Problem modeling faults - Full block matrix for faults or drift #804

lucianomarquetto · 2023-05-30T14:25:39Z

lucianomarquetto
May 30, 2023

Hi all,

I am having a hard time figuring out how to deal with a problem modeling three faults. This has been troubling me for a while, but as the faults have amost the same azimuth, I was putting azimuth & dip (180,85) for the three (in my rotated model - see #776). Nonetheless, now I wanted to use their real azimuth, but then I get an error. First I thought it was something related to my rotated model but even when I do not rotate the model I still get the problem.

My faults have 2 points each (2 surfaces, 2 orientations), which worked fine if they are all 180 azimuth and 85 dip.

The error I get when trying to put different azimuths for each is the huge text below.

Any idea what is happening here and how can I solve it?

Thanks!

EDIT1: I checked both #733 and #583, but in my case the error only arrives when I change those three parameters (azimuth of each fault), so I do not know how I can step-wise increase the complexity of the model. If I change one azimuth it already give me this error.

scan_perform.pyx in theano.scan_module.scan_perform.perform()

~/mambaforge/envs/gempy/lib/python3.7/site-packages/theano/gof/op.py in rval(p, i, o, n)
891 def rval(p=p, i=node_input_storage, o=node_output_storage, n=node):
--> 892 r = p(n, [x[0] for x in i], o)
893 for o in node.outputs:

~/mambaforge/envs/gempy/lib/python3.7/site-packages/theano/tensor/slinalg.py in perform(self, node, inputs, output_storage)
254 else:
--> 255 rval = scipy.linalg.solve(A, b)
256 output_storage[0][0] = rval

~/mambaforge/envs/gempy/lib/python3.7/site-packages/scipy/linalg/basic.py in solve(a, b, sym_pos, lower, overwrite_a, overwrite_b, debug, check_finite, assume_a, transposed)
218 lu, ipvt, info = getrf(a1, overwrite_a=overwrite_a)
--> 219 _solve_check(n, info)
220 x, info = getrs(lu, ipvt, b1,

~/mambaforge/envs/gempy/lib/python3.7/site-packages/scipy/linalg/basic.py in _solve_check(n, info, lamch, rcond)
28 elif 0 < info:
---> 29 raise LinAlgError('Matrix is singular.')
30

LinAlgError: Matrix is singular.
Apply node that caused the error: Solve{A_structure='general', lower=False, overwrite_A=False, overwrite_b=False}(Block Covariance Matrix, b vector)
Toposort index: 609
Inputs types: [TensorType(float64, matrix), TensorType(float64, vector)]
Inputs shapes: [(121, 121), (121,)]
Inputs strides: [(968, 8), (8,)]
Inputs values: ['not shown', 'not shown']
Inputs type_num: [12, 12]
Outputs clients: [[Shape(Solve{A_structure='general', lower=False, overwrite_A=False, overwrite_b=False}.0), Reshape{1}(Solve{A_structure='general', lower=False, overwrite_A=False, overwrite_b=False}.0, MakeVector{dtype='int64'}.0)]]

Backtrace when the node is created(use Theano flag traceback.limit=N to make it longer):
File "/tmp/ipykernel_44248/3227243222.py", line 2, in
gp.set_interpolator(geo_model, theano_optimizer='fast_compile', verbose=[])
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/gempy/api_modules/setters.py", line 108, in set_interpolator
geo_model._interpolator.compile_th_fn_geo(inplace=True, grid=grid)
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/gempy/core/interpolator.py", line 1124, in compile_th_fn_geo
self.theano_graph.theano_output(),
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/gempy/core/theano_modules/theano_graph_pro.py", line 631, in theano_output
solutions[:9] = self.compute_series()
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/gempy/core/theano_modules/theano_graph_pro.py", line 496, in compute_series
profile=False
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/theano/scan_module/scan.py", line 774, in scan
condition, outputs, updates = scan_utils.get_updates_and_outputs(fn(*args))
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/gempy/core/theano_modules/theano_graph_pro.py", line 2137, in compute_a_series
self.solve_kriging(b),
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/gempy/core/theano_modules/theano_graph_pro.py", line 1396, in solve_kriging
DK_parameters = theano.tensor.slinalg.solve(C_matrix, b)

Debugprint of the apply node:
Solve{A_structure='general', lower=False, overwrite_A=False, overwrite_b=False} [id A] <TensorType(float64, vector)> ''
|IncSubtensor{Set;int64::, int64:int64:} [id B] <TensorType(float64, matrix)> 'Block Covariance Matrix'
|IncSubtensor{Set;int64:int64:} [id C] <TensorType(float64, vector)> 'b vector'

HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.

During handling of the above exception, another exception occurred:

LinAlgError Traceback (most recent call last)
/tmp/ipykernel_44248/3766651416.py in
1 #Compute model
----> 2 gp.compute_model(geo_model)

~/mambaforge/envs/gempy/lib/python3.7/site-packages/gempy/gempy_api.py in compute_model(model, output, at, compute_mesh, reset_weights, reset_scalar, reset_block, sort_surfaces, debug, set_solutions, **kwargs)
289 model._interpolator.reset_flow_control_initial_results(reset_weights, reset_scalar, reset_block)
290
--> 291 sol = model._interpolator.theano_function(*i)
292
293 if debug is True or set_solutions is False:

~/mambaforge/envs/gempy/lib/python3.7/site-packages/theano/compile/function_module.py in call(self, *args, **kwargs)
915 node=self.fn.nodes[self.fn.position_of_error],
916 thunk=thunk,
--> 917 storage_map=getattr(self.fn, 'storage_map', None))
918 else:
919 # old-style linkers raise their own exceptions

~/mambaforge/envs/gempy/lib/python3.7/site-packages/theano/gof/link.py in raise_with_op(node, thunk, exc_info, storage_map)
323 # extra long error message in that case.
324 pass
--> 325 reraise(exc_type, exc_value, exc_trace)
326
327

~/mambaforge/envs/gempy/lib/python3.7/site-packages/six.py in reraise(tp, value, tb)
716 value = tp()
717 if value.traceback is not tb:
--> 718 raise value.with_traceback(tb)
719 raise value
720 finally:

~/mambaforge/envs/gempy/lib/python3.7/site-packages/theano/compile/function_module.py in call(self, *args, **kwargs)
901 try:
902 outputs =
--> 903 self.fn() if output_subset is None else
904 self.fn(output_subset=output_subset)
905 except Exception:

~/mambaforge/envs/gempy/lib/python3.7/site-packages/theano/scan_module/scan_op.py in rval(p, i, o, n, allow_gc)
961 def rval(p=p, i=node_input_storage, o=node_output_storage, n=node,
962 allow_gc=allow_gc):
--> 963 r = p(n, [x[0] for x in i], o)
964 for o in node.outputs:
965 compute_map[o][0] = True

~/mambaforge/envs/gempy/lib/python3.7/site-packages/theano/scan_module/scan_op.py in p(node, args, outs)
950 args,
951 outs,
--> 952 self, node)
953 except (ImportError, theano.gof.cmodule.MissingGXX):
954 p = self.execute

scan_perform.pyx in theano.scan_module.scan_perform.perform()

~/mambaforge/envs/gempy/lib/python3.7/site-packages/theano/gof/link.py in raise_with_op(node, thunk, exc_info, storage_map)
323 # extra long error message in that case.
324 pass
--> 325 reraise(exc_type, exc_value, exc_trace)
326
327

~/mambaforge/envs/gempy/lib/python3.7/site-packages/six.py in reraise(tp, value, tb)
716 value = tp()
717 if value.traceback is not tb:
--> 718 raise value.with_traceback(tb)
719 raise value
720 finally:

scan_perform.pyx in theano.scan_module.scan_perform.perform()

~/mambaforge/envs/gempy/lib/python3.7/site-packages/theano/gof/op.py in rval(p, i, o, n)
890 # default arguments are stored in the closure of rval
891 def rval(p=p, i=node_input_storage, o=node_output_storage, n=node):
--> 892 r = p(n, [x[0] for x in i], o)
893 for o in node.outputs:
894 compute_map[o][0] = True

~/mambaforge/envs/gempy/lib/python3.7/site-packages/theano/tensor/slinalg.py in perform(self, node, inputs, output_storage)
253 A, b, lower=False)
254 else:
--> 255 rval = scipy.linalg.solve(A, b)
256 output_storage[0][0] = rval
257

~/mambaforge/envs/gempy/lib/python3.7/site-packages/scipy/linalg/basic.py in solve(a, b, sym_pos, lower, overwrite_a, overwrite_b, debug, check_finite, assume_a, transposed)
217 (a1, b1))
218 lu, ipvt, info = getrf(a1, overwrite_a=overwrite_a)
--> 219 _solve_check(n, info)
220 x, info = getrs(lu, ipvt, b1,
221 trans=trans, overwrite_b=overwrite_b)

