def time_budgets(experiment, obs_period, htype, bin_type, err_type, ignore):
days = obs_period_to_days[experiment.name][obs_period]
res_subdir = experiment.path_to_results(subdir=os.path.join('time_budget'))
# load data
fname = "{}_time_budget_{}.json".format(experiment.name, bin_type)
df_all = experiment.load_json_data(days,
akind='time_budget',
fname=fname,
subakind=bin_type,
sub_index1='timebin',
ignore=ignore)
# percents
df = df_all.apply(lambda row: 100. * row / row['total time'],
axis=1).drop(columns=['active state', 'total time'])
# tidy form
xvar = "percent"
yvar = "timebin"
dft = pd.melt(df.reset_index(),
id_vars=['group', 'mouse', 'day', 'timebin'],
var_name='event',
value_name=xvar)
dft['obs_period'] = dft.apply(days_to_obs_period,
args=(experiment, ),
axis=1)
# dft['budget type'] = dft.apply(budget_type, axis=1)
hue = 'group'
col = 'event'
num_groups = len(dft.group.unique())
height = 5 if experiment.name in ['StrainSurvey'] else 4
fontsize = 4 if experiment.name in ['StrainSurvey'] else 6
ci = to_seaborn_ci[err_type]
act_types = ['F', 'D', 'L', 'O', 'IS']
palettes = [
get_features_palette(act_to_actlabel[x], num_groups) for x in act_types
]
kws1 = dict(height=height, aspect=1, ci=ci, sharex='col', legend=False)
if htype == 'groups':
g = sns.catplot(kind='bar',
x=xvar,
y=yvar,
row='obs_period',
col=col,
hue=hue,
data=dft,
**kws1)
set_layout(g, xvar, yvar, palettes, hue, fontsize)
set_legends(g, palettes, hue)
title = "{}\ntime budgets\ngroup averages$\pm${}\ndays: {}: {}" \
.format(str(experiment), err_type, obs_period, days)
add_figtitle(g.fig, title, y=1.15, xpad=-0.1, ypad=-0.05)
plt.subplots_adjust(wspace=0.3)
# save
fname = "{}_time_budget_{}_groups_{}_days_test8" \
.format(experiment.name, bin_type, err_type)
save_figure(experiment, g.fig, res_subdir, fname)
def facets_groups_comparison_hue_group(experiment, obs_period, bin_type, err_type, ignore):
res_subdir = os.path.join('features', 'groups', 'days_comparison_hue_group')
days = obs_period_to_days[experiment.name][obs_period]
bins_label = '24H, DC and LC' if bin_type == '3cycles' else bin_type
height = 2
# load AS data
feature_type = 'AS'
dft, _ = load_tidy_data(experiment, obs_period, bin_type, feature_type=feature_type, ignore=ignore)
dft['obs_period'] = dft.apply(days_to_obs_period, args=(experiment,), axis=1)
row = 'feature'
col = 'obs_period'
hue = 'group'
num_groups = len(dft.group.unique())
palette = get_features_palette(ftype='active_state', num_items=num_groups)
# draw
g = draw_facets(dft, bin_type, err_type, palette, height, row=row, col=col, hue=hue,
num_items=16, add_kws=dict(sharey='row'))
# layout
set_as_layout(g, bin_type, lw=1)
set_as_legend(g, palette, hue)
# title
title = "{}\nactive state features, {}\ngroup averages $\pm$ {}\ndays comparison:\n{}".format(
str(experiment), bin_type, err_type, days)
add_figtitle(g.fig, title, y=1.1, xpad=-0.05, ypad=-0.02)
plt.subplots_adjust(wspace=0.2) # necessary, or custom legend will screw the layout up
# save
fname = "{}_active_state_features_vs_CT_{}_grp_avg_{}_days_comparison".format(
experiment.name, bin_type, err_type)
save_figure(experiment, g.fig, res_subdir, fname)
# load FDL data
feature_type = 'FDL'
dft, _ = load_tidy_data(experiment, obs_period, bin_type, feature_type=feature_type, ignore=ignore)
# label dataframe in groups-days
dft[col] = dft.apply(days_to_obs_period, args=(experiment,), axis=1)
all_days = dft.obs_period.unique()
for ftype, dfg in dft.groupby('feature_type'):
palette = get_features_palette(ftype, num_items=len(all_days))
