def create_simple_pie():
global slee
data_m = slee['time']
train_data_m = np.array(data_m)
train_t_list = train_data_m.tolist() # list
data_m = slee['ti']
train_data_m = np.array(data_m)
train_w_list = train_data_m.tolist() # list
slee['ti'] = slee['ti'] * 0.85
data_m = slee['ti']
train_data_m = np.array(data_m)
train_l_list = train_data_m.tolist() # list
data_m = slee['wear_type']
train_data_m = np.array(data_m)
train_k_list = train_data_m.tolist() # list
bar = Bar("睡眠数据", '有效时长：', height=360)
bar.add("时长/s",
train_t_list,
train_w_list,
yaxis_max=20,
yaxis_min=0,
bar_category_gap=-10,
legend_top="5%")
#bar.add("校正后/s", train_t_list, train_l_list, yaxis_max=20, yaxis_min=0,bar_category_gap=-20,legend_top="5%")
bar1 = Bar("佩戴状态", title_top="35%")
bar1.add("佩戴状态",
train_t_list,
train_k_list,
legend_top="35%",
bar_category_gap=0)
grid = Grid()
grid.add(bar, grid_top="10%", grid_bottom="70%")
grid.add(bar1, grid_top="40%", grid_bottom="35%")
grid.renderer = 'svg'
return grid
def Data_for_static():
global username
global date_pro
global name
global str_path
global rate_save
global day
global temp_static
global checklist
year = int(str(date_pro)[0:4])
month = int(str(date_pro)[5:7])
day = int(str(date_pro)[8:10])
lastDay = getMonthFirstDayAndLastDay(year, month)
str_date2 = str(year) + '-' + str(month) + '-' + str(1)
str_date1 = str(year) + '-' + str(month) + '-' + str(lastDay)
if len(rate_save) > 15 and rate_save.iloc[
0, 4] == username and rate_save.iloc[
1, 4] == month and day == rate_save.iloc[2, 4]:
rate_save['username'] = username
else:
if len(rate_save) > 15 and rate_save.iloc[
0, 4] == username and rate_save.iloc[1, 4] == month:
rate_save['username'] = username
else:
cnt = 0
while cnt <= 2:
if name == 0:
str_path = str_path2
name = 1
elif name == 1:
str_path = str_path1
name = 2
else:
str_path = str_path3
name = 0
if cnt <= 0:
temp_static = Scrapy(str_date1, str_date2)
else:
df2 = Scrapy(str_date1, str_date2)
temp_static = pd.merge(temp_static, df2, how='outer')
cnt = cnt + 1
temp_static['date'] = temp_static['datetime'].map(Time_get(0))
temp_static['Hour'] = temp_static['datetime'].map(Time_get(1))
rate_save = pd.DataFrame(np.arange(lastDay + 1),
columns=['timesta'])
rate_save['rate'] = 0
rate_save['rate_night'] = 0
rate_save['rate_day'] = 0
rate_save['username'] = username
rate_save.iloc[1, 4] = month
rate_save.iloc[2, 4] = day
start, end = Choose_time(checklist)
for i in range(lastDay):
da = temp_static[temp_static['date'] == i]
data = da[da['wear_type'] == 1]
rate = Get_rate(data)
data_night1 = data[data['Hour'] <= start]
data_night2 = data[data['Hour'] >= end]
data_night = pd.merge(data_night1, data_night2, how='outer')
rate_night = Get_rate(data_night)
data_day = data[data['Hour'] > start]
data_day = data_day[data_day['Hour'] < end]
rate_day = Get_rate(data_day)
rate_save.iloc[i, 0] = i
rate_save.iloc[i, 1] = rate
rate_save.iloc[i, 2] = rate_night
rate_save.iloc[i, 3] = rate_day
rate1 = rate_save[rate_save['rate'] > 0]['rate'].mean()
rate1 = float('%.2f' % rate1)
rate2 = rate_save[rate_save['rate_night'] > 0]['rate_night'].mean()
rate2 = float('%.2f' % rate2)
rate3 = rate_save[rate_save['rate_day'] > 0]['rate_day'].mean()
rate3 = float('%.2f' % rate3)
data_m = rate_save['timesta']
train_data_m = np.array(data_m)
train_k_list = train_data_m.tolist() # list
data_m = rate_save['rate']
train_data_m = np.array(data_m)
train_l_list = train_data_m.tolist() # list
data_m = rate_save['rate_night']
train_data_m = np.array(data_m)
train_m_list = train_data_m.tolist() # list
