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R4670 PySONIC (old)
utils.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author: Theo Lemaire
# @Date: 2016-09-19 22:30:46
# @Email: theo.lemaire@epfl.ch
# @Last Modified by: Theo Lemaire
# @Last Modified time: 2019-04-03 20:32:03
''' Definition of generic utility functions used in other modules '''
import
operator
import
os
import
math
import
pickle
import
tkinter
as
tk
from
tkinter
import
filedialog
import
numpy
as
np
import
colorlog
from
scipy.interpolate
import
interp1d
# Package logger
def
setLogger
():
log_formatter
=
colorlog
.
ColoredFormatter
(
'
%(log_color)s
%(asctime)s
%(message)s
'
,
datefmt
=
'
%d
/%m/%Y %H:%M:%S:'
,
reset
=
True
,
log_colors
=
{
'DEBUG'
:
'green'
,
'INFO'
:
'white'
,
'WARNING'
:
'yellow'
,
'ERROR'
:
'red'
,
'CRITICAL'
:
'red,bg_white'
,
},
style
=
'%'
)
log_handler
=
colorlog
.
StreamHandler
()
log_handler
.
setFormatter
(
log_formatter
)
color_logger
=
colorlog
.
getLogger
(
'PySONIC'
)
color_logger
.
addHandler
(
log_handler
)
return
color_logger
logger
=
setLogger
()
# File naming conventions
def
ESTIM_filecode
(
neuron
,
Astim
,
tstim
,
PRF
,
DC
):
return
'ESTIM_{}_{}_{:.1f}mA_per_m2_{:.0f}ms{}'
.
format
(
neuron
,
'CW'
if
DC
==
1
else
'PW'
,
Astim
,
tstim
*
1e3
,
'_PRF{:.2f}Hz_DC{:.2f}%'
.
format
(
PRF
,
DC
*
1e2
)
if
DC
<
1.
else
''
)
def
ASTIM_filecode
(
neuron
,
a
,
Fdrive
,
Adrive
,
tstim
,
PRF
,
DC
,
method
):
return
'ASTIM_{}_{}_{:.0f}nm_{:.0f}kHz_{:.2f}kPa_{:.0f}ms_{}{}'
.
format
(
neuron
,
'CW'
if
DC
==
1
else
'PW'
,
a
*
1e9
,
Fdrive
*
1e-3
,
Adrive
*
1e-3
,
tstim
*
1e3
,
'PRF{:.2f}Hz_DC{:.2f}%_'
.
format
(
PRF
,
DC
*
1e2
)
if
DC
<
1.
else
''
,
method
)
def
MECH_filecode
(
a
,
Fdrive
,
Adrive
,
Qm
):
return
'MECH_{:.0f}nm_{:.0f}kHz_{:.1f}kPa_{:.1f}nCcm2'
.
format
(
a
*
1e9
,
Fdrive
*
1e-3
,
Adrive
*
1e-3
,
Qm
*
1e5
)
# Figure naming conventions
def
figtitle
(
meta
):
''' Return appropriate title based on simulation metadata. '''
if
'Cm0'
in
meta
:
return
'{:.0f}nm radius BLS structure: MECH-STIM {:.0f}kHz, {:.2f}kPa, {:.1f}nC/cm2'
.
format
(
meta
[
'a'
]
*
1e9
,
meta
[
'Fdrive'
]
*
1e-3
,
meta
[
'Adrive'
]
*
1e-3
,
meta
[
'Qm'
]
*
1e5
)
else
:
if
meta
[
'DC'
]
<
1
:
wavetype
=
'PW'
suffix
=
', {:.2f}Hz PRF, {:.0f}% DC'
.
format
(
meta
[
'PRF'
],
meta
[
'DC'
]
*
1e2
)
else
:
wavetype
=
'CW'
suffix
=
''
if
'Astim'
in
meta
:
return
'{} neuron: {} E-STIM {:.2f}mA/m2, {:.0f}ms{}'
.
format
(
meta
[
'neuron'
],
wavetype
,
meta
[
'Astim'
],
meta
[
'tstim'
]
*
1e3
,
suffix
)
else
:
return
'{} neuron ({:.1f}nm): {} A-STIM {:.0f}kHz {:.2f}kPa, {:.0f}ms{} - {} model'
.
