python – matplotlib和numpy – 直方图条颜色和规范化

所以我有两个问题：

1-我有一个2D直方图w / 1D直方图沿着x& y轴.这些直方图总计了它们各自的x和y值,而主直方图总计了对数x-y区间的值.代码如下.我用pcolormesh来生成2D直方图……我已经生成了一个范围为vmin = 1,vmax = 14的颜色条……我将这些颜色条保持不变,因为我正在生成一组这些图.广泛的数据范围 – 我希望颜色在它们之间保持一致.

我还想根据相同的标准化对1D直方图条进行着色.我已经设置了一个函数来进行映射,但它是固执线性的 – 甚至我为映射指定了LogNorm.

我附上了几个图表,显示了我认为1D直方图的线性比例.查看大约10 ^ 4(或10 ^ 6)的x轴直方图值……它们在颜色条中的1/2路点处着色,而不是在对数刻度点处.

我究竟做错了什么？

2-我还希望最终通过bin宽度(xrange或yrange)将1D直方图标准化.但是,我认为我不能直接在matplotlib.hist中进行.也许我应该使用np hist,但后来我不知道如何使用对数刻度和彩色条进行matplotlib.bar绘图(再次,映射我用于2D hist的颜色).

这是代码：

## 20 Oct 2015# Rick Sarmento## Purpose:#  Reads star particle data and creates phase plots#  Place histograms of x and y axis along axes#  Uses pcolormesh norm=LogNorm(vmin=1,vmax=8)## Method:#  Main plot uses np.hist2d then takes log of result## Revision history## ########################################################### Generate colors for histogram bars based on height# This is not right! # ##########################################################def colorHistOnHeight(N, patches):    # we need to normalize the data to 0..1 for the full    # range of the colormap    print("N max: %.2lf"%N.max())    fracs = np.log10(N.astype(float))/9.0 # normalize colors to the top of our scale    print("fracs max: %.2lf"%fracs.max())    norm = mpl.colors.LogNorm(2.0, 9.0)    # NOTE this color mapping is different from the one below.    for thisfrac, thispatch in zip(fracs, patches):        color = mpl.cm.jet(thisfrac)        thispatch.set_facecolor(color)    return# ########################################################### Generate a combo contour/density plot# ##########################################################def genDensityPlot(x, y, mass, pf, z, filename, xaxislabel):    """    :rtype : none    """    nullfmt = NullFormatter()    # Plot location and size    fig = plt.figure(figsize=(20, 20))    ax2dhist = plt.axes(rect_2dhist)    axHistx = plt.axes(rect_histx)    axHisty = plt.axes(rect_histy)    # Fix any "log10(0)" points...    x[x == np.inf] = 0.0    y[y == np.inf] = 0.0    y[y > 1.0] = 1.0 # Fix any minor numerical errors that could result in y>1    # Bin data in log-space    xrange = np.logspace(minX,maxX,xbins)    yrange = np.logspace(minY,maxY,ybins)    # Note axis order: y then x    # H is the binned data... counts normalized by star particle mass    # TODO -- if we're looking at x = log Z, don't weight by mass * f_p... just mass!    H, xedges, yedges = np.histogram2d(y, x, weights=mass * (1.0 - pf), # We have log bins, so we take                                         bins=(yrange,xrange))    # Use the bins to find the extent of our plot    extent = [yedges[0], yedges[-1], xedges[0], xedges[-1]]    # levels = (5, 4, 3) # Needed for contours only...     X,Y=np.meshgrid(xrange,yrange) # Create a mess over our range of bins    # Take log of the bin data    H = np.log10(H)    masked_array = np.ma.array(H, mask=np.isnan(H))  # mask out all nan, i.e. log10(0.0)    # Fix colors -- white for values of 1.0.     cmap = copy.copy(mpl.cm.jet)    cmap.set_bad('w', 1.)  # w is color, for values of 1.0    # Create a plot of the binned    cax = (ax2dhist.pcolormesh(X,Y,masked_array, cmap=cmap, norm=LogNorm(vmin=1,vmax=8)))    print("Normalized H max %.2lf"%masked_array.max())    # Setup the color bar    cbar = fig.colorbar(cax, ticks=[1, 2, 4, 6, 8])    cbar.ax.set_yticklabels(['1', '2', '4', '6', '8'], size=24)    cbar.set_label('$log\, M_{sp, pol,\odot}$', size=30)    ax2dhist.tick_params(axis='x', labelsize=22)    ax2dhist.tick_params(axis='y', labelsize=22)    ax2dhist.set_xlabel(xaxislabel, size=30)    ax2dhist.set_ylabel('$log\, Z_{pri}/Z$', size=30)    ax2dhist.set_xlim([10**minX,10**maxX])    