Python pyplot.colorbar函数代码示例

	def __call__(self,iteration):
		
		p = self.t.calcPress(iteration,pOnly=True)

		plt.imshow(p[:,0,:],origin=0)
		plt.axis('tight')
		plt.colorbar()

def plotmapdBtime():
    pcolormesh(yoko, time*1e6, dB(S11c), vmin=-65, vmax=-30)
    title("Reflection (dB) vs flux \n and time (1 us pulse) at 4.46 GHz")
    xlabel("Flux (V)")
    ylabel("Time (us)")
    #ylim(0, 1.5)
    colorbar()

def test_minimized_rasterized():
    # This ensures that the rasterized content in the colorbars is
    # only as thick as the colorbar, and doesn't extend to other parts
    # of the image.  See #5814.  While the original bug exists only
    # in Postscript, the best way to detect it is to generate SVG
    # and then parse the output to make sure the two colorbar images
    # are the same size.
    from xml.etree import ElementTree

    np.random.seed(0)
    data = np.random.rand(10, 10)

    fig, ax = plt.subplots(1, 2)
    p1 = ax[0].pcolormesh(data)
    p2 = ax[1].pcolormesh(data)

    plt.colorbar(p1, ax=ax[0])
    plt.colorbar(p2, ax=ax[1])

    buff = io.BytesIO()
    plt.savefig(buff, format='svg')

    buff = io.BytesIO(buff.getvalue())
    tree = ElementTree.parse(buff)
    width = None
    for image in tree.iter('image'):
        if width is None:
            width = image['width']
        else:
            if image['width'] != width:
                assert False

def implot(plt, x, y, Z, ax=None, colorbar=True, **kwargs):
    """Image plot of general data (like imshow but with non-pixel axes).

    Parameters
    ----------
    plt : plot object
        Plot object, typically `matplotlib.pyplot`.
    x : (M,) array_like
        Vector of x-axis points, must be linear (equally spaced).
    y : (N,) array_like
        Vector of y-axis points, must be linear (equally spaced).
    Z : (M, N) array_like
        Matrix of data to be displayed, the value at each (x, y) point.
    ax : axis object (optional)
        A specific axis to plot on (defaults to `plt.gca()`).
    colorbar: boolean (optional)
        Whether to plot a colorbar.
    **kwargs
        Additional arguments for `ax.imshow`.
    """
    ax = plt.gca() if ax is None else ax

    def is_linear(x):
        diff = np.diff(x)
        return np.allclose(diff, diff[0])

    assert is_linear(x) and is_linear(y)
    image = ax.imshow(Z, aspect='auto', extent=(x[0], x[-1], y[-1], y[0]),
                      **kwargs)
    if colorbar:
        plt.colorbar(image, ax=ax)

def plot_jacobian(A, name, cmap= plt.cm.coolwarm, normalize=True, precision=1e-6):

    """
    Customized visualization of jacobian matrices for observing
    sparsity patterns
    """
    
    plt.figure()
    fig, ax = plt.subplots()
    
    if normalize is True:
        plt.imshow(A, interpolation='none', cmap=cmap,
                   norm = mpl.colors.Normalize(vmin=-1.,vmax=1.))
    else:
        plt.imshow(A, interpolation='none', cmap=cmap)        
    plt.colorbar(format=ticker.FuncFormatter(fmt))
    
    ax.spy(A, marker='.', markersize=0,  precision=precision)
    
    ax.spines['right'].set_visible(True)
    ax.spines['bottom'].set_visible(True)
    ax.xaxis.set_ticks_position('top')
    ax.yaxis.set_ticks_position('left')

    xlabels = np.linspace(0, A.shape[0], 5, True, dtype=int)
    ylabels = np.linspace(0, A.shape[1], 5, True, dtype=int)

    plt.xticks(xlabels)
    plt.yticks(ylabels)

    plt.savefig(name, bbox_inches='tight', pad_inches=0.05)
    
    plt.close()

    return

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')

def streamlineBxz_dens():
    fig=plt.figure()
    ppy=yt.ProjectionPlot(ds, "y", "Bxz", weight_field="density") #Project X-component of B-field from z-direction
    By=ppy._frb["density"]
    ax=fig.add_subplot(111)
    plt.xticks(tick_locs,tick_lbls)
    plt.yticks(tick_locs,tick_lbls)
    Bymag=ax.pcolormesh(np.log10(By), cmap="YlGn")
    cbar_m=plt.colorbar(Bymag)
    cbar_m.set_label("density")
    res=800

