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've spent entirely too long researching how to get two subplots to share the same y-axis with a single colorbar shared between the two in Matplotlib.
What was happening was that when I called the colorbar() function in either subplot1 or subplot2, it would autoscale the plot such that the colorbar plus the plot would fit inside the 'subplot' bounding box, causing the two side-by-side plots to be two very different sizes.
To get around this, I tried to create a third subplot which I then hacked to render no plot with just a colorbar present.
The only problem is, now the heights and widths of the two plots are uneven, and I can't figure out how to make it look okay.
Here is my code:
from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import patches
from matplotlib.ticker import NullFormatter
# SIS Functions
TE = 1 # Einstein radius
g1 = lambda x,y: (TE/2) * (y**2-x**2)/((x**2+y**2)**(3/2))
g2 = lambda x,y: -1*TE*x*y / ((x**2+y**2)**(3/2))
kappa = lambda x,y: TE / (2*np.sqrt(x**2+y**2))
coords = np.linspace(-2,2,400)
X,Y = np.meshgrid(coords,coords)
g1out = g1(X,Y)
g2out = g2(X,Y)
kappaout = kappa(X,Y)
for i in range(len(coords)):
for j in range(len(coords)):
if np.sqrt(coords[i]**2+coords[j]**2) <= TE:
g1out[i][j]=0
g2out[i][j]=0
fig = plt.figure()
fig.subplots_adjust(wspace=0,hspace=0)
# subplot number 1
ax1 = fig.add_subplot(1,2,1,aspect='equal',xlim=[-2,2],ylim=[-2,2])
plt.title(r"$\gamma_{1}$",fontsize="18")
plt.xlabel(r"x ($\theta_{E}$)",fontsize="15")
plt.ylabel(r"y ($\theta_{E}$)",rotation='horizontal',fontsize="15")
plt.xticks([-2.0,-1.5,-1.0,-0.5,0,0.5,1.0,1.5])
plt.xticks([-2.0,-1.5,-1.0,-0.5,0,0.5,1.0,1.5])
plt.imshow(g1out,extent=(-2,2,-2,2))
plt.axhline(y=0,linewidth=2,color='k',linestyle="--")
plt.axvline(x=0,linewidth=2,color='k',linestyle="--")
e1 = patches.Ellipse((0,0),2,2,color='white')
ax1.add_patch(e1)
# subplot number 2
ax2 = fig.add_subplot(1,2,2,sharey=ax1,xlim=[-2,2],ylim=[-2,2])
plt.title(r"$\gamma_{2}$",fontsize="18")
plt.xlabel(r"x ($\theta_{E}$)",fontsize="15")
ax2.yaxis.set_major_formatter( NullFormatter() )
plt.axhline(y=0,linewidth=2,color='k',linestyle="--")
plt.axvline(x=0,linewidth=2,color='k',linestyle="--")
plt.imshow(g2out,extent=(-2,2,-2,2))
e2 = patches.Ellipse((0,0),2,2,color='white')
ax2.add_patch(e2)
# subplot for colorbar
ax3 = fig.add_subplot(1,1,1)
ax3.axis('off')
cbar = plt.colorbar(ax=ax2)
plt.show()
Just place the colorbar in its own axis and use subplots_adjust to make room for it.
As a quick example:
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2)
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
fig.colorbar(im, cax=cbar_ax)
plt.show()
Note that the color range will be set by the last image plotted (that gave rise to im) even if the range of values is set by vmin and vmax. If another plot has, for example, a higher max value, points with higher values than the max of im will show in uniform color.
You can simplify Joe Kington's code using the axparameter of figure.colorbar() with a list of axes.
From the documentation:
ax
None | parent axes object(s) from which space for a new colorbar axes will be stolen. If a list of axes is given they will all be resized to make room for the colorbar axes.
