matplotlib plotting multiple lines in 3D - python

I am trying to plot multiple lines in a 3D plot using matplotlib. I have 6 datasets with x and y values. What I've tried so far was, to give each point in the data sets a z-value. So all points in data set 1 have z=1 all points of data set 2 have z=2 and so on.
Then I exported them into three files. "X.txt" containing all x-values, "Y.txt" containing all y-values, same for "Z.txt".
Here's the code so far:
#!/usr/bin/python
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
import pylab
xdata = '/X.txt'
ydata = '/Y.txt'
zdata = '/Z.txt'
X = np.loadtxt(xdata)
Y = np.loadtxt(ydata)
Z = np.loadtxt(zdata)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_wireframe(X,Y,Z)
plt.show()
What I get looks pretty close to what I need. But when using wireframe, the first point and the last point of each dataset are connected. How can I change the colour of the line for each data set and how can I remove the connecting lines between the datasets?
Is there a better plotting style then wireframe?


Load the data sets individually, and then plot each one individually.
I don't know what formats you have, but you want something like this
from mpl_toolkits.mplot3d.axes3d import Axes3D
import matplotlib.pyplot as plt
fig, ax = plt.subplots(subplot_kw={'projection': '3d'})
datasets = [{"x":[1,2,3], "y":[1,4,9], "z":[0,0,0], "colour": "red"} for _ in range(6)]
for dataset in datasets:
ax.plot(dataset["x"], dataset["y"], dataset["z"], color=dataset["colour"])
plt.show()
Each time you call plot (or plot_wireframe but i don't know what you need that) on an axes object, it will add the data as a new series. If you leave out the color argument matplotlib will choose them for you, but it's not too smart and after you add too many series' it will loop around and start using the same colours again.
n.b. i haven't tested this - can't remember if color is the correct argument. Pretty sure it is though.

Related

Set output range of matplotlib - imshow

For a project I'm working on I have created two sets of data which is made from a function that takes two input and returns a 3rd. I am currently using a matplotlib imshow graph to show the data. As one of the data sets contains far higher values than the other data set so I was hoping to set a range for both meaning the colours would represent the same value across the two charts. Is there a good way to do this? thankyou
Here is the code I am currently using:
import matplotlib.pyplot as plt
import json
import numpy as np
with open("multi_testing\out_put\\bit_shift.txt","r") as f:
n = json.loads(f.read())
n = n[0]
inp = np.array(n)
fig, ax = plt.subplots()
im = ax.imshow(inp)
ax.invert_yaxis()
ax.set_title("bit shifting")
fig.tight_layout()
plt.show()
and here are the two data sets:
[[[7,7,7,7,7,7,7,7,7,7,7],[11,11,11,11,11,11,11,11,11,11,11],[15,15,15,15,15,15,15,15,15,15,15],[19,19,19,19,19,19,19,19,19,19,19],[23,23,23,23,23,23,23,23,23,23,23],[27,27,27,27,27,27,27,27,27,27,27],[31,31,31,31,31,31,31,31,31,31,31],[35,35,35,35,35,35,35,35,35,35,35],[39,39,39,39,39,39,39,39,39,39,39],[43,43,43,43,43,43,43,43,43,43,43],[47,47,47,47,47,47,47,47,47,47,47]]]
and
[[[10,19,26,28,33,35,35,37,40,42,42],[10,19,26,28,33,35,35,37,40,42,42],[10,19,26,28,33,35,35,37,40,42,42],[10,19,26,28,33,35,35,37,40,42,42],[10,19,26,28,33,35,35,37,40,42,42],[10,19,26,28,33,35,35,37,40,42,42],[10,19,26,28,33,35,35,37,40,42,42],[10,19,26,28,33,35,35,37,40,42,42],[10,19,26,28,33,35,35,37,40,42,42],[10,19,26,28,33,35,35,37,40,42,42],[10,19,26,28,33,35,35,37,40,42,42]]]

You can use vmin and vmax for this while using ax.imshow(). For example:
im = ax.imshow(inp, vmin=0, vmax=50)

Making a matplotlib graph partially invisible

Look at this pretty graph.
Is there a way, in matplotlib, to make parts of the red and green graph invisible (where f(x)=0)?
Not just those, but also the single line segment where the flat part connects to the sine curve.
Basically, is it possible to tell matplotlib to only plot graph on a certain interval and not draw the rest (or vice versa)?

