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test_pykriging1D.py
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text/x-python
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R4670 PySONIC (old)
test_pykriging1D.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author: Theo Lemaire
# @Date: 2017-04-24 11:04:39
# @Email: theo.lemaire@epfl.ch
# @Last Modified by: Theo Lemaire
# @Last Modified time: 2017-05-26 13:44:14
''' Predict a 1D Vmeff profile using the PyKriging module. '''
import
pickle
import
numpy
as
np
import
matplotlib.pyplot
as
plt
from
pyKriging.krige
import
kriging
class
Variable
:
''' dummy class to contain information about the variable '''
name
=
''
unit
=
''
lookup
=
''
factor
=
1.
max_error
=
0.
def
__init__
(
self
,
var_name
,
var_unit
,
var_lookup
,
var_factor
,
var_max_error
):
self
.
name
=
var_name
self
.
unit
=
var_unit
self
.
factor
=
var_factor
self
.
lookup
=
var_lookup
self
.
max_error
=
var_max_error
# Set data variable and Kriging parameters
varinf
=
Variable
(
'V_{m, eff}'
,
'mV'
,
'V_eff'
,
1.
,
1e-1
)
# varinf = Variable('\\alpha_{m, eff}', 'ms^{-1}', 'alpha_m_eff', 1e-3, 1e1)
# varinf = Variable('\\beta_{m, eff}', 'ms^{-1}', 'beta_m_eff', 1e-3, 5e0)
# varinf = Variable('\\alpha_{h, eff}', 'ms^{-1}', 'alpha_h_eff', 1e-3, 1e1)
# varinf = Variable('\\beta_{h, eff}', 'ms^{-1}', 'beta_h_eff', 1e-3, 1e1)
# Define true function by interpolation from specific profile
def
f
(
x
):
return
np
.
interp
(
x
,
Qm
,
xvect
)
# Load coefficient profile
dirpath
=
'C:/Users/admin/Google Drive/PhD/NBLS model/Output/lookups 0.35MHz charge extended/'
filepath
=
dirpath
+
'lookups_a32.0nm_f350.0kHz_A100.0kPa_dQ1.0nC_cm2.pkl'
filepath0
=
dirpath
+
'lookups_a32.0nm_f350.0kHz_A0.0kPa_dQ1.0nC_cm2.pkl'
with
open
(
filepath
,
'rb'
)
as
fh
:
lookup
=
pickle
.
load
(
fh
)
Qm
=
lookup
[
'Q'
]
xvect
=
lookup
[
varinf
.
lookup
]
with
open
(
filepath0
,
'rb'
)
as
fh
:
lookup
=
pickle
.
load
(
fh
)
xvect0
=
lookup
[
varinf
.
lookup
]
# xvect = xvect - xvect0
print
(
'defining estimation vector'
)
x
=
np
.
atleast_2d
(
np
.
linspace
(
-
150.
,
150.
,
1000
)
*
1e-5
)
.
T
y
=
f
(
x
)
.
ravel
()
print
(
'defining prediction vector'
)
X0
=
np
.
atleast_2d
(
np
.
linspace
(
-
150.
,
150.
,
10
)
*
1e-5
)
.
T
Y0
=
f
(
X0
)
.
ravel
()
print
(
'creating kriging model'
)
k
=
kriging
(
X0
,
Y0
)
print
(
'training kriging model'
)
k
.
train
()
print
(
'predicting'
)
y_pred0
=
np
.
array
([
k
.
predict
(
xx
)
for
xx
in
x
])
X
=
X0
Y
=
Y0
numiter
=
10
for
i
in
range
(
numiter
):
print
(
'Infill iteration {0} of {1}....'
.
format
(
i
+
1
,
numiter
))
newpoints
=
k
.
infill
(
1
,
method
=
'error'
)
for
point
in
newpoints
:
newX
=
k
.
inversenormX
(
point
)
newY
=
f
(
newX
)[
0
]
print
(
'adding point ({:.3f}, {:.3f})'
.
format
(
newX
[
0
]
*
1e5
,
newY
*
varinf
.
factor
))
X
=
np
.
append
(
X
,
[
newX
],
axis
=
0
)
Y
=
np
.
append
(
Y
,
newY
)
k
.
addPoint
(
newX
,
newY
,
norm
=
True
)
k
.
train
()
y_pred
=
np
.
array
([
k
.
predict
(
xx
)
for
xx
in
x
])
fig
,
ax
=
plt
.
subplots
()
ax
.
plot
(
x
*
1e5
,
y
*
varinf
.
factor
,
'r:'
,
label
=
u'true function'
)
ax
.
plot
(
X0
*
1e5
,
Y0
*
varinf
.
factor
,
'r.'
,
markersize
=
10
,
label
=
u'Initial observations'
)
ax
.
plot
(
x
*
1e5
,
y_pred0
*
varinf
.
factor
,
'b-'
,
label
=
u'Initial prediction'
)
ax
.
set_xlabel
(
'$Q_m\ (nC/cm^2)$'
)
ax
.
set_ylabel
(
'$'
+
varinf
.
name
+
'\ ('
+
varinf
.
unit
+
')$'
)
ax
.
legend
()
fig
,
ax
=
plt
.
subplots
()
ax
.
plot
(
x
*
1e5
,
y
*
varinf
.
factor
,
'r:'
,
label
=
u'true function'
)
ax
.
plot
(
X
*
1e5
,
Y
*
varinf
.
factor
,
'r.'
,
markersize
=
10
,
label
=
u'Final observations'
)
ax
.
plot
(
x
*
1e5
,
y_pred
*
varinf
.
factor
,
'b-'
,
label
=
u'Final prediction'
)
ax
.
set_xlabel
(
'$Q_m\ (nC/cm^2)$'
)
ax
.
set_ylabel
(
'$'
+
varinf
.
name
+
'\ ('
+
varinf
.
unit
+
')$'
)
ax
.
legend
()
plt
.
show
()
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