### Get Current OMP Threads

Source: https://rmjarvis.github.io/TreeCorr

Get the current number of OpenMP threads being used by TreeCorr.

```python
import treecorr
print(treecorr.get_omp_threads())

```

--------------------------------

### Run Corr3 with Python API

Source: https://rmjarvis.github.io/TreeCorr

Execute the `corr3` function using the Python API. This allows for the calculation of three-point correlation functions.

```python
import treecorr
# Example usage with dummy data
cat1 = treecorr.Catalog(x=[1,2,3], y=[4,5,6], g1=[0.1,0.2,0.3], g2=[0.3,0.2,0.1])
cat2 = treecorr.Catalog(x=[7,8,9], y=[1,2,3], g1=[0.1,0.2,0.3], g2=[0.3,0.2,0.1])
cat3 = treecorr.Catalog(x=[4,5,6], y=[7,8,9], g1=[0.1,0.2,0.3], g2=[0.3,0.2,0.1])

# NNN correlation
nn = treecorr.NNNCorrelation(nbins=10, min=0.1, max=10.0)
nn.process(cat1, cat2, cat3)
print(f"NNN correlation: {nn.mean:.3f} +/- {nn.sigma:.3f}")

# GGG correlation
ggg = treecorr.GGGCorrelation(nbins=10, min=0.1, max=10.0)
ggg.process(cat1, cat2, cat3)
print(f"GGG correlation: {ggg.mean:.3f} +/- {ggg.sigma:.3f}")

```

--------------------------------

### Run Corr2 with Python API

Source: https://rmjarvis.github.io/TreeCorr

Execute the `corr2` function using the Python API. This is an alternative to using the command-line executable.

```python
import treecorr
# Example usage with dummy data
cat1 = treecorr.Catalog(x=[1,2,3], y=[4,5,6], g1=[0.1,0.2,0.3], g2=[0.3,0.2,0.1])
cat2 = treecorr.Catalog(x=[7,8,9], y=[1,2,3], g1=[0.1,0.2,0.3], g2=[0.3,0.2,0.1])

# NN correlation
nn = treecorr.NNCorrelation(nbins=10, min=0.1, max=10.0)
nn.process(cat1, cat2)
print(f"NN correlation: {nn.mean:.3f} +/- {nn.sigma:.3f}")

# GG correlation
gg = treecorr.GGCorrelation(nbins=10, min=0.1, max=10.0)
gg.process(cat1, cat2)
print(f"GG correlation: {gg.mean:.3f} +/- {gg.sigma:.3f}")

```

--------------------------------

### Build Multi-Covariance Design Matrix

Source: https://rmjarvis.github.io/TreeCorr

Build the design matrix for estimating the covariance of multiple correlation functions. This is a prerequisite for using `estimate_multi_cov`.

```python
import treecorr
# Assuming 'corr' is a pre-defined Corr2 object
design_matrix = corr.build_multi_cov_design_matrix()

```

--------------------------------

### Estimate Multi-Covariance Matrix

Source: https://rmjarvis.github.io/TreeCorr

Estimate the covariance matrix for multiple correlation functions. This function is useful for understanding the statistical uncertainties in correlation measurements.

```python
import treecorr
# Assuming 'corr' is a pre-defined Corr2 object
cov = corr.estimate_multi_cov()

```

--------------------------------

### Set Maximum OMP Threads

Source: https://rmjarvis.github.io/TreeCorr

Set the maximum number of OpenMP threads that can be used by TreeCorr. This should be called before any other TreeCorr functions.

```python
import treecorr
treecorr.set_max_omp_threads(4)

```

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