~/mambaforge/envs/gempy/lib/python3.7/site-packages/scipy/linalg/basic.py in _solve_check(n, info, lamch, rcond)
27 '.'.format(-info))
28 elif 0 < info:
---> 29 raise LinAlgError('Matrix is singular.')
30
31 if lamch is None:

LinAlgError: Matrix is singular.
Apply node that caused the error: Solve{A_structure='general', lower=False, overwrite_A=False, overwrite_b=False}(Block Covariance Matrix, b vector)
Toposort index: 609
Inputs types: [TensorType(float64, matrix), TensorType(float64, vector)]
Inputs shapes: [(121, 121), (121,)]
Inputs strides: [(968, 8), (8,)]
Inputs values: ['not shown', 'not shown']
Inputs type_num: [12, 12]
Outputs clients: [[Shape(Solve{A_structure='general', lower=False, overwrite_A=False, overwrite_b=False}.0), Reshape{1}(Solve{A_structure='general', lower=False, overwrite_A=False, overwrite_b=False}.0, MakeVector{dtype='int64'}.0)]]

Backtrace when the node is created(use Theano flag traceback.limit=N to make it longer):
File "/tmp/ipykernel_44248/3227243222.py", line 2, in
gp.set_interpolator(geo_model, theano_optimizer='fast_compile', verbose=[])
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/gempy/api_modules/setters.py", line 108, in set_interpolator
geo_model._interpolator.compile_th_fn_geo(inplace=True, grid=grid)
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/gempy/core/interpolator.py", line 1124, in compile_th_fn_geo
self.theano_graph.theano_output(),
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/gempy/core/theano_modules/theano_graph_pro.py", line 631, in theano_output
solutions[:9] = self.compute_series()
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/gempy/core/theano_modules/theano_graph_pro.py", line 496, in compute_series
profile=False
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/theano/scan_module/scan.py", line 774, in scan
condition, outputs, updates = scan_utils.get_updates_and_outputs(fn(*args))
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/gempy/core/theano_modules/theano_graph_pro.py", line 2137, in compute_a_series
self.solve_kriging(b),
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/gempy/core/theano_modules/theano_graph_pro.py", line 1396, in solve_kriging
DK_parameters = theano.tensor.slinalg.solve(C_matrix, b)

Debugprint of the apply node:
Solve{A_structure='general', lower=False, overwrite_A=False, overwrite_b=False} [id A] <TensorType(float64, vector)> ''
|IncSubtensor{Set;int64::, int64:int64:} [id B] <TensorType(float64, matrix)> 'Block Covariance Matrix'
|IncSubtensor{Set;int64:int64:} [id C] <TensorType(float64, vector)> 'b vector'

