I am supposed to use python to solve a Matrix A^1000. I have tried numpy's built in matrix_power function to compute the output, but when I try to compute the result step by step according to the formula P*(D^1000) *P^-1 , I get an incomplete result. I am trying to figure out if my precision options are just too tight or if I am doing something else wrong completely
I have tried the matrix_power function to definitely get the result I want. But I need to be able to show how the calculation is done, and when I do so, I get an incomplete result
import numpy as np
np.set_printoptions(precision=6) # set the precision of the output
np.set_printoptions(suppress=True) # suppress the use of scientific notation
from numpy import diag, allclose, corrcoef
from numpy.random import randint, randn
from numpy.linalg import eig, matrix_rank, inv, cholesky, qr, norm, matrix_power
from sympy import Matrix, init_printing, matrix2numpy
A = np.array([[0.9,0.15,0.25],[0.075,0.8,0.25],[0.025,0.05,0.5]])
A
#python way
A_1000 = matrix_power(A,1000)
A_1000
D , P = eig(A)
P * np.diag(D**1000) * np.linalg.inv(p)
Related
I have wrote this code to solve an equation , I know the behavior of this function has very rapid oscillations, when I RUN it gives bogus values for some "m[x]" and some "t"'s, with this error:
C:\Users\dani\anaconda3\lib\site-packages\scipy\integrate\odepack.py:247: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.
warnings.warn(warning_msg, ODEintWarning)
I don't know what is the problem.
how can I get correct results? or at least as accurate as possible? or maybe I should rewrite the code in another form?
thank you.
import scipy as sio
import numpy as np
import mpmath as mp
import scipy.integrate as spi
import matplotlib.pyplot as plt
import time
from scipy.integrate import quad
initial_value=np.logspace(24,27,100)
t=np.logspace(-20,6,100)
m=np.logspace(0,6,100)
start_time=time.perf_counter()
phi_m={}
phi_m_prime={}
phi=[]
phi_prime=[]
j=0
i=np.pi*2.435*initial_value[0]
while i<(np.pi*(2.435*10**(27))):
i=np.pi*2.435*initial_value[j]
phi=[]
phi_prime=[]
for x in range (len(m)):
def dzdt(z,T):
return [z[1], -3*1.4441*(10**(-6))*m[x]*np.sqrt(0.69)*(mp.coth(1.5*np.sqrt(0.69)*(10**(-6))*1.4441*m[x]*T))*z[1] - z[0]]
z0 = [i,0]
ts = t/m[x]
zs = spi.odeint(dzdt, z0, ts)
phi.append(zs[99,0])
phi_prime.append(zs[99,1])
phi_m[j]=phi
phi_m_prime[j]=phi_prime
j+=1
end_time=time.perf_counter()
print(end_time-start_time,"seconds")
When I run this code in Python 3，why it shows name “lags” not defined？
from numpy import log,sqrt,std,subtract,cumsum,polyfit
def hurst(ts):
# Create the range of lag values
lags = range(2, 100)
# Calculate the array of the variances of the lagged differences
tau = [sqrt(std(subtract(ts[lag:], ts[:-lag]))) for lag in lags] # This line throws the Error
# Use a linear fit to estimate the Hurst Exponent
poly = polyfit(log(lags), log(tau), 1)
# Return the Hurst exponent from the polyfit output
return poly[0]*2.0
It looks like you didnt import numpy correctly.
Add this to the top of your code:
import numpy as np
and then change all calls to be:
np.polyfit(...)
np.sqrt(...)
or if you only want to import the ones you need:
from numpy import log, sqrt, std, subtract, cumsum, polyfit
I wish to perform a fourier transform of the function 'stress' from 0 to infinity and extract the real and imaginary parts. I have the following code that does it using a numerical integration technique:
import numpy as np
from scipy.integrate import trapz
import fileinput
import sys,string
window = 200000 # length of the array I wish to transform (number of data points)
time = np.linspace(1,window,window)
freq = np.logspace(-5,2,window)
output = [0]*len(freq)
for index,f in enumerate(freq):
visco = trapz(stress*np.exp(-1j*f*t),t)
soln = visco*(1j*f)
output[index] = soln
print 'f storage loss'
for i in range(len(freq)):
print freq[i],output[i].real,output[i].imag
This gives me a nice transformation of my input data.
Now I have an array of size 2x10^6, and using the above technique is not feasible(computation time scales as O(N^2)), so I have turned to the inbuilt fft function in numpy.
There aren't too many arguments that you can specify to change this function, and so I'm finding it difficult to customize it to my needs.
