def loadExptGrps(mouse_set):
expts = lab.ExperimentSet(os.path.join(metadata_path, 'expt_metadata.xml'),
behaviorDataPath=os.path.join(
data_path, 'behavior'),
dataPath=os.path.join(data_path, 'imaging'))
mouse_sets = {}
mouse_sets['GOL'] = ('jz096', 'jz097', 'jz098', 'jz100', 'jz101', 'jz102',
'jz106', 'jz113', 'jz114', 'jz121', 'jz135', 'jz136')
mouse_sets['RF'] = ('jz049', 'jz051', 'jz052', 'jz053', 'jz054', 'jz058',
'jz059', 'jz060', 'jz064', 'jz066', 'jz067')
if mouse_set not in mouse_sets:
raise ValueError('Unrecognized mouse set')
exptGrps = {}
WT = []
Df = []
for mouse in [expts.grabMouse(m) for m in mouse_sets[mouse_set]]:
genotype = mouse.get('genotype').lower()
if 'df16ap' in genotype:
Df.append(mouse)
elif 'df16an' in genotype:
WT.append(mouse)
exptGrps['WT_mice'] = WT
exptGrps['Df_mice'] = Df
if mouse_set == 'GOL':
exptGrps['WT_hidden_behavior_set'] = \
lab.classes.HiddenRewardExperimentGroup.from_json(
WT_behavior_set, expts, label=WT_label)
exptGrps['Df_hidden_behavior_set'] = \
lab.classes.HiddenRewardExperimentGroup.from_json(
Df_behavior_set, expts, label=Df_label)
exptGrps['WT_place_set'] = \
lab.classes.pcExperimentGroup.from_json(
WT_imaging_set, expts, imaging_label=IMAGING_LABEL,
label=WT_label)
exptGrps['Df_place_set'] = \
lab.classes.pcExperimentGroup.from_json(
Df_imaging_set, expts, imaging_label=IMAGING_LABEL,
label=Df_label)
elif mouse_set == 'RF':
exptGrps['WT_place_set'] = \
lab.classes.pcExperimentGroup.from_json(
WT_RF_set, expts, imaging_label=IMAGING_LABEL,
label=WT_label).pair()
exptGrps['Df_place_set'] = \
lab.classes.pcExperimentGroup.from_json(
Df_RF_set, expts, imaging_label=IMAGING_LABEL,
label=Df_label).pair()
return exptGrps
def main():
expts = lab.ExperimentSet(
os.path.join(df.metadata_path, 'expt_metadata.xml'),
behaviorDataPath=os.path.join(df.data_path, 'behavior'),
dataPath=os.path.join(df.data_path, 'imaging'))
sal_grp = lab.classes.HiddenRewardExperimentGroup.from_json(
sal_json, expts, label='saline to muscimol')
mus_grp = lab.classes.HiddenRewardExperimentGroup.from_json(
mus_json, expts, label='muscimol to saline')
fig = plt.figure(figsize=(8.5, 11))
gs = plt.GridSpec(1, 1, top=0.9, bottom=0.7, left=0.1, right=0.4)
ax = fig.add_subplot(gs[0, 0])
for expt in mus_grp:
if 'saline' in expt.get('drug'):
expt.attrib['drug_condition'] = 'reversal'
elif 'muscimol' in expt.get('drug'):
expt.attrib['drug_condition'] = 'learning'
for expt in sal_grp:
if 'saline' in expt.get('drug'):
expt.attrib['drug_condition'] = 'learning'
elif 'muscimol' in expt.get('drug'):
expt.attrib['drug_condition'] = 'reversal'
plotting.plot_metric(
ax, [sal_grp, mus_grp], metric_fn=ra.fraction_licks_in_reward_zone,
label_groupby=False, plotby=['X_drug_condition'],
plot_method='swarm', rotate_labels=False,
activity_label='Fraction of licks in reward zone',
colors=sns.color_palette('deep'), plot_bar=True)
ax.set_yticks([0, 0.1, 0.2, 0.3, 0.4])
ax.set_ylim(top=0.4)
ax.set_xticklabels(['Days 1-3', 'Day 4'])
sns.despine(fig)
ax.set_title('')
ax.set_xlabel('')
misc.save_figure(
fig, filename, save_dir=save_dir)
plt.close('all')
def main():
raw_data, data = emd.load_data('wt',
session_filter='C',
root=os.path.join(df.data_path,
'enrichment_model'))
expts = lab.ExperimentSet(os.path.join(df.metadata_path,
'expt_metadata.xml'),
behaviorDataPath=os.path.join(
df.data_path, 'behavior'),
dataPath=os.path.join(df.data_path, 'imaging'))
params = pickle.load(open(params_path))
fig = plt.figure(figsize=(8.5, 11))
gs1 = plt.GridSpec(1,
2,
left=0.1,
bottom=0.65,
right=0.9,
top=0.9,
wspace=0.05)
fov1_ax = fig.add_subplot(gs1[0, 0])
fov2_ax = fig.add_subplot(gs1[0, 1])
cmap_ax = fig.add_axes([0.49, 0.65, 0.02, 0.25])
gs2 = plt.GridSpec(2,
2,
left=0.1,
bottom=0.3,
right=0.5,
top=0.6,
wspace=0.5,
hspace=0.5)
recur_ax = fig.add_subplot(gs2[0, 0])
shift_ax = fig.add_subplot(gs2[0, 1])
shift_compare_ax = fig.add_subplot(gs2[1, 0])
var_compare_ax = fig.add_subplot(gs2[1, 1])
#
# Tuning maps
#
e1 = expts.grabExpt('jz135', '2015-10-12-14h33m47s')
e2 = expts.grabExpt('jz135', '2015-10-12-15h34m38s')
cmap = mpl.colors.ListedColormap(sns.color_palette("husl", 256))
for ax, expt in ((fov1_ax, e1), (fov2_ax, e2)):
place.plot_spatial_tuning_overlay(ax,
lab.classes.pcExperimentGroup(
[expt], imaging_label='soma'),
labels_visible=False,
alpha=0.9,
lw=0.1,
cmap=cmap)
plot_ROI_outlines(ax,
expt,
channel='Ch2',
label='soma',
roi_filter=None,
ls='-',
color='k',
lw=0.1)
# Add a 50-um scale bar
plotting.add_scalebar(
ax=ax,
matchx=False,
matchy=False,
sizey=0,
sizex=50 / expt.imagingParameters()['micronsPerPixel']['XAxis'],
bar_color='w',
bar_thickness=3)
fov1_ax.set_title('Session 1')
fov2_ax.set_title('Session 2')
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient)).T
cmap_ax.imshow(gradient, aspect='auto', cmap=cmap)
sns.despine(ax=cmap_ax, top=True, left=True, right=True, bottom=True)
cmap_ax.tick_params(left=False,
labelleft=False,
bottom=False,
labelbottom=False)
