def __init__(self, xv, yv, mask, **kwargs):
     assert xv.shape == yv.shape, 'xv and yv must have the same shape'
     for dx, dq in zip(xv.shape, mask.shape):
          assert dx==dq+1, \
          '''xv and yv must be cell verticies
          (i.e., one cell bigger in each dimension)'''
     
     self.xv = xv
     self.yv = yv
     
     self.mask = mask
     
     land_color = kwargs.pop('land_color', (0.6, 1.0, 0.6))
     sea_color = kwargs.pop('sea_color', (0.6, 0.6, 1.0))
     
     cm = plt.matplotlib.colors.ListedColormap([land_color, sea_color], 
                                              name='land/sea')
     self._pc = plt.pcolor(xv, yv, mask, cmap=cm, vmin=0, vmax=1, **kwargs)
     self._xc = 0.25*(xv[1:,1:]+xv[1:,:-1]+xv[:-1,1:]+xv[:-1,:-1])
     self._yc = 0.25*(yv[1:,1:]+yv[1:,:-1]+yv[:-1,1:]+yv[:-1,:-1])
     
     if isinstance(self.xv, np.ma.MaskedArray):
         self._mask = mask[~self._xc.mask]
     else:
         self._mask = mask.flatten()
     
     plt.connect('button_press_event', self._on_click)
     plt.connect('key_press_event', self._on_key)
     self._clicking = False
     plt.title('Editing %s -- click "e" to toggle' % self._clicking)
     plt.draw()

def analisis_grafica(carpeta, nombre):
    fig = plt.figure()
    ax = plt.subplot(111)

    # prueba=cm.comtrade(carpeta, nombre)
    # prueba.config()
    # prueba.extraerDatos()

    # A=prueba.oscilografia[:,11]
    # B=prueba.oscilografia[:,10]
    # C=prueba.oscilografia[:,9]

    plt.plot(A, "g", label="A")
    plt.plot(B, "r", label="B")
    plt.plot(C, "y", label="C")
    plt.suptitle(u"Seleccione el ciclo prefalla")  # Ponemos un titulo superior
    plt.legend()  # Creamos la caja con la leyenda

    binding_id = plt.connect("motion_notify_event", on_move)
    plt.connect("button_press_event", on_click)

    """if "test_disconnect" in sys.argv:
        print("disconnecting console coordinate printout...")
        plt.disconnect(binding_id)"""

    # plt.ion()  # Ponemos el modo interactivo
    # plt.axvspan(-0.5,0.5, alpha = 0.25)
    plt.show()

    def twoDplots(self, ind1, ind2):
        """
        Provides a plot of a two-dimensional slice of eigenvector space.
	"""
        afs = []
        fig = plt.figure()
        s1 = fig.add_subplot(111)
        s1.scatter(self.eigvecs[:, ind1], self.eigvecs[:, ind2], c="w")
        afs.append(
            AnnoteModule.AnnoteFinder(self.eigvecs[:, ind1], self.eigvecs[:, ind2], self.resnames, xtol=5, ytol=5)
        )
        plt.connect("button_press_event", afs[-1])
        s1.set_xlabel("$|" + str(ind1) + ">$")
        s1.set_ylabel("$|" + str(ind2) + ">$")
        # fig.suptitle('Eigenvector projections')
        try:
            for sec in self.listofsecs:
                s1.scatter(
                    self.eigvecs[sec.members, ind1],
                    self.eigvecs[sec.members, ind2],
                    c=sec.weights,
                    cmap=sec.seccolor,
                    norm=matplotlib.colors.Normalize(vmin=0, vmax=0.3),
                )
        except AttributeError:
            print "No sectors have been defined for this matrix yet. Call twoDxcc to get them if desired."

        return fig

 def analisis_grafica(self):
     
     
     #prueba=cm.comtrade(carpeta, nombre)
     #prueba.config()
     #prueba.extraerDatos()
     
     #A=prueba.oscilografia[:,11]
     #B=prueba.oscilografia[:,10]
     #C=prueba.oscilografia[:,9]
     
     
     alt.plot(self.A,'g',label='A')
     alt.plot(self.B,'r',label='B')
     alt.plot(self.C,'y',label='C')
     alt.suptitle(u'Seleccione el ciclo prefalla')  # Ponemos un titulo superior
     alt.legend()  # Creamos la caja con la leyenda
     
     binding_id = alt.connect('motion_notify_event', on_move)
     alt.connect('button_press_event', on_click)
     
     '''if "test_disconnect" in sys.argv:
         print("disconnecting console coordinate printout...")
         plt.disconnect(binding_id)'''
     
     
     #plt.ion()  # Ponemos el modo interactivo
     #plt.axvspan(-0.5,0.5, alpha = 0.25)
     alt.show()

def ROI_define():
    im=mpimg.imread(image)
    height=im.shape[0] / nrow
    width=im.shape[1] / ncol

    # Axis adjustable in function of views during acquisition
    fig=plt.figure(figsize=(10, 10 * height / width))
    plt.imshow(im)
    plt.grid(True)
    plt.xticks([width * i for i in range(1, ncol)])
    plt.yticks([height * i for i in range(1, nrow)])

    # Add mouse click event to set area of interest
    ax2=fig.add_subplot(111)
    ax2.patch.set_alpha(0.5)
    cnv=Canvas(ax2)
    plt.connect('button_press_event', cnv.update_path)
    plt.connect('motion_notify_event', cnv.set_location)
    plt.show()

    # Deal with polygon
    poly=cnv.extract_poly()
    poly, scaleX, scaleY=coordinate_conversion(
        poly, im.shape[1], im.shape[0])
    return poly, scaleX, scaleY

 def plot(self, multiview=False):
     """
     Plotten der Oberflaeche:
 
     plot(multiview=False)
 
         multiview: True: Two plotfields side by side; False: One single plotfield
 
     - Space-bar: show next surface.
     - '[1-9]': show every 2nd surface
     - '0': show last surface
     - 'r': show first surface
     - 'a': switch plot aspect 1:1 <==> auto
     - 'c': switch between two modi, single plot, multiple plot for surfaces
     - 'f': save plot as (png-)file with the name of the xxx.srf file
     - 'b': switch between fixed axis and auto axis of new surface
     - 'q': Quit.
     """
     help(plot.plot)
     if not multiview:
         self.multi = False
         self.ax1 = plt.subplot(1, 1, 1)
     else:
         self.multi = True
         self.ax1 = plt.subplot(1, 2, 1)
         self.ax2 = plt.subplot(1, 2, 2)
     self.event_handler(None)
     plt.connect("key_press_event", self.event_handler)
     plt.show()

def imrect(image):
    """
    Best python hack of MATLAB's imrect that I could come up with

    :param image: a numpy array
    :return: returns the bounds of the selected roi
    """
    print('Click and drag to select ROI. Press "Enter" to proceed.')
    fig = plt.figure('ROI selection')
    plt.imshow(image)
    ax = plt.axes()

    def nothing(eclick, erelease):
        pass

    bounds = None
    def on_enter(event):
        if event.key == 'enter' and selector._rect_bbox != (0., 0., 0., 1.):
            nonlocal bounds
            bounds = [int(i) for i in selector._rect_bbox]
            plt.close(fig)

    selector = widgets.RectangleSelector(ax, nothing, drawtype='box', interactive=True)
    plt.connect('key_press_event', on_enter)
    plt.show()  # blocks execution until on_enter() completes
    return bounds

def plot_vi_breakdown_panel(px, h, title, xlab, ylab, hlines, scatter_size,
                            **kwargs):
    """Plot a single panel (over or undersegmentation) of VI breakdown plot.

