So far we have shown the basics to create geological models by importing data from an external source (in especial GeoModeller 3D). In this chapter, we will explore the option available in GemPy to create the data directly in GemPy or to modify existing one. In this respect we will delve into the pandas DataFrames that contain the necessary data.
In this example we will make use of the library qgrid. From its documentation :
Qgrid is a Jupyter notebook widget which uses SlickGrid to render pandas DataFrames within a Jupyter notebook. This allows you to explore your DataFrames with intuitive scrolling, sorting, and filtering controls, as well as edit your DataFrames by double clicking cells.
In practice these library allows us to use pandas.DataFrames in Jupyter Notebooks as an excel table. Be aware that Qgrid requires of enabling nbextensions so it is important to test that the installation (https://github.com/quantopian/qgrid) was successful before trying to execute the rest of the notebook.
Let's create a bit of default data:
Let's start as always by importing the necessary dependencies:
In [1]:
# Importing gempy
import gempy as gp
# Embedding matplotlib figures into the notebooks
#%matplotlib inline
# Aux imports
import numpy as np
import pandas as pn
import matplotlib.pyplot as plt
import theano
import qgrid
In [2]:
geo_model = gp.create_model('Tutorial_ch1-6_CreatingModels')
gp.init_data(geo_model, [0, 1000, 0, 1000, -1000, 0], [50, 50, 50])
geo_model.set_default_surfaces()
geo_model.set_default_orientation()
geo_model.add_surface_points(400, 300, -500, 'surface1')
geo_model.add_surface_points(600, 300, -500, 'surface1')
Active grids: ['regular']
Out[2]:
X
Y
Z
smooth
surface
0
400.0
300.0
-500.0
0.000001
surface1
1
600.0
300.0
-500.0
0.000001
surface1
Some default values but to make the model a bit faster but they are not necessary:
In [3]:
gp.set_interpolator(geo_model, theano_optimizer='fast_run', verbose=[])
Setting kriging parameters to their default values.
Compiling theano function...
Level of Optimization: fast_run
Device: cpu
Precision: float64
Number of faults: 0
Compilation Done!
Kriging values:
values
range 1732.05
$C_o$ 71428.6
drift equations [3]
Out[3]:
<gempy.core.interpolator.InterpolatorModel at 0x257f226ceb8>
In [4]:
geo_model.additional_data
Out[4]:
values
Structure
isLith
True
isFault
False
number faults
0
number surfaces
1
number series
1
number surfaces per series
[1]
len surfaces surface_points
[2]
len series surface_points
[2]
len series orientations
[1]
Options
dtype
float64
output
geology
theano_optimizer
fast_run
device
cpu
verbosity
[]
Kriging
range
1732.05
$C_o$
71428.6
drift equations
[3]
Rescaling
rescaling factor
1200
centers
[300.000005, 150.000005, -249.999995]
In [5]:
gp.compute_model(geo_model, debug=False,compute_mesh=False, sort_surfaces=False)
Out[5]:
Lithology ids
[2. 2. 2. ... 1. 1. 1.]
In [6]:
gp.plot_2d(geo_model, cell_number=25,
direction='y', show_data=True)
i:\pycharmprojects\gempy\gempy\plot\plot_api.py:261: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.
p.fig.show()
Out[6]:
<gempy.plot.visualization_2d.Plot2D at 0x25797fbb2e8>
In [7]:
gp.plot_2d(geo_model, cell_number= 25, direction='y', series_n=0, show_scalar=True)
Out[7]:
<gempy.plot.visualization_2d.Plot2D at 0x257976ee860>
In [8]:
vtk_object = gp.plot_3d(geo_model, plotter_type='background')
In [9]:
vtk_object.toggle_live_updating()
Out[9]:
True
In [10]:
geo_model.surfaces
Out[10]:
surface series order_surfaces color id
0
surface1
Default series
1
#015482
1
1
surface2
Default series
2
#9f0052
2
In [11]:
geo_model.modify_surface_points(0, Z=-300, plot_object=vtk_object)
Out[11]:
X
Y
Z
smooth
surface
0
400.0
300.0
-300.0
0.000001
surface1
1
600.0
300.0
-500.0
0.000001
surface1
In [12]:
geo_model.add_orientations(1,1,1, 'surface1', pole_vector=(1,1,1), plot_object=vtk_object)
Out[12]:
X
Y
Z
G_x
G_y
G_z
smooth
surface
0
0.00001
0.00001
0.00001
0.0
0.0
1.0
0.01
surface1
1
1.00000
1.00000
1.00000
1.0
1.0
1.0
0.01
surface1
In [13]:
gp.activate_interactive_df(geo_model, vtk_object)
Out[13]:
<gempy.core.qgrid_integration.QgridModelIntegration at 0x25796b9e470>
It is important to get df with get to update the models sinde the activate_interactive method is called
In [14]:
geo_model.qi.get('orientations')
i:\pycharmprojects\gempy\gempy\core\solution.py:315: UserWarning: Surfaces not computed due to: No surface found at the given iso value.. The surface is: Series: No surface found at the given iso value.; Surface Number:1
'; Surface Number:' + str(s_n))
In [15]:
geo_model.qi.get('surface_points')
AssertionError: Model not computed. Laking data in some surface
WARNING:root:Encountered issue in callback: Input dimension mis-match. (input[0].shape[1] = 0, input[1].shape[1] = 1)
Apply node that caused the error: Elemwise{sub,no_inplace}(Subtensor{::, int64:int64:}.0, Subtensor{::, int64:int64:}.0)
Toposort index: 111
Inputs types: [TensorType(float64, matrix), TensorType(float64, matrix)]
Inputs shapes: [(0, 0), (0, 1)]
Inputs strides: [(1000016, 8), (1000016, 8)]
Inputs values: [array([], shape=(0, 0), dtype=float64), array([], shape=(0, 1), dtype=float64)]
Inputs type_num: [12, 12]
Outputs clients: [[Elemwise{add,no_inplace}(Elemwise{sub,no_inplace}.0, TensorConstant{(1, 1) of 0.0001})]]
Backtrace when the node is created(use Theano flag traceback.limit=N to make it longer):
File "i:\pycharmprojects\gempy\gempy\core\interpolator.py", line 1027, in compile_th_fn_geo
self.theano_graph.theano_output(),
File "i:\pycharmprojects\gempy\gempy\core\theano_modules\theano_graph_pro.py", line 558, in theano_output
solutions[:9] = self.compute_series()
File "i:\pycharmprojects\gempy\gempy\core\theano_modules\theano_graph_pro.py", line 432, in compute_series
profile=False
File "C:\Users\legui\miniconda3\envs\gp-dev\lib\site-packages\theano\scan_module\scan.py", line 774, in scan
condition, outputs, updates = scan_utils.get_updates_and_outputs(fn(*args))
File "i:\pycharmprojects\gempy\gempy\core\theano_modules\theano_graph_pro.py", line 1881, in compute_a_series
self.solve_kriging(b),
File "i:\pycharmprojects\gempy\gempy\core\theano_modules\theano_graph_pro.py", line 1188, in solve_kriging
C_matrix = self.covariance_matrix()
File "i:\pycharmprojects\gempy\gempy\core\theano_modules\theano_graph_pro.py", line 1090, in covariance_matrix
F_I, F_G = self.faults_matrix()
File "i:\pycharmprojects\gempy\gempy\core\theano_modules\theano_graph_pro.py", line 1063, in faults_matrix
F_I = (self.fault_drift_at_surface_points_ref - self.fault_drift_at_surface_points_rest) + 0.0001
Debugprint of the apply node:
Elemwise{sub,no_inplace} [id A] <TensorType(float64, matrix)> ''
|Subtensor{::, int64:int64:} [id B] <TensorType(float64, matrix)> ''
| |Elemwise{mul,no_inplace} [id C] <TensorType(float64, matrix)> ''
| | |AdvancedSubtensor [id D] <TensorType(float64, matrix)> ''
| | | |<TensorType(float64, 3D)> [id E] <TensorType(float64, 3D)>
| | | |Subtensor{int64} [id F] <TensorType(int64, vector)> ''
| | | | |Nonzero [id G] <TensorType(int64, matrix)> ''
| | | | | |Elemwise{Cast{int8}} [id H] <TensorType(int8, vector)> ''
| | | | | |Subtensor{::, int8} [id I] <TensorType(int32, vector)> ''
| | | | | |fault relation matrix_copy [id J] <TensorType(int32, matrix)>
| | | | | |ScalarFromTensor [id K] <int8> ''
| | | | | |Elemwise{Cast{int8}} [id L] <TensorType(int8, scalar)> ''
| | | | | |<TensorType(int32, scalar)> [id M] <TensorType(int32, scalar)>
| | | | |Constant{0} [id N] <int64>
| | | |TensorConstant{0} [id O] <TensorType(int64, scalar)>
| | | |MakeSlice [id P] <slice> ''
| | | |TensorConstant{0} [id Q] <TensorType(int8, scalar)>
| | | |Elemwise{add,no_inplace} [id R] <TensorType(int64, scalar)> ''
| | | | |Elemwise{add,no_inplace} [id S] <TensorType(int64, scalar)> ''
| | | | | |Subtensor{int64} [id T] <TensorType(int64, scalar)> ''
| | | | | | |Shape [id U] <TensorType(int64, vector)> ''
| | | | | | | |Coordinates of the grid points to interpolate_copy [id V] <TensorType(float64, matrix)>
| | | | | | |Constant{0} [id N] <int64>
| | | | | |Elemwise{mul,no_inplace} [id W] <TensorType(int64, scalar)> ''
| | | | | |TensorConstant{2} [id X] <TensorType(int8, scalar)>
| | | | | |Elemwise{sub,no_inplace} [id Y] <TensorType(int64, scalar)> ''
| | | | | |Subtensor{int64} [id Z] <TensorType(int64, scalar)> ''
| | | | | | |Shape [id BA] <TensorType(int64, vector)> ''
| | | | | | | |All the surface_points points at once_copy [id BB] <TensorType(float64, matrix)>
| | | | | | |Constant{0} [id N] <int64>
| | | | | |Subtensor{int64} [id BC] <TensorType(int64, scalar)> ''
| | | | | |Shape [id BD] <TensorType(int64, vector)> ''
| | | | | | |Number of points per surface used to split rest-ref_copy [id BE] <TensorType(int32, vector)>
| | | | | |Constant{0} [id N] <int64>
| | | | |TensorConstant{0} [id Q] <TensorType(int8, scalar)>
| | | |NoneConst [id BF] <NoneTypeT>
| | |InplaceDimShuffle{x,x} [id BG] <TensorType(float64, (True, True))> ''
| | |<TensorType(float64, scalar)> [id BH] <TensorType(float64, scalar)>
| |ScalarFromTensor [id BI] <int64> ''
| | |Elemwise{add,no_inplace} [id BJ] <TensorType(int64, scalar)> ''
| | |Elemwise{add,no_inplace} [id BK] <TensorType(int64, scalar)> ''
| | | |Subtensor{int64} [id T] <TensorType(int64, scalar)> ''
| | | |Elemwise{sub,no_inplace} [id Y] <TensorType(int64, scalar)> ''
| | |Length of surface_points in every series[t] [id BL] <TensorType(int32, scalar)>
| |ScalarFromTensor [id BM] <int64> ''
| |Elemwise{add,no_inplace} [id BN] <TensorType(int64, scalar)> ''
| |Elemwise{add,no_inplace} [id BK] <TensorType(int64, scalar)> ''
| |Length of surface_points in every series[t+1] [id BO] <TensorType(int32, scalar)>
|Subtensor{::, int64:int64:} [id BP] <TensorType(float64, matrix)> ''
|Elemwise{mul,no_inplace} [id C] <TensorType(float64, matrix)> ''
|ScalarFromTensor [id BQ] <int64> ''
| |Elemwise{add,no_inplace} [id BR] <TensorType(int64, scalar)> ''
| |Subtensor{int64} [id T] <TensorType(int64, scalar)> ''
| |Length of surface_points in every series[t] [id BL] <TensorType(int32, scalar)>
|ScalarFromTensor [id BS] <int64> ''
|Elemwise{add,no_inplace} [id BT] <TensorType(int64, scalar)> ''
|Subtensor{int64} [id T] <TensorType(int64, scalar)> ''
|Length of surface_points in every series[t+1] [id BO] <TensorType(int32, scalar)>
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.
