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[python-control-discuss] SF.net SVN: python-control:[170] trunk
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From: <mur...@us...> - 2011-07-26 02:41:10
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Revision: 170
http://python-control.svn.sourceforge.net/python-control/?rev=170&view=rev
Author: murrayrm
Date: 2011-07-26 02:41:03 +0000 (Tue, 26 Jul 2011)
Log Message:
-----------
* Updated phaseplot module with new (more pythonic) call signature
* Added boxplot function to generate initial conditions around a box
* New genetic switch example (from FBS), demonstrating phase plots
Modified Paths:
--------------
trunk/ChangeLog
trunk/examples/phaseplots.py
trunk/src/__init__.py
trunk/src/phaseplot.py
Added Paths:
-----------
trunk/examples/genswitch.py
Modified: trunk/ChangeLog
===================================================================
--- trunk/ChangeLog 2011-07-25 05:00:41 UTC (rev 169)
+++ trunk/ChangeLog 2011-07-26 02:41:03 UTC (rev 170)
@@ -1,3 +1,17 @@
+2011-07-25 Richard Murray <murray@malabar.local>
+
+ * examples/phaseplots.py: updated calls to PhasePlot to use new
+ argument structure
+
+ * src/phaseplot.py (PhasePlot): Updated call signature to be
+ more pythonic and fixed up documentation.
+
+ * examples/genswitch.py (genswitch): added new example showing
+ PhasePlot functionality
+
+ * src/phaseplot.py (boxgrid): added function to compute initial
+ conditions around the edges of a box
+
2011-07-24 Richard Murray <murray@malabar.local>
* tests/margin_test.py: added simple unit tests for margin functions
Added: trunk/examples/genswitch.py
===================================================================
--- trunk/examples/genswitch.py (rev 0)
+++ trunk/examples/genswitch.py 2011-07-26 02:41:03 UTC (rev 170)
@@ -0,0 +1,81 @@
+# genswitch_plot.m - run the Collins genetic switch model
+# RMM, 24 Jan 07
+#
+# This file contains an example from FBS of a simple dynamical model
+# of a genetic switch. Plots time traces and a phase portrait using
+# the python-control library.
+
+import numpy as np
+import matplotlib.pyplot as mpl
+from scipy.integrate import odeint
+from matplotlib.mlab import frange
+from control import PhasePlot, boxgrid
+
+# 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])
+
+# Run a simulation from an initial condition
+tim1 = np.linspace(0, 10, 100)
+sol1 = odeint(genswitch, [1, 5], tim1)
+
+# Extract the equlibirum 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)
+
+# First plot out the curves that define the equilibria
+u = frange(0, 4.5, 0.1)
+f = np.divide(mu, (1 + u**n)) # mu / (1 + u^n), elementwise
+
+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)');
+
+# 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.hold(True);
+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]);
+
+mpl.xlabel('Time {\itt} [scaled]');
+mpl.ylabel('Protein concentrations [scaled]');
+mpl.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]);
