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some stuff done

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Dominik Demuth 2023-05-16 17:45:39 +02:00
parent 996b0b2ae2
commit bc215ce32b
2 changed files with 46 additions and 240 deletions
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236
src/nmreval/dsc/TNMH_v0c-tau.py
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@ -1,41 +1,26 @@
# Tool-Naranayaswamy-Moynihan-Hodge model
# by Florian Pabst 2020
import re
import os
import sys import sys
import time
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from scipy.integrate import quad, cumulative_trapezoid from scipy.integrate import cumulative_trapezoid
from scipy.optimize import curve_fit, fsolve from scipy.optimize import curve_fit, fsolve
from scipy.stats import linregress from scipy.stats import linregress
from nmreval.data import Points from nmreval.data import Points
def slope(x, t, m):
return t+x*m
def tau_k(tau_0, deltaE, temp_k, xx, T_f_km1):
return tau_0 * np.exp(xx*deltaE / (R*temp_k) + (1-xx) * deltaE / (R*T_f_km1))
# Read data # Read data
dataName = sys.argv[1] dataName = sys.argv[1]
try: try:
data = np.loadtxt(dataName, skiprows=0).T data = np.loadtxt(dataName, skiprows=0).T
# print("Loading")
temp = data[0] temp = data[0]
heat_capacity = data[1] heat_capacity = data[1]
except IndexError: except IndexError:
# print("File not found")
exit() exit()
print(np.round(temp, decimals=3)[:30])
def get_fictive_temperature(pts: Points, glass: tuple[float, float], liquid: tuple[float, float]): def get_fictive_temperature(pts: Points, glass: tuple[float, float], liquid: tuple[float, float]):
min_glass, max_glass = min(glass), max(glass) min_glass, max_glass = min(glass), max(glass)
@ -51,8 +36,8 @@ def get_fictive_temperature(pts: Points, glass: tuple[float, float], liquid: tup
region.y -= glass_extrapolation region.y -= glass_extrapolation
liquid_regime = (min_liquid < region.x) & (region.x < max_liquid) liquid_regime = (min_liquid < region.x) & (region.x < max_liquid)
regress = linregress(region.x[liquid_regime], region.y[liquid_regime]) regress2 = linregress(region.x[liquid_regime], region.y[liquid_regime])
liquid_extrapolation = regress.slope * region.x + regress.intercept liquid_extrapolation = regress2.slope * region.x + regress2.intercept
real_area = -cumulative_trapezoid(region.y, region.x, initial=0) real_area = -cumulative_trapezoid(region.y, region.x, initial=0)
real_area -= real_area[-1] real_area -= real_area[-1]
@ -60,10 +45,12 @@ def get_fictive_temperature(pts: Points, glass: tuple[float, float], liquid: tup
equivalent_area -= equivalent_area[-1] equivalent_area -= equivalent_area[-1]
equivalent_area *= -1 equivalent_area *= -1
return region.x, region.x[np.argmin(np.abs(real_area[:, None] - equivalent_area[None, :]), axis=1)] return region.x, np.round(region.x[np.argmin(np.abs(real_area[:, None] - equivalent_area[None, :]), axis=1)], decimals=4)
curve = Points(x=temp, y=heat_capacity, name=dataName) curve = Points(x=temp, y=heat_capacity, name=dataName)
# plt.plot(curve.x, curve.y)
# plt.show()
# First step: Calculate fictive Temperature T_f(T) from Cp data, then dT_f/dT # First step: Calculate fictive Temperature T_f(T) from Cp data, then dT_f/dT
@ -75,21 +62,19 @@ initial_Tf = np.mean(fictiveTemp[:20])
rate = curve.value rate = curve.value
# Calculate dTf/dT # Calculate dTf/dT
with np.errstate(all='ignore'): # with np.errstate(all='ignore'):
