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make fit_freq work when df_master=None #7

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30e15ec
make fit_freq work when df_master=None
Simarilius-uk May 12, 2026
80d4886
change to use .loc for assigment to get rid of the FutureWarnings
Simarilius-uk May 14, 2026
667fa29
Changed the allocations in shift.py to use .loc so
Simarilius-uk May 14, 2026
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47 changes: 32 additions & 15 deletions pyvisco/prony.py
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Original file line number Diff line number Diff line change
Expand Up @@ -459,24 +459,41 @@ def fit_freq(df_dis, df_master, opt=False):

#Estimate instantaneous (E_0) and equilibrium (E_inf) modulus
E_0 = df_dis.E_0
if opt:
#Get measurement data
E_freq_meas = np.concatenate((df_master[stor]/E_0,
df_master[loss]/E_0))
omega_meas = df_master['omega'].values

#Get measurement data
E_freq_meas = np.concatenate((df_master[stor]/E_0,
df_master[loss]/E_0))
omega_meas = df_master['omega'].values

#Get Prony series
tau_i = df_dis['tau_i'].values
x0 = np.hstack((alpha_i, tau_i))
#Get Prony series
tau_i = df_dis['tau_i'].values
x0 = np.hstack((alpha_i, tau_i))

#Define bounds
bnd_a = ((0,1),)*alpha_i.shape[0]
#Define bounds
bnd_a = ((0,1),)*alpha_i.shape[0]

#Perform minimization to obtain alpha_i
res = minimize(ls_res(E_freq_norm), alpha_i,
args=(tau_i, E_freq_meas, omega_meas), method='L-BFGS-B', bounds=bnd_a)
alpha_i = res.x
err = res.fun
#Perform minimization to obtain alpha_i
res = minimize(ls_res(E_freq_norm), alpha_i,
args=(tau_i, E_freq_meas, omega_meas), method='L-BFGS-B', bounds=bnd_a)
alpha_i = res.x
err = res.fun
else:
E_inf = df_dis.E_inf
#Assembly 'K_global' matrix [Kraus 2017, Eq. 22]
N = df_dis.nprony
K_stor = np.tril(np.ones((N,N)), -1) + np.diag([0.5] * N)
K_loss = (np.diag([0.5] * N)
+ np.diag([0.1] * (N-1), 1) + np.diag([0.1] * (N-1), -1)
+ np.diag([0.01] * (N-2), 2) + np.diag([0.01] * (N-2), -2)
+ np.diag([0.001] * (N-3), 3) + np.diag([0.001] * (N-3), -3))
K_global = np.vstack([K_stor, K_loss, np.ones((1,N))])
#Assembly right-hand vector
E = np.concatenate((df_dis[stor]/(E_0-E_inf),
df_dis[loss]/(E_0-E_inf),
np.array([1])))

#Solve equation system
alpha_i, err = nnls(K_global, E)

###############################################################################
# DEPRECATED - Error gets large for certain viscoelastic materials
Expand Down
16 changes: 8 additions & 8 deletions pyvisco/shift.py
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Original file line number Diff line number Diff line change
Expand Up @@ -204,10 +204,10 @@ def fit_poly(df_aT):
xdata = df_aT['T'].values+273.15
ydata = df_aT['log_aT'].values

df_K['D4'][['C0', 'C1', 'C2', 'C3', 'C4']], _pcov = curve_fit(poly4, xdata, ydata)
df_K['D3'][['C0', 'C1', 'C2', 'C3']], _pcov = curve_fit(poly3, xdata, ydata)
df_K['D2'][['C0', 'C1', 'C2']], _pcov = curve_fit(poly2, xdata, ydata)
df_K['D1'][['C0', 'C1']], _pcov = curve_fit(poly1, xdata, ydata)
df_K.loc[['C0', 'C1', 'C2', 'C3', 'C4'], 'D4'], _pcov = curve_fit(poly4, xdata, ydata)
df_K.loc[['C0', 'C1', 'C2', 'C3'], 'D3'], _pcov = curve_fit(poly3, xdata, ydata)
df_K.loc[['C0', 'C1', 'C2'], 'D2'], _pcov = curve_fit(poly2, xdata, ydata)
df_K.loc[['C0', 'C1'], 'D1'], _pcov = curve_fit(poly1, xdata, ydata)

# Celsius
df_C = pd.DataFrame(
Expand All @@ -220,10 +220,10 @@ def fit_poly(df_aT):
xdata = df_aT['T'].values
ydata = df_aT['log_aT'].values

df_C['D4'][['C0', 'C1', 'C2', 'C3', 'C4']], _pcov = curve_fit(poly4, xdata, ydata)
df_C['D3'][['C0', 'C1', 'C2', 'C3']], _pcov = curve_fit(poly3, xdata, ydata)
df_C['D2'][['C0', 'C1', 'C2']], _pcov = curve_fit(poly2, xdata, ydata)
df_C['D1'][['C0', 'C1']], _pcov = curve_fit(poly1, xdata, ydata)
df_C.loc[['C0', 'C1', 'C2', 'C3', 'C4'], 'D4'], _pcov = curve_fit(poly4, xdata, ydata)
df_C.loc[['C0', 'C1', 'C2', 'C3'], 'D3'], _pcov = curve_fit(poly3, xdata, ydata)
df_C.loc[['C0', 'C1', 'C2'], 'D2'], _pcov = curve_fit(poly2, xdata, ydata)
df_C.loc[['C0', 'C1'], 'D1'], _pcov = curve_fit(poly1, xdata, ydata)

# Prep dataframes and add units for output
df_C = df_C.T
Expand Down
26 changes: 13 additions & 13 deletions verification/verify_freq_master.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -29,7 +29,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -45,7 +45,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -55,7 +55,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand All @@ -72,7 +72,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -111,7 +111,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -149,7 +149,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -186,7 +186,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -223,7 +223,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -259,7 +259,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -299,7 +299,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -341,7 +341,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -429,7 +429,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"metadata": {
"scrolled": false
},
Expand Down