# ---
# jupyter:
#   jupytext:
#     text_representation:
#       extension: .py
#       format_name: light
#       format_version: '1.5'
#       jupytext_version: 1.14.4
#   kernelspec:
#     display_name: Python 3
#     language: python
#     name: python3
# ---

# +
# %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns

import pandas as pd

sns.set_style('white')
#Input data
in_file = '../data/wholesample_aware_86.csv'
df = pd.read_csv(in_file)

# +
df['awareness'] = df['awareness'].map({'AWARE': 'aware', 'UNAWARE': 'unaware'})

df.head()

# +
pal = sns.color_palette(['teal', 'darkblue'])

fig, ax = plt.subplots(figsize=(4, 3.5))
sns.barplot(y='L. PCC', x='awareness', 
            data=df, ax=ax, capsize=0.1, 
            errcolor='black', ci=95, palette=pal, errwidth=2, linewidth=2.5, edgecolor="black")
def change_width(ax, new_value) :
    locs = ax.get_xticks()
    for i,patch in enumerate(ax.patches):
        current_width = patch.get_width()
        diff = current_width - new_value

        # we change the bar width
        patch.set_width(new_value)

        # we recenter the bar
        patch.set_x(patch.get_x() + diff * .5)
change_width(ax, .4) 
ax.set_ylabel('% BOLD 'r'$\Delta$', fontsize=20, fontweight='bold')
ax.set_xlabel('AWARENESS', fontsize=20, fontweight='bold', labelpad=10)
ax.set_yticks([-1, -0.5, 0, 0.5, 1])
ax.set_ylim(-1, 1)
ax.tick_params(axis='both', labelsize=16, direction='out', length=5, left=True, width=2.5)
ax.tick_params(axis='x', labelsize=20)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_linewidth(2.5)
ax.axhline(0, color='black', linewidth=2.5)
#for tick in ax.xaxis.get_major_ticks():
    #tick.label1.set_fontweight('bold')
fig.tight_layout()
fig.savefig(fname='./figure_3/aware_LPCC_bar.png', dpi=300)

###############   #################

fig, ax = plt.subplots(figsize=(4, 3.5))
sns.barplot(y='R. PFC', x='awareness', 
            data=df, ax=ax, capsize=0.1, 
            errcolor='black', ci=95, palette=pal, errwidth=2, linewidth=2.5, edgecolor="black")
def change_width(ax, new_value) :
    locs = ax.get_xticks()
    for i,patch in enumerate(ax.patches):
        current_width = patch.get_width()
        diff = current_width - new_value

        # we change the bar width
        patch.set_width(new_value)

        # we recenter the bar
        patch.set_x(patch.get_x() + diff * .5)
change_width(ax, .4) 
ax.set_ylabel('% BOLD 'r'$\Delta$', fontsize=20, fontweight='bold')
ax.set_xlabel('AWARENESS', fontsize=20, fontweight='bold', labelpad=10)
ax.set_yticks([-1, -0.5, 0, 0.5, 1])
ax.set_ylim(-1, 1)
ax.tick_params(axis='both', labelsize=16, direction='out', length=5, left=True, width=2.5)
ax.tick_params(axis='x', labelsize=20)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_linewidth(2.5)
ax.axhline(0, color='black', linewidth=2.5)
#for tick in ax.xaxis.get_major_ticks():
    #tick.label1.set_fontweight('bold')
fig.tight_layout()
fig.savefig(fname='./figure_3/aware_RPFC_bar.png', dpi=300)

###############   #################
fig, ax = plt.subplots(figsize=(4, 3.5))
sns.barplot(y='<NAME>', x='awareness', 
            data=df, ax=ax, capsize=0.1, 
            errcolor='black', ci=95, palette=pal, errwidth=2, linewidth=2.5, edgecolor="black")
def change_width(ax, new_value) :
    locs = ax.get_xticks()
    for i,patch in enumerate(ax.patches):
        current_width = patch.get_width()
        diff = current_width - new_value

        # we change the bar width
        patch.set_width(new_value)

        # we recenter the bar
        patch.set_x(patch.get_x() + diff * .5)
change_width(ax, .4) 
ax.set_ylabel('% BOLD 'r'$\Delta$', fontsize=20, fontweight='bold')
ax.set_xlabel('AWARENESS', fontsize=20, fontweight='bold', labelpad=10)
ax.set_yticks([-1, -0.5, 0, 0.5, 1])
ax.set_ylim(-1, 1)
ax.tick_params(axis='both', labelsize=16, direction='out', length=5, left=True, width=2.5)
ax.tick_params(axis='x', labelsize=20)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_linewidth(2.5)
ax.axhline(0, color='black', linewidth=2.5)
#for tick in ax.xaxis.get_major_ticks():
    #tick.label1.set_fontweight('bold')
fig.tight_layout()
fig.savefig(fname='./figure_3/aware_RPut_bar.png', dpi=300)

###############   #################

fig, ax = plt.subplots(figsize=(4, 3.5))
sns.barplot(y='<NAME>', x='awareness', 
            data=df, ax=ax, capsize=0.1, 
            errcolor='black', ci=95, palette=pal, errwidth=2, linewidth=2.5, edgecolor="black")
def change_width(ax, new_value) :
    locs = ax.get_xticks()
    for i,patch in enumerate(ax.patches):
        current_width = patch.get_width()
        diff = current_width - new_value

        # we change the bar width
        patch.set_width(new_value)

        # we recenter the bar
        patch.set_x(patch.get_x() + diff * .5)
change_width(ax, .4) 

ax.set_ylabel('% BOLD 'r'$\Delta$', fontsize=20, fontweight='bold')
ax.set_xlabel('AWARENESS', fontsize=20, fontweight='bold', labelpad=10)
ax.set_yticks([0, 0.5, 1, 1.5, 2])
ax.set_ylim(0, 2)
ax.tick_params(axis='both', labelsize=16, direction='out', length=5, left=True, width=2.5)
ax.tick_params(axis='x', labelsize=20)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_linewidth(2.5)
ax.spines['left'].set_linewidth(2.5)
#for tick in ax.xaxis.get_major_ticks():
    #tick.label1.set_fontweight('bold')
fig.tight_layout()
fig.savefig(fname='./figure_3/aware_LPI_bar.png', dpi=300)


# +
# %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns

import pandas as pd

sns.set_style('white')
#Input data
in_file = '../data/wholesample_corr_aware_86.csv'
df = pd.read_csv(in_file)
df.head()

# +

jgrid = sns.jointplot(x='unaware_log10', y='R_P_Insula_AU', 
                      data=df, kind='reg',
                      height=6, color='darkslategray',
                      joint_kws=dict(scatter_kws=dict(edgecolor='white', s=50)),
                      marginal_kws=dict(hist=False, kde_kws={'shade': True}),  # you can control axes with dicts
                      xlim=(-100, 500), ylim=(-100, 500), truncate=False, ci=None)  # note the huge limits
fig = jgrid.fig
ax_joint = jgrid.ax_joint
ax_x = jgrid.ax_marg_x
ax_y = jgrid.ax_marg_y

# Clean up the axes
ax_y.set_ylabel(None)
ax_x.set_xlabel(None)
ax_joint.set_xlim(0,2.5)
ax_joint.set_xticks([0, 0.5, 1, 1.5, 2, 2.5])
ax_joint.lines[0].set_linewidth(3)
ax_joint.spines['left'].set_linewidth(2.5)
ax_joint.spines['bottom'].set_linewidth(2.5)
ax_joint.set_ylim(-2, 4)
ax_joint.set_yticks([-2, 0, 2, 4])
ax_joint.lines[0].set_linestyle(":")
ax_joint.tick_params(axis='both', which='major', labelsize=16,direction='out', length=5, left=True, bottom=True, width=2.5)
# Add a legend
ax_joint.set_xlabel('% UNAWARE (log10)', fontsize=20, fontweight='bold')
ax_joint.set_ylabel(r'AWARE-UNAWARE $\beta$', fontsize=20, fontweight='bold')
fig.set_size_inches((4.5, 4))
fig.tight_layout()
fig.savefig(fname='./figure_3/R_PI-AU_unaware')
fig.show()

