Source code for gempy.API.compute_API
from typing import Optional
import numpy as np
import gempy_engine
from gempy_engine.core.backend_tensor import BackendTensor
from gempy.API.gp2_gp3_compatibility.gp3_to_gp2_input import gempy3_to_gempy2
from gempy_engine.config import AvailableBackends
from gempy_engine.core.data import Solutions
from .grid_API import set_custom_grid
from ..core.data.gempy_engine_config import GemPyEngineConfig
from ..core.data.geo_model import GeoModel
from ..optional_dependencies import require_gempy_legacy
[docs]
def compute_model(gempy_model: GeoModel, engine_config: Optional[GemPyEngineConfig] = None) -> Solutions:
"""
Compute the geological model given the provided GemPy model.
Args:
gempy_model (GeoModel): The GemPy model to compute.
engine_config (Optional[GemPyEngineConfig]): Configuration for the computational engine. Defaults to None, in which case a default configuration will be used.
Raises:
ValueError: If the provided backend in the engine_config is not supported.
Returns:
Solutions: The computed geological model.
"""
engine_config = engine_config or GemPyEngineConfig(use_gpu=False)
match engine_config.backend:
case AvailableBackends.numpy | AvailableBackends.PYTORCH:
BackendTensor.change_backend_gempy(
engine_backend=engine_config.backend,
use_gpu=engine_config.use_gpu,
dtype=engine_config.dtype
)
# TODO: To decide what to do with this.
interpolation_input = gempy_model.interpolation_input_copy
gempy_model.taped_interpolation_input = interpolation_input # * This is used for gradient tape
gempy_model.solutions = gempy_engine.compute_model(
interpolation_input=interpolation_input,
options=gempy_model.interpolation_options,
data_descriptor=gempy_model.input_data_descriptor,
geophysics_input=gempy_model.geophysics_input,
)
case AvailableBackends.aesara | AvailableBackends.legacy:
gempy_model.legacy_model = _legacy_compute_model(gempy_model)
case _:
raise ValueError(f'Backend {engine_config} not supported')
return gempy_model.solutions
def compute_model_at(gempy_model: GeoModel, at: np.ndarray,
engine_config: Optional[GemPyEngineConfig] = None) -> np.ndarray:
"""
Compute the geological model at specific coordinates.
Note: This function sets a custom grid and computes the model so be wary of side effects.
Args:
gempy_model (GeoModel): The GemPy model to compute.
at (np.ndarray): The coordinates at which to compute the model.
engine_config (Optional[GemPyEngineConfig], optional): Configuration for the computational engine. Defaults to None, in which case a default configuration will be used.
Returns:
np.ndarray: The computed geological model at the specified coordinates.
"""
set_custom_grid(
grid=gempy_model.grid,
xyz_coord=at
)
sol = compute_model(gempy_model, engine_config)
return sol.raw_arrays.custom
def optimize_and_compute(geo_model: GeoModel, engine_config: GemPyEngineConfig, max_epochs: int = 10,
convergence_criteria: float = 1e5):
if engine_config.backend != AvailableBackends.PYTORCH:
raise ValueError(f'Only PyTorch backend is supported for optimization. Received {engine_config.backend}')
BackendTensor.change_backend_gempy(
engine_backend=engine_config.backend,
use_gpu=engine_config.use_gpu,
dtype=engine_config.dtype
)
import torch
from gempy_engine.core.data.continue_epoch import ContinueEpoch
interpolation_input = geo_model.interpolation_input_copy
geo_model.taped_interpolation_input = interpolation_input
nugget_effect_scalar = geo_model.taped_interpolation_input.surface_points_copy.nugget_effect_scalar
optimizer = torch.optim.Adam(
params=[nugget_effect_scalar],
lr=0.01,
)
# Optimization loop
geo_model.interpolation_options.kernel_options.optimizing_condition_number = True
def _check_convergence_criterion(conditional_number: float, condition_number_old: float, conditional_number_target: float = 1e5):
reached_conditional_target = conditional_number < conditional_number_target
if reached_conditional_target == False and epoch > 10:
condition_number_change = torch.abs(conditional_number - condition_number_old) / condition_number_old
if condition_number_change < 0.01:
reached_conditional_target = True
return reached_conditional_target
previous_condition_number = 0
for epoch in range(max_epochs):
optimizer.zero_grad()
try:
geo_model.taped_interpolation_input.grid = geo_model.interpolation_input_copy.grid
gempy_engine.compute_model(
interpolation_input=geo_model.taped_interpolation_input,
options=geo_model.interpolation_options,
data_descriptor=geo_model.input_data_descriptor,
geophysics_input=geo_model.geophysics_input,
)
except ContinueEpoch:
# Get absolute values of gradients
grad_magnitudes = torch.abs(nugget_effect_scalar.grad)
# Get indices of the 10 largest gradients
grad_magnitudes.size
# * This ignores 90 percent of the gradients
# To int
n_values = int(grad_magnitudes.size()[0] * 0.9)
_, indices = torch.topk(grad_magnitudes, n_values, largest=False)
# Zero out gradients that are not in the top 10
mask = torch.ones_like(nugget_effect_scalar.grad)
mask[indices] = 0
nugget_effect_scalar.grad *= mask
# Update the vector
optimizer.step()
nugget_effect_scalar.data = nugget_effect_scalar.data.clamp_(min=1e-7) # Replace negative values with 0
# optimizer.zero_grad()
# Monitor progress
if epoch % 1 == 0:
# print(f"Epoch {epoch}: Condition Number = {condition_number.item()}")
print(f"Epoch {epoch}")
if _check_convergence_criterion(
conditional_number=geo_model.interpolation_options.kernel_options.condition_number,
condition_number_old=previous_condition_number,
conditional_number_target=convergence_criteria,
):
break
previous_condition_number = geo_model.interpolation_options.kernel_options.condition_number
continue
geo_model.interpolation_options.kernel_options.optimizing_condition_number = False
geo_model.solutions = gempy_engine.compute_model(
interpolation_input=geo_model.taped_interpolation_input,
options=geo_model.interpolation_options,
data_descriptor=geo_model.input_data_descriptor,
geophysics_input=geo_model.geophysics_input,
)
return geo_model.solutions
def _legacy_compute_model(gempy_model: GeoModel) -> 'gempy_legacy.Project':
gpl = require_gempy_legacy()
legacy_model: gpl.Project = gempy3_to_gempy2(gempy_model)
gpl.set_interpolator(legacy_model)
gpl.compute_model(legacy_model)
return legacy_model