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, 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)>, <TensorType(float64, scalar)>, Nugget effect of gradients, Nugget effect of scalar, Attenuation factor, Sigmoid Outside, Sigmoid slope, <TensorType(int64, vector)>, <TensorType(bool, vector)>, <TensorType(int64, 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: 167
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(int64, vector), TensorType(int64, vector), TensorType(int32, vector), TensorType(float64, vector), TensorType(float64, 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(int64, matrix), 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(int64, vector), TensorType(bool, vector), TensorType(int64, vector), TensorType(float64, matrix), TensorType(float64, matrix), TensorType(float64, matrix), TensorType(float64, vector), TensorType(float64, vector), TensorType(float64, vector), TensorType(float64, matrix)]
Inputs shapes: [(), (9,), (9,), (9,), (9,), (9,), (9,), (9,), (9,), (9,), (9,), (9,), (9,), (9,), (9,), (9,), (9,), (9,), (9,), (10, 9, 1, 343618), (10, 1293), (10, 9, 343618), (10, 9, 9), (10, 9, 343618), (10, 9, 343618), (10, 9, 1, 343618), (10,), (9,), (9, 9), (), (), (), (954,), (318,), (), (), (), (9,), (9,), (9,), (343000, 3), (318, 3), (318, 3), (318,), (318,), (318,), (1, 10)]
Inputs strides: [(), (4,), (4,), (4,), (4,), (4,), (4,), (4,), (4,), (4,), (1,), (1,), (1,), (4,), (8,), (8,), (4,), (8,), (8,), (24740496, 2748944, 2748944, 8), (10344, 8), (24740496, 2748944, 8), (648, 72, 8), (3092562, 343618, 1), (3092562, 343618, 1), (24740496, 2748944, 2748944, 8), (8,), (4,), (8, 72), (), (), (), (8,), (8,), (), (), (), (8,), (1,), (8,), (24, 8), (8, 2544), (8, 2544), (8,), (8,), (8,), (80, 8)]
Inputs values: [array(9), 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', array(10.), array(2.), array(4.), 'not shown', 'not shown', array(2.), array(50.), array(50000.), 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown']
Inputs type_num: [7, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 5, 7, 7, 5, 12, 12, 12, 12, 12, 12, 0, 0, 12, 7, 5, 7, 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 "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3257, in run_cell_async
interactivity=interactivity, compiler=compiler, result=result)
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3472, in run_ast_nodes
if (await self.run_code(code, result, async_=asy)):
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3552, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "/tmp/ipykernel_44248/3227243222.py", line 2, in
gp.set_interpolator(geo_model, theano_optimizer='fast_compile', verbose=[])
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/gempy/api_modules/setters.py", line 108, in set_interpolator
geo_model._interpolator.compile_th_fn_geo(inplace=True, grid=grid)
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/gempy/core/interpolator.py", line 1124, in compile_th_fn_geo
self.theano_graph.theano_output(),
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/gempy/core/theano_modules/theano_graph_pro.py", line 631, in theano_output
solutions[:9] = self.compute_series()
File "/home/luciano/mambaforge/envs/gempy/lib/python3.7/site-packages/gempy/core/theano_modules/theano_graph_pro.py", line 496, 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)> ''
| | | | | | | | | | | | | | | |Elemwise{minimum,no_inplace} [id Q] <TensorType(int64, scalar)> ''
| | | | | | | | | | | | | | | | |Elemwise{minimum,no_inplace} [id R] <TensorType(int64, scalar)> ''
| | | | | | | | | | | | | | | | | |Subtensor{int64} [id S] <TensorType(int64, scalar)> ''
| | | | | | | | | | | | | | | | | | |Shape [id T] <TensorType(int64, vector)> ''
| | | | | | | | | | | | | | | | | | | |Subtensor{int64:int64:} [id U] <TensorType(int32, vector)> 'Length of surface_points in every series[0:-1]'
| | | | | | | | | | | | | | | | | | |Constant{0} [id V]
| | | | | | | | | | | | | | | | | |Subtensor{int64} [id W] <TensorType(int64, scalar)> ''
| | | | | | | | | | | | | | | | | |Shape [id X] <TensorType(int64, vector)> ''
| | | | | | | | | | | | | | | | | | |Subtensor{int64::} [id Y] <TensorType(int32, vector)> 'Length of surface_points in every series[1:]'
| | | | | | | | | | | | | | | | | |Constant{0} [id V]
| | | | | | | | | | | | | | | | |Subtensor{int64} [id Z] <TensorType(int64, scalar)> ''
| | | | | | | | | | | | | | | | |Shape [id BA] <TensorType(int64, vector)> ''
| | | | | | | | | | | | | | | | | |Subtensor{int64:int64:} [id BB] <TensorType(int32, vector)> 'Length of foliations in every series[0:-1]'
| | | | | | | | | | | | | | | | |Constant{0} [id V]
| | | | | | | | | | | | | | | |Subtensor{int64} [id BC] <TensorType(int64, scalar)> ''
| | | | | | | | | | | | | | | |Shape [id BD] <TensorType(int64, vector)> ''
| | | | | | | | | | | | | | | | |Subtensor{int64::} [id BE] <TensorType(int32, vector)> 'Length of foliations in every series[1:]'
| | | | | | | | | | | | | | | |Constant{0} [id V]
| | | | | | | | | | | | | | |Subtensor{int64} [id BF] <TensorType(int64, scalar)> ''
| | | | | | | | | | | | | | |Shape [id BG] <TensorType(int64, vector)> ''
| | | | | | | | | | | | | | | |Subtensor{int64:int64:} [id BH] <TensorType(int32, vector)> 'Length of weights in every series[0:-1]'
| | | | | | | | | | | | | | |Constant{0} [id V]
| | | | | | | | | | | | | |Subtensor{int64} [id BI] <TensorType(int64, scalar)> ''
| | | | | | | | | | | | | |Shape [id BJ] <TensorType(int64, vector)> ''
| | | | | | | | | | | | | | |Subtensor{int64::} [id BK] <TensorType(int32, vector)> 'Length of weights in every series[1:]'
| | | | | | | | | | | | | |Constant{0} [id V]
| | | | | | | | | | | | |Subtensor{int64} [id BL] <TensorType(int64, scalar)> ''
| | | | | | | | | | | | |Shape [id BM] <TensorType(int64, vector)> ''
| | | | | | | | | | | | | |Subtensor{int64:int64:} [id BN] <TensorType(int32, vector)> 'List with the number of surfaces[0:-1]'
| | | | | | | | | | | | |Constant{0} [id V]
| | | | | | | | | | | |Subtensor{int64} [id BO] <TensorType(int64, scalar)> ''
| | | | | | | | | | | |Shape [id BP] <TensorType(int64, vector)> ''
| | | | | | | | | | | | |Subtensor{int64::} [id BQ] <TensorType(int32, vector)> 'List with the number of surfaces[1:]'
| | | | | | | | | | | |Constant{0} [id V]
| | | | | | | | | | |Subtensor{int64} [id BR] <TensorType(int64, scalar)> ''
| | | | | | | | | | |Shape [id BS] <TensorType(int64, vector)> ''
| | | | | | | | | | | |Subtensor{int64::} [id BT] <TensorType(int32, vector)> 'Grade of the universal drift[0:]'
| | | | | | | | | | |Constant{0} [id V]
| | | | | | | | | |Subtensor{int64} [id BU] <TensorType(int64, scalar)> ''
| | | | | | | | | |Shape [id BV] <TensorType(int64, vector)> ''
| | | | | | | | | | |Subtensor{int64::} [id BW] <TensorType(bool, vector)> 'Vector controlling if weights must be recomputed[0:]'
| | | | | | | | | |Constant{0} [id V]
| | | | | | | | |Subtensor{int64} [id BX] <TensorType(int64, scalar)> ''
| | | | | | | | |Shape [id BY] <TensorType(int64, vector)> ''
| | | | | | | | | |Subtensor{int64::} [id BZ] <TensorType(bool, vector)> 'Vector controlling if scalar matrix must be recomputed[0:]'
| | | | | | | | |Constant{0} [id V]
| | | | | | | |Subtensor{int64} [id CA] <TensorType(int64, scalar)> ''
| | | | | | | |Shape [id CB] <TensorType(int64, vector)> ''
| | | | | | | | |Subtensor{int64::} [id CC] <TensorType(bool, vector)> 'Vector controlling if block matrix must be recomputed[0:]'
| | | | | | | |Constant{0} [id V]
| | | | | | |Subtensor{int64} [id CD] <TensorType(int64, scalar)> ''
| | | | | | |Shape [id CE] <TensorType(int64, vector)> ''
| | | | | | | |Subtensor{int64::} [id CF] <TensorType(int32, vector)> 'The series (fault) is finite[0:]'
| | | | | | |Constant{0} [id V]
| | | | | |Subtensor{int64} [id CG] <TensorType(int64, scalar)> ''
| | | | | |Shape [id CH] <TensorType(int64, vector)> ''
| | | | | | |Subtensor{int64::} [id CI] <TensorType(int64, vector)> ''
| | | | | | |<TensorType(int64, vector)> [id CJ] <TensorType(int64, vector)>
| | | | | | |Constant{0} [id V]
| | | | | |Constant{0} [id V]
| | | | |Subtensor{int64} [id CK] <TensorType(int64, scalar)> ''
| | | | |Shape [id CL] <TensorType(int64, vector)> ''
| | | | | |Subtensor{int64::} [id CM] <TensorType(int64, vector)> ''
| | | | | |<TensorType(int64, vector)> [id CN] <TensorType(int64, vector)>
| | | | | |Constant{0} [id V]
| | | | |Constant{0} [id V]