# draw
g = draw_facets(dfg, bin_type, err_type, palette, height, row=row, col=col, hue=hue, num_items=16,
add_kws=dict(sharey='row'))
# layout
set_as_layout(g, bin_type, lw=1)
set_as_legend(g, palette, hue)
title = "{}\n{} features, {}\ngroup averages $\pm$ {}\ndays comparison:\n{}".format(
str(experiment), ftype, bins_label, err_type, days)
add_figtitle(g.fig, title, y=1.1, xpad=-0.05, ypad=-0.02)
plt.subplots_adjust(wspace=0.2) # necessary, or custom legend will screw the layout up
# save
fname = "{}_{}_features_vs_CT_{}_grp_avg_{}_days_comparison_hue_group".format(
experiment.name, ftype, bin_type, err_type)
save_figure(experiment, g.fig, res_subdir, fname)
def plot_as(md, as_num, vel=None):
# active state data
as_tset = md.data['preprocessing']['AS_timeset'][as_num - 1]
vel = vel or vel_index(as_tset)
fig = plot_active_state_zoom_in(md, as_num, vel)
figtitle = 'Experiment: {}\ngroup: {}, {}, day: {}\nActive State #{}'.format(
md.experiment.name, md.group.name, md.mouse.name, md.day, as_num)
plot_utils.add_figtitle(fig, figtitle, y=1.12)
return fig
def within_as_structure_mice(experiment, num_mins):
if experiment.name in ['HiFat2']:
for nmins in num_mins:
res_subdir = os.path.join('within_as_structure')
days_dict = OrderedDict(
(l, v) for l, v in obs_period_to_days[experiment.name].items()
if l not in ['acclimated', 'comparison'])
xmax = nmins if num_mins is not None else 200
nrows = len(days_dict)
# figure
for group in list(experiment.groups):
ncols = len(list(group.valid_mice))
fig, axes = plt.subplots(nrows,
ncols,
figsize=(20, 5),
sharex=True,
sharey=True)
for num, (frame, days) in enumerate(
obs_period_to_days[experiment.name].iteritems()):
if frame not in ['acclimated', 'comparison']:
m = 0
for mouse in list(group.mice):
if not mouse.is_ignored:
print "drawing within_as_structure for: {}, {} days".format(
mouse, frame)
ax = axes[num][m]
data = mouse.create_within_as_structure(
days, nmins)
draw_structure(ax, data)
set_layout(ax, xmax, num_as=len(data))
m += 1
row_labels = [k for k in days_dict.keys() if k != 'acclimated']
col_labels = [m[1] for m in group.valid_mice]
xlabel = "AS time from onset [min]"
ylabel = "active state counts"
set_subplots_labels(fig,
ncols,
row_labels,
col_labels,
xlabel,
ylabel,
rxpad=-0.5)
days = obs_period_to_days[experiment.name]['acclimated']
title = "{}\nwithin active state structure\ngroup{}: {}\ndays comparison:\n{}" \
.format(str(experiment), group.number, group.name, days)
add_figtitle(fig, title, ypad=-0.03)
# plt.subplots_adjust(hspace=0.5, wspace=0.4)
# save
fname = '{}_within_as_structure_group{}_{}_days_comparison_{}mins'\
.format(experiment.name, group.number, group.name, nmins)
save_figure(experiment, fig, res_subdir, fname)
def within_as_structure_groups(experiment, num_mins):
if experiment.name in ['HiFat2']:
for nmins in num_mins:
res_subdir = os.path.join('within_as_structure')
days_dict = OrderedDict(
(l, v) for l, v in obs_period_to_days[experiment.name].items()
if l not in ['acclimated', 'comparison'])
xmax = nmins if num_mins is not None else 200
# figure
nrows = len(days_dict)
ncols = len(list(experiment.groups))
fig, axes = plt.subplots(nrows,
ncols,
figsize=(12, 16),
sharex=True,
sharey=True)
for g, group in enumerate(list(experiment.groups)):
for num, (frame, days) in enumerate(
obs_period_to_days[experiment.name].iteritems()):
if frame not in ['acclimated', 'comparison']:
print "drawing within_as_structure for: {}, {} days".format(
group, frame)
ax = axes[num][g]
