def test_profileplot():
"""
profileplot should return an axes object when a DiffProfile is passed
"""
dis = np.linspace(0, 100, 101)
X = np.linspace(0, 1, 101)
If = [30.5, 60.5]
profile = DiffProfile(dis=dis, X=X, If=If)
ax = profileplot(profile, label='test')
assert isinstance(ax, Axes)
def FSA(profile_exp,
profile_sm,
diffsys,
time,
Xlim=[],
n=[400, 500],
w=None,
f=None,
alpha=0.3,
name=''):
"""
Forward Simulation Analysis
Extract diffusion coefficients based on a diffusion profile.
Please do not close any plot window during the FSA process.
This is the final step of FSA.
Parameters
----------
profile_exp : DiffProfile
Experimental diffusion profile, used for comparison with simulation
results.
profile_sm : DiffProfile
Diffusion profile after data smooth on experimental profile.
diffsys : DiffSystem
Diffusion coefficients
time : float
Diffusion time in seconds
Xlim : list (float), optional
Passed to 'pydiffusion.Dtools.SF', 'pydiffusion.utils.step'.
Indicates the left and right concentration limits for calculation.
Default value = [profile.X[0], profile.X[-1]].
n : list. optional
Passed to 'pydiffusion.utils.automesh'.
Meshing number range, default = [400, 500].
w : list, optional
Weights of each phase to calculate error.
Passed to 'pydiffusion.utils.error_profile'.
f : function of Meshing
Keyword argument of automesh()
alpha : float
Keyword argument of automesh()
name : str, optional
Name the output DiffProfile
Returns
-------
profile_sim : DiffProfile
Simulated diffusion profile after FSA.
diffsys_sim : DiffSystem
Calculated diffusion efficients by FSA.
Examples
--------
After datasmooth() and Dmodel(), FSA can be performed to calculate accurate diffusion coefficients:
>>> ds = datasmooth(exp)
>>> dsys = Dmodel(ds, time)
>>> fsa = FSA(exp, ds, dsys, time)
"""
# Create step profile on meshed grids
dism = automesh(profile=profile_sm, diffsys=diffsys, n=n, f=f, alpha=alpha)
matano = matanocalc(profile_sm, Xlim)
if Xlim == [] and profile_sm.X[-1] < profile_sm.X[0]:
profile_init = step(dism, matano, diffsys,
[diffsys.Xr[-1, 1], diffsys.Xr[0, 0]])
else:
profile_init = step(dism, matano, diffsys, Xlim)
# Determine the stop criteria of forward simulations
error_sm = error_profile(profile_sm, profile_exp)
ipt = input(
'Default error = %.6f\nInput the stop criteria of error: [%.6f]\n' %
(error_sm, error_sm * 2))
error_stop = error_sm * 2 if ipt == '' else float(ipt)
# If there is no Xspl info in diffsys, use Phase Mode
# else: ask if use Phase or Point Mode
if diffsys.Xspl is not None:
ipt = input(
'Use Phase Mode? [n]\n(The shape of diffusivity curve does not change)\n'
)
pp = False if 'y' in ipt or 'Y' in ipt else True
else:
pp = False
if name == '':
name = profile_exp.name + '_FSA'
# Diffusion coefficients used for forward simulations
diffsys_sim = DiffSystem(diffsys.Xr,
diffsys.Dfunc,
Xspl=diffsys.Xspl,
name=name)
# Plot FSA status
fig = plt.figure('FSA', figsize=(16, 6))
ax1, ax2 = fig.add_subplot(121), fig.add_subplot(122)
profileplot(profile_exp,
ax1,
ls='none',
marker='o',
c='b',
fillstyle='none')
profileplot(profile_sm, ax1, ls='-', c='g', lw=1)
SFplot(profile_sm, time, Xlim, ax2, ls='none', c='b', marker='.')
DCplot(diffsys_sim, ax2, ls='-', c='r', lw=2)
plt.draw()
plt.tight_layout()
plt.pause(0.1)
n_sim = 0
while True:
# Simulation
n_sim += 1
profile_sim = mphSim(profile_init, diffsys_sim, time, name=name)
error_sim = error_profile(profile_sim, profile_exp, w)
print('Simulation %i, error = %f(%f)' % (n_sim, error_sim, error_stop))
# Plot simulation results
ax1.cla()
ax2.cla()
profileplot(profile_exp,
ax1,
ls='none',
marker='o',
c='b',
fillstyle='none')
profileplot(profile_sm, ax1, ls='-', c='g', lw=1)
profileplot(profile_sim, ax1, ls='-', c='r', lw=2)
SFplot(profile_sm, time, Xlim, ax2, ls='none', c='b', marker='.')
