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Matplotlib: how to scale the y axis according to the y-value? [duplicate]

I'm trying to create a histogram of a data column and plot it logarithmically (y-axis) and I'm not sure why the following code does not work:
import numpy as np
import matplotlib.pyplot as plt
data = np.loadtxt('foo.bar')
fig = plt.figure()
ax = fig.add_subplot(111)
plt.hist(data, bins=(23.0, 23.5,24.0,24.5,25.0,25.5,26.0,26.5,27.0,27.5,28.0))
ax.set_xlim(23.5, 28)
ax.set_ylim(0, 30)
ax.grid(True)
plt.yscale('log')
plt.show()
I've also tried instead of plt.yscale('log') adding Log=true in the plt.hist line and also I tried ax.set_yscale('log'), but nothing seems to work. I either get an empty plot, either the y-axis is indeed logarithmic (with the code as shown above), but there is no data plotted (no bins).

try
plt.yscale('log', nonposy='clip')
http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.yscale
The issue is with the bottom of bars being at y=0 and the default is to mask out in-valid points (log(0) -> undefined) when doing the log transformation (there was discussion of changing this, but I don't remember which way it went) so when it tries to draw the rectangles for you bar plot, the bottom edge is masked out -> no rectangles.

The hist constructor accepts the log parameter.
You can do this:
plt.hist(data, bins=bins, log=True)

np.logspace returns bins in [1-10], logarithmically spaced - in my case xx is a npvector >0 so the following code does the trick
logbins=np.max(xx)*(np.logspace(0, 1, num=1000) - 1)/9
hh,ee=np.histogram(xx, density=True, bins=logbins)

How do you change the values of a colorbar without changing the coloring of the plot?

I am wondering if there is a way to change the values of a colorbar in a pcolormesh plot without changing the colors of the plot itself. When I manually set the values of the colorbar it always changes how it draws the picture. The original image is below:
The picture looks right, but I would like to have the colorbar match the y-axis to the first plot. So, I manually changed vmin/vmax to those values. What I get instead is
Clearly, it is taking the values of the image, which are all about zero, relatively speaking. Is there an easy way to manually adjust the displayed values of the colorbar without changing how it draws the image?
My original code for the first image is as follows:
import matplotlib.pyplot as plt
from scipy import signal
CF = 2400
fs = 100
plt.figure(figsize=(15,10))
ax1 = plt.subplot(211)
plt.ylabel('Amplitude [dBm]')
plt.xlabel('Frequency [MHz]')
plt.plot(freqArray, dbmArray)
ax2 = plt.subplot(212, sharex = ax1)
f, time, Sxx = signal.spectrogram(dataArray, fs, nfft=4096)
plt.gca().set_xlim([min(f+CF), max(f+CF)])
plt.pcolormesh(f+CF, time*(1e-3), Sxx.T)
plt.xlabel('Frequency [MHz]')
plt.ylabel('Time [ms]')
plt.gca().invert_yaxis()
plt.colorbar()
plt.show()
For the second image, the only difference is
plt.pcolormesh(f+CF, time*(1e-3), Sxx.T, vmin=min(dbmArray), vmax=max(dbmArray))

Matplotlib ylim and xlim not working in combination of scatter plot and line plot or fill_between

I encounter a plotting issue I don't understand. Below code shall draw a straight line, fill the area above the line with a colour and plot several scattered dots in it. That all works but if I combine scatter and any of line or fill_between I cannot set the plot limits. The plot area is much larger than it had to be.
So how do I set the plot limits?
from matplotlib import pyplot as plt
import numpy as np
x = np.linspace(0,160,100)
MCSample = np.random.normal(112,10,1000)
YSample = np.random.normal(100,2.41,1000)
y_limit = max(160, np.max(YSample))
fig, ax = plt.subplots(1, 1)
ax.plot(x,x, label="Limit State function")
ax.scatter(MCSample,YSample, marker='.', color='b', alpha=0.5)
ax.fill_between(x,y_limit,x, alpha=0.1, color='r')
ax.set_xlim=(0,160)
ax.set_ylim=(0,y_limit)
plt.show()
I'm using Python 3.5.1 and Matplotlib 1.5.1.

In your code you are setting ax.set_xlim to equal (0,160).
All you have to do to make your code work is to get rid of the equal signs as shown below:
ax.set_xlim(0,160)
ax.set_ylim(0,y_limit) # no equals sign on these 2 lines
Now you are applying those limits to the graph rather than defining them to equal the limits.

Logarithmic y-axis bins in python

I'm trying to create a histogram of a data column and plot it logarithmically (y-axis) and I'm not sure why the following code does not work:
import numpy as np
import matplotlib.pyplot as plt
data = np.loadtxt('foo.bar')
fig = plt.figure()
ax = fig.add_subplot(111)
plt.hist(data, bins=(23.0, 23.5,24.0,24.5,25.0,25.5,26.0,26.5,27.0,27.5,28.0))
ax.set_xlim(23.5, 28)
ax.set_ylim(0, 30)
ax.grid(True)
plt.yscale('log')
plt.show()
I've also tried instead of plt.yscale('log') adding Log=true in the plt.hist line and also I tried ax.set_yscale('log'), but nothing seems to work. I either get an empty plot, either the y-axis is indeed logarithmic (with the code as shown above), but there is no data plotted (no bins).

try
plt.yscale('log', nonposy='clip')
http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.yscale
The issue is with the bottom of bars being at y=0 and the default is to mask out in-valid points (log(0) -> undefined) when doing the log transformation (there was discussion of changing this, but I don't remember which way it went) so when it tries to draw the rectangles for you bar plot, the bottom edge is masked out -> no rectangles.

The hist constructor accepts the log parameter.
You can do this:
plt.hist(data, bins=bins, log=True)

np.logspace returns bins in [1-10], logarithmically spaced - in my case xx is a npvector >0 so the following code does the trick
logbins=np.max(xx)*(np.logspace(0, 1, num=1000) - 1)/9
hh,ee=np.histogram(xx, density=True, bins=logbins)

Plotting point on top of filled contour plot adds lots of blank space

I have the following Python code which I am using to plot a filled contour plot:
def plot_polar_contour(values, azimuths, zeniths):
theta = np.radians(azimuths)
zeniths = np.array(zeniths)
values = np.array(values)
values = values.reshape(len(azimuths), len(zeniths))
r, theta = np.meshgrid(zeniths, np.radians(azimuths))
fig, ax = subplots(subplot_kw=dict(projection='polar'))
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
cax = ax.contourf(theta, r, values, 30)
autumn()
cb = fig.colorbar(cax)
cb.set_label("Pixel reflectance")
show()
This gives me a plot like:
However, when I add the line ax.plot(0, 30, 'p') just before show() I get the following:
It seems that just adding that one point (which is well within the original axis range) screws up the axis range on the radius axis.
Is this by design, or is this a bug? What would you suggest doing to fix it? Do I need to manually adjust the axis ranges, or is there a way to stop the extra plot command doing this?

If the axis auto-scaling mode isn't explicitly specified, plot will use "loose" autoscaling and contourf will use "tight" autoscaling.
The same things happens for non-polar axes. E.g.
import matplotlib.pyplot as plt
import numpy as np
plt.imshow(np.random.random((10,10)))
plt.plot([7], [7], 'ro')
plt.show()
You have a number of options.
Explicitly call ax.axis('image') or ax.axis('tight') at some point in the code.
Pass in scalex=False and scaley=False as keyword arguments to plot.
Manually set the axis limits.
The easiest and most readable is to just explicitly call ax.axis('tight'), i.m.o.