# generate simulated data
d = dict()
all_vals = []
siteID = []
for s in range(nSites):
# should each site have a different true mean?
vals = np.random.normal(trueDiff, sd, nPairs[s])
d[s] = vals
siteID.extend(s * np.ones(nPairs[s]).tolist())
all_vals.extend(vals)
grouped_vals = [d[s].mean() for s in range(nSites)]
# generate bootstrapped samples
bs_even = stats.get_bootstrapped_sample(d, even_sample=True, nboot=1000)
bs_weighted = stats.get_bootstrapped_sample(d,
even_sample=False,
nboot=1000)
# get pvalue for each method (and for standard Wilcoxon over the population, ignoring sites, one sided)
p_even = round(
1 - stats.get_direct_prob(np.zeros(len(bs_even)), bs_even)[0], 5)
p_weighted = round(
1 - stats.get_direct_prob(np.zeros(len(bs_even)), bs_weighted)[0], 5)
p_wilcox = round(
ss.wilcoxon(all_vals, np.zeros(len(all_vals)),
alternative='greater').pvalue, 5)
p_wilcoxg = round(
ss.wilcoxon(grouped_vals,
np.zeros(len(grouped_vals)),
print(
f"Mean MI: {MI.mean()}, sem: {MI.std() / np.sqrt(MI.shape[0])} \n n total units: {MI.shape[0]}"
)
miax.hist(MI, bins=mi_bins, edgecolor='white', color='grey')
miax.axvline(0, linestyle='--', color='k')
miax.set_xlabel('Modulation Index')
miax.set_ylabel(r"$n$ units")
# get bootstrapped p-value
np.random.normal(123)
d = {
s: MI[np.argwhere(df['site'].values == s).squeeze()]
for s in df['site'].unique()
}
bootsample = get_bootstrapped_sample(d, even_sample=False, nboot=1000)
p = get_direct_prob(bootsample, np.zeros(len(bootsample)))[0]
miax.text(mi_bins.min(), miax.get_ylim()[-1] - 2, r"p=%s" % round(p, 4))
# plot noise correlation
rsc_path = '/auto/users/hellerc/results/nat_pupil_ms/noise_correlations/'
rsc_df = ld.load_noise_correlation('rsc_ev',
xforms_model='NULL',
path=rsc_path)
mask = ~(rsc_df['bp'].isna() | rsc_df['sp'].isna())
rsc_df = rsc_df[mask]
d = {
s: rsc_df.loc[rsc_df.site == s]['sp'].values -
rsc_df.loc[rsc_df.site == s]['bp'].values
for s in rsc_df.site.unique()
}
lab = r"$\Delta d'^2$"
# plot by just collapsing over all sites
# generate error bars with hierarchical bootstrap
print('generating bootstrap SE, will take a moment')
cos_val_delt = ss.binned_statistic(df['cos_dU_evec_test'],
df['state_MI'],
statistic='mean',
bins=cbins).statistic
ds = [{
s: df[(df.site == s) & (df['cos_dU_evec_test'] > cbins[i]) &
(df['cos_dU_evec_test'] < cbins[i + 1])]['state_MI'].values
for s in df.site.unique()
} for i in range(len(cbins) - 1)]
cos_val_delt_se = np.stack([
np.std(stats.get_bootstrapped_sample(ds[i], nboot=100))
for i in range(len(ds))
])
du_val_delt = ss.binned_statistic(df['dU_mag_test'],
df['state_MI'],
statistic='mean',
bins=dbins).statistic
ds = [{
s: df[(df.site == s) & (df['dU_mag_test'] > dbins[i]) &
(df['dU_mag_test'] < dbins[i + 1])]['state_MI'].values
for s in df.site.unique()
} for i in range(len(cbins) - 1)]
du_val_delt_se = np.stack([
np.std(stats.get_bootstrapped_sample(ds[i], nboot=100))
for i in range(len(ds))
gridsize=nbins,
cmap=cmap_second,
vmin=-1,
vmax=1)
plt.show()
# stats tests
# noise variance change
np.random.seed(123)
nboots = 1000
ds = {
s: df_cut[(df_cut.site == s)]['lambda_diff'].values
for s in df_cut.site.unique()
}
ds_boot = stats.get_bootstrapped_sample(ds, nboot=nboots)
p = 1 - stats.get_direct_prob(ds_boot, np.zeros(nboots))[0]
print("big pupil variance vs. small pupil variance: \n" + \
f"p = {p}\n" + \
f"mean = {np.mean(ds_boot)}\n" + \
f"sem = {np.std(ds_boot)/np.sqrt(nboots)}\n")
nboots = 1000
ds = {
s: df_cut[(df_cut.site == s)]['mag_diff'].values
for s in df_cut.site.unique()
}
ds_boot = stats.get_bootstrapped_sample(ds, nboot=nboots)
ax[0].set_xlabel('Number of dimensions')
ax[1].set_ylabel(r"$\Delta d'^2$")
ax[1].set_xticks([0, 1, 2])
ax[1].set_xticklabels(['TDR', 'TDR+1', 'TDR+2'])
ax[1].set_xlabel('Number of dimensions')
f.tight_layout()
f.savefig(fig_fn)
# compute stats
np.random.seed(123)
d = {s: dfn1_dp.loc[dfn1_dp.site==s, 'dp_opt_test'].values - df_dp.loc[df_dp.site==s, 'dp_opt_test'].values
for s in df_dp.site.unique()}
bootstat = stats.get_bootstrapped_sample(d, nboot=5000)
p = 1 - stats.get_direct_prob(np.zeros(len(bootstat)), bootstat)[0]
print("========== OVERALL DPRIME ================")
print("TDR+1 vs. TDR: pval: {0}".format(p))
print("Mean percent improvement: {0}".format((100 * (dfn1_dp.groupby(by='site').mean()['dp_opt_test'] - \
df_dp.groupby(by='site').mean()['dp_opt_test']) / \
df_dp.groupby(by='site').mean()['dp_opt_test'] ).mean()))
d = {s: dfn2_dp.loc[dfn2_dp.site==s, 'dp_opt_test'].values - dfn1_dp.loc[dfn1_dp.site==s, 'dp_opt_test'].values
for s in df_dp.site.unique()}
bootstat = stats.get_bootstrapped_sample(d, nboot=5000)
p = 1 - stats.get_direct_prob(np.zeros(len(bootstat)), bootstat)[0]
print("TDR+2 vs. TDR+1: pval: {0}".format(p))