update fig

README.md

@@ -14,7 +14,7 @@ The `fastgwr` program is dependent on `mpi4py` package and a working MPI impleme
 $ conda install mpi4py
 ```
 
-By installing `mpi4py`, `conda` will also install an MPI implementation based on your computer system (OpenMPI for Mac/Linux; MPICH for Windows). Users may want to check whether the MPI implementation is successfully installed and is on your path by running the `mpiexec` command. Then the `fastgwr` program can be installed from PyPi:
+By installing `mpi4py`, `conda` will also install an MPI implementation based on your computer system (OpenMPI for Mac/Linux; MPICH/MS-MPI for Windows). Users may want to check whether the MPI implementation is successfully installed and is on your path by running the `mpiexec` command. Then the `fastgwr` program can be installed from PyPi:
 
 ```bash
 $ pip install fastgwr

fastgwr/FastGWR.py

@@ -104,16 +104,16 @@ def parse_gwr_args(self):
                 self.minbw = int(parser_arg.minbw)
 
         if self.comm.rank == 0:
-            print("-"*60)
-            print("Starting FastGWR with",self.comm.size,"Processors")
+            print("-"*60,flush=True)
+            print("Starting FastGWR with",self.comm.size,"Processors",flush=True)
             if self.fixed:
-                print("Spatial Kernel: Fixed Gaussian")
+                print("Spatial Kernel: Fixed Gaussian",flush=True)
             else:
-                print("Spatial Kernel: Adaptive Bisquare")
+                print("Spatial Kernel: Adaptive Bisquare",flush=True)
 
-            print("Data Input Path:",self.fname)
-            print("Output Result Path:",self.fout)
-            print("Intercept:",self.constant)
+            print("Data Input Path:",self.fname,flush=True)
+            print("Output Result Path:",self.fout,flush=True)
+            print("Intercept:",self.constant,flush=True)
 
 
     def read_file(self):
@@ -299,7 +299,7 @@ def mpi_gwr_fit(self, y, X, bw, final=False, mgwr=False):
             if self.comm.rank ==0:
                 data = np.vstack(Betas_list)
 
-                print("Fitting GWR Using Bandwidth:",bw)
+                print("Fitting GWR Using Bandwidth:",bw,flush=True)
 
                 RSS = np.sum(data[:,1]**2)
                 TSS = np.sum((y - np.mean(y))**2)
@@ -344,7 +344,7 @@ def mpi_gwr_fit(self, y, X, bw, final=False, mgwr=False):
             trS = sum(trS_list)
             aicc = self.compute_aicc(RSS, trS)
             if not mgwr:
-                print("BW, AICc",bw, aicc)
+                print("BW, AICc",bw, aicc,flush=True)
             return aicc
 
         return
@@ -366,8 +366,8 @@ def fit(self, y=None, X=None, init_mgwr=False, mgwr=False):
         if self.comm.rank ==0:
             self.set_search_range(init_mgwr=init_mgwr,mgwr=mgwr)
             if not mgwr:
-                print("Optimal Bandwidth Searching...")
-                print("Range:",self.minbw,self.maxbw)
+                print("Optimal Bandwidth Searching...",flush=True)
+                print("Range:",self.minbw,self.maxbw,flush=True)
         self.minbw = self.comm.bcast(self.minbw,root=0)
         self.maxbw = self.comm.bcast(self.maxbw,root=0)
 

fastgwr/FastMGWR.py

@@ -68,16 +68,16 @@ def backfitting(self):
         """
 
         if self.comm.rank ==0:
-            print("MGWR Backfitting...")
-            print("Data are standardized")
+            print("MGWR Backfitting...",flush=True)
+            print("Data are standardized",flush=True)
 
         #Initalization
         betas,bw = self.fit(init_mgwr=True,mgwr=True)
 
         self.bw_init = bw
 
         if self.comm.rank ==0:
-            print("Initialization Done...")
+            print("Initialization Done...",flush=True)
         XB = betas*self.X
         err = self.y.reshape(-1) - np.sum(XB,axis=1)
         bws = [None]*self.k
@@ -116,8 +116,8 @@ def backfitting(self):
             XB = newXB
 
             if self.comm.rank ==0:
-                print("Iter:",mgwr_iters,"SOC:","{:.2e}".format(score))
-                print("bws:",bws)
+                print("Iter:",mgwr_iters,"SOC:","{:.2e}".format(score),flush=True)
+                print("bws:",bws,flush=True)
 
             if score < 1e-5:
                 break
@@ -205,7 +205,7 @@ def mgwr_fit(self,n_chunks=2):
         if self.estonly:
             return
         if self.comm.rank ==0:
-            print("Computing Inference with",n_chunks,"Chunk(s)")
+            print("Computing Inference with",n_chunks,"Chunk(s)",flush=True)
         self.n_chunks = self.comm.size * n_chunks
         self.chunks = np.arange(self.comm.rank*n_chunks, (self.comm.rank+1)*n_chunks)
 

fastgwr/__main__.py

@@ -3,7 +3,7 @@
 import fastgwr
 
 @click.group()
-@click.version_option("0.2.8")
+@click.version_option("0.2.9")
 def main():
     pass
 
@@ -86,8 +86,9 @@ def testmgwr():
     """
     Testing MGWR with zillow data
     """
-
     print("Testing MGWR with zillow data:")
+    mpi_path = os.path.dirname(fastgwr.__file__) + '/fastgwr_mpi.py'
+
     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