Apply node that caused the error: for{cpu,Looping}(Elemwise{minimum,no_inplace}.0, Subtensor{:int64:}.0, Subtensor{:int64:}.0, Subtensor{:int64:}.0, Subtensor{:int64:}.0, Subtensor{:int64:}.0, Subtensor{:int64:}.0, Subtensor{:int64:}.0, Subtensor{:int64:}.0, Subtensor{:int64:}.0, Subtensor{:int64:}.0, Subtensor{:int64:}.0, Subtensor{:int64:}.0, Subtensor{:int64:}.0, Subtensor{:int64:}.0, Subtensor{:int64:}.0, Subtensor{:int64:}.0, IncSubtensor{Set;:int64:}.0, IncSubtensor{Set;:int64:}.0, IncSubtensor{Set;:int64:}.0, IncSubtensor{Set;:int64:}.0, IncSubtensor{Set;:int64:}.0, IncSubtensor{Set;:int64:}.0, IncSubtensor{Set;:int64:}.0, IncSubtensor{Set;:int64:}.0, Number of points per surface used to split rest-ref, fault relation matrix, <TensorType(float64, scalar)>, <TensorType(float64, scalar)>, Range, Covariance at 0, <TensorType(float64, scalar)>, Nugget effect of gradients, Nugget effect of scalar, Attenuation factor, Sigmoid Outside, Sigmoid slope, <TensorType(int32, vector)>, <TensorType(bool, vector)>, <TensorType(int32, vector)>, Coordinates of the grid points to interpolate, All the surface_points points at once, Position of the dips, Angle of every dip, Azimuth, Polarity, Values that the blocks are taking)
Toposort index: 157
Inputs types: [TensorType(int64, scalar), TensorType(int32, vector), TensorType(int32, vector), TensorType(int32, vector), TensorType(int32, vector), TensorType(int32, vector), TensorType(int32, vector), TensorType(int32, vector), TensorType(int32, vector), TensorType(int32, vector), TensorType(bool, vector), TensorType(bool, vector), TensorType(bool, vector), TensorType(int32, vector), TensorType(int32, vector), TensorType(int32, vector), TensorType(int32, vector), TensorType(float64, 4D), TensorType(float64, matrix), TensorType(float64, 3D), TensorType(float64, 3D), TensorType(bool, 3D), TensorType(bool, 3D), TensorType(float64, 4D), TensorType(int64, vector), TensorType(int32, vector), TensorType(int32, matrix), TensorType(float64, scalar), TensorType(float64, scalar), TensorType(float64, scalar), TensorType(float64, scalar), TensorType(float64, scalar), TensorType(float64, vector), TensorType(float64, vector), TensorType(float64, scalar), TensorType(float64, scalar), TensorType(float64, scalar), TensorType(int32, vector), TensorType(bool, vector), TensorType(int32, vector), TensorType(float64, matrix), TensorType(float64, matrix), TensorType(float64, matrix), TensorType(float64, vector), TensorType(float64, vector), TensorType(float64, vector), TensorType(float64, matrix)]
Inputs shapes: [(), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (2, 1, 1, 125002), (2, 10), (2, 1, 125002), (2, 1, 1), (2, 1, 125002), (2, 1, 125002), (2, 1, 1, 125002), (2,), (2,), (1, 1), (), (), (), (), (), (6,), (4,), (), (), (), (1,), (1,), (1,), (125000, 3), (4, 3), (2, 3), (2,), (2,), (2,), (1, 3)]
Inputs strides: [(), (4,), (4,), (4,), (4,), (4,), (4,), (4,), (4,), (4,), (1,), (1,), (1,), (4,), (4,), (4,), (4,), (1000016, 1000016, 1000016, 8), (80, 8), (1000016, 1000016, 8), (8, 8, 8), (125002, 125002, 1), (125002, 125002, 1), (1000016, 1000016, 1000016, 8), (8,), (4,), (4, 4), (), (), (), (), (), (8,), (8,), (), (), (), (4,), (1,), (4,), (24, 8), (8, 32), (8, 16), (8,), (8,), (8,), (24, 8)]
Inputs values: [array(1, dtype=int64), array([0]), array([1]), array([0]), array([2]), array([0]), array([10]), array([0]), array([1]), array([3]), array([ True]), array([ True]), array([ True]), array([0]), array([0]), array([0]), array([0]), 'not shown', 'not shown', 'not shown', array([[[0.e+000]],
[[5.e-323]]]), 'not shown', 'not shown', 'not shown', array([ 0, 10], dtype=int64), array([1, 1]), array([[0]]), array(10.), array(2.), array(1.41068677), array(58.17574207), array(4.), 'not shown', array([1.e-06, 1.e-06, 1.e-06, 1.e-06]), array(2.), array(50.), array(50000.), array([0]), array([False]), array([0]), 'not shown', 'not shown', 'not shown', array([0., 0.]), array([0., 0.]), array([1., 1.]), array([[1., 2., 3.]])]
