The question
I am trying to draw grid lines from the ticks of my SecondaryAxis with
ax2.grid(color=color,linestyle='--')
nothing shows up on the figure, I believe I am in the same situation as for Format SecondaryAxis ticklabels Matplotlib, aren't I ?
However, does anybody have a workaround for the issue without reversing the scales ? I mean by reversing the scale is to have the percentages scale on the main axis and the normal scale on the secondary axis.
The full code
import matplotlib.pyplot as plt
import numpy as np
#generate dummy load duration curve
dur=2500
load = np.random.normal(60,30,dur+1)
load[::-1].sort()
x=range(0,dur+1)
perticks = np.linspace(0,1,11)
xticks = perticks*dur
# get yticks from xticks
yticks = np.interp(xticks, range(0,dur+1), load)
print(yticks)
# create figure object with axe object
fig, ax1 = plt.subplots(figsize=(16, 8))
ax1.plot(x, load)
#create second axis
ax2 = ax1.secondary_yaxis('right')
# label and color of the secondaryaxis
perlabels = ['0%', '10%', '20%', '30%', '40%', '50%', '60%', '70%', '80%', '90%', '100%']
color ='tab:blue'
ax2.set_yticks(yticks)
ax2.set_yticklabels(labels=perlabels)
ax2.tick_params(axis='y', color=color, labelcolor=color)
# draw grid lines on the secondaryaxis
ax2.grid(color=color,linestyle='--')
# do the same for x axis
ax3 = ax1.secondary_xaxis('top')
ax3.set_xticks(xticks)
ax3.set_xticklabels(labels=perlabels)
ax3.tick_params(axis='x', color=color, labelcolor=color)
ax3.grid(color=color,linestyle='--')
The output
I did some digging on this topic, and opened an issue on GitHub. Here's what I found out:
The SecondaryAxis is "quite new thing", added in matplotlib 3.1.0. (May 2019). Even the v.3.3.3 docs say that the secondary_xaxis() method is experimental.
The SecondaryAxis inherits from _AxesBase, which is an "implementation detail". It is not supposed (as of v.3.3.3) to work as Axes object, and the SecondaryAxis.grid() is not supposed to draw anything (like _AxesBase.grid() does). Although, I agree it is misleading that there is a non-working method.
Therefore, at the time of writing, .grid() is only assumed to work on primaxy axes.
Making the blue axis primary
Since .grid() only works on non-secondary axis, you make the primary axis blue, and move it to top & right.
Code
# Take the x and y-ticks for transfering them to secondary axis
xticks_orig = ax1.get_xticks()
yticks_orig = ax1.get_yticks()
# Make the primary axis blue since we want to draw grid on it
ax1.xaxis.tick_top()
ax1.yaxis.tick_right()
ax1.set_xticks(xticks)
ax1.set_yticks(yticks)
ax1.set_yticklabels(labels=perlabels)
ax1.set_xticklabels(labels=perlabels)
ax1.tick_params(axis="both", color=color, labelcolor=color)
ax1.grid(color=color, linestyle="--")
# Draw the black secondary axis
ax2 = ax1.secondary_yaxis("left")
ax3 = ax1.secondary_xaxis("bottom")
ax2.set_yticks(yticks_orig)
ax3.set_xticks(xticks_orig)
Adding grid lines manually
You could add the grid lines also manually, like this
xlim = ax1.get_xlim()
for y in ax2.get_yticks():
ax1.plot(xlim, (y, y), ls="--", color=color, lw=0.5)
ax1.set_xlim(xlim)
ylim = ax1.get_ylim()
for x in ax3.get_xticks():
ax1.plot((x, x), ylim, ls="--", color=color, lw=0.5)
ax1.set_ylim(ylim)
The output would look like this:
The difference here is now we draw lines on the figure which look like the grid lines.
To add on np8's answer, you can also use axvline to draw the lines. This has the advantage that you do not need to keep track of the y limits manually:
for x in ax2.get_xticks():
ax1.axvline(x, color=color, zorder=-1, linestyle="--", linewidth=0.5)
Note also that you will need to appropriately transform the x-coordinate to match the transform you do from ax1 to ax2.
