7. First Level GLM (from Nilearn)#
In this tutorial, we will go through a simple workflow of the first level general linear modeling with a BIDS dataset from openneuro. This analysis is only performed on one subject.
This tutorial is based on the Nilearn GLM tutorial.
import nest_asyncio
nest_asyncio.apply()
7.1. Preparation#
Import packages that will be used globally and set up output directory
import warnings
import sys
if not sys.warnoptions:
warnings.simplefilter("ignore")
import os
import typing as ty
from pathlib import Path
import pydra
from pydra import Workflow
from pydra.engine.specs import File
import pandas as pd
from scipy.stats import norm
import nibabel as nib
from nilearn.datasets import (
fetch_openneuro_dataset_index,
fetch_openneuro_dataset,
select_from_index,
)
from nilearn.interfaces.fsl import get_design_from_fslmat
from nilearn.glm.first_level import first_level_from_bids
from nilearn.reporting import get_clusters_table, make_glm_report
from nilearn.plotting import (
plot_glass_brain,
plot_img_comparison,
plot_contrast_matrix,
)
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
Cell In[2], line 17
14 from scipy.stats import norm
16 import nibabel as nib
---> 17 from nilearn.datasets import (
18 fetch_openneuro_dataset_index,
19 fetch_openneuro_dataset,
20 select_from_index,
21 )
22 from nilearn.interfaces.fsl import get_design_from_fslmat
23 from nilearn.glm.first_level import first_level_from_bids
ImportError: cannot import name 'fetch_openneuro_dataset_index' from 'nilearn.datasets' (/usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/nilearn/datasets/__init__.py)
# get current directory
pydra_tutorial_dir = os.path.dirname(os.getcwd())
# set up output directory
workflow_dir = Path(pydra_tutorial_dir) / 'outputs'
workflow_out_dir = workflow_dir / '6_glm'
# create the output directory if not exit
os.makedirs(workflow_out_dir, exist_ok=True)
workflow_out_dir
PosixPath('/tmp/outputs/6_glm')
7.2. Create tasks#
In this section, we converte major steps into tasks. Each pydra task can have multiple python functions. We recommand to put those logically more related functions into the same task.
It is very important to keep in mind what adjacent tasks of your current task will be.
Your previous task will decide your arguments in the current task
Your next task will be impacted by the returns in the current task
7.2.1. fetch openneuro BIDS dataset#
In this task, we do the following:
get openneuro dataset index
specify exclusion patterns and number of subjects
download the data we need
Notes: Here we still use n_subjects as an argument. Given that we will only analyze one subject, you can also remove this argument and specify n_subjects =1 in select_from_index. If you do, do not forget to modify the argument in the workflow later.
@pydra.mark.task
@pydra.mark.annotate(
{
'exclusion_patterns': list,
'n_subjects': int,
'return': {'data_dir': str},
}
)
def get_openneuro_dataset(exclusion_patterns, n_subjects):
_, urls = fetch_openneuro_dataset_index()
urls = select_from_index(
urls, exclusion_filters=exclusion_patterns, n_subjects=n_subjects
)
data_dir, _ = fetch_openneuro_dataset(urls=urls)
return data_dir
7.2.2. obtain FirstLevelModel objects automatically and fit arguments#
To get the first level model(s) we have to specify
the dataset directory
the task_label
the space_label
the folder with the desired derivatives (fMRIPrep)
In our case, we only have one subject so we will only have one first level model. Then, for this model, we will obtain
the list of run images
events
confound regressors
Those are inferred from the confounds.tsv files available in the BIDS dataset.