data = group.create_within_as_structure(days)
draw_structure(ax, data)
set_layout(ax, xmax, num_as=len(data))
row_labels = [
'{}:\n{}'.format(k, v) for k, v in days_dict.items()
if k != 'acclimated'
]
col_labels = [g.name for g in experiment.groups]
xlabel = "AS time from onset [min]"
ylabel = "active state counts"
set_subplots_labels(fig,
ncols,
row_labels,
col_labels,
xlabel,
ylabel,
ylpad=-0.05)
title = "{}\nwithin active state structure\ngroups, acclimated days comparison:\n{}"\
.format(str(experiment), group.number, group.name, obs_period_to_days[experiment.name]['acclimated'])
add_figtitle(fig, title, ypad=-0.03)
# plt.subplots_adjust(wspace=0.3)
# save
fname = '{}_within_as_structure_groups_days_comparison_{}mins' \
.format(experiment.name, nmins)
save_figure(experiment, fig, res_subdir, fname)
def group_rasters(experiment, obs_period=(), write_days=True, as_only=False):
"""Draws individual rasters in a panel for all individuals in a group """
days = obs_period_to_days[experiment.name][obs_period]
num_days = len(days)
suffix = obs_period.replace('-', '_').replace(' ', '_')
for group in experiment.groups:
num_mice = len(list(group.valid_mice))
figsize, nrows, ncols = figsubplots(num_mice)
fig, axes = plt.subplots(nrows, ncols, figsize=figsize)
m = 0
for mouse in group.mice:
ax = axes.flatten()[m]
if not mouse.is_ignored:
cnt = 0
for md in mouse.mousedays(days):
if not md.is_ignored:
print "plotting {}".format(md)
y_offset = (num_days - cnt) * 10
md.load_npy_data(load_keys)
draw_timesets(ax, md, y_offset, as_only)
if write_days:
ax.text(3.5,
y_offset - 5,
'day{}'.format(md.day),
ha='left',
va='bottom',
fontsize=4)
cnt += 1
set_layout(ax, num_days)
ax.set_title("{}".format(mouse.name), y=1.15)
m += 1
# turn ignored mice axis off
for ax in axes.flatten()[len(group.valid_mice):]:
ax.axis('off')
figtitle = "{}\n{}\n{} days: {}".format(str(experiment), str(group),
suffix, days)
plot_utils.add_figtitle(fig, figtitle, y=0.97)
plt.subplots_adjust(hspace=0.4)
text = '' if not as_only else "AS_only"
filename = "{}_raster_group{}_{}_{}_days_{}".format(
experiment.name, group.number, group.name, suffix, text)
plot_utils.save_figure(experiment,
fig,
subdir='rasters',
filename=filename)
plt.close()
def plot_entire_day(md):
md.load_npy_data(keys=load_keys)
md_data = md.data['preprocessing']
fig, ax = plt.subplots(figsize=(10, 3))
plot_md(ax, md_data, lw=0.1)
draw_raster_labels(ax)
draw_feeder_lickometer_position(ax, label_xpos=30)
draw_sctive_state_durations(ax, md_data)
# draw_rectangle_around_active_state(ax, tstart, tend, as_num)
figtitle = 'Experiment: {}, group{}: {}\nindv{}: {}, day: {}'.format(
md.experiment.name, md.group.number, md.group.name, md.mouse.number,
md.mouse.name, md.day)
plot_utils.add_figtitle(fig, figtitle, ypad=-0.05)
return fig
def plot_manual_zoomin(md, tstart, dt, tstep, VEL=True):
md_data = md.data['preprocessing']
tend = tstart + dt
ts, te = [
utils.hcm_time_to_ct_string((x + 7) * 3600) for x in (tstart, tend)
]
print "plotting manual Zoom-In: CT{} to CT{} ..".format(ts, te)
fig, ax = plt.subplots(figsize=(10, 3))
plot_md(ax, md_data, lw=1)
if VEL:
plot_velocity(ax, md_data)
zoom_in_xaxis(ax, tstart, tend, tstep)
show_timeset_durations(ax, md_data, tstart, tend)
figtitle = 'Experiment: {}\ngroup: {}, {}, day: {}\nZoom-In: CT{} to CT{}'.format(
md.experiment.name, md.group.name, md.mouse.name, md.day, ts, te)
plot_utils.add_figtitle(fig, figtitle, y=1.12)
return fig, ts, te
def facets_group_avgs(experiment, obs_period, bin_type, err_type, ignore):
