[TST] Tests and better debugging

.gitignore

@@ -1,4 +1,3 @@
-data/
 docs/generated/
 
 # Byte-compiled / optimized / DLL files

.travis.yml

@@ -2,11 +2,39 @@ language: python
 sudo: false
 
 python:
-  - 3.6
+    - 3.6
+
+matrix:
+  include:
+    - python: 3.6
+      env:
+        - STYLE=1
+    - python: 3.6
+      env:
+        - COVERAGE=1
 
 before_install:
-  - python -m pip install --upgrade pip
-  - pip install "flake8<3.0" flake8-putty
+    - python -m pip install --upgrade pip
+    - pip install --upgrade virtualenv
+    - if [ "${STYLE}" == "1" ]; then
+      pip install "flake8<3.0" flake8-putty;
+      fi
+    - if [ "${COVERAGE}" == "1" ]; then
+      pip install coverage coveralls codecov pytest pytest-cov;
+      fi
 
 script:
-- flake8 tedana
+    - |
+      if [ "${STYLE}" == "1" ]; then
+          flake8 tedana
+      else
+          if [ "${COVERAGE}" == "1" ]; then
+              TEST_ARGS="--cov-report term-missing --cov=tedana";
+          fi
+          py.test --ignore=tedana/tests/test_tedana.py ${TEST_ARGS} tedana;
+      fi
+
+after_success:
+    - if [ "${COVERAGE}" == "1" ]; then
+          coveralls; codecov;
+      fi

Dockerfile

@@ -31,44 +31,9 @@ RUN apt-get update -qq && apt-get install -yq --no-install-recommends  \
     && chmod -R 777 /neurodocker && chmod a+s /neurodocker
 ENTRYPOINT ["/neurodocker/startup.sh"]
 
-RUN apt-get update -qq && apt-get install -yq --no-install-recommends git vim libxml2-dev libnlopt-dev libxslt-dev\
+RUN apt-get update -qq && apt-get install -yq --no-install-recommends git vim \
     && apt-get clean \
-    && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-
-#--------------------
-# Install AFNI latest
-#--------------------
-ENV PATH=/opt/afni:$PATH
-RUN apt-get update -qq && apt-get install -yq --no-install-recommends ed gsl-bin libglu1-mesa-dev libglib2.0-0 libglw1-mesa \
-    libgomp1 libjpeg62 libxm4 netpbm tcsh xfonts-base xvfb \
-    && libs_path=/usr/lib/x86_64-linux-gnu \
-    && if [ -f $libs_path/libgsl.so.19 ]; then \
-           ln $libs_path/libgsl.so.19 $libs_path/libgsl.so.0; \
-       fi \
-    # Install libxp (not in all ubuntu/debian repositories) \
-    && apt-get install -yq --no-install-recommends libxp6 \
-    || /bin/bash -c " \
-       curl --retry 5 -o /tmp/libxp6.deb -sSL http://mirrors.kernel.org/debian/pool/main/libx/libxp/libxp6_1.0.2-2_amd64.deb \
-       && dpkg -i /tmp/libxp6.deb && rm -f /tmp/libxp6.deb" \
-    # Install libpng12 (not in all ubuntu/debian repositories) \
-    && apt-get install -yq --no-install-recommends libpng12-0 \
-    || /bin/bash -c " \
-       curl -o /tmp/libpng12.deb -sSL http://mirrors.kernel.org/debian/pool/main/libp/libpng/libpng12-0_1.2.49-1%2Bdeb7u2_amd64.deb \
-       && dpkg -i /tmp/libpng12.deb && rm -f /tmp/libpng12.deb" \
-    # Install R \
-    && apt-get install -yq --no-install-recommends \
-    	r-base-dev r-cran-rmpi \
-     || /bin/bash -c " \
-        curl -o /tmp/install_R.sh -sSL https://gist.githubusercontent.com/kaczmarj/8e3792ae1af70b03788163c44f453b43/raw/0577c62e4771236adf0191c826a25249eb69a130/R_installer_debian_ubuntu.sh \
-        && /bin/bash /tmp/install_R.sh" \
-    && apt-get clean \
-    && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* \
-    && echo "Downloading AFNI ..." \
-    && mkdir -p /opt/afni \
-    && curl -sSL --retry 5 https://afni.nimh.nih.gov/pub/dist/tgz/linux_openmp_64.tgz \
-    | tar zx -C /opt/afni --strip-components=1 \
-    && /opt/afni/rPkgsInstall -pkgs ALL \
-    && rm -rf /tmp/*
+&& rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
 
