def plot_diagram(fname, fout, title=None, **kwargs):
df = load_table(fname, sort='T', fill=0)
df['T'] -= 273.15
# A1 = df['NP(BCC_A2)']
# A1 = df['NP(FCC_A1#1)']
fig, ax = plt.subplots(figsize=(4, 4))
ax.plot(df['T'], df['NP(BCC_A2)'], label=r'$\alpha$ (ferrita)')
ax.plot(df['T'], df['NP(FCC_A1#1)'], label=r'$\gamma$ (austenita)')
try:
ax.plot(df['T'], df['NP(CEMENTITE)'], label=r'$Fe_3C$ (cementita)')
except:
pass
A1 = df['T'][df['NP(FCC_A1#1)'] == 0].max()
ax.axvline(A1, ls=':', color='k')
ax.annotate('A1', (A1, 1), va='bottom', ha='right')
A3 = df['T'][df['NP(FCC_A1#1)'] == 1].min()
ax.axvline(A3, ls=':', color='k')
ax.annotate('A3', (A3, 1), va='bottom', ha='left')
try:
Tcem = df['T'][df['NP(CEMENTITE)'] == 0].min()
Tbcc = df['T'][df['NP(BCC_A2)'] == 0].min()
except:
pass
else:
A1prime = Tcem if Tcem < Tbcc else Tbcc
ax.axvline(A1prime, ls=':', color='k')
ax.annotate('A1\'', (A1prime, 1), va='bottom', ha='left')
ax.set_xlim(400, 1200)
ax.set_ylim(-.05, 1.07)
ax.set_xlabel(u'Temperatura (°C)')
ax.set_ylabel(u'Fração molar da fase')
if title is not None:
ax.set_title(title)
ax.legend()
fig.savefig(fout, **kwargs)
return fig, ax
"""
Plot all RESULT_####.TXT files
"""
import glob
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tctools import load_table, plot_table
files = pd.read_csv('../databases/compositions_files.csv', comment='#')
fig, ax = plt.subplots()
for i, fname in enumerate(files['file']):
if i % 10 == 0:
try:
df = load_table(fname, 'T', fill=0)
x = df['T']
if 'NP(FCC_A1#1)' in df.columns:
y = df['NP(FCC_A1#1)']
if 'NP(FCC_A1#2)' in df.columns:
y = df['NP(FCC_A1#2)']
ax.plot(x, y, label=fname)
except Exception as ex:
print(fname, ex)
# plt.legend()
plt.show()
# plt.plot(T, bcc.DVV0(T, P), label='Ferrite')
# plt.plot(T, fcc.DVV0(T, P), label='Austenite')
# plt.plot(T, cem.DVV0(T, P), label='Cementite')
# plt.legend(loc='best')
# plt.show()
import tctools as tt
files = [
'/home/arthur/Dropbox/{Shared} Lucas/Thermo-Calc/Trilhos/NP_HC.TXT',
'/home/arthur/Dropbox/{Shared} Lucas/Thermo-Calc/Trilhos/NP_S1.TXT',
'/home/arthur/Dropbox/{Shared} Lucas/Thermo-Calc/Trilhos/NP_NIPPON.TXT'
]
labels = ['HC', 'S1', 'Nippon']
for fname, lbl in zip(files, labels):
data = tt.load_table(fname, sort=0)
Ts = []
Vs = []
P = 101325.
V0 = data[0, 1] * bcc.Vm(25, P) + data[0, 3] * cem.Vm(25, P) / 4.
for line in data:
T, npbcc, npfcc, npcem = line
V = npbcc * bcc.Vm(T, P) + npfcc * fcc.Vm(T, P) + npcem * cem.Vm(
T, P) / 4.
