def kdeplot(x, color='w', **kwargs):
global row_count, col_count
if color != 'w':
color = sns.light_palette(
color, n_colors=len(data) + 1)[row_count + 1]
sns.kdeplot(x, color=color, **kwargs)
col_count = (col_count + 1) % n_feat
if col_count == 0:
row_count = (row_count + 1) % len(data)
def main():
all_grps = df.loadExptGrps('GOL')
WT_expt_grp = all_grps['WT_hidden_behavior_set']
Df_expt_grp = all_grps['Df_hidden_behavior_set']
behavior_fn = ra.fraction_licks_in_reward_zone
behavior_kwargs = {}
activity_label = 'Fraction of licks in reward zone'
WT_colors = sns.light_palette(WT_color, 8)[::-1]
Df_colors = sns.light_palette(Df_color, 7)[::-1]
markers = ('o', 'v', '^', 'D', '*', 's')
fig, axs = plt.subplots(4, 2, figsize=(8.5, 11))
sns.despine(fig)
wt_ax = axs[0, 0]
df_ax = axs[0, 1]
for ax in list(axs.flat)[2:]:
ax.set_visible(False)
wt_expt_grps = [
WT_expt_grp.subGroup(list(expts['expt']), label=mouse)
for mouse, expts in WT_expt_grp.dataframe(
WT_expt_grp, include_columns=['mouseID']).groupby('mouseID')
]
df_expt_grps = [
Df_expt_grp.subGroup(list(expts['expt']), label=mouse)
for mouse, expts in Df_expt_grp.dataframe(
Df_expt_grp, include_columns=['mouseID']).groupby('mouseID')
]
plotting.plot_metric(wt_ax,
wt_expt_grps,
metric_fn=behavior_fn,
activity_kwargs=behavior_kwargs,
groupby=[['expt'], ['condition_day']],
plotby=['condition_day'],
plot_method='line',
ms=5,
activity_label=activity_label,
colors=WT_colors,
markers=markers,
label_every_n=1,
label_groupby=False,
rotate_labels=False)
wt_ax.set_xlabel('Day in Condition')
wt_ax.set_title(WT_expt_grp.label())
wt_ax.set_yticks([0, 0.1, 0.2, 0.3, 0.4, 0.5])
wt_ax.set_xticklabels(['1', '2', '3', '1', '2', '3', '1', '2', '3'])
label_conditions(wt_ax)
wt_ax.get_legend().set_visible(False)
wt_ax.tick_params(length=3, pad=2)
plotting.plot_metric(df_ax,
df_expt_grps,
metric_fn=behavior_fn,
activity_kwargs=behavior_kwargs,
groupby=[['expt'], ['condition_day']],
plotby=['condition_day'],
plot_method='line',
ms=5,
activity_label=activity_label,
colors=Df_colors,
markers=markers,
label_every_n=1,
label_groupby=False,
rotate_labels=False)
df_ax.set_xlabel('Day in Condition')
df_ax.set_title(Df_expt_grp.label())
df_ax.set_yticks([0, 0.1, 0.2, 0.3, 0.4, 0.5])
df_ax.set_xticklabels(['1', '2', '3', '1', '2', '3', '1', '2', '3'])
label_conditions(df_ax)
df_ax.get_legend().set_visible(False)
df_ax.tick_params(length=3, pad=2)
misc.save_figure(fig, filename, save_dir=save_dir)
def main():
all_grps = df.loadExptGrps('GOL')
WT_expt_grp = all_grps['WT_place_set']
Df_expt_grp = all_grps['Df_place_set']
expt_grps = [WT_expt_grp, Df_expt_grp]
if MALES_ONLY:
for expt_grp in expt_grps:
expt_grp.filter(lambda expt: expt.parent.get('sex') == 'M')
WT_label = WT_expt_grp.label()
Df_label = Df_expt_grp.label()
fig = plt.figure(figsize=(8.5, 11))
gs1 = plt.GridSpec(2,
5,
left=0.1,
right=0.3,
top=0.90,
bottom=0.67,
hspace=0.2)
gs1_2 = plt.GridSpec(2,
5,
left=0.3,
right=0.5,
top=0.90,
bottom=0.67,
hspace=0.2)
WT_1_heatmap_ax = fig.add_subplot(gs1[0, :-1])
WT_3_heatmap_ax = fig.add_subplot(gs1_2[0, :-1])
Df_1_heatmap_ax = fig.add_subplot(gs1[1, :-1])
