def plot_all_waterfalls(df, savepath, scale='blank_subtracted_NLL'):
areas, cres = dataset_params()
for area in areas:
for cre in cres:
session_IDs = get_sessions(df, area, cre)
if len(session_IDs) > 0:
#sort by direction using event magnitude
direction_order = get_cell_order_direction_sorted(
df, area, cre, savepath)
#display response significance
resp, blank, p_all = pool_sessions(session_IDs,
area + '_' + cre,
savepath,
scale=scale)
resp = center_direction_zero(resp)
condition_responses = resp[p_all < SIG_THRESH]
dirXcon_mat = concatenate_contrasts(condition_responses)
dirXcon_mat = dirXcon_mat[direction_order]
dirXcon_mat = move_all_negative_to_bottom(dirXcon_mat)
plot_full_waterfall(
dirXcon_mat, cre,
shorthand(area) + '_' + shorthand(cre) + '_full', scale,
savepath)
def get_cell_order_direction_sorted(df, area, cre, savepath):
session_IDs = get_sessions(df, area, cre)
resp, blank, p_all = pool_sessions(session_IDs,
area + '_' + cre,
savepath,
scale='event')
resp = center_direction_zero(resp)
condition_responses = resp[p_all < SIG_THRESH]
peak_dir, peak_con = get_peak_conditions(condition_responses)
direction_mat = select_peak_contrast(condition_responses, peak_con)
direction_order = sort_by_weighted_peak_direction(direction_mat)
return direction_order
def plot_DSI_distribution(df, savepath, curve='cdf'):
areas, cres = dataset_params()
cre_colors = get_cre_colors()
area = 'VISp'
pooled_DSI = []
colors = []
alphas = []
cre_labels = []
for cre in cres:
session_IDs = get_sessions(df, area, cre)
resp, blank, p_all = pool_sessions(session_IDs,
area + '_' + cre,
savepath,
scale='event')
dsi = calc_DSI(resp[p_all < SIG_THRESH])
pooled_DSI.append(dsi)
colors.append(cre_colors[cre])
alphas.append(1.0)
cre_labels.append(shorthand(cre))
xticks = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
plot_cdf(metric=pooled_DSI,
metric_labels=cre_labels,
colors=colors,
alphas=alphas,
hist_range=(0.0, 1.0),
hist_bins=200,
x_label='DSI',
x_ticks=xticks,
x_tick_labels=[str(x) for x in xticks],
save_name='V1_DSI_' + curve,
savepath=savepath,
do_legend=False)
def plot_direction_vector_sum_by_contrast(df, savepath):
areas, cres = dataset_params()
directions, contrasts = grating_params()
for area in areas:
for cre in cres:
session_IDs = get_sessions(df, area, cre)
if len(session_IDs) > 0:
resp, blank, p_all = pool_sessions(session_IDs,
area + '_' + cre,
savepath,
scale='event')
sig_resp = resp[p_all < SIG_THRESH]
pref_dir_mat = calc_pref_direction_dist_by_contrast(sig_resp)
pref_dir_mat = pref_dir_mat / np.sum(
pref_dir_mat, axis=0, keepdims=True)
resultant_mag = []
resultant_theta = []
for i_con, contrast in enumerate(contrasts):
mag, theta = calc_vector_sum(pref_dir_mat[:, i_con])
resultant_mag.append(mag)
resultant_theta.append(theta)
#bootstrap CI for distribution at 5% contrast
num_cells = len(sig_resp)
uniform_LB, uniform_UB = uniform_direction_vector_sum(
num_cells)
radial_direction_figure(
np.zeros((len(directions), )), np.zeros(
(len(directions), )), resultant_mag, resultant_theta,
uniform_LB, uniform_UB, cre, num_cells,
shorthand(area) + '_' + shorthand(cre) + '_combined',
savepath)
def plot_contrast_CoM(df, savepath, curve='cdf'):
areas, cres = dataset_params()
cre_colors = get_cre_colors()
area = 'VISp'
pooled_resp = []
colors = []
alphas = []
cre_labels = []
for cre in cres:
session_IDs = get_sessions(df, area, cre)
resp, blank, p_all = pool_sessions(session_IDs,
area + '_' + cre,
savepath,
scale='event')
pooled_resp.append(resp[p_all < SIG_THRESH])
colors.append(cre_colors[cre])
alphas.append(1.0)
cre_labels.append(shorthand(cre))
center_of_mass = center_of_mass_for_list(pooled_resp)
contrasts = [5, 10, 20, 40, 60, 80]
plot_cdf(metric=center_of_mass,
metric_labels=cre_labels,
colors=colors,
alphas=alphas,
hist_range=(np.log(5.0), np.log(70.0)),
hist_bins=200,
x_label='Contrast (CoM)',
x_ticks=np.log(contrasts),
x_tick_labels=[str(x) for x in contrasts],
save_name=shorthand(area) + '_contrast_' + curve,
savepath=savepath,
do_legend=True)