Python LineCollection.set_linewidth方法代码示例

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_linewidth [as 别名]
def plot_Kiel_diagram(starl):
    """
    Plot Kiel diagram.
    """
    x = starl['temperature']
    y = starl['g']
    age = starl['age']/1e6
    points = np.array([x, y]).T.reshape(-1, 1, 2)
    segments = np.concatenate([points[:-1], points[1:]], axis=1)
    
    cmap = pl.cm.spectral
    norm = pl.Normalize(age.min(), age.max())
    lc = LineCollection(segments, cmap=cmap,norm=norm)
    lc.set_array(age)
    lc.set_linewidth(3)
    pl.gca().add_collection(lc)
    pl.xlim(x.max(), x.min())
    pl.ylim(y.max(), y.min())
    pl.xlabel('Effective temperature [K]')
    pl.ylabel('log(surface gravity [cm s$^{-2}$]) [dex]')
    
    ax0 = pl.gca()
    ax1 = pl.mpl.colorbar.make_axes(ax0)[0]
    norm = pl.mpl.colors.Normalize(age.min(), age.max())
    cb1 = pl.mpl.colorbar.ColorbarBase(ax1, cmap=cmap,
                                   norm=norm,orientation='vertical')
    cb1.set_label('Age [Myr]')
    pl.axes(ax0)

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_linewidth [as 别名]
def hello():

    rtimes, rt, rp = np.loadtxt("data/data.txt").T
    rtimes = map(datetime.datetime.fromtimestamp, rtimes)
    rtimes = matplotlib.dates.date2num(rtimes)
    fig = Figure()
    axis = fig.add_subplot(1, 1, 1)
    axis.xaxis_date()
    fig.autofmt_xdate()

    forecast_list = []
    for fname in glob.glob("data/forecast.*.txt"):
        stamp = fname.split(".")[1]
        times, tempa = np.loadtxt(fname).T
        times = map(datetime.datetime.fromtimestamp, times)
        times = matplotlib.dates.date2num(times)

        points = np.array([times, tempa]).T.reshape(-1, 1, 2)
        segments = np.concatenate([points[:-1], points[1:]], axis=1)
        lc = LineCollection(segments, cmap=plt.get_cmap("jet"),
                            norm=plt.Normalize(0, 1))
        lc.set_array(np.linspace(0,1,len(times)))
        lc.set_linewidth(1)
        axis.add_collection(lc)

        axis.plot_date(times, tempa, "-", linewidth=0)

    axis.plot_date(rtimes, rt, "-",linewidth=1, color="black")

    canvas = FigureCanvas(fig)
    output = StringIO.StringIO()
    canvas.print_png(output)
    response = make_response(output.getvalue())
    response.mimetype = 'image/png'
    return response

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_linewidth [as 别名]
def show_mf_wave(**kwargs):
    ion()
    mjfile='data/mf_W1%s_W2%s_U%s_N%s_dmu%s.npy'%(kwargs.get('K1'),kwargs.get('K2'),kwargs.get('U'),kwargs.get('nsite'),kwargs.get('dmu'))
    pls=load(mjfile)
    ampl=(pls[:2]*pls[:2].conj()).real
    print 'Magnituede %s'%sum(ampl,axis=1)
    if ONSV:
        return
    #mjfile2='data/mf_W1%s_W2%s_U%s_N%s_dmu%s.npy'%(kwargs.get('K1'),kwargs.get('K2'),kwargs.get('U'),kwargs.get('nsite'),-kwargs.get('dmu'))
    #pls2=load(mjfile2)
    #overlap=(pls2[:2].dot(pls[:2].T.conj()))
    #print overlap
    #subplot(211)

    #plot(abs(ket_even.state))
    #ylim(0,0.5)
    #subplot(212)
    #plot(abs(ket_odd.state))
    #ylim(0,0.5)
    #pdb.set_trace()
    lw=2
    lc='r'
    nsite=pls.shape[1]
    for n in xrange(2):
        pln=ampl[n]
        ax=subplot(121+n)
        lc=LineCollection([[(i,0),(i,pln[i].item())] for i in xrange(nsite)])
        lc.set_linewidth(lw)
        ax.add_collection(lc)

        ax.autoscale()
        ax.margins(0.1)
    pdb.set_trace()

