first_count, integrand_frequency_first_index, integrand_frequency_second_value, integrand_frequency_second_count, integrand_frequency_second_index, integrand_frequency_first_second_middle_value, integrand_frequency_first_second_middle_count, integrand_frequency_first_second_middle_index = \\
#             integrand_rssi_model[\'gauss_rssi_model_type\'], integrand_rssi_model[\'frequency_first_value\'], \\
#             integrand_rssi_model[\'frequency_first_count\'], integrand_rssi_model[\'frequency_first_index\'], \\
#             integrand_rssi_model[\'frequency_second_value\'], integrand_rssi_model[\'frequency_second_count\'], \\
#             integrand_rssi_model[\'frequency_second_index\'], integrand_rssi_model[\'frequency_first_second_middle_value\'], \\
#             integrand_rssi_model[\'frequency_first_second_middle_count\'], integrand_rssi_model[
#                 \'frequency_first_second_middle_index\']
#
#         res_single, res_double = report_dic[direction_][type_][\'simple_dic\'][\'res_single\'], \\
#                                  report_dic[direction_][type_][\'simple_dic\'][\'res_double\']
#
#         ll = direction_, type_, res_single, res_double, to_rssi_list_np_average, to_rssi_list_np_std, to_quartern_index_1, to_quartern_value_1, to_quartern_index_3, to_quartern_value_3, to_gauss_rssi_model_type, to_frequency_first_value, to_frequency_first_count, to_frequency_first_index, to_frequency_second_value, to_frequency_second_count, to_frequency_second_index, to_frequency_first_second_middle_value, to_frequency_first_second_middle_count, to_frequency_first_second_middle_index, integrand_rssi_list_np_average, integrand_rssi_list_np_std, integrand_quartern_index_1, integrand_quartern_value_1, integrand_quartern_index_3, integrand_quartern_value_3, integrand_gauss_rssi_model_type, integrand_frequency_first_value, integrand_frequency_first_count, integrand_frequency_first_index, integrand_frequency_second_value, integrand_frequency_second_count, integrand_frequency_second_index, integrand_frequency_first_second_middle_value, integrand_frequency_first_second_middle_count, integrand_frequency_first_second_middle_index
#         worksheet.append(ll)
# file_name = \'自采集数据-单双峰-概率计算结果\'
# localtime_ = time.strftime("%y%m%d%H%M%S", time.localtime())
# file_name_save = \'%s%s%s\' % (file_name, localtime_, \'.xlsx\')
# wb.save(file_name_save)
#
# print(\'ok-finished\', localtime_)

　　

 

 

 

import matplotlib

matplotlib.use(\'Agg\')
import numpy as np
from numpy import array
from matplotlib import pyplot
from scipy import integrate
import math
import time
import matplotlib.mlab as mlab

zhfont1 = matplotlib.font_manager.FontProperties(fname=\'C:\\Windows\\Fonts\\STKAITI.TTF\')


def draw_frequency_hist_probability_density(rssi_list, title_, xlabel=\'rssi\', dir_=\'./savefig/\'):
    np_std = np.std(rssi_list)
    np_average = np.average(rssi_list)
    x_list = sorted(list(set(rssi_list)))
    len_ = len(rssi_list)
    loop_ = len(x_list)
    x, y1, y2 = [], [], []

    for i in range(0, loop_, 1):
        val = x_list[i]
        frequency, probability_density = rssi_list.count(val), rssi_list.count(val) / len_
        x.append(val)
        y1.append(frequency)
        y2.append(probability_density)

    fig, (ax1, ax2, ax3, ax4) = pyplot.subplots(4, 1)
    fig.set_size_inches(16, 16)

    ax1.set_ylabel(\'Frequency\')
    localtime_ = time.strftime("%y%m%d%H%M%S", time.localtime())
    title_ = \'%s%s\' % (title_, localtime_)
    ax1.set_title(title_, fontproperties=zhfont1)

    ax2.set_ylabel(\'Frequency\')

    xlabel_3 = \'%s--std=%s,average=%s,sample_number=%s\' % (xlabel, np_std, np_average, len(rssi_list))
    ax3.set_xlabel(xlabel_3)
    ax3.set_ylabel(\'ProbabilityDensity\')

    ax1.plot(x, y1, \'bo\')
    ax2.plot(x, y1)
    ax3.plot(x, y2)

    # Tweak spacing to prevent clipping of ylabel

    sigma = np.std(rssi_list)
    mu = np.average(rssi_list)
    x = array(rssi_list)
    # num_bins = 100
    # n, bins, patches = ax4.hist(x, num_bins, normed=1)
    num_bins = len(x_list)
    n, bins, patches = ax4.hist(x, num_bins, normed=1)
    # n, bins, patches = ax4.hist(x, normed=1)
    # add a \'best fit\' line
    y = mlab.normpdf(bins, mu, sigma)
    ax4.plot(bins, y, \'--\')
    xlabel_4 = \'%s--std=%s,average=%s\' % (\'normpdf\', np_std, np_average)
    ax4.set_xlabel(xlabel_4)
    ylabel_4 = \'normpdf\'
    ax4.set_ylabel(ylabel_4)
    # str_= \'%s: $\\mu=$s, $\\sigma=$s$\' % (\'te\')
    # ax4.set_title(str_)
    ax4.plot(bins, y)

    fig.tight_layout()
    # pyplot.plot()
    dir_ = \'%s%s\' % (dir_, title_)
    pyplot.show()
    pyplot.savefig(dir_)
    pyplot.close()