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mediation_lavaan_plot.py
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mediation_lavaan_plot.py
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#%%
import numpy as np
import pandas as pd
import os
import seaborn as sns
import scipy as sp
import matplotlib.pyplot as plt
from hddmnn_funcs import seaborn_style
sns.set(style='ticks')
seaborn_style()
import pingouin as pg
#%% GET DATA
usr = os.environ['USER']
if 'aeurai' in usr: # lisa
datapath = '/home/aeurai/Data/MEG_HDDM'
elif 'urai' in usr: # mbp laptop
datapath = '/Users/urai/Data/projects/0/neurodec/Data/MEG-PL/'
# add repetition probability to split groups
data = pd.read_csv(os.path.join(datapath, 'CSV', 'allsubjects_meg.csv'))
rep = data.groupby(['subj_idx'])['repeat', 'group'].mean().reset_index()
#%%
# add lavaan output
for gridx, groups in enumerate(['repeaters', 'alternators', 'allsj']):
group_markers = ['^', 'o', 's']
plt.close('all')
for trials in ['all', 'error', 'correct']:
df_lavaan = pd.read_csv(os.path.join(datapath, 'CSV',
'mediation', 'lavaan_threemediators_%s.csv'%trials))
df_lavaan2 = df_lavaan.pivot_table(values='est', index='subj_idx', columns='label').reset_index()
# merge these different ones
df = pd.merge(df_lavaan2, rep, on='subj_idx')
# subselect participants
if groups == 'repeaters':
df = df[df.group == 1]
elif groups == 'alternators':
df = df[df.group == -1]
# else, do nothing
# one plot for the main figure 3b
plt.close('all')
df['x'] = 0
ylims = [[0.005, 0.005, 0.005, 0.25], [0.15, 0.15, 0.15, 0.15, 0.15, 0.15]]
for vsidx, varsets in enumerate([['indirect_gamma', 'indirect_alpha', 'indirect_betalat', 'direct'],
['a1', 'a2', 'a3', 'b1', 'b2', 'b3']]):
fig, ax = plt.subplots(nrows=1, ncols=len(varsets), figsize=(len(varsets)*1.3, 2))
for vidx, v in enumerate(varsets):
# now the summary
ttest_against_zero = pg.ttest(df[v] * 10000, 0)
if ttest_against_zero['p-val'].item() < 0.05:
plotvars = {'mec': 'w', 'mfc': 'k', 'ms': 8}
else:
plotvars = {'mec': 'k', 'mfc': 'w', 'ms': 6}
yerr = np.array(df[v].mean() - ttest_against_zero['CI95%'].item()[0] / 10000,
ttest_against_zero['CI95%'].item()[1] / 10000 - df[v].mean()).T
ax[vidx].errorbar(y=df[v].mean(), x=0, yerr=yerr,
xerr=None, marker=group_markers[gridx], color='k', **plotvars)
ax[vidx].axhline(0, linestyle=':', color='.15', zorder=-100)
ax[vidx].set(ylim=[-ylims[vsidx][vidx], ylims[vsidx][vidx]],
xlim=[-0.1, 0.1],
# ylim=[-np.max(np.abs(df[v])) * 1.1, np.max(np.abs(df[v])) * 1.1],
xlabel='', xticklabels=[],
ylabel=v)
#ax[vidx].legend_.remove()
# do stats on the posterior distribution
pval = ttest_against_zero['p-val'].item()
txt = "p = {:.4f}".format(pval)
# if pval < 0.001:
# txt = "***"
# elif pval < 0.01:
# txt = "**"
# elif pval < 0.05:
# txt = "*"
# else:
# txt = ''
if df[v].mean() > 0:
star_y = ttest_against_zero['CI95%'].item()[1] / 10000 * 1.2
else:
star_y = ttest_against_zero['CI95%'].item()[0] / 10000 * 1.6
ax[vidx].text(0, star_y, txt, fontsize='small', ha='center',
fontweight='bold')
ax[vidx].tick_params(axis='x', colors='w')
sns.despine(trim=True)
plt.tight_layout()
plt.show()
fig.savefig(os.path.join(datapath,
'figures', 'lavaanMulti_3mediators_tr%s_v%s_gr%s.pdf'%(trials, vsidx, groups)),
facecolor='white')
# ====================================================== #
#%% another way: repeaters + alternators in 1 plot