cmap_ax.set_ylabel('belt position')
# Figure out the reward window width
reward_poss, windows = [], []
for expt in [e1, e2]:
reward_poss.append(expt.rewardPositions(units='normalized')[0])
track_length = expt[0].behaviorData()['trackLength']
window = float(expt.get('operantSpatialWindow'))
windows.append(window / track_length)
reward_pos = np.mean(reward_poss)
window = np.mean(windows)
# Add reward zone
cmap_ax.plot([0, 1], [reward_pos, reward_pos],
transform=cmap_ax.transAxes,
color='k',
ls=':')
cmap_ax.plot([0, 1], [reward_pos + window, reward_pos + window],
transform=cmap_ax.transAxes,
color='k',
ls=':')
cmap_ax.set_ylim(0, 256)
#
# Recurrence by position
#
recur_x_vals = np.linspace(-np.pi, np.pi, 1000)
recur_data = emd.recurrence_by_position(data, method='cv')
recur_knots = np.linspace(-np.pi, np.pi,
params['position_recurrence']['n_knots'])
recur_splines = splines.CyclicSpline(recur_knots)
recur_n = recur_splines.design_matrix(recur_x_vals)
recur_fit = splines.prob(params['position_recurrence']['theta'], recur_n)
recur_boots_fits = [
splines.prob(boot, recur_n)
for boot in params['position_recurrence']['boots_theta']
]
recur_ci_up_fit = np.percentile(recur_boots_fits, 95, axis=0)
recur_ci_low_fit = np.percentile(recur_boots_fits, 5, axis=0)
recur_ax.plot(recur_x_vals, recur_fit, color=WT_color)
recur_ax.fill_between(recur_x_vals,
recur_ci_low_fit,
recur_ci_up_fit,
facecolor=WT_color,
alpha=0.5)
sns.regplot(recur_data[:, 0],
recur_data[:, 1],
ax=recur_ax,
color=WT_color,
y_jitter=0.2,
fit_reg=False,
scatter_kws={'s': 1},
marker=WT_marker)
recur_ax.axvline(ls='--', color='0.4', lw=0.5)
recur_ax.set_xlim(-np.pi, np.pi)
recur_ax.set_xticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi])
recur_ax.set_xticklabels(['-0.50', '-0.25', '0', '0.25', '0.50'])
recur_ax.set_ylim(-0.3, 1.3)
recur_ax.set_yticks([0, 0.5, 1])
recur_ax.tick_params(length=3, pad=1, top=False)
recur_ax.set_xlabel('Initial distance from reward (fraction of belt)')
recur_ax.set_ylabel('Place cell recurrence probability')
recur_ax.set_title('')
recur_ax_2 = recur_ax.twinx()
recur_ax_2.tick_params(length=3, pad=1, top=False)
recur_ax_2.set_ylim(-0.3, 1.3)
recur_ax_2.set_yticks([0, 1])
recur_ax_2.set_yticklabels(['non-recur', 'recur'])
#
# Place field stability
#
shift_x_vals = np.linspace(-np.pi, np.pi, 1000)
shift_knots = params['position_stability']['all_pairs']['knots']
shift_spline = splines.CyclicSpline(shift_knots)
shift_n = shift_spline.design_matrix(shift_x_vals)
shift_theta_b = params['position_stability']['all_pairs']['theta_b']
shift_b_fit = np.dot(shift_n, shift_theta_b)
shift_theta_k = params['position_stability']['all_pairs']['theta_k']
shift_k_fit = splines.get_k(shift_theta_k, shift_n)
shift_fit_var = 1. / shift_k_fit
shift_data = emd.paired_activity_centroid_distance_to_reward(data)
shift_data = shift_data.dropna()
shifts = shift_data['second'] - shift_data['first']
shifts[shifts < -np.pi] += 2 * np.pi
shifts[shifts >= np.pi] -= 2 * np.pi
shift_ax.plot(shift_x_vals, shift_b_fit, color=WT_color)
shift_ax.fill_between(shift_x_vals,
shift_b_fit - shift_fit_var,
shift_b_fit + shift_fit_var,
facecolor=WT_color,
alpha=0.5)
sns.regplot(shift_data['first'],
shifts,
ax=shift_ax,
color=WT_color,
fit_reg=False,
scatter_kws={'s': 1},
marker=WT_marker)
shift_ax.axvline(ls='--', color='0.4', lw=0.5)
shift_ax.axhline(ls='--', color='0.4', lw=0.5)
shift_ax.plot([-np.pi, np.pi], [np.pi, -np.pi], color='g', ls=':', lw=2)
shift_ax.tick_params(length=3, pad=1, top=False)
shift_ax.set_xlabel('Initial distance from reward (fraction of belt)')
shift_ax.set_ylabel(r'$\Delta$ position (fraction of belt)')
shift_ax.set_xlim(-np.pi, np.pi)
shift_ax.set_xticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi])
shift_ax.set_xticklabels(['-0.50', '-0.25', '0', '0.25', '0.50'])
shift_ax.set_ylim(-np.pi, np.pi)
shift_ax.set_yticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi])
shift_ax.set_yticklabels(['-0.50', '-0.25', '0', '0.25', '0.50'])
shift_ax.set_title('')
#
# Stability by distance to reward
#
shift_x_vals = np.linspace(-np.pi, np.pi, 1000)
shift_knots = params['position_stability']['all_pairs']['knots']
shift_spline = splines.CyclicSpline(shift_knots)
shift_n = shift_spline.design_matrix(shift_x_vals)
shift_theta_b = params['position_stability']['all_pairs']['theta_b']
shift_b_fit = np.dot(shift_n, shift_theta_b)
shift_boots_b_fit = [
np.dot(shift_n, boot)
for boot in params['position_stability']['all_pairs']['boots_theta_b']
]
shift_b_ci_up_fit = np.percentile(shift_boots_b_fit, 95, axis=0)
shift_b_ci_low_fit = np.percentile(shift_boots_b_fit, 5, axis=0)
shift_compare_ax.plot(shift_x_vals, shift_b_fit, color=WT_color)
shift_compare_ax.fill_between(shift_x_vals,
shift_b_ci_low_fit,
shift_b_ci_up_fit,
facecolor=WT_color,
alpha=0.5)
shift_compare_ax.axvline(ls='--', color='0.4', lw=0.5)
shift_compare_ax.axhline(ls='--', color='0.4', lw=0.5)