Df_3_heatmap_ax = fig.add_subplot(gs1_2[1, :-1])
gs_cbar = plt.GridSpec(2,
10,
left=0.3,
right=0.5,
top=0.90,
bottom=0.67,
hspace=0.2)
WT_colorbar_ax = fig.add_subplot(gs_cbar[0, -1])
Df_colorbar_ax = fig.add_subplot(gs_cbar[1, -1])
gs2 = plt.GridSpec(1, 10, left=0.1, right=0.5, top=0.6, bottom=0.45)
pf_close_fraction_ax = fig.add_subplot(gs2[0, :4])
pf_close_behav_corr_ax = fig.add_subplot(gs2[0, 5:])
frac_near_range_2 = (-0.051, 0.551)
behav_range_2 = (-0.051, 0.551)
#
# Heatmaps
#
WT_cmap = sns.light_palette(WT_color, as_cmap=True)
WT_dataframe = lab.ExperimentGroup.dataframe(
WT_expt_grp, include_columns=['X_condition', 'X_day', 'X_session'])
WT_1_expt_grp = WT_expt_grp.subGroup(
list(WT_dataframe[(WT_dataframe['X_condition'] == 'C')
& (WT_dataframe['X_day'] == '0') &
(WT_dataframe['X_session'] == '0')]['expt']))
place.plotPositionHeatmap(WT_1_expt_grp,
roi_filter=WT_filter,
ax=WT_1_heatmap_ax,
norm='individual',
cbar_visible=False,
cmap=WT_cmap,
plotting_order='place_cells_only',
show_belt=False,
reward_in_middle=True)
fix_heatmap_ax(WT_1_heatmap_ax, WT_1_expt_grp)
WT_1_heatmap_ax.set_title(r'Condition $\mathrm{III}$: Day 1')
WT_1_heatmap_ax.set_ylabel(WT_label)
WT_1_heatmap_ax.set_xlabel('')
WT_3_expt_grp = WT_expt_grp.subGroup(
list(WT_dataframe[(WT_dataframe['X_condition'] == 'C')
& (WT_dataframe['X_day'] == '2') &
(WT_dataframe['X_session'] == '0')]['expt']))
place.plotPositionHeatmap(WT_3_expt_grp,
roi_filter=WT_filter,
ax=WT_3_heatmap_ax,
norm='individual',
cbar_visible=True,
cax=WT_colorbar_ax,
cmap=WT_cmap,
plotting_order='place_cells_only',
show_belt=False,
reward_in_middle=True)
fix_heatmap_ax(WT_3_heatmap_ax, WT_3_expt_grp)
WT_3_heatmap_ax.set_title(r'Condition $\mathrm{III}$: Day 3')
WT_3_heatmap_ax.set_ylabel('')
WT_3_heatmap_ax.set_xlabel('')
WT_colorbar_ax.set_yticklabels(['Min', 'Max'])
Df_cmap = sns.light_palette(Df_color, as_cmap=True)
Df_dataframe = lab.ExperimentGroup.dataframe(
Df_expt_grp, include_columns=['X_condition', 'X_day', 'X_session'])
Df_1_expt_grp = Df_expt_grp.subGroup(
list(Df_dataframe[(Df_dataframe['X_condition'] == 'C')
& (Df_dataframe['X_day'] == '0') &
(Df_dataframe['X_session'] == '2')]['expt']))
place.plotPositionHeatmap(Df_1_expt_grp,
roi_filter=Df_filter,
ax=Df_1_heatmap_ax,
norm='individual',
cbar_visible=False,
cmap=Df_cmap,
plotting_order='place_cells_only',
show_belt=False,
reward_in_middle=True)
fix_heatmap_ax(Df_1_heatmap_ax, Df_1_expt_grp)
Df_1_heatmap_ax.set_ylabel(Df_label)
Df_3_expt_grp = Df_expt_grp.subGroup(
list(Df_dataframe[(Df_dataframe['X_condition'] == 'C')
& (Df_dataframe['X_day'] == '2') &
(Df_dataframe['X_session'] == '0')]['expt']))
place.plotPositionHeatmap(Df_3_expt_grp,
roi_filter=Df_filter,
ax=Df_3_heatmap_ax,
norm='individual',
cbar_visible=True,
cax=Df_colorbar_ax,
cmap=Df_cmap,
plotting_order='place_cells_only',
show_belt=False,
reward_in_middle=True)
fix_heatmap_ax(Df_3_heatmap_ax, Df_3_expt_grp)
Df_3_heatmap_ax.set_ylabel('')
Df_colorbar_ax.set_yticklabels(['Min', 'Max'])
#
# Fraction of PCs near reward
#
activity_metric = place.centroid_to_position_threshold