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_linewidth [as 别名]
def d3():
    rtimes, rt, rp = np.loadtxt("data/data.txt").T
    mask = np.logical_and(rtimes>1391000000, rtimes<1393000000)
    rtimes = rtimes[mask]
    rt = rt[mask]
    rtimes = map(datetime.datetime.fromtimestamp, rtimes)
    rtimes = matplotlib.dates.date2num(rtimes)
    fig, axis = plt.subplots()

    axis.xaxis_date()
    fig.autofmt_xdate()
    axis.plot_date(rtimes, rt, "-",linewidth=3, color="black")

    forecast_list = []
    for fname in glob.glob("data/forecast.1391*.txt"):
        stamp = fname.split(".")[1]
        times, tempa = np.loadtxt(fname).T
        times = map(datetime.datetime.fromtimestamp, times)
        times = matplotlib.dates.date2num(times)

        points = np.array([times, tempa]).T.reshape(-1, 1, 2)
        segments = np.concatenate([points[:-1], points[1:]], axis=1)
        lc = LineCollection(segments, cmap=plt.get_cmap("jet"),
                            norm=plt.Normalize(0, 1))
        lc.set_array(np.linspace(0,1,len(times)))
        lc.set_linewidth(1)
        axis.add_collection(lc)

        axis.plot_date(times, tempa, "-", linewidth=2)



    return fig_to_html(fig)

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_linewidth [as 别名]
def add_data(globe, axes, color_dict):
    """Add shapefile polygons to the matplotlib axes"""
    file_object = shapefile.Reader(filename)
    shapes = file_object.shapes()
    records = file_object.records()
    #iterate over all but the first 20 polygons (they're junk)
    for record, shape in zip(records[20:],shapes[20:]):
        #this entry is the colour code
        description = record[6]
        lons,lats = zip(*shape.points)
        #transform the lat/long coords to the right projection
        data = np.array(globe(lons, lats)).T
        #shapefile shapes can have disconnected parts, we have
        #to check
        if len(shape.parts) == 1:
            segs = [data,]
        else:
            segs = []
            for i in range(1,len(shape.parts)):
                #add all the parts
                index = shape.parts[i-1]
                index2 = shape.parts[i]
                segs.append(data[index:index2])
            segs.append(data[index2:])
        #Add all the parts we've found as a set of lines
        lines = LineCollection(segs,antialiaseds=(1,))
        lines.set_facecolors(color_dict[description])
        lines.set_edgecolors('k')
        lines.set_linewidth(0.1)
        #add the collection to the active axes
        axes.add_collection(lines)

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_linewidth [as 别名]
    def __plot_all(self, spectrum):
        total = len(spectrum)
        count = 0.0
        for timeStamp in spectrum:
            if self.settings.fadeScans:
                alpha = (total - count) / total
            else:
                alpha = 1

            data = spectrum[timeStamp].items()
            peakF, peakL = self.extent.get_peak_fl()

            segments, levels = self.__create_segments(data)
            if segments is not None:
                lc = LineCollection(segments)
                lc.set_array(numpy.array(levels))
                lc.set_norm(self.__get_norm(self.settings.autoL, self.extent))
                lc.set_cmap(self.colourMap)
                lc.set_linewidth(self.lineWidth)
                lc.set_gid('plot')
                lc.set_alpha(alpha)
                self.axes.add_collection(lc)
                count += 1

        return peakF, peakL

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_linewidth [as 别名]
def doTracelines(xstart,ystart,zstart,step,tmax,Nmax):
    global ActiveAxis, ActiveCanvas, ActiveTimmlModel, ActiveSettings
    setActiveWindow()
    win = getActiveWindow()
    ActiveAxis.set_autoscale_on(False)
    width = 0.5
    color = []
    for j in range(getActiveNumberLayers()):
        color.append( ActiveSettings.get_color('Trace',j) )
        color[j] = colorConverter.to_rgba( color[j] )
    for i in range( len(xstart) ):
        xyz, time, reason, pylayers = ActiveTimmlModel.\
            traceline(xstart[i],ystart[i],zstart[i],step,tmax,Nmax,tstart=0.0,window=win,labfrac = 2.0, Hfrac = 2.0)
        trace_color = []
        for j in range(len(xyz)-1):  # Number of segments one less than number of points
            trace_color.append( color[ pylayers[j] ] )
        points = zip( xyz[:,0], xyz[:,1] )
        segments = zip( points[:-1], points[1:] )
        LC = LineCollection(segments, colors = trace_color)
        LC.set_linewidth(width)
        ActiveAxis.add_collection(LC)
        #ActiveAxis.plot( xyz[:,0], xyz[:,1], 'b' )
    ActiveAxis.set_xlim(win[0],win[2])
    ActiveAxis.set_ylim(win[1],win[3])
    ActiveCanvas.draw()

