[ENH] Generate BIDS Derivatives-compatible outputs

.circleci/config.yml

@@ -28,92 +28,125 @@ jobs:
           paths:
               - /opt/conda/envs/tedana_py37
 
-  unittest_35:
+  unittest_36:
     docker:
       - image: continuumio/miniconda3
     working_directory: /tmp/src/tedana
     steps:
       - checkout
       - restore_cache:
-          key: conda-py35-v1-{{ checksum "tedana/info.py" }}
+          key: conda-py36-v1-{{ checksum "tedana/info.py" }}
       - run:
           name: Generate environment
           command: |
             apt-get install -yqq make
-            if [ ! -d /opt/conda/envs/tedana_py35 ]; then
-              conda create -yq -n tedana_py35 python=3.5
-              source activate tedana_py35
+            if [ ! -d /opt/conda/envs/tedana_py36 ]; then
+              conda create -yq -n tedana_py36 python=3.6
+              source activate tedana_py36
               pip install .[tests]
             fi
       - run:
           name: Running unit tests
           command: |
-            source activate tedana_py35
+            source activate tedana_py36
             make unittest
             mkdir /tmp/src/coverage
-            mv /tmp/src/tedana/.coverage /tmp/src/coverage/.coverage.py35
+            mv /tmp/src/tedana/.coverage /tmp/src/coverage/.coverage.py36
       - save_cache:
-          key: conda-py35-v1-{{ checksum "tedana/info.py" }}
+          key: conda-py36-v1-{{ checksum "tedana/info.py" }}
           paths:
-              - /opt/conda/envs/tedana_py35
+              - /opt/conda/envs/tedana_py36
       - persist_to_workspace:
           root: /tmp
           paths:
-              - src/coverage/.coverage.py35
+              - src/coverage/.coverage.py36
 
-  unittest_36:
+  unittest_37:
     docker:
       - image: continuumio/miniconda3
     working_directory: /tmp/src/tedana
     steps:
       - checkout
       - restore_cache:
-          key: conda-py36-v1-{{ checksum "tedana/info.py" }}
+          key: conda-py37-v1-{{ checksum "tedana/info.py" }}
+      - run:
+          name: Running unit tests
+          command: |
+            apt-get install -y make
+            source activate tedana_py37  # depends on makeenv_37
+            make unittest
+            mkdir /tmp/src/coverage
+            mv /tmp/src/tedana/.coverage /tmp/src/coverage/.coverage.py37
+      - persist_to_workspace:
+          root: /tmp
+          paths:
+              - src/coverage/.coverage.py37
+
+  unittest_38:
+    docker:
+      - image: continuumio/miniconda3
+    working_directory: /tmp/src/tedana
+    steps:
+      - checkout
+      - restore_cache:
+          key: conda-py38-v1-{{ checksum "tedana/info.py" }}
       - run:
           name: Generate environment
           command: |
             apt-get install -yqq make
-            if [ ! -d /opt/conda/envs/tedana_py36 ]; then
-              conda create -yq -n tedana_py36 python=3.6
-              source activate tedana_py36
+            if [ ! -d /opt/conda/envs/tedana_py38 ]; then
+              conda create -yq -n tedana_py38 python=3.8
+              source activate tedana_py38
               pip install .[tests]
             fi
       - run:
           name: Running unit tests
           command: |
-            source activate tedana_py36
+            source activate tedana_py38
             make unittest
             mkdir /tmp/src/coverage
-            mv /tmp/src/tedana/.coverage /tmp/src/coverage/.coverage.py36
+            mv /tmp/src/tedana/.coverage /tmp/src/coverage/.coverage.py38
       - save_cache:
-          key: conda-py36-v1-{{ checksum "tedana/info.py" }}
+          key: conda-py38-v1-{{ checksum "tedana/info.py" }}
           paths:
-              - /opt/conda/envs/tedana_py36
+              - /opt/conda/envs/tedana_py38
       - persist_to_workspace:
           root: /tmp
           paths:
-              - src/coverage/.coverage.py36
+              - src/coverage/.coverage.py38
 
-  unittest_37:
+  unittest_39:
     docker:
       - image: continuumio/miniconda3
     working_directory: /tmp/src/tedana
     steps:
       - checkout
       - restore_cache:
-          key: conda-py37-v1-{{ checksum "tedana/info.py" }}
+          key: conda-py39-v1-{{ checksum "tedana/info.py" }}
+      - run:
+          name: Generate environment
+          command: |
+            apt-get install -yqq make
+            if [ ! -d /opt/conda/envs/tedana_py39 ]; then
+              conda create -yq -n tedana_py39 python=3.9
+              source activate tedana_py39
+              pip install .[tests]
+            fi
       - run:
           name: Running unit tests
           command: |
-            apt-get install -y make
-            source activate tedana_py37  # depends on makeenv_37
+            source activate tedana_py39
             make unittest
             mkdir /tmp/src/coverage
-            mv /tmp/src/tedana/.coverage /tmp/src/coverage/.coverage.py37
+            mv /tmp/src/tedana/.coverage /tmp/src/coverage/.coverage.py39
+      - save_cache:
+          key: conda-py39-v1-{{ checksum "tedana/info.py" }}
+          paths:
+              - /opt/conda/envs/tedana_py39
       - persist_to_workspace:
           root: /tmp
           paths:
-              - src/coverage/.coverage.py37
+              - src/coverage/.coverage.py39
 
   style_check:
     docker:
@@ -254,7 +287,6 @@ workflows:
   build_test:
     jobs:
       - makeenv_37
-      - unittest_35
       - unittest_36
       - unittest_37:
           requires:
@@ -274,11 +306,14 @@ workflows:
       - t2smap:
           requires:
             - makeenv_37
+      - unittest_38
+      - unittest_39
       - merge_coverage:
           requires:
-            - unittest_35
             - unittest_36
             - unittest_37
+            - unittest_38
+            - unittest_39
             - three-echo
             - four-echo
             - five-echo

