def horiz_time_const(d, params):
'''
TO DO:
'''
fig = plt.figure()
fig.set_tight_layout(True)
ax = fig.add_subplot(111)
pf.AxisFormat()
pf.TufteAxis(ax, ['bottom', 'left'], [3, 3])
time = util.get_time(d)
inds = np.where(np.logical_and(time >= 450, time <= 600))[0]
time_ = time[inds] - 500
for t in range(d['ntrial']):
h1 = util.get_cell_data(d['tr'][t], 'h1')
h2 = util.get_cell_data(d['tr'][t], 'h2')
h1dat = d['tr'][t]['r'][h1[0]]['x'][inds]
h2dat = d['tr'][t]['r'][h2[0]]['x'][inds]
ax.plot(time_, h1dat, label='H1') # only use 1st response
ax.plot(time_, h2dat, label='H2') # only use 1st response
#ax[1].set_ylabel('response')
ax.set_xlabel('time (ms)')
ax.legend(fontsize=22, loc='lower right')
savedir = util.get_save_dirname(params, check_randomized=True)
fig.savefig(savedir + 'h_time_const' + str(t) + '.eps', edgecolor='none')
plt.show(block=params['block_plots'])
def dist(data, params, normalize=True):
'''
'''
deg_per_pix, mm_per_deg = util.conversion_factors(params['species'])
fig = plt.figure(figsize=(5.5, 6))
fig.set_tight_layout(True)
ax = fig.add_subplot(111)
pf.AxisFormat(markersize=8, linewidth=3)
pf.TufteAxis(ax, ['bottom', 'left'], [4, 5])
for d in data:
dat = data[d]
dat = dat[dat[:, 0].argsort()]
x_val_h = dat[:, 0] * deg_per_pix * mm_per_deg * 1000
yval = dat[:, 1]
xval = np.hstack([x_val_h[::-1][:-1] * -1, x_val_h])
yval = np.hstack([yval[::-1][:-1], yval])
if normalize:
yval /= yval.max()
ax.plot(xval, yval, '-', label=d.upper())
ax.set_xlabel('distance ($\mu$m)')
ax.set_xlim([-200, 200])
ax.legend(fontsize=22)
savedir = util.get_save_dirname(params, check_randomized=True)
fig.savefig(savedir + 'h_space_const.svg', edgecolor='none')
plt.show(block=params['block_plots'])
def mosaic(params, return_ax=False):
mosaic = np.genfromtxt(params['mosaic_file'])
fig = plt.figure(figsize=(9, 9))
fig.set_tight_layout(True)
ax = fig.add_subplot(111)
pf.AxisFormat()
pf.TufteAxis(ax, [''], [5, 5])
s_cones = mosaic[np.where(mosaic[:, 1] == 0)[0]]
m_cones = mosaic[np.where(mosaic[:, 1] == 1)[0]]
l_cones = mosaic[np.where(mosaic[:, 1] == 2)[0]]
ax.plot(s_cones[:, 2], s_cones[:, 3], 'bo', markersize=6)
ax.plot(m_cones[:, 2], m_cones[:, 3], 'go', markersize=6)
ax.plot(l_cones[:, 2], l_cones[:, 3], 'ro', markersize=6)
ax.set_xlim([-1, 128])
ax.set_ylim([-1, 128])
if not return_ax:
savedir = util.get_save_dirname(params, check_randomized=True)
fig.savefig(savedir + 'mosaic.eps', edgecolor='none')
plt.show(block=params['block_plots'])
else:
return ax, fig
def plot_cone_weights(r, celldat, celllist, params):
'''
'''
fig = plt.figure(figsize=(6,6))
fig.set_tight_layout(True)
ax = fig.add_subplot(111)
# build diamond plot
pf.AxisFormat(markersize=8, fontsize=9, tickdirection='inout')
pf.centerAxes(ax)
ax.spines['left'].set_smart_bounds(False)
ax.spines['bottom'].set_smart_bounds(False)
ax.axis('equal')
# Add in diamond boarders
ax.plot([-1, 0], [0, 1], 'k')
ax.plot([0, 1], [1, 0], 'k')
ax.plot([1, 0], [0, -1], 'k')
ax.plot([0, -1], [-1, 0], 'k')
# set come fig params
ax.set_xlim([-1.1, 1.1])
ax.set_ylim([-1.1, 1.1])
# get mosaic plot
mos, mos_fig = mosaic(params, return_ax=True)
for c in r: # for each cell type in results