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_linewidth [as 别名]
def addLine(shapefilename):

    r = shapefile.Reader(shapefilename)
    shapes = r.shapes()
    records = r.records()

    cnt = 0
    for record, shape in zip(records, shapes):
        print(cnt)

        lons,lats = zip(*shape.points)
        data = np.array(m(lons, lats)).T

        if len(shape.parts) == 1:
            segs = [data,]
        else:
            segs = []
            for i in range(1,len(shape.parts)):
                index = shape.parts[i-1]
                index2 = shape.parts[i]
                segs.append(data[index:index2])
            segs.append(data[index2:])

        lines = LineCollection(segs,antialiaseds=(1,), zorder=3)
        # lines.set_facecolors(np.random.rand(3, 1) * 0.5 + 0.5)
        lines.set_edgecolors('k')
        lines.set_linewidth(0.3)
        ax.add_collection(lines)
        cnt += 1

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_linewidth [as 别名]
def colorline(ax, x,y,z,linewidth=1, colormap='jet', norm=None, zorder=1, alpha=1, linestyle='solid'):
        cmap = plt.get_cmap(colormap)
        
        if type(linewidth) is list or type(linewidth) is np.array or type(linewidth) is np.ndarray:
            linewidths = linewidth
        else:
            linewidths = np.ones_like(z)*linewidth
        
        if norm is None:
            norm = plt.Normalize(np.min(z), np.max(z))
        else:
            norm = plt.Normalize(norm[0], norm[1])
        
        '''
        if self.hide_colorbar is False:
            if self.cb is None:
                self.cb = matplotlib.colorbar.ColorbarBase(self.ax1, cmap=cmap, norm=norm, orientation='vertical', boundaries=None)
        '''
            
        # Create a set of line segments so that we can color them individually
        # This creates the points as a N x 1 x 2 array so that we can stack points
        # together easily to get the segments. The segments array for line collection
        # needs to be numlines x points per line x 2 (x and y)
        points = np.array([x, y]).T.reshape(-1, 1, 2)
        segments = np.concatenate([points[:-1], points[1:]], axis=1)
        
        # Create the line collection object, setting the colormapping parameters.
        # Have to set the actual values used for colormapping separately.
        lc = LineCollection(segments, linewidths=linewidths, cmap=cmap, norm=norm, zorder=zorder, alpha=alpha, linestyles=linestyle )
        lc.set_array(z)
        lc.set_linewidth(linewidth)
        
        ax.add_collection(lc)

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_linewidth [as 别名]
    def __plot_all(self):
        total = len(self.data)
        count = 0.0
        for timeStamp in self.data:
            if len(self.data[timeStamp]) < 2:
                self.parent.threadPlot = None
                return None, None

            if self.fade:
                alpha = (total - count) / total
            else:
                alpha = 1

            data = self.data[timeStamp].items()
            peakF, peakL = self.extent.get_peak_fl()

            segments, levels = self.__create_segments(data)
            lc = LineCollection(segments)
            lc.set_array(numpy.array(levels))
            lc.set_norm(self.__get_norm(self.autoL, self.extent))
            lc.set_cmap(self.colourMap)
            lc.set_linewidth(self.lineWidth)
            lc.set_gid('plot')
            lc.set_alpha(alpha)
            self.axes.add_collection(lc)
            count += 1

        return peakF, peakL

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_linewidth [as 别名]
def load_colorado_shapes(m):

    # read all US counties
    rdr = shapefile.Reader("../USA_adm/USA_adm2")
    shapes = rdr.shapes()
    records = rdr.records()

    # only keep Colorado counties
    ndx = filter(lambda i: records[i][4] == 'Colorado', np.arange(len(shapes)))
    shapes = [shapes[i] for i in ndx]
    records = [records[i] for i in ndx]

    # modified from web tutorial
    # http://www.geophysique.be/2013/02/12/matplotlib-basemap-tutorial-10-shapefiles-unleached-continued/
    line_col = []
    for record, shape in zip(records, shapes):
        lons,lats = zip(*shape.points)
        data = np.array(m(lons, lats)).T
 
        if len(shape.parts) == 1:
            segs = [data,]
        else:
            segs = []
            for i in range(1,len(shape.parts)):
                index = shape.parts[i-1]
                index2 = shape.parts[i]
                segs.append(data[index:index2])
                segs.append(data[index2:])
 
        lines = LineCollection(segs, antialiaseds=(1,))
        lines.set_edgecolors('k')
        lines.set_linewidth(0.8)
        line_col.append(lines)

    return line_col

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_linewidth [as 别名]
def plot_spectrum(el,x=arange(2),offset=[0.,0.],ax=None,lw=3,**kwargs):
    '''
    Plot spectrum.