""" plots features vs CT (bins) and 24H/DC/LC (3cycles) """
# load data
dft, _ = load_tidy_data(experiment,
obs_period,
bin_type,
feature_type='FDL',
ignore=ignore)
res_subdir = os.path.join('features', 'groups')
days = obs_period_to_days[experiment.name][obs_period]
days_label = obs_period.replace('-', '_').replace(' ', '_')
bins_label = '24H, DC and LC' if bin_type == '3cycles' else bin_type
row = 'feature_type'
col = 'feature'
hue = 'group'
height = 2
groups = dft.group.unique()
palettes = [
get_features_palette(act_to_actlabel[x], len(groups)) for x in 'FDL'
]
# draw
g = draw_facets(dft,
bin_type,
err_type,
height,
row=row,
col=col,
hue=hue,
num_items=len(groups))
# layout
set_fdl_layout(g, bin_type, palettes)
set_fdl_legends(g, palettes, hue='group')
title = "{}\nfeeding, drinking and locomotion features, {}\ngroup averages $\pm$ {}\n{} days:\n{}".format(
str(experiment), bins_label, err_type, days_label, days)
add_figtitle(g.fig, title, y=1.1, xpad=-0.05, ypad=-0.03)
plt.subplots_adjust(
wspace=0.3) # necessary, or custom legend will screw the layout up
# save
fname = "{}_feeding_drinking_locomotion_features_vs_CT_{}_grp_avg_{}_{}_days" \
.format(experiment.name, bin_type, err_type, days_label)
save_figure(experiment, g.fig, res_subdir, fname)
def mouse_raster(experiment,
obs_period=(),
mouse_label=None,
write_days=True,
as_only=False):
"""Draws a raster plot for one individual. """
days = obs_period_to_days[experiment.name][obs_period]
num_days = len(days)
suffix = obs_period.replace('-', '_').replace(' ', '_')
mouse = experiment.mouse_object(('', mouse_label))
fig, ax = plt.subplots(figsize=(12, 8))
for cnt, md in enumerate(mouse.mousedays(days)):
print "plotting {}".format(md)
y_offset = (num_days - cnt) * 10
md.load_npy_data(load_keys)
draw_timesets(ax, md, y_offset, as_only)
if write_days:
ax.text(3.5,
y_offset - 5,
'day{}'.format(md.day),
ha='left',
va='bottom',
fontsize=8)
set_layout(ax, num_days, labelsize=10, height=2)
figtitle = "{}\n{}\n{} days: {}".format(str(experiment), str(mouse),
suffix, days)
plot_utils.add_figtitle(fig, figtitle, y=0.97)
text = '' if not as_only else "_AS_only"
filename = "{}_raster_group{}_{}_indv{}_{}_{}_days{}" \
.format(experiment.name, mouse.group.number, mouse.group.name, mouse.number, mouse.name, suffix, text)
plot_utils.save_figure(experiment,
fig,
subdir='rasters/mice',
filename=filename)
plt.close()
def within_as_structure(experiment, obs_period, num_mins=15):
res_subdir = os.path.join('within_as_structure')
days = obs_period_to_days[experiment.name][obs_period]
xmax = num_mins if num_mins is not None else 200
for group in experiment.groups:
figsize, nrows, ncols = figsubplots(group.tot_mice)
fig, axes = plt.subplots(nrows, ncols, figsize=figsize)
m = 0
for mouse in group.mice:
if not mouse.is_ignored:
print "drawing within_as_structure for: {}".format(mouse)
# fig, ax = plt.subplots()
ax = axes.flatten()[m]
data = mouse.create_within_as_structure(days, num_mins)
draw_structure(ax, data)
set_layout(ax, xmax, num_as=len(data))
m += 1
row_labels = [
'{}:\n{}'.format(k, v) for k, v in days_dict.items()
if k != 'acclimated'
]
col_labels = [g.name for g in experiment.groups]
xlabel = "AS time from onset [min]"
ylabel = "active state counts"
set_subplots_labels(fig, ncols, row_labels, col_labels, xlabel, ylabel)
title = "{}\nwithin active state structure\ngroup{}: {}\n{} days:\n{}"\
.format(str(experiment), group.number, group.name, obs_period, days)
plt.subplots_adjust(hspace=0.5, wspace=0.4)
add_figtitle(fig, title)
