def plot_probability_if_twins_belong_to_same_group():
"""
Plot the probability whether a twin pair both belong to the same group
"""
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from nibrain.util.plotfig import auto_bar_width, plot_stacked_bar
hemis = ('lh', 'rh')
gids = (0, 1, 2)
hatchs = [None, '//', '*'] # [None, '//', '*']
limit_name = f"limit_G{''.join(map(str, gids))}"
zygosity = ('MZ', 'DZ')
zyg2color = {'MZ': (0.33, 0.33, 0.33, 1), 'DZ': (0.66, 0.66, 0.66, 1)}
count_file = pjoin(work_dir, 'count_if_same_group.csv')
n_hemi = len(hemis)
n_gid = len(gids)
n_zyg = len(zygosity)
df = pd.read_csv(count_file, index_col=0)
x = np.arange(n_hemi)
width = auto_bar_width(x, n_zyg)
offset = -(n_zyg - 1) / 2
_, ax = plt.subplots(figsize=(12.8, 7))
for zyg in zygosity:
ys = np.zeros((n_gid, n_hemi))
items = [f'{zyg}_lh', f'{zyg}_rh']
labels = []
for gid_idx, gid in enumerate(gids):
ys[gid_idx] = df.loc[f'G{gid}', items]
labels.append(f'G{gid}_{zyg}')
ys = ys / np.array([df.loc[limit_name, items]])
plot_stacked_bar(x + width * offset,
ys,
width,
label=labels,
ec=zyg2color[zyg],
fc='w',
hatch=hatchs,
ax=ax)
offset += 1
print(f'{zyg}_{hemis}:\n', ys)
print(f'{zyg}_{hemis}:\n', np.sum(ys, 0))
ax.set_xticks(x)
ax.set_xticklabels(hemis)
ax.set_ylabel('the ratio of twins')
ax.legend()
plt.tight_layout()
plt.show()
def plot_twinsID_distribution_G0G1G2():
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from nibrain.util.plotfig import auto_bar_width, plot_stacked_bar
hemis = ('lh', 'rh')
hemi2color = {'lh': (0.33, 0.33, 0.33, 1), 'rh': (0.66, 0.66, 0.66, 1)}
gids = (0, 1, 2)
zygosity = ('MZ', 'DZ')
twins_gid_file = pjoin(work_dir, 'twins_gid_1080.csv')
gid_file = pjoin(
proj_dir, 'analysis/s2/1080_fROI/refined_with_Kevin/'
'grouping/group_id_{hemi}.npy')
n_hemi = len(hemis)
n_gid = len(gids)
n_zyg = len(zygosity)
df = pd.read_csv(twins_gid_file)
zyg2indices = {}
for zyg in zygosity:
zyg2indices[zyg] = df['zygosity'] == zyg
x = np.arange(n_zyg)
width = auto_bar_width(x, n_hemi)
offset = -(n_hemi - 1) / 2
_, axes = plt.subplots(1, 2, figsize=(12.8, 7))
for hemi in hemis:
cols = [f'twin1_gid_{hemi}', f'twin2_gid_{hemi}']
ys = np.zeros((n_gid, n_zyg))
gid_vec = np.load(gid_file.format(hemi=hemi))
n_subjs = np.zeros((n_gid, 1))
for zyg_idx, zyg in enumerate(zygosity):
data = np.array(df.loc[zyg2indices[zyg], cols])
for gid_idx, gid in enumerate(gids):
ys[gid_idx, zyg_idx] = np.sum(data == gid)
if zyg_idx == 0:
n_subjs[gid_idx, 0] = np.sum(gid_vec == gid)
x_tmp = x + width * offset
offset += 1
labels = [f'G0_{hemi}', f'G1_{hemi}', f'G2_{hemi}']
face_colors = ['w', 'w', hemi2color[hemi]]
hatchs = ['//', '*', None]
ax = axes[0]
plot_stacked_bar(x_tmp,
ys,
width,
label=labels,
ec=hemi2color[hemi],
fc=face_colors,
hatch=hatchs,
ax=ax)
ax = axes[1]
plot_stacked_bar(x_tmp,
ys / n_subjs,
width,
label=labels,
ec=hemi2color[hemi],
fc=face_colors,
hatch=hatchs,
ax=ax)
axes[0].set_xticks(x)
axes[0].set_xticklabels(zygosity)
axes[0].set_ylabel('the number of subjects')
axes[1].set_xticks(x)
axes[1].set_xticklabels(zygosity)
axes[1].set_ylabel('the ratio of subjects')
axes[1].legend()
plt.tight_layout()
plt.show()