Let's say I have made a plot, and in that plot there is a specific point where I draw vertical line from to the x-axis. This point has the x-value 33.55 for example. However, my tick separation is something like 10 or 20 from 0 to 100.
So basically: Is there a way in which I can add this single custom value to the tick axis, so it shows together with all the other values that where there before ?
Use np.append to add to the array of ticks:
import numpy as np
from matplotlib import pyplot as plt
x = np.random.rand(100) * 100
y = np.random.rand(100) * 100
fig, ax = plt.subplots(figsize=(8, 6))
ax.scatter(x, y)
ax.set_xticks(np.append(ax.get_xticks(), 33.55))
Note that if your plot is not big enough, the tick labels may overlap.
If you want the new tick to "clear its orbit", so to speak:
special_value = 33.55
black_hole_radius = 10
new_ticks = [value for value in ax.get_xticks() if abs(value - special_value) > black_hole_radius] + [special_value]
ax.set_xticks(new_ticks)
Related
I have a plot in which I have to divide my data points into several groups, so I made customized sticks for this plot.
For instance, I have to group data points into multiples of 12, this is what I did
my_xticks = []
for x_ele in range(len(all_points)):
if x_ele % 12 == 0:
my_xticks.append(x_ele//12 + 1)
else:
my_xticks.append('')
ax.set_xticks(range(len(my_xticks)))
ax.set_xticklabels(my_xticks)
And the x-axis of the plot looks as
However, I wish to remove those spikes with empty labels, as circled in red
So the final x-axis could look like
Any idea? Thanks!
You didn't provide any data so i solved this by using some data i created. the idea is to use the range function to create the same gap between each tick.
Here is my code:
from matplotlib import pyplot as plt
import numpy as np
# create sample data
x = np.linspace(1, 60, 100)
y = x*x
# define the space of ticks
space = 12
# get minimum x value
min_val = int(min(x))
# get maximum x value
max_val = int(max(x))
# define our ticks
xticks = list(range(min_val, max_val, space))
# define labels for each tick
xticklabels = list(range(1, len(xticks) + 1, 1))
# create plot
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels)
plt.show()
And output:
Python beginner so apologies if incorrect terminology at any point.
I am using the legend(loc='best', ...) method and it works 99% of the time. However, when stacking more than 9 plots (i.e. i>9 in example below) on a single figure, with individual labels, it defaults to center and covers the data.
Is there a way to run a test in the script that will give a true/false value if the legend is covering any data points?
Very simplified code:
fig = plt.figure()
for i in data:
plt.plot(i[x, y], label=LABEL)
fig.legend(loc='best')
fig.savefig()
Example of legend covering data
One way is to add some extra space at the bottom/top/left or right side of the axis (in your case I would prefer top or bottom), by changing the limits slightly. Doing so makes the legend fit below the data. Add extra space by setting a different y-limit with ax.set_ylim(-3e-4, 1.5e-4) (the upper limit is approximately what it is in your figure and -3 is a estimate of what you need).
What you also need to do is to add split the legend into more columns, with the keyword ncol=N when creating the legend.
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111)
x = np.linspace(0, 1, 100)
y = 3.5 * x - 2
for i in range(9):
ax.plot(x, y + i / 10., label='iiiiiiiiiiii={}'.format(i))
ax.set_ylim(-3, 1.5)
ax.legend(loc='lower center', ncol=3) # ncol=3 looked nice for me, maybe you need to change this
plt.show()
EDIT
Another solution is to put the legend in a separate axis like I do in the code below. The data-plot does not need to care about making space for the legend or anything and you should have enough space in the axis below to put all your line-labels. If you need more space, you can easily change the ratio of the upper axis to the lower axis.
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(211)
ax_leg = fig.add_subplot(212)
x = np.linspace(0, 1, 100)
y = 3.5 * x - 2
lines = []
for i in range(9): #for plotting the actual data
li, = ax.plot(x, y + i / 10., label='iiiiiiiiiiii={}'.format(i))
lines.append(li)
for line in lines: # just to make the legend plot
ax_leg.plot([], [], line.get_color(), label=line.get_label())
ax_leg.legend(loc='center', ncol=3, ) # ncol=3 looked nice for me, maybe you need to change this
ax_leg.axis('off')
fig.show()
I am creating a plot in python. Is there a way to re-scale the axis by a factor? The yscale and xscale commands only allow me to turn log scale off.
