import importlib
from inspect import getmembers, isfunction
import logging
import os
import sys
import warnings
import numpy as np
import trx.trx_file_memmap as tmm
from dipy.core.gradients import (
extract_b0,
extract_dwi_shell,
gradient_table,
mask_non_weighted_bvals,
)
from dipy.core.sphere import Sphere
from dipy.data import get_sphere
from dipy.io.gradients import read_bvals_bvecs
from dipy.io.image import load_nifti, save_nifti
from dipy.io.peaks import (
load_pam,
niftis_to_pam,
pam_to_niftis,
tensor_to_pam,
)
from dipy.io.streamline import load_tractogram, save_tractogram
from dipy.reconst.shm import convert_sh_descoteaux_tournier
from dipy.reconst.utils import convert_tensors
from dipy.tracking.streamlinespeed import length
from dipy.utils.optpkg import optional_package
from dipy.utils.tractogram import concatenate_tractogram
from dipy.workflows.utils import handle_vol_idx
from dipy.workflows.workflow import Workflow
ne, have_ne, _ = optional_package("numexpr")
[docs]
class IoInfoFlow(Workflow):
[docs]
@classmethod
def get_short_name(cls):
return "io_info"
[docs]
def run(
self,
input_files,
b0_threshold=50,
bvecs_tol=0.01,
bshell_thr=100,
reference=None,
):
"""Provides useful information about different files used in
medical imaging. Any number of input files can be provided. The
program identifies the type of file by its extension.
Parameters
----------
input_files : variable string
Any number of Nifti1, bvals or bvecs files.
b0_threshold : float, optional
Threshold used to find b0 volumes.
bvecs_tol : float, optional
Threshold used to check that norm(bvec) = 1 +/- bvecs_tol
b-vectors are unit vectors.
bshell_thr : float, optional
Threshold for distinguishing b-values in different shells.
reference : string, optional
Reference anatomy for tck/vtk/fib/dpy file.
support (.nii or .nii.gz).
"""
np.set_printoptions(3, suppress=True)
io_it = self.get_io_iterator()
for input_path in io_it:
mult_ = len(input_path)
logging.info(f"-----------{mult_ * '-'}")
logging.info(f"Looking at {input_path}")
logging.info(f"-----------{mult_ * '-'}")
ipath_lower = input_path.lower()
extension = os.path.splitext(ipath_lower)[1]
if ipath_lower.endswith(".nii") or ipath_lower.endswith(".nii.gz"):
data, affine, img, vox_sz, affcodes = load_nifti(
input_path, return_img=True, return_voxsize=True, return_coords=True
)
logging.info(f"Data size {data.shape}")
logging.info(f"Data type {data.dtype}")
if data.ndim == 3:
logging.info(
f"Data min {data.min()} max {data.max()} avg {data.mean()}"
)
logging.info(
f"2nd percentile {np.percentile(data, 2)} "
f"98th percentile {np.percentile(data, 98)}"
)
if data.ndim == 4:
logging.info(
f"Data min {data[..., 0].min()} "
f"max {data[..., 0].max()} "
f"avg {data[..., 0].mean()} of vol 0"
)
msg = (
f"2nd percentile {np.percentile(data[..., 0], 2)} "
f"98th percentile {np.percentile(data[..., 0], 98)} "
f"of vol 0"
)
logging.info(msg)
logging.info(f"Native coordinate system {''.join(affcodes)}")
logging.info(f"Affine Native to RAS matrix \n{affine}")
logging.info(f"Voxel size {np.array(vox_sz)}")
if np.sum(np.abs(np.diff(vox_sz))) > 0.1:
msg = "Voxel size is not isotropic. Please reslice.\n"
logging.warning(msg, stacklevel=2)
if os.path.basename(input_path).lower().find("bval") > -1:
bvals = np.loadtxt(input_path)
logging.info(f"b-values \n{bvals}")
logging.info(f"Total number of b-values {len(bvals)}")
shells = np.sum(np.diff(np.sort(bvals)) > bshell_thr)
logging.info(f"Number of gradient shells {shells}")
logging.info(
f"Number of b0s {np.sum(bvals <= b0_threshold)} "
f"(b0_thr {b0_threshold})\n"
)
if os.path.basename(input_path).lower().find("bvec") > -1:
bvecs = np.loadtxt(input_path)
logging.info(f"Bvectors shape on disk is {bvecs.shape}")
rows, cols = bvecs.shape
if rows < cols:
bvecs = bvecs.T
logging.info(f"Bvectors are \n{bvecs}")
norms = np.array([np.linalg.norm(bvec) for bvec in bvecs])
