Note

Click here to download the full example code

1.2: Data Structure and Manipulation¶

# Importing GemPy
import gempy as gp
import gempy

# Importing auxiliary libraries
import numpy as np
import pandas as pd
pd.set_option('precision', 2)

Series¶

Series is the object that contains the properties associated with each independent scalar field. Right now it is simply the order of the series (which is inferred by the index order). But in the future will be add the unconformity relation or perhaps the type of interpolator

Series and Faults classes are quite entangled since fault are a view of series

faults = gp.Faults()
series = gp.Series(faults)
series.df

	order_series	BottomRelation	isActive
Default series	1	Erosion	False

We can modify the series bt using set_series_index:

series.set_series_index(['foo', 'foo2', 'foo5', 'foo7'])
series

	order_series	BottomRelation	isActive
foo	1	Erosion	False
foo2	2	Erosion	False
foo5	3	Erosion	False
foo7	4	Erosion	False

The index of series are pandas categories. These provides quite handy backend functionality (see pandas.Categorical).

series.df.index

Out:

CategoricalIndex(['foo', 'foo2', 'foo5', 'foo7'], categories=['foo', 'foo2', 'foo5', 'foo7'], ordered=False, dtype='category')

For adding new series:

series.add_series('foo3')
series

	order_series	BottomRelation	isActive
foo	1	Erosion	False
foo2	2	Erosion	False
foo5	3	Erosion	False
foo7	4	Erosion	False
foo3	5	Erosion	False

Delete series

series.delete_series('foo3')
series

	order_series	BottomRelation	isActive
foo	1	Erosion	False
foo2	2	Erosion	False
foo5	3	Erosion	False
foo7	4	Erosion	False

Rename series:

series.rename_series({'foo': 'boo'})
series

	order_series	BottomRelation	isActive
boo	1	Erosion	False
foo2	2	Erosion	False
foo5	3	Erosion	False
foo7	4	Erosion	False

Reorder series:

series.reorder_series(['foo2', 'boo', 'foo7', 'foo5'])
series

	order_series	BottomRelation	isActive
foo2	1	Erosion	False
boo	2	Erosion	False
foo7	3	Erosion	False
foo5	4	Erosion	False

Faults¶

The df faults is used to characterize which mathematical series behave as fault and if mentioned faults are finite or infinite. Both dataframes get updated automatically as we modify the series object linked to the fault object (by passing it when a Series object is created).

faults

	isFault	isFinite
foo2	False	False
boo	False	False
foo7	False	False
foo5	False	False

Finally we have the faults relations df which captures which mathematical series a given fault offset in order to reproduce complex faulting networks

faults.faults_relations_df

	foo2	boo	foo7	foo5
foo2	False	False	False	False
boo	False	False	False	False
foo7	False	False	False	False
foo5	False	False	False	False

We can use set_is_fault to choose which of our series are faults:

faults.set_is_fault(['boo'])

	isFault	isFinite
foo2	False	False
boo	True	False
foo7	False	False
foo5	False	False

Similar thing for the fault relations:

fr = np.zeros((4, 4))
fr[2, 2] = True
fr[1, 2] = True

faults.set_fault_relation(fr)

	foo2	boo	foo7	foo5
foo2	False	False	False	False
boo	False	False	True	False
foo7	False	False	False	False
foo5	False	False	False	False

Now if we change the series df and we update the series already defined will conserve their values while the new ones will be set to false:

series.add_series('foo20')

	order_series	BottomRelation	isActive
foo2	1	Erosion	False
boo	2	Erosion	False
foo7	3	Erosion	False
foo5	4	Erosion	False
foo20	5	Erosion	False

series

	order_series	BottomRelation	isActive
foo2	1	Erosion	False
boo	2	Erosion	False
foo7	3	Erosion	False
foo5	4	Erosion	False
foo20	5	Erosion	False

faults

	isFault	isFinite
foo2	False	False
boo	True	False
foo7	False	False
foo5	False	False
foo20	False	False

faults.faults_relations_df

	foo2	boo	foo7	foo5	foo20
foo2	False	False	False	False	False
boo	False	False	True	False	False
foo7	False	False	False	False	False
foo5	False	False	False	False	False
foo20	False	False	False	False	False

When we add new series the values switch to NaN. We will be careful not having any NaNs in the DataFrames or we will raise errors down the line.

