diff --git a/.gitignore b/.gitignore
index b9b7fa8..c3dbfe0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,137 @@
 Pendings/
 *.m~
-__pycache__
-*.pyc
 *.txt
 
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+*.DS_STORE
+
+.idea/
\ No newline at end of file
diff --git a/Python/Demo/DSE/VarTS/DSE_Avar_ts.txt b/Python/Demo/DSE/VarTS/DSE_Avar_ts.txt
deleted file mode 100644
index 5a79f57..0000000
--- a/Python/Demo/DSE/VarTS/DSE_Avar_ts.txt
+++ /dev/null
@@ -1,1200 +0,0 @@
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diff --git a/Python/Demo/DSE/VarTS/DSE_Dvar_ts.txt b/Python/Demo/DSE/VarTS/DSE_Dvar_ts.txt
deleted file mode 100644
index 24f5eeb..0000000
--- a/Python/Demo/DSE/VarTS/DSE_Dvar_ts.txt
+++ /dev/null
@@ -1,1199 +0,0 @@
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diff --git a/Python/Demo/DSE/VarTS/DSE_Evar_ts.txt b/Python/Demo/DSE/VarTS/DSE_Evar_ts.txt
deleted file mode 100644
index dba8ad3..0000000
--- a/Python/Demo/DSE/VarTS/DSE_Evar_ts.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-   6.34352
-   5.58454
diff --git a/Python/Demo/DSE/VarTS/DSE_Percents.txt b/Python/Demo/DSE/VarTS/DSE_Percents.txt
deleted file mode 100644
index 902b162..0000000
--- a/Python/Demo/DSE/VarTS/DSE_Percents.txt
+++ /dev/null
@@ -1,8 +0,0 @@
- 100.00000
-  44.40298
-  55.50918
-   0.08784
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-   0.03177
-   0.73309
-   0.00208
diff --git a/Python/Demo/DSE/VarTS/DSE_Relatives.txt b/Python/Demo/DSE/VarTS/DSE_Relatives.txt
deleted file mode 100644
index 95dba82..0000000
--- a/Python/Demo/DSE/VarTS/DSE_Relatives.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-   1.00000
-   0.88880
-   1.11111
-   1.05405
-1768.45644
- 146.62406
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diff --git a/Python/Demo/DSE/VarTS/DSE_RootMeanOfSquares.txt b/Python/Demo/DSE/VarTS/DSE_RootMeanOfSquares.txt
deleted file mode 100644
index 80eff9b..0000000
--- a/Python/Demo/DSE/VarTS/DSE_RootMeanOfSquares.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-  11.31640
-   5.02482
-   6.28164
-   0.00994
-   0.08679
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-   0.08296
-   0.00023
diff --git a/Python/Demo/DSE/VarTS/DSE_SumOfSquares.txt b/Python/Demo/DSE/VarTS/DSE_SumOfSquares.txt
deleted file mode 100644
index 6797470..0000000
--- a/Python/Demo/DSE/VarTS/DSE_SumOfSquares.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-3131325697.77786
-1390402023.84503
-1738173195.64653
-2750478.28630
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-65005.53765
diff --git a/Python/Demo/DSE/VarTS/DSE_Svar_ts.txt b/Python/Demo/DSE/VarTS/DSE_Svar_ts.txt
deleted file mode 100644
index 44ea44c..0000000
--- a/Python/Demo/DSE/VarTS/DSE_Svar_ts.txt
+++ /dev/null
@@ -1,1199 +0,0 @@
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diff --git a/Python/Demo/MatlabTest/DSE/VarTS/DSE_Avar_ts.txt b/Python/Demo/MatlabTest/DSE/VarTS/DSE_Avar_ts.txt
deleted file mode 100644
index 5a79f57..0000000
--- a/Python/Demo/MatlabTest/DSE/VarTS/DSE_Avar_ts.txt
+++ /dev/null
@@ -1,1200 +0,0 @@
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diff --git a/Python/Demo/MatlabTest/DSE/VarTS/DSE_Dvar_ts.txt b/Python/Demo/MatlabTest/DSE/VarTS/DSE_Dvar_ts.txt
deleted file mode 100644
index 24f5eeb..0000000
--- a/Python/Demo/MatlabTest/DSE/VarTS/DSE_Dvar_ts.txt
+++ /dev/null
@@ -1,1199 +0,0 @@
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diff --git a/Python/Demo/MatlabTest/DSE/VarTS/DSE_Evar_ts.txt b/Python/Demo/MatlabTest/DSE/VarTS/DSE_Evar_ts.txt
