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phaseplot.py
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phaseplot.py
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#! TODO: add module docstring
# phaseplot.py - generate 2D phase portraits
#
# Author: Richard M. Murray
# Date: 24 July 2011, converted from MATLAB version (2002); based on
# a version by Kristi Morgansen
#
# Copyright (c) 2011 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. The name of the author may not be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``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 THE AUTHOR 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.
import numpy as np
import matplotlib.pyplot as mpl
from scipy.integrate import odeint
from .exception import ControlNotImplemented
__all__ = ['phase_plot', 'box_grid']
def _find(condition):
"""Returns indices where ravel(a) is true.
Private implementation of deprecated matplotlib.mlab.find
"""
return np.nonzero(np.ravel(condition))[0]
def phase_plot(odefun, X=None, Y=None, scale=1, X0=None, T=None,
lingrid=None, lintime=None, logtime=None, timepts=None,
parms=(), verbose=True):
"""Phase plot for 2D dynamical systems
Produces a vector field or stream line plot for a planar system.
Call signatures:
phase_plot(func, X, Y, ...) - display vector field on meshgrid
phase_plot(func, X, Y, scale, ...) - scale arrows
phase_plot(func. X0=(...), T=Tmax, ...) - display stream lines
phase_plot(func, X, Y, X0=[...], T=Tmax, ...) - plot both
phase_plot(func, X0=[...], T=Tmax, lingrid=N, ...) - plot both
phase_plot(func, X0=[...], lintime=N, ...) - stream lines with arrows
Parameters
----------
func : callable(x, t, ...)
Computes the time derivative of y (compatible with odeint).
The function should be the same for as used for
:mod:`scipy.integrate`. Namely, it should be a function of the form
dxdt = F(x, t) that accepts a state x of dimension 2 and
returns a derivative dx/dt of dimension 2.
X, Y: 3-element sequences, optional, as [start, stop, npts]
Two 3-element sequences specifying x and y coordinates of a
grid. These arguments are passed to linspace and meshgrid to
generate the points at which the vector field is plotted. If
absent (or None), the vector field is not plotted.
scale: float, optional
Scale size of arrows; default = 1
X0: ndarray of initial conditions, optional
List of initial conditions from which streamlines are plotted.
Each initial condition should be a pair of numbers.
T: array-like or number, optional
Length of time to run simulations that generate streamlines.
If a single number, the same simulation time is used for all
initial conditions. Otherwise, should be a list of length
len(X0) that gives the simulation time for each initial
condition. Default value = 50.
lingrid : integer or 2-tuple of integers, optional
Argument is either N or (N, M). If X0 is given and X, Y are missing,
a grid of arrows is produced using the limits of the initial
conditions, with N grid points in each dimension or N grid points in x
and M grid points in y.
lintime : integer or tuple (integer, float), optional
If a single integer N is given, draw N arrows using equally space time
points. If a tuple (N, lambda) is given, draw N arrows using
exponential time constant lambda
timepts : array-like, optional
Draw arrows at the given list times [t1, t2, ...]
parms: tuple, optional
List of parameters to pass to vector field: `func(x, t, *parms)`
See also
--------
box_grid : construct box-shaped grid of initial conditions
"""
#
# Figure out ranges for phase plot (argument processing)
#
#! TODO: need to add error checking to arguments
#! TODO: think through proper action if multiple options are given
#
autoFlag = False; logtimeFlag = False; timeptsFlag = False; Narrows = 0;
if lingrid is not None:
autoFlag = True;
Narrows = lingrid;
if (verbose):
print('Using auto arrows\n')
elif logtime is not None:
logtimeFlag = True;
Narrows = logtime[0];
timefactor = logtime[1];
if (verbose):
print('Using logtime arrows\n')
elif timepts is not None:
timeptsFlag = True;
Narrows = len(timepts);
# Figure out the set of points for the quiver plot
#! TODO: Add sanity checks
elif (X is not None and Y is not None):
(x1, x2) = np.meshgrid(
np.linspace(X[0], X[1], X[2]),
np.linspace(Y[0], Y[1], Y[2]))
Narrows = len(x1)
else:
# If we weren't given any grid points, don't plot arrows
Narrows = 0;
if ((not autoFlag) and (not logtimeFlag) and (not timeptsFlag)
and (Narrows > 0)):
# Now calculate the vector field at those points
(nr,nc) = x1.shape;
dx = np.empty((nr, nc, 2))
