update readme

README.md

@@ -1,16 +1,16 @@
 # dmipy: Diffusion Microstructure Imaging in Python
 NOTE: PACKAGE NOT FUNCTIONAL YET
 
-The Dmipy software package facilitates state-of-the-art diffusion MRI Microstructure Imaging using a "Building Block" philosophy. In this philosophy, any combination of biophysical models, typically representing intra- and/or extra-axonal tissue compartments, can be easy combined into a multi-compartment Microstructure Model in just a few lines of code. The created model can be used to both simulate and fit data for any PGSE-based dMRI acquisition, including single shell, multi-shell and qtau-dMRI acquisition schemes.
+The dmipy software package facilitates state-of-the-art diffusion MRI Microstructure Imaging using a "Building Block" philosophy. In this philosophy, any combination of biophysical models, typically representing intra- and/or extra-axonal tissue compartments, can be easy combined into a multi-compartment Microstructure Model in just a few lines of code. The created model can be used to both simulate and fit data for any PGSE-based dMRI acquisition, including single shell, multi-shell and qtau-dMRI acquisition schemes.
 
 Model fitting algorithms are also implemented modularly. Currently, any created model can be fitted using either gradient-descent based fitting, or more recent Microstructure Imaging of Crossings (MIX) fitting.
 
-Mipy stands apart from other packages is the complete freedom the user has to design and use a microstructure model. Dmipy allows the user to do Microstructure Imaging research at the highest level, while the package automatically takes care of all the coding architecture that is needed to fit a designed model to a data set.
+Mipy stands apart from other packages is the complete freedom the user has to design and use a microstructure model. dmipy allows the user to do Microstructure Imaging research at the highest level, while the package automatically takes care of all the coding architecture that is needed to fit a designed model to a data set.
 
-*For Dipy users*: Dmipy is completely compatible, complementary and similar in usage to Dipy!
-- Dipy gradient tables can be directly used when setting up Dmipy models.
-- Dipy models can be used to give initial conditions for Dmipy models, e.g. using DTI or CSD to give a starting point for orientations or diffusivities for NODDI or NODDIx.
-- Setting up and fitting Dmipy models is similar as in Dipy models - using fit=model.fit(data) and then exploring the fitted model parameters from the fit object.
+*For Dipy users*: dmipy is completely compatible, complementary and similar in usage to Dipy!
+- Dipy gradient tables can be directly used when setting up dmipy models.
+- Dipy models can be used to give initial conditions for dmipy models, e.g. using DTI or CSD to give a starting point for orientations or diffusivities for NODDI or NODDIx.
+- Setting up and fitting dmipy models is similar as in Dipy models - using fit=model.fit(data) and then exploring the fitted model parameters from the fit object.
 - Fitted models that estimate dispersion/orientations can generate Fiber Orientation Densities (FODs) and peaks for every fitted voxel in the same format as Dipy, which can be directly used for fiber tractography!
 - Fitted models also provide easy functions to study fitting errors in terms of MSE and $R^2$ error.
 
@@ -29,14 +29,14 @@ Recommended to use Anaconda Python distribution.
 - [numba](https://numba.pydata.org/) (optional for faster functions)
 
 ## Getting Started
-To get a feeling for how to use Dmipy, we provide a few tutorial notebooks:
+To get a feeling for how to use dmipy, we provide a few tutorial notebooks:
 - [Setting up an acquisition scheme](https://github.com/AthenaEPI/mipy/blob/master/examples/tutorial_setting_up_acquisition_scheme.ipynb)
 - [Simulating and fitting data using a simple Stick model](https://github.com/AthenaEPI/mipy/blob/master/examples/tutorial_simulating_and_fitting_using_a_simple_model.ipynb)
 - [Combining biophysical models into a Microstructure model](https://github.com/AthenaEPI/mipy/blob/master/examples/tutorial_combining_biophysical_models_into_microstructure_model.ipynb)
 - [Creating a dispersed axon bundle representation and imposing parameter links](https://github.com/AthenaEPI/mipy/blob/master/examples/tutorial_imposing_parameter_links.ipynb)
 - [Parameter Cascading: Using a simple model to initialize a complex one](https://github.com/AthenaEPI/mipy/blob/master/examples/tutorial_parameter_cascading_and_simulating_nd_datasets.ipynb)
 
-## Explanations and Illustrations of Dmipy Contents
+## Explanations and Illustrations of dmipy Contents
 ### Biophysical Models and Distributions
 - [Cylinder models (Axons, e.g. [Assaf et al. 2004])](https://github.com/AthenaEPI/mipy/blob/master/examples/example_cylinder_models.ipynb)
 - [Sphere Models (Tumor cells, e.g. [Panagiotaki et al. 2014])](https://github.com/AthenaEPI/mipy/blob/master/examples/example_sphere_models.ipynb)
@@ -47,8 +47,8 @@ To get a feeling for how to use Dmipy, we provide a few tutorial notebooks:
 ### Global Optimizers
 - Brute Force (Brute2Fine)
 - Stochastic (MIX) [Farooq et al. 2016]
-## Dmipy implementations of Microstructure Models in Literature
-Dmipy uses HCP data to illustrate microstructure model examples. To reproduce these examples, Dmipy provides a direct way to download HCP data (using your own AWS credentials) in the [HCP tutorial](https://github.com/AthenaEPI/mipy/blob/master/examples/tutorial_human_connectome_project_aws.ipynb).
+## dmipy implementations of Microstructure Models in Literature
+dmipy uses HCP data to illustrate microstructure model examples. To reproduce these examples, dmipy provides a direct way to download HCP data (using your own AWS credentials) in the [HCP tutorial](https://github.com/AthenaEPI/mipy/blob/master/examples/tutorial_human_connectome_project_aws.ipynb).
 ### Single Bundle Microstructure Models
 - [Ball and Stick [Behrens et al. 2003]](https://github.com/AthenaEPI/mipy/blob/master/examples/example_ball_and_stick.ipynb)
 - [Ball and Racket [Sotiropoulos et al. 2012]](https://github.com/AthenaEPI/mipy/blob/master/examples/example_ball_and_racket.ipynb)
@@ -62,9 +62,9 @@ Dmipy uses HCP data to illustrate microstructure model examples. To reproduce th
 ### Spherical Mean-Based Microstructure Models
 - [Multi-Compartment Microscopic Diffusion Imaging [Kaden et al. 2016]](https://github.com/AthenaEPI/mipy/blob/master/examples/example_multi_compartment_spherical_mean_technique.ipynb)
 
-## How to contribute to Dmipy
-Dmipy's design is completely modular and can easily be extended with new models, distributions or optimizers. To contribute, view our contribution guidelines[link] and wanted additions[link].
-## How to cite Dmipy
+## How to contribute to dmipy
+dmipy's design is completely modular and can easily be extended with new models, distributions or optimizers. To contribute, view our contribution guidelines[link] and wanted additions[link].
+## How to cite dmipy
 Fick, Rutger. *Advanced dMRI signal modeling for tissue microstructure characterization*. Diss. Université Côte d'Azur; Inria Sophia Antipolis, 2017.
 
 NOTE: PACKAGE NOT FUNCTIONAL YET