Is there an easy way to avoid pyplot zooming far into noisy data?
Something like a lower boundary for the axis limits.
I am not trying to set a fix boundary to my axis, as this will fully disable automatic scaling.
Maybe a "minimum tick distance" would also work.
Right now I am using an additional 'invisible' plot in my graph that will define the maximum zoom.
Some example that illustrates what I want to achieve:
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 100, 1)
noise = np.random.randn(len(x))*0.1
y = 10+noise
y_dummy_low = [0]*len(x)
y_dummy_high = [20]*len(x)
plt.figure()
plt.plot(x, y) # noise data i actually want to plot
plt.plot(x, y_dummy_low, y_dummy_high, marker="None", linestyle="None") # this will avoid zooming too much
plt.show()
Zooming too far
Zooming OK
I want to plot some coordinates using Plotly express because it allows me a more interactive approach, but I can not find the way to control the scale in the axis in the way I can manage with matplotlib.pyplot in one single line
plt.axis("scaled")
Could you please share some suggestions? Thanks.
Here is the code using Plotly express:
fig = px.scatter(coordinates_utm, x='EASTING', y='NORTHING', title=name,
hover_name=coordinates_utm.index,
hover_data={'NORTHING':':.6f','EASTING': ':.6f'})
fig.add_trace(px.scatter(coordinates_utm_lineal, x='x', y='ylineal',color_discrete_sequence=['red']).data[0])
Here is the code using plt:
fig.show()
plt.figure()
plt.scatter(coordinates_utm_lineal.x,coordinates_utm_lineal.ylineal,s=2)
plt.scatter(coordinates_utm.EASTING,coordinates_utm.NORTHING, s=2)
plt.axis("scaled")
plt.show()
This is my current output
Sadly, you didn't provide a fully reproducible example, so I'm going to create my own.
Also, I'm not really familiar with plt.axis("scaled"), as I usually use plt.axis("equal"). Reading the documentation associated to plt.axis, they appear to be somewhat similar. See if the following answer can satisfy your needs.
import plotly.express as px
import numpy as np
t = np.linspace(0, 2*np.pi)
x = np.cos(t)
y = np.sin(t)
fig = px.scatter(x=x, y=y)
fig.layout.yaxis.scaleanchor="x"
fig.show()
Python version: 3.6.4 (Anaconda on Windows)
Seaborn: 0.8.1
Matplotlib: 2.1.2
I'm trying to create a 2D Kernel Density plot using Seaborn but I want each step in the colourmap to have a different alpha value. I had a look at this question to create a matplotlib colourmap with alpha values: Add alpha to an existing matplotlib colormap.
I have a problem in that the lines between contours are visible. The result I get is here:
I thought that I had found the answer when I found this question: Hide contour linestroke on pyplot.contourf to get only fills. I tried the method outlined in the answer (using set_edgecolor("face") but it did not work in this case. That question also seemed to be related to vector graphics formats and I am just writing out a PNG.
Here is my script:
import numpy as np
import seaborn as sns
import matplotlib.colors as cols
import matplotlib.pyplot as plt
def alpha_cmap(cmap):
my_cmap = cmap(np.arange(cmap.N))
# Set a square root alpha.
x = np.linspace(0, 1, cmap.N)
my_cmap[:,-1] = x ** (0.5)
my_cmap = cols.ListedColormap(my_cmap)
return my_cmap
xs = np.random.uniform(size=100)
ys = np.random.uniform(size=100)
kplot = sns.kdeplot(data=xs, data2=ys,
cmap=alpha_cmap(plt.cm.viridis),
shade=True,
shade_lowest=False,
n_levels=30)
plt.savefig("example_plot.png")
Guided by some comments on this question I have tried some other methods that have been successful when this problem has come up. Based on this question (Matplotlib Contourf Plots Unwanted Outlines when Alpha < 1) I have tried altering the plot call to:
sns.kdeplot(data=xs, data2=ys,
cmap=alpha_cmap(plt.cm.viridis),
shade=True,
shade_lowest=False,
n_levels=30,
antialiased=True)
With antialiased=True the lines between contours are replaced by a narrow white line:
I have also tried an approach similar to this question - Pyplot pcolormesh confused when alpha not 1. This approach is based on looping over the PathCollections in kplot.collections and tuning the parameters of the edges so that they become invisible. I have tried adding this code and tweaking the linewidth -
for thing in kplot.collections:
thing.set_edgecolor("face")
thing.set_linewidth(0.01)
fig.canvas.draw()
This results in a mix of white and dark lines - .
