Open Mining Format

https://omf.readthedocs.io/en/latest/
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The Open Mining Format (OMF) is an API library that provides an open-source serialization format and...
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### Install OMF using pip

Source: https://omf.readthedocs.io/en/latest/index.html

Install the OMF library using pip. This command installs the pre-release version.

```bash
pip install --pre omf

```

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

### Install OMF from GitHub

Source: https://omf.readthedocs.io/en/latest/index.html

Clone the OMF repository from GitHub and install it locally using pip. This is useful for development or to use the latest unreleased code.

```bash
git clone https://github.com/gmggroup/omf.git
cd omf
pip install -e .

```

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

### Build OMF Project Elements All at Once

Source: https://omf.readthedocs.io/en/latest/content/examples.html

This snippet demonstrates how to create and configure multiple OMF element types within a single project. It includes setting up sample files, defining points, lines, surfaces, and volumetric data with associated attributes and textures, and finally saving the project.

```python
import datetime
import numpy as np
import os
import png
import omf

# setup sample files
dir = os.getcwd()
png_file = os.path.join(dir, "example.png")
omf_file = os.path.join(dir, "example.omf")
for f in (png_file, omf_file):
    if os.path.exists(f):
        os.remove(f)
img = ["110010010011", "101011010100", "110010110101", "100010010011"]
img = [[int(val) for val in value] for value in img]
writer = png.Writer(len(img[0]), len(img), greyscale=True, bitdepth=16)
with open(png_file, "wb") as file:
    writer.write(file, img)

proj = omf.Project(
    name="Test project",
    description="Just some assorted elements",
)
pts = omf.PointSet(
    name="Random Points",
    description="Just random points",
    vertices=np.random.rand(100, 3),
    attributes=[
        omf.NumericAttribute(
            name="rand attr",
            array=np.random.rand(100),
            location="vertices",
        ),
        omf.NumericAttribute(
            name="More rand attr",
            array=np.random.rand(100),
            location="vertices",
        ),
    ],
    textures=[
        omf.ProjectedTexture(
            name="test image",
            image=png_file,
            origin=[0, 0, 0],
            axis_u=[1, 0, 0],
            axis_v=[0, 1, 0],
        ),
        omf.ProjectedTexture(
            name="test image",
            image=png_file,
            origin=[0, 0, 0],
            axis_u=[1, 0, 0],
            axis_v=[0, 0, 1],
        ),
    ],
    metadata={
        "color": "green",
    },
)
lin = omf.LineSet(
    name="Random Line",
    vertices=np.random.rand(100, 3),
    segments=np.floor(np.random.rand(50, 2) * 100).astype(int),
    attributes=[
        omf.NumericAttribute(
            name="rand vert attr",
            array=np.random.rand(100),
            location="vertices",
        ),
        omf.NumericAttribute(
            name="rand segment attr",
            array=np.random.rand(50),
            location="segments",
        ),
    ],
    metadata={
        "color": "#0000FF",
    },
)
surf = omf.Surface(
    name="trisurf",
    vertices=np.random.rand(100, 3),
    triangles=np.floor(np.random.rand(50, 3) * 100).astype(int),
    attributes=[
        omf.NumericAttribute(
            name="rand vert attr",
            array=np.random.rand(100),
            location="vertices",
        ),
        omf.NumericAttribute(
            name="rand face attr",
            array=np.random.rand(50),
            location="faces",
        ),
    ],
    metadata={
        "color": [100, 200, 200],
    },
)
grid = omf.TensorGridSurface(
    name="gridsurf",
    tensor_u=np.ones(10).astype(float),
    tensor_v=np.ones(15).astype(float),
    origin=[50.0, 50.0, 50.0],
    axis_u=[1.0, 0, 0],
    axis_v=[0, 0, 1.0],
    offset_w=np.random.rand(11 * 16),
    attributes=[
        omf.NumericAttribute(
            name="rand vert attr",
            array=np.random.rand(11 * 16),
            location="vertices",
        ),
        omf.NumericAttribute(
            name="rand face attr",
            array=np.random.rand(10 * 15),
            location="faces",
        ),
    ],
    textures=[
        omf.ProjectedTexture(
            name="test image",
            image=png_file,
            origin=[2.0, 2.0, 2.0],
            axis_u=[5.0, 0, 0],
            axis_v=[0, 2.0, 5.0],
        ),
    ],
)
vol = omf.TensorGridBlockModel(
    name="vol",
    tensor_u=np.ones(10).astype(float),
    tensor_v=np.ones(15).astype(float),
    tensor_w=np.ones(20).astype(float),
    origin=[10.0, 10.0, -10],
    attributes=[
        omf.NumericAttribute(
            name="random attr", location="cells", array=np.random.rand(10 * 15 * 20)
        ),
    ],
)

proj.elements = [pts, lin, surf, grid, vol]

proj.metadata = {
    "coordinate_reference_system": "epsg 3857",
    "date_created": datetime.datetime.utcnow(),
    "version": "v1.3",
    "revision": "10",
}

assert proj.validate()

omf.save(proj, omf_file)

```

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

### Piecewise Building of OMF PointSet

Source: https://omf.readthedocs.io/en/latest/content/examples.html

This snippet illustrates an alternative method for creating OMF elements by initializing an element and then setting its properties individually. This approach can be useful for more dynamic or incremental construction of elements.

```python
pts = omf.PointSet()
pts.name = 'Random Points'
pts.vertices = np.random.rand(100, 3)
...
```
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