Inputs type_num: [9, 7, 7, 7, 7, 7, 7, 7, 7, 7, 0, 0, 0, 7, 7, 7, 7, 12, 12, 12, 12, 0, 0, 12, 9, 7, 7, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 7, 0, 7, 12, 12, 12, 12, 12, 12, 12]
Outputs clients: [[Subtensor{int64::}(for{cpu,Looping}.0, Constant{1})], [Subtensor{int64::}(for{cpu,Looping}.1, Constant{1})], [Subtensor{int64::}(for{cpu,Looping}.2, Constant{1})], [Subtensor{int64::}(for{cpu,Looping}.3, Constant{1})], [Subtensor{int64::}(for{cpu,Looping}.4, Constant{1})], [Subtensor{int64::}(for{cpu,Looping}.5, Constant{1})], [], []]
Backtrace when the node is created(use Theano flag traceback.limit=N to make it longer):
File "C:\Users\legui\miniconda3\envs\gp-dev\lib\site-packages\IPython\core\interactiveshell.py", line 3063, in run_cell_async
interactivity=interactivity, compiler=compiler, result=result)
File "C:\Users\legui\miniconda3\envs\gp-dev\lib\site-packages\IPython\core\interactiveshell.py", line 3254, in run_ast_nodes
if (await self.run_code(code, result, async_=asy)):
File "C:\Users\legui\miniconda3\envs\gp-dev\lib\site-packages\IPython\core\interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-3-4c4b16d0ba3b>", line 1, in <module>
gp.set_interpolator(geo_model, theano_optimizer='fast_run', verbose=[])
File "i:\pycharmprojects\gempy\gempy\api_modules\setters.py", line 108, in set_interpolator
geo_model._interpolator.compile_th_fn_geo(inplace=True, grid=grid)
File "i:\pycharmprojects\gempy\gempy\core\interpolator.py", line 1027, in compile_th_fn_geo
self.theano_graph.theano_output(),
File "i:\pycharmprojects\gempy\gempy\core\theano_modules\theano_graph_pro.py", line 558, in theano_output
solutions[:9] = self.compute_series()
File "i:\pycharmprojects\gempy\gempy\core\theano_modules\theano_graph_pro.py", line 432, in compute_series
profile=False
Debugprint of the apply node:
for{cpu,Looping}.0 [id A] <TensorType(float64, 4D)> ''
|Elemwise{minimum,no_inplace} [id B] <TensorType(int64, scalar)> ''
| |Elemwise{minimum,no_inplace} [id C] <TensorType(int64, scalar)> ''
| | |Elemwise{minimum,no_inplace} [id D] <TensorType(int64, scalar)> ''
| | | |Elemwise{minimum,no_inplace} [id E] <TensorType(int64, scalar)> ''
| | | | |Elemwise{minimum,no_inplace} [id F] <TensorType(int64, scalar)> ''
| | | | | |Elemwise{minimum,no_inplace} [id G] <TensorType(int64, scalar)> ''
| | | | | | |Elemwise{minimum,no_inplace} [id H] <TensorType(int64, scalar)> ''
| | | | | | | |Elemwise{minimum,no_inplace} [id I] <TensorType(int64, scalar)> ''
| | | | | | | | |Elemwise{minimum,no_inplace} [id J] <TensorType(int64, scalar)> ''
| | | | | | | | | |Elemwise{minimum,no_inplace} [id K] <TensorType(int64, scalar)> ''
| | | | | | | | | | |Elemwise{minimum,no_inplace} [id L] <TensorType(int64, scalar)> ''
| | | | | | | | | | | |Elemwise{minimum,no_inplace} [id M] <TensorType(int64, scalar)> ''
| | | | | | | | | | | | |Elemwise{minimum,no_inplace} [id N] <TensorType(int64, scalar)> ''
| | | | | | | | | | | | | |Elemwise{minimum,no_inplace} [id O] <TensorType(int64, scalar)> ''
| | | | | | | | | | | | | | |Elemwise{minimum,no_inplace} [id P] <TensorType(int64, scalar)> ''
| | | | | | | | | | | | | | | |Subtensor{int64} [id Q] <TensorType(int64, scalar)> ''
| | | | | | | | | | | | | | | | |Shape [id R] <TensorType(int64, vector)> ''
| | | | | | | | | | | | | | | | | |Subtensor{int64:int64:} [id S] <TensorType(int32, vector)> 'Length of surface_points in every series[0:-1]'
| | | | | | | | | | | | | | | | |Constant{0} [id T] <int64>
| | | | | | | | | | | | | | | |Subtensor{int64} [id U] <TensorType(int64, scalar)> ''
| | | | | | | | | | | | | | | |Shape [id V] <TensorType(int64, vector)> ''
| | | | | | | | | | | | | | | | |Subtensor{int64::} [id W] <TensorType(int32, vector)> 'Length of surface_points in every series[1:]'
| | | | | | | | | | | | | | | |Constant{0} [id T] <int64>
| | | | | | | | | | | | | | |Subtensor{int64} [id X] <TensorType(int64, scalar)> ''
| | | | | | | | | | | | | | |Shape [id Y] <TensorType(int64, vector)> ''
| | | | | | | | | | | | | | | |Subtensor{int64:int64:} [id Z] <TensorType(int32, vector)> 'Length of foliations in every series[0:-1]'
| | | | | | | | | | | | | | |Constant{0} [id T] <int64>
| | | | | | | | | | | | | |Subtensor{int64} [id BA] <TensorType(int64, scalar)> ''
| | | | | | | | | | | | | |Shape [id BB] <TensorType(int64, vector)> ''
| | | | | | | | | | | | | | |Subtensor{int64::} [id BC] <TensorType(int32, vector)> 'Length of foliations in every series[1:]'
| | | | | | | | | | | | | |Constant{0} [id T] <int64>
| | | | | | | | | | | | |Subtensor{int64} [id BD] <TensorType(int64, scalar)> ''
| | | | | | | | | | | | |Shape [id BE] <TensorType(int64, vector)> ''
| | | | | | | | | | | | | |Subtensor{int64:int64:} [id BF] <TensorType(int32, vector)> 'Length of weights in every series[0:-1]'
| | | | | | | | | | | | |Constant{0} [id T] <int64>
| | | | | | | | | | | |Subtensor{int64} [id BG] <TensorType(int64, scalar)> ''
| | | | | | | | | | | |Shape [id BH] <TensorType(int64, vector)> ''
| | | | | | | | | | | | |Subtensor{int64::} [id BI] <TensorType(int32, vector)> 'Length of weights in every series[1:]'
| | | | | | | | | | | |Constant{0} [id T] <int64>
| | | | | | | | | | |Subtensor{int64} [id BJ] <TensorType(int64, scalar)> ''
| | | | | | | | | | |Shape [id BK] <TensorType(int64, vector)> ''
| | | | | | | | | | | |Subtensor{int64:int64:} [id BL] <TensorType(int32, vector)> 'List with the number of surfaces[0:-1]'
| | | | | | | | | | |Constant{0} [id T] <int64>
| | | | | | | | | |Subtensor{int64} [id BM] <TensorType(int64, scalar)> ''
| | | | | | | | | |Shape [id BN] <TensorType(int64, vector)> ''
| | | | | | | | | | |Subtensor{int64::} [id BO] <TensorType(int32, vector)> 'List with the number of surfaces[1:]'
| | | | | | | | | |Constant{0} [id T] <int64>
| | | | | | | | |Subtensor{int64} [id BP] <TensorType(int64, scalar)> ''
| | | | | | | | |Shape [id BQ] <TensorType(int64, vector)> ''
| | | | | | | | | |Subtensor{int64::} [id BR] <TensorType(int32, vector)> 'Grade of the universal drift[0:]'
| | | | | | | | |Constant{0} [id T] <int64>
| | | | | | | |Subtensor{int64} [id BS] <TensorType(int64, scalar)> ''
| | | | | | | |Shape [id BT] <TensorType(int64, vector)> ''
| | | | | | | | |Subtensor{int64::} [id BU] <TensorType(bool, vector)> 'Vector controlling if weights must be recomputed[0:]'
| | | | | | | |Constant{0} [id T] <int64>
| | | | | | |Subtensor{int64} [id BV] <TensorType(int64, scalar)> ''
| | | | | | |Shape [id BW] <TensorType(int64, vector)> ''
| | | | | | | |Subtensor{int64::} [id BX] <TensorType(bool, vector)> 'Vector controlling if scalar matrix must be recomputed[0:]'
| | | | | | |Constant{0} [id T] <int64>
| | | | | |Subtensor{int64} [id BY] <TensorType(int64, scalar)> ''
| | | | | |Shape [id BZ] <TensorType(int64, vector)> ''
| | | | | | |Subtensor{int64::} [id CA] <TensorType(bool, vector)> 'Vector controlling if block matrix must be recomputed[0:]'
| | | | | |Constant{0} [id T] <int64>
| | | | |Subtensor{int64} [id CB] <TensorType(int64, scalar)> ''
| | | | |Shape [id CC] <TensorType(int64, vector)> ''
| | | | | |Subtensor{int64::} [id CD] <TensorType(int32, vector)> 'The series (fault) is finite[0:]'
| | | | |Constant{0} [id T] <int64>
| | | |Subtensor{int64} [id CE] <TensorType(int64, scalar)> ''
| | | |Shape [id CF] <TensorType(int64, vector)> ''
| | | | |Subtensor{int64::} [id CG] <TensorType(int32, vector)> ''
| | | | |<TensorType(int32, vector)> [id CH] <TensorType(int32, vector)>
| | | | |Constant{0} [id T] <int64>
| | | |Constant{0} [id T] <int64>
| | |Subtensor{int64} [id CI] <TensorType(int64, scalar)> ''
| | |Shape [id CJ] <TensorType(int64, vector)> ''
| | | |Subtensor{int64::} [id CK] <TensorType(int32, vector)> ''
| | | |<TensorType(int32, vector)> [id CL] <TensorType(int32, vector)>
| | | |Constant{0} [id T] <int64>
| | |Constant{0} [id T] <int64>
| |TensorConstant{5000} [id CM] <TensorType(int64, scalar)>
|Subtensor{:int64:} [id CN] <TensorType(int32, vector)> ''
| |Subtensor{int64:int64:} [id S] <TensorType(int32, vector)> 'Length of surface_points in every series[0:-1]'
| |ScalarFromTensor [id CO] <int64> ''
| |Elemwise{minimum,no_inplace} [id B] <TensorType(int64, scalar)> ''
|Subtensor{:int64:} [id CP] <TensorType(int32, vector)> ''
| |Subtensor{int64::} [id W] <TensorType(int32, vector)> 'Length of surface_points in every series[1:]'
| |ScalarFromTensor [id CO] <int64> ''
|Subtensor{:int64:} [id CQ] <TensorType(int32, vector)> ''
| |Subtensor{int64:int64:} [id Z] <TensorType(int32, vector)> 'Length of foliations in every series[0:-1]'
| |ScalarFromTensor [id CO] <int64> ''
|Subtensor{:int64:} [id CR] <TensorType(int32, vector)> ''
| |Subtensor{int64::} [id BC] <TensorType(int32, vector)> 'Length of foliations in every series[1:]'
| |ScalarFromTensor [id CO] <int64> ''
|Subtensor{:int64:} [id CS] <TensorType(int32, vector)> ''
