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phaseplot.py
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phaseplot.py
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# -*- coding: utf-8 -*-
# @Author: Theo Lemaire
# @Email: theo.lemaire@epfl.ch
# @Date: 2018-10-01 20:40:28
# @Last Modified by: Theo Lemaire
# @Last Modified time: 2019-11-14 19:34:28
import numpy as np
import matplotlib.pyplot as plt
from ..core import getModel
from ..utils import *
from ..constants import *
from .pltutils import *
class PhasePlot(GenericPlot):
''' Generic interface to build a phase plot displaying the evolution of 2 variables resulting from model simulations. '''
@classmethod
def createBackBone(cls, pltvar, tbounds, fs, prettify):
# Create figure
fig, axes = plt.subplots(1, 2, figsize=(8, 4))
# 1st axis: variable as function of time
ax = axes[0]
ax.set_xlabel('$\\rm time\ (ms)$', fontsize=fs)
ax.set_ylabel('$\\rm {}$'.format(pltvar['label']), fontsize=fs)
ax.set_xlim(tbounds)
ax.set_ylim(pltvar['lim'])
# 2nd axis: phase plot (derivative of variable vs variable)
ax = axes[1]
ax.set_xlabel('$\\rm {}$'.format(pltvar['label']), fontsize=fs)
ax.set_ylabel('$\\rm {}$'.format(pltvar['dlabel']), fontsize=fs)
ax.set_xlim(pltvar['lim'])
ax.set_ylim(pltvar['dlim'])
ax.plot([0, 0], [pltvar['dlim'][0], pltvar['dlim'][1]], '--', color='k', linewidth=1)
ax.plot([pltvar['lim'][0], pltvar['lim'][1]], [0, 0], '--', color='k', linewidth=1)
if prettify:
cls.prettify(axes[0], xticks=tbounds, yticks=pltvar['lim'])
cls.prettify(axes[1], xticks=pltvar['lim'], yticks=pltvar['dlim'])
for ax in axes:
cls.removeSpines(ax)
cls.setTickLabelsFontSize(ax, fs)
return fig, axes
def checkInputs(self, labels):
self.checkLabels(labels)
@staticmethod
def extractSpikesData(t, y, tbounds, rel_tbounds, tspikes):
spikes_tvec, spikes_yvec, spikes_dydtvec = [], [], []
for j, (tspike, tbound) in enumerate(zip(tspikes, tbounds)):
left_bound = max(tbound[0], rel_tbounds[0] + tspike)
right_bound = min(tbound[1], rel_tbounds[1] + tspike)
inds = np.where((t > left_bound) & (t < right_bound))[0]
spikes_tvec.append(t[inds] - tspike)
spikes_yvec.append(y[inds])
dinds = np.hstack(([inds[0] - 1], inds, [inds[-1] + 1]))
dydt = np.diff(y[dinds]) / np.diff(t[dinds])
spikes_dydtvec.append((dydt[:-1] + dydt[1:]) / 2) # average of the two
return spikes_tvec, spikes_yvec, spikes_dydtvec
def addLegend(self, fig, axes, handles, labels, fs):
fig.subplots_adjust(top=0.8)
if len(self.filepaths) > 1:
axes[0].legend(handles, labels, fontsize=fs, frameon=False,
loc='upper center', bbox_to_anchor=(1.0, 1.35))
else:
fig.suptitle(labels[0], fontsize=fs)
def render(self, no_offset=False, no_first=False, labels=None, colors=None,
fs=10, lw=2, trange=None, rel_tbounds=None, prettify=False,
cmap=None, cscale='lin'):
self.checkInputs(labels)
if rel_tbounds is None:
rel_tbounds = np.array((-1.5e-3, 1.5e-3))
# Check pltvar
if self.varname not in self.phaseplotvars:
raise KeyError(
'Unknown plot variable: "{}". Possible plot variables are: {}'.format(
self.varname, ', '.join(['"{}"'.format(p) for p in self.phaseplotvars.keys()])))
pltvar = self.phaseplotvars[self.varname]
fig, axes = self.createBackBone(pltvar, rel_tbounds * 1e3, fs, prettify)
# Loop through data files
comp_values, full_labels = [], []
handles0, handles1 = [], []
for i, filepath in enumerate(self.filepaths):
# Load data
data, meta = self.getData(filepath, trange=trange)
meta.pop('tcomp')
# Extract model
model = getModel(meta)
full_labels.append(self.figtitle(model, meta))
# Check consistency of sim types and check differing inputs
comp_values = self.checkConsistency(meta, comp_values)
# Extract time and y-variable
t = data['t'].values
y = data[self.varname].values
# Detect spikes in signal
ispikes, properties = detectSpikes(
data, key=self.varname, mph=pltvar['thr_amp'], mpp=pltvar['thr_prom'])
nspikes = ispikes.size
tspikes = t[ispikes]
yspikes = y[ispikes]
properties = convertPeaksProperties(t, properties)
tbounds = np.array(list(zip(properties['left_bases'], properties['right_bases'])))
if nspikes == 0:
logger.warning('No spikes detected')
else:
# Store spikes in dedicated lists
spikes_tvec, spikes_yvec, spikes_dydtvec = self.extractSpikesData(
t, y, tbounds, rel_tbounds, tspikes)
# Plot spikes temporal profiles and phase-plane diagrams
lh0, lh1 = [], []
for j in range(nspikes):
if colors is None:
color = 'C{}'.format(i if len(self.filepaths) > 1 else j % 10)
else:
color = colors[i]
lh0.append(axes[0].plot(
spikes_tvec[j] * 1e3, spikes_yvec[j] * pltvar['factor'],
linewidth=lw, c=color)[0])
lh1.append(axes[1].plot(
spikes_yvec[j] * pltvar['factor'], spikes_dydtvec[j] * pltvar['dfactor'],
linewidth=lw, c=color)[0])
handles0.append(lh0)
handles1.append(lh1)
# Determine labels
if self.comp_ref_key is not None:
self.comp_info = model.inputs().get(self.comp_ref_key, None)
comp_values, comp_labels = self.getCompLabels(comp_values)
labels = self.chooseLabels(labels, comp_labels, full_labels)
# Post-process figure
fig.tight_layout()
# Add labels or colorbar legend
if cmap is not None:
if not self.is_unique_comp:
raise ValueError('Colormap mode unavailable for multiple differing parameters')
if self.comp_info is None:
raise ValueError('Colormap mode unavailable for qualitative comparisons')
cmap_handles = [h0 + h1 for h0, h1 in zip(handles0, handles1)]
self.addCmap(
fig, cmap, cmap_handles, comp_values, self.comp_info, fs, prettify, zscale=cscale)
else:
leg_handles = [x[0] for x in handles0]
self.addLegend(fig, axes, leg_handles, labels, fs)
return fig

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