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2)
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
fig.colorbar(im, ax=axes.ravel().tolist())
plt.show()
This solution does not require manual tweaking of axes locations or colorbar size, works with multi-row and single-row layouts, and works with tight_layout(). It is adapted from a gallery example, using ImageGrid from matplotlib's AxesGrid Toolbox.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid
# Set up figure and image grid
fig = plt.figure(figsize=(9.75, 3))
grid = ImageGrid(fig, 111, # as in plt.subplot(111)
nrows_ncols=(1,3),
axes_pad=0.15,
share_all=True,
cbar_location="right",
cbar_mode="single",
cbar_size="7%",
cbar_pad=0.15,
)
# Add data to image grid
for ax in grid:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
# Colorbar
ax.cax.colorbar(im)
ax.cax.toggle_label(True)
#plt.tight_layout() # Works, but may still require rect paramater to keep colorbar labels visible
plt.show()
Using make_axes is even easier and gives a better result. It also provides possibilities to customise the positioning of the colorbar.
Also note the option of subplots to share x and y axes.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
cax,kw = mpl.colorbar.make_axes([ax for ax in axes.flat])
plt.colorbar(im, cax=cax, **kw)
plt.show()
As a beginner who stumbled across this thread, I'd like to add a python-for-dummies adaptation of abevieiramota's very neat answer (because I'm at the level that I had to look up 'ravel' to work out what their code was doing):
import numpy as np
import matplotlib.pyplot as plt
fig, ((ax1,ax2,ax3),(ax4,ax5,ax6)) = plt.subplots(2,3)
axlist = [ax1,ax2,ax3,ax4,ax5,ax6]
first = ax1.imshow(np.random.random((10,10)), vmin=0, vmax=1)
third = ax3.imshow(np.random.random((12,12)), vmin=0, vmax=1)
fig.colorbar(first, ax=axlist)
plt.show()
Much less pythonic, much easier for noobs like me to see what's actually happening here.
Shared colormap and colorbar
This is for the more complex case where the values are not just between 0 and 1; the cmap needs to be shared instead of just using the last one.
import numpy as np
from matplotlib.colors import Normalize
import matplotlib.pyplot as plt
import matplotlib.cm as cm
fig, axes = plt.subplots(nrows=2, ncols=2)
cmap=cm.get_cmap('viridis')
normalizer=Normalize(0,4)
im=cm.ScalarMappable(norm=normalizer)
for i,ax in enumerate(axes.flat):
ax.imshow(i+np.random.random((10,10)),cmap=cmap,norm=normalizer)
ax.set_title(str(i))
fig.colorbar(im, ax=axes.ravel().tolist())
plt.show()
As pointed out in other answers, the idea is usually to define an axes for the colorbar to reside in. There are various ways of doing so; one that hasn't been mentionned yet would be to directly specify the colorbar axes at subplot creation with plt.subplots(). The advantage is that the axes position does not need to be manually set and in all cases with automatic aspect the colorbar will be exactly the same height as the subplots. Even in many cases where images are used the result will be satisfying as shown below.
When using plt.subplots(), the use of gridspec_kw argument allows to make the colorbar axes much smaller than the other axes.
fig, (ax, ax2, cax) = plt.subplots(ncols=3,figsize=(5.5,3),
gridspec_kw={"width_ratios":[1,1, 0.05]})
Example:
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
fig, (ax, ax2, cax) = plt.subplots(ncols=3,figsize=(5.5,3),
gridspec_kw={"width_ratios":[1,1, 0.05]})
fig.subplots_adjust(wspace=0.3)
im = ax.imshow(np.random.rand(11,8), vmin=0, vmax=1)
im2 = ax2.imshow(np.random.rand(11,8), vmin=0, vmax=1)
ax.set_ylabel("y label")
fig.colorbar(im, cax=cax)
plt.show()
This works well, if the plots' aspect is autoscaled or the images are shrunk due to their aspect in the width direction (as in the above). If, however, the images are wider then high, the result would look as follows, which might be undesired.
A solution to fix the colorbar height to the subplot height would be to use mpl_toolkits.axes_grid1.inset_locator.InsetPosition to set the colorbar axes relative to the image subplot axes.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
from mpl_toolkits.axes_grid1.inset_locator import InsetPosition
fig, (ax, ax2, cax) = plt.subplots(ncols=3,figsize=(7,3),
gridspec_kw={"width_ratios":[1,1, 0.05]})
fig.subplots_adjust(wspace=0.3)
im = ax.imshow(np.random.rand(11,16), vmin=0, vmax=1)
im2 = ax2.imshow(np.random.rand(11,16), vmin=0, vmax=1)
ax.set_ylabel("y label")
ip = InsetPosition(ax2, [1.05,0,0.05,1])
cax.set_axes_locator(ip)
fig.colorbar(im, cax=cax, ax=[ax,ax2])
plt.show()
New in matplotlib 3.4.0
Shared colorbars can now be implemented using subfigures:
New Figure.subfigures and Figure.add_subfigure allow ... localized figure artists (e.g., colorbars and suptitles) that only pertain to each subfigure.