format
(
meta
[
'neuron'
],
meta
[
'a'
]
*
1e9
,
wavetype
,
meta
[
'Fdrive'
]
*
1e-3
,
meta
[
'Adrive'
]
*
1e-3
,
meta
[
'tstim'
]
*
1e3
,
suffix
,
meta
[
'method'
])
# SI units prefixes
si_prefixes
=
{
'y'
:
1e-24
,
# yocto
'z'
:
1e-21
,
# zepto
'a'
:
1e-18
,
# atto
'f'
:
1e-15
,
# femto
'p'
:
1e-12
,
# pico
'n'
:
1e-9
,
# nano
'u'
:
1e-6
,
# micro
'm'
:
1e-3
,
# mili
''
:
1e0
,
# None
'k'
:
1e3
,
# kilo
'M'
:
1e6
,
# mega
'G'
:
1e9
,
# giga
'T'
:
1e12
,
# tera
'P'
:
1e15
,
# peta
'E'
:
1e18
,
# exa
'Z'
:
1e21
,
# zetta
'Y'
:
1e24
,
# yotta
}
def
loadData
(
fpath
,
frequency
=
1
):
''' Load dataframe and metadata dictionary from pickle file. '''
logger
.
info
(
'Loading data from "
%s
"'
,
os
.
path
.
basename
(
fpath
))
with
open
(
fpath
,
'rb'
)
as
fh
:
frame
=
pickle
.
load
(
fh
)
df
=
frame
[
'data'
]
.
iloc
[::
frequency
]
meta
=
frame
[
'meta'
]
return
df
,
meta
def
si_format
(
x
,
precision
=
0
,
space
=
' '
):
''' Format a float according to the SI unit system, with the appropriate prefix letter. '''
if
isinstance
(
x
,
float
)
or
isinstance
(
x
,
int
)
or
isinstance
(
x
,
np
.
float
)
or
\
isinstance
(
x
,
np
.
int32
)
or
isinstance
(
x
,
np
.
int64
):
if
x
==
0
:
factor
=
1e0
prefix
=
''
else
:
sorted_si_prefixes
=
sorted
(
si_prefixes
.
items
(),
key
=
operator
.
itemgetter
(
1
))
vals
=
[
tmp
[
1
]
for
tmp
in
sorted_si_prefixes
]
# vals = list(si_prefixes.values())
ix
=
np
.
searchsorted
(
vals
,
np
.
abs
(
x
))
-
1
if
np
.
abs
(
x
)
==
vals
[
ix
+
1
]:
ix
+=
1
factor
=
vals
[
ix
]
prefix
=
sorted_si_prefixes
[
ix
][
0
]
# prefix = list(si_prefixes.keys())[ix]
return
'{{:.{}f}}{}{}'
.
format
(
precision
,
space
,
prefix
)
.
format
(
x
/
factor
)
elif
isinstance
(
x
,
list
)
or
isinstance
(
x
,
tuple
):
return
[
si_format
(
item
,
precision
,
space
)
for
item
in
x
]
elif
isinstance
(
x
,
np
.
ndarray
)
and
x
.
ndim
==
1
:
return
[
si_format
(
float
(
item
),
precision
,
space
)
for
item
in
x
]
else
:
print
(
type
(
x
))
def
pow10_format
(
number
,
precision
=
2
):
''' Format a number in power of 10 notation. '''
ret_string
=
'{0:.{1:d}e}'
.
format
(
number
,
precision
)
a
,
b
=
ret_string
.
split
(
"e"
)
a
=
float
(
a
)
b
=
int
(
b
)
return
'{}10^{{{}}}'
.
format
(
'{} * '
.
format
(
a
)
if
a
!=
1.
else
''
,
b
)
def
rmse
(
x1
,
x2
):
''' Compute the root mean square error between two 1D arrays '''
return
np
.
sqrt
(((
x1
-
x2
)
**
2
)
.
mean
())
def
rsquared
(
x1
,
x2
):
''' compute the R-squared coefficient between two 1D arrays '''
residuals
=
x1
-
x2
ss_res
=
np
.
sum
(
residuals
**
2
)
ss_tot
=
np
.
sum
((
x1
-
np
.
mean
(
x1
))
**
2
)
return
1
-
(
ss_res
/
ss_tot
)
def
Pressure2Intensity
(
p
,
rho
=
1075.0
,
c
=
1515.0
):
''' Return the spatial peak, pulse average acoustic intensity (ISPPA)
associated with the specified pressure amplitude.
