formatting fixes from last commit
@@ -278,7 +278,7 @@ Parameter Scans
~~~~~~~~~~~~~~~
To study the consequences of varying a specific parameter value or initial concentration on a simulation,
iteratively adjust the given parameter over a range of values of interest and re-run the simulation.
-Using the ``show`` parameter and ``te.show()`` we can plot all these simulations on a single figure.
+Using the ``show`` parameter and ``te.show`` we can plot all these simulations on a single figure.
.. code-block:: python
@@ -331,6 +331,8 @@ results in some measure of an output variable are deemed to be sensitive.
def plot_param_uncertainty(model, startVal, name, num_sims):
stdDev = 0.6
+
+ # assumes initial parameter estimate as mean and iterates 60% above and below.
vals = np.linspace((1-stdDev)*startVal, (1+stdDev)*startVal, 100)
for val in vals:
r.resetToOrigin()
@@ -358,3 +360,6 @@ results in some measure of an output variable are deemed to be sensitive.
In the above code, the ``exec`` command is used to set the model parameters to their given value (i.e. ``r.K1 = 1.5``) and
the code sweeps through all the given parameters of interests (names).
+Above, we see that the K3 parameter produces the widest distribution of outcomes, and is thus the most sensitive
+under the given model, taking into account its assumptions and approximate parameter values. On the other hand, variations in K_1, K1, and K_3
+seem to have very little effect on the outcome of the system.
