Im trying to understand How to use loop and range function in python( matplotlib package ) to visualize the cdf of the Poisson distribution in a single density plot??
𝜆 = 3,4,5,6,7
import numpy as np
import matplotlib.pyplot as plt
plt.gcf().set_size_inches(12,6)
for l in range(3,8,1):
seq = np.arrange(0,20)
How can i build the rest of the code ?
import numpy as np
You define the range of values to calculate the cdf outside the loop, and for every iteration, you have the values as x, and the cdf as y:
import matplotlib.pyplot as plt
from scipy.stats import poisson
Seq = np.arange(0,20)
plt.gcf().set_size_inches(12,6)
for l in range(3,8,1):
plt.plot(Seq,poisson.cdf(Seq,l),label=l)
plt.legend(loc="lower right")
Related
Want to plot normalised values in array but getting empty plot
import numpy as np
x_array = np.array([2,3,5,6,7,4,8,7,6])
normalized_arr = preprocessing.normalize([x_array])
print(normalized_arr)
plt.plot(normalized_arr)
plt.show()
Empty plot - https://i.stack.imgur.com/NnSbI.png
Is there function that can fill the empty plot with values?
You probably need to change your code into:
import numpy as np
import matplotlib.pyplot as plt
from sklearn import preprocessing
x_array = np.array([2,3,5,6,7,4,8,7,6])
normalized_arr = preprocessing.normalize([x_array])
print(normalized_arr)
plt.plot(x_array.reshape(-1,1),normalized_arr.reshape(-1,1))
plt.show()
Output
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)
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.mlab as mlab`
mu = np.loadtxt('my_data/corr.txt')
d = mu[:,2]
y=[]
tot=0
min=999
for i in d:
y.append(float(i))
tot=tot+float(i)
if (min>float(i)):
min=float(i)
av=tot/len(y)
z=[]
m=[]
for i in y:
z.append(i-av)
m.append(i-min)
plt.acorr(z,usevlines=True,maxlags=None,normed=True)
plt.show()
WIth this code I have the output showing a bar chart.
Now,
1) How do I change this plot style to give just the trend line? I cant modify the line properties by any means.
2) How do I write this output data to a dat or txt file?
this should be a working minimal example:
import matplotlib.pyplot as plt
import numpy as np
from numpy.random import normal
data = normal(0, 1, 1000)
# return values are lags, correlation vector and the drawn line
lags, corr, line, rest = plt.acorr(data, marker=None, linestyle='-', color='red', usevlines=False)
plt.show()
np.savetxt("correlations.txt", np.transpose((lags, corr)), header='Lags\tCorrelation')
But i would recommand not to connect the points.
I have a signal that is not sampled equidistant; for further processing it needs to be. I thought that scipy.signal.resample would do it, but I do not understand its behavior.
The signal is in y, corresponding time in x.
The resampled is expected in yy, with all corresponding time in xx. Does anyone know what I do wrong or how to achieve what I need?
This code does not work: xx is not time:
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
x = np.array([0,1,2,3,4,5,6,6.5,7,7.5,8,8.5,9])
y = np.cos(-x**2/4.0)
num=50
z=signal.resample(y, num, x, axis=0, window=None)
yy=z[0]
xx=z[1]
plt.plot(x,y)
plt.plot(xx,yy)
plt.show()
Even when you give the x coordinates (which corresponds to the t argument), resample assumes that the sampling is uniform.
Consider using one of the univariate interpolators in scipy.interpolate.
For example, this script:
import numpy as np
from scipy import interpolate
import matplotlib.pyplot as plt
x = np.array([0,1,2,3,4,5,6,6.5,7,7.5,8,8.5,9])
y = np.cos(-x**2/4.0)
f = interpolate.interp1d(x, y)
num = 50
xx = np.linspace(x[0], x[-1], num)
yy = f(xx)
plt.plot(x,y, 'bo-')
plt.plot(xx,yy, 'g.-')
plt.show()
generates this plot:
Check the docstring of interp1d for options to control the interpolation, and also check out the other interpolation classes.