python控制系统仿真库control（一）伯德图
1、安装
我使用的是pycharm，因此直接搜索control安装即可。注意，最好已经事先安装了scipy,numpy以及matplotlib这几个库以备不时之需。
2、文档地址
https://python-control.readthedocs.io/en/0.8.2/
http://python-control.sourceforge.net/manual/
（第二个我觉得更好一些，第一个版本更新，但是没有代码实例）
3、使用例子
1、创建控制系统：
import control as ctrl
sysTf=ctrl.tf([1],[1,2,1])
sysTf为由传递函数定义的系统。但是按照文档说明，这个库还支持用状态空间定义的系统。不过，作为一个自控的初学者，目前只用得到传递函数。
这个传递函数定义了一个振荡环节，相当于：
G ( s ) = 1 ( s ω n ) 2 + 2 ψ ω n s + 1 G(s)=\frac{1}{(\frac{s}{\omega_n})^2+2\frac{\psi}{\omega_n}s+1} G(s)=(ωns)2+2ωnψs+11
很多课本上都有这个环节的伯德图。若要画出这个图，则需要这么做：
import control as ctrl
import matplotlib.pyplot as plt
import numpy as np
psiList = [0.05,0.2,0.5,0.707,1.0]
sysTfList=[]
for psi in psiList:
sysTfList.append(ctrl.tf([1],[1,2*psi,1]))
mag,phase,omega=ctrl.bode(sysTfList,omega=np.logspace(start=-1,stop=1,num=200),dB=True,deg=False)
plt.show()
当然，图片还是有美中不足的地方：缺少图例。
4、适当修改源码中的一些部分
以下是我修改过的freqplot.py，在pycharm中按下ctrl+B追踪control.bode_plot()这个方法即可找到这个文件。
主要修改是增加了bode()的参数表，比原来的代码增加了一个legend变量，传入一个与sysList等长度的字符串列表作为图例的文字。
原理是新建了一个名为lineList的列表，保存循环中sysList的每一项所绘出的线条对象。
目前仅修改了margins!=True情况时绘出的图形,因此在伯德图中可以显示图例。用法如下：
import control as ctrl
import matplotlib.pyplot as plt
import math
import numpy as np
psiList = [0.05,0.2,0.5,0.707,1.0]
sysTfList=[]
for psi in psiList:
sysTfList.append(ctrl.tf([1],[1,2*psi,1]))
mag,phase,omega=ctrl.bode(sysTfList,omega=np.logspace(start=-1,stop=1,num=200),dB=True,deg=False,
legends=['0.05','0.2','0.5','0.707','1.0'])
plt.show()
——————————————————————————————————————
生成的图片如下：
以下是源代码：
# freqplot.py - frequency domain plots for control systems
#
# Author: Richard M. Murray
# Date: 24 May 09
#
# This file contains some standard control system plots: Bode plots,
# Nyquist plots and pole-zero diagrams. The code for Nichols charts
# is in nichols.py.
#
# Copyright (c) 2010 by California Institute of Technology
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the California Institute of Technology nor
# the names of its contributors may be used to endorse or promote
# products derived from this software without specific prior
# written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH
# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
# SUCH DAMAGE.
#
# $Id$
import matplotlib
import matplotlib.pyplot as plt
import scipy as sp
import numpy as np
import math
from .ctrlutil import unwrap
from .bdalg import feedback
from .margins import stability_margins
__all__ = ['bode_plot', 'nyquist_plot', 'gangof4_plot',
'bode', 'nyquist', 'gangof4']
#
# Main plotting functions
#
# This section of the code contains the functions for generating
# frequency domain plots
#
# Bode plot
def bode_plot(syslist, omega=None, dB=None, Hz=None, deg=None,
Plot=True, omega_limits=None, omega_num=None, margins=None,legends=[],*args, **kwargs):
"""
Bode plot for a system
Plots a Bode plot for the system over a (optional) frequency range.