The matplotlib gallery includes demos on how to plot subfigures.
Here is a minimal example with 2 subfigures, each with a shared colorbar:
fig = plt.figure(constrained_layout=True)
(subfig_l, subfig_r) = fig.subfigures(nrows=1, ncols=2)
axes_l = subfig_l.subplots(nrows=1, ncols=2, sharey=True)
for ax in axes_l:
im = ax.imshow(np.random.random((10, 10)), vmin=0, vmax=1)
# shared colorbar for left subfigure
subfig_l.colorbar(im, ax=axes_l, location='bottom')
axes_r = subfig_r.subplots(nrows=3, ncols=1, sharex=True)
for ax in axes_r:
mesh = ax.pcolormesh(np.random.randn(30, 30), vmin=-2.5, vmax=2.5)
# shared colorbar for right subfigure
subfig_r.colorbar(mesh, ax=axes_r)
The solution of using a list of axes by abevieiramota works very well until you use only one row of images, as pointed out in the comments. Using a reasonable aspect ratio for figsize helps, but is still far from perfect. For example:
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(9.75, 3))
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
fig.colorbar(im, ax=axes.ravel().tolist())
plt.show()
The colorbar function provides the shrink parameter which is a scaling factor for the size of the colorbar axes. It does require some manual trial and error. For example:
fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.75)
To add to #abevieiramota's excellent answer, you can get the euqivalent of tight_layout with constrained_layout. You will still get large horizontal gaps if you use imshow instead of pcolormesh because of the 1:1 aspect ratio imposed by imshow.
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2, constrained_layout=True)
for ax in axes.flat:
im = ax.pcolormesh(np.random.random((10,10)), vmin=0, vmax=1)
fig.colorbar(im, ax=axes.flat)
plt.show()
I noticed that almost every solution posted involved ax.imshow(im, ...) and did not normalize the colors displayed to the colorbar for the multiple subfigures. The im mappable is taken from the last instance, but what if the values of the multiple im-s are different? (I'm assuming these mappables are treated in the same way that the contour-sets and surface-sets are treated.) I have an example using a 3d surface plot below that creates two colorbars for a 2x2 subplot (one colorbar per one row). Although the question asks explicitly for a different arrangement, I think the example helps clarify some things. I haven't found a way to do this using plt.subplots(...) yet because of the 3D axes unfortunately.
If only I could position the colorbars in a better way... (There is probably a much better way to do this, but at least it should be not too difficult to follow.)