@pydra.mark.task
@pydra.mark.annotate(
{
'data_dir': str,
'task_label': str,
'space_label': str,
'derivatives_folder': str,
'smoothing_fwhm': float,
'return': {'model': ty.Any, 'imgs': list, 'subject': str},
}
)
def get_info_from_bids(
data_dir, task_label, space_label, smoothing_fwhm, derivatives_folder
):
(
models,
models_run_imgs,
models_events,
models_confounds,
) = first_level_from_bids(
dataset_path=data_dir,
task_label=task_label,
space_label=space_label,
smoothing_fwhm=smoothing_fwhm,
derivatives_folder=derivatives_folder,
)
model, imgs, events, confounds = (
models[0],
models_run_imgs[0],
models_events[0],
models_confounds[0],
)
subject = 'sub-' + model.subject_label
return model, imgs, subject
7.2.3. Get design matrix#
This task does the following:
read the design matrix in
.matrename the column
save the new design matrix as
.csv
Think: What if we don’t save the new design matrix, but return it directly? In other words, we return a pandas.DataFrame instead of a path. What will happen? Worth a try :)
@pydra.mark.task
@pydra.mark.annotate(
{'data_dir': str, 'subject': str, 'return': {'dm_path': str}}
)
def get_designmatrix(data_dir, subject):
fsl_design_matrix_path = os.path.join(
data_dir,
'derivatives',
'task',
subject,
'stopsignal.feat',
'design.mat',
)
design_matrix = get_design_from_fslmat(
fsl_design_matrix_path, column_names=None
)
design_columns = [
'cond_%02d' % i for i in range(len(design_matrix.columns))
]
design_columns[0] = 'Go'
design_columns[4] = 'StopSuccess'
design_matrix.columns = design_columns
dm_path = os.path.join(workflow_out_dir, 'designmatrix.csv')
design_matrix.to_csv(dm_path, index=None)
return dm_path
7.2.4. Fit the first level model#
What we are doing here is:
use the design matrix to fit the first level model
compute the contrast
save the z_map and masker for futher use
generate a glm report (HTML file)
@pydra.mark.task
@pydra.mark.annotate(
{
'model': ty.Any,
'imgs': ty.Any,
'dm_path': ty.Any,
'contrast': str,
'return': {'model': ty.Any, 'z_map_path': str, 'masker': ty.Any, 'glm_report_file': str},
}
)
def model_fit(model, imgs, dm_path, contrast):
design_matrix = pd.read_csv(dm_path)
model.fit(imgs, design_matrices=[design_matrix])
z_map = model.compute_contrast(contrast)
z_map_path = os.path.join(workflow_out_dir, 'firstlevel_z_map.nii.gz')
z_map.to_filename(z_map_path)
masker_path = os.path.join(workflow_out_dir, 'firstlevel_masker.nii.gz')
masker = model.masker_
glm_report_file = os.path.join(workflow_out_dir, 'glm_report.html')
report = make_glm_report(model, contrast)
report.save_as_html(glm_report_file)
return model, z_map_path, masker, glm_report_file
7.2.5. Get cluster table#
For publication purposes, we obtain a cluster table.
@pydra.mark.task
@pydra.mark.annotate({'z_map_path': str, 'return': {'output_file': str}})
def cluster_table(z_map_path):
stat_img = nib.load(z_map_path)
output_file = os.path.join(workflow_out_dir, 'cluster_table.csv')
df = get_clusters_table(
stat_img, stat_threshold=norm.isf(0.001), cluster_threshold=10
)
df.to_csv(output_file, index=None)
return output_file
7.2.6. Make plots#
Here we want to make some plots to display our results and compare the result from FSL.
plot nilearn z-map
plot fsl z-map
plot nilearn and fsl comparison
plot design matrix contrast
You can also seperate this task into multiple sub-tasks. But it makes more sense to put them into one task as they use the same files and function nilearn.plotting repeatedly.
@pydra.mark.task
@pydra.mark.annotate(
{
'data_dir': str,
'dm_path': str,
'z_map_path': str,
'contrast': str,
'subject': str,
'masker': ty.Any,
'return': {
'output_file1': str,
'output_file2': str,
'output_file3': str,
'output_file4': str,
},
}
)
def plots(data_dir,dm_path,z_map_path,contrast,subject,masker):
# plot and save nilearn z-map
z_map = nib.load(z_map_path)
output_file1 = os.path.join(workflow_out_dir, 'nilearn_z_map.jpg')
plot_glass_brain(
z_map,
output_file=output_file1,
colorbar=True,
threshold=norm.isf(0.001),
title='Nilearn Z map of "StopSuccess - Go" (unc p<0.001)',
plot_abs=False,
display_mode='ortho',
)
# plot and save fsl z-map
fsl_z_map = nib.load(
os.path.join(
data_dir,
'derivatives',
'task',
subject,
'stopsignal.feat',
'stats',
'zstat12.nii.gz',
)
)
output_file2 = os.path.join(workflow_out_dir, 'fsl_z_map.jpg')
plot_glass_brain(
fsl_z_map,
output_file=output_file2,
colorbar=True,
threshold=norm.isf(0.001),
title='FSL Z map of "StopSuccess - Go" (unc p<0.001)',
plot_abs=False,
display_mode='ortho',
)
# plot and save nilearn and fsl comparison
plot_img_comparison(
[z_map],
[fsl_z_map],
masker,
output_dir=workflow_out_dir,
ref_label='Nilearn',
src_label='FSL',
)
old = os.path.join(workflow_out_dir, '0000.png')
new = os.path.join(workflow_out_dir, 'nilearn_fsl_comp.jpg')
os.rename(old, new)
output_file3 = new
print(output_file3)
# plot and save design matrix contrast
design_matrix = pd.read_csv(dm_path)
output_file4 = os.path.join(workflow_out_dir, 'firstlevel_contrast.jpg')
plot_contrast_matrix(contrast, design_matrix, output_file=output_file4)
return output_file1, output_file2, output_file3, output_file4
7.3. Make a workflow from tasks#
Now we have created all tasks we need for this first level analysis, and there are two choices for our next step.
