How to produce an exponentially scaled axis?
Consider the following code:
from numpy import log2
import matplotlib.pyplot as plt
xdata = [log2(x)*(10/log2(10)) for x in range(1,11)]
ydata = range(10)
plt.plot(xdata, ydata)
plt.show()
This produces the following plot:
My question is, how can I modify this, so that the plot, with the exact same data as input, appears as a straight line? This basically re开发者_如何学Cquires scaling the x-axis appropriately, but I cannot figure how to do this. The reason for doing this is that I am displaying a function that changes very little at the beginning, but starts to fluctuate more towards the end of the valid interval, so I want to have a higher horizontal resolution towards the end. If anyone can propose an alternative solution to my approach, feel free to do so!Here is how it is done. A good example to follow. You just subclass the ScaleBase class.
Here's your transform. It's not too complicated when you whittle out all the custom formatters and stuff. Just a little verbose.
from numpy import log2
import matplotlib.pyplot as plt
from matplotlib import scale as mscale
from matplotlib import transforms as mtransforms
class CustomScale(mscale.ScaleBase):
name = 'custom'
def __init__(self, axis, **kwargs):
mscale.ScaleBase.__init__(self)
self.thresh = None #thresh
def get_transform(self):
return self.CustomTransform(self.thresh)
def set_default_locators_and_formatters(self, axis):
pass
class CustomTransform(mtransforms.Transform):
input_dims = 1
output_dims = 1
is_separable = True
def __init__(self, thresh):
mtransforms.Transform.__init__(self)
self.thresh = thresh
def transform_non_affine(self, a):
return 10**(a/10)
def inverted(self):
return CustomScale.InvertedCustomTransform(self.thresh)
class InvertedCustomTransform(mtransforms.Transform):
input_dims = 1
output_dims = 1
is_separable = True
def __init__(self, thresh):
mtransforms.Transform.__init__(self)
self.thresh = thresh
def transform_non_affine(self, a):
return log2(a)*(10/log2(10))
def inverted(self):
return CustomScale.CustomTransform(self.thresh)
mscale.register_scale(CustomScale)
xdata = [log2(x)*(10/log2(10)) for x in range(1,11)]
ydata = range(10)
plt.plot(xdata, ydata)
plt.gca().set_xscale('custom')
plt.show()
The easiest way is to use semilogy
from numpy import log2
import matplotlib.pyplot as plt
xdata = log2(range(1,11)) * (10/log2(10))
ydata = range(10)
plt.semilogy(xdata, ydata)
plt.show()
精彩评论