shift_compare_ax.tick_params(length=3, pad=1, top=False)
shift_compare_ax.set_xlim(-np.pi, np.pi)
shift_compare_ax.set_xticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi])
shift_compare_ax.set_xticklabels(['-0.50', '-0.25', '0', '0.25', '0.50'])
shift_compare_ax.set_ylim(-0.10 * 2 * np.pi, 0.10 * 2 * np.pi)
y_ticks = np.array(['-0.10', '-0.05', '0', '0.05', '0.10'])
shift_compare_ax.set_yticks(y_ticks.astype('float') * 2 * np.pi)
shift_compare_ax.set_yticklabels(y_ticks)
shift_compare_ax.set_xlabel(
'Initial distance from reward (fraction of belt)')
shift_compare_ax.set_ylabel(r'$\Delta$ position (fraction of belt)')
shift_theta_k = params['position_stability']['all_pairs']['theta_k']
shift_k_fit = splines.get_k(shift_theta_k, shift_n)
shift_boots_k_fit = [
splines.get_k(boot, shift_n)
for boot in params['position_stability']['all_pairs']['boots_theta_k']
]
shift_k_ci_up_fit = np.percentile(shift_boots_k_fit, 95, axis=0)
shift_k_ci_low_fit = np.percentile(shift_boots_k_fit, 5, axis=0)
var_compare_ax.plot(shift_x_vals, 1. / shift_k_fit, color=WT_color)
var_compare_ax.fill_between(shift_x_vals,
1. / shift_k_ci_low_fit,
1. / shift_k_ci_up_fit,
facecolor=WT_color,
alpha=0.5)
var_compare_ax.axvline(ls='--', color='0.4', lw=0.5)
var_compare_ax.tick_params(length=3, pad=1, top=False)
var_compare_ax.set_xlim(-np.pi, np.pi)
var_compare_ax.set_xticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi])
var_compare_ax.set_xticklabels(['-0.50', '-0.25', '0', '0.25', '0.50'])
y_ticks = np.array(['0.005', '0.010', '0.015', '0.020'])
var_compare_ax.set_yticks(y_ticks.astype('float') * (2 * np.pi)**2)
var_compare_ax.set_yticklabels(y_ticks)
var_compare_ax.set_xlabel(
'Initial distance from reward (fraction of belt)')
var_compare_ax.set_ylabel(r'$\Delta$ position variance')
misc.save_figure(fig, filename, save_dir=save_dir)
plt.close('all')
def main():
all_grps = df.loadExptGrps('GOL')
expts = lab.ExperimentSet(os.path.join(df.metadata_path,
'expt_metadata.xml'),
behaviorDataPath=os.path.join(
df.data_path, 'behavior'),
dataPath=os.path.join(df.data_path, 'imaging'))
WT_expt_grp = all_grps['WT_place_set']
Df_expt_grp = all_grps['Df_place_set']
expt_grps = [WT_expt_grp, Df_expt_grp]
WT_label = WT_expt_grp.label()
Df_label = Df_expt_grp.label()
fig = plt.figure(figsize=(8.5, 11))
gs2 = plt.GridSpec(48, 1, right=.6)
wt_trace_ax = fig.add_subplot(gs2[12:16, 0])
wt_position_ax = fig.add_subplot(gs2[16:18, 0])
df_trace_ax = fig.add_subplot(gs2[18:22, 0])
df_position_ax = fig.add_subplot(gs2[22:24, 0])
gs3 = plt.GridSpec(2, 2, hspace=0.3, left=.62, bottom=0.5, top=0.7)
wt_transients_ax = fig.add_subplot(gs3[0, 0], polar=True)
df_transients_ax = fig.add_subplot(gs3[1, 0], polar=True)
wt_vector_ax = fig.add_subplot(gs3[0, 1], polar=True)
df_vector_ax = fig.add_subplot(gs3[1, 1], polar=True)
gs4 = plt.GridSpec(1, 4, top=0.48, bottom=0.35, wspace=0.3)
pf_fraction_ax = fig.add_subplot(gs4[0, 0])
pf_per_cell_ax = fig.add_subplot(gs4[0, 1])
pf_width_ax = fig.add_subplot(gs4[0, 2])
circ_var_ax = fig.add_subplot(gs4[0, 3])
pf_fraction_inset_ax = fig.add_axes([0.22, 0.36, 0.04, 0.06])
pf_width_inset_ax = fig.add_axes([0.63, 0.41, 0.04, 0.06])
circ_var_inset_ax = fig.add_axes([0.84, 0.36, 0.04, 0.06])
#
# PC Examples
#
wt_expt = expts.grabExpt('jz121', '2015-02-21-16h06m30s')
df_expt = expts.grabExpt('jz098', '2014-11-08-16h14m02s')
pc_expt_grp = place.pcExperimentGroup([wt_expt, df_expt],
imaging_label='soma')
wt_id = '0422-0349'
df_id = '0074-0339'
wt_idx = wt_expt.roi_ids().index(wt_id)
df_idx = df_expt.roi_ids().index(df_id)
wt_imaging_data = wt_expt.imagingData(channel='Ch2',
label='soma',
dFOverF='from_file')
df_imaging_data = df_expt.imagingData(channel='Ch2',
label='soma',
dFOverF='from_file')
wt_transients = wt_expt.transientsData(threshold=95,
channel='Ch2',
label='soma')
df_transients = df_expt.transientsData(threshold=95,
channel='Ch2',
label='soma')
place.plotImagingData(roi_tSeries=wt_imaging_data[wt_idx, :, 0],
ax=wt_trace_ax,
roi_transients=wt_transients[wt_idx][0],
position=None,
imaging_interval=wt_expt.frame_period(),
placeField=None,
xlabel_visible=False,
ylabel_visible=True,
right_label=True,
placeFieldColor=None,
title='',
rasterized=False,
color='.4',
transients_color=WT_color)
sns.despine(ax=wt_trace_ax, top=True, left=False, bottom=True, right=True)
wt_trace_ax.set_ylabel(WT_label, rotation='horizontal', ha='right')
wt_trace_ax.tick_params(bottom=False, labelbottom=False)
wt_trace_ax.tick_params(axis='y', direction='in', length=3, pad=3)
wt_trace_ax.spines['left'].set_linewidth(1)
wt_trace_ax.spines['left'].set_position(('outward', 5))
place.plotImagingData(roi_tSeries=df_imaging_data[df_idx, :, 0],
ax=df_trace_ax,
roi_transients=df_transients[df_idx][0],
position=None,
imaging_interval=df_expt.frame_period(),
placeField=None,
xlabel_visible=False,
ylabel_visible=True,
right_label=True,
placeFieldColor=None,
title='',
rasterized=False,
color='.4',
transients_color=Df_color)
