I am currently have a nx3 matrix array. I want plot the three columns as three axis's.
How can I do that?
I have googled and people suggested using Matlab, but I am really having a hard time with understanding it. I also need it be a scatter plot.
Can someone teach me?
You can use matplotlib for this. matplotlib has a mplot3d module that will do exactly what you want.
import matplotlib.pyplot as plt
import random
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(projection='3d')
sequence_containing_x_vals = list(range(0, 100))
sequence_containing_y_vals = list(range(0, 100))
sequence_containing_z_vals = list(range(0, 100))
random.shuffle(sequence_containing_x_vals)
random.shuffle(sequence_containing_y_vals)
random.shuffle(sequence_containing_z_vals)
ax.scatter(sequence_containing_x_vals, sequence_containing_y_vals, sequence_containing_z_vals)
plt.show()
The code above generates a figure like:
Use the following code it worked for me:
# Create the figure
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# Generate the values
x_vals = X_iso[:, 0:1]
y_vals = X_iso[:, 1:2]
z_vals = X_iso[:, 2:3]
# Plot the values
ax.scatter(x_vals, y_vals, z_vals, c = 'b', marker='o')
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')
ax.set_zlabel('Z-axis')
plt.show()
while X_iso is my 3-D array and for X_vals, Y_vals, Z_vals I copied/used 1 column/axis from that array and assigned to those variables/arrays respectively.
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = plt.axes(projection='3d')
scatter plot
zdata = 15 * np.random.random(100)
xdata = np.sin(zdata) + 0.1 * np.random.randn(100)
ydata = np.cos(zdata) + 0.1 * np.random.randn(100)
ax.scatter3D(xdata, ydata, zdata);
Colab notebook
Using plotly - Easiest and most functional and nice plots
import plotly.express as px
df = px.data.iris()
fig = px.scatter_3d(df, x='sepal_length', y='sepal_width', z='petal_width',
color='species')
fig.show()
https://plotly.com/python/3d-scatter-plots/
Related
I would like to use the grid in multiple matplotlib figures, but if I just use plt.grid() the grid would only show up in one of the charts.
How can I change the code below, so that the grid shows up in both figures, please?
import matplotlib.pyplot as plt
import numpy as np
rng = np.random.default_rng(19680801)
N_points = 100000
dist1 = rng.standard_normal(N_points)
fig = plt.figure()
axis = fig.add_subplot(1,1,1)
fig1 = plt.figure()
ax = fig1.add_subplot(1,1,1)
axis.hist(dist1)
ax.hist(dist1)
plt.grid()
plt.show()
import matplotlib.pyplot as plt
import numpy as np
rng = np.random.default_rng(19680801)
N_points = 100000
dist1 = rng.standard_normal(N_points)
fig = plt.figure()
axis = fig.add_subplot(1,1,1)
axis.grid()
fig1 = plt.figure()
ax = fig1.add_subplot(1,1,1)
ax.grid()
axis.hist(dist1)
ax.hist(dist1)
# plt.grid()
plt.show()
I'm trying to plot 23 graphs in a 6x4 grid, with one figure taking up twice the width of the other figures. I'm using gridspec and my current code is:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec
x = np.arange(0, 7, 0.01)
fig = plt.figure(figsize=(6, 4))
gs = gridspec.GridSpec(nrows=6, ncols=4)
for n in range(22):
ax = fig.add_subplot(gs[n])
ax.plot(x, np.sin(0.2*n*x))
corrax = fig.add_subplot(gs[22])
fig.tight_layout()
plt.show()
This produces the following:
I want to increase the width of the rightmost plot in the bottom row so it takes up the remaining space in that row. Is there a way to accomplish this?
You can use slices to select several positions from the gridspec, e.g. gs[22:24].
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec
x = np.arange(0, 7, 0.01)
fig = plt.figure(figsize=(6, 4))
gs = gridspec.GridSpec(nrows=6, ncols=4)
for n in range(22):
ax = fig.add_subplot(gs[n])
ax.plot(x, np.sin(0.2*n*x))
corrax = fig.add_subplot(gs[22:24])
corrax.plot(x,np.sin(0.2*22*x), color="crimson", lw=3)
fig.tight_layout()
plt.show()
You can also slice the gridspec two-dimensionally. E.g. to create a 3x3 grid and make the plot in the lower right corner span two columns and two rows, you could slice like gs[1:,1:].
