def draw_subplot(experiment, avgs, fig_title, subtitles, plot_type):
E = experiment
figsize, nrows, ncols, _, _ = get_subplot_specs(E, num_subplots=4)
labels = ['M', 'F', 'W', 'other', 'IS']
colors = [E.fcolors[x][0] for x in labels]
fig, axes = plt.subplots(nrows, ncols, figsize=figsize)
for c in xrange(avgs.shape[0]):
ax = axes.flatten()[c]
if plot_type == 'bar':
stop
elif plot_type == 'pie':
plot_as_a_pie(ax, avgs[c], colors, labels, subtitles[c])
if c == 0:
h, l = ax.get_legend_handles_labels()
set_legend(fig, h, l)
subplot_labels(E, fig, xlabel='', ylabel='', fig_title=fig_title, txpad=.1, typad=.03)
plt.subplots_adjust(hspace=0.4, wspace=.1)
return fig
def draw_subplot_compare(experiment,
avgs,
errs,
fig_title,
subtitles,
ONSET=True,
num_mins=15,
tbin_size=5,
ADD_SOURCE=False):
E = experiment
figsize, nrows, ncols, _, _ = get_subplot_specs(E, num_subplots=2)
if E.num_strains == 2:
pass
# colors = [[get_16_colors()[x] for x in idx] for idx in [[2, 3, 8, 9], [0, 1, 14, 15]]
elif E.num_strains == 4:
colors = [[get_16_colors()[x] for x in idx]
for idx in [[2, 3, 8, 9], [0, 1, 14, 15]]]
elif E.num_strains == 16:
colors = get_16_colors()
# probability
fig, axes = plt.subplots(nrows, ncols, figsize=figsize)
every_tbins = 2 if num_mins > 5 else 1
xticklim = (0, 15) if ONSET else (-15, 0)
xs = np.arange(xticklim[0], xticklim[1], tbin_size * every_tbins / 60.)
k = 0
for act in ['F', 'W']:
ax = axes[k]
for c in xrange(avgs.shape[0]):
# plotting every 5 tbins
ax.errorbar(xs,
avgs[c, ::every_tbins, k],
yerr=errs[c, ::every_tbins, k],
lw=.3,
color=colors[k][c],
label=E.strain_names[c],
elinewidth=.5,
capsize=.5,
capthick=.2)
set_compare_layout(E, ax, act, colors[k][c], ONSET, k, num_mins,
tbin_size)
set_compare_legend(ax, colors[k])
ax.set_title(subtitles[k], fontsize=10)
k += 1
# set subplot labels
on_text = 'from AS onset' if ONSET else 'to AS offset'
xlabel = 'Time ' + on_text + ' [min]'
ylabel = 'Probability'
subplot_labels(fig, xlabel, ylabel, fig_title, ADD_SOURCE)
plt.subplots_adjust(bottom=.12, hspace=0.6, wspace=0.3)
return fig
def draw_subplot(experiment,
avgs,
labels,
level,
cycle,
err_type,
fname,
plot_subtype,
plot_type,
ADD_SOURCE=True):
E = experiment
figsize, nrows, ncols, _, _ = get_subplot_specs(E,
num_subplots=len(E.groups))
fig, axes = plt.subplots(nrows, ncols, figsize=figsize)
slice_labels = ['M', 'F', 'W', 'other', 'IS']
colors = [E.fcolors[x][0] for x in slice_labels]
title, subtitles = get_figure_titles(E, labels, level, cycle, err_type,
plot_type)
for c in xrange(avgs.shape[0]):
try:
ax = axes.flatten()[c]
if plot_subtype == 'bar':
stop
elif plot_subtype == 'pie':
plot_as_a_pie(ax, avgs[c], colors, slice_labels, subtitles[c])
if c == 0:
h, l = ax.get_legend_handles_labels()
except IndexError:
ax.axis('off')
continue
set_legend(fig, h, l)
# plt.subplots_adjust(hspace=.4)
add_titles_notes(E, fig, title=title, typad=-.15, tlxpad=0, tlypad=-.15)
save_plot(fname, fig, ADD_SOURCE=ADD_SOURCE, labelsize=6, sypad=.25)
def plot_explained_variance_ratio(experiment, X, pca, level, lastcomp, fname,
EXCLUDE_SS):
E = experiment
components = pca.components_
num_components = pca.n_components_
expl_variance = pca.explained_variance_ratio_
figsize, nrows, ncols, _, _ = get_subplot_specs(E, num_subplots=2)