for cone in range(0, len(r[c][:, 0])):
ind = np.where(celldat[:, 0] == float(celllist[cone]))[0]
type = round(celldat[ind, 1])
sym = find_shape_color(type, c)
loc = celldat[ind, 2:4][0]
# add data to diamond plot
ax.plot(r[c][cone, 2], r[c][cone, 1], sym)
ax.plot(-r[c][cone, 2], -r[c][cone, 1], sym, mec='k', mew=2,
alpha=0.5)
# compute s weight for mosaic plot
s_weight = r[c][cone, 0] #compute_s_weight(r, c, cone)
s_rgb = 1 - np.abs(s_weight)
mos.plot(loc[0], loc[1], 'o', fillstyle='none', mew=4,
mec=[s_rgb, s_rgb, s_rgb])
mos.set_xlim([35, 90])
mos.set_ylim([35, 90])
# save figs
savedir = util.get_save_dirname(params, check_randomized=True)
fig.savefig(savedir + 'cone_inputs.svg', edgecolor='none')
mos_fig.savefig(savedir + 'cone_inputs_mosaic.eps', edgecolor='none')
def knn(d, params):
'''
TO DO:
'''
celllist = util.get_cell_list(d)
celldat = np.genfromtxt('results/txt_files/nn_results.txt')
cellIDs = celldat[:, 0]
fig = plt.figure()
fig.set_tight_layout(True)
ax = fig.add_subplot(111)
pf.AxisFormat(markersize=8)
pf.TufteAxis(ax, ['bottom', 'left'], [5, 5])
N = util.num(d['const']['MOO_tn']['val']) # time steps
tf = util.num(d['const']['tf']['val']) # temporal frequency (Hz)
keys = util.get_cell_data(d['tr'][0], 'h2')
for i, r in enumerate(keys):
# find distance to S
cellID = int(celllist[i])
ind = np.where(cellIDs == cellID)[0]
distance = celldat[ind, 4][0]
# find amplitude of signal
cell = d['tr'][0]['r'][r]['x']
fft = np.fft.fft(cell)
amp = np.abs(fft[tf]) * 2 / N
if celldat[ind, 1] == 0:
ax.plot(distance, amp, 'bo')
if celldat[ind, 1] == 1:
ax.plot(distance, amp, 'go')
if celldat[ind, 1] == 2:
ax.plot(distance, amp, 'ro')
savedir = util.get_save_dirname(params, check_randomized=True)
fig.savefig(savedir + 'knn.svg', edgecolor='none')
plt.show(block=params['block_plots'])
def plot_color_names(rg, purity_thresh, results, params):
'''
'''
# --- plot color names on a diamond plot --- #
fig1 = plt.figure(figsize=(6,6))
fig1.set_tight_layout(True)
ax = fig1.add_subplot(111)
# build diamond plot
pf.AxisFormat(markersize=8, fontsize=20)
pf.centerAxes(ax)
ax.spines['left'].set_smart_bounds(False)
ax.spines['bottom'].set_smart_bounds(False)
ax.axis('equal')
# Add in diamond boarders
ax.plot([-1, 0], [0, 1], 'k')
ax.plot([0, 1], [1, 0], 'k')
ax.plot([1, 0], [0, -1], 'k')
ax.plot([0, -1], [-1, 0], 'k')
for i in range(len(rg)):
maxind = results[i, 5:10].argmax()
symbol = 'o'
# check if cone has a purity greater threshold
if purity_thresh is not None and purity_thresh < 1:
symbol = '^' # means low purity
if rg[i] < 0:
color = [0, np.abs(rg[i]), 0]
else:
color = [np.abs(rg[i]), 0, 0]
ax.plot(results[i, 3], results[i, 2], symbol, color=color,
markeredgewidth=2, markeredgecolor='k', alpha=0.5)
savedir = util.get_save_dirname(params, check_randomized=True)
fig1.savefig(savedir + 'cone_inputs_w_naming.svg',
edgecolor='none')
def tuning_curve(d, params):
'''
'''
# Get this with conversion call
deg_per_pix, mm_per_deg = util.conversion_factors(params['species'])
deg2um = mm_per_deg / 1000 # conversion (micron / deg)
if params['analysis_type'] == 'sf':
figsize = (7, 7)
else:
figsize = (6, 5)
fig = plt.figure(figsize=figsize)