activity_kwargs = {
'method': 'resultant_vector',
'positions': 'reward',
'pcs_only': True,
'threshold': np.pi / 8
}
behavior_fn = ra.fraction_licks_in_reward_zone
behavior_kwargs = {}
behavior_label = 'Fraction of licks in reward zone'
plotting.plot_metric(pf_close_fraction_ax,
expt_grps,
metric_fn=activity_metric,
roi_filters=roi_filters,
groupby=[['expt', 'X_condition', 'X_day']],
plotby=['X_condition', 'X_day'],
plot_abs=False,
plot_method='line',
activity_kwargs=activity_kwargs,
rotate_labels=False,
activity_label='Fraction of place cells near reward',
label_every_n=1,
colors=colors,
markers=markers,
markersize=5,
return_full_dataframes=False,
linestyles=linestyles)
pf_close_fraction_ax.axhline(1 / 8., linestyle='--', color='k')
pf_close_fraction_ax.set_title('')
sns.despine(ax=pf_close_fraction_ax)
pf_close_fraction_ax.set_xlabel('Day in Condition')
day_number_only_label(pf_close_fraction_ax)
label_conditions(pf_close_fraction_ax)
pf_close_fraction_ax.legend(loc='upper left', fontsize=6)
pf_close_fraction_ax.set_ylim(0, 0.40)
pf_close_fraction_ax.set_yticks([0, 0.1, 0.2, 0.3, 0.4])
scatter_kws = {'s': 5}
colorby_list = [(expt_grp.label(), 'C') for expt_grp in expt_grps]
pf_close_behav_corr_ax.set_xlim(frac_near_range_2)
pf_close_behav_corr_ax.set_ylim(behav_range_2)
plotting.plot_paired_metrics(
expt_grps,
first_metric_fn=place.centroid_to_position_threshold,
second_metric_fn=behavior_fn,
roi_filters=roi_filters,
groupby=(('expt', ), ),
colorby=('expt_grp', 'X_condition'),
filter_fn=lambda df: df['X_condition'] == 'C',
filter_columns=['X_condition'],
first_metric_kwargs=activity_kwargs,
second_metric_kwargs=behavior_kwargs,
first_metric_label='Fraction of place cells near reward',
second_metric_label=behavior_label,
shuffle_colors=False,
fit_reg=True,
plot_method='regplot',
colorby_list=colorby_list,
colors=colors,
markers=markers,
ax=pf_close_behav_corr_ax,
scatter_kws=scatter_kws,
truncate=False,
linestyles=linestyles)
pf_close_behav_corr_ax.set_xlim(frac_near_range_2)
pf_close_behav_corr_ax.set_ylim(behav_range_2)
pf_close_behav_corr_ax.tick_params(direction='in')
pf_close_behav_corr_ax.get_legend().set_visible(False)
pf_close_behav_corr_ax.legend(loc='upper left', fontsize=6)
misc.save_figure(fig, filename, save_dir=save_dir)
plt.close('all')
def main():
hidden_grps = df.loadExptGrps('GOL')
WT_expt_grp_hidden = hidden_grps['WT_place_set']
Df_expt_grp_hidden = hidden_grps['Df_place_set']
expt_grps_hidden = [WT_expt_grp_hidden, Df_expt_grp_hidden]
acute_grps = df.loadExptGrps('RF')
WT_expt_grp_acute = acute_grps['WT_place_set'].unpair()
Df_expt_grp_acute = acute_grps['Df_place_set'].unpair()
expt_grps_acute = [WT_expt_grp_acute, Df_expt_grp_acute]
WT_label = WT_expt_grp_hidden.label()
Df_label = Df_expt_grp_hidden.label()
labels = [WT_label, Df_label]
fig = plt.figure(figsize=(8.5, 11))
gs1 = plt.GridSpec(1, 1, top=0.9, bottom=0.7, left=0.1, right=0.20)
across_ctx_ax = fig.add_subplot(gs1[0, 0])
gs2 = plt.GridSpec(3, 1, top=0.9, bottom=0.7, left=0.25, right=0.35)
wt_pie_ax = fig.add_subplot(gs2[0, 0])
df_pie_ax = fig.add_subplot(gs2[1, 0])
