def compare_shapes_between_hemispheres():
import os
import numpy as np
import pandas as pd
# For plotting:
from math import pi
from bokeh.models import HoverTool
from bokeh.plotting import ColumnDataSource, figure, show, save, output_file
from mindboggle.mio.colors import viridis_colormap
from mindboggle.mio.labels import DKTprotocol
titles = [
"Fractional difference between interhemispheric volumes",
"Fractional difference between interhemispheric thickinthehead cortical thicknesses",
"Fractional difference between interhemispheric cortical label median areas",
"Fractional difference between interhemispheric cortical label median travel depths",
"Fractional difference between interhemispheric cortical label median geodesic depths",
"Fractional difference between interhemispheric cortical label median mean curvatures",
"Fractional difference between interhemispheric cortical label median FreeSurfer curvatures",
"Fractional difference between interhemispheric cortical label median FreeSurfer thicknesses"
]
names = [
"volume_for_each_freesurfer_label",
"thickinthehead_per_freesurfer_cortex_label",
"median_area_per_freesurfer_cortex_label",
"median_travel_depth_per_freesurfer_cortex_label",
"median_geodesic_depth_per_freesurfer_cortex_label",
"median_mean_curvatures_per_freesurfer_cortex_label",
"median_freesurfer_curvature_per_freesurfer_cortex_label",
"median_freesurfer_thickness_per_freesurfer_cortex_label"
]
table_dir = '/Users/arno/Data/manual_tables'
tablesL = [
os.path.join('tables', 'volume_for_each_freesurfer_label.csv'),
os.path.join('tables',
'thickinthehead_per_freesurfer_cortex_label.csv'),
os.path.join('tables', 'left_cortical_surface',
'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface',
'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface',
'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface',
'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface',
'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface', 'label_shapes.csv')
]
tablesR = [
os.path.join('tables', 'volume_for_each_freesurfer_label.csv'),
os.path.join('tables',
'thickinthehead_per_freesurfer_cortex_label.csv'),
os.path.join('tables', 'right_cortical_surface',
'label_shapes.csv'),
os.path.join('tables', 'right_cortical_surface',
'label_shapes.csv'),
os.path.join('tables', 'right_cortical_surface',
'label_shapes.csv'),
os.path.join('tables', 'right_cortical_surface',
'label_shapes.csv'),
os.path.join('tables', 'right_cortical_surface',
'label_shapes.csv'),
os.path.join('tables', 'right_cortical_surface',
'label_shapes.csv')
]
column_indices = [1, 1, 1, 2, 10, 18, 26, 34]
# --------------------------------------------------------------------
# Alternating left, right cortex label numbers (for volume shapes):
# --------------------------------------------------------------------
dkt = DKTprotocol()
labels_left = dkt.left_cerebrum_cortex_DKT31_numbers
labels_right = dkt.right_cerebrum_cortex_DKT31_numbers
label_names = dkt.DKT31_names
#exclude_sulci = [20] # Sulcus 20 removed from protocol since initial run
# --------------------------------------------------------------------
# Colors:
# --------------------------------------------------------------------
colors = viridis_colormap()
#from matplotlib import cm as cmaps
#import matplotlib.pyplot as plt
#plt.register_cmap(name='viridis', cmap=cmaps.viridis)
#plt.set_cmap(cmaps.viridis)
scale_rect = 20
# --------------------------------------------------------------------
# Subjects:
# --------------------------------------------------------------------
subject_list = '/Users/arno/Data/subject_list_Mindboggle101.txt'
fid = open(subject_list, 'r')
subjects = [x.strip() for x in fid.readlines()]
# --------------------------------------------------------------------
# Loop through tables:
# --------------------------------------------------------------------
data_means = np.zeros((len(labels_left), len(titles)))
data_summaries = np.zeros((len(titles), 8))
for ititle, title in enumerate(titles):
tableL_file = tablesL[ititle]
tableR_file = tablesR[ititle]
name = names[ititle]
index = column_indices[ititle]
# ----------------------------------------------------------------
# Loop through subjects:
# ----------------------------------------------------------------
subject_shapesL = np.zeros((len(subjects), len(labels_left)))
subject_shapesR = np.zeros((len(subjects), len(labels_right)))
for isubject, subject in enumerate(subjects):
tableL = os.path.join(table_dir, subject, tableL_file)
tableR = os.path.join(table_dir, subject, tableR_file)
columnsL = pd.read_csv(tableL, sep=",", index_col='name')
columnsR = pd.read_csv(tableR, sep=",", index_col='name')
# ------------------------------------------------------------
# Loop through labels:
# ------------------------------------------------------------
for ilabel, labelL in enumerate(labels_left):
for irow in range(columnsL.shape[0]):
if int(columnsL.iloc[irow][0]) == int(labelL):
valueL = columnsL.iloc[irow][index]
subject_shapesL[isubject, ilabel] = valueL
for ilabel, labelR in enumerate(labels_right):
for irow in range(columnsR.shape[0]):
if int(columnsR.iloc[irow][0]) == int(labelR):
valueR = columnsR.iloc[irow][index]
subject_shapesR[isubject, ilabel] = valueR
# ----------------------------------------------------------------
# Save csv files:
# ----------------------------------------------------------------
data = pd.DataFrame(subject_shapesL,
index=subjects,
columns=labels_left)
data.to_csv(name + '_left.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name + '_left_summary.csv')
data = pd.DataFrame(subject_shapesR,
index=subjects,
columns=labels_right)
data.to_csv(name + '_right.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name + '_right_summary.csv')
subject_shape_diffs = subject_shapesL - subject_shapesR
data = pd.DataFrame(subject_shape_diffs,
index=subjects,
columns=label_names)
data.to_csv(name + '_differences.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name + '_differences_summary.csv')
subject_shape_abs_diffs = np.abs(subject_shape_diffs)
subject_shape_frac_diffs = subject_shape_diffs / subject_shapesL
data = pd.DataFrame(subject_shape_frac_diffs,
index=subjects,
columns=label_names)
data.to_csv(name + '_fractional_differences.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name + '_fractional_differences_summary.csv')
subject_shape_frac_abs_diffs = np.abs(subject_shape_abs_diffs /
subject_shapesL)
data = pd.DataFrame(subject_shape_frac_abs_diffs,
index=subjects,
columns=label_names)
n50 = len(np.where(data.values > 0.5)[0])
n25 = len(np.where(data.values > 0.25)[0])
n10 = len(np.where(data.values > 0.1)[0])
print(title)
print("Fractional absolute differences above "
"0.5: {0}; 0.25: {1}; 0.1: {2}".format(n50, n25, n10))
print("")
data.to_csv(name + '_fractional_abs_differences.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name +
'_fractional_abs_differences_summary.csv')
data_means[:, ititle] = data_summary.loc['mean'].values
data_summaries[ititle, :] = data_summary.mean(axis=1)
# ----------------------------------------------------------------
# Plot heatmap for labels X subjects array for each table:
# ----------------------------------------------------------------
html_file = name + '_fractional_abs_differences.html'
print(html_file)