create one workflow to connect all tasks together
create sub-workflows with some closely related tasks, and connect these workflows along with other tasks into a larger workflow.
We recommand the second approach as it is alway a good practice to group tasks, especially when there are a large number of tasks in the analysis.
Our analysis can be divided into three parts: (1) get/read the data, (2) analyze the data, and (3) plot the result, where (1) and (3) only have one task each. So we can put all tasks in (2) into one workflow and name it as firstlevel or whatever you prefer.
# initiate a workflow
wf_firstlevel = Workflow(
name='wf_firstlevel',
input_spec=[
'data_dir',
'task_label',
'space_label',
'derivatives_folder',
'smoothing_fwhm',
'contrast',
'output_dir',
],
)
# specify input
wf_firstlevel.inputs.task_label = 'stopsignal'
wf_firstlevel.inputs.space_label = 'MNI152NLin2009cAsym'
wf_firstlevel.inputs.derivatives_folder = 'derivatives/fmriprep'
wf_firstlevel.inputs.smoothing_fwhm = 5.0
# add task - get_info_from_bids
wf_firstlevel.add(
get_info_from_bids(
name='get_info_from_bids',
data_dir=wf_firstlevel.lzin.data_dir,
task_label=wf_firstlevel.lzin.task_label,
space_label=wf_firstlevel.lzin.space_label,
derivatives_folder=wf_firstlevel.lzin.derivatives_folder,
smoothing_fwhm=wf_firstlevel.lzin.smoothing_fwhm,
)
)
# add task - get_designmatrix
wf_firstlevel.add(
get_designmatrix(
name='get_designmatrix',
data_dir=wf_firstlevel.lzin.data_dir,
subject=wf_firstlevel.get_info_from_bids.lzout.subject,
)
)
wf_firstlevel.add(
model_fit(
name='l1estimation',
model=wf_firstlevel.get_info_from_bids.lzout.model,
imgs=wf_firstlevel.get_info_from_bids.lzout.imgs,
dm_path=wf_firstlevel.get_designmatrix.lzout.dm_path,
contrast=wf_firstlevel.lzin.contrast,
)
)
# add task - cluster_table
wf_firstlevel.add(
cluster_table(
name='cluster_table',
z_map_path=wf_firstlevel.l1estimation.lzout.z_map_path,
)
)
# specify output
wf_firstlevel.set_output(
[
('z_map', wf_firstlevel.l1estimation.lzout.z_map_path),
('masker', wf_firstlevel.l1estimation.lzout.masker),
('subject', wf_firstlevel.get_info_from_bids.lzout.subject),
('dm_path', wf_firstlevel.get_designmatrix.lzout.dm_path),
('cluster_table', wf_firstlevel.cluster_table.lzout.output_file),
('glm_report', wf_firstlevel.l1estimation.lzout.glm_report_file),
]
)
7.4. The overaching workflow#
Connect other tasks and the above workflow into one
Now we need to create the overaching glm workflow that connects the above workflow and other tasks (e.g., get/read the data and plot the result)
wf = Workflow(
name='firstlevel_glm',
input_spec=['exclusion_patterns', 'n_subjects', 'contrast', 'output_dir'],
)
wf.inputs.exclusion_patterns = [
'*group*',
'*phenotype*',
'*mriqc*',
'*parameter_plots*',
'*physio_plots*',
'*space-fsaverage*',
'*space-T1w*',
'*dwi*',
'*beh*',
'*task-bart*',
'*task-rest*',
'*task-scap*',
'*task-task*',
]
wf.inputs.n_subjects = 1
wf.inputs.output_dir = workflow_out_dir
wf.inputs.contrast = 'StopSuccess - Go'
wf.add(
get_openneuro_dataset(
name='get_openneuro_dataset',
exclusion_patterns=wf.lzin.exclusion_patterns,
n_subjects=wf.lzin.n_subjects,
)
)
wf_firstlevel.inputs.data_dir = wf.get_openneuro_dataset.lzout.data_dir
wf_firstlevel.inputs.contrast = wf.inputs.contrast
wf_firstlevel.inputs.output_dir = wf.inputs.output_dir
wf.add(wf_firstlevel)
wf.add(
plots(
name='plots',
data_dir=wf.get_openneuro_dataset.lzout.data_dir,
dm_path=wf_firstlevel.lzout.dm_path,
z_map_path=wf_firstlevel.lzout.z_map,
contrast=wf.lzin.contrast,
subject=wf_firstlevel.lzout.subject,
masker=wf_firstlevel.lzout.masker,
)
)
wf.set_output(
[
('output1', wf.plots.lzout.output_file1),
('output2', wf.plots.lzout.output_file2),
('output3', wf.plots.lzout.output_file3),
('output4', wf.plots.lzout.output_file4),