res = np.where((norms <= 1 + bvecs_tol) & (norms >= 1 - bvecs_tol))
ncl1 = np.sum(norms < 1 - bvecs_tol)
logging.info(f"Total number of unit bvectors {len(res[0])}")
logging.info(f"Total number of non-unit bvectors {ncl1}\n")
if extension in [".trk", ".tck", ".trx", ".vtk", ".vtp", ".fib", ".dpy"]:
sft = None
if extension in [".trk", ".trx"]:
sft = load_tractogram(input_path, "same", bbox_valid_check=False)
else:
sft = load_tractogram(input_path, reference, bbox_valid_check=False)
lengths_mm = list(length(sft.streamlines))
sft.to_voxmm()
lengths, steps = [], []
for streamline in sft.streamlines:
lengths += [len(streamline)]
steps += [np.sqrt(np.sum(np.diff(streamline, axis=0) ** 2, axis=1))]
steps = np.hstack(steps)
logging.info(f"Number of streamlines: {len(sft)}")
logging.info(f"min_length_mm: {float(np.min(lengths_mm))}")
logging.info(f"mean_length_mm: {float(np.mean(lengths_mm))}")
logging.info(f"max_length_mm: {float(np.max(lengths_mm))}")
logging.info(f"std_length_mm: {float(np.std(lengths_mm))}")
logging.info(f"min_length_nb_points: {float(np.min(lengths))}")
logging.info("mean_length_nb_points: " f"{float(np.mean(lengths))}")
logging.info(f"max_length_nb_points: {float(np.max(lengths))}")
logging.info(f"std_length_nb_points: {float(np.std(lengths))}")
logging.info(f"min_step_size: {float(np.min(steps))}")
logging.info(f"mean_step_size: {float(np.mean(steps))}")
logging.info(f"max_step_size: {float(np.max(steps))}")
logging.info(f"std_step_size: {float(np.std(steps))}")
logging.info(
"data_per_point_keys: " f"{list(sft.data_per_point.keys())}"
)
logging.info(
"data_per_streamline_keys: "
f"{list(sft.data_per_streamline.keys())}"
)
np.set_printoptions()
[docs]
class FetchFlow(Workflow):
[docs]
@classmethod
def get_short_name(cls):
return "fetch"
[docs]
@staticmethod
def get_fetcher_datanames():
"""Gets available dataset and function names.
Returns
-------
available_data: dict
Available dataset and function names.
"""
fetcher_module = FetchFlow.load_module("dipy.data.fetcher")
available_data = dict(
{
(name.replace("fetch_", ""), func)
for name, func in getmembers(fetcher_module, isfunction)
if name.lower().startswith("fetch_")
and func is not fetcher_module.fetch_data
}
)
return available_data
[docs]
@staticmethod
def load_module(module_path):
"""Load / reload an external module.
Parameters
----------
module_path: string
the path to the module relative to the main script
Returns
-------
module: module object
"""
if module_path in sys.modules:
return importlib.reload(sys.modules[module_path])
else:
return importlib.import_module(module_path)
[docs]
def run(
self,
data_names,
subjects=None,
include_optional=False,
include_afq=False,
hcp_bucket="hcp-openaccess",
hcp_profile_name="hcp",
hcp_study="HCP_1200",
hcp_aws_access_key_id=None,
hcp_aws_secret_access_key=None,
out_dir="",
):
"""Download files to folder and check their md5 checksums.
To see all available datasets, please type "list" in data_names.
Parameters
----------
data_names : variable string
Any number of Nifti1, bvals or bvecs files.
subjects : variable string, optional
Identifiers of the subjects to download. Used only by the HBN & HCP dataset.
For example with HBN dataset: --subject NDARAA948VFH NDAREK918EC2
include_optional : bool, optional
Include optional datasets.
include_afq : bool, optional
Whether to include pyAFQ derivatives. Used only by the HBN dataset.
hcp_bucket : string, optional
The name of the HCP S3 bucket.
hcp_profile_name : string, optional
The name of the AWS profile used for access.
hcp_study : string, optional
Which HCP study to grab.
hcp_aws_access_key_id : string, optional
AWS credentials to HCP AWS S3. Will only be used if `profile_name` is
set to False.
hcp_aws_secret_access_key : string, optional
AWS credentials to HCP AWS S3. Will only be used if `profile_name` is
set to False.
out_dir : string, optional
Output directory.