faults.set_is_fault()

	isFault	isFinite
foo2	False	False
boo	True	False
foo7	False	False
foo5	False	False
foo20	False	False

faults.set_fault_relation()

	foo2	boo	foo7	foo5	foo20
foo2	False	False	False	False	False
boo	False	False	False	False	False
foo7	False	False	False	False	False
foo5	False	False	False	False	False
foo20	False	False	False	False	False

Surfaces:¶

The df surfaces contains three properties. id refers to the order of the surfaces on the sequential pile, i.e. the strict order of computation. values on the other hand is the final value that each voxel will have after discretization. This may be useful for example in the case we want to map a specific geophysical property (such as density) to a given unit. By default both are the same since to discretize lithological units the value is arbitrary.

From an empty df¶

The Surfaces class needs to have an associate series object. This will limit the name of the series since they are a pandas.Categorical.

surfaces = gp.Surfaces(series)

We can set any number of formations by passing a list with the names. By default they will take the name or the first series.

surfaces.set_surfaces_names(['foo', 'foo2', 'foo5'])

	surface	series	order_surfaces	color	id
0	foo	foo20	1	#015482	1
1	foo2	foo20	2	#9f0052	2
2	foo5	foo20	3	#ffbe00	3

series

	order_series	BottomRelation	isActive
foo2	1	Erosion	False
boo	2	Erosion	False
foo7	3	Erosion	False
foo5	4	Erosion	False
foo20	5	Erosion	False

We can add new formations:

surfaces.add_surface(['feeeee'])
surfaces

	surface	series	order_surfaces	color	id
0	foo	foo20	1	#015482	1
1	foo2	foo20	2	#9f0052	2
2	foo5	foo20	3	#ffbe00	3
3	feeeee	foo20	4	#728f02	4

The column formation is also a ‘’pandas.Categorical’’. This will be important for the Data classes (surface_points and Orientations)

surfaces.df['surface']

Out:

     foo
    foo2
    foo5
  feeeee
Name: surface, dtype: object

surfaces

	surface	series	order_surfaces	color	id
0	foo	foo20	1	#015482	1
1	foo2	foo20	2	#9f0052	2
2	foo5	foo20	3	#ffbe00	3
3	feeeee	foo20	4	#728f02	4

Set values¶

To set the values we do it with the following method

surfaces.set_surfaces_values([2, 2, 2, 5])

	surface	series	order_surfaces	color	id	value_0
0	foo	foo20	1	#015482	1	2
1	foo2	foo20	2	#9f0052	2	2
2	foo5	foo20	3	#ffbe00	3	2
3	feeeee	foo20	4	#728f02	4	5

surfaces

	surface	series	order_surfaces	color	id	value_0
0	foo	foo20	1	#015482	1	2
1	foo2	foo20	2	#9f0052	2	2
2	foo5	foo20	3	#ffbe00	3	2
3	feeeee	foo20	4	#728f02	4	5

Set values with a given name:¶

We can give specific names to the properties (i.e. density)

surfaces.add_surfaces_values([[2, 2, 2, 6], [2, 2, 1, 8]], ['val_foo', 'val2_foo'])

	surface	series	order_surfaces	color	id	value_0	val_foo	val2_foo
0	foo	foo20	1	#015482	1	2	2	2
1	foo2	foo20	2	#9f0052	2	2	2	2
2	foo5	foo20	3	#ffbe00	3	2	2	1
3	feeeee	foo20	4	#728f02	4	5	6	8

surfaces

	surface	series	order_surfaces	color	id	value_0	val_foo	val2_foo
0	foo	foo20	1	#015482	1	2	2	2
1	foo2	foo20	2	#9f0052	2	2	2	2
2	foo5	foo20	3	#ffbe00	3	2	2	1
3	feeeee	foo20	4	#728f02	4	5	6	8

Delete formations values¶

To delete a full property:

surfaces.delete_surface_values(['val_foo', 'value_0'])

Out:

True

One of the formations must be set be the basement:¶

surfaces.set_basement()
surfaces

	surface	series	order_surfaces	color	id	val2_foo
0	foo	foo20	1	#015482	1	2
1	foo2	foo20	2	#9f0052	2	2
2	foo5	foo20	3	#ffbe00	3	1
3	feeeee	foo20	4	#728f02	4	8