DCplot(diffsys_sim, ax2, ls='-', c='r', lw=2)
plt.draw()
plt.tight_layout()
# DC adjust
Dfunc_adjust = [0] * diffsys_sim.Np
# If error > stop criteria, continue simulation by auto DC adjustment
if error_sim > error_stop:
for ph in range(diffsys_sim.Np):
try:
Dfunc_adjust[ph] = Dadjust(profile_sm, profile_sim,
diffsys_sim, ph, pp)
except (ValueError, TypeError) as error:
ita_finish()
raise error
diffsys_sim.Dfunc = Dfunc_adjust
# If error < stop criteria or simulate too many times
if error_sim <= error_stop or n_sim > 9:
ita_start()
# Ask if exit
ipt = ask_input('Satisfied with FSA? [n]')
if 'y' in ipt or 'Y' in ipt:
ita_finish()
break
# If use Point Mode
if diffsys_sim.Xspl is not None:
ipt = ask_input('Use Point Mode (y) or Phase Mode (n)? [y]')
pp = False if 'n' in ipt or 'N' in ipt else True
if pp:
for ph in range(diffsys_sim.Np):
try:
Dfunc_adjust[ph] = Dadjust(profile_sm, profile_sim,
diffsys_sim, ph, pp)
except (ValueError, TypeError) as error:
ita_finish()
raise error
diffsys_sim.Dfunc = Dfunc_adjust
DCplot(diffsys_sim, ax2, ls='-', c='m', lw=2)
plt.draw()
plt.pause(0.1)
ita_finish()
continue
# Phase Mode, ask if use manual input for each phase
pp = False
ipt = input('Phase Mode\nManually input for each phase? [n]')
manual = True if 'y' in ipt or 'Y' in ipt else False
for ph in range(diffsys_sim.Np):
if manual:
ipt = input(
'Input deltaD for phase # %i:\n(DC = DC * 10^deltaD, default deltaD = auto)\n'
% (ph + 1))
deltaD = float(ipt) if ipt != '' else None
else:
deltaD = None
try:
Dfunc_adjust[ph] = Dadjust(profile_sm, profile_sim,
diffsys_sim, ph, pp, deltaD)
except (ValueError, TypeError) as error:
ita_finish()
raise error
# Apply the adjustment to diffsys_sim
diffsys_sim.Dfunc = Dfunc_adjust
DCplot(diffsys_sim, ax2, ls='-', c='m', lw=2)
plt.draw()
plt.pause(0.1)
ita_finish()
return profile_sim, diffsys_sim
from pydiffusion.plot import profileplot, DCplot
from pydiffusion.io import read_csv, save_csv
# Create diffusion system with constant DC
diffsys = DiffSystem(Xr=[0, 1], X=[0, 1], DC=[1e-14, 1e-14], name='Constant D')
# Create initial step profile
dis = mesh(0, 1000, 501)
profile_init = step(dis, 500, diffsys, name='Intitial step profile')
fig = plt.figure(figsize=(16, 6))
ax1, ax2 = fig.add_subplot(121), fig.add_subplot(122)
ax1.set_title('Diffusion Coefficients', fontsize=15)
ax2.set_title('Initial Step Profile', fontsize=15)
DCplot(diffsys, ax1)
profileplot(profile_init, ax2)
# Diffusion simulation using the setups
time = 200 * 3600
profile_final = mphSim(profile_init, diffsys, time)
ax = profileplot(profile_init, ls='--')
profileplot(profile_final, ax, c='r')
# Read diffusion coefficients data of Ni-Mo system
data = pd.read_csv('examples/data/NiMo.csv')
X, DC = data['X'], data['DC']
Xr = np.array([[0, 0.25], [0.49, 0.53], [0.97, 1]])
diffsys_NiMo = DiffSystem(Xr=Xr, X=X, DC=DC)
# Read diffusion coefficients from saved data file by pydiffusion.io.save_csv
import matplotlib.pyplot as plt
from pydiffusion.io import read_csv, save_csv
from pydiffusion.plot import profileplot, DCplot, SFplot
