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Copy pathPyroFit_v1.0.0.py
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PyroFit_v1.0.0.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Universal Script for PyroData Evaluation
# (Use only for individual data records -- all files are contained in one single folder!!!)
# Start with Version Numbers on: 03.09.2014 to distinguish between different versions in filename
#---------------------------------------------------------------------------------------------------------------------------
# Author: Sven Jachalke
# Mail: [email protected]
# Adress: Institut fuer Experimentelle Physik
# Leipziger Strasse 23
# 09596 Freiberg
#---------------------------------------------------------------------------------------------------------------------------
# Necessary Python Packages:
# - Anaconda Distribution (Python 3.6 and higher)
# - scipy.interpolate
# - pylab (matplotlib, numpy, scipy), etc.
# - lmfit (https://github.com/lmfit/lmfit-py)
# - tubafcdpy(https://github.com/SvenJachalke/tubafcdpy)
#---------------------------------------------------------------------------------------------------------------------------
# chose plotting backend
import matplotlib
matplotlib.use('TkAgg')
# Import modules------------------------------------------------------------------------------------------------------------
import matplotlib.pyplot as plt
from matplotlib import __version__
from tubafcdpy import *
import glob, sys, os, datetime
from numpy import *
from scipy.interpolate import interp1d
from scipy.integrate import cumtrapz
from lmfit import minimize, Parameters, report_errors, fit_report
from matplotlib.offsetbox import AnchoredText
from time import sleep
version = '0.9.9'
plt.ion() # interactive on
plt.close('all') # close all current/open figures
# Operator Information------------------------------------------------------------------------------------------------------
now = datetime.datetime.now()
operator = {
'name':'Sven Jachalke',
'mail':'[email protected]',
'company':'TU Bergakademie Freiberg',
'tel':'+49 (0) 3731 / 39-3787',
'date': now.strftime('%Y-%m-%d')
}
# Areas for pyroKoeff-------------------------------------------------------------------------------------------------------
area_d5 = pi/4.0*(5.385/1000)**2 # D -- for small Edwards shadow mask (d=5.385mm)
area_d13 = pi/4.0*(12.68/1000)**2 # A -- for big Edwards shadow mask (d=12.68mm)
area_d15 = pi/4.0*(15.0/1000)**2 # B -- for single crystals with d=15mm
area_a5 = 1.4668e-5 # C -- for 5x5mm samples, e.g. SrTiO3, ...
#areas from older skript versions
area_d13_old = 1.3994e-4 # Aold -- for large Edwards shadow mask (d=13,...mm), e.g. for PVDF, ...
area_d15_old = 1.761e-4 # Bold -- for single crystals with d=15mm
#costums area and error (in m2) # CUSTOM --
custom = 1.51227e-7
custom_error = 5.203042e-9
# User Settings-------------------------------------------------------------------------------------------------------------
start_index = 200 #start index for fit/plot (100 = 50s, because 2 indices = 1s)
fit_periods = 2 #how many periods have to fitted with sine wave in SinLinRamp
sigma = 3 #error level
upper_I_lim = 1e-3 #limitation of current in plot and fit (for spikes, ...)
temp_filter_flag = True #True = no plot/fit of second temperature (top PT100)
current_filter_flag = True
calculate_data_from_fit_flag = False #True = saving fit as data points to txt file for I_pyro and I_TSC
PS_flag = False #flag if PS should be calculated from p(T)
BR_flag = False #Flag for ByerRoundy Plot (False=not plotting)
single_crystal = False #for single crystals phase=90deg ... thermal contact correction
interpolation_step = 0.5 #time grid for interpolation (in sec)
Ifit_counter_limit = 5 #repeat number when I-fit insufficient
# Alternatives for calculations --------------------------------------------------------------------------------------------
Formation = False #If TRUE and OnPerm / SineWave Method will be evaluated as SinLinRamp by p(t) instead of p(T)
#Used for SrTiO3 Formation (under electric field)
Resistance = True #If True and OnPerm / Calculation of R(T)
#Maybe needs some testing
AbsResistance = True #If True absolute values of set voltage and measured current is calculated
#(usefull is current changes singn)
PartWiseTFit = True #If TRUE the temperature of a SineWave + LinRamp/TrangleHat will be fitted part wise
#as the current (same interval!) and not over the whole range
#In order to keep the increasing error low, it is recommended to use more than 1 fit period!
BaselineCorrection = False #If TRUE addtional baseline dataset is substracted from current measurement (needs TEMP and CURR log!)