"""
if out_dir:
dipy_home = os.environ.get("DIPY_HOME", None)
os.environ["DIPY_HOME"] = out_dir
available_data = FetchFlow.get_fetcher_datanames()
data_names = [name.lower() for name in data_names]
if "all" in data_names:
logging.warning("Skipping HCP and HBN datasets.")
available_data.pop("hcp", None)
available_data.pop("hbn", None)
for name, fetcher_function in available_data.items():
if name in ["hcp", "hbn"]:
continue
logging.info("------------------------------------------")
logging.info(f"Fetching at {name}")
logging.info("------------------------------------------")
fetcher_function(include_optional=include_optional)
elif "list" in data_names:
logging.info(
"Please, select between the following data names: \n"
f"{', '.join(available_data.keys())}"
)
else:
skipped_names = []
for data_name in data_names:
if data_name not in available_data.keys():
skipped_names.append(data_name)
continue
logging.info("------------------------------------------")
logging.info(f"Fetching at {data_name}")
logging.info("------------------------------------------")
if data_name == "hcp":
if not subjects:
logging.error(
"Please provide the subjects to download the HCP dataset."
)
continue
try:
available_data[data_name](
subjects=subjects,
bucket=hcp_bucket,
profile_name=hcp_profile_name,
study=hcp_study,
aws_access_key_id=hcp_aws_access_key_id,
aws_secret_access_key=hcp_aws_secret_access_key,
)
except Exception as e:
logging.error(
f"Error while fetching HCP dataset: {str(e)}", exc_info=True
)
elif data_name == "hbn":
if not subjects:
logging.error(
"Please provide the subjects to download the HBN dataset."
)
continue
try:
available_data[data_name](
subjects=subjects, include_afq=include_afq
)
except Exception as e:
logging.error(
f"Error while fetching HBN dataset: {str(e)}", exc_info=True
)
else:
available_data[data_name](include_optional=include_optional)
nb_success = len(data_names) - len(skipped_names)
print("\n")
logging.info(f"Fetched {nb_success} / {len(data_names)} Files ")
if skipped_names:
logging.warn(f"Skipped data name(s): {' '.join(skipped_names)}")
logging.warn(
"Please, select between the following data names: "
f"{', '.join(available_data.keys())}"
)
if out_dir:
if dipy_home:
os.environ["DIPY_HOME"] = dipy_home
else:
os.environ.pop("DIPY_HOME", None)
# We load the module again so that if we run another one of these
# in the same process, we don't have the env variable pointing
# to the wrong place
self.load_module("dipy.data.fetcher")
[docs]
class SplitFlow(Workflow):
[docs]
@classmethod
def get_short_name(cls):
return "split"
[docs]
def run(self, input_files, vol_idx=0, out_dir="", out_split="split.nii.gz"):
"""Splits the input 4D file and extracts the required 3D volume.
Parameters
----------
input_files : variable string
Any number of Nifti1 files
vol_idx : int, optional
Index of the 3D volume to extract.
out_dir : string, optional
Output directory.
out_split : string, optional
Name of the resulting split volume
"""
io_it = self.get_io_iterator()
for fpath, osplit in io_it:
logging.info(f"Splitting {fpath}")
data, affine, image = load_nifti(fpath, return_img=True)
if vol_idx == 0:
logging.info("Splitting and extracting 1st b0")
split_vol = data[..., vol_idx]
save_nifti(osplit, split_vol, affine, hdr=image.header)
logging.info(f"Split volume saved as {osplit}")
[docs]
class ConcatenateTractogramFlow(Workflow):
[docs]
@classmethod
def get_short_name(cls):
return "concatracks"
[docs]
def run(
self,
tractogram_files,
reference=None,
delete_dpv=False,
delete_dps=False,
delete_groups=False,
check_space_attributes=True,
preallocation=False,
out_dir="",
out_extension="trx",
out_tractogram="concatenated_tractogram",
):
"""Concatenate multiple tractograms into one.