sns.despine(ax=df_trace_ax, top=True, left=False, bottom=True, right=True)
df_trace_ax.set_ylabel(Df_label, rotation='horizontal', ha='right')
df_trace_ax.tick_params(bottom=False, labelbottom=False)
df_trace_ax.tick_params(axis='y', direction='in', length=3, pad=3)
df_trace_ax.spines['left'].set_linewidth(1)
df_trace_ax.spines['left'].set_position(('outward', 5))
y_min = min(wt_trace_ax.get_ylim()[0], df_trace_ax.get_ylim()[0])
y_max = max(wt_trace_ax.get_ylim()[1], df_trace_ax.get_ylim()[1])
wt_trace_ax.set_ylim(y_min, y_max)
df_trace_ax.set_ylim(y_min, y_max)
wt_trace_ax.set_yticks([0, y_max])
df_trace_ax.set_yticks([0, y_max])
wt_trace_ax.set_yticklabels(['0', '{:0.1f}'.format(y_max)])
df_trace_ax.set_yticklabels(['0', '{:0.1f}'.format(y_max)])
wt_trace_ax.set_xlim(0, 600)
df_trace_ax.set_xlim(0, 600)
place.plotPosition(wt_expt.find('trial'),
ax=wt_position_ax,
rasterized=False,
position_kwargs={'color': 'k'})
sns.despine(ax=wt_position_ax,
top=True,
left=True,
bottom=True,
right=True)
wt_position_ax.set_ylabel('')
wt_position_ax.set_xlabel('')
wt_position_ax.tick_params(left=False,
bottom=False,
top=False,
right=False,
labelleft=False,
labelbottom=False,
labelright=False,
labeltop=False)
plotting.add_scalebar(wt_position_ax,
matchx=False,
matchy=False,
hidex=False,
hidey=False,
sizex=60,
labelx='1 min',
bar_thickness=.02,
pad=0,
loc=4)
wt_position_ax.set_yticks([0, 1])
wt_position_ax.set_ylim(0, 1)
place.plotPosition(df_expt.find('trial'),
ax=df_position_ax,
rasterized=False,
position_kwargs={'color': 'k'})
sns.despine(ax=df_position_ax, top=True, bottom=True, right=True)
df_position_ax.tick_params(bottom=False,
top=False,
right=False,
labelbottom=False,
labelright=False,
labeltop=False,
direction='in',
length=3,
pad=3)
df_position_ax.spines['left'].set_linewidth(1)
df_position_ax.spines['left'].set_position(('outward', 5))
df_position_ax.set_ylabel('Position')
df_position_ax.set_xlabel('')
df_position_ax.set_yticks([0, 1])
df_position_ax.set_ylim(0, 1)
trans_kwargs = {
'color': WT_color,
'marker': 'o',
'linestyle': 'None',
'markersize': 3
}
wt_pf = [pc_expt_grp.pfs_n()[wt_expt][wt_idx]]
place.plotPosition(wt_expt.find('trial'),
ax=wt_transients_ax,
polar=True,
placeFields=wt_pf,
placeFieldColors=[WT_color],
trans_roi_filter=lambda roi: roi.id == wt_id,
rasterized=False,
running_trans_only=True,
demixed=False,
position_kwargs={'color': '0.5'},
trans_kwargs=trans_kwargs)
wt_transients_ax.set_xlabel('')
wt_transients_ax.set_ylabel('')
wt_transients_ax.set_rticks([])
prep_polar_ax(wt_transients_ax)
trans_kwargs['color'] = Df_color
df_pf = [pc_expt_grp.pfs_n()[df_expt][df_idx]]
place.plotPosition(df_expt.find('trial'),
ax=df_transients_ax,
polar=True,
placeFields=df_pf,
placeFieldColors=[Df_color],
trans_roi_filter=lambda roi: roi.id == df_id,
rasterized=False,
running_trans_only=True,
demixed=False,
position_kwargs={'color': '0.5'},
trans_kwargs=trans_kwargs)
df_transients_ax.set_xlabel('')
df_transients_ax.set_ylabel('')
df_transients_ax.set_rticks([])
prep_polar_ax(df_transients_ax)
place.plotTransientVectors(place.pcExperimentGroup([wt_expt],
imaging_label='soma'),
wt_idx,
wt_vector_ax,
mean_zorder=99,
color=WT_color,
mean_color='g')
place.plotTransientVectors(place.pcExperimentGroup([df_expt],
imaging_label='soma'),
df_idx,
df_vector_ax,
mean_zorder=99,
color=Df_color,
mean_color='g')
#
# Stats
#
groupby = [['expt'], ['mouseID']]
plotting.plot_metric(pf_fraction_ax,
expt_grps,
metric_fn=place.place_cell_percentage,
groupby=None,
plotby=None,
colorby=None,
plot_method='cdf',
roi_filters=roi_filters,
activity_kwargs=None,
colors=colors,
activity_label='Place cell fraction',
rotate_labels=False,
return_full_dataframes=False,
linestyles=linestyles)
pf_fraction_ax.legend(loc='upper left', fontsize=6)
pf_fraction_ax.set_title('')
pf_fraction_ax.set_ylabel('Cumulative fraction')
pf_fraction_ax.set_xticks([0, .2, .4, .6, .8])
pf_fraction_ax.set_xlim(0, .8)
pf_fraction_ax.spines['left'].set_linewidth(1)
pf_fraction_ax.spines['bottom'].set_linewidth(1)
plotting.plot_metric(pf_fraction_inset_ax,
expt_grps,
metric_fn=place.place_cell_percentage,
groupby=groupby,
plotby=None,
colorby=None,
plot_method='swarm',
roi_filters=roi_filters,
activity_kwargs=None,
colors=colors,
activity_label='Place cell fraction',
rotate_labels=False,
linewidth=0.2,
edgecolor='gray')
pf_fraction_inset_ax.set_title('')
pf_fraction_inset_ax.set_ylabel('')
pf_fraction_inset_ax.set_xlabel('')
pf_fraction_inset_ax.set_yticks([0, 0.5])
pf_fraction_inset_ax.set_ylim([0, 0.5])
pf_fraction_inset_ax.get_legend().set_visible(False)
sns.despine(ax=pf_fraction_inset_ax)
pf_fraction_inset_ax.tick_params(bottom=False, labelbottom=False)
pf_fraction_inset_ax.spines['left'].set_linewidth(1)
pf_fraction_inset_ax.spines['bottom'].set_linewidth(1)
pf_fraction_inset_ax.set_xlim(-0.6, 0.6)
n_pf_kwargs = {'per_mouse_fractions': True, 'max_n_place_fields': 3}
plotting.plot_metric(pf_per_cell_ax,