fig, (ax0, ax1) = plt.subplots(nrows=nrows, ncols=ncols, figsize=figsize)
ax0.plot(range(len(components)), expl_variance, color='.4')
ax1.plot(range(len(components))[-lastcomp:],
expl_variance[-lastcomp:],
color='.4')
ax0.set_ylim([0, .35])
ax1.set_ylim([0, .0006])
ax1.xaxis.set_major_locator(MultipleLocator(1))
for ax in [ax0, ax1]:
ax.xaxis.set_major_formatter(FormatStrFormatter('%d'))
ax_cleanup(ax)
# annotate close-up
text = 'close up,\nlast %d components' % lastcomp
ax1.text(.5, .7, text, fontsize=8, transform=ax1.transAxes)
text = '%d strains, ' %E.num_strains if not EXCLUDE_SS \
else '%d strains (no AKR, CAST, CZECH), ' %(E.num_strains-3)
level_ = 'mice' if level == 'mouse' else level
fig_title = 'Explained variance ratio, %d principal components\n%s%d %s, 24H avg data' % (
num_components, text, X.shape[0], level_)
subplot_labels(E,
fig,
xlabel='Principal component',
ylabel='explained variance',
fig_title=fig_title)
plt.subplots_adjust(bottom=.15, wspace=.4)
save_plot(fname, fig)
def draw_subplot1(experiment,
all_avgs,
all_errs,
fig_title,
subtitles,
ONSET=True,
num_mins=15,
tbin_size=5):
E = experiment
act = ['F', 'W']
colors = [
E.fcolors['F'][1], E.fcolors['F'][0], E.fcolors['W'][1],
E.fcolors['W'][0]
]
tick_colors = [E.fcolors[x][1] for x in act]
leg_labels = ['F-DC', 'W-DC', 'F-LC', 'W-LC']
# subplot labels
on_text = 'from AS onset' if ONSET else 'to AS offset'
xlabel = 'Time ' + on_text + ' [min]'
ylabel = 'Probability'
every_tbins = 2 if num_mins > 5 else 1
xticklim = (0, 15) if ONSET else (-15, 0)
xs = np.arange(xticklim[0], xticklim[1], tbin_size * every_tbins / 60.)
figsize, nrows, ncols, sharex, _ = get_subplot_specs(E, num_subplots=4)
fig, axes = plt.subplots(nrows, ncols, figsize=figsize, sharex=sharex)
hls = []
for n in xrange(all_avgs.shape[0]
): # shape: (groups * chow/hifat, F/W, DC/LC, prob values)
ax0 = axes.flatten()[n] # F
ax1 = ax0.twinx() # W
twin_axes = [ax0, ax1]
data, yerrs = all_avgs[n], all_errs[n]
c = 0
for avgs, errs in zip(data, yerrs): # DC/LC
k = 0
for avg, err in zip(avgs, errs): # F/W
ax = twin_axes[k]
# plotting every 5 tbins
ax.errorbar(
xs,
avg[::every_tbins],
yerr=err[::every_tbins],
lw=.8,
color=colors[c],
label=leg_labels[c], #fmt='.', ms=.3,
elinewidth=.8,
capsize=.8,
capthick=.3,
zorder=5 - c)
set_layout(E, ax, act[k], tick_colors[k], ONSET, k, num_mins,
tbin_size)
k += 1
c += 1
if n == 0:
hls.append(ax.get_legend_handles_labels())
ax0.set_xlabel(xlabel, fontsize=8)
if n % 2 == 0:
ax0.set_ylabel(ylabel, fontsize=8, labelpad=10)
if n == 1:
set_legend(ax0, hls, colors)
ax0.set_title(subtitles[n], fontsize=10)
subplot_labels(E, fig, fig_title=fig_title, txpad=0, typad=0)
plt.subplots_adjust(bottom=.12, hspace=0.6, wspace=0.3)
return fig
def draw_subplot2(experiment,
avgs,
errs,
fig_title,
subtitles,
ONSET=True,
num_mins=15,
tbin_size=5,
act='F'):
E = experiment
colors = [E.fcolors[act][1], E.fcolors[act][0]]
tick_colors = colors[0]
text = 'drink' if act == 'W' else 'feed'
leg_labels = ['%s-%s' % (text, x) for x in ['DC', 'LC']]
# subplot labels
on_text = 'from AS onset' if ONSET else 'to AS offset'
xlabel = 'Time ' + on_text + ' [min]'
ylabel = 'Probability'
every_tbins = 2 if num_mins > 5 else 1
xticklim = (0, 15) if ONSET else (-15, 0)
xs = np.arange(xticklim[0], xticklim[1], tbin_size * every_tbins / 60.)
