Merge pull request #338 from swaythe/eg_mod

Eg mod

.circleci/config.yml

@@ -63,9 +63,8 @@ jobs:
             echo 'export SPM_MCR="$SPM_ROOT_DIR/spm12.sh"' >> $BASH_ENV
             echo 'export PATH="/home/circleci/.local/bin:$PATH"' >> $BASH_ENV
             pip install --upgrade pip
-            pip install scipy sklearn nibabel configobj coverage pytest -q
+            pip install scipy sklearn nibabel nilearn configobj coverage pytest -q
             pip install matplotlib pandas nipype --ignore-installed -q
-            git clone https://github.com/swaythe/nilearn.git; cd nilearn; pip install -e . -q; cd ..
             python setup.py install --user
             python -c "from pypreprocess import datasets; datasets.fetch_spm_auditory(); datasets.fetch_spm_multimodal_fmri(); datasets.fetch_fsl_feeds()"
             sudo chown -R $USER:$USER /home/circleci/

README.rst

@@ -21,6 +21,7 @@ pypreprocess
 * support for precompiled SPM (besides the usual matlab-dependent flavor).
 
 pypreprocess relies on nipype's interfaces to SPM (both precompiled SPM  and matlab-dependent SPM flavors). It also has pure-Python (no C extensions, no compiled code, just Python) modules and scripts for slice-timing correction, motion correction, coregistration, and smoothing, without need for nipype or matlab.
+It has been developed in the Linux environment and is tested with Ubuntu 16 and 18. No guarantees with other OSes.
 
 License
 =======
@@ -55,18 +56,15 @@ To begin with, you may also want to install the pre-compiled version of SPM (in
 
 Second, install the python packages pip, scipy, pytest, nibabel, sklearn, nipype, pandas, matplotlib, nilearn and configobj.  If you have a python virtual environment, just run::
 
-     $ pip install scipy sklearn nibabel configobj coverage pytest matplotlib pandas nipype
-     $ git clone https://github.com/swaythe/nilearn.git; cd nilearn; pip install -e . ; cd ..
+     $ pip install scipy sklearn nibabel nilearn configobj coverage pytest matplotlib pandas nipype
 
 If not, make sure to install pip (run: 'sudo apt-get install python-pip'). If you want to install these locally, use the --user option::
 
-     $ pip install scipy sklearn nibabel configobj coverage pytest matplotlib pandas nipype --ignore-installed --user
-     $ git clone https://github.com/swaythe/nilearn.git; cd nilearn; pip install -e . --user; cd ..
+     $ pip install scipy sklearn nibabel nilearn configobj coverage pytest matplotlib pandas nipype --ignore-installed --user
 
 If you want to install these for all users, use sudo::
 
-     $ pip install scipy sklearn nibabel configobj coverage pytest matplotlib pandas nipype --ignore-installed
-     $ git clone https://github.com/swaythe/nilearn.git; cd nilearn; pip install -e . --user; cd ..
+     $ pip install scipy sklearn nibabel nilearn configobj coverage pytest matplotlib pandas nipype --ignore-installed
 
 Finally, install pypreprocess itself by running the following in the pypreprocess::
 
@@ -103,7 +101,7 @@ If you find nipype errors like "could not configure SPM", this is most likely th
 
 Layout of examples
 ==================
-We have written some examplary scripts for preprocessing some popular datasets.
+We have written some example scripts for preprocessing some popular datasets.
 The **examples** directory contains a set of scripts, each demoing an aspect of pypreprocessing. Some scripts even provide use-cases for the nipy-based GLM. The examples use publicly available sMRI and fMRI data. Data fetchers are based on the nilearn API.
 The main examples scripts can be summarized as follows:
 
@@ -119,8 +117,6 @@ More advanced examples
 
 * **examples/pipelining/nistats_glm_fsl_feeds_fmri.py**: demos preprocessing + first-level GLM on FSL FEEDS dataset using nistats python package.
 