expt_grps,
metric_fn=place.n_place_fields,
groupby=None,
plotby=['number'],
plot_method='swarm',
roi_filters=roi_filters,
activity_kwargs=n_pf_kwargs,
colors=colors,
activity_label='Fraction of place cells',
rotate_labels=False,
plot_bar=True,
edgecolor='k',
linewidth=0.5,
size=3)
sns.despine(ax=pf_per_cell_ax)
pf_per_cell_ax.set_title('')
pf_per_cell_ax.set_xlabel('Place fields per cell')
pf_per_cell_ax.set_ylabel('Fraction of place cells')
pf_per_cell_ax.set_xticklabels(['1', '2', '3+'])
pf_per_cell_ax.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 1])
plotting.plot_metric(pf_width_ax,
expt_grps,
metric_fn=place.place_field_width,
groupby=[['roi_id', 'expt']],
plotby=None,
plot_method='hist',
roi_filters=roi_filters,
activity_kwargs=None,
activity_label='Place field width (cm)',
normed=True,
plot_mean=True,
bins=20,
range=(0, 120),
colors=colors,
rotate_labels=False,
filled=False,
mean_kwargs={'ls': ':'},
return_full_dataframes=False,
linestyles=linestyles)
pf_width_ax.set_title('')
pf_width_ax.legend(loc='lower right', fontsize=6)
pf_width_ax.set_xticks([0, 40, 80, 120])
pf_width_ax.set_yticks([0, 0.02, 0.04, 0.06, 0.08])
pf_width_ax.set_ylim(0, 0.08)
pf_width_ax.set_ylabel('Normalized density')
pf_width_ax.spines['left'].set_linewidth(1)
pf_width_ax.spines['bottom'].set_linewidth(1)
plotting.plot_metric(pf_width_inset_ax,
expt_grps,
metric_fn=place.place_field_width,
groupby=[['roi_id', 'expt'], ['expt'], ['mouseID']],
plotby=None,
plot_method='swarm',
roi_filters=roi_filters,
activity_kwargs=None,
activity_label='Place field width (cm)',
colors=colors,
rotate_labels=False,
linewidth=0.2,
edgecolor='gray')
pf_width_inset_ax.set_title('')
pf_width_inset_ax.set_ylabel('')
pf_width_inset_ax.set_xlabel('')
pf_width_inset_ax.get_legend().set_visible(False)
sns.despine(ax=pf_width_inset_ax)
pf_width_inset_ax.tick_params(bottom=False, labelbottom=False)
pf_width_inset_ax.set_ylim(25, 40)
pf_width_inset_ax.set_yticks([25, 40])
pf_width_inset_ax.spines['left'].set_linewidth(1)
pf_width_inset_ax.spines['bottom'].set_linewidth(1)
pf_width_inset_ax.set_xlim(-0.6, 0.6)
plotting.plot_metric(circ_var_ax,
expt_grps,
metric_fn=place.circular_variance,
groupby=[['roi_id', 'expt']],
plotby=None,
plot_method='cdf',
roi_filters=roi_filters,
activity_kwargs=None,
activity_label='Circular variance',
colors=colors,
rotate_labels=False,
return_full_dataframes=False,
linestyles=linestyles)
circ_var_ax.set_title('')
circ_var_ax.legend(loc='upper left', fontsize=6)
circ_var_ax.set_ylabel('Cumulative fraction')
circ_var_ax.set_xlim(-0.1, 1)
circ_var_ax.set_xticks([0, 0.5, 1])
circ_var_ax.spines['left'].set_linewidth(1)
circ_var_ax.spines['bottom'].set_linewidth(1)
plotting.plot_metric(circ_var_inset_ax,
expt_grps,
metric_fn=place.circular_variance,
groupby=groupby,
plotby=None,
plot_method='swarm',
roi_filters=roi_filters,
activity_kwargs=None,
activity_label='Circular variance',
colors=colors,
rotate_labels=False,
linewidth=0.2,
edgecolor='gray')
circ_var_inset_ax.set_title('')
circ_var_inset_ax.set_ylabel('')
circ_var_inset_ax.set_xlabel('')
circ_var_inset_ax.get_legend().set_visible(False)
sns.despine(ax=circ_var_inset_ax)
circ_var_inset_ax.tick_params(bottom=False, labelbottom=False)
circ_var_inset_ax.set_ylim(0, 0.6)
circ_var_inset_ax.set_yticks([0, 0.6])
circ_var_inset_ax.spines['left'].set_linewidth(1)
circ_var_inset_ax.spines['bottom'].set_linewidth(1)
circ_var_inset_ax.set_xlim(-0.6, 0.6)
misc.save_figure(fig, filename, save_dir=save_dir)
plt.close('all')
def main():
all_expt_grps = df.loadExptGrps('GOL')
WT_expt_grp_hidden = all_expt_grps['WT_place_set']
Df_expt_grp_hidden = all_expt_grps['Df_place_set']
expt_grps = [WT_expt_grp_hidden, Df_expt_grp_hidden]
paired_grps = [grp.pair(
'consecutive groups', groupby=['condition_day']) for grp in expt_grps]
fig = plt.figure(figsize=(8.5, 11))
gs1 = plt.GridSpec(
3, 8, top=0.9, bottom=0.7, left=0.1, right=0.9, wspace=0.4)
ax2 = fig.add_subplot(gs1[:, :2])
pf_fraction_ax = fig.add_subplot(gs1[:, 2:4])
circ_var_ax = fig.add_subplot(gs1[:, 4:6])
trans_ax1 = fig.add_subplot(gs1[0, 6], polar=True)
trans_ax2 = fig.add_subplot(gs1[0, 7], polar=True)
trans_ax3 = fig.add_subplot(gs1[1, 6], polar=True)
trans_ax4 = fig.add_subplot(gs1[1, 7], polar=True)
trans_ax5 = fig.add_subplot(gs1[2, 6], polar=True)
trans_ax6 = fig.add_subplot(gs1[2, 7], polar=True)
trans_axs = [
trans_ax1, trans_ax2, trans_ax3, trans_ax4, trans_ax5, trans_ax6]
#
# Stability by distance to fabric transitions
#
filter_fn = lambda df: df['second_condition_day_session'] == 'B_0_0'
filter_columns = ['second_condition_day_session']
label_order = ['before', 'middle', 'after']
data_to_plot = [[], [], []]
all_data, shuffles = [], []
for expt_grp, roi_filter in zip(paired_grps, roi_filters):
fabric_map = {expt: expt.belt().fabric_transitions(
units='normalized') for expt in expt_grp}
def norm_diff(n1, n2):
d = n1 - n2
d = d + 1.0 if d < -0.5 else d
d = d - 1.0 if d >= 0.5 else d
