Python 如何使用matplotlib获取密度图的对数轴？

我正在尝试使用matplotlib制作2D密度图（来自一些模拟数据）。我的x和y数据被定义为某些量的log10。如何获取对数轴（带有对数小刻度）

下面是我的代码示例：

import numpy as np
import matplotlib.pyplot as plt

Data = np.genfromtxt("data") # A 2-column data file
x = np.log10(Data[:,0])
y = np.log10(Data[:,1])

xmin = x.min()
xmax = x.max()
ymin = y.min()
ymax = y.max()

fig = plt.figure()
ax = fig.add_subplot(111)

hist = ax.hexbin(x,y,bins='log', gridsize=(30,30), cmap=cm.Reds)
ax.axis([xmin, xmax, ymin, ymax])

plt.savefig('plot.pdf')

---

从matplotlib.pyplot.hist docstring中，如果您希望轴上的日志比例，似乎有一个“log”参数要设置为“True”

hist(x, bins=10, range=None, normed=False, cumulative=False,
     bottom=None, histtype='bar', align='mid',
     orientation='vertical', rwidth=None, log=False, **kwargs)

log:
If True, the histogram axis will be set to a log scale. If log is True and x is a 1D
array, empty bins will be filtered out and only the non-empty (n, bins, patches) will be
returned.

---

还有一个pyplot.loglog函数，用于在x轴和y轴上绘制具有对数缩放的绘图。

来自@gcalmettes的答案指的是

pyplot.hist

。

pyplot.hexbin

的签名有点不同：

hexbin(x, y, C = None, gridsize = 100, bins = None,
             xscale = 'linear', yscale = 'linear',
             cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None,
             edgecolors='none', reduce_C_function = np.mean, mincnt=None, marginals=True,
             **kwargs)

您对

xscale

参数感兴趣：

*xscale*: [ 'linear' | 'log' ]
    Use a linear or log10 scale on the horizontal axis.

---

非常感谢你的建议

下面，我加入我自己的解决方案。这几乎不是一个“最低限度的工作示例”，但我已经将我的脚本剥离了很多

简而言之，我使用imshow来绘制“图像”（一个带有日志箱的二维直方图），并删除了轴。然后，我在第一个绘图的正上方绘制了第二个空（透明）绘图，以获取日志轴，因为imshow似乎不允许这样做。如果你问我的话，相当复杂

我的代码可能远远不是最佳的，因为我对python和matplotlib还不熟悉

顺便说一下，我不使用hexbin有两个原因： 1） 它太慢了，无法在像我这样的大数据文件上运行。 2） 在我使用的版本中，六边形稍微过大，即它们重叠，导致形状和大小不规则的“像素”。 此外，我希望能够将直方图数据写入文本格式的文件中

#!/usr/bin/python

# How to get log axis with a 2D colormap (i.e. an "image") ??
#############################################################
#############################################################

import numpy as np
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import math

# Data file containing 2D data in log-log coordinates.
# The format of the file is 3 columns : x y v
# where v is the value to plotted for coordinate (x,y)
# x and y are already log values
# For instance, this can be a 2D histogram with log bins.
input_file="histo2d.dat"

# Parameters to set space for the plot ("bounding box")
x1_bb, y1_bb, x2_bb, y2_bb = 0.125, 0.12, 0.8, 0.925

# Parameters to set space for colorbar
cb_fraction=0.15
cb_pad=0.05

# Return unique values from a sorted list, will be required later
def uniq(seq, idfun=None): 
    # order preserving
    if idfun is None:
        def idfun(x): return x
    seen = {}
    result = []
    for item in seq:
        marker = idfun(item)
        # in old Python versions:
        # if seen.has_key(marker)
        # but in new ones:
        if marker in seen: continue
        seen[marker] = 1
        result.append(item)
    return result

# Read data from file. The format of the file is 3 columns : x y v
# where v is the value to plotted for coordinate (x,y)

Data = np.genfromtxt(input_file)
x = Data[:,0]
y = Data[:,1]
v = Data[:,2]

# Determine x and y limits and resolution of data

x_uniq = np.array(uniq(np.sort(x)))
y_uniq = np.array(uniq(np.sort(y)))

x_resolution = x_uniq.size
y_resolution = y_uniq.size

x_interval_length = x_uniq[1]-x_uniq[0]
y_interval_length = y_uniq[1]-y_uniq[0]

xmin = x.min()
xmax = x.max()+0.5*x_interval_length
ymin = y.min()
ymax = y.max()+0.5*y_interval_length

# Reshape 1D data to turn it into a 2D "image"

v = v.reshape([x_resolution, y_resolution])
v = v[:,range(y_resolution-1,-1,-1)].transpose()

# Plot 2D "image" 
# ---------------

# I use imshow which only work with linear axes.
# We will have to change the axes later...

axis_lim=[xmin, xmax, ymin, ymax]

fig = plt.figure()
ax = fig.add_subplot(111)
extent = [xmin, xmax, ymin, ymax]
img = plt.imshow(v, extent=extent, interpolation='nearest', cmap=cm.Reds, aspect='auto')
ax.axis(axis_lim)

# Make space for the colorbar
x2_bb_eff = (x2_bb-(cb_fraction+cb_pad)*x1_bb)/(1.0-(cb_fraction+cb_pad))
ax.set_position([x1_bb, y1_bb, x2_bb_eff-x1_bb, y2_bb-y1_bb])
position = ax.get_position()

# Remove axis ticks so that we can put log ticks on top
ax.set_xticks([])
ax.set_yticks([])

# Add colorbar
cb = fig.colorbar(img,fraction=cb_fraction,pad=cb_pad)
cb.set_label('Value [unit]')

# Add logarithmic axes
# --------------------

# Empty plot on top of previous one. Only used to add log axes.
ax = fig.add_subplot(111,frameon=False)
ax.set_xscale('log')
ax.set_yscale('log')
plt.plot([])
ax.set_position([x1_bb, y1_bb, x2_bb-x1_bb, y2_bb-y1_bb])

axis_lim_log=map(lambda x: 10.**x, axis_lim)
ax.axis(axis_lim_log)

plt.grid(b=True, which='major', linewidth=1)
plt.ylabel('Some quantity [unit]')
plt.xlabel('Another quantity [unit]')

plt.show()