# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""utils for geometry"""
from __future__ import absolute_import
import numpy as np
import scipy.stats as ss
from .geometry_base import PartSamplingConfig, SamplingConfig, GEOM_TYPES
[文档]def create_config_from_edict(edict_config):
"""
Convert from dict to SamplingConfig.
Args:
edict_config (dict): dict containing configuration info.
Returns:
geometry_base.SamplingConfig, sampling configuration.
Raises:
ValueError: If part_config_dict can not be generated from input dict.
Supported Platforms:
``Ascend``
Examples:
>>> from easydict import EasyDict as edict
>>> from mindelec.geometry import create_config_from_edict
>>> rect_config = edict({
... 'domain': edict({
... 'random_sampling': True,
... 'size': 200,
... }),
... 'BC': edict({
... 'random_sampling': True,
... 'size': 50,
... 'with_normal': True,
... })
... })
>>> sampling_config = create_config_from_edict(rect_config)
"""
if not isinstance(edict_config, dict):
raise TypeError("Input: {} should be dictionary, but got: {}".format(edict_config, type(edict_config)))
part_config_dict = {}
for geom_type in edict_config.keys():
if geom_type in GEOM_TYPES and edict_config[geom_type] is not None:
part_config_dict[geom_type] = PartSamplingConfig(edict_config[geom_type].get("size", 1),
edict_config[geom_type].get("random_sampling", True),
edict_config[geom_type].get("sampler", "uniform"),
edict_config[geom_type].get("random_merge", True),
edict_config[geom_type].get("with_normal", False))
if part_config_dict is not None:
return SamplingConfig(part_config_dict)
raise ValueError("Unknown sampling info, please check your config")
_sampler_method = {
"lhs": ss.qmc.LatinHypercube,
"halton": ss.qmc.Halton,
"sobol": ss.qmc.Sobol,
"uniform": np.random.rand
}
def sample(size, dimension, sampler="uniform"):
"""function for sampling points by different random methods"""
sampler = sampler.lower()
if sampler not in _sampler_method.keys():
raise ValueError("Unknown sampler method {}, only support: {}".format(sampler, _sampler_method.keys()))
sample_method = _sampler_method[sampler]
if sampler == "uniform":
data = sample_method(size, dimension)
else:
data = sample_method(d=dimension).random(size)
if not isinstance(data, np.ndarray):
data = np.array(data)
return data
def polar_sample(r_theta):
"""convert polar coordinate system to rectangle coordinate system"""
r, theta = r_theta[:, 0], 2 * np.pi * r_theta[:, 1]
coord_xy = np.sqrt(r) * np.vstack([np.cos(theta), np.sin(theta)])
return coord_xy.T
def generate_mesh(coord_min, coord_max, mesh_size, endpoint=True):
"""generate regularly distributed mesh"""
dimension = len(coord_min)
if dimension != len(coord_max) or dimension != len(mesh_size):
raise ValueError("Inconsistent dimension info, coord_min: {}, coord_max: {}, mesh_size: {}"
.format(coord_min, coord_max, mesh_size))
axis_x = np.linspace(coord_min[0], coord_max[0], mesh_size[0], endpoint=endpoint)
mesh = None
if dimension == 1:
mesh = axis_x[:, np.newaxis].astype(np.float32)
return mesh
axis_y = np.linspace(coord_min[1], coord_max[1], mesh_size[1], endpoint=endpoint)
if dimension == 2:
mesh_x, mesh_y = np.meshgrid(axis_x, axis_y)
mesh = np.hstack((mesh_x.flatten()[:, None], mesh_y.flatten()[:, None])).astype(np.float32)
return mesh
axis_z = np.linspace(coord_min[2], coord_max[2], mesh_size[2], endpoint=endpoint)
if dimension == 3:
mesh_x, mesh_y, mesh_z = np.meshgrid(axis_x, axis_y, axis_z)
mesh = np.hstack((mesh_x.flatten()[:, None], mesh_y.flatten()[:, None],
mesh_z.flatten()[:, None])).astype(np.float32)
return mesh
return mesh