For some reason when I use a zorder with my scatter plot the edges of the points overlap the axis. I tried some of the solutions from [here] (matplotlib axis tick labels covered by scatterplot (using spines)) but they didn't work for me. Is there a way from preventing this from happening?
I understand I could also add an ax.axvline() at my boundaries but that would be an annoying workaround for lots of plots.
xval = np.array([0,0,0,3,3,3,0,2,3,0])
yval = np.array([0,2,3,5,1,0,1,0,4,5])
zval = yval**2-4
fig = plt.figure(figsize=(6,6))
ax = plt.subplot(111)
ax.scatter(xval,yval,cmap=plt.cm.rainbow,c=zval,s=550,zorder=20)
ax.set_ylim(0,5)
ax.set_xlim(0,3)
#These don't work
ax.tick_params(labelcolor='k', zorder=100)
ax.tick_params(direction='out', length=4, color='k', zorder=100)
#This will work but I don't want to have to do this for the plot edges every time
ax.axvline(0,c='k',zorder=100)
plt.show()
For me the solution you linked to works; that is, setting the z-order of the scatter plot to a negative number. E.g.
xval = np.array([0,0,0,3,3,3,0,2,3,0])
yval = np.array([0,2,3,5,1,0,1,0,4,5])
zval = yval**2-4
fig = plt.figure(figsize=(6,6))
ax = plt.subplot(111)
ax.scatter(xval,yval,cmap=plt.cm.rainbow,c=zval,s=550,zorder=-1)
ax.set_ylim(0,5)
ax.set_xlim(0,3)
plt.show()
]1
You can fix the overlap using the following code with a large number for the zorder. This will work on both the x- and y-axis.
for k,spine in ax.spines.items():
spine.set_zorder(1000)
This works for me
import numpy as np
import matplotlib.pyplot as plt
xval = np.array([0,0,0,3,3,3,0,2,3,0])
yval = np.array([0,2,3,5,1,0,1,0,4,5])
zval = yval**2-4
fig = plt.figure(figsize=(6,6))
ax = plt.subplot(111)
ax.scatter(xval,yval,cmap=plt.cm.rainbow,c=zval,s=550,zorder=20)
ax.set_ylim(-1,6)
ax.set_xlim(-1,4)
#These don't work
ax.tick_params(labelcolor='k', zorder=100)
ax.tick_params(direction='out', length=4, color='k', zorder=100)
#This will work but I don't want to have to do this for the plot edges every time
ax.axvline(0,c='k',zorder=100)
plt.show()
Your circle sizes are big enough that they go beyond the axis scope. So we simply change the ylim and xlim
Changed
ax.set_ylim(0,5)
ax.set_xlim(0,3)
to
ax.set_ylim(-1,6)
ax.set_xlim(-1,4)
Also, zorder doesn't play a role in pushing the points to edges.
Related
I have a parallel coordinates plot with lots of data points so I'm trying to use a continuous colour bar to represent that, which I think I have worked out. However, I haven't been able to remove the default key that is put in when creating the plot, which is very long and hinders readability. Is there a way to remove this table to make the graph much easier to read?
This is the code I'm currently using to generate the parallel coordinates plot:
parallel_coordinates(data[[' male_le','
female_le','diet','activity','obese_perc','median_income']],'median_income',colormap = 'rainbow',
alpha = 0.5)
fig, ax = plt.subplots(figsize=(6, 1))
fig.subplots_adjust(bottom=0.5)
cmap = mpl.cm.rainbow
bounds = [0.00,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N,)
plt.colorbar(mpl.cm.ScalarMappable(norm = norm, cmap=cmap),cax = ax, orientation = 'horizontal',
label = 'normalised median income', alpha = 0.5)
plt.show()
Current Output:
I want my legend to be represented as a color bar, like this:
Any help would be greatly appreciated. Thanks.
You can use ax.legend_.remove() to remove the legend.
The cax parameter of plt.colorbar indicates the subplot where to put the colorbar. If you leave it out, matplotlib will create a new subplot, "stealing" space from the current subplot (subplots are often referenced to by ax in matplotlib). So, here leaving out cax (adding ax=ax isn't necessary, as here ax is the current subplot) will create the desired colorbar.
The code below uses seaborn's penguin dataset to create a standalone example.
