Say I have some input data:
data = np.random.normal(loc=100,scale=10,size=(500,1,32)) hist = np.ones((32,20)) # initialise hist for z in range(32): hist[z],edges = np.histogram(data[:,0,z],bins=np.arange(80,122,2))
I can plot it using imshow()
:
plt.imshow(hist,cmap='Reds')
getting:
However, the x-axis values do not match the input data (i.e. mean of 100, range from 80 to 122). Therefore, I'd like to change the x-axis to show the values in edges
.
I have tried:
ax = plt.gca() ax.set_xlabel([80,122]) # range of values in edges ... # this shifts the plot so that nothing is visible
and
ax.set_xticklabels(edges) ... # this labels the axis but does not centre around the mean:
Any ideas on how I can change the axis values to reflect the input data I am using?
To set labels on the x-axis and y-axis, use the plt. xlabel() and plt. ylabel() methods.
Use ax. invert_yaxis() to invert the y-axis, or ax. invert_xaxis() to invert the x-axis.
I would try to avoid changing the xticklabels
if possible, otherwise it can get very confusing if you for example overplot your histogram with additional data.
Defining the range of your grid is probably the best and with imshow
it can be done by adding the extent
keyword. This way the axes gets adjusted automatically. If you want to change the labels i would use set_xticks
with perhaps some formatter. Altering the labels directly should be the last resort.
fig, ax = plt.subplots(figsize=(6,6)) ax.imshow(hist, cmap=plt.cm.Reds, interpolation='none', extent=[80,120,32,0]) ax.set_aspect(2) # you may also use am.imshow(..., aspect="auto") to restore the aspect ratio
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