# ====================================================== #
cmap1 = sns.color_palette('ch:s=.25,rot=-.25', n_colors=2)
cmap1 = sns.color_palette('PuOr', n_colors=7)
cmap = [(0.15,0.15,0.15), cmap1[1], cmap1[len(cmap1)-2]]
for trials in ['all', 'error', 'correct']:
df_lavaan = pd.read_csv(os.path.join(datapath, 'CSV',
'mediation', 'lavaan_threemediators_%s.csv'%trials))
df_lavaan2 = df_lavaan.pivot_table(values='est', index='subj_idx', columns='label').reset_index()
df_keep = pd.merge(df_lavaan2, rep, on='subj_idx')
df['x'] = 0
ylims = [[0.0051, 0.0051, 0.0051, 0.25], [0.15, 0.15, 0.15, 0.15, 0.15, 0.15]]
for vsidx, varsets in enumerate([['indirect_gamma', 'indirect_alpha', 'indirect_betalat', 'direct'],
['a1', 'a2', 'a3', 'b1', 'b2', 'b3']]):
plt.close('all')
fig, ax = plt.subplots(nrows=1, ncols=len(varsets), figsize=(len(varsets)*1.3, 2))
for gridx, groups in enumerate(['all', 'alternators', 'repeaters']):
group_markers = ['s', 'o', '^']
# merge these different ones, create new repo
df = pd.merge(df_lavaan2, rep, on='subj_idx')
# subselect participants
if groups == 'alternators':
df = df[df.group == -1]
print('removing repeaters')
print(df.subj_idx.nunique())
elif groups == 'repeaters':
df = df[df.group == 1]
print('keeping repeaters')
print(df.subj_idx.nunique())
elif groups == 'all':
print('keeping all subjects in')
print(df.subj_idx.nunique())
for vidx, v in enumerate(varsets):
# now the summary
ttest_against_zero = pg.ttest(df[v] * 10000, 0)
if ttest_against_zero['p-val'].item() < 0.05:
plotvars = {'mec': 'w', 'mfc': cmap[gridx], 'ms': 7}
else:
plotvars = {'mec': cmap[gridx], 'mfc': 'w', 'ms': 5}
yerr = np.array(df[v].mean() - ttest_against_zero['CI95%'].item()[0] / 10000,
ttest_against_zero['CI95%'].item()[1] / 10000 - df[v].mean()).T
ax[vidx].errorbar(y=df[v].mean(), x=gridx, yerr=yerr,
xerr=None, marker=group_markers[gridx], color=cmap[gridx], **plotvars)
ax[vidx].axhline(0, linestyle='--', color='darkgrey', zorder=-100)
ax[vidx].set(ylim=[-ylims[vsidx][vidx], ylims[vsidx][vidx]],
xlim=[-0.5, 2.2],
xlabel='', xticks=[0,1,2],
xticklabels=['all', 'alt', 'rep'],
ylabel=v)
# do stats on the posterior distribution
pval = ttest_against_zero['p-val'].item()
print('%s trials, %s %s: t(%d) = %.3f, p = %.4f'%(trials, v, groups,
ttest_against_zero['dof'].item(),
ttest_against_zero['T'].item(),
ttest_against_zero['p-val'].item()))
if ttest_against_zero['p-val'].item() < 0.0001:
print(np.format_float_scientific(ttest_against_zero['p-val'].item(), precision = 2, exp_digits=3))
txt = "p={:.4f}".format(pval)
if pval < 0.001:
txt = "***"
elif pval < 0.01:
txt = "**"
elif pval < 0.05:
txt = "*"
else:
txt = ''
if df[v].mean() > 0:
star_y = ttest_against_zero['CI95%'].item()[1] / 10000 * 1.2
else:
star_y = ttest_against_zero['CI95%'].item()[0] / 10000 * 1.8
#star_y = -ylims[vsidx][vidx] / 1.3
ax[vidx].text(gridx, star_y, txt, fontsize='small', ha='center',
#rotation=-80,
fontweight='bold',
color=cmap[gridx])
#ax[vidx].tick_params(axis='x', colors='w')
# stats on the groupdiff
if gridx == 2:
ttest2 = pg.ttest(df_keep.loc[df_keep.group == 1, v],
df_keep.loc[df_keep.group == -1, v], paired=False)
pval = ttest2['p-val'].item()
txt = "p = {:.4f}".format(pval)
print(ttest2[['T', 'dof', 'p-val']])
if pval < 0.001:
txt = "-***-"
elif pval < 0.01:
txt = "-**-"
elif pval < 0.05:
txt = "-*-"
else:
txt = ''