-* **examples/pipelining/nipype_preproc_spm_nyu.py**: preprocessing of NYU resting-state dataset
-
 Examples using pure Python (no SPM, FSL, etc. required)
 -------------------------------------------------------
 * **examples/pure_python/slice_timing_demos.py, examples/pure_python/realign_demos.py, examples/pure_python/coreg_demos.py**: demos Slice-Timing Correction (STC), motion-correction, and coregistration on various datasets, using modules written in pure Python

examples/easy_start/nipype_preproc_spm_auditory.py

@@ -13,7 +13,8 @@
 import pandas as pd
 from nilearn.glm.first_level.design_matrix import (make_first_level_design_matrix,
                                                          check_design_matrix)
-from nilearn.reporting import plot_design_matrix
+
+from nilearn.plotting.matrix_plotting import plot_design_matrix
 from nilearn.glm.first_level import FirstLevelModel
 import matplotlib.pyplot as plt
 

examples/easy_start/nipype_preproc_spm_haxby.py

@@ -27,7 +27,7 @@
 
 # fetch HAXBY dataset
 N_SUBJECTS = 2
-haxby_data = fetch_haxby(n_subjects=N_SUBJECTS)
+haxby_data = fetch_haxby(subjects=N_SUBJECTS)
 
 # set output dir
 OUTPUT_DIR = os.path.join(os.path.dirname(haxby_data.mask),

examples/pure_python/coreg_demos.py

@@ -36,19 +36,6 @@ def _spm_auditory_factory():
     sd = fetch_spm_auditory()
     return sd.func[0], sd.anat
 
-
-def _abide_factory(institute="KKI"):
-    for scans in sorted(glob.glob(
-            "/home/elvis/CODE/datasets/ABIDE/%s_*/%s_*/scans" % (
-                institute, institute))):
-        subject_id = os.path.basename(os.path.dirname(os.path.dirname(scans)))
-        func = os.path.join(scans, "rest/resources/NIfTI/files/rest.nii")
-        anat = os.path.join(scans,
-                            "anat/resources/NIfTI/files/mprage.nii")
-
-        yield subject_id, func, anat
-
-
 def _nyu_rest_factory(session=1):
     from pypreprocess.nipype_preproc_spm_utils import SubjectData
 
@@ -79,7 +66,7 @@ def _nyu_rest_factory(session=1):
         # set subject output directory
         subject_data.output_dir = "/tmp/%s" % subject_id
 
-        subject_data.sanitize(deleteorient=True, niigz2nii=False)
+        subject_data.sanitize(deleteorient=False, niigz2nii=False)
 
         yield (subject_data.subject_id, subject_data.func[0],
                subject_data.anat)
@@ -91,8 +78,3 @@ def _nyu_rest_factory(session=1):
 for subject_id, func, anat in _nyu_rest_factory():
     print("%s +++NYU rest %s+++\r\n" % ("\t" * 5, subject_id))
     mem.cache(_run_demo)(func, anat)
-
-# ABIDE demo
-for subject_id, func, anat in _abide_factory():
-    print("%s +++ABIDE %s+++\r\n" % ("\t" * 5, subject_id))
-    mem.cache(_run_demo)(func, anat)

examples/pure_python/pure_python_preproc_demo.py

@@ -10,7 +10,8 @@
 sd = fetch_spm_multimodal_fmri()
 sd.output_dir = "/tmp/sub001"
 sd.func = [sd.func1, sd.func2]
+sd.session_output_dirs = ["/tmp/sub001/session1", "/tmp/sub001/session2"]
 
 # preproc data
-do_subject_preproc(sd.__dict__, concat=False, coregister=True, stc=True,
-                   tsdiffana=True, realign=True, report=True, reslice=True)
+do_subject_preproc(sd, concat=False, coregister=True, stc=True,
+                   tsdiffana=True, realign=True, report=False, reslice=True)

examples/pure_python/slice_timing_demos.py

@@ -174,7 +174,7 @@ def demo_HRF(output_dir, n_slices=10,
     # create time values scaled at 1%
     timescale = .01
     n_timepoints = 24
-    time = np.linspace(0, n_timepoints, num=1 + (n_timepoints - 0) / timescale)
+    time = np.linspace(0, n_timepoints, num=int(1 + (n_timepoints - 0) / timescale))
 
     # create gamma functions
     n1 = 4
@@ -202,7 +202,7 @@ def compute_hrf(t):
 
     # sample the time and the signal
     freq = 100
-    TR = 3.
+    TR = 3
     acquisition_time = time[::TR * freq]
     n_scans = len(acquisition_time)