Data¶
The geoh5
format allows storing data (values) on different parts of an Object
. The data types currently supported by geoh5py
are
Float
Integer
Text
Colormap
Well log
[1]:
from geoh5py.workspace import Workspace
import numpy as np
# Re-use the previous workspace
workspace = Workspace("my_project.geoh5")
# Get the curve from previous section
curve = workspace.get_entity("Curve")[0]
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.
[2]:
curve.add_data({
"my_cell_values": {
"association":"CELL",
"values": np.random.randn(curve.n_cells)
}
})
[2]:
<geoh5py.data.float_data.FloatData at 0x7f5ba43b6ef0>
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
[3]:
# 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.)
}
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.
[4]:
curve.add_data({
"my_comment": {
"association":"OBJECT",
"values": "hello_world"
}
})
[4]:
<geoh5py.data.text_data.TextData at 0x7f5b7953f4f0>
Colormap¶
The colormap data type can be used to store or customize the color palette used by Geoscience ANALYST.
[5]:
from geoh5py.data.color_map import ColorMap
# Create some data on a grid2D entity.
grid = workspace.get_entity("Grid2D")[0]
# Add data
radius = grid.add_data({
"radial": {"values": np.linalg.norm(grid.centroids, axis=1)}
})
[6]:
# 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 ColorMap from the EntityType
.
[7]:
# 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
.
[8]:
radius.entity_type.color_map._values
[8]:
rec.array([( 56.32334484, 0, 255, 125, 255),
( 62.73899671, 28, 226, 112, 255),
( 69.15464858, 56, 198, 100, 255),
( 75.57030046, 85, 170, 88, 255),
( 81.98595233, 113, 141, 76, 255),
( 88.4016042 , 141, 113, 63, 255),
( 94.81725607, 170, 85, 51, 255),
(101.23290795, 198, 56, 39, 255),
(107.64855982, 226, 28, 27, 255),
(114.06421169, 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
.
[9]:
file_data = grid.add_file("./c_data.ipynb")
The information can easily be re-exported out to disk with the save
method.
[10]:
file_data.save_file(path="./temp", name="new_name.ipynb")
Well Data¶
In the case of Drillhole
objects, data are always stored as from-to
interval values.
Depth Data¶
Depth data are used to represent measurements recorded at discrete depths along the well path. A depth
attribute is required on creation. Depth markers are converted internally to from-to
intervals by adding a small depth values defined by the collocation_distance
. If the Drillhole
object already holds depth data at the same location, geoh5py
will group the datasets under the same PropertyGroup
.
[12]:
well = workspace.get_entity("Drillhole")[0]
depths_A = np.arange(0, 50.) # 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]),
}
})
[12]:
[<abc.ConcatenatedFloatData at 0x7f5ba43b7a30>,
<abc.ConcatenatedFloatData at 0x7f5b7953e1d0>]
Interval (From-To) Data¶
Interval data are defined by constant values bounded by a start (FROM) and an end (TO) depth. A from-to
attribute defined as a numpy.ndarray (nD, 2)
is expected on creation. Subsequent data are appended to the same interval PropertyGroup
if the from-to
values match within the collocation distance parameter. Users can control the tolerance for matching intervals by supplying a collocation_distance
argument in meters, or by setting the default on the drillhole entity
(default_collocation_distance = 1e-2
meters).
[13]:
# 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,
}
})
[13]:
<abc.ConcatenatedFloatData at 0x7f5b791c1060>
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
.
[14]:
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 0x7f5b7953f550>]
Property Groups¶
Data
entities sharing the same parent Object
and association
can be linked within a property_groups
and made available through profiling. This can be used to group data that would normally be stored as 2D array.
[15]:
# Add another VERTEX data and create a group with previous
curve.add_data_to_group([obj.uid for obj in data_list], "my_trig_group")
[15]:
<geoh5py.groups.property_group.PropertyGroup at 0x7f5b791c18a0>
[16]:
workspace.close()