import matplotlib
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
cmap = 'plasma'
ncontours = 5
def get_data(row, col):
""" get X, Y, Z, and plot number of subplot
Z > 0 for top row, Z < 0 for bottom row """
if row == 0:
x = np.linspace(1, 10, 10, dtype=int)
X, Y = np.meshgrid(x, x)
Z = np.sqrt(X**2 + Y**2)
if col == 0:
pnum = 1
else:
pnum = 2
elif row == 1:
x = np.linspace(1, 10, 10, dtype=int)
X, Y = np.meshgrid(x, x)
Z = -np.sqrt(X**2 + Y**2)
if col == 0:
pnum = 3
else:
pnum = 4
print("\nPNUM: {}, Zmin = {}, Zmax = {}\n".format(pnum, np.min(Z), np.max(Z)))
return X, Y, Z, pnum
fig = plt.figure()
nrows, ncols = 2, 2
zz = []
axes = []
for row in range(nrows):
for col in range(ncols):
X, Y, Z, pnum = get_data(row, col)
ax = fig.add_subplot(nrows, ncols, pnum, projection='3d')
ax.set_title('row = {}, col = {}'.format(row, col))
fhandle = ax.plot_surface(X, Y, Z, cmap=cmap)
zz.append(Z)
axes.append(ax)
## get full range of Z data as flat list for top and bottom rows
zz_top = zz[0].reshape(-1).tolist() + zz[1].reshape(-1).tolist()
zz_btm = zz[2].reshape(-1).tolist() + zz[3].reshape(-1).tolist()
## get top and bottom axes
ax_top = [axes[0], axes[1]]
ax_btm = [axes[2], axes[3]]
## normalize colors to minimum and maximum values of dataset
norm_top = matplotlib.colors.Normalize(vmin=min(zz_top), vmax=max(zz_top))
norm_btm = matplotlib.colors.Normalize(vmin=min(zz_btm), vmax=max(zz_btm))
cmap = cm.get_cmap(cmap, ncontours) # number of colors on colorbar
mtop = cm.ScalarMappable(cmap=cmap, norm=norm_top)
mbtm = cm.ScalarMappable(cmap=cmap, norm=norm_btm)
for m in (mtop, mbtm):
m.set_array([])
# ## create cax to draw colorbar in
# cax_top = fig.add_axes([0.9, 0.55, 0.05, 0.4])
# cax_btm = fig.add_axes([0.9, 0.05, 0.05, 0.4])
cbar_top = fig.colorbar(mtop, ax=ax_top, orientation='vertical', shrink=0.75, pad=0.2) #, cax=cax_top)
cbar_top.set_ticks(np.linspace(min(zz_top), max(zz_top), ncontours))
cbar_btm = fig.colorbar(mbtm, ax=ax_btm, orientation='vertical', shrink=0.75, pad=0.2) #, cax=cax_btm)
cbar_btm.set_ticks(np.linspace(min(zz_btm), max(zz_btm), ncontours))
plt.show()
plt.close(fig)
## orientation of colorbar = 'horizontal' if done by column
This topic is well covered but I still would like to propose another approach in a slightly different philosophy.
It is a bit more complex to set-up but it allow (in my opinion) a bit more flexibility. For example, one can play with the respective ratios of each subplots / colorbar:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.gridspec import GridSpec
# Define number of rows and columns you want in your figure
nrow = 2
ncol = 3
# Make a new figure
fig = plt.figure(constrained_layout=True)
# Design your figure properties
widths = [3,4,5,1]
gs = GridSpec(nrow, ncol + 1, figure=fig, width_ratios=widths)
# Fill your figure with desired plots
axes = []
for i in range(nrow):
for j in range(ncol):
axes.append(fig.add_subplot(gs[i, j]))
im = axes[-1].pcolormesh(np.random.random((10,10)))
# Shared colorbar
axes.append(fig.add_subplot(gs[:, ncol]))
fig.colorbar(im, cax=axes[-1])
plt.show()
The answers above are great, but most of them use the fig.colobar() method applied to a fig object. This example shows how to use the plt.colobar() function, applied directly to pyplot:
def shared_colorbar_example():
fig, axs = plt.subplots(nrows=3, ncols=3)
for ax in axs.flat:
plt.sca(ax)
color = np.random.random((10))
plt.scatter(range(10), range(10), c=color, cmap='viridis', vmin=0, vmax=1)
plt.colorbar(ax=axs.ravel().tolist(), shrink=0.6)
plt.show()
shared_colorbar_example()
Since most answers above demonstrated usage on 2D matrices, I went with a simple scatter plot. The shrink keyword is optional and resizes the colorbar.
If vmin and vmax are not specified this approach will automatically analyze all of the subplots for the minimum and maximum value to be used on the colorbar. The above approaches when using fig.colorbar(im) scan only the image passed as argument for min and max values of the colorbar.
Result:
I'm working with matplotlib to plot a variable in latitude longitude coordinates. The problem is that this image cannot include axes or borders. I have been able to remove axis, but the white padding around my image has to be completely removed (see example images from code below here: http://imgur.com/a/W0vy9) .
I have tried several methods from Google searches, including these StackOverflow methodologies:
Remove padding from matplotlib plotting
How to remove padding/border in a matplotlib subplot (SOLVED)
Matplotlib plots: removing axis, legends and white spaces
but nothing has worked in removing the white space. If you have any advice (even if it is to ditch matplotlib and to try another plotting library instead) I would appreciate it!