# Plot Settings-------------------------------------------------------------------------------------------------------------
# Check Matplotlib Version--------------------------------------------------------------------------------------------------
if int(__version__[0]) == 2:
matplotlib.style.use('classic') #get old mpl style, if installed also my 'science' style file can be used
color_style = 'TUBAF' #TUBAF = CD colors, Standard = Matplotlib standard colors
matplotlib.rcParams['legend.fancybox'] = True #plot fancy box (round edges)
print_signature = True #print the operators signature at the bottom of the plot
enable_title = True #enable/disable title in plot
label_size = '16' #font size of x,y labels in plot in standard style
title_size = '15' #font size of the figure title in standard style
fontsize_box = '11' #font size in the text box in standard style
fig_size = (12.0,9.0) #size of figures (general aspect ratio 4:3!!!)
set_dpi = 150 #dpi for exporting figures as png
skip_points = 0 #number of points to skip in plotting to speed up plotting and zooming (not interpol, fit)
transparency_flag = False #exporting figures with transparent background?
facecolor_legends = 'white'
colorlist = ['m','g', 'c', 'r']
linestylelist = ['x','*','o ', 'x']
temp_linestyle=['o',''] #[makerstyle, linestyle] for temperature
curr_linestyle = ['o',''] #[makerstyle, linestyle] for current
volt_linestyle = ['*',''] #[makerstyle, linestyle] for voltage
line = "-----------------------------------"
export_format = 'png' #figure output format (png,jpeg,pdf,eps)
# Functions-----------------------------------------------------------------------------------------------------------------
def prompt(string):
python_version = int(sys.version[0])
if python_version==2:
answer = raw_input(string)
elif python_version==3:
answer = input(string)
return answer
# file functions -----------------------------------------------------------------------------------------------------------
def extract_date(filename):
"""
Returns the date of the filename, wich is located at the first 16 characters of the filename
input: filename [str]
output: date [str]
"""
return filename[:16]
def extract_samplename(filename):
"""
Returns samplename, which is located after the second "_"
input: filename [str]
output: samplename [str]
"""
return filename.split("_")[2]
def extract_datatype(filename):
"""
Check's ending of recorded files and returns string with data type of the file
input: filename [str]
output: data type [str]:
Current, Voltage, Charge, Temperature, Powersupply, Vaccum, HighVoltage, GBIP-Errors
"""
if filename.endswith("ELT-Curr-t-I.log") or filename.endswith("ELT-Curr-t-I-VB.log") and 'Baseline' not in filename:
return "Current"
elif filename.endswith("ELT-Volt-t-V.log") and 'Baseline' not in filename:
return "Voltage"
elif filename.endswith("ELT-Char-t-Q.log") and 'Baseline' not in filename:
return "Charge"
elif filename.endswith("TEMP-t-Tpelt-Tsoll-Tsample.log") and 'Baseline' not in filename:
return "Temperature"
elif filename.endswith("PWR-t-I-U.log") and 'Baseline' not in filename:
return "Powersupply"
elif filename.endswith("VAC-t-pressure.log") and 'Baseline' not in filename:
return "Vacuum"
elif filename.endswith("HiV-t-HVsetVoltage-HVmeasVoltage.log") and 'Baseline' not in filename:
return "HighVoltage"
elif filename.endswith("GPIB-t-addr-errcount-cmd-data-atconvdat-STBerrque.log") and 'Baseline' not in filename:
return "GBIP-Errors"
else:
return None
def extract_measurementmode(filename):
"""
Opens temperature file and extract the Waveform and HV-Status
input: filename [str]
output: hv_mode, waveform [str]
"""
if filename.endswith("TEMP-t-Tpelt-Tsoll-Tsample.log"):
datei = open(filename, "r")
line = datei.readline() #first line = HV status
hv_mode = (line.strip()).split(" ")[1]
line = datei.readline() #second line = waveform
waveform = (line.strip()).split(" ")[1]
datei.close()
return hv_mode, waveform
else:
print("Could not find measurement type. Check temperature file!")
def extract_T_stimulation_params(filename):
"""
Return set parameters of the temperature stimulation, which are located in the header of the Temperature file
input: filename [str]
output: T_stimulation_params_dict [dict] (contains key:value pair for each finding)
"""
if filename.endswith("TEMP-t-Tpelt-Tsoll-Tsample.log"):
datei = open(filename, 'r')
T_stimulation_params_dict = {}
try:
hv_mode = datei.readline().strip().split(" ")[1]
T_stimulation_params_dict.update({"hv_mode":hv_mode})
except:
pass
try:
waveform = datei.readline().strip().split(" ")[1]
T_stimulation_params_dict.update({"waveform":waveform})
except:
pass
try:
if T_stimulation_params_dict["waveform"]=="PWRSquareWave":
I = datei.readline().strip().split(" ")[1]
T_stimulation_params_dict.update({"I":float(I)})
else:
amp = datei.readline().strip().split(" ")[1]
T_stimulation_params_dict.update({"amp":float(amp)})
except:
pass
try:
freq = datei.readline().strip().split(" ")[1]
T_stimulation_params_dict.update({"freq":float(freq)})
except:
pass
try:
if T_stimulation_params_dict["waveform"]=="PWRSquareWave":
V = datei.readline().strip().split(" ")[1]
T_stimulation_params_dict.update({"V":float(V)})
else:
offs = datei.readline().strip().split(" ")[1]
T_stimulation_params_dict.update({"offs":float(offs)})
except:
pass
try:
heat_rate = datei.readline().strip().split(" ")[1]
T_stimulation_params_dict.update({"heat_rate":float(heat_rate)})
except:
pass
try:
cool_rate = datei.readline().strip().split(" ")[1]
T_stimulation_params_dict.update({"cool_rate":float(cool_rate)})
except:
pass
try:
T_Limit_H = datei.readline().strip().split(" ")[1]
T_stimulation_params_dict.update({"T_Limit_H":float(T_Limit_H)})
except:
pass
try:
T_Limit_L = datei.readline().strip().split(" ")[1]
T_stimulation_params_dict.update({"T_Limit_L":float(T_Limit_L)})
except:
pass
datei.close()
return T_stimulation_params_dict
def extract_HV_params(filename):
"""
Returns a list with set HV-settings
input: filename [str]
output: list of HV parameters [[float],...]
