We Keep Coding

Prevent grid lines from twin axis to be drawn on top of artists from original axis

I have an axis on which I plot some data and I have another twin axis which I use to draw grid lines at specific tick positions (other than the ticks of the original axis):
import matplotlib.pyplot as plt
import numpy as np
f, ax = plt.subplots()
ax.set_xlim([0, 1])
ax2 = ax.twiny()
ax2.set_xlim([0, 1])
ax2.set_xticks(np.linspace(0, 1, 11))
ax2.xaxis.grid()
x = np.linspace(0, 1, 100)
ax.plot(x, np.sin(x), label='sin(x)')
ax.legend()
plt.show()
Now this has the undesirable effect that the grid lines of the twin axes are drawn on top of the legend and line plot of the original axis. As far as I understand this is because matplotlib draws the axes in the order they were created and for that reason zorder won't help (because zorder only specifies the order among the artists of a single axis).
I know I could plot the data on the twin axis ax2 instead (followed by ax2.legend()) but I'd prefer to have the setup as is. Instead changing the order in which the two axes are drawn should solve the problem, but I couldn't figure out how to do that. There is f.get_axes() which seems to return the axes in the order they were created but no option to revert it.
Or maybe there exists even another solution?

You can change the zorder of the axes themselves.
ax.set_zorder(2)
ax2.set_zorder(1)
ax.patch.set_visible(False)

Two subplots coming out too long (length)

I'm attempting to plot two bar charts using matplotlib.pyplot.subplots. I've created subplots within a function, but when I output the subplots they are too long in height and not long enough in width.
Here's the function that I wrote:
def corr_bar(data1, data2, method='pearson'):
# Basic configuration.
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(7, 7))
ax1, ax2 = axes
corr_matrix1 = data1.corr(method=method)
corr_matrix2 = data2.corr(method=method)
cmap = cm.get_cmap('coolwarm')
major_ticks = np.arange(0, 1.1, 0.1)
minor_ticks = np.arange(0, 1.1, 0.05)
# Values for plotting.
x1 = corr_matrix1['price'].sort_values(ascending=False).index
x2 = corr_matrix2['price'].sort_values(ascending=False).index
values1 = corr_matrix1['price'].sort_values(ascending=False).values
values2 = corr_matrix2['price'].sort_values(ascending=False).values
im1 = ax1.bar(x1, values1, color=cmap(values1))
im2 = ax2.bar(x2, values2, color=cmap(values2))
# Formatting for plot 1.
ax1.set_yticks(major_ticks)
ax1.set_yticks(minor_ticks, minor=True)
plt.setp(ax1.get_xticklabels(), rotation=45, ha='right', rotation_mode='anchor')
ax1.grid(which='both')
ax1.grid(which='minor', alpha=0.4)
ax1.grid(which='major', alpha=0.7)
ax1.xaxis.grid(False)
# Formatting for plot 2.
ax2.set_yticks(major_ticks)
ax2.set_yticks(minor_ticks, minor=True)
plt.setp(ax2.get_xticklabels(), rotation=45, ha='right', rotation_mode='anchor')
ax2.grid(which='both')
ax2.grid(which='minor', alpha=0.4)
ax2.grid(which='major', alpha=0.7)
ax2.xaxis.grid(False)
fig.tight_layout()
plt.show()
This function (when run with two Pandas DataFrames) outputs an image like the following:
I purposely captured the blank right side of the image as well in an attempt to better depict my predicament. What I want is for the bar charts to be appropriately sized in height and width as to take up the entire space, rather than be elongated and pushed to the left.
I've tried to use the ax.set(aspect='equal') method but it "scrunches up" the bar chart. Would anybody happen to know what I could do to solve this issue?
Thank you.

When you define figsize=(7,7) you are setting the size of the entire figure and not the subplots. So your entire figure must be a square in this case. You should change it to figsize=(14,7) or use a number larger than 14 to get a little bit of extra space.

Two y axes for a single plot

I'm trying to create a plot with two Y axes (left and right) for the same data, that is, one is a scaled version of the other. I would like also to preserve the tick positions and grid positions, so the grid will match the ticks at both sides.
I'm trying to do this by plotting twice the same data, one as-is and the other scaled, but they are not coincident.
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(17, 27, 0.1)
y1 = 0.05 * x + 100
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.plot(x, y1, 'g-')
ax2.plot(x, y1/max(y1), 'g-')
ax1.set_xlabel('X data')
ax1.set_ylabel('Y data', color='g')
ax2.set_ylabel('Y data normalized', color='b')
plt.grid()
plt.show()
Any help will be appreciated.