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
import numpy as np
from pandas.plotting import parallel_coordinates
penguins = sns.load_dataset('penguins')
fig, ax = plt.subplots(figsize=(10, 4))
cmap = plt.get_cmap('rainbow')
bounds = np.arange(penguins['body_mass_g'].min(), penguins['body_mass_g'].max() + 200, 200)
norm = mpl.colors.BoundaryNorm(bounds, 256)
penguins = penguins.dropna(subset=['body_mass_g'])
parallel_coordinates(penguins[['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g']],
'body_mass_g', colormap=cmap, alpha=0.5, ax=ax)
ax.legend_.remove()
plt.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),
ax=ax, orientation='horizontal', label='body mass', alpha=0.5)
plt.show()
I want to fix the position of the ticks on the logarithmic scale, such that they are the same in each subplot (see red annotation in image).
My code looks like this:
ax = fig.add_subplot(2,2, axis)
ax2 = ax.twinx()
ax2.set_yscale('log')
ax2.set_ylim(0,100)
Right now, set_yscale=('log') optimizes the tick spacing for each subplot. I prefer to adopt the tick spacing of the upper right subplot.
You can achieve this by getting the limits of the left twin axis and setting it as the limits of the right twin axis.
Consider the following working example. Follow this procedure for the subplots you want to align the axes of.
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(8, 3))
axl = fig.add_subplot(121)
axr = fig.add_subplot(122)
ax1 = axl.twinx()
ax1.plot(np.logspace(-2, 3, 5))
ax1.set_yscale('log')
ax2 = axr.twinx()
ax2.plot(np.logspace(0, 3, 5))
ax2.set_yscale('log')
ax2.set_ylim(ax1.get_ylim()) # <-- This is the key line
plt.tight_layout()
plt.show()
OP's solution:
Plot a dummy curve and set alpha=0. Make sure the curve spans y_min and y_max.
fig = plt.figure()
axes = [1,2,3,4]
for axis in axes:
ax = fig.add_subplot(2,2, axis)
ax2 = ax.twinx()
ax2.set_yscale('log')
ax2.plot(x_dummy, y_dummy, alpha=0) # <-- dummy plot
x_real, y_real = func_that_loads_data() # <-- your interesting plot
curve1 = ax2.plot(x_real, y_real)
plt.show()
The solution provided by Sheldore was impractical to implement because I plot my data using a for-loop (unavoidable unless I escalate the number of variables).
Since I overwrite the ax variable on every iteration, I would have to save the y-limit as a global variable. Read here why global variables should be avoided.
ax = fig.add_subplot(2,2, axis)
ax2 = ax.twinx()
ax2.set_yscale('log')
if axis == 1:
global yscale
yscale = ax2.get_ylim() # <-- where the magic happens
elif axis > 1:
ax2.set_ylim(yscale)
I am trying to plot a large dataset with a scatter plot.
I want to use matplotlib to plot it with single pixel marker.
It seems to have been solved.
https://github.com/matplotlib/matplotlib/pull/695
But I cannot find a mention of how to get a single pixel marker.
My simplified dataset (data.csv)
Length,Time
78154393,139.324091
84016477,229.159305
84626159,219.727537
102021548,225.222662
106399706,221.022827
107945741,206.760239
109741689,200.153263
126270147,220.102802
207813132,181.67058
610704756,50.59529
623110004,50.533158
653383018,52.993885
659376270,53.536834
680682368,55.97628
717978082,59.043843
My code is below.
import pandas as pd
import os
import numpy
import matplotlib.pyplot as plt
inputfile='data.csv'
iplevel = pd.read_csv(inputfile)
base = os.path.splitext(inputfile)[0]
fig = plt.figure()
plt.yscale('log')
#plt.xscale('log')
plt.title(' My plot: '+base)
plt.xlabel('x')
plt.ylabel('y')
plt.scatter(iplevel['Time'], iplevel['Length'],color='black',marker=',',lw=0,s=1)
fig.tight_layout()
fig.savefig(base+'_plot.png', dpi=fig.dpi)
You can see below that the points are not single pixel.
Any help is appreciated
The problem
I fear that the bugfix discussed at matplotlib git repository that you're citing is only valid for plt.plot() and not for plt.scatter()
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(4,2))
ax = fig.add_subplot(121)
ax2 = fig.add_subplot(122, sharex=ax, sharey=ax)
ax.plot([1, 2],[0.4,0.4],color='black',marker=',',lw=0, linestyle="")
ax.set_title("ax.plot")
ax2.scatter([1,2],[0.4,0.4],color='black',marker=',',lw=0, s=1)
ax2.set_title("ax.scatter")
ax.set_xlim(0,8)
ax.set_ylim(0,1)
fig.tight_layout()
print fig.dpi #prints 80 in my case
fig.savefig('plot.png', dpi=fig.dpi)
The solution: Setting the markersize
The solution is to use a usual "o" or "s" marker, but set the markersize to be exactly one pixel. Since the markersize is given in points, one would need to use the figure dpi to calculate the size of one pixel in points. This is 72./fig.dpi.