    Parameters
    ----------
    px : np.ndarray of float, shape (N,)
        The probability (size) of each segment.
    h : np.ndarray of float, shape (N,)
        The conditional entropy of that segment.
    title, xlab, ylab : string
        Parameters for `matplotlib.plt.plot`.
    hlines : iterable of float
        Plot hyperbolic lines of same VI contribution. For each value `v` in
        `hlines`, draw the line `h = v/px`.
    scatter_size : int, optional
    **kwargs : dict
        Additional keyword arguments for `matplotlib.pyplot.plot`.

    Returns
    -------
    None
    """
    x = np.arange(max(min(px),1e-10), max(px), (max(px)-min(px))/100.0)
    for val in hlines:
        plt.plot(x, val/x, color='gray', ls=':', **kwargs) 
    plt.scatter(px, h, label=title, s=scatter_size, **kwargs)
    # Make points clickable to identify ID. This section needs work.
    af = AnnotationFinder(px, h, [str(i) for i in range(len(px))])
    plt.connect('button_press_event', af)
    plt.xlim(xmin=-0.05*max(px), xmax=1.05*max(px))
    plt.ylim(ymin=-0.05*max(h), ymax=1.05*max(h))
    plt.xlabel(xlab)
    plt.ylabel(ylab)
    plt.title(title)

	def turnon(self, event, current_ax):
		self.Fiddle = True
		toggle_selector.RS = RectangleSelector(current_ax, self.line_select_callback,
									   drawtype='box', useblit=True,
									   button=[3], # don't use middle button
									   minspanx=5, minspany=5,
									   spancoords='pixels')
		plt.connect('key_press_event', toggle_selector)

 def view(self,comparer=None,grayScale=False):
     self.grayScale= grayScale
     self.comparer = comparer
     self.fig = plt.figure("Image Viewer")
     self.ax = plt.subplot(111)
     self._event_fct(None) # initial setup
     plt.connect('key_press_event', self._event_fct)
     plt.show()

 def configureFigure(self):
     #http://stackoverflow.com/questions/12439588/how-to-maximize-a-plt-show-window-using-python
     mng = plt.get_current_fig_manager()
     #mng.resize(*mng.window.maxsize())
     mng.window.wm_geometry("+0+0")
     #http://stackoverflow.com/questions/6541123/improve-subplot-size-spacing-with-many-subplots-in-matplotlib
     plt.subplots_adjust(left=0.09, right=0.95, bottom=0.05, top=0.95, wspace=0.2, hspace=0.5)
     #http://matplotlib.org/examples/event_handling/keypress_demo.html
     plt.connect('key_press_event', pressHandler)

 def __init__(self, environment):
     self.signal = False
     self.environment = environment
     plt.ion()
     plt.axis([0,environment.width,0,environment.height])
     plt.connect('key_press_event', self.event)
     plt.show()
     self.draw()
     plt.pause(100)

def point(x,y,annotation,r,p,c,cmap,ytol):
  a=[x]
  b=[y]
  n=[annotation]
  s=[r*r]
  color=matplotlib.colors.colorConverter.to_rgb(cmap(1.+log10(c))) 
  #sctpt=ax.scatter(a,b,s=s,color=color,cmap=cmap,edgecolors='black')
  sctpt=pylab.gca().scatter(a,b,s=s,color=color,cmap=cmap,alpha=1.,picker=5)
  af1 = AnnoteFinder(a,b,n,xtol=r,ytol=ytol,pos=p)
  P.connect('button_press_event',af1)
  return sctpt

def signalPlot(time, data, names):
    plt.figure(1)
    colors =['r', '#FCBDB1', 'g', '#B6FAC4', 'b', '#CAB6FA', 'c']
    ax = plt.subplot(111)
    for x in range(len(data)):
        ax.plot(time, data[x], c=colors[x], label=names[x])
    plt.legend()
    selector.keyPressed.RS = RectangleSelector(ax, selector.onselect, drawtype='box', rectprops=dict(facecolor='red', edgecolor = 'black',
                 alpha=0.5, fill=True))
    plt.connect('key_press_event', selector.keyPressed)
    plt.show()
    plt.show()

def plotEvents(catalog, client=None, **kwargs):
    """
    Plot events on map.