]
)
7.5. Run Workflow Run#
from pydra import Submitter
with Submitter(plugin='cf', n_procs=4) as submitter:
submitter(wf)
results = wf.result()
print(results)
Show code cell output
Task exception was never retrieved
future: <Task finished name='Task-2' coro=<ConcurrentFuturesWorker.exec_as_coro() done, defined at /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/pydra/engine/workers.py:173> exception=NameError("name 'fetch_openneuro_dataset_index' is not defined")>
concurrent.futures.process._RemoteTraceback:
"""
Traceback (most recent call last):
File "/usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/concurrent/futures/process.py", line 254, in _process_worker
r = call_item.fn(*call_item.args, **call_item.kwargs)
File "/usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/pydra/engine/core.py", line 529, in _run
self._run_task()
~~~~~~~~~~~~~~^^
File "/usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/pydra/engine/task.py", line 202, in _run_task
output = cp.loads(self.inputs._func)(**inputs)
File "/tmp/ipykernel_2783/965776143.py", line 10, in get_openneuro_dataset
_, urls = fetch_openneuro_dataset_index()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
NameError: name 'fetch_openneuro_dataset_index' is not defined
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/asyncio/tasks.py", line 306, in __step_run_and_handle_result
result = coro.throw(exc)
File "/usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/pydra/engine/workers.py", line 176, in exec_as_coro
res = await self.loop.run_in_executor(self.pool, runnable._run, rerun)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/asyncio/futures.py", line 286, in __await__
yield self # This tells Task to wait for completion.
^^^^^^^^^^
File "/usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/asyncio/tasks.py", line 375, in __wakeup
future.result()
~~~~~~~~~~~~~^^
File "/usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/asyncio/futures.py", line 199, in result
raise self._exception.with_traceback(self._exception_tb)
NameError: name 'fetch_openneuro_dataset_index' is not defined
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/pydra/engine/core.py:1365, in Workflow._collect_outputs(self)
1364 try:
-> 1365 val_out = val.get_value(self)
1366 output_wf[name] = val_out
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/pydra/engine/specs.py:1030, in LazyOutField.get_value(self, wf, state_index)
1028 return val
-> 1030 value = get_nested_results(result, depth=split_depth)
1031 value = self._apply_cast(value)
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/pydra/engine/specs.py:1020, in LazyOutField.get_value.<locals>.get_nested_results(res, depth)
1019 if res.errored:
-> 1020 raise ValueError(
1021 f"Cannot retrieve value for {self.field} from {self.name} as "
1022 "the node errored"
1023 )
1024 val = res.get_output_field(self.field)
ValueError: Cannot retrieve value for output_file1 from plots as the node errored
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
Cell In[13], line 4
1 from pydra import Submitter
3 with Submitter(plugin='cf', n_procs=4) as submitter:
----> 4 submitter(wf)
6 results = wf.result()
8 print(results)
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/pydra/engine/submitter.py:42, in Submitter.__call__(self, runnable, cache_locations, rerun)
40 if cache_locations is not None:
41 runnable.cache_locations = cache_locations
---> 42 self.loop.run_until_complete(self.submit_from_call(runnable, rerun))
43 return runnable.result()
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/nest_asyncio.py:98, in _patch_loop.<locals>.run_until_complete(self, future)
95 if not f.done():
96 raise RuntimeError(
97 'Event loop stopped before Future completed.')