"""
if filename.endswith("HiV-t-HVsetVoltage-HVmeasVoltage.log"):
datei = open(filename, 'r')
HVmax = datei.readline().strip().split(" ")[4]
zeile = datei.readline()
if zeile!='' and zeile!='\r\n' and zeile !='\n':
HVcomp = zeile.strip().split(" ")[5]
datei.close()
return [float(HVmax), float(HVcomp)]
datei.close()
return [float(HVmax)]
def set_interpolation_range(a,b):
"""
Function to find die interpolation range of two variables, e.g. temperature and current
"""
boundries = [0.0,0.0]
if min(a) <= min(b):
boundries[0] = min(b)
else:
boundries[0] = min(a)
if max(a) >= max(b):
boundries[1] = max(b)
else:
boundries[1] = max(a)
return boundries
def interpolate_data(temp_array, curr_array, steps, temp_filter_flag,boundries=None):
"""
interpolates current and temperature data for plotting and fitting
input: temperature array [ndarray]
current array [ndarray]
steps [float]
temp_filter_flag [bool]
output: interpolated arrays
"""
if BaselineCorrection == True:
boundries = boundries
else:
boundries = set_interpolation_range(curr_array[:,0],temp_array[:,0]) #find interpolation range
#new = arange(boundries[0], boundries[1], steps) #arange new time axis in 0.5s steps
tnew = arange(boundries[0],boundries[1], steps)
#Temperature
Tinterpol_down = interp1d(temp_array[:,0],temp_array[:,1]) #interpolation of lower temperature
Tnew_down = Tinterpol_down(tnew)
Tnew_top = zeros(len(Tnew_down))
if temp_filter_flag == False:
Tinterpol_top = interp1d(temp_array[::5,0],temp_array[::5,3])
Tnew_top = Tinterpol_top(tnew[:-5])
#array ist zu kurz fuer vstack!!!! Was tun? was tun wenn soweit!
Tnew = vstack([Tnew_down,Tnew_top]).T
#Interpolation current data #same for current
Iinterpol = interp1d(curr_array[:,0],curr_array[:,1])
Inew = Iinterpol(tnew)
return tnew, Tnew, Inew
def fileprint_fit(log, fit, name):
"""
Writes fit values into file
Input: log [filehandle] - previously generated
fit [dicts] - Params dict (lmfit)
name [str] - what was fitted? (Temp, Curr, ...)
Output: None
"""
log.write("#%s fit data\n#----------\n" % name)
log.write(fit_report(fit))
log.write("\n#----------\n")
return None
def consoleprint_fit(fit, name):
"""
Writes fit value in shell window
Input: fit [dict] - Parameters-dict from lmfit
name [str] - what was fitted= (Temp, Curr, ...)