from pydiffusion.Dtools import FSA
# Read required data for FSA
NiMo_sm, diffsys_init = read_csv('examples/data/NiMo_DC_init.csv')
NiMo_exp, _ = read_csv('examples/data/NiMo_exp.csv')
Xp = [[.05, .2], [.5, .515], [.985]]
diffsys_init.Xspl = Xp
time = 3600 * 1000
fig = plt.figure(figsize=(16, 6))
ax1, ax2 = fig.add_subplot(121), fig.add_subplot(122)
profileplot(NiMo_exp, ax1, ls='none', c='b', marker='o', fillstyle='none')
profileplot(NiMo_sm, ax1, c='g', lw=2, label='NiMo_exp_smoothed')
SFplot(NiMo_sm, time, Xlim=[0, 1], ax=ax2, c='b', label='Sauer-Freise')
DCplot(diffsys_init, ax2, c='r', lw=2)
plt.pause(1.0)
# FSA
NiMo_sim, diffsys_fsa = FSA(NiMo_exp,
NiMo_sm,
diffsys_init,
time,
Xlim=[0, 1],
n=[250, 300])
# Plot the results
fig = plt.figure(figsize=(16, 6))
ax1, ax2 = fig.add_subplot(121), fig.add_subplot(122)
from pydiffusion.io import read_csv, save_csv
from pydiffusion.plot import profileplot, DCplot, SFplot
from pydiffusion.Dtools import Dmodel
# Read smoothed NiMo data
NiMo_sm, _ = read_csv('examples/data/NiMo_sm.csv')
profileplot(NiMo_sm)
# DC modeling
time = 1000 * 3600
diffsys_init = Dmodel(NiMo_sm, time, Xlim=[0, 1])
# Plot results
ax = SFplot(NiMo_sm, time, Xlim=[0, 1])
DCplot(diffsys_init, ax)
# DC modeling automatically
Xspl = [[.05, .2], [.5, .515], [.985]]
diffsys_init_auto = Dmodel(NiMo_sm, time, Xspl=Xspl, Xlim=[0, 1])
# Save result
save_csv('examples/data/NiMo_DC_init.csv',
profile=NiMo_sm,
diffsys=diffsys_init_auto)
import pandas as pd
from pydiffusion.core import DiffProfile
from pydiffusion.io import read_csv, save_csv
from pydiffusion.plot import profileplot
from pydiffusion.smooth import datasmooth
# Read raw data
data = pd.read_csv('examples/data/NiMo_exp.csv')
dis, X = data['dis'], data['X']
NiMo_exp = DiffProfile(dis=dis, X=X, name='NiMo_exp')
# Another way to read profile data, .csv must be created by pydiffusion.io.save_csv
NiMo_exp, _ = read_csv('examples/data/NiMo_exp.csv')
ax = profileplot(NiMo_exp, c='b', marker='o', ls='none', fillstyle='none')
# Data smoothing
NiMo_sm = datasmooth(NiMo_exp, [311.5, 340.5], n=500)
# Plot result
profileplot(NiMo_sm, ax, c='r')
# Save result
save_csv('examples/data/NiMo_sm.csv', profile=NiMo_sm)
dism = automesh(profile_fsa, diffsys_TiZr, [300, 350])
mp = matanocalc(profile_fsa, [0, 1])
profile_init = step(dism, mp, diffsys_TiZr)
time = 100 * 3600
error_result = ErrorAnalysis(profile_exp,
profile_init,
diffsys_TiZr,
time,
loc=3,
accuracy=1e-2,
output=True)
fig = plt.figure(figsize=(16, 6))
ax1, ax2 = fig.add_subplot(121), fig.add_subplot(122)
DCplot(diffsys_TiZr, ax1, error_result)
profileplot(profile_fsa, ax2, error_result)
profileplot(profile_exp, ax2, marker='o', ls='none', fillstyle='none')
plt.pause(1.0)
# Error analysis with high accuracy
error_result2 = ErrorAnalysis(profile_exp,
profile_init,
diffsys_TiZr,
time,
loc=21,
accuracy=1e-3,
output=False)
fig = plt.figure(figsize=(16, 6))
ax1, ax2 = fig.add_subplot(121), fig.add_subplot(122)
DCplot(diffsys_TiZr, ax1, error_result2)