Parameters
----------
syslist : linsys
List of linear input/output systems (single system is OK)
omega : list
List of frequencies in rad/sec to be used for frequency response
dB : boolean
If True, plot result in dB
Hz : boolean
If True, plot frequency in Hz (omega must be provided in rad/sec)
deg : boolean
If True, plot phase in degrees (else radians)
Plot : boolean
If True, plot magnitude and phase
omega_limits: tuple, list, ... of two values
Limits of the to generate frequency vector.
If Hz=True the limits are in Hz otherwise in rad/s.
omega_num: int
number of samples
margins : boolean
If True, plot gain and phase margin
\*args, \**kwargs:
Additional options to matplotlib (color, linestyle, etc)
Returns
-------
mag : array (list if len(syslist) > 1)
magnitude
phase : array (list if len(syslist) > 1)
phase in radians
omega : array (list if len(syslist) > 1)
frequency in rad/sec
Notes
-----
1. Alternatively, you may use the lower-level method (mag, phase, freq)
= sys.freqresp(freq) to generate the frequency response for a system,
but it returns a MIMO response.
2. If a discrete time model is given, the frequency response is plotted
along the upper branch of the unit circle, using the mapping z = exp(j
\omega dt) where omega ranges from 0 to pi/dt and dt is the discrete
timebase. If not timebase is specified (dt = True), dt is set to 1.
Examples
--------
>>> sys = ss("1. -2; 3. -4", "5.; 7", "6. 8", "9.")
>>> mag, phase, omega = bode(sys)
"""
# Set default values for options
from . import config
if dB is None:
dB = config.bode_dB
if deg is None:
deg = config.bode_deg
if Hz is None:
Hz = config.bode_Hz
# If argument was a singleton, turn it into a list
if not getattr(syslist, '__iter__', False):
syslist = (syslist,)
if omega is None:
if omega_limits is None:
# Select a default range if none is provided
omega = default_frequency_range(syslist, Hz=Hz, number_of_samples=omega_num)
else:
omega_limits = np.array(omega_limits)
if Hz:
omega_limits *= 2. * math.pi
if omega_num:
omega = sp.logspace(np.log10(omega_limits[0]),
np.log10(omega_limits[1]),
num=omega_num,
endpoint=True)
else:
omega = sp.logspace(np.log10(omega_limits[0]),
np.log10(omega_limits[1]),
endpoint=True)
mags, phases, omegas, nyquistfrqs = [], [], [], []
lineList = []
for sys in syslist:
if sys.inputs > 1 or sys.outputs > 1:
# TODO: Add MIMO bode plots.
raise NotImplementedError("Bode is currently only implemented for SISO systems.")
else:
omega_sys = np.array(omega)
if sys.isdtime(True):
nyquistfrq = 2. * math.pi * 1. / sys.dt / 2.
omega_sys = omega_sys[omega_sys < nyquistfrq]
# TODO: What distance to the Nyquist frequency is appropriate?
else:
nyquistfrq = None
# Get the magnitude and phase of the system
mag_tmp, phase_tmp, omega_sys = sys.freqresp(omega_sys)
mag = np.atleast_1d(np.squeeze(mag_tmp))
phase = np.atleast_1d(np.squeeze(phase_tmp))
phase = unwrap(phase)
mags.append(mag)
phases.append(phase)
omegas.append(omega_sys)
nyquistfrqs.append(nyquistfrq)
# Get the dimensions of the current axis, which we will divide up
# TODO: Not current implemented; just use subplot for now
if Plot:
nyquistfrq_plot = None
if Hz:
omega_plot = omega_sys / (2. * math.pi)
if nyquistfrq:
nyquistfrq_plot = nyquistfrq / (2. * math.pi)
else:
omega_plot = omega_sys
if nyquistfrq:
nyquistfrq_plot = nyquistfrq
# Set up the axes with labels so that multiple calls to
# bode_plot will superimpose the data. This was implicit
# before matplotlib 2.1, but changed after that (See
# https://github.com/matplotlib/matplotlib/issues/9024).
# The code below should work on all cases.
# Get the current figure
if 'sisotool' in kwargs:
fig = kwargs['fig']
ax_mag = fig.axes[0]
ax_phase = fig.axes[2]
sisotool = kwargs['sisotool']
del kwargs['fig']
del kwargs['sisotool']
else:
fig = plt.gcf()
ax_mag = None
ax_phase = None
sisotool = False
# Get the current axes if they already exist
for ax in fig.axes:
if ax.get_label() == 'control-bode-magnitude':
ax_mag = ax
elif ax.get_label() == 'control-bode-phase':
ax_phase = ax
# If no axes present, create them from scratch
if ax_mag is None or ax_phase is None:
plt.clf()
ax_mag = plt.subplot(211,
label='control-bode-magnitude')
ax_phase = plt.subplot(212,
label='control-bode-phase',
sharex=ax_mag)
# Magnitude plot
if dB:
pltline = ax_mag.semilogx(omega_plot, 20 * np.log10(mag),
*args, **kwargs)
else:
pltline = ax_mag.loglog(omega_plot, mag, *args, **kwargs)
if nyquistfrq_plot:
ax_mag.axvline(nyquistfrq_plot,
color=pltline[0].get_color())
# Add a grid to the plot + labeling
ax_mag.grid(False if margins else True, which='both')
ax_mag.set_ylabel("Magnitude (dB)" if dB else "Magnitude")
# Phase plot
if deg:
phase_plot = phase * 180. / math.pi
else:
phase_plot = phase
a=ax_phase.semilogx(omega_plot, phase_plot, *args, **kwargs)
lineList.extend(a)
# Show the phase and gain margins in the plot
if margins:
margin = stability_margins(sys)
gm, pm, Wcg, Wcp = margin[0], margin[1], margin[3], margin[4]
# TODO: add some documentation describing why this is here
phase_at_cp = phases[0][(np.abs(omegas[0] - Wcp)).argmin()]
if phase_at_cp >= 0.:
phase_limit = 180.
else:
phase_limit = -180.
if Hz:
Wcg, Wcp = Wcg/(2*math.pi),Wcp/(2*math.pi)
ax_mag.axhline(y=0 if dB else 1, color='k', linestyle=':',
zorder=-20)
a=ax_phase.axhline(y=phase_limit if deg else math.radians(phase_limit),
color='k', linestyle=':', zorder=-20)
mag_ylim = ax_mag.get_ylim()
phase_ylim = ax_phase.get_ylim()
if pm != float('inf') and Wcp != float('nan'):
if dB:
ax_mag.semilogx([Wcp, Wcp], [0.,-1e5],
color='k', linestyle=':',
zorder=-20)
else:
ax_mag.loglog([Wcp,Wcp], [1.,1e-8],color='k',
linestyle=':', zorder=-20)
if deg:
ax_phase.semilogx([Wcp, Wcp],
[1e5, phase_limit+pm],
color='k', linestyle=':',
zorder=-20)
ax_phase.semilogx([Wcp, Wcp],
[phase_limit + pm, phase_limit],
color='k', zorder=-20)
else:
ax_phase.semilogx([Wcp, Wcp],
[1e5, math.radians(phase_limit) +
math.radians(pm)],
color='k', linestyle=':',
zorder=-20)
ax_phase.semilogx([Wcp, Wcp],
[math.radians(phase_limit) +
math.radians(pm),
math.radians(phase_limit)],
color='k', zorder=-20)
if gm != float('inf') and Wcg != float('nan'):
if dB:
ax_mag.semilogx([Wcg, Wcg],
[-20.*np.log10(gm), -1e5],
color='k', linestyle=':',
zorder=-20)
ax_mag.semilogx([Wcg, Wcg], [0,-20*np.log10(gm)],
color='k', zorder=-20)
else:
ax_mag.loglog([Wcg, Wcg],
[1./gm,1e-8],color='k',
linestyle=':', zorder=-20)
ax_mag.loglog([Wcg, Wcg],
[1.,1./gm],color='k', zorder=-20)
if deg:
a=ax_phase.semilogx([Wcg, Wcg], [1e-8, phase_limit],
color='k', linestyle=':',
zorder=-20)
else:
a=ax_phase.semilogx([Wcg, Wcg],
[1e-8, math.radians(phase_limit)],
color='k', linestyle=':',
zorder=-20)
ax_mag.set_ylim(mag_ylim)
ax_phase.set_ylim(phase_ylim)
if sisotool:
ax_mag.text(0.04, 0.06,
'G.M.: %.2f %s\nFreq: %.2f %s' %
(20*np.log10(gm) if dB else gm,
'dB ' if dB else '',
Wcg, 'Hz' if Hz else 'rad/s'),
horizontalalignment='left',
verticalalignment='bottom',
transform=ax_mag.transAxes,
fontsize=8 if int(matplotlib.__version__[0]) == 1 else 6)
ax_phase.text(0.04, 0.06,
'P.M.: %.2f %s\nFreq: %.2f %s' %
(pm if deg else math.radians(pm),
'deg' if deg else 'rad',
Wcp, 'Hz' if Hz else 'rad/s'),
horizontalalignment='left',
verticalalignment='bottom',
transform=ax_phase.transAxes,
fontsize=8 if int(matplotlib.__version__[0]) == 1 else 6)
else:
plt.suptitle('Gm = %.2f %s(at %.2f %s), Pm = %.2f %s (at %.2f %s)' %
(20*np.log10(gm) if dB else gm,
'dB ' if dB else '\b',
Wcg, 'Hz' if Hz else 'rad/s',
pm if deg else math.radians(pm),
'deg' if deg else 'rad',
Wcp, 'Hz' if Hz else 'rad/s'))
if nyquistfrq_plot:
a=ax_phase.axvline(nyquistfrq_plot, color=pltline[0].get_color())
# Add a grid to the plot + labeling
ax_phase.set_ylabel("Phase (deg)" if deg else "Phase (rad)")
def gen_zero_centered_series(val_min, val_max, period):
v1 = np.ceil(val_min / period - 0.2)
v2 = np.floor(val_max / period + 0.2)
return np.arange(v1, v2 + 1) * period
if deg:
ylim = ax_phase.get_ylim()
ax_phase.set_yticks(gen_zero_centered_series(ylim[0],
ylim[1], 45.))
ax_phase.set_yticks(gen_zero_centered_series(ylim[0],
ylim[1], 15.),
minor=True)
else:
ylim = ax_phase.get_ylim()
ax_phase.set_yticks(gen_zero_centered_series(ylim[0],
ylim[1],
math.pi / 4.))
ax_phase.set_yticks(gen_zero_centered_series(ylim[0],
ylim[1],
math.pi / 12.),
minor=True)
ax_phase.grid(False if margins else True, which='both')
# ax_mag.grid(which='minor', alpha=0.3)
# ax_mag.grid(which='major', alpha=0.9)
# ax_phase.grid(which='minor', alpha=0.3)
# ax_phase.grid(which='major', alpha=0.9)
# Label the frequency axis
ax_phase.set_xlabel("Frequency (Hz)" if Hz
else "Frequency (rad/sec)")
if legends!=[]:
# print(lineList,legends)
ax_phase.legend(lineList,legends)
ax_mag.legend(lineList,legends)
if len(syslist) == 1:
return mags[0], phases[0], omegas[0]
else:
return mags, phases, omegas
def nyquist_plot(syslist, omega=None, Plot=True, color=None,
labelFreq=0, *args, **kwargs):
"""
Nyquist plot for a system
Plots a Nyquist plot for the system over a (optional) frequency range.