"""
print("---------------")
print("Fit: %s"%name)
print("---------------")
report_errors(fit)
return None
# plot functions ---------------------------------------------------------------------------------------------------------------
def set_skip_points():
if len(tnew) < 10000:
return 1
elif len(tnew) >= 10000 and len(tnew) <= 100000:
return 3
else:
return 6
def plot_graph(tnew, Tnew, Inew, T_profile):
head = date+"_"+samplename+"_"+T_profile
bild = plt.figure(head, figsize=fig_size)
ax1 = bild.add_subplot(111)
ax2 = ax1.twinx()
if enable_title == True:
ax1.set_title(samplename, size=title_size)
#Plot Temperature
ax1.set_xlabel("Time (s)",size=label_size)
ax1.set_ylabel("Temperature (K)",color=temp_color,size=label_size)
ax1.set_xlim(tnew[0],tnew[-1])
ax1.grid(b=None, which='major', axis='both', color='grey', linewidth=1,linestyle=':')
ax1.tick_params(axis='y', colors=temp_color)
if temp_filter_flag == True:
Tline = ax1.plot(tnew[start_index::set_skip_points()], Tnew[start_index::set_skip_points(),0], color=temp_color,marker=temp_linestyle[0],linestyle=temp_linestyle[1], label="data")
else:
Tline = ax1.plot(tnew[start_index:-5:skip_points], Tnew[start_index::skip_points,1],color=temp_color,marker=temp_linestyle[0],linestyle=temp_linestyle[1], label="data (top)")
ax1.autoscale(enable=True, axis='y', tight=None)
legT = ax1.legend(title="Temperatures", loc='upper right')
ax1.set_xlim(tnew[start_index])
ax1.locator_params(nbins=10)
#Plot Current
ax2.set_ylabel("Current (A)",color=curr_color,size=label_size)
Iline = ax2.plot(tnew[start_index::set_skip_points()], Inew[start_index::set_skip_points()], marker=curr_linestyle[0], linestyle=curr_linestyle[1] ,color=curr_color, label="data")
ax2.legend(title="Currents", loc='lower right')
ax2.set_xlim(tnew[start_index])
ax2.locator_params(nbins=10,axis = 'y')
ax2.set_ylim(min(Inew[start_index:]),max(Inew[start_index:]))
ax2.tick_params(axis='y', colors=curr_color)
#ax1.set_zorder(+1)
#ax2.autoscale(enable=True, axis='y', tight=None)
#ax2.add_artist(legT)
bild.tight_layout()
if print_signature == True:
bild.subplots_adjust(bottom=0.125)
signature = operator['name']+' | '+operator['mail']+' | ' + operator['tel'] + ' | ' +operator['company'] + ' | ' + operator['date']
plt.figtext(0.15,0.02,signature)
plt.show()
return bild, ax1, ax2
def plot_textbox(boxtext):
"""
Plots anchored text box with measurement informations into the graph.
Input: boxtext [str]
Output: box [instance]
"""
#box = figtext(x,y,boxtext,fontdict=None, bbox=properties)
box = AnchoredText(boxtext,
prop=dict(size=fontsize_box), frameon=True,
loc=3,
)
box.patch.set_boxstyle("round,pad=0.,rounding_size=0.2")
return box
def current_custom_legend(ax,loc=4):
np_line = plt.Line2D(range(10), range(10), linestyle='-', marker='', color = np_color)
p_line = plt.Line2D(range(10), range(10), linestyle='-', marker='', color = p_color)
Imeas_line = plt.Line2D(range(10), range(10), linestyle='', marker='o', color = curr_color)
Ifit_line = plt.Line2D(range(10), range(10), linestyle='-', marker='', color = curr_color)
legend = ax.legend((Imeas_line,Ifit_line,np_line,p_line), ('data','fit','non-pyro','pyro'),loc=loc,title="currents")
return legend
def temperature_custom_legend(ax,loc=1):
Tmeas_line = plt.Line2D(range(10),range(10), linestyle='', marker='o', color = temp_color)
Tfit_line = plt.Line2D(range(10),range(10), linestyle='-', marker='', color = temp_color)
legend = ax.legend((Tmeas_line,Tfit_line),('data','fit'),loc=loc,title='temperatures')
return legend
def saving_figure(bild, pbild=False):
"""
saves figure with individual filename composed of date, filename, T_profile and print on console
input: bild - figure instance
pbild - bool, when True pyroelectric coefficient figure in SinLimRamp will be plotted
return: None
"""
print("saving ...")
if pbild == False:
image_name = date+"_"+samplename+"_"+T_profile+"_T-I"
print("...Temperature/Current Plot\n%s.%s" % (image_name,export_format))
elif pbild == "Polarize":
image_name = date+'_'+samplename+'_Polarize'
print("...Temperature/Polarization Plot\n%s.%s" % (image_name,export_format))
elif pbild == 'Resistance':
image_name = date+'_'+samplename+'_Resistance'
print("...Temperature/Resistance Plot\n%s.%s" % (image_name,export_format))
else:
image_name = date+"_"+samplename+"_"+T_profile+"_p"
print("...Pyro Plot\n%s.%s" %(image_name,export_format))
if export_format == 'png':
bild.savefig(image_name+'.png', dpi=set_dpi, transparent=transparency_flag)
elif export_format == 'pdf':
bild.savefig(image_name+'.pdf')
return None
def Lissajous(ax, x, y):
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
axins = inset_axes(ax, width="15%", height="20%", loc=2)
axins.set_xticklabels([])
axins.set_yticklabels([])
axins.plot(x,y,color=tubafcyan(),linestyle='-')
axins.grid(linestyle=':')
# fit functions ---------------------------------------------------------------------------------------------------------------
def sinfunc(params, x, data=None):
"""
Model for sine function using the lmfit model
input: Parameters Dict (lmfit)
output: sine wave model
"""
amp = params['amp'].value
freq = params['freq'].value
phase = params['phase'].value
offs = params['offs'].value
slope = params['slope'].value
model = amp*sin(2*pi*freq*x+phase)+offs+slope*x