I believe that I will not be able to tune the line width to make the lines disappear because of the variable width of the contour bands.
Using both methods (antialiasing + linewidth) makes this version, which looks cool but isn't quite what I want:
I also found this question - Changing Transparency of/Remove Contour Lines in Matplotlib
This one suggests overplotting a second plot with a different number of contour levels on the same axis, like:
kplot = sns.kdeplot(data=xs, data2=ys,
ax=ax,
cmap=alpha_cmap(plt.cm.viridis),
shade=True,
shade_lowest=False,
n_levels=30,
antialiased=True)
kplot = sns.kdeplot(data=xs, data2=ys,
ax=ax,
cmap=alpha_cmap(plt.cm.viridis),
shade=True,
shade_lowest=False,
n_levels=35,
antialiased=True)
This results in:
This is better, and almost works. The problem here is I need variable (and non-linear) alpha throughout the colourmap. The variable banding and lines seem to be a result of the combinations of alpha when contours are plotted over each other. I also still see some clear/white lines in the result.
I can't figure out how to rotate the text on the X Axis. Its a time stamp, so as the number of samples increase, they get closer and closer until they overlap. I'd like to rotate the text 90 degrees so as the samples get closer together, they aren't overlapping.
Below is what I have, it works fine with the exception that I can't figure out how to rotate the X axis text.
import sys
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import datetime
font = {'family' : 'normal',
'weight' : 'bold',
'size' : 8}
matplotlib.rc('font', **font)
values = open('stats.csv', 'r').readlines()
time = [datetime.datetime.fromtimestamp(float(i.split(',')[0].strip())) for i in values[1:]]
delay = [float(i.split(',')[1].strip()) for i in values[1:]]
plt.plot(time, delay)
plt.grid(b='on')
plt.savefig('test.png')
This works for me:
plt.xticks(rotation=90)
Many "correct" answers here but I'll add one more since I think some details are left out of several. The OP asked for 90 degree rotation but I'll change to 45 degrees because when you use an angle that isn't zero or 90, you should change the horizontal alignment as well; otherwise your labels will be off-center and a bit misleading (and I'm guessing many people who come here want to rotate axes to something other than 90).
Easiest / Least Code
Option 1
plt.xticks(rotation=45, ha='right')
As mentioned previously, that may not be desirable if you'd rather take the Object Oriented approach.
Option 2
Another fast way (it's intended for date objects but seems to work on any label; doubt this is recommended though):
fig.autofmt_xdate(rotation=45)
fig you would usually get from:
fig = plt.gcf()
fig = plt.figure()
fig, ax = plt.subplots()
fig = ax.figure
Object-Oriented / Dealing directly with ax
Option 3a
If you have the list of labels:
labels = ['One', 'Two', 'Three']
ax.set_xticks([1, 2, 3])
ax.set_xticklabels(labels, rotation=45, ha='right')
In later versions of Matplotlib (3.5+), you can just use set_xticks alone:
ax.set_xticks([1, 2, 3], labels, rotation=45, ha='right')
Option 3b
If you want to get the list of labels from the current plot:
# Unfortunately you need to draw your figure first to assign the labels,
# otherwise get_xticklabels() will return empty strings.
plt.draw()
ax.set_xticks(ax.get_xticks())
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
As above, in later versions of Matplotlib (3.5+), you can just use set_xticks alone:
ax.set_xticks(ax.get_xticks(), ax.get_xticklabels(), rotation=45, ha='right')
Option 4
Similar to above, but loop through manually instead.
for label in ax.get_xticklabels():
label.set_rotation(45)
label.set_ha('right')
Option 5
We still use pyplot (as plt) here but it's object-oriented because we're changing the property of a specific ax object.
plt.setp(ax.get_xticklabels(), rotation=45, ha='right')
Option 6
This option is simple, but AFAIK you can't set label horizontal align this way so another option might be better if your angle is not 90.
ax.tick_params(axis='x', labelrotation=45)
Edit:
There's discussion of this exact "bug" but a fix hasn't been released (as of 3.4.0):
https://github.com/matplotlib/matplotlib/issues/13774
Easy way
As described here, there is an existing method in the matplotlib.pyplot figure class that automatically rotates dates appropriately for you figure.