deleted file mode 100644
index dba8ad3..0000000
--- a/Python/Demo/MatlabTest/DSE/VarTS/DSE_Evar_ts.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-   6.34352
-   5.58454
diff --git a/Python/Demo/MatlabTest/DSE/VarTS/DSE_Percents.txt b/Python/Demo/MatlabTest/DSE/VarTS/DSE_Percents.txt
deleted file mode 100644
index 902b162..0000000
--- a/Python/Demo/MatlabTest/DSE/VarTS/DSE_Percents.txt
+++ /dev/null
@@ -1,8 +0,0 @@
- 100.00000
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diff --git a/Python/Demo/MatlabTest/DSE/VarTS/DSE_Relatives.txt b/Python/Demo/MatlabTest/DSE/VarTS/DSE_Relatives.txt
deleted file mode 100644
index 95dba82..0000000
--- a/Python/Demo/MatlabTest/DSE/VarTS/DSE_Relatives.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-   1.00000
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- 146.62406
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diff --git a/Python/Demo/MatlabTest/DSE/VarTS/DSE_RootMeanOfSquares.txt b/Python/Demo/MatlabTest/DSE/VarTS/DSE_RootMeanOfSquares.txt
deleted file mode 100644
index 80eff9b..0000000
--- a/Python/Demo/MatlabTest/DSE/VarTS/DSE_RootMeanOfSquares.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-  11.31640
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diff --git a/Python/Demo/MatlabTest/DSE/VarTS/DSE_SumOfSquares.txt b/Python/Demo/MatlabTest/DSE/VarTS/DSE_SumOfSquares.txt
deleted file mode 100644
index 6797470..0000000
--- a/Python/Demo/MatlabTest/DSE/VarTS/DSE_SumOfSquares.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-3131325697.77786
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diff --git a/Python/Demo/MatlabTest/DSE/VarTS/DSE_Svar_ts.txt b/Python/Demo/MatlabTest/DSE/VarTS/DSE_Svar_ts.txt
deleted file mode 100644
index 44ea44c..0000000
--- a/Python/Demo/MatlabTest/DSE/VarTS/DSE_Svar_ts.txt
+++ /dev/null
@@ -1,1199 +0,0 @@
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diff --git a/README.md b/README.md
index 0859b12..7de3be1 100644
--- a/README.md
+++ b/README.md
@@ -1,159 +1 @@
-# Introduction
-[![DOI](https://zenodo.org/badge/75120155.svg)](https://zenodo.org/badge/latestdoi/75120155)
-
-Series of scripts, mainly in MATLAB and BASH, to support DVARS inference and DSE variance decomposition proposed in
-
- __Afyouni S. & Nichols T.E, *Insight and Inference for DVARS*, 2017__
-http://www.biorxiv.org/content/early/2017/04/06/125021.1
-
-The toolbox can be used to:
-
-* generate DVARS and five standardised variants of the measure.
-* generate p-values for spikes to facilitate the decision whether a spike is corrupted and should be scrubbed.
-* explain variance of 4D fMRI images via three variance components: fast (D-var), slow (S-var) and edge (E-var).
-* explain what share of the whole variance each component occupies.
-
-All materials presented in Afyouni&Nichols (2017) can be reproduced using codes presented in [DVARS_Paper17](https://github.com/asoroosh/DVARS_Paper17).
-
-# Configuration
-
-## Dependencies
-* `MATLAB2013b` (or later) statistical toolbox [required].
-* `'/Nifti_Util'` for neuroimaging analysis. This folder contains selective function from  [optional].
-* `FSL 5.0.9` (or later) to produce DSE variance images [optional].
-
-### Octave <a name="octave"></a>
-Both DVARS inference and DSE decomposition methods are also available for Octave via scripts under `Octave/`. Note that you require statistics package to run the script in Octave:
-```
-pkg install -forge io
-pkg install -forge statistics
-```
-We have only tested the script on Octave 4.4.1.
