peterlu14
86 lines
3.4 KiB
Python
86 lines
3.4 KiB
Python
from ana_func import dataAnalyticFunc
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import matplotlib.pyplot as plt
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import csv
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import pandas as pd
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import time
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async def CVmode_run():
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df = read_data_csv()
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print(df[0][:10])
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data = [[df[0][i] for i in range(len(df[0])) if df[2][i] == 5], [df[1][i] for i in range(len(df[1])) if df[2][i] == 5]]
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print(data[0][:10]) #y:Current[nA]/x:Voltage[uV]
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CVmode = dataAnalyticFunc(data[1], data[0])
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cal_slop_data = CVmode.guess_func()
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print(len(cal_slop_data))
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integel = CVmode.integral_func('x_data','y_data')
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print(integel)
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# return [data[1],data[0]]
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return [[i for i in range(len(data[0]))],cal_slop_data]
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# return [[i for i in range(len(df[0]))],[i/50 for i in df[0]]]
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# CVmode()
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def slop_run(id, channel_list, win):
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# id = 166
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# win = 20
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print(win)
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channel_data = read_data(id, channel_list)
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slop_mode = dataAnalyticFunc(channel_data[0], channel_data[1])
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cal_slop_data = slop_mode.cal_slop_array(window=win)
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# return [[i for i in range(len(channel_data[0]))], channel_data[1]]
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return [[i for i in range(len(channel_data[0]))], cal_slop_data]
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def VTmode_run(args):
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x_data, y_data, percentage = args
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data_list = {}
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### create dataAnalyticFunc instance
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# print('x', x_data, 'y', y_data)
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VT_mode = dataAnalyticFunc(x_data, y_data)
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peak_wid ,_ = VT_mode.histogram_find_peak()
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if len(peak_wid) != 2:
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print('check if data fit VT mode.')
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return [[], [[], []]],{}
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VT_data_list = {'ground':round(min(peak_wid)), 'ceiling':round(max(peak_wid)) }
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# print('peak list =', data_list['ground'])
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data_list.update(VT_data_list)
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peak_list = list(data_list.values())
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trigger_line_down = (data_list['ceiling']-data_list['ground'])*percentage / 1e2 + data_list['ground']
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trigger_line_up = (data_list['ceiling']-data_list['ground'])*(1-percentage / 1e2) + data_list['ground']
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trigger = {str(percentage)+'% point':trigger_line_down, str(100-percentage)+'% point':trigger_line_up}
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data_list.update(trigger)
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peak_list.append(trigger_line_down)
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peak_list.append(trigger_line_up)
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start_time = time.time()
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triggerpoint = [VT_mode.trigger_point('y_data','x_data',trigger_line_down,gte = False,head = True), VT_mode.trigger_point('y_data','x_data',trigger_line_up,gte=True,head=False)]
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print("trigger end：%f 秒" % (time.time() - start_time))
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# slope_tan = math.atan2(triggerpoint[0][1]-triggerpoint[1][1],triggerpoint[0][0]-triggerpoint[1][0])
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center_point = [(triggerpoint[0][0]+triggerpoint[1][0])/2,(triggerpoint[0][1]+triggerpoint[1][0])/2]
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slope = (triggerpoint[0][1]-triggerpoint[1][1])/(triggerpoint[0][0]-triggerpoint[1][0])
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center_point_dict = {'center point key': center_point[0], 'center point value':center_point[1], 'slope': slope}
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print('trigger point',triggerpoint, slope, center_point)
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data_list.update(center_point_dict)
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# Plot
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# plt.plot(channel_data[0],channel_data[1],'o',markersize=2)
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# print(peak_list)
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# for i in peak_list[1:]:
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# plt.hlines(i,min(channel_data[0]),max(channel_data[0]),color='r')
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# for j in triggerpoint:
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# plt.plot(j[0],j[1],'o',markersize=5)
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# plt.savefig('fig/test'+ str(id) +'.png')
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# plt.close()
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print([peak_list, triggerpoint] , data_list)
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return [peak_list, triggerpoint] , data_list
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def movstd_run(channel_data):
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movs_mode = dataAnalyticFunc(channel_data[0], channel_data[1])
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result = movs_mode.SMA(20,5)
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return result
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