Parameters
----------
tractogram_list : variable string
The stateful tractogram filenames to concatenate
reference : string, optional
Reference anatomy for tck/vtk/fib/dpy file.
support (.nii or .nii.gz).
delete_dpv : bool, optional
Delete dpv keys that do not exist in all the provided TrxFiles
delete_dps : bool, optional
Delete dps keys that do not exist in all the provided TrxFile
delete_groups : bool, optional
Delete all the groups that currently exist in the TrxFiles
check_space_attributes : bool, optional
Verify that dimensions and size of data are similar between all the
TrxFiles
preallocation : bool, optional
Preallocated TrxFile has already been generated and is the first
element in trx_list (Note: delete_groups must be set to True as
well)
out_dir : string, optional
Output directory.
out_extension : string, optional
Extension of the resulting tractogram
out_tractogram : string, optional
Name of the resulting tractogram
"""
io_it = self.get_io_iterator()
trx_list = []
has_group = False
for fpath, _, _ in io_it:
if fpath.lower().endswith(".trx") or fpath.lower().endswith(".trk"):
reference = "same"
if not reference:
raise ValueError(
"No reference provided. It is needed for tck,"
"fib, dpy or vtk files"
)
tractogram_obj = load_tractogram(fpath, reference, bbox_valid_check=False)
if not isinstance(tractogram_obj, tmm.TrxFile):
tractogram_obj = tmm.TrxFile.from_sft(tractogram_obj)
elif len(tractogram_obj.groups):
has_group = True
trx_list.append(tractogram_obj)
trx = concatenate_tractogram(
trx_list,
delete_dpv=delete_dpv,
delete_dps=delete_dps,
delete_groups=delete_groups or not has_group,
check_space_attributes=check_space_attributes,
preallocation=preallocation,
)
valid_extensions = ["trk", "trx", "tck", "fib", "dpy", "vtk"]
if out_extension.lower() not in valid_extensions:
raise ValueError(
f"Invalid extension. Valid extensions are: {valid_extensions}"
)
out_fpath = os.path.join(out_dir, f"{out_tractogram}.{out_extension}")
save_tractogram(trx.to_sft(), out_fpath, bbox_valid_check=False)
[docs]
class ConvertSHFlow(Workflow):
[docs]
@classmethod
def get_short_name(cls):
return "convert_dipy_mrtrix"
[docs]
def run(
self,
input_files,
out_dir="",
out_file="sh_convert_dipy_mrtrix_out.nii.gz",
):
"""Converts SH basis representation between DIPY and MRtrix3 formats.
Because this conversion is equal to its own inverse, it can be used to
convert in either direction: DIPY to MRtrix3 or vice versa.
Parameters
----------
input_files : string
Path to the input files. This path may contain wildcards to
process multiple inputs at once.
out_dir : string, optional
Where the resulting file will be saved. (default '')
out_file : string, optional
Name of the result file to be saved.
(default 'sh_convert_dipy_mrtrix_out.nii.gz')
"""
io_it = self.get_io_iterator()
for in_file, out_file in io_it:
data, affine, image = load_nifti(in_file, return_img=True)
data = convert_sh_descoteaux_tournier(data)
save_nifti(out_file, data, affine, hdr=image.header)
[docs]
class ConvertTensorsFlow(Workflow):
[docs]
@classmethod
def get_short_name(cls):
return "convert_tensors"
[docs]
def run(
self,
tensor_files,
from_format="mrtrix",
to_format="dipy",
out_dir=".",
out_tensor="converted_tensor",
):
"""Converts tensor representation between different formats.
Parameters
----------
tensor_files : variable string
Any number of tensor files
from_format : string, optional
Format of the input tensor files. Valid options are 'dipy',
'mrtrix', 'ants', 'fsl'.
to_format : string, optional
Format of the output tensor files. Valid options are 'dipy',
'mrtrix', 'ants', 'fsl'.
out_dir : string, optional
Output directory.
out_tensor : string, optional
Name of the resulting tensor file
"""
io_it = self.get_io_iterator()
for fpath, otensor in io_it:
logging.info(f"Converting {fpath}")
data, affine, image = load_nifti(fpath, return_img=True)
data = convert_tensors(data, from_format, to_format)
save_nifti(otensor, data, affine, hdr=image.header)
[docs]
class ConvertTractogramFlow(Workflow):
[docs]
@classmethod
def get_short_name(cls):
return "convert_tractogram"
[docs]
def run(
self,
input_files,
reference=None,
pos_dtype="float32",
offsets_dtype="uint32",
out_dir="",
out_tractogram="converted_tractogram.trk",
):
"""Converts tractogram between different formats.