+PhasePlot(genswitch, X0 = boxgrid([0, 5, 6], [0, 5, 6]), T = 10,
+ timepts = [0.2, 0.6, 1.2])
+
+# Add the stable equilibrium points
+mpl.hold(True);
+mpl.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);
+
Modified: trunk/examples/phaseplots.py
===================================================================
--- trunk/examples/phaseplots.py 2011-07-25 05:00:41 UTC (rev 169)
+++ trunk/examples/phaseplots.py 2011-07-26 02:41:03 UTC (rev 170)
@@ -27,16 +27,16 @@
# Outer trajectories
PhasePlot(invpend_ode,
- 'logtime', (3, 0.7), None,
- [ [-2*pi, 1.6], [-2*pi, 0.5], [-1.8, 2.1],
- [-1, 2.1], [4.2, 2.1], [5, 2.1],
- [2*pi, -1.6], [2*pi, -0.5], [1.8, -2.1],
- [1, -2.1], [-4.2, -2.1], [-5, -2.1] ],
- np.linspace(0, 40, 200))
+ X0 = [ [-2*pi, 1.6], [-2*pi, 0.5], [-1.8, 2.1],
+ [-1, 2.1], [4.2, 2.1], [5, 2.1],
+ [2*pi, -1.6], [2*pi, -0.5], [1.8, -2.1],
+ [1, -2.1], [-4.2, -2.1], [-5, -2.1] ],
+ T = np.linspace(0, 40, 200),
+ logtime = (3, 0.7) )
# Separatrices
mpl.hold(True);
-PhasePlot(invpend_ode, 'auto', 0, None, [[-2.3056, 2.1], [2.3056, -2.1]], 6)
+PhasePlot(invpend_ode, X0 = [[-2.3056, 2.1], [2.3056, -2.1]], T=6, lingrid=0)
mpl.show();
#
@@ -57,10 +57,10 @@
mpl.figure(); mpl.clf();
mpl.axis([-1, 1, -1, 1]); # set(gca, 'DataAspectRatio', [1, 1, 1]);
PhasePlot(oscillator_ode,
- 'timepts', [0.25, 0.8, 2, 3], None, [
- [-1, 1], [-0.3, 1], [0, 1], [0.25, 1], [0.5, 1], [0.75, 1], [1, 1],
- [1, -1], [0.3, -1], [0, -1], [-0.25, -1], [-0.5, -1], [-0.75, -1], [-1, -1]
- ], np.linspace(0, 8, 80))
+ X0 = [
+ [-1, 1], [-0.3, 1], [0, 1], [0.25, 1], [0.5, 1], [0.75, 1], [1, 1],
+ [1, -1], [0.3, -1], [0, -1], [-0.25, -1], [-0.5, -1], [-0.75, -1], [-1, -1]
+ ], T = np.linspace(0, 8, 80), timepts = [0.25, 0.8, 2, 3])
mpl.hold(True); mpl.plot([0], [0], 'k.'); # 'MarkerSize', AM_data_markersize*3);
# set(gca,'DataAspectRatio',[1,1,1]);
mpl.xlabel('x1'); mpl.ylabel('x2');
@@ -81,11 +81,13 @@
m = 1; b = 1; k = 1; # default values
mpl.figure(); mpl.clf();
mpl.axis([-1, 1, -1, 1]); # set(gca, 'DataAspectRatio', [1 1 1]);
-PhasePlot(oscillator_ode, 'timepts', [0.3, 1, 2, 3], None,
- [[-1,1], [-0.3,1], [0,1], [0.25,1], [0.5,1], [0.7,1], [1,1], [1.3,1],
- [1,-1], [0.3,-1], [0,-1], [-0.25,-1], [-0.5,-1], [-0.7,-1], [-1,-1],
- [-1.3,-1]],
- np.linspace(0, 10, 100), parms = (m, b, k));
+PhasePlot(oscillator_ode,
+ X0 = [
+ [-1,1], [-0.3,1], [0,1], [0.25,1], [0.5,1], [0.7,1], [1,1], [1.3,1],
+ [1,-1], [0.3,-1], [0,-1], [-0.25,-1], [-0.5,-1], [-0.7,-1], [-1,-1],
+ [-1.3,-1]
+ ], T = np.linspace(0, 10, 100),
+ timepts = [0.3, 1, 2, 3], parms = (m, b, k));
mpl.hold(True); mpl.plot([0], [0], 'k.'); # 'MarkerSize', AM_data_markersize*3);
# set(gca,'FontSize', 16);
mpl.xlabel('{\itx}_1'); mpl.ylabel('{\itx}_2');
@@ -93,14 +95,14 @@
# Saddle
mpl.figure(); mpl.clf();
mpl.axis([-1, 1, -1, 1]); # set(gca, 'DataAspectRatio', [1 1 1]);
-PhasePlot(saddle_ode, 'timepts', [0.2, 0.5, 0.8], None,
+PhasePlot(saddle_ode, scale = 2, timepts = [0.2, 0.5, 0.8], X0 =
[ [-1, -1], [1, 1],
