【基于rssi室内定位报告】rssi分布情况标识位置

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()