-
-# fMRI Diagnostics
-Here we show the proposed diagnostic figure and table can be used to examine the quality of the fMRI datasets. We start with basic example, on minimally pre-processed HCP subject 115320, which can be used to perform _DVARS inference_, _DSE variance decomposition_ and eventually _fMRI diagnostic_.
-
-Firstly, load an fMRI image into your Matlab using `Nifti_Util` tool (or any other external toolbox) available.
-
-```
-Path_to_Nifti='~/your/nifti/file/118730/rfMRI_REST1_LR.nii.gz';
-V1 = load_untouch_nii(Path_to_Nifti);
-V2 = V1.img;
-X0 = size(V2,1); Y0 = size(V2,2); Z0 = size(V2,3); T0 = size(V2,4);
-I0 = prod([X0,Y0,Z0]);
-Y  = reshape(V2,[I0,T0]); clear V2 V1;
-```
-
-## DVARS Inference
-`DVARSCalc.m` allows you to estimate the DVARS-related statistics (e.g. D-var, p-values for spikes and standardised variants of the fast variance including Δ%D-var for practical significance).
-
-```
-[DVARS,DVARS_Stat]=DVARSCalc(Y,'scale',1/100,'TransPower',1/3,'RDVARS','verbose',1);
-```
-For detailed description of each input argument, type `help DVARSCalc`. DVARSCalc prints out a log as
-
-```
--Input is a Matrix.
--Extra-cranial areas removed: 224998x1200
--Intensity Scaled by 0.01.
--Data centred. Untouched Grand Mean: 9999.2939, Post-norm Grand Mean: 99.9927
--Robust estimate of autocorrelation...
---voxel: 100000
---voxel: 200000
---AC robust estimate was failed on 1 voxels.
--Settings:
---Test Method:          X2
---ExpVal method:        median
---VarEst method:        hIQRd
---Power Transformation: 0.33333
-
-Settings: TestMethod=X2  I=224998  T=1200
-----Expected Values----------------------------------
-    sig2bar    sig2median    median    sigbar2     xbar
-    _______    __________    ______    _______    ______
-
-    26.577     19.723        26.488    23.095     27.028
-
-----Variances----------------------------------------
-      S2       IQRd      hIQRd
-    ______    ______    _______
-
-    6.1229    1.1031    0.84496
-```
-which includes Variance and Expected Values tables shows the results of different methods of estimating the first two moments discussed in Afyouni & Nichols (2017). For mass analysis you can turn off the log by `...,verbose,0,...`.
-
-`DVARS_Stat` is a structure containing test statistics and standardised D-var measures. For example, use `DVARS_Stat.RDVARS` for Relative DVARS or `Stat.DeltapDvar` for Δ%D-var.
-
-
-## DSE Variance Decomposition
-Using `DSEvars.m` decomposes the image variance (A-var) into three components; fast (D), slow (S) and edge (E) variance.
- ```
-[V,DSE_Stat]=DSEvars(Y,'scale',1/100);
- ```
-For detailed description of each input argument, type `help DSEvars`. DSEvars prints out a log as:
- ```
- -Input is a Matrix.
- -Extra-cranial areas removed: 224998x1200
- -Intensity Scaled by 0.01.
- -Data centred. Untouched Grand Mean: 9999.2939, Post-norm Grand Mean: 99.9927, Post demean: 1.9908e-07
- -Variance images will NOT be saved:
- -- Either destination directory was not set OR the input is not a nifti.
- ----------------------
- Sum-of-Mean-Squared (SMS) Table
-                   Avar     Dvar    Svar     Evar
-                   _____    ____    _____    ____
-
-     Whole         30188    8101    22049    37  
-     Global          206       4      199     1  
-     non-Global    29981    8096    21849    35  
-
- ------------DSE ANOVA Table
-                  MS          RMS       Percentage_of_whole    Relative_to_iid
-               _________    ________    ___________________    _______________
-
-     Avar         25.157      5.0157          100                    1        
-     Dvar         6.7514      2.5984       26.837              0.53719        
-     Svar         18.374      4.2865       73.039                1.462        
-     Evar       0.031147     0.17648      0.12381               1.4857        
-     g_Avar      0.17212     0.41487      0.68418               1539.4        
-     g_Dvar     0.003973    0.063032     0.015793               71.126        
-     g_Svar       0.1665     0.40805      0.66185               2980.8        
-     g_Evar    0.0016433    0.040538    0.0065323                17637        
-
- ----------------------
- ```
- Which includes the DSE ANOVA table. Similarly, log can be turned off by `...,verbose,0,...`.
