geolime.plotting.vario_plot

Classes:

Covariance(dim, angles, 0.0, 0.0], scales, …)
Enum(value)	Generic enumeration.
ScalarMappable([norm, cmap])	A mixin class to map scalar data to RGBA.

Data:

Dict	The central part of internal API.
List	The central part of internal API.

Functions:

ceil(x, /)	Return the ceiling of x as an Integral.
directional_vector(dimension, …[, deg])	Compute directional vector from angles
pairs_plot_2d(coords, index, projection)	Debug function that displays pairs of points on a 2D projection plane.
pairs_plot_3d(coords, index, title)	Debug function that displays pairs of points on a 3D space.
plot_semivariogram(variograms[, model, …])	Semivariogram plot
shift(input, shift[, output, order, mode, …])	Shift an array.
vario_contour(coords, values, lags, tol, …)	Plot VarioContour as Pie Chart.
vario_map(coords, values, lags, tol, n_az, atol)	Plot VarioMap as Pie Chart.
variogram(coords, values, lags, tol, …[, …])	Given boundary input arguments (lag and its associated tolerance, angular reference and its associated tolerance), compute the variogram for a set of points (value associated with cartesian coordinates for a specific point)

geolime.plotting.vario_plot.pairs_plot_2d(coords: numpy.ndarray, index: List[int], projection: enum.Enum)

Debug function that displays pairs of points on a 2D projection plane. Mostly for debugging purposes.

Parameters

• coords (np.ndarray) – cartesian coordinates

• index (List[int]) – list of pairs

• projection (Enum) – projection plane to be considered (‘xy’, ‘xz’ or yz)

Returns

A 2D matplotlib plot

Return type

None

geolime.plotting.vario_plot.pairs_plot_3d(coords: numpy.ndarray, index: List[int], title: str)

Debug function that displays pairs of points on a 3D space. Mostly for debugging purposes.

Parameters

• coords (np.ndarray) – cartesian coordinates

• index (List[int]) – list of pairs

• title (str) – a title string to be displayed on the plot

Returns

A 3D matplotlib plot

Return type

None

geolime.plotting.vario_plot.plot_semivariogram(variograms: List, model: geolime.geostats.models.covariance.Covariance = None, model_angles: List[Dict[str, float]] = None, display_npairs: bool = False, method: enum.Enum = 'semivariogram', nlag: int = 100, border_ratio: float = 1)

Semivariogram plot

Parameters

• variograms (List[pd.DataFrame]) – A pandas DataFrame with at least ‘vario’ column in it

• model (Covariance) – optional, covariance model to be applied

• model_angles (List[Dict[str, float]]) – optional, display variogram model along other orientations specified as dict of {azi: float, dip: float, pitch: float, label: str}

• display_npairs (bool) – Display barplot with number of pairs if True

• method (Enum) – “covariogram” or “semivariogram”

• nlag (int) – Number of lag samples

• border_ratio (float) – Border extent ratio

Returns

A matplotlib plot

Return type

None

geolime.plotting.vario_plot.vario_contour(coords: numpy.ndarray, values: numpy.ndarray, lags: List[float], tol: float, n_az: int, atol: float)

Plot VarioContour as Pie Chart. Angle of slice equals angular tolerance and slice radius equals lag distance.

Parameters

• coords (np.ndarray) – Cartesian coordiantes of samples values

• values (np.ndarray) – Sample values

• lags (List[float]) – List of lags, in meters

• tol (float) – Lag tolerance, in percent

• n_az (int) – Number of azimuth to compute variogram

• atol (float) – Angular tolerance for variogram computation

geolime.plotting.vario_plot.vario_map(coords: numpy.ndarray, values: numpy.ndarray, lags: List[float], tol: float, n_az: int, atol: float)

Plot VarioMap as Pie Chart. Angle of slice equals angular tolerance and slice radius equals lag distance.

Parameters

• coords (np.ndarray) – Cartesian coordiantes of samples values

• values (np.ndarray) – Sample values

• lags (List[float]) – List of lags, in meters

• tol (float) – Lag tolerance, in percent

• n_az (int) – Number of azimuth to compute variogram

• atol (float) – Angular tolerance for variogram computation