Here is a basic form of the code I'm using that shows this behavior:
import numpy as np
import matplotlib
from mpl_toolkits.basemap import Basemap
from scipy import stats
lat = np.random.randint(-60.5, high=60.5, size=257087)
lon = np.random.randint(-179.95, high=180, size=257087)
maxnsz = np.random.randint(12, 60, size=257087)
percRange = np.arange(100,40,-1)
percStr=percRange.astype(str)
val_percentile=np.percentile(maxnsz, percRange, interpolation='nearest')
#Rank all values
all_percentiles=stats.rankdata(maxnsz)/len(maxnsz)
#Figure setup
fig = matplotlib.pyplot.figure(frameon=False, dpi=600)
#Basemap code can go here
x=lon
y=lat
cmap = matplotlib.cm.get_cmap('cool')
h=np.where(all_percentiles >= 0.999)
hl=np.where((all_percentiles < 0.999) & (all_percentiles > 0.90))
mh=np.where((all_percentiles > 0.75) & (all_percentiles < 0.90))
ml=np.where((all_percentiles >= 0.4) & (all_percentiles < 0.75))
l=np.where(all_percentiles < 0.4)
all_percentiles[h]=0
all_percentiles[hl]=0.25
all_percentiles[mh]=0.5
all_percentiles[ml]=0.75
all_percentiles[l]=1
rgba_low=cmap(1)
rgba_ml=cmap(0.75)
rgba_mh=cmap(0.51)
rgba_hl=cmap(0.25)
rgba_high=cmap(0)
matplotlib.pyplot.axis('off')
matplotlib.pyplot.scatter(x[ml],y[ml], c=rgba_ml, s=3, marker=',',edgecolor='none', alpha=0.4)
matplotlib.pyplot.scatter(x[mh],y[mh], c=rgba_mh, s=3, marker='o', edgecolor='none', alpha=0.5)
matplotlib.pyplot.scatter(x[hl],y[hl], c=rgba_hl, s=4, marker='*',edgecolor='none', alpha=0.6)
matplotlib.pyplot.scatter(x[h],y[h], c=rgba_high, s=5, marker='^', edgecolor='none',alpha=0.75)
fig.savefig('/home/usr/code/python/testfig.jpg', bbox_inches=0, nbins=0, transparent="True", pad_inches=0.0)
fig.canvas.draw()
The problem is that all the solutions given at Matplotlib plots: removing axis, legends and white spaces are actually meant to work with imshow.
So, the following clearly works
import matplotlib.pyplot as plt
fig = plt.figure()
ax=fig.add_axes([0,0,1,1])
ax.set_axis_off()
im = ax.imshow([[2,3,4,1], [2,4,4,2]], origin="lower", extent=[1,4,2,8])
ax.plot([1,2,3,4], [2,3,4,8], lw=5)
ax.set_aspect('auto')
plt.show()
and produces
But here, you are using scatter. Adding a scatter plot
import matplotlib.pyplot as plt
fig = plt.figure()
ax=fig.add_axes([0,0,1,1])
ax.set_axis_off()
im = ax.imshow([[2,3,4,1], [2,4,4,2]], origin="lower", extent=[1,4,2,8])
ax.plot([1,2,3,4], [2,3,4,8], lw=5)
ax.scatter([2,3,4,1], [2,3,4,8], c="r", s=2500)
ax.set_aspect('auto')
plt.show()
produces
Scatter has the particularity that matplotlib tries to make all points visible by default, which means that the axes limits are set such that all scatter points are visible as a whole.
To overcome this, we need to specifically set the axes limits:
import matplotlib.pyplot as plt
fig = plt.figure()
ax=fig.add_axes([0,0,1,1])
ax.set_axis_off()
im = ax.imshow([[2,3,4,1], [2,4,4,2]], origin="lower", extent=[1,4,2,8])
ax.plot([1,2,3,4], [2,3,4,8], lw=5)
ax.scatter([2,3,4,1], [2,3,4,8], c="r", s=2500)
ax.set_xlim([1,4])
ax.set_ylim([2,8])
ax.set_aspect('auto')
plt.show()
such that we will get the desired behaviour.
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