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# -*- coding:latin-1 -*-
#-----------------------------------------------------------------------------
# Name: PlotFigure.py
# Purpose: Plotting frame that contains the plots generated by Model Buider
#
# Author: Flavio C. Coelho
#
# Created: 2004/09/01
# RCS-ID: $Id: PlotFigure.py,v 1.1 2004/01/13 10:51:43 fccoelho Exp $
# Copyright: (c) 2003
# Licence: GPL
# Obs: This code was based on Jeremy Donoghue's embedding_in_wx.py included with
# matplotlib.
#-----------------------------------------------------------------------------
#Boa:Frame:PlotFigure
import matplotlib
matplotlib.use('WXAgg')
from matplotlib.backends.backend_wx import NavigationToolbar2Wx, FigureManager
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.axes import Subplot
import matplotlib.numerix as numpy
#from RandomArray import *
#from numpy.random.old import *
from pylab import *
from numpy.random import *
from numpy import *
import wxversion
wxversion.select('2.8')
import wx
def create(parent):
return PlotFigure(parent)
[wxID_PLOTFIGURE] = [wx.NewId() for _init_ctrls in range(1)]
class PlotFigure(wx.Frame):
def _init_ctrls(self, prnt):
# generated method, don't edit
wx.Frame.__init__(self, id=wxID_PLOTFIGURE, name='Output', parent=prnt,
pos=wx.Point(311, 209), size=wx.Size(1280, 893),
style=wx.DEFAULT_FRAME_STYLE, title='Results')
self.SetClientSize(wx.Size(1280, 893))
def __init__(self, parent):
self._init_ctrls(parent)
self.fig = Figure((10,8), 75)
self.canvas = FigureCanvas(self,-1, self.fig)
self.toolbar = NavigationToolbar2Wx(self.canvas)
self.toolbar.Realize()
# On Windows, default frame size behaviour is incorrect
# you don't need this under Linux
tw, th = self.toolbar.GetSizeTuple()
fw, fh = self.canvas.GetSizeTuple()
self.toolbar.SetSize(wx.Size(fw, th))
# Create a figure manager to manage things
self.figmgr = FigureManager(self.canvas, 1, self)
# Now put all into a sizer
sizer = wx.BoxSizer(wx.VERTICAL)
# This way of adding to sizer prevents resizing
#sizer.Add(self.fig, 0, wxLEFT|wxTOP)
# This way of adding to sizer allows resizing
sizer.Add(self.canvas, 1, wx.LEFT|wx.TOP|wx.GROW)
# Best to allow the toolbar to resize!
sizer.Add(self.toolbar, 0, wx.GROW)
self.SetSizer(sizer)
self.Fit()
def plot(self,x,y,leg=[]):
"""
This function will plot one or more lines.
y is a list of sequances or a single vector.
x is a vector.
"""
# Use this line if using a toolbar
a = self.fig.add_subplot(111)
# Or this one if there is no toolbar
#a = Subplot(self.fig, 111)
if str(type(y)) == "<type 'list'>":
nlin = len(y)
for i in range(nlin):
a.plot (x,y[i])
else:
a.plot(x,y)
a.set_xlabel('Time', fontsize=9)
#---generating tuple of legends-------------------------------------------------
if str(type(y)) == "<type 'list'>":
if leg ==[]:
b = range(nlin)
leg = tuple(['$y_{'+str(i)+'}$' for i in b])
else:
pass
#-------------------------------------------------------------------------------
a.legend(leg)
self.toolbar.update()
def vectorField(self,x,y,u,v,c=0.2,xlabel='',ylabel='',trajectory=[]):
"""
Plot a vector field for State space visualizations
"""
# Use this line if using a toolbar
a = self.fig.add_subplot(111)
# Or this one if there is no toolbar
#a = Subplot(self.fig, 111)
q = a.quiver2(x,y,u,v,c,units='x')
a.set_xlabel(xlabel)
a.set_ylabel(ylabel)
a.set_title('State Space Diagram')
if trajectory:
for i in range(len(trajectory))[::2]:
lt = '-'
if i != 0:
lt = ':'
a.plot(trajectory[i],trajectory[i+1],lt,linewidth=2)
# Do contours for isoclines
print x.shape
sq = int(sqrt(len(x)))
x.shape = (sq,sq)
y.shape = (sq,sq)
u.shape = (sq,sq)
v.shape = (sq,sq)
z1 = sqrt(u**2+v**2) #basic arrow lengths
z2 = u #dx/dt
z3 = v #dy/dt
lev = (0,)
cs1 = a.contour(x,y,z1,20,origin='lower')#general isoclines
# im = a.imshow(z1, interpolation='bilinear',alpha=0.9, origin='lower',cmap=cm.gray)
cs1l = a.contour(x,y,z1,lev,linewidths=2,colors='k',origin='lower')
cs2l = a.contour(x,y,z2,lev,linewidths=2,colors='k',origin='lower')
cs3l = a.contour(x,y,z3,lev,linewidths=2,colors='k',origin='lower')
#doing contour labels
a.clabel(cs1l,fmt = 'Nullcline %2.1f',inline=0, colors = 'r', fontsize=14)
a.clabel(cs2l,fmt = 'dx/dt=%2.1f',inline=0, colors = 'r', fontsize=14)
a.clabel(cs3l,fmt = 'dy/dt=%2.1f',inline=0, colors = 'r', fontsize=14)
def plotSpecg(self,x, name):
"""
Makes a Spectrogram plot of x
"""
# Use this line if using a toolbar
a = self.fig.add_subplot(111)
# Or this one if there is no toolbar
#a = Subplot(self.fig, 111)
a.specgram(x)
a.set_xlabel('time')
a.set_ylabel('frequency')
a.set_title('Spectrogram of %s'%name)
def plotSpec(self,x, name):
"""
Makes a power spectrum plot of x
"""
# Use this line if using a toolbar
a = self.fig.add_subplot(111)
# Or this one if there is no toolbar
#a = Subplot(self.fig, 111)
a.psd(x)
a.set_xlabel('frequency')
a.set_ylabel('Power(dB)')
a.set_title('Power Spectrum of %s'%name)
def plot_data(self, x,y,leg=None):
"""
This function will plot the time series as output by odeint.
"""
# Use this line if using a toolbar
a = self.fig.add_subplot(111)
# Or this one if there is no toolbar
#a = Subplot(self.fig, 111)
nvar = min(y[0].shape)
for i in range(nvar):
a.plot (x,y[0][:,i])
a.set_xlabel('Time', fontsize=9)
a.set_ylabel('$State variables$', fontsize=9)
a.set_title('Time series')
#---generating tuple of legends-------------------------------------------------
if not leg:
b = range(nvar)
leg = tuple(['$y_{'+str(i)+'}$' for i in b])
#-------------------------------------------------------------------------------
a.legend(leg)
self.toolbar.update()
def plotStats(self,x, ts,vnames=[]):
"""
This function will plot multiple time series
ts is a list of lists: [list of median time-series, list of lower credible intervals, list of upper credible intervals]
"""
# Use this line if using a toolbar
#a = self.figmgr.add_subplot(111)
# Or this one if there is no toolbar
#a = Subplot(self.fig, 111)
nvar = len(ts[0])
print len(x), len(ts[0])
c=['r','g','b','c','m','y','k']
for i in range(nvar):
a = self.fig.add_subplot(nvar*100+10+i+1)
a.plot(x,ts[0][i], '%s-o'%c[i%len(c)],x,ts[1][i],'%s-.'%c[i%len(c)], x,ts[2][i],'%s-.'%c[i%len(c)])
a.set_xlabel('Time', fontsize=9)
if vnames:
a.set_ylabel(vnames[i], fontsize=9)
else:
a.set_ylabel('$y_{'+str(i)+'}$', fontsize=9)
a.legend(('$median$',))
#---generating tuple of legends-------------------------------------------------
## b = range(nvar)
## leg = tuple(['$y_{'+str(i)+'}$' for i in b])
#-------------------------------------------------------------------------------
self.toolbar.update()
def plotDist(self,data,vname):
"""
Plots histograms of each line of 'data'
the name of the variables are the elements of the list 'vname'
"""
nvar = len(data)
for i in range(nvar):
# Use this line if using a toolbar
a = self.fig.add_subplot(nvar*100+10+i+1)
nb, bins, patches = a.hist(data[i],bins=50, normed=1)
a.set_ylabel(vname[i], fontsize=9)
self.toolbar.update()
def plotMeldout(self,meldout,vnames=[]):
"""
Plots histograms of the posterior distributions of the model components
meldOut is the output of the Melding.SIR function: (w,qtiltheta,qtilphi,q1est)
"""
nvar = len(meldout[1])
data = meldout[1]
for i in range(nvar):
# Use this line if using a toolbar
a = self.fig.add_subplot(nvar*100+10+i+1)
# Or this one if there is no toolbar
#a = Subplot(self.fig, 111)
nb, bins, patches = a.hist(data[i],bins=50, normed=1)
a.set_title('Posterior Distribution of %s$'%vnames[i], fontsize=9)
self.toolbar.update()
def plotEquation(self, eqlist):
"""
Plot list of equations typeset with mathtext on an equation box
"""
a = self.fig.add_subplot(111)
a.plot([0,10],'w.')
a.set_xlim((0,10))
a.set_ylim((0,10))
a.set_title('Model Equations')
a.set_xticklabels([])
a.set_yticklabels([])
a.set_xticks([])
a.set_yticks([])
for i in range(len(eqlist)):
print eqlist[i]
a.text(1,9-i,eqlist[i], fontsize=15)
self.toolbar.update()
def GetToolBar(self):
# You will need to override GetToolBar if you are using an
# unmanaged toolbar in your frame
return self.toolbar
if __name__ == '__main__':
app = wx.PySimpleApp()
frame = create(None)
x = normal(0,1,50)
y = normal(0,1,(50,5))
frame.plot_data(x,[y])
frame.Show()
app.MainLoop()
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