Metric🍋
AziDipPitch
dataclass
🍋
Bases: Convention
Azimuth Dip Pitch Convention class.
BearingPlungeDip
dataclass
🍋
Bases: Convention
Bearing Plunge Dip Convention class.
Convention
dataclass
🍋
Bases: Entity
Convention class.
Attributes:
| Name | Type | Description |
|---|---|---|
axes_seq |
str
|
order of axis. |
orientation |
Tuple[AngleDirection, AngleDirection, AngleDirection]
|
orientation of angles. |
angle_units |
AngleUnit
|
unit of angles. |
Metric
🍋
Bases: Entity
Metric class.
Attributes:
| Name | Type | Description |
|---|---|---|
scales |
Vector
|
Major axes of ellipsoid. |
angles |
Angles
|
Azimuth/dip/pitch of main direction. |
anisotropy |
Convention
|
Metric anisotropy matrix. |
rot_mat |
ndarray
|
Rotation matrix. |
dimension |
int
|
Metric dimension. |
convention |
Convention
|
Metric convention. |
angles
property
writable
🍋
Vector of the ellipsoid used for the anisotropy. 1 value in 2 dimensions(Azimuth), 3 values in 3 dimensions(Azimuth/Dip/Pitch).
Returns:
| Type | Description |
|---|---|
Angles
|
Ellipsoid angles. |
anisotropy
property
🍋
Matrix used for distance computation in a Mahalanobis definition. See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.mahalanobis.html#scipy.spatial.distance.mahalanobis.
Returns:
| Type | Description |
|---|---|
ndarray
|
Matrix of anisotropy. |
convention
property
🍋
scales
property
writable
🍋
Vector of the ellipsoid used for the anisotropy. 2 values in 2 dimensions, 3 values in 3 dimension.
Returns:
| Type | Description |
|---|---|
Vector
|
Ellipsoid scales. |
distance_matrix(x, y)
🍋
Computes the distance matrix of two sets of points in the Euclidean space according to a distance.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x
|
Vector
|
A numpy matrix with the vectors to be analized in its rows. |
required |
y
|
Vector
|
A numpy matrix with the vectors to be analized in its rows (it must have the same number of columns as X1) |
required |
Returns:
| Type | Description |
|---|---|
ndarray
|
This functions returns a numpy matrix, where the (i,j)-th element |
ndarray
|
corresponds to the distance between the i-th row of X1 and the |
ndarray
|
j-th row of X2. |
eval(x, y)
🍋
Compute the Euclidean distance between two points, given ellipsoid anisotropy.
x and y must be
- Two numpy arrays of same dimension
- One numpy matrix and one numpy array, obtaining a numpy vector of distances between the rows of the matrix and the numpy vector (there must be a column compatibility)
- Two numpy matrices of the same shape, obtaining a numpy vector with the distances between their rows.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x
|
Vector
|
First array for distance computation. |
required |
y
|
Vector
|
Second array for distance computation. |
required |
Returns:
| Type | Description |
|---|---|
float
|
Metric vector. |
to_pyvista(center=[0.0, 0.0, 0.0])
🍋
Export Metric to Pyvista PolyData.
Returns:
| Type | Description |
|---|---|
|
PolyData object. |