Note
Example connectome files are provided in the GitHub repository cffdata.
Import the library. Subsequently, we assume that this has been done:
from cfflib import *
Load the dataset from the file system:
a=load_from_meta_cml('example_dataset_01/meta.cml')
You can print all the loaded connectome objects:
print a.get_all()
If you have a zipped file with ending .cff, you can load it as well with:
a=load_from_cff('datasets/ds1/connectomefile.cff')
You can get the first network and load it like this:
mynetwork = a.get_by_name('Network Lausanne83')[0]
mynetwork.load()
The loaded network object accessible through the data attribute (a NetworkX object):
print mynetwork.data
You see that it is a NetworkX graph. You can modify it as you like.
After modification, you can store it, which stores it in the corresponding file that this CNetwork was referenced to (relative path):
print mynetwork.src
mynetwork.save()
Show other attributes:
print mynetwork.name
print mynetwork.dtype
print mynetwork.description
To show the metadata attributes as dictionary:
print mynetwork.get_metadata_as_dict()
You can save the currently loaded connectome file:
save_to_cff('myconnectome.cff', a)
The same you can do for other connectome objects, if the corresponding Python libraries are installed correctly:
# CVolume
obj = a.get_by_name('Example Volume')
obj.load()
print obj.data
# CSurface
obj = a.get_by_name('Example Surface')
obj.load()
print obj.data
# CTrack
obj = a.get_by_name('Tractography')
obj.load()
# You do not want to display all fibers, just show the header
print obj.data[1]
# CTimeseries
obj = a.get_by_name('Generated timeseries data')
obj.load()
print obj.data
# CData
obj = a.get_by_name('Arbitrary data file')
obj.load()
print obj.data
# CScript
obj = a.get_by_name('Analysis Script MMXXXIV')
obj.load()
print obj.data
# CImagestack
obj = a.get_by_name('FIB Rat Striatum')
obj.load()
print obj.data