:param p: pressure amplitude (Pa)
:param rho: medium density (kg/m3)
:param c: speed of sound in medium (m/s)
:return: spatial peak, pulse average acoustic intensity (W/m2)
'''
return
p
**
2
/
(
2
*
rho
*
c
)
def
Intensity2Pressure
(
I
,
rho
=
1075.0
,
c
=
1515.0
):
''' Return the pressure amplitude associated with the specified
spatial peak, pulse average acoustic intensity (ISPPA).
:param I: spatial peak, pulse average acoustic intensity (W/m2)
:param rho: medium density (kg/m3)
:param c: speed of sound in medium (m/s)
:return: pressure amplitude (Pa)
'''
return
np
.
sqrt
(
2
*
rho
*
c
*
I
)
def
OpenFilesDialog
(
filetype
,
dirname
=
''
):
''' Open a FileOpenDialogBox to select one or multiple file.
The default directory and file type are given.
:param dirname: default directory
:param filetype: default file type
:return: tuple of full paths to the chosen filenames
'''
root
=
tk
.
Tk
()
root
.
withdraw
()
filenames
=
filedialog
.
askopenfilenames
(
filetypes
=
[(
filetype
+
" files"
,
'.'
+
filetype
)],
initialdir
=
dirname
)
if
filenames
:
par_dir
=
os
.
path
.
abspath
(
os
.
path
.
join
(
filenames
[
0
],
os
.
pardir
))
else
:
par_dir
=
None
return
(
filenames
,
par_dir
)
def
selectDirDialog
():
''' Open a dialog box to select a directory.
:return: full path to selected directory
'''
root
=
tk
.
Tk
()
root
.
withdraw
()
return
filedialog
.
askdirectory
()
def
SaveFileDialog
(
filename
,
dirname
=
None
,
ext
=
None
):
''' Open a dialog box to save file.
:param filename: filename
:param dirname: initial directory
:param ext: default extension
:return: full path to the chosen filename
'''
root
=
tk
.
Tk
()
root
.
withdraw
()
filename_out
=
filedialog
.
asksaveasfilename
(
defaultextension
=
ext
,
initialdir
=
dirname
,
initialfile
=
filename
)
return
filename_out
def
downsample
(
t_dense
,
y
,
nsparse
):
''' Decimate periodic signals to a specified number of samples.'''
if
(
y
.
ndim
)
>
1
:
nsignals
=
y
.
shape
[
0
]
else
:
nsignals
=
1
y
=
np
.
array
([
y
])
# determine time step and period of input signal
T
=
t_dense
[
-
1
]
-
t_dense
[
0
]
dt_dense
=
t_dense
[
1
]
-
t_dense
[
0
]
# resample time vector linearly
t_ds
=
np
.
linspace
(
t_dense
[
0
],
t_dense
[
-
1
],
nsparse
)
# create MAV window
nmav
=
int
(
0.03
*
T
/
dt_dense
)
if
nmav
%
2
==
0
:
nmav
+=
1
mav
=
np
.
ones
(
nmav
)
/
nmav
# determine signals padding
npad
=
int
((
nmav
-
1
)
/
2
)
# determine indexes of sampling on convolved signals
ids
=
np
.
round
(
np
.
linspace
(
0
,
t_dense
.
size
-
1
,
nsparse
))
.
astype
(
int
)
y_ds
=
np
.
empty
((
nsignals
,
nsparse
))
# loop through signals
for
i
in
range
(
nsignals
):
# pad, convolve and resample
pad_left
=
y
[
i
,
-
(
npad
+
2
):
-
2
]
pad_right
=
y
[
i
,
1
:
npad
+
1
]
y_ext
=
np
.
concatenate
((
pad_left
,
y
[
i
,
:],
pad_right
),
axis
=
0
)
y_mav
=
np
.
convolve
(
y_ext
,
mav
,
mode
=
'valid'
)
y_ds
[
i
,
:]
=
y_mav
[
ids
]
if
nsignals
==
1
:
y_ds
=
y_ds
[
0
,
:]
return
(
t_ds
,
y_ds
)
def
rescale
(
x
,
lb
=
None
,
ub
=
None
,
lb_new
=
0
,
ub_new
=
1
):
''' Rescale a value to a specific interval by linear transformation. '''
if
lb
is
None
:
lb
=
x
.
min
()
if
ub
is
None
:
ub
=
x
.
max
()
xnorm
=
(
x
-
lb
)
/
(
ub
-
lb
)
return
xnorm
*
(
ub_new
-
lb_new
)
+
lb_new
def
getNeuronLookupsFile
(
mechname
,
a
=
None
,
Fdrive
=
None
,
Adrive
=
None
,
fs
=
False
):
fpath
=
os
.
