I am making a 3D plot with two sets of axes (in particular, an animation for a rotating cone).
First, I make one set of axes using fig.add_subplot, and plot the rotating cone using ax.plot.
fig=plt.figure()
ax = fig.add_subplot(111, projection="3d")
But when I add this declaration for ax2 after the above two lines (like below), I just see a white plot (no rotating cone).
fig=plt.figure()
ax = fig.add_subplot(111, projection="3d")
ax2 = fig.add_axes(ax.get_position(), projection="3d", frame_on=False)
Can anyone help with what went wrong here? Why is what I plotted using ax get covered or erased when I declared an ax2?
Thanks!
The second plot background color is covering the first one. You can remove the background by doing:
ax2.patch.set_visible(False)
However, you will still see the second axes on top of the first one, and you will only be able to interact with the top axes and not the bottom one. Are you sure that's what you are trying to do?
Related
I'm trying to plot a figure without tickmarks or numbers on either of the axes (I use axes in the traditional sense, not the matplotlib nomenclature!). An issue I have come across is where matplotlib adjusts the x(y)ticklabels by subtracting a value N, then adds N at the end of the axis.
This may be vague, but the following simplified example highlights the issue, with '6.18' being the offending value of N:
import matplotlib.pyplot as plt
import random
prefix = 6.18
rx = [prefix+(0.001*random.random()) for i in arange(100)]
ry = [prefix+(0.001*random.random()) for i in arange(100)]
plt.plot(rx,ry,'ko')
frame1 = plt.gca()
for xlabel_i in frame1.axes.get_xticklabels():
xlabel_i.set_visible(False)
xlabel_i.set_fontsize(0.0)
for xlabel_i in frame1.axes.get_yticklabels():
xlabel_i.set_fontsize(0.0)
xlabel_i.set_visible(False)
for tick in frame1.axes.get_xticklines():
tick.set_visible(False)
for tick in frame1.axes.get_yticklines():
tick.set_visible(False)
plt.show()
The three things I would like to know are:
How to turn off this behaviour in the first place (although in most cases it is useful, it is not always!) I have looked through matplotlib.axis.XAxis and cannot find anything appropriate
How can I make N disappear (i.e. X.set_visible(False))
Is there a better way to do the above anyway? My final plot would be 4x4 subplots in a figure, if that is relevant.
Instead of hiding each element, you can hide the whole axis:
frame1.axes.get_xaxis().set_visible(False)
frame1.axes.get_yaxis().set_visible(False)
Or, you can set the ticks to an empty list:
frame1.axes.get_xaxis().set_ticks([])
frame1.axes.get_yaxis().set_ticks([])
In this second option, you can still use plt.xlabel() and plt.ylabel() to add labels to the axes.
If you want to hide just the axis text keeping the grid lines:
frame1 = plt.gca()
frame1.axes.xaxis.set_ticklabels([])
frame1.axes.yaxis.set_ticklabels([])
Doing set_visible(False) or set_ticks([]) will also hide the grid lines.
If you are like me and don't always retrieve the axes, ax, when plotting the figure, then a simple solution would be to do
plt.xticks([])
plt.yticks([])
I've colour coded this figure to ease the process.
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
You can have full control over the figure using these commands, to complete the answer I've add also the control over the spines:
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
# X AXIS -BORDER
ax.spines['bottom'].set_visible(False)
# BLUE
ax.set_xticklabels([])
# RED
ax.set_xticks([])
# RED AND BLUE TOGETHER
ax.axes.get_xaxis().set_visible(False)
# Y AXIS -BORDER
ax.spines['left'].set_visible(False)
# YELLOW
ax.set_yticklabels([])
# GREEN
ax.set_yticks([])
# YELLOW AND GREEN TOGHETHER
ax.axes.get_yaxis().set_visible(False)
I was not actually able to render an image without borders or axis data based on any of the code snippets here (even the one accepted at the answer). After digging through some API documentation, I landed on this code to render my image
plt.axis('off')
plt.tick_params(axis='both', left=False, top=False, right=False, bottom=False, labelleft=False, labeltop=False, labelright=False, labelbottom=False)
plt.savefig('foo.png', dpi=100, bbox_inches='tight', pad_inches=0.0)
I used the tick_params call to basically shut down any extra information that might be rendered and I have a perfect graph in my output file.
