diff --git a/analysis.py b/analysis.py
index 5c99f26..4d26a53 100644
--- a/analysis.py
+++ b/analysis.py
@@ -7,8 +7,6 @@ from contextlib import contextmanager
 import os
 import Pk_library as PKL
 import glob
-import pylab as pl
-pl.ion()
 
 @contextmanager
 def suppress_stdout():
@@ -130,30 +128,3 @@ def get_spectra(ini_name, dirname, mcmc_steps, which_field='BORG_final_density',
         all_r[i,:] = Pk.XPk[:,0,0] / np.sqrt(Pk.Pk[:,0,1] * Pk.Pk[:,0,0])
         
     return k, Pk_true, all_pk, all_r
-
-def plot_dens_fields(mean_field, std_field, true_field, slice):
-    """
-    """
-    sides = mean_field.shape[0]
-    x = np.arange(sides)
-    y = x
-    X, Y = np.meshgrid(x, y)
-    fig, axes = pl.subplots(figsize=(16,7), ncols=3) 
-    _, _, _, im0 = axes[0].hist2d(X.ravel(), Y.ravel(), bins=(sides, sides), weights=true_field[slice].ravel())
-    axes[0].set_xlabel('x')
-    axes[0].set_ylabel('y')
-    axes[0].set_title('Generated mock density')
-    pl.colorbar(im0)
-
-    _, _, _, im1 = axes[1].hist2d(X.ravel(), Y.ravel(), bins=(sides, sides), weights=mean_field[slice].ravel())
-    axes[1].set_xlabel('x')
-    axes[1].set_ylabel('y') 
-    axes[1].set_title('Mean reconstructed density')	
-    pl.colorbar(im1)
-
-    _, _, _, im2 = axes[2].hist2d(X.ravel(), Y.ravel(), bins=(sides, sides), weights=std_field[slice].ravel())
-    axes[2].set_xlabel('x')
-    axes[2].set_ylabel('y') 
-    axes[2].set_title('Std of reconstructed density')	
-    pl.colorbar(im2)
-
diff --git a/example0.py b/example0.py
index 756d547..dea70f8 100644
--- a/example0.py
+++ b/example0.py
@@ -238,7 +238,7 @@ class MyLikelihood(borg.likelihood.BaseLikelihood):
 
 
 @borg.registerGravityBuilder
-def build_gravity_model(state: borg.likelihood.MarkovState, box: borg.forward.BoxModel, ini_fname=None) -> borg.forward.BaseForwardModel:
+def build_gravity_model(state: borg.likelihood.MarkovState, box: borg.forward.BoxModel) -> borg.forward.BaseForwardModel:
     """
     Builds the gravity model and returns the forward model chain.
 
@@ -255,17 +255,13 @@ def build_gravity_model(state: borg.likelihood.MarkovState, box: borg.forward.Bo
     global chain
     myprint("Building gravity model")
 
-    if ini_fname is None:
-        ini_fname=borg.getIniConfigurationFilename()
-    config = configparser.ConfigParser()
-    config.read(ini_fname)
     # READ FROM INI FILE
-    which_model = config['gravity']['which_model']
-    ai = float(config['gravity']['ai']) # Initial scale factor
-    af = float(config['gravity']['af']) # Final scale factor
-    supersampling = int(config['gravity']['supersampling'])
-    forcesampling = int(config['gravity']['forcesampling'])
-    nsteps = int(config['gravity']['nsteps']) # Number of steps in the PM solver
+    which_model = 'lpt'
+    ai = 0.05 # Initial scale factor
+    af = 1.0 # Final scale factor
+    supersampling = 2
+    forcesampling = 2
+    nsteps = 20 # Number of steps in the PM solver
 
     chain = borg.forward.ChainForwardModel(box)
 
diff --git a/ini_file.ini b/ini_file.ini
index 1adb910..8259c0a 100644
--- a/ini_file.ini
+++ b/ini_file.ini
@@ -1,9 +1,9 @@
 [system]
 console_output = borg_log
 VERBOSE_LEVEL = 2
-N0 = 64
-N1 = 64
-N2 = 64
+N0 = 32
+N1 = 32
+N2 = 32
 L0 = 500.0
 L1 = 500.0
 L2 = 500.0
@@ -20,7 +20,7 @@ bias_sampler_blocked= true
 ares_heat = 1.0
 
 [mcmc]
-number_to_generate = 50
+number_to_generate = 20
 random_ic = false
 init_random_scaling = 0.1
 
@@ -31,7 +31,7 @@ max_timesteps = 50
 mixing = 1
 
 [python]
-likelihood_path = /home/mosman/borg_examples/example0.py
+likelihood_path = /home/bartlett/borg_examples/example0.py
 
 [run]
 run_type = mock
@@ -53,12 +53,12 @@ beta = 1.5
 z0 = 0
 
 [mock]
-sigma_dens = 1.
+sigma_dens = 1
 
 [gravity]
 which_model = lpt
-ai = 0.05
-af = 1.0
+ai = 0.05 # Initial scale factor
+af = 1.0 # Final scale factor
 supersampling = 2
 forcesampling = 2
-nsteps = 20
+nsteps = 20 # Number of steps in the PM solver
\ No newline at end of file