# where to put this one?
if vidx == 0:
y_pos = star_y * 1.4
elif vidx == 3:
y_pos = star_y * 1.7
else:
y_pos = star_y * 1.5
ax[vidx].text(1.5, y_pos, txt, fontsize='small', ha='center', va='top',
fontweight='bold', color='.1')
sns.despine(trim=True)
fig.tight_layout()
fig.savefig(os.path.join(datapath, 'figures', 'lavaan_grcomp_tr%s_v%s.pdf'%(trials, vsidx)),
facecolor='white')
# ====================================================== #
#%% a new figure with correct and error side-by-side (+ stats)
# ====================================================== #
df_lavaan_corr = pd.read_csv(os.path.join(datapath, 'CSV',
'mediation', 'lavaan_threemediators_%s.csv'%'correct'))
df_lavaan2_corr = df_lavaan_corr.pivot_table(values='est', index='subj_idx', columns='label').reset_index()
df_lavaan_err = pd.read_csv(os.path.join(datapath, 'CSV',
'mediation', 'lavaan_threemediators_%s.csv'%'error'))
df_lavaan2_err = df_lavaan_err.pivot_table(values='est', index='subj_idx', columns='label').reset_index()
# merge these different ones
df = pd.merge(df_lavaan2_corr, df_lavaan2_err, on='subj_idx',
suffixes=("_correct", "_error"))
x = [-0.1, 0.1]
ylims = [[0.01, 0.01, 0.01, 0.1]]
for vsidx, varsets in enumerate([['indirect_gamma', 'indirect_alpha', 'indirect_betalat', 'direct']]):
fig, ax = plt.subplots(nrows=1, ncols=len(varsets), figsize=(len(varsets) * 1.3, 2))
for vidx, v in enumerate(varsets):
for pfbidx, prevfb in enumerate(['_correct', '_error']):
cols = ['darkgreen', 'darkred']
# now the summary
ttest_against_zero = pg.ttest(df[v + prevfb] * 10000, 0)
if ttest_against_zero['p-val'].item() < 0.05:
plotvars = {'mec': 'w', 'mfc': cols[pfbidx], 'ms': 14}
else:
plotvars = {'mec': cols[pfbidx], 'mfc': 'w', 'ms': 10}
yerr = np.array(df[v + prevfb].mean() - ttest_against_zero['CI95%'].item()[0] / 10000,
ttest_against_zero['CI95%'].item()[1] / 10000 - df[v + prevfb].mean()).T
ax[vidx].errorbar(y=df[v + prevfb].mean(), x=x[pfbidx], yerr=yerr,
xerr=None, marker='.', color=cols[pfbidx], **plotvars)
ax[vidx].axhline(0, linestyle=':', color='.15', zorder=-100)
ax[vidx].set(ylim=[-ylims[vsidx][vidx], ylims[vsidx][vidx]],
xlim=[-0.3, 0.3], xticks=x,
# ylim=[-np.max(np.abs(df[v])) * 1.1, np.max(np.abs(df[v])) * 1.1],
xlabel='', ylabel=v)
ax[vidx].set_xticklabels(['correct', 'error'], rotation=-30)
[t.set_color(i) for (i, t) in zip(cols, ax[vidx].xaxis.get_ticklabels())]
# do stats on the posterior distribution
pval = ttest_against_zero['p-val'].item()
if pval < 0.001:
txt = "***"
elif pval < 0.01:
txt = "**"
elif pval < 0.05:
txt = "*"
else:
txt = ''
ax[vidx].text(x[pfbidx], ttest_against_zero['CI95%'].item()[1] / 10000 * 1.1,
txt,
fontsize='small', ha='center',
fontweight='bold', color=cols[pfbidx])
# paired ttest between correct and error
ttest_paired = pg.ttest(df[v + '_correct'] * 10000, df[v + '_error'] * 10000)
pval = ttest_paired['p-val'].item()
if pval < 0.001:
txt = "***"
elif pval < 0.01:
txt = "**"
elif pval < 0.05:
txt = "*"
else:
txt = ''
ax[vidx].text(0.5, 1, txt,
fontsize='small', transform=ax[vidx].transAxes, ha='center',
fontweight='bold', color='k')
sns.despine(trim=True)
plt.tight_layout()