[-1, -0.95], [-1, -0.9], [-1, -0.8], [-1, -0.6], [-1, -0.4], [-1, -0.2],
[-0.95, -1], [-0.9, -1], [-0.8, -1], [-0.6, -1], [-0.4, -1], [-0.2, -1],
[1, 0.95], [1, 0.9], [1, 0.8], [1, 0.6], [1, 0.4], [1, 0.2],
[0.95, 1], [0.9, 1], [0.8, 1], [0.6, 1], [0.4, 1], [0.2, 1],
[-0.5, -0.45], [-0.45, -0.5], [0.5, 0.45], [0.45, 0.5],
- [-0.04, 0.04], [0.04, -0.04] ], np.linspace(0, 2, 20));
+ [-0.04, 0.04], [0.04, -0.04] ], T = np.linspace(0, 2, 20));
mpl.hold(True); mpl.plot([0], [0], 'k.'); # 'MarkerSize', AM_data_markersize*3);
# set(gca,'FontSize', 16);
mpl.xlabel('{\itx}_1'); mpl.ylabel('{\itx}_2');
@@ -109,10 +111,10 @@
m = 1; b = 0; k = 1; # zero damping
mpl.figure(); mpl.clf();
mpl.axis([-1, 1, -1, 1]); # set(gca, 'DataAspectRatio', [1 1 1]);
-PhasePlot(oscillator_ode, 'timepts',
- [pi/6, pi/3, pi/2, 2*pi/3, 5*pi/6, pi, 7*pi/6, 4*pi/3, 9*pi/6, 5*pi/3, 11*pi/6, 2*pi], None,
- [ [0.2,0], [0.4,0], [0.6,0], [0.8,0], [1,0], [1.2,0], [1.4,0] ],
- np.linspace(0, 20, 200), parms = (m, b, k));
+PhasePlot(oscillator_ode, timepts =
+ [pi/6, pi/3, pi/2, 2*pi/3, 5*pi/6, pi, 7*pi/6, 4*pi/3, 9*pi/6, 5*pi/3, 11*pi/6, 2*pi],
+ X0 = [ [0.2,0], [0.4,0], [0.6,0], [0.8,0], [1,0], [1.2,0], [1.4,0] ],
+ T = np.linspace(0, 20, 200), parms = (m, b, k));
mpl.hold(True); mpl.plot([0], [0], 'k.') # 'MarkerSize', AM_data_markersize*3);
# set(gca,'FontSize', 16);
mpl.xlabel('{\itx}_1'); mpl.ylabel('{\itx}_2');
Modified: trunk/src/__init__.py
===================================================================
--- trunk/src/__init__.py 2011-07-25 05:00:41 UTC (rev 169)
+++ trunk/src/__init__.py 2011-07-26 02:41:03 UTC (rev 170)
@@ -64,6 +64,7 @@
from mateqn import *
from modelsimp import *
from nichols import *
+from phaseplot import PhasePlot, boxgrid
from rlocus import *
from statefbk import *
from statesp import *
Modified: trunk/src/phaseplot.py
===================================================================
--- trunk/src/phaseplot.py 2011-07-25 05:00:41 UTC (rev 169)
+++ trunk/src/phaseplot.py 2011-07-26 02:41:03 UTC (rev 170)
@@ -1,7 +1,8 @@
# phaseplot.py - generate 2D phase portraits
#
# Author: Richard M. Murray
-# Date: Fall 2002 (MATLAB version), based on a version by Kristi Morgansen
+# 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.
@@ -38,99 +39,107 @@
from exception import ControlNotImplemented
from scipy.integrate import odeint
-def PhasePlot(odefun, xlimv, ylimv, scale=1, xinit=None, T=None, parms=(),
- verbose=True):
+def PhasePlot(odefun, X=None, Y=None, scale=1, X0=None, T=None,
+ lingrid=None, lintime=None, logtime=None, timepts=None,
+ parms=(), verbose=True):
"""
- PhasePlot Phase plot for 2D dynamical systems
+ Phase plot for 2D dynamical systems
- PhasePlot(F, X1range, X2range, scale) produces a quiver plot for
- the function F. X1range and X2range have the form [min, max, num]
- and specify the axis limits for the plot, along with the number of
- subdivisions in each axis, used to produce an quiver plot for the
- vector field. The vector field is scaled by a factor 'scale'
- (default = 1).
