Readme update

README.md

@@ -2,5 +2,5 @@
 ###### A library to estimate and simulate Stochastic Differential Equations.
 
 This library is a collection of statistical methods to simulate and estimate non-deterministic differential equations. Inference of SDEs is a topic I personally find very interesting but while the tools for their deterministic counterparts are well-developed (and especially well-implemented), stochastic differential equations lack a common tool set in Python.
-
+![Alt text](misc/cirpaths.png?raw=true)
 Currently implemented are strong schemes with different properties and besides the Euler scheme they all have first-order convergence.

examples/simulate_cir.py

@@ -15,24 +15,24 @@ def cir_diffusion(x, c):
     return np.sqrt(x) * c
 
 
-cir_process = SDE(cir_drift, cir_diffusion, timerange=[0,10])
+cir_process = SDE(cir_drift, cir_diffusion, timerange=[0,2])
 
-euler_path = np.zeros([100, 2001])
-platen_path = np.zeros([100, 2001])
+euler_path = np.zeros([10, 2001])
+platen_path = np.zeros([10, 2001])
 
 print("Run time estimation between Euler and Platen discretization of an CIR process.")
 
-parameter = {'a': 2, 'b': 0.5, 'c' : 0.2}
+parameter = {'a': 2, 'b': 2.5, 'c' : 0.2}
 t = time()
-for i in range(100):
+for i in range(10):
     tmp = []
     for path in Euler(cir_process, parameter, steps = 2000):
         tmp.append(path)
     euler_path[i] = tmp
 print("Euler: " + str(time() - t))
 
 t = time()
-for i in range(100):
+for i in range(10):
     tmp = []
     for path in Platen(cir_process, parameter, steps = 2000):
         tmp.append(path)