# Set up the data for plotting. We will need to have values for every
# pair of subject/label names. Map the value to a color.
subjectx = []
label_namex = []
value1x = []
value2x = []
differencex = []
fractionx = []
colorx = []
for ilabel in range(len(labels_left)):
for isubject, subject in enumerate(subjects):
label_namex.append(label_names[ilabel])
subjectx.append(subject)
value1 = subject_shapesL[isubject, ilabel]
value2 = subject_shapesR[isubject, ilabel]
difference = subject_shape_diffs[isubject, ilabel]
value1x.append(value1)
value2x.append(value2)
differencex.append(difference)
fraction = subject_shape_frac_diffs[isubject, ilabel]
fractionx.append(fraction)
abs_fraction = subject_shape_frac_abs_diffs[isubject,
ilabel]
if np.isnan(value1) or np.isnan(value2) or np.isnan(
abs_fraction):
rgb = [0, 0, 0]
elif abs_fraction > 1.0:
rgb = [1, 1, 1]
else:
rgb = [
np.int(255 * x)
for x in colors[np.int(255 * abs_fraction)]
]
hex = "#%02x%02x%02x" % tuple(rgb)
colorx.append(hex)
output_file(html_file, title=title)
source = ColumnDataSource(
dict(subject=subjectx,
label_name=label_namex,
color=colorx,
value1=value1x,
value2=value2x,
difference=differencex,
fraction=fractionx))
TOOLS = "hover,save,pan,box_zoom,wheel_zoom"
plot_width = len(subjects) * scale_rect
plot_height = len(labels_left) * scale_rect
p = figure(title=title,
x_range=subjects,
y_range=list(reversed(label_names)),
plot_width=plot_width,
plot_height=plot_height,
x_axis_location="above",
tools=TOOLS)
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_text_font_size = "10pt"
p.axis.major_label_standoff = 0
p.xaxis.major_label_orientation = pi / 3
p.rect(x="subject",
y="label_name",
width=1,
height=1,
source=source,
color="color",
line_color=None)
p.select_one(HoverTool).tooltips = [
('subject', '@subject'),
('label', '@label_name'),
('value1', '@value1'),
('value2', '@value2'),
('difference', '@difference'),
('fraction', '@fraction'),
]
#show(p) # show the plot
#import sys; sys.exit()
save(p) # save the plot
data_means_df = pd.DataFrame(data_means,
index=label_names,
columns=names)
data_means_df.to_csv(
'means_of_interhemispheric_fractional_abs_shape_differences.csv')
data_summaries_df = pd.DataFrame(data_summaries,
index=names,
columns=data_summary.index)
data_summaries_df.to_csv(
'summary_of_interhemispheric_fractional_abs_shape_differences.csv')
def compare_shapes_between_scans():
import os
import numpy as np
import pandas as pd
# For plotting:
from math import pi
from bokeh.models import HoverTool
from bokeh.plotting import ColumnDataSource, figure, show, save, output_file
from mindboggle.mio.colors import viridis_colormap
from mindboggle.mio.labels import DKTprotocol
#from mindboggle.mio.plots import histograms_of_lists
titles = [
"Fractional difference between re/scan volumes",
"Fractional difference between re/scan thickinthehead cortical thicknesses",
"Fractional difference between re/scan left cortical label median areas",
"Fractional difference between re/scan left cortical label median travel depths",
"Fractional difference between re/scan left cortical label median geodesic depths",
"Fractional difference between re/scan left cortical label median mean curvatures",
"Fractional difference between re/scan left cortical label median FreeSurfer curvatures",
"Fractional difference between re/scan left cortical label median FreeSurfer thicknesses"
]
# "Fractional difference between re/scan right cortical label median areas",
# "Fractional difference between re/scan right cortical label median travel depths",
# "Fractional difference between re/scan right cortical label median geodesic depths",
# "Fractional difference between re/scan right cortical label median mean curvatures",
# "Fractional difference between re/scan right cortical label median FreeSurfer curvatures",
# "Fractional difference between re/scan right cortical label median FreeSurfer thicknesses"]
# "Fractional difference between re/scan right cortical label median FreeSurfer convexities"]
names = [
"volume_for_each_freesurfer_label",
"thickinthehead_per_freesurfer_cortex_label",
"median_area_per_freesurfer_left_cortex_label",
"median_travel_depth_per_freesurfer_left_cortex_label",
"median_geodesic_depth_per_freesurfer_left_cortex_label",
"median_mean_curvatures_per_freesurfer_left_cortex_label",
"median_freesurfer_curvature_per_freesurfer_left_cortex_label",
"median_freesurfer_thickness_per_freesurfer_left_cortex_label"
]
# "median_area_per_freesurfer_right_cortex_label",
# "median_travel_depth_per_freesurfer_right_cortex_label",
# "median_geodesic_depth_per_freesurfer_right_cortex_label",
# "median_mean_curvatures_per_freesurfer_right_cortex_label",
# "median_freesurfer_curvature_per_freesurfer_right_cortex_label",
# "median_freesurfer_thickness_per_freesurfer_right_cortex_label"]
# "median_freesurfer_convexity_per_freesurfer_right_cortex_label"]
table_dir = '/Users/arno/Data/shape_tables_for_auto_labels_of_Mindboggle101_rescans'
tables = [
os.path.join('tables', 'volume_for_each_freesurfer_label.csv'),
os.path.join('tables',
'thickinthehead_per_freesurfer_cortex_label.csv'),
os.path.join('tables', 'left_cortical_surface',
'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface',
'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface',
'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface',
'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface',
'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface', 'label_shapes.csv')
]
