Float#

Numerical float data can be attached to the various elements making up object. Data can be added to an Object entity using the add_data method.

curve.add_data(
    {
        "my_cell_values": {
            "association": "CELL",
            "values": np.random.randn(curve.n_cells),
        }
    }
)

<geoh5py.data.float_data.FloatData at 0x70c5769eece0>

The association can be one of:

• OBJECT: Single element characterizing the parent object

• VERTEX: Array of values associated with the parent object vertices

• CELL: Array of values associated with the parent object cells

The length and order of the array of values must be consistent with the corresponding element of association. If the association argument is omited, geoh5py will attempt to assign the data to the correct part based on the shape of the data values, either object.n_values or object.n_cells

# Add multiple data vectors on a single call
data = {}
for ii in range(8):
    data[f"Period:{ii}"] = {
        "association": "VERTEX",
        "values": (ii + 1)
        * np.cos(ii * curve.vertices[:, 0] * np.pi / curve.vertices[:, 0].max() / 4.0),
    }

data_list = curve.add_data(data)
print([obj.name for obj in data_list])

['Period:0', 'Period:1', 'Period:2', 'Period:3', 'Period:4', 'Period:5', 'Period:6', 'Period:7']

The newly created data is directly added to the project’s geoh5 file and available for visualization:

Integer#

Same implementation as for Float data type but with values provided as integer (int32).

Text#

Text (string) data can only be associated to the object itself.

curve.add_data({"my_comment": {"association": "OBJECT", "values": "hello_world"}})

<geoh5py.data.text_data.TextData at 0x70c56c7fe5f0>

Colormap#

The colormap data type can be used to store or customize the color palette used by Geoscience ANALYST.

from geoh5py.data.color_map import ColorMap
from geoh5py.objects import Grid2D


# Create some data on a grid2D entity.
# Create the Surface object
grid = Grid2D.create(
    workspace,
    u_cell_size=2.5,
    v_cell_size=2.5,
    u_count=64,
    v_count=16,
)
# Add data
radius = grid.add_data({"radial": {"values": np.linalg.norm(grid.centroids, axis=1)}})

# Create a simple colormap that spans the data range
nc = 10
rgba = np.vstack(
    [
        np.linspace(radius.values.min(), radius.values.max(), nc),  # Values
        np.linspace(0, 255, nc),  # Red
        np.linspace(255, 0, nc),  # Green
        np.linspace(125, 15, nc),  # Blue,
        np.ones(nc) * 255,  # Alpha,
    ]
).T

We now have an array that contains a range of integer values for red, green, blue and alpha (RGBA) over the span of the data values. This array can be used to implicitly create a MyColorMap from the EntityType.

# Assign the colormap to the data type
radius.entity_type.color_map = rgba

The resulting ColorMap stores the values to geoh5 as a numpy.recarray with fields for Value, Red, Green, Blue and Alpha.

radius.entity_type.color_map._values

rec.array([(  1.76776695,   0, 255, 125, 255),
           ( 19.72811601,  28, 226, 112, 255),
           ( 37.68846506,  56, 198, 100, 255),
           ( 55.64881412,  85, 170,  88, 255),
           ( 73.60916317, 113, 141,  76, 255),
           ( 91.56951223, 141, 113,  63, 255),
           (109.52986128, 170,  85,  51, 255),
           (127.49021034, 198,  56,  39, 255),
           (145.45055939, 226,  28,  27, 255),
           (163.41090845, 255,   0,  15, 255)],
          dtype=[('Value', '<f8'), ('Red', 'u1'), ('Green', 'u1'), ('Blue', 'u1'), ('Alpha', 'u1')])

Files#

Raw files can be added to groups and objects and stored as blob (bytes) data in geoh5.

with open("docs.txt", mode="w") as file:
    file.write("Hello world")

file_data = grid.add_file("docs.txt")

The information can easily be re-exported out to disk with the save method.

file_data.save_file(path="./temp", name="exported.txt")

import shutil


shutil.rmtree("./temp")

Well Data#

In the case of Drillhole objects, data are always stored as from-to interval values.

from geoh5py.groups import DrillholeGroup
from geoh5py.objects import Drillhole


dh_group = DrillholeGroup.create(workspace)
well = Drillhole.create(workspace, collar=(0, 0, 0), parent=dh_group)

depths_A = np.arange(0, 50.0)  # First list of depth

# Second list slightly offsetted on the first few depths
depths_B = np.arange(0.01, 50.01)

# Add both set of log data with 0.5 m tolerance
well.add_data(
    {
        "my_log_values": {
            "depth": depths_A,
            "values": np.random.randn(depths_A.shape[0]),
        },
        "log_wt_tolerance": {
            "depth": depths_B,
            "values": np.random.randn(depths_B.shape[0]),
        },
    }
)

[<abc.ConcatenatedFloatData at 0x70c56c49d150>,
 <abc.ConcatenatedFloatData at 0x70c56c49c190>]

# Define a from-to array
from_to = np.vstack([[0.25, 25.5], [30.1, 55.5], [56.5, 80.2]])

# Add some reference data
well.add_data(
    {
        "interval_values": {
            "values": np.asarray([1, 2, 3]),
            "from-to": from_to,
            "value_map": {1: "Unit_A", 2: "Unit_B", 3: "Unit_C"},
            "type": "referenced",
        }
    }
)

# Add float data on the same intervals
well.add_data(
    {
        "random_values": {
            "values": np.random.randn(from_to.shape[0]),
            "from-to": from_to,
        }
    }
)

<abc.ConcatenatedFloatData at 0x70c56c49d5d0>

Get data#

Just like any Entity, data can be retrieved from the Workspace using the get_entity method. For convenience, Objects also have a get_data_list and get_data method that focusses only on their respective children Data.

my_list = curve.get_data_list()
print(my_list, curve.get_data(my_list[0]))

['Period:0', 'Period:1', 'Period:2', 'Period:3', 'Period:4', 'Period:5', 'Period:6', 'Period:7', 'my_cell_values', 'my_comment'] [<geoh5py.data.float_data.FloatData object at 0x70c56c7fed40>]