Minor changes to the mcdriver.py example.

docs/examples/kernel/mcdriver.py

@@ -1,70 +1,71 b''
 #!/usr/bin/env python
 # encoding: utf-8
 """Run a Monte-Carlo options pricer in parallel."""
 
 from IPython.kernel import client
 import numpy as N
 from mcpricer import MCOptionPricer
 
 
 tc = client.TaskClient()
 rc = client.MultiEngineClient()
 
 # Initialize the common code on the engines
 rc.run('mcpricer.py')
 
-# Push the variables that won't change (stock print, interest rate, days and MC paths)
+# Push the variables that won't change 
+#(stock print, interest rate, days and MC paths)
 rc.push(dict(S=100.0, r=0.05, days=260, paths=10000))
 
 task_string = """\
 op = MCOptionPricer(S,K,sigma,r,days,paths)
 op.run()
 vp, ap, vc, ac = op.vanilla_put, op.asian_put, op.vanilla_call, op.asian_call
 """
 
 # Create arrays of strike prices and volatilities
-K_vals = N.arange(90.0,110.0,2.0)
-sigma_vals = N.arange(0.02, 0.3, 0.02)
+K_vals = N.linspace(90.0,100.0,5)
+sigma_vals = N.linspace(0.0, 0.2,5)
 
 # Submit tasks
 taskids = []
 for K in K_vals:
     for sigma in sigma_vals:
         t = client.Task(task_string, 
             push=dict(sigma=sigma,K=K),
             pull=('vp','ap','vc','ac','sigma','K'))
         taskids.append(tc.run(t))
 
 print "Submitted tasks: ", taskids
 
 # Block until tasks are completed
 tc.barrier(taskids)
 
 # Get the results
 results = [tc.get_task_result(tid) for tid in taskids]
 
 # Assemble the result
 vc = N.empty(K_vals.shape[0]*sigma_vals.shape[0],dtype='float64')
 vp = N.empty(K_vals.shape[0]*sigma_vals.shape[0],dtype='float64')
 ac = N.empty(K_vals.shape[0]*sigma_vals.shape[0],dtype='float64')
 ap = N.empty(K_vals.shape[0]*sigma_vals.shape[0],dtype='float64')
 for i, tr in enumerate(results):
     ns = tr.ns
     vc[i] = ns.vc
     vp[i] = ns.vp
     ac[i] = ns.ac
     ap[i] = ns.ap
 vc.shape = (K_vals.shape[0],sigma_vals.shape[0])
 vp.shape = (K_vals.shape[0],sigma_vals.shape[0])
 ac.shape = (K_vals.shape[0],sigma_vals.shape[0])
 ap.shape = (K_vals.shape[0],sigma_vals.shape[0])
 
 
 def plot_options(K_vals, sigma_vals, prices):
     """Make a contour plot of the option prices."""
     import pylab
     pylab.contourf(sigma_vals, K_vals, prices)
     pylab.colorbar()
     pylab.title("Option Price")
     pylab.xlabel("Volatility")
     pylab.ylabel("Strike Price")