""" Groups data according to grouping_keys, computes

asymmetry index for key, and graphs."""

group_names, grouping_index = get_groupings(data, grouping_keys)

x_data = np.asarray(data[xaxis_key].tolist())

prop_ai = get_asymmetry_index(data, yaxis_key)

n_subplots = len(group_names)

n_rows = 1 # int(np.round(np.sqrt(n_subplots)))

n_cols = n_subplots # int(np.ceil(n_subplots / float(n_rows)))

if 'regressions' in plots:

fh1 = plt.figure(figsize=(18, 7))

# fh1.suptitle('%s regression' % yaxis_key)

if 'distributions' in plots:

fh2 = plt.figure(figsize=(18, 7))

fh2.suptitle('%s distributions' % yaxis_key)

n_bins = 15

bins = np.linspace(prop_ai[~np.isnan(prop_ai)].min(),

prop_ai[~np.isnan(prop_ai)].max(),

n_bins + 2)[1:-1]

group_samples = []

regressions = []

for gi, group_name in enumerate(group_names):

# Index the current group

if group_name == 'all':

idx = np.ones((len(data.values()[0]),), dtype=bool)

else:

idx = grouping_index[gi]

# Select data within the group

cur_x = x_data[idx]

group_ai = prop_ai[idx]

# Remove bad data

bad_idx = np.logical_or(np.isnan(cur_x), np.isnan(group_ai))

good_idx = np.logical_not(bad_idx)

cur_x = cur_x[good_idx]

group_ai = group_ai[good_idx]

group_samples.append(group_ai)

regressions.append(scipy.stats.linregress(cur_x, group_ai))

# Plot the regression result

params = dict(xlabel=xaxis_key, ylabel='Asymmetry Index (LH - RH)',

title='Group: %s (n=%d)' % (group_name, len(group_ai)))

if 'regressions' in plots:

if gi > 0:

del params['ylabel']

# ax1 = fh1.add_subplot(n_rows, n_cols, gi + 1)

ax1 = fh1.gca()

do_and_plot_regression(cur_x, group_ai, ax=ax1, colori=gi,

show_std=(len(cur_x) > 200), **params)

ax1.set_title(measure_key) # ax1.get_title().split('\n')[0])

# Plot the distribution result

if 'distributions' in plots:

ax2 = fh2.add_subplot(n_rows, n_cols, gi + 1)

plot_normalized_hist(group_ai, ax2, bins=bins)

ax2.set_title(params['title'])

ax2.set_xlabel(params['ylabel'])

ax2.set_ylims([0, 0.25])

regressions = np.asarray(regressions)

# stats[:, n]: 0:mean_stat: 1:mean_pval; 2:std_stat, 3:std_pval

# shape: n_compares x 4

stats = np.asarray([(scipy.stats.ttest_ind(gsamps1, gsamps2) +

scipy.stats.levene(gsamps1, gsamps2))

for gi, gsamps1 in enumerate(group_samples)

for gsamps2 in group_samples[gi + 1:]])

# Test whether variances differ

if 'stats' in plots:

dist_mat = scipy.spatial.distance.squareform(stats[:, 2])

sig_mat = stats[:, 3] <= (0.05 / stats.shape[0])

fh3 = plt.figure()

fh3.suptitle(str(['%.2e' % s for s in stats[:, 3]]))

ax1 = fh3.add_subplot(1, 2, 1)

plot_symmetric_matrix_as_triangle(dist_mat, ax=ax1, labels=group_names)

ax2 = fh3.add_subplot(1, 2, 2, axisbg=fh3.get_facecolor())

plot_symmetric_matrix_as_triangle(sig_mat, ax=ax2, labels=group_names)

if 'distributions' in plots:

equalize_xlims(fh2)

equalize_ylims(fh2)

# Dump a tsv of group rvals, pvals, and coeff of variation

n_measures = len(regressions)

stat_names = ['rval', 'pval', 'coeff_of_var']

for mi, measure_name in enumerate(sorted(measure_names)):

if mi == 0:

header_vals = ['%s_%s' % (group_name, stat_name)

for si, stat_name in enumerate(stat_names)

for group_name in group_names]

print('\t'.join([''] + header_vals))

row_vals = ['%.10f' % regressions[mi][gi, si + 2]

for si, stat_name in enumerate(stat_names)

for gi, group_name in enumerate(group_names)]

# Dump a tsv of group rvals, pvals, and coeff of variation

n_measures = len(regressions)

stat_names = ['rval', 'pval']

color_arr = ['b', 'g', 'r', 'k', 'y', 'c'][:len(group_names)]

prefix = np.unique([m[:12] for m in measure_names])

all_xvals = dict([(gn, []) for gn in group_names])

all_yvals = dict([(gn, []) for gn in group_names])

for p in prefix:

prefix_idx = np.asarray([m.startswith(p) for m in measure_names])

p_xvals = dict()

p_yvals = dict()

for gi, group_name in enumerate(group_names):

