Error message #733

anneschulz1991 · 2022-11-10T13:23:36Z

anneschulz1991
Nov 10, 2022

Hello to all,
again I have a question. I started to create a model and after running the following command I got the following error message:

**/home/anne/anaconda3/envs/gempy-environment/lib/python3.9/site-packages/theano/tensor/slinalg.py:255: LinAlgWarning: Ill-conditioned matrix (rcond=5.04289e-14): result may not be accurate.

rval = scipy.linalg.solve(A, b)

IndexError Traceback (most recent call last)
scan_perform.pyx in theano.scan_module.scan_perform.perform()

IndexError: index 277 is out of bounds for axis 0 with size 277

During handling of the above exception, another exception occurred:

IndexError Traceback (most recent call last)
~/anaconda3/envs/gempy-environment/lib/python3.9/site-packages/theano/compile/function_module.py in call(self, *args, **kwargs)
902 outputs =
--> 903 self.fn() if output_subset is None else
904 self.fn(output_subset=output_subset)

~/anaconda3/envs/gempy-environment/lib/python3.9/site-packages/theano/scan_module/scan_op.py in rval(p, i, o, n, allow_gc)
962 allow_gc=allow_gc):
--> 963 r = p(n, [x[0] for x in i], o)
964 for o in node.outputs:

~/anaconda3/envs/gempy-environment/lib/python3.9/site-packages/theano/scan_module/scan_op.py in p(node, args, outs)
927 def p(node, args, outs):
--> 928 return scan_perform_ext.perform(self.n_shared_outs,
929 self.n_mit_mot_outs,

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

~/anaconda3/envs/gempy-environment/lib/python3.9/site-packages/theano/gof/link.py in raise_with_op(node, thunk, exc_info, storage_map)
324 pass
--> 325 reraise(exc_type, exc_value, exc_trace)
326

~/anaconda3/envs/gempy-environment/lib/python3.9/site-packages/six.py in reraise(tp, value, tb)
717 if value.traceback is not tb:
--> 718 raise value.with_traceback(tb)
719 raise value

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

IndexError: index 277 is out of bounds for axis 0 with size 277
Apply node that caused the error: AdvancedSubtensor1(Subtensor{int64:int64:}.0, Subtensor{int32:int32:}.0)
Toposort index: 661
Inputs types: [TensorType(float32, vector), TensorType(int32, vector)]
Inputs shapes: [(277,), (10,)]
Inputs strides: [(4,), (4,)]
Inputs values: ['not shown', 'not shown']
Inputs type_num: [11, 5]
Outputs clients: [[Join(TensorConstant{0}, TensorConstant{(1,) of 0}, AdvancedSubtensor1.0, TensorConstant{(1,) of 0}), Join(TensorConstant{0}, MakeVector{dtype='float32'}.0, AdvancedSubtensor1.0, MakeVector{dtype='float32'}.0), AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True}(<TensorType(float32, matrix)>, AdvancedSubtensor1.0, <TensorType(int32, scalar)>, Elemwise{sub,no_inplace}.0), Elemwise{neg,no_inplace}(AdvancedSubtensor1.0), MaxAndArgmax{axis=(0,)}(AdvancedSubtensor1.0)]]

Backtrace when the node is created(use Theano flag traceback.limit=N to make it longer):
File "/tmp/ipykernel_3560/960449288.py", line 1, in
interp_data = gp.set_interpolator(geo_model,
File "/home/anne/gempy/gempy/api_modules/setters.py", line 108, in set_interpolator
geo_model._interpolator.compile_th_fn_geo(inplace=True, grid=grid)
File "/home/anne/gempy/gempy/core/interpolator.py", line 1124, in compile_th_fn_geo
self.theano_graph.theano_output(),
File "/home/anne/gempy/gempy/core/theano_modules/theano_graph_pro.py", line 631, in theano_output
solutions[:9] = self.compute_series()
File "/home/anne/gempy/gempy/core/theano_modules/theano_graph_pro.py", line 465, in compute_series
series, self.updates1 = theano.scan(
File "/home/anne/anaconda3/envs/gempy-environment/lib/python3.9/site-packages/theano/scan_module/scan.py", line 774, in scan
condition, outputs, updates = scan_utils.get_updates_and_outputs(fn(*args))
File "/home/anne/gempy/gempy/core/theano_modules/theano_graph_pro.py", line 2148, in compute_a_series
scalar_field_at_surface_points = self.get_scalar_field_at_surface_points(Z_x,
File "/home/anne/gempy/gempy/core/theano_modules/theano_graph_pro.py", line 1756, in get_scalar_field_at_surface_points
Z_x[-2 * self.len_points: -self.len_points][npf_op]