| |Subtensor{int64:int64:} [id BF] <TensorType(int32, vector)> 'Length of weights in every series[0:-1]'
| |ScalarFromTensor [id CO] <int64> ''
|Subtensor{:int64:} [id CT] <TensorType(int32, vector)> ''
| |Subtensor{int64::} [id BI] <TensorType(int32, vector)> 'Length of weights in every series[1:]'
| |ScalarFromTensor [id CO] <int64> ''
|Subtensor{:int64:} [id CU] <TensorType(int32, vector)> ''
| |Subtensor{int64:int64:} [id BL] <TensorType(int32, vector)> 'List with the number of surfaces[0:-1]'
| |ScalarFromTensor [id CO] <int64> ''
|Subtensor{:int64:} [id CV] <TensorType(int32, vector)> ''
| |Subtensor{int64::} [id BO] <TensorType(int32, vector)> 'List with the number of surfaces[1:]'
| |ScalarFromTensor [id CO] <int64> ''
|Subtensor{:int64:} [id CW] <TensorType(int32, vector)> ''
| |Subtensor{int64::} [id BR] <TensorType(int32, vector)> 'Grade of the universal drift[0:]'
| |ScalarFromTensor [id CO] <int64> ''
|Subtensor{:int64:} [id CX] <TensorType(bool, vector)> ''
| |Subtensor{int64::} [id BU] <TensorType(bool, vector)> 'Vector controlling if weights must be recomputed[0:]'
| |ScalarFromTensor [id CO] <int64> ''
|Subtensor{:int64:} [id CY] <TensorType(bool, vector)> ''
| |Subtensor{int64::} [id BX] <TensorType(bool, vector)> 'Vector controlling if scalar matrix must be recomputed[0:]'
| |ScalarFromTensor [id CO] <int64> ''
|Subtensor{:int64:} [id CZ] <TensorType(bool, vector)> ''
| |Subtensor{int64::} [id CA] <TensorType(bool, vector)> 'Vector controlling if block matrix must be recomputed[0:]'
| |ScalarFromTensor [id CO] <int64> ''
|Subtensor{:int64:} [id DA] <TensorType(int32, vector)> ''
| |Subtensor{int64::} [id CD] <TensorType(int32, vector)> 'The series (fault) is finite[0:]'
| |ScalarFromTensor [id CO] <int64> ''
|Subtensor{:int64:} [id DB] <TensorType(int32, vector)> ''
| |Subtensor{int64::} [id CG] <TensorType(int32, vector)> ''
| |ScalarFromTensor [id CO] <int64> ''
|Subtensor{:int64:} [id DC] <TensorType(int32, vector)> ''
| |Subtensor{int64::} [id CK] <TensorType(int32, vector)> ''
| |ScalarFromTensor [id CO] <int64> ''
|Subtensor{:int64:} [id DD] <TensorType(int32, vector)> ''
| |TensorConstant{[ 0 1..4998 4999]} [id DE] <TensorType(int32, vector)>
| |ScalarFromTensor [id CO] <int64> ''
|IncSubtensor{Set;:int64:} [id DF] <TensorType(float64, 4D)> ''
| |AllocEmpty{dtype='float64'} [id DG] <TensorType(float64, 4D)> ''
| | |Elemwise{add,no_inplace} [id DH] <TensorType(int64, scalar)> ''
| | | |Elemwise{minimum,no_inplace} [id B] <TensorType(int64, scalar)> ''
| | | |Subtensor{int64} [id DI] <TensorType(int64, scalar)> ''
| | | |Shape [id DJ] <TensorType(int64, vector)> ''
| | | | |Rebroadcast{0} [id DK] <TensorType(float64, 4D)> ''
| | | | |InplaceDimShuffle{x,0,1,2} [id DL] <TensorType(float64, (True, False, False, False))> ''
| | | | |block matrix [id DM] <TensorType(float64, 3D)>
| | | |Constant{0} [id T] <int64>
| | |Subtensor{int64} [id DN] <TensorType(int64, scalar)> ''
| | | |Shape [id DJ] <TensorType(int64, vector)> ''
| | | |Constant{1} [id DO] <int64>
| | |Subtensor{int64} [id DP] <TensorType(int64, scalar)> ''
| | | |Shape [id DJ] <TensorType(int64, vector)> ''
| | | |Constant{2} [id DQ] <int64>
| | |Subtensor{int64} [id DR] <TensorType(int64, scalar)> ''
| | |Shape [id DJ] <TensorType(int64, vector)> ''
| | |Constant{3} [id DS] <int64>
| |Rebroadcast{0} [id DK] <TensorType(float64, 4D)> ''
| |ScalarFromTensor [id DT] <int64> ''
| |Subtensor{int64} [id DI] <TensorType(int64, scalar)> ''
|IncSubtensor{Set;:int64:} [id DU] <TensorType(float64, matrix)> ''
| |AllocEmpty{dtype='float64'} [id DV] <TensorType(float64, matrix)> ''
| | |Elemwise{add,no_inplace} [id DW] <TensorType(int64, scalar)> ''
| | | |Elemwise{minimum,no_inplace} [id B] <TensorType(int64, scalar)> ''
| | | |Subtensor{int64} [id DX] <TensorType(int64, scalar)> ''
| | | |Shape [id DY] <TensorType(int64, vector)> ''
| | | | |Rebroadcast{0} [id DZ] <TensorType(float64, matrix)> ''
| | | | |InplaceDimShuffle{x,0} [id EA] <TensorType(float64, row)> ''
| | | | |Weights vector [id EB] <TensorType(float64, vector)>
| | | |Constant{0} [id T] <int64>
| | |Subtensor{int64} [id EC] <TensorType(int64, scalar)> ''
| | |Shape [id DY] <TensorType(int64, vector)> ''
| | |Constant{1} [id DO] <int64>
| |Rebroadcast{0} [id DZ] <TensorType(float64, matrix)> ''
| |ScalarFromTensor [id ED] <int64> ''
| |Subtensor{int64} [id DX] <TensorType(int64, scalar)> ''
|IncSubtensor{Set;:int64:} [id EE] <TensorType(float64, 3D)> ''
| |AllocEmpty{dtype='float64'} [id EF] <TensorType(float64, 3D)> ''
| | |Elemwise{add,no_inplace} [id EG] <TensorType(int64, scalar)> ''
| | | |Elemwise{minimum,no_inplace} [id B] <TensorType(int64, scalar)> ''
| | | |Subtensor{int64} [id EH] <TensorType(int64, scalar)> ''
| | | |Shape [id EI] <TensorType(int64, vector)> ''
| | | | |Rebroadcast{0} [id EJ] <TensorType(float64, 3D)> ''
| | | | |InplaceDimShuffle{x,0,1} [id EK] <TensorType(float64, (True, False, False))> ''
| | | | |Scalar matrix [id EL] <TensorType(float64, matrix)>
| | | |Constant{0} [id T] <int64>
| | |Subtensor{int64} [id EM] <TensorType(int64, scalar)> ''
| | | |Shape [id EI] <TensorType(int64, vector)> ''
| | | |Constant{1} [id DO] <int64>
| | |Subtensor{int64} [id EN] <TensorType(int64, scalar)> ''
| | |Shape [id EI] <TensorType(int64, vector)> ''
| | |Constant{2} [id DQ] <int64>
| |Rebroadcast{0} [id EJ] <TensorType(float64, 3D)> ''
| |ScalarFromTensor [id EO] <int64> ''
| |Subtensor{int64} [id EH] <TensorType(int64, scalar)> ''
|IncSubtensor{Set;:int64:} [id EP] <TensorType(float64, 3D)> ''
| |AllocEmpty{dtype='float64'} [id EQ] <TensorType(float64, 3D)> ''
| | |Elemwise{add,no_inplace} [id ER] <TensorType(int64, scalar)> ''
| | | |Elemwise{minimum,no_inplace} [id B] <TensorType(int64, scalar)> ''
| | | |Subtensor{int64} [id ES] <TensorType(int64, scalar)> ''
| | | |Shape [id ET] <TensorType(int64, vector)> ''
| | | | |Rebroadcast{0} [id EU] <TensorType(float64, 3D)> ''
| | | | |InplaceDimShuffle{x,0,1} [id EV] <TensorType(float64, (True, False, False))> ''
| | | | |Alloc [id EW] <TensorType(float64, matrix)> ''
| | | | |TensorConstant{0.0} [id EX] <TensorType(float64, scalar)>
| | | | |Subtensor{int64} [id EY] <TensorType(int64, scalar)> ''
| | | | | |Shape [id EZ] <TensorType(int64, vector)> ''
| | | | | | |<TensorType(int32, vector)> [id CH] <TensorType(int32, vector)>
| | | | | |Constant{0} [id T] <int64>
| | | | |Subtensor{int64} [id FA] <TensorType(int32, scalar)> ''
| | | | |List with the number of surfaces [id FB] <TensorType(int32, vector)>
| | | | |Constant{-1} [id FC] <int64>
| | | |Constant{0} [id T] <int64>
| | |Subtensor{int64} [id FD] <TensorType(int64, scalar)> ''
| | | |Shape [id ET] <TensorType(int64, vector)> ''
| | | |Constant{1} [id DO] <int64>
| | |Subtensor{int64} [id FE] <TensorType(int64, scalar)> ''
| | |Shape [id ET] <TensorType(int64, vector)> ''
| | |Constant{2} [id DQ] <int64>
| |Rebroadcast{0} [id EU] <TensorType(float64, 3D)> ''
| |ScalarFromTensor [id FF] <int64> ''
| |Subtensor{int64} [id ES] <TensorType(int64, scalar)> ''
|IncSubtensor{Set;:int64:} [id FG] <TensorType(bool, 3D)> ''
| |AllocEmpty{dtype='bool'} [id FH] <TensorType(bool, 3D)> ''
| | |Elemwise{add,no_inplace} [id FI] <TensorType(int64, scalar)> ''
| | | |Elemwise{minimum,no_inplace} [id B] <TensorType(int64, scalar)> ''
| | | |Subtensor{int64} [id FJ] <TensorType(int64, scalar)> ''
| | | |Shape [id FK] <TensorType(int64, vector)> ''
| | | | |Rebroadcast{0} [id FL] <TensorType(bool, 3D)> ''
| | | | |InplaceDimShuffle{x,0,1} [id FM] <TensorType(bool, (True, False, False))> ''
| | | | |mask matrix [id FN] <TensorType(bool, matrix)>
| | | |Constant{0} [id T] <int64>
| | |Subtensor{int64} [id FO] <TensorType(int64, scalar)> ''
| | | |Shape [id FK] <TensorType(int64, vector)> ''
| | | |Constant{1} [id DO] <int64>
| | |Subtensor{int64} [id FP] <TensorType(int64, scalar)> ''
| | |Shape [id FK] <TensorType(int64, vector)> ''
| | |Constant{2} [id DQ] <int64>
| |Rebroadcast{0} [id FL] <TensorType(bool, 3D)> ''
| |ScalarFromTensor [id FQ] <int64> ''
| |Subtensor{int64} [id FJ] <TensorType(int64, scalar)> ''
|IncSubtensor{Set;:int64:} [id FR] <TensorType(bool, 3D)> ''
| |AllocEmpty{dtype='bool'} [id FS] <TensorType(bool, 3D)> ''
| | |Elemwise{add,no_inplace} [id FT] <TensorType(int64, scalar)> ''
| | | |Elemwise{minimum,no_inplace} [id B] <TensorType(int64, scalar)> ''
| | | |Subtensor{int64} [id FU] <TensorType(int64, scalar)> ''
| | | |Shape [id FV] <TensorType(int64, vector)> ''
| | | | |Rebroadcast{0} [id FW] <TensorType(bool, 3D)> ''
| | | | |InplaceDimShuffle{x,0,1} [id FX] <TensorType(bool, (True, False, False))> ''
| | | | |Elemwise{second,no_inplace} [id FY] <TensorType(bool, matrix)> ''
| | | | |mask matrix [id FN] <TensorType(bool, matrix)>
| | | | |TensorConstant{(1, 1) of False} [id FZ] <TensorType(bool, (True, True))>
| | | |Constant{0} [id T] <int64>
| | |Subtensor{int64} [id GA] <TensorType(int64, scalar)> ''