Not sure if you can achieve this without getting ugly-looking numbers on your normalized axis. But if that doesn't bother you, try adding this to your code:
ax2.set_ylim([ax1.get_ylim()[0]/max(y1),ax1.get_ylim()[1]/max(y1)])
ax2.set_yticks(ax1.get_yticks()/max(y1))
Probably not the most elegant solution, but it scales your axis limits and tick positions similarly to what you do with the data itself so the grid matches both axes.

Matplotlib: how to set ticks of twinned axis in log plot

In my plot, a secondary x axis is used to display the value of another variable for some data. Now, the original axis is log scaled. Unfortunaltely, the twinned axis puts the ticks (and the labels) referring to the linear scale of the original axis and not as intended to the log scale. How can this be overcome?
Here the code example that should put the ticks of the twinned axis in the same (absolute axes) position as the ones for the original axis:
def conv(x):
"""some conversion function"""
# ...
return x2
ax = plt.subplot(1,1,1)
ax.set_xscale('log')
# get the location of the ticks of ax
axlocs,axlabels = plt.xticks()
# twin axis and set limits as in ax
ax2 = ax.twiny()
ax2.set_xlim(ax.get_xlim())
#Set the ticks, should be set referring to the log scale of ax, but are set referring to the linear scale
ax2.set_xticks(axlocs)
# put the converted labels
ax2.set_xticklabels(map(conv,axlocs))
An alternative way would be (the ticks are then not set in the same position, but that doesn't matter):
from matplotlib.ticker import FuncFormatter
ax = plt.subplot(1,1,1)
ax.set_xscale('log')
ax2 = ax.twiny()
ax2.set_xlim(ax.get_xlim())
ax2.xaxis.set_major_formatter(FuncFormatter(lambda x,pos:conv(x)))
Both approaches work well as long as no log scale is used.
Perhaps there exists an easy fix. Is there something I missed in the documentation?
As a workaround, I tried to obtain the ax.transAxes coordinates of the ticks of ax and put the ticks at the very same position in ax2. But there does not exist something like
ax2.set_xticks(axlocs,transform=ax2.transAxes)
TypeError: set_xticks() got an unexpected keyword argument 'transform'

This has been asked a while ago, but I stumbled over it with the same question.
I eventually managed to solve the problem by introducing a logscaled (semilogx) transparent (alpha=0) dummy plot.
Example:
import numpy as np
import matplotlib.pyplot as plt
def conversion_func(x): # some arbitrary transformation function
return 2 * x**0.5 # from x to z
x = np.logspace(0, 5, 100)
y = np.sin(np.log(x))
fig = plt.figure()
ax = plt.gca()
ax.semilogx(x, y, 'k')
ax.set_xlim(x[0], x[-1]) # this is important in order that limits of both axes match
ax.set_ylabel("$y$")
ax.set_xlabel("$x$", color='C0')
ax.tick_params(axis='x', which='both', colors='C0')
ax.axvline(100, c='C0', lw=3)
ticks_x = np.logspace(0, 5, 5 + 1) # must span limits of first axis with clever spacing
ticks_z = conversion_func(ticks_x)
ax2 = ax.twiny() # get the twin axis
ax2.semilogx(ticks_z, np.ones_like(ticks_z), alpha=0) # transparent dummy plot
ax2.set_xlim(ticks_z[0], ticks_z[-1])
ax2.set_xlabel("$z \equiv f(x)$", color='C1')
ax2.xaxis.label.set_color('C1')
ax2.tick_params(axis='x', which='both', colors='C1')
ax2.axvline(20, ls='--', c='C1', lw=3) # z=20 indeed matches x=100 as desired
fig.show()
In the above example the vertical lines demonstrate that first and second axis are indeed shifted to one another as wanted. x = 100 gets shifted to z = 2*x**0.5 = 20. The colours are just to clarify which vertical line goes with which axis.

