Python pylab.ion函数代码示例

def hinton(W, maxWeight=None):
    """
    Draws a Hinton diagram for visualizing a weight matrix. 
    Temporarily disables matplotlib interactive mode if it is on, 
    otherwise this takes forever.
    """
    reenable = False
    if P.isinteractive():
        P.ioff()
    P.clf()
    height, width = W.shape
    if not maxWeight:
        maxWeight = 2**N.ceil(N.log(N.max(N.abs(W)))/N.log(2))

    P.fill(N.array([0,width,width,0]),N.array([0,0,height,height]),'gray')
    P.axis('off')
    P.axis('equal')
    for x in xrange(width):
        for y in xrange(height):
            _x = x+1
            _y = y+1
            w = W[y,x]
            if w > 0:
                _blob(_x - 0.5, height - _y + 0.5, min(1,w/maxWeight),'white')
            elif w < 0:
                _blob(_x - 0.5, height - _y + 0.5, min(1,-w/maxWeight),'black')
    if reenable:
        P.ion()
    P.show()

def test_path():
  "generate and draw a random path"
  path = genpath()
  P.ion()
  P.clf()
  draw_path(P.gca(), path)
  P.draw()

def cmap_plot(cmdLine):

    pylab.figure(figsize=[5,10])
    a=outer(ones(10),arange(0,1,0.01))
    subplots_adjust(top=0.99,bottom=0.00,left=0.01,right=0.8)
    maps=[m for m in cm.datad if not m.endswith("_r")]
    maps.sort()
    l=len(maps)+1
    for i, m in enumerate(maps):
        print m
        subplot(l,1,i+1)
        pylab.setp(pylab.gca(),xticklabels=[],xticks=[],yticklabels=[],yticks=[])
        imshow(a,aspect='auto',cmap=get_cmap(m),origin="lower")
        pylab.text(100.85,0.5,m,fontsize=10)

# render plot

    if cmdLine: 
        pylab.show(block=True)
    else: 
        pylab.ion()
        pylab.plot([])
        pylab.ioff()
	
    status = 1
    return status

    def __init__(self, ca, cmap=None):
        """
        CAPlotter() constructor keeps a reference to the CA model, and
        optionally a colormap to be used with plots.

        Parameters
        ----------
        ca : LandlabCellularAutomaton object
            Reference to a CA model
        cmap : Matplotlib colormap (optional)
            Colormap to be used in plotting
        """
        import matplotlib

        # Set the colormap; default to matplotlib's "jet" colormap
        if cmap is None:
            self._cmap = matplotlib.cm.jet
        else:
            self._cmap = cmap

        # Keep a reference to the CA model
        self.ca = ca

        # Initialize the plot and remember the grid type
        plt.ion()
        plt.figure(1)
        if type(ca.grid) is landlab.grid.hex.HexModelGrid:
            self.gridtype = 'hex'
        else:
            self.gridtype = 'rast'

	def __init__(self, window_size, easting_offset, northing_offset):
		self.rad_to_deg = 180.0/pi
        # Get parameters
		self.plot_pose = plot_pose
		self.plot_gnss = plot_gnss
		self.plot_odometry = plot_odometry
		self.plot_yaw = plot_yaw
		self.trkpt_threshold = 0.1 # [m]
        
		# Initialize map
		self.offset_e = easting_offset
		self.offset_n = northing_offset
		self.window_size = window_size
		self.map_title = map_title
		self.odo = []
		self.gnss = []
		self.pose_pos = []
		self.odo_yaw = []
		self.gnss_yaw = []
		self.ahrs_yaw = []
		self.pose_yaw = []
		self.wpt_mode = 0
		self.wpt_destination = False
		self.wpt_target = False

		self.pose_image_save = True # save an image for time-lapse video generation
		self.pose_image_count = 0

		ion() # turn interaction mode on

def qqplotp(pv,fileout = None, pnames=pnames(), rownames=rownames(),alphalevel = 0.05,legend=None,xlim=None,ylim=None,ycoord=10,plotsize="652x526",title=None,dohist=True):
     '''
     Read in p-values from filein and make a qqplot adn histogram.
     If fileout is provided, saves the qqplot only at present.
     Searches through p until one is found.   '''       
     
     import pylab as pl     
     pl.ion()     
          
     fs=8     
     h1=qqplot(pv, fileout, alphalevel,legend,xlim,ylim,addlambda=True)
     #lambda_gc=estimate_lambda(pv)
     #pl.legend(["gc="+ '%1.3f' % lambda_gc],loc=2)     
     pl.title(title,fontsize=fs)
     #wm=pl.get_current_fig_manager()
     #e.g. "652x526+100+10
     xcoord=100
     #wm.window.wm_geometry(plotsize + "+" + str(xcoord) + "+" + str(ycoord))

     if dohist:
         h2=pvalhist(pv)
         pl.title(title,fontsize=fs)
         #wm=pl.get_current_fig_manager()
         width_height=plotsize.split("x")
         buffer=10
         xcoord=int(xcoord + float(width_height[0])+buffer)
         #wm.window.wm_geometry(plotsize + "+" + str(xcoord) + "+" + str(ycoord))
     else: h2=None

     return h1,h2

 def drawModel(self):
     PL.ion() # bug fix by Alex Hill in 2013
     if self.modelFigure == None or self.modelFigure.canvas.manager.window == None:
         self.modelFigure = PL.figure()
     self.modelDrawFunc()
     self.modelFigure.canvas.manager.window.update()
     PL.show() # bug fix by Hiroki Sayama in 2016

     def run(self):
         plts = {}
         graphs = {}
         pos  = 0

         plt.ion()
         plt.style.use('ggplot')

         for name in sorted(self.names.values()):
             p = plt.subplot(math.ceil(len(self.names) / 2), 2, pos+1)
             p.set_ylim([0, 100])
             p.set_title(self.machine_classes[name] + " " + name)

             p.get_xaxis().set_visible(False)

             X = range(0, NUM_ENTRIES, 1)
             Y = NUM_ENTRIES * [0]
             graphs[name] = p.plot(X, Y)[0]

             plts[name] = p
             pos += 1 

         plt.tight_layout()
         
         while True:
             for name, p in plts.items():
                 graphs[name].set_ydata(self.loads[name])

             plt.draw()
             plt.pause(0.05)

def animate_1D(time, var, x, ylab=' '): 
    """Animate a 2d array with a sequence of 1d plots

     Input: time = one-dimensional time vector;
            var =  array with first dimension = len(time) ;
            x = (optional) vector width dimension equal to var.shape[1];
            ylab = ylabel for plot
    """
    
    import pylab
    import numpy
   
    

    pylab.close()
    pylab.ion()

    # Initial plot
    vmin=var.min()
    vmax=var.max()
    line, = pylab.plot( (x.min(), x.max()), (vmin, vmax), 'o')

    # Lots of plots
    for i in range(len(time)):
        line.set_xdata(x)
        line.set_ydata(var[i,:])
        pylab.draw()
        pylab.xlabel('x')
        pylab.ylabel(ylab)
        pylab.title('time = ' + str(time[i]))
    
    return

 def sintest(self, AMP=0.5, FREQ=1, TIME=5, mode='velocity'):
 
     # freq in hertz
     # amp in radians
     
     self.tstart = time.time()
     t = time.time()-self.tstart
     self.setpos(0)
     pylab.ion()
     
     print('**Running Sin Test**')
     
     while t<TIME:
         t = time.time()-self.tstart
         ctrl = AMP*np.sin(FREQ*2.0*np.pi*t)
         
         if mode == 'velocity':
             self.setvel(ctrl)
             output = self.getvel()
         if mode == 'pos':
             self.setpos(ctrl)
             output = self.getpos()
         
         pylab.plot([t],[output],color='blue', marker='*')
         pylab.plot([t],[ctrl],color='red')
         pylab.draw()
     self.stop()

 def __init__(self, net, task, valueNetwork=None, **args):
     self.net = net
     self.task = task
     self.setArgs(**args)
     if self.valueLearningRate == None:
         self.valueLearningRate = self.learningRate
     if self.valueMomentum == None:
         self.valueMomentum = self.momentum        
     if self.supervisedPlotting:
         from pylab import ion
         ion() 
     
     # adaptive temperature:
     self.tau = 1.
     
     # prepare the datasets to be used
     self.weightedDs = ImportanceDataSet(self.task.outdim, self.task.indim)
     self.rawDs = ReinforcementDataSet(self.task.outdim, self.task.indim)
     self.valueDs = SequentialDataSet(self.task.outdim, 1)
     
     # prepare the supervised trainers
     self.bp = BackpropTrainer(self.net, self.weightedDs, self.learningRate,
                               self.momentum, verbose=False,
                               batchlearning=True)            
     
     # CHECKME: outsource
     self.vnet = valueNetwork
     if valueNetwork != None:
         self.vbp = BackpropTrainer(self.vnet, self.valueDs, self.valueLearningRate,
                                    self.valueMomentum, verbose=self.verbose)
         
