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dat_ident.py
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# -*- coding: utf-8 -*- |
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import csv, time, glob, datetime, os import matplotlib.pyplot as plt import matplotlib.dates as md #from scipy import signal #from scipy.optimize import curve_fit tic = time.time() os.chdir('/home/user/sicav_data/Manip/2016/2016-03/') def getColumn(filename, column): results = [] for dat_file in sorted(glob.glob(filename)): file_result = csv.reader(open(dat_file), delimiter='\t') results = results + map(float,[result[column] for result in file_result]) return results t = getColumn('*-lakeshore.dat', 0) T1 = getColumn('*-lakeshore.dat', 2) T2 = getColumn('*-lakeshore.dat', 3) T3 = getColumn('*-lakeshore.dat', 4) T4 = getColumn('*-lakeshore.dat', 5) """ n = 400 t = [t[n*i] for i in range(len(t)/n)] T1 = [T1[n*i] for i in range(len(T1)/n)] T2 = [T2[n*i] for i in range(len(T2)/n)] T3 = [T3[n*i] for i in range(len(T3)/n)] T4 = [T4[n*i] for i in range(len(T4)/n)] def func(U, a0, b1, b2, y0): sys = signal.lti([a0],[b2, b1, 1]) y = sys.output(U, t, y0) return y[1] print('Fitting...') popt, cov = curve_fit(func, T1, T4) print(popt) Yfit = func(T1, *popt) """ timetamps = [datetime.datetime.fromtimestamp(ti) for ti in t] datenums=md.date2num(timetamps) plt.subplots_adjust(bottom=0.35) plt.xticks(rotation=90) ax=plt.gca() xfmt = md.DateFormatter('%Y-%m-%d %H:%M:%S') ax.xaxis.set_major_formatter(xfmt) plt.plot(datenums, T1, label = 'Table') plt.plot(datenums, T2, label = 'Link st.') plt.plot(datenums, T3, label = 'PT2') plt.plot(datenums, T4, label = 'Reg. st.') #plt.plot(datenums, Yfit, label = 'Fit') plt.legend() plt.grid() plt.show() toc = time.time() - tic print(toc) |