data_m = rate_save['rate_day']
train_data_m = np.array(data_m)
train_n_list = train_data_m.tolist() # list
strplot = '数据有效率：' + str(rate1) + '% 晚间(' + str(end) + ':00--' + str(
start) + ':00)有效率：' + str(rate2) + '% 白天(' + str(
start) + ':00--' + str(end) + ':00)有效率：' + str(rate3) + '%'
str1 = username + '-' + title + '--' + str(year) + '年' + str(
month) + '月' + ' ' + strplot
bar = Bar("数据有效率统计(%)", str1)
bar.add("有效率/%", train_k_list, train_l_list, xaxis_min=1, legend_top="5%")
bar1 = Bar("晚间数据有效率统计(%)", title_top="30%")
bar1.add("rate_night/%",
train_k_list,
train_m_list,
xaxis_min=1,
legend_top="30%")
bar1.add("rate_day/%",
train_k_list,
train_n_list,
xaxis_min=1,
legend_top="30%")
bar2 = Bar("白天数据有效率统计(%)", title_top="55%")
bar2.add("rate_day /%",
train_k_list,
train_n_list,
xaxis_min=1,
legend_top="55%")
grid = Grid()
grid.add(bar, grid_top="10%", grid_bottom="75%")
grid.add(bar1, grid_top="35%", grid_bottom="50%")
grid.add(bar2, grid_top="60%", grid_bottom="25%")
grid.renderer = 'svg'
return grid
def create_simple_kline():
global username
global temp_spo2
global temp3
global date_pro
global rate_all
global rate_night1
global rate_day1
global checklist
global title
global da
temp_spo2 = Data_martix(da)
if len(temp_spo2) > 1:
train_x_list = data_to_list_spo2(temp_spo2, 'timestamp')
train_y_list = data_to_list_spo2(temp_spo2, 'spo2')
train_z_list = data_to_list_spo2(temp_spo2, 'hr')
temp_spo3 = temp_spo2[temp_spo2['healthindex'] > 0]
data_x = temp_spo3['healthindex']
train_data = np.array(data_x)
train_e_list = train_data.tolist() # list
data_x = temp_spo3['timestamp']
train_data = np.array(data_x)
train_r_list = train_data.tolist() # list
dd = temp_spo2[temp_spo2['healthindex'] == 15]
train_t_list = data_to_list_spo2(temp_spo2, 'timestamp')
train_k_list = data_to_list_spo2(temp_spo2, 'wear_type')
cost_matrix = temp_spo2[temp_spo2['cost_all'] > 0]
cost_matrix1 = cost_matrix[cost_matrix['cost_all'] > 0]
cost_all = cost_matrix1['cost_all'].mean()
cost_all = float('%.1f' % cost_all)
h = '平均耗时:' + str(cost_all) + 's'
else:
train_x_list = []
train_y_list = []
train_z_list = []
train_e_list = []
train_r_list = []
train_k_list = []
dd = []
h = '平均耗时:' + str(0) + 's'
if len(dd) >= 10:
h1 = Error_pro(date_pro)
else:
h1 = ''
start, end = Choose_time(checklist)
plotstr = str(
date_pro
) + '/' + username + '-' + title + ': ' + 'The useful data rate is ' + str(
rate_all) + '% 晚间(' + str(end) + ':00--' + str(
start) + ':00)有效率：' + str(rate_night1) + '% ; 白天 ：' + str(
rate_day1) + '%)'
line = Scatter("血氧/心率值", plotstr, title_top="0%")
line.add("血氧",
train_x_list,
train_y_list,
xaxis_min=0,
xaxis_max=24,
legend_top="1%")
line.add("心率",
train_x_list,
train_z_list,
xaxis_min=0,
xaxis_max=24,
legend_top="1%")
line.add("信号不佳",
train_r_list,
train_e_list,
xaxis_min=0,
xaxis_max=24,
legend_top="1%")
line.xaxis_min = 0
line.xaxis_max = 24
if len(temp_spo2) < 144:
train_t_list, train_w_list = fill_data(temp_spo2, 'cost')
train_t_list, train_j_list = fill_data(temp_spo2, 'cost_all')
train_f_list, train_h_list = fill_data(temp_spo2, 'cost_cnt')
else:
train_w_list = data_to_list_spo2(temp_spo2, 'cost')
train_h_list = data_to_list_spo2(temp_spo2, 'cost_cnt')
train_j_list = data_to_list_spo2(temp_spo2, 'cost_all')
line1 = Scatter("佩戴状态", h1, title_top="25%")
line1.add("佩戴状态",
train_x_list,
train_k_list,
xaxis_min=0,
xaxis_max=24,
legend_top="26%")
bar = Bar("每次计算耗时", title_top="45%")
bar.add("时长/s",
train_t_list,
train_w_list,
yaxis_max=100,
yaxis_min=0,
is_datazoom_show=True,
