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fake_models.py
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fake_models.py
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from typing import Tuple
import numpy
import numpy as np
from bmipy import Bmi
from grpc4bmi.constants import GRPC_MAX_MESSAGE_LENGTH
class SomeException(Exception):
pass
class FailingModel(Bmi):
def __init__(self, exc):
self.exc = exc
def initialize(self, filename):
raise self.exc
def update(self):
raise self.exc
def update_until(self, time: float) -> None:
raise self.exc
def finalize(self):
raise self.exc
def get_component_name(self):
raise self.exc
def get_input_item_count(self) -> int:
raise self.exc
def get_output_item_count(self) -> int:
raise self.exc
def get_input_var_names(self):
raise self.exc
def get_output_var_names(self):
raise self.exc
def get_start_time(self):
raise self.exc
def get_current_time(self):
raise self.exc
def get_end_time(self):
raise self.exc
def get_time_step(self):
raise self.exc
def get_time_units(self):
raise self.exc
def get_var_type(self, name):
raise self.exc
def get_var_units(self, name):
raise self.exc
def get_var_itemsize(self, name):
raise self.exc
def get_var_nbytes(self, name):
raise self.exc
def get_var_grid(self, name):
raise self.exc
def get_value(self, name, dest):
raise self.exc
def get_value_ptr(self, name):
raise self.exc
def get_value_at_indices(self, name, dest, inds):
raise self.exc
def set_value(self, name, src):
raise self.exc
def set_value_at_indices(self, name, inds, src):
raise self.exc
def get_grid_shape(self, grid, shape):
raise self.exc
def get_grid_x(self, grid, x):
raise self.exc
def get_grid_y(self, grid, y):
raise self.exc
def get_grid_z(self, grid, z):
raise self.exc
def get_grid_spacing(self, grid, spacing):
raise self.exc
def get_grid_origin(self, grid, origin):
raise self.exc
def get_grid_rank(self, grid):
raise self.exc
def get_grid_size(self, grid):
raise self.exc
def get_grid_type(self, grid):
raise self.exc
def get_var_location(self, name: str) -> str:
raise self.exc
def get_grid_node_count(self, grid: int) -> int:
raise self.exc
def get_grid_edge_count(self, grid: int) -> int:
raise self.exc
def get_grid_face_count(self, grid: int) -> int:
raise self.exc
def get_grid_edge_nodes(self, grid: int, edge_nodes: np.ndarray) -> np.ndarray:
raise self.exc
def get_grid_face_nodes(self, grid: int, face_nodes: np.ndarray) -> np.ndarray:
raise self.exc
def get_grid_nodes_per_face(self, grid: int, nodes_per_face: np.ndarray) -> np.ndarray:
raise self.exc
def get_grid_face_edges(self, grid: int, face_edges: np.ndarray) -> np.ndarray:
raise self.exc
class GridModel(FailingModel):
def __init__(self):
super(GridModel, self).__init__(SomeException('not used'))
def initialize(self, filename):
pass
def get_output_var_names(self) -> Tuple[str]:
return 'plate_surface__temperature',
def get_var_grid(self, name):
return 0
class UniRectGridModel(GridModel):
def get_grid_type(self, grid):
return 'uniform_rectilinear'
def get_grid_rank(self, grid):
return 3
def get_grid_size(self, grid):
return 24
def get_grid_shape(self, grid: int, shape: np.ndarray) -> np.ndarray:
numpy.copyto(src=[2, 3, 4], dst=shape)
return shape
def get_grid_origin(self, grid, dest):
numpy.copyto(src=[0.1, 1.1, 2.1], dst=dest)
return dest
def get_grid_spacing(self, grid, dest):
numpy.copyto(src=[0.1, 0.2, 0.3], dst=dest)
return dest
class Rect3DGridModel(GridModel):
def get_grid_type(self, grid):
return 'rectilinear'
def get_grid_size(self, grid):
return 24
def get_grid_rank(self, grid: int) -> int:
return 3
def get_grid_shape(self, grid: int, shape: np.ndarray) -> np.ndarray:
numpy.copyto(src=[2, 3, 4], dst=shape)
return shape
def get_grid_x(self, grid: int, x: np.ndarray) -> np.ndarray:
numpy.copyto(src=[0.1, 0.2, 0.3, 0.4], dst=x)
return x
def get_grid_y(self, grid: int, y: np.ndarray) -> np.ndarray:
numpy.copyto(src=[1.1, 1.2, 1.3], dst=y)
return y
def get_grid_z(self, grid: int, z: np.ndarray) -> np.ndarray:
numpy.copyto(src=[2.1, 2.2], dst=z)
return z
class Rect2DGridModel(Rect3DGridModel):
def get_grid_size(self, grid):
return 12 # 4*3
def get_grid_rank(self, grid: int) -> int:
return 2
def get_grid_shape(self, grid: int, shape: np.ndarray) -> np.ndarray:
numpy.copyto(src=[3, 4], dst=shape)
return shape
def get_grid_z(self, grid: int, z: np.ndarray) -> np.ndarray:
raise NotImplementedError('Do not know what z is')
class Structured3DQuadrilateralsGridModel(GridModel):
# Grid shape:
# 0
# / \
# / \
# 3 1
# \ /
# \ /
# 2
#
def get_grid_type(self, grid):
return 'structured_quadrilateral'