So far I have
import numpy as np
import fileinput
import sys, string
np.set_printoptions(threshold='nan')
N = len(stress)
fvi = np.fft.fft(stress,n=N)
gprime = fvi.real
gdoubleprime = fvi.imag
for i in range(len(stress)):
print gprime[i], gdoubleprime[i]
And it's not giving me accurate results.
The DFT in python is of the form A_k = summation(a_m * exp(-2*piimk/n)) where the summation is from m = 0 to m = n-1 (http://docs.scipy.org/doc/numpy-1.10.1/reference/routines.fft.html). How can I change it to the form that I have mentioned in my first code, i.e. exp(-1jfreq*t) (freq is the frequency and t is the time which have already been predefined)? Or is there a post processing of the data that I have to do?
Thanks in advance for all your help.
Here is my first steps within the NumPy world.
As a matter of fact the target is plotting below 2-D function as a 3-D mesh:
N = \frac{n}{2\sigma\sqrt{\pi}}\exp^{-\frac{n^{2}x^{2}}{4\sigma^{2}}}
That could been done as a piece a cake in Matlab with below snippet:
[x,n] = meshgrid(0:0.1:20, 1:1:100);
mu = 0;
sigma = sqrt(2)./n;
f = normcdf(x,mu,sigma);
mesh(x,n,f);
But the bloody result is ugly enough to drive me trying Python capabilities to generate scientific plots.
I searched something and found that the primary steps to hit above mark in Pyhton might be acquired by below snippet:
from matplotlib.patches import Polygon
import numpy as np
from scipy.integrate import quad
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
sigma = 1
def integrand(x,n):
return (n/(2*sigma*np.sqrt(np.pi)))*np.exp(-(n**2*x**2)/(4*sigma**2))
t = np.linespace(0, 20, 0.01)
n = np.linespace(1, 100, 1)
lower_bound = -100000000000000000000 #-inf
upper_bound = t
tt, nn = np.meshgrid(t,n)
real_integral = quad(integrand(tt,nn), lower_bound, upper_bound)
Axes3D.plot_trisurf(real_integral, tt,nn)
Edit: With due attention to more investigations on Greg's advices, above code is the most updated snippet.
Here is the generated exception:
RuntimeError: infinity comparisons don't work for you
It is seemingly referring to the quad call...
Would you please helping me to handle this integrating-plotting problem?!...
Best
Just a few hints to get you in the right direction.
numpy.meshgrid can do the same as MatLABs function:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.meshgrid.html
When you have x and n you can do math just like in matlab:
sigma = numpy.sqrt(2)/n
(in python multiplication/division is default index by index - no dot needed)
scipy has a lot more advanced functions, see for example How to calculate cumulative normal distribution in Python for a 1D case.
For plotting you can use matplotlibs pcolormesh:
import matplotlib.pyplot as plt
plt.pcolormesh(x,n,real_integral)
Hope this helps until someone can give you a more detailed answer.
I intend for part of a program I'm writing to automatically generate Gaussian distributions of various statistics over multiple raw text sources, however I'm having some issues generating the graphs as per the guide at:
python pylab plot normal distribution
The general gist of the plot code is as follows.
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as pyplot
meanAverage = 222.89219487179491 # typical value calculated beforehand
standardDeviation = 3.8857889432054091 # typical value calculated beforehand
x = np.linspace(-3,3,100)
pyplot.plot(x,mlab.normpdf(x,meanAverage,standardDeviation))
pyplot.show()
All it does is produce a rather flat looking and useless y = 0 line!
Can anyone see what the problem is here?
Cheers.
If you read documentation of matplotlib.mlab.normpdf, this function is deprycated and you should use scipy.stats.norm.pdf instead.
Deprecated since version 2.2: scipy.stats.norm.pdf
And because your distribution mean is about 222, you should use np.linspace(200, 220, 100).
So your code will look like:
import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as pyplot
meanAverage = 222.89219487179491 # typical value calculated beforehand
standardDeviation = 3.8857889432054091 # typical value calculated beforehand
x = np.linspace(200, 220, 100)
pyplot.plot(x, norm.pdf(x, meanAverage, standardDeviation))
pyplot.show()
It looks like you made a few small but significant errors. You either are choosing your x vector wrong or you swapped your stddev and mean. Since your mean is at 222, you probably want your x vector in this area, maybe something like 150 to 300. This way you get all the good stuff, right now you are looking at -3 to 3 which is at the tail of the distribution. Hope that helps.
I see that, for the *args which are sending meanAverage, standardDeviation, the correct thing to be sent is:
mu : a numdims array of means of a
sigma : a numdims array of atandard deviation of a
Does this help?