shuffle_pie_ax = fig.add_subplot(gs2[2, 0])
pie_axs = (wt_pie_ax, df_pie_ax, shuffle_pie_ax)
gs3 = plt.GridSpec(1, 1, top=0.9, bottom=0.7, left=0.4, right=0.5)
cue_cell_bar_ax = fig.add_subplot(gs3[0, 0])
gs5 = plt.GridSpec(1, 1, top=0.5, bottom=0.3, left=0.1, right=0.3)
acute_stability_ax = fig.add_subplot(gs5[0, 0])
acute_stability_inset_ax = fig.add_axes([0.23, 0.32, 0.05, 0.08])
gs6 = plt.GridSpec(1, 1, top=0.5, bottom=0.3, left=0.4, right=0.5)
task_compare_ax = fig.add_subplot(gs6[0, 0])
#
# RF Compare
#
params = {}
params['filename'] = filename
params_cent_shift_pc = {}
params_cent_shift_pc['stability_fn'] = place.activity_centroid_shift
params_cent_shift_pc['stability_kwargs'] = {
'activity_filter': 'pc_both',
'circ_var_pcs': False,
'units': 'norm',
'shuffle': True
}
params_cent_shift_pc['stability_label'] = \
'Centroid shift (fraction of belt)'
params_cent_shift_all = {}
params_cent_shift_all['stability_fn'] = place.activity_centroid_shift
params_cent_shift_all['stability_kwargs'] = {
'activity_filter': 'active_both',
'circ_var_pcs': False,
'units': 'norm',
'shuffle': True
}
params_cent_shift_all['stability_label'] = \
'Centroid shift (fraction of belt)'
params_cent_shift_all['stability_inset_ylim'] = (0.15, 0.30)
params_cent_shift_all['stability_cdf_range'] = (0.15, 0.35)
params_cent_shift_all['stability_cdf_ticks'] = \
(0.15, 0.20, 0.25, 0.30, 0.35)
params_cent_shift_all['stability_compare_ylim'] = (0.15, 0.27)
params_cent_shift_all['stability_compare_yticks'] = (0.15, 0.20, 0.25)
params_cent_shift_all['ctx_compare_ylim'] = (0.10, 0.30)
params_cent_shift_all['ctx_compare_yticks'] = \
(0.10, 0.15, 0.20, 0.25, 0.30)
params_cent_shift_cm = {}
params_cent_shift_cm['stability_fn'] = place.activity_centroid_shift
params_cent_shift_cm['stability_kwargs'] = {
'activity_filter': 'active_both',
'circ_var_pcs': False,
'units': 'cm',
'shuffle': True
}
params_cent_shift_cm['stability_label'] = 'Centroid shift (cm)'
params_pop_vect_corr = {}
params_pop_vect_corr['stability_fn'] = place.population_vector_correlation
params_pop_vect_corr['stability_kwargs'] = {
'method': 'corr',
'activity_filter': 'pc_both',
'min_pf_density': 0.05,
'circ_var_pcs': False
}
params_pop_vect_corr['stability_label'] = 'Population vector correlation'
params_pf_corr = {}
params_pf_corr['stability_fn'] = place.place_field_correlation
params_pf_corr['stability_kwargs'] = {'activity_filter': 'pc_either'}
params_pf_corr['stability_label'] = 'Place field correlation'
params_pf_corr['stability_inset_ylim'] = (0, 0.50)
params_pf_corr['stability_cdf_range'] = (0.15, 0.55)
params_pf_corr['stability_cdf_ticks'] = (0.15, 0.25, 0.35, 0.45, 0.55)
params_pf_corr['stability_compare_ylim'] = (0.22, 0.40)
params_pf_corr['stability_compare_yticks'] = (0.25, 0.30, 0.35, 0.40)
params_pf_corr['hidden_ctx_compare_ylim'] = (0.22, 0.40)
params_pf_corr['hidden_ctx_compare_yticks'] = (0.25, 0.30, 0.35, 0.40)
params_pf_corr['ctx_compare_ylim'] = (0.22, 0.40)
params_pf_corr['ctx_compare_yticks'] = (0.25, 0.30, 0.35, 0.40)
params.update(params_cent_shift_all)
day_paired_grps_acute = [