figsize, nrows, ncols, sharex, _ = get_subplot_specs(E, num_subplots=4)
fig, axes = plt.subplots(nrows, ncols, figsize=figsize, sharex=sharex)
hls = []
for n in xrange(avgs.shape[0]
): # shape: (groups * chow/hifat, F/W, DC/LC, prob values)
ax = axes.flatten()[n]
data, yerrs = avgs[n], errs[n]
c = 0
for avg, err in zip(data, yerrs): # DC/LC
# plotting every 5 tbins
ax.errorbar(
xs,
avg[::every_tbins],
yerr=err[::every_tbins],
lw=.8,
color=colors[c],
label=leg_labels[c], #fmt='.', ms=.3,
elinewidth=.8,
capsize=.8,
capthick=.3,
zorder=5 - c)
c += 1
set_layout(E,
ax,
act,
tick_colors,
ONSET,
item_num=None,
num_mins=num_mins,
tbin_size=tbin_size)
if n == 0:
hls.append(ax.get_legend_handles_labels())
ax.set_xlabel(xlabel, fontsize=8)
if n % 2 == 0:
ax.set_ylabel(ylabel, fontsize=8, labelpad=10)
if n == 1:
set_legend(ax, hls, colors)
ax.set_title(subtitles[n], fontsize=10)
subplot_labels(E, fig, fig_title=fig_title, txpad=0, typad=0)
plt.subplots_adjust(bottom=.12, hspace=0.6, wspace=0.3)
return fig
def plot_component_loadings(E,
X,
pca_components,
feature_names,
level,
fname,
EXCLUDE_SS=False,
BY_STRAIN=False,
strain=None,
ADD_SOURCE=True):
num_items = X.shape[0]
num_components = len(pca_components)
num_features = len(feature_names)
figsize, nrows, ncols, sharex, sharey = get_subplot_specs(
E, num_subplots=num_components)
fig, axes = plt.subplots(nrows,
ncols,
figsize=figsize,
sharex=sharex,
sharey=sharey)
xs = range(1, num_components + 1)
for c, comp in enumerate(pca_components):
ax = axes.flatten()[c]
ax.plot(xs, comp, color='.4')
ax.set_xlim(0, ax.get_xlim()[1])
ax.set_xticks(range(num_features))
ax.set_xticklabels([])
ax.set_title('PC%d' % c, fontsize=8, position=(.5, 1.15), ha='center')
ax.axhline(xmin=0, xmax=num_features, lw=.5, color='k')
ax.spines['bottom'].set_position('zero')
ax.spines['bottom'].set_visible(False)
ax_cleanup(ax)
# display feature names on top
heights = list(np.linspace(1.05, .55, 4)) * 8
for p, feat in enumerate(feature_names):
ax.text(xs[p],
heights[p],
feat,
fontsize=4,
ha='center',
transform=ax.transData)
ax.axvline(x=xs[p], ymin=.4, ymax=heights[p], lw=.2, color='.5')
# title
text = '%d strains,' %E.num_strains if not EXCLUDE_SS \
else '%d strains (no AKR, CAST, CZECH),' %(E.num_strains-3)
level_ = 'mice' if level == 'mouse' else level
fig_title = 'Principal Component loadings, %d components\n' %num_components \
+ '%s %d %s, 24H avg data\n%s days:%dto%d' %(
text, num_items, level_, E.use_days, E.daysToUse[0], E.daysToUse[-1])
if BY_STRAIN:
fig_title = 'Principal Component loadings, %d components\n' %num_components \
+ '%s %d %s, 24H avg data\ngroup%d: %s\n%s days:%dto%d' %(
text, num_items, level_, strain, E.strain_names[strain],
E.use_days, E.daysToUse[0], E.daysToUse[-1])
subplot_labels(E, fig, xlabel='', ylabel='Loading', fig_title=fig_title)
plt.subplots_adjust(hspace=.4, wspace=.3)
save_plot(fname, fig, ADD_SOURCE=ADD_SOURCE)
def plot_within_AS_structure(experiment,
level='strain',
EARLY=True,
num_mins=15,
days=None,
cycle='24H',
ADD_SOURCE=True): #, s_start=0):
E = experiment
print "%s, level: %s, EARLY: %s, %s" % (plot_within_AS_structure.__name__,
level, EARLY, cycle)
if EARLY: print "first %d minutes" % num_mins