 # Create new user: neuro
 RUN useradd --no-user-group --create-home --shell /bin/bash neuro
@@ -93,25 +58,14 @@ RUN echo "Downloading Miniconda installer ..." \
 #-----------------------------
 # Create py3 conda environment
 #-----------------------------
-RUN conda create -y -q --name default --channel vida-nyu python=3.6.1 \
-    	numpy pandas reprozip traits \
-    && sync && conda clean -tipsy && sync \
-    && /bin/bash -c "source activate default \
-    	&& pip install -q --no-cache-dir \
-    	nipype ipython scikit-learn scipy ipdb mdp nilearn nibabel>=2.1.0" \
-    && sync
-ENV PATH=/opt/conda/envs/default/bin:$PATH
-
-#------------------------------
-# Create py27 conda environment
-#------------------------------
-RUN conda create -y -q --name py27 python=2.7 \
-    numpy pandas reprozip traits \
+RUN conda create -y -q --name py36 python=3.6 \
+    	numpy scikit-learn mdp nilearn scipy 'nibabel>=2.1.0' \
     && sync && conda clean -tipsy && sync \
-    && /bin/bash -c "source activate default \
-        && pip install -q --no-cache-dir \
-        nipype ipython scikit-learn scipy ipdb mdp nilearn nibabel>=2.1.0" \
-    && sync
+    && bash -c "source activate py36 \
+    && pip install -q --no-cache-dir \
+        https://github.com/ME-ICA/tedana/archive/master.tar.gz" \
+    && sync \
+    && sed -i '$isource activate py36' $ND_ENTRYPOINT
 
 USER root
 

README.md

@@ -1,25 +1,28 @@
 # tedana
 
-TE-Dependent Analysis (_tedana_) is a Python module for denoising multi-echo fMRI data.
+`TE`-`de`pendent `ana`lysis (_tedana_) is a Python module for denoising multi-echo functional magnetic resonance imaging (fMRI) data.
 
 [![CircleCI](https://circleci.com/gh/ME-ICA/tedana.svg?style=shield)](https://circleci.com/gh/ME-ICA/tedana)
 [![Documentation Status](https://readthedocs.org/projects/tedana/badge/?version=latest)](http://tedana.readthedocs.io/en/latest/?badge=latest)
+[![Codecov](https://codecov.io/gh/me-ica/tedana/branch/master/graph/badge.svg)](https://codecov.io/gh/me-ica/tedana)
 [![License](https://img.shields.io/badge/License-LGPL%202.0-blue.svg)](https://opensource.org/licenses/LGPL-2.1)
 [![Join the chat at https://gitter.im/ME-ICA/tedana](https://badges.gitter.im/ME-ICA/tedana.svg)](https://gitter.im/ME-ICA/tedana?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
 
 
 ## About
 
-tedana is part of the ME-ICA pipeline, and therefore assumes that you're working with already preprocessed data. If you're in need of a preprocessing pipeline, we recommend [FMRIPREP](https://github.com/poldracklab/fmriprep/), which has been tested for compatibility with multi-echo fMRI data.
+`tedana` originally came about as a part of the [`ME-ICA`](https://github.com/me-ica/me-ica) pipeline.
+The ME-ICA pipeline orignially performed both pre-processing and TE-dependent analysis of multi-echo fMRI data; however, `tedana` now assumes that you're working with data which has been previously preprocessed.
+If you're in need of a pre-processing pipeline, we recommend [`fmriprep`](https://github.com/poldracklab/fmriprep/) which has been tested for compatibility with multi-echo fMRI data and `tedana`.
 
 ### Why Multi-Echo?
 
-Multi-echo fMRI data collection entails acquires multiple TEs (commonly called [echo times](http://mriquestions.com/tr-and-te.html)) for each collected fMRI volume.
-Our signal of interest, Blood Oxygen-Level Dependent or [BOLD signal](http://www.fil.ion.ucl.ac.uk/spm/course/slides10-zurich/Kerstin_BOLD.pdf), is known to decay at a set rate within each fMRI volume.
-Collecting multiple echos therefore allows us to infer if components of fMRI signal are BOLD-related or driven by acquisition artifacts, like participant motion.
-For a review, see [Kundu et al. (2017), _NeuroImage_](https://paperpile.com/shared/eH3PPu).
+Multi-echo fMRI data is obtained by acquiring multiple TEs (commonly called [echo times](http://mriquestions.com/tr-and-te.html)) for each MRI volume during data collection.
+While fMRI signal contains important neural information (termed the blood oxygen-level dependent, or [BOLD signal](http://www.fil.ion.ucl.ac.uk/spm/course/slides10-zurich/Kerstin_BOLD.pdf)), it also contains "noise" (termed non-BOLD signal) caused by things like participant motion and changes in breathing.
+Because the BOLD signal is known to decay at a set rate, collecting multiple echos allows us to assess whether components of the fMRI signal are BOLD- or non-BOLD.
+For a comprehensive review, see [Kundu et al. (2017), _NeuroImage_](https://paperpile.com/shared/eH3PPu).
 
