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Updated examples to be PEP compliant#307

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Merged
murrayrm merged 12 commits intopython-control:masterfrombilltubbs:bt-tests
Jun 9, 2019
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6 changes: 3 additions & 3 deletionsexamples/bdalg-matlab.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -10,8 +10,8 @@
sys1ss = ss(A1, B1, C1, 0)
sys1tf = ss2tf(sys1ss)

sys2tf = tf([1, 0.5], [1, 5]);
sys2ss = tf2ss(sys2tf);
sys2tf = tf([1, 0.5], [1, 5])
sys2ss = tf2ss(sys2tf)

# Series composition
series1 = sys1ss + sys2ss;
series1 = sys1ss + sys2ss
123 changes: 78 additions & 45 deletionsexamples/bode-and-nyquist-plots.ipynb
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20 changes: 10 additions & 10 deletionsexamples/check-controllability-and-observability.py
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Expand Up@@ -6,25 +6,25 @@

from __future__ import print_function

from scipy import * # Load the scipy functions
import numpy as np # Load the scipy functions
from control.matlab import * # Load the controls systems library

# Parameters defining the system

m = 250.0 # system mass
k = 40.0 # spring constant
b = 60.0 # damping constant
k = 40.0# spring constant
b = 60.0# damping constant

# System matrices
A =matrix([[1, -1, 1.],
[1, -k /m, -b /m],
[1, 1, 1]])
A =np.array([[1, -1, 1.],
[1, -k/m, -b/m],
[1, 1, 1]])

B =matrix([[0],
[1 /m],
[1]])
B =np.array([[0],
[1/m],
[1]])

C =matrix([[1., 0, 1.]])
C =np.array([[1., 0, 1.]])

sys = ss(A, B, C, 0)

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89 changes: 45 additions & 44 deletionsexamples/genswitch.py
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Expand Up@@ -8,75 +8,76 @@
import os

import numpy as np
import matplotlib.pyplot asmpl
import matplotlib.pyplot asplt
from scipy.integrate import odeint
from control import phase_plot, box_grid

# Simple model of a genetic switch
#

# This function implements the basic model of the genetic switch
# Parameters taken from Gardner, Cantor and Collins, Nature, 2000
def genswitch(y, t, mu=4, n=2):
return(mu /(1 + y[1]**n) - y[0], mu /(1 + y[0]**n) - y[1])
returnmu/(1 + y[1]**n) - y[0], mu/(1 + y[0]**n) - y[1]

# Run a simulation from an initial condition
tim1 = np.linspace(0, 10, 100)
sol1 = odeint(genswitch, [1, 5], tim1)

# Extract theequlibirum points
mu = 4; n = 2;# switch parameters
eqpt = np.empty(3);
eqpt[0] = sol1[0,-1]
eqpt[1] = sol1[1,-1]
eqpt[2] = 0;# fzero(@(x) mu/(1+x^2) - x, 2);
# Extract theequilibrium points
mu = 4; n = 2# switch parameters
eqpt = np.empty(3)
eqpt[0] = sol1[0,-1]
eqpt[1] = sol1[1,-1]
eqpt[2] = 0 # fzero(@(x) mu/(1+x^2) - x, 2)

# Run another simulation showing switching behavior
tim2 = np.linspace(11, 25, 100);
sol2 = odeint(genswitch, sol1[-1,:] + [2, -2], tim2)
tim2 = np.linspace(11, 25, 100)
sol2 = odeint(genswitch, sol1[-1,:] + [2, -2], tim2)

# First plot out the curves that define the equilibria
u = np.linspace(0, 4.5, 46)
f = np.divide(mu, (1 + u**n)) # mu /(1 + u^n),elementwise
f = np.divide(mu, (1 + u**n)) # mu/(1 + u^n),element-wise

