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Merge pull request #113 from brainlife/rf/ezbids_core
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[RF] WIP: refactor ezBIDS core
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@dlevitas
dlevitas authored Feb 15, 2024
2 parents 2203407 + 9ba0c06 commit 83f771a
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150 changes: 69 additions & 81 deletions handler/ezBIDS_core/createThumbnailsMovies.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
import nibabel as nib
from PIL import Image
from math import floor
from natsort import natsorted
from pathlib import Path
matplotlib.use('Agg')
plt.style.use('dark_background')

Expand All @@ -26,62 +26,50 @@
# Functions


def create_MEG_thumbnail():
def create_MEG_thumbnail(meg_img_file):
"""
Generate a simple visualization of the MEG data.
"""
import mne.viz

uploaded_json_list = natsorted(pd.read_csv("list", header=None, lineterminator='\n').to_numpy().flatten().tolist())
ext = Path(meg_img_file).suffix
fname = meg_img_file
raw = mne.io.read_raw(fname, verbose=0)
mne.viz.set_browser_backend('matplotlib', verbose=None)

"""
Get the MEG data organized
"""
data_files = [x.split("./")[-1] for x in uploaded_json_list]
MEG_data_files = []
for data in data_files:
if any(x in data for x in MEG_extensions):
MEG_data_files.append(data)

if len(MEG_data_files):
for meg in MEG_data_files:
fname = f"{data_dir}/{meg}"
raw = mne.io.read_raw(fname, verbose=0)
mne.viz.set_browser_backend('matplotlib', verbose=None)
png_types = ["channels", "psd"]
for png_type in png_types:
output_file = fname.split(ext)[0] + f"_{png_type}.png"

png_types = ["channels", "psd"]
for png_type in png_types:
output_file = fname.split(".")[0] + f"_{png_type}.png"
if png_type == "channels":
fig = raw.plot()
elif png_type == "psd":
fig = raw.compute_psd().plot()

if png_type == "channels":
fig = raw.plot()
elif png_type == "psd":
fig = raw.compute_psd().plot()
fig.savefig(output_file, bbox_inches='tight')

fig.savefig(output_file, bbox_inches='tight')


def create_thumbnail(nifti_file, image):
def create_thumbnail(img_file, image):
"""
Generates a PNG screenshot of a NIfTI data file. If data is 4D,
the 2nd volume is used.
Parameters
----------
nifti_file : string
img_file : string
path of 4D NIfTI file.
image: nibabel.nifti1.Nifti1Image
result of nib.load(nifti_file).
result of nib.load(img_file).
"""

if image.ndim == 4:
object_img_array = image.dataobj[..., 1]
else:
object_img_array = image.dataobj[:]

output_file = nifti_file.split(".nii.gz")[0] + ".png"
output_file = img_file.split(".nii.gz")[0] + ".png"

slice_x = object_img_array[floor(object_img_array.shape[0] / 2), :, :]
slice_y = object_img_array[:, floor(object_img_array.shape[1] / 2), :]
Expand All @@ -107,24 +95,24 @@ def create_thumbnail(nifti_file, image):
png.save(output_file)


def create_DWIshell_thumbnails(nifti_file, image, bval_file):
def create_DWIshell_thumbnails(img_file, image, bval_file):
"""
Generates a PNG for each unique DWI acquisition shell.
Parameters
----------
nifti_file : string
img_file : string
path of 4D NIfTI file.
image: nibabel.nifti1.Nifti1Image
result of nib.load(nifti_file).
result of nib.load(img_file).
bval_file: string
path of corresponding NIfTI file's bval.
"""

output_file = nifti_file.split(".nii.gz")[0] + ".png"
output_file = img_file.split(".nii.gz")[0] + ".png"
bvals = [floor(float(x)) for x in pd.read_csv(bval_file, delim_whitespace=True).columns.tolist()]
modified_bvals = [round(x, -2) for x in bvals]
unique_bvals = np.unique(modified_bvals).tolist()
Expand Down Expand Up @@ -158,59 +146,59 @@ def create_DWIshell_thumbnails(nifti_file, image, bval_file):

