GCPDS - visualizations¶
Installation¶
pip install -U git+https://github.com/UN-GCPDS/python-gcpds.visualizations.git
Navigation¶
Brain connectivities¶
Note:
Colab users must ensure to install matplotlib via apt-get with the following command:
!apt-get install python3-matplotlib[3]:
from gcpds.visualizations.connectivities import CircosConnectivity
The channels that are used to display the connectivities:
[4]:
channels = ['Fp2', 'C4', 'T4', 'T6', 'O2', 'O1', 'T3', 'T5', 'C3', 'Fp1']
The areas are used to group a set of electrodes:
[5]:
areas = {
'Frontal': ['Fp2'],
'Central': ['C4'],
'Temporal': ['T4', 'T6'],
'Occipital': ['O2'],
'Occipital_': ['O1'],
'Temporal_': ['T5', 'T3'],
'Central_': ['C3'],
'Frontal_': ['Fp1'],
}
Random conectivities values:
[6]:
N = len(channels) # channels
connectivities_s = np.random.normal(size=(N, N)) # Matrix form
connectivities_s.shape
[6]:
(10, 10)
[7]:
connectivities_v = connectivities_s[np.triu_indices(N, k=1)].reshape(-1) # vector form
connectivities_v.shape
[7]:
(45,)
Inter-hemispheric plots¶
[8]:
conn = CircosConnectivity(
connectivities_v, channels, areas=areas, threshold=0.7,
# cmaps and themes
areas_cmap='Set3',
arcs_cmap='Wistia',
hemisphere_color='lightgray',
channel_color='#f8f9fa',
min_alpha=0,
# Texts
width={'hemispheres':35, 'areas':100, 'channels':60},
text={'hemispheres':40, 'areas':20, 'channels':40},
separation={'hemispheres':10, 'areas':-30, 'channels':5},
labelposition={'hemispheres':60, 'areas':0, 'channels':-10},
size=10,
labelsize=15,
# Shapes
show_emisphere=True,
arcs_separation=30,
connection_width=0.1,
small_separation=5,
big_separation=10,
offset=0,
)
conn.figure;
Topoplot reference:
[9]:
conn.topoplot_reference(
montage_name='standard_1005',
size=10,
fontsize=20,
markersize=40,
markerfacecolor='#ffffff88',
markeredgecolor='#000000',
)
[9]:
<Axes: >
Cross-hemispheric plot¶
[10]:
areas = {
'Frontal': ['Fp1', 'Fp2'],
'Central': ['C4'],
'Temporal': ['T4', 'T6'],
'Occipital': ['O2','O1'],
'Temporal_': ['T5', 'T3'],
'Central_': ['C3'],
}
[11]:
conn = CircosConnectivity(
connectivities_v, channels, areas=areas, threshold=0.3,
# cmaps and themes
areas_cmap='Set3',
arcs_cmap='Wistia',
hemisphere_color='lightgray',
channel_color='#f8f9fa',
min_alpha=0,
# Texts
width={'areas':100, 'channels':60},
text={'areas':20, 'channels':40},
separation={'areas':-30, 'channels':5},
labelposition={'areas':0, 'channels':-10},
size=10,
labelsize=15,
# Shapes
show_emisphere=False,
arcs_separation=30,
connection_width=0.1,
small_separation=5,
big_separation=10,
offset=-1,
)
conn.figure;
[12]:
conn.topoplot_reference(
montage_name='standard_1005',
size=10,
fontsize=20,
markersize=40,
markerfacecolor='#ffffff88',
markeredgecolor='#000000',
)
[12]:
<Axes: >
Accuracy and Gain Comparison (AGCO)¶
[7]:
from gcpds.visualizations.accuracy import agco
Input data:
[8]:
ticks = np.array(['S01', 'S02', 'S03', 'S04', 'S05', 'S06', 'S07', 'S08', 'S09', 'S10',
'S11', 'S12', 'S13', 'S14', 'S15', 'S16', 'S17', 'S18', 'S19', 'S20',
'S21', 'S22', 'S23', 'S24', 'S25', 'S26', 'S27', 'S28', 'S30', 'S31',
'S32', 'S33', 'S35', 'S36', 'S37', 'S38', 'S39', 'S40', 'S41', 'S42',
'S43', 'S44', 'S45', 'S46', 'S47', 'S48', 'S49', 'S50', 'S51', 'S52'])
method_1 = np.array([78.33, 50. , 78.33, 81.67, 75. , 74.07, 44.44, 55. , 59.72,
53.33, 41.67, 58.49, 76.67, 93.33, 55.17, 61.67, 45. , 63.33,
55.81, 76. , 58.33, 61.67, 61.67, 58.33, 40.68, 73.33, 55.36,
64.41, 56.76, 68.33, 43.33, 53.33, 65. , 71.19, 73.33, 41.38,
61.67, 40. , 66.67, 50. , 86.67, 65. , 53.33, 77.78, 58.62,
78.33, 65.52, 73.33, 45.76, 60.])