| | | |Shape [id FV] <TensorType(int64, vector)> ''
| | | |Constant{1} [id DO] <int64>
| | |Subtensor{int64} [id GB] <TensorType(int64, scalar)> ''
| | |Shape [id FV] <TensorType(int64, vector)> ''
| | |Constant{2} [id DQ] <int64>
| |Rebroadcast{0} [id FW] <TensorType(bool, 3D)> ''
| |ScalarFromTensor [id GC] <int64> ''
| |Subtensor{int64} [id FU] <TensorType(int64, scalar)> ''
|IncSubtensor{Set;:int64:} [id GD] <TensorType(float64, 4D)> ''
| |AllocEmpty{dtype='float64'} [id GE] <TensorType(float64, 4D)> ''
| | |Elemwise{add,no_inplace} [id GF] <TensorType(int64, scalar)> ''
| | | |Elemwise{minimum,no_inplace} [id B] <TensorType(int64, scalar)> ''
| | | |Subtensor{int64} [id GG] <TensorType(int64, scalar)> ''
| | | |Shape [id GH] <TensorType(int64, vector)> ''
| | | | |Rebroadcast{0} [id GI] <TensorType(float64, 4D)> ''
| | | | |InplaceDimShuffle{x,0,1,2} [id GJ] <TensorType(float64, (True, False, False, False))> ''
| | | | |Elemwise{second,no_inplace} [id GK] <TensorType(float64, 3D)> ''
| | | | |block matrix [id DM] <TensorType(float64, 3D)>
| | | | |TensorConstant{(1, 1, 1) of 0.0} [id GL] <TensorType(float64, (True, True, True))>
| | | |Constant{0} [id T] <int64>
| | |Subtensor{int64} [id GM] <TensorType(int64, scalar)> ''
| | | |Shape [id GH] <TensorType(int64, vector)> ''
| | | |Constant{1} [id DO] <int64>
| | |Subtensor{int64} [id GN] <TensorType(int64, scalar)> ''
| | | |Shape [id GH] <TensorType(int64, vector)> ''
| | | |Constant{2} [id DQ] <int64>
| | |Subtensor{int64} [id GO] <TensorType(int64, scalar)> ''
| | |Shape [id GH] <TensorType(int64, vector)> ''
| | |Constant{3} [id DS] <int64>
| |Rebroadcast{0} [id GI] <TensorType(float64, 4D)> ''
| |ScalarFromTensor [id GP] <int64> ''
| |Subtensor{int64} [id GG] <TensorType(int64, scalar)> ''
|IncSubtensor{Set;:int64:} [id GQ] <TensorType(int64, vector)> ''
| |AllocEmpty{dtype='int64'} [id GR] <TensorType(int64, vector)> ''
| | |Elemwise{add,no_inplace} [id GS] <TensorType(int64, scalar)> ''
| | |Elemwise{minimum,no_inplace} [id B] <TensorType(int64, scalar)> ''
| | |TensorConstant{1} [id GT] <TensorType(int64, scalar)>
| |TensorConstant{(1,) of 0} [id GU] <TensorType(int64, vector)>
| |Constant{1} [id DO] <int64>
|Number of points per surface used to split rest-ref [id GV] <TensorType(int32, vector)>
|fault relation matrix [id GW] <TensorType(int32, matrix)>
|<TensorType(float64, scalar)> [id GX] <TensorType(float64, scalar)>
|<TensorType(float64, scalar)> [id GY] <TensorType(float64, scalar)>
|Range [id GZ] <TensorType(float64, scalar)>
|Covariance at 0 [id HA] <TensorType(float64, scalar)>
|<TensorType(float64, scalar)> [id HB] <TensorType(float64, scalar)>
|Nugget effect of gradients [id HC] <TensorType(float64, vector)>
|Nugget effect of scalar [id HD] <TensorType(float64, vector)>
|Attenuation factor [id HE] <TensorType(float64, scalar)>
|Sigmoid Outside [id HF] <TensorType(float64, scalar)>
|Sigmoid slope [id HG] <TensorType(float64, scalar)>
|<TensorType(int32, vector)> [id CL] <TensorType(int32, vector)>
|<TensorType(bool, vector)> [id HH] <TensorType(bool, vector)>
|<TensorType(int32, vector)> [id CH] <TensorType(int32, vector)>
|Coordinates of the grid points to interpolate [id HI] <TensorType(float64, matrix)>
|All the surface_points points at once [id HJ] <TensorType(float64, matrix)>
|Position of the dips [id HK] <TensorType(float64, matrix)>
|Angle of every dip [id HL] <TensorType(float64, vector)>
|Azimuth [id HM] <TensorType(float64, vector)>
|Polarity [id HN] <TensorType(float64, vector)>
|Values that the blocks are taking [id HO] <TensorType(float64, matrix)>
for{cpu,Looping}.1 [id A] <TensorType(float64, matrix)> ''
for{cpu,Looping}.2 [id A] <TensorType(float64, 3D)> ''
for{cpu,Looping}.3 [id A] <TensorType(float64, 3D)> ''
for{cpu,Looping}.4 [id A] <TensorType(bool, 3D)> ''
for{cpu,Looping}.5 [id A] <TensorType(bool, 3D)> ''
for{cpu,Looping}.6 [id A] <TensorType(float64, 4D)> ''
for{cpu,Looping}.7 [id A] <TensorType(int64, vector)> ''
Inner graphs of the scan ops:
for{cpu,Looping}.0 [id A] <TensorType(float64, 4D)> ''
>IncSubtensor{Set;int32, ::, int8:int64:} [id HP] <TensorType(float64, 3D)> ''
> |block matrix[t-1] [id HQ] <TensorType(float64, 3D)> -> [id DF]
> |if{} [id HR] <TensorType(float64, matrix)> ''
> | |Vector controlling if block matrix must be recomputed[t] [id HS] <TensorType(bool, scalar)> -> [id CZ]
> | |if{} [id HT] <TensorType(float64, matrix)> ''
> | | |The series (fault) is finite[t] [id HU] <TensorType(int32, scalar)> -> [id DA]
> | | |Sum{axis=[0], acc_dtype=float64} [id HV] <TensorType(float64, matrix)> 'The chunk of block model of a specific series'
> | | |Sum{axis=[0], acc_dtype=float64} [id HW] <TensorType(float64, matrix)> 'The chunk of block model of a specific series'
> | |Subtensor{int32, ::} [id HX] <TensorType(float64, matrix)> ''
> | |block matrix[t-1] [id HQ] <TensorType(float64, 3D)> -> [id DF]
> | |ScalarFromTensor [id HY] <int32> ''
> | |<TensorType(int32, scalar)> [id HZ] <TensorType(int32, scalar)> -> [id DD]
> |ScalarFromTensor [id HY] <int32> ''
> |Constant{0} [id IA] <int8>
> |ScalarFromTensor [id IB] <int64> ''
> |Elemwise{add,no_inplace} [id IC] <TensorType(int64, scalar)> ''
> |Elemwise{add,no_inplace} [id ID] <TensorType(int64, scalar)> ''
> | |Subtensor{int64} [id IE] <TensorType(int64, scalar)> ''
> | | |Shape [id IF] <TensorType(int64, vector)> ''
> | | | |Coordinates of the grid points to interpolate_copy [id IG] <TensorType(float64, matrix)> -> [id HI]
> | | |Constant{0} [id IH] <int64>
> | |Elemwise{mul,no_inplace} [id II] <TensorType(int64, scalar)> ''
> | |TensorConstant{2} [id IJ] <TensorType(int8, scalar)>
> | |Elemwise{sub,no_inplace} [id IK] <TensorType(int64, scalar)> ''
> | |Subtensor{int64} [id IL] <TensorType(int64, scalar)> ''
> | | |Shape [id IM] <TensorType(int64, vector)> ''
> | | | |All the surface_points points at once_copy [id IN] <TensorType(float64, matrix)> -> [id HJ]
> | | |Constant{0} [id IH] <int64>
> | |Subtensor{int64} [id IO] <TensorType(int64, scalar)> ''
> | |Shape [id IP] <TensorType(int64, vector)> ''
> | | |Number of points per surface used to split rest-ref_copy [id IQ] <TensorType(int32, vector)> -> [id GV]
> | |Constant{0} [id IH] <int64>
> |TensorConstant{0} [id IR] <TensorType(int8, scalar)>
>IncSubtensor{Set;int32:int32:} [id IS] <TensorType(float64, vector)> ''
> |Weights vector[t-1] [id IT] <TensorType(float64, vector)> -> [id DU]
> |if{} [id IU] <TensorType(float64, vector)> ''
> | |Vector controlling if weights must be recomputed[t] [id IV] <TensorType(bool, scalar)> -> [id CX]
> | |Reshape{1} [id IW] <TensorType(float64, vector)> 'Dual Kriging parameters'
> | |Subtensor{int32:int32:} [id IX] <TensorType(float64, vector)> ''
> | |Weights vector[t-1] [id IT] <TensorType(float64, vector)> -> [id DU]
> | |ScalarFromTensor [id IY] <int32> ''
> | | |Length of weights in every series[t] [id IZ] <TensorType(int32, scalar)> -> [id CS]
> | |ScalarFromTensor [id JA] <int32> ''
> | |Length of weights in every series[t+1] [id JB] <TensorType(int32, scalar)> -> [id CT]
> |ScalarFromTensor [id IY] <int32> ''
> |ScalarFromTensor [id JA] <int32> ''
>IncSubtensor{Set;int32, int8:int64:} [id JC] <TensorType(float64, matrix)> ''
> |Scalar matrix[t-1] [id JD] <TensorType(float64, matrix)> -> [id EE]
> |if{} [id JE] <TensorType(float64, vector)> ''
> | |Vector controlling if scalar matrix must be recomputed[t] [id JF] <TensorType(bool, scalar)> -> [id CY]
> | |Subtensor{int64} [id JG] <TensorType(float64, vector)> 'Value of the potential field at every point'
> | |Subtensor{int32} [id JH] <TensorType(float64, vector)> ''
> | |Scalar matrix[t-1] [id JD] <TensorType(float64, matrix)> -> [id EE]
> | |ScalarFromTensor [id HY] <int32> ''
> |ScalarFromTensor [id HY] <int32> ''
> |Constant{0} [id IA] <int8>
> |ScalarFromTensor [id IB] <int64> ''
>AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JI] <TensorType(float64, matrix)> ''
> |<TensorType(float64, matrix)> [id JJ] <TensorType(float64, matrix)> -> [id EP]
> |AdvancedSubtensor1 [id JK] <TensorType(float64, vector)> ''
> | |Subtensor{int64:int64:} [id JL] <TensorType(float64, vector)> ''
> | | |if{} [id JE] <TensorType(float64, vector)> ''
> | | |ScalarFromTensor [id JM] <int64> ''
> | | | |Elemwise{mul,no_inplace} [id JN] <TensorType(int64, scalar)> ''
> | | | |TensorConstant{-2} [id JO] <TensorType(int8, scalar)>
> | | | |Elemwise{sub,no_inplace} [id IK] <TensorType(int64, scalar)> ''
> | | |ScalarFromTensor [id JP] <int64> ''
> | | |Elemwise{neg,no_inplace} [id JQ] <TensorType(int64, scalar)> ''
> | | |Elemwise{sub,no_inplace} [id IK] <TensorType(int64, scalar)> ''
> | |Subtensor{int32:int32:} [id JR] <TensorType(int32, vector)> ''
> | |CumOp{None, add} [id JS] <TensorType(int32, vector)> 'Number of points per surfaces after rest-ref. This is used for finding the differentsurface points withing a layer.'
