diff --git a/src/riptide.py b/src/riptide.py
index 7d93ee3..661557a 100644
--- a/src/riptide.py
+++ b/src/riptide.py
@@ -8,7 +8,6 @@
 import pandas
 import warnings
 from random import seed
-from copy import deepcopy
 from datetime import datetime
 
 import cobra
@@ -64,15 +63,16 @@ def save_output(riptide_obj='NULL', path='NULL', file_type='JSON', silent=False)
     if riptide_obj == 'NULL':
         raise ValueError('ERROR: Did not provide a RIPTiDe object')
     
+    curr_wd = str(os.getcwd())
     if path == 'NULL':
         if silent == False:
             print('WARNING: Did not provide an output directory. Using default riptide_files in working directory')
-        path = riptide_obj.model.id + '_' + riptide_obj.run_start
+        path = curr_wd + '/' + riptide_obj.model.id + '_' + riptide_obj.run_start
     else:
         path = path + '_' + riptide_obj.run_start
     try:
         # Recursively build dirs if possible
-        os.mkdirs(path)
+        os.mkdir(path)
         if silent == False: print('Saving results to', path)
     except:
         if silent == False:
@@ -636,7 +636,6 @@ def _screen_tasks(model, tasks, silent):
     
     return screened_tasks
 
-
 # Create context-specific model based on transcript distribution
 def contextualize(model, transcriptome = 'none', samples = 1000, silent = False, prune = True,
     fraction = 0.8, minimum = False, conservative = False, objective = True, additive = False, direct = False,
@@ -810,7 +809,7 @@ def contextualize(model, transcriptome = 'none', samples = 1000, silent = False,
         riptide_model = _prune_model(model, inactive_rxns, conservative)
         riptide_object.pruned = _record_pruned_elements(model, riptide_model)
     else:
-        riptide_model = deepcopy(model)
+        riptide_model = model.copy()
          
     # Find optimal solution space based on transcription and final constraints
     if silent == False:
@@ -1007,7 +1006,7 @@ def _weighted_expression(model, coefficients={}):
 # Determine those reactions that carry flux in a pFBA objective set to a threshold of maximum
 def _constrain_for_pruning(model, min_coefficients, objective, obj_fraction, tasks, task_fraction, minimum_flux, silent):
     
-    constrained_model = deepcopy(model)
+    constrained_model = model.copy()
     
     # Add objective/task constraints
     min_coefficient = min(list(min_coefficients.values()))
@@ -1034,7 +1033,7 @@ def _constrain_for_pruning(model, min_coefficients, objective, obj_fraction, tas
 # Determine those reactions that carry flux in a pFBA objective set to a threshold of maximum
 def _constrain_for_sampling(model, max_coefficients, sampling_depth, objective, obj_frac, tasks, task_frac, min_flux, silent):
     
-    constrained_model = deepcopy(model)
+    constrained_model = model.copy()
     
     # Apply weigths to new expression, allow deviation
     if isinstance(objective, str) == True:
@@ -1048,17 +1047,21 @@ def _constrain_for_sampling(model, max_coefficients, sampling_depth, objective,
     constrained_model.solver.update()
     
     flux_sum = constrained_model.slim_optimize(error_value=0.)
-    flux_sum_constraint = constrained_model.problem.Constraint(sampling_expr, lb=min_flux, ub=flux_sum)
-    model.add_cons_vars(flux_sum_constraint)
-    constrained_model.solver.update()
-    
-    # Analyze flux ranges and calculate concordance
-    try:
-        flux_samples = _gapsplit(constrained_model, depth=sampling_depth)
-        concordance = _calc_concordance(flux_samples, max_coefficients)
-    except:
+    if flux_sum <= min_flux:
         flux_samples = 'Not performed'
         concordance = 'Not performed'
+    else:
+        flux_sum_constraint = constrained_model.problem.Constraint(sampling_expr, lb=min_flux, ub=flux_sum)
+        constrained_model.add_cons_vars(flux_sum_constraint)
+        constrained_model.solver.update()
+        
+        # Analyze flux ranges and calculate concordance
+        try:
+            flux_samples = _gapsplit(constrained_model, depth=sampling_depth)
+            concordance = _calc_concordance(flux_samples, max_coefficients)
+        except:
+            flux_samples = 'Not performed'
+            concordance = 'Not performed'
 
     return flux_samples, concordance
 
@@ -1087,7 +1090,7 @@ def _calc_concordance(flux_samples, coefficient_dict):
 # Prune model based on blocked reactions from minimization as well as user-defined reactions
 def _prune_model(model, rm_rxns, conserve):
     
-    new_model = deepcopy(model)
+    new_model = model.copy()
     
     if str(new_model.id).strip() == '':
         new_model.id = 'model_riptide'