10
120 lines
4.7 KiB
Python
120 lines
4.7 KiB
Python
import psycopg2
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import ana_func
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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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def search_id_by_str(keyword):
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conn = psycopg2.connect(database="postgres", user="biopro", password="BioProControlBox", host="127.0.0.1", port="5432")
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cur = conn.cursor()
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sql_str = f"select id from recording_data_metas WHERE name ILIKE '%"+keyword+"%'"
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cur.execute(sql_str)
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data = cur.fetchall()
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data = [i[0] for i in data]
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return data
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def read_data(id,channel_list):
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conn = psycopg2.connect(database="postgres", user="biopro", password="BioProControlBox", host="127.0.0.1", port="5432")
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cur = conn.cursor()
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sql_str = f'select "raw_data" from "recording_data_metas" WHERE id = {id}'
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cur.execute(sql_str)
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data = cur.fetchall()[0][0]
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channel_data = []
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for channel in channel_list:
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i = str(channel)
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channel_data_temp = []
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for j in data[i]:
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# print(j)
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sql_str = f'select "data" from "{i}_recording_data_raws" WHERE id = {j}'
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cur.execute(sql_str)
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raw_data = cur.fetchall()[0][0]
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raw_data = raw_data.replace('"***"',' ').split(" ")
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channel_data_temp =channel_data_temp + [int(raw_data[k]) for k in range(len(raw_data)) if k%2]
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# print(len(channel_data_temp))
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channel_data.append(channel_data_temp)
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return channel_data
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def read_data_csv(file_name=0, start_row=0):
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df = pd.read_excel("read_file/2021-8-9-15-42-46-0_CV discussion.xlsx",skiprows = 63, usecols="B,D,F,H")
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data = [list(df['Current[nA]']),list(df['Voltage[uV]']),list(df['Unnamed: 7'])]
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# print(df)
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return data
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# read_data_csv()
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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 = ana_func.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 = ana_func.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(id, channel_list, perc):
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# id = 166
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percentage = perc/100
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data_list = {}
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print('channel_list', channel_list)
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print(percentage)
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channel_data = read_data(id, channel_list)
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VT_mode = ana_func.dataAnalyticFunc(channel_data[0], channel_data[1])
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VT_data_list ,_ = VT_mode.histogram_find_peak()
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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['ground']-data_list['ceiling'])*percentage + data_list['ceiling']
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trigger_line_up = (data_list['ground']-data_list['ceiling'])*(1-percentage) + data_list['ceiling']
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trigger = {str(100-perc)+'% point':trigger_line_down, str(perc)+'% 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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triggerpoint = [VT_mode.trigger_point('y_data','x_data',trigger_line_down, order=-1,drop=-1), VT_mode.trigger_point('y_data','x_data',trigger_line_up)]
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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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resultList = [id , *channel_data, peak_list, triggerpoint]
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channel_data.append(peak_list)
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channel_data.append(triggerpoint)
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return resultList, data_list
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# if __name__ == '__main__':
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# VTmode_run() |