+ Produces a vector field or stream line plot for a planar system.
- The function F should be the same for as used for 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 dxdt of
- dimension 2.
+ Call signatures:
+ PhasePlot(func, X, Y, ...) - display vector field on meshgrid
+ PhasePlot(func, X, Y, scale, ...) - scale arrows
+ PhasePlot(func. X0=(...), T=Tmax, ...) - display stream lines
+ PhasePlot(func, X, Y, X0=[...], T=Tmax, ...) - plot both
+ PhasePlot(func, X0=[...], T=Tmax, lingrid=N, ...) - plot both
+ PhasePlot(func, X0=[...], lintime=N, ...) - stream lines with arrows
- PhasePlot(F, X1range, X2range, scale, Xinit) produces a phase
- plot for the function F, consisting of the quiver plot plus stream
- lines. The streamlines start at the initial conditions listed in
- Xinit, which should be a matrix whose rows give the desired inital
- conditions for x1 and x2. X1range or X2range is 'auto', the arrows
- are produced based on the stream lines. If 'scale' is negative,
- dots are placed at the base of the arrows. If 'scale' is zero, no
- dots are produced.
-
- PhasePlot(F, X1range, X2range, scale, boxgrid(X1range2, X2range2))
- produces a phase plot with stream lines generated at the edges of
- the rectangle defined by X1range2, X2range2. These ranges are in
- the same form as X1range, X2range.
+ Parameters
+ ----------
+ func : callable(x, t, ...)
+ Computes the time derivative of y (compatible with odeint).
+ The function should be the same for as used for
+ 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.
- PhasePlot(F, X1range, X2range, scale, Xinit, T) produces a phase
- plot where the streamlines are simluted for time T (default = 50).
+ X, Y: ndarray, optional
+ Two 1-D arrays representing x and y coordinates of a grid.
+ These arguments are passed to meshgrid and generate the lists
+ of points at which the vector field is plotted. If absent (or
+ None), the vector field is not plotted.
- PhasePlot(F, X1range, X2range, scale, Xinit, T, P1, P2, ...)
- passes additional parameters to the function F, in the same way as
- ODE45.
+ scale: float, optional
+ Scale size of arrows; default = 1
- Instead of drawing arrows on a grid, arrows can also be drawn on
- streamlines by usinge the X1range and X2range arguments as follows:
+ X0: ndarray of initial conditions, optional
+ List of initial conditions from which streamlines are plotted.
+ Each initial condition should be a pair of numbers.
- X1range X2range
- ------- -------
- 'auto' N Draw N arrows using equally space time points
- 'logtime' [N, lam] Draw N arrows using exponential time constant lam
- 'timepts' [t1, t2, ...] Draw arrows at the list times
- """
+ 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.
- #
- # Parameters defining the plot
- #
- # The constants below define the parameters that control how the
- # plot looks. These can be modified to customize the look of the
- # phase plot.
- #
+ lingrid = N or (N, M): integer or 2-tuple of integers, optional
+ 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.