# Plot rval vs. pval

group_xvals = np.asarray([regressions[mi][gi, 2]

for mi in np.nonzero(prefix_idx)[0]])

group_yvals = np.asarray([regressions[mi][gi, 3]

for mi in np.nonzero(prefix_idx)[0]])

good_idx = ~np.logical_or(np.isnan(group_xvals),

np.isnan(group_yvals))

p_xvals[group_name] = group_xvals[good_idx]

p_yvals[group_name] = group_yvals[good_idx]

all_xvals[group_name] += group_xvals[good_idx].tolist()

all_yvals[group_name] += group_yvals[good_idx].tolist()

fh = plt.figure(figsize=(18, 8))

ax1 = fh.add_subplot(1, 2, 1)

plot_normalized_hist(

np.asarray([p_xvals[gn] for gn in group_names]).T,

ax=ax1,

bins=20,

color=color_arr)

ax1.legend(group_names)

ax2 = fh.add_subplot(1, 2, 2)

plot_normalized_hist(

np.asarray([p_yvals[gn] for gn in group_names]).T,

ax=ax2,

bins=20,

color=color_arr)

fh.suptitle(p)

# Bar plot (total)

fh = plt.figure(figsize=(18, 8))

ax1 = fh.add_subplot(1, 2, 1)

plot_normalized_hist(np.asarray([all_xvals[gn] for gn in group_names]).T,

ax=ax1,

bins=50,

color=color_arr)

ax1.legend(group_names)

ax2 = fh.add_subplot(1, 2, 2)

plot_normalized_hist(np.asarray([all_yvals[gn] for gn in group_names]).T,

ax=ax2,

bins=50,

color=color_arr)

fh.suptitle('Over all measures')

#ax = plt.figure(figsize=(18, 8)).gca()

ax1.scatter(np.asarray([all_xvals[gn] for gn in group_names]).T,

30 * np.asarray([all_yvals[gn] for gn in group_names]).T,

# Show the distribution of stats, to see if there are

# reliable group differences.

fh4 = plt.figure(figsize=(18, 8))

lbls = ['%s vs. %s' % (gn1, gn2)

for gi, gn1 in enumerate(group_names)

for gn2 in group_names[gi + 1:]]

pi = 1

for si, stat_name in enumerate(['mean', 'var']):

stat_vals = np.asarray([ss[:, si * 2] for ss in stats])

pvals = np.asarray([ss[:, si * 2 + 1] for ss in stats])

for li, lbl in enumerate(lbls):

ax1 = fh4.add_subplot(2, len(lbls), pi)

plot_normalized_hist(pvals[:, li], ax=ax1, bins=[0.0001, 0.001, 0.01, 0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60])

ax1.set_title(lbl)

if li == 0:

ax1.set_ylabel(stat_name)

pi += 1

equalize_xlims(fh4)

equalize_ylims(fh4)

means = np.asarray([ss[:, 0] for ss in stats])

stds = np.asarray([ss[:, 2] for ss in stats])

good_idx = ~np.logical_or(np.isnan(means.sum(1)), np.isnan(stds.sum(1)))

fh = plt.figure(figsize=(18, 8))

ax1 = fh.add_subplot(1, 2, 1)

plot_normalized_hist(means[good_idx], ax=ax1, bins=25)

ax1.set_xlabel('mean')

ax2 = fh.add_subplot(1, 2, 2)

plot_normalized_hist(stds[good_idx], ax=ax2, bins=25)

ax2.set_xlabel('standard deviation')

grouping_keys=['Gender', 'FDH_23_Handedness_Prtcpnt'],

xaxis_key='Age_At_IMGExam',

plots='regressions'):

""" Loop over all properties to show asymmetry."""

data = get_all_data()

data.filter(lambda k, v: 'fuzzy' not in k) # Remove 'fuzzy'

data.filter([partial(lambda k, v, p: (k.startswith(p) or

k in grouping_keys or

k == xaxis_key),

p=p)

for p in prefix])

# Process & plot the data.

stats = []

regressions = []

group_samples = []

measure_keys = data.get_twohemi_keys()

for pi, key in enumerate(sorted(measure_keys)):

print("Comparing %d (%s)..." % (pi, key))

gn, ss, rv, gs = compare_group_asymmetry(data.data_dict, xaxis_key=xaxis_key,

yaxis_key=key, plots=plots,

grouping_keys=grouping_keys,

measure_key=key)

stats.append(ss)

regressions.append(rv)

group_samples.append(gs)

if 'regression_stats' in plots:

dump_regressions_csv(regressions,

group_names=gn,

measure_names=measure_keys)

plot_regressions_scatter(regressions,

group_names=gn,

measure_names=measure_keys)

if 'stat_distributions' in plots:

plot_stat_distributions(stats, group_names=gn)

if error_msg is not None:

print("*** ERROR *** : %s" % error_msg)

print("\nUsage: %s prefixes group_keys [xaxis] [plots]" % args[0])

print("\tProduce plots for each group")

print("\n\tprefixes: simple selector for groups of measures to include. Popular choices include:")

print("\t\tMRI_cort_area.ctx: ")

print("\t\tMRI_cort_thick.ctx: ")

print("\n\tgroups: comma separated list of keys for grouping. Popular ones include:")

print("\t\tGender: ")

print("\t\tFDH_23_Handedness_Prtcpnt: ")

print("\txaxis: (optional) value to regress against (default=Age_At_IMGExam)")

print("\tplots: (optional) list of plots (default=regressions); select from:")

print("\t\tregressions:")

print("\t\tdistributions:")

print("\t\tstats:")

print("\t\tregression_stats:")