if data is None:
return model
else:
return model-data
def expdecay(params, x, data=None):
"""
Model for exponetial decay function using the lmfit model
input: Parameters Dict (lmfit)
output: decay model
"""
model = params['factor'].value * exp(-x/ params['decay'].value) + params['offs'].value
if data is None:
return model
else:
return model-data
def linear(params, x, data=None):
"""
Model for linear function using lmfit module
input: Parameter dict (lmfit)
output: model
"""
a = params['slope'].value
b = params['offs'].value
model = a*x + b
if data is None:
return model
else:
return model-data
# misc functions --------------------------------------------------------------------------------------------------------------
def extract_fit_relerr_params(params):
"""
Extract the fitted parameters from the Paramters Dict and put it into lists (values and errors)
input: params [Params Dict]
return: fit [list], err[list]
"""
fit = [params['amp'].value,params['freq'].value,params['phase'].value,params['offs'].value,params['slope'].value]
err = [abs(params['amp'].stderr),abs(params['freq'].stderr),abs(params['phase'].stderr),abs(params['offs'].stderr),abs(params['slope'].stderr)]
return fit, err
def listtoparam(liste, parameterdic):
"""
Adds a list of temperature parameters to the Parameters Dict of lmfit
input: liste [list]
paramterdic [Parameters Dict]
"""
parameterdic.add('amp', value=liste[0])
parameterdic.add('freq', value=liste[1])
parameterdic.add('phase', value=liste[2])
parameterdic.add('offs', value=liste[3])
parameterdic.add('slope', value=liste[4])
return None
def fit(x, y, start, end, slice, start_parameters, vary_freq=True, heating=True):
"""
Peforms fit for y(x) with start and end values (indices) and returns fit dictionary
Input: t [ndarray]
T [ndarray]
start [int]
end [int]
start_paramters [dict]
vary_freq [bool]
heating [bool], - heating rate (True) or cool rate (False) as start parameter
Return: results [minimize instance]
Params [lmfit dict]
"""
#initialize list and dicts for fit
Params = Parameters()
Params.add('amp', value=start_parameters['amp'],min=0.0)
Params.add('freq', value=start_parameters['freq'], min=1e-5, max=0.2, vary=vary_freq)
Params.add('phase', value=0.1)
Params.add('offs', value=start_parameters['offs'], min=0.0)
if heating==True:
Params.add('slope', value=start_parameters['heat_rate'])
else:
Params.add('slope', value=-start_parameters['cool_rate'])
#perform fit
result = minimize(sinfunc, Params, args=(x[start:end:slice], y[start:end:slice]), method="leastsq")
return result
def rel_err(Tfit, Terror, Ifit, Ierror, area, area_error, phasediff, Xsigma=1):
"""
Calculates relative maximum error
"""
phasediff_error = abs(Terror[2])+abs(Ierror[2])
# rel err = dp/p .... I_Amp phi A T_Amp f
rel_err = Xsigma*(abs(Ierror[0]/Ifit[0]) + abs(phasediff_error/(tan(phasediff))) + abs(area_error/area) + abs(Terror[0]/Tfit[0]) + abs(Terror[1]/Tfit[1]))
return rel_err
def get_area():
"""
function to get the active area of several pyroelectric materials, depending which mask was used
input: None
output: area [float]
"""
area_input = prompt("Area [m2]:")
if area_input == "A": #d13
return area_d13, 0.0082*area_d13
elif area_input == "Aold":
return area_d13_old, 0.0082*area_d13_old
elif area_input == "B": #d15
return area_d15, 0.0082*area_d15
elif area_input == "Bold":
return area_d15_old, 0.0082*area_d15_old
elif area_input == "C": #a5
return area_a5, 0.0082*area_a5
elif area_input == "D": #d5
return area_d5, 0.0082*area_d5
elif area_input == "PMNPT":
return 1.65e-4, 1.35292e-6 #area of PMN-PT samples with SputterShadowMaskContacts
elif area_input == "CUSTOM": #custom defined values
return custom, custom_error
else:
return float(area_input), 0.0082*float(area_input) #direct area input
def amp_phase_correction(fit_dict):
"""
Correction if neg. amplitudes and phases >< 2 pi are fitted
Input: Parameters dict
Output: Parameters dict
"""
if fit_dict['amp'].value < 0.0:
fit_dict['amp'].value = abs(fit_dict['amp'].value)
fit_dict['phase'].value = fit_dict['phase'].value + pi #add value of pi if amplitude negativ
else:
pass
fit_dict['phase'].value = phase_correction(fit_dict['phase'].value)
return fit_dict
def phase_correction(phase):
"""
Brings phase in the range of 0 to 2*pi
"""
if phase > 2*pi:
while phase > +2*pi:
phase = phase - 2*pi
elif phase < 2*pi or phase<0.0:
while phase < 0.0:
phase = phase + 2*pi
else:
pass
return phase
#Main Program------------------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------------------------------
print(line)
print("PyroFit - UnivseralScript - v%s" % version)
print(line)
#Init Plot Colors-----------------------------------------------------------------------------------------------------------------
if color_style == 'TUBAF':
other = 'k'
temp_color = TUBAFblue()
curr_color = TUBAFred()
p_color = TUBAForange()
np_color = TUBAFcyan()
volt_color = TUBAFgreen()
elif color_style == 'Standard':
other = 'k'
temp_color = 'b'
curr_color = 'r'
p_color = 'c'
np_color = 'm'
volt_color = 'g'
# File Reading-----------------------------------------------------------------------------------------------------------------
filelist = glob.glob('*.log')
filecounter = 0
#check folder for files and read files!