Set formation values¶

We can also use set_surface_values instead adding. This will delete the previous properties and add the new one

surfaces.set_surfaces_values([[2, 2, 2, 6], [2, 2, 1, 8]], ['val_foo', 'val2_foo'])
surfaces

	surface	series	order_surfaces	color	id	val_foo	val2_foo
0	foo	foo20	1	#015482	1	2	2
1	foo2	foo20	2	#9f0052	2	2	2
2	foo5	foo20	3	#ffbe00	3	2	1
3	feeeee	foo20	4	#728f02	4	6	8

The last property is the correspondant series that each formation belong to. series and formation are pandas categories. To get a overview of what this mean check https://pandas.pydata.org/pandas-docs/stable/categorical.html.

surfaces.df['series']

Out:

0    foo20
1    foo20
2    foo20
3    foo20
Name: series, dtype: category
Categories (5, object): ['foo2', 'boo', 'foo7', 'foo5', 'foo20']

surfaces.df['surface']

Out:

     foo
    foo2
    foo5
  feeeee
Name: surface, dtype: object

Map series to formation¶

To map a series to a formation we can do it by passing a dict:

surfaces

	surface	series	order_surfaces	color	id	val_foo	val2_foo
0	foo	foo20	1	#015482	1	2	2
1	foo2	foo20	2	#9f0052	2	2	2
2	foo5	foo20	3	#ffbe00	3	2	1
3	feeeee	foo20	4	#728f02	4	6	8

series

	order_series	BottomRelation	isActive
foo2	1	Erosion	False
boo	2	Erosion	False
foo7	3	Erosion	False
foo5	4	Erosion	False
foo20	5	Erosion	False

If a series does not exist in the Series object, we rise a warning and we set those formations to nans

d = {"foo7": 'foo', "booX": ('foo2', 'foo5', 'fee')}

surfaces.map_series(d)

	surface	series	order_surfaces	color	id	val_foo	val2_foo
0	foo	foo7	1	#015482	1	2	2
1	foo2	foo20	1	#9f0052	2	2	2
2	foo5	foo20	2	#ffbe00	3	2	1
3	feeeee	foo20	3	#728f02	4	6	8

surfaces.map_series({"foo7": 'foo', "boo": ('foo2', 'foo5', 'fee')})

	surface	series	order_surfaces	color	id	val_foo	val2_foo
1	foo2	boo	1	#9f0052	1	2	2
2	foo5	boo	2	#ffbe00	2	2	1
0	foo	foo7	1	#015482	3	2	2
3	feeeee	foo20	1	#728f02	4	6	8

surfaces

	surface	series	order_surfaces	color	id	val_foo	val2_foo
1	foo2	boo	1	#9f0052	1	2	2
2	foo5	boo	2	#ffbe00	2	2	1
0	foo	foo7	1	#015482	3	2	2
3	feeeee	foo20	1	#728f02	4	6	8

An advantage of categories is that they are order so no we can tidy the df by series and formation

Modify surface name¶

surfaces.rename_surfaces({'foo2': 'lala'})

	surface	series	order_surfaces	color	id	val_foo	val2_foo
1	lala	boo	1	#9f0052	1	2	2
2	foo5	boo	2	#ffbe00	2	2	1
0	foo	foo7	1	#015482	3	2	2
3	feeeee	foo20	1	#728f02	4	6	8

surfaces

	surface	series	order_surfaces	color	id	val_foo	val2_foo
1	lala	boo	1	#9f0052	1	2	2
2	foo5	boo	2	#ffbe00	2	2	1
0	foo	foo7	1	#015482	3	2	2
3	feeeee	foo20	1	#728f02	4	6	8

surfaces.df.loc[2, 'val_foo'] = 22

surfaces

	surface	series	order_surfaces	color	id	val_foo	val2_foo
1	lala	boo	1	#9f0052	1	2	2
2	foo5	boo	2	#ffbe00	2	22	1
0	foo	foo7	1	#015482	3	2	2
3	feeeee	foo20	1	#728f02	4	6	8

Modify surface color¶

The surfaces DataFrame also contains a column for the color in which the surfaces are displayed. To change the color, call

surfaces.colors.change_colors()

Out:

Click to select new colors.
VBox(children=(ColorPicker(value='#015482', description='foo'), ColorPicker(value='#9f0052', description='foo2'), ColorPicker(value='#ffbe00', description='foo5'), ColorPicker(value='#728f02', description='feeeee')))