-In tedana, we combine all collected echos, then decompose the resulting time series into components that can be classified as BOLD or non-BOLD based. This is performed in a series of steps including:
+In `tedana`, we take the time series from all the collected TEs, combine them, and decompose the resulting data into components that can be classified as BOLD or non-BOLD. This is performed in a series of steps including:
 
 * Principal components analysis
 * Independent components analysis
@@ -29,7 +32,9 @@ More information and documentation can be found at https://tedana.readthedocs.io
 
 ## Installation
 
-You'll need to set up a working development environment to use tedana. We provide a Dockerfile for this purpose (check out [tips on using Docker](https://neurohackweek.github.io/docker-for-scientists/)), or you can set up your environment locally. If you choose the latter, make sure the following packages are installed:
+You'll need to set up a working development environment to use `tedana`.
+We provide a Dockerfile for this purpose (check out [tips on using Docker](https://neurohackweek.github.io/docker-for-scientists/)), but you can also set up your environment locally.
+If you choose the latter, the following packages will need to be installed as dependencies:
 
 mdp  
 nilearn  
@@ -38,21 +43,20 @@ numpy
 pybids>=0.4.0  
 scikit-learn  
 
-tedana will eventually be hosted on PyPi. In the mean time, you can still install it with `pip` using:
+`tedana` will eventually be hosted on PyPi. In the interim, you can still install it with `pip` using:
 
 ```
 pip install https://github.com/ME-ICA/tedana/archive/master.tar.gz
 ```
 
-## Development
+## Getting involved
 
-We :yellow_heart: new contributors ! To get started, check out [our contributing guidelines](https://github.com/ME-ICA/tedana/blob/master/CONTRIBUTING.md).
+We :yellow_heart: new contributors !
+To get started, check out [our contributing guidelines](https://github.com/ME-ICA/tedana/blob/master/CONTRIBUTING.md).
 
-Want to learn more about our plans for developing tedana ? Check out [our roadmap](https://github.com/ME-ICA/tedana/projects). Have a question, comment, or suggestion ? Open or comment on one of [our issues](https://github.com/ME-ICA/tedana/issues) !
+Want to learn more about our plans for developing `tedana` ?
+Check out [our roadmap](https://github.com/ME-ICA/tedana/projects).
+Have a question, comment, or suggestion ?
+Open or comment on one of [our issues](https://github.com/ME-ICA/tedana/issues) !
 
-We ask that all contributions to tedana respect our [code of conduct](https://github.com/ME-ICA/tedana/blob/master/Code_of_Conduct.md).
-
-### :earth_americas: Mozilla Global Sprint (10-11 May, 2018) :earth_africa:
-
-This year, tedana will be participating in the [Mozilla Global Sprint](https://foundation.mozilla.org/opportunity/global-sprint/) !
-Check out issues tagged [![mozsprint](https://img.shields.io/badge/-mozsprint-0052cc.svg)](https://github.com/ME-ICA/tedana/labels/mozsprint) for good places to get started during the sprint.
+We ask that all contributions to `tedana` respect our [code of conduct](https://github.com/ME-ICA/tedana/blob/master/Code_of_Conduct.md).

circle.yml

@@ -14,28 +14,27 @@ jobs:
       - checkout
 
       - run:
-          name: download data
+          name: Download input test data
           command: curl -L -o /home/neuro/data/zcat_ffd.nii.gz https://www.dropbox.com/s/ljeqskdmnc6si9d/zcat_ffd.nii.gz?dl=0
 
       - run:
-          name: download and unzip processed data
+          name: Download expected output data
           command: |
             curl -L -o /home/neuro/data/test_TED.tar.gz https://www.dropbox.com/s/x5xhzs3x6p3ukjl/test_TED.tar.gz?dl=0
             tar -xvzf /home/neuro/data/test_TED.tar.gz --no-same-owner -C /home/neuro/data/
 
 
       - run:
-          name: install dependencies
+          name: Create test environment
           command: |
             conda create --name venv python=3.6
             source activate venv
             pip install pytest
             pip install -r requirements.txt
             python setup.py install
 
-      # run tests!
       - run:
-          name: run tests
+          name: Run tests
           command: |
             source activate venv
             py.test ./tedana/tests/test_tedana.py

requirements.txt

@@ -1,10 +1,9 @@
 numpy
 scikit-learn
+scipy
 mdp
 nilearn
 nibabel>=2.1.0
-pybids>=0.4.0
-nipype
 duecredit
 sphinx-argparse
 numpydoc

setup.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 # @Author: oesteban
-""" meica setup script """
+""" tedana setup script """
 
 
 def main():
@@ -16,7 +16,7 @@ def main():
     # For Python 3: use a locals dictionary
     # http://stackoverflow.com/a/1463370/6820620
     ldict = locals()
-    # Get version and release info, which is all stored in meica/info.py
+    # Get version and release info, which is all stored in tedana/info.py
     module_file = op.join(this_path, 'tedana', 'info.py')
     with open(module_file) as infofile:
         pythoncode = [line for line in infofile.readlines() if not

tedana/cli/run_tedana.py

@@ -7,6 +7,9 @@
 
 from tedana import workflows
 
+import logging
+logging.basicConfig(format='[%(levelname)s]: ++ %(message)s', level=logging.INFO)
+
 
 def is_valid_file(parser, arg):
     """
@@ -138,7 +141,7 @@ def get_parser():
                         dest='filecsdata',
                         help='Save component selection data',
                         action='store_true',
-                        default=True)
+                        default=False)
     parser.add_argument('--label',
                         dest='label',
                         type=str,
@@ -149,13 +152,37 @@ def get_parser():
                         type=int,
                         help='Seeded value for ICA, for reproducibility.',
                         default=42)
+    parser.add_argument('--debug',
+                        dest='debug',
+                        help=argparse.SUPPRESS,
+                        action='store_true',
+                        default=False)
+    parser.add_argument('--quiet',
+                        dest='quiet',
+                        help=argparse.SUPPRESS,
+                        action='store_true',
+                        default=False)
     return parser
 
 
 def main(argv=None):
-    """Entry point"""
+    """Tedana entry point"""
+    options = get_parser().parse_args(argv)
+    if options.debug and not options.quiet:
+        logging.getLogger().setLevel(logging.DEBUG)
+    elif options.quiet:
+        logging.getLogger().setLevel(logging.WARNING)
+    workflows.tedana.main(**vars(options))
+
+
+def run_t2smap(argv=None):
+    """T2smap entry point"""
     options = get_parser().parse_args(argv)
-    workflows.tedana(**vars(options))
+    if options.debug and not options.quiet:
+        logging.getLogger().setLevel(logging.DEBUG)
+    elif options.quiet:
+        logging.getLogger().setLevel(logging.WARNING)
+    workflows.t2smap.main(**vars(options))
 
 
 if __name__ == '__main__':

tedana/decomposition/_utils.py

@@ -6,12 +6,8 @@
 import numpy as np
 from scipy import stats
 
-logging.basicConfig(format='[%(levelname)s]: %(message)s', level=logging.INFO)
 LGR = logging.getLogger(__name__)
 
-F_MAX = 500
-Z_MAX = 8
-
 
 def eimask(dd, ees=None):
     """
@@ -34,12 +30,12 @@ def eimask(dd, ees=None):
         ees = range(dd.shape[1])
     imask = np.zeros([dd.shape[0], len(ees)], dtype=bool)
     for ee in ees:
-        LGR.info('++ Creating eimask for echo {}'.format(ee))
+        LGR.debug('Creating eimask for echo {}'.format(ee))
         perc98 = stats.scoreatpercentile(dd[:, ee, :].flatten(), 98,
                                          interpolation_method='lower')
         lthr, hthr = 0.001 * perc98, 5 * perc98
-        LGR.info('++ Eimask threshold boundaries: '
-                 '{:.03f} {:.03f}'.format(lthr, hthr))
+        LGR.debug('Eimask threshold boundaries: '
+                  '{:.03f} {:.03f}'.format(lthr, hthr))
         m = dd[:, ee, :].mean(axis=1)
         imask[np.logical_and(m > lthr, m < hthr), ee] = True