return d
def closest_transition(row):
expt = row['first_expt']
centroid = complex_to_norm(row['first_centroid'])
positions = fabric_map[expt]['position']
distances = [norm_diff(centroid, t) for t in positions]
row['closest'] = distances[np.argmin(np.abs(distances))]
return row
data, shuffle = place.activity_centroid_shift(
expt_grp, roi_filter=roi_filter, activity_filter='active_both',
circ_var_pcs=False, units='norm', shuffle=True)
plotting.prepare_dataframe(data, include_columns=filter_columns)
data = data[filter_fn(data)]
plotting.prepare_dataframe(shuffle, include_columns=filter_columns)
data = data.apply(closest_transition, axis=1)
shuffle = shuffle.apply(closest_transition, axis=1)
def categorize(row):
if row['closest'] < 0 and -1 / 9. < row['closest']:
row['category'] = 'before'
elif row['closest'] > 0 and 1 / 9. > row['closest']:
row['category'] = 'after'
else:
row['category'] = 'middle'
return row
data = data.apply(categorize, axis=1)
shuffle = shuffle.apply(categorize, axis=1)
groupby = [
['second_condition_day_session', 'second_mouse', 'category']]
for gb in groupby:
plotting.prepare_dataframe(data, include_columns=gb)
plotting.prepare_dataframe(shuffle, include_columns=gb)
data = data.groupby(gb, as_index=False).mean()
shuffle = shuffle.groupby(gb, as_index=False).mean()
for category, group in data.groupby(['category']):
idx = label_order.index(category)
data_to_plot[idx].append(group['value'])
shuffles.append(shuffle)
all_data.append(data)
shuffle_df = pd.concat(shuffles, ignore_index=True)
for category, group in shuffle_df.groupby(['category']):
idx = label_order.index(category)
data_to_plot[idx].append(group['value'])
plotting.grouped_bar(
ax2, data_to_plot, condition_labels=label_order,
cluster_labels=df.labels + ('shuffle',),
bar_colors=sns.color_palette('deep')[3:], scatter_points=False,
scatterbar_colors=None, jitter_x=False, loc='best', error_bars='sem')
sns.despine(ax=ax2)
ax2.set_yticks([0, 0.1, 0.2, 0.3])
ax2.set_ylabel('Centroid shift (fraction of belt)')
#
# Burlap belt
#
expts = lab.ExperimentSet(
os.path.join(df.metadata_path, 'expt_metadata.xml'),
behaviorDataPath=os.path.join(df.data_path, 'behavior'),
dataPath=os.path.join(df.data_path, 'imaging'))
burlap_expt_grp = lab.classes.pcExperimentGroup.from_json(
cue_free_json, expts, imaging_label=df.IMAGING_LABEL, label='cue-free')
acute_grps = df.loadExptGrps('RF')
WT_expt_grp_acute = acute_grps['WT_place_set'].unpair()
WT_expt_grp_acute.label('cue-rich')
burlap_colors = ('k', '0.9')
example_expt = expts.grabExptByPath('/jz128/TSeries-07262015-burlap-000')
cv = place.circular_variance_p(burlap_expt_grp)
cv = cv[cv['expt'] == example_expt]
cv = cv.sort_values(by=['value'])
trans_kwargs = {
'color': '0.9', 'marker': 'o', 'linestyle': 'None',
'markersize': 3}
for ax, (idx, row) in zip(trans_axs, cv.iloc[:6].iterrows()):
expt = row['expt']
roi_idx = expt.rois().index(row['roi'])
pf = None
place.plotPosition(
expt.find('trial'), ax=ax, polar=True,
placeFields=pf, placeFieldColors=['0.9'],
trans_roi_filter=lambda roi: roi.id == expt.roi_ids()[roi_idx],
rasterized=False, running_trans_only=True, demixed=False,
position_kwargs={'color': '0.5'}, trans_kwargs=trans_kwargs)
ax.set_xlabel('')
ax.set_ylabel('')
ax.set_rticks([])
prep_polar_ax(ax)
for ax in trans_axs[:-1]:
ax.set_xticklabels(['', '', '', ''])
activity_kwargs = {'circ_var': True}
plotting.plot_metric(
pf_fraction_ax, [WT_expt_grp_acute, burlap_expt_grp],
metric_fn=place.place_cell_percentage,
groupby=None, plotby=None, colorby=None, plot_method='swarm',
roi_filters=[WT_filter, WT_filter], activity_kwargs=activity_kwargs,
colors=burlap_colors, activity_label='Place cell fraction',
rotate_labels=False, plot_bar=True)
pf_fraction_ax.set_title('')
pf_fraction_ax.set_xlabel('')
sns.despine(ax=pf_fraction_ax)
pf_fraction_ax.set_yticks([0.0, 0.1, 0.2, 0.3, 0.4])
plotting.plot_metric(
circ_var_ax, [WT_expt_grp_acute, burlap_expt_grp],
metric_fn=place.circular_variance,
groupby=[['roi_id', 'expt']], plotby=None, plot_method='cdf',
roi_filters=[WT_filter, WT_filter], activity_kwargs=None,
activity_label='Circular variance', colors=burlap_colors,
rotate_labels=False)
circ_var_ax.set_title('')
circ_var_ax.get_legend().set_visible(False)
circ_var_ax.set_xticks([0, 0.5, 1])
save_figure(fig, filename, save_dir=save_dir)
plt.close('all')
def main():
all_grps = df.loadExptGrps('GOL')
expts = lab.ExperimentSet(os.path.join(df.metadata_path,
'expt_metadata.xml'),
behaviorDataPath=os.path.join(
df.data_path, 'behavior'),
dataPath=os.path.join(df.data_path, 'imaging'))
WT_expt_grp = all_grps['WT_hidden_behavior_set']
Df_expt_grp = all_grps['Df_hidden_behavior_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')
labels = [expt_grp.label() for expt_grp in expt_grps]
fig = plt.figure(figsize=(8.5, 11))
HORIZONTAL = False
if HORIZONTAL:
gs1 = plt.GridSpec(8, 6)
wt_lick_axs = [
fig.add_subplot(gs1[0, 0]),
fig.add_subplot(gs1[0, 1]),
