#!/usr/bin/env python
# coding: utf-8

# In[1]:


get_ipython().run_line_magic('matplotlib', 'ipympl')
import pyvisco as visco
from pyvisco import styles
styles.format_fig()


# ***
# # Verification - Comparison with ANSYS APDL 2021 R1

# ## Master curve in time domain

# In[2]:


#Load user master curve in frequency domain
data = visco.load.file('./time_master/time_user_master.csv')
RefT = -5
domain = 'time'
modul = 'E'
df_master, units = visco.load.user_master(data, domain, RefT, modul)


# In[3]:


#Smooth
win = 1
df_master = visco.master.smooth(df_master, win)
fig_smooth = visco.master.plot_smooth(df_master, units)


# In[4]:


#Discretize number of Prony terms
df_dis = visco.prony.discretize(df_master)
fig_dis = visco.prony.plot_dis(df_master, df_dis, units)


# In[5]:


#Fit Prony series parameter
prony, df_GMaxw = visco.prony.fit(df_dis, df_master)
fig_fit = visco.prony.plot_fit(df_master, df_GMaxw, units)


# In[6]:


#Plot Generalized Maxwell model
fig_GMaxw = visco.prony.plot_GMaxw(df_GMaxw, units);


# In[7]:


#Compare Python fit with ANSYS APDL
df_prony_ANSYS = visco.verify.load_prony_ANSYS('./time_master/prony_terms.MPL')
prony_ANSYS = visco.verify.prep_prony_ANSYS(df_prony_ANSYS, prony, E_0 = 1739.03)
nprony = prony_ANSYS['df_terms'].shape[0]
df_GMaxw_ANSYS = visco.prony.calc_GMaxw(**prony_ANSYS, decades = nprony)
fig_fit_ANSYS = visco.verify.plot_fit_ANSYS(df_master, df_GMaxw, df_GMaxw_ANSYS, units)


# In[8]:


fig_coeff = visco.prony.plot_param([prony, prony_ANSYS], ['Python', 'Ansys'])


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