2.3 Prepare the Data#
mode = "svg" # output format for figs
import matplotlib
font = {'family' : 'Dejavu Sans',
'weight' : 'normal',
'size' : 20}
matplotlib.rc('font', **font)
import matplotlib
from matplotlib import pyplot as plt
import os
import urllib
import boto3
from botocore import UNSIGNED
from botocore.client import Config
from graspologic.utils import import_edgelist
import numpy as np
import glob
from tqdm import tqdm
# the AWS bucket the data is stored in
BUCKET_ROOT = "open-neurodata"
parcellation = "Schaefer400"
FMRI_PREFIX = "m2g/Functional/BNU1-11-12-20-m2g-func/Connectomes/" + parcellation + "_space-MNI152NLin6_res-2x2x2.nii.gz/"
FMRI_PATH = os.path.join("datasets", "fmri") # the output folder
DS_KEY = "abs_edgelist" # correlation matrices for the networks to exclude
def fetch_fmri_data(bucket=BUCKET_ROOT, fmri_prefix=FMRI_PREFIX,
output=FMRI_PATH, name=DS_KEY):
"""
A function to fetch fMRI connectomes from AWS S3.
"""
# check that output directory exists
if not os.path.isdir(FMRI_PATH):
os.makedirs(FMRI_PATH)
# start boto3 session anonymously
s3 = boto3.client('s3', config=Config(signature_version=UNSIGNED))
# obtain the filenames
bucket_conts = s3.list_objects(Bucket=bucket,
Prefix=fmri_prefix)["Contents"]
for s3_key in tqdm(bucket_conts):
# get the filename
s3_object = s3_key['Key']
# verify that we are grabbing the right file
if name not in s3_object:
op_fname = os.path.join(FMRI_PATH, str(s3_object.split('/')[-1]))
if not os.path.exists(op_fname):
s3.download_file(bucket, s3_object, op_fname)
def read_fmri_data(path=FMRI_PATH):
"""
A function which loads the connectomes as adjacency matrices.
"""
fnames = glob.glob(os.path.join(path, "*.csv"))
fnames.sort()
# import edgelists with graspologic
# edgelists will be all of the files that end in a csv
networks = [import_edgelist(fname) for fname in tqdm(fnames)]
return np.stack(networks, axis=0)
fetch_fmri_data()
As = read_fmri_data()
A = As[0]
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def remove_isolates(A):
"""
A function which removes isolated nodes from the
adjacency matrix A.
"""
degree = A.sum(axis=0) # sum along the rows to obtain the node degree
out_degree = A.sum(axis=1)
A_purged = A[~(degree == 0),:]
A_purged = A_purged[:,~(degree == 0)]
print("Purging {:d} nodes...".format((degree == 0).sum()))
return A_purged
A = remove_isolates(A)
Purging 0 nodes...
import matplotlib.pyplot as plt
from graphbook_code import heatmap
A_abs = np.abs(A)
fig, axs = plt.subplots(1,3, figsize=(21, 6))
heatmap(A, ax=axs[0], title="(A) Human Connectome, Raw", vmin=np.min(A), vmax=1)
heatmap(A_abs, ax=axs[1], title="(B) Human Connectome, Absolute", vmin=np.min(A), vmax=1)
heatmap(A_abs - A, ax=axs[2], title="(C) Difference(Absolute - Raw)", vmin=0, vmax=1)
<Axes: title={'left': '(C) Difference(Absolute - Raw)'}>
from sklearn.base import TransformerMixin, BaseEstimator
class CleanData(BaseEstimator, TransformerMixin):
def fit(self, X):
return self
def transform(self, X):
print("Cleaning data...")
Acleaned = remove_isolates(X)
A_abs_cl = np.abs(Acleaned)
self.A_ = A_abs_cl
return self.A_
data_cleaner = CleanData()
A_clean = data_cleaner.transform(A)
Cleaning data...
Purging 0 nodes...
from graspologic.utils import binarize
threshold = 0.4
A_bin = binarize(A_clean > threshold)
from graspologic.utils import pass_to_ranks
A_ptr = pass_to_ranks(A_clean)
xtick = [0, 199, 399]; ytick = xtick
xlabs = [1, 200, 400]; ylabs = xlabs
fig, axs = plt.subplots(1,3, figsize=(18, 5))
heatmap(A, ax=axs[0], title="(A) Raw Connectome", vmin=-1, vmax=1, xticks=xtick, xticklabels=xlabs,
yticks=ytick, yticklabels=ylabs, xtitle="Brain Area", ytitle="Brain Area")
heatmap(A_bin.astype(int), ax=axs[1], title="(B) Binarized Connectome", xticks=xtick, xticklabels=xlabs,
yticks=ytick, yticklabels=ylabs, xtitle="Brain Area", ytitle="Brain Area")
heatmap(A_ptr, ax=axs[2], title="(C) Ranked Connectome", vmin=0, vmax=1, xticks=xtick, xticklabels=xlabs,
yticks=ytick, yticklabels=ylabs, xtitle="Brain Area", ytitle="Brain Area")
fig.tight_layout()
fname = "cleaning_connectomes"
if mode != "png":
os.makedirs(f"Figures/{mode:s}", exist_ok=True)
fig.savefig(f"Figures/{mode:s}/{fname:s}.{mode:s}")
os.makedirs("Figures/png", exist_ok=True)
fig.savefig(f"Figures/png/{fname:s}.png")
import seaborn as sns
fig, axs = plt.subplots(2,1, figsize=(10, 10))
sns.histplot(A_clean[A_clean > 0].flatten(), ax=axs[0], color="gray")
axs[0].set_xlabel("Edge weight")
axs[0].set_title("(A) Histogram of human connectome, \n non-zero edge weights")
sns.histplot(A_ptr[A_ptr > 0].flatten(), ax=axs[1], color="gray")
axs[1].set_xlabel("ptr(Edge weight)")
axs[1].set_title("(B) Histogram of human connectome, \n passed-to-ranks")
fig.tight_layout()
fname = "ptrhists"
if mode != "png":
fig.savefig(f"Figures/{mode:s}/{fname:s}.{mode:s}")
fig.savefig(f"Figures/png/{fname:s}.png")
class FeatureScaler(BaseEstimator, TransformerMixin):
def fit(self, X):
return self
def transform(self, X):
print("Scaling edge-weights...")
A_scaled = pass_to_ranks(X)
return (A_scaled)
feature_scaler = FeatureScaler()
A_cleaned_scaled = feature_scaler.transform(A_clean)
# Scaling edge-weights...
Scaling edge-weights...
from sklearn.pipeline import Pipeline
num_pipeline = Pipeline([
('cleaner', CleanData()),
('scaler', FeatureScaler()),
])
A_xfm = num_pipeline.fit_transform(A)
Cleaning data...
Purging 0 nodes...
Scaling edge-weights...
A_xfm2 = num_pipeline.fit_transform(As[1])
Cleaning data...
Purging 0 nodes...
Scaling edge-weights...