for filename in filelist:
date=extract_date(filename)
datatype=extract_datatype(filename)
if datatype=="Temperature":
HV_status, T_profile = extract_measurementmode(filename)
measurement_info = extract_T_stimulation_params(filename)
samplename = extract_samplename(filename)
Tdata = loadtxt(filename, skiprows=9)
#previous filter of Tdata
erase_bools_T = (Tdata[:,1]!=9.9e39) #overflow on down temperature
Tdata = Tdata[erase_bools_T]
filecounter = filecounter + 1
sys.stdout.write("\rReading: %d/%d completed" % (filecounter,len(filelist)))
sys.stdout.flush()
elif datatype=="Current":
Idata = loadtxt(filename)
#previous filtering of data
erase_bools_I = (Idata[:,1]!=9.9e39) #overflow on Keithley amperemeter
Idata = Idata[erase_bools_I]
erase_bools_I = (Idata[:,1]!=9.9e37)
Idata = Idata[erase_bools_I]
erase_bools_I = (Idata[:,1]!=0.015) #overflow in measurement program
Idata = Idata[erase_bools_I]
filecounter = filecounter + 1
sys.stdout.write("\rReading: %d/%d completed" % (filecounter,len(filelist)))
if current_filter_flag == True:
erase_bools_I = (abs(Idata[:,1])< upper_I_lim) #user defined low pass filter with upper_I_lim variable
Idata = Idata[erase_bools_I]
sys.stdout.write("\rData filter applied")
sys.stdout.flush()
elif datatype=="Charge":
Qdata = loadtxt(filename)
filecounter = filecounter + 1
sys.stdout.write("\rReading: %d/%d completed" % (filecounter,len(filelist)))
sys.stdout.flush()
elif datatype=="Voltage":
Vdata = loadtxt(filename)
filecounter = filecounter +1
sys.stdout.write("\rReading: %d/%d completed" % (filecounter,len(filelist)))
sys.stdout.flush()
elif datatype=="HighVoltage":
if os.path.getsize(filename)>4:
HV_set = extract_HV_params(filename)
if os.path.getsize(filename)>32:
HVdata = loadtxt(filename,skiprows=2)
filecounter = filecounter + 1
sys.stdout.write("\rReading: %d/%d completed" % (filecounter,len(filelist)))
sys.stdout.flush()
elif datatype=="Vacuum":
Vacdata = loadtxt(filename)
filecounter = filecounter + 1
sys.stdout.write("\rReading: %d/%d completed" % (filecounter,len(filelist)))
sys.stdout.flush()
elif datatype=="Powersupply":
Powerdata = loadtxt(filename)
filecounter = filecounter + 1
sys.stdout.write("\rReading: %d/%d completed" % (filecounter,len(filelist)))
sys.stdout.flush()
elif datatype=="GBIP-Errors":
filecounter = filecounter + 1
sys.stdout.write("\rReading: %d/%d completed" % (filecounter,len(filelist)))
sys.stdout.flush()
elif datatype==None:
continue
print("\n"+line)
#----------------------------------------------------------------------------------------------------------------------------
if filelist == []:
print("No measurement data files in Folder!")
else:
#Routines for every measurement_type-------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------------------------
#normal measurement routines without HV (SinWave, LinRamp, ...)