method_2 = np.array([71.5, 58.5, 88.5, 86.2, 75.8, 63.3, 60.2, 54.8, 63.1, 76.5, 67. ,
65.4, 75.2, 93.5, 65.4, 58.5, 52.5, 65. , 57.9, 68.8, 83.8, 65.2,
85. , 70. , 63.5, 78.8, 54.1, 64.2, 62.8, 66.8, 54.8, 63.7, 78.8,
69.2, 77.8, 53.1, 63.7, 61.8, 71.8, 66. , 97. , 75.2, 57.7, 77.9,
67.7, 90.8, 72.8, 83. , 51.3, 69.5])
labels = ['CSP+LDA', 'GFC+WDCNN']
Plot arguments¶
[9]:
fig = agco(
method_1, method_2, ticks, labels, sort='method_1',
# styles
reference_c='C1',
gain_c='C0',
loss_c='C3',
barwidth=6,
# labels
ylabel='Accuracy [%]',
xlabel='Subjects',
gain_labels=['gain', 'loss'],
# figure options
size=(12, 5),
fig=None,
ax=None,
dpi=90,
)
## To change the legend position
# legend = plt.gca().artists[0]
# l2 = plt.legend(plt.gca().get_children(), labels[:4], loc='upper right', ncol=2)
# plt.gca().add_artist(l2)
# legend.remove()
Reverse sort¶
[6]:
agco(method_1, method_2, ticks, labels, sort='method_1r', size=(12, 5));
Subplots¶
[11]:
plt.figure(figsize=(12, 8), dpi=90)
ax1 = plt.subplot(211)
agco(method_1, method_2, ticks, labels, sort='method_1', ax=ax1, fig=plt.gcf())
ax2 = plt.subplot(212)
agco(method_1, method_2, ticks, labels, sort='method_2', ax=ax2, fig=plt.gcf())
plt.subplots_adjust(hspace=0.5)
Plot modifications¶
[8]:
fig = agco(method_1, method_2, ticks, labels, sort='method_1', size=(10, 5))
pastel2 = plt.get_cmap('Pastel2')
f1 = plt.fill_betweenx([35, 100], 40, 50, color=pastel2(1), alpha=0.8, label='4444')
f2 = plt.fill_betweenx([35, 100], 15, 40, color=pastel2(5), alpha=0.8)
f3 = plt.fill_betweenx([35, 100], -1, 15, color=pastel2(0), alpha=0.8)
l2 = plt.legend([f1, f2, f3], ["2%", '3%', '5%'], loc=4, ncol=1, bbox_to_anchor =(1.07, 0.5))
plt.gca().add_artist(l2);
All gains¶
[9]:
agco(method_1, method_1+10, ticks, labels, sort=None, size=(12, 5));
All loss¶
[10]:
agco(method_1, method_1-10, ticks, labels, sort=None, size=(12, 5));
EEG plot¶
[3]:
from gcpds.visualizations.series import plot_eeg
Input data:
[4]:
channels = ['Fp1','Fpz','Fp2',
'F7','F5','F3','F1','Fz','F2','F4','F6','F8',
'C5','C3','C1','Cz','C2','C4','C6',
'P7', 'P3','P1','Pz','P2','P4','P8',
'O1','Oz','O2',
'Iz']
sample_rate = 250
[5]:
data = np.random.normal(size=(len(channels), sample_rate*5))
data.shape
[5]:
(30, 1250)
Plot arguments¶
[10]:
plot_eeg(
data, channels, sample_rate,
# color theme
cmap='viridis',
# axis options
nxlabel=10,
sca=0.7,
offset=-0.1,
# figure options
fig=None,
ax=None,
size=(10, 15),
dpi=90
);
Colors¶
[11]:
plot_eeg(
data[::3], channels[::3], sample_rate, size=(10, 7), sca=0.8,
# color theme
cmap='cool',
);
[15]:
plot_eeg(
data[::3], channels[::3], sample_rate, size=(10, 7), sca=0.5,
# color theme
cmap='Set3',
);
[13]:
plot_eeg(
data[::3], channels[::3], sample_rate, size=(10, 7), sca=0.8,
# color theme
cmap='tab20', # default
);
EEG topoplots¶
[3]:
from gcpds.visualizations.topoplots import topoplot
[4]:
channels = ['Fp1','Fpz','Fp2',
'F7','F5','F3','F1','Fz','F2','F4','F6','F8',
'C5','C3','C1','Cz','C2','C4','C6',
'P7', 'P3','P1','Pz','P2','P4','P8',
'O1','Oz','O2',
'Iz']
data = np.random.normal(size=len(channels))
[5]:
ax = topoplot(data, channels, contours=3, cmap='viridis_r', names=channels, sensors=False)
ax.get_figure();
[18]:
ax = topoplot(data, channels, cmap='viridis_r', res=10, sensors=False, show=False, contours=0, outlines=None)
ax.get_images()[0].set_interpolation('none')
ax.get_figure();