This allow to change the colors interactively. If you already know which colors you want to use, you can also update them with a dictionary mapping the surface name to a hex color string:

new_colors = {'foo': '#ff8000', 'foo5': '#4741be'}
surfaces.colors.change_colors(new_colors)

Data¶

surface_points¶

These two DataFrames (df from now on) will contain the individual information of each point at an interface or orientation. Some properties of this table are mapped from the df below.

surface_points = gempy.core.data_modules.geometric_data.SurfacePoints(surfaces)

surface_points

	X	Y	Z	smooth	surface

surface_points.set_surface_points(pd.DataFrame(np.random.rand(6, 3)),
                                  ['foo', 'foo5', 'lala', 'foo5', 'lala', 'feeeee'])

	X	Y	Z	smooth	surface
0	0.39	0.41	0.82	2.00e-06	foo
1	0.69	0.89	0.06	2.00e-06	foo5
2	0.53	0.55	0.03	2.00e-06	lala
3	0.02	0.23	0.55	2.00e-06	foo5
4	0.95	0.83	0.94	2.00e-06	lala
5	0.45	0.66	0.41	2.00e-06	feeeee

surface_points

	X	Y	Z	smooth	surface
0	0.39	0.41	0.82	2.00e-06	foo
1	0.69	0.89	0.06	2.00e-06	foo5
2	0.53	0.55	0.03	2.00e-06	lala
3	0.02	0.23	0.55	2.00e-06	foo5
4	0.95	0.83	0.94	2.00e-06	lala
5	0.45	0.66	0.41	2.00e-06	feeeee

surface_points.map_data_from_surfaces(surfaces, 'series')
surface_points

	X	Y	Z	smooth	surface
0	0.39	0.41	0.82	2.00e-06	foo
1	0.69	0.89	0.06	2.00e-06	foo5
2	0.53	0.55	0.03	2.00e-06	lala
3	0.02	0.23	0.55	2.00e-06	foo5
4	0.95	0.83	0.94	2.00e-06	lala
5	0.45	0.66	0.41	2.00e-06	feeeee

surface_points.map_data_from_surfaces(surfaces, 'id')
surface_points

	X	Y	Z	smooth	surface
0	0.39	0.41	0.82	2.00e-06	foo
1	0.69	0.89	0.06	2.00e-06	foo5
2	0.53	0.55	0.03	2.00e-06	lala
3	0.02	0.23	0.55	2.00e-06	foo5
4	0.95	0.83	0.94	2.00e-06	lala
5	0.45	0.66	0.41	2.00e-06	feeeee

series

	order_series	BottomRelation	isActive
foo2	1	Erosion	False
boo	2	Erosion	False
foo7	3	Erosion	False
foo5	4	Erosion	False
foo20	5	Erosion	False

surface_points.map_data_from_series(series, 'order_series')
surface_points

	X	Y	Z	smooth	surface
0	0.39	0.41	0.82	2.00e-06	foo
1	0.69	0.89	0.06	2.00e-06	foo5
2	0.53	0.55	0.03	2.00e-06	lala
3	0.02	0.23	0.55	2.00e-06	foo5
4	0.95	0.83	0.94	2.00e-06	lala
5	0.45	0.66	0.41	2.00e-06	feeeee

surface_points.sort_table()
surface_points

	X	Y	Z	smooth	surface
2	0.53	0.55	0.03	2.00e-06	lala
4	0.95	0.83	0.94	2.00e-06	lala
1	0.69	0.89	0.06	2.00e-06	foo5
3	0.02	0.23	0.55	2.00e-06	foo5
0	0.39	0.41	0.82	2.00e-06	foo
5	0.45	0.66	0.41	2.00e-06	feeeee

faults

	isFault	isFinite
foo2	False	False
boo	True	False
foo7	False	False
foo5	False	False
foo20	False	False

Orientations¶

orientations = gempy.core.data_modules.geometric_data.Orientations(surfaces)

orientations

	X	Y	Z	G_x	G_y	G_z	smooth	surface

Set values passing pole vectors:¶

orientations.set_orientations(np.random.rand(6, 3) * 10,
                              np.random.rand(6, 3),
                              surface=['foo', 'foo5', 'lala', 'foo5',
                                       'lala', 'feeeee'])

orientations

	X	Y	Z	G_x	G_y	G_z	smooth	surface
0	8.48	3.08	8.24	0.40	0.06	0.37	0.01	foo
1	6.87	0.41	8.03	0.84	0.27	0.31	0.01	foo5
2	8.21	3.42	1.50	0.89	0.56	0.38	0.01	lala
3	9.39	9.91	0.13	0.72	0.84	0.38	0.01	foo5
4	2.07	8.72	3.94	0.36	0.70	0.93	0.01	lala
5	2.60	0.10	4.96	1.00	0.75	0.97	0.01	feeeee