# os.path.join('tables', 'right_cortical_surface', 'label_shapes.csv'),
# os.path.join('tables', 'right_cortical_surface', 'label_shapes.csv'),
# os.path.join('tables', 'right_cortical_surface', 'label_shapes.csv'),
# os.path.join('tables', 'right_cortical_surface', 'label_shapes.csv'),
# os.path.join('tables', 'right_cortical_surface', 'label_shapes.csv'),
# os.path.join('tables', 'right_cortical_surface', 'label_shapes.csv')]
column_indices = [1, 1, 1, 2, 10, 18, 26,
34] #, 1, 2, 10, 18, 26, 34] #, 42]
# --------------------------------------------------------------------
# Alternating left, right cortex label numbers (for volume shapes):
# --------------------------------------------------------------------
dkt = DKTprotocol()
labels_left = dkt.left_cerebrum_cortex_DKT31_numbers
labels_right = dkt.right_cerebrum_cortex_DKT31_numbers
DKT31_names = dkt.DKT31_names
# label_list = []
# label_name_list = []
# for ilabel, label_left in enumerate(labels_left):
# label_list.append(label_left)
# label_list.append(labels_right[ilabel])
# label_name_list.append(DKT31_names[ilabel] + ' (left)')
# label_name_list.append(DKT31_names[ilabel] + ' (right)')
#label_list = [str(x) for x in label_list]
##exclude_sulci = [20] # Sulcus 20 removed from protocol since initial run
#label_lists = [label_list,
# label_list,
label_lists = [
labels_left, labels_left, labels_left, labels_left, labels_left,
labels_left, labels_left, labels_left, labels_left, labels_right,
labels_right, labels_right, labels_right, labels_right,
labels_right, labels_right
]
label_name_lists = [DKT31_names for x in range(len(titles))]
#label_name_lists[0] = label_name_list
#label_name_lists[1] = label_name_list
# --------------------------------------------------------------------
# Colors:
# --------------------------------------------------------------------
colors = viridis_colormap()
#from matplotlib import cm as cmaps
#import matplotlib.pyplot as plt
#plt.register_cmap(name='viridis', cmap=cmaps.viridis)
#plt.set_cmap(cmaps.viridis)
scale_rect = 40
# --------------------------------------------------------------------
# Subjects with second scans:
# --------------------------------------------------------------------
groups = ['OASIS-TRT-20', 'MMRR-21']
numbers = [20, 21]
nsubjects = sum(numbers)
subjects = []
subjects2 = []
for igroup, group in enumerate(groups):
for n in range(1, numbers[igroup] + 1):
subjects.append(group + '-' + str(n))
subjects2.append(group + '-rescan-' + str(n))
# --------------------------------------------------------------------
# Loop through tables:
# --------------------------------------------------------------------
data_means = np.zeros((len(labels_left), len(titles)))
data_summaries = np.zeros((len(titles), 8))
for ititle, title in enumerate(titles):
table = tables[ititle]
name = names[ititle]
index = column_indices[ititle]
labels = label_lists[ititle]
label_names = label_name_lists[ititle]
# ----------------------------------------------------------------
# Loop through subjects:
# ----------------------------------------------------------------
subject_shapes = np.zeros((nsubjects, len(labels)))
subject2_shapes = np.zeros((nsubjects, len(labels)))
for isubject, subject in enumerate(subjects):
subject2 = subjects2[isubject]
table_file = os.path.join(table_dir, subject, table)
table_file2 = os.path.join(table_dir, subject2, table)
columns = pd.read_csv(table_file, sep=",", index_col='name')
columns2 = pd.read_csv(table_file2, sep=",", index_col='name')
# ------------------------------------------------------------
# Loop through labels:
# ------------------------------------------------------------
for ilabel, label in enumerate(labels):
for irow in range(columns.shape[0]):
if int(columns.iloc[irow][0]) == int(label):
value = columns.iloc[irow][index]
value2 = columns2.iloc[irow][index]
subject_shapes[isubject, ilabel] = value
subject2_shapes[isubject, ilabel] = value2
# ----------------------------------------------------------------
# Save csv files:
# ----------------------------------------------------------------
data = pd.DataFrame(subject_shapes, index=subjects, columns=labels)
data.to_csv(name + '_scans.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name + '_scans_summary.csv')
data = pd.DataFrame(subject2_shapes,
index=subjects,
columns=labels)
data.to_csv(name + '_rescans.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name + '_rescans_summary.csv')
subject_shape_diffs = subject2_shapes - subject_shapes
data = pd.DataFrame(subject_shape_diffs,
index=subjects,
columns=labels)
data.to_csv(name + '_differences.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name + '_differences_summary.csv')
subject_shape_abs_diffs = np.abs(subject_shape_diffs)
max_diffs = subject_shape_abs_diffs.max(axis=0)
subject_shape_frac_diffs = subject_shape_diffs / subject_shapes
data = pd.DataFrame(subject_shape_frac_diffs,
index=subjects,
columns=labels)
#iInf, jInf = np.where(data.values == np.inf)
#data.iloc[iInf, jInf] = 'NaN'
data.to_csv(name + '_fractional_differences.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name + '_fractional_differences_summary.csv')
# max_array = np.zeros((nsubjects, len(labels), 2))
# max_array[:, :, 0] = subject_shapes
# max_array[:, :, 1] = subject2_shapes
# max_diff = np.abs(np.max(max_array, axis=2))
# maxs = np.max(np.max([subject_shapes, subject2_shapes], axis=0), axis=0)
# mins = np.min(np.min([subject_shapes, subject2_shapes], axis=0), axis=0)
# max_diff = np.abs(maxs - mins)
# max_diff = np.max([subject_shapes, subject2_shapes]) -\
# np.min([subject_shapes, subject2_shapes])
subject_shape_frac_abs_diffs = np.abs(subject_shape_abs_diffs /