grp.pair('consecutive groups', groupby=['day_in_df'])
for grp in expt_grps_acute
]
paired_grps_acute = day_paired_grps_acute
paired_grps_hidden = [
grp.pair('consecutive groups', groupby=['X_condition', 'X_day'])
for grp in expt_grps_hidden
]
filter_fn = lambda df: (df['expt_pair_label'] == 'SameAll')
filter_columns = ['expt_pair_label']
acute_stability = plotting.plot_metric(
acute_stability_ax,
paired_grps_acute,
metric_fn=params['stability_fn'],
groupby=[['expt_pair_label', 'second_expt']],
plotby=None,
plot_method='cdf',
plot_abs=True,
roi_filters=roi_filters,
activity_kwargs=params['stability_kwargs'],
plot_shuffle=True,
shuffle_plotby=False,
pool_shuffle=True,
activity_label=params['stability_label'],
colors=colors,
rotate_labels=False,
filter_fn=filter_fn,
filter_columns=filter_columns,
return_full_dataframes=False,
linestyles=linestyles)
acute_stability_ax.set_xlabel(params['stability_label'])
acute_stability_ax.set_title('')
sns.despine(ax=acute_stability_ax)
acute_stability_ax.set_xlim(params['stability_cdf_range'])
acute_stability_ax.set_xticks(params['stability_cdf_ticks'])
acute_stability_ax.legend(loc='upper left', fontsize=6)
plotting.plot_metric(acute_stability_inset_ax,
paired_grps_acute,
metric_fn=params['stability_fn'],
groupby=[['second_expt'], ['second_mouseID']],
plotby=None,
plot_method='swarm',
plot_abs=True,
roi_filters=roi_filters,
activity_kwargs=params['stability_kwargs'],
plot_shuffle=True,
shuffle_plotby=False,
pool_shuffle=True,
activity_label=params['stability_label'],
colors=colors,
rotate_labels=False,
filter_fn=filter_fn,
filter_columns=filter_columns,
linewidth=0.2,
edgecolor='gray',
plot_shuffle_as_hline=True)
acute_stability_inset_ax.get_legend().set_visible(False)
sns.despine(ax=acute_stability_inset_ax)
acute_stability_inset_ax.set_title('')
acute_stability_inset_ax.set_ylabel('')
acute_stability_inset_ax.set_xlabel('')
acute_stability_inset_ax.tick_params(bottom=False, labelbottom=False)
acute_stability_inset_ax.set_ylim(params['stability_inset_ylim'])
acute_stability_inset_ax.set_yticks(params['stability_inset_ylim'])
tmp_fig = plt.figure()
tmp_ax = tmp_fig.add_subplot(111)
hidden_stability = plotting.plot_metric(
tmp_ax,
paired_grps_hidden,
metric_fn=params['stability_fn'],
groupby=[['expt_pair_label', 'second_expt']],
plotby=('expt_pair_label', ),
plot_method='line',
plot_abs=True,
roi_filters=roi_filters,
activity_kwargs=params['stability_kwargs'],
plot_shuffle=True,
shuffle_plotby=False,
pool_shuffle=True,
activity_label=params['stability_label'],
colors=colors,
rotate_labels=False,
filter_fn=filter_fn,
filter_columns=filter_columns,
return_full_dataframes=False)
plt.close(tmp_fig)
wt_acute = acute_stability[WT_label]['dataframe']
wt_acute_shuffle = acute_stability[WT_label]['shuffle']
df_acute = acute_stability[Df_label]['dataframe']
df_acute_shuffle = acute_stability[Df_label]['shuffle']
wt_hidden = hidden_stability[WT_label]['dataframe']
wt_hidden_shuffle = hidden_stability[WT_label]['shuffle']
df_hidden = hidden_stability[Df_label]['dataframe']
df_hidden_shuffle = hidden_stability[Df_label]['shuffle']