######################################################################################

jgrid = sns.jointplot(x='unaware_log10', y='R_Putamen_AU', 
                      data=df, kind='reg',
                      height=6, color='darkslategray',
                      joint_kws=dict(scatter_kws=dict(edgecolor='white', s=50)),
                      marginal_kws=dict(hist=False, kde_kws={'shade': True}),  # you can control axes with dicts
                      xlim=(-100, 500), ylim=(-100, 500), truncate=False, ci=None)  # note the huge limits
fig = jgrid.fig
ax_joint = jgrid.ax_joint
ax_x = jgrid.ax_marg_x
ax_y = jgrid.ax_marg_y

# Clean up the axes
ax_y.set_ylabel(None)
ax_x.set_xlabel(None)
ax_joint.lines[0].set_linestyle(":")
ax_joint.lines[0].set_linewidth(3)
ax_joint.spines['left'].set_linewidth(2.5)
ax_joint.spines['bottom'].set_linewidth(2.5)
ax_joint.set_xlim(0,2.5)
ax_joint.set_xticks([0, 0.5, 1, 1.5, 2, 2.5])
ax_joint.set_ylim(-2, 4)
ax_joint.set_yticks([-2, 0, 2, 4])
ax_joint.tick_params(axis='both', which='major', labelsize=16,direction='out', length=5, left=True, bottom=True, width=2.5)
# Add a legend
ax_joint.set_xlabel('% UNAWARE (log10)', fontsize=20, fontweight='bold')
ax_joint.set_ylabel(r'AWARE-UNAWARE $\beta$', fontsize=20, fontweight='bold')
fig.set_size_inches((4.5, 4))
fig.tight_layout()
fig.savefig(fname='./figure_3/R_put-AU_unaware')
fig.show()

######################################################################################

jgrid = sns.jointplot(x='unaware_log10', y='L_PCC_AU', 
                      data=df, kind='reg',
                      height=6, color='darkslategray',
                      joint_kws=dict(scatter_kws=dict(edgecolor='white', s=50)),
                      marginal_kws=dict(hist=False, kde_kws={'shade': True}),  # you can control axes with dicts
                      xlim=(-100, 500), ylim=(-100, 500), truncate=False, ci=None)  # note the huge limits
fig = jgrid.fig
ax_joint = jgrid.ax_joint
ax_x = jgrid.ax_marg_x
ax_y = jgrid.ax_marg_y

# Clean up the axes
ax_y.set_ylabel(None)
ax_x.set_xlabel(None)
ax_joint.set_xlim(0,2.5)
ax_joint.set_xticks([0, 0.5, 1, 1.5, 2, 2.5])
ax_joint.lines[0].set_linewidth(3)
ax_joint.spines['left'].set_linewidth(2.5)
ax_joint.spines['bottom'].set_linewidth(2.5)
ax_joint.set_ylim(-4, 2)
ax_joint.set_yticks([-4, -2, 0, 2])
ax_joint.lines[0].set_linestyle(":")
ax_joint.tick_params(axis='both', which='major', labelsize=16, direction='out', length=5, left=True, bottom=True, width=2.5)
# Add a legend
ax_joint.set_xlabel(r'% UNAWARE (log10)', fontsize=20, fontweight='bold')
ax_joint.set_ylabel(r'AWARE-UNAWARE $\beta$', fontsize=20, fontweight='bold')
fig.set_size_inches((4.5, 4))
fig.tight_layout()
fig.savefig(fname='./figure_3/L_PCC-AU_unaware')
fig.show()

######################################################################################

jgrid = sns.jointplot(x='unaware_log10', y='R_PFC_AU', 
                      data=df, kind='reg',
                      height=6, color='darkslategray',
                      joint_kws=dict(scatter_kws=dict(edgecolor='white', s=50)),
                      marginal_kws=dict(hist=False, kde_kws={'shade': True}),  # you can control axes with dicts
                      xlim=(-100, 500), ylim=(-100, 500), truncate=False, ci=None)  # note the huge limits
fig = jgrid.fig
ax_joint = jgrid.ax_joint
ax_x = jgrid.ax_marg_x
ax_y = jgrid.ax_marg_y