subject_shapes)
data = pd.DataFrame(subject_shape_frac_abs_diffs,
index=subjects,
columns=labels)
n50 = len(np.where(data.values > 0.5)[0])
n25 = len(np.where(data.values > 0.25)[0])
n10 = len(np.where(data.values > 0.1)[0])
print(title)
print("Fractional absolute differences above "
"0.5: {0}; 0.25: {1}; 0.1: {2}".format(n50, n25, n10))
print("")
#iInf, jInf = np.where(data.values == np.inf)
#data.iloc[iInf, jInf] = 'NaN'
data.to_csv(name + '_fractional_abs_differences.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name +
'_fractional_abs_differences_summary.csv')
data_means[:, ititle] = data_summary.loc['mean'].values
data_summaries[ititle, :] = data_summary.mean(axis=1)
# ignore_columns = []
# nbins = 100
# axis_limits = []
# histograms_of_lists(subject_shape_diffs, title, ignore_columns,
# nbins, axis_limits, [title])
# ----------------------------------------------------------------
# Plot heatmap for labels X subjects array for each table:
# ----------------------------------------------------------------
html_file = name + '_fractional_abs_differences.html'
print(html_file)
# Set up the data for plotting. We will need to have values for every
# pair of subject/label names. Map the value to a color.
subjectx = []
labelx = []
label_namex = []
value1x = []
value2x = []
differencex = []
fractionx = []
colorx = []
for ilabel, label in enumerate(labels):
for isubject, subject in enumerate(subjects):
labelx.append(label)
label_namex.append(label_names[ilabel])
subjectx.append(subject)
value1 = subject_shapes[isubject, ilabel]
value2 = subject2_shapes[isubject, ilabel]
difference = subject_shape_diffs[isubject, ilabel]
value1x.append(value1)
value2x.append(value2)
differencex.append(difference)
fraction = subject_shape_frac_diffs[isubject, ilabel]
fractionx.append(fraction)
abs_fraction = subject_shape_frac_abs_diffs[isubject,
ilabel]
if np.isnan(value) or np.isnan(abs_fraction):
rgb = [0, 0, 0]
elif abs_fraction > 1.0:
rgb = [1, 1, 1]
else:
rgb = [
np.int(255 * x)
for x in colors[np.int(255 * abs_fraction)]
]
hex = "#%02x%02x%02x" % tuple(rgb)
colorx.append(hex)
output_file(html_file, title=title)
source = ColumnDataSource(
dict(subject=subjectx,
label=labelx,
label_name=label_namex,
color=colorx,
value1=value1x,
value2=value2x,
difference=differencex,
fraction=fractionx))
TOOLS = "hover,save,pan,box_zoom,wheel_zoom"
plot_width = len(subjects) * scale_rect
plot_height = len(labels) * scale_rect
p = figure(title=title,
x_range=subjects,
y_range=list(reversed(label_names)),
plot_width=plot_width,
plot_height=plot_height,
x_axis_location="above",
tools=TOOLS)
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_text_font_size = "10pt"
p.axis.major_label_standoff = 0
p.xaxis.major_label_orientation = pi / 3
p.rect(x="subject",
y="label_name",
width=1,
height=1,
source=source,
color="color",
line_color=None)
p.select_one(HoverTool).tooltips = [
('subject', '@subject'),
('label', '@label'),
('label name', '@label_name'),
('value1', '@value1'),
('value2', '@value2'),
('difference', '@difference'),
('fraction', '@fraction'),
]
#show(p) # show the plot
#import sys; sys.exit()
save(p) # save the plot
data_means_df = pd.DataFrame(data_means,
index=label_names,
columns=names)
data_means_df.to_csv(
'means_of_rescan_fractional_abs_shape_differences.csv')
data_summaries_df = pd.DataFrame(data_summaries,
index=names,
columns=data_summary.index)
data_summaries_df.to_csv(
'summary_of_rescan_fractional_abs_shape_differences.csv')
def compare_shapes_between_hemispheres():
import os
import numpy as np
import pandas as pd
# For plotting:
from math import pi
from bokeh.models import HoverTool
from bokeh.plotting import ColumnDataSource, figure, show, save, output_file
from mindboggle.mio.colors import viridis_colormap
from mindboggle.mio.labels import DKTprotocol
titles = ["Fractional difference between interhemispheric volumes",
"Fractional difference between interhemispheric thickinthehead cortical thicknesses",
"Fractional difference between interhemispheric cortical label median areas",
"Fractional difference between interhemispheric cortical label median travel depths",
"Fractional difference between interhemispheric cortical label median geodesic depths",
"Fractional difference between interhemispheric cortical label median mean curvatures",
"Fractional difference between interhemispheric cortical label median FreeSurfer curvatures",
"Fractional difference between interhemispheric cortical label median FreeSurfer thicknesses"]
names = ["volume_for_each_freesurfer_label",
"thickinthehead_per_freesurfer_cortex_label",
"median_area_per_freesurfer_cortex_label",
"median_travel_depth_per_freesurfer_cortex_label",
"median_geodesic_depth_per_freesurfer_cortex_label",
"median_mean_curvatures_per_freesurfer_cortex_label",
"median_freesurfer_curvature_per_freesurfer_cortex_label",
"median_freesurfer_thickness_per_freesurfer_cortex_label"]
table_dir = '/Users/arno/Data/manual_tables'
tablesL = [os.path.join('tables', 'volume_for_each_freesurfer_label.csv'),
os.path.join('tables', 'thickinthehead_per_freesurfer_cortex_label.csv'),
os.path.join('tables', 'left_cortical_surface', 'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface', 'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface', 'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface', 'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface', 'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface', 'label_shapes.csv')]