Somewhat of an old thread but, this seems to be a faster method using the latest version of matplotlib:
set the major formatter for the x-axis
ax.xaxis.set_major_formatter(plt.NullFormatter())
One trick could be setting the color of tick labels as white to hide it!
plt.xticks(color='w')
plt.yticks(color='w')
or to be more generalized (#Armin Okić), you can set it as "None".
When using the object oriented API, the Axes object has two useful methods for removing the axis text, set_xticklabels() and set_xticks().
Say you create a plot using
fig, ax = plt.subplots(1)
ax.plot(x, y)
If you simply want to remove the tick labels, you could use
ax.set_xticklabels([])
or to remove the ticks completely, you could use
ax.set_xticks([])
These methods are useful for specifying exactly where you want the ticks and how you want them labeled. Passing an empty list results in no ticks, or no labels, respectively.
You could simply set xlabel to None, straight in your axis. Below an working example using seaborn
from matplotlib import pyplot as plt
import seaborn as sns
tips = sns.load_dataset("tips")
ax = sns.boxplot(x="day", y="total_bill", data=tips)
ax.set(xlabel=None)
plt.show()
Just do this in case you have subplots
fig, axs = plt.subplots(1, 2, figsize=(16, 8))
ax[0].set_yticklabels([]) # x-axis
ax[0].set_xticklabels([]) # y-axis
I'm trying to plot a two-dimensional array in matplotlib using imshow(), and overlay it with a scatterplot on a second y axis.
oneDim = np.array([0.5,1,2.5,3.7])
twoDim = np.random.rand(8,4)
plt.figure()
ax1 = plt.gca()
ax1.imshow(twoDim, cmap='Purples', interpolation='nearest')
ax1.set_xticks(np.arange(0,twoDim.shape[1],1))
ax1.set_yticks(np.arange(0,twoDim.shape[0],1))
ax1.set_yticklabels(np.arange(0,twoDim.shape[0],1))
ax1.grid()
#This is the line that causes problems
ax2 = ax1.twinx()
#That's not really part of the problem (it seems)
oneDimX = oneDim.shape[0]
oneDimY = 4
ax2.plot(np.arange(0,oneDimX,1),oneDim)
ax2.set_yticks(np.arange(0,oneDimY+1,1))
ax2.set_yticklabels(np.arange(0,oneDimY+1,1))
If I only run everything up to the last line, I get my array fully visualised:
However, if I add a second y axis (ax2=ax1.twinx()) as preparation for the scatterplot, it changes to this incomplete rendering:
What's the problem? I've left a few lines in the code above describing the addition of the scatterplot, although it doesn't seem to be part of the issue.
Following the GitHub discussion which Thomas Kuehn has pointed at, the issue has been fixed few days ago. In the absence of a readily available built, here's a fix using the aspect='auto' property. In order to get nice regular boxes, I adjusted the figure x/y using the array dimensions. The axis autoscale feature has been used to remove some additional white border.
oneDim = np.array([0.5,1,2.5,3.7])
twoDim = np.random.rand(8,4)
plt.figure(figsize=(twoDim.shape[1]/2,twoDim.shape[0]/2))
ax1 = plt.gca()
ax1.imshow(twoDim, cmap='Purples', interpolation='nearest', aspect='auto')
ax1.set_xticks(np.arange(0,twoDim.shape[1],1))
ax1.set_yticks(np.arange(0,twoDim.shape[0],1))
ax1.set_yticklabels(np.arange(0,twoDim.shape[0],1))
ax1.grid()
ax2 = ax1.twinx()
#Required to remove some white border
ax1.autoscale(False)
ax2.autoscale(False)
Result:
I'm trying to plot a figure without tickmarks or numbers on either of the axes (I use axes in the traditional sense, not the matplotlib nomenclature!). An issue I have come across is where matplotlib adjusts the x(y)ticklabels by subtracting a value N, then adds N at the end of the axis.