def get_grid_rank(self, grid: int) -> int:
return 3
def get_grid_size(self, grid):
return 4
def get_grid_shape(self, grid, shape):
numpy.copyto(src=[1, 2, 2], dst=shape)
return shape
def get_grid_x(self, grid, x):
numpy.copyto(src=[1.1, 0.1, 1.1, 2.1], dst=x)
return x
def get_grid_y(self, grid: int, y: np.ndarray) -> np.ndarray:
numpy.copyto(src=[2.2, 1.2, 0.2, 2.2], dst=y)
return y
def get_grid_z(self, grid: int, z: np.ndarray) -> np.ndarray:
numpy.copyto(src=[1.1, 2.2, 3.3, 4.4], dst=z)
return z
class Structured2DQuadrilateralsGridModel(GridModel):
# Grid shape:
# 0
# / \
# / \
# 3 1
# \ /
# \ /
# 2
#
def get_grid_type(self, grid):
return 'structured_quadrilateral'
def get_grid_rank(self, grid: int) -> int:
return 2
def get_grid_size(self, grid):
return 4
def get_grid_shape(self, grid, shape):
numpy.copyto(src=[2, 2], dst=shape)
return shape
def get_grid_x(self, grid, x):
numpy.copyto(src=[1.1, 0.1, 1.1, 2.1], dst=x)
return x
def get_grid_y(self, grid: int, y: np.ndarray) -> np.ndarray:
numpy.copyto(src=[2.2, 1.2, 0.2, 2.2], dst=y)
return y
def get_grid_z(self, grid: int, z: np.ndarray) -> np.ndarray:
raise NotImplementedError('Do not know what z is')
class UnstructuredGridBmiModel(GridModel):
# Uses grid example at https://bmi.readthedocs.io/en/latest/model_grids.html#unstructured-grids
# Grid shape:
# 3-----\
# / \ \
# 0 \ \---5
# \ 2--/ /
# \ / /
# \ / /
# 1---\ /
# 4
def get_grid_type(self, grid):
return 'unstructured'
def get_grid_shape(self, grid, dest):
raise NotImplementedError('Do not know what shape is')
def get_grid_size(self, grid):
return 6
def get_grid_rank(self, grid: int) -> int:
return 2
def get_grid_node_count(self, grid: int) -> int:
return 6
def get_grid_edge_count(self, grid: int) -> int:
return 8
def get_grid_face_count(self, grid: int) -> int:
return 3
def get_grid_edge_nodes(self, grid: int, edge_nodes: np.ndarray) -> np.ndarray:
numpy.copyto(src=(0, 1, 1, 2, 2, 3, 3, 0, 1, 4, 4, 5, 5, 2, 5, 3), dst=edge_nodes)
return edge_nodes
def get_grid_face_nodes(self, grid: int, face_nodes: np.ndarray) -> np.ndarray:
numpy.copyto(src=(0, 1, 2, 3, 1, 4, 5, 2, 2, 5, 3), dst=face_nodes)
return face_nodes
def get_grid_face_edges(self, grid: int, face_edges: np.ndarray) -> np.ndarray:
numpy.copyto(src=(0, 1, 2, 3, 4, 5, 6, 1, 6, 7, 2), dst=face_edges)
return face_edges
def get_grid_nodes_per_face(self, grid: int, nodes_per_face: np.ndarray) -> np.ndarray:
numpy.copyto(src=(4, 4, 3), dst=nodes_per_face)
return nodes_per_face
def get_grid_x(self, grid: int, x: np.ndarray) -> np.ndarray:
numpy.copyto(src=[0., 1., 2., 1., 3., 4.], dst=x)
return x
def get_grid_y(self, grid: int, y: np.ndarray) -> np.ndarray:
numpy.copyto(src=[3., 1., 2., 4., 0., 3.], dst=y)
return y
def get_grid_z(self, grid: int, z: np.ndarray) -> np.ndarray:
raise NotImplementedError('Do not know what z is')
class DTypeModel(GridModel):
def __init__(self):
super().__init__()
self.dtype = numpy.dtype('float32')
self.value = numpy.array((1.1, 2.2, 3.3), dtype=self.dtype)
def get_var_type(self, name):
return str(self.dtype)
def get_var_itemsize(self, name):
return self.dtype.itemsize
def get_var_nbytes(self, name):
return self.dtype.itemsize * self.value.size
def get_value(self, name, dest):
numpy.copyto(src=self.value, dst=dest)
return dest
def get_value_at_indices(self, name, dest, inds):
numpy.copyto(src=self.value[inds], dst=dest)
return dest
def set_value(self, name, src):
self.value[:] = src
def set_value_at_indices(self, name, inds, src):
self.value[inds] = src
class Float32Model(DTypeModel):
pass
class Int32Model(DTypeModel):
def __init__(self):
super().__init__()
self.dtype = numpy.dtype('int32')
self.value = numpy.array((12, 24, 36), dtype=self.dtype)
class BooleanModel(DTypeModel):
def __init__(self):
super().__init__()
self.dtype = numpy.dtype('bool')
self.value = numpy.array((True, False, True), dtype=self.dtype)
class HugeModel(DTypeModel):
"""Model which has value which does not fit in single message body
Can be run from command line with
..code-block:: bash
run-bmi-server --path $PWD/test --name fake_models.HugeModel --port 55555 --debug
"""
def __init__(self):
super().__init__()
self.dtype = numpy.dtype('float64')
# Create value which is bigger than 4Mb
dimension = (3 * GRPC_MAX_MESSAGE_LENGTH) // self.dtype.itemsize + 1000
self.value = numpy.ones((dimension,), dtype=self.dtype)
class WithItemSizeZeroAndVarTypeFloat32Model(Float32Model):
def get_var_itemsize(self, name):
return 0
class WithItemSizeZeroAndUnknownVarType(WithItemSizeZeroAndVarTypeFloat32Model):
def get_var_type(self, name):
return 'real'