> | |ScalarFromTensor [id JT] <int32> ''
> | | |List with the number of surfaces[t] [id JU] <TensorType(int32, scalar)> -> [id CU]
> | |ScalarFromTensor [id JV] <int32> ''
> | |List with the number of surfaces[t+1] [id JW] <TensorType(int32, scalar)> -> [id CV]
> |<TensorType(int32, scalar)> [id HZ] <TensorType(int32, scalar)> -> [id DD]
> |Elemwise{sub,no_inplace} [id JX] <TensorType(int32, vector)> ''
> |Subtensor{int32:int32:} [id JY] <TensorType(int32, vector)> ''
> | |TensorConstant{[ 1 2..4998 4999]} [id JZ] <TensorType(int32, vector)>
> | |ScalarFromTensor [id JT] <int32> ''
> | |ScalarFromTensor [id JV] <int32> ''
> |TensorConstant{(1,) of 1} [id KA] <TensorType(int8, (True,))>
>IncSubtensor{Set;int32, int8:int64:} [id KB] <TensorType(bool, matrix)> ''
> |IncSubtensor{Set;int64:int32:, int8:int64:} [id KC] <TensorType(bool, matrix)> ''
> | |IncSubtensor{Set;int32:int32:, int8:int64:} [id KD] <TensorType(bool, matrix)> ''
> | | |mask matrix[t-1] [id KE] <TensorType(bool, matrix)> -> [id FG]
> | | |if{} [id KF] <TensorType(bool, vector)> ''
> | | | |<TensorType(int32, scalar)> [id KG] <TensorType(int32, scalar)> -> [id DC]
> | | | |Elemwise{gt,no_inplace} [id KH] <TensorType(bool, vector)> ''
> | | | | |if{} [id JE] <TensorType(float64, vector)> ''
> | | | | |InplaceDimShuffle{x} [id KI] <TensorType(float64, (True,))> ''
> | | | | |MaxAndArgmax{axis=(0,)}.0 [id KJ] <TensorType(float64, scalar)> 'max'
> | | | |Subtensor{int32, int8:int64:} [id KK] <TensorType(bool, vector)> ''
> | | | |mask matrix[t-1] [id KE] <TensorType(bool, matrix)> -> [id FG]
> | | | |ScalarFromTensor [id KL] <int32> ''
> | | | | |Elemwise{sub,no_inplace} [id KM] <TensorType(int32, scalar)> ''
> | | | | |<TensorType(int32, scalar)> [id HZ] <TensorType(int32, scalar)> -> [id DD]
> | | | | |TensorConstant{1} [id KN] <TensorType(int8, scalar)>
> | | | |Constant{0} [id IA] <int8>
> | | | |ScalarFromTensor [id IB] <int64> ''
> | | |ScalarFromTensor [id KL] <int32> ''
> | | |ScalarFromTensor [id HY] <int32> ''
> | | |Constant{0} [id IA] <int8>
> | | |ScalarFromTensor [id IB] <int64> ''
> | |Subtensor{::int64} [id KO] <TensorType(bool, matrix)> ''
> | | |CumOp{0, mul} [id KP] <TensorType(bool, matrix)> ''
> | | | |Subtensor{::int64} [id KQ] <TensorType(bool, matrix)> ''
> | | | |Subtensor{int64:int32:, int8:int64:} [id KR] <TensorType(bool, matrix)> ''
> | | | | |IncSubtensor{Set;int32:int32:, int8:int64:} [id KD] <TensorType(bool, matrix)> ''
> | | | | |ScalarFromTensor [id KS] <int64> ''
> | | | | | |Elemwise{sub,no_inplace} [id KT] <TensorType(int64, scalar)> ''
> | | | | | |<TensorType(int32, scalar)> [id HZ] <TensorType(int32, scalar)> -> [id DD]
> | | | | | |Elemwise{mul,no_inplace} [id KU] <TensorType(int64, scalar)> ''
> | | | | | |Elemwise{mul,no_inplace} [id KV] <TensorType(int64, scalar)> ''
> | | | | | | |Elemwise{add,no_inplace} [id KW] <TensorType(int64, scalar)> ''
> | | | | | | | |<TensorType(int64, scalar)> [id KX] <TensorType(int64, scalar)> -> [id GQ]
> | | | | | | | |Elemwise{add,no_inplace} [id KY] <TensorType(int32, scalar)> ''
> | | | | | | | |Subtensor{int32} [id KZ] <TensorType(int32, scalar)> ''
> | | | | | | | | |<TensorType(int32, vector)> [id LA] <TensorType(int32, vector)> -> [id CL]
> | | | | | | | | |ScalarFromTensor [id HY] <int32> ''
> | | | | | | | |Subtensor{int32} [id LB] <TensorType(bool, scalar)> ''
> | | | | | | | |<TensorType(bool, vector)> [id LC] <TensorType(bool, vector)> -> [id HH]
> | | | | | | | |ScalarFromTensor [id HY] <int32> ''
> | | | | | | |Elemwise{add,no_inplace} [id KY] <TensorType(int32, scalar)> ''
> | | | | | |Subtensor{int64} [id LD] <TensorType(int32, scalar)> ''
> | | | | | |<TensorType(int32, vector)> [id LA] <TensorType(int32, vector)> -> [id CL]
> | | | | | |ScalarFromTensor [id LE] <int64> ''
> | | | | | |Elemwise{sub,no_inplace} [id LF] <TensorType(int64, scalar)> ''
> | | | | | |<TensorType(int32, scalar)> [id HZ] <TensorType(int32, scalar)> -> [id DD]
> | | | | | |<TensorType(int64, scalar)> [id KX] <TensorType(int64, scalar)> -> [id GQ]
> | | | | |ScalarFromTensor [id HY] <int32> ''
> | | | | |Constant{0} [id IA] <int8>
> | | | | |ScalarFromTensor [id IB] <int64> ''
> | | | |Constant{-1} [id LG] <int64>
> | | |Constant{-1} [id LG] <int64>
> | |ScalarFromTensor [id KS] <int64> ''
> | |ScalarFromTensor [id HY] <int32> ''
> | |Constant{0} [id IA] <int8>
> | |ScalarFromTensor [id IB] <int64> ''
> |if{} [id LH] <TensorType(bool, vector)> ''
> | |<TensorType(int32, scalar)> [id LI] <TensorType(int32, scalar)> -> [id DB]
> | |Elemwise{gt,no_inplace} [id LJ] <TensorType(bool, vector)> ''
> | | |if{} [id JE] <TensorType(float64, vector)> ''
> | | |InplaceDimShuffle{x} [id LK] <TensorType(float64, (True,))> ''
> | | |Elemwise{neg,no_inplace} [id LL] <TensorType(float64, scalar)> ''
> | | |MaxAndArgmax{axis=(0,)}.0 [id LM] <TensorType(float64, scalar)> 'max'
> | |Elemwise{mul,no_inplace} [id LN] <TensorType(bool, vector)> ''
> | |InplaceDimShuffle{x} [id LO] <TensorType(bool, (True,))> ''
> | | |Elemwise{invert,no_inplace} [id LP] <TensorType(bool, scalar)> ''
> | | |Subtensor{int32} [id LB] <TensorType(bool, scalar)> ''
> | |Elemwise{second,no_inplace} [id LQ] <TensorType(bool, vector)> ''
> | |if{} [id JE] <TensorType(float64, vector)> ''
> | |TensorConstant{(1,) of True} [id LR] <TensorType(bool, (True,))>
> |ScalarFromTensor [id HY] <int32> ''
> |Constant{0} [id IA] <int8>
> |ScalarFromTensor [id IB] <int64> ''
>AdvancedBooleanIncSubtensor{inplace=False, set_instead_of_inc=True} [id LS] <TensorType(bool, matrix)> ''
> |<TensorType(bool, matrix)> [id LT] <TensorType(bool, matrix)> -> [id FR]
> |Elemwise{add,no_inplace} [id LU] <TensorType(bool, vector)> ''
> | |if{} [id LH] <TensorType(bool, vector)> ''
> | |if{} [id LV] <TensorType(bool, vector)> ''
> | |Subtensor{int32} [id LB] <TensorType(bool, scalar)> ''
> | |Elemwise{gt,no_inplace} [id LJ] <TensorType(bool, vector)> ''
> | |Elemwise{second,no_inplace} [id LW] <TensorType(bool, vector)> ''
> | |if{} [id JE] <TensorType(float64, vector)> ''
> | |TensorConstant{(1,) of False} [id LX] <TensorType(bool, (True,))>
> |Elemwise{mul,no_inplace} [id LY] <TensorType(bool, vector)> ''
> | |Subtensor{:int64:} [id LZ] <TensorType(bool, vector)> ''
> | | |<TensorType(bool, vector)> [id LC] <TensorType(bool, vector)> -> [id HH]
> | | |ScalarFromTensor [id MA] <int64> ''
> | | |Subtensor{int64} [id MB] <TensorType(int64, scalar)> ''
> | | |Shape [id MC] <TensorType(int64, vector)> ''
> | | | |<TensorType(int32, vector)> [id MD] <TensorType(int32, vector)> -> [id CH]
> | | |Constant{0} [id IH] <int64>
> | |Elemwise{invert,no_inplace} [id ME] <TensorType(bool, vector)> ''
> | |Elemwise{Cast{bool}} [id MF] <TensorType(bool, vector)> ''
> | |Subtensor{:int64:} [id MG] <TensorType(int32, vector)> ''
> | |Subtensor{::, int8} [id MH] <TensorType(int32, vector)> ''
> | | |fault relation matrix_copy [id MI] <TensorType(int32, matrix)> -> [id GW]
> | | |ScalarFromTensor [id MJ] <int8> ''
> | | |Elemwise{Cast{int8}} [id MK] <TensorType(int8, scalar)> ''
> | | |<TensorType(int32, scalar)> [id HZ] <TensorType(int32, scalar)> -> [id DD]
> | |ScalarFromTensor [id MA] <int64> ''
> |MakeSlice [id ML] <slice> ''
> |TensorConstant{0} [id IR] <TensorType(int8, scalar)>
> |Elemwise{add,no_inplace} [id IC] <TensorType(int64, scalar)> ''
> |NoneConst [id MM] <NoneTypeT>
>IncSubtensor{Set;int32, ::, int8:int64:} [id MN] <TensorType(float64, 3D)> ''
> |<TensorType(float64, 3D)> [id MO] <TensorType(float64, 3D)> -> [id GD]
> |if{} [id HR] <TensorType(float64, matrix)> ''
