def makeXCorr(self, spk_times: np.array, ax: matplotlib.axes = None,
**kwargs) -> matplotlib.axes:
# spk_times in samples provided in seconds but convert to
# ms for a more display friendly scale
spk_times = spk_times / 3e4 * 1000.
S = SpikeCalcsGeneric(spk_times)
y = S.xcorr(spk_times)
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.hist(
y[y != 0], bins=201, range=[-500, 500],
color='k', histtype='stepfilled')
ax.set_xlim(-500, 500)
ax.set_xticks((-500, 0, 500))
ax.set_xticklabels('')
ax.tick_params(
axis='both', which='both', left=False, right=False,
bottom=False, top=False)
ax.set_yticklabels('')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.xaxis.set_ticks_position('bottom')
return ax
def plot_visit_durations(data: list, ax: Axes):
"""
Plot the visit durations from the raw data.
:param data: Raw data
:param ax: Axes object in which to put the plot
:return: None
"""
# Histogram options
min_visit_s = 30
max_visit_s = 60 * 60 * 3
nbins = 150
# Regression line options
fit_start = 6
fit_stop = 144
# Create histogram
durations = get_visit_durations(data)
n, bins, _ = ax.hist(durations, bins=linspace(min_visit_s, max_visit_s, nbins))
# Create regression line
bin_width = (bins[fit_stop - 1] - bins[fit_start]) / (fit_start - fit_stop)
lin_fitting_bins = [b + bin_width / 2 for b in bins[fit_start:fit_stop]]
lin_fitting_n = n[fit_start:fit_stop]
[a, b] = polyfit(lin_fitting_bins, log(lin_fitting_n), 1, w=sqrt(lin_fitting_n))
fitted_n = [exp(b + a * t) for t in lin_fitting_bins]
regression_line_opts = {"linestyle": "--", "color": "black", "linewidth": 2}
regression_label_text = "y={:.0f}exp({:.6f}*t)".format(exp(b), a)
regression_label_coords = (max_visit_s * 0.6, max(n) * 0.5)
ax.plot(lin_fitting_bins, fitted_n, **regression_line_opts)
ax.text(*regression_label_coords, regression_label_text)
# Label axes
ax.set_xlabel("Varighet for visitt (s)")
ax.set_ylabel("Andel visitter (vilkårlig)")
ax.set_yscale("log")
def plot_histogram(data: List[np.float64], bins: int, axes: matplotlib.axes):
axes.hist(x=data, bins=bins)
axes.set_title('KDE plot')
axes.set_xlabel('x')
axes.set_ylabel('f')