Parameters
----------
input_files : variable string
Any number of tractogram files
reference : string, optional
Reference anatomy for tck/vtk/fib/dpy file.
support (.nii or .nii.gz).
pos_dtype : string, optional
Data type of the tractogram points, used for vtk files.
offsets_dtype : string, optional
Data type of the tractogram offsets, used for vtk files.
out_dir : string, optional
Output directory.
out_tractogram : string, optional
Name of the resulting tractogram
"""
io_it = self.get_io_iterator()
for fpath, otracks in io_it:
in_extension = fpath.lower().split(".")[-1]
out_extension = otracks.lower().split(".")[-1]
if in_extension == out_extension:
warnings.warn(
"Input and output are the same file format. Skipping...",
stacklevel=2,
)
continue
if not reference and in_extension in ["trx", "trk"]:
reference = "same"
if not reference and in_extension not in ["trx", "trk"]:
raise ValueError(
"No reference provided. It is needed for tck,"
"fib, dpy or vtk files"
)
sft = load_tractogram(fpath, reference, bbox_valid_check=False)
if out_extension != "trx":
if out_extension == "vtk":
if sft.streamlines._data.dtype.name != pos_dtype:
sft.streamlines._data = sft.streamlines._data.astype(pos_dtype)
if offsets_dtype == "uint64" or offsets_dtype == "uint32":
offsets_dtype = offsets_dtype[1:]
if sft.streamlines._offsets.dtype.name != offsets_dtype:
sft.streamlines._offsets = sft.streamlines._offsets.astype(
offsets_dtype
)
save_tractogram(sft, otracks, bbox_valid_check=False)
else:
trx = tmm.TrxFile.from_sft(sft)
if trx.streamlines._data.dtype.name != pos_dtype:
trx.streamlines._data = trx.streamlines._data.astype(pos_dtype)
if trx.streamlines._offsets.dtype.name != offsets_dtype:
trx.streamlines._offsets = trx.streamlines._offsets.astype(
offsets_dtype
)
tmm.save(trx, otracks)
trx.close()
[docs]
class NiftisToPamFlow(Workflow):
[docs]
@classmethod
def get_short_name(cls):
return "niftis_to_pam"
[docs]
def run(
self,
peaks_dir_files,
peaks_values_files,
peaks_indices_files,
shm_files=None,
gfa_files=None,
sphere_files=None,
default_sphere_name="repulsion724",
out_dir="",
out_pam="peaks.pam5",
):
"""Convert multiple nifti files to a single pam5 file.
Parameters
----------
peaks_dir_files : string
Path to the input peaks directions volume. This path may contain
wildcards to process multiple inputs at once.
peaks_values_files : string
Path to the input peaks values volume. This path may contain
wildcards to process multiple inputs at once.
peaks_indices_files : string
Path to the input peaks indices volume. This path may contain
wildcards to process multiple inputs at once.
shm_files : string, optional
Path to the input spherical harmonics volume. This path may
contain wildcards to process multiple inputs at once.
gfa_files : string, optional
Path to the input generalized FA volume. This path may contain
wildcards to process multiple inputs at once.
sphere_files : string, optional
Path to the input sphere vertices. This path may contain
wildcards to process multiple inputs at once. If it is not define,
default_sphere option will be used.
default_sphere_name : string, optional
Specify default sphere to use for spherical harmonics
representation. This option can be superseded by
sphere_files option. Possible options: ['symmetric362', 'symmetric642',
'symmetric724', 'repulsion724', 'repulsion100', 'repulsion200'].
out_dir : string, optional
Output directory (default input file directory).
out_pam : string, optional
Name of the peaks volume to be saved.