for dataframe in (wt_acute, wt_acute_shuffle, df_acute, df_acute_shuffle):
dataframe['task'] = 'RF'
for dataframe in (wt_hidden, wt_hidden_shuffle, df_hidden,
df_hidden_shuffle):
dataframe['task'] = 'GOL'
WT_data = wt_acute.append(wt_hidden, ignore_index=True)
Df_data = df_acute.append(df_hidden, ignore_index=True)
WT_shuffle = wt_acute_shuffle.append(wt_hidden_shuffle, ignore_index=True)
Df_shuffle = df_acute_shuffle.append(df_hidden_shuffle, ignore_index=True)
filter_columns = ('expt_pair_label', )
filter_fn = lambda df: (df['expt_pair_label'] == 'SameAll')
order_dict = {'RF': 0, 'GOL': 1}
WT_data['order'] = WT_data['task'].map(order_dict)
Df_data['order'] = Df_data['task'].map(order_dict)
line_kwargs = {'markersize': 4}
plotting.plot_dataframe(task_compare_ax, [WT_data, Df_data],
[WT_shuffle, Df_shuffle],
labels=labels,
activity_label='',
groupby=[['task', 'second_mouseID']],
plotby=('task', ),
plot_method='box_and_line',
colors=colors,
filter_fn=filter_fn,
filter_columns=filter_columns,
plot_shuffle=True,
shuffle_plotby=False,
pool_shuffle=True,
orderby='order',
notch=False,
plot_shuffle_as_hline=True,
markers=markers,
linestyles=linestyles,
line_kwargs=line_kwargs,
flierprops={
'markersize': 3,
'marker': 'o'
},
whis='range')
task_compare_ax.set_title('')
sns.despine(ax=task_compare_ax)
task_compare_ax.set_ylim(params['stability_compare_ylim'])
task_compare_ax.set_yticks(params['stability_compare_yticks'])
task_compare_ax.set_xlabel('')
task_compare_ax.set_ylabel(params['stability_label'])
task_compare_ax.legend(loc='upper right', fontsize=6)
#
# Stability across transition
#
groupby = [['second_expt'], ['second_mouse']]
filter_fn = lambda df: (df['X_first_condition'] == 'A') \
& (df['X_second_condition'] == 'B')
filter_columns = ('X_first_condition', 'X_second_condition')
plotting.plot_metric(across_ctx_ax,
paired_grps_hidden,
metric_fn=params['stability_fn'],
groupby=groupby,
plotby=None,
plot_method='swarm',
activity_kwargs=params['stability_kwargs'],
plot_shuffle=True,
shuffle_plotby=False,
pool_shuffle=True,
colors=colors,
activity_label=params['stability_label'],
rotate_labels=False,
filter_fn=filter_fn,
filter_columns=filter_columns,
plot_shuffle_as_hline=True,
return_full_dataframes=False,
plot_bar=True,
roi_filters=roi_filters)
sns.despine(ax=across_ctx_ax)
across_ctx_ax.set_ylim(0.0, 0.3)
across_ctx_ax.set_yticks([0.0, 0.1, 0.2, 0.3])
across_ctx_ax.set_xticklabels([])
across_ctx_ax.set_xlabel('')
across_ctx_ax.set_title('')
across_ctx_ax.get_legend().set_visible(False)
plotting.stackedText(across_ctx_ax, labels, colors=colors, loc=2, size=10)
#
# Cue remapping
#
THRESHOLD = 0.05 * 2 * np.pi
CUENESS_THRESHOLD = 0.33
def first_cue_position(row):
expt = row['first_expt']
cue = row['cue']
cues = expt.belt().cues(normalized=True)
first_cue = cues.ix[cues['cue'] == cue]
pos = (first_cue['start'] + first_cue['stop']) / 2
angle = pos * 2 * np.pi
return np.complex(np.cos(angle), np.sin(angle))
def dotproduct(v1, v2):
return sum((a * b) for a, b in zip(v1, v2))
def length(v):
return math.sqrt(dotproduct(v, v))
def angle(v1, v2):
return math.acos(