Don't need to cover them, just Eliminate the ticks!
d= [7,9,14,17,35,70];
j= [100,80,50,40,20,10];
plt.figure()
plt.xscale('log')
plt.plot(freq, freq*spec) #plot some spectrum
ax1 = plt.gca() #define my first axis
ax1.yaxis.set_ticks_position('both')
ax1.tick_params(axis='y',which='both',direction='in');
ax1.tick_params(axis='x',which='both',direction='in');
ax2 = ax1.twiny() #generates second axis (top)
ax2.set_xlim(ax1.get_xlim()); #same limits
plt.xscale('log') #make it log
ax2.set_xticks(freq[d]); #my own 'major' ticks OVERLAPS!!!
ax2.set_xticklabels(j); #change labels
ax2.tick_params(axis='x',which='major',direction='in');
ax2.tick_params(axis='x',which='minor',top=False); #REMOVE 'MINOR' TICKS
ax2.grid()

I think you can fix your issue by calling ax2.set_xscale('log').
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
ax.semilogx(np.logspace(1.0, 5.0, 20), np.random.random([20]))
new_tick_locations = np.array([10., 100., 1000., 1.0e4])
def tick_function(X):
V = X / 1000.
return ["%.3f" % z for z in V]
ax2 = ax.twiny()
ax2.set_xscale('log')
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(new_tick_locations)
ax2.set_xticklabels(tick_function(new_tick_locations))
ax2.set_xlabel(r"Modified x-axis: $X/1000$")

Add second axis to polar plot

I try to plot two polar plots in one figure. See code below:
fig = super(PlotWindPowerDensity, self).get_figure()
rect = [0.1, 0.1, 0.8, 0.8]
ax = WindSpeedDirectionAxes(fig, rect)
self.values_dict = collections.OrderedDict(sorted(self.values_dict.items()))
values = self.values_dict.items()
di, wpd = zip(*values)
wpd = np.array(wpd).astype(np.double)
wpdmask = np.isfinite(wpd)
theta = self.radar_factory(int(len(wpd)))
# spider plot
ax.plot(theta[wpdmask], wpd[wpdmask], color = 'b', alpha = 0.5)
ax.fill(theta[wpdmask], wpd[wpdmask], facecolor = 'b', alpha = 0.5)
# bar plot
ax.plot_bar(table=self.table, sectors=self.sectors, speedbins=self.wpdbins, option='wind_power_density', colorfn=get_sequential_colors)
fig.add_axes(ax)
return fig
The length of the bar is the data base (how many sampling points for this sector). The colors of the bars show the frequency of certain value bins (eg. 2.5-5 m/s) in the correspondent sector (blue: low, red: high). The blue spider plot shows the mean value for each sector.
In the shown figure, the values of each plot are similar, but this is rare. I need to assign the second plot to another axis and show this axis in another direction.
EDIT:
After the nice answer of Joe, i get the result of the figure.
That's almost everything i wanted to achieve. But there are some points i wasn't able to figure out.
The plot is made for dynamicly changing data bases. Therefore i need a dynamic way to get the same location of the circles. Till now I solve it with:
start, end = ax2.get_ylim()
ax2.yaxis.set_ticks(np.arange(0, end, end / len(ax.yaxis.get_ticklocs())))
means: for second axis i alter the ticks in order to fit the ticklocs to the one's of first axis.
In most cases i get some decimal places, but i don't want that, because it corrupts the clearness of the plot. Is there a way to solve this problem more smartly?
The ytics (the radial one's) range from 0 to the next-to-last circle. How can i achieve that the values range from the first circle to the very last (the border)? The same like for the first axis.