For aplot`, the markersize is directly
ax.plot(..., marker="o", ms=72./fig.dpi)
For a scatter the markersize is given through the s argument, which is in square points,
ax.scatter(..., marker='o', s=(72./fig.dpi)**2)
Complete example:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(4,2))
ax = fig.add_subplot(121)
ax2 = fig.add_subplot(122, sharex=ax, sharey=ax)
ax.plot([1, 2],[0.4,0.4], marker='o',ms=72./fig.dpi, mew=0,
color='black', linestyle="", lw=0)
ax.set_title("ax.plot")
ax2.scatter([1,2],[0.4,0.4],color='black', marker='o', lw=0, s=(72./fig.dpi)**2)
ax2.set_title("ax.scatter")
ax.set_xlim(0,8)
ax.set_ylim(0,1)
fig.tight_layout()
fig.savefig('plot.png', dpi=fig.dpi)
For anyone still trying to figure this out, the solution I found was to specify the s argument in plt.scatter.
The s argument refers to the area of the point you are plotting.
It doesn't seem to be quite perfect, since s=1 seems to cover about 4 pixels of my screen, but this definitely makes them smaller than anything else I've been able to find.
https://matplotlib.org/devdocs/api/_as_gen/matplotlib.pyplot.scatter.html
s : scalar or array_like, shape (n, ), optional
size in points^2. Default is rcParams['lines.markersize'] ** 2.
Set the plt.scatter() parameter to linewidths=0 and figure out the right value for the parameter s.
Source: https://stackoverflow.com/a/45803960/4063622
I am trying to work out how to set/correct the position of tick labels for a 3D matplotlib plot. Tick labels do not align with the ticks. The issue seems to be especially prominent when many tick labels are required.
I have modified an example (http://matplotlib.org/examples/mplot3d/polys3d_demo.html) from the matplotlib documentation to illustrate my question.
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.collections import PolyCollection
from matplotlib.colors import colorConverter
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure(figsize=(10,10))
ax = fig.gca(projection='3d')
cc = lambda arg: colorConverter.to_rgba(arg, alpha=0.6)
xs = np.arange(0, 10, 0.4)
verts = []
zs = np.arange(50)
for z in zs:
ys = np.ones(len(xs))*z
ys[0], ys[-1] = 0, 0
verts.append(list(zip(xs, ys)))
poly = PolyCollection(verts,facecolor='c')
poly.set_alpha(0.7)
ax.add_collection3d(poly, zs=zs, zdir='y')
ax.set_xlabel('X')
ax.set_xlim3d(0, 10)
ax.set_ylabel('Y')
ax.set_ylim3d(-1, len(zs))
ax.set_yticks(np.arange(len(zs)))
labels = {}
for l_c in zs:
labels[l_c] = 'This Looks Bad'
ax.set_yticklabels(labels,rotation=-15)
ax.set_zlabel('Z')
ax.set_zlim3d(0, ys.max())
plt.show()
So the question is: how can I get the tick labels to align with the tick positions?
By using these alignments, I get much better placements:
ax.set_yticklabels(labels,rotation=-15,
verticalalignment='baseline',
horizontalalignment='left')
I've modified the example with less tick markers so you can see the placement:
They do align, but with the horizontal position centered at the tick. Because of the 3D view this makes them appear a bit below where you would expect them to be. The effect is not related to the amount of ticks but to the width.
Specifically setting the alignment will help. Try adding:
ax.set_yticklabels(labels,rotation=-15, va='center', ha='left')
Play around a bit with the different alignments to see which you prefer, i think you're after ha='left'.
Reducing the padding, distance from the tick, might also help.
You can also set the pad argument as negative in the tick_params options for each axis. Like this:
ax.tick_params(axis='x', which='major', pad=-3)
This might help to adjust the distance between tick labels and axis.