path
.
join
(
os
.
path
.
split
(
__file__
)[
0
],
'neurons'
,
'{}_lookups'
.
format
(
mechname
)
)
if
a
is
not
None
:
fpath
+=
'_{:.0f}nm'
.
format
(
a
*
1e9
)
if
Fdrive
is
not
None
:
fpath
+=
'_{:.0f}kHz'
.
format
(
Fdrive
*
1e-3
)
if
Adrive
is
not
None
:
fpath
+=
'_{:.0f}kPa'
.
format
(
Adrive
*
1e-3
)
if
fs
is
True
:
fpath
+=
'_fs'
return
'{}.pkl'
.
format
(
fpath
)
def
getLookups4D
(
mechname
):
''' Retrieve 4D lookup tables and reference vectors for a given membrane mechanism.
:param mechname: name of membrane density mechanism
:return: 4-tuple with 1D numpy arrays of reference input vectors (charge density and
one other variable), a dictionary of associated 2D lookup numpy arrays, and
a dictionary with information about the other variable.
'''
# Check lookup file existence
lookup_path
=
getNeuronLookupsFile
(
mechname
)
if
not
os
.
path
.
isfile
(
lookup_path
):
raise
FileNotFoundError
(
'Missing lookup file: "{}"'
.
format
(
lookup_path
))
# Load lookups dictionary
logger
.
debug
(
'Loading lookup table'
)
with
open
(
lookup_path
,
'rb'
)
as
fh
:
df
=
pickle
.
load
(
fh
)
inputs
=
df
[
'input'
]
lookups4D
=
df
[
'lookup'
]
# Retrieve 1D inputs from lookups dictionary
aref
=
inputs
[
'a'
]
Fref
=
inputs
[
'f'
]
Aref
=
inputs
[
'A'
]
Qref
=
inputs
[
'Q'
]
return
aref
,
Fref
,
Aref
,
Qref
,
lookups4D
def
getLookupsOff
(
mechname
):
''' Retrieve appropriate US-OFF lookup tables and reference vectors
for a given membrane mechanism.
:param mechname: name of membrane density mechanism
:return: 2-tuple with 1D numpy array of reference charge density
and dictionary of associated 1D lookup numpy arrays.
'''
# Get 4D lookups and input vectors
aref
,
Fref
,
Aref
,
Qref
,
lookups4D
=
getLookups4D
(
mechname
)
# Perform 2D projection in appropriate dimensions
logger
.
debug
(
'Interpolating lookups at A = 0'
)
lookups_off
=
{
key
:
y4D
[
0
,
0
,
0
,
:]
for
key
,
y4D
in
lookups4D
.
items
()}
return
Qref
,
lookups_off
def
getLookups2D
(
mechname
,
a
=
None
,
Fdrive
=
None
,
Adrive
=
None
):
''' Retrieve appropriate 2D lookup tables and reference vectors
for a given membrane mechanism, projected at a specific combination
of sonophore radius, US frequency and/or acoustic pressure amplitude.
:param mechname: name of membrane density mechanism
:param a: sonophore radius (m)
:param Fdrive: US frequency (Hz)
:param Adrive: Acoustic peak pressure amplitude (Hz)
:return: 4-tuple with 1D numpy arrays of reference input vectors (charge density and
one other variable), a dictionary of associated 2D lookup numpy arrays, and
a dictionary with information about the other variable.
'''
# Get 4D lookups and input vectors
aref
,
Fref
,
Aref
,
Qref
,
lookups4D
=
getLookups4D
(
mechname
)
# Check that inputs are within lookup range
if
a
is
not
None
:
a
=
isWithin
(
'radius'
,
a
,
(
aref
.
min
(),
aref
.
max
()))
if
Fdrive
is
not
None
:
Fdrive
=
isWithin
(
'frequency'
,
Fdrive
,
(
Fref
.
min
(),
Fref
.
max
()))
if
Adrive
is
not
None
:
Adrive
=
isWithin
(
'amplitude'
,
Adrive
,
(
Aref
.
min
(),
Aref
.
