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saudzahirr merged 1 commit into from
Apr 18, 2026
Merged

Enhance README and index documentation formatting for clarity #32

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4 changes: 3 additions & 1 deletion README.md
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@@ -1,4 +1,6 @@
# ``mcerp`` Real-time latin-hypercube sampling-based Monte Carlo ERror Propagation for Python
# mcerp

**Real-time latin-hypercube sampling-based Monte Carlo ERror Propagation for Python**

[![Tests](https://github.com/eggzec/mcerp/actions/workflows/code_test.yml/badge.svg)](https://github.com/eggzec/mcerp/actions/workflows/code_test.yml)
[![Documentation](https://github.com/eggzec/mcerp/actions/workflows/docs_build.yml/badge.svg)](https://github.com/eggzec/mcerp/actions/workflows/docs_build.yml)
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4 changes: 2 additions & 2 deletions docs/index.md
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Expand Up @@ -2,8 +2,8 @@

![MCERP](./_static/logo.png)

Real-time latin-hypercube-sampling-based Monte Carlo error propagation for
Python.
**Real-time latin-hypercube-sampling-based Monte Carlo error propagation for
Python.**

`mcerp` is a stochastic calculator for uncertainty analysis. It represents
inputs as probability distributions, samples them with Latin hypercube
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12 changes: 4 additions & 8 deletions docs/quickstart.md
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Expand Up @@ -11,9 +11,9 @@ from mcerp import *
Construct uncertain variables from probability distributions:

```python
x1 = N(24, 1) # normal distribution: mean 24, standard deviation 1
x2 = N(37, 4) # normal distribution: mean 37, standard deviation 4
x3 = Exp(2) # exponential distribution: lambda 2
x1 = N(24, 1) # normal distribution: mean 24, standard deviation 1
x2 = N(37, 4) # normal distribution: mean 37, standard deviation 4
x3 = Exp(2) # exponential distribution: lambda 2
```

The first four moments are available as properties:
Expand Down Expand Up @@ -227,11 +227,7 @@ plotcorr([x1, x2, x3], labels=["x1", "x2", "x3"], show=True)
Now impose a target correlation matrix:

```python
c = np.array([
[1.0, -0.75, 0.0],
[-0.75, 1.0, 0.0],
[0.0, 0.0, 1.0],
])
c = np.array([[1.0, -0.75, 0.0], [-0.75, 1.0, 0.0], [0.0, 0.0, 1.0]])

correlate([x1, x2, x3], c)
correlation_matrix([x1, x2, x3])
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6 changes: 1 addition & 5 deletions docs/theory.md
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Expand Up @@ -387,11 +387,7 @@ x1 = N(24, 1)
x2 = N(37, 4)
x3 = Exp(2)

R = np.array([
[1.0, -0.75, 0.0],
[-0.75, 1.0, 0.0],
[0.0, 0.0, 1.0],
])
R = np.array([[1.0, -0.75, 0.0], [-0.75, 1.0, 0.0], [0.0, 0.0, 1.0]])

correlate([x1, x2, x3], R)

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