I have an array containing 5 different numbers:
array([2.40064633, 4.10132553, 8.59968518, 2.40290345, 1.39988773]
and I want to plot the lines on the x axis (parallel to the y axis) equal to each of these numbers i.e.
x = 2.4006463
x = 4.10132553 so on and so forth for all of the numbers in the array.
I tried using plot(x = array[...]) but to no solution.
Is there a clean way of doing this using numpy or mathlab?
This will work:
import matplotlib.pyplot as plt
b =([2.40064633, 4.10132553, 8.59968518, 2.40290345, 1.39988773])
for l in b:
plt.axvline(l)
plt.show()
or is it an numpy array then:
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(1,4)
for l in x:
plt.axvline(l)
plt.show()
here is my take. quite the similar as Rahul's only with the lines harshed.
import matplotlib.pyplot as plt
import numpy as np
xcoords = np.array([2.40064633, 4.10132553, 8.59968518, 2.40290345, 1.39988773])
for xc in xcoords:
plt.axvline(x=xc, color='k', linestyle='--')
Related
I have some lists that each of which has a different shape and I would like to plot all of them together in one polar scatter plot. I also tried to use iter tools but I could not find the solution.
import numpy as np
import matplotlib.pyplot as plt
a1=[1,2,3,4,5,6]
a2=[2,3,5,6]
a3=[1,2,3]
a4=[1,2,3,4,4,56,7,8]
ax1 = plt.subplot(111,polar= True)
for i in range (0,3):
theta = 4 * np.pi * np.random.rand(len(a[i]))
ax1.set_ylim(0,0.1)
ax1.set_rlabel_position(180)
for i in range (0,3):
ax1.scatter(theta,a[i], cmap='hsv', alpha=0.5)
Be carefull i modified your lists for a better visual exmaple!
I hope I understood your question correctly...
import numpy as np
import matplotlib.pyplot as plt
a1=[1,2,3,4,5,6]
a2=[2,3,5,6]
a3=[1,2,3]
a4=[1,2,3,4,4,7,7,8]
ax1 = plt.subplot(111,polar= True)
for onelist in [a1,a2,a3,a4]:
theta_list = np.linspace(0,2*np.pi,len(onelist))
ax1.plot(theta_list,onelist,marker="x")
plt.show()
I have numpy array with values 0,1,2. I want to separate them in different arrays and plot them. How can I do that?
for i in range(2):
if i==0
z = [i]
elif i==1
y = [i]
else
w = [i]
this is what i tried
just use the histogram function from pyplot
import numpy as np
import matplotlib.pyplot as plt
y = np.random.randint(0,3,100)
plt.hist(y)
plt.show()
I have pandas series of complex numbers, which I would like to plot. Currently, I am looping through each point and assigning it a color. I would prefer to generate the plot without the need to loop over each point... Using Series.plot() would be preferable. Converting series to numpy is ok though.
Here is an example of what I currently have:
import pandas as pd
import numpy as np
from matplotlib import pyplot
s = pd.Series((1+np.random.randn(500)*0.05)*np.exp(1j*np.linspace(-np.pi, np.pi, 500)))
cmap = pyplot.cm.viridis
for i, val in enumerate(s):
pyplot.plot(np.real(val), np.imag(val), 'o', ms=10, color=cmap(i/(len(s)-1)))
pyplot.show()
You can use pyplot.scatter, which allows coloring of points based on a value.
pyplot.scatter(np.real(s), np.imag(s), s=50, c=np.arange(len(s)), cmap='viridis')
Here, we set c to an increasing sequence to get the same result as in the question.
You can simply plot the real and imaginary part of the series without a loop.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
s = pd.Series((1+np.random.randn(500)*0.05)*np.exp(1j*np.linspace(-np.pi, np.pi, 500)))
plt.plot(s.values.real,s.values.imag, marker="o", ls="")
plt.show()
However, you need to use a scatter plot if you want to have different colors:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
s = pd.Series((1+np.random.randn(500)*0.05)*np.exp(1j*np.linspace(-np.pi, np.pi, 500)))
plt.scatter(s.values.real,s.values.imag, c = range(len(s)), cmap=plt.cm.viridis)
plt.show()
I am using the below codes to quantise the input signal for quantisation interval of 0.5 and this should give me staircase signal.The algorithm used here is same as used in Simulink.Could any one help me plot the quantised signal.
import numpy as np
import matplotlib.pyplot as plt
for i in range(0,10):
q=0.5;
x=q*np.round(i/q);
plt.plot(i,x)
plt.xlim([0,10])
plt.ylim([0,10])
plt.hold()
plt.grid()
plt.show()
Do you mean something like this?
import numpy as np
import matplotlib.pyplot as plt
q = 0.5
x = np.linspace(0, 10, 1000)
y = q * np.round(x/q)
plt.plot(x,y)
I want to plot ECG graph using matplotlib . y values from a file having float values and x value is incrementing(ie x ranges from 1 to 1000). went through tutorials and couldn't find any solutions.
Demo Code
import numpy as np
import matplotlib.pyplot as plt
import random
import pickle
#Y Axis : Generate 1000 random numbers
yAxisNumbers = np.random.uniform(1,100,1000)
#Save numbers to a file for demo purpose
with open('numpyData.txt', 'wb') as myFile:
pickle.dump(yAxisNumbers,myFile)
#X Axis :Generate 1000 random numbers
xNumbers = [ x for x in range(1000)]
#Load file data to a list
with open('numpyData.txt', 'rb') as aFile:
yNumbers = pickle.load(aFile)
#Plot and label Graph
plt.plot(xNumbers,yNumbers)
plt.ylabel("Random Float Numbers")
plt.xlabel("Number Count")
plt.title("ECG Graph")
plt.show()
Graph
Here's a minimal answer, based on the scant details provided.
import numpy as np
import matplotlib.pyplot as plt
plt.ion()
Y = np.loadtxt(filename, other needed options)
plt.plot(np.arange(len(Y))+1,Y)
import numpy as np
import pylab as p
aa=np.loadtxt('....your file ....')
x,y= aa.T # transpose data into 2 columns, assuming you have 2 columns
p.plot(x,y)
p.show()