Ts.append(T)
Vs.append((V - V0) / V0)
# Vs.append(V)
plt.plot(np.array(Ts) - 273.15, Vs, label=lbl)
import os
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tctools import load_table, plot_table
if __name__ == '__main__':
if len(sys.argv) > 1:
T_min, T_max, T_step = 673, 1473, 10
df = pd.read_csv('../databases/compositions_files.csv', comment='#')
for idx in sys.argv[1:]:
fname = '../results/{:05d}.DAT'.format(int(idx))
fig, ax = plt.subplots()
result = load_table(fname, sort='T', fill=0)
plot_table(df=result, xaxis='T', ax=ax, colpattern='NP(*)')
idx, = np.where(fname == df['file'].values)
if len(idx) > 0:
idx = int(idx[0])
sel = df.iloc[idx]
title = 'Fe-{:g}C-{:g}Mn-{:g}Si-{:g}Ni-{:g}Cr'.format(
100 * sel['C'], 100 * sel['Mn'], 100 * sel['Si'],
100 * sel['Ni'], 100 * sel['Cr'])
ax.set_title(title)
plt.show()
def extract_Tcrit(fname):
"""
Extract the critical temperature (Tcrit) from Thermo-Calc
generated table
Arguments
---------
fname : Path to the Thermo-Calc table file
Return
------
A1, A1prime, A3, eutectoid : tuple containing the extracted
values of A1, A1prime, and A3, and a string eutectoid
(hipo/hiper) determining whether the alloy is hipo or hiper
eutectoid.
"""
A1 = np.nan
A1prime = np.nan
A3 = np.nan
eutectoid = None
try:
df = load_table(fname, sort='T', fill=0)
except:
print('Failed to load {}'.format(fname))
else:
# print('{} successfully loaded'.format(fname))
# temperature
T = df['T']
mf_fer = np.array([])
mf_aus = np.array([])
mf_cem = np.array([])
if 'NP(BCC_A2)' in df.columns:
# mole fraction of ferrite
mf_fer = df['NP(BCC_A2)']
max_aus = 0 # 0 K
if 'NP(FCC_A1#1)' in df.columns:
# mole fraction of austenite
mf_aus = df['NP(FCC_A1#1)']
max_aus = mf_aus.max()
if 'NP(FCC_A1#2)' in df.columns:
# Chooses the column with higher NP values
if df['NP(FCC_A1#2)'].max() > max_aus:
mf_aus = df['NP(FCC_A1#2)']
if 'NP(CEMENTITE)' in df.columns:
# mole fraction of cementite
mf_cem = df['NP(CEMENTITE)']
# Determine A1 and A3
if len(mf_aus) > 0:
# A1 temperature
# highest temperature where mf_aus is 0
idx, = np.where(mf_aus == 0)
if len(idx) > 0:
A1 = T[idx[-1]]
# A3 temperature
# lowest temperature where mf_aus is 1
idx, = np.where(mf_aus == 1)
if len(idx) > 0:
A3 = T[idx[0]]
# Determine A1'
idx_fer, = np.where(mf_fer == 0)
idx_cem, = np.where(mf_cem == 0)
if len(idx_fer) > 0:
if len(idx_cem) > 0:
# A1' and A3 are related to the temperatures where
# mf_fer and mf_cem are 0, namely T_fer and T_cem
# A1' equals to min(T_fer, T_cem)
# A3 equals to max(T_fer, T_cem)
T_fer = T[idx_fer[0]]
T_cem = T[idx_cem[0]]
if T_fer > T_cem:
A1prime = T_cem
A3 = T_fer
eutectoid = 'hipo'
else:
A1prime = T_fer
A3 = T_cem
eutectoid = 'hiper'
else:
# If cementite is not defined, there's no intecritical
# phase field (ferrite + austenite + cementite).
# In this case, A1 and A1' do not make sense and are
# set as np.nan
A1 = np.nan
A1prime = np.nan
eutectoid = 'hipo'
else:
if len(mf_cem) > 0:
eutectoid = 'hiper'
else:
print('{} has no austenite'.format(fname))
return A1, A1prime, A3, eutectoid