Debugprint of the apply node:
AdvancedSubtensor1 [id A] <TensorType(float32, vector)> ''
|Subtensor{int64:int64:} [id B] <TensorType(float32, vector)> ''
| |if{} [id C] <TensorType(float32, vector)> ''
| | |Vector controlling if scalar matrix must be recomputed[t] [id D] <TensorType(bool, scalar)>
| | |Subtensor{int64} [id E] <TensorType(float32, vector)> 'Value of the potential field at every point'
| | |Subtensor{int32} [id F] <TensorType(float32, vector)> ''
| | |Scalar matrix[t-1] [id G] <TensorType(float32, matrix)>
| | |ScalarFromTensor [id H] ''
| | |<TensorType(int32, scalar)> [id I] <TensorType(int32, scalar)>
| |ScalarFromTensor [id J] ''
| | |Elemwise{mul,no_inplace} [id K] <TensorType(int64, scalar)> ''
| | |TensorConstant{-2} [id L] <TensorType(int8, scalar)>
| | |Elemwise{sub,no_inplace} [id M] <TensorType(int64, scalar)> ''
| | |Subtensor{int64} [id N] <TensorType(int64, scalar)> ''
| | | |Shape [id O] <TensorType(int64, vector)> ''
| | | | |All the surface_points points at once_copy [id P] <TensorType(float32, matrix)>
| | | |Constant{0} [id Q]
| | |Subtensor{int64} [id R] <TensorType(int64, scalar)> ''
| | |Shape [id S] <TensorType(int64, vector)> ''
| | | |Number of points per surface used to split rest-ref_copy [id T] <TensorType(int32, vector)>
| | |Constant{0} [id Q]
| |ScalarFromTensor [id U] ''
| |Elemwise{neg,no_inplace} [id V] <TensorType(int64, scalar)> ''
| |Elemwise{sub,no_inplace} [id M] <TensorType(int64, scalar)> ''
|Subtensor{int32:int32:} [id W] <TensorType(int32, vector)> ''
|CumOp{None, add} [id X] <TensorType(int32, vector)> 'Number of points per surfaces after rest-ref. This is used for finding the differentsurface points withing a layer.'
|ScalarFromTensor [id Y] ''
| |List with the number of surfaces[t] [id Z] <TensorType(int32, scalar)>
|ScalarFromTensor [id BA] ''
|List with the number of surfaces[t+1] [id BB] <TensorType(int32, scalar)>

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:

IndexError Traceback (most recent call last)
/tmp/ipykernel_3560/1433740912.py in
----> 1 gp.compute_model(geo_model)

~/gempy/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:

~/anaconda3/envs/gempy-environment/lib/python3.9/site-packages/theano/compile/function_module.py in call(self, *args, **kwargs)
912 if hasattr(self.fn, 'thunks'):
913 thunk = self.fn.thunks[self.fn.position_of_error]
--> 914 gof.link.raise_with_op(
915 node=self.fn.nodes[self.fn.position_of_error],
916 thunk=thunk,

~/anaconda3/envs/gempy-environment/lib/python3.9/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

~/anaconda3/envs/gempy-environment/lib/python3.9/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:

~/anaconda3/envs/gempy-environment/lib/python3.9/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:

~/anaconda3/envs/gempy-environment/lib/python3.9/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

~/anaconda3/envs/gempy-environment/lib/python3.9/site-packages/theano/scan_module/scan_op.py in p(node, args, outs)
926
927 def p(node, args, outs):
--> 928 return scan_perform_ext.perform(self.n_shared_outs,
929 self.n_mit_mot_outs,
930 self.n_seqs,

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

~/anaconda3/envs/gempy-environment/lib/python3.9/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

~/anaconda3/envs/gempy-environment/lib/python3.9/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()

IndexError: index 277 is out of bounds for axis 0 with size 277
Apply node that caused the error: AdvancedSubtensor1(Subtensor{int64:int64:}.0, Subtensor{int32:int32:}.0)
Toposort index: 661
Inputs types: [TensorType(float32, vector), TensorType(int32, vector)]
Inputs shapes: [(277,), (10,)]
Inputs strides: [(4,), (4,)]
Inputs values: ['not shown', 'not shown']
Inputs type_num: [11, 5]
Outputs clients: [[Join(TensorConstant{0}, TensorConstant{(1,) of 0}, AdvancedSubtensor1.0, TensorConstant{(1,) of 0}), Join(TensorConstant{0}, MakeVector{dtype='float32'}.0, AdvancedSubtensor1.0, MakeVector{dtype='float32'}.0), AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True}(<TensorType(float32, matrix)>, AdvancedSubtensor1.0, <TensorType(int32, scalar)>, Elemwise{sub,no_inplace}.0), Elemwise{neg,no_inplace}(AdvancedSubtensor1.0), MaxAndArgmax{axis=(0,)}(AdvancedSubtensor1.0)]]

