dff = (region_response.to_numpy() - np.mean(region_response.to_numpy(), axis=1)[:, None]) / np.mean(region_response.to_numpy(), axis=1)[:, None]
# trim and filter
resp = functional_connectivity.filterRegionResponse(dff, cutoff=cutoff, fs=fs)
resp = functional_connectivity.trimRegionResponse(file_id, resp)
region_dff = pd.DataFrame(data=resp, index=name_list_ito)
# fig2_1, ax = plt.subplots(4, 1, figsize=(3.5, 4))
fig2_1, ax = plt.subplots(4, 1, figsize=(8, 4))
ax = ax.ravel()
[x.set_axis_off() for x in ax]
[x.set_ylim([-0.2, 0.29]) for x in ax]
[x.set_xlim([-15, timevec[-1]]) for x in ax]
for p_ind, pr in enumerate(pull_regions):
ax[p_ind].plot(timevec, region_dff.loc[pr, x_start:(x_start+dt-1)], color=colors[p_ind])
ax[p_ind].annotate(bridge.displayName(pr), (-10, 0), rotation=90, fontsize=10)
plotting.addScaleBars(ax[0], dT=10, dF=0.10, T_value=-2.5, F_value=-0.10)
fig2_1.subplots_adjust(hspace=0.02, wspace=0.02)
fig2_2, ax = plt.subplots(3, 3, figsize=(2.5, 2.5))
[x.set_xticks([]) for x in ax.ravel()]
[x.set_yticks([]) for x in ax.ravel()]
for ind_1, eg1 in enumerate(pull_regions):
for ind_2, eg2 in enumerate(pull_regions):
if ind_1 > ind_2:
r, p = pearsonr(region_dff.loc[eg1, :], region_dff.loc[eg2, :])
# normed xcorr plot
c='b',
s=5 + 40 * G_anat.degree(weight='weight')[key],
edgecolors='k',
alpha=0.25)
ax_fxn.scatter(xi,
yi,
zi,
c='b',
s=5 + 20 * G_fxn.degree(weight='weight')[key],
edgecolors='k',
alpha=0.25)
if name_list_ito[key] not in roilabels_to_skip:
ax_anat.text(xi,
yi,
zi + 2,
bridge.displayName(name_list_ito[key]),
zdir=(0, 0, 0),
fontsize=7,
fontweight='bold')
ax_fxn.text(xi,
yi,
zi + 2,
bridge.displayName(name_list_ito[key]),
zdir=(0, 0, 0),
fontsize=7,
fontweight='bold')
# plot connections
for i, j in enumerate(G_anat.edges()):
x = np.array((anat_position[j[0]][0], anat_position[j[1]][0]))
y = np.array((anat_position[j[0]][1], anat_position[j[1]][1]))
def doAlignmentTest(cell_type, neuprint_search):
# (1) Get TBar count based on atlas aligntment
tbar = pd.read_csv(os.path.join(data_dir, 'hemi_2_atlas', '{}_ito_tbar.csv'.format(cell_type)), header=0).iloc[:, 1:]
tbar.index = np.arange(1, 87)
include_inds_ito, name_list_ito = bridge.getItoNames()
tbar = tbar.loc[include_inds_ito, :]
tbar.index = name_list_ito
tbar = pd.DataFrame(tbar.sum(axis=1), columns=['sum'])
fh, ax = plt.subplots(2, 1, figsize=(4, 2))
vmin = 1
vmax = np.nanmax(tbar.to_numpy())
sns.heatmap(np.log10(tbar.T).replace(-np.inf, 0), ax=ax[1], cmap='cividis', cbar=False, xticklabels=[bridge.displayName(x) for x in tbar.index], yticklabels=False, vmin=0, vmax=np.log10(vmax))
# ax[1].set_title('Atlas\nregistration')
ax[1].tick_params(axis='both', which='major', labelsize=8)
# (2) Get TBar count according to Neuprint & Janelia region tags
Neur, _ = fetch_neurons(NeuronCriteria(type=neuprint_search, status='Traced', regex=True))
neuprint_rois = [bridge.ito_to_neuprint(x) for x in name_list_ito]
tbar_count = np.zeros((Neur.shape[0], len(neuprint_rois)))
for roi_ind, nr in enumerate(neuprint_rois):
for n_ind, roiInfo in enumerate(Neur.roiInfo):
if len(nr) > 1:
new_tbars = np.sum([roiInfo.get(x, {'pre': 0}).get('pre', 0) for x in nr])
else:
new_tbars = roiInfo.get(nr[0], {'pre': 0}).get('pre', 0)
tbar_count[n_ind, roi_ind] = new_tbars
tbar_neuprint = pd.DataFrame(tbar_count.sum(axis=0).T, index=name_list_ito, columns=['ct'])
sns.heatmap(np.log10(tbar_neuprint.T).replace(-np.inf, 0), ax=ax[0], cmap='cividis', cbar=False, xticklabels=False, yticklabels=False, vmin=0, vmax=np.log10(vmax))
# ax[0].set_title('Neuprint')
ax[0].tick_params(axis='both', which='major', labelsize=8)
cb = fh.colorbar(matplotlib.cm.ScalarMappable(norm=matplotlib.colors.SymLogNorm(vmin=vmin, vmax=vmax, base=10, linthresh=0.1, linscale=1), cmap="cividis"), ax=ax, shrink=1.0, label='T-Bars')
r, p = pearsonr(tbar.to_numpy().ravel(), tbar_neuprint.to_numpy().ravel())
print('r = {}'.format(r))
print(tbar.sum().sum())
print(tbar_neuprint.sum().sum())
return fh
ax.plot([-3.5, 3.5], [-3.5, 3.5], 'k-')
ax.axhline(color='k', zorder=0, alpha=0.5)
ax.axvline(color='k', zorder=0, alpha=0.5)
ax.set_xticks([-2, 2])
ax.set_yticks([-2, 2])
ax.set_xticklabels(['-2$\sigma$', '+2$\sigma$'])
ax.set_yticklabels(['-2$\sigma$', '+2$\sigma$'])
ax.set_xlabel('SC', fontsize=10)
ax.set_ylabel('FC', fontsize=10)
ax.tick_params(axis='both', which='major', labelsize=8)
ax.set_aspect(1)
fig4_2, ax = plt.subplots(1, 1, figsize=(4, 4))
sns.heatmap(sorted_diff,
ax=ax,
yticklabels=[bridge.displayName(x) for x in sorted_diff.columns],
xticklabels=[bridge.displayName(x) for x in sorted_diff.index],
cbar_kws={
'label': 'Difference (FC - SC)',
'shrink': .65
},
cmap="RdBu_r",
rasterized=True,
vmin=-lim,
vmax=lim)
ax.set_aspect('equal')
ax.tick_params(axis='both', which='major', labelsize=6)
fig4_0.savefig(os.path.join(analysis_dir, 'figpanels', 'fig4_0.svg'),
format='svg',
transparent=True,
for it in range(iterations):
new_samples = np.sort(lognorm_model.rvs(size=len(ct)))[::-1]
samples.append(new_samples)
samples = np.vstack(samples)
# Note order of operations matters here, get mean and std before going back out of log
mod_mean = 10**np.mean(samples, axis=0)
err_down = 10**(np.mean(samples, axis=0) - 2*np.std(samples, axis=0))
err_up = 10**(np.mean(samples, axis=0) + 2*np.std(samples, axis=0))
axS1[p_ind].set_axis_on()
axS1[p_ind].fill_between(list(range(len(mod_mean))), err_up, err_down, color='k', alpha=0.4, rasterized=False)
axS1[p_ind].plot(mod_mean, 'k--', rasterized=False)
axS1[p_ind].plot(ct, marker='.', linestyle='none', rasterized=False)
axS1[p_ind].set_xticks([])
axS1[p_ind].annotate('{}'.format(bridge.displayName(pr)), (12, 6e3), fontsize=8)
axS1[p_ind].set_yscale('log')
axS1[p_ind].set_ylim([0.5, 5e4])
axS1[p_ind].set_yticks([1e0, 1e2, 1e4])
if pr == pull_region:
ax[0].fill_between(list(range(len(mod_mean))), err_up, err_down, color='k', alpha=0.4)
ax[0].plot(mod_mean, 'k--')
ax[0].plot(ct, marker='o', linestyle='none')
ax[0].set_xticks(list(range(len(ct))))
ax[0].tick_params(axis='both', which='major', labelsize=10)
ax[0].set_xticklabels([bridge.displayName(x) for x in ct.index])
ax[0].set_yscale('log')
ax[0].set_ylim([1, 8e3])
for tick in ax[0].get_xticklabels():