| | | |TensorConstant{5000} [id CO] <TensorType(int64, scalar)>
| | |Subtensor{int64} [id CP] <TensorType(int64, scalar)> ''
| | |Shape [id CQ] <TensorType(int64, vector)> ''
| | | |Subtensor{int64::} [id CR] <TensorType(float64, vector)> 'Range[0:]'
| | |Constant{0} [id V]
| |Subtensor{int64} [id CS] <TensorType(int64, scalar)> ''
| |Shape [id CT] <TensorType(int64, vector)> ''
| | |Subtensor{int64::} [id CU] <TensorType(float64, vector)> 'Covariance at 0[0:]'
| |Constant{0} [id V]
|Subtensor{:int64:} [id CV] <TensorType(int32, vector)> ''
| |Subtensor{int64:int64:} [id U] <TensorType(int32, vector)> 'Length of surface_points in every series[0:-1]'
| |ScalarFromTensor [id CW] ''
| |Elemwise{minimum,no_inplace} [id B] <TensorType(int64, scalar)> ''
|Subtensor{:int64:} [id CX] <TensorType(int32, vector)> ''
| |Subtensor{int64::} [id Y] <TensorType(int32, vector)> 'Length of surface_points in every series[1:]'
| |ScalarFromTensor [id CW] ''
|Subtensor{:int64:} [id CY] <TensorType(int32, vector)> ''
| |Subtensor{int64:int64:} [id BB] <TensorType(int32, vector)> 'Length of foliations in every series[0:-1]'
| |ScalarFromTensor [id CW] ''
|Subtensor{:int64:} [id CZ] <TensorType(int32, vector)> ''
| |Subtensor{int64::} [id BE] <TensorType(int32, vector)> 'Length of foliations in every series[1:]'
| |ScalarFromTensor [id CW] ''
|Subtensor{:int64:} [id DA] <TensorType(int32, vector)> ''
| |Subtensor{int64:int64:} [id BH] <TensorType(int32, vector)> 'Length of weights in every series[0:-1]'
| |ScalarFromTensor [id CW] ''
|Subtensor{:int64:} [id DB] <TensorType(int32, vector)> ''
| |Subtensor{int64::} [id BK] <TensorType(int32, vector)> 'Length of weights in every series[1:]'
| |ScalarFromTensor [id CW] ''
|Subtensor{:int64:} [id DC] <TensorType(int32, vector)> ''
| |Subtensor{int64:int64:} [id BN] <TensorType(int32, vector)> 'List with the number of surfaces[0:-1]'
| |ScalarFromTensor [id CW] ''
|Subtensor{:int64:} [id DD] <TensorType(int32, vector)> ''
| |Subtensor{int64::} [id BQ] <TensorType(int32, vector)> 'List with the number of surfaces[1:]'
| |ScalarFromTensor [id CW] ''
|Subtensor{:int64:} [id DE] <TensorType(int32, vector)> ''
| |Subtensor{int64::} [id BT] <TensorType(int32, vector)> 'Grade of the universal drift[0:]'
| |ScalarFromTensor [id CW] ''
|Subtensor{:int64:} [id DF] <TensorType(bool, vector)> ''
| |Subtensor{int64::} [id BW] <TensorType(bool, vector)> 'Vector controlling if weights must be recomputed[0:]'
| |ScalarFromTensor [id CW] ''
|Subtensor{:int64:} [id DG] <TensorType(bool, vector)> ''
| |Subtensor{int64::} [id BZ] <TensorType(bool, vector)> 'Vector controlling if scalar matrix must be recomputed[0:]'
| |ScalarFromTensor [id CW] ''
|Subtensor{:int64:} [id DH] <TensorType(bool, vector)> ''
| |Subtensor{int64::} [id CC] <TensorType(bool, vector)> 'Vector controlling if block matrix must be recomputed[0:]'
| |ScalarFromTensor [id CW] ''
|Subtensor{:int64:} [id DI] <TensorType(int32, vector)> ''
| |Subtensor{int64::} [id CF] <TensorType(int32, vector)> 'The series (fault) is finite[0:]'
| |ScalarFromTensor [id CW] ''
|Subtensor{:int64:} [id DJ] <TensorType(int64, vector)> ''
| |Subtensor{int64::} [id CI] <TensorType(int64, vector)> ''
| |ScalarFromTensor [id CW] ''
|Subtensor{:int64:} [id DK] <TensorType(int64, vector)> ''
| |Subtensor{int64::} [id CM] <TensorType(int64, vector)> ''
| |ScalarFromTensor [id CW] ''
|Subtensor{:int64:} [id DL] <TensorType(int32, vector)> ''
| |TensorConstant{[ 0 1..4998 4999]} [id DM] <TensorType(int32, vector)>
| |ScalarFromTensor [id CW] ''
|Subtensor{:int64:} [id DN] <TensorType(float64, vector)> ''
| |Subtensor{int64::} [id CR] <TensorType(float64, vector)> 'Range[0:]'
| |ScalarFromTensor [id CW] ''
|Subtensor{:int64:} [id DO] <TensorType(float64, vector)> ''
| |Subtensor{int64::} [id CU] <TensorType(float64, vector)> 'Covariance at 0[0:]'
| |ScalarFromTensor [id CW] ''
|IncSubtensor{Set;:int64:} [id DP] <TensorType(float64, 4D)> ''
| |AllocEmpty{dtype='float64'} [id DQ] <TensorType(float64, 4D)> ''
| | |Elemwise{add,no_inplace} [id DR] <TensorType(int64, scalar)> ''
| | | |Elemwise{minimum,no_inplace} [id B] <TensorType(int64, scalar)> ''
| | | |Subtensor{int64} [id DS] <TensorType(int64, scalar)> ''
| | | |Shape [id DT] <TensorType(int64, vector)> ''
| | | | |Rebroadcast{0} [id DU] <TensorType(float64, 4D)> ''
| | | | |InplaceDimShuffle{x,0,1,2} [id DV] <TensorType(float64, (True, False, False, False))> ''
| | | | |block matrix [id DW] <TensorType(float64, 3D)>
| | | |Constant{0} [id V]
| | |Subtensor{int64} [id DX] <TensorType(int64, scalar)> ''
| | | |Shape [id DT] <TensorType(int64, vector)> ''
| | | |Constant{1} [id DY]
| | |Subtensor{int64} [id DZ] <TensorType(int64, scalar)> ''
| | | |Shape [id DT] <TensorType(int64, vector)> ''
| | | |Constant{2} [id EA]
| | |Subtensor{int64} [id EB] <TensorType(int64, scalar)> ''
| | |Shape [id DT] <TensorType(int64, vector)> ''
| | |Constant{3} [id EC]
| |Rebroadcast{0} [id DU] <TensorType(float64, 4D)> ''
| |ScalarFromTensor [id ED] ''
| |Subtensor{int64} [id DS] <TensorType(int64, scalar)> ''
|IncSubtensor{Set;:int64:} [id EE] <TensorType(float64, matrix)> ''
| |AllocEmpty{dtype='float64'} [id EF] <TensorType(float64, matrix)> ''
| | |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, matrix)> ''
| | | | |InplaceDimShuffle{x,0} [id EK] <TensorType(float64, row)> ''
| | | | |Weights vector [id EL] <TensorType(float64, vector)>
| | | |Constant{0} [id V]
| | |Subtensor{int64} [id EM] <TensorType(int64, scalar)> ''
| | |Shape [id EI] <TensorType(int64, vector)> ''
| | |Constant{1} [id DY]
| |Rebroadcast{0} [id EJ] <TensorType(float64, matrix)> ''
| |ScalarFromTensor [id EN] ''
| |Subtensor{int64} [id EH] <TensorType(int64, scalar)> ''
|IncSubtensor{Set;:int64:} [id EO] <TensorType(float64, 3D)> ''
| |AllocEmpty{dtype='float64'} [id EP] <TensorType(float64, 3D)> ''
| | |Elemwise{add,no_inplace} [id EQ] <TensorType(int64, scalar)> ''
| | | |Elemwise{minimum,no_inplace} [id B] <TensorType(int64, scalar)> ''
| | | |Subtensor{int64} [id ER] <TensorType(int64, scalar)> ''
| | | |Shape [id ES] <TensorType(int64, vector)> ''
| | | | |Rebroadcast{0} [id ET] <TensorType(float64, 3D)> ''
| | | | |InplaceDimShuffle{x,0,1} [id EU] <TensorType(float64, (True, False, False))> ''
| | | | |Scalar matrix [id EV] <TensorType(float64, matrix)>
| | | |Constant{0} [id V]
| | |Subtensor{int64} [id EW] <TensorType(int64, scalar)> ''
| | | |Shape [id ES] <TensorType(int64, vector)> ''
| | | |Constant{1} [id DY]
| | |Subtensor{int64} [id EX] <TensorType(int64, scalar)> ''
| | |Shape [id ES] <TensorType(int64, vector)> ''
| | |Constant{2} [id EA]
| |Rebroadcast{0} [id ET] <TensorType(float64, 3D)> ''
| |ScalarFromTensor [id EY] ''
| |Subtensor{int64} [id ER] <TensorType(int64, scalar)> ''
|IncSubtensor{Set;:int64:} [id EZ] <TensorType(float64, 3D)> ''
| |AllocEmpty{dtype='float64'} [id FA] <TensorType(float64, 3D)> ''
| | |Elemwise{add,no_inplace} [id FB] <TensorType(int64, scalar)> ''
| | | |Elemwise{minimum,no_inplace} [id B] <TensorType(int64, scalar)> ''
| | | |Subtensor{int64} [id FC] <TensorType(int64, scalar)> ''
| | | |Shape [id FD] <TensorType(int64, vector)> ''
| | | | |Rebroadcast{0} [id FE] <TensorType(float64, 3D)> ''
| | | | |InplaceDimShuffle{x,0,1} [id FF] <TensorType(float64, (True, False, False))> ''
| | | | |Alloc [id FG] <TensorType(float64, matrix)> ''
| | | | |TensorConstant{0.0} [id FH] <TensorType(float64, scalar)>
| | | | |Subtensor{int64} [id FI] <TensorType(int64, scalar)> ''
| | | | | |Shape [id FJ] <TensorType(int64, vector)> ''
| | | | | | |<TensorType(int64, vector)> [id CJ] <TensorType(int64, vector)>
| | | | | |Constant{0} [id V]
| | | | |Subtensor{int64} [id FK] <TensorType(int32, scalar)> ''
| | | | |List with the number of surfaces [id FL] <TensorType(int32, vector)>
| | | | |Constant{-1} [id FM]
| | | |Constant{0} [id V]
| | |Subtensor{int64} [id FN] <TensorType(int64, scalar)> ''
| | | |Shape [id FD] <TensorType(int64, vector)> ''
| | | |Constant{1} [id DY]
| | |Subtensor{int64} [id FO] <TensorType(int64, scalar)> ''
| | |Shape [id FD] <TensorType(int64, vector)> ''
| | |Constant{2} [id EA]
| |Rebroadcast{0} [id FE] <TensorType(float64, 3D)> ''
| |ScalarFromTensor [id FP] ''
| |Subtensor{int64} [id FC] <TensorType(int64, scalar)> ''
|IncSubtensor{Set;:int64:} [id FQ] <TensorType(bool, 3D)> ''
| |AllocEmpty{dtype='bool'} [id FR] <TensorType(bool, 3D)> ''