"""
io_it = self.get_io_iterator()
msg = f"pam5 files saved in {out_dir or 'current directory'}"
for fpeak_dirs, fpeak_values, fpeak_indices, opam in io_it:
logging.info("Converting nifti files to pam5")
peak_dirs, affine = load_nifti(fpeak_dirs)
peak_values, _ = load_nifti(fpeak_values)
peak_indices, _ = load_nifti(fpeak_indices)
if sphere_files:
xyz = np.loadtxt(sphere_files)
sphere = Sphere(xyz=xyz)
else:
sphere = get_sphere(name=default_sphere_name)
niftis_to_pam(
affine=affine,
peak_dirs=peak_dirs,
sphere=sphere,
peak_values=peak_values,
peak_indices=peak_indices,
pam_file=opam,
)
logging.info(msg.replace("pam5", opam))
[docs]
class TensorToPamFlow(Workflow):
[docs]
@classmethod
def get_short_name(cls):
return "tensor_to_niftis"
[docs]
def run(
self,
evals_files,
evecs_files,
sphere_files=None,
default_sphere_name="repulsion724",
out_dir="",
out_pam="peaks.pam5",
):
"""Convert multiple tensor files(evals, evecs) to pam5 files.
Parameters
----------
evals_files : string
Path to the input eigen values volumes. This path may contain
wildcards to process multiple inputs at once.
evecs_files : string
Path to the input eigen vectors volumes. This path may contain
wildcards to process multiple inputs at once.
sphere_files : string, optional
Path to the input sphere vertices. This path may contain
wildcards to process multiple inputs at once. If it is not define,
default_sphere option will be used.
default_sphere_name : string, optional
Specify default sphere to use for spherical harmonics
representation. This option can be superseded by sphere_files
option. Possible options: ['symmetric362', 'symmetric642',
'symmetric724', 'repulsion724', 'repulsion100', 'repulsion200'].
out_dir : string, optional
Output directory (default input file directory).
out_pam : string, optional
Name of the peaks volume to be saved.
"""
io_it = self.get_io_iterator()
msg = f"pam5 files saved in {out_dir or 'current directory'}"
for fevals, fevecs, opam in io_it:
logging.info("Converting tensor files to pam5...")
evals, affine = load_nifti(fevals)
evecs, _ = load_nifti(fevecs)
if sphere_files:
xyz = np.loadtxt(sphere_files)
sphere = Sphere(xyz=xyz)
else:
sphere = get_sphere(name=default_sphere_name)
tensor_to_pam(evals, evecs, affine, sphere=sphere, pam_file=opam)
logging.info(msg.replace("pam5", opam))
[docs]
class PamToNiftisFlow(Workflow):
[docs]
@classmethod
def get_short_name(cls):
return "pam_to_niftis"
[docs]
def run(
self,
pam_files,
out_dir="",
out_peaks_dir="peaks_dirs.nii.gz",
out_peaks_values="peaks_values.nii.gz",
out_peaks_indices="peaks_indices.nii.gz",
out_shm="shm.nii.gz",
out_gfa="gfa.nii.gz",
out_sphere="sphere.txt",
out_b="B.nii.gz",
out_qa="qa.nii.gz",
):
"""Convert pam5 files to multiple nifti files.
Parameters
----------
pam_files : string
Path to the input peaks volumes. This path may contain wildcards to
process multiple inputs at once.
out_dir : string, optional
Output directory (default input file directory).
out_peaks_dir : string, optional
Name of the peaks directions volume to be saved.
out_peaks_values : string, optional
Name of the peaks values volume to be saved.
out_peaks_indices : string, optional
Name of the peaks indices volume to be saved.
out_shm : string, optional
Name of the spherical harmonics volume to be saved.
out_gfa : string, optional
Generalized FA volume name to be saved.
out_sphere : string, optional
Sphere vertices name to be saved.
out_b : string, optional
Name of the B Matrix to be saved.
out_qa : string, optional
Name of the Quantitative Anisotropy file to be saved.
"""
io_it = self.get_io_iterator()
msg = f"Nifti files saved in {out_dir or 'current directory'}"
for (
ipam,
opeaks_dir,
opeaks_values,
opeaks_indices,
oshm,
ogfa,
osphere,
ob,
oqa,
) in io_it:
logging.info("Converting %s file to niftis...", ipam)
pam = load_pam(ipam)
pam_to_niftis(
pam,
fname_peaks_dir=opeaks_dir,
fname_shm=oshm,
fname_peaks_values=opeaks_values,
fname_peaks_indices=opeaks_indices,
fname_sphere=osphere,
fname_gfa=ogfa,
fname_b=ob,
fname_qa=oqa,
)
logging.info(msg)
[docs]
class MathFlow(Workflow):
[docs]
@classmethod
def get_short_name(cls):
return "math_flow"
[docs]
def run(
self, operation, input_files, dtype=None, out_dir="", out_file="math_out.nii.gz"
):
"""Perform mathematical operations on volume input files.