dTfdT = np.gradient(fictiveTemp, temperatures) # dTfdT = np.diff(fictiveTemp)/np.diff(temperatures) # np.gradient(fictiveTemp, temperatures)
nan_filter = ~np.isnan(dTfdT) # nan_filter = ~np.isnan(dTfdT)
temperatures = np.array(temperatures)[nan_filter] # temperatures = 0.5 * (temperatures[:-1] + temperatures[1:])[nan_filter]
dTfdT = dTfdT[nan_filter] # # fictiveTemp = fictiveTemp[nan_filter]
# dTfdT = dTfdT[nan_filter]
print(fictiveTemp[:30])
exponents = []
taus = []
temps = [0]
T_f_ns = []
R = 8.314 R = 8.314
def tnmfunc(temperature, tau_g, x, beta, deltaE): def tnm_function(temperature, tau_g, x, beta, energy, tg):
step = len(temperature) step = len(temperature)
Tf = np.empty(step) Tf = np.empty(step)
@ -103,7 +88,7 @@ def tnmfunc(temperature, tau_g, x, beta, deltaE):
temp_0 = temperature[0] temp_0 = temperature[0]
for i in range(0, step-1): for i in range(0, step-1):
tau[i] = relax(temperature[i+1], Tf[i], tau_g, T_g, deltaE, x) tau[i] = relax(temperature[i+1], Tf[i], tau_g, tg, energy, x)
dttau[:i] += delt[i] / tau[i] dttau[:i] += delt[i] / tau[i]
Tf[i+1] = np.sum(delT[:i] * (1-np.exp(-dttau[:i]**beta))) + temp_0 Tf[i+1] = np.sum(delT[:i] * (1-np.exp(-dttau[:i]**beta))) + temp_0
@ -115,147 +100,6 @@ def relax(t, tf, tau_g, t_glass, ea, x):
return tau_g * np.exp((x*h / t) + ((1 - x) * h / tf) - h / t_glass) return tau_g * np.exp((x*h / t) + ((1 - x) * h / tf) - h / t_glass)
# TNMH Code (adapted from Badrinarayanan's PhD Thesis):
def tnmfunc2(xdata, tau_0, xx, beta, deltaE):
delhR = deltaE/8.314
T = []
Tf = []
t = []
delt = []
tau = []
dttau = []
delT = []
step = len(xdata)
for k in range(0, step):
dttau.append(0)
T.append(240)
Tf.append(240)
t.append(0)
delt.append(0)
tau.append(0)
dttau.append(0)
delT.append(0)
T[1] = xdata[1]
Tf[1] = T[1]
t[1] = 0
delt[1] = 0
tau[1] = tau_0 * np.exp((xx*delhR / T[1]) + ((1 - xx)*delhR / Tf[1])-delhR/T_g)
dttau[1] = delt[1] / tau[1]
delT[1] = 0
Tfinit = T[1]
T_f_ns = [xdata[0]]
for i in range(1, step):
T[i] = xdata[i]
delT[i] = xdata[i] - xdata[i-1]
delt[i] = abs(delT[i]) / (rate/60)
t[i] = t[i-1] + delt[i]
tau[i] = tau_0 * np.exp((delhR*xx / T[i]) + ((1 - xx)*delhR / Tf[i-1])-delhR/T_g)
# print(tau[i], T[i], Tf[i-1], dttau)
for j in np.arange(2, i).reshape(-1):
dttau[j] = dttau[j] + (delt[i] / tau[i])
Tfinit = Tfinit + (delT[j] * (1 - np.exp(- (dttau[j] ** beta))))
Tf[i] = Tfinit
T_f_ns.append(Tfinit)
Tfinit = Tf[1]
return T_f_ns
def tnmfunc3(xdata, tau_0, xx, beta, deltaE):
delhR = deltaE/8.314
step = len(xdata)
dttau = []
T = []
Tf = []
t = []
delT = []
delt = []
tau = []
for _ in range(step):
T.append(np.nan)
Tf.append(np.nan)
t.append(np.nan)
delT.append(np.nan)
delt.append(np.nan)
tau.append(np.nan)
dttau.append(0)
T[0] = xdata[0]
Tf[0] = T[0]
t[0] = 0
delT[0] = 0
delt[0] = 0
tau[0] = tau_0 * np.exp(xx * delhR/T[0] + (1-xx)*delhR/Tf[0] - delhR/T_g)
dttau[0] = delt[0]/tau[0]
Tfinit = T[0]
for i in range(1, step):
T[i] = xdata[i]
delT[i] = xdata[i] - xdata[i-1]
delt[i] = abs(delT[i]) * 60 / rate
t[i] = t[i-1] + delt[i]
tau[i] = tau_0 * np.exp(xx * delhR/T[i] + (1-xx)*delhR/Tf[i-1] - delhR/T_g)
for j in range(1, i):
dttau[j] += delt[i]/tau[i]
Tfinit += delT[j] * (1-np.exp(-dttau[j]**beta))
Tf[i] = Tfinit
Tfinit = T[0]
return Tf
def tnmfunc2(xdata, tau_0, xx, beta, deltaE):
delhR = deltaE/8.314
T = []
Tf = []
t = []
delt = []
tau = []
dttau = []
delT = []
step = len(xdata)
for k in range(0, step):
dttau.append(0)
T.append(240)
