I'm fairly new to scatter plots and python in general. I am trying to plot a third variable against an x and a y, however, I'm not quite sure how to about specifying that argument? So I would have X values which are ints, y values which are also ints and then on the graph itself I want the model scores to show. Is there any way to do this sort of thing?
Thank you.
You can use color to plot a third value. Here is a very minimal example :
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)
plt.scatter(x,y, c=z, s=5, cmap=cm.hsv)
cbar= plt.colorbar()
plt.show()
Edit
You could also use the size of markers, their transparency, hue or rgb values to depict even more information. Here is an example with marker size, alpha level and color on a perceptually uniform colormap.
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.colors as colors
import matplotlib.cm as cmx
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)
t = np.random.rand(100)
w = np.random.rand(100)
fig, ax = plt.subplots(1, 1)
cmap = plt.get_cmap('plasma')
cNorm = colors.Normalize(vmin=0, vmax=max(z))
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cmap)
for i in range(100):
ax.scatter(x[i],y[i], c=scalarMap.to_rgba(z[i]), s=t[i]*100, cmap=cmx.plasma, alpha=w[i], edgecolor='none')
scalarMap.set_array([])
fig.colorbar(scalarMap,ax=ax)
for a in [0.1, 0.5, 0.9]:
ax.scatter([], [], c='k', alpha=0.5, s=a*100, label=str(a), edgecolors='none')
l1 = ax.legend(scatterpoints=1, frameon=True, loc='lower left' ,markerscale=1)
for b in [0.25, 0.5, 0.75]:
ax.scatter([], [], c='k', alpha=b, s=50, label=str(b), edgecolors='none')
ax.legend(scatterpoints=1, frameon=True, loc='lower right' ,markerscale=1)
fig.show()
At face value, that question doesn't really make sense because a conventional scatterplot has only two axes, and of course you can't plot points with three dimensions (x, y and accuracy).
However, there are alternative ways to do so.
Use colours
import numpy as np
from matplotlib import pyplot as plt
x = np.random.rand(200)
y = np.random.rand(200)
fig, ax = plt.subplots(figsize=(5, 5))
ax.scatter(x, y, c=(x + y), cmap='RdPu')
scatter takes a c argument, which can be a numeric value, as well as a cmap argument, which can be a string referencing a colormap.
The colormap object translates the numbers provided in c into points along a colour mapping, which you can think of as a gradient bar.
Use 3D axes
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(5, 5))
ax = Axes3D(fig)
ax.scatter(x, y, (x + y))
This turns your 3rd dimension, accuracy, into an ordinary spatial dimension.
Use size of the markers
Very similar to the color option in the first part, you can change the size of the scatter markers (given you have some idea about the scale of the values). So based on the first example, you can also do;
import numpy as np
from matplotlib import pyplot as plt
x = np.random.rand(200)
y = np.random.rand(200)
fig, ax = plt.subplots(figsize=(5, 5))
ax.scatter(x, y, c='k', s=5*(x + y), cmap='RdPu')
scatter takes also the s argument, that changes the size of the markers.
Related
I have computed a lot (~5000) of 3d points (x,y,z) in a quite complicated way so I have no function such that z = f(x,y). I can plot the 3d surface using
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
X = surface_points[:,0]
Y = surface_points[:,1]
Z = surface_points[:,2]
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
surf = ax.plot_trisurf(X, Y, Z, cmap=cm.coolwarm, vmin=np.nanmin(Z), vmax=np.nanmax(Z))
I would like to plot this also in 2d, with a colorbar indicating the z-value. I know there is a simple solution using ax.contour if my z is a matrix, but here I only have a vector.
Attaching the plot_trisurf result when rotated to xy-plane. This is what I what like to achieve without having to rotate a 3d plot. In this, my variable surface_points is an np.array with size 5024 x 3.