# save
fname = '{}_within_as_structure_group{}_{}_{}_days_{}mins_test3' \
.format(experiment.name, group.number, group.name, obs_period, num_mins)
save_figure(experiment, fig, res_subdir, fname)
def position_density(experiment,
obs_period,
htype='group',
bin_type='7cycles',
xbins=2,
ybins=4,
ignore=False,
csv_file=False):
"""Draws position density plots. """
days = obs_period_to_days[experiment.name][obs_period]
timebins = cycle_timebins[bin_type]
res_subdir = os.path.join('position_density', htype)
var_name = 'xy_bin'
value_name = 'value'
kwargs = dict(xticks=[], yticks=[], title='', xlabel='', ylabel='')
days_label = obs_period.replace('-', '_').replace(' ', '_')
if csv_file:
write_to_csv(experiment, obs_period, bin_type, xbins, ybins, ignore)
else:
# load data
hb = True if htype == 'mousedays' else False
df_all = load_data(experiment,
days,
bin_type,
xbins=xbins,
ybins=ybins,
occupancy=True,
hb=hb,
ignore=ignore)
if htype == 'groups':
# figure1: group averages
# tidy form
id_vars = ['group', 'timebin']
dfm = df_all.groupby(id_vars).agg(np.mean)
dft = pd.melt(dfm.reset_index(),
id_vars=id_vars,
var_name=var_name,
value_name=value_name)
# figure
g = sns.FacetGrid(dft,
row='group',
col='timebin',
col_order=timebins,
height=2,
gridspec_kws={'wspace': 0.05})
g = g.map_dataframe(draw_imshow,
xbins,
ybins,
hb=hb,
annotate=True).set(**kwargs)
# layout
colorbar(g)
set_facetgrid_labels(g) # rows and cols labels
title = "{}\nposition density {}x{}, {}\ngroup averages\n{} days:\n{}" \
.format(str(experiment), xbins, ybins, bin_type, obs_period, days)
add_figtitle(g.fig, title)
# save
fname = "{}_position_{}_xbins{}_ybins{}_groups_{}_days" \
.format(experiment.name, bin_type, xbins, ybins, days_label)
save_figure(experiment, g.fig, res_subdir, filename=fname)
plt.close()
# figure2: group averages, day by day
res_subdir = os.path.join(res_subdir, 'days_breakdown')
id_vars = ['group', 'day', 'timebin']
dfm = df_all.groupby(id_vars).agg(np.mean)
dft = pd.melt(dfm.reset_index(),
id_vars=id_vars,
var_name=var_name,
value_name=value_name)
for timebin, dfg in dft.groupby(['timebin']):
# figure
g = sns.FacetGrid(dfg,
row='group',
col='day',
height=2,
gridspec_kws={'wspace': 0.05})
g = g.map_dataframe(draw_imshow,
xbins,
ybins,
hb=hb,
annotate=True).set(**kwargs)
# layout
colorbar(g)
set_facetgrid_labels(g) # rows and cols labels
title = "{}\nposition density {}x{}, {}\ngroup averages, days\n{} days breakdown:\n{}".format(
str(experiment), xbins, ybins, timebin, obs_period, days)
add_figtitle(g.fig, title)
# save
fname = "{}_position_{}_xbins{}_ybins{}_{}_grp_avg_{}_days_breakdown" \
.format(experiment.name, bin_type, xbins, ybins, timebin, days_label)
save_figure(experiment, g.fig, res_subdir, fname)
plt.close()
elif htype == 'mice':
id_vars = ['group', 'mouse', 'timebin']
dfm = df_all.groupby(id_vars).agg(np.mean)
dft = pd.melt(dfm.reset_index(),
id_vars=id_vars,
var_name=var_name,
value_name=value_name)
for group_name, dfg in dft.groupby(['group']):
group_number = experiment.group_number(group_name)
# figure
g = sns.FacetGrid(dfg,
row='timebin',
col='mouse',
height=2,
gridspec_kws={'wspace': 0.05})
g = g.map_dataframe(draw_imshow,
xbins,
ybins,
hb=False,
annotate=True).set(**kwargs)
# layout
colorbar(g)
set_facetgrid_labels(g) # rows and cols labels
title = "{}\nposition density {}x{}, {}\ngroup{}: {}\n{} days:\n{}" \
.format(str(experiment), xbins, ybins, bin_type, group_number, group_name, obs_period, days)
add_figtitle(g.fig, title, y=0.95)
# save
fname = "{}_position_{}_xbins{}_ybins{}_grp{}_{}_avg_{}_days" \