Parameters
----------
syslist : list of LTI
List of linear input/output systems (single system is OK)
omega : freq_range
Range of frequencies (list or bounds) in rad/sec
Plot : boolean
If True, plot magnitude
color : string
Used to specify the color of the plot
labelFreq : int
Label every nth frequency on the plot
\*args, \**kwargs:
Additional options to matplotlib (color, linestyle, etc)
Returns
-------
real : array
real part of the frequency response array
imag : array
imaginary part of the frequency response array
freq : array
frequencies
Examples
--------
>>> sys = ss("1. -2; 3. -4", "5.; 7", "6. 8", "9.")
>>> real, imag, freq = nyquist_plot(sys)
"""
# If argument was a singleton, turn it into a list
if not getattr(syslist, '__iter__', False):
syslist = (syslist,)
# Select a default range if none is provided
if omega is None:
omega = default_frequency_range(syslist)
# Interpolate between wmin and wmax if a tuple or list are provided
elif isinstance(omega, list) or isinstance(omega, tuple):
# Only accept tuple or list of length 2
if len(omega) != 2:
raise ValueError("Supported frequency arguments are (wmin,wmax) tuple or list, "
"or frequency vector. ")
omega = np.logspace(np.log10(omega[0]), np.log10(omega[1]),
num=50, endpoint=True, base=10.0)
for sys in syslist:
if sys.inputs > 1 or sys.outputs > 1:
# TODO: Add MIMO nyquist plots.
raise NotImplementedError("Nyquist is currently only implemented for SISO systems.")
else:
# Get the magnitude and phase of the system
mag_tmp, phase_tmp, omega = sys.freqresp(omega)
mag = np.squeeze(mag_tmp)
phase = np.squeeze(phase_tmp)
# Compute the primary curve
x = sp.multiply(mag, sp.cos(phase))
y = sp.multiply(mag, sp.sin(phase))
if Plot:
# Plot the primary curve and mirror image
p = plt.plot(x, y, '-', color=color, *args, **kwargs)
c = p[0].get_color()
ax = plt.gca()
# Plot arrow to indicate Nyquist encirclement orientation
ax.arrow(x[0], y[0], (x[1]-x[0])/2, (y[1]-y[0])/2, fc=c, ec=c,
head_width=0.2, head_length=0.2)
plt.plot(x, -y, '-', color=c, *args, **kwargs)
ax.arrow(x[-1], -y[-1], (x[-1]-x[-2])/2, (y[-1]-y[-2])/2, fc=c, ec=c,
head_width=0.2, head_length=0.2)
# Mark the -1 point
plt.plot([-1], [0], 'r+')
# Label the frequencies of the points
if labelFreq:
ind = slice(None, None, labelFreq)
for xpt, ypt, omegapt in zip(x[ind], y[ind], omega[ind]):
# Convert to Hz
f = omegapt / (2 * sp.pi)
# Factor out multiples of 1000 and limit the
# result to the range [-8, 8].
pow1000 = max(min(get_pow1000(f), 8), -8)
# Get the SI prefix.
prefix = gen_prefix(pow1000)
# Apply the text. (Use a space before the text to
# prevent overlap with the data.)
#
# np.round() is used because 0.99... appears
# instead of 1.0, and this would otherwise be
# truncated to 0.
plt.text(xpt, ypt, ' ' + str(int(np.round(f / 1000 ** pow1000, 0))) + ' ' +
prefix + 'Hz')
if Plot:
ax = plt.gca()
ax.set_xlabel("Real axis")
ax.set_ylabel("Imaginary axis")
ax.grid(color="lightgray")
return x, y, omega
# TODO: think about how (and whether) to handle lists of systems
def gangof4_plot(P, C, omega=None):
"""Plot the "Gang of 4" transfer functions for a system
Generates a 2x2 plot showing the "Gang of 4" sensitivity functions
[T, PS; CS, S]
Parameters
----------
P, C : LTI
Linear input/output systems (process and control)
omega : array
Range of frequencies (list or bounds) in rad/sec
Returns
-------
None
"""
if P.inputs > 1 or P.outputs > 1 or C.inputs > 1 or C.outputs > 1:
# TODO: Add MIMO go4 plots.
raise NotImplementedError("Gang of four is currently only implemented for SISO systems.")