# Clean up the axes
ax_y.set_ylabel(None)
ax_x.set_xlabel(None)
ax_joint.set_xlim(0,2.5)
ax_joint.set_xticks([0, 0.5, 1, 1.5, 2, 2.5])
ax_joint.lines[0].set_linewidth(3)
ax_joint.spines['left'].set_linewidth(2.5)
ax_joint.spines['bottom'].set_linewidth(2.5)
ax_joint.set_ylim(-4, 2)
ax_joint.set_yticks([-4, -2, 0, 2])
ax_joint.lines[0].set_linestyle(":")
ax_joint.tick_params(axis='both', which='major', labelsize=16, direction='out', length=5, left=True, bottom=True, width=2.5)
# Add a legend
ax_joint.set_xlabel(r'% UNAWARE (log10)', fontsize=20, fontweight='bold')
ax_joint.set_ylabel(r'AWARE-UNAWARE $\beta$', fontsize=20, fontweight='bold')
fig.set_size_inches((4.5, 4))
fig.tight_layout()
fig.savefig(fname='./figure_3/R_PFC-AU_unaware')
fig.show()

# +
# %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns

import pandas as pd

sns.set_style('white')
#Input data
in_file = '../data/cog_fail_86.csv'
df = pd.read_csv(in_file)
df.head()

# +
df[['HIV', 'CB']]
df['HIV-status'] = df['HIV'].map({1: 'HIV-', 2: 'HIV+'})
df['CB-status'] = df['CB'].map({1: 'non-user', 2: 'user'})
df['group'] = df['HIV-status'] + ', ' + df['CB-status']

# We need a separate dataframe for each group
hiv_pos_df = df.loc[df['HIV-status'] == 'HIV+']
hiv_neg_df = df.loc[df['HIV-status'] == 'HIV-']

# +
# We need a separate dataframe for each group
hiv_pos_df = df.loc[df['HIV-status'] == 'HIV+']
hiv_neg_df = df.loc[df['HIV-status'] == 'HIV-']

# Then we make the jointplot for one of the groups only
jgrid = sns.jointplot(x='cog_fail_total', y='A_U_L_PCC', 
                      data=hiv_pos_df, kind='reg',
                      color='purple', label='HIV positive',
                      joint_kws=dict(scatter_kws=dict(edgecolor='white', s=50)),
                      marginal_kws=dict(hist=False, kde_kws={'shade': True}),  # you can control axes with dicts
                      xlim=(-100, 500), ylim=(-100, 500), truncate=False, ci=None)  # note the huge limits
fig = jgrid.fig
ax_joint = jgrid.ax_joint
ax_x = jgrid.ax_marg_x
ax_y = jgrid.ax_marg_y

# Add the HIV-negative central regression plot to the joint axis
sns.regplot(x='cog_fail_total', y='A_U_L_PCC', data=hiv_neg_df, ax=ax_joint,
            color='green', label='HIV negative', scatter_kws=dict(edgecolor='white', s=50, linewidth=0.6), truncate=False, ci=None)

# Add the full sample central regression plot to the joint axis
sns.regplot(x='cog_fail_total', y='A_U_L_PCC', data=df, ax=ax_joint,
            color='darkslategray', label='all', truncate=False, scatter=False)

# Clean up the axes
ax_y.set_ylabel(None)
ax_x.set_xlabel(None)
ax_joint.set_xticks([0, 20, 40, 60, 80, 100])
ax_joint.set_xlim(-2,100)
ax_joint.set_yticks([-4, -2, 0, 2, 4])
ax_joint.set_ylim(-4, 4)
ax_joint.tick_params(axis='both', which='major', labelsize=16)
# Add a legend
ax_joint.legend(fontsize=16)
ax_joint.spines['left'].set_linewidth(2.5)
ax_joint.spines['bottom'].set_linewidth(2.5)
ax_joint.set_xlabel('CFQ', fontsize=20, fontweight='bold')
ax_joint.set_ylabel(r'AWARE-UNAWARE $\beta$', fontsize=20, fontweight='bold')
#fig.set_size_inches((8, 6))
ax_joint.lines[0].set_linestyle("")
ax_joint.lines[1].set_linestyle("")
ax_joint.tick_params(axis='both', which='major', labelsize=16,direction='out', length=5, left=True, bottom=True, width=2.5)
ax_joint.legend().set_visible(False)
fig.set_size_inches((4.5, 4))
fig.tight_layout()
fig.savefig(fname='./figure_3/L_PCC_A_U_cog_fail')
fig.show()