text = 'full_AS' if not EARLY else 'first_%dmin' % num_mins
labels = ['F', 'W', 'M', 'other']
colors = [E.fcolors[x][0] for x in labels]
colors[3] = E.fcolors['other'][1]
if level == 'strain':
# dirname = E.figures_dir_subpar + 'within_AS/examples/'
dirname = E.figures_dir_subpar + 'within_AS/%s_days/%s/%s/%s/' % (
E.use_days, level, cycle, text)
if not os.path.isdir(dirname): os.makedirs(dirname)
for group in E.groups: #[s_start:s_start+4]:
print '%d: %s' % (group.number, group.name)
mice = E.get_mouseNumbers_from_groupNumber(group.number)
figsize, nrows, ncols, sharex, sharey = get_subplot_specs(
E, num_subplots=len(mice))
fig, axes = plt.subplots(nrows,
ncols,
figsize=figsize,
sharex=sharex,
sharey=sharey)
m = 0
for mouse in group.individuals:
if not mouse.ignored:
print mouse
ax = axes.flatten()[m]
plot_mouse_AS_structure(E, level, mouse, ax, colors, EARLY,
num_mins, days, cycle)
m += 1
remove_axes_not_used(group, axes, nrows, ncols, m)
set_legend(fig, colors)
title = '%s: %s, %s' % (group.name, E.use_days, cycle)
fig.suptitle(title, y=1.03)
fig_title = ''
# fig_title = 'Within-AS structure, across %s\ngroup%d: %s\n%s_days: %d to %d' %(
# cycle, group.number, E.strain_names[group.number], E.use_days, E.daysToUse[0], E.daysToUse[-1])
xlabel = 'Time from AS onset [min]'
ylabel = 'AS sorted by duration'
subplot_labels(E,
fig,
xlabel,
ylabel,
fig_title,
txpad=-0.03,
typad=.03)
fname = dirname + 'within_AS_structure_strain%d_%s_%s' % (
group.number, text, cycle)
save_plot(fname, fig, ADD_SOURCE=ADD_SOURCE, ypad=-.04)
elif level == 'mouse':
for group in E.groups: #[s_start:s_start+4]:
print '%d: %s' % (group.number, group.name)
dirname = E.figures_dir_subpar + 'within_AS/%s_days/%s/%s/%s/strain%d/' % (
E.use_days, level, cycle, text, group.number)
if not os.path.isdir(dirname): os.makedirs(dirname)
for mouse in group.individuals:
if not mouse.ignored:
print mouse
figsize = get_subplot_specs(E)
fig, ax = plt.subplots(figsize=figsize)
plot_mouse_AS_structure(E, level, mouse, ax, colors, EARLY,
num_mins, days, cycle)
fig_title = 'Within-AS structure, across %s\ngroup %d: %s\n%s_days: %d to %d' % (
cycle, group.number, E.strain_names[group.number],
E.use_days, E.daysToUse[0], E.daysToUse[-1])
xlabel = 'Time from AS onset [min]'
ylabel = 'AS sorted by duration'
subplot_labels(E, fig, xlabel, ylabel, fig_title)
if ADD_SOURCE:
add_source(fig)
fname = dirname + 'within_AS_structure_individual%d_%s_%s' % (
mouse.mouseNumber, text, cycle)
save_plot(fname, fig)
def draw_subplot(experiment,
all_avgs,
all_errs,
fig_title,
subtitles,
ONSET=True,
num_mins=15,
tbin_size=5,
ADD_SOURCE=False):
E = experiment
figsize, nrows, ncols, sharex, sharey = get_subplot_specs(E,
num_subplots=len(
E.groups))
# cy_labels = ['24hrs', 'Dark Cycle', 'Light Cycle']
act = ['F', 'W']
colors = [
'.4', E.fcolors['F'][0], E.fcolors['F'][1], '.65', E.fcolors['W'][0],
E.fcolors['W'][1]
]
tick_colors = [E.fcolors[x][0] for x in act]
leg_labels = ['F-24H', 'W-24H', 'F-DC', 'W-DC', 'F-LC', 'W-LC']
# probability
fig, axes = plt.subplots(nrows,
ncols,
figsize=figsize,
sharex=sharex,
sharey=sharey)
every_tbins = 2 if num_mins > 5 else 1
xticklim = (0, 15) if ONSET else (-15, 0)
xs = np.arange(xticklim[0], xticklim[1], tbin_size * every_tbins / 60.)