plt.show()
fig.savefig(os.path.join(datapath,
'figures', 'lavaanMulti_correctVerror.pdf'),
facecolor='white')
# ====================================================== #
#%% working memory mediation, as main figure 7
# ====================================================== #
cmap1 = sns.color_palette('ch:s=.25,rot=-.25', n_colors=2)
cmap1 = sns.color_palette('PuOr', n_colors=7)
cmap = [(0.15,0.15,0.15), cmap1[1], cmap1[len(cmap1)-2]]
for trials in ['all']:
df_lavaan = pd.read_csv(os.path.join(datapath, 'CSV',
'mediation', 'lavaan_wm2_%s.csv'%trials))
df_lavaan2 = df_lavaan.pivot_table(values='est', index='subj_idx', columns='label').reset_index()
df_keep = pd.merge(df_lavaan2, rep, on='subj_idx')
df['x'] = 0
ylims = [[0.0051, 0.0051, 0.0051, 0.25], [0.15, 0.15, 0.15, 0.15, 0.15, 0.15]]
for vsidx, varsets in enumerate([['indirect_gamma_stim', 'indirect_gamma_delay']]):
plt.close('all')
fig, ax = plt.subplots(nrows=1, ncols=len(varsets), figsize=(len(varsets)*1.3, 2))
for gridx, groups in enumerate(['all', 'alternators', 'repeaters']):
group_markers = ['s', 'o', '^']
# merge these different ones
df = pd.merge(df_lavaan2, rep, on='subj_idx')
# subselect participants
if groups == 'alternators':
df = df[df.group == -1]
print('removing repeaters')
elif groups == 'repeaters':
df = df[df.group == 1]
print('keeping repeaters')
elif groups == 'all':
print('keeping all subjects in')
for vidx, v in enumerate(varsets):
# now the summary
ttest_against_zero = pg.ttest(df[v] * 10000, 0)
if ttest_against_zero['p-val'].item() < 0.05:
plotvars = {'mec': 'w', 'mfc': cmap[gridx], 'ms': 7}
else:
plotvars = {'mec': cmap[gridx], 'mfc': 'w', 'ms': 5}
yerr = np.array(df[v].mean() - ttest_against_zero['CI95%'].item()[0] / 10000,
ttest_against_zero['CI95%'].item()[1] / 10000 - df[v].mean()).T
ax[vidx].errorbar(y=df[v].mean(), x=gridx, yerr=yerr,
xerr=None, marker=group_markers[gridx], color=cmap[gridx], **plotvars)
ax[vidx].axhline(0, linestyle='--', color='darkgrey', zorder=-100)
ax[vidx].set(ylim=[-ylims[vsidx][vidx], ylims[vsidx][vidx]],
xlim=[-0.5, 2.2],
xlabel='', xticks=[0,1,2],
xticklabels=['all', 'alt', 'rep'],
ylabel=v)
# do stats on the posterior distribution
pval = ttest_against_zero['p-val'].item()
txt = "p = {:.4f}".format(pval)
if pval < 0.001:
txt = "***"
elif pval < 0.01:
txt = "**"
elif pval < 0.05:
txt = "*"
else:
txt = ''
if df[v].mean() > 0:
star_y = ttest_against_zero['CI95%'].item()[1] / 10000 * 1.2
else:
star_y = ttest_against_zero['CI95%'].item()[0] / 10000 * 1.4
ax[vidx].text(gridx, star_y, txt, fontsize='small', ha='center',
fontweight='bold', color=cmap[gridx])
#ax[vidx].tick_params(axis='x', colors='w')
print('WM %s %s: t(%d) = %.3f, p = %.4f'%(v, groups,
ttest_against_zero['dof'].item(),
ttest_against_zero['T'].item(),
ttest_against_zero['p-val'].item()))
# stats on the groupdiff
if gridx == 2:
ttest2 = pg.ttest(df_keep.loc[df_keep.group == 1, v],
df_keep.loc[df_keep.group == -1, v], paired=False)
pval = ttest2['p-val'].item()
txt = "p = {:.4f}".format(pval)
if pval < 0.001:
txt = "-***-"
elif pval < 0.01:
txt = "-**-"
elif pval < 0.05:
txt = "-*-"
else:
txt = ''
