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Add pyproject.toml to make project pip installable #5

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b781705
Add pyproject.toml to make project pip installable
kmdouglass Jan 17, 2023
f7f4713
Fix minor errors in the code and README
kmdouglass Jan 17, 2023
864d51e
Add pyproject.toml to make project pip installable
kmdouglass Jan 17, 2023
d771549
Merge branch 'fix-minor-errors'
kmdouglass Apr 24, 2023
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2 changes: 1 addition & 1 deletion README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ Below are some examples of using the \MaxEnt software. Starting with an example
from pymaxent import *
mu = [1,3.5]
x = [1,2,3,4,5,6]
sol, lambdas = reconstruct(mu,ivars=x)
sol, lambdas = reconstruct(mu,rndvar=x)
```

Similarly, for a continuous distribution, one passes a list of input moments.
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18 changes: 18 additions & 0 deletions pyproject.toml
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@@ -0,0 +1,18 @@
[build-system]
requires = ["setuptools"]
build-backend = "setuptools.build_meta"

[project]
name = "PyMaxEnt"
version = "1.0.0"
authors = [
{name = "Tony Saad", email = "tony.saad@chemeng.utah.edu"},
{name = "Giovanna Ruai"},
]
license = { text = "MIT" }
description = "Implements a maximum entropy reconstruction of distributions with known moments."

dependencies = [
"numpy",
"scipy",
]
8 changes: 3 additions & 5 deletions src/pymaxent.py
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Expand Up @@ -56,7 +56,7 @@ def moments_d(f,k,x):
for i in range(0,k):
xp = np.power(x,i) # compute x^p
xpf = np.dot(xp,f) # compute x^p * f(x)
mom.append(np.sum(xpf)) # compute moment: sum(x^p * f(x))
moms.append(np.sum(xpf)) # compute moment: sum(x^p * f(x))
return np.array(moms)

def moments(f, k, rndvar=None, bnds=None):
Expand Down Expand Up @@ -101,14 +101,13 @@ def integrand(x, lamb, k=0, discrete=False):
else:
return x**k * np.exp(np.dot(lamb, xi))

def residual_d(lamb,x,k,mu):
def residual_d(lamb,x,mu):
'''
Calculates the residual of the moment approximation function.

Parameters:
lamb (array): an array of Lagrange constants used to approximate the distribution
x (array):
k (integer): order of the moment
mu (array): an array of the known moments needed to approximate the distribution function

Returns:
Expand All @@ -133,8 +132,7 @@ def maxent_reconstruct_d(rndvar, mu):
'''
lambguess = np.zeros(len(mu))
lambguess[0] = -np.log(np.sqrt(2*np.pi))
k = len(mu)
lambsol = fsolve(residual_d, lambguess, args = (rndvar,k,mu))
lambsol = fsolve(residual_d, lambguess, args = (rndvar,mu))
probabilites = integrand(rndvar, lambsol, k=0, discrete=True)
return probabilites, lambsol

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