#######################################################################


# Then we make the jointplot for one of the groups only
jgrid = sns.jointplot(x='cog_fail_total', y='A_U_R_PFC', 
                      data=hiv_pos_df, kind='reg',
                      color='purple', label='HIV positive',
                      joint_kws=dict(scatter_kws=dict(edgecolor='white', s=50)),
                      marginal_kws=dict(hist=False, kde_kws={'shade': True}),  # you can control axes with dicts
                      xlim=(-100, 500), ylim=(-100, 500), truncate=False, ci=None)  # note the huge limits
fig = jgrid.fig
ax_joint = jgrid.ax_joint
ax_x = jgrid.ax_marg_x
ax_y = jgrid.ax_marg_y

# Add the HIV-negative central regression plot to the joint axis
sns.regplot(x='cog_fail_total', y='A_U_R_PFC', data=hiv_neg_df, ax=ax_joint,
            color='green', label='HIV negative', scatter_kws=dict(edgecolor='white', s=50, linewidth=0.6), truncate=False, ci=None)

# Add the full sample central regression plot to the joint axis
sns.regplot(x='cog_fail_total', y='A_U_R_PFC', data=df, ax=ax_joint,
            color='darkslategray', label='all', truncate=False, scatter=False, ci=None)


# Clean up the axes
ax_y.set_ylabel(None)
ax_x.set_xlabel(None)
ax_joint.set_xticks([0, 20, 40, 60, 80, 100])
ax_joint.set_xlim(-2,100)
ax_joint.set_yticks([-4, -2, 0, 2])
ax_joint.set_ylim(-4, 2)
ax_joint.tick_params(axis='both', which='major', labelsize=16)
# Add a legend
ax_joint.legend(fontsize=16)
ax_joint.spines['left'].set_linewidth(2.5)
ax_joint.spines['bottom'].set_linewidth(2.5)
ax_joint.set_xlabel('CFQ', fontsize=20, fontweight='bold')
ax_joint.set_ylabel(r'AWARE-UNAWARE $\beta$', fontsize=20, fontweight='bold')
#fig.set_size_inches((8, 6))
ax_joint.lines[0].set_linestyle("")
ax_joint.lines[1].set_linestyle("")
ax_joint.lines[2].set_linestyle(":")
ax_joint.tick_params(axis='both', which='major', labelsize=16,direction='out', length=5, left=True, bottom=True, width=2.5)
ax_joint.legend().set_visible(False)
fig.set_size_inches((4.5, 4))
fig.tight_layout()
fig.savefig(fname='./figure_3/R_PFC_A_U_cog_fail')
fig.show()

#######################################################################

# We need a separate dataframe for each group
hiv_pos_df = df.loc[df['HIV-status'] == 'HIV+']
hiv_neg_df = df.loc[df['HIV-status'] == 'HIV-']

# Then we make the jointplot for one of the groups only
jgrid = sns.jointplot(x='cog_fail_total', y='A_U_R_Insula', 
                      data=hiv_pos_df, kind='reg',
                      color='purple', label='HIV positive',
                      joint_kws=dict(scatter_kws=dict(edgecolor='white', s=50)),
                      marginal_kws=dict(hist=False, kde_kws={'shade': True}),  # you can control axes with dicts
                      xlim=(-100, 500), ylim=(-100, 500), truncate=False, ci=None)  # note the huge limits
fig = jgrid.fig
ax_joint = jgrid.ax_joint
ax_x = jgrid.ax_marg_x
ax_y = jgrid.ax_marg_y

# Add the HIV-negative central regression plot to the joint axis
sns.regplot(x='cog_fail_total', y='A_U_R_Insula', data=hiv_neg_df, ax=ax_joint,
            color='green', label='HIV negative', scatter_kws=dict(edgecolor='white', s=50, linewidth=0.6), truncate=False, ci=None)

# Add the full sample central regression plot to the joint axis
sns.regplot(x='cog_fail_total', y='A_U_R_Insula', data=df, ax=ax_joint,
            color='darkslategray', label='all', truncate=False, scatter=False, ci=None)