max
()))
# Determine projection dimensions based on inputs
var_a
=
{
'name'
:
'a'
,
'label'
:
'sonophore radius'
,
'val'
:
a
,
'unit'
:
'm'
,
'factor'
:
1e9
,
'ref'
:
aref
,
'axis'
:
0
}
var_Fdrive
=
{
'name'
:
'f'
,
'label'
:
'frequency'
,
'val'
:
Fdrive
,
'unit'
:
'Hz'
,
'factor'
:
1e-3
,
'ref'
:
Fref
,
'axis'
:
1
}
var_Adrive
=
{
'name'
:
'A'
,
'label'
:
'amplitude'
,
'val'
:
Adrive
,
'unit'
:
'Pa'
,
'factor'
:
1e-3
,
'ref'
:
Aref
,
'axis'
:
2
}
if
not
isinstance
(
Adrive
,
float
):
var1
=
var_a
var2
=
var_Fdrive
var3
=
var_Adrive
elif
not
isinstance
(
Fdrive
,
float
):
var1
=
var_a
var2
=
var_Adrive
var3
=
var_Fdrive
elif
not
isinstance
(
a
,
float
):
var1
=
var_Fdrive
var2
=
var_Adrive
var3
=
var_a
# Perform 2D projection in appropriate dimensions
logger
.
debug
(
'Interpolating lookups at (
%s
=
%s%s
,
%s
=
%s%s
)'
,
var1
[
'name'
],
si_format
(
var1
[
'val'
],
space
=
' '
),
var1
[
'unit'
],
var2
[
'name'
],
si_format
(
var2
[
'val'
],
space
=
' '
),
var2
[
'unit'
])
lookups3D
=
{
key
:
interp1d
(
var1
[
'ref'
],
y4D
,
axis
=
var1
[
'axis'
])(
var1
[
'val'
])
for
key
,
y4D
in
lookups4D
.
items
()}
if
var2
[
'axis'
]
>
var1
[
'axis'
]:
var2
[
'axis'
]
-=
1
lookups2D
=
{
key
:
interp1d
(
var2
[
'ref'
],
y3D
,
axis
=
var2
[
'axis'
])(
var2
[
'val'
])
for
key
,
y3D
in
lookups3D
.
items
()}
if
var3
[
'val'
]
is
not
None
:
logger
.
debug
(
'Interpolating lookups at
%d
new
%s
values between
%s%s
and
%s%s
'
,
len
(
var3
[
'val'
]),
var3
[
'name'
],
si_format
(
min
(
var3
[
'val'
]),
space
=
' '
),
var3
[
'unit'
],
si_format
(
max
(
var3
[
'val'
]),
space
=
' '
),
var3
[
'unit'
])
lookups2D
=
{
key
:
interp1d
(
var3
[
'ref'
],
y2D
,
axis
=
0
)(
var3
[
'val'
])
for
key
,
y2D
in
lookups2D
.
items
()}
var3
[
'ref'
]
=
np
.
array
(
var3
[
'val'
])
return
var3
[
'ref'
],
Qref
,
lookups2D
,
var3
def
getLookups2Dfs
(
mechname
,
a
,
Fdrive
,
fs
):
# Check lookup file existence
lookup_path
=
getNeuronLookupsFile
(
mechname
,
a
=
a
,
Fdrive
=
Fdrive
,
fs
=
True
)
if
not
os
.
path
.
isfile
(
lookup_path
):
raise
FileNotFoundError
(
'Missing lookup file: "{}"'
.
format
(
lookup_path
))
# Load lookups dictionary
logger
.
debug
(
'Loading lookup table'
)
with
open
(
lookup_path
,
'rb'
)
as
fh
:
df
=
pickle
.
load
(
fh
)
inputs
=
df
[
'input'
]
lookups3D
=
df
[
'lookup'
]
# Retrieve 1D inputs from lookups dictionary
fsref
=
inputs
[
'fs'
]
Aref
=
inputs
[
'A'
]
Qref
=
inputs
[
'Q'
]
# Check that fs is within lookup range
fs
=
isWithin
(
'coverage'
,
fs
,
(
fsref
.
min
(),
fsref
.
max
()))
# Perform projection at fs
logger
.
debug
(
'Interpolating lookups at fs =
%s%%
'
,
fs
*
1e2
)
lookups2D
=
{
key
:
interp1d
(
fsref
,
y3D
,
axis
=
2
)(
fs
)
for
key
,
y3D
in
lookups3D
.
items
()}
return
Aref
,
Qref
,
lookups2D
def
isWithin
(
name
,
val
,
bounds
,
rel_tol
=
1e-9
):
''' Check if a floating point number is within an interval.
If the value falls outside the interval, an error is raised.
If the value falls just outside the interval due to rounding errors,
the associated interval bound is returned.
