From 9fb345d491e710a88ed1a1c8cc155376208aec5e Mon Sep 17 00:00:00 2001
From: Andrea Zanelli <znllandrea@gmail.com>
Date: Wed, 20 May 2020 13:23:39 +0200
Subject: [PATCH] fix typo in README.md

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 531d812..f0e0e15 100644
--- a/README.md
+++ b/README.md
@@ -24,7 +24,7 @@ the Python run-time library.
 
       
 ### performance
-Since prometeo programs transpile to pure C code that calls the high performance linear algebra library BLASFEO ([https://arxiv.org/abs/1704.02457](https://arxiv.org/abs/1704.02457)), execution time can be comparable to hand-written high-performance code. The figure below shows a comparison of the CPU time necessary to carry out a Riccati factorization using highly optimized hand-written C code with calls to BLASFEO and the ones obtained with prometeo transpiled code from [this example](https://github.com/zanellia/prometeo/blob/master/examples/riccati_example/riccati_mass_spring.py). The computation times obtained with NumPy and Julia are added too for comparison - notice however that these last two implementations of the Riccati factorization are not as easily embeddable as the C code generated by prometeo and the hand-coded C implementation. All the benchmarks have been run on a Dell XPS-9360 equipped with an i7-7560U CPU running at 2.30 GHz (to avoid frequency fluctuations due to overheat). 
+Since prometeo programs transpile to pure C code that calls the high performance linear algebra library BLASFEO ([https://arxiv.org/abs/1704.02457](https://arxiv.org/abs/1704.02457)), execution time can be comparable to hand-written high-performance code. The figure below shows a comparison of the CPU time necessary to carry out a Riccati factorization using highly optimized hand-written C code with calls to BLASFEO and the ones obtained with prometeo transpiled code from [this example](https://github.com/zanellia/prometeo/blob/master/examples/riccati_example/riccati_mass_spring.py). The computation times obtained with NumPy and Julia are added too for comparison - notice however that these last two implementations of the Riccati factorization are **not as easily embeddable** as the C code generated by prometeo and the hand-coded C implementation. All the benchmarks have been run on a Dell XPS-9360 equipped with an i7-7560U CPU running at 2.30 GHz (to avoid frequency fluctuations due to overheating). 
 
 <p align="center">
 <img src="https://github.com/zanellia/prometeo/blob/master/benchmarks/riccati_benchmark.png" width="70%" >
@@ -39,8 +39,8 @@ If you want to install prometeo building the sources on your local machine you c
 
 - Run `git submodule update --init` to clone the submodules.
 - Run `make install_shared` from `<prometeo_root>/prometeo/cpmt` to compile and install the shared library associated with the C backend. Notice that the default installation path is `<prometeo_root>/prometeo/cpmt/install`.
-- You need Python 3.7. or later.
-- Optional: to keep things clean you can setup a virtual environment with `virtualenv --python=<path_to_python3.7 <path_to_new_virtualenv>`.
+- You need Python 3.6. or later.
+- Optional: to keep things clean you can setup a virtual environment with `virtualenv --python=<path_to_python3.6> <path_to_new_virtualenv>`.
 - Run `pip install -e .` from `<prometeo_root>` to install the Python package.
 
 Finally, you can run the examples in `<root>/examples` with `pmt <example_name>.py --cgen=<True/False>`, where the `--cgen` flag determines whether the code is executed by the Python interpreter or C code is generated compiled and run.