---> 98 return f.result()
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/asyncio/futures.py:199, in Future.result(self)
197 self.__log_traceback = False
198 if self._exception is not None:
--> 199 raise self._exception.with_traceback(self._exception_tb)
200 return self._result
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/asyncio/tasks.py:304, in Task.__step_run_and_handle_result(***failed resolving arguments***)
300 try:
301 if exc is None:
302 # We use the `send` method directly, because coroutines
303 # don't have `__iter__` and `__next__` methods.
--> 304 result = coro.send(None)
305 else:
306 result = coro.throw(exc)
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/pydra/engine/submitter.py:68, in Submitter.submit_from_call(self, runnable, rerun)
66 # 1
67 if runnable.state is None:
---> 68 await runnable._run(self, rerun=rerun)
69 # 3
70 else:
71 await self.expand_runnable(runnable, wait=True, rerun=rerun)
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/pydra/engine/core.py:1237, in Workflow._run(self, submitter, rerun, **kwargs)
1235 self.audit.monitor()
1236 await self._run_task(submitter, rerun=rerun)
-> 1237 result.output = self._collect_outputs()
1238 except Exception:
1239 etype, eval, etr = sys.exc_info()
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/pydra/engine/core.py:1371, in Workflow._collect_outputs(self)
1369 # checking if the tasks has predecessors that raises error
1370 if isinstance(getattr(self, val.name)._errored, list):
-> 1371 raise ValueError(
1372 f"Tasks {getattr(self, val.name)._errored} raised an error"
1373 )
1374 else:
1375 if isinstance(getattr(self, val.name).output_dir, list):
ValueError: Tasks ['get_openneuro_dataset'] raised an error
7.6. Visualization#
If you arrive here without any errors, yay, you just made your first pydra workflow for a first-level GLM!
7.7. Examine folder structure#
Let’s take a look at what you have got.
!ls ../outputs/6_glm
7.7.1. Plot figures#
7.7.1.1. First level contrast#
Show code cell source
from IPython.display import Image
Image(filename='../outputs/6_glm/firstlevel_contrast.jpg')
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[15], line 3
1 from IPython.display import Image
----> 3 Image(filename='../outputs/6_glm/firstlevel_contrast.jpg')
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/IPython/core/display.py:1053, in Image.__init__(self, data, url, filename, format, embed, width, height, retina, unconfined, metadata, alt)
1051 self.unconfined = unconfined
1052 self.alt = alt
-> 1053 super(Image, self).__init__(data=data, url=url, filename=filename,
1054 metadata=metadata)
1056 if self.width is None and self.metadata.get('width', {}):
1057 self.width = metadata['width']
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/IPython/core/display.py:371, in DisplayObject.__init__(self, data, url, filename, metadata)
368 elif self.metadata is None:
369 self.metadata = {}
--> 371 self.reload()
372 self._check_data()
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/IPython/core/display.py:1088, in Image.reload(self)
1086 """Reload the raw data from file or URL."""
1087 if self.embed:
-> 1088 super(Image,self).reload()
1089 if self.retina:
1090 self._retina_shape()
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/IPython/core/display.py:397, in DisplayObject.reload(self)
395 if self.filename is not None:
396 encoding = None if "b" in self._read_flags else "utf-8"
--> 397 with open(self.filename, self._read_flags, encoding=encoding) as f:
398 self.data = f.read()
399 elif self.url is not None:
400 # Deferred import
FileNotFoundError: [Errno 2] No such file or directory: '../outputs/6_glm/firstlevel_contrast.jpg'
7.7.1.2. Nilearn Z map#
Show code cell source
Image(filename='../outputs/6_glm/nilearn_z_map.jpg')
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[16], line 1
----> 1 Image(filename='../outputs/6_glm/nilearn_z_map.jpg')
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/IPython/core/display.py:1053, in Image.__init__(self, data, url, filename, format, embed, width, height, retina, unconfined, metadata, alt)
1051 self.unconfined = unconfined
1052 self.alt = alt
-> 1053 super(Image, self).__init__(data=data, url=url, filename=filename,
1054 metadata=metadata)
1056 if self.width is None and self.metadata.get('width', {}):
1057 self.width = metadata['width']
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/IPython/core/display.py:371, in DisplayObject.__init__(self, data, url, filename, metadata)
368 elif self.metadata is None:
369 self.metadata = {}
--> 371 self.reload()
372 self._check_data()
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/IPython/core/display.py:1088, in Image.reload(self)
1086 """Reload the raw data from file or URL."""
1087 if self.embed:
-> 1088 super(Image,self).reload()
1089 if self.retina:
1090 self._retina_shape()
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/IPython/core/display.py:397, in DisplayObject.reload(self)
395 if self.filename is not None:
396 encoding = None if "b" in self._read_flags else "utf-8"
--> 397 with open(self.filename, self._read_flags, encoding=encoding) as f:
398 self.data = f.read()
399 elif self.url is not None:
400 # Deferred import
FileNotFoundError: [Errno 2] No such file or directory: '../outputs/6_glm/nilearn_z_map.jpg'
7.7.1.3. FSL Z map#
Show code cell source
Image(filename='../outputs/6_glm/fsl_z_map.jpg')
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[17], line 1
----> 1 Image(filename='../outputs/6_glm/fsl_z_map.jpg')
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/IPython/core/display.py:1053, in Image.__init__(self, data, url, filename, format, embed, width, height, retina, unconfined, metadata, alt)
1051 self.unconfined = unconfined
1052 self.alt = alt
-> 1053 super(Image, self).__init__(data=data, url=url, filename=filename,
1054 metadata=metadata)
1056 if self.width is None and self.metadata.get('width', {}):
1057 self.width = metadata['width']
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/IPython/core/display.py:371, in DisplayObject.__init__(self, data, url, filename, metadata)
368 elif self.metadata is None:
369 self.metadata = {}
--> 371 self.reload()
372 self._check_data()
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/IPython/core/display.py:1088, in Image.reload(self)
1086 """Reload the raw data from file or URL."""
1087 if self.embed:
-> 1088 super(Image,self).reload()
1089 if self.retina:
1090 self._retina_shape()
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/IPython/core/display.py:397, in DisplayObject.reload(self)
395 if self.filename is not None:
396 encoding = None if "b" in self._read_flags else "utf-8"
--> 397 with open(self.filename, self._read_flags, encoding=encoding) as f:
398 self.data = f.read()
399 elif self.url is not None:
400 # Deferred import
FileNotFoundError: [Errno 2] No such file or directory: '../outputs/6_glm/fsl_z_map.jpg'
7.7.1.4. Nilearn FSL comparison#
Show code cell source
Image(filename='../outputs/6_glm/nilearn_fsl_comp.jpg')
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[18], line 1
----> 1 Image(filename='../outputs/6_glm/nilearn_fsl_comp.jpg')
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/IPython/core/display.py:1053, in Image.__init__(self, data, url, filename, format, embed, width, height, retina, unconfined, metadata, alt)
1051 self.unconfined = unconfined
1052 self.alt = alt
-> 1053 super(Image, self).__init__(data=data, url=url, filename=filename,
1054 metadata=metadata)
1056 if self.width is None and self.metadata.get('width', {}):
1057 self.width = metadata['width']
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/IPython/core/display.py:371, in DisplayObject.__init__(self, data, url, filename, metadata)
368 elif self.metadata is None:
369 self.metadata = {}
--> 371 self.reload()
372 self._check_data()
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/IPython/core/display.py:1088, in Image.reload(self)
1086 """Reload the raw data from file or URL."""
1087 if self.embed:
-> 1088 super(Image,self).reload()
1089 if self.retina:
1090 self._retina_shape()
File /usr/share/miniconda/envs/pydra-tutorial/lib/python3.13/site-packages/IPython/core/display.py:397, in DisplayObject.reload(self)
395 if self.filename is not None:
396 encoding = None if "b" in self._read_flags else "utf-8"
--> 397 with open(self.filename, self._read_flags, encoding=encoding) as f:
398 self.data = f.read()
399 elif self.url is not None:
400 # Deferred import
FileNotFoundError: [Errno 2] No such file or directory: '../outputs/6_glm/nilearn_fsl_comp.jpg'
7.8. Exercise#
What if we need to run the first-level GLM on multiple subject? We will need the splitter.
So, where should we add .split?