# where to put this one?
if vidx == 0:
y_pos = star_y * 1.4
elif vidx == 3:
y_pos = star_y * 1.7
else:
y_pos = star_y * 1.5
ax[vidx].text(1.5, y_pos, txt, fontsize='small', ha='center', va='top',
fontweight='bold', color='darkgrey')
sns.despine(trim=True)
fig.tight_layout()
fig.savefig(os.path.join(datapath, 'figures', 'lavaan_wm2_tr%s_v%s.pdf'%(trials, vsidx)),
facecolor='white')
# # ====================================================== #
# #%% ADD WORKING MEMORY MEDIATION
# # ====================================================== #
# df_lavaan_corr = pd.read_csv(os.path.join(datapath, 'CSV',
# 'mediation', 'lavaan_wm2_%s.csv'%'all'))
# df_lavaan2_corr = df_lavaan_corr.pivot_table(values='est', index='subj_idx', columns='label').reset_index()
# # compute each person's repetition probability
# data = pd.read_csv(os.path.join(datapath, 'MEG_HDDM_all_clean', 'allsubjects_megall_4hddm_norm_flip.csv'))
# rep = data.groupby(['subj_idx'])['repeat'].mean().reset_index()
# rep['repeaters'] = (rep['repeat'] > 0.5)
# df_keep = pd.merge(df_lavaan2_corr, rep, on='subj_idx')
# group_markers = ['o', '^', 's']
# for gridx, groups in enumerate(['alternators', 'repeaters']):
# # subselect participants
# if groups == 'repeaters':
# df = df_keep[df_keep.repeaters]
# elif groups == 'alternators':
# df = df_keep[~df_keep.repeaters]
# # else, do nothing
# x = [-0.1, 0.1]
# ylims = [[0.005, 0.005, 0.15]]
# plt.close('all')
# fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(3 * 1.3, 2))
# for vsidx, varsets in enumerate([['indirect_gamma_stim', 'indirect_gamma_delay', 'direct']]):
# for vidx, v in enumerate(varsets):
# # now the summary
# ttest_against_zero = pg.ttest(df[v] * 10000, 0)
# if ttest_against_zero['p-val'].item() < 0.05:
# plotvars = {'mec': 'w', 'mfc': 'k', 'ms': 8}
# else:
# plotvars = {'mec': 'k', 'mfc': 'w', 'ms': 6}
# yerr = np.array(df[v].mean() - ttest_against_zero['CI95%'].item()[0] / 10000,
# ttest_against_zero['CI95%'].item()[1] / 10000 - df[v].mean()).T
# ax[vidx].errorbar(y=df[v].mean(), x=0, yerr=yerr,
# xerr=None, marker=group_markers[gridx], color='k', **plotvars)
# ax[vidx].axhline(0, linestyle=':', color='.15', zorder=-100)
# ax[vidx].set(ylim=[-ylims[vsidx][vidx], ylims[vsidx][vidx]],
# xticks=[],
# # xlim=[-0.3, 0.3],
# # ylim=[-np.max(np.abs(df[v])) * 1.1, np.max(np.abs(df[v])) * 1.1],
# xlabel='', ylabel=v)
# # ax[vidx].set_xticklabels(['correct', 'error'], rotation=-30)
# # [t.set_color(i) for (i, t) in zip(cols, ax[vidx].xaxis.get_ticklabels())]
# # do stats on the posterior distribution
# pval = ttest_against_zero['p-val'].item()
# if pval < 0.001:
# txt = "***"
# elif pval < 0.01:
# txt = "**"
# elif pval < 0.05:
# txt = "*"
# else:
# txt = ''
# ax[vidx].text(0, ttest_against_zero['CI95%'].item()[1] / 10000 * 1.1,
# txt,
# fontsize='small', ha='center',