This may be vague, but the following simplified example highlights the issue, with '6.18' being the offending value of N:
import matplotlib.pyplot as plt
import random
prefix = 6.18
rx = [prefix+(0.001*random.random()) for i in arange(100)]
ry = [prefix+(0.001*random.random()) for i in arange(100)]
plt.plot(rx,ry,'ko')
frame1 = plt.gca()
for xlabel_i in frame1.axes.get_xticklabels():
xlabel_i.set_visible(False)
xlabel_i.set_fontsize(0.0)
for xlabel_i in frame1.axes.get_yticklabels():
xlabel_i.set_fontsize(0.0)
xlabel_i.set_visible(False)
for tick in frame1.axes.get_xticklines():
tick.set_visible(False)
for tick in frame1.axes.get_yticklines():
tick.set_visible(False)
plt.show()
The three things I would like to know are:
How to turn off this behaviour in the first place (although in most cases it is useful, it is not always!) I have looked through matplotlib.axis.XAxis and cannot find anything appropriate
How can I make N disappear (i.e. X.set_visible(False))
Is there a better way to do the above anyway? My final plot would be 4x4 subplots in a figure, if that is relevant.
Instead of hiding each element, you can hide the whole axis:
frame1.axes.get_xaxis().set_visible(False)
frame1.axes.get_yaxis().set_visible(False)
Or, you can set the ticks to an empty list:
frame1.axes.get_xaxis().set_ticks([])
frame1.axes.get_yaxis().set_ticks([])
In this second option, you can still use plt.xlabel() and plt.ylabel() to add labels to the axes.
If you want to hide just the axis text keeping the grid lines:
frame1 = plt.gca()
frame1.axes.xaxis.set_ticklabels([])
frame1.axes.yaxis.set_ticklabels([])
Doing set_visible(False) or set_ticks([]) will also hide the grid lines.
If you are like me and don't always retrieve the axes, ax, when plotting the figure, then a simple solution would be to do
plt.xticks([])
plt.yticks([])
I've colour coded this figure to ease the process.
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
You can have full control over the figure using these commands, to complete the answer I've add also the control over the spines:
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
# X AXIS -BORDER
ax.spines['bottom'].set_visible(False)
# BLUE
ax.set_xticklabels([])
# RED
ax.set_xticks([])
# RED AND BLUE TOGETHER
ax.axes.get_xaxis().set_visible(False)
# Y AXIS -BORDER
ax.spines['left'].set_visible(False)
# YELLOW
ax.set_yticklabels([])
# GREEN
ax.set_yticks([])
# YELLOW AND GREEN TOGHETHER
ax.axes.get_yaxis().set_visible(False)
I was not actually able to render an image without borders or axis data based on any of the code snippets here (even the one accepted at the answer). After digging through some API documentation, I landed on this code to render my image
plt.axis('off')
plt.tick_params(axis='both', left=False, top=False, right=False, bottom=False, labelleft=False, labeltop=False, labelright=False, labelbottom=False)
plt.savefig('foo.png', dpi=100, bbox_inches='tight', pad_inches=0.0)
I used the tick_params call to basically shut down any extra information that might be rendered and I have a perfect graph in my output file.
Somewhat of an old thread but, this seems to be a faster method using the latest version of matplotlib:
set the major formatter for the x-axis
ax.xaxis.set_major_formatter(plt.NullFormatter())
One trick could be setting the color of tick labels as white to hide it!
plt.xticks(color='w')
plt.yticks(color='w')
or to be more generalized (#Armin Okić), you can set it as "None".
When using the object oriented API, the Axes object has two useful methods for removing the axis text, set_xticklabels() and set_xticks().
Say you create a plot using
fig, ax = plt.subplots(1)
ax.plot(x, y)
If you simply want to remove the tick labels, you could use
ax.set_xticklabels([])
or to remove the ticks completely, you could use
ax.set_xticks([])
These methods are useful for specifying exactly where you want the ticks and how you want them labeled. Passing an empty list results in no ticks, or no labels, respectively.
You could simply set xlabel to None, straight in your axis. Below an working example using seaborn
from matplotlib import pyplot as plt
import seaborn as sns
tips = sns.load_dataset("tips")
ax = sns.boxplot(x="day", y="total_bill", data=tips)
ax.set(xlabel=None)
plt.show()
Just do this in case you have subplots
fig, axs = plt.subplots(1, 2, figsize=(16, 8))
ax[0].set_yticklabels([]) # x-axis
ax[0].set_xticklabels([]) # y-axis
I have searched on google but didn't get an answer. I created a subplot consisting of 2 axes and called plt.gca() but every time it only referred to the last axis in the axes of my subplots. I then started to wonder if it is possible to get a particular axis by passing in some kwargs but didn't find such parameter. I would really like to know how plt.gca() works and why you can't specify which axis to get.
gca means "get current axes".