Edit:
For example. If I have a plot where the x scales goes from 1 nm to 50 nm, the x scale will range from 1x10^(-9) to 50x10^(-9) and I want it to change from 1 to 50. Thus, I want the plot function to divide the x values placed on the plot by 10^(-9)
As you have noticed, xscale and yscale does not support a simple linear re-scaling (unfortunately). As an alternative to Hooked's answer, instead of messing with the data, you can trick the labels like so:
ticks = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x*scale))
ax.xaxis.set_major_formatter(ticks)
A complete example showing both x and y scaling:
import numpy as np
import pylab as plt
import matplotlib.ticker as ticker
# Generate data
x = np.linspace(0, 1e-9)
y = 1e3*np.sin(2*np.pi*x/1e-9) # one period, 1k amplitude
# setup figures
fig = plt.figure()
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
# plot two identical plots
ax1.plot(x, y)
ax2.plot(x, y)
# Change only ax2
scale_x = 1e-9
scale_y = 1e3
ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/scale_x))
ax2.xaxis.set_major_formatter(ticks_x)
ticks_y = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/scale_y))
ax2.yaxis.set_major_formatter(ticks_y)
ax1.set_xlabel("meters")
ax1.set_ylabel('volt')
ax2.set_xlabel("nanometers")
ax2.set_ylabel('kilovolt')
plt.show()
And finally I have the credits for a picture:
Note that, if you have text.usetex: true as I have, you may want to enclose the labels in $, like so: '${0:g}$'.
Instead of changing the ticks, why not change the units instead? Make a separate array X of x-values whose units are in nm. This way, when you plot the data it is already in the correct format! Just make sure you add a xlabel to indicate the units (which should always be done anyways).
from pylab import *
# Generate random test data in your range
N = 200
epsilon = 10**(-9.0)
X = epsilon*(50*random(N) + 1)
Y = random(N)
# X2 now has the "units" of nanometers by scaling X
X2 = (1/epsilon) * X
subplot(121)
scatter(X,Y)
xlim(epsilon,50*epsilon)
xlabel("meters")
subplot(122)
scatter(X2,Y)
xlim(1, 50)
xlabel("nanometers")
show()
To set the range of the x-axis, you can use set_xlim(left, right), here are the docs
Update:
It looks like you want an identical plot, but only change the 'tick values', you can do that by getting the tick values and then just changing them to whatever you want. So for your need it would be like this:
ticks = your_plot.get_xticks()*10**9
your_plot.set_xticklabels(ticks)
I'm working on a graphic with matplotlib in Python 3.4 that represents:
x = (months)
y = (12 values)
import matplotlib.pyplot as plt
import numpy as np
import calendar
N = 12
mult = 12500
x = np.arange(N)
y = mult *np.random.randn(12)
plt.plot(x, y, 'r')
plt.xticks(x, calendar.month_name[1:13], rotation=20 )
plt.yticks(y, y)
plt.grid('on')
plt.margins(0.05)
plt.show()
Tha labels of the yticks are the values in y, but when some values are very similar the ylabels overlap.
Example:
I've tried linespacing property, but it just works with each label it doesnt affect to the set.
How do I give some spacing to the ylabels or avoid that overlapping?
As pointed out in a comment by #jme: "As a person who reads a lot of graphs, I'd suggest that labeling the y-axis this way is kind of disorienting. I'd label the y-axis with regular intervals, and label the individual points with their y-coords. Like this, for example:
I'm trying to combine a normal matplotlib.pyplot plt.plot(x,y) with variable y as a function of variable x with a boxplot. However, I only want a boxplot on certain (variable) locations of x but this does not seem to work in matplotlib?
Are you wanting something like this? The positions kwarg to boxplot allows you to place the boxplots at arbitrary positions.
import matplotlib.pyplot as plt
import numpy as np
# Generate some data...
data = np.random.random((100, 5))
y = data.mean(axis=0)
x = np.random.random(y.size) * 10
x -= x.min()
x.sort()
# Plot a line between the means of each dataset
plt.plot(x, y, 'b-')
# Save the default tick positions, so we can reset them...
locs, labels = plt.xticks()
plt.boxplot(data, positions=x, notch=True)
# Reset the xtick locations.
plt.xticks(locs)
plt.show()
This is what has worked for me:
plot box-plot
get boxt-plot x-axis tick locations
use box-plot x-axis tick locations as x-axis values for the line plot
# Plot Box-plot
ax.boxplot(data, positions=x, notch=True)
# Get box-plot x-tick locations
locs=ax.get_xticks()
# Plot a line between the means of each dataset
# x-values = box-plot x-tick locations
# y-values = means
ax.plot(locs, y, 'b-')