added first draft of logging UT-CHG#155

bet/calculateP/simpleFunP.py

@@ -8,7 +8,7 @@
 from bet.Comm import comm, MPI 
 import numpy as np
 import bet.calculateP.voronoiHistogram as vHist
-import collections
+import collections, logging
 import bet.util as util
 import bet.sample as samp
 
@@ -190,9 +190,9 @@ def normal_normal(data_set, Q_ref, M, std, num_d_emulate=1E6):
     covariance = std**2
 
     d_distr_samples = np.zeros((M, len(Q_ref)))
-    print "d_distr_samples.shape", d_distr_samples.shape
-    print "Q_ref.shape", Q_ref.shape
-    print "std.shape", std.shape
+    logging.info("d_distr_samples.shape", d_distr_samples.shape)
+    logging.info("Q_ref.shape", Q_ref.shape)
+    logging.info("std.shape", std.shape)
 
     if comm.rank == 0:
         for i in range(len(Q_ref)):
@@ -422,14 +422,15 @@ def uniform_hyperrectangle_binsize(data_set, Q_ref, bin_size,
     else:
         if not len(center_pts_per_edge) == dim: 
             center_pts_per_edge = np.ones((dim,))
-            print 'Warning: center_pts_per_edge dimension mismatch.'
-            print 'Using 1 in each dimension.'
+            msg = 'center_pts_per_edge dimension mismatch.'
+            msg += 'Using 1 in each dimension.'
+            logging.warning(msg)
     if np.any(np.less(center_pts_per_edge, 0)):
-        print 'Warning: center_pts_per_edge must be greater than 0'
+        logging.warning('center_pts_per_edge must be greater than 0')
     if not isinstance(bin_size, collections.Iterable):
         bin_size = bin_size*np.ones((dim,))
     if np.any(np.less(bin_size, 0)):
-        print 'Warning: center_pts_per_edge must be greater than 0'
+        logging.warning('center_pts_per_edge must be greater than 0')
 
     sur_domain = np.array([np.min(data, 0), np.max(data, 0)]).transpose()
 

bet/calculateP/voronoiHistogram.py

@@ -7,6 +7,7 @@
 volumes of these cells.
 """
 
+import logging
 import numpy as np
 import bet.util as util
 import bet.sample as samp
@@ -50,7 +51,7 @@ def center_and_layer1_points_binsize(center_pts_per_edge, center, r_size,
     if np.any(np.greater(r_size, rect_width)):
         msg = "The hyperrectangle defined by this size extends outside the "
         msg += "original domain."
-        print msg
+        logging.warning(msg)
 
     # determine the locations of the points for the 1st bounding layer
     layer1_left = rect_domain[:, 0]-rect_width/(2*center_pts_per_edge)
@@ -101,7 +102,7 @@ def center_and_layer1_points(center_pts_per_edge, center, r_ratio, sur_domain):
     if np.all(np.greater(r_ratio, 1)):
         msg = "The hyperrectangle defined by this ratio is larger than the"
         msg += " original domain."
-        print msg
+        logging.warning(msg)
 
     # determine r_size from the width of the surrounding domain
     r_size = r_ratio*(sur_domain[:, 1]-sur_domain[:, 0])
@@ -141,11 +142,11 @@ def edges_regular(center_pts_per_edge, rect_domain, sur_domain):
     if np.any(np.greater_equal(sur_domain[:, 0], rect_domain[:, 0])):
         msg = "The hyperrectangle defined by this size is larger than the"
         msg += " original domain."
-        print msg
+        logging.warning(msg)
     elif np.any(np.less_equal(sur_domain[:, 1], rect_domain[:, 1])):
         msg = "The hyperrectangle defined by this size is larger than the"
         msg += " original domain."
-        print msg
+        logging.warning(msg)
 
     rect_edges = list()
     rect_and_sur_edges = list()

bet/postProcess/postTools.py

@@ -7,6 +7,7 @@
 import numpy as np
 import scipy.io as sio
 import bet.sample as sample
+import logging
 
 class dim_not_matching(Exception):
     """
@@ -380,9 +381,9 @@ def compare_yield(sort_ind, sample_quality, run_param, column_headings=None):
     """
     if column_headings == None:
         column_headings = "Run parameters"
-    print "Sample Set No., Quality, "+ str(column_headings)
+    logging.info("Sample Set No., Quality, "+ str(column_headings))
     for i in reversed(sort_ind):
-        print i, sample_quality[i], np.round(run_param[i], 3)
+        logging.info(i, sample_quality[i], np.round(run_param[i], 3))
 
 def in_high_prob(data, rho_D, maximum, sample_nos=None):
     """
@@ -411,7 +412,7 @@ def in_high_prob(data, rho_D, maximum, sample_nos=None):
     else:
         rD = rho_D(data[sample_nos, :])
     adjusted_total_prob = int(sum(rD)/maximum)
-    print "Samples in box "+str(adjusted_total_prob)
+    logging.info("Samples in box "+str(adjusted_total_prob))
     return adjusted_total_prob
 
 def in_high_prob_multi(results_list, rho_D, maximum, sample_nos_list=None):

bet/sample.py

@@ -8,7 +8,7 @@
     :class:`bet.sample.dim_not_matching`
 """
 
-import os, warnings
+import os, logging
 import numpy as np
 import scipy.spatial as spatial
 import scipy.io as sio
@@ -82,7 +82,7 @@ def load_sample_set(file_name, sample_set_name=None):
     if sample_set_name+"_dim" in mdat.keys():
         loaded_set = sample_set(np.squeeze(mdat[sample_set_name+"_dim"]))
     else:
-        warnings.warn("No sample_set named {} with _dim in file".\
+        logging.info("No sample_set named {} with _dim in file".\
                 format(sample_set_name))
         return None
 
@@ -911,7 +911,7 @@ def __init__(self, input_sample_set, output_sample_set,
         if output_sample_set is not None:
             self.check_nums()
         else:
-            warnings.warn("No output_sample_set")
+            logging.info("No output_sample_set")
 
     def check_nums(self):
         """

bet/sampling/adaptiveSampling.py

@@ -16,7 +16,7 @@
 import scipy.io as sio
 import bet.sampling.basicSampling as bsam
 import bet.util as util
-import math, os, glob
+import math, os, glob, logging
 from bet.Comm import comm 
 import bet.sample as sample
 
@@ -274,7 +274,7 @@ def generalized_chains(self, input_obj, t_set, kern,
         min_ratio = t_set.min_ratio
 
         if not hot_start:
-            print "COLD START"
+            logging.info("COLD START")
             step_ratio = t_set.init_ratio*np.ones(self.num_chains_pproc)
 
             # Initiative first batch of N samples (maybe taken from latin
@@ -302,14 +302,14 @@ def generalized_chains(self, input_obj, t_set, kern,
             # LOAD FILES
             if hot_start == 1: # HOT START FROM PARTIAL RUN
                 if comm.rank == 0:
-                    print "HOT START from partial run"
+                    logging.info("HOT START from partial run")
                 # Find and open save files
                 save_dir = os.path.dirname(savefile)
                 base_name = os.path.dirname(savefile)
                 mdat_files = glob.glob(os.path.join(save_dir,
                         "proc*_{}".format(base_name)))
                 if len(mdat_files) == 0:
-                    print "HOT START using serial file"
+                    logging.info("HOT START using serial file")
                     mdat = sio.loadmat(savefile)
                     disc = sample.load_discretization(savefile)
                     kern_old = np.squeeze(mdat['kern_old'])
@@ -331,7 +331,7 @@ def generalized_chains(self, input_obj, t_set, kern,
                         all_step_ratios = np.reshape(all_step_ratios,
                                  (self.num_chains, -1), 'F')
                 elif hot_start == 1 and len(mdat_files) == comm.size:
-                    print "HOT START using parallel files (same nproc)"
+                    logging.info("HOT START using parallel files (same nproc)")
                     # if the number of processors is the same then set mdat to
                     # be the one with the matching processor number (doesn't
                     # really matter)
@@ -340,7 +340,7 @@ def generalized_chains(self, input_obj, t_set, kern,
                     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:
-                    print "HOT START using parallel files (diff nproc)"
+                    logging.info("HOT START using parallel files (diff nproc)")
                     # Determine how many processors the previous data used
                     # otherwise gather the data from mdat and then scatter
                     # among the processors and update mdat
@@ -387,7 +387,7 @@ def generalized_chains(self, input_obj, t_set, kern,
                     kern_old = np.concatenate(kern_old)
             if hot_start == 2: # HOT START FROM COMPLETED RUN:
                 if comm.rank == 0:
-                    print "HOT START from completed run"
+                    logging.info("HOT START from completed run")
                 mdat = sio.loadmat(savefile)
                 disc = sample.load_discretization(savefile)
                 kern_old = np.squeeze(mdat['kern_old'])
@@ -459,10 +459,10 @@ def generalized_chains(self, input_obj, t_set, kern,
             if self.chain_length < 4:
                 pass
             elif comm.rank == 0 and (batch+1)%(self.chain_length/4) == 0:
-                print "Current chain length: "+\
+                logging.info("Current chain length: "+\
                             str(batch+1)+"/"+str(self.chain_length)
             disc._input_sample_set.append_values_local(input_new.\
-                    get_values_local())
+                    get_values_local()))
             disc._output_sample_set.append_values_local(output_new_values)
             all_step_ratios = np.concatenate((all_step_ratios, step_ratio))
             mdat['step_ratios'] = all_step_ratios

bet/sensitivity/chooseQoIs.py

@@ -283,7 +283,7 @@ def chooseOptQoIs_verbose(input_set, qoiIndices=None, num_qois_return=None,
     if comm.rank == 0:
         qoi_combs = np.array(list(combinations(list(qoiIndices),
                         num_qois_return)))
-        print 'Possible sets of QoIs : ', qoi_combs.shape[0]
+        logging.info('Possible sets of QoIs : ', qoi_combs.shape[0])
         qoi_combs = np.array_split(qoi_combs, comm.size)
     else:
         qoi_combs = None
@@ -396,10 +396,10 @@ def find_unique_vecs(input_set, inner_prod_tol, qoiIndices=None,
     G = G/np.tile(norm_G, (input_dim, 1, 1)).transpose(1, 2, 0)
 
     if comm.rank == 0:
-        print '*** find_unique_vecs ***'
-        print 'num_zerovec : ', len(indz), 'of (', G.shape[1],\
-            ') original QoIs'
-        print 'Possible QoIs : ', len(qoiIndices) - len(indz)
+        logging.info('*** find_unique_vecs ***')
+        logging.info('num_zerovec : ', len(indz), 'of (', G.shape[1],\
+            ') original QoIs')
+        logging.info('Possible QoIs : ', len(qoiIndices) - len(indz))
     qoiIndices = list(set(qoiIndices) - set(indz))
 
     # Find all num_qois choose 2 pairs of QoIs
@@ -423,7 +423,7 @@ def find_unique_vecs(input_set, inner_prod_tol, qoiIndices=None,
 
     unique_vecs = np.array(list(set(qoiIndices) - set(repeat_vec)))
     if comm.rank == 0:
-        print 'Unique QoIs : ', unique_vecs.shape[0]
+        logging.info('Unique QoIs : ', unique_vecs.shape[0])
 
     return unique_vecs
 
@@ -552,10 +552,10 @@ def find_good_sets(input_set, good_sets_prev, unique_indices,
             good_sets_new = np.append(good_sets_new, each[1:], axis=0)
         good_sets = good_sets_new
 
-        print 'Possible sets of QoIs of size %i : '%good_sets.shape[1],\
-            np.sum(count_qois)
-        print 'Good sets of QoIs of size %i : '%good_sets.shape[1],\
-            good_sets.shape[0] - 1
+        logging.info('Possible sets of QoIs of size %i : '%good_sets.shape[1],\
+            np.sum(count_qois))
+        logging.info('Good sets of QoIs of size %i : '%good_sets.shape[1],\
+            good_sets.shape[0] - 1)
 
     comm.Barrier()
     best_sets = comm.bcast(best_sets, root=0)
@@ -664,7 +664,7 @@ def chooseOptQoIs_large_verbose(input_set, qoiIndices=None,
     unique_indices = find_unique_vecs(input_set, inner_prod_tol, qoiIndices,
         remove_zeros)
     if comm.rank == 0:
-        print 'Unique Indices are : ', unique_indices
+        logging.info('Unique Indices are : ', unique_indices)
 
     good_sets_curr = util.fix_dimensions_vector_2darray(unique_indices)
     best_sets = []
@@ -678,6 +678,6 @@ def chooseOptQoIs_large_verbose(input_set, qoiIndices=None,
         best_sets.append(best_sets_curr)
         optsingvals_list.append(optsingvals_tensor_curr)
         if comm.rank == 0:
-            print best_sets_curr
+            logging.info(best_sets_curr)
 
     return (best_sets, optsingvals_list)