    el:
        the data.
    x:
        the lower and upper limit of x.
    offset:
        the displace of data.
    ax:
        the ax.
    '''
    N=len(el)
    if ax==None:
        ax=gca()
    #x=repeat([array(x)+offset[0]],N,axis=0).T
    x=array(x)+offset[0]
    el=el+offset[1]
    lc=LineCollection([[(x[0],el[i]),(x[1],el[i])] for i in xrange(N)],**kwargs)
    lc.set_linewidth(lw)
    #pl=ax.plot(x,concatenate([el[newaxis,...],el[newaxis,...]],axis=0))
    ax.add_collection(lc)
    ax.autoscale()
    ax.margins(0.1)
    #for i in xrange(N):
        #axhline(y=el[i],xmin=x[0],xmax=x[1])
    return ax

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_linewidth [as 别名]
def traceShape(file_shapefile):
    r = shapefile.Reader(file_shapefile)
    shapes = r.shapes()
    records = r.records()
    #sc_fac = 100000
    for record, shape in zip(records,shapes):
        #print shape.points
        lonsh,latsh = zip(*shape.points)
        # lonsh = [x/sc_fac for x in lonsh]
        # latsh = [x/sc_fac for x in latsh]
        data = np.array(m(lonsh, latsh)).T
     
        if len(shape.parts) == 1:
            segs = [data,]
        else:
            segs = []
            for i in range(1,len(shape.parts)):
                index = shape.parts[i-1]
                index2 = shape.parts[i]
                segs.append(data[index:index2])
            segs.append(data[index2:])
     
        lines = LineCollection(segs,antialiaseds=(1,))
        # lines.set_facecolors(cm.jet(np.random.rand(1)))
        lines.set_edgecolors('k')
        lines.set_linewidth(0.1)
        ax.add_collection(lines)

    return None

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_linewidth [as 别名]
def plotflow(nm,j,ylabel='Congestion Window Size',state=False):
  i=0

  if state and not isinstance(nm,list):
    r = (1,0,0)
    g = (0,1,0)
    b = (0,0,1)
    clrs = np.zeros((flows[nm][3].shape[0],3))
    clrs[flows[nm][-1]=='SS']=g
    clrs[flows[nm][-1]=='CA']=b
    clrs[flows[nm][-1]=='FR']=r
    points = np.array([flows[nm][i], flows[nm][j]]).T.reshape(-1, 1, 2)
    segments = np.concatenate([points[:-1], points[1:]], axis=1)

    lc = LineCollection(segments, colors=clrs)
    lc.set_linewidth(1.7)
    fig, ax = plt.subplots()
    ax.add_collection(lc)
    ax.autoscale_view()

    line_ss = mlines.Line2D([], [], color='green', label='Slow Start')
    line_ca = mlines.Line2D([], [], color='blue', label='Congestion Avoidance')
    line_fr = mlines.Line2D([], [], color='red', label='Fast Recovery')
    plt.legend(handles=[line_ss,line_ca,line_fr])
  else:
    if isinstance(nm,list):
      for n in nm:
        if n in flows:
          plt.plot(flows[n][i],flows[n][j],label=n)
    else:
      plt.plot(flows[nm][i],flows[nm][j])
    plt.legend()
  plt.xlabel('time (s)')
  plt.ylabel(ylabel)
  plt.show()

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_linewidth [as 别名]
def plot(ax, x, y, time, sim_type):
	assert(len(x) == len(y) == len(time))

	l = len(time)
	if use_hf_coloration:
		time_to_grayscale = 0.8 / 23.6 # for HF coloring
	else:
		time_to_grayscale = 0.8 / time[l-1]
	colors = []
	for i in range(l-1):
		if use_hf_coloration:
			color = get_hf_color(time[i])  # time[] is really HF
		else:
			g = 0.8 - (time[i] * time_to_grayscale)**2.0
			if sim_type == 'driven':
				color = (g, 1.0, g, 0.8)
			else:
				color = (g, g, 1.0, 1.0)
		colors.append(color)
	
	points = zip(x,y)
	segments = zip(points[:-1], points[1:])
	lc = LineCollection(segments, colors=colors)
	lc.set_alpha(1.0)
	lc.set_linewidth(1.0)
	lc.set_antialiased(True)
	ax.add_collection(lc)
	if use_hf_coloration:
		end_points.append((x[l-1], y[l-1], get_hf_color(time[l-1])))
	else:
		end_points.append((x[l-1], y[l-1], COLOR[sim_type]))