.format(experiment.name, bin_type, xbins, ybins, group_number, group_name, days_label)
save_figure(experiment, g.fig, res_subdir, filename=fname)
plt.close()
elif htype == 'mousedays':
res_subdir = os.path.join(res_subdir,
'xbins{}_ybins{}'.format(xbins, ybins))
id_vars = [
'group', 'mouse', 'day', 'timebin', 'detected', 'observed'
]
dft = pd.melt(df_all.reset_index(),
id_vars=id_vars,
var_name=var_name,
value_name=value_name)
for name, dfg in dft.groupby(['group', 'mouse']):
group_name, mouse_name = name
group_number = experiment.group_number(group_name)
# figure
g = sns.FacetGrid(dfg,
row='timebin',
col='day',
height=2,
gridspec_kws={'wspace': 0.05})
g = g.map_dataframe(draw_imshow,
xbins,
ybins,
hb=hb,
annotate=True).set(**kwargs)
# layout
colorbar(g)
set_facetgrid_labels(g) # rows and cols labels
title = "{}\nposition density {}x{}, {}\ngroup{}: {}, {}\n{} days:\n{}".format(
str(experiment), xbins, ybins, bin_type, group_number,
group_name, mouse_name, obs_period, days)
add_figtitle(g.fig, title, y=0.95)
# save
fname = "{}_position_{}_xbins{}_ybins{}_grp{}_{}_{}_{}_days".format(
experiment.name, bin_type, xbins, ybins, group_number,
group_name, mouse_name, days_label)
save_figure(experiment, g.fig, res_subdir, filename=fname)
plt.close()
def facets_groups_comparison_hue_day(experiment, obs_period, htype, timepoint,
err_type, ignore):
days = obs_period_to_days[experiment.name][obs_period]
ci = to_seaborn_ci[err_type]
# load data
fname = '{}_breakfast_{}_binary_counts_tbinsize30s.json'.format(
experiment.name, timepoint)
df_all = experiment.load_json_data(days,
akind='breakfast',
fname=fname,
subakind=timepoint,
sub_index1='as_num',
sub_index2='event',
PROB=True,
ignore=ignore)
# tidy form
xvar = "AS time from {} [min]".format(timepoint)
yvar = "probability"
dft = pd.melt(df_all.reset_index(),
id_vars=['group', 'mouse', 'day', 'event'],
var_name=xvar,
value_name=yvar)
tbin_size = dft[xvar].min()
dft[xvar] = dft[xvar] / 60. # convert to minutes
dft['obs_period'] = dft.apply(days_to_obs_period,
args=(experiment, ),
axis=1)
row = 'event'
col = 'group'
hue = 'obs_period'
num_periods = len(dft.obs_period.unique())
height = 2
palettes = [
get_features_palette(act_to_actlabel[x], num_periods) for x in 'FD'
]
kws1 = dict(height=height,
aspect=1.5,
ci=ci,
sharey='row',
legend=False,
markers=[''] * len(dft[xvar].unique()))
# draw
if htype == 'groups':
g = sns.catplot(kind='point',
data=dft,
x=xvar,
y=yvar,
row=row,
col=col,
hue=hue,
**kws1)
# layout
set_layout(g, xvar, yvar, palettes, hue=hue)
set_legends(g, palettes, hue=hue)
# title
title = "{}\nprobability of event from AS {}\ngroup averages$\pm${}\ndays comparison:\n{}".format(
str(experiment), timepoint, err_type, days)
add_figtitle(g.fig, title, y=1.05, ypad=-0.05)
plt.subplots_adjust(wspace=0.3)
# save
res_subdir = experiment.path_to_results(
subdir=os.path.join('breakfast'))
fname = "{}_breakfast_{}_tbinsize{}s_groups_{}_days_comparison_hue_day".format(
experiment.name, timepoint, tbin_size, err_type)
save_figure(experiment, g.fig, res_subdir, fname)
def facets_groups_day_breakdown(experiment, obs_period, bin_type, err_type,
ignore):
res_subdir = os.path.join('features', 'groups', 'days_breakdown')
days = obs_period_to_days[experiment.name][obs_period]
days_label = obs_period.replace('-', '_').replace(' ', '_')
bins_label = '24H, DC and LC' if bin_type == '3cycles' else bin_type
height = 2
# load AS data
dft, _ = load_tidy_data(experiment,
obs_period,
bin_type,
feature_type='AS',