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec
x = np.arange(0, 7, 0.01)
fig = plt.figure(figsize=(6, 4))
gs = gridspec.GridSpec(nrows=3, ncols=3)
for n in range(3):
ax = fig.add_subplot(gs[0,n])
ax.plot(x, np.sin(0.2*n*x))
if n !=0:
ax = fig.add_subplot(gs[n,0])
ax.plot(x, np.sin(0.2*n*x))
corrax = fig.add_subplot(gs[1:,1:])
corrax.plot(x,np.sin(0.2*22*x), color="crimson", lw=3)
fig.tight_layout()
plt.show()
#corrax = fig.add_subplot(gs[5,2:])
corrax = fig.add_subplot(6,4,(23,24))
both shold work.
see examples
I have a series of lines representing the change of a variable; each with a unique color. For that reason I want to add a colorbar next to the plot. The desired output is shown below.
The problem is that plot is a non-mappable object, i.e. the colorbar has to be added manually. I consider my current solution (below) sub-optimal as it involves size parameters of which I have no interest in controlling. I'd prefer a similar solution as for a mappable object (example below current solution).
Desired output
Current solution
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
x = np.linspace(0, 5, 100)
N = 20
cmap = plt.get_cmap('jet',N)
fig = plt.figure(figsize=(8,6))
ax1 = fig.add_axes([0.10,0.10,0.70,0.85])
for i,n in enumerate(np.linspace(0,2,N)):
y = np.sin(x)*x**n
ax1.plot(x,y,c=cmap(i))
plt.xlabel('x')
plt.ylabel('y')
ax2 = fig.add_axes([0.85,0.10,0.05,0.85])
norm = mpl.colors.Normalize(vmin=0,vmax=2)
cb1 = mpl.colorbar.ColorbarBase(ax2,cmap=cmap,norm=norm,orientation='vertical')
plt.show()
Desired solution
(obviously replacing imshow)
fig,ax = plt.subplots()
cax = ax.imshow(..)
cbar = fig.colorbar(cax,aspect=10)
plt.show()
You may define your own ScalarMappable and use it just as if it was present in the plot.
(Note that I changed the numbero f colors to 21 to have nice spacings of 0.1)
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
x = np.linspace(0, 5, 100)
N = 21
cmap = plt.get_cmap('jet',N)
fig = plt.figure(figsize=(8,6))
ax1 = fig.add_axes([0.10,0.10,0.70,0.85])
for i,n in enumerate(np.linspace(0,2,N)):
y = np.sin(x)*x**n
ax1.plot(x,y,c=cmap(i))
plt.xlabel('x')
plt.ylabel('y')
norm = mpl.colors.Normalize(vmin=0,vmax=2)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
plt.colorbar(sm, ticks=np.linspace(0,2,N),
boundaries=np.arange(-0.05,2.1,.1))
plt.show()
I have a figure showing the contourf plot and another showing a plot i've made earlier and I want to plot both on the same figure what should I do?
Here is the code of my contourf plot:
import pylab as pl
from pylab import *
import xlrd
import math
import itertools
from matplotlib import collections as mc
import matplotlib.pyplot as plt
import copy as dc
import pyexcel
from pyexcel.ext import xlsx
import decimal
x_list = linspace(0, 99, 100)
y_list = linspace(0, 99, 100)
X, Y = meshgrid(x_list, y_list, indexing='xy')
Z = [[0 for x in range(len(x_list))] for x in range(len(y_list))]
for each_axes in range(len(Z)):
for each_point in range(len(Z[each_axes])):
Z[len(Z)-1-each_axes][each_point] = power_at_each_point(each_point, each_axes)
figure()
CP2 = contourf(X, Y, Z, cmap=plt.get_cmap('Reds'))
colorbar(CP2)
title('Coverage Plot')
xlabel('x (m)')
ylabel('y (m)')
show()
This is the code of my previously plotted plot:
lc = mc.LineCollection(lines, linewidths=3)
fig, ax = pl.subplots()
ax.add_collection(lc)
ax.autoscale()
ax.margins(0.05)
#The code blow is just for drawing the final plot of the building.