fig.add_subplot(gs1[0, 2]),
fig.add_subplot(gs1[0, 3]),
fig.add_subplot(gs1[0, 4]),
fig.add_subplot(gs1[0, 5])
]
df_lick_axs = [
fig.add_subplot(gs1[1, 0]),
fig.add_subplot(gs1[1, 1]),
fig.add_subplot(gs1[1, 2]),
fig.add_subplot(gs1[1, 3]),
fig.add_subplot(gs1[1, 4]),
fig.add_subplot(gs1[1, 5])
]
gs2 = plt.GridSpec(4, 2, hspace=0.5, wspace=0.2)
reward_zone_ax = fig.add_subplot(gs2[1, 0])
else:
gs1 = plt.GridSpec(10, 6)
wt_lick_axs = [
fig.add_subplot(gs1[0, 0]),
fig.add_subplot(gs1[1, 0]),
fig.add_subplot(gs1[2, 0]),
fig.add_subplot(gs1[3, 0]),
fig.add_subplot(gs1[4, 0]),
fig.add_subplot(gs1[5, 0])
]
df_lick_axs = [
fig.add_subplot(gs1[0, 1]),
fig.add_subplot(gs1[1, 1]),
fig.add_subplot(gs1[2, 1]),
fig.add_subplot(gs1[3, 1]),
fig.add_subplot(gs1[4, 1]),
fig.add_subplot(gs1[5, 1])
]
gs2 = plt.GridSpec(10, 1, hspace=0.5, wspace=0.8, left=0.47, right=0.9)
reward_zone_ax = fig.add_subplot(gs2[0:4, :])
gs3 = plt.GridSpec(10, 3, hspace=0.5, wspace=0.1, left=0.47, right=0.9)
fraction_licks_by_session_A_ax = fig.add_subplot(gs3[5:7, 0])
fraction_licks_by_session_B_ax = fig.add_subplot(gs3[5:7, 1])
fraction_licks_by_session_C_ax = fig.add_subplot(gs3[5:7, 2])
#
# Lick plots
#
wt_lick_expts = [
expts.grabExpt('jz101', '2014-11-06-23h37m54s'),
expts.grabExpt('jz101', '2014-11-08-22h53m27s'),
expts.grabExpt('jz101', '2014-11-09-23h06m56s'),
expts.grabExpt('jz101', '2014-11-11-23h13m16s'),
expts.grabExpt('jz101', '2014-11-12-19h29m41s'),
expts.grabExpt('jz101', '2014-11-14-19h59m09s')
]
df_lick_expts = [
expts.grabExpt('jz106', '2014-12-11-17h06m49s'),
expts.grabExpt('jz106', '2014-12-13-19h00m01s'),
expts.grabExpt('jz106', '2014-12-14-17h17m17s'),
expts.grabExpt('jz106', '2014-12-16-17h43m05s'),
expts.grabExpt('jz106', '2014-12-17-17h57m51s'),
expts.grabExpt('jz106', '2014-12-19-17h13m52s')
]
shade_color = sns.xkcd_rgb['light green']
for ax, expt in zip(wt_lick_axs, wt_lick_expts):
expt.licktogram(ax=ax,
plot_belt=False,
nPositionBins=20,
color=WT_color,
linewidth=0,
shade_reward=True,
shade_color=shade_color)
for ax, expt in zip(df_lick_axs, df_lick_expts):
expt.licktogram(ax=ax,
plot_belt=False,
nPositionBins=20,
color=Df_color,
linewidth=0,
shade_reward=True,
shade_color=shade_color)
for ax in wt_lick_axs + df_lick_axs:
ax.set_ylim(0, 0.6)
ax.set_yticks([0, 0.3, 0.6])
ax.set_xticks([0, 0.5, 1])
ax.set_xticklabels(['0.0', '0.5', '1.0'])
sns.despine(ax=ax)
ax.set_title('')
if HORIZONTAL:
for ax in wt_lick_axs:
ax.tick_params(labelbottom=False)
ax.set_xlabel('')
for ax in df_lick_axs[1:]:
ax.set_xlabel('')
for ax, label in zip(wt_lick_axs, [
r'Condition $\mathrm{I}$' + '\nDay 1',
r'Condition $\mathrm{I}$' + '\nDay 3',
r'Condition $\mathrm{II}$' + '\nDay 1',
r'Condition $\mathrm{II}$' + '\nDay 3',
r'Condition $\mathrm{III}$' + '\nDay 1',
r'Condition $\mathrm{III}$' + '\nDay 3'
]):
ax.set_title(label)
for ax in it.chain(wt_lick_axs[1:], df_lick_axs[1:]):
ax.set_ylabel('')
sns.despine(ax=ax, left=True, top=True, right=True)
for ax in wt_lick_axs + df_lick_axs:
ax.spines['bottom'].set_linewidth(0.5)
wt_lick_axs[0].tick_params(labelbottom=False)
for ax in wt_lick_axs[1:]:
ax.tick_params(labelleft=False, left=False, labelbottom=False)
for ax in df_lick_axs[1:]:
ax.tick_params(labelleft=False, left=False)
right_label(wt_lick_axs[-1], labels[0])
right_label(df_lick_axs[-1], labels[1])
df_lick_axs[0].set_yticks([0, 0.6])
wt_lick_axs[0].set_yticks([0, 0.6])
df_lick_axs[0].set_ylabel('Fraction of licks')
wt_lick_axs[0].set_ylabel('')
df_lick_axs[0].spines['left'].set_linewidth(0.5)
wt_lick_axs[0].spines['left'].set_linewidth(0.5)
else:
for ax in wt_lick_axs + df_lick_axs:
ax.spines['bottom'].set_linewidth(0.5)
for ax in wt_lick_axs + df_lick_axs[1:]:
sns.despine(ax=ax, left=True, top=True, right=True)
sns.despine(ax=df_lick_axs[0], left=True, top=True, right=False)
for ax in wt_lick_axs[:-1] + df_lick_axs[:-1]:
ax.set_xlabel('')
for ax in df_lick_axs:
ax.set_ylabel('')
for ax, label in zip(wt_lick_axs, [
r'Condition $\mathrm{I}$' + '\nDay 1',
r'Condition $\mathrm{I}$' + '\nDay 3',
r'Condition $\mathrm{II}$' + '\nDay 1',
r'Condition $\mathrm{II}$' + '\nDay 3',
r'Condition $\mathrm{III}$' + '\nDay 1',
r'Condition $\mathrm{III}$' + '\nDay 3'
]):
ax.set_ylabel(label,
rotation='horizontal',
ha='right',
multialignment='center',
labelpad=3,
va='center')
for ax in wt_lick_axs[:-1] + df_lick_axs[:-1]:
ax.tick_params(labelleft=False, left=False, labelbottom=False)
for ax in (wt_lick_axs[-1], df_lick_axs[-1]):
ax.tick_params(labelleft=False, left=False)
wt_lick_axs[0].set_title(labels[0])
df_lick_axs[0].set_title(labels[1])
df_lick_axs[0].yaxis.tick_right()
df_lick_axs[0].yaxis.set_label_position("right")
df_lick_axs[0].set_yticks([0, 0.6])
df_lick_axs[0].tick_params(axis='y', length=2, pad=2, direction='in')
df_lick_axs[0].set_ylabel('Fraction of licks')
df_lick_axs[0].spines['right'].set_linewidth(0.5)
filter_fn = None
filter_columns = None
behavior_fn = ra.fraction_licks_in_reward_zone