.gitignore

@@ -108,4 +108,7 @@ ENV/
 
 # jupyter notebooks
 .ipynb_checkpoints/
-*.ipynb
+*.ipynb
+
+# vim swap files
+*.swp

README.md

@@ -33,7 +33,7 @@ More information and documentation can be found at https://tedana.readthedocs.io
 ### Use `tedana` with your local Python environment
 
 You'll need to set up a working development environment to use `tedana`.
-To set up a local environment, you will need Python >=3.5 and the following packages will need to be installed:
+To set up a local environment, you will need Python >=3.6 and the following packages will need to be installed:
 
 * [numpy>=1.14](http://www.numpy.org/)
 * [scipy](https://www.scipy.org/)

docs/api.rst

@@ -161,7 +161,6 @@ API
    tedana.io.add_decomp_prefix
    tedana.io.split_ts
    tedana.io.write_split_ts
-   tedana.io.writefeats
    tedana.io.writeresults
    tedana.io.writeresults_echoes
 

docs/approach.rst

@@ -69,7 +69,7 @@ estimate voxel-wise :math:`T_{2}^*` and :math:`S_0`.
 :math:`S_0` corresponds to the total signal in each voxel before decay and can reflect coil sensivity.
 :math:`T_{2}^*` corresponds to the rate at which a voxel decays over time, which
 is related to signal dropout and BOLD sensitivity.
-Estimates of the parameters are saved as **t2sv.nii.gz** and **s0v.nii.gz**.
+Estimates of the parameters are saved as **T2starmap.nii.gz** and **S0map.nii.gz**.
 
 While :math:`T_{2}^*` and :math:`S_0` in fact fluctuate over time, estimating
 them on a volume-by-volume basis with only a small number of echoes is not
@@ -153,7 +153,7 @@ between the distributions for other echoes.
 
 The time series for the optimally combined data also looks like a combination
 of the other echoes (which it is).
-This optimally combined data is written out as **ts_OC.nii.gz**
+This optimally combined data is written out as **desc-optcom_bold.nii.gz**
 
 .. image:: /_static/a10_optimal_combination_timeseries.png
 
@@ -194,7 +194,7 @@ component analysis (PCA).
 The goal of this step is to make it easier for the later ICA decomposition to converge.
 Dimensionality reduction is a common step prior to ICA.
 TEDPCA applies PCA to the optimally combined data in order to decompose it into component maps and
-time series (saved as **mepca_mix.1D**).
+time series (saved as **desc-PCA_mixing.tsv**).
 Here we can see time series for some example components (we don't really care about the maps):
 
 .. image:: /_static/a11_pca_component_timeseries.png
@@ -277,9 +277,9 @@ Next, ``tedana`` applies TE-dependent independent component analysis (ICA) in
 order to identify and remove TE-independent (i.e., non-BOLD noise) components.
 The dimensionally reduced optimally combined data are first subjected to ICA in
 order to fit a mixing matrix to the whitened data.
-This generates a number of independent timeseries (saved as **meica_mix.1D**),
-as well as beta maps which show the spatial loading of these components on the
-brain (**betas_OC.nii.gz**).
+This generates a number of independent timeseries (saved as **desc-ICA_mixing.tsv**),
+as well as parameter estimate maps which show the spatial loading of these components on the
+brain (**desc-ICA_components.nii.gz**).
 
 .. image:: /_static/a13_ica_component_timeseries.png
 
@@ -312,13 +312,13 @@ The blue and red lines show the predicted values for the :math:`S_0` and
 A decision tree is applied to :math:`\kappa`, :math:`\rho`, and other metrics in order to
 classify ICA components as TE-dependent (BOLD signal), TE-independent
 (non-BOLD noise), or neither (to be ignored).
-These classifications are saved in `comp_table_ica.txt`.
+These classifications are saved in **desc-tedana_metrics.tsv**.
 The actual decision tree is dependent on the component selection algorithm employed.
 ``tedana`` includes the option `kundu` (which uses hardcoded thresholds
 applied to each of the metrics).
 
 Components that are classified as noise are projected out of the optimally combined data,
-yielding a denoised timeseries, which is saved as `dn_ts_OC.nii.gz`.
+yielding a denoised timeseries, which is saved as **desc-optcomDenoised_bold.nii.gz**.
 
 .. image:: /_static/a15_denoised_data_timeseries.png
 

docs/installation.rst

@@ -2,7 +2,7 @@ Installation
 ------------
 
 You'll need to set up a working development environment to use ``tedana``.
-To set up a local environment, you will need Python >=3.5 and the following
+To set up a local environment, you will need Python >=3.6 and the following
 packages will need to be installed:
 
 - nilearn