You can call it after you plot your data (i.e.ax.plot(dates,ydata) :
fig.autofmt_xdate()
If you need to format the labels further, checkout the above link.
Non-datetime objects
As per languitar's comment, the method I suggested for non-datetime xticks would not update correctly when zooming, etc. If it's not a datetime object used as your x-axis data, you should follow Tommy's answer:
for tick in ax.get_xticklabels():
tick.set_rotation(45)
Try pyplot.setp. I think you could do something like this:
x = range(len(time))
plt.xticks(x, time)
locs, labels = plt.xticks()
plt.setp(labels, rotation=90)
plt.plot(x, delay)
Appart from
plt.xticks(rotation=90)
this is also possible:
plt.xticks(rotation='vertical')
I came up with a similar example. Again, the rotation keyword is.. well, it's key.
from pylab import *
fig = figure()
ax = fig.add_subplot(111)
ax.bar( [0,1,2], [1,3,5] )
ax.set_xticks( [ 0.5, 1.5, 2.5 ] )
ax.set_xticklabels( ['tom','dick','harry'], rotation=45 ) ;
If you want to apply rotation on the axes object, the easiest way is using tick_params. For example.
ax.tick_params(axis='x', labelrotation=90)
Matplotlib documentation reference here.
This is useful when you have an array of axes as returned by plt.subplots, and it is more convenient than using set_xticks because in that case you need to also set the tick labels, and also more convenient that those that iterate over the ticks (for obvious reasons)
If using plt:
plt.xticks(rotation=90)
In case of using pandas or seaborn to plot, assuming ax as axes for the plot:
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
Another way of doing the above:
for tick in ax.get_xticklabels():
tick.set_rotation(45)
My answer is inspired by cjohnson318's answer, but I didn't want to supply a hardcoded list of labels; I wanted to rotate the existing labels:
for tick in ax.get_xticklabels():
tick.set_rotation(45)
The simplest solution is to use:
plt.xticks(rotation=XX)
but also
# Tweak spacing to prevent clipping of tick-labels
plt.subplots_adjust(bottom=X.XX)
e.g for dates I used rotation=45 and bottom=0.20 but you can do some test for your data
import pylab as pl
pl.xticks(rotation = 90)
To rotate the x-axis label to 90 degrees
for tick in ax.get_xticklabels():
tick.set_rotation(45)
It will depend on what are you plotting.
import matplotlib.pyplot as plt
x=['long_text_for_a_label_a',
'long_text_for_a_label_b',
'long_text_for_a_label_c']
y=[1,2,3]
myplot = plt.plot(x,y)
for item in myplot.axes.get_xticklabels():
item.set_rotation(90)
For pandas and seaborn that give you an Axes object:
df = pd.DataFrame(x,y)
#pandas
myplot = df.plot.bar()
#seaborn
myplotsns =sns.barplot(y='0', x=df.index, data=df)
# you can get xticklabels without .axes cause the object are already a
# isntance of it
for item in myplot.get_xticklabels():
item.set_rotation(90)
If you need to rotate labels you may need change the font size too, you can use font_scale=1.0 to do that.
I'm trying to create a box & whisker plot of a set of data binning y versus x. I found an useful example in making binned boxplot in matplotlib with numpy and scipy in Python. The question is now very simple. How can I specify the color of the boxes in matplotlib.pyplot.boxplot as I would like to set it transparent in order to let the reader also to see the original data. I know there exists the example shown in http://matplotlib.sourceforge.net/examples/pylab_examples/boxplot_demo2.html but is anything simpler than this? It looks strange the impossibility to set the color of the boxes directly in boxplot
Thank you in advance
You could just render the original data as a scatter plot behind the boxplot and then hide the fliers of the boxplot.
import pylab
import numpy
pylab.figure()
data = [numpy.random.normal(i, size=50) for i in xrange(5)]
for x, y in enumerate(data):
pylab.scatter([x + 1 for i in xrange(50)], y, alpha=0.5, edgecolors='r', marker='+')
bp = pylab.boxplot(data)
pylab.setp(bp['boxes'], color='black')
pylab.setp(bp['whiskers'], color='black')
pylab.setp(bp['fliers'], marker='None')
pylab.xlim(0,6)
pylab.show()