So, as I understand it, you want to display data with very different magnitudes on the same polar plot. Basically you're asking how to do something similar to twinx for polar axes.
As an example to illustrate the problem, it would be nice to display the green series on the plot below at a different scale than the blue series, while keeping them on the same polar axes for easy comparison.:
import numpy as np
import matplotlib.pyplot as plt
numpoints = 30
theta = np.linspace(0, 2*np.pi, numpoints)
r1 = np.random.random(numpoints)
r2 = 5 * np.random.random(numpoints)
params = dict(projection='polar', theta_direction=-1, theta_offset=np.pi/2)
fig, ax = plt.subplots(subplot_kw=params)
ax.fill_between(theta, r2, color='blue', alpha=0.5)
ax.fill_between(theta, r1, color='green', alpha=0.5)
plt.show()
However, ax.twinx() doesn't work for polar plots.
It is possible to work around this, but it's not very straight-forward. Here's an example:
import numpy as np
import matplotlib.pyplot as plt
def main():
numpoints = 30
theta = np.linspace(0, 2*np.pi, numpoints)
r1 = np.random.random(numpoints)
r2 = 5 * np.random.random(numpoints)
params = dict(projection='polar', theta_direction=-1, theta_offset=np.pi/2)
fig, ax = plt.subplots(subplot_kw=params)
ax2 = polar_twin(ax)
ax.fill_between(theta, r2, color='blue', alpha=0.5)
ax2.fill_between(theta, r1, color='green', alpha=0.5)
plt.show()
def polar_twin(ax):
ax2 = ax.figure.add_axes(ax.get_position(), projection='polar',
label='twin', frameon=False,
theta_direction=ax.get_theta_direction(),
theta_offset=ax.get_theta_offset())
ax2.xaxis.set_visible(False)
# There should be a method for this, but there isn't... Pull request?
ax2._r_label_position._t = (22.5 + 180, 0.0)
ax2._r_label_position.invalidate()
# Ensure that original axes tick labels are on top of plots in twinned axes
for label in ax.get_yticklabels():
ax.figure.texts.append(label)
return ax2
main()
That does what we want, but it looks fairly bad at first. One improvement would be to the tick labels to correspond to what we're plotting:
plt.setp(ax2.get_yticklabels(), color='darkgreen')
plt.setp(ax.get_yticklabels(), color='darkblue')
However, we still have the double-grids, which are rather confusing. One easy way around this is to manually set the r-limits (and/or r-ticks) such that the grids will fall on top of each other. Alternately, you could write a custom locator to do this automatically. Let's stick with the simple approach here:
ax.set_rlim([0, 5])
ax2.set_rlim([0, 1])
Caveat: Because shared axes don't work for polar plots, the implmentation I have above will have problems with anything that changes the position of the original axes. For example, adding a colorbar to the figure will cause all sorts of problems. It's possible to work around this, but I've left that part out. If you need it, let me know, and I'll add an example.
At any rate, here's the full, stand-alone code to generate the final figure:
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1977)
def main():
numpoints = 30
theta = np.linspace(0, 2*np.pi, numpoints)
r1 = np.random.random(numpoints)
r2 = 5 * np.random.random(numpoints)
params = dict(projection='polar', theta_direction=-1, theta_offset=np.pi/2)
fig, ax = plt.subplots(subplot_kw=params)
ax2 = polar_twin(ax)
ax.fill_between(theta, r2, color='blue', alpha=0.5)
ax2.fill_between(theta, r1, color='green', alpha=0.5)
plt.setp(ax2.get_yticklabels(), color='darkgreen')
plt.setp(ax.get_yticklabels(), color='darkblue')
ax.set_ylim([0, 5])
ax2.set_ylim([0, 1])
plt.show()
def polar_twin(ax):
ax2 = ax.figure.add_axes(ax.get_position(), projection='polar',
label='twin', frameon=False,
theta_direction=ax.get_theta_direction(),
theta_offset=ax.get_theta_offset())
ax2.xaxis.set_visible(False)
# There should be a method for this, but there isn't... Pull request?
ax2._r_label_position._t = (22.5 + 180, 0.0)
ax2._r_label_position.invalidate()
# Bit of a hack to ensure that the original axes tick labels are on top of
# whatever is plotted in the twinned axes. Tick labels will be drawn twice.
for label in ax.get_yticklabels():
ax.figure.texts.append(label)
return ax2
if __name__ == '__main__':
main()

Just to add onto #JoeKington 's (great) answer, I found that the "hack to ensure that the original axes tick labels are on top of whatever is plotted in the twinned axes" didn't work for me so as an alternative I've used:
from matplotlib.ticker import MaxNLocator
#Match the tick point locations by setting the same number of ticks in the
# 2nd axis as the first
ax2.yaxis.set_major_locator(MaxNLocator(nbins=len(ax1.get_yticks())))
#Set the last tick as the plot limit
ax2.set_ylim(0, ax2.get_yticks()[-1])
#Remove the tick label at zero
ax2.yaxis.get_major_ticks()[0].label1.set_visible(False)