diff --git a/README.md b/README.md
index 50c9f56..c237af3 100644
--- a/README.md
+++ b/README.md
@@ -35,6 +35,7 @@ Some more advanced talks of interested:
   * David Beazley, "Discovering python" https://www.youtube.com/watch?v=RZ4Sn-Y7AP8.
   * Thomas Ballinger, "Finding closure with closures" https://www.youtube.com/watch?v=E9wS6LdXM8Y
 
+
 ## Getting Python
 
 | Name         | Use             |
@@ -81,24 +82,18 @@ General information through talks tthat maybe  useful on PyData (various confere
   * Enthought: https://www.youtube.com/user/EnthoughtMedia
   * Continuum Analytics: https://www.youtube.com/channel/UCND4vKhJssAtK8p1Blfj14Q
 
+
 ## Particle Physics packages
 | Name         | Use             |
 | ------------ | --------------- |
 | [numpythia](https://github.com/scikit-hep/numpythia) | Interface between FastJet and NumPy. |
 | [pyjet](https://github.com/scikit-hep/pyjet) | Interface between PYTHIA and NumPy. |
 
-## Jupyter extensions
-
-Jupyter has a wide ecosystem of extensions that can be used to extend the functionality. Some useful extensions for HEP data analysis are summarised here.
-
-| Name         | Use             |
-| ------------ | --------------- |
-| nbdime       | Simplifies diffing and merging of jupyter notebooks that are stored in version control. |
-| jupytext     | Splits notebooks into a `.ipynb` and `.py` file for easier version control and to allow them to be run as scripts idependently of jupyter. |
 
 ## Tutorials
 
-See tutorials here and other resources collected by [IML HEP-ML Resources](https://github.com/iml-wg/HEP-ML-Resources#tutorials)
+See tutorials here and other resources collected by [IML HEP-ML Resources](https://github.com/iml-wg/HEP-ML-Resources#tutorials).
+
 
 ## ROOT and interoperability with ROOT
 
@@ -106,6 +101,7 @@ For many particle physics experiments, a lot of data is stored within ROOT files
 
 | Package name | Use | Pro | Con | Further information |
 | ------------ | --- | --- | --- | ------------------- |
+| [ostap](https://github.com/OstapHEP/ostap) | User-friendly & more intuitive interface to(Py)ROOT | Many decorations to ROOT classes | Requires C++ code compilation |  |
 | [uproot](https://github.com/scikit-hep/uproot) | Native Python ROOT I/O | Easy to install, fast, no dependence on C++ ROOT | Although can read all ROOT files, can only write ROOT files with specific objects. | |
 | root_numpy, root_pandas | ROOT to/from Numpy and Pandas, like uproot | full ROOT functionality, like TFormula | slower than uproot, binary incompatibilities with different versions of ROOT | |
 | [ROOT conda](https://nlesc.gitbooks.io/cern-root-conda-recipes/content/installing_root_via_anaconda.html) | Using ROOT within Anaconda | Easy to get ROOT installed with PyROOT support | Not all features of ROOT and getting dated (6.04 Py2.7/3.4 since XENON1T uses that) | [Recipes](https://github.com/NLeSC/root-conda-recipes) |
@@ -115,6 +111,16 @@ For many particle physics experiments, a lot of data is stored within ROOT files
 | [pyhf](https://github.com/diana-hep/pyhf) | statistical analysis / fitting | pure python implementation of HistFactory specification with auto-diff enabled backends in tensorflow, pytorch, and MXNet | not yet interoperable with ROOT-based RooFit models | [pyhf](https://github.com/diana-hep/pyhf) |
 
 
+## Jupyter extensions
+
+Jupyter has a wide ecosystem of extensions that can be used to extend the functionality. Some useful extensions for HEP data analysis are summarised here.
+
+| Name         | Use             |
+| ------------ | --------------- |
+| nbdime       | Simplifies diffing and merging of jupyter notebooks that are stored in version control. |
+| jupytext     | Splits notebooks into a `.ipynb` and `.py` file for easier version control and to allow them to be run as scripts idependently of jupyter. |
+
+
 ## Speeding up code
 
 Often, it is not needed anymore to write C++/C routines that get wrapped since there are other ways to speed up your Python code.  Namely:
@@ -126,6 +132,7 @@ Often, it is not needed anymore to write C++/C routines that get wrapped since t
 | [numpy](http://www.numpy.org) | Expressing your code as array options means you get native-C speeds. |
 | NumExpr | single pass "mapper" operations (one input → one output). |
 
+
 ## Binding C/C++ to Python
 
 *Before you read this, realize that this is for existing C++ code.  If you want to write new C/C++ code for speed, see section above.*
@@ -145,6 +152,7 @@ At present, the best summary of how to bind code in HEP applications comes from
 | swig | Wrapping C++ code  | Widely used. | Have to write wrapper file and feels dated. | |
 | Boost | Wrapping C++ code | Widely used.  | Giant dependency since Boost does many other things.| |
 
+
 ## Experimental codes
 
 Stealing code from other physicists is its own sign of flattery.  Codes that are abandoned more than two years will get struck through.: