def plot_histogram(image,
title="Image Histogram",
height=400,
width=1000,
view_in_browser=True,
bins=25,
remove_zeros=True):
'''plot interactive histogram (in browser)'''
image = get_nii_obj(image)[0]
data = image.get_data()
histogram = get_histogram_data(data, bins=bins, remove_zeros=remove_zeros)
bins = '"%s"' % ('","'.join(["%.2f" % (x) for x in histogram["bins"]]))
counts = '"%s"' % ('","'.join(["%s" % (x) for x in histogram["counts"]]))
template = get_template("histogram")
elements = [{
"HISTOGRAM_DATA": counts
}, {
"HISTOGRAM_LABELS": bins
}, {
"HISTOGRAM_TITLE": title
}, {
"HISTOGRAM_HEIGHT": height
}, {
"HISTOGRAM_WIDTH": width
}]
for element in elements:
template = add_string(element, template)
if view_in_browser == True:
view(template)
else:
return template
def plot_histogram(image,
title="Image Histogram",
height=400,
width=1000,
view_in_browser=True,
bins=25,
remove_zeros=True):
'''plot interactive histogram (in browser)'''
image = get_nii_obj(image)[0]
data = image.get_data()
histogram = get_histogram_data(data,bins=bins,remove_zeros=remove_zeros)
bins = '"%s"' %('","'.join(["%.2f" %(x) for x in histogram["bins"]]))
counts = '"%s"' %('","'.join(["%s" %(x) for x in histogram["counts"]]))
template = get_template("histogram")
elements = [{"HISTOGRAM_DATA":counts},
{"HISTOGRAM_LABELS":bins},
{"HISTOGRAM_TITLE":title},
{"HISTOGRAM_HEIGHT":height},
{"HISTOGRAM_WIDTH":width}]
for element in elements:
template = add_string(element,template)
if view_in_browser==True:
view(template)
else:
return template
# Make a connectogram d3 visualization from a square connectivity matrix
from pybraincompare.compare.network import connectogram
from pybraincompare.template.visual import view
import pandas
# A square matrix, tab separated file, with row and column names corresponding to node names
connectivity_matrix = "data/parcel_matrix.tsv"
parcel_info = pandas.read_csv("data/parcels.csv")
networks = list(parcel_info["Community"])
# should be in format "L-1" for hemisphere (L or R) and group number (1..N)
groups = ["%s-%s" %(parcel_info["Hem"][x],parcel_info["ID"][x]) for x in range(0,parcel_info.shape[0])]
# A threshold value for the connectivity matrix to determine neighbors, eg, a value of .95 means we only keep top 5% of positive and negative connections, and the user can explore this top percent
threshold = 0.99
html_snippet = connectogram(matrix_file=connectivity_matrix,groups=groups,threshold=threshold,network_names=networks)
view(html_snippet)
#!/usr/bin/env python2 from pybraincompare.ontology.tree import named_ontology_tree_from_tsv from pybraincompare.template.visual import view # This defines a "mock" ontology for the openfmri proof of concept set - names should be unique relationship_table = "data/cogatlas_annotate_triples.tsv" # Create a data structure for d3 visualization and data analysis (no output json, specify with output_json=) data_structure = named_ontology_tree_from_tsv(relationship_table) html_snippet = make_ontology_tree_d3(data_structure) # View in browser! view(html_snippet)