Homec4science
Diffusion PySONIC (old) (master) Browse

R4670/PySONIC/plt/divmaps.py
4f17c4046cf6master

Last Change
Raw File

divmaps.py
View Options

# -*- coding: utf-8 -*-
# @Author: Theo Lemaire
# @Email: theo.lemaire@epfl.ch
# @Date: 2020-06-29 18:11:24
# @Last Modified by: Theo Lemaire
# @Last Modified time: 2021-06-08 21:23:36
import numpy as np
import matplotlib.pyplot as plt
from ..utils import logger, si_format
from .xymap import XYMap
class DivergenceMap(XYMap):
''' Interface to a 2D map showing divergence of the SONIC output from a
cycle-averaged NICE output, for various combinations of parameters.
'''
def __init__(self, benchmark, xvec, yvec, sim_args, eval_mode, eval_args, *args, **kwargs):
self.benchmark = benchmark
self.sim_args = sim_args
self.eval_mode = eval_mode
self.eval_args = eval_args
super().__init__(self.benchmark.outdir, xvec, yvec, *args, **kwargs)
@property
def eval_mode(self):
return self._eval_mode
@eval_mode.setter
def eval_mode(self, value):
if value not in self.benchmark.eval_funcs().keys():
raise ValueError(f'unknown evalation mode: {value}')
self._eval_mode = value
@property
def zkey(self):
return self.eval_mode
@property
def zunit(self):
return self.benchmark.eval_funcs()[self.eval_mode][1]
@property
def zfactor(self):
if self.eval_mode == 'ss':
return 1e5
else:
return 1e0
@property
def suffix(self):
s = self.eval_mode
if len(self.eval_args) > 0:
s = f'{s}_{"_".join([str(x) for x in self.eval_args])}'
return s
def descPair(self, x1, x2):
raise NotImplementedError
def logDiv(self, x, div):
''' Log divergence for a particular inputs combination. '''
logger.info(f'{self.descPair(*x)}: {self.eval_mode} = {div:.2e} {self.zunit}')
def compute(self, x):
data, _ = self.benchmark.getModelAndRunSims(*self.sim_args, *x)
div = self.benchmark.computeDivergence(data, self.eval_mode, *self.eval_args)
self.logDiv(x, div)
return div
def callbackPltFunc(self):
raise NotImplementedError
def onClick(self, event):
x = self.getOnClickXY(event)
data, _ = self.benchmark.getModelAndRunSims(*self.sim_args, *x)
fig, ax = plt.subplots(figsize=(4, 4))
ax.set_xlabel('time (ms)')
ax.set_ylabel('Qm (nC/cm2)')
for sk in ['top', 'right']:
ax.spines[sk].set_visible(False)
ax.set_title(self.descPair(*x))
self.callbackPltFunc()(ax, data)
plt.show()
def render(self, zscale='log', zbounds=(1e-1, 1e1), extend_under=True, extend_over=True,
cmap='Spectral_r', figsize=(6, 4), fs=12, ax=None, **kwargs):
''' Render with default log scale, zbounds, cmap and cbar properties. '''
fig = super().render(
zscale=zscale, zbounds=zbounds, extend_under=extend_under, extend_over=extend_over,
cmap=cmap, figsize=figsize, fs=fs, ax=ax, **kwargs)
if ax is None:
fig.canvas.manager.set_window_title(f'{self.corecode()} - {self.eval_mode}')
return fig
class PassiveDivergenceMap(DivergenceMap):
''' Divergence map of a passive model for various combinations of
membrane time constants (taum) and axial time constant (tauax)
'''
xkey = 'taum'
xfactor = 1e0
xunit = 's'
ykey = 'tauax'
yfactor = 1e0
yunit = 's'
@property
def title(self):
return f'passive divmap - {self.eval_mode}'
def corecode(self):
return f'divmap_{self.benchmark.code()}'
def descPair(self, taum, tauax):
return f'taum = {si_format(taum, 2)}s, tauax = {si_format(tauax, 2)}s'
@staticmethod
def addPeriodicityLines(ax, T, dims='xy', color='k', pattern='cross'):
xmin, ymin = 0, 0
xmax, ymax = 1, 1
if pattern in ['upper-square', 'lower-square']:
data_to_axis = ax.transData + ax.transAxes.inverted()
xc, yc = data_to_axis.transform((T, T))
if pattern == 'upper-square':
xmin, ymin = xc, yc
else:
xmax, ymax = xc, yc
if 'x' in dims:
ax.axvline(T, ymin=ymin, ymax=ymax, color=color, linestyle='--', linewidth=1.5)
if 'y' in dims:
ax.axhline(T, xmin=xmin, xmax=xmax, color=color, linestyle='--', linewidth=1.5)
def render(self, xscale='log', yscale='log', T=None, ax=None, **kwargs):
''' Render with drive periodicty indicator. '''
fig = super().render(xscale=xscale, yscale=yscale, ax=ax, **kwargs)
if ax is None:
ax = fig.axes[0]
if T is not None:
self.addPeriodicityLines(ax, T)
return fig
def callbackPltFunc(self):
return {
'ss': self.benchmark.plotQm,
'transient': self.benchmark.plotQnorm
}[self.eval_mode]
class FiberDivergenceMap(DivergenceMap):
''' Divergence map of a fiber model for various combinations of
acoustic pressure amplitudes in both compartments
'''
xkey = 'A1'
xfactor = 1e0
xunit = 'Pa'
ykey = 'A2'
yfactor = 1e0
yunit = 'Pa'
def __init__(self, benchmark, Avec, *args, **kwargs):
super().__init__(benchmark, Avec, Avec, *args, **kwargs)
@property
def title(self):
return f'fiber divmap - {self.eval_mode}'
def corecode(self):
return f'divmap_{self.benchmark.code()}'
def descPair(self, *amps):
return f"A = {', '.join(f'{si_format(A, 2)}Pa' for A in amps)}"
def compute(self, x):
if x[0] < x[1]:
return np.nan
return super().compute(x)
def render(self, Ascale='log', **kwargs):
super().render(xscale=Ascale, yscale=Ascale, **kwargs)
c4science · Help