### Run Persistent Path Homology via CLI

Source: https://github.com/weilabmsu/pathhom/blob/main/README.md

Execute persistent path homology calculations using distance or angle-based filtration from the command line.

```bash
# dis-based
python "./code/pathhomology.py" --input_type cloudpoints --input_data "./data/B12N12_cage.csv" --filtration_type distance --max_distance 5 --save_name 'betti_numbers_dppt.npy' --max_path 2
# angle-based
python "./code/pathhomology.py" --input_type cloudpoints --input_data "./data/B12N12_cage.csv" --filtration_type angle --save_name 'betti_numbers_dppt.npy' --max_path 2
```

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

### Compute Basic Path Homology

Source: https://github.com/weilabmsu/pathhom/blob/main/README.md

Calculate Betti numbers from a set of nodes and edges using the PathHomology class.

```python
import numpy as np
from pathhomology import PathHomology

nodes = np.array([1, 2, 3, 4, 5, 6])
edges = np.array(
    [
        [1, 2],
        [3, 4],
        [3, 2],
        [5, 2],
        [5, 1],
    ]
)
max_path = 2
betti_num = PathHomology().path_homology(edges, nodes, max_path)
print(f'Betti numbers for 0 to max {max_path} path: {betti_num}')
```

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

### Compute Persistent Path Homology in Python

Source: https://github.com/weilabmsu/pathhom/blob/main/README.md

Calculate persistent path homology from cloudpoints or predefined digraphs using the PathHomology API.

```python

# From cloudpoints
import numpy as np
from pathhomology import PathHomology
max_path = 2
cloudpoints = np.array([
    [-1.1, 0, 0],
    [1.2, 0, 0],
    [0, -1.3, 0],
    [0, 1.4, 0],
    [1.5, 0, 0],
    [-1.6, 0, 0]
])
points_weight = [1, 2, 9, 4, 5, 6]
betti_nums_dppt = PathHomology().persistent_path_homology(
        cloudpoints, points_weight, max_path, filtration=None)
betti_nums_appt = PathHomology().persistent_angle_path_homology(
        cloudpoints, points_weight, max_path)

# #############

# From digraph (predefined digraph)
all_edges = np.array([
    [1, 3, 4, 4, 5, 5, 3, 5, 6, 2, 3, 4],
    [2, 2, 3, 1, 1, 2, 5, 4, 1, 6, 6, 6],
]).T - 1
betti_num_appt_digraph = PathHomology().persistent_angle_path_homology_from_digraph(
    cloudpoints, all_edges, max_path, filtration_angle_step=30)

filtration_for_dis_filtration = np.arange(0, 5, 0.2)
betti_num_dppt_digraph = PathHomology().persistent_path_homology_from_digraph(
        cloudpoints, all_edges, max_path, filtration=filtration_for_dis_filtration)
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.