| | |Elemwise{add,no_inplace} [id FS] <TensorType(int64, scalar)> ''
| | | |Elemwise{minimum,no_inplace} [id B] <TensorType(int64, scalar)> ''
| | | |Subtensor{int64} [id FT] <TensorType(int64, scalar)> ''
| | | |Shape [id FU] <TensorType(int64, vector)> ''
| | | | |Rebroadcast{0} [id FV] <TensorType(bool, 3D)> ''
| | | | |InplaceDimShuffle{x,0,1} [id FW] <TensorType(bool, (True, False, False))> ''
| | | | |mask matrix [id FX] <TensorType(bool, matrix)>
| | | |Constant{0} [id V]
| | |Subtensor{int64} [id FY] <TensorType(int64, scalar)> ''
| | | |Shape [id FU] <TensorType(int64, vector)> ''
| | | |Constant{1} [id DY]
| | |Subtensor{int64} [id FZ] <TensorType(int64, scalar)> ''
| | |Shape [id FU] <TensorType(int64, vector)> ''
| | |Constant{2} [id EA]
| |Rebroadcast{0} [id FV] <TensorType(bool, 3D)> ''
| |ScalarFromTensor [id GA] ''
| |Subtensor{int64} [id FT] <TensorType(int64, scalar)> ''
|IncSubtensor{Set;:int64:} [id GB] <TensorType(bool, 3D)> ''
| |AllocEmpty{dtype='bool'} [id GC] <TensorType(bool, 3D)> ''
| | |Elemwise{add,no_inplace} [id GD] <TensorType(int64, scalar)> ''
| | | |Elemwise{minimum,no_inplace} [id B] <TensorType(int64, scalar)> ''
| | | |Subtensor{int64} [id GE] <TensorType(int64, scalar)> ''
| | | |Shape [id GF] <TensorType(int64, vector)> ''
| | | | |Rebroadcast{0} [id GG] <TensorType(bool, 3D)> ''
| | | | |InplaceDimShuffle{x,0,1} [id GH] <TensorType(bool, (True, False, False))> ''
| | | | |Elemwise{second,no_inplace} [id GI] <TensorType(bool, matrix)> ''
| | | | |mask matrix [id FX] <TensorType(bool, matrix)>
| | | | |TensorConstant{(1, 1) of False} [id GJ] <TensorType(bool, (True, True))>
| | | |Constant{0} [id V]
| | |Subtensor{int64} [id GK] <TensorType(int64, scalar)> ''
| | | |Shape [id GF] <TensorType(int64, vector)> ''
| | | |Constant{1} [id DY]
| | |Subtensor{int64} [id GL] <TensorType(int64, scalar)> ''
| | |Shape [id GF] <TensorType(int64, vector)> ''
| | |Constant{2} [id EA]
| |Rebroadcast{0} [id GG] <TensorType(bool, 3D)> ''
| |ScalarFromTensor [id GM] ''
| |Subtensor{int64} [id GE] <TensorType(int64, scalar)> ''
|IncSubtensor{Set;:int64:} [id GN] <TensorType(float64, 4D)> ''
| |AllocEmpty{dtype='float64'} [id GO] <TensorType(float64, 4D)> ''
| | |Elemwise{add,no_inplace} [id GP] <TensorType(int64, scalar)> ''
| | | |Elemwise{minimum,no_inplace} [id B] <TensorType(int64, scalar)> ''
| | | |Subtensor{int64} [id GQ] <TensorType(int64, scalar)> ''
| | | |Shape [id GR] <TensorType(int64, vector)> ''
| | | | |Rebroadcast{0} [id GS] <TensorType(float64, 4D)> ''
| | | | |InplaceDimShuffle{x,0,1,2} [id GT] <TensorType(float64, (True, False, False, False))> ''
| | | | |Elemwise{second,no_inplace} [id GU] <TensorType(float64, 3D)> ''
| | | | |block matrix [id DW] <TensorType(float64, 3D)>
| | | | |TensorConstant{(1, 1, 1) of 0.0} [id GV] <TensorType(float64, (True, True, True))>
| | | |Constant{0} [id V]
| | |Subtensor{int64} [id GW] <TensorType(int64, scalar)> ''
| | | |Shape [id GR] <TensorType(int64, vector)> ''
| | | |Constant{1} [id DY]
| | |Subtensor{int64} [id GX] <TensorType(int64, scalar)> ''
| | | |Shape [id GR] <TensorType(int64, vector)> ''
| | | |Constant{2} [id EA]
| | |Subtensor{int64} [id GY] <TensorType(int64, scalar)> ''
| | |Shape [id GR] <TensorType(int64, vector)> ''
| | |Constant{3} [id EC]
| |Rebroadcast{0} [id GS] <TensorType(float64, 4D)> ''
| |ScalarFromTensor [id GZ] ''
| |Subtensor{int64} [id GQ] <TensorType(int64, scalar)> ''
|IncSubtensor{Set;:int64:} [id HA] <TensorType(int64, vector)> ''
| |AllocEmpty{dtype='int64'} [id HB] <TensorType(int64, vector)> ''
| | |Elemwise{add,no_inplace} [id HC] <TensorType(int64, scalar)> ''
| | |Elemwise{minimum,no_inplace} [id B] <TensorType(int64, scalar)> ''
| | |TensorConstant{1} [id HD] <TensorType(int64, scalar)>
| |TensorConstant{(1,) of 0} [id HE] <TensorType(int64, vector)>
| |Constant{1} [id DY]
|Number of points per surface used to split rest-ref [id HF] <TensorType(int32, vector)>
|fault relation matrix [id HG] <TensorType(int64, matrix)>
|<TensorType(float64, scalar)> [id HH] <TensorType(float64, scalar)>
|<TensorType(float64, scalar)> [id HI] <TensorType(float64, scalar)>
|<TensorType(float64, scalar)> [id HJ] <TensorType(float64, scalar)>
|Nugget effect of gradients [id HK] <TensorType(float64, vector)>
|Nugget effect of scalar [id HL] <TensorType(float64, vector)>
|Attenuation factor [id HM] <TensorType(float64, scalar)>
|Sigmoid Outside [id HN] <TensorType(float64, scalar)>
|Sigmoid slope [id HO] <TensorType(float64, scalar)>
|<TensorType(int64, vector)> [id CN] <TensorType(int64, vector)>
|<TensorType(bool, vector)> [id HP] <TensorType(bool, vector)>
|<TensorType(int64, vector)> [id CJ] <TensorType(int64, vector)>
|Coordinates of the grid points to interpolate [id HQ] <TensorType(float64, matrix)>
|All the surface_points points at once [id HR] <TensorType(float64, matrix)>
|Position of the dips [id HS] <TensorType(float64, matrix)>
|Angle of every dip [id HT] <TensorType(float64, vector)>
|Azimuth [id HU] <TensorType(float64, vector)>
|Polarity [id HV] <TensorType(float64, vector)>
|Values that the blocks are taking [id HW] <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 HX] <TensorType(float64, 3D)> ''
|block matrix[t-1] [id HY] <TensorType(float64, 3D)> -> [id DP]
|if{} [id HZ] <TensorType(float64, matrix)> ''
| |Vector controlling if block matrix must be recomputed[t] [id IA] <TensorType(bool, scalar)> -> [id DH]
| |if{} [id IB] <TensorType(float64, matrix)> ''
| | |The series (fault) is finite[t] [id IC] <TensorType(int32, scalar)> -> [id DI]
| | |Sum{axis=[0], acc_dtype=float64} [id ID] <TensorType(float64, matrix)> 'The chunk of block model of a specific series'
| | |Sum{axis=[0], acc_dtype=float64} [id IE] <TensorType(float64, matrix)> 'The chunk of block model of a specific series'
| |Subtensor{int32, ::} [id IF] <TensorType(float64, matrix)> ''
| |block matrix[t-1] [id HY] <TensorType(float64, 3D)> -> [id DP]
| |ScalarFromTensor [id IG] ''
| |<TensorType(int32, scalar)> [id IH] <TensorType(int32, scalar)> -> [id DL]
|ScalarFromTensor [id IG] ''
|Constant{0} [id II]
|ScalarFromTensor [id IJ] ''
|Elemwise{add,no_inplace} [id IK] <TensorType(int64, scalar)> ''
|Elemwise{add,no_inplace} [id IL] <TensorType(int64, scalar)> ''
| |Subtensor{int64} [id IM] <TensorType(int64, scalar)> ''
| | |Shape [id IN] <TensorType(int64, vector)> ''
| | | |Coordinates of the grid points to interpolate_copy [id IO] <TensorType(float64, matrix)> -> [id HQ]
| | |Constant{0} [id IP]
| |Elemwise{mul,no_inplace} [id IQ] <TensorType(int64, scalar)> ''
| |TensorConstant{2} [id IR] <TensorType(int8, scalar)>
| |Elemwise{sub,no_inplace} [id IS] <TensorType(int64, scalar)> ''
| |Subtensor{int64} [id IT] <TensorType(int64, scalar)> ''
| | |Shape [id IU] <TensorType(int64, vector)> ''
| | | |All the surface_points points at once_copy [id IV] <TensorType(float64, matrix)> -> [id HR]
| | |Constant{0} [id IP]
| |Subtensor{int64} [id IW] <TensorType(int64, scalar)> ''
| |Shape [id IX] <TensorType(int64, vector)> ''
| | |Number of points per surface used to split rest-ref_copy [id IY] <TensorType(int32, vector)> -> [id HF]
| |Constant{0} [id IP]
|TensorConstant{0} [id IZ] <TensorType(int8, scalar)>
IncSubtensor{Set;int32:int32:} [id JA] <TensorType(float64, vector)> ''
|Weights vector[t-1] [id JB] <TensorType(float64, vector)> -> [id EE]
|if{} [id JC] <TensorType(float64, vector)> ''
| |Vector controlling if weights must be recomputed[t] [id JD] <TensorType(bool, scalar)> -> [id DF]
| |Reshape{1} [id JE] <TensorType(float64, vector)> 'Dual Kriging parameters'
| |Subtensor{int32:int32:} [id JF] <TensorType(float64, vector)> ''
| |Weights vector[t-1] [id JB] <TensorType(float64, vector)> -> [id EE]
| |ScalarFromTensor [id JG] ''
| | |Length of weights in every series[t] [id JH] <TensorType(int32, scalar)> -> [id DA]
| |ScalarFromTensor [id JI] ''
| |Length of weights in every series[t+1] [id JJ] <TensorType(int32, scalar)> -> [id DB]
|ScalarFromTensor [id JG] ''
|ScalarFromTensor [id JI] ''
IncSubtensor{Set;int32, int8:int64:} [id JK] <TensorType(float64, matrix)> ''
|Scalar matrix[t-1] [id JL] <TensorType(float64, matrix)> -> [id EO]
|if{} [id JM] <TensorType(float64, vector)> ''
| |Vector controlling if scalar matrix must be recomputed[t] [id JN] <TensorType(bool, scalar)> -> [id DG]
| |Subtensor{int64} [id JO] <TensorType(float64, vector)> 'Value of the potential field at every point'