Backtrace when the node is created(use Theano flag traceback.limit=N to make it longer):
File "/tmp/ipykernel_3560/960449288.py", line 1, in
interp_data = gp.set_interpolator(geo_model,
File "/home/anne/gempy/gempy/api_modules/setters.py", line 108, in set_interpolator
geo_model._interpolator.compile_th_fn_geo(inplace=True, grid=grid)
File "/home/anne/gempy/gempy/core/interpolator.py", line 1124, in compile_th_fn_geo
self.theano_graph.theano_output(),
File "/home/anne/gempy/gempy/core/theano_modules/theano_graph_pro.py", line 631, in theano_output
solutions[:9] = self.compute_series()
File "/home/anne/gempy/gempy/core/theano_modules/theano_graph_pro.py", line 465, in compute_series
series, self.updates1 = theano.scan(
File "/home/anne/anaconda3/envs/gempy-environment/lib/python3.9/site-packages/theano/scan_module/scan.py", line 774, in scan
condition, outputs, updates = scan_utils.get_updates_and_outputs(fn(*args))
File "/home/anne/gempy/gempy/core/theano_modules/theano_graph_pro.py", line 2148, in compute_a_series
scalar_field_at_surface_points = self.get_scalar_field_at_surface_points(Z_x,
File "/home/anne/gempy/gempy/core/theano_modules/theano_graph_pro.py", line 1756, in get_scalar_field_at_surface_points
Z_x[-2 * self.len_points: -self.len_points][npf_op]