"Current" here means that it provides a handle to the last active axes. If there is no axes yet, an axes will be created. If you create two subplots, the subplot that is created last is the current one.
There is no such thing as gca(something), because that would translate into "get current axes which is not the current one" - sound unlogical already, doesn't it?
The easiest way to make sure you have a handle to any axes in the plot is to create that handle yourself. E.g.
ax = plt.subplot(121)
ax2 = plt.subplot(122)
You may then use ax or ax2 at any point after that to manipulate the axes of choice.
Also consider using the subplots (note the s) command,
fig, (ax, ax2) = plt.subplots(ncols=2)
If you don't have a handle or forgot to create one, you may get one e.g. via
all_axes = plt.gcf().get_axes()
ax = all_axes[0]
to get the first axes. Since there is no natural order of axes in a plot, this should only be used if no other option is available.
As a supplement to Importance's very fine answer, I thought I would point out the pyplot command sca, which stands for "set current axes".
It takes an axes as an argument and sets it as the current axes, so you still need references to your axes. But the thing about sca that some may find useul is that you can have multiple axes and work on all of them while still using the pyplot interface rather than the object-oriented approach.
import matplotlib.pyplot as plt
fig = plt.figure()
ax = plt.subplot(121)
ax2 = plt.subplot(122)
# Check if ax2 is current axes
print(ax2 is plt.gca())
# >>> True
# Plot on ax2
plt.plot([0,1],[0,1])
plt.xlabel('X')
plt.ylabel('Y')
# Now set ax as current axes
plt.sca(ax)
print(ax2 is plt.gca())
# >>> False
print(ax is plt.gca())
# >>> True
# We can call the exact same commands as we did for ax2, but draw on ax
plt.plot([0,1],[0,1])
plt.xlabel('X')
plt.ylabel('Y')
plt.show()
So you'll notice that we were able to reuse the same code to plot and add labels to both axes.
I am wondering if there is a way to control which plot lies on top of other plots if one makes multiple plots on one axis. An example:
As you can see, the green series is on top of the blue series, and both series are on top of the black dots (which I made with a scatter plot). I would like the black dots to be on top of both series (lines).
I first did the above with the following code
plt.plot(series1_x, series1_y)
plt.plot(series2_x, series2_y)
plt.scatter(series2_x, series2_y)
Then I tried the following
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.plot(series1_x, series1_y)
ax2 = fig.add_subplot(111)
ax2.plot(series2_x, series2_y)
ax3 = fig.add_subplot(111)
ax3.scatter(series2_x, series2_y)
And some variations on that, but no luck.
Swapping around the plot functions has an effect on which plot is on top, but no matter where I put the scatter function, the lines are on top of the dots.
NOTE:
I am using Python 3.5 on Windows 10 (this example), but mostly Python 3.4 on Ubuntu.
NOTE 2:
I know this may seem like a trivial issue, but I have a case where the series on top of the dots are so dense that the colour of the dots get obscured, and in those cases I need my readers to clearly see which dots are what colour, hence why I need the dots to be on top.
Use the zorder kwarg where the lower the zorder the further back the plot, e.g.
plt.plot(series1_x, series1_y, zorder=1)
plt.plot(series2_x, series2_y, zorder=2)
plt.scatter(series2_x, series2_y, zorder=3)
Yes, you can. Just use zorder parameter. The higher the value, more on top the plot shall be.
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.plot(series1_x, series1_y, zorder=3)
ax2 = fig.add_subplot(111)
ax2.plot(series2_x, series2_y, zorder=4)
ax3 = fig.add_subplot(111)
ax3.scatter(series2_x, series2_y, zorder=5)
Alternatively, you can do line and marker plot at the same time. You can even set different colors for line and marker face.
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.plot(series1_x, series1_y)
ax2 = fig.add_subplot(111)
ax2.plot(series2_x, series2_y, '-o', color='b', mfc='k')
The '-o' sets plot style to line and circle markers, color='b' sets line color to blue and mfc='k' sets the marker face color to black.
Another solution besides using zorder, and worth knowing: You can simply plot a scatter of points using the plot command. Something like plot(series2_x, series2_y, ' o'). Note the ' o' with a space means no lines but circle points. This way the order of plotting them on the axes does put them on top.