| |Subtensor{int32} [id JP] <TensorType(float64, vector)> ''
| |Scalar matrix[t-1] [id JL] <TensorType(float64, matrix)> -> [id EO]
| |ScalarFromTensor [id IG] ''
|ScalarFromTensor [id IG] ''
|Constant{0} [id II]
|ScalarFromTensor [id IJ] ''
AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JQ] <TensorType(float64, matrix)> ''
|<TensorType(float64, matrix)> [id JR] <TensorType(float64, matrix)> -> [id EZ]
|AdvancedSubtensor1 [id JS] <TensorType(float64, vector)> ''
| |Subtensor{int64:int64:} [id JT] <TensorType(float64, vector)> ''
| | |if{} [id JM] <TensorType(float64, vector)> ''
| | |ScalarFromTensor [id JU] ''
| | | |Elemwise{mul,no_inplace} [id JV] <TensorType(int64, scalar)> ''
| | | |TensorConstant{-2} [id JW] <TensorType(int8, scalar)>
| | | |Elemwise{sub,no_inplace} [id IS] <TensorType(int64, scalar)> ''
| | |ScalarFromTensor [id JX] ''
| | |Elemwise{neg,no_inplace} [id JY] <TensorType(int64, scalar)> ''
| | |Elemwise{sub,no_inplace} [id IS] <TensorType(int64, scalar)> ''
| |Subtensor{int32:int32:} [id JZ] <TensorType(int32, vector)> ''
| |CumOp{None, add} [id KA] <TensorType(int32, vector)> 'Number of points per surfaces after rest-ref. This is used for finding the differentsurface points withing a layer.'
| |ScalarFromTensor [id KB] ''
| | |List with the number of surfaces[t] [id KC] <TensorType(int32, scalar)> -> [id DC]
| |ScalarFromTensor [id KD] ''
| |List with the number of surfaces[t+1] [id KE] <TensorType(int32, scalar)> -> [id DD]
|<TensorType(int32, scalar)> [id IH] <TensorType(int32, scalar)> -> [id DL]
|Elemwise{sub,no_inplace} [id KF] <TensorType(int32, vector)> ''
|Subtensor{int32:int32:} [id KG] <TensorType(int32, vector)> ''
| |TensorConstant{[ 1 2..4998 4999]} [id KH] <TensorType(int32, vector)>
| |ScalarFromTensor [id KB] ''
| |ScalarFromTensor [id KD] ''
|TensorConstant{(1,) of 1} [id KI] <TensorType(int8, (True,))>
IncSubtensor{Set;int32, int8:int64:} [id KJ] <TensorType(bool, matrix)> ''
|IncSubtensor{Set;int64:int32:, int8:int64:} [id KK] <TensorType(bool, matrix)> ''
| |IncSubtensor{Set;int32:int32:, int8:int64:} [id KL] <TensorType(bool, matrix)> ''
| | |mask matrix[t-1] [id KM] <TensorType(bool, matrix)> -> [id FQ]
| | |if{} [id KN] <TensorType(bool, vector)> ''
| | | |<TensorType(int64, scalar)> [id KO] <TensorType(int64, scalar)> -> [id DK]
| | | |Elemwise{gt,no_inplace} [id KP] <TensorType(bool, vector)> ''
| | | | |if{} [id JM] <TensorType(float64, vector)> ''
| | | | |InplaceDimShuffle{x} [id KQ] <TensorType(float64, (True,))> ''
| | | | |MaxAndArgmax{axis=(0,)}.0 [id KR] <TensorType(float64, scalar)> 'max'
| | | |Subtensor{int32, int8:int64:} [id KS] <TensorType(bool, vector)> ''
| | | |mask matrix[t-1] [id KM] <TensorType(bool, matrix)> -> [id FQ]
| | | |ScalarFromTensor [id KT] ''
| | | | |Elemwise{sub,no_inplace} [id KU] <TensorType(int32, scalar)> ''
| | | | |<TensorType(int32, scalar)> [id IH] <TensorType(int32, scalar)> -> [id DL]
| | | | |TensorConstant{1} [id KV] <TensorType(int8, scalar)>
| | | |Constant{0} [id II]
| | | |ScalarFromTensor [id IJ] ''
| | |ScalarFromTensor [id KT] ''
| | |ScalarFromTensor [id IG] ''
| | |Constant{0} [id II]
| | |ScalarFromTensor [id IJ] ''
| |Subtensor{::int64} [id KW] <TensorType(bool, matrix)> ''
| | |CumOp{0, mul} [id KX] <TensorType(bool, matrix)> ''
| | | |Subtensor{::int64} [id KY] <TensorType(bool, matrix)> ''
| | | |Subtensor{int64:int32:, int8:int64:} [id KZ] <TensorType(bool, matrix)> ''
| | | | |IncSubtensor{Set;int32:int32:, int8:int64:} [id KL] <TensorType(bool, matrix)> ''
| | | | |ScalarFromTensor [id LA] ''
| | | | | |Elemwise{sub,no_inplace} [id LB] <TensorType(int64, scalar)> ''
| | | | | |<TensorType(int32, scalar)> [id IH] <TensorType(int32, scalar)> -> [id DL]
| | | | | |Elemwise{mul,no_inplace} [id LC] <TensorType(int64, scalar)> ''
| | | | | |Elemwise{mul,no_inplace} [id LD] <TensorType(int64, scalar)> ''
| | | | | | |Elemwise{add,no_inplace} [id LE] <TensorType(int64, scalar)> ''
| | | | | | | |<TensorType(int64, scalar)> [id LF] <TensorType(int64, scalar)> -> [id HA]
| | | | | | | |Elemwise{add,no_inplace} [id LG] <TensorType(int64, scalar)> ''
| | | | | | | |Subtensor{int32} [id LH] <TensorType(int64, scalar)> ''
| | | | | | | | |<TensorType(int64, vector)> [id LI] <TensorType(int64, vector)> -> [id CN]
| | | | | | | | |ScalarFromTensor [id IG] ''
| | | | | | | |Subtensor{int32} [id LJ] <TensorType(bool, scalar)> ''
| | | | | | | |<TensorType(bool, vector)> [id LK] <TensorType(bool, vector)> -> [id HP]
| | | | | | | |ScalarFromTensor [id IG] ''
| | | | | | |Elemwise{add,no_inplace} [id LG] <TensorType(int64, scalar)> ''
| | | | | |Subtensor{int64} [id LL] <TensorType(int64, scalar)> ''
| | | | | |<TensorType(int64, vector)> [id LI] <TensorType(int64, vector)> -> [id CN]
| | | | | |ScalarFromTensor [id LM] ''
| | | | | |Elemwise{sub,no_inplace} [id LN] <TensorType(int64, scalar)> ''
| | | | | |<TensorType(int32, scalar)> [id IH] <TensorType(int32, scalar)> -> [id DL]
| | | | | |<TensorType(int64, scalar)> [id LF] <TensorType(int64, scalar)> -> [id HA]
| | | | |ScalarFromTensor [id IG] ''
| | | | |Constant{0} [id II]
| | | | |ScalarFromTensor [id IJ] ''
| | | |Constant{-1} [id LO]
| | |Constant{-1} [id LO]
| |ScalarFromTensor [id LA] ''
| |ScalarFromTensor [id IG] ''
| |Constant{0} [id II]
| |ScalarFromTensor [id IJ] ''
|if{} [id LP] <TensorType(bool, vector)> ''
| |<TensorType(int64, scalar)> [id LQ] <TensorType(int64, scalar)> -> [id DJ]
| |Elemwise{gt,no_inplace} [id LR] <TensorType(bool, vector)> ''
| | |if{} [id JM] <TensorType(float64, vector)> ''
| | |InplaceDimShuffle{x} [id LS] <TensorType(float64, (True,))> ''
| | |Elemwise{neg,no_inplace} [id LT] <TensorType(float64, scalar)> ''
| | |MaxAndArgmax{axis=(0,)}.0 [id LU] <TensorType(float64, scalar)> 'max'
| |Elemwise{mul,no_inplace} [id LV] <TensorType(bool, vector)> ''
| |InplaceDimShuffle{x} [id LW] <TensorType(bool, (True,))> ''
| | |Elemwise{invert,no_inplace} [id LX] <TensorType(bool, scalar)> ''
| | |Subtensor{int32} [id LJ] <TensorType(bool, scalar)> ''
| |Elemwise{second,no_inplace} [id LY] <TensorType(bool, vector)> ''
| |if{} [id JM] <TensorType(float64, vector)> ''
| |TensorConstant{(1,) of True} [id LZ] <TensorType(bool, (True,))>
|ScalarFromTensor [id IG] ''
|Constant{0} [id II]
|ScalarFromTensor [id IJ] ''
AdvancedBooleanIncSubtensor{inplace=False, set_instead_of_inc=True} [id MA] <TensorType(bool, matrix)> ''
|<TensorType(bool, matrix)> [id MB] <TensorType(bool, matrix)> -> [id GB]
|Elemwise{add,no_inplace} [id MC] <TensorType(bool, vector)> ''
| |if{} [id LP] <TensorType(bool, vector)> ''
| |if{} [id MD] <TensorType(bool, vector)> ''
| |Subtensor{int32} [id LJ] <TensorType(bool, scalar)> ''
| |Elemwise{gt,no_inplace} [id LR] <TensorType(bool, vector)> ''
| |Elemwise{second,no_inplace} [id ME] <TensorType(bool, vector)> ''
| |if{} [id JM] <TensorType(float64, vector)> ''
| |TensorConstant{(1,) of False} [id MF] <TensorType(bool, (True,))>
|Elemwise{mul,no_inplace} [id MG] <TensorType(bool, vector)> ''
| |Subtensor{:int64:} [id MH] <TensorType(bool, vector)> ''
| | |<TensorType(bool, vector)> [id LK] <TensorType(bool, vector)> -> [id HP]
| | |ScalarFromTensor [id MI] ''
| | |Subtensor{int64} [id MJ] <TensorType(int64, scalar)> ''
| | |Shape [id MK] <TensorType(int64, vector)> ''
| | | |<TensorType(int64, vector)> [id ML] <TensorType(int64, vector)> -> [id CJ]
| | |Constant{0} [id IP]
| |Elemwise{invert,no_inplace} [id MM] <TensorType(bool, vector)> ''
| |Elemwise{Cast{bool}} [id MN] <TensorType(bool, vector)> ''
| |Subtensor{:int64:} [id MO] <TensorType(int64, vector)> ''
| |Subtensor{::, int8} [id MP] <TensorType(int64, vector)> ''
| | |fault relation matrix_copy [id MQ] <TensorType(int64, matrix)> -> [id HG]
| | |ScalarFromTensor [id MR] ''
| | |Elemwise{Cast{int8}} [id MS] <TensorType(int8, scalar)> ''
| | |<TensorType(int32, scalar)> [id IH] <TensorType(int32, scalar)> -> [id DL]
| |ScalarFromTensor [id MI] ''
|MakeSlice [id MT] ''
|TensorConstant{0} [id IZ] <TensorType(int8, scalar)>
|Elemwise{add,no_inplace} [id IK] <TensorType(int64, scalar)> ''
|NoneConst [id MU]
IncSubtensor{Set;int32, ::, int8:int64:} [id MV] <TensorType(float64, 3D)> ''
|<TensorType(float64, 3D)> [id MW] <TensorType(float64, 3D)> -> [id GN]
|if{} [id HZ] <TensorType(float64, matrix)> ''
|ScalarFromTensor [id IG] ''
|Constant{0} [id II]
|ScalarFromTensor [id IJ] ''
Elemwise{mul,no_inplace} [id LC] <TensorType(int64, scalar)> ''