I had the same problems in one of my codes, I solved it this way:
import numpy as np
from scipy.interpolate import griddata
import matplotlib.pylab as plt
from matplotlib import cm
N = 10000
surface_points = np.random.rand(N,3)
X = surface_points[:,0]
Y = surface_points[:,1]
Z = surface_points[:,2]
nx = 10*int(np.sqrt(N))
xg = np.linspace(X.min(), X.max(), nx)
yg = np.linspace(Y.min(), Y.max(), nx)
xgrid, ygrid = np.meshgrid(xg, yg)
ctr_f = griddata((X, Y), Z, (xgrid, ygrid), method='linear')
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.contourf(xgrid, ygrid, ctr_f, cmap=cm.coolwarm)
plt.show()
You could use a scatter plot to display a projection of your z color onto the x-y axis.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
N = 10000
surface_points = np.random.rand(N,3)
X = surface_points[:,0]
Y = surface_points[:,1]
Z = surface_points[:,2]
# fig = plt.figure()
# ax = fig.add_subplot(projection='3d')
# surf = ax.plot_trisurf(X, Y, Z, cmap=cm.coolwarm, vmin=np.nanmin(Z), vmax=np.nanmax(Z))
fig = plt.figure()
cmap = cm.get_cmap('coolwarm')
color = cmap(Z)[..., :3]
plt.scatter(X,Y,c=color)
plt.show()
Since you seem to have a 3D shape that is hollow, you could split the projection into two like if you cur the shape in two pieces.
fig = plt.figure()
plt.subplot(121)
plt.scatter(X[Z<0.5],Y[Z<0.5],c=color[Z<0.5])
plt.title('down part')
plt.subplot(122)
plt.scatter(X[Z>=0.5],Y[Z>=0.5],c=color[Z>+0.5])
plt.title('top part')
plt.show()
I created 3d bar graph with matplotlib with the following code:
fig = plt.figure()
cmap = get_cmap(len(os.listdir(conv1d_kernel_path)))
ax = fig.add_subplot(111, projection='3d')
for f in os.listdir(conv1d_kernel_path):
step = int(re.findall(r'\d+', f)[0])
conv1d_kernel_histo[f]['bins'] = convert_bins(30, min_weight, max_weight, conv1d_kernel_histo[f])
bin_counts = conv1d_kernel_histo[f]['bins'][:, 2]
width = (max_weight-min_weight)/30 #ToDo change 30 to numbins
xs = conv1d_kernel_histo[f]['bins'][:, 0] + width / 2
ax.bar(xs, list(bin_counts), width=width, zs=step, zdir='y', color=cmap(step), ec=cmap(step+20), alpha=0.8)
ax.set_xlabel('weights')
ax.set_ylabel('step')
ax.set_zlabel('count')
plt.show()
The directory and convert bins function isn't so important, just that it gives me the info I can use to define the input data that I iteratively pass to the ax.bar function. Anyways, I receive the following output:
I want to invert the axis titled 'steps', and this question seems to be nearly what I need; however, when I used ax.invert_yaxis() the axis titled 'weights' is inverted. When I alternatively use ax.invert_xaxis() the same axis is inverted. Out of curiosity, I tried ax.invert_zaxis() but that worked as one would think, and flipped the whole graph upside down. Does anybody have a solution to this problem? A different method for inverting the axis? Thanks, all help is appreciated
Well inverting the axis limits like ax.set_ylim(150,0) should work fine. Sample plot:
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import numpy as np
fig = plt.figure()
ax = fig.gca(projection='3d')
# Make data.
X = np.arange(-5, 5, 0.25)
Y = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
# Plot the surface.
surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
# Invert Y-Axis
ax.set_ylim(4,-4)
# Customize the z axis.
ax.set_zlim(-1.01, 1.01)
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
# Add a color bar which maps values to colors.
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
Original:
Inverted Y-Axis:
I have 2 variables (x,y) that change with time (t). I want to plot x vs. t and color the ticks based on the value of y. e.g. for highest values of y the tick color is dark green, for lowest value is dark red, and for intermediate values the color will be scaled in between green and red.
Can this be done with matplotlib in python?
This is what matplotlib.pyplot.scatter is for.
If no colormap is specified, scatter will use whatever the default colormap is set to. To specify which colormap scatter should use, use the cmap kwarg (e.g. cmap="jet").