np.round(dotproduct(v1, v2) / (length(v1) * length(v2)), 3))
def distance_to_first_cue(row):
centroid = row['second_centroid']
pos = row['first_cue_position']
return angle((pos.real, pos.imag), (centroid.real, centroid.imag))
WT_copy = copy(WT_expt_grp_hidden)
WT_copy.filterby(lambda df: ~df['X_condition'].str.contains('C'),
['X_condition'])
WT_paired = WT_copy.pair('consecutive groups',
groupby=['X_condition',
'X_day']).pair('consecutive groups',
groupby=['X_condition'])
Df_copy = copy(Df_expt_grp_hidden)
Df_copy.filterby(lambda df: ~df['X_condition'].str.contains('C'),
['X_condition'])
Df_paired = Df_copy.pair('consecutive groups',
groupby=['X_condition',
'X_day']).pair('consecutive groups',
groupby=['X_condition'])
WT_df, WT_shuffle_df = place.cue_cell_remapping(
WT_paired,
roi_filter=WT_filter,
near_threshold=THRESHOLD,
activity_filter='active_both',
circ_var_pcs=False,
shuffle=True)
Df_df, Df_shuffle_df = place.cue_cell_remapping(
Df_paired,
roi_filter=Df_filter,
near_threshold=THRESHOLD,
activity_filter='active_both',
circ_var_pcs=False,
shuffle=True)
shuffle_df = pd.concat([WT_shuffle_df, Df_shuffle_df], ignore_index=True)
cueness, cueness_fraction = [], []
cue_n, place_n, neither_n = [], [], []
for grp_df in (WT_df, Df_df, shuffle_df):
grp_df['first_cue_position'] = grp_df.apply(first_cue_position, axis=1)
grp_df['second_distance_to_first_cue_position'] = grp_df.apply(
distance_to_first_cue, axis=1)
grp_df['cueness'] = grp_df['second_distance_to_first_cue_position'] / \
(grp_df['value'] + grp_df['second_distance_to_first_cue_position'])
plotting.prepare_dataframe(grp_df, ['first_mouse'])
cueness_fraction.append([[]])
cue_n.append([])
place_n.append([])
neither_n.append([])
for mouse, mouse_df in grp_df.groupby('first_mouse'):
cue_n[-1].append((mouse_df['cueness'] >
(1 - CUENESS_THRESHOLD)).sum())
place_n[-1].append((mouse_df['cueness'] < CUENESS_THRESHOLD).sum())
neither_n[-1].append(mouse_df.shape[0] - cue_n[-1][-1] -
place_n[-1][-1])
cueness_fraction[-1][0].append(cue_n[-1][-1] /
float(place_n[-1][-1]))
cueness.append([grp_df['cueness']])
cue_labels = labels + ['shuffle']
plotting.swarm_plot(cue_cell_bar_ax,
cueness_fraction[:2],
condition_labels=labels,
colors=colors,
plot_bar=True)
cue_cell_bar_ax.axhline(np.mean(cueness_fraction[-1][0]),
ls='--',
color='k')
sns.despine(ax=cue_cell_bar_ax)
cue_cell_bar_ax.set_ylim(0, 1.5)
cue_cell_bar_ax.set_yticks([0, 0.5, 1.0, 1.5])
cue_cell_bar_ax.set_xticklabels([])
cue_cell_bar_ax.set_xlabel('')
cue_cell_bar_ax.set_ylabel('Cue-to-position ratio')
cue_cell_bar_ax.get_legend().set_visible(False)
plotting.stackedText(cue_cell_bar_ax,
labels,
colors=colors,
loc=2,
size=10)
WT_colors = sns.light_palette(WT_color, 7)[:-6:-2]
Df_colors = sns.light_palette(Df_color, 7)[:-6:-2]
shuffle_colors = sns.light_palette('k', 7)[:-6:-2]
pie_colors = (WT_colors, Df_colors, shuffle_colors)
pie_labels = ['cue', 'position', 'neither']
orig_size = mpl.rcParams.get('xtick.labelsize')
mpl.rcParams['xtick.labelsize'] = 5
for grp_ax, grp_label, grp_cue_n, grp_place_n, grp_neither_n, p_cs in zip(
pie_axs, cue_labels, cue_n, place_n, neither_n, pie_colors):