ignore=ignore)
groups = dft.group.unique()
palette = get_features_palette(ftype='active state', num_items=len(groups))
# draw
g = draw_facets(dft,
bin_type,
err_type,
palette,
height,
row='feature',
col='day',
hue='group',
num_items=len(groups),
add_kws=dict(sharey='row'))
# layout
set_as_layout(g, bin_type)
set_as_legend(g, palette, hue='group')
title = "{}\nactive state features, {}\ngroup averages $\pm$ {}\n{} days breakdown:\n{}"\
.format(str(experiment), bins_label, err_type, days_label, days)
add_figtitle(g.fig, title, y=1.1, xpad=-0.05, ypad=-0.02)
plt.subplots_adjust(
wspace=0.2) # necessary, or custom legend will screw the layout up
# save
fname = "{}_active_state_features_vs_CT_{}_grp_avg_{}_{}_days_breakdown"\
.format(experiment.name, bin_type, err_type, days_label)
save_figure(experiment, g.fig, res_subdir, fname)
# load FDL data
dft, _ = load_tidy_data(experiment,
obs_period,
bin_type,
feature_type='FDL',
ignore=ignore)
row = 'feature'
col = 'day'
hue = 'group'
groups = dft.group.unique()
for ftype, dfg in dft.groupby('feature_type'):
palette = get_features_palette(ftype, num_items=len(groups))
# draw
g = draw_facets(dfg,
bin_type,
err_type,
palette,
height,
row=row,
col=col,
hue=hue,
num_items=len(groups),
add_kws=dict(sharey='row'))
# layout
set_as_layout(g, bin_type)
set_as_legend(g, palette, hue='group')
title = "{}\n{} features, {}\ngroup averages $\pm$ {}\n{} days breakdown:\n{}" \
.format(str(experiment), ftype, bins_label, err_type, days_label, days)
add_figtitle(g.fig, title, y=1.1, xpad=-0.05, ypad=-0.02)
plt.subplots_adjust(
wspace=0.2) # necessary, or custom legend will screw the layout up
# save
fname = "{}_{}_features_vs_CT_{}_grp_avg_{}_{}_days_breakdown" \
.format(experiment.name, ftype, bin_type, err_type, days_label)
save_figure(experiment, g.fig, res_subdir, fname)
def breakfast(experiment,
obs_period,
htype='groups',
timepoint='onset',
err_type='sem',
ignore=True):
days = obs_period_to_days[experiment.name][obs_period]
res_subdir = experiment.path_to_results(subdir=os.path.join('breakfast'))
# load data
fname = '{}_breakfast_{}_binary_counts_tbinsize30s.json'.format(
experiment.name, timepoint)
df_all = experiment.load_json_data(days,
akind='breakfast',
fname=fname,
subakind=timepoint,
sub_index1='as_num',
sub_index2='event',
PROB=True,
ignore=ignore)
# tidy form
xvar = "AS time from {} [min]".format(timepoint)
yvar = "probability"
dft = pd.melt(df_all.reset_index(),
id_vars=['group', 'mouse', 'day', 'event'],
var_name=xvar,
value_name=yvar)
tbin_size = dft[xvar].min()
dft[xvar] = dft[xvar] / 60. # convert to minutes
col = 'group'
hue = 'event'
height = 2
palette = sns.color_palette([fcolors[act_to_actlabel[x]][1] for x in 'FD'])
ci = to_seaborn_ci[err_type]
col_wrap = 4 if experiment.name == 'StrainSurvey' else None
kws1 = dict(height=height,
aspect=1.5,
ci=ci,
palette=palette,
sharey='row',
legend=False,
col_wrap=col_wrap,
markers=[''] * len(dft[xvar].unique()))
if htype == 'groups':
# draw
g = sns.catplot(kind='point',
x=xvar,
y=yvar,
col=col,
hue=hue,
data=dft,
**kws1)
set_layout(g, xvar, yvar)
set_legend(g, palette, hue='event')
title = "{}\nprobability of event from AS {}\ngroup averages$\pm${}\ndays: {}: {}" \
.format(str(experiment), timepoint, err_type, obs_period, days)
add_figtitle(g.fig, title, y=1.05, ypad=-0.03)
plt.subplots_adjust(wspace=0.3)
# save
fname = "{}_breakfast_{}_tbinsize{}s_groups_{}_days" \
.format(experiment.name, timepoint, tbin_size, err_type)
save_figure(experiment, g.fig, res_subdir, fname)