This workflow allows the user to perform mathematical operations on
multiple input files. e.g. to add two volumes together, subtract one:
``dipy_math "vol1 + vol2 - vol3" t1.nii.gz t1_a.nii.gz t1_b.nii.gz``
The input files must be in Nifti format and have the same shape.
Parameters
----------
operation : string
Mathematical operation to perform. supported operators are:
- Bitwise operators (and, or, not, xor): ``&, |, ~, ^``
- Comparison operators: ``<, <=, ==, !=, >=, >``
- Unary arithmetic operators: ``-``
- Binary arithmetic operators: ``+, -, *, /, **, <<, >>``
Supported functions are:
- ``where(bool, number1, number2) -> number``: number1 if the bool
condition is true, number2 otherwise.
- ``{sin,cos,tan}(float|complex) -> float|complex``: trigonometric sine,
cosine or tangent.
- ``{arcsin,arccos,arctan}(float|complex) -> float|complex``:
trigonometric inverse sine, cosine or tangent.
- ``arctan2(float1, float2) -> float``: trigonometric inverse tangent of
float1/float2.
- ``{sinh,cosh,tanh}(float|complex) -> float|complex``: hyperbolic
sine, cosine or tangent.
- ``{arcsinh,arccosh,arctanh}(float|complex) -> float|complex``:
hyperbolic inverse sine, cosine or tangent.
- ``{log,log10,log1p}(float|complex) -> float|complex``: natural,
base-10 and log(1+x) logarithms.
- ``{exp,expm1}(float|complex) -> float|complex``: exponential and
exponential minus one.
- ``sqrt(float|complex) -> float|complex``: square root.
- ``abs(float|complex) -> float|complex``: absolute value.
- ``conj(complex) -> complex``: conjugate value.
- ``{real,imag}(complex) -> float``: real or imaginary part of complex.
- ``complex(float, float) -> complex``: complex from real and imaginary
parts.
- ``contains(np.str, np.str) -> bool``: returns True for every string
in op1 that contains op2.
input_files : variable string
Any number of Nifti1 files
dtype : string, optional
Data type of the resulting file.
out_dir : string, optional
Output directory
out_file : string, optional
Name of the resulting file to be saved.
"""
vol_dict = {}
ref_affine = None
ref_shape = None
info_msg = ""
have_errors = False
for i, fname in enumerate(input_files, start=1):
if not os.path.isfile(fname):
logging.error(f"Input file {fname} does not exist.")
raise SystemExit()
if not (fname.endswith(".nii.gz") or fname.endswith(".nii")):
msg = (
f"Wrong volume type: {fname}. Only Nifti files are supported"
" (*.nii or *.nii.gz)."
)
logging.error(msg)
raise SystemExit()
data, affine = load_nifti(fname)
vol_dict[f"vol{i}"] = data
info_msg += f"{fname}:\n- vol index: {i}\n- shape: {data.shape}"
info_msg += f"\n- affine:\n{affine}\n"
if ref_affine is None:
ref_affine = affine
ref_shape = data.shape
continue
have_errors = (
have_errors
or not np.all(np.isclose(ref_affine, affine, rtol=1e-05, atol=1e-08))
or not np.array_equal(ref_shape, data.shape)
)
if have_errors:
logging.warning(info_msg)
msg = "All input files must have the same shape and affine matrix."
logging.error(msg)
raise SystemExit()
try:
res = ne.evaluate(operation, local_dict=vol_dict)
except KeyError as e:
msg = (
f"Impossible key {e} in the operation. You have {len(input_files)}"
f" volumes available with the following keys: {list(vol_dict.keys())}"
)
logging.error(msg)
raise SystemExit() from e
if dtype:
try:
res = res.astype(dtype)
except TypeError as e:
msg = (
f"Impossible to cast to {dtype}. Check possible numpy type here:"
"https://numpy.org/doc/stable/reference/arrays.interface.html"
)
logging.error(msg)
raise SystemExit() from e
out_fname = os.path.join(out_dir, out_file)
logging.info(f"Saving result to {out_fname}")
save_nifti(out_fname, res, affine)