> |ScalarFromTensor [id HY] <int32> ''
> |Constant{0} [id IA] <int8>
> |ScalarFromTensor [id IB] <int64> ''
>Elemwise{mul,no_inplace} [id KU] <TensorType(int64, scalar)> ''
for{cpu,Looping}.1 [id A] <TensorType(float64, matrix)> ''
>IncSubtensor{Set;int32, ::, int8:int64:} [id HP] <TensorType(float64, 3D)> ''
>IncSubtensor{Set;int32:int32:} [id IS] <TensorType(float64, vector)> ''
>IncSubtensor{Set;int32, int8:int64:} [id JC] <TensorType(float64, matrix)> ''
>AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JI] <TensorType(float64, matrix)> ''
>IncSubtensor{Set;int32, int8:int64:} [id KB] <TensorType(bool, matrix)> ''
>AdvancedBooleanIncSubtensor{inplace=False, set_instead_of_inc=True} [id LS] <TensorType(bool, matrix)> ''
>IncSubtensor{Set;int32, ::, int8:int64:} [id MN] <TensorType(float64, 3D)> ''
>Elemwise{mul,no_inplace} [id KU] <TensorType(int64, scalar)> ''
for{cpu,Looping}.2 [id A] <TensorType(float64, 3D)> ''
>IncSubtensor{Set;int32, ::, int8:int64:} [id HP] <TensorType(float64, 3D)> ''
>IncSubtensor{Set;int32:int32:} [id IS] <TensorType(float64, vector)> ''
>IncSubtensor{Set;int32, int8:int64:} [id JC] <TensorType(float64, matrix)> ''
>AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JI] <TensorType(float64, matrix)> ''
>IncSubtensor{Set;int32, int8:int64:} [id KB] <TensorType(bool, matrix)> ''
>AdvancedBooleanIncSubtensor{inplace=False, set_instead_of_inc=True} [id LS] <TensorType(bool, matrix)> ''
>IncSubtensor{Set;int32, ::, int8:int64:} [id MN] <TensorType(float64, 3D)> ''
>Elemwise{mul,no_inplace} [id KU] <TensorType(int64, scalar)> ''
for{cpu,Looping}.3 [id A] <TensorType(float64, 3D)> ''
>IncSubtensor{Set;int32, ::, int8:int64:} [id HP] <TensorType(float64, 3D)> ''
>IncSubtensor{Set;int32:int32:} [id IS] <TensorType(float64, vector)> ''
>IncSubtensor{Set;int32, int8:int64:} [id JC] <TensorType(float64, matrix)> ''
>AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JI] <TensorType(float64, matrix)> ''
>IncSubtensor{Set;int32, int8:int64:} [id KB] <TensorType(bool, matrix)> ''
>AdvancedBooleanIncSubtensor{inplace=False, set_instead_of_inc=True} [id LS] <TensorType(bool, matrix)> ''
>IncSubtensor{Set;int32, ::, int8:int64:} [id MN] <TensorType(float64, 3D)> ''
>Elemwise{mul,no_inplace} [id KU] <TensorType(int64, scalar)> ''
for{cpu,Looping}.4 [id A] <TensorType(bool, 3D)> ''
>IncSubtensor{Set;int32, ::, int8:int64:} [id HP] <TensorType(float64, 3D)> ''
>IncSubtensor{Set;int32:int32:} [id IS] <TensorType(float64, vector)> ''
>IncSubtensor{Set;int32, int8:int64:} [id JC] <TensorType(float64, matrix)> ''
>AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JI] <TensorType(float64, matrix)> ''
>IncSubtensor{Set;int32, int8:int64:} [id KB] <TensorType(bool, matrix)> ''
>AdvancedBooleanIncSubtensor{inplace=False, set_instead_of_inc=True} [id LS] <TensorType(bool, matrix)> ''
>IncSubtensor{Set;int32, ::, int8:int64:} [id MN] <TensorType(float64, 3D)> ''
>Elemwise{mul,no_inplace} [id KU] <TensorType(int64, scalar)> ''
for{cpu,Looping}.5 [id A] <TensorType(bool, 3D)> ''
>IncSubtensor{Set;int32, ::, int8:int64:} [id HP] <TensorType(float64, 3D)> ''
>IncSubtensor{Set;int32:int32:} [id IS] <TensorType(float64, vector)> ''
>IncSubtensor{Set;int32, int8:int64:} [id JC] <TensorType(float64, matrix)> ''
>AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JI] <TensorType(float64, matrix)> ''
>IncSubtensor{Set;int32, int8:int64:} [id KB] <TensorType(bool, matrix)> ''
>AdvancedBooleanIncSubtensor{inplace=False, set_instead_of_inc=True} [id LS] <TensorType(bool, matrix)> ''
>IncSubtensor{Set;int32, ::, int8:int64:} [id MN] <TensorType(float64, 3D)> ''
>Elemwise{mul,no_inplace} [id KU] <TensorType(int64, scalar)> ''
for{cpu,Looping}.6 [id A] <TensorType(float64, 4D)> ''
>IncSubtensor{Set;int32, ::, int8:int64:} [id HP] <TensorType(float64, 3D)> ''
>IncSubtensor{Set;int32:int32:} [id IS] <TensorType(float64, vector)> ''
>IncSubtensor{Set;int32, int8:int64:} [id JC] <TensorType(float64, matrix)> ''
>AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JI] <TensorType(float64, matrix)> ''
>IncSubtensor{Set;int32, int8:int64:} [id KB] <TensorType(bool, matrix)> ''
>AdvancedBooleanIncSubtensor{inplace=False, set_instead_of_inc=True} [id LS] <TensorType(bool, matrix)> ''
>IncSubtensor{Set;int32, ::, int8:int64:} [id MN] <TensorType(float64, 3D)> ''
>Elemwise{mul,no_inplace} [id KU] <TensorType(int64, scalar)> ''
for{cpu,Looping}.7 [id A] <TensorType(int64, vector)> ''
>IncSubtensor{Set;int32, ::, int8:int64:} [id HP] <TensorType(float64, 3D)> ''
>IncSubtensor{Set;int32:int32:} [id IS] <TensorType(float64, vector)> ''
>IncSubtensor{Set;int32, int8:int64:} [id JC] <TensorType(float64, matrix)> ''
>AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JI] <TensorType(float64, matrix)> ''
>IncSubtensor{Set;int32, int8:int64:} [id KB] <TensorType(bool, matrix)> ''
>AdvancedBooleanIncSubtensor{inplace=False, set_instead_of_inc=True} [id LS] <TensorType(bool, matrix)> ''
>IncSubtensor{Set;int32, ::, int8:int64:} [id MN] <TensorType(float64, 3D)> ''
>Elemwise{mul,no_inplace} [id KU] <TensorType(int64, scalar)> ''
Storage map footprint:
- Coordinates of the grid points to interpolate, Input, Shape: (125000, 3), ElemSize: 8 Byte(s), TotalSize: 3000000 Byte(s)
- IncSubtensor{Set;:int64:}.0, Shape: (2, 1, 1, 125002), ElemSize: 8 Byte(s), TotalSize: 2000032 Byte(s)
- IncSubtensor{Set;:int64:}.0, Shape: (2, 1, 125002), ElemSize: 8 Byte(s), TotalSize: 2000032 Byte(s)
- IncSubtensor{Set;:int64:}.0, Shape: (2, 1, 1, 125002), ElemSize: 8 Byte(s), TotalSize: 2000032 Byte(s)
- for{cpu,Looping}.0, Shape: (2, 1, 1, 125002), ElemSize: 8 Byte(s), TotalSize: 2000032 Byte(s)
- for{cpu,Looping}.2, Shape: (2, 1, 125002), ElemSize: 8 Byte(s), TotalSize: 2000032 Byte(s)
- for{cpu,Looping}.6, Shape: (2, 1, 1, 125002), ElemSize: 8 Byte(s), TotalSize: 2000032 Byte(s)
- block matrix, Shared Input, Shape: (1, 1, 125002), ElemSize: 8 Byte(s), TotalSize: 1000016 Byte(s)
- Scalar matrix, Shared Input, Shape: (1, 125002), ElemSize: 8 Byte(s), TotalSize: 1000016 Byte(s)
- IncSubtensor{Set;:int64:}.0, Shape: (2, 1, 125002), ElemSize: 1 Byte(s), TotalSize: 250004 Byte(s)
- IncSubtensor{Set;:int64:}.0, Shape: (2, 1, 125002), ElemSize: 1 Byte(s), TotalSize: 250004 Byte(s)
- for{cpu,Looping}.4, Shape: (2, 1, 125002), ElemSize: 1 Byte(s), TotalSize: 250004 Byte(s)
- for{cpu,Looping}.5, Shape: (2, 1, 125002), ElemSize: 1 Byte(s), TotalSize: 250004 Byte(s)
- mask matrix, Shared Input, Shape: (1, 125002), ElemSize: 1 Byte(s), TotalSize: 125002 Byte(s)
- TensorConstant{[ 0 1..4998 4999]}, Shape: (5000,), ElemSize: 4 Byte(s), TotalSize: 20000 Byte(s)
- Vector controlling if weights must be recomputed, Input, Shape: (1000,), ElemSize: 1 Byte(s), TotalSize: 1000 Byte(s)
- Vector controlling if scalar matrix must be recomputed, Input, Shape: (1000,), ElemSize: 1 Byte(s), TotalSize: 1000 Byte(s)
- Vector controlling if block matrix must be recomputed, Input, Shape: (1000,), ElemSize: 1 Byte(s), TotalSize: 1000 Byte(s)
- IncSubtensor{Set;:int64:}.0, Shape: (2, 10), ElemSize: 8 Byte(s), TotalSize: 160 Byte(s)
- for{cpu,Looping}.1, Shape: (2, 10), ElemSize: 8 Byte(s), TotalSize: 160 Byte(s)
- All the surface_points points at once, Input, Shape: (4, 3), ElemSize: 8 Byte(s), TotalSize: 96 Byte(s)
- Weights vector, Shared Input, Shape: (10,), ElemSize: 8 Byte(s), TotalSize: 80 Byte(s)
- Position of the dips, Input, Shape: (2, 3), ElemSize: 8 Byte(s), TotalSize: 48 Byte(s)
- Nugget effect of gradients, Shared Input, Shape: (6,), ElemSize: 8 Byte(s), TotalSize: 48 Byte(s)
- Nugget effect of scalar, Shared Input, Shape: (4,), ElemSize: 8 Byte(s), TotalSize: 32 Byte(s)
- Values that the blocks are taking, Input, Shape: (1, 3), ElemSize: 8 Byte(s), TotalSize: 24 Byte(s)
- Angle of every dip, Input, Shape: (2,), ElemSize: 8 Byte(s), TotalSize: 16 Byte(s)
- Azimuth, Input, Shape: (2,), ElemSize: 8 Byte(s), TotalSize: 16 Byte(s)
- Polarity, Input, Shape: (2,), ElemSize: 8 Byte(s), TotalSize: 16 Byte(s)
- IncSubtensor{Set;:int64:}.0, Shape: (2,), ElemSize: 8 Byte(s), TotalSize: 16 Byte(s)
- IncSubtensor{Set;:int64:}.0, Shape: (2, 1, 1), ElemSize: 8 Byte(s), TotalSize: 16 Byte(s)
- for{cpu,Looping}.3, Shape: (2, 1, 1), ElemSize: 8 Byte(s), TotalSize: 16 Byte(s)
- for{cpu,Looping}.7, Shape: (2,), ElemSize: 8 Byte(s), TotalSize: 16 Byte(s)
- Length of surface_points in every series, Shared Input, Shape: (2,), ElemSize: 4 Byte(s), TotalSize: 8 Byte(s)
- Length of foliations in every series, Shared Input, Shape: (2,), ElemSize: 4 Byte(s), TotalSize: 8 Byte(s)
- Length of weights in every series, Shared Input, Shape: (2,), ElemSize: 4 Byte(s), TotalSize: 8 Byte(s)
- List with the number of surfaces, Shared Input, Shape: (2,), ElemSize: 4 Byte(s), TotalSize: 8 Byte(s)
- The series (fault) is finite, Shared Input, Shape: (2,), ElemSize: 4 Byte(s), TotalSize: 8 Byte(s)
- Number of points per surface used to split rest-ref, Shared Input, Shape: (2,), ElemSize: 4 Byte(s), TotalSize: 8 Byte(s)
- <TensorType(float64, scalar)>, Shared Input, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- <TensorType(float64, scalar)>, Shared Input, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- Range, Shared Input, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- Covariance at 0, Shared Input, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- <TensorType(float64, scalar)>, Shared Input, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- Attenuation factor, Shared Input, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- Sigmoid Outside, Shared Input, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- Sigmoid slope, Shared Input, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- <TensorType(float64, scalar)>, Shared Input, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- Constant{0}, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- Constant{1}, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- Constant{-1}, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- TensorConstant{5000}, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- Elemwise{minimum,no_inplace}.0, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- TensorConstant{1}, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- TensorConstant{(1,) of 0}, Shape: (1,), ElemSize: 8 Byte(s), TotalSize: 8 Byte(s)
- TensorConstant{(1, 1, 1) of 0.0}, Shape: (1, 1, 1), ElemSize: 8 Byte(s), TotalSize: 8 Byte(s)
- Constant{3}, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- Constant{2}, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- TensorConstant{0.0}, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
- Grade of the universal drift, Shared Input, Shape: (1,), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
- <TensorType(int32, vector)>, Shared Input, Shape: (1,), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
- <TensorType(int32, vector)>, Shared Input, Shape: (1,), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
- fault relation matrix, Shared Input, Shape: (1, 1), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
- Subtensor{:int64:}.0, Shape: (1,), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
- Subtensor{:int64:}.0, Shape: (1,), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
- Subtensor{:int64:}.0, Shape: (1,), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
- Subtensor{:int64:}.0, Shape: (1,), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
- Subtensor{:int64:}.0, Shape: (1,), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
- Subtensor{:int64:}.0, Shape: (1,), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
- Subtensor{:int64:}.0, Shape: (1,), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
- Subtensor{:int64:}.0, Shape: (1,), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
- Subtensor{:int64:}.0, Shape: (1,), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
- Subtensor{:int64:}.0, Shape: (1,), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
- Subtensor{:int64:}.0, Shape: (1,), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
- Subtensor{:int64:}.0, Shape: (1,), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
- Subtensor{:int64:}.0, Shape: (1,), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
- TensorConstant{(1,) of -1}, Shape: (1,), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
- <TensorType(bool, vector)>, Shared Input, Shape: (1,), ElemSize: 1 Byte(s), TotalSize: 1 Byte(s)
- TensorConstant{(1, 1) of False}, Shape: (1, 1), ElemSize: 1 Byte(s), TotalSize: 1 Byte(s)
- Subtensor{:int64:}.0, Shape: (1,), ElemSize: 1 Byte(s), TotalSize: 1 Byte(s)
- Subtensor{:int64:}.0, Shape: (1,), ElemSize: 1 Byte(s), TotalSize: 1 Byte(s)
- Subtensor{:int64:}.0, Shape: (1,), ElemSize: 1 Byte(s), TotalSize: 1 Byte(s)
- TensorConstant{0}, Shape: (), ElemSize: 1 Byte(s), TotalSize: 1.0 Byte(s)
- Full block matrix for faults or drift. We take 2 times len points for the faultdrift., Input, Shape: (0, 125002), ElemSize: 8 Byte(s), TotalSize: 0 Byte(s)
TotalSize: 18149284.0 Byte(s) 0.017 GB
TotalSize inputs: 5148633.0 Byte(s) 0.005 GB
In [16]:
geo_model.qi.get('surfaces')
In [17]:
geo_model.qi.get('series')
In [18]:
geo_model.qi.get('faults')
Fault colors changed. If you do not like this behavior, set change_color to False.
i:\pycharmprojects\gempy\gempy\core\solution.py:315: UserWarning: Surfaces not computed due to: No surface found at the given iso value.. The surface is: Series: No surface found at the given iso value.; Surface Number:0
'; Surface Number:' + str(s_n))
Fault colors changed. If you do not like this behavior, set change_color to False.
Fault colors changed. If you do not like this behavior, set change_color to False.
In [18]:
geo_model.qi.get('faults_relations')
In [19]:
geo_model.stack
Out[19]:
order_series
BottomRelation
isActive
isFault
isFinite
Default series
1
Erosion
True
False
False
series1
2
Fault
True
True
False
In [22]:
gp.compute_model(geo_model)
Out[22]:
Lithology ids
[0. 0. 0. ... 0. 0. 0.]
In [21]:
geo_model.surface_points
Out[21]:
X
Y
Z
smooth
surface
0
299.600201
772.615765
-552.816467
0.000001
surface1
1
582.115016
663.868433
-740.618860
0.000001
surface1
4
758.235692
11.568112
-629.601263
0.000001
surface1
5
473.917077
79.266347
-337.568765
0.000001
surface1
3
77.856401
473.182159
-240.869901
0.000001
surface2
2
484.397244
447.213061
-335.870346
0.000001
surface2
i:\pycharmprojects\gempy\gempy\core\solution.py:315: UserWarning: Surfaces not computed due to: No surface found at the given iso value.. The surface is: Series: No surface found at the given iso value.; Surface Number:0
'; Surface Number:' + str(s_n))
In [19]:
geo_model.interpolator.theano_graph.not_l.set_value(1.)
vtk_object.update_model()
../../..\gempy\core\solution.py:284: UserWarning: Attribute error. Using non masked marching cubesmarching_cubes_lewiner() got an unexpected keyword argument 'mask'.
warnings.warn('Attribute error. Using non masked marching cubes' + str(e)+'.')
In [20]:
geo_model.interpolator.theano_graph.ellipse_factor_exponent.set_value(50)
In [21]:
vtk_object.update_model()
Content source: cgre-aachen/gempy
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