Debugprint of the apply node:
AdvancedSubtensor1 [id A] <TensorType(float32, vector)> ''
|Subtensor{int64:int64:} [id B] <TensorType(float32, vector)> ''
| |if{} [id C] <TensorType(float32, vector)> ''
| | |Vector controlling if scalar matrix must be recomputed[t] [id D] <TensorType(bool, scalar)>
| | |Subtensor{int64} [id E] <TensorType(float32, vector)> 'Value of the potential field at every point'
| | |Subtensor{int32} [id F] <TensorType(float32, vector)> ''
| | |Scalar matrix[t-1] [id G] <TensorType(float32, matrix)>
| | |ScalarFromTensor [id H] ''
| | |<TensorType(int32, scalar)> [id I] <TensorType(int32, scalar)>
| |ScalarFromTensor [id J] ''
| | |Elemwise{mul,no_inplace} [id K] <TensorType(int64, scalar)> ''
| | |TensorConstant{-2} [id L] <TensorType(int8, scalar)>
| | |Elemwise{sub,no_inplace} [id M] <TensorType(int64, scalar)> ''
| | |Subtensor{int64} [id N] <TensorType(int64, scalar)> ''
| | | |Shape [id O] <TensorType(int64, vector)> ''
| | | | |All the surface_points points at once_copy [id P] <TensorType(float32, matrix)>
| | | |Constant{0} [id Q]
| | |Subtensor{int64} [id R] <TensorType(int64, scalar)> ''
| | |Shape [id S] <TensorType(int64, vector)> ''
| | | |Number of points per surface used to split rest-ref_copy [id T] <TensorType(int32, vector)>
| | |Constant{0} [id Q]
| |ScalarFromTensor [id U] ''
| |Elemwise{neg,no_inplace} [id V] <TensorType(int64, scalar)> ''
| |Elemwise{sub,no_inplace} [id M] <TensorType(int64, scalar)> ''
|Subtensor{int32:int32:} [id W] <TensorType(int32, vector)> ''
|CumOp{None, add} [id X] <TensorType(int32, vector)> 'Number of points per surfaces after rest-ref. This is used for finding the differentsurface points withing a layer.'
|ScalarFromTensor [id Y] ''
| |List with the number of surfaces[t] [id Z] <TensorType(int32, scalar)>
|ScalarFromTensor [id BA] ''
|List with the number of surfaces[t+1] [id BB] <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, 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(float32, scalar)>, <TensorType(float32, 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(float32, vector), TensorType(float32, vector), TensorType(float32, 4D), TensorType(float32, matrix), TensorType(float32, 3D), TensorType(float32, 3D), TensorType(bool, 3D), TensorType(bool, 3D), TensorType(float32, 4D), TensorType(int64, vector), TensorType(int32, vector), TensorType(int64, matrix), TensorType(float64, scalar), TensorType(float32, scalar), TensorType(float32, scalar), TensorType(float32, vector), TensorType(float32, vector), TensorType(float32, scalar), TensorType(float32, scalar), TensorType(float32, scalar), TensorType(int64, vector), TensorType(bool, vector), TensorType(int64, vector), TensorType(float32, matrix), TensorType(float32, matrix), TensorType(float32, matrix), TensorType(float32, vector), TensorType(float32, vector), TensorType(float32, vector), TensorType(float32, matrix)]
Inputs shapes: [(), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (1,), (2, 1, 1, 100554), (2, 1141), (2, 1, 100554), (2, 1, 10), (2, 1, 100554), (2, 1, 100554), (2, 1, 1, 100554), (2,), (10,), (1, 1), (), (), (), (861,), (287,), (), (), (), (1,), (1,), (1,), (100000, 3), (287, 3), (287, 3), (287,), (287,), (287,), (1, 11)]
Inputs strides: [(), (4,), (4,), (4,), (4,), (4,), (4,), (4,), (4,), (4,), (1,), (1,), (1,), (4,), (8,), (8,), (4,), (4,), (4,), (402216, 402216, 402216, 4), (4564, 4), (402216, 402216, 4), (40, 40, 4), (100554, 100554, 1), (100554, 100554, 1), (402216, 402216, 402216, 4), (8,), (4,), (8, 8), (), (), (), (4,), (4,), (), (), (), (8,), (1,), (8,), (12, 4), (4, 1148), (4, 1148), (4,), (4,), (4,), (44, 4)]
Inputs values: [array(1), array([0], dtype=int32), array([277], dtype=int32), array([0], dtype=int32), array([287], dtype=int32), array([0], dtype=int32), array([1141], dtype=int32), array([0], dtype=int32), array([10], dtype=int32), array([3], dtype=int32), array([ True]), array([ True]), array([ True]), array([0], dtype=int32), array([0]), array([0]), array([0], dtype=int32), array([0.50211763], dtype=float32), array([765.3707], dtype=float32), 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown', array([ 0, 1141]), 'not shown', array([[0]]), array(10.), array(2., dtype=float32), array(4., dtype=float32), 'not shown', 'not shown', array(2., dtype=float32), array(50., dtype=float32), array(50000., dtype=float32), array([0]), array([False]), array([0]), '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, 11, 11, 11, 11, 11, 11, 0, 0, 11, 7, 5, 7, 12, 11, 11, 11, 11, 11, 11, 11, 7, 0, 7, 11, 11, 11, 11, 11, 11, 11]
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/anne/anaconda3/envs/gempy-environment/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3172, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "/home/anne/anaconda3/envs/gempy-environment/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3364, in run_ast_nodes
if (await self.run_code(code, result, async_=asy)):
File "/home/anne/anaconda3/envs/gempy-environment/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3444, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "/tmp/ipykernel_3560/960449288.py", line 1, in
interp_data = gp.set_interpolator(geo_model,
File "/home/anne/gempy/gempy/api_modules/setters.py", line 108, in set_interpolator
geo_model._interpolator.compile_th_fn_geo(inplace=True, grid=grid)
File "/home/anne/gempy/gempy/core/interpolator.py", line 1124, in compile_th_fn_geo
self.theano_graph.theano_output(),
File "/home/anne/gempy/gempy/core/theano_modules/theano_graph_pro.py", line 631, in theano_output
solutions[:9] = self.compute_series()
File "/home/anne/gempy/gempy/core/theano_modules/theano_graph_pro.py", line 465, in compute_series
series, self.updates1 = theano.scan(

