def makeSpeedVsHeadDirectionPlot(self, spk_times: np.array,
ax: matplotlib.axes = None,
**kwargs) -> matplotlib.axes:
self.initialise()
spk_times_in_pos_samples = self.getSpikePosIndices(spk_times)
idx = np.array(spk_times_in_pos_samples, dtype=int)
if np.ma.is_masked(self.speed):
w = self.speed.mask
w = np.array(~w, dtype=int)
else:
w = np.bincount(idx, minlength=self.speed.shape[0])
dir_bins = np.arange(0, 360, 6)
spd_bins = np.arange(0, 30, 1)
h = np.histogram2d(
self.dir, self.speed, [dir_bins, spd_bins], weights=w)
from ephysiopy.common.utils import blurImage
im = blurImage(h[0], 5, ftype='gaussian')
im = np.ma.MaskedArray(im)
# mask low rates...
im = np.ma.masked_where(im <= 1, im)
# ... and where less than 0.5% of data is accounted for
x, y = np.meshgrid(dir_bins, spd_bins)
vmax = np.max(np.ravel(im))
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.pcolormesh(x, y, im.T, cmap=plt.cm.get_cmap("jet"),
edgecolors='face',
vmax=vmax, shading='auto')
plt.xticks([90, 180, 270], fontweight='normal', size=6)
plt.yticks([10, 20], fontweight='normal', size=6)
return ax
def makeRateMap(self, spk_times: np.array,
ax: matplotlib.axes = None) -> matplotlib.axes:
self.initialise()
spk_times_in_pos_samples = self.getSpikePosIndices(spk_times)
spk_weights = np.bincount(
spk_times_in_pos_samples, minlength=self.npos)
rmap = self.RateMapMaker.getMap(spk_weights)
ratemap = np.ma.MaskedArray(rmap[0], np.isnan(rmap[0]), copy=True)
x, y = np.meshgrid(rmap[1][1][0:-1], rmap[1][0][0:-1])
vmax = np.nanmax(np.ravel(ratemap))
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.pcolormesh(
x, y, ratemap, cmap=plt.cm.get_cmap("jet"), edgecolors='face',
vmax=vmax, shading='auto')
ax.set_aspect('equal')
return ax
def show_SAC(self, A: np.array, inDict: dict,
ax: matplotlib.axes = None, **kwargs) -> matplotlib.axes:
"""
Displays the result of performing a spatial autocorrelation (SAC)
on a grid cell.
Uses the dictionary containing measures of the grid cell SAC to
make a pretty picture
Parameters
----------
A : array_like
The spatial autocorrelogram
inDict : dict
The dictionary calculated in getmeasures
ax : matplotlib.axes._subplots.AxesSubplot, optional
If given the plot will get drawn in these axes. Default None
Returns
-------
fig : matplotlib.Figure instance
The Figure on which the SAC is shown
See Also
--------
ephysiopy.common.binning.RateMap.autoCorr2D()
ephysiopy.common.ephys_generic.FieldCalcs.getMeaures()
"""
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
Am = A.copy()
Am[~inDict['dist_to_centre']] = np.nan
Am = np.ma.masked_invalid(np.atleast_2d(Am))
x, y = np.meshgrid(
np.arange(0, np.shape(A)[1]),
np.arange(0, np.shape(A)[0]))
vmax = np.nanmax(np.ravel(A))
ax.pcolormesh(
x, y, A, cmap=plt.cm.get_cmap("gray_r"),
edgecolors='face', vmax=vmax, shading='auto')
import copy
cmap = copy.copy(plt.cm.get_cmap("jet"))
cmap.set_bad('w', 0)
ax.pcolormesh(
x, y, Am, cmap=cmap,
edgecolors='face', vmax=vmax, shading='auto')
# horizontal green line at 3 o'clock
_y = (np.shape(A)[0]/2, np.shape(A)[0]/2)
_x = (np.shape(A)[1]/2, np.shape(A)[0])
ax.plot(_x, _y, c='g')
mag = inDict['scale'] * 0.5
th = np.linspace(0, inDict['orientation'], 50)
from ephysiopy.common.utils import rect
[x, y] = rect(mag, th, deg=1)
# angle subtended by orientation
ax.plot(
x + (inDict['dist_to_centre'].shape[1] / 2),
(inDict['dist_to_centre'].shape[0] / 2) - y, 'r', **kwargs)
# plot lines from centre to peaks above middle
for p in inDict['closest_peak_coords']:
if p[0] <= inDict['dist_to_centre'].shape[0] / 2:
ax.plot(
(inDict['dist_to_centre'].shape[1]/2, p[1]),
(inDict['dist_to_centre'].shape[0] / 2, p[0]), 'k', **kwargs)
ax.invert_yaxis()
all_ax = ax.axes
all_ax.set_aspect('equal')
all_ax.set_xlim((0.5, inDict['dist_to_centre'].shape[1]-1.5))
all_ax.set_ylim((inDict['dist_to_centre'].shape[0]-.5, -.5))
return ax
