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pltutils.py
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Fri, May 10, 21:56

pltutils.py
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# -*- coding: utf-8 -*-
# @Author: Theo Lemaire
# @Date: 2017-08-21 14:33:36
# @Last Modified by: Theo Lemaire
# @Last Modified time: 2019-06-06 18:26:50
''' Useful functions to generate plots. '''
import numpy as np
import matplotlib
# Matplotlib parameters
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
matplotlib.rcParams['font.family'] = 'arial'
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'])
def cm2inch(*tupl):
inch = 2.54
if isinstance(tupl[0], tuple):
return tuple(i / inch for i in tupl[0])
else:
return tuple(i / inch for i in tupl)
def extractPltVar(model, pltvar, df, meta=None, nsamples=0, name=''):
if 'func' in pltvar:
s = 'model.{}'.format(pltvar['func'])
try:
var = eval(s)
except AttributeError:
var = eval(s.replace('model', 'model.neuron'))
elif 'key' in pltvar:
var = df[pltvar['key']]
elif 'constant' in pltvar:
var = eval(pltvar['constant']) * np.ones(nsamples)
else:
var = df[name]
var = var.values.copy()
if var.size == nsamples - 2:
var = np.hstack((np.array([pltvar.get('y0', var[0])] * 2), var))
var *= pltvar.get('factor', 1)
return var
def computeMeshEdges(x, scale='lin'):
''' Compute the appropriate edges of a mesh that quads a linear or logarihtmic distribution.
:param x: the input vector
:param scale: the type of distribution ('lin' for linear, 'log' for logarihtmic)
:return: the edges vector
'''
if scale == 'log':
x = np.log10(x)
dx = x[1] - x[0]
n = x.size + 1
return {'lin': np.linspace, 'log': np.logspace}[scale](x[0] - dx / 2, x[-1] + dx / 2, n)

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