# Clean up the axes
ax_y.set_ylabel(None)
ax_x.set_xlabel(None)
ax_joint.set_xticks([0, 20, 40, 60, 80, 100])
ax_joint.set_xlim(-2,100)
ax_joint.set_yticks([-2, 0, 2, 4])
ax_joint.set_ylim(-2, 4)
ax_joint.tick_params(axis='both', which='major', labelsize=16)
# Add a legend
ax_joint.legend(fontsize=16)
ax_joint.spines['left'].set_linewidth(2.5)
ax_joint.spines['bottom'].set_linewidth(2.5)
ax_joint.set_xlabel('CFQ', fontsize=20, fontweight='bold')
ax_joint.set_ylabel(r'AWARE-UNAWARE $\beta$', fontsize=20, fontweight='bold')
#fig.set_size_inches((8, 6))
ax_joint.lines[0].set_linestyle("")
ax_joint.lines[1].set_linestyle("")
ax_joint.lines[2].set_linestyle(":")
ax_joint.tick_params(axis='both', which='major', labelsize=16,direction='out', length=5, left=True, bottom=True, width=2.5)
ax_joint.legend().set_visible(False)
fig.set_size_inches((4.5, 4))
fig.tight_layout()
fig.savefig(fname='./figure_3/R_insula_A_U_cog_fail')
fig.show()


# We need a separate dataframe for each group
hiv_pos_df = df.loc[df['HIV-status'] == 'HIV+']
hiv_neg_df = df.loc[df['HIV-status'] == 'HIV-']

# Then we make the jointplot for one of the groups only
jgrid = sns.jointplot(x='cog_fail_total', y='A_U_R_Putamen', 
                      data=hiv_pos_df, kind='reg',
                      color='purple', label='HIV positive',
                      joint_kws=dict(scatter_kws=dict(edgecolor='white', s=50)),
                      marginal_kws=dict(hist=False, kde_kws={'shade': True}),  # you can control axes with dicts
                      xlim=(-100, 500), ylim=(-100, 500), truncate=False, ci=None)  # note the huge limits
fig = jgrid.fig
ax_joint = jgrid.ax_joint
ax_x = jgrid.ax_marg_x
ax_y = jgrid.ax_marg_y

# Add the HIV-negative central regression plot to the joint axis
sns.regplot(x='cog_fail_total', y='A_U_R_Putamen', data=hiv_neg_df, ax=ax_joint,
            color='green', label='HIV negative', scatter_kws=dict(edgecolor='white', s=50, linewidth=0.6), truncate=False, ci=None)

# Add the full sample central regression plot to the joint axis
sns.regplot(x='cog_fail_total', y='A_U_R_Putamen', data=df, ax=ax_joint,
            color='darkslategray', label='all', truncate=False, scatter=False, ci=None)

# Clean up the axes
ax_y.set_ylabel(None)
ax_x.set_xlabel(None)
ax_joint.set_xticks([0, 20, 40, 60, 80, 100])
ax_joint.set_xlim(-2,100)
ax_joint.set_yticks([-2, 0, 2, 4])
ax_joint.set_ylim(-2, 4)
ax_joint.tick_params(axis='both', which='major', labelsize=16)
# Add a legend
ax_joint.legend(fontsize=16)
ax_joint.spines['left'].set_linewidth(2.5)
ax_joint.spines['bottom'].set_linewidth(2.5)
ax_joint.set_xlabel('CFQ', fontsize=20, fontweight='bold')
ax_joint.set_ylabel(r'AWARE-UNAWARE $\beta$', fontsize=20, fontweight='bold')
#fig.set_size_inches((8, 6))
ax_joint.lines[0].set_linestyle("")
ax_joint.lines[1].set_linestyle("")
ax_joint.lines[2].set_linestyle(":")
ax_joint.tick_params(axis='both', which='major', labelsize=16,direction='out', length=5, left=True, bottom=True, width=2.5)
ax_joint.legend().set_visible(False)
fig.set_size_inches((4.5, 4))
fig.tight_layout()
fig.savefig(fname='./figure_3/R_put_A_U_cog_fail')
fig.show()
# -



StarcoderdataJupyterScriptsDedupFiltered