Tf.append(240)
t.append(0)
delt.append(0)
tau.append(0)
dttau.append(0)
delT.append(0)
T[1] = xdata[1]
Tf[1] = T[1]
t[1] = 0
delt[1] = 0
tau[1] = tau_0 * np.exp((xx*delhR / T[1]) + ((1 - xx)*delhR / Tf[1])-delhR/T_g)
dttau[1] = delt[1] / tau[1]
delT[1] = 0
Tfinit = T[1]
T_f_ns = [xdata[0]]
for i in range(1, step):
T[i] = xdata[i]
delT[i] = xdata[i] - xdata[i-1]
delt[i] = abs(delT[i]) / (rate/60)
t[i] = t[i-1] + delt[i]
tau[i] = tau_0 * np.exp((delhR*xx / T[i]) + ((1 - xx)*delhR / Tf[i-1])-delhR/T_g)
# print(tau[i], T[i], Tf[i-1], dttau)
for j in np.arange(2, i).reshape(-1):
dttau[j] = dttau[j] + (delt[i] / tau[i])
Tfinit = Tfinit + (delT[j] * (1 - np.exp(- (dttau[j] ** beta))))
Tf[i] = Tfinit
T_f_ns.append(Tfinit)
Tfinit = Tf[1]
return T_f_ns
# Use T_g = T_fictive for tau determination # Use T_g = T_fictive for tau determination
T_g = initial_Tf T_g = initial_Tf
@ -263,48 +107,26 @@ T_g = initial_Tf
p0 = [10, 0.5, 0.4, 225085] p0 = [10, 0.5, 0.4, 225085]
def fitTNMH(x, tau_0, xx, beta, deltaE): def tnmh_fit(tg):
dTNMHdT = [] def wrap(x, tau_0, xx, beta, energy):
modelTemp = np.append(x[::-1], x[1:]) modelTemp = np.r_[x[::-1], x[1:]]
TNMH = tnmfunc2(modelTemp, tau_0, xx, beta, deltaE) TNMH = tnm_function(modelTemp, tau_0, xx, beta, energy, tg)
for i in range(len(modelTemp)-1): res = np.gradient(TNMH, modelTemp)
dTNMHdT.append((TNMH[i+1]-TNMH[i]) / (modelTemp[i+1]-modelTemp[i]))
res = dTNMHdT[len(modelTemp)//2-1:]
plt.plot(x, res) return res[len(x)-1:]
plt.show()
return np.array(res) return wrap
# Use only every 20th data point to reduce fitting time # Use only every 20th data point to reduce fitting time
temperaturesInterpol = np.linspace(temperatures[0], temperatures[-1], 20) temperaturesInterpol = np.linspace(temperatures[0], temperatures[-1], 200)
temperaturesInterpol = np.linspace(150, 200, 51)
temperaturesInterpol = np.append(temperaturesInterpol[::-1], temperaturesInterpol[1:])
start = time.time()
tnmfunc2(temperaturesInterpol, *p0)
print('Flo', time.time()-start)
start = time.time()
tnmfunc3(temperaturesInterpol, *p0)
print('list', time.time()-start)
start = time.time()
tnmfunc(temperaturesInterpol, *p0)
print('Array', time.time()-start)
dTfdTInterpol = np.interp(temperaturesInterpol, temperatures, dTfdT) dTfdTInterpol = np.interp(temperaturesInterpol, temperatures, dTfdT)
plt.plot(tnmfunc2(temperaturesInterpol, *p0), label='Flo') res = curve_fit(tnmh_fit(T_g), temperaturesInterpol, dTfdTInterpol, p0)
plt.plot(tnmfunc3(temperaturesInterpol, *p0), label='List')
plt.plot(tnmfunc(temperaturesInterpol, *p0), label='array')
plt.legend()
plt.show()
plt.plot(np.diff(tnmfunc2(temperaturesInterpol, *p0)), label='Flo')
plt.plot(np.diff(tnmfunc3(temperaturesInterpol, *p0)), label='List') plt.plot(temperatures, dTfdT, label='dTf/dT')
plt.plot(np.diff(tnmfunc(temperaturesInterpol, *p0)), label='array') plt.plot(temperaturesInterpol, tnmh_fit(T_g)(temperaturesInterpol, *res[0]), label='fit')
plt.legend() plt.legend()
plt.show() plt.show()
# print(fitTNMH(temperaturesInterpol, *p0))

48
src/nmreval/dsc/TNMH_v0c-tau2.py
View File

@ -26,7 +26,6 @@ except IndexError:
dataOutName = os.path.splitext(str(dataName))[0] + "_Tfict+TNMH.dat" dataOutName = os.path.splitext(str(dataName))[0] + "_Tfict+TNMH.dat"
# First step: Calculate fictive Temperature T_f(T) from Cp data, then dT_f/dT # First step: Calculate fictive Temperature T_f(T) from Cp data, then dT_f/dT
# Find start and end point for glass Cp linear fit # Find start and end point for glass Cp linear fit
@ -138,7 +137,7 @@ intStop = fitLimitRight_Lq