You could try replacing your points of interest with np.nan as shown below:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# here is some example data because none was provided in the question;
# it is a quadratic from x=-5:5
x = np.arange(-5, 6)
s = pd.Series(x**2, index=x)
# replace all y values less than 4 with np.nan and store in a new Series object
s_mod = s.apply(lambda y: np.nan if y < 4 else y)
# plot the modified data with the original data
fig, ax = plt.subplots()
s.plot(marker='o', markersize=16, ax=ax, label='original')
s_mod.plot(marker='s', ax=ax, label='modified')
ax.legend()
fig # displays as follows

Matplotlib 3D plot use colormap

I am trying to use ax.scatter to plot a 3D scattering plot. I've read the data from a fits file and stored data from three column into x,y,z. And I have made sure x,y,z data are the same size. z has been normolized between 0 and 1.
import numpy as np
import matplotlib
from matplotlib import pylab,mlab,pyplot,cm
plt = pyplot
import pyfits as pf
from mpl_toolkits.mplot3d import Axes3D
import fitsio
data = fitsio.read("xxx.fits")
x=data["x"]
y=data["y"]
z=data["z"]
z = (z-np.nanmin(z)) /(np.nanmax(z) - np.nanmin(z))
Cen3D = plt.figure()
ax = Cen3D.add_subplot(111, projection='3d')
cmap=cm.ScalarMappable(norm=z, cmap=plt.get_cmap('hot'))
ax.scatter(x,y,z,zdir=u'z',cmap=cmap)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
plt.show()
What I am trying to achieve is use color to indicate the of size of z. Like higher value of z will get darker color. But I am keep getting a plot without the colormap I want, they are all the same default blue color. What did I do wrong? Thanks.

You can use the c keyword in the scatter command, to tell it how to color the points.
You don't need to set zdir, as that is for when you are plotting a 2d set
As #Lenford pointed out, you can use cmap='hot' in this case too, since you have already normalized your data.
I've modified your example to use some random data rather than your fits file.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)
z = (z-np.nanmin(z)) /(np.nanmax(z) - np.nanmin(z))
Cen3D = plt.figure()
ax = Cen3D.add_subplot(111, projection='3d')
ax.scatter(x,y,z,cmap='hot',c=z)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
plt.show()

As per the pyplot.scatter documentation, the points specified to be plotted must be in the form of an array of floats for cmap to apply, otherwise the default colour (in this case, jet) will continue to apply.
As an aside, simply stating cmap='hot' will work for this code, as the colour map hot is a registered colour map in matplotlib.

2D Color coded scatter plot with user defined color range and static colormap

I have 3 vectors - x,y,vel each having some 8k values. I also have quite a few files containing these 3 vectors. All the files have different x,y,vel. I want to get multiple scatter plots with the following conditions:
Color coded according to the 3rd variable i.e vel.
Once the ranges have been set for the colors (for the data from the 1st file), they should remain constant for all the remaining files. i don't want a dynamically changing (color code changing with each new file).
Want to plot a colorbar.
I greatly appreciate all your thoughts!!
I have attached the code for a single file.
import numpy as np
import matplotlib.pyplot as plt
# Create Map
cm = plt.cm.get_cmap('RdYlBu')
x,y,vel = np.loadtxt('finaldata_temp.txt', skiprows=0, unpack=True)
vel = [cm(float(i)/(8000)) for i in xrange(8000)] # 8000 is the no. of values in each of x,y,vel vectors.
# 2D Plot
plt.scatter(x, y, s=27, c=vel, marker='o')
plt.axis('equal')
plt.savefig('testfig.png', dpi=300)
plt.show()
quit()