     # keep information:
     self.totalSteps = 0
     self.totalEpisodes = 0

	def histoCase( self ):
		print "Building Histogram"
		
		header = self.histoAxis.get()
		
		if ( self.filterNum.get() == "None" ):
			data = self.dataInstance.getNumericAxis( self.histoAxis.get() )
		else:
			labels = []
			data = []
			for set in self.main.histoCols:
				labels.append( set[0] )
				data.append( set[1] )
		
		pylab.ion()
		if ( self.filterNum.get() == "None" ):
			pylab.hist( data, bins=self.bins.get(), label=header )
		else:
			pylab.hist( data, bins=self.bins.get(), label=labels )

		pylab.legend()
		pylab.xlabel( header )
		pylab.ylabel("Frequency")
		pylab.title("Histogram" )

		pylab.show()

def plotInit(Plotting, Elements):
	if (Plotting == 2):
		loc = [i.xy for i in Elements]
		x = [i.real for i in loc]
		y = [i.imag for i in loc]
		x = list(sorted(set(x))) 
		x.remove(-10)
		y = list(sorted(set(y)))

		X, Y = pylab.meshgrid(x, y)
		U = pylab.ones(shape(X))
		V = pylab.ones(shape(Y))

		pylab.ion()
		fig, ax = pylab.subplots(1,1)
		graph = ax.quiver(X, Y, U, V)
		pylab.draw()
	else:
		pylab.ion()
		graph, = pylab.plot(1, 'ro', markersize = 2) 
		x = 2
		pylab.axis([-x,x,x,-x])

		graph.set_xdata(0)
		graph.set_ydata(0)
		pylab.draw()

	return graph

 def __init__(self,file):
     # Task parameters
     self.running = True
     
     # Class variables
     self.origin = Vector(0,0)
     self.position = Vector(0,0)
     self.position_list = []
     
     # init plot
     self.fig = pyplot.figure(num=None, figsize=(8, 8), dpi=80, facecolor='w', edgecolor='k')
     self.area = 2
     ion()
     
     # Init transform
     self.tf = tf.TransformListener()
     self.br = tf.TransformBroadcaster()
     self.quaternion = np.empty((4, ), dtype=np.float64)
     
     # Init node
     self.rate = rospy.Rate(10)
     
     # Init subscriber
     self.imu_sub = rospy.Subscriber('/fmInformation/imu', Imu, self.onImu )
     
     # Init stat
     self.file = file
     self.deviations = []    

def plotDirections(aabb=(),mask=0,bins=20,numHist=True,noShow=False,sphSph=False):
	"""Plot 3 histograms for distribution of interaction directions, in yz,xz and xy planes and
	(optional but default) histogram of number of interactions per body. If sphSph only sphere-sphere interactions are considered for the 3 directions histograms.

	:returns: If *noShow* is ``False``, displays the figure and returns nothing. If *noShow*, the figure object is returned without being displayed (works the same way as :yref:`yade.plot.plot`).
	"""
	import pylab,math
	from yade import utils
	for axis in [0,1,2]:
		d=utils.interactionAnglesHistogram(axis,mask=mask,bins=bins,aabb=aabb,sphSph=sphSph)
		fc=[0,0,0]; fc[axis]=1.
		subp=pylab.subplot(220+axis+1,polar=True);
		# 1.1 makes small gaps between values (but the column is a bit decentered)
		pylab.bar(d[0],d[1],width=math.pi/(1.1*bins),fc=fc,alpha=.7,label=['yz','xz','xy'][axis])
		#pylab.title(['yz','xz','xy'][axis]+' plane')
		pylab.text(.5,.25,['yz','xz','xy'][axis],horizontalalignment='center',verticalalignment='center',transform=subp.transAxes,fontsize='xx-large')
	if numHist:
		pylab.subplot(224,polar=False)
		nums,counts=utils.bodyNumInteractionsHistogram(aabb if len(aabb)>0 else utils.aabbExtrema())
		avg=sum([nums[i]*counts[i] for i in range(len(nums))])/(1.*sum(counts))
		pylab.bar(nums,counts,fc=[1,1,0],alpha=.7,align='center')
		pylab.xlabel('Interactions per body (avg. %g)'%avg)
		pylab.axvline(x=avg,linewidth=3,color='r')
		pylab.ylabel('Body count')
	if noShow: return pylab.gcf()
	else:
		pylab.ion()
		pylab.show()