behavior_kwargs = {}
activity_label = 'Fraction of licks in reward zone'
plot_metric(reward_zone_ax,
expt_grps,
metric_fn=behavior_fn,
activity_kwargs=behavior_kwargs,
groupby=[['expt'], ['mouseID', 'X_condition', 'X_day']],
plotby=['X_condition', 'X_day'],
plot_method='line',
activity_label=activity_label,
colors=colors,
linestyles=linestyles,
label_every_n=1,
label_groupby=False,
markers=markers,
markersize=5,
rotate_labels=False,
filter_fn=filter_fn,
filter_columns=filter_columns,
return_full_dataframes=False)
reward_zone_ax.set_yticks([0, .1, .2, .3, .4])
sns.despine(ax=reward_zone_ax)
reward_zone_ax.set_xlabel('Day in Condition')
reward_zone_ax.set_title('')
day_number_only_label(reward_zone_ax)
label_conditions(reward_zone_ax)
reward_zone_ax.legend(loc='lower left', fontsize=8)
# reward_zone_ax.get_legend().set_visible(False)
# stackedText(reward_zone_ax, labels, colors=colors, loc=3, size=10)
groupby = [['expt']]
plotby = ['X_condition', 'X_session']
filter_fn = lambda df: (df['X_session'] != '1') & (df['X_condition'] == 'A'
)
filter_columns = ['X_session', 'X_condition']
line_kwargs = {'markersize': 4}
plot_metric(fraction_licks_by_session_A_ax,
expt_grps,
metric_fn=behavior_fn,
activity_kwargs=behavior_kwargs,
groupby=groupby,
plotby=plotby,
plot_method='box_and_line',
activity_label=activity_label,
colors=colors,
notch=False,
label_every_n=1,
label_groupby=False,
markers=markers,
rotate_labels=False,
line_kwargs=line_kwargs,
linestyles=linestyles,
filter_fn=filter_fn,
filter_columns=filter_columns,
flierprops={
'markersize': 2,
'marker': 'o'
},
box_width=0.4,
box_spacing=0.2,
return_full_dataframes=False,
whis='range')
sns.despine(ax=fraction_licks_by_session_A_ax, top=True, right=True)
fraction_licks_by_session_A_ax.set_xticklabels(['first', 'last'])
fraction_licks_by_session_A_ax.set_xlabel('')
fraction_licks_by_session_A_ax.set_ylim(-0.02, 0.6)
fraction_licks_by_session_A_ax.set_yticks([0, 0.2, 0.4, 0.6])
fraction_licks_by_session_A_ax.set_title('')
fraction_licks_by_session_A_ax.legend(loc='upper left', fontsize=6)
# fraction_licks_by_session_A_ax.get_legend().set_visible(False)
fraction_licks_by_session_A_ax.text(
0.5,
.95,
r'$\mathrm{I}$',
ha='center',
va='center',
transform=fraction_licks_by_session_A_ax.transAxes,
fontsize=12)
filter_fn = lambda df: (df['X_session'] != '1') & (df['X_condition'] == 'B'
)
filter_columns = ['X_session', 'X_condition']
plot_metric(fraction_licks_by_session_B_ax,
expt_grps,
metric_fn=behavior_fn,
activity_kwargs=behavior_kwargs,
groupby=groupby,
plotby=plotby,
plot_method='box_and_line',
activity_label=activity_label,
colors=colors,
label_every_n=1,
label_groupby=False,
markers=markers,
rotate_labels=False,
line_kwargs=line_kwargs,
linestyles=linestyles,
filter_fn=filter_fn,
filter_columns=filter_columns,
notch=False,
flierprops={
'markersize': 2,
'marker': 'o'
},
box_width=0.4,
box_spacing=0.2,
return_full_dataframes=False,
whis='range')
sns.despine(ax=fraction_licks_by_session_B_ax,
top=True,
right=True,
left=True)
fraction_licks_by_session_B_ax.tick_params(left=False, labelleft=False)
fraction_licks_by_session_B_ax.set_xticklabels(['first', 'last'])
fraction_licks_by_session_B_ax.set_xlabel('Session in day')
fraction_licks_by_session_B_ax.set_ylabel('')
fraction_licks_by_session_B_ax.set_ylim(-0.02, 0.6)
fraction_licks_by_session_B_ax.set_title('')
fraction_licks_by_session_B_ax.get_legend().set_visible(False)
fraction_licks_by_session_B_ax.text(
0.5,
.95,
r'$\mathrm{II}$',
ha='center',
va='center',
transform=fraction_licks_by_session_B_ax.transAxes,
fontsize=12)
filter_fn = lambda df: (df['X_session'] != '1') & (df['X_condition'] == 'C'
)
filter_columns = ['X_session', 'X_condition']
plot_metric(fraction_licks_by_session_C_ax,
expt_grps,
metric_fn=behavior_fn,
activity_kwargs=behavior_kwargs,
groupby=groupby,
plotby=plotby,
plot_method='box_and_line',
activity_label=activity_label,
colors=colors,
notch=False,
label_every_n=1,
label_groupby=False,
markers=markers,
rotate_labels=False,
line_kwargs=line_kwargs,
linestyles=linestyles,
filter_fn=filter_fn,
filter_columns=filter_columns,
return_full_dataframes=False,
flierprops={
'markersize': 2,
'marker': 'o'
},
box_width=0.4,
box_spacing=0.2,
whis='range')
sns.despine(ax=fraction_licks_by_session_C_ax,
top=True,
right=True,
left=True)
fraction_licks_by_session_C_ax.tick_params(left=False, labelleft=False)
fraction_licks_by_session_C_ax.set_xticklabels(['first', 'last'])
fraction_licks_by_session_C_ax.set_xlabel('')
fraction_licks_by_session_C_ax.set_ylabel('')
fraction_licks_by_session_C_ax.set_ylim(-0.02, 0.6)
fraction_licks_by_session_C_ax.set_title('')
fraction_licks_by_session_C_ax.get_legend().set_visible(False)
fraction_licks_by_session_C_ax.text(
0.5,
.95,
r'$\mathrm{III}$',
ha='center',
va='center',
transform=fraction_licks_by_session_C_ax.transAxes,
fontsize=12)
misc.save_figure(fig, filename, save_dir=save_dir)
plt.close('all')