Define data
x = np.linspace(0,2*np.pi,100)
y = 2*np.sin(x)
Plot
fig = plt.figure()
ax = plt.axes()
fig.add_subplot(ax)
ax.plot(x,y)
Add second axis
newax = plt.axes(axisbg='none')
Gives me ValueError: Unknown element o, even though it does the same thing as what I am about to describe. I can also see that this works (no error) to do the same thing:
newax = plt.axes()
fig.add_subplot(newax)
newax.set_axis_bgcolor('none')
However, it turns the background color of the original figure "gray" (or whatever the figure background is)? I don't understand, as I thought this would make newax transparent except for the axes and box around the figure. Even if I switch the order, same thing:
plt.close('all')
fig = plt.figure()
newax = plt.axes()
fig.add_subplot(newax)
newax.set_axis_bgcolor('none')
ax = plt.axes()
fig.add_subplot(ax)
ax.plot(x,y)
This is surprising because I thought the background of one would be overlaid on the other, but in either case it is the newax background that appears to be visible (or at least this is the color I see).
What is going on here?
You're not actually adding a new axes.
Matplotlib is detecting that there's already a plot in that position and returning it instead of a new axes object.
(Check it for yourself. ax and newax will be the same object.)
There's probably not a reason why you'd want to, but here's how you'd do it.
(Also, don't call newax = plt.axes() and then call fig.add_subplot(newax) You're doing the same thing twice.)
Edit: With newer (>=1.2, I think?) versions of matplotlib, you can accomplish the same thing as the example below by using the label kwarg to fig.add_subplot. E.g. newax = fig.add_subplot(111, label='some unique string')
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
# If you just call `plt.axes()` or equivalently `fig.add_subplot()` matplotlib
# will just return `ax` again. It _won't_ create a new axis unless we
# call fig.add_axes() or reset fig._seen
newax = fig.add_axes(ax.get_position(), frameon=False)
ax.plot(range(10), 'r-')
newax.plot(range(50), 'g-')
newax.axis('equal')
plt.show()
Of course, this looks awful, but it's what you're asking for...
I'm guessing from your earlier questions that you just want to add a second x-axis? If so, this is a completely different thing.
If you want the y-axes linked, then do something like this (somewhat verbose...):
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
newax = ax.twiny()
# Make some room at the bottom
fig.subplots_adjust(bottom=0.20)
# I'm guessing you want them both on the bottom...
newax.set_frame_on(True)
newax.patch.set_visible(False)
newax.xaxis.set_ticks_position('bottom')
newax.xaxis.set_label_position('bottom')
newax.spines['bottom'].set_position(('outward', 40))
ax.plot(range(10), 'r-')
newax.plot(range(21), 'g-')
ax.set_xlabel('Red Thing')
newax.set_xlabel('Green Thing')
plt.show()
If you want to have a hidden, unlinked y-axis, and an entirely new x-axis, then you'd do something like this:
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
newax = fig.add_axes(ax.get_position())
newax.patch.set_visible(False)
newax.yaxis.set_visible(False)
for spinename, spine in newax.spines.iteritems():
if spinename != 'bottom':
spine.set_visible(False)
newax.spines['bottom'].set_position(('outward', 25))
ax.plot(range(10), 'r-')
x = np.linspace(0, 6*np.pi)
newax.plot(x, 0.001 * np.cos(x), 'g-')
plt.show()
Note that the y-axis values for anything plotted on newax are never shown.
If you wanted, you could even take this one step further, and have independent x and y axes (I'm not quite sure what the point of it would be, but it looks neat...):
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2, right=0.85)
newax = fig.add_axes(ax.get_position())
newax.patch.set_visible(False)
newax.yaxis.set_label_position('right')
newax.yaxis.set_ticks_position('right')
newax.spines['bottom'].set_position(('outward', 35))
ax.plot(range(10), 'r-')
ax.set_xlabel('Red X-axis', color='red')
ax.set_ylabel('Red Y-axis', color='red')
x = np.linspace(0, 6*np.pi)
newax.plot(x, 0.001 * np.cos(x), 'g-')
newax.set_xlabel('Green X-axis', color='green')
newax.set_ylabel('Green Y-axis', color='green')
plt.show()
You can also just add an extra spine at the bottom of the plot. Sometimes this is easier, especially if you don't want ticks or numerical things along it. Not to plug one of my own answers too much, but there's an example of that here: How do I plot multiple X or Y axes in matplotlib?
As one last thing, be sure to look at the parasite axes examples if you want to have the different x and y axes linked through a specific transformation.