Set values pasing orientation data: azimuth, dip, pole (dip direction)¶

orientations.set_orientations(np.random.rand(6, 3) * 10,
                              orientation=np.random.rand(6, 3) * 20,
                              surface=['foo', 'foo5', 'lala', 'foo5',
                                       'lala', 'feeeee'])

orientations

	X	Y	Z	G_x	G_y	G_z	smooth	surface
0	8.33	8.79	5.68	0.43	4.03	12.22	0.01	foo
1	8.25	6.22	5.13	0.02	0.06	7.72	0.01	foo5
2	3.37	6.46	0.18	0.69	5.60	16.39	0.01	lala
3	3.82	1.27	8.42	0.15	4.21	13.70	0.01	foo5
4	5.79	9.10	0.89	0.54	6.43	18.63	0.01	lala
5	2.46	3.97	8.81	0.02	0.33	4.14	0.01	feeeee

Mapping data from the other df¶

orientations.map_data_from_surfaces(surfaces, 'series')
orientations

	X	Y	Z	G_x	G_y	G_z	smooth	surface
0	8.33	8.79	5.68	0.43	4.03	12.22	0.01	foo
1	8.25	6.22	5.13	0.02	0.06	7.72	0.01	foo5
2	3.37	6.46	0.18	0.69	5.60	16.39	0.01	lala
3	3.82	1.27	8.42	0.15	4.21	13.70	0.01	foo5
4	5.79	9.10	0.89	0.54	6.43	18.63	0.01	lala
5	2.46	3.97	8.81	0.02	0.33	4.14	0.01	feeeee

orientations.map_data_from_surfaces(surfaces, 'id')
orientations

	X	Y	Z	G_x	G_y	G_z	smooth	surface
0	8.33	8.79	5.68	0.43	4.03	12.22	0.01	foo
1	8.25	6.22	5.13	0.02	0.06	7.72	0.01	foo5
2	3.37	6.46	0.18	0.69	5.60	16.39	0.01	lala
3	3.82	1.27	8.42	0.15	4.21	13.70	0.01	foo5
4	5.79	9.10	0.89	0.54	6.43	18.63	0.01	lala
5	2.46	3.97	8.81	0.02	0.33	4.14	0.01	feeeee

orientations.map_data_from_series(series, 'order_series')
orientations

	X	Y	Z	G_x	G_y	G_z	smooth	surface
0	8.33	8.79	5.68	0.43	4.03	12.22	0.01	foo
1	8.25	6.22	5.13	0.02	0.06	7.72	0.01	foo5
2	3.37	6.46	0.18	0.69	5.60	16.39	0.01	lala
3	3.82	1.27	8.42	0.15	4.21	13.70	0.01	foo5
4	5.79	9.10	0.89	0.54	6.43	18.63	0.01	lala
5	2.46	3.97	8.81	0.02	0.33	4.14	0.01	feeeee

orientations.update_annotations()

	X	Y	Z	G_x	G_y	G_z	smooth	surface
0	8.33	8.79	5.68	0.43	4.03	12.22	0.01	foo
1	8.25	6.22	5.13	0.02	0.06	7.72	0.01	foo5
2	3.37	6.46	0.18	0.69	5.60	16.39	0.01	lala
3	3.82	1.27	8.42	0.15	4.21	13.70	0.01	foo5
4	5.79	9.10	0.89	0.54	6.43	18.63	0.01	lala
5	2.46	3.97	8.81	0.02	0.33	4.14	0.01	feeeee

Grid¶

grid = gp.Grid()
grid.create_regular_grid([0, 10, 0, 10, 0, 10], [50, 50, 50])

Out:

<gempy.core.grid_modules.grid_types.RegularGrid object at 0x7ff9cd9aa990>

grid.values

Out:

array([[0.1, 0.1, 0.1],
       [0.1, 0.1, 0.3],
       [0.1, 0.1, 0.5],
       ...,
       [9.9, 9.9, 9.5],
       [9.9, 9.9, 9.7],
       [9.9, 9.9, 9.9]])

