python画图matplotlib直方图条怎么变宽_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 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", "$log\, Z_{\odot}/f_{pol}$")

这里有几个图显示了1D轴直方图着色的问题

解决方法:

0)您的代码非常好(并且有帮助！)记录,但是对于您减少到最小的工作示例将非常有帮助.

1)colorHistOnHeight中的fracs数组不包括1e2的下限.

2)不同LogNorm颜色映射的范围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.

希望其中一个有帮助！

标签：python,matplotlib,numpy,histogram,histogram2d