Debugprint of the apply node:
for{cpu,Looping}.0 [id A] <TensorType(float32, 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(float32, 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(float32, 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(float32, vector)> ''
| |Subtensor{int64::} [id CR] <TensorType(float32, vector)> 'Range[0:]'
| |ScalarFromTensor [id CW] ''
|Subtensor{:int64:} [id DO] <TensorType(float32, vector)> ''
| |Subtensor{int64::} [id CU] <TensorType(float32, vector)> 'Covariance at 0[0:]'
| |ScalarFromTensor [id CW] ''
|IncSubtensor{Set;:int64:} [id DP] <TensorType(float32, 4D)> ''
| |AllocEmpty{dtype='float32'} [id DQ] <TensorType(float32, 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(float32, 4D)> ''
| | | | |InplaceDimShuffle{x,0,1,2} [id DV] <TensorType(float32, (True, False, False, False))> ''
| | | | |block matrix [id DW] <TensorType(float32, 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(float32, 4D)> ''
| |ScalarFromTensor [id ED] ''
| |Subtensor{int64} [id DS] <TensorType(int64, scalar)> ''
|IncSubtensor{Set;:int64:} [id EE] <TensorType(float32, matrix)> ''
| |AllocEmpty{dtype='float32'} [id EF] <TensorType(float32, 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(float32, matrix)> ''
| | | | |InplaceDimShuffle{x,0} [id EK] <TensorType(float32, row)> ''
| | | | |Weights vector [id EL] <TensorType(float32, 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(float32, matrix)> ''
| |ScalarFromTensor [id EN] ''
| |Subtensor{int64} [id EH] <TensorType(int64, scalar)> ''
|IncSubtensor{Set;:int64:} [id EO] <TensorType(float32, 3D)> ''
| |AllocEmpty{dtype='float32'} [id EP] <TensorType(float32, 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(float32, 3D)> ''
| | | | |InplaceDimShuffle{x,0,1} [id EU] <TensorType(float32, (True, False, False))> ''
| | | | |Scalar matrix [id EV] <TensorType(float32, 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(float32, 3D)> ''
| |ScalarFromTensor [id EY] ''
| |Subtensor{int64} [id ER] <TensorType(int64, scalar)> ''
|IncSubtensor{Set;:int64:} [id EZ] <TensorType(float32, 3D)> ''
| |AllocEmpty{dtype='float32'} [id FA] <TensorType(float32, 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(float32, 3D)> ''
| | | | |InplaceDimShuffle{x,0,1} [id FF] <TensorType(float32, (True, False, False))> ''
| | | | |Alloc [id FG] <TensorType(float32, matrix)> ''
| | | | |TensorConstant{0.0} [id FH] <TensorType(float32, 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(float32, 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(float32, 4D)> ''
| |AllocEmpty{dtype='float32'} [id GO] <TensorType(float32, 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(float32, 4D)> ''
| | | | |InplaceDimShuffle{x,0,1,2} [id GT] <TensorType(float32, (True, False, False, False))> ''
| | | | |Elemwise{second,no_inplace} [id GU] <TensorType(float32, 3D)> ''
| | | | |block matrix [id DW] <TensorType(float32, 3D)>
| | | | |TensorConstant{(1, 1, 1) of 0.0} [id GV] <TensorType(float32, (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(float32, 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(float32, scalar)> [id HI] <TensorType(float32, scalar)>
|<TensorType(float32, scalar)> [id HJ] <TensorType(float32, scalar)>
|Nugget effect of gradients [id HK] <TensorType(float32, vector)>
|Nugget effect of scalar [id HL] <TensorType(float32, vector)>
|Attenuation factor [id HM] <TensorType(float32, scalar)>
|Sigmoid Outside [id HN] <TensorType(float32, scalar)>
|Sigmoid slope [id HO] <TensorType(float32, 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(float32, matrix)>
|All the surface_points points at once [id HR] <TensorType(float32, matrix)>
|Position of the dips [id HS] <TensorType(float32, matrix)>
|Angle of every dip [id HT] <TensorType(float32, vector)>
|Azimuth [id HU] <TensorType(float32, vector)>
|Polarity [id HV] <TensorType(float32, vector)>
|Values that the blocks are taking [id HW] <TensorType(float32, matrix)>
for{cpu,Looping}.1 [id A] <TensorType(float32, matrix)> ''
for{cpu,Looping}.2 [id A] <TensorType(float32, 3D)> ''
for{cpu,Looping}.3 [id A] <TensorType(float32, 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(float32, 4D)> ''
for{cpu,Looping}.7 [id A] <TensorType(int64, vector)> ''

Inner graphs of the scan ops:

for{cpu,Looping}.0 [id A] <TensorType(float32, 4D)> ''

