[英]How to get log axes for a density plot with matplotlib?
I am trying to make a 2D density plot (from some simulation data) with matplotlib. 我试图用matplotlib制作2D密度图(来自一些模拟数据)。 My x and y data are defined as the log10 of some quantities.
我的x和y数据被定义为某些数量的log10。 How can I get logarithmic axes (with log minor ticks)?
如何获得对数轴(带有对数小刻度)?
Here is an exemple of my code: 这是我的代码的例子:
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')
From the matplotlib.pyplot.hist docstring, it looks like there is a 'log' argument to set to 'True' if you want log scale on axis. 从matplotlib.pyplot.hist docstring看,如果你想在轴上使用对数刻度,看起来有一个'log'参数设置为'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.
There is also a pyplot.loglog function to make a plot with log scaling on the x and y axis. 还有一个pyplot.loglog函数可以在x和y轴上创建具有对数缩放的绘图。
Thank you very much for suggestions. 非常感谢您的建议。
Below, I join my own solution. 下面,我加入我自己的解决方案。 It is hardly "a minimum working example" but I have already stripped my script quite a lot!
它几乎不是“最低限度的工作示例”,但我已经剥离了我的剧本很多!
In a nutshell, I used imshow to plot the "image" (a 2D histogram with log bins) and I remove the axes. 简而言之,我使用imshow绘制“图像”(带有日志箱的2D直方图)并删除轴。 Then, I draw a second, empty (and transparent), plot, exactly on top of the first plot just to get log axes as imshow doesn't seem to allow it.
然后,我绘制第二个空的(和透明的)绘图,正好在第一个绘图的顶部,只是为了获得日志轴,因为imshow似乎不允许它。 Quite complicated if you ask me!
如果你问我,那很复杂!
My code is probably far from optimal as I am new to python and matplotlib... 我的代码可能远非最佳,因为我是python和matplotlib的新手......
By the way, I don't use hexbin for two reasons: 1) It is too slow to run on very big data files like the kind I have. 顺便说一句,我不使用hexbin有两个原因:1)在我这样的非常大的数据文件上运行它太慢了。 2) With the version I use, the hexagons are slightly too large, ie they overlap, resulting in "pixels" of irregular shapes and sizes.
2)对于我使用的版本,六边形稍微过大,即它们重叠,导致不规则形状和尺寸的“像素”。 Also, I want to be able to write the histogram data into a file in text format.
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()
The answer from @gcalmettes refers to pyplot.hist
. @gcalmettes的答案是指
pyplot.hist
。 The signature for pyplot.hexbin
is a bit different: 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)
You are interested on the xscale
parameter: 您对
xscale
参数感兴趣:
*xscale*: [ 'linear' | 'log' ]
Use a linear or log10 scale on the horizontal axis.
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