    When client is given it shows the waveform when the event is clicked.
    IPython may not be started in pylab mode for this feature working.
    """

    def onpress(event):
        if not event.inaxes or event.key != "shift":
            return
        lon, lat = map(event.xdata, event.ydata, inverse=True)
        dlon = 0.05
        dlat = 0.05
        filter = "%f < latitude < %f and %f < longitude < %f" % (lat - dlat, lat + dlat, lon - dlon, lon + dlon)
        ev = catalog.filter2(filter)
        if len(ev) == 0:
            print "No event picked"
            return
        from sito import Stream

        st = Stream()
        print "Selcet", ev
        for arrival in ev[0].origins[0].arrivals:
            arrival.pick_id.convertIDToQuakeMLURI()
            pick = arrival.pick_id.getReferredObject()
            if not pick:
                print "FAIL"
                return
            time = pick.time
            seed_id = pick.waveform_id.getSEEDString()
            try:
                st1 = Stream(client.getWaveform(*(seed_id.split(".") + [time - 50, time + 250])))
            except Exception as ex:
                print "%s for %s" % (ex, seed_id)
                continue

            st1.merge()
            print "load %s %s %.1f" % (seed_id, pick.phase_hint, time - ev[0].origins[0].time)
            st1[0].stats["label"] = "%s %s %.1f" % (seed_id, pick.phase_hint, time - ev[0].origins[0].time)
            st += st1
        st.setHI("filter", "")
        st.filter2(2, None)
        # st.plot(automerge=False, method='fast', type='relative')
        im = st.plot_()
        plt.show()

    if client is None:
        catalog.plot(**kwargs)
    else:
        map, ax = catalog.plot(handle=True, **kwargs)
        plt.connect("button_press_event", onpress)
    plt.show()

def plot(fname,multiview=False):

    '''
    Plotten der Oberflaeche:

    plot(fname,multiview=False)

        fname is the file name e.g. trench.srf
        multiview: True: Two plotfields side by side; False: One single plotfield

    - Space-bar: show next surface.
    - '[1-9]': show every 2nd surface
    - '0': show last surface
    - 'r': show first surface
    - 'a': switch plot aspect 1:1 <==> auto
    - 'c': switch between two modi, single plot, multiple plot for surfaces
    - 'f': save plot as (png-)file with the name of the xxx.srf file
    - 'b': switch between fixed axis and auto axis of new surface
    - 'q': Quit.
    '''
    help(plot)
    global abs_filepath
    global number_of_surfaces
    global ax1
    global ax2
    global filename
    global multi

    filename = fname
    multi = multiview
    abs_filepath = get_filepath(fname)
    count_surfaces()

    number_of_surfaces = count_surfaces()
    number = 1
    
    while  get_surfacedata(number) != None:
		# get data from surfaces to plot them
        x_Vals_tmp, y_Vals_tmp = get_surfacedata(number)
        x_Vals.append(x_Vals_tmp)
        y_Vals.append(y_Vals_tmp)
        number = number + 1
        if number > number_of_surfaces:
            break

    if multi:
        f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
    else:
        ax1 = plt.subplot(111)
    event_handler(None)
    plt.connect('key_press_event', event_handler)
    plt.show()

def main():
    global rgb, hsv, fig, window
    args = parse_arguments()
    image = cv2.imread(args.image)
    if args.blur > 0:
        image = cv2.GaussianBlur(image, (args.blur, args.blur), 0)
    window = args.window
    rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    fig = plt.figure()
    plot(fig, rgb, hsv, (window, window), no_labels=args.no_labels)
    plt.connect('pick_event', onpick)
    plt.show()

