matplotlib和numpy直方图条颜色和规格化

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

3条回答

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1楼 · 编辑于 2024-09-28 22:34:17

0）你的代码很好（而且很有帮助！）有文档记录，但将其简化为一个最小的工作示例将非常有帮助。
1） colorHistOnHeight中的fracs数组不包括1e2的下界。
2）不同的LogNorm颜色映射的边界在整个代码中都在变化（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）创建一个标量可映射对象^{}对象，使用to_rgba方法将标量值直接转换为颜色。在

希望其中一个能帮上忙！在

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2楼 · 编辑于 2024-09-28 22:34:17

我想出了如何使用上面的建议：matplotlib.sm.ScalarMappable。就这样！映射与我的colorbar比例匹配。在

# ##########################################################
# Generate colors for histogram bars based on height
# Method:
#  Take log of the histogram values (weighted counts)..
#  Create a LogNorm mapping between 1->9
#  Use the norm to map scalar values between 1 & 9 to rgb
# ##########################################################
def colorHistOnHeight(N, patches):
    cleanN = np.ma.masked_where(N == 0.0, N)
    fracs  = np.log10(cleanN) # normalize colors to the top of our scale
    norm   = mpl.colors.LogNorm(vmin=1.0, vmax=9.0)
    sm     = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.jet)
    sm.set_clim([1.0,9.0])
    for thisfrac, thispatch in zip(fracs, patches):
        color = sm.to_rgba(thisfrac)
        thispatch.set_facecolor(color)
    return

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3楼 · 编辑于 2024-09-28 22:34:17

下面是我如何通过对数bin宽度（模拟的体积是标量值）规范化matplotlib hist。有人请检查我的解决方案。在

yrange = np.logspace(minY,maxY,ybins)
N, bins, patches = axHisty.hist(y, bins=yrange, log=True, weights=mass * (1.0 - pf))

widths = np.diff(bins)
for item,dbx in zip(patches,widths):
    item.set_height(item.get_height()/dbx/cmvol)

我将直方图矩形的高度通过模拟的bin width（dbx）和comoving vol（cmvol）规范化。我想这就行了！在

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