Nodes = xlrd.open_workbook(Node_file_location)
sheet = Nodes.sheet_by_index(0)
Node_Order_Counter = range(1, sheet.nrows + 1)
In_Node_Order_Counter = 0
for counter in range(len(Node_Positions_Ascending)):
plt.plot(Node_Positions_Ascending[counter][0], Node_Positions_Ascending[counter][1], marker='o', color='r',
markersize=6)
pl.text(Node_Positions_Ascending[counter][0], Node_Positions_Ascending[counter][1],
str(Node_Order_Counter[In_Node_Order_Counter]),
color="black", fontsize=15)
In_Node_Order_Counter += 1
#Plotting the different node positions on our plot & numbering them
pl.show()
Without your data we can't see what the plot is supposed to look like, but I have some general recommendations.
Don't use pylab. And if you absolutely must use it, use it within its namespace, and don't do from pylab import *. It makes for very sloppy code - for example, linspace and meshgrid are actually from numpy, but it's hard to tell that when you use pylab.
For complicated plotting, don't even use pyplot. Instead, use the direct object plotting interface. For example, to make a normal plot on top of a contour plot, (such as you want to do) you could do the following:
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
x = np.linspace(1, 5, 20)
y = np.linspace(2, 5, 20)
z = x[:,np.newaxis] * (y[np.newaxis,:])**2
xx, yy = np.meshgrid(x, y)
ax.contourf(xx, yy, z, cmap='Reds')
ax.plot(x, 0.2*y**2)
plt.show()
Notice that I only used pyplot to create the figure and axes, and show them. The actual plotting is done using the AxesSubplot object.
I am updating a 3d scatter plot with every iteration of a loop. When the plot is redrawn, the gridlines "go through" or "cover" the points, which makes my data more difficult to visualize. If I build a single 3d plot (no loop updating) this does not happen. The code below demonstrates the simplest case:
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import time
X = np.random.rand(100, 3)*10
Y = np.random.rand(100, 3)*5
plt.ion()
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X[:, 0], X[:, 1], X[:, 2])
plt.draw()
for i in range(0, 20):
time.sleep(3) #make changes more apparent/easy to see
Y = np.random.rand(100, 3)*5
ax.cla()
ax.scatter(Y[:, 0], Y[:, 1], Y[:, 2])
plt.draw()
Has anyone else encountered this problem?
It looks like MaxNoe is right in the sense that the problem is in the ax.cla()or plt.cla() call. In fact it seems it is something like a known issue.
Then there is a problem, since the clear axes method doesn't work in 3D plots and for 3D scatters there is no clean way to change the coordinates of the data points (a la sc.set_data(new_values)), as discussed in this mail list (I didn't find anything more recent).
In the mail list, however, Ben Roon points to a workaround that might be useful for you, too.
Workaround:
You need to set the new coordinates of the datapoints in the internal _ofsets3d variable of the Line3DCollectionobject returned by the scatter function.
Your example adapted would look like:
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import time
X = np.random.rand(100, 3)*10
Y = np.random.rand(100, 3)*5
plt.ion()
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
sc = ax.scatter(X[:, 0], X[:, 1], X[:, 2])
fig.show()
for i in range(0, 20):
plt.pause(1)
Y = np.random.rand(100, 3)*5
sc._offsets3d = (Y[:,0], Y[:,1], Y[:,2])
plt.draw()
I could narrow it down to the use of cla():
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x, y = np.meshgrid(np.linspace(-2,2), np.linspace(-2,2))
ax.plot_surface(x,y, x**2+y**2)
fig.savefig("fig_a.png")
ax.cla()
ax.plot_surface(x,y, x**2+y**2)
fig.savefig("fig_b.png")
these are the resulting plots:
This is but a workaround, as it does not resolve the issue with ax.cla() pointed out by MaxNoe. It is also not particularly pretty since it clears the entire figure, however it does the desired task:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig1 = plt.figure()
ax1 = fig1.add_subplot(111, projection='3d')
x, y = np.meshgrid(np.linspace(-2,2), np.linspace(-2,2))
ax1.plot_surface(x,y, x**2+y**2)
fig1.savefig("fig_a.png")
fig1.clf()
ax1 = fig1.add_subplot(111, projection='3d')
ax1.plot_surface(x,y, x**2+y**2)
fig1.savefig("fig_b.png")
I'd suggest using ax = fig.gca(projection='3d') instead of ax = fig.add_subplot(111, projection='3d') .