    #densxy=Density2D(0,1) #integrated density along given axis
    U=Flattenx(0,1) #X-magnetic field integrated along given axis
    V=Flattenz(0,1) #Z-magnetic field
    #U=np.asarray(zip(*x2)[::-1]) #rotate the matrix 90 degrees to correct orientation to match projected plots
    #V=np.asarray(zip(*y2)[::-1])
    norm=np.sqrt(U**2+V**2) #magnitude of the vector
    Unorm=U/norm #normalise vectors 
    Vnorm=V/norm
    #mask_Unorm=np.ma.masked_where(densxy<np.mean(densxy),Unorm) #create a masked array of Unorm values only in high density regions
    #mask_Vnorm=np.ma.masked_where(densxy<np.mean(densxy),Vnorm)
    X,Y=np.meshgrid(np.linspace(0,res,64, endpoint=True),np.linspace(0,res,64,endpoint=True))
    streams=plt.streamplot(X,Y,Unorm,Vnorm,color=norm*1e6,density=(3,3),cmap=plt.cm.autumn)
    cbar=plt.colorbar(orientation="horizontal")
    cbar.set_label('Bxz streamlines (uG)')
    plt.title("Bxz streamlines on weighted density projection")
    plt.xlabel("(1e4 AU)")
    plt.ylabel("(1e4 AU)")

def plot_heat_net(net_mat, sectors):
    """Plot a heat map of the net relations.

    Parameters
    ----------
    net_mat: np.ndarray
        the net represented in a matrix way.
    sectors: list
        the name of the elements of the adjacency matrix network.

    Returns
    -------
    fig: matplotlib.pyplot.figure
        the figure of the matrix heatmap.

    """
    vmax = np.sort([np.abs(net_mat.max()), np.abs(net_mat.min())])[::-1][0]
    n_sectors = len(sectors)
    assert(net_mat.shape[0] == net_mat.shape[1])
    assert(n_sectors == len(net_mat))

    fig = plt.figure()
    plt.imshow(net_mat, interpolation='none', cmap=plt.cm.RdYlGn,
               vmin=-vmax, vmax=vmax)
    plt.xticks(range(n_sectors), sectors)
    plt.yticks(range(n_sectors), sectors)
    plt.xticks(rotation=90)
    plt.colorbar()
    return fig

    def test_unimodality_of_GEV(self):
        x0 = 1500

        mu = 1000
        data = np.array([x0])

        ksi = np.arange(-2, 2, 0.01)
        sigma = np.arange(10, 8000, 10)

        n_ksi = len(ksi)
        n_sigma = len(sigma)

        z = np.zeros((n_ksi, n_sigma))

        for i, the_ksi in enumerate(ksi):
            for j, the_sigma in enumerate(sigma):
                z[i, j] = gevfit.objective_function_stationary_high([the_sigma, mu, the_ksi], data)


        sigma, ksi = np.meshgrid(sigma, ksi)
        z = np.ma.masked_where(z == gevfit.BIG_NUM, z)
        z = np.ma.masked_where(z > 9, z)

        plt.figure()
        plt.pcolormesh(ksi, sigma, z)
        plt.colorbar()
        plt.xlabel('$\\xi$')
        plt.ylabel('$\\sigma$')
        plt.title('$\\mu = %.1f, x = %.1f$' % (mu, x0))

        plt.show()


        pass

def corrplot(C, cmap=None, cmap_range=(0.,1.), cbar=True, fontsize=14, **kwargs):
    """
    Plots values in a correlation matrix

    """

    ax = kwargs['ax']
    n = len(C)

    # defaults
    if cmap is None:
        if min(cmap_range) >= 0:
            cmap = "OrRd"
        elif max(cmap_range) <= 0:
            cmap = "RdBu"
        else:
            cmap = "gray"

    # remove values
    rr, cc = np.triu_indices(n, k=1)
    C[rr, cc] = np.nan

    vmin, vmax = cmap_range
    img = ax.imshow(C, cmap=cmap, vmin=vmin, vmax=vmax, aspect='equal')

    if cbar:
        plt.colorbar(img) #, shrink=0.75)

    for j in range(n):
        for i in range(j+1,n):
            ax.text(i, j, '{:0.2f}'.format(C[i,j]), fontsize=fontsize,
                    fontdict={'ha': 'center', 'va': 'center'})

    noticks(ax=ax)

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=None,
                          zmin=1):
    """This function prints and plots the confusion matrix for the intent classification.

    Normalization can be applied by setting `normalize=True`."""
    import numpy as np

    zmax = cm.max()
    plt.imshow(cm, interpolation='nearest', cmap=cmap if cmap else plt.cm.Blues, aspect='auto',
               norm=LogNorm(vmin=zmin, vmax=zmax))
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=90)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        logger.info("Normalized confusion matrix: \n{}".format(cm))
    else:
        logger.info("Confusion matrix, without normalization: \n{}".format(cm))

