We Keep Coding

Python plotting clearing the graph in for loop

I am plotting within a for loop. So that I get a new graph each iteration. Of course I want to clear the graph from the previous iteration. When I use plt.cla() the axis labels and title is also cleared. How can I just remove the graph but keep the axis labels and title?
for n in range(N):
ax.plot(x[n],t) # plot
plt.savefig(f'fig_{n}.png') # save the plot
plt.cla()

Try plt.clf() - clear figure
for n in range(N):
ax.plot(x[n],t) # plot
plt.savefig(f'fig_{n}.png') # save the plot
plt.clf()

Remove lines at the bottom of the loop.
from matplotlib import pyplot as plt
import numpy as np
y = np.arange(10)
fig,ax = plt.subplots()
ax.set_title('foo')
ax.set_ylabel('wye')
ax.set_xlabel('EX')
for n in range(3):
ln, = ax.plot(y*n)
fig.savefig(f'fig{n}')
ln.remove()

Plots not visible when using a line plot

I am new to python and I am trying to plot x and y (both have a large number of data) but when I use a plt.plot there is not plot visible on the output.
The code I have been using is
for i in range(len(a)):
plt.plot(a[i],b[i])
plt.figure()
plt.show()
when I tried a scatter plot
for i in range(len(a)):
plt.scatter(a[i],b[i])
plt.figure()
plt.show()
I am not able to understand the reason for missing the line plot and even when I try seaborn it showing me an error ValueError: If using all scalar values, you must pass an index

import numpy as np
import matplotlib.pyplot as plt
a = np.linspace(0,5,100)
b = np.linspace(0,10,100)
plt.plot(a,b)
plt.show()
I think this answers your question. I have taken sample values of a and b. The matplotlib line plots are not required to run in loops

A line is created between two points. If you are plotting single values, a line can't be constructed.
Well, you might say "but I am plotting many points," which already contains part of the answer (points). Actually, matplotlib.plot() plots line-objects. So every time, you call plot, it creates a new one (no matter if you are calling it on the same or on a new axis). The reason why you don't get lines is that only single points are plotted. The reason why you're not even seeing the these points is that plot() does not indicate the points with markers per default. If you add marker='o' to plot(), you will end up with the same figure as with scatter.
A scatter-plot on the other hand is an unordered collection of points. There characteristic is that there are no lines between these points because they are usually not a sequence. Nonetheless, because there are no lines between them, you can plot them all at once. Per default, they have all the same color but you can even specify a color vector so that you can encode a third information in it.
import matplotlib.pyplot as plt
import numpy as np
# create random data
a = np.random.rand(10)
b = np.random.rand(10)
# open figure + axes
fig,axs = plt.subplots(1,2)
# standard scatter-plot
axs[0].scatter(a,b)
axs[0].set_title("scatter plot")
# standard line-plot
axs[1].plot(a,b)
axs[1].set_title("line plot")

Show only the last plot in Python using MatPlotLib

I am using Python 3.5 with MatPlotLib package. My problem is as follows:
I generate, say 50 plots, which I save each to a PNG file. Then I generate 2 summarizing plots, which I want to both save and show on display. However, when I use the plt.show() command, it also shows all the previous 50 plots, which I don't want to display, just save them. How to suppress the show on these previous 50 plots and show only the last one?
Here is an example code:
import matplotlib.pyplot as plt
import numpy as np
for i in range(50):
fig = plt.figure()
plt.plot(np.arange(10),np.arange(10)) # just plot something
plt.savefig(f"plot_{i}.png")
# I want to save these plots but not show them
# summarizing plot
fig = plt.figure()
plt.plot(np.arange(100),np.arange(100))
plt.show() # now it shows this fig and the previous 50 figs, but I want only to show this one!

Close all after the loop:
plt.close("all") #this is the line to be added
fig = plt.figure()
plt.plot(np.arange(100),np.arange(100))
plt.show()

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

matplotlib plotting multiple lines in 3D

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.