grp_ax.pie([sum(grp_cue_n),
sum(grp_place_n),
sum(grp_neither_n)],
autopct='%1.0f%%',
shadow=False,
frame=False,
labels=pie_labels,
colors=p_cs,
textprops={'fontsize': 5})
grp_ax.set_title(grp_label)
plotting.square_axis(grp_ax)
mpl.rcParams['xtick.labelsize'] = orig_size
misc.save_figure(fig, params['filename'], save_dir=save_dir)
plt.close('all')
def main():
fig = plt.figure(figsize=(8.5, 11))
gs1 = plt.GridSpec(2,
2,
left=0.1,
right=0.7,
top=0.9,
bottom=0.5,
hspace=0.4,
wspace=0.4)
WT_enrich_ax = fig.add_subplot(gs1[0, 0])
Df_enrich_ax = fig.add_subplot(gs1[0, 1])
WT_final_dist_ax = fig.add_subplot(gs1[1, 0])
Df_final_dist_ax = fig.add_subplot(gs1[1, 1])
simulations_path_A = os.path.join(
df.data_path, 'enrichment_model',
'WT_Df_enrichment_model_simulation_A.pkl')
simulations_path_B = os.path.join(
df.data_path, 'enrichment_model',
'WT_Df_enrichment_model_simulation_B.pkl')
simulations_path_C = os.path.join(
df.data_path, 'enrichment_model',
'WT_Df_enrichment_model_simulation_C.pkl')
m_A = pickle.load(open(simulations_path_A))
m_B = pickle.load(open(simulations_path_B))
m_C = pickle.load(open(simulations_path_C))
WT_colors = sns.light_palette(WT_color, 7)[2::2]
Df_colors = sns.light_palette(Df_color, 7)[2::2]
condition_labels = [
r'Condition $\mathrm{I}$', r'Condition $\mathrm{II}$',
r'Condition $\mathrm{III}$'
]
WT_final_dists, Df_final_dists = [], []
for m, WT_c, Df_c in zip((m_A, m_B, m_C), WT_colors, Df_colors):
WT_enrich = emp.calc_enrichment(m['WT_no_swap_pos'],
m['WT_no_swap_masks'])
Df_enrich = emp.calc_enrichment(m['Df_no_swap_pos'],
m['Df_no_swap_masks'])
WT_final_dists.append(
emp.calc_final_distributions(m['WT_no_swap_pos'],
m['WT_no_swap_masks']))
Df_final_dists.append(
emp.calc_final_distributions(m['Df_no_swap_pos'],
m['Df_no_swap_masks']))
emp.plot_enrichment(WT_enrich_ax, WT_enrich, WT_c, title='', rad=False)
emp.plot_enrichment(Df_enrich_ax, Df_enrich, Df_c, title='', rad=False)
WT_enrich_ax.set_xlabel("Iteration ('session' #)")
Df_enrich_ax.set_xlabel("Iteration ('session' #)")
plotting.stackedText(WT_enrich_ax,
condition_labels,
colors=WT_colors,
loc=2,
size=8)
plotting.stackedText(Df_enrich_ax,
condition_labels,
colors=Df_colors,
loc=2,
size=8)
emp.plot_final_distributions(WT_final_dist_ax,
WT_final_dists,
WT_colors,
labels=condition_labels,
title='',
rad=False)
emp.plot_final_distributions(Df_final_dist_ax,
Df_final_dists,
Df_colors,
labels=condition_labels,
title='',
rad=False)
WT_final_dist_ax.set_xlabel('Distance from reward\n(fraction of belt)')
Df_final_dist_ax.set_xlabel('Distance from reward\n(fraction of belt)')
plotting.stackedText(WT_final_dist_ax,
condition_labels,
colors=WT_colors,
loc=2,
size=8)
plotting.stackedText(Df_final_dist_ax,
condition_labels,
colors=Df_colors,
loc=2,
size=8)
WT_final_dist_ax.set_yticks([0, 0.1, 0.2, 0.3])
Df_final_dist_ax.set_yticks([0, 0.1, 0.2, 0.3])
save_figure(fig, filename, save_dir=save_dir)
def get_cmap(base_color, n_colors=256):
colors = [np.array([1., 1., 1., 0])] + \
sns.light_palette(base_color, n_colors=n_colors - 1)
return mpl_colors.ListedColormap(colors)