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.ylabel('True label')
    plt.xlabel('Predicted label')

def plot_map(AX, fname, mmin=0,mmax=1, annot='A', xoff=False,yoff=False, xlab='None', ylab='None', aspect=False, row_lab=None, col_lab=None, log=False):

	global xtix, ytix, xtix_loc, ytix_loc, fsa, fs

	mmap = np.genfromtxt(fname, delimiter=',')
	if log:
		mmap = np.log(mmap)
	im = AX.pcolor(mmap, vmin=mmin, vmax=mmax)
	plt.colorbar(im, ax=AX)
	AX.annotate(annot, (0,0), (0.02,0.9), color='white', fontsize= fsa, fontweight='bold', xycoords='data', textcoords='axes fraction')
	if row_lab != None:
		AX.annotate(row_lab, xy=(0.0, 0.5), size='x-large', ha='right', va='center', xytext= (-4.5, 5))#(-ax.yaxis.labelpad - pad, 0),xycoords=ax.yaxis.label, textcoords='offset points'
	if col_lab != None:
		AX.set_title(col_lab)

	AX.set_xticks(xtix_loc)
	AX.set_yticks(ytix_loc)
	if xoff:
		AX.set_xticklabels('')
	else:
		AX.set_xticklabels(xtix)
	if yoff:
		AX.set_yticklabels('')
	else:
		AX.set_yticklabels(ytix)

	if xlab != 'None':
		AX.set_xlabel(xlab, fontsize = fs)
	if ylab != 'None':
		AX.set_ylabel(ylab, fontsize = fs)

	if aspect:
		AX.set_aspect('equal', 'datalim')

def get_heatmap(data_mat, name_for_saving_files,  pp,stimulus_on_time, stimulus_off_time,delta_ff, f0_start, f0_end):
    
    #Plot heatmap for validation 
    A1 = np.reshape(data_mat, (np.size(data_mat,0)*np.size(data_mat,1), np.size(data_mat,2)))
    if delta_ff == 1:
        delta_ff_A1 = np.zeros(np.shape(A1))
        for ii in xrange(0,np.size(A1,0)):
            delta_ff_A1[ii,:] = (A1[ii,:]-np.mean(A1[ii,f0_start:f0_end]))/(np.std(A1[ii,f0_start:f0_end])+0.1)
        B = np.argsort(np.mean(delta_ff_A1, axis=1))  
        print np.max(delta_ff_A1)
    else:
        B = np.argsort(np.mean(A1, axis=1)) 
        print np.max(A1)

    with sns.axes_style("white"):
        C = A1[B,:][-2000:,:]

        fig2 = plt.imshow(C,aspect='auto', cmap='jet', vmin = np.min(C), vmax = np.max(C))
        
        plot_vertical_lines_onset(stimulus_on_time)
        plot_vertical_lines_offset(stimulus_off_time)
        plt.title(name_for_saving_files)
        plt.colorbar()
        fig2 = plt.gcf()
        pp.savefig(fig2)
        plt.close()

def plotTimeseries(mytimes,myyears,times,mydata,myvar,depthlevels,mytype):
    
    depthlevels=-np.asarray(depthlevels)
    ax = figure().add_subplot(111)
    
    y,x = np.meshgrid(depthlevels,times)
    
    mydata=np.rot90(mydata)
    if myvar=='temp':
        levels = np.arange(-2,16,1)
    if myvar=='salt':
        levels = np.arange(mydata.min(),mydata.max()+0.1,0.05)
    if mytype=="T-CLASS3-IC_CHANGE":
             levels = np.arange(mydata.min(),mydata.max()+0.1,0.1)
    if mytype=="S-CLASS3-IC_CHANGE":
             levels = np.arange(mydata.min(),mydata.max()+0.1,0.05)
             
    print mydata.min(), mydata.max()
    cs=contourf(x,y,mydata,levels,cmap=cm.get_cmap('RdBu_r',len(levels)-1))
    plt.colorbar(cs)
    xticks(mytimes,myyears,rotation=-90)

    plotfile='figures/'+str(mytype)+'_'+str(myvar)+'_alldepths.pdf'
    plt.savefig(plotfile,dpi=300)
    print 'Saved figure file %s\n'%(plotfile)

def lasso_regression(features, solutions, verbose=0):
    columns = solutions.columns

    clf = Lasso(alpha=1e-4, max_iter=5000)

    print('Training Model... ')
    clf.fit(features, solutions)
    
    feature_coeff = clf.coef_
    features_importances = np.zeros((169, 3))
    for idx in range(3):
        features_importance = np.reshape(feature_coeff[idx, :], (169, 8))
        features_importance = np.max(features_importance, axis=1)
        features_importances[:, idx] = features_importance
        
    features_importance_max = np.max(features_importances, axis=1)
    features_importance_max = np.reshape(features_importance_max, (13, 13))
    plt.pcolor(features_importance_max)
    plt.title("Feature importance for HoG")
    plt.colorbar()
    plt.xticks(arange(0.5,13.5), range(1, 14))
    plt.yticks(arange(0.5,13.5), range(1, 14))
    plt.axis([0, 13, 0, 13])
    plt.show()
    
    print('Done Training')
    return (clf, columns)