for{cpu,Looping}.1 [id A] <TensorType(float64, matrix)> ''

IncSubtensor{Set;int32, ::, int8:int64:} [id HX] <TensorType(float64, 3D)> ''
IncSubtensor{Set;int32:int32:} [id JA] <TensorType(float64, vector)> ''
IncSubtensor{Set;int32, int8:int64:} [id JK] <TensorType(float64, matrix)> ''
AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JQ] <TensorType(float64, matrix)> ''
IncSubtensor{Set;int32, int8:int64:} [id KJ] <TensorType(bool, matrix)> ''
AdvancedBooleanIncSubtensor{inplace=False, set_instead_of_inc=True} [id MA] <TensorType(bool, matrix)> ''
IncSubtensor{Set;int32, ::, int8:int64:} [id MV] <TensorType(float64, 3D)> ''
Elemwise{mul,no_inplace} [id LC] <TensorType(int64, scalar)> ''

for{cpu,Looping}.2 [id A] <TensorType(float64, 3D)> ''

IncSubtensor{Set;int32, ::, int8:int64:} [id HX] <TensorType(float64, 3D)> ''
IncSubtensor{Set;int32:int32:} [id JA] <TensorType(float64, vector)> ''
IncSubtensor{Set;int32, int8:int64:} [id JK] <TensorType(float64, matrix)> ''
AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JQ] <TensorType(float64, matrix)> ''
IncSubtensor{Set;int32, int8:int64:} [id KJ] <TensorType(bool, matrix)> ''
AdvancedBooleanIncSubtensor{inplace=False, set_instead_of_inc=True} [id MA] <TensorType(bool, matrix)> ''
IncSubtensor{Set;int32, ::, int8:int64:} [id MV] <TensorType(float64, 3D)> ''
Elemwise{mul,no_inplace} [id LC] <TensorType(int64, scalar)> ''

for{cpu,Looping}.3 [id A] <TensorType(float64, 3D)> ''

IncSubtensor{Set;int32, ::, int8:int64:} [id HX] <TensorType(float64, 3D)> ''
IncSubtensor{Set;int32:int32:} [id JA] <TensorType(float64, vector)> ''
IncSubtensor{Set;int32, int8:int64:} [id JK] <TensorType(float64, matrix)> ''
AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JQ] <TensorType(float64, matrix)> ''
IncSubtensor{Set;int32, int8:int64:} [id KJ] <TensorType(bool, matrix)> ''
AdvancedBooleanIncSubtensor{inplace=False, set_instead_of_inc=True} [id MA] <TensorType(bool, matrix)> ''
IncSubtensor{Set;int32, ::, int8:int64:} [id MV] <TensorType(float64, 3D)> ''
Elemwise{mul,no_inplace} [id LC] <TensorType(int64, scalar)> ''

for{cpu,Looping}.4 [id A] <TensorType(bool, 3D)> ''

IncSubtensor{Set;int32, ::, int8:int64:} [id HX] <TensorType(float64, 3D)> ''
IncSubtensor{Set;int32:int32:} [id JA] <TensorType(float64, vector)> ''
IncSubtensor{Set;int32, int8:int64:} [id JK] <TensorType(float64, matrix)> ''
AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JQ] <TensorType(float64, matrix)> ''
IncSubtensor{Set;int32, int8:int64:} [id KJ] <TensorType(bool, matrix)> ''
AdvancedBooleanIncSubtensor{inplace=False, set_instead_of_inc=True} [id MA] <TensorType(bool, matrix)> ''
IncSubtensor{Set;int32, ::, int8:int64:} [id MV] <TensorType(float64, 3D)> ''
Elemwise{mul,no_inplace} [id LC] <TensorType(int64, scalar)> ''

for{cpu,Looping}.5 [id A] <TensorType(bool, 3D)> ''

IncSubtensor{Set;int32, ::, int8:int64:} [id HX] <TensorType(float64, 3D)> ''
IncSubtensor{Set;int32:int32:} [id JA] <TensorType(float64, vector)> ''
IncSubtensor{Set;int32, int8:int64:} [id JK] <TensorType(float64, matrix)> ''
AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JQ] <TensorType(float64, matrix)> ''
IncSubtensor{Set;int32, int8:int64:} [id KJ] <TensorType(bool, matrix)> ''
AdvancedBooleanIncSubtensor{inplace=False, set_instead_of_inc=True} [id MA] <TensorType(bool, matrix)> ''
IncSubtensor{Set;int32, ::, int8:int64:} [id MV] <TensorType(float64, 3D)> ''
Elemwise{mul,no_inplace} [id LC] <TensorType(int64, scalar)> ''

for{cpu,Looping}.6 [id A] <TensorType(float64, 4D)> ''

IncSubtensor{Set;int32, ::, int8:int64:} [id HX] <TensorType(float64, 3D)> ''
IncSubtensor{Set;int32:int32:} [id JA] <TensorType(float64, vector)> ''
IncSubtensor{Set;int32, int8:int64:} [id JK] <TensorType(float64, matrix)> ''
AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JQ] <TensorType(float64, matrix)> ''
IncSubtensor{Set;int32, int8:int64:} [id KJ] <TensorType(bool, matrix)> ''
AdvancedBooleanIncSubtensor{inplace=False, set_instead_of_inc=True} [id MA] <TensorType(bool, matrix)> ''
IncSubtensor{Set;int32, ::, int8:int64:} [id MV] <TensorType(float64, 3D)> ''
Elemwise{mul,no_inplace} [id LC] <TensorType(int64, scalar)> ''

for{cpu,Looping}.7 [id A] <TensorType(int64, vector)> ''

IncSubtensor{Set;int32, ::, int8:int64:} [id HX] <TensorType(float64, 3D)> ''
IncSubtensor{Set;int32:int32:} [id JA] <TensorType(float64, vector)> ''
IncSubtensor{Set;int32, int8:int64:} [id JK] <TensorType(float64, matrix)> ''
AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JQ] <TensorType(float64, matrix)> ''
IncSubtensor{Set;int32, int8:int64:} [id KJ] <TensorType(bool, matrix)> ''
AdvancedBooleanIncSubtensor{inplace=False, set_instead_of_inc=True} [id MA] <TensorType(bool, matrix)> ''
IncSubtensor{Set;int32, ::, int8:int64:} [id MV] <TensorType(float64, 3D)> ''
Elemwise{mul,no_inplace} [id LC] <TensorType(int64, scalar)> ''

Storage map footprint:

IncSubtensor{Set;:int64:}.0, Shape: (10, 9, 1, 343618), ElemSize: 8 Byte(s), TotalSize: 247404960 Byte(s)
IncSubtensor{Set;:int64:}.0, Shape: (10, 9, 343618), ElemSize: 8 Byte(s), TotalSize: 247404960 Byte(s)
IncSubtensor{Set;:int64:}.0, Shape: (10, 9, 1, 343618), ElemSize: 8 Byte(s), TotalSize: 247404960 Byte(s)
for{cpu,Looping}.0, Shape: (10, 9, 1, 343618), ElemSize: 8 Byte(s), TotalSize: 247404960 Byte(s)
for{cpu,Looping}.2, Shape: (10, 9, 343618), ElemSize: 8 Byte(s), TotalSize: 247404960 Byte(s)
for{cpu,Looping}.6, Shape: (10, 9, 1, 343618), ElemSize: 8 Byte(s), TotalSize: 247404960 Byte(s)
IncSubtensor{Set;:int64:}.0, Shape: (10, 9, 343618), ElemSize: 1 Byte(s), TotalSize: 30925620 Byte(s)
IncSubtensor{Set;:int64:}.0, Shape: (10, 9, 343618), ElemSize: 1 Byte(s), TotalSize: 30925620 Byte(s)
for{cpu,Looping}.4, Shape: (10, 9, 343618), ElemSize: 1 Byte(s), TotalSize: 30925620 Byte(s)
for{cpu,Looping}.5, Shape: (10, 9, 343618), ElemSize: 1 Byte(s), TotalSize: 30925620 Byte(s)
block matrix, Shared Input, Shape: (9, 1, 343618), ElemSize: 8 Byte(s), TotalSize: 24740496 Byte(s)
Scalar matrix, Shared Input, Shape: (9, 343618), ElemSize: 8 Byte(s), TotalSize: 24740496 Byte(s)
Coordinates of the grid points to interpolate, Input, Shape: (343000, 3), ElemSize: 8 Byte(s), TotalSize: 8232000 Byte(s)
mask matrix, Shared Input, Shape: (9, 343618), ElemSize: 1 Byte(s), TotalSize: 3092562 Byte(s)
IncSubtensor{Set;:int64:}.0, Shape: (10, 1293), ElemSize: 8 Byte(s), TotalSize: 103440 Byte(s)
for{cpu,Looping}.1, Shape: (10, 1293), ElemSize: 8 Byte(s), TotalSize: 103440 Byte(s)
TensorConstant{[ 0 1..4998 4999]}, Shape: (5000,), ElemSize: 4 Byte(s), TotalSize: 20000 Byte(s)
Weights vector, Shared Input, Shape: (1293,), ElemSize: 8 Byte(s), TotalSize: 10344 Byte(s)
Position of the dips, Input, Shape: (318, 3), ElemSize: 8 Byte(s), TotalSize: 7632 Byte(s)
All the surface_points points at once, Input, Shape: (318, 3), ElemSize: 8 Byte(s), TotalSize: 7632 Byte(s)
Nugget effect of gradients, Shared Input, Shape: (954,), ElemSize: 8 Byte(s), TotalSize: 7632 Byte(s)
IncSubtensor{Set;:int64:}.0, Shape: (10, 9, 9), ElemSize: 8 Byte(s), TotalSize: 6480 Byte(s)
for{cpu,Looping}.3, Shape: (10, 9, 9), ElemSize: 8 Byte(s), TotalSize: 6480 Byte(s)
Angle of every dip, Input, Shape: (318,), ElemSize: 8 Byte(s), TotalSize: 2544 Byte(s)
Azimuth, Input, Shape: (318,), ElemSize: 8 Byte(s), TotalSize: 2544 Byte(s)
Polarity, Input, Shape: (318,), ElemSize: 8 Byte(s), TotalSize: 2544 Byte(s)
Nugget effect of scalar, Shared Input, Shape: (318,), ElemSize: 8 Byte(s), TotalSize: 2544 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)
fault relation matrix, Shared Input, Shape: (9, 9), ElemSize: 8 Byte(s), TotalSize: 648 Byte(s)
Values that the blocks are taking, Input, Shape: (1, 10), ElemSize: 8 Byte(s), TotalSize: 80 Byte(s)
Range, Shared Input, Shape: (10,), ElemSize: 8 Byte(s), TotalSize: 80 Byte(s)
Covariance at 0, Shared Input, Shape: (10,), ElemSize: 8 Byte(s), TotalSize: 80 Byte(s)
IncSubtensor{Set;:int64:}.0, Shape: (10,), ElemSize: 8 Byte(s), TotalSize: 80 Byte(s)
for{cpu,Looping}.7, Shape: (10,), ElemSize: 8 Byte(s), TotalSize: 80 Byte(s)
<TensorType(int64, vector)>, Shared Input, Shape: (9,), ElemSize: 8 Byte(s), TotalSize: 72 Byte(s)
<TensorType(int64, vector)>, Shared Input, Shape: (9,), ElemSize: 8 Byte(s), TotalSize: 72 Byte(s)
Subtensor{:int64:}.0, Shape: (9,), ElemSize: 8 Byte(s), TotalSize: 72 Byte(s)
Subtensor{:int64:}.0, Shape: (9,), ElemSize: 8 Byte(s), TotalSize: 72 Byte(s)
Subtensor{:int64:}.0, Shape: (9,), ElemSize: 8 Byte(s), TotalSize: 72 Byte(s)
Subtensor{:int64:}.0, Shape: (9,), ElemSize: 8 Byte(s), TotalSize: 72 Byte(s)
Length of surface_points in every series, Shared Input, Shape: (10,), ElemSize: 4 Byte(s), TotalSize: 40 Byte(s)
Length of foliations in every series, Shared Input, Shape: (10,), ElemSize: 4 Byte(s), TotalSize: 40 Byte(s)
Length of weights in every series, Shared Input, Shape: (10,), ElemSize: 4 Byte(s), TotalSize: 40 Byte(s)
List with the number of surfaces, Shared Input, Shape: (10,), ElemSize: 4 Byte(s), TotalSize: 40 Byte(s)
The series (fault) is finite, Shared Input, Shape: (10,), ElemSize: 4 Byte(s), TotalSize: 40 Byte(s)
Grade of the universal drift, Shared Input, Shape: (9,), ElemSize: 4 Byte(s), TotalSize: 36 Byte(s)
Number of points per surface used to split rest-ref, Shared Input, Shape: (9,), ElemSize: 4 Byte(s), TotalSize: 36 Byte(s)
Subtensor{:int64:}.0, Shape: (9,), ElemSize: 4 Byte(s), TotalSize: 36 Byte(s)
Subtensor{:int64:}.0, Shape: (9,), ElemSize: 4 Byte(s), TotalSize: 36 Byte(s)
Subtensor{:int64:}.0, Shape: (9,), ElemSize: 4 Byte(s), TotalSize: 36 Byte(s)
Subtensor{:int64:}.0, Shape: (9,), ElemSize: 4 Byte(s), TotalSize: 36 Byte(s)
Subtensor{:int64:}.0, Shape: (9,), ElemSize: 4 Byte(s), TotalSize: 36 Byte(s)
Subtensor{:int64:}.0, Shape: (9,), ElemSize: 4 Byte(s), TotalSize: 36 Byte(s)
Subtensor{:int64:}.0, Shape: (9,), ElemSize: 4 Byte(s), TotalSize: 36 Byte(s)
Subtensor{:int64:}.0, Shape: (9,), ElemSize: 4 Byte(s), TotalSize: 36 Byte(s)
Subtensor{:int64:}.0, Shape: (9,), ElemSize: 4 Byte(s), TotalSize: 36 Byte(s)
Subtensor{:int64:}.0, Shape: (9,), ElemSize: 4 Byte(s), TotalSize: 36 Byte(s)
Subtensor{:int64:}.0, Shape: (9,), ElemSize: 4 Byte(s), TotalSize: 36 Byte(s)
<TensorType(bool, vector)>, Shared Input, Shape: (9,), ElemSize: 1 Byte(s), TotalSize: 9 Byte(s)
Subtensor{:int64:}.0, Shape: (9,), ElemSize: 1 Byte(s), TotalSize: 9 Byte(s)
Subtensor{:int64:}.0, Shape: (9,), ElemSize: 1 Byte(s), TotalSize: 9 Byte(s)
Subtensor{:int64:}.0, Shape: (9,), ElemSize: 1 Byte(s), TotalSize: 9 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)
<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)
TensorConstant{(1,) of -1}, Shape: (1,), ElemSize: 8 Byte(s), TotalSize: 8 Byte(s)
TensorConstant{(1, 1) of False}, Shape: (1, 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, 343618), ElemSize: 8 Byte(s), TotalSize: 0 Byte(s)
TotalSize: 1669226352.0 Byte(s) 1.555 GB
TotalSize inputs: 60873429.0 Byte(s) 0.057 GB

Answered by lucianomarquetto

May 30, 2023

Ok, for some reason sharing my frustration here somehow solved my problem, haha! I have no idea what I did but not the error does not appear anymore. So, marked as solved.

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lucianomarquetto · 2023-05-30T17:18:04Z

lucianomarquetto
May 30, 2023
Author

Ok, for some reason sharing my frustration here somehow solved my problem, haha! I have no idea what I did but not the error does not appear anymore. So, marked as solved.

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Problem modeling faults - Full block matrix for faults or drift #804

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GemPy

Problem modeling faults - Full block matrix for faults or drift #804

lucianomarquetto May 30, 2023

Replies: 1 comment

lucianomarquetto May 30, 2023 Author

lucianomarquetto
May 30, 2023

lucianomarquetto
May 30, 2023
Author