else:
# Select a default range if none is provided
# TODO: This needs to be made more intelligent
if omega is None:
omega = default_frequency_range((P, C))
# Compute the senstivity functions
L = P * C
S = feedback(1, L)
T = L * S
# Set up the axes with labels so that multiple calls to
# gangof4_plot will superimpose the data. See details in bode_plot.
plot_axes = {'t': None, 's': None, 'ps': None, 'cs': None}
for ax in plt.gcf().axes:
label = ax.get_label()
if label.startswith('control-gangof4-'):
key = label[len('control-gangof4-'):]
if key not in plot_axes:
raise RuntimeError("unknown gangof4 axis type '{}'".format(label))
plot_axes[key] = ax
# if any of the axes are missing, start from scratch
if any((ax is None for ax in plot_axes.values())):
plt.clf()
plot_axes = {'t': plt.subplot(221, label='control-gangof4-t'),
'ps': plt.subplot(222, label='control-gangof4-ps'),
'cs': plt.subplot(223, label='control-gangof4-cs'),
's': plt.subplot(224, label='control-gangof4-s')}
#
# Plot the four sensitivity functions
#
# TODO: Need to add in the mag = 1 lines
mag_tmp, phase_tmp, omega = T.freqresp(omega)
mag = np.squeeze(mag_tmp)
plot_axes['t'].loglog(omega, mag)
mag_tmp, phase_tmp, omega = (P * S).freqresp(omega)
mag = np.squeeze(mag_tmp)
plot_axes['ps'].loglog(omega, mag)
mag_tmp, phase_tmp, omega = (C * S).freqresp(omega)
mag = np.squeeze(mag_tmp)
plot_axes['cs'].loglog(omega, mag)
mag_tmp, phase_tmp, omega = S.freqresp(omega)
mag = np.squeeze(mag_tmp)
plot_axes['s'].loglog(omega, mag)
#
# Utility functions
#
# This section of the code contains some utility functions for
# generating frequency domain plots
#
# Compute reasonable defaults for axes
def default_frequency_range(syslist, Hz=None, number_of_samples=None,
feature_periphery_decade=None):
"""Compute a reasonable default frequency range for frequency
domain plots.
Finds a reasonable default frequency range by examining the features
(poles and zeros) of the systems in syslist.
Parameters
----------
syslist : list of LTI
List of linear input/output systems (single system is OK)
Hz: boolean
If True, the limits (first and last value) of the frequencies
are set to full decades in Hz so it fits plotting with logarithmic
scale in Hz otherwise in rad/s. Omega is always returned in rad/sec.
number_of_samples: int
Number of samples to generate
feature_periphery_decade: float
Defines how many decades shall be included in the frequency range on
both sides of features (poles, zeros).
Example: If there is a feature, e.g. a pole, at 1Hz and feature_periphery_decade=1.
then the range of frequencies shall span 0.1 .. 10 Hz.
The default value is read from config.bode_feature_periphery_decade.
Returns
-------
omega : array
Range of frequencies in rad/sec
Examples
--------
>>> from matlab import ss
>>> sys = ss("1. -2; 3. -4", "5.; 7", "6. 8", "9.")
>>> omega = default_frequency_range(sys)
"""
# This code looks at the poles and zeros of all of the systems that
# we are plotting and sets the frequency range to be one decade above
# and below the min and max feature frequencies, rounded to the nearest
# integer. It excludes poles and zeros at the origin. If no features
# are found, it turns logspace(-1, 1)
# Set default values for options
from . import config
if number_of_samples is None:
number_of_samples = config.bode_number_of_samples
if feature_periphery_decade is None:
feature_periphery_decade = config.bode_feature_periphery_decade
# Find the list of all poles and zeros in the systems
features = np.array(())
freq_interesting = []
# detect if single sys passed by checking if it is sequence-like
if not getattr(syslist, '__iter__', False):
syslist = (syslist,)
for sys in syslist:
try:
# Add new features to the list
if sys.isctime():
features_ = np.concatenate((np.abs(sys.pole()),
np.abs(sys.zero())))
# Get rid of poles and zeros at the origin
features_ = features_[features_ != 0.0]
features = np.concatenate((features, features_))
elif sys.isdtime(strict=True):
fn = math.pi * 1. / sys.dt
# TODO: What distance to the Nyquist frequency is appropriate?
freq_interesting.append(fn * 0.9)
features_ = np.concatenate((sys.pole(),
sys.zero()))