for i in range(nr):
for j in range(nc):
dx[i, j, :] = np.squeeze(odefun((x1[i,j], x2[i,j]), 0, *parms))
# Plot the quiver plot
#! TODO: figure out arguments to make arrows show up correctly
if scale is None:
mpl.quiver(x1, x2, dx[:,:,1], dx[:,:,2], angles='xy')
elif (scale != 0):
#! TODO: optimize parameters for arrows
#! TODO: figure out arguments to make arrows show up correctly
xy = mpl.quiver(x1, x2, dx[:,:,0]*np.abs(scale),
dx[:,:,1]*np.abs(scale), angles='xy')
# set(xy, 'LineWidth', PP_arrow_linewidth, 'Color', 'b');
#! TODO: Tweak the shape of the plot
# a=gca; set(a,'DataAspectRatio',[1,1,1]);
# set(a,'XLim',X(1:2)); set(a,'YLim',Y(1:2));
mpl.xlabel('x1'); mpl.ylabel('x2');
# See if we should also generate the streamlines
if X0 is None or len(X0) == 0:
return
# Convert initial conditions to a numpy array
X0 = np.array(X0);
(nr, nc) = np.shape(X0);
# Generate some empty matrices to keep arrow information
x1 = np.empty((nr, Narrows)); x2 = np.empty((nr, Narrows));
dx = np.empty((nr, Narrows, 2))
# See if we were passed a simulation time
if T is None:
T = 50
# Parse the time we were passed
TSPAN = T;
if (isinstance(T, (int, float))):
TSPAN = np.linspace(0, T, 100);
# Figure out the limits for the plot
if scale is None:
# Assume that the current axis are set as we want them
alim = mpl.axis();
xmin = alim[0]; xmax = alim[1];
ymin = alim[2]; ymax = alim[3];
else:
# Use the maximum extent of all trajectories
xmin = np.min(X0[:,0]); xmax = np.max(X0[:,0]);
ymin = np.min(X0[:,1]); ymax = np.max(X0[:,1]);
# Generate the streamlines for each initial condition
for i in range(nr):
state = odeint(odefun, X0[i], TSPAN, args=parms);
time = TSPAN
mpl.plot(state[:,0], state[:,1])
#! TODO: add back in colors for stream lines
# PP_stream_color(np.mod(i-1, len(PP_stream_color))+1));
# set(h[i], 'LineWidth', PP_stream_linewidth);
# Plot arrows if quiver parameters were 'auto'
if (autoFlag or logtimeFlag or timeptsFlag):
# Compute the locations of the arrows
#! TODO: check this logic to make sure it works in python
for j in range(Narrows):
# Figure out starting index; headless arrows start at 0
k = -1 if scale is None else 0;
# Figure out what time index to use for the next point
if (autoFlag):
# Use a linear scaling based on ODE time vector
tind = np.floor((len(time)/Narrows) * (j-k)) + k;
elif (logtimeFlag):
# Use an exponential time vector
# MATLAB: tind = find(time < (j-k) / lambda, 1, 'last');
tarr = _find(time < (j-k) / timefactor);
tind = tarr[-1] if len(tarr) else 0;
elif (timeptsFlag):
# Use specified time points
# MATLAB: tind = find(time < Y[j], 1, 'last');
tarr = _find(time < timepts[j]);
tind = tarr[-1] if len(tarr) else 0;
# For tailless arrows, skip the first point
if tind == 0 and scale is None:
continue;
# Figure out the arrow at this point on the curve
x1[i,j] = state[tind, 0];
x2[i,j] = state[tind, 1];
# Skip arrows outside of initial condition box
if (scale is not None or
(x1[i,j] <= xmax and x1[i,j] >= xmin and
x2[i,j] <= ymax and x2[i,j] >= ymin)):
v = odefun((x1[i,j], x2[i,j]), 0, *parms)
dx[i, j, 0] = v[0]; dx[i, j, 1] = v[1];
else:
dx[i, j, 0] = 0; dx[i, j, 1] = 0;
# Set the plot shape before plotting arrows to avoid warping
# a=gca;
# if (scale != None):
# set(a,'DataAspectRatio', [1,1,1]);
# if (xmin != xmax and ymin != ymax):
# mpl.axis([xmin, xmax, ymin, ymax]);
# set(a, 'Box', 'on');
# Plot arrows on the streamlines
if scale is None and Narrows > 0:
# Use a tailless arrow
#! TODO: figure out arguments to make arrows show up correctly
mpl.quiver(x1, x2, dx[:,:,0], dx[:,:,1], angles='xy')
elif (scale != 0 and Narrows > 0):
#! TODO: figure out arguments to make arrows show up correctly
xy = mpl.quiver(x1, x2, dx[:,:,0]*abs(scale), dx[:,:,1]*abs(scale),
angles='xy')
# set(xy, 'LineWidth', PP_arrow_linewidth);
# set(xy, 'AutoScale', 'off');
# set(xy, 'AutoScaleFactor', 0);
if (scale < 0):
bp = mpl.plot(x1, x2, 'b.'); # add dots at base
# set(bp, 'MarkerSize', PP_arrow_markersize);
return;
# Utility function for generating initial conditions around a box
def box_grid(xlimp, ylimp):
"""box_grid generate list of points on edge of box
list = box_grid([xmin xmax xnum], [ymin ymax ynum]) generates a
list of points that correspond to a uniform grid at the end of the
box defined by the corners [xmin ymin] and [xmax ymax].
"""
sx10 = np.linspace(xlimp[0], xlimp[1], xlimp[2])
sy10 = np.linspace(ylimp[0], ylimp[1], ylimp[2])
sx1 = np.hstack((0, sx10, 0*sy10+sx10[0], sx10, 0*sy10+sx10[-1]))
sx2 = np.hstack((0, 0*sx10+sy10[0], sy10, 0*sx10+sy10[-1], sy10))
return np.transpose( np.vstack((sx1, sx2)) )