fig.set_tight_layout(True)
ax1 = fig.add_subplot(111)
if params['analysis_type'] == 'sf':
ax2 = ax1.twiny()
pf.AxisFormat()
pf.TufteAxis(ax1, [
'bottom',
'left',
], [4, 4])
if params['analysis_type'] == 'sf':
pf.TufteAxis(ax2, [
'top',
], [4, 4])
# get the data
r = an.response(d, params)
colors = ['k', 'gray', 'r', 'b', 'g', 'c', 'm']
cells = util.get_cell_type(params['cell_type'])
# set some smart axes for second axis
ymax = -100 # start small
ymin = 1000 # start large
if params['analysis_type'] == 'sf':
x = util.num(d['const']['VAR_sf']['val']) # spatial freq (cpd)
elif params['analysis_type'] == 'tf':
x = util.num(d['const']['VAR_tf']['val']) # temp freq
for i, c in enumerate(cells):
ax1.loglog(x, r[c][i, :], 'o-', color=colors[i], label=c)
vec = np.reshape(r[c], -1)
max = np.max(vec)
min = np.min(vec)
if max > ymax:
ymax = max
if min < ymin:
ymin = min
ymax += 0.1
ymin -= 0.1
ax1.axis([x[0] - 0.05, x[-1] + 1, ymin, ymax])
ax1.set_ylabel('amplitude')
ax1.legend(fontsize=22, loc='lower center')
if params['analysis_type'] == 'tf':
ax1.set_xlabel('temporal frequency (Hz)')
elif params['analysis_type'] == 'sf':
ax1.set_xlabel('cycles / degree')
ax2.xaxis.tick_top()
ax2.yaxis.tick_left()
ax2.axis([(x[0] - 0.05) * deg2um, (x[-1] + 1) * deg2um, ymin, ymax])
ax2.set_xlabel('cycles / $\mu$m')
ax2.set_xscale('log')
savedir = util.get_save_dirname(params, check_randomized=True)
fig.savefig(savedir + params['cell_type'] + '_' + params['analysis_type'] +
'_tuning.eps',
edgecolor='none')
plt.show(block=params['block_plots'])
def stack(d, params):
'''
TO DO:
* Add rgc option
'''
ylim = [[0, 0], [0, 0], [0, 0]]
figs = {}
for t in range(d['ntrial']):
figs[t] = {}
figs[t]['f'] = plt.figure(figsize=(6.5, 9))
figs[t]['f'].set_tight_layout(True)
ax = {}
ax[0] = figs[t]['f'].add_subplot(311)
ax[1] = figs[t]['f'].add_subplot(312)
ax[2] = figs[t]['f'].add_subplot(313)
pf.AxisFormat()
pf.TufteAxis(ax[0], [
'left',
], [3, 3])
pf.TufteAxis(ax[1], [
'left',
], [3, 3])
pf.TufteAxis(ax[2], ['bottom', 'left'], [3, 3])
time = util.get_time(d)
# Subtract 10 points in so that zero offset
for i in ax:
ax[i].set_prop_cycle(
cycler('color', ['r', 'g', 'b', 'c', 'm', 'y', 'k']))
keys = util.get_cell_data(d['tr'][t], 'cone')
for r in keys:
dat = d['tr'][t]['r'][r]['x']
y = dat - dat[10]
ax[0].plot(time, y)
ylim = check_lims(y, ylim, 0)
keys = util.get_cell_data(d['tr'][t], 'h1')
for r in keys:
dat = d['tr'][t]['r'][r]['x']
y = dat - dat[10] + 0.2
ax[1].plot(time, y)
ylim = check_lims(y, ylim, 1)
keys = util.get_cell_data(d['tr'][t], 'h2')
for r in keys:
dat = d['tr'][t]['r'][r]['x']
y = dat - dat[10] + 0.1
ax[1].plot(time, y)
ylim = check_lims(y, ylim, 1)
keys = util.get_cell_data(d['tr'][t], 'bp')
for r in keys:
dat = d['tr'][t]['r'][r]['x']
y = dat - dat[10]
ax[2].plot(time, y)
ylim = check_lims(y, ylim, 2)
ax[2].set_xlabel('time (ms)')
figs[t]['ax'] = ax
for t in figs:
for i in ax:
figs[t]['ax'][i].set_ylim(ylim[i])
savedir = util.get_save_dirname(params, check_randomized=True)
for i in ax:
#pf.invert(ax[i], fig, bk_color='k')
figs[t]['f'].savefig(savedir + 'stack_t' + str(t) + '.eps',
edgecolor='none')
plt.show(block=params['block_plots'])