-
- `V` is a structure containing all the variance components and `DSE_Stat` is a structure containing columns of the ANOVA table as well as Δ%D-var.  
-
-## Visualisation
-We suggest to summarise all the results via a simple visualisation of the DSE variance components and significant DVARS data-points
-as proposed in paper (see Fig. 3, Fig. 4 and Fig. 5). Using `V` and `DVARS_Stat`, this can easily be done as below:
-
-```
-fMRIDiag_plot(V,DVARS_Stat)
-```
-
-Although by passing more input arguments, a richer diagnostic figure can be produced. For example, to add the BOLD intensity image, you can use
-
-```
-fMRIDiag_plot(V,DVARS_Stat,'BOLD',Y)
-```
-
-and also, the displacement information, such as Frame-wise Displacement (FD), can be added to the this figure.
-
-```
-%--Movement Parameters--------------------------------
-%replace this path with the path to the text files with movement regressors
-%of the image on your machine
-%Note that MovPartextImport only works safe with the HCP files, you have to
-%insert the movement regressors mannually (just drag the text file into the
-%workspace!) to the Matlab.
-
-MovPar=MovPartextImport(['~/115320/MNINonLinear/Results/rfMRI_REST1_LR/Movement_Regressors.txt']);
-[FDts,FD_Stat]=FDCalc(MovPar);
-%--------------------------------
-
-fMRIDiag_plot(V,DVARS_Stat,'BOLD',Y,'FD',FDts,'AbsMov',[FD_Stat.AbsRot FD_Stat.AbsTrans])
-```
-![alt tag](fMRIDiag_115320.png)
+# The Python README!
\ No newline at end of file
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+include LICENSE
+
+prune matlab
\ No newline at end of file
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diff --git a/matlab/README.md b/matlab/README.md
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+# Introduction
+[![DOI](https://zenodo.org/badge/75120155.svg)](https://zenodo.org/badge/latestdoi/75120155)
+
+Series of scripts, mainly in MATLAB and BASH, to support DVARS inference and DSE variance decomposition proposed in
+
+ __Afyouni S. & Nichols T.E, *Insight and Inference for DVARS*, 2017__
+http://www.biorxiv.org/content/early/2017/04/06/125021.1
+
+The toolbox can be used to:
+
+* generate DVARS and five standardised variants of the measure.
+* generate p-values for spikes to facilitate the decision whether a spike is corrupted and should be scrubbed.
+* explain variance of 4D fMRI images via three variance components: fast (D-var), slow (S-var) and edge (E-var).
+* explain what share of the whole variance each component occupies.
+
+All materials presented in Afyouni&Nichols (2017) can be reproduced using codes presented in [DVARS_Paper17](https://github.com/asoroosh/DVARS_Paper17).
+
+# Configuration
+
+## Dependencies
+* `MATLAB2013b` (or later) statistical toolbox [required].
+* `'/Nifti_Util'` for neuroimaging analysis. This folder contains selective function from  [optional].
+* `FSL 5.0.9` (or later) to produce DSE variance images [optional].
+
+### Octave <a name="octave"></a>
+Both DVARS inference and DSE decomposition methods are also available for Octave via scripts under `Octave/`. Note that you require statistics package to run the script in Octave:
+```
+pkg install -forge io
+pkg install -forge statistics
+```
+We have only tested the script on Octave 4.4.1.
+
+# fMRI Diagnostics
+Here we show the proposed diagnostic figure and table can be used to examine the quality of the fMRI datasets. We start with basic example, on minimally pre-processed HCP subject 115320, which can be used to perform _DVARS inference_, _DSE variance decomposition_ and eventually _fMRI diagnostic_.
+
+Firstly, load an fMRI image into your Matlab using `Nifti_Util` tool (or any other external toolbox) available.