if measurement_info['hv_mode'] == "Off":
#Thermostat Method
#--------------------------------------------------------------------------------------------------------------------
if measurement_info['waveform'] == "Thermostat":
print("Mode:\t\t%s"%measurement_info['waveform'])
print("Temperature:\t%.1fK" % measurement_info['T_Limit_H'])
#Interpolation and plotting of data ----
print(line)
print("...plotting")
print(line)
# pre-fit plot
tnew, Tnew, Inew = interpolate_data(Tdata, Idata, interpolation_step, temp_filter_flag)
bild, ax1, ax2 = plot_graph(tnew, Tnew, Inew, T_profile)
#text box
box_text = "Temperature: "+str(measurement_info['T_Limit_H']) + "K"
box = plot_textbox(box_text)
ax2.add_artist(box)
plt.show()
#saving figure
saving_figure(bild)
#---------------------------------------------------------------------------------------------------------------------
#LinearRamp Method
elif measurement_info['waveform'] == "LinearRamp":
print("Mode:\t\t%s"%measurement_info['waveform'])
print("Temperature:\t%.1fK - %.1fK\nSlope:\t%.1fK/h" % (measurement_info['offs'],max(Tdata[:,1]),measurement_info['heat_rate']*3600))
#Interpolation and plotting of data ----
print(line)
print("...plotting")
print(line)
# pre-fit plot
tnew, Tnew, Inew = interpolate_data(Tdata, Idata, interpolation_step, temp_filter_flag)
bild1, ax1, ax2 = plot_graph(tnew, Tnew, Inew, T_profile)
#text box
box_text = "Temperature: "+str(measurement_info['offs']) +' - ' + str(round(max(Tdata[:,1]),2)) + "K \nSlope: " + str(measurement_info['heat_rate']*3600) + " K/h"
box = plot_textbox(box_text)
ax2.add_artist(box)
plt.show()
# Perform Byer-Roundy Fit and calc p
#---------------------------------------------------------------------------------------------------------------
answer = prompt("fit? [y/n]")
if answer == "y":
area, area_error = get_area()
print(line)
print("... fitting")
#Byer Roundy evaluation
head = date+"_"+samplename+"_"+T_profile+'p(T)'
bild2 = plt.figure(head, figsize=fig_size)
axp = bild2.add_subplot(111)
#check when ramp run into T_Limit_H
if max(Tnew[:,0]) < measurement_info['T_Limit_H']:
maxT_ind = Tnew[:,0]>max(Tnew[:,0])-1
else:
maxT_ind = Tnew[:,0]>(measurement_info['T_Limit_H']-1)
number_of_lim = maxT_ind.tolist().count(True)
limit = len(Tnew[:,0])-number_of_lim
# init Params dict
Params = Parameters()
Params.add('offs', value=measurement_info['offs'])
Params.add('slope', value=measurement_info['heat_rate'])
if PartWiseTFit == False:
Tresults = minimize(linear, Params, args=(tnew[start_index:limit], Tnew[start_index:limit,0]), method="leastsq")
ax1.plot(tnew[start_index:limit],linear(Tresults.params,tnew[start_index:limit]),color=temp_color,linestyle='-',label="T-Fit (down)")
plt.draw()
pyro_koeff = abs(Inew[start_index:limit]) / (area * Tresults.params['slope'].value)
pyro_koeff_err = pyro_koeff * (0.02 + area_error/area + Tresults.params['slope'].stderr/Tresults.params['slope'].value)
p = vstack((tnew[start_index:limit],Tnew[start_index:limit,0],pyro_koeff, pyro_koeff_err)).T
else:
pyro_koeff = array([])
pyro_koeff_err = array([])
time = array([])
Temp = array([])
# T Fit in 10 parts (for wide T range!)
cut = 10
satzlaenge = int((limit-start_index)/cut)
for i in arange(1,cut):
start = start_index+int((i*satzlaenge)-satzlaenge)
ende = start_index+int(i*satzlaenge)
t = tnew[start:ende]
T = Tnew[start:ende,0]
Tresults = minimize(linear, Params, args=(t,T), method="leastsq")
ax1.plot(tnew[start:ende],linear(Tresults.params,tnew[start:ende]),color=temp_color,linestyle='-',label="T-Fit (down)")
plt.draw()
pyro_koeff_temp = abs(Inew[start:ende]) / (area * Tresults.params['slope'].value)
pyro_koeff_err_temp = pyro_koeff_temp * (0.02 + area_error/area + Tresults.params['slope'].stderr/Tresults.params['slope'].value)
pyro_koeff = append(pyro_koeff,pyro_koeff_temp)
pyro_koeff_err = append(pyro_koeff_err, pyro_koeff_err_temp)
time = append(time, t)
Temp = append(Temp, T)
p = vstack((time, Temp, pyro_koeff, pyro_koeff_err)).T
axp.plot(p[:,1],p[:,2]*1e6,color=temp_color,marker=".",linestyle="-", label='p (BR)')
axp.set_xlabel('Temperature (K)',size=label_size)
axp.set_ylabel(u"p (yC/Km²)",color=temp_color,size=label_size)
axp.grid(b=None, which='major', axis='both', color='grey',linestyle=':')
axp.set_xlim(273,max(Tnew[:,0]))
#Calculating PR ---------------------------------------------------------------------------------------
print("remanent Polarization ...")