fictiveTemp = [] fictiveTemp = []
temperatures = [] temperatures = []
for i,tempStart in enumerate(dataTemp): for i, tempStart in enumerate(dataTemp):
if fitLimitLeft_Gl < i < fitLimitRight_Lq: if fitLimitLeft_Gl < i < fitLimitRight_Lq:
intStart = i intStart = i
@ -172,20 +171,15 @@ for i in range(len(temperatures)-1):
dTfdT.append((fictiveTemp[i+1]-fictiveTemp[i]) / (temperatures[i+1]-temperatures[i])) dTfdT.append((fictiveTemp[i+1]-fictiveTemp[i]) / (temperatures[i+1]-temperatures[i]))
temperaturesCut.append(temperatures[i+1]) temperaturesCut.append(temperatures[i+1])
""" print(fictiveTemp[:30])
fig, (ax1) = P.subplots(1)
fig, ax1 = plt.subplots(1)
ax1.set_xlabel('Temperature / K') ax1.set_xlabel('Temperature / K')
ax1.set_ylabel('Fictive Temperature / K') ax1.set_ylabel('Fictive Temperature / K')
#ax1.set_xlim(left=coordsGl[0][0]) # ax1.plot(temperatures, fictiveTemp, 'ro')
#ax1.set_xlim(right=coordsLq[1][0]) ax1.plot(temperaturesCut, dTfdT)
#ax1.set_ylim(bottom=coordsGl[0][1]-0.1) # ax1.axhline(y=T_f_0[0], color='b', linestyle='-')
#ax1.set_ylim(top=coordsLq[1][1]+0.1) plt.show()
ax1.plot(temperatures,fictiveTemp, 'ro')
#ax1.plot(temperatures,(T_f_0+temperatures*0), 'b-')
ax1.axhline(y=T_f_0[0], color='b', linestyle='-')
#ax1.plot(temperatures[:-1],dTfdT, 'ro')
P.show()
"""
# Second step: Calcualte and Fit TNMH-model # Second step: Calcualte and Fit TNMH-model
try: try:
@ -207,6 +201,7 @@ T_f_ns = []
markovs = [coordsLq[1][0]] markovs = [coordsLq[1][0]]
R = 8.31 R = 8.31
# TNMH Code (adapted from Badrinarayanan's PhD Thesis): # TNMH Code (adapted from Badrinarayanan's PhD Thesis):
def tnmfunc2(xdata, tau_0, xx, beta, deltaE): def tnmfunc2(xdata, tau_0, xx, beta, deltaE):
delhR = deltaE/8.314 delhR = deltaE/8.314
@ -250,7 +245,6 @@ def tnmfunc2(xdata, tau_0, xx, beta, deltaE):
T_f_ns.append(Tfinit) T_f_ns.append(Tfinit)
Tfinit = Tf[1] Tfinit = Tf[1]
print(T_f_ns[:10])
return T_f_ns return T_f_ns
@ -297,25 +291,15 @@ tau_0, xx, beta, deltaE = p0
fitResult = fitTNMH(p0, temperaturesInterpol) fitResult = fitTNMH(p0, temperaturesInterpol)
"""
fig, (ax1) = P.subplots(1) fig, ax1 = plt.subplots()
ax1.set_xlabel('Temperature / K') ax1.set_xlabel('Temperature / K')
ax1.set_ylabel('Fictive Temperature / K') ax1.set_ylabel('Fictive Temperature / K')
#ax1.set_xlim(left=coordsGl[0][0]) ax1.plot(temperaturesCut, dTfdT, 'bo')
#ax1.set_xlim(right=coordsLq[1][0]) ax1.plot(temperaturesInterpol, dTfdTInterpol, 'yo')
#ax1.set_ylim(bottom=coordsGl[0][1]-0.1) ax1.plot(temperaturesInterpol, fitResult, 'r-')
#ax1.set_ylim(top=coordsLq[1][1]+0.1) plt.show()
#ax1.plot(temperatures,fictiveTemp, 'bo')
#ax1.plot(temperatures,T_f_ns, 'r-')
ax1.plot(temperaturesCut,dTfdT, 'bo')
#ax1.plot(temperatures[0:5454],dTNMHdT[0:5454], 'bo')
#ax1.plot(temperatures[5456:],dTNMHdT[5455:], 'ro')
#ax1.plot(temperaturesModel[0:99],dTNMHdT[0:99], 'b-')
#ax1.plot(temperaturesModel[100:199],dTNMHdT[100:199], 'r-')
ax1.plot(temperaturesInterpol,dTfdTInterpol, 'yo')
ax1.plot(temperaturesInterpol,fitResult, 'r-')
P.show()
"""
dataOutName = os.path.splitext(str(dataName))[0] + "_dTfdT_tau.fit" dataOutName = os.path.splitext(str(dataName))[0] + "_dTfdT_tau.fit"
dataOut = np.column_stack((temperaturesInterpol, dTfdTInterpol, fitResult)) dataOut = np.column_stack((temperaturesInterpol, dTfdTInterpol, fitResult))