How to use validate_field_names method in autotest

Best Python code snippet using autotest_python

test_cpp_regression.py

Source:test_cpp_regression.py

1# GT4Py - GridTools4Py - GridTools for Python2#3# Copyright (c) 2014-2022, ETH Zurich4# All rights reserved.5#6# This file is part the GT4Py project and the GridTools framework.7# GT4Py is free software: you can redistribute it and/or modify it under8# the terms of the GNU General Public License as published by the9# Free Software Foundation, either version 3 of the License, or any later10# version. See the LICENSE.txt file at the top-level directory of this11# distribution for a copy of the license or check <https://www.gnu.org/licenses/>.12#13# SPDX-License-Identifier: GPL-3.0-or-later14import itertools15import os16import hypothesis as hyp17import hypothesis.strategies as hyp_st18import numpy as np19import pytest20import gt4py.backend as gt_backend21import gt4py.storage as gt_store22from gt4py.storage.utils import cpu_copy23from ..definitions import ALL_BACKENDS24from ..reference_cpp_regression import reference_module25from .utils import id_version  # import fixture used by pytest26from .utils import generate_test_module27REGISTRY = list()28def register(cpp_regression_test):29    REGISTRY.append(cpp_regression_test)30    return cpp_regression_test31def get_reference(test_name, backend, domain, origins, shapes, masks=None):32    reference_data = reference_module.__dict__[test_name](*domain)33    res = {}34    for k, data in reference_data.items():35        if np.isscalar(data):36            res[k] = np.float_(data)37        else:38            try:39                field = gt_store.from_array(40                    data,41                    dtype=np.float_,42                    aligned_index=origins[k],43                    backend=backend.name,44                )45            except KeyError:46                field = gt_store.from_array(47                    data,48                    dtype=np.float_,49                    aligned_index=origins[k[: -len("_reference")]],50                    backend=backend.name,51                )52            res[k] = field53    return res54@register55@hyp.given(domain=hyp_st.tuples(*([hyp_st.integers(min_value=1, max_value=8)] * 3)))56def run_horizontal_diffusion(backend, id_version, domain):57    validate_field_names = ["out_field"]58    origins = {"in_field": (2, 2, 0), "out_field": (0, 0, 0), "coeff": (0, 0, 0)}59    shapes = {60        name: tuple(domain[i] + 2 * origin[i] for i in range(3)) for name, origin in origins.items()61    }62    name = "horizontal_diffusion"63    arg_fields = get_reference(name, backend, domain, origins, shapes)64    validate_fields = {65        name + "_reference": arg_fields.pop(name + "_reference") for name in validate_field_names66    }67    testmodule = generate_test_module(68        "horizontal_diffusion", backend, id_version=id_version, rebuild=False69    )70    testmodule.run(71        **arg_fields,72        _domain_=domain,73        _origin_=origins,74        exec_info=None,75    )76    for k in validate_field_names:77        np.testing.assert_allclose(78            cpu_copy(arg_fields[k]), cpu_copy(validate_fields[k + "_reference"])79        )80@register81@hyp.given(82    domain=hyp_st.tuples(83        *(84            [hyp_st.integers(min_value=1, max_value=32)] * 285            + [hyp_st.integers(min_value=2, max_value=32)]86        )87    )88)89def run_tridiagonal_solver(backend, id_version, domain):90    validate_field_names = ["out"]91    origins = {92        "inf": (0, 0, 0),93        "diag": (0, 0, 0),94        "sup": (0, 0, 0),95        "rhs": (0, 0, 0),96        "out": (0, 0, 0),97    }98    shapes = {99        name: tuple(domain[i] + 2 * origin[i] for i in range(3)) for name, origin in origins.items()100    }101    name = "tridiagonal_solver"102    arg_fields = get_reference(name, backend, domain, origins, shapes)103    validate_fields = {104        name + "_reference": arg_fields.pop(name + "_reference") for name in validate_field_names105    }106    testmodule = generate_test_module(107        "tridiagonal_solver", backend, id_version=id_version, rebuild=False108    )109    testmodule.run(110        **arg_fields,111        _domain_=domain,112        _origin_=origins,113        exec_info=None,114    )115    for k in validate_field_names:116        if hasattr(arg_fields[k], "synchronize"):117            arg_fields[k].device_to_host(force=True)118        np.testing.assert_allclose(119            cpu_copy(arg_fields[k]), cpu_copy(validate_fields[k + "_reference"])120        )121@register122@hyp.given(123    domain=hyp_st.tuples(124        *(125            [hyp_st.integers(min_value=1, max_value=32)] * 2126            + [hyp_st.integers(min_value=2, max_