# # Zero-Order Hold and Interpolation
#
# Implementation of control systems generally requires an interface between the sampled data computations of process control and the continuous time dynamics of the real world. A zero-order hold is the most common model for the reconstruction of a continuous time signal from sampled data.
# ## Interpolation Function
# In[1]:
import numpy as np
def interp0(x, xp, yp):
"""Zeroth order hold interpolation w/ same
(base) signature as numpy.interp."""
def func(x0):
if x0 <= xp[0]:
return yp[0]
if x0 >= xp[-1]:
return yp[-1]
k = 0
while x0 > xp[k]:
k += 1
return yp[k-1]
if isinstance(x,float):
return func(x)
elif isinstance(x, list):
return [func(x) for x in x]
elif isinstance(x, np.ndarray):
return np.asarray([func(x) for x in x])
else:
raise TypeError('argument must be float, list, or ndarray')
# ## Demonstration
# In[2]:
get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt
import numpy as np
# choose a function
f = np.sin
# sampled signal
xp = np.linspace(0,10,20)
yp = f(xp)
# interpolation grid with 'true' function
x = np.linspace(0,12,1000)
plt.plot(x,f(x),'--')
# plot
plt.hold(True)
plt.scatter(xp,yp)
plt.plot(x,interp0(x,xp,yp),'r')
plt.xlim([x.min(),x.max()])
plt.title('Zero Order Hold/Interpolation')
plt.xlabel('Time')
plt.ylabel('Value')
plt.legend(['Signal','ZOH'])
# In[ ]:
#
# < [Predictive Control](http://nbviewer.jupyter.org/github/jckantor/CBE30338/blob/master/notebooks/08.00-Predictive-Control.ipynb) | [Contents](toc.ipynb) | [Discrete Events](http://nbviewer.jupyter.org/github/jckantor/CBE30338/blob/master/notebooks/09.00-Discrete-Events.ipynb) >