diff --git a/pymc/examples/disaster_model_arbitrary_determinisitc.py b/pymc/examples/disaster_model_arbitrary_determinisitc.py
new file mode 100644
index 0000000000..ab85cf70c0
--- /dev/null
+++ b/pymc/examples/disaster_model_arbitrary_determinisitc.py
@@ -0,0 +1,66 @@
+"""
+Similar to disaster_model.py, but for arbitrary 
+determinsitics which are not not working with Theano.
+Note that gradient based samplers will not work.
+"""
+
+
+from pymc import *
+import theano.tensor as t
+from numpy import arange, array, ones, concatenate, empty
+from numpy.random import randint
+
+__all__ = ['disasters_data', 'switchpoint', 'early_mean', 'late_mean', 'rate',
+             'disasters']
+
+# Time series of recorded coal mining disasters in the UK from 1851 to 1962
+disasters_data = array([4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,
+                            3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,
+                            2, 2, 3, 4, 2, 1, 3, 2, 2, 1, 1, 1, 1, 3, 0, 0,
+                            1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,
+                            0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,
+                            3, 3, 1, 1, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,
+                            0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1])
+years = len(disasters_data)
+
+#here is the trick
+@theano.compile.ops.as_op(itypes=[t.lscalar, t.dscalar, t.dscalar],otypes=[t.dvector])
+def rateFunc(switchpoint,early_mean, late_mean):
+    ''' Concatenate Poisson means '''
+    out = empty(years)
+    out[:switchpoint] = early_mean
+    out[switchpoint:] = late_mean
+    return out
+
+
+with Model() as model:
+
+    # Prior for distribution of switchpoint location
+    switchpoint = DiscreteUniform('switchpoint', lower=0, upper=years)
+    # Priors for pre- and post-switch mean number of disasters
+    early_mean = Exponential('early_mean', lam=1.)
+    late_mean = Exponential('late_mean', lam=1.)
+
+    # Allocate appropriate Poisson rates to years before and after current
+    # switchpoint location
+    idx = arange(years)
+    #theano style:
+    #rate = switch(switchpoint >= idx, early_mean, late_mean)
+    #non-theano style
+    rate = rateFunc(switchpoint, early_mean, late_mean)
+
+    # Data likelihood
+    disasters = Poisson('disasters', rate, observed=disasters_data)
+
+    # Initial values for stochastic nodes
+    start = {'early_mean': 2., 'late_mean': 3.}
+
+    # Use slice sampler for means
+    step1 = Slice([early_mean, late_mean])
+    # Use Metropolis for switchpoint, since it accomodates discrete variables
+    step2 = Metropolis([switchpoint])
+
+    # njobs>1 works only with most recent (mid August 2014) Thenao version:
+    # https://github.com/Theano/Theano/pull/2021
+    tr = sample(1000, tune=500, start=start, step=[step1, step2],njobs=1)
+    traceplot(tr)
diff --git a/pymc/plots.py b/pymc/plots.py
index 42d9b005a2..fa41d7c3aa 100644
--- a/pymc/plots.py
+++ b/pymc/plots.py
@@ -129,7 +129,7 @@ def kde2plot(x, y, grid=200):
     return f
 
 
-def autocorrplot(trace, vars=None, fontmap=None, max_lag=100):
+def autocorrplot(trace, vars=None, fontmap=None, max_lag=100,burn=0, thin=1):
     """Bar plot of the autocorrelation function for a trace"""
     import matplotlib.pyplot as plt
     if fontmap is None:
@@ -148,7 +148,7 @@ def autocorrplot(trace, vars=None, fontmap=None, max_lag=100):
 
     for i, v in enumerate(vars):
         for j in range(chains):
-            d = np.squeeze(trace.get_values(v, chains=[j]))
+            d = np.squeeze(trace.get_values(v, chains=[j],burn=burn,thin=thin))
 
             ax[i, j].acorr(d, detrend=plt.mlab.detrend_mean, maxlags=max_lag)