IncSubtensor{Set;int32, ::, int8:int64:} [id HX] <TensorType(float32, 3D)> ''
|block matrix[t-1] [id HY] <TensorType(float32, 3D)> -> [id DP]
|if{} [id HZ] <TensorType(float32, matrix)> ''
| |Vector controlling if block matrix must be recomputed[t] [id IA] <TensorType(bool, scalar)> -> [id DH]
| |if{} [id IB] <TensorType(float32, matrix)> ''
| | |The series (fault) is finite[t] [id IC] <TensorType(int32, scalar)> -> [id DI]
| | |Sum{axis=[0], acc_dtype=float64} [id ID] <TensorType(float32, matrix)> 'The chunk of block model of a specific series'
| | |Sum{axis=[0], acc_dtype=float64} [id IE] <TensorType(float32, matrix)> 'The chunk of block model of a specific series'
| |Subtensor{int32, ::} [id IF] <TensorType(float32, matrix)> ''
| |block matrix[t-1] [id HY] <TensorType(float32, 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(float32, 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(float32, 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(float32, vector)> ''
|Weights vector[t-1] [id JB] <TensorType(float32, vector)> -> [id EE]
|if{} [id JC] <TensorType(float32, vector)> ''
| |Vector controlling if weights must be recomputed[t] [id JD] <TensorType(bool, scalar)> -> [id DF]
| |Reshape{1} [id JE] <TensorType(float32, vector)> 'Dual Kriging parameters'
| |Subtensor{int32:int32:} [id JF] <TensorType(float32, vector)> ''
| |Weights vector[t-1] [id JB] <TensorType(float32, 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(float32, matrix)> ''
|Scalar matrix[t-1] [id JL] <TensorType(float32, matrix)> -> [id EO]
|if{} [id JM] <TensorType(float32, vector)> ''
| |Vector controlling if scalar matrix must be recomputed[t] [id JN] <TensorType(bool, scalar)> -> [id DG]
| |Subtensor{int64} [id JO] <TensorType(float32, vector)> 'Value of the potential field at every point'
| |Subtensor{int32} [id JP] <TensorType(float32, vector)> ''
| |Scalar matrix[t-1] [id JL] <TensorType(float32, 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(float32, matrix)> ''
|<TensorType(float32, matrix)> [id JR] <TensorType(float32, matrix)> -> [id EZ]
|AdvancedSubtensor1 [id JS] <TensorType(float32, vector)> ''
| |Subtensor{int64:int64:} [id JT] <TensorType(float32, vector)> ''
| | |if{} [id JM] <TensorType(float32, 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(float32, vector)> ''
| | | | |InplaceDimShuffle{x} [id KQ] <TensorType(float32, (True,))> ''
| | | | |MaxAndArgmax{axis=(0,)}.0 [id KR] <TensorType(float32, 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(float32, vector)> ''
| | |InplaceDimShuffle{x} [id LS] <TensorType(float32, (True,))> ''
| | |Elemwise{neg,no_inplace} [id LT] <TensorType(float32, scalar)> ''
| | |MaxAndArgmax{axis=(0,)}.0 [id LU] <TensorType(float32, 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(float32, 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(float32, 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(float32, 3D)> ''
|<TensorType(float32, 3D)> [id MW] <TensorType(float32, 3D)> -> [id GN]
|if{} [id HZ] <TensorType(float32, 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(float32, matrix)> ''

IncSubtensor{Set;int32, ::, int8:int64:} [id HX] <TensorType(float32, 3D)> ''
IncSubtensor{Set;int32:int32:} [id JA] <TensorType(float32, vector)> ''
IncSubtensor{Set;int32, int8:int64:} [id JK] <TensorType(float32, matrix)> ''
AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JQ] <TensorType(float32, 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(float32, 3D)> ''
Elemwise{mul,no_inplace} [id LC] <TensorType(int64, scalar)> ''

for{cpu,Looping}.2 [id A] <TensorType(float32, 3D)> ''

IncSubtensor{Set;int32, ::, int8:int64:} [id HX] <TensorType(float32, 3D)> ''
IncSubtensor{Set;int32:int32:} [id JA] <TensorType(float32, vector)> ''
IncSubtensor{Set;int32, int8:int64:} [id JK] <TensorType(float32, matrix)> ''
AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JQ] <TensorType(float32, 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(float32, 3D)> ''
Elemwise{mul,no_inplace} [id LC] <TensorType(int64, scalar)> ''

for{cpu,Looping}.3 [id A] <TensorType(float32, 3D)> ''

IncSubtensor{Set;int32, ::, int8:int64:} [id HX] <TensorType(float32, 3D)> ''
IncSubtensor{Set;int32:int32:} [id JA] <TensorType(float32, vector)> ''
IncSubtensor{Set;int32, int8:int64:} [id JK] <TensorType(float32, matrix)> ''
AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JQ] <TensorType(float32, 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(float32, 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(float32, 3D)> ''
IncSubtensor{Set;int32:int32:} [id JA] <TensorType(float32, vector)> ''
IncSubtensor{Set;int32, int8:int64:} [id JK] <TensorType(float32, matrix)> ''
AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JQ] <TensorType(float32, 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(float32, 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(float32, 3D)> ''
IncSubtensor{Set;int32:int32:} [id JA] <TensorType(float32, vector)> ''
IncSubtensor{Set;int32, int8:int64:} [id JK] <TensorType(float32, matrix)> ''
AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JQ] <TensorType(float32, 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(float32, 3D)> ''
Elemwise{mul,no_inplace} [id LC] <TensorType(int64, scalar)> ''

for{cpu,Looping}.6 [id A] <TensorType(float32, 4D)> ''