:param val: float value
:param bounds: interval bounds (float tuple)
:return: original or corrected value
'''
if
isinstance
(
val
,
list
)
or
isinstance
(
val
,
np
.
ndarray
)
or
isinstance
(
val
,
tuple
):
return
[
isWithin
(
name
,
v
,
bounds
,
rel_tol
)
for
v
in
val
]
if
val
>=
bounds
[
0
]
and
val
<=
bounds
[
1
]:
return
val
elif
val
<
bounds
[
0
]
and
math
.
isclose
(
val
,
bounds
[
0
],
rel_tol
=
rel_tol
):
logger
.
warning
(
'Rounding
%s
value (
%s
) to interval lower bound (
%s
)'
,
name
,
val
,
bounds
[
0
])
return
bounds
[
0
]
elif
val
>
bounds
[
1
]
and
math
.
isclose
(
val
,
bounds
[
1
],
rel_tol
=
rel_tol
):
logger
.
warning
(
'Rounding
%s
value (
%s
) to interval upper bound (
%s
)'
,
name
,
val
,
bounds
[
1
])
return
bounds
[
1
]
else
:
raise
ValueError
(
'{} value ({}) out of [{}, {}] interval'
.
format
(
name
,
val
,
bounds
[
0
],
bounds
[
1
]))
def
getLookupsCompTime
(
mechname
):
# Check lookup file existence
lookup_path
=
getNeuronLookupsFile
(
mechname
)
if
not
os
.
path
.
isfile
(
lookup_path
):
raise
FileNotFoundError
(
'Missing lookup file: "{}"'
.
format
(
lookup_path
))
# Load lookups dictionary
logger
.
debug
(
'Loading comp times'
)
with
open
(
lookup_path
,
'rb'
)
as
fh
:
df
=
pickle
.
load
(
fh
)
tcomps4D
=
df
[
'tcomp'
]
return
np
.
sum
(
tcomps4D
)
def
getLowIntensitiesSTN
():
''' Return an array of acoustic intensities (W/m2) used to study the STN neuron in
Tarnaud, T., Joseph, W., Martens, L., and Tanghe, E. (2018). Computational Modeling
of Ultrasonic Subthalamic Nucleus Stimulation. IEEE Trans Biomed Eng.
'''
return
np
.
hstack
((
np
.
arange
(
10
,
101
,
10
),
np
.
arange
(
101
,
131
,
1
),
np
.
array
([
140
])
))
# W/m2
def
getIndex
(
container
,
value
):
''' Return the index of a float / string value in a list / array
:param container: list / 1D-array of elements
:param value: value to search for
:return: index of value (if found)
'''
if
isinstance
(
value
,
float
):
container
=
np
.
array
(
container
)
imatches
=
np
.
where
(
np
.
isclose
(
container
,
value
,
rtol
=
1e-9
,
atol
=
1e-16
))[
0
]
if
len
(
imatches
)
==
0
:
raise
ValueError
(
'{} not found in {}'
.
format
(
value
,
container
))
return
imatches
[
0
]
elif
isinstance
(
value
,
str
):
return
container
.
index
(
value
)
def
titrate
(
target_func
,
args
,
xbounds
,
dx_thr
):
''' Use a binary search to determine a target value x within a specific search interval.
At each iteration, a target function is called that can return one of 3 options:
-1: refine search to the upper-half of the search interval
+1: refine search to the lower-half of the search interval
0: criterion is reached
If the target function returns 0, the function returns x.
Otherwise, it keeps iterating by calling itself recursively.
:param target_func: function to be called at each iteration.
:param args: list of function arguments other than x
:param xbounds: search interval for x (progressively refined)
:param dx_thr: interval span below which the iteration stops
:return: target value x
'''
# Determine current x as the middle of the search interval
x
=
(
xbounds
[
0
]
+
xbounds
[
1
])
/
2
# Call the target function with x
y
=
target_func
(
x
,
*
args
)
# If titration interval is small enough, stop recursion
if
(
xbounds
[
1
]
-
xbounds
[
0
])
<=
dx_thr
:
# If criterion is reached, return threshold value
if
y
==
0
:
return
x
# Otherwise, return NaN
else
:
logger
.
warning
(
'titration does not converge within this interval'
)
return
np
.
nan
# Otherwise, refine titration interval and iterate recursively
else
:
xbounds
=
(
x
,
xbounds
[
1
])
if
y
==
-
1
else
(
xbounds
[
0
],
x
)
return
titrate
(
target_func
,
args
,
xbounds
,
dx_thr
)
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