python - matplotlib 和 numpy - 直方图条形颜色和标准化

Question

所以我有两个问题:

1- 我有一个 2D 直方图，沿 x 和 y 轴有 1D 直方图。这些直方图汇总了它们各自的 x 和 y 值，而主直方图汇总了对数 x-y bin 中的值。代码如下。我使用 pcolormesh 生成 2D 直方图...并且生成了 vmin=1、vmax=14 范围内的颜色条...我将这些设置为常数，因为我在 a 上生成了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。

我还想根据相同的归一化为一维直方图条着色。我已经设置了一个函数来进行映射，但它是顽固的线性——即使我为映射指定了 LogNorm。

我附上了一些图表，它们显示了我认为是一维直方图的线性比例。查看 10^4(或 10^6)左右的 x 轴直方图值...它们在颜色条中的 1/2 方向点着色，而不是在对数刻度点。

我做错了什么？

2- 我还想最终通过 bin 宽度(xrange 或 yrange)对 1D 直方图进行归一化。但是，我不认为我可以直接在 matplotlib.hist 中完成它。也许我应该使用 np hist，但我不知道如何使用对数刻度和彩色条绘制 matplotlib.bar 图(同样，映射我用于 2D hist 的颜色)。

代码如下:

#
# 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 mpl
import matplotlib.pyplot as plt
#import matplotlib.colors as colors # For the colored 1d histogram routine
from matplotlib.ticker import NullFormatter
from matplotlib.colors import LogNorm
from matplotlib.ticker import LogFormatterMathtext
import numpy as np
import copy as copy

files = [
    "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 - global
left, width = 0.1, 0.63
bottom, height = 0.1, 0.63
bottom_h = left_h = left + width + 0.01

xbins = ybins = 100

rect_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", "$log\, Z_{\odot}/f_{pol}$")

这里有几张图显示了一维轴直方图的着色问题

Answer 1

0) 您的代码记录得非常好(而且很有用!)，但是如果您精简为最小 工作示例，将会非常有帮助。
1) colorHistOnHeight中的fracs数组不包含1e2的下界。
2) 您的不同 LogNorm 颜色图的边界在整个代码中发生变化(例如 [1,8] 与 [2,9])。将这些参数设置为变量，并根据需要传递这些变量。
3) 创建标量可映射对象 matplotlib.cm.ScalarMappable对象使用 to_rgba 方法将标量值直接转换为颜色。

希望其中之一能有所帮助!