IncSubtensor{Set;int32, ::, int8:int64:} [id HX] <TensorType(float32, 3D)> ''
IncSubtensor{Set;int32:int32:} [id JA] <TensorType(float32, vector)> ''
IncSubtensor{Set;int32, int8:int64:} [id JK] <TensorType(float32, matrix)> ''
AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JQ] <TensorType(float32, 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(float32, 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(float32, 3D)> ''
IncSubtensor{Set;int32:int32:} [id JA] <TensorType(float32, vector)> ''
IncSubtensor{Set;int32, int8:int64:} [id JK] <TensorType(float32, matrix)> ''
AdvancedIncSubtensor{inplace=False, set_instead_of_inc=True} [id JQ] <TensorType(float32, 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(float32, 3D)> ''
Elemwise{mul,no_inplace} [id LC] <TensorType(int64, scalar)> ''

Storage map footprint:

Coordinates of the grid points to interpolate, Input, Shape: (100000, 3), ElemSize: 4 Byte(s), TotalSize: 1200000 Byte(s)
IncSubtensor{Set;:int64:}.0, Shape: (2, 1, 1, 100554), ElemSize: 4 Byte(s), TotalSize: 804432 Byte(s)
IncSubtensor{Set;:int64:}.0, Shape: (2, 1, 100554), ElemSize: 4 Byte(s), TotalSize: 804432 Byte(s)
IncSubtensor{Set;:int64:}.0, Shape: (2, 1, 1, 100554), ElemSize: 4 Byte(s), TotalSize: 804432 Byte(s)
for{cpu,Looping}.0, Shape: (2, 1, 1, 100554), ElemSize: 4 Byte(s), TotalSize: 804432 Byte(s)
for{cpu,Looping}.2, Shape: (2, 1, 100554), ElemSize: 4 Byte(s), TotalSize: 804432 Byte(s)
for{cpu,Looping}.6, Shape: (2, 1, 1, 100554), ElemSize: 4 Byte(s), TotalSize: 804432 Byte(s)
block matrix, Shared Input, Shape: (1, 1, 100554), ElemSize: 4 Byte(s), TotalSize: 402216 Byte(s)
Scalar matrix, Shared Input, Shape: (1, 100554), ElemSize: 4 Byte(s), TotalSize: 402216 Byte(s)
IncSubtensor{Set;:int64:}.0, Shape: (2, 1, 100554), ElemSize: 1 Byte(s), TotalSize: 201108 Byte(s)
IncSubtensor{Set;:int64:}.0, Shape: (2, 1, 100554), ElemSize: 1 Byte(s), TotalSize: 201108 Byte(s)
for{cpu,Looping}.4, Shape: (2, 1, 100554), ElemSize: 1 Byte(s), TotalSize: 201108 Byte(s)
for{cpu,Looping}.5, Shape: (2, 1, 100554), ElemSize: 1 Byte(s), TotalSize: 201108 Byte(s)
mask matrix, Shared Input, Shape: (1, 100554), ElemSize: 1 Byte(s), TotalSize: 100554 Byte(s)
TensorConstant{[ 0 1..4998 4999]}, Shape: (5000,), ElemSize: 4 Byte(s), TotalSize: 20000 Byte(s)
IncSubtensor{Set;:int64:}.0, Shape: (2, 1141), ElemSize: 4 Byte(s), TotalSize: 9128 Byte(s)
for{cpu,Looping}.1, Shape: (2, 1141), ElemSize: 4 Byte(s), TotalSize: 9128 Byte(s)
Weights vector, Shared Input, Shape: (1141,), ElemSize: 4 Byte(s), TotalSize: 4564 Byte(s)
Position of the dips, Input, Shape: (287, 3), ElemSize: 4 Byte(s), TotalSize: 3444 Byte(s)
All the surface_points points at once, Input, Shape: (287, 3), ElemSize: 4 Byte(s), TotalSize: 3444 Byte(s)
Nugget effect of gradients, Shared Input, Shape: (861,), ElemSize: 4 Byte(s), TotalSize: 3444 Byte(s)
Angle of every dip, Input, Shape: (287,), ElemSize: 4 Byte(s), TotalSize: 1148 Byte(s)
Azimuth, Input, Shape: (287,), ElemSize: 4 Byte(s), TotalSize: 1148 Byte(s)
Polarity, Input, Shape: (287,), ElemSize: 4 Byte(s), TotalSize: 1148 Byte(s)
Nugget effect of scalar, Shared Input, Shape: (287,), ElemSize: 4 Byte(s), TotalSize: 1148 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, 1, 10), ElemSize: 4 Byte(s), TotalSize: 80 Byte(s)
for{cpu,Looping}.3, Shape: (2, 1, 10), ElemSize: 4 Byte(s), TotalSize: 80 Byte(s)
Values that the blocks are taking, Input, Shape: (1, 11), ElemSize: 4 Byte(s), TotalSize: 44 Byte(s)
Number of points per surface used to split rest-ref, Shared Input, Shape: (10,), ElemSize: 4 Byte(s), TotalSize: 40 Byte(s)
IncSubtensor{Set;:int64:}.0, Shape: (2,), 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)
<TensorType(int64, vector)>, Shared Input, Shape: (1,), ElemSize: 8 Byte(s), TotalSize: 8 Byte(s)
<TensorType(int64, vector)>, Shared Input, Shape: (1,), ElemSize: 8 Byte(s), TotalSize: 8 Byte(s)
Range, Shared Input, Shape: (2,), ElemSize: 4 Byte(s), TotalSize: 8 Byte(s)
Covariance at 0, Shared Input, Shape: (2,), ElemSize: 4 Byte(s), TotalSize: 8 Byte(s)
fault relation matrix, Shared Input, Shape: (1, 1), ElemSize: 8 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)
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)
Constant{3}, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
Constant{2}, Shape: (), ElemSize: 8 Byte(s), TotalSize: 8.0 Byte(s)
Subtensor{:int64:}.0, Shape: (1,), ElemSize: 8 Byte(s), TotalSize: 8 Byte(s)
Subtensor{:int64:}.0, Shape: (1,), ElemSize: 8 Byte(s), TotalSize: 8 Byte(s)
TensorConstant{(1,) of -1}, Shape: (1,), ElemSize: 8 Byte(s), TotalSize: 8 Byte(s)
Grade of the universal drift, Shared Input, Shape: (1,), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
<TensorType(float32, scalar)>, Shared Input, Shape: (), ElemSize: 4 Byte(s), TotalSize: 4.0 Byte(s)
<TensorType(float32, scalar)>, Shared Input, Shape: (), ElemSize: 4 Byte(s), TotalSize: 4.0 Byte(s)
Attenuation factor, Shared Input, Shape: (), ElemSize: 4 Byte(s), TotalSize: 4.0 Byte(s)
Sigmoid Outside, Shared Input, Shape: (), ElemSize: 4 Byte(s), TotalSize: 4.0 Byte(s)
Sigmoid slope, Shared Input, Shape: (), ElemSize: 4 Byte(s), TotalSize: 4.0 Byte(s)
TensorConstant{(1, 1, 1) of 0.0}, Shape: (1, 1, 1), ElemSize: 4 Byte(s), TotalSize: 4 Byte(s)
TensorConstant{0.0}, Shape: (), ElemSize: 4 Byte(s), TotalSize: 4.0 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)
<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, 100554), ElemSize: 4 Byte(s), TotalSize: 0 Byte(s)
TotalSize: 7797308.0 Byte(s) 0.007 GB
TotalSize inputs: 2147761.0 Byte(s) 0.002 GB**