tablesR = [os.path.join('tables', 'volume_for_each_freesurfer_label.csv'),
os.path.join('tables', 'thickinthehead_per_freesurfer_cortex_label.csv'),
os.path.join('tables', 'right_cortical_surface', 'label_shapes.csv'),
os.path.join('tables', 'right_cortical_surface', 'label_shapes.csv'),
os.path.join('tables', 'right_cortical_surface', 'label_shapes.csv'),
os.path.join('tables', 'right_cortical_surface', 'label_shapes.csv'),
os.path.join('tables', 'right_cortical_surface', 'label_shapes.csv'),
os.path.join('tables', 'right_cortical_surface', 'label_shapes.csv')]
column_indices = [1, 1, 1, 2, 10, 18, 26, 34]
# --------------------------------------------------------------------
# Alternating left, right cortex label numbers (for volume shapes):
# --------------------------------------------------------------------
dkt = DKTprotocol()
labels_left = dkt.left_cerebrum_cortex_DKT31_numbers
labels_right = dkt.right_cerebrum_cortex_DKT31_numbers
label_names = dkt.DKT31_names
#exclude_sulci = [20] # Sulcus 20 removed from protocol since initial run
# --------------------------------------------------------------------
# Colors:
# --------------------------------------------------------------------
colors = viridis_colormap()
#from matplotlib import cm as cmaps
#import matplotlib.pyplot as plt
#plt.register_cmap(name='viridis', cmap=cmaps.viridis)
#plt.set_cmap(cmaps.viridis)
scale_rect = 20
# --------------------------------------------------------------------
# Subjects:
# --------------------------------------------------------------------
subject_list = '/Users/arno/Data/subject_list_Mindboggle101.txt'
fid = open(subject_list, 'r')
subjects = [x.strip() for x in fid.readlines()]
# --------------------------------------------------------------------
# Loop through tables:
# --------------------------------------------------------------------
data_means = np.zeros((len(labels_left), len(titles)))
data_summaries = np.zeros((len(titles), 8))
for ititle, title in enumerate(titles):
tableL_file = tablesL[ititle]
tableR_file = tablesR[ititle]
name = names[ititle]
index = column_indices[ititle]
# ----------------------------------------------------------------
# Loop through subjects:
# ----------------------------------------------------------------
subject_shapesL = np.zeros((len(subjects), len(labels_left)))
subject_shapesR = np.zeros((len(subjects), len(labels_right)))
for isubject, subject in enumerate(subjects):
tableL = os.path.join(table_dir, subject, tableL_file)
tableR = os.path.join(table_dir, subject, tableR_file)
columnsL = pd.read_csv(tableL, sep=",", index_col='name')
columnsR = pd.read_csv(tableR, sep=",", index_col='name')
# ------------------------------------------------------------
# Loop through labels:
# ------------------------------------------------------------
for ilabel, labelL in enumerate(labels_left):
for irow in range(columnsL.shape[0]):
if int(columnsL.iloc[irow][0]) == int(labelL):
valueL = columnsL.iloc[irow][index]
subject_shapesL[isubject, ilabel] = valueL
for ilabel, labelR in enumerate(labels_right):
for irow in range(columnsR.shape[0]):
if int(columnsR.iloc[irow][0]) == int(labelR):
valueR = columnsR.iloc[irow][index]
subject_shapesR[isubject, ilabel] = valueR
# ----------------------------------------------------------------
# Save csv files:
# ----------------------------------------------------------------
data = pd.DataFrame(subject_shapesL, index=subjects, columns=labels_left)
data.to_csv(name + '_left.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name + '_left_summary.csv')
data = pd.DataFrame(subject_shapesR, index=subjects, columns=labels_right)
data.to_csv(name + '_right.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name + '_right_summary.csv')
subject_shape_diffs = subject_shapesL - subject_shapesR
data = pd.DataFrame(subject_shape_diffs, index=subjects, columns=label_names)
data.to_csv(name + '_differences.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name + '_differences_summary.csv')
subject_shape_abs_diffs = np.abs(subject_shape_diffs)
subject_shape_frac_diffs = subject_shape_diffs / subject_shapesL
data = pd.DataFrame(subject_shape_frac_diffs,
index=subjects, columns=label_names)
data.to_csv(name + '_fractional_differences.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name + '_fractional_differences_summary.csv')
subject_shape_frac_abs_diffs = np.abs(subject_shape_abs_diffs / subject_shapesL)
data = pd.DataFrame(subject_shape_frac_abs_diffs,
index=subjects, columns=label_names)
n50 = len(np.where(data.values > 0.5)[0])
n25 = len(np.where(data.values > 0.25)[0])
n10 = len(np.where(data.values > 0.1)[0])
print(title)
print("Fractional absolute differences above "
"0.5: {0}; 0.25: {1}; 0.1: {2}".format(n50, n25, n10))
print("")
data.to_csv(name + '_fractional_abs_differences.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name + '_fractional_abs_differences_summary.csv')
data_means[:, ititle] = data_summary.loc['mean'].values
data_summaries[ititle, :] = data_summary.mean(axis=1)
# ----------------------------------------------------------------
# Plot heatmap for labels X subjects array for each table:
# ----------------------------------------------------------------
html_file = name + '_fractional_abs_differences.html'
print(html_file)
