diff --git a/.DS_Store b/.DS_Store
index 146f0b7..9dba8f7 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/fastgwr/__main__.py b/fastgwr/__main__.py
index f52959b..4e5840a 100644
--- a/fastgwr/__main__.py
+++ b/fastgwr/__main__.py
@@ -88,8 +88,6 @@ def testmgwr():
     """
     
     print("Testing MGWR with zillow data:")
-    mpi_path = os.path.dirname(fastgwr.__file__) + '/fastgwr_mpi.py'
-    print(mpi_path)
     command = "mpiexec -np 2 python " + mpi_path + " -data https://raw.github.com/Ziqi-Li/FastGWR/master/Zillow-test-dataset/zillow_1k.csv -mgwr -c"
     os.system(command)
     pass
diff --git a/paper/paper.md b/paper/paper.md
index 0671b67..805b7cf 100644
--- a/paper/paper.md
+++ b/paper/paper.md
@@ -33,7 +33,7 @@ As geospatial data are increasingly available from different sources such as rem
 
 There are currently existing packages that allow users to fit GWR and MGWR models. Two most popular open-source options are `mgwr` in python [@oshan2019mgwr] and `GWmodel` in R [@gollini2013gwmodel], both of which provide friendly APIs and are actively maintained. `GWmodel` supports a wide array of geographically weighted models and analysis tools; however, the performance of `GWmodel` is lagged behind and not suitable for large datasets. A comprehensive performance comparison between `GWmodel` and `fastgwr` can be found in @li2019fast and @li2020computational. As for `mgwr`, the parallelism of `fastgwr` has been built into `mgwr` by leveraging the `multiprocessing` package. For small and moderate sized problems, the performance between `mgwr` and `fastgwr` is comparable. Nevertheless, the major advantage of `fastgwr` is that the use of MPI-based parallelism allows the program to run in parallel across multiple computer nodes. In this way, `fastgwr` is the only option if the analyst wants to run the GWR program on a high performance computing cluster, which empowers larger-scale analysis that is impossible for a single workstation. To demostrate this, `fastgwr` has been tested on the University of Arizona's [Ocelote](https://public.confluence.arizona.edu/display/UAHPC/Ocelote+Quick+Start) cluster using the [Zillow datasets](https://github.com/Ziqi-Li/FastGWR/tree/master/Zillow-test-dataset), and the scalability can be seen in \autoref{fig:example}. It is expected that the scalability will further increase with larger datasets as the ratio between computation and data transfer will become larger. Additionally, the model fitting results of `fastgwr` have been validated against `mgwr` which can be found in the [notebooks](https://github.com/Ziqi-Li/FastGWR/tree/master/validation%20notebook) in the attached [Gituhb repository](https://github.com/Ziqi-Li/FastGWR).
 
-![Scalability of `fastgwr`. GWR model is fitted with 20,000 observations and MGWR model is fitted with 5,000 observations. \label{fig:example}](scalability.png){ width=50%}
+![Scalability of `fastgwr`. GWR model is fitted with 20,000 observations and MGWR model is fitted with 10,000 observations. \label{fig:example}](scalability.png){ width=50%}
 
 
 # Installation
diff --git a/requirements.txt b/requirements.txt
index a95e1cf..ff540fa 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,5 @@
-mpi4py==3.0.3
-numpy==1.19.2
+mpi4py
+scipy
+numpy
 click
 twine