diff --git a/AssayTools/parser.py b/AssayTools/parser.py
index 4e7af54..9fa0b95 100755
--- a/AssayTools/parser.py
+++ b/AssayTools/parser.py
@@ -83,7 +83,7 @@ def get_data_using_inputs(inputs):
                 except:
                     pass
     
-        for i in range(0,7,2):
+        for i in range(0,len(inputs['ligand_order']*2),2):
             protein_row = ALPHABET[i]
             buffer_row = ALPHABET[i+1]
 
diff --git a/AssayTools/pymcmodels.py b/AssayTools/pymcmodels.py
index a0aa37e..81184eb 100644
--- a/AssayTools/pymcmodels.py
+++ b/AssayTools/pymcmodels.py
@@ -779,8 +779,8 @@ def run_mcmc(pymc_model, nthin=20, nburn=0, niter=20000, map=True, db='ram', dbn
         mcmc.use_step_method(pymc.Metropolis, getattr(pymc_model, 'epsilon_ex'), proposal_sd=10000.0, proposal_distribution='Normal')
     if hasattr(pymc_model, 'epsilon_em'):
         mcmc.use_step_method(pymc.Metropolis, getattr(pymc_model, 'epsilon_em'), proposal_sd=10000.0, proposal_distribution='Normal')
-    # log_F_PL is highly correlated with DeltaG
-    mcmc.use_step_method(pymc.AdaptiveMetropolis, [pymc_model.log_F_PL, pymc_model.DeltaG], scales={ pymc_model.log_F_PL : 0.1, pymc_model.DeltaG : 0.1  })
+    # Uncomment below to use log_F_PL highly correlated with DeltaG to improve sampling somewhat.
+    #mcmc.use_step_method(pymc.AdaptiveMetropolis, [pymc_model.log_F_PL, pymc_model.DeltaG], scales={ pymc_model.log_F_PL : 0.1, pymc_model.DeltaG : 0.1  })
 
     mcmc.sample(iter=(nburn+niter), burn=nburn, thin=nthin, progress_bar=False, tune_throughout=True)
 
diff --git a/examples/direct-fluorescence-assay/README.md b/examples/direct-fluorescence-assay/README.md
index f495acb..2379606 100644
--- a/examples/direct-fluorescence-assay/README.md
+++ b/examples/direct-fluorescence-assay/README.md
@@ -23,11 +23,11 @@ To plot raw data using `xml2png`, navigate into the data folder and run:
 
 To get a deltaG estimate from direct fluorescence data using our Bayesian modeling framework, via the `quickmodel` script run:
 
-  `env PYTHONPATH="./" quickmodel --inputs 'inputs_p38_singlet'`
+  `quickmodel --inputs 'inputs_p38_singlet'`
 
 or
  
-  `env PYTHONPATH="./" quickmodel --inputs 'inputs_Gef_WIP' --type 'spectra'`
+  `quickmodel --inputs 'inputs_Gef_WIP'`
 
 The two inputs.py files here are required to run these, but they can be changed if you want to run other types of analyses (different sections of the xml file, different wavelenghts, etc.).
  
diff --git a/scripts/inputs_example.py b/scripts/inputs_example.py
index 6e1b2c3..01651b4 100644
--- a/scripts/inputs_example.py
+++ b/scripts/inputs_example.py
@@ -10,6 +10,8 @@
     'ligand_order'  :  ['Bosutinib','Bosutinib Isomer','Erlotinib','Gefitinib','Bosutinib','Bosutinib Isomer','Erlotinib','Gefitinib'],
     'Lstated'       :  np.array([20.0e-6,14.0e-6,9.82e-6,6.88e-6,4.82e-6,3.38e-6,2.37e-6,1.66e-6,1.16e-6,0.815e-6,0.571e-6,0.4e-6,0.28e-6,0.196e-6,0.138e-6,0.0964e-6,0.0676e-6,0.0474e-6,0.0320e-6,0.0240e-6,0.0160e-6,0.0120e-6,0.008e-6,0.0], np.float64), # ligand concentration, M
     'Pstated'       :  0.5e-6 * np.ones([24],np.float64), # protein concentration, M
+    'P_error'       :  0.15,
+    'L_error'       :  0.08,
     'assay_volume'  :  50e-6, # assay volume, L
     'well_area'     :  0.1369, # well area, cm^2 for 4ti-0203 [http://4ti.co.uk/files/3113/4217/2464/4ti-0201.pdf]
     }
diff --git a/scripts/quickmodel.py b/scripts/quickmodel.py
index 6c3fc43..7fc3368 100644
--- a/scripts/quickmodel.py
+++ b/scripts/quickmodel.py
@@ -46,8 +46,15 @@ def quick_model(inputs, nsamples=1000, nthin=20):
             #these need to be changed so they are TAKEN FROM INPUTS!!!
 
             # Uncertainties in protein and ligand concentrations.
-            dPstated = 0.35 * inputs['Pstated'] # protein concentration uncertainty
-            dLstated = 0.08 * inputs['Lstated'] # ligand concentraiton uncertainty (due to gravimetric preparation and HP D300 dispensing)
+            try:
+                 dPstated = inputs['P_error'] * inputs['Pstated'] # protein concentration uncertainty               
+            except:
+                 dPstated = 0.35 * inputs['Pstated'] # protein concentration uncertainty
+            
+            try:
+                 dLstated = inputs['L_error'] * inputs['Lstated']
+            except:
+                 dLstated = 0.08 * inputs['Lstated'] # ligand concentraiton uncertainty (due to gravimetric preparation and HP D300 dispensing)
 
             from assaytools import pymcmodels
             pymc_model = pymcmodels.make_model(inputs['Pstated'], dPstated, inputs['Lstated'], dLstated,