mpl.figure(1);mpl.clf();
mpl.axis([0, 5, 0, 5]); # box on;
mpl.plot(u, f, '-', f, u, '--') # 'LineWidth', AM_data_linewidth);
mpl.legend(('z1, f(z1)', 'z2, f(z2)')) # legend(lgh, 'boxoff');
mpl.plot([0, 3], [0, 3], 'k-') # 'LineWidth', AM_ref_linewidth);
mpl.plot(eqpt[0], eqpt[1], 'k.', eqpt[1], eqpt[0], 'k.',
eqpt[2], eqpt[2], 'k.') # 'MarkerSize', AM_data_markersize*3);
mpl.xlabel('z1, f(z2)');
mpl.ylabel('z2, f(z1)');
plt.figure(1);plt.clf()
plt.axis([0, 5, 0, 5]) # box on;
plt.plot(u, f, '-', f, u, '--') # 'LineWidth', AM_data_linewidth)
plt.legend(('z1, f(z1)', 'z2, f(z2)')) # legend(lgh, 'boxoff')
plt.plot([0, 3], [0, 3], 'k-') # 'LineWidth', AM_ref_linewidth)
plt.plot(eqpt[0], eqpt[1], 'k.', eqpt[1], eqpt[0], 'k.',
eqpt[2], eqpt[2], 'k.') # 'MarkerSize', AM_data_markersize*3)
plt.xlabel('z1, f(z2)')
plt.ylabel('z2, f(z1)')

# Time traces
mpl.figure(3);mpl.clf();# subplot(221);
mpl.plot(tim1, sol1[:,0], 'b-', tim1, sol1[:,1], 'g--');
# set(pl, 'LineWidth', AM_data_linewidth);
mpl.plot([tim1[-1], tim1[-1]+1],
[sol1[-1,0], sol2[0,1]], 'ko:',
[tim1[-1], tim1[-1]+1], [sol1[-1,1], sol2[0,0]], 'ko:');
# set(pl, 'LineWidth', AM_data_linewidth, 'MarkerSize', AM_data_markersize);
mpl.plot(tim2, sol2[:,0], 'b-', tim2, sol2[:,1], 'g--');
# set(pl, 'LineWidth', AM_data_linewidth);
mpl.axis([0, 25, 0, 5]);
plt.figure(3);plt.clf()# subplot(221)
plt.plot(tim1, sol1[:,0], 'b-', tim1, sol1[:,1], 'g--')
# set(pl, 'LineWidth', AM_data_linewidth)
plt.plot([tim1[-1], tim1[-1] +1],
[sol1[-1,0], sol2[0,1]], 'ko:',
[tim1[-1], tim1[-1] +1], [sol1[-1,1], sol2[0,0]], 'ko:')
# set(pl, 'LineWidth', AM_data_linewidth, 'MarkerSize', AM_data_markersize)
plt.plot(tim2, sol2[:,0], 'b-', tim2, sol2[:,1], 'g--')
# set(pl, 'LineWidth', AM_data_linewidth)
plt.axis([0, 25, 0, 5])

mpl.xlabel('Time {\itt} [scaled]');
mpl.ylabel('Protein concentrations [scaled]');
mpl.legend(('z1 (A)', 'z2 (B)')) # 'Orientation', 'horizontal');
# legend(legh, 'boxoff');
plt.xlabel('Time {\itt} [scaled]')
plt.ylabel('Protein concentrations [scaled]')
plt.legend(('z1 (A)', 'z2 (B)')) # 'Orientation', 'horizontal')
# legend(legh, 'boxoff')

# Phase portrait
mpl.figure(2); mpl.clf(); # subplot(221);
mpl.axis([0, 5, 0, 5]); # set(gca, 'DataAspectRatio', [1, 1, 1]);
phase_plot(genswitch, X0 = box_grid([0, 5, 6], [0, 5, 6]), T = 10,
timepts = [0.2, 0.6, 1.2])
plt.figure(2)
plt.clf() # subplot(221)
plt.axis([0, 5, 0, 5]) # set(gca, 'DataAspectRatio', [1, 1, 1])
phase_plot(genswitch, X0=box_grid([0, 5, 6], [0, 5, 6]), T=10,
timepts=[0.2, 0.6, 1.2])

# Add the stable equilibrium points
mpl.plot(eqpt[0], eqpt[1], 'k.', eqpt[1], eqpt[0], 'k.',
eqpt[2], eqpt[2], 'k.') # 'MarkerSize', AM_data_markersize*3);
plt.plot(eqpt[0], eqpt[1], 'k.', eqpt[1], eqpt[0], 'k.',
eqpt[2], eqpt[2], 'k.') # 'MarkerSize', AM_data_markersize*3)

mpl.xlabel('Protein A [scaled]');
mpl.ylabel('Protein B [scaled]'); # 'Rotation', 90);
plt.xlabel('Protein A [scaled]')
plt.ylabel('Protein B [scaled]') # 'Rotation', 90)

if 'PYCONTROL_TEST_EXAMPLES' not in os.environ:
mpl.show()
plt.show()
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