2.2 Get 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()
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from graphbook_code import heatmap
from matplotlib import pyplot as plt
A = As[0]
fig, axs = plt.subplots(1, 2, gridspec_kw={"width_ratios": [1.4, 2]}, figsize=(16, 5.5))
heatmap(A, vmin=-1, vmax=1, ax=axs[0], xticks=[0, 199, 399], xticklabels=[1, 200, 400],
yticks=[0, 199, 399], yticklabels=[1, 200, 400], ytitle="Brain Area", xtitle="Brain Area",
title="(A) Heatmap of Functional Connectome", shrink=0.6, legend_title="Edge weight")
import seaborn as sns
sns.histplot(A.flatten(), ax=axs[1], bins=50, color="gray")
axs[1].set_xlabel("Edge weight")
axs[1].set_title("(B) Histogram of functional connectome edge-weights", size=20, pad=15)
fig.tight_layout()
fname = "raw"
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")