# Set up the data for plotting. We will need to have values for every
# pair of subject/label names. Map the value to a color.
subjectx = []
label_namex = []
value1x = []
value2x = []
differencex = []
fractionx = []
colorx = []
for ilabel in range(len(labels_left)):
for isubject, subject in enumerate(subjects):
label_namex.append(label_names[ilabel])
subjectx.append(subject)
value1 = subject_shapesL[isubject, ilabel]
value2 = subject_shapesR[isubject, ilabel]
difference = subject_shape_diffs[isubject, ilabel]
value1x.append(value1)
value2x.append(value2)
differencex.append(difference)
fraction = subject_shape_frac_diffs[isubject, ilabel]
fractionx.append(fraction)
abs_fraction = subject_shape_frac_abs_diffs[isubject, ilabel]
if np.isnan(value1) or np.isnan(value2) or np.isnan(abs_fraction):
rgb = [0, 0, 0]
elif abs_fraction > 1.0:
rgb = [1, 1, 1]
else:
rgb = [np.int(255 * x) for x in
colors[np.int(255 * abs_fraction)]]
hex = "#%02x%02x%02x" % tuple(rgb)
colorx.append(hex)
output_file(html_file, title=title)
source = ColumnDataSource(dict(subject=subjectx,
label_name=label_namex,
color=colorx,
value1=value1x,
value2=value2x,
difference=differencex,
fraction=fractionx))
TOOLS = "hover,save,pan,box_zoom,wheel_zoom"
plot_width = len(subjects) * scale_rect
plot_height = len(labels_left) * scale_rect
p = figure(title=title, x_range=subjects, y_range=list(reversed(label_names)),
plot_width=plot_width, plot_height=plot_height,
x_axis_location="above", tools=TOOLS)
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_text_font_size = "10pt"
p.axis.major_label_standoff = 0
p.xaxis.major_label_orientation = pi/3
p.rect(x="subject", y="label_name", width=1, height=1,
source=source, color="color", line_color=None)
p.select_one(HoverTool).tooltips = [
('subject', '@subject'),
('label', '@label_name'),
('value1', '@value1'),
('value2', '@value2'),
('difference', '@difference'),
('fraction', '@fraction'),
]
#show(p) # show the plot
#import sys; sys.exit()
save(p) # save the plot
data_means_df = pd.DataFrame(data_means,
index=label_names,
columns=names)
data_means_df.to_csv('means_of_interhemispheric_fractional_abs_shape_differences.csv')
data_summaries_df = pd.DataFrame(data_summaries,
index=names,
columns=data_summary.index)
data_summaries_df.to_csv('summary_of_interhemispheric_fractional_abs_shape_differences.csv')
def compare_shapes_between_scans():
import os
import numpy as np
import pandas as pd
# For plotting:
from math import pi
from bokeh.models import HoverTool
from bokeh.plotting import ColumnDataSource, figure, show, save, output_file
from mindboggle.mio.colors import viridis_colormap
from mindboggle.mio.labels import DKTprotocol
#from mindboggle.mio.plots import histograms_of_lists
titles = ["Fractional difference between re/scan volumes",
"Fractional difference between re/scan thickinthehead cortical thicknesses",
"Fractional difference between re/scan left cortical label median areas",
"Fractional difference between re/scan left cortical label median travel depths",
"Fractional difference between re/scan left cortical label median geodesic depths",
"Fractional difference between re/scan left cortical label median mean curvatures",
"Fractional difference between re/scan left cortical label median FreeSurfer curvatures",
"Fractional difference between re/scan left cortical label median FreeSurfer thicknesses"]
# "Fractional difference between re/scan right cortical label median areas",
# "Fractional difference between re/scan right cortical label median travel depths",
# "Fractional difference between re/scan right cortical label median geodesic depths",
# "Fractional difference between re/scan right cortical label median mean curvatures",
# "Fractional difference between re/scan right cortical label median FreeSurfer curvatures",
# "Fractional difference between re/scan right cortical label median FreeSurfer thicknesses"]
# "Fractional difference between re/scan right cortical label median FreeSurfer convexities"]
names = ["volume_for_each_freesurfer_label",
"thickinthehead_per_freesurfer_cortex_label",
"median_area_per_freesurfer_left_cortex_label",
"median_travel_depth_per_freesurfer_left_cortex_label",
"median_geodesic_depth_per_freesurfer_left_cortex_label",
"median_mean_curvatures_per_freesurfer_left_cortex_label",
"median_freesurfer_curvature_per_freesurfer_left_cortex_label",
"median_freesurfer_thickness_per_freesurfer_left_cortex_label"]
# "median_area_per_freesurfer_right_cortex_label",
# "median_travel_depth_per_freesurfer_right_cortex_label",
# "median_geodesic_depth_per_freesurfer_right_cortex_label",
# "median_mean_curvatures_per_freesurfer_right_cortex_label",
# "median_freesurfer_curvature_per_freesurfer_right_cortex_label",
# "median_freesurfer_thickness_per_freesurfer_right_cortex_label"]
# "median_freesurfer_convexity_per_freesurfer_right_cortex_label"]
table_dir = '/Users/arno/Data/shape_tables_for_auto_labels_of_Mindboggle101_rescans'
tables = [os.path.join('tables', 'volume_for_each_freesurfer_label.csv'),
os.path.join('tables', 'thickinthehead_per_freesurfer_cortex_label.csv'),
os.path.join('tables', 'left_cortical_surface', 'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface', 'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface', 'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface', 'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface', 'label_shapes.csv'),
os.path.join('tables', 'left_cortical_surface', 'label_shapes.csv')]
# os.path.join('tables', 'right_cortical_surface', 'label_shapes.csv'),
# os.path.join('tables', 'right_cortical_surface', 'label_shapes.csv'),
# os.path.join('tables', 'right_cortical_surface', 'label_shapes.csv'),
# os.path.join('tables', 'right_cortical_surface', 'label_shapes.csv'),
# os.path.join('tables', 'right_cortical_surface', 'label_shapes.csv'),
# os.path.join('tables', 'right_cortical_surface', 'label_shapes.csv')]
column_indices = [1, 1, 1, 2, 10, 18, 26, 34] #, 1, 2, 10, 18, 26, 34] #, 42]
# --------------------------------------------------------------------
# Alternating left, right cortex label numbers (for volume shapes):
# --------------------------------------------------------------------
dkt = DKTprotocol()