ax2dhist.set_ylim([10**minY,10**maxY])    ax2dhist.set_xscale('log')    ax2dhist.set_yscale('log')    ax2dhist.grid(color='0.75', linestyle=':', linewidth=2)    # Generate the xy axes histograms    ylims = ax2dhist.get_ylim()    xlims = ax2dhist.get_xlim()    ##########################################################    # Create the axes histograms    ##########################################################    # Note that even with log=True, the array N is NOT log of the weighted counts    # Eventually we want to normalize these value (in N) by binwidth and overall    # simulation volume... but I don't know how to do that.    N, bins, patches = axHistx.hist(x, bins=xrange, log=True, weights=mass * (1.0 - pf))    axHistx.set_xscale("log")    colorHistOnHeight(N, patches)    N, bins, patches = axHisty.hist(y, bins=yrange, log=True, weights=mass * (1.0 - pf),                                    orientation='horizontal')    axHisty.set_yscale('log')    colorHistOnHeight(N, patches)    # Setup format of the histograms    axHistx.set_xlim(ax2dhist.get_xlim())  # Match the x range on the horiz hist    axHistx.set_ylim([100.0,10.0**9])       # Constant range for all histograms    axHistx.tick_params(labelsize=22)    axHistx.yaxis.set_ticks([1e2,1e4,1e6,1e8])    axHistx.grid(color='0.75', linestyle=':', linewidth=2)    axHisty.set_xlim([100.0,10.0**9])       # We're rotated, so x axis is the value    axHisty.set_ylim([10**minY,10**maxY])  # Match the y range on the vert hist    axHisty.tick_params(labelsize=22)    axHisty.xaxis.set_ticks([1e2,1e4,1e6,1e8])    axHisty.grid(color='0.75', linestyle=':', linewidth=2)    # no labels    axHistx.xaxis.set_major_formatter(nullfmt)    axHisty.yaxis.set_major_formatter(nullfmt)    if z[0] == '0': z = z[1:]    axHistx.set_title('z='   z, size=40)    plt.savefig(filename   "-z_"   z   ".png", dpi=fig.dpi)    #    plt.show()    plt.close(fig) # Release memory assoc'd with the plot    return# ########################################################### ############################################################## Main program### ########################################################### ##########################################################import matplotlib as mplimport matplotlib.pyplot as plt#import matplotlib.colors as colors # For the colored 1d histogram routinefrom matplotlib.ticker import NullFormatterfrom matplotlib.colors import LogNormfrom matplotlib.ticker import LogFormatterMathtextimport numpy as npimport copy as copyfiles = [    "18.00",    "17.00",    "16.00",    "15.00",    "14.00",    "13.00",    "12.00",    "11.00",    "10.00",    "09.00",    "08.50",    "08.00",    "07.50",    "07.00",    "06.50",    "06.00",    "05.50",    "05.09"]# Plot parameters - globalleft, width = 0.1, 0.63bottom, height = 0.1, 0.63bottom_h = left_h = left   width   0.01xbins = ybins = 100rect_2dhist = [left, bottom, width, height]rect_histx = [left, bottom_h, width, 0.15]rect_histy = [left_h, bottom, 0.2, height]prefix = "./"# prefix="20Sep-BIG/"for indx, z in enumerate(files):    spZ = np.loadtxt(prefix   "spZ_"   z   ".txt", skiprows=1)    spPZ = np.loadtxt(prefix   "spPZ_"   z   ".txt", skiprows=1)    spPF = np.loadtxt(prefix   "spPPF_"   z   ".txt", skiprows=1)    spMass = np.loadtxt(prefix   "spMass_"   z   ".txt", skiprows=1)    print ("Generating phase diagram for z=%s" % z)    minY = -4.0    maxY = 0.5    minX = -8.0    maxX = 0.5    genDensityPlot(spZ, spPZ / spZ, spMass, spPF, z,                   "Z_PMassZ-MassHistLogNorm", "$log\, Z_{\odot}$")    minX = -5.0    genDensityPlot((spZ) / (1.0 - spPF), spPZ / spZ, spMass, spPF, z,                   "Z_PMassZ1-PGF-MassHistLogNorm", "$log\, Z_{\odot}/f_{pol}$")

这里有几个图显示了1D轴直方图着色的问题