From dd9d6c71e61626a642a88909716f8bd3b9a1f87a Mon Sep 17 00:00:00 2001
From: Lindley Graham <lichgraham@gmail.com>
Date: Tue, 17 May 2016 01:58:57 -0400
Subject: [PATCH] fixed all TODO LG

---
 README.md                                   | 11 +++++++++++
 bet/sampling/adaptiveSampling.py            | 10 ++++++----
 test/test_sampling/test_adaptiveSampling.py |  3 ++-
 3 files changed, 19 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 103f2b71..9f8d89dd 100644
--- a/README.md
+++ b/README.md
@@ -20,6 +20,17 @@ You will need to run sphinx-apidoc AND reinstall BET anytime a new module or met
 
 Useful scripts are contained in ``examples/``
 
+Tests
+-----
+
+To run tests in serial call::
+
+    nosetests tests
+
+To run tests in parallel call::
+
+    mpirun -np NPROC nosetets tests
+
 Dependencies
 ------------
 
diff --git a/bet/sampling/adaptiveSampling.py b/bet/sampling/adaptiveSampling.py
index c674c7f2..f916b48c 100644
--- a/bet/sampling/adaptiveSampling.py
+++ b/bet/sampling/adaptiveSampling.py
@@ -229,6 +229,10 @@ def generalized_chains(self, input_obj, t_set, kern,
             hot_start=0): 
         """
         Basic adaptive sampling algorithm using generalized chains.
+
+        .. todo::
+
+            Test HOTSTART from parallel files using different and same num proc
        
         :param string initial_sample_type: type of initial sample random (or r),
             latin hypercube(lhs), or space-filling curve(TBD)
@@ -331,7 +335,7 @@ def generalized_chains(self, input_obj, t_set, kern,
                     # be the one with the matching processor number (doesn't
                     # really matter)
                     mdat = sio.loadmat(mdat_files[comm.rank])
-                    disc = sample.load_discretization(savefile)
+                    disc = sample.load_discretization(mdat_files[comm.rank])
                     kern_old = np.squeeze(mdat['kern_old'])
                     all_step_ratios = np.squeeze(mdat['step_ratios'])
                 elif hot_start == 1 and len(mdat_files) != comm.size:
@@ -388,7 +392,6 @@ def generalized_chains(self, input_obj, t_set, kern,
                 kern_old = np.squeeze(mdat['kern_old'])
                 all_step_ratios = np.squeeze(mdat['step_ratios'])
                 chain_length = disc.check_nums()/self.num_chains
-                #mdat_files = []
                 # reshape if parallel
                 if comm.size > 1:
                     temp_input = np.reshape(disc._input_sample_set.\
@@ -397,7 +400,6 @@ def generalized_chains(self, input_obj, t_set, kern,
                     temp_output = np.reshape(disc._output_sample_set.\
                                 get_values(), (self.num_chains, chain_length,
                                     -1), 'F')
-
                     all_step_ratios = np.reshape(all_step_ratios,
                             (self.num_chains, chain_length), 'F')
             # SPLIT DATA IF NECESSARY
@@ -427,7 +429,7 @@ def generalized_chains(self, input_obj, t_set, kern,
                     get_values_local()[-self.num_chains_pproc:, :])
 
             # Determine how many batches have been run
-            start_ind = disc.check_nums()/self.num_chains_pproc
+            start_ind = disc._input_sample_set.get_values_local().shape[0]/self.num_chains_pproc
         
         mdat = dict()
         self.update_mdict(mdat)
diff --git a/test/test_sampling/test_adaptiveSampling.py b/test/test_sampling/test_adaptiveSampling.py
index ad68107c..66679b18 100644
--- a/test/test_sampling/test_adaptiveSampling.py
+++ b/test/test_sampling/test_adaptiveSampling.py
@@ -154,6 +154,7 @@ def ifun(outputs):
     assert np.all(all_step_ratios <= t_set.max_ratio)
     
     # did the savefiles get created? (proper number, contain proper keys)
+    comm.barrier()
     mdat = dict()
     if comm.rank == 0:
         mdat = sio.loadmat(savefile)
@@ -229,6 +230,7 @@ def map_10t4(x):
 
 
     def tearDown(self):
+        comm.barrier()
         for f in self.savefiles:
             if comm.rank == 0 and os.path.exists(f+".mat"):
                 os.remove(f+".mat")
@@ -424,7 +426,6 @@ def ifun(outputs):
             assert asr > t_set.min_ratio
             assert asr < t_set.max_ratio
 
-    #TODO: LG Fix
     def test_generalized_chains(self):
         """
         Test :met:`bet.sampling.adaptiveSampling.sampler.generalized_chains`