hls = []
for c in xrange(all_avgs.shape[1]):
try:
ax0 = axes.flatten()[c]
ax1 = ax0.twinx()
k = 0 # F, W
m = 0 # colors and leg_labels
for ax in [ax0, ax1]:
# plotting every 5 tbins
for avgs, errs in zip(all_avgs, all_errs):
ax.errorbar(
xs,
avgs[c, ::every_tbins, k],
yerr=errs[c, ::every_tbins, k],
lw=.3,
color=colors[m],
label=leg_labels[m], #fmt='.', ms=.3,
elinewidth=.5,
capsize=.5,
capthick=.2,
zorder=5 - k)
m += 1
if c == 0:
h, l = ax.get_legend_handles_labels()
hls.append([h[0][0], l[0]])
set_layout(E, ax, act[k], tick_colors[k], ONSET, c, num_mins,
tbin_size)
k += 1
ax.set_title(subtitles[c], fontsize=10)
# if c == 0:
# set_legend(ax, hls, colors)
except IndexError:
ax.axis('off')
continue
# set subplot labels
on_text = 'from AS onset' if ONSET else 'to AS offset'
xlabel = 'Time ' + on_text + ' [min]'
ylabel = 'Probability'
subplot_labels(E, fig, xlabel, ylabel, fig_title)
if ADD_SOURCE:
add_source(fig)
plt.subplots_adjust(bottom=.12, hspace=0.6, wspace=0.3)
return fig
def draw_subplot(experiment,
data,
labels,
ONSET=True,
num_mins=15,
tbin_size=5):
E = experiment
figsize, nrows, ncols, _, _ = get_subplot_specs(E,
num_subplots=len(E.groups))
fig, axes = plt.subplots(nrows,
ncols,
figsize=figsize,
sharex=sharex,
sharey=sharey)
act = ['F', 'W']
fd_text = ['Feeding', 'Drinking']
colors = [E.fcolors[x][0] for x in act]
every_tbins = 2 if num_mins > 5 else 1
xticklim = (0, 15) if ONSET else (-15, 0)
xs = np.arange(xticklim[0], xticklim[1], tbin_size * every_tbins / 60.)
avgs = data[:, :, 0]
errs = data[:, :, 1]
hls = []
for c in xrange(avgs.shape[0]):
try:
ax0 = axes.flatten()[c]
ax1 = ax0.twinx()
k = 0
for ax in [ax0, ax1]:
# plotting every 5 tbins
ax.errorbar(
xs,
avgs[c, ::every_tbins, k],
yerr=errs[c, ::every_tbins, k],
lw=.3,
color=colors[k],
label=fd_text[k], #fmt='.', ms=.3,
elinewidth=.5,
capsize=.5,
capthick=.2,
zorder=5 - k)
set_layout(E, ax, act[k], colors[k], ONSET, c, num_mins,
tbin_size)
if c == 0:
h, l = ax.get_legend_handles_labels()
hls.append([h[0][0], l[0]])
k += 1
ax.set_title(subtitles[c], fontsize=10)
if c == 0:
set_legend(ax, hls, colors)
except IndexError:
ax.axis('off')
continue
# set subplot labels
on_text = 'from AS onset' if ONSET else 'to AS offset'
xlabel = 'Time ' + on_text + ' [min]'
ylabel = 'Probability'
subplot_labels(E, fig, xlabel, ylabel, fig_title)
plt.subplots_adjust(bottom=.12, hspace=0.6, wspace=0.3)
return fig