 def __init__(self, ax, x, y, rescale, shift, offsets=(-20, 20)):
     self.x = np.asarray(x, dtype='float')
     y = np.asarray(y, dtype='float')
     self._points = np.column_stack((x, y))
     self.rescale = rescale
     self.shift = shift
     self.tree = spatial.cKDTree(self._points)
     self.ax = ax
     self.fig = ax.figure
     self.dot = ax.scatter([0], [y.min()], s=130, color='green', alpha=0.7,animated=True)
     self.annotation = self.ax.annotate('', xy=(0, 0), ha = 'right', xytext = offsets, textcoords = 'offset points', va = 'bottom', bbox = dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.75,animated=True), arrowprops = dict(arrowstyle='->', connectionstyle='arc3,rad=0',animated=True),animated=True)
     self.background = None
     plt.connect('motion_notify_event', self)

def selectable_plot(*args, **kwargs):
    fig, current_ax = plt.subplots()                    # make a new plotingrange

    plt.plot(*args, **kwargs)  # plot something

    # drawtype is 'box' or 'line' or 'none'
    toggle_selector.RS = RectangleSelector(current_ax, line_select_callback,
                                           drawtype='box', useblit=True,
                                           button=[1,3], # don't use middle button
                                           minspanx=5, minspany=5,
                                           spancoords='pixels')
    plt.connect('key_press_event', toggle_selector)
    plt.show()

def plot_matrix(A, title=None, labels=None, vocab=None, fig=None):
    # cmap = [[1, 0, 0], [0, 1, 0], [0, 0, 1], [0.25, 0.75, 0], [0.25, 0, 0.75], [0, 0.5, 0.5], [0.75, 0.25, 0], [0.75, 0, 0.25], [0, 0.75, 0.25], [0, 0.25, 0.75]]
    cmap = [
        [1, 0, 0],
        [0, 1, 0],
        [0, 0, 1],
        [1, 1, 0],
        [0, 1, 1],
    ]  # , [1, 0, 1], [1, 0.5, 0], [0.5, 0, 1], [0.5, 1, 0], [0.98, 0.39, 0]]
    cl = len(cmap)
    markers = ["o", "d", ">", (5, 1)]
    ml = len(markers)
    if not vocab:
        vocab = range(A.shape[0])
    res = np.array([x for x in bh_tsne(A, verbose=True)])
    if not fig:
        plt.figure()
    else:
        plt.figure(fig)
    if title:
        plt.title(title)
    # if all(labels) != None:
    #    plt.scatter(res[:, 0], res[:, 1], s=20, c=labels, alpha=0.5)
    # else:
    #    plt.scatter(res[:, 0], res[:, 1], s=20, alpha=0.5)
    for col in xrange(A.shape[1]):
        top_word = np.argmax(A[:, col])
        mk = (col // cl) % ml
        colors = np.zeros((A.shape[0], 4))
        colors[:, 0] = cmap[col % cl][0]
        colors[:, 1] = cmap[col % cl][1]
        colors[:, 2] = cmap[col % cl][2]
        colors[:, -1] = A[:, col] / A[top_word, col]
        plt.scatter(res[:, 0], res[:, 1], c=colors, marker=markers[mk], s=30, edgecolor="none")
        plt.scatter(
            res[top_word, 0],
            res[top_word, 1],
            c=cmap[col % cl],
            marker=markers[mk],
            s=30,
            edgecolor="none",
            label=u"тема #" + str(col),
        )
    if all(vocab) != None:
        af = AnnoteFinder(res[:, 0], res[:, 1], vocab, xtol=0.1, ytol=0.1)
        plt.connect("button_press_event", af)
    plt.legend(scatterpoints=1, loc="best", ncol=3, fontsize=9)
    plt.draw()
    return res