labels_left = dkt.left_cerebrum_cortex_DKT31_numbers
labels_right = dkt.right_cerebrum_cortex_DKT31_numbers
DKT31_names = dkt.DKT31_names
# label_list = []
# label_name_list = []
# for ilabel, label_left in enumerate(labels_left):
# label_list.append(label_left)
# label_list.append(labels_right[ilabel])
# label_name_list.append(DKT31_names[ilabel] + ' (left)')
# label_name_list.append(DKT31_names[ilabel] + ' (right)')
#label_list = [str(x) for x in label_list]
##exclude_sulci = [20] # Sulcus 20 removed from protocol since initial run
#label_lists = [label_list,
# label_list,
label_lists = [labels_left,
labels_left,
labels_left, labels_left, labels_left,
labels_left, labels_left, labels_left, labels_left,
labels_right, labels_right, labels_right,
labels_right, labels_right, labels_right, labels_right]
label_name_lists = [DKT31_names for x in range (len(titles))]
#label_name_lists[0] = label_name_list
#label_name_lists[1] = label_name_list
# --------------------------------------------------------------------
# Colors:
# --------------------------------------------------------------------
colors = viridis_colormap()
#from matplotlib import cm as cmaps
#import matplotlib.pyplot as plt
#plt.register_cmap(name='viridis', cmap=cmaps.viridis)
#plt.set_cmap(cmaps.viridis)
scale_rect = 40
# --------------------------------------------------------------------
# Subjects with second scans:
# --------------------------------------------------------------------
groups = ['OASIS-TRT-20', 'MMRR-21']
numbers = [20, 21]
nsubjects = sum(numbers)
subjects = []
subjects2 = []
for igroup, group in enumerate(groups):
for n in range(1, numbers[igroup]+1):
subjects.append(group+'-'+str(n))
subjects2.append(group+'-rescan-'+str(n))
# --------------------------------------------------------------------
# Loop through tables:
# --------------------------------------------------------------------
data_means = np.zeros((len(labels_left), len(titles)))
data_summaries = np.zeros((len(titles), 8))
for ititle, title in enumerate(titles):
table = tables[ititle]
name = names[ititle]
index = column_indices[ititle]
labels = label_lists[ititle]
label_names = label_name_lists[ititle]
# ----------------------------------------------------------------
# Loop through subjects:
# ----------------------------------------------------------------
subject_shapes = np.zeros((nsubjects, len(labels)))
subject2_shapes = np.zeros((nsubjects, len(labels)))
for isubject, subject in enumerate(subjects):
subject2 = subjects2[isubject]
table_file = os.path.join(table_dir, subject, table)
table_file2 = os.path.join(table_dir, subject2, table)
columns = pd.read_csv(table_file, sep=",", index_col='name')
columns2 = pd.read_csv(table_file2, sep=",", index_col='name')
# ------------------------------------------------------------
# Loop through labels:
# ------------------------------------------------------------
for ilabel, label in enumerate(labels):
for irow in range(columns.shape[0]):
if int(columns.iloc[irow][0]) == int(label):
value = columns.iloc[irow][index]
value2 = columns2.iloc[irow][index]
subject_shapes[isubject, ilabel] = value
subject2_shapes[isubject, ilabel] = value2
# ----------------------------------------------------------------
# Save csv files:
# ----------------------------------------------------------------
data = pd.DataFrame(subject_shapes, index=subjects, columns=labels)
data.to_csv(name + '_scans.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name + '_scans_summary.csv')
data = pd.DataFrame(subject2_shapes, index=subjects, columns=labels)
data.to_csv(name + '_rescans.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name + '_rescans_summary.csv')
subject_shape_diffs = subject2_shapes - subject_shapes
data = pd.DataFrame(subject_shape_diffs, index=subjects, columns=labels)
data.to_csv(name + '_differences.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name + '_differences_summary.csv')
subject_shape_abs_diffs = np.abs(subject_shape_diffs)
max_diffs = subject_shape_abs_diffs.max(axis=0)
subject_shape_frac_diffs = subject_shape_diffs / subject_shapes
data = pd.DataFrame(subject_shape_frac_diffs,
index=subjects, columns=labels)
#iInf, jInf = np.where(data.values == np.inf)
#data.iloc[iInf, jInf] = 'NaN'
data.to_csv(name + '_fractional_differences.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name + '_fractional_differences_summary.csv')
# max_array = np.zeros((nsubjects, len(labels), 2))
# max_array[:, :, 0] = subject_shapes
# max_array[:, :, 1] = subject2_shapes
# max_diff = np.abs(np.max(max_array, axis=2))
# maxs = np.max(np.max([subject_shapes, subject2_shapes], axis=0), axis=0)
# mins = np.min(np.min([subject_shapes, subject2_shapes], axis=0), axis=0)
# max_diff = np.abs(maxs - mins)
# max_diff = np.max([subject_shapes, subject2_shapes]) -\
# np.min([subject_shapes, subject2_shapes])
subject_shape_frac_abs_diffs = np.abs(subject_shape_abs_diffs / subject_shapes)
data = pd.DataFrame(subject_shape_frac_abs_diffs,
index=subjects, columns=labels)
n50 = len(np.where(data.values > 0.5)[0])
n25 = len(np.where(data.values > 0.25)[0])
n10 = len(np.where(data.values > 0.1)[0])
print(title)
print("Fractional absolute differences above "
"0.5: {0}; 0.25: {1}; 0.1: {2}".format(n50, n25, n10))
print("")
#iInf, jInf = np.where(data.values == np.inf)
#data.iloc[iInf, jInf] = 'NaN'
data.to_csv(name + '_fractional_abs_differences.csv')
data_summary = data.describe(include='all')
data_summary.to_csv(name + '_fractional_abs_differences_summary.csv')
data_means[:, ititle] = data_summary.loc['mean'].values
data_summaries[ititle, :] = data_summary.mean(axis=1)
# ignore_columns = []
# nbins = 100
# axis_limits = []
# histograms_of_lists(subject_shape_diffs, title, ignore_columns,
# nbins, axis_limits, [title])
# ----------------------------------------------------------------
# Plot heatmap for labels X subjects array for each table:
# ----------------------------------------------------------------
html_file = name + '_fractional_abs_differences.html'
print(html_file)