As a quick example:
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
# Generate data...
t = np.linspace(0, 2 * np.pi, 20)
x = np.sin(t)
y = np.cos(t)
plt.scatter(t, x, c=y, ec='k')
plt.show()
One may specify a custom color map and norm
cmap, norm = mcolors.from_levels_and_colors([0, 2, 5, 6], ['red', 'green', 'blue'])
plt.scatter(x, y, c=t, cmap=cmap, norm=norm)
If you want to plot lines instead of points, see this example, modified here to plot good/bad points representing a function as a black/red as appropriate:
def plot(xx, yy, good):
"""Plot data
Good parts are plotted as black, bad parts as red.
Parameters
----------
xx, yy : 1D arrays
Data to plot.
good : `numpy.ndarray`, boolean
Boolean array indicating if point is good.
"""
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
from matplotlib.colors import from_levels_and_colors
from matplotlib.collections import LineCollection
cmap, norm = from_levels_and_colors([0.0, 0.5, 1.5], ['red', 'black'])
points = np.array([xx, yy]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
lines = LineCollection(segments, cmap=cmap, norm=norm)
lines.set_array(good.astype(int))
ax.add_collection(lines)
plt.show()
Suppose I have gridded data with dimensions (x,y) and values are in z.
so simply we can make scatter plot for third dimension by:
import numpy as np
import matplotlib.pyplot as plt
x = np.random.random(10)
y = np.random.random(10)
z = np.random.random(10)
plt.scatter(x, y, c = z, s=150, cmap = 'jet')
plt.show()
what i am thinking now is to remove the line color of each circular scatter plot. And also instead of circle can we make it square??
I did not find any way to do that. your help will be highly appreciated.
Pass the argument edgecolors='none' to plt.scatter. The patch boundary will not be drawn.
Pass the argument marker='s' to plt.scatter. The marker style will be square.
Then, we have,
The source code,
import numpy as np
import matplotlib.pyplot as plt
x = np.random.random(10)
y = np.random.random(10)
z = np.random.random(10)
plt.scatter(x, y, c = z, s=150, cmap = 'jet', edgecolors='none', marker='s')
plt.show()
Refer to matplotlib.pyplot.scatter for more information.
How can I create a 3D plot with a color gradient for the points? See the example below, which works for a 2D scatter plot.
Edit (thanks to Chris): What I'm expecting to see from the 3D plot is a color gradient of the points ranging from red to green as in the 2D scatter plot.
What I see in the 3D scatter plot are only red points.
Solution: for some reasons (related to the gradient example I copied elsewhere) I set xrange to len-1, which messes everything in the 3D plot.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Create Map
cm = plt.get_cmap("RdYlGn")
x = np.random.rand(30)
y = np.random.rand(30)
z = np.random.rand(30)
#col = [cm(float(i)/(29)) for i in xrange(29)] # BAD!!!
col = [cm(float(i)/(30)) for i in xrange(30)]
# 2D Plot
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(x, y, s=10, c=col, marker='o')
# 3D Plot
fig = plt.figure()
ax3D = fig.add_subplot(111, projection='3d')
ax3D.scatter(x, y, z, s=10, c=col, marker='o')
plt.show()
Here is an example for 3d scatter with gradient colors:
import matplotlib.cm as cmx
from mpl_toolkits.mplot3d import Axes3D
def scatter3d(x,y,z, cs, colorsMap='jet'):
cm = plt.get_cmap(colorsMap)
cNorm = matplotlib.colors.Normalize(vmin=min(cs), vmax=max(cs))
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(x, y, z, c=scalarMap.to_rgba(cs))
scalarMap.set_array(cs)
fig.colorbar(scalarMap)
plt.show()
Of course, you can choose the scale to range between different values, like 0 and 1.
Following works: I can't figure out why yours doesn't. You should be able to set color as a sequence of RGBA floats, or just sequence of floats.
# Create Map
cm = plt.get_cmap("RdYlGn")
x = np.random.rand(30)
y = np.random.rand(30)
z = np.random.rand(30)
col = np.arange(30)
# 2D Plot
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(x, y, s=10, c=col, marker='o')
# 3D Plot
fig = plt.figure()
ax3D = fig.add_subplot(111, projection='3d')
p3d = ax3D.scatter(x, y, z, s=30, c=col, marker='o')
plt.show()
However, in help of scatter, I see the following, it may be related.
A :class:`matplotlib.colors.Colormap` instance or registered
name. If *None*, defaults to rc ``image.cmap``. *cmap* is
only used if *c* is an array of floats.