# Begin:
data_dir = sys.argv[1]
json_file = sys.argv[2]
img_file = sys.argv[2]
MEG_extensions = [".ds", ".fif", ".sqd", ".con", ".raw", ".ave", ".mrk", ".kdf", ".mhd", ".trg", ".chn", ".dat"]
os.chdir(data_dir)

nifti_file = json_file.split(".json")[0] + ".nii.gz"

create_MEG_thumbnail()

if not os.path.isfile(f"{data_dir}/{nifti_file}"):
print(f"{json_file} does not have a corresponding NIfTI file, cannot generate screenshot")
if img_file.endswith(tuple(MEG_extensions)):
print("")
print(f"Creating thumbnails for {img_file}")
print("")
create_MEG_thumbnail(img_file)
else:
output_dir = nifti_file.split(".nii.gz")[0]
image = nib.load(nifti_file)
if not img_file.endswith('blood.json'):
output_dir = img_file.split(".nii.gz")[0]
image = nib.load(img_file)

if len([x for x in image.shape if x < 0]): # image has negative dimension(s), cannot process
print(f"{nifti_file} has negative dimension(s), cannot process")
else:
# if image.get_data_dtype() == [('R', 'u1'), ('G', 'u1'), ('B', 'u1')]:
if image.get_data_dtype() not in ["<i2", "<u2", "<f4", "int16", "uint16"]:
# Likely non-imaging acquisition. Example: "facMapReg" sequences in NYU_Shanghai dataset
print(
f"{nifti_file} doesn't appear to be an "
"imaging acquisition and therefore will "
"not be converted to BIDS. Please modify "
"if incorrect."
)
if len([x for x in image.shape if x < 0]): # image has negative dimension(s), cannot process
print(f"{img_file} has negative dimension(s), cannot process")
else:
# object_img_array = image.dataobj[:]

bval_file = json_file.split(".json")[0].split("./")[-1] + ".bval"
if not os.path.isfile(f"{data_dir}/{bval_file}"):
bval_file = "n/a"
# if image.get_data_dtype() == [('R', 'u1'), ('G', 'u1'), ('B', 'u1')]:
if image.get_data_dtype() not in ["<i2", "<u2", "<f4", "int16", "uint16"]:
# Likely non-imaging acquisition. Example: "facMapReg" sequences in NYU_Shanghai dataset
print(
f"{img_file} doesn't appear to be an "
"imaging acquisition and therefore will "
"not be converted to BIDS. Please modify "
"if incorrect."
)
else:
bvals = [x.split(" ") for x in pd.read_csv(bval_file).columns.tolist()][0]
bvals = [floor(float(x)) for x in bvals if not isinstance(x, str)]
# object_img_array = image.dataobj[:]

if len(bvals) <= 1: # just b0, so unhelpful
bval_file = img_file.split(".nii.gz")[0].split("./")[-1] + ".bval"
if not os.path.isfile(f"{data_dir}/{bval_file}"):
bval_file = "n/a"

# Create thumbnail
if nifti_file != "n/a":
print("")
print(f"Creating thumbnail for {nifti_file}")
print("")
create_thumbnail(nifti_file, image)

# Create thumbnail of each DWI's unique shell
if bval_file != "n/a":
print("")
print(f"Creating thumbnail(s) for each DWI shell in {nifti_file}")
print("")
create_DWIshell_thumbnails(nifti_file, image, bval_file)

# Remove the folder containing the PNGs for movie generation; don't need them anymore
if os.path.isdir(output_dir):
shutil.rmtree(output_dir)
else:
bvals = [x.split(" ") for x in pd.read_csv(bval_file).columns.tolist()][0]
bvals = [floor(float(x)) for x in bvals if not isinstance(x, str)]

if len(bvals) <= 1: # just b0, so unhelpful
bval_file = "n/a"

# Create thumbnail
if img_file != "n/a":
print("")
print(f"Creating thumbnail for {img_file}")
print("")
create_thumbnail(img_file, image)

# Create thumbnail of each DWI's unique shell
if bval_file != "n/a":
print("")
print(f"Creating thumbnail(s) for each DWI shell in {img_file}")
print("")
create_DWIshell_thumbnails(img_file, image, bval_file)

# Remove the folder containing the PNGs for movie generation; don't need them anymore
if os.path.isdir(output_dir):
shutil.rmtree(output_dir)
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