Also, in my case I first had to render the canvas in order for the tick labels to be generated:
fig1.canvas.draw()
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 want to plot a grid onto the background of 2D plot line, similar as it is for ECG presentations, i.e. at specific points in regular interval dots are shown, e.g. as in this image
In this example there are precisely 4 dots spaced between to major dots. Want I don't want is sth as this Plotting a grid with Matplotlib, i.e. just dotted grid lines
What I did so far (coming from ancient matlab knowledge) is this:
xg = np.linspace(iStart/fs, iEnd/fs, len(y))
yrange = ax.get_ylim()
yg = np.linspace(yrange[0], yrange[1], 4)
xx, yy = np.meshgrid(xg, yg)
gridpoints, = plt.plot(xx.reshape(1,-1),yy.reshape(1,-1),linewidth=0.3,color='0.75',marker=".",markersize=10)
But it gets me this:
What am I not getting right?
As said by jpnadas you can use plt.grid()
here an example on how you can put and customize grid
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
img = plt.imread(imagename)
_, ax = plt.subplots(ncols=1,nrows=1)
ax.imshow(img)
plt.gca().xaxis.set_major_locator(MultipleLocator(16))
plt.gca().yaxis.set_major_locator(MultipleLocator(16))
plt.gca().xaxis.set_minor_locator(MultipleLocator(32))
plt.gca().yaxis.set_minor_locator(MultipleLocator(32))
# Don't allow the axis to be on top of your data
ax.set_axisbelow(True)
# Turn on the minor TICKS, which are required for the minor GRID
ax.minorticks_on()
# Customize the major grid
ax.grid(which='major', linestyle='-', linewidth='4', color='yellow')
# Customize the minor grid
ax.grid(which='minor', linestyle=':', linewidth='2', color='blue')
plt.show()
I found my mistake. It was not an error in my thinking, but that my len(y) in the linspace of x was referencing the wrong vector, hencing producing a too fine grid, which looked like a line.
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 creating a plot using python 3.5.1 and matplotlib 1.5.1 that has two subplots (side by side) with a shared Y axis. A sample output image is shown below:
Notice the extra white space at the top and bottom of each set of axes. Try as I might I can't seem to get rid of it. The overall goal of the figure is to have a waterfall type plot on the left with a shared Y axes with the plot on the right.
Here's some sample code to reproduce the image above.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
# create some X values
periods = np.linspace(1/1440, 1, 1000)
# create some Y values (will be datetimes, not necessarily evenly spaced
# like they are in this example)
day_ints = np.linspace(1, 100, 100)
days = pd.to_timedelta(day_ints, 'D') + pd.to_datetime('2016-01-01')
# create some fake data for the number of points
points = np.random.random(len(day_ints))
# create some fake data for the color mesh
Sxx = np.random.random((len(days), len(periods)))
# Create the plots
fig = plt.figure(figsize=(8, 6))
# create first plot
ax1 = plt.subplot2grid((1,5), (0,0), colspan=4)
im = ax1.pcolormesh(periods, days, Sxx, cmap='viridis', vmin=0, vmax=1)
ax1.invert_yaxis()
ax1.autoscale(enable=True, axis='Y', tight=True)
# create second plot and use the same y axis as the first one
ax2 = plt.subplot2grid((1,5), (0,4), sharey=ax1)
ax2.scatter(points, days)
ax2.autoscale(enable=True, axis='Y', tight=True)
# Hide the Y axis scale on the second plot
plt.setp(ax2.get_yticklabels(), visible=False)
#ax1.set_adjustable('box-forced')
#ax2.set_adjustable('box-forced')
fig.colorbar(im, ax=ax1)
As you can see in the commented out code I've tried a number of approaches, as suggested by posts like https://github.com/matplotlib/matplotlib/issues/1789/ and Matplotlib: set axis tight only to x or y axis.
As soon as I remove the sharey=ax1 part of the second subplot2grid call the problem goes away, but then I also don't have a common Y axis.
Autoscale tends to add a buffer to the data so that all of the data points are easily visible and not part-way cut off by the axes.
Change:
ax1.autoscale(enable=True, axis='Y', tight=True)
to:
ax1.set_ylim(days.min(),days.max())
and
ax2.autoscale(enable=True, axis='Y', tight=True)
to:
ax2.set_ylim(days.min(),days.max())
To get:
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))