- #! TODO: convert to keywords
- #! TODO: eliminate old parameters that aren't used
- # PP color = ['m', 'c', 'r', 'g', 'b', 'k', 'y'];
- PP_stream_color = ('b'); # color array for streamlines
- PP_stream_linewidth = 1; # line width for stream lines
+ lintime = N: integer, optional
+ Draw N arrows using equally space time points
- PP_arrow_linewidth = 1; # line width for arrows (quiver)
- PP_arrow_markersize = 10; # size of arrow base marker
+ logtime = (N, lambda): (integer, float), optional
+ Draw N arrows using exponential time constant lambda
+ timepts = [t1, t2, ...]: array-like, optional
+ Draw arrows at the given list times
+
+ parms: tuple, optional
+ List of parameters to pass to vector field: func(x, t, *parms)
+
+ See also
+ --------
+ boxgrid(X, Y): construct box-shaped grid of initial conditions
+
+ Examples
+ --------
+ """
+
#
# Figure out ranges for phase plot (argument processing)
#
- auto = 0; logtime = 0; timepts = 0; Narrows = 0;
- if (isinstance(xlimv, str) and xlimv == 'auto'):
- auto = 1;
- Narrows = ylimv;
+ #! TODO: need to add error checking to arguments
+ autoFlag = False; logtimeFlag = False; timeptsFlag = False; Narrows = 0;
+ if (lingrid != None):
+ autoFlag = True;
+ Narrows = lingrid;
if (verbose):
print 'Using auto arrows\n';
- elif (isinstance(xlimv, str) and xlimv == 'logtime'):
- logtime = 1;
- Narrows = ylimv[0];
- timefactor = ylimv[1];
+ elif (logtime != None):
+ logtimeFlag = True;
+ Narrows = logtime[0];
+ timefactor = logtime[1];
if (verbose):
print 'Using logtime arrows\n';
- elif (isinstance(xlimv, str) and xlimv == 'timepts'):
- timepts = 1;
- Narrows = len(ylimv);
+ elif (timepts != None):
+ timeptsFlag = True;
+ Narrows = len(timepts);
else:
# Figure out the set of points for the quiver plot
+ #! TODO: Add sanity checks
(x1, x2) = np.meshgrid(
- frange(xlimv[0], xlimv[1], float(xlimv[1]-xlimv[0])/xlimv[2]),
- frange(ylimv[0], ylimv[1], float(ylimv[1]-ylimv[0])/ylimv[2]));
+ frange(X[0], X[1], float(X[1]-X[0])/X[2]),
+ frange(Y[0], Y[1], float(Y[1]-Y[0])/Y[2]));
- if ((not auto) and (not logtime) and (not timepts)):
+ if ((not autoFlag) and (not logtimeFlag) and (not timeptsFlag)):
# Now calculate the vector field at those points
(nr,nc) = x1.shape;
dx = np.empty((nr, nc, 2))
@@ -151,16 +160,16 @@
#! TODO: Tweak the shape of the plot
# a=gca; set(a,'DataAspectRatio',[1,1,1]);
- # set(a,'XLim',xlimv(1:2)); set(a,'YLim',ylimv(1:2));
+ # 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 (xinit == None or len(xinit) == 0):
+ if (X0 == None or len(X0) == 0):
return
# Convert initial conditions to a numpy array
- xinit = np.array(xinit);
- (nr, nc) = np.shape(xinit);
+ 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));
@@ -183,12 +192,12 @@
ymin = alim[2]; ymax = alim[3];
else:
# Use the maximum extent of all trajectories
- xmin = np.min(xinit[:,0]); xmax = np.max(xinit[:,0]);
- ymin = np.min(xinit[:,1]); ymax = np.max(xinit[:,1]);
+ 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, xinit[i], TSPAN, args=parms);
+ state = odeint(odefun, X0[i], TSPAN, args=parms);
time = TSPAN
mpl.hold(True);
mpl.plot(state[:,0], state[:,1])
@@ -197,7 +206,7 @@
# set(h[i], 'LineWidth', PP_stream_linewidth);
# Plot arrows if quiver parameters were 'auto'
- if (auto or logtime or timepts):
+ 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):
@@ -206,18 +215,18 @@
k = -1 if scale == None else 0;
# Figure out what time index to use for the next point
- if (auto):
+ if (autoFlag):
# Use a linear scaling based on ODE time vector
tind = np.floor((len(time)/Narrows) * (j-k)) + k;
- elif (logtime):
+ 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 (timepts):
+ elif (timeptsFlag):
# Use specified time points
- # MATLAB: tind = find(time < ylimv[j], 1, 'last');
- tarr = find(time < ylimv[j]);
+ # 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
@@ -263,3 +272,20 @@
# set(bp, 'MarkerSize', PP_arrow_markersize);
return;
+
+# Utility function for generating initial conditions around a box
+def boxgrid(xlimp, ylimp):
+ """BOXGRID generate list of points on edge of box
+
+ list = BOXGRID([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 = frange(xlimp[0], xlimp[1], float(xlimp[1]-xlimp[0])/xlimp[2])
+ sy10 = frange(ylimp[0], ylimp[1], float(ylimp[1]-ylimp[0])/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)) )
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