def plot_trial(dat, info, ax = None): ax, _ = misc.axis_check(ax) # plot the pre-track plot_track(dat['pre_x'], dat['pre_y'], ax = ax, color = 'b') ax.plot(dat['pre_x'][0], dat['pre_y'][0], marker = '*', ms = 20, color = 'b') # if there is a post-track, plot it if len(dat['post_x']) > 0: plot_track(dat['post_x'], dat['post_y'], ax = ax, color = 'g') ax.plot(dat['post_x'][0], dat['post_y'][0], marker = '*', ms = 20, color = 'g') # draw the target radius cir = mpl.patches.Circle((dat['target_x'], dat['target_y']), radius = info['target_radius'], color = 'r', fill = False, linestyle = 'dotted') ax.add_patch(cir) # draw the arena radius cir = mpl.patches.Circle((info['arena_x'], info['arena_y']), radius = info['arena_radius'], color = 'k', fill = False) ax.add_patch(cir) misc.target(dat['target_x'], dat['target_y'], ax, color = 'r') misc.target(dat['spout_x'], dat['spout_y'], ax, color = 'g') # set the axis limits ax.set_xlim([info['arena_x']-info['arena_radius']-20, info['arena_x']+info['arena_radius']+20]) ax.set_ylim([info['arena_y']-info['arena_radius']-20, info['arena_y']+info['arena_radius']+20]) plt.show()
def circ_psth(rast_, rr, npips, onset = 0.05, bins = 20, color = 'b', remove_first = False, ax = None): ''' Input: rast_ : subset of block raster corresponding to only the stimuli you want plotted rr : the repetition rate npips : number of pips presented at this rate onset : in seconds, onset time of first pip bins : number of bins to include remove_first : exclude the respose to the first pip ''' spktimes_ = Spikes.rast2spktimes(rast_) period = 1. / rr train_end = float(npips) / rr spktimes_chunk = spktimes_ - onset spktimes_chunk = spktimes_chunk[spktimes_chunk > 0] spktimes_chunk = spktimes_chunk[spktimes_chunk < train_end] if remove_first: spktimes_chunk = spktimes_chunk[spktimes_chunk > period] spktimes_pol = spktimes_chunk * 2*np.pi * rr # convert to radians spktimes_pol = np.mod(spktimes_pol, 2*np.pi) # force range 0-2pi nspks = spktimes_pol.size r, V, theta = circ_stat(spktimes_pol) ax, fig = misc.axis_check(ax, polar = True) if spktimes_pol.size > 0: ax.hist(spktimes_pol, bins = bins, color = color, histtype = 'stepfilled') ax.plot([0, theta], [0, V], 'r.-') ax.plot([0, 0], [0, V*np.cos(theta)], 'r.-') return r, V, theta
def plot_heatmap(data, epoch = 'pre', ax = None, gridsize = 30, rel_to_target = False):
'''
plots a heatmap from x and y locations in one of the trial epochs.
uses hexagonal bins
'''
xy = np.empty((0, 2), dtype = np.int32)
x_key = epoch + '_x'
y_key = epoch + '_y'
if rel_to_target is True:
for dat in data:
xy = np.vstack((xy, np.vstack((dat[x_key] - dat['target_x'], dat[y_key] - dat['target_y'])).T))
elif rel_to_target is False:
for dat in data:
xy = np.vstack((xy, np.vstack((dat[x_key], dat[y_key])).T))
ax, _ = misc.axis_check(ax)
ax.set_aspect('equal')
ax.hexbin(xy[:, 0], xy[:, 1], gridsize = gridsize)
if rel_to_target is False:
misc.target(data[0]['spout_x'], data[0]['spout_y'], ax)
if rel_to_target is True:
misc.target(0, 0, ax)
plt.show();
def plot_blob(blob, Pxx, ax = None): ax, fig = misc.axis_check(ax) plot_spec(np.power(Pxx, 0.5), ax = ax) ax.set_xlim((max(blob['ij'][0][1] - 100, 0), blob['ij'][-1][1]+100)) plt.show()
def plot_spec(Pxx, F = None, T = None, ax = None, F_range = None, **kwargs): ''' Plots and correctly formats the spectrogram (axis tick labels) Input: Pxx : MxN spectrogram array (M frequencies, N time bins) F : vector of length M indicating the frequencies included in the spectrogram ax : axis on which to plot lowpass : boolean, whether or not to exclude frequencies lower than 2 kHz kwargs : properties for plotting Output: ax : axis containing the spectrogram ''' ax, fig = misc.axis_check(ax) if T is None: extent = [0, Pxx.shape[1]-1, None, None] else: extent = [T[0], T[-1], None, None] if F is None: extent[2:4] = [0, Pxx.shape[0]-1] else: extent[2:4] = [F[0], F[-1]] ax.imshow(Pxx, aspect = 'auto', origin = 'lower', interpolation = 'nearest', extent = extent, **kwargs) # ax.set_yticks(np.arange(0, F.size, 20)) # ax.set_yticklabels(F[np.arange(0, F.size, 20)]) # fig.tight_layout() return ax
def plot_track(x, y, ax = None, **kwargs):
'''
shows the path for one trial
'''
ax, _ = misc.axis_check(ax)
ax.set_aspect('equal')
ax.plot(x, y, '.-', **kwargs)
plt.show()
def plot_startleampl(df, ax = None):
'''
plots the startle amplitude as a function of trial number
uncued: red
cued: black
'''
if not hasattr(df, 'startleampl'):
df = add_startleampl(df)
ax, fig = misc.axis_check(ax)
cuegp = df.groupby('cued')
colors = dict(cued ='k', uncued='r')
for i, j in cuegp:
ax.plot(j.index, j.startleampl, '.', color = colors[i])
def plot_raster(rast, lineheight = None, ax = None): ax, _ = misc.axis_check(ax) ntrials, nbins = rast.shape spk = rast.nonzero() spktrials = spk[0] spktimes = spk[1]/1000. ax.plot(spktimes, spktrials, marker = '|', linestyle = '', ms = 1) if not lineheight is None: [ax.axhline(i, color = 'r', lw = 0.5) for i in lineheight] ax.set_xlim([0, nbins/1000.]) ax.set_ylim([0, ntrials]) return ax
def plot_hist2d(df, ax = None): ax, fig = misc.axis_check(ax) ax.hist2d(-df.LocY, df.LocX, bins = 1000) return ax
def plot_loc(df, ax = None, color = 'b', ms = 0.5, **kwargs): ax, fig = misc.axis_check(ax) ax.plot(-df.LocY, df.LocX, '.', color = color, ms = ms, **kwargs) return ax
def plot_rrtf(t, psth, rr, npips, onset = 0.05, duration = 0.025, ax = None): ax, fig = misc.axis_check(ax) ax.plot(t, Spikes.exp_smoo(psth)) plot_tone_pips(rr, npips, onset = onset, duration = duration, ax = ax, color = 'r')