datazoom_xaxis_index=[2, 2],
legend_top="45%")
bar1 = Bar("计算总耗时", h, title_top="62%")
bar1.add("总时长/s",
train_t_list,
train_j_list,
yaxis_min=0,
legend_top="65%")
bar2 = Bar("计算次数", title_top="83%")
bar2.add("总次数", train_t_list, train_h_list, yaxis_min=0, legend_top="85%")
grid = Grid()
grid.add(line, grid_bottom="78%")
grid.add(line1, grid_top="31%", grid_bottom="58%")
grid.add(bar, grid_top="50%", grid_bottom="40%")
grid.add(bar1, grid_top="68%", grid_bottom="20%")
grid.add(bar2, grid_top="88%", grid_bottom="2%")
grid.renderer = 'svg'
return grid
def create_simple_bar():
global temp3
global str_path
global name
global flag
global com
global date_pro
global username
global title
global max_channel1
global max_channel2
global max_channel3
global h
global leng
Data_Acquire()
temp3 = Change_Str_Data(da)
if flag == 0:
com = temp3
flag = 1
"""三个通道的电容值进行归一化处理"""
if len(temp3) > 0:
max_channel1 = temp3['channel1'].max()
max_channel2 = temp3['channel2'].max()
max_channel3 = temp3['channel3'].max()
"""
temp3['channel1'] = temp3['channel1']/max_channel1*1000
temp3['channel2'] = temp3['channel2'] / max_channel2 * 1000
temp3['channel3'] = temp3['channel3'] / max_channel3 * 1000
"""
temp3['channel1'] = temp3['channel1'].map(Normalize(1))
temp3['channel2'] = temp3['channel2'].map(Normalize(2))
temp3['channel3'] = temp3['channel3'].map(Normalize(3))
"""数据未达到指定组数时进行数据填充"""
if len(temp3) < 280:
train_x_list = data_fill('timestamp') # list
train_y_list = data_fill('wear_data')
train_z_list = data_fill('Wear_type')
train_k_list = data_fill('channel1')
train_l_list = data_fill('channel2')
train_a_list = data_fill('channel3')
train_o_list = data_fill('Charge')
train_b_list = data_fill('Activity')
train_c_list = data_fill('activity')
else:
"""将dataFrame格式转为list"""
train_x_list = data_to_list('timestamp') # list
train_y_list = data_to_list('wear_data')
train_z_list = data_to_list('Wear_type')
train_k_list = data_to_list('channel1')
train_l_list = data_to_list('channel2')
train_a_list = data_to_list('channel3')
train_b_list = data_to_list('Activity')
train_c_list = data_to_list('activity')
train_o_list = data_to_list('Charge')
plotstr2 = str(leng) + str(
date_pro) + '/' + username + '-' + title + ': ' + "检测结论----该段时间内数据正常"
line2 = Line("Mems波动值", plotstr2)
strm = "Mems波动值"
strn = "活动量"
line2.add(strm,
train_x_list,
train_b_list,
yaxis_min="dataMin",
legend_top="1%")
line2.add(strn,
train_x_list,
train_c_list,
yaxis_min="dataMin",
legend_top="1%")
line3 = Line("三通道归一化后变化", title_top="50%")
strc1 = "Chnanel1"
strc2 = "Chnanel2"
strc3 = "Chnanel3"
line3.add(strc1,
train_x_list,
train_k_list,
yaxis_min=700,
legend_top="51%")
line3.add(strc2,
train_x_list,
train_l_list,
yaxis_min=700,
legend_top="51%")
line3.add(strc3,
train_x_list,
train_a_list,
yaxis_min=700,
legend_top="51%")
line1 = Scatter("佩戴/充电状态", title_top="75%")
strm = "佩戴状态"
line1.add(strm,
train_x_list,
train_z_list,
xaxis_max=24,
yaxis_min=0,
yaxis_max=1.2,
legend_top="76%")
line1.add('充电状态',
train_x_list,
train_o_list,
xaxis_max=24,
yaxis_min=0,
yaxis_max=1.2,
legend_top="76%",
symbol_size=5,
xaxis_name='Time/H')
line = Line("电容波动情况", title_top="25%")
strm = "电容值波动情况"
line.add(strm,
train_x_list,
train_y_list,
yaxis_min=0,
legend_top="26%",
label_color="#40ff27")
page = Grid()
page.add(line3, grid_top="55%", grid_bottom="30%")
page.add(line1, grid_top="80%", grid_bottom="5%")
page.add(line, grid_top="30%", grid_bottom="55%")
page.add(line2, grid_top="5%", grid_bottom="80%")
page.renderer = 'svg'
return page