# fontweight='bold')
# sns.despine(trim=True)
# plt.tight_layout()
# # fig.savefig(os.path.join(datapath, 'Figures', 'lavaan_wm_group%s.pdf'%groups), facecolor='white')
# # ====================================================== #
# #%% ADD WORKING MEMORY MEDIATION - repeaters and alternators together
# # ====================================================== #
# group_markers = ['o', '^', 's']
# df_lavaan_corr = pd.read_csv(os.path.join(datapath, 'CSV',
# 'mediation', 'lavaan_wm2_%s.csv'%'all'))
# df_lavaan2_corr = df_lavaan_corr.pivot_table(values='est', index='subj_idx', columns='label').reset_index()
# # compute each person's repetition probability
# df_keep = pd.merge(df_lavaan2_corr, rep, on='subj_idx')
# # cmap = sns.color_palette('ch:s=.25,rot=-.25', n_colors=2)
# cmap1 = sns.color_palette('PuOr', n_colors=7)
# cmap = [cmap1[1], cmap1[len(cmap1)-2]]
# x = [-0.1, 0.1]
# ylims = [[0.005, 0.005, 0.15]]
# plt.close('all')
# fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(3 * 1.3, 2))
# for gridx, groups in enumerate(['alternators', 'repeaters']):
# # subselect participants
# if groups == 'repeaters':
# df = df_keep[df_keep.repeaters == 1.]
# elif groups == 'alternators':
# df = df_keep[df_keep.repeaters == 0.]
# # else, do nothing
# for vsidx, varsets in enumerate([['indirect_gamma_stim', 'indirect_gamma_delay', 'direct']]):
# for vidx, v in enumerate(varsets):
# # now the summary
# ttest_against_zero = pg.ttest(df[v] * 10000, 0)
# if ttest_against_zero['p-val'].item() < 0.05:
# plotvars = {'mec': 'w', 'mfc': cmap[gridx], 'ms': 8}
# else:
# plotvars = {'mec': cmap[gridx], 'mfc': 'w', 'ms': 6}
# yerr = np.array(df[v].mean() - ttest_against_zero['CI95%'].item()[0] / 10000,
# ttest_against_zero['CI95%'].item()[1] / 10000 - df[v].mean()).T
# ax[vidx].errorbar(y=df[v].mean(), x=x[gridx], yerr=yerr,
# xerr=None, marker=group_markers[gridx], color=cmap[gridx], **plotvars)
# ax[vidx].axhline(0, linestyle=':', color='.15', zorder=-100)
# ax[vidx].set(ylim=[-ylims[vsidx][vidx], ylims[vsidx][vidx]],
# xlim=[-0.3, 0.3], xticks=x,
# # ylim=[-np.max(np.abs(df[v])) * 1.1, np.max(np.abs(df[v])) * 1.1],
# xlabel='', ylabel=v)
# ax[vidx].set_xticklabels(['alt', 'rep'], rotation=-30)
# # [t.set_color(i) for (i, t) in zip(cols, ax[vidx].xaxis.get_ticklabels())]
# # do stats on the posterior distribution
# pval = ttest_against_zero['p-val'].item()
# if pval < 0.001:
# txt = "***"
# elif pval < 0.01:
# txt = "**"
# elif pval < 0.05:
# txt = "*"
# else:
# txt = ''
# ax[vidx].text(x[gridx], ttest_against_zero['CI95%'].item()[1] / 10000 * 1.1,
# txt,
# fontsize='small', ha='center',
# fontweight='bold', color=cmap[gridx])
# sns.despine(trim=True)
# plt.tight_layout()
# fig.savefig(os.path.join(datapath,
# 'figures', 'lavaan_wm_groups.pdf'), facecolor='white')