Rescaling Data¶

rescaling = gempy.core.data_modules.geometric_data.RescaledData(
    surface_points, orientations, grid)

surface_points

	X	Y	Z	smooth	surface
2	0.53	0.55	0.03	2.00e-06	lala
4	0.95	0.83	0.94	2.00e-06	lala
1	0.69	0.89	0.06	2.00e-06	foo5
3	0.02	0.23	0.55	2.00e-06	foo5
0	0.39	0.41	0.82	2.00e-06	foo
5	0.45	0.66	0.41	2.00e-06	feeeee

orientations

	X	Y	Z	G_x	G_y	G_z	smooth	surface
0	8.33	8.79	5.68	0.43	4.03	12.22	0.01	foo
1	8.25	6.22	5.13	0.02	0.06	7.72	0.01	foo5
2	3.37	6.46	0.18	0.69	5.60	16.39	0.01	lala
3	3.82	1.27	8.42	0.15	4.21	13.70	0.01	foo5
4	5.79	9.10	0.89	0.54	6.43	18.63	0.01	lala
5	2.46	3.97	8.81	0.02	0.33	4.14	0.01	feeeee

Additional Data¶

ad = gp.AdditionalData(surface_points, orientations, grid, faults, surfaces, rescaling)

ad

		values
Structure	isLith	True
	isFault	True
	number faults	1
	number surfaces	4
	number series	5
	number surfaces per series	[0, 2, 1, 0, 1]
	len surfaces surface_points	[2, 2, 1, 1]
	len series surface_points	[0, 4, 1, 0, 1]
	len series orientations	[0, 4, 1, 0, 1]
Options	dtype	float64
	output	geology
	theano_optimizer	fast_compile
	device	cpu
	verbosity	None
Kriging	range	17
	$C_o$	7.1
	drift equations	[3, 3, 3, 3, 3]
Rescaling	rescaling factor	18
Rescaling	centers	[4.1742456039270355, 4.664036206917114, 4.423674404271726]

ad.structure_data

	values
isLith	True
isFault	True
number faults	1
number surfaces	4
number series	5
number surfaces per series	[0, 2, 1, 0, 1]
len surfaces surface_points	[2, 2, 1, 1]
len series surface_points	[0, 4, 1, 0, 1]
len series orientations	[0, 4, 1, 0, 1]

ad.options

	values
dtype	float64
output	geology
theano_optimizer	fast_compile
device	cpu
verbosity	None

ad.options.df

	dtype	output	theano_optimizer	device	verbosity
values	float64	geology	fast_compile	cpu	None

ad.options.df.dtypes

Out:

dtype               category
output              category
theano_optimizer    category
device              category
verbosity             object
dtype: object

ad.kriging_data

	values
range	17
$C_o$	7.1
drift equations	[3, 3, 3, 3, 3]

ad.rescaling_data

	values
rescaling factor	18
centers	[4.1742456039270355, 4.664036206917114, 4.423674404271726]

Interpolator¶

faults.df['isFault'].values

Out:

array([False,  True, False, False, False])

interp = gp.InterpolatorModel(surface_points, orientations, grid, surfaces, series, faults, ad)

interp.compile_th_fn_geo()

Out:

Compiling theano function...
Level of Optimization:  fast_compile
Device:  cpu
Precision:  float64
Number of faults:  1
Compilation Done!

<theano.compile.function_module.Function object at 0x7ff9b9f52d90>

interp.print_theano_shared()

Out:

len sereies i [0 2]
len sereies o [0 4]
len sereies w [ 0 17]
n surfaces per series [0 2]
n universal eq [3]
is finite [0 0 0 0 0]
is erosion [0]
is onlap [0]

Total running time of the script: ( 0 minutes 4.618 seconds)

Gallery generated by Sphinx-Gallery

1.2: Data Structure and Manipulation¶

Series¶

Faults¶

Surfaces:¶

From an empty df¶

Set values¶

Set values with a given name:¶

Delete formations values¶

One of the formations must be set be the basement:¶

Set formation values¶

Map series to formation¶

Modify surface name¶

Modify surface color¶

Data¶

surface_points¶

Orientations¶

Set values passing pole vectors:¶

Set values pasing orientation data: azimuth, dip, pole (dip direction)¶

Mapping data from the other df¶

Grid¶

Rescaling Data¶

Additional Data¶

Interpolator¶

GemPy

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