You will have to iterate over all your data files to get the maximum value for vel, I have added a few lines of code (that need to be adjusted to fit your case) that will do that.
Therefore, your colorbar line has been changed to use the max_vel, allowing you to get rid of that code using the fixed value of 8000.
Additionally, I took the liberty to remove the black edges around the points, because I find that they 'obfuscate' the color of the point.
Lastly, I have added adjusted your plot code to use an axis object, which is required to have a colorbar.
import numpy as np
import matplotlib.pyplot as plt
# This is needed to iterate over your data files
import glob
# Loop over all your data files to get the maximum value for 'vel'.
# You will have to adjust this for your code
"""max_vel = 0
for i in glob.glob(<your files>,'r') as fr:
# Iterate over all lines
if <vel value> > max_vel:
max_vel = <vel_value>"""
# Create Map
cm = plt.cm.get_cmap('RdYlBu')
x,y,vel = np.loadtxt('finaldata_temp.txt', skiprows=0, unpack=True)
# Plot the data
fig=plt.figure()
fig.patch.set_facecolor('white')
# Here we switch to an axis object
# Additionally, you can plot several of your files in the same figure using
# the subplot option.
ax=fig.add_subplot(111)
s = ax.scatter(x,y,c=vel,edgecolor=''))
# Here we assign the color bar to the axis object
cb = plt.colorbar(mappable=s,ax=ax,cmap=cm)
# Here we set the range of the color bar based on the maximum observed value
# NOTE: This line only changes the calculated color and not the display
# 'range' of the legend next to the plot, for that we need to switch to
# ColorbarBase (see second code snippet).
cb.setlim(0,max_vel)
cb.set_label('Value of \'vel\'')
plt.show()
Snippet, demonstrating ColorbarBase
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
cm = plt.cm.get_cmap('RdYlBu')
x = [1,5,10]
y = [2,6,9]
vel = [7,2,1]
# Plot the data
fig=plt.figure()
fig.patch.set_facecolor('white')
ax=fig.add_subplot(111)
s = ax.scatter(x,y,c=vel,edgecolor=''))
norm = mpl.colors.Normalize(vmin=0, vmax=10)
ax1 = fig.add_axes([0.95, 0.1, 0.01, 0.8])
cb = mpl.colorbar.ColorbarBase(ax1,norm=norm,cmap=cm,orientation='vertical')
cb.set_clim(vmin = 0, vmax = 10)
cb.set_label('Value of \'vel\'')
plt.show()
This produces the following plot
For more examples of what you can do with the colorbar, specifically the more flexible ColorbarBase, I would suggest that you check the documentation -> http://matplotlib.org/examples/api/colorbar_only.html

Overlaying a lineCollection on a plot in matplotlib - how to get the two to line up.

I'm trying to do a heat map over a shape file in python. I need to make quite a few of these so don't want to read in the .shp every time.
Instead, I thought I could create a lineCollection instance of the map boundaries and overlay the two images. Problem is - I can't seem to get the two to line up correctly.
Here is the code, where linecol is the lineCollection object.
fig = plt.figure()
ax = fig.add_subplot(111)
ax.contourf(xi,yi,zi)
ax.add_collection(linecol, autolim = False)
plt.show()
Is there an easy way to fix the limits of linecol to match those of the other plot? I've had a play with set_xlim and transforms.Bbox, but can't seem to manage it.
Thank you very much for your help!

Transforms are tricky because of the various coordinate systems involved. See http://matplotlib.sourceforge.net/users/transforms_tutorial.html.
I managed to scale a LineCollection to the appropriate size like this. The key was to realize that I needed to add + ax.transData to the new transform I set on the LineCollection. (When you don't set any transform on an artist object, ax.transData is the default. It converts data coordinates into display coordinates.)
from matplotlib import cm
import matplotlib.pyplot as plt
import matplotlib.collections as mc
import matplotlib.transforms as tx
import numpy as np
fig = plt.figure()
# Heat map spans 1 x 1.
ax = fig.add_subplot(111)
xs = ys = np.arange(0, 1.01, 0.01)
zs = np.random.random((101,101))
ax.contourf(xs, ys, zs, cmap=cm.autumn)
lines = mc.LineCollection([[(5,1), (9,5), (5,9), (1,5), (5,1)]])
# Shape spans 10 x 10. Resize it to 1 x 1 before applying the transform from
# data coords to display coords.
trans = tx.Affine2D().scale(0.1) + ax.transData
lines.set_transform(trans)
ax.add_collection(lines)
plt.show()
(Output here: http://i.stack.imgur.com/hDNN8.png Not enough reputation to post inline.)
It should be easy to modify this if you need the shape translated or scaled unequally on x and y.

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