PS_plot = prompt("Calculate? (y/n):")
if PS_plot == "y":
PS_flag = True
#generate new ax
axP = axp.twinx()
number_of_maxima = prompt("How many max?: ")
number_of_maxima = int(number_of_maxima)
print("select TC(s) from the p(T) plot")
TC = plt.ginput(number_of_maxima)
#get index from array where temp is 300K
T300 = abs(p[:]-300).argmin()
P = []
TC_index_list = []
#loop for each selected temperature
for i in range(number_of_maxima):
TC_index = abs(p[:,1]-TC[i][0]).argmin()
TC_index_list.append(TC_index)
#calc PS with partial trapezoidal integration
for f in range(TC_index):
# reverse pyroKoeff
P = list(cumtrapz(p[:TC_index,2][::-1],p[:TC_index,1],initial=0))
P = P[::-1]
#fill rest of array legth with zeros
for f in range(TC_index,len(p)):
P.append(0.0)
#make array type
Polarization = array(P)
#append to p array
p = column_stack((p,Polarization)) #letzte Spalte ist Polarizationsverlauf
#unit conversion for Plot
Pout = array(P) * 100 # yC/cm2
#Pout = array(P) * 1000 # mC/m2
#user message
#print("%d:\tTC: %.2f K / %.2f C\n\tPR(300 K): %.3f yC/km2" % (i+1,TC[i][0],(TC[i][0]-273.15),abs(Pout[T300])*1e3))
#Plot
axP.plot(p[:,1],abs(Pout), linestylelist[i], color=p_color, label="rem. Polarization")
cur_ylim = axP.get_ylim()
#axP.set_ylim(1e0,1e3)
axP.set_xlim(axp.get_xbound())
axP.set_ylabel(u'$P_{\mathrm{R}}$ (yC/m²)',color=p_color,size=label_size)
plt.draw()
bild2.tight_layout()
saving_figure(bild2,pbild=True)
#writing log files
print(line)
print("...writing log files")
# temperature
log = open(date+"_"+samplename+"_"+T_profile+"_T-Fit.txt", 'w+')
log.write("#Results\n#----------\n")
fileprint_fit(log,Tresults.params,"Temperature")
log.close()
# p and PS
header_string = "time [s]\t\t\tTemp [K]\t\t\tp_BR [C/Km2]\t\t\tp_err [C/Km2]"
if PS_flag == True:
for k in range(number_of_maxima):
pol_string = "\t\tPS [C/m2] - TC %.2fK" % p[TC_index_list[k],1]
header_string = header_string + pol_string
savetxt(date+"_"+samplename+"_"+T_profile+"_"+"PyroData.txt", p, delimiter="\t", header=header_string)
saving_figure(bild1)
#---------------------------------------------------------------------------------------------------------------------
#SineWave Method
elif measurement_info['waveform'] == "SineWave":
print("Mode:\t\t%s"%measurement_info['waveform'])
print("Stimulation:\tA=%.1fK\n\t\tf=%.1fmHz\n\t\tO=%.1fK" % (measurement_info['amp'], measurement_info['freq']*1000, measurement_info['offs']))
#Interpolation and plotting of data ----
print(line)
print("...plotting")
print(line)
# pre-fit plot
tnew, Tnew, Inew = interpolate_data(Tdata, Idata, interpolation_step, temp_filter_flag)
bild1, ax1, ax2 = plot_graph(tnew, Tnew, Inew, T_profile)
Lissajous(ax1, Tnew[start_index:,0], Inew[start_index:])
#---------------------------------------------------------------------------------------------------------------
answer = prompt("fit? [y/n]")
if answer == "y":
area, area_error = get_area()
print(line)
print("... fitting")
#Fit temperature----------------------------------------------------------------------------------------
Tresult_down = fit(tnew, Tnew[:,0], start_index, len(Tnew[:,0])-1,1,measurement_info, True, True)
#correction of phase and amplitudes
Tparams_down = amp_phase_correction(Tresult_down.params)
#extract params dict to lists
Tfit_down, Terror_down = extract_fit_relerr_params(Tparams_down)
#Plot
ax1.plot(tnew[start_index:], sinfunc(Tparams_down, tnew[start_index:]), color=temp_color, linestyle='-', label="T-Fit (down)")
plt.draw()
#absolute T_high Error
total_Terror_down = abs(Tparams_down['amp'].stderr/Tparams_down['amp'].value)+abs(Tparams_down['phase'].stderr/Tparams_down['phase'].value)+abs(Tparams_down['freq'].stderr/Tparams_down['freq'].value)+abs(Tparams_down['offs'].stderr/Tparams_down['offs'].value)+abs(Tparams_down['slope'].stderr/Tparams_down['slope'].value)
#for top temperature
if temp_filter_flag == False:
Tresult_high = fit(tnew, Tnew[:,1], start_index, len(Tnew_top)-1,5,measurement_info, True, True)
#correction of phase and amplitude
Tparams_high = amp_phase_correction(Tresult_high.params)
#extract params dict to lists
Tfit_high, Terror_high = extract_fit_relerr_params(Tparams_high)
#plot of second fit
ax1.plot(tnew[start_index:-5], sinfunc(Tparams_high, tnew[start_index:-5]), color=volt_color,linestyle='-', label='T-Fit (top)')
plt.draw()
#absolute T_high Error
total_Terror_high = abs(Tparams_high['amp'].stderr/Tparams_high['amp'].value)+abs(Tparams_high['phase'].stderr/Tparams_high['phase'].value)+abs(Tparams_high['freq'].stderr/Tparams_high['freq'].value)+abs(Tparams_high['offs'].stderr/Tparams_high['offs'].value)+abs(Tparams_high['slope'].stderr/Tparams_high['slope'].value)
#Fit current ---------------------------------------------------------------------------------------------
#initialize parameters dict for current fit
Iparams = Parameters()
Iparams.add('amp', value=1e-11)