# Set up the data for plotting. We will need to have values for every
# pair of subject/label names. Map the value to a color.
subjectx = []
labelx = []
label_namex = []
value1x = []
value2x = []
differencex = []
fractionx = []
colorx = []
for ilabel, label in enumerate(labels):
for isubject, subject in enumerate(subjects):
labelx.append(label)
label_namex.append(label_names[ilabel])
subjectx.append(subject)
value1 = subject_shapes[isubject, ilabel]
value2 = subject2_shapes[isubject, ilabel]
difference = subject_shape_diffs[isubject, ilabel]
value1x.append(value1)
value2x.append(value2)
differencex.append(difference)
fraction = subject_shape_frac_diffs[isubject, ilabel]
fractionx.append(fraction)
abs_fraction = subject_shape_frac_abs_diffs[isubject, ilabel]
if np.isnan(value) or np.isnan(abs_fraction):
rgb = [0, 0, 0]
elif abs_fraction > 1.0:
rgb = [1, 1, 1]
else:
rgb = [np.int(255 * x) for x in
colors[np.int(255 * abs_fraction)]]
hex = "#%02x%02x%02x" % tuple(rgb)
colorx.append(hex)
output_file(html_file, title=title)
source = ColumnDataSource(dict(subject=subjectx,
label=labelx,
label_name=label_namex,
color=colorx,
value1=value1x,
value2=value2x,
difference=differencex,
fraction=fractionx))
TOOLS = "hover,save,pan,box_zoom,wheel_zoom"
plot_width = len(subjects) * scale_rect
plot_height = len(labels) * scale_rect
p = figure(title=title, x_range=subjects, y_range=list(reversed(label_names)),
plot_width=plot_width, plot_height=plot_height,
x_axis_location="above", tools=TOOLS)
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_text_font_size = "10pt"
p.axis.major_label_standoff = 0
p.xaxis.major_label_orientation = pi/3
p.rect(x="subject", y="label_name", width=1, height=1,
source=source, color="color", line_color=None)
p.select_one(HoverTool).tooltips = [
('subject', '@subject'),
('label', '@label'),
('label name', '@label_name'),
('value1', '@value1'),
('value2', '@value2'),
('difference', '@difference'),
('fraction', '@fraction'),
]
#show(p) # show the plot
#import sys; sys.exit()
save(p) # save the plot
data_means_df = pd.DataFrame(data_means,
index=label_names,
columns=names)
data_means_df.to_csv('means_of_rescan_fractional_abs_shape_differences.csv')
data_summaries_df = pd.DataFrame(data_summaries,
index=names,
columns=data_summary.index)
data_summaries_df.to_csv('summary_of_rescan_fractional_abs_shape_differences.csv')
from mindboggle.evaluate.evaluate_features import evaluate_deep_features
# For plotting:
from math import pi
from bokeh.models import HoverTool
from bokeh.plotting import ColumnDataSource, figure, show, save, output_file
from mindboggle.mio.colors import viridis_colormap
measure_feature_distances = False
maxd = 53
feature_dir = '/homedir/fundus_evaluation_2014/fundi_vtk'
plot_and_describe_fundus_distances = False
use_all_subjects = True
exclude_sulci = [20] # Sulcus 20 removed from protocol since initial run
colors = viridis_colormap()
#from matplotlib import cm as cmaps
#import matplotlib.pyplot as plt
#plt.register_cmap(name='viridis', cmap=cmaps.viridis)
#plt.set_cmap(cmaps.viridis)
compute_results = True
mindboggled = '/mnt/nfs-share/Mindboggle101/mindboggled/manual'
labels_dir = '/mnt/nfs-share/Mindboggle101/mindboggled/manual'
# --------------------------------------------------------------------
# Subjects:
# --------------------------------------------------------------------
# Select only those subjects for which all methods were applied:
names = [
from mindboggle.evaluate.evaluate_features import evaluate_deep_features
# For plotting:
from math import pi
from bokeh.models import HoverTool
from bokeh.plotting import ColumnDataSource, figure, show, save, output_file
from mindboggle.mio.colors import viridis_colormap
measure_feature_distances = False
maxd = 53
feature_dir = '/homedir/fundus_evaluation_2014/fundi_vtk'
plot_and_describe_fundus_distances = False
use_all_subjects = True
exclude_sulci = [20] # Sulcus 20 removed from protocol since initial run
colors = viridis_colormap()
#from matplotlib import cm as cmaps
#import matplotlib.pyplot as plt
#plt.register_cmap(name='viridis', cmap=cmaps.viridis)
#plt.set_cmap(cmaps.viridis)
compute_results = True
mindboggled = '/mnt/nfs-share/Mindboggle101/mindboggled/manual'
labels_dir = '/mnt/nfs-share/Mindboggle101/mindboggled/manual'
# --------------------------------------------------------------------
# Subjects:
# --------------------------------------------------------------------
# Select only those subjects for which all methods were applied:
names = ['OASIS-TRT-20', 'MMRR-21', 'NKI-RS-22', 'NKI-TRT-20',