Unfortunately I could not identify the source of the error. Can anyone help me?
Many greetings
Anne

Answered by Japhiolite

Nov 10, 2022

Hi Anne,
these looooong tenso - theano errors about an ill-conditioned matrix usually point at a very instable, errorneous model, where no real solution can be calculated. The causes for this can be manifold unfortunately, so there's no straight up solution I can provide.

Having a look at the amount of input data, and whether there are some "contradicting" data may be a worthwhile approach, or first reducing model complexity and step-wise increasing it.

Best,
Jan

View full answer

Japhiolite · 2022-11-10T13:56:55Z

Japhiolite
Nov 10, 2022
Maintainer

Hi Anne,
these looooong tenso - theano errors about an ill-conditioned matrix usually point at a very instable, errorneous model, where no real solution can be calculated. The causes for this can be manifold unfortunately, so there's no straight up solution I can provide.

Having a look at the amount of input data, and whether there are some "contradicting" data may be a worthwhile approach, or first reducing model complexity and step-wise increasing it.

Best,
Jan

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GemPy

Error message #733

anneschulz1991 Nov 10, 2022

**/home/anne/anaconda3/envs/gempy-environment/lib/python3.9/site-packages/theano/tensor/slinalg.py:255: LinAlgWarning: Ill-conditioned matrix (rcond=5.04289e-14): result may not be accurate.

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Japhiolite Nov 10, 2022 Maintainer

anneschulz1991
Nov 10, 2022

Japhiolite
Nov 10, 2022
Maintainer