# Get rid of poles and zeros
# * at the origin and real <= 0 & imag==0: log!
# * at 1.: would result in omega=0. (logaritmic plot!)
features_ = features_[(features_.imag != 0.0) | (features_.real > 0.)]
features_ = features_[np.bitwise_not((features_.imag == 0.0) &
(np.abs(features_.real - 1.0) < 1.e-10))]
# TODO: improve
features__ = np.abs(np.log(features_) / (1.j * sys.dt))
features = np.concatenate((features, features__))
else:
# TODO
raise NotImplementedError('type of system in not implemented now')
except:
pass
# Make sure there is at least one point in the range
if features.shape[0] == 0:
features = np.array([1.])
if Hz:
features /= 2. * math.pi
features = np.log10(features)
lsp_min = np.floor(np.min(features) - feature_periphery_decade)
lsp_max = np.ceil(np.max(features) + feature_periphery_decade)
lsp_min += np.log10(2. * math.pi)
lsp_max += np.log10(2. * math.pi)
else:
features = np.log10(features)
lsp_min = np.floor(np.min(features) - feature_periphery_decade)
lsp_max = np.ceil(np.max(features) + feature_periphery_decade)
if freq_interesting:
lsp_min = min(lsp_min, np.log10(min(freq_interesting)))
lsp_max = max(lsp_max, np.log10(max(freq_interesting)))
# TODO: Add a check in discrete case to make sure we don't get aliasing
# (Attention: there is a list of system but only one omega vector)
# Set the range to be an order of magnitude beyond any features
if number_of_samples:
omega = sp.logspace(lsp_min, lsp_max, num=number_of_samples, endpoint=True)
else:
omega = sp.logspace(lsp_min, lsp_max, endpoint=True)
return omega
#
# KLD 5/23/11: Two functions to create nice looking labels
#
def get_pow1000(num):
"""Determine the exponent for which the significand of a number is within the
range [1, 1000).
"""
# Based on algorithm from http://www.mail-archive.com/[email protected]/msg14433.html, accessed 2010/11/7
# by Jason Heeris 2009/11/18
from decimal import Decimal
from math import floor
dnum = Decimal(str(num))
if dnum == 0:
return 0
elif dnum < 0:
dnum = -dnum
return int(floor(dnum.log10() / 3))
def gen_prefix(pow1000):
"""Return the SI prefix for a power of 1000.
"""
# Prefixes according to Table 5 of [BIPM 2006] (excluding hecto,
# deca, deci, and centi).
if pow1000 < -8 or pow1000 > 8:
raise ValueError("Value is out of the range covered by the SI prefixes.")
return ['Y', # yotta (10^24)
'Z', # zetta (10^21)
'E', # exa (10^18)
'P', # peta (10^15)
'T', # tera (10^12)
'G', # giga (10^9)
'M', # mega (10^6)
'k', # kilo (10^3)
'', # (10^0)
'm', # milli (10^-3)
r'$\mu$', # micro (10^-6)
'n', # nano (10^-9)
'p', # pico (10^-12)
'f', # femto (10^-15)
'a', # atto (10^-18)
'z', # zepto (10^-21)
'y'][8 - pow1000] # yocto (10^-24)
def find_nearest_omega(omega_list, omega):
omega_list = np.asarray(omega_list)
idx = (np.abs(omega_list - omega)).argmin()
return omega_list[(np.abs(omega_list - omega)).argmin()]
# Function aliases
bode = bode_plot
nyquist = nyquist_plot
gangof4 = gangof4_plot