Hi I have a text file containing three columns of numbers; one column each for the x,y,z coordinates of a bunch of points. All numbers are between 0 ad 1.
I want to plot all these points in the unit cube [0,1]x[0,1]x[0,1].
Please let me know how I can do this in Octave or MatPlot lib, whichever prduces a better quality image.
If I understand your question correctly, this is how it looks in Matplotlib:
This is the code to produce this plot:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
np.random.seed(101)
x,y,z = np.random.rand(3,20)
fig = plt.figure()
# version 1.0.x syntax:
#ax = fig.add_subplot(111, projection='3d')
# version 0.99.x syntax: (accepted by 1.0.x as well)
ax = Axes3D(fig)
ax.scatter(x,y,z)
fig.savefig('scatter3d.png')
As the code suggests, there are slight differences in how matplotlib version 0.99.1.1 and version 1.0.1 behave, as noted in this SO question/answer. I am using 0.99.1.1, and I had trouble using all the options available to 2D scatter plots, which should be the same for 3D plots as well. The full list of scatter features are listed here.
The above code resulted from looking at the matplotlib tutorial on 3D plotting.
Related
I want to plot some coordinates using Plotly express because it allows me a more interactive approach, but I can not find the way to control the scale in the axis in the way I can manage with matplotlib.pyplot in one single line
plt.axis("scaled")
Could you please share some suggestions? Thanks.
Here is the code using Plotly express:
fig = px.scatter(coordinates_utm, x='EASTING', y='NORTHING', title=name,
hover_name=coordinates_utm.index,
hover_data={'NORTHING':':.6f','EASTING': ':.6f'})
fig.add_trace(px.scatter(coordinates_utm_lineal, x='x', y='ylineal',color_discrete_sequence=['red']).data[0])
Here is the code using plt:
fig.show()
plt.figure()
plt.scatter(coordinates_utm_lineal.x,coordinates_utm_lineal.ylineal,s=2)
plt.scatter(coordinates_utm.EASTING,coordinates_utm.NORTHING, s=2)
plt.axis("scaled")
plt.show()
This is my current output
Sadly, you didn't provide a fully reproducible example, so I'm going to create my own.
Also, I'm not really familiar with plt.axis("scaled"), as I usually use plt.axis("equal"). Reading the documentation associated to plt.axis, they appear to be somewhat similar. See if the following answer can satisfy your needs.
import plotly.express as px
import numpy as np
t = np.linspace(0, 2*np.pi)
x = np.cos(t)
y = np.sin(t)
fig = px.scatter(x=x, y=y)
fig.layout.yaxis.scaleanchor="x"
fig.show()
I am using python 3.8 on Windows 10; trying to make a plot with about 700M points in it, sound wave analysis. Here: Interactive large plot with ~20 million sample points and gigabytes of data
Vaex was highly recommended. I am trying to use examples from the Vaex tutorial but the graph does not appear. I could not find a good example on Internet.
import vaex
import numpy as np
df = vaex.example()
df.plot1d(df.x, limits='99.7%');
The Vaex documents don't mention that pyplot.show() should be used to display. Plot1d plots a histogram. How to plot just connected points?
I am pretty sure that the vaex documentation explains that the (now deprecated) method .plot1d(...) is a wrapper around matplotlib plotting routines.
If you would like to create custom plots using the binned data, you can take this approach (I also found it in their docs)
import vaex
import numpy as np
import pylab as plt
# Load example data
df = vaex.example()
# Do the binning yourself
counts = df.count(binby=df.x, shape=64, limits='99.7%')
# Take care of the x-axis
limits = df.limits_percentage(df.x, percentage=99.7)
xvals = np.linspace(limits[0], limits[1], num=64)
# Create your custom plot via matplotlib, plotly or your favorite tool
p.plot(xvals, counts, marker='o', ms=5);
I want to create a 3d plot like the following, such that axes pass through the origin with ticks on them.
PS: I could do that for 2D plots using matplotlib (the following figure). I searched a lot to do the same for 3D plots but I did not find any info.
If you want to restrict yourself to just matplotlib then we can use quiver3d plot as shown below. But the results may not be very visually appealing. You can see here how to add 3D text annotations.
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111,projection='3d')
ax.set_xlim(0,2)
ax.set_ylim(0,2)
ax.set_zlim(0,2)
ax.view_init(elev=20., azim=32)
# Make a 3D quiver plot
x, y, z = np.zeros((3,3))
u, v, w = np.array([[1,1,0],[1,0,1],[0,1,1]])
ax.quiver(x,y,z,u,v,w,arrow_length_ratio=0.1)
plt.show()
When I try to obtain plots in which the axis (both formulae and text) are written in LaTeX standard roman font, I keep not obtaining the plot, but the code runs without warnings. In particular, this simple scatter with TeX code in the axis labels, in which I have put my better understanding of the documentation:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
x = np.linspace(0,1,100)
y = np.random.rand(100,1)
plt.rc('text', usetex=True)
plt.rc('font', family='roman')
plt.scatter(x, y, c='b', s=10)
plt.xlabel(r'$\lambda$ ($\AA$)',size='12')
plt.ylabel(r'$F_\alpha (W/m^2)$ ',size='12')
plt.title(r'A title in \LaTeX typography')
plt.show()
keeps yielding a message like <matplotlib.figure.Figure at 0x1f75d4750>, which I have met before, but I keep failing when trying to remedy this one. In addition, saving the plot (png or pdf) would not solve the issue, and if the problem is related to TeX, I have definitely not found any resource that can help. I use MacOS Sierra.
I'm attempting to follow the violin plot example in bokeh, but am unable to add x-axis labels to my violins. According to the Seaborn documentation it looks like I should be able to add x-axis labels via the "names" argument, however, the following code does not add x-axis labels:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from bokeh import mpl
from bokeh.plotting import show
# generate some random data
data = 1 + np.random.randn(20, 6)
# Use Seaborn and Matplotlib normally
sns.violinplot(data, color="Set3", names=["kirk","spock","bones","scotty","uhura","sulu"])
plt.title("Seaborn violin plot in Bokeh")
# Convert to interactive Bokeh plot with one command
show(mpl.to_bokeh(name="violin"))
I believe that the issue is that I'm converting a figure from seaborn to matplotlib to bokeh, but I'm not sure at what level the x-axis labels go in.
I've confirmed that the labels are showing up in matplotlib before conversion to bokeh. I've also tried adding the labels to bokeh after conversion, but this results in a weird plot. I've created an issue for this problem with the bokeh developers here.
Since Bokeh 12.5 (April 2017), support for Matplotlib has been deprecated, so mpl.to_bokeh() is no longer available.