+
+```
+Path_to_Nifti='~/your/nifti/file/118730/rfMRI_REST1_LR.nii.gz';
+V1 = load_untouch_nii(Path_to_Nifti);
+V2 = V1.img;
+X0 = size(V2,1); Y0 = size(V2,2); Z0 = size(V2,3); T0 = size(V2,4);
+I0 = prod([X0,Y0,Z0]);
+Y  = reshape(V2,[I0,T0]); clear V2 V1;
+```
+
+## DVARS Inference
+`DVARSCalc.m` allows you to estimate the DVARS-related statistics (e.g. D-var, p-values for spikes and standardised variants of the fast variance including Δ%D-var for practical significance).
+
+```
+[DVARS,DVARS_Stat]=DVARSCalc(Y,'scale',1/100,'TransPower',1/3,'RDVARS','verbose',1);
+```
+For detailed description of each input argument, type `help DVARSCalc`. DVARSCalc prints out a log as
+
+```
+-Input is a Matrix.
+-Extra-cranial areas removed: 224998x1200
+-Intensity Scaled by 0.01.
+-Data centred. Untouched Grand Mean: 9999.2939, Post-norm Grand Mean: 99.9927
+-Robust estimate of autocorrelation...
+--voxel: 100000
+--voxel: 200000
+--AC robust estimate was failed on 1 voxels.
+-Settings:
+--Test Method:          X2
+--ExpVal method:        median
+--VarEst method:        hIQRd
+--Power Transformation: 0.33333
+
+Settings: TestMethod=X2  I=224998  T=1200
+----Expected Values----------------------------------
+    sig2bar    sig2median    median    sigbar2     xbar
+    _______    __________    ______    _______    ______
+
+    26.577     19.723        26.488    23.095     27.028
+
+----Variances----------------------------------------
+      S2       IQRd      hIQRd
+    ______    ______    _______
+
+    6.1229    1.1031    0.84496
+```
+which includes Variance and Expected Values tables shows the results of different methods of estimating the first two moments discussed in Afyouni & Nichols (2017). For mass analysis you can turn off the log by `...,verbose,0,...`.
+
+`DVARS_Stat` is a structure containing test statistics and standardised D-var measures. For example, use `DVARS_Stat.RDVARS` for Relative DVARS or `Stat.DeltapDvar` for Δ%D-var.
+
+
+## DSE Variance Decomposition
+Using `DSEvars.m` decomposes the image variance (A-var) into three components; fast (D), slow (S) and edge (E) variance.
+ ```
+[V,DSE_Stat]=DSEvars(Y,'scale',1/100);
+ ```
+For detailed description of each input argument, type `help DSEvars`. DSEvars prints out a log as:
+ ```
+ -Input is a Matrix.
+ -Extra-cranial areas removed: 224998x1200
+ -Intensity Scaled by 0.01.
+ -Data centred. Untouched Grand Mean: 9999.2939, Post-norm Grand Mean: 99.9927, Post demean: 1.9908e-07
+ -Variance images will NOT be saved:
+ -- Either destination directory was not set OR the input is not a nifti.
+ ----------------------
+ Sum-of-Mean-Squared (SMS) Table
+                   Avar     Dvar    Svar     Evar
+                   _____    ____    _____    ____
+
+     Whole         30188    8101    22049    37  
+     Global          206       4      199     1  
+     non-Global    29981    8096    21849    35  
+
+ ------------DSE ANOVA Table
+                  MS          RMS       Percentage_of_whole    Relative_to_iid
+               _________    ________    ___________________    _______________
+
+     Avar         25.157      5.0157          100                    1        
+     Dvar         6.7514      2.5984       26.837              0.53719        
+     Svar         18.374      4.2865       73.039                1.462        
+     Evar       0.031147     0.17648      0.12381               1.4857        
+     g_Avar      0.17212     0.41487      0.68418               1539.4        
+     g_Dvar     0.003973    0.063032     0.015793               71.126        
+     g_Svar       0.1665     0.40805      0.66185               2980.8        
+     g_Evar    0.0016433    0.040538    0.0065323                17637        
+
+ ----------------------
+ ```
+ Which includes the DSE ANOVA table. Similarly, log can be turned off by `...,verbose,0,...`.
+
+ `V` is a structure containing all the variance components and `DSE_Stat` is a structure containing columns of the ANOVA table as well as Δ%D-var.  
+
+## Visualisation
+We suggest to summarise all the results via a simple visualisation of the DSE variance components and significant DVARS data-points
+as proposed in paper (see Fig. 3, Fig. 4 and Fig. 5). Using `V` and `DVARS_Stat`, this can easily be done as below:
+
+```
+fMRIDiag_plot(V,DVARS_Stat)
+```
+
+Although by passing more input arguments, a richer diagnostic figure can be produced. For example, to add the BOLD intensity image, you can use
+
+```
+fMRIDiag_plot(V,DVARS_Stat,'BOLD',Y)
+```
+
+and also, the displacement information, such as Frame-wise Displacement (FD), can be added to the this figure.
+
+```
+%--Movement Parameters--------------------------------
+%replace this path with the path to the text files with movement regressors
+%of the image on your machine
+%Note that MovPartextImport only works safe with the HCP files, you have to
+%insert the movement regressors mannually (just drag the text file into the
+%workspace!) to the Matlab.
+
+MovPar=MovPartextImport(['~/115320/MNINonLinear/Results/rfMRI_REST1_LR/Movement_Regressors.txt']);
+[FDts,FD_Stat]=FDCalc(MovPar);
+%--------------------------------
+
+fMRIDiag_plot(V,DVARS_Stat,'BOLD',Y,'FD',FDts,'AbsMov',[FD_Stat.AbsRot FD_Stat.AbsTrans])
+```
+![alt tag](fMRIDiag_115320.png)
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diff --git a/pyproject.toml b/pyproject.toml
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--- /dev/null
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+[project]
+name = "dvars"
+authors = [
+    {name = "Soroosh Afyouni", email = "srafyouni@gmail.com"},
+]
+description = "Three-part variance decomposition of DVARS spatiotemporal rs-fMRI diagnostics yeilding units and p-values."
+readme = "README.md"
+requires-python = ">=3.8.0"
+dynamic = ["version"]
+license = {text = "BSD-3-Clause"}
+
+dependencies = [
+    "numpy",
+    "nibabel",
+    "matplotlib",
+    "scipy",
+]
+
+classifiers = [
+    "Development Status :: 2 - Pre-Alpha",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Operating System :: OS Independent",
+    "License :: OSI Approved :: MIT License",
+]
+
+[project.urls]
+"Bug Tracker" = "https://github.com/asoroosh/DVARS/issues"
+"Documentation" = "https://github.com/asoroosh/DVARS"
+"Source Code" = "https://github.com/asoroosh/DVARS"
+"User support" = "https://github.com/asoroosh/DVARS/issues"
+
+[project.optional-dependencies]
+dev = [
+    "pytest",
+    "pytest-cov",
+    "coverage",
+    "black",
+    "isort",
+    "mypy",
+    "pre-commit",
+    "ruff",
+]
+
+[build-system]
+requires = [
+    "setuptools>=45",
+    "wheel",
+    "setuptools_scm[toml]>=6.2",
+]
+build-backend = "setuptools.build_meta"
+
+[tool.mypy]
+exclude = [
+    "tests/",
+    "matlab/",
+]
+
+[tool.setuptools]
+include-package-data = true
+
+[tool.setuptools.packages.find]
+include = ["dvars*"]
+exclude = ["tests*", "docs*"]
+
+[tool.pytest.ini_options]
+addopts = "--cov=dvars"
+
+[tool.black]
+target-version = ['py38', 'py39', 'py310', 'py311']
+skip-string-normalization = false
+line-length = 120
+
+[tool.ruff]
+line-length = 120
+exclude = ["__init__.py","build",".eggs"]
+select = ["I", "E", "F", "TCH", "TID252"]
+fix = true
+ignore = ["E203","E501","E731","C901","W291","W293","E402","E722"]
+
+[tool.ruff.per-file-ignores]
+"__init__.py" = ["F401"]
+
+[tool.ruff.mccabe]
+max-complexity = 18
+
+[tool.ruff.lint.flake8-tidy-imports]
+ban-relative-imports = "all"
+
+[tool.setuptools_scm]
+
+[tool.check-manifest]
+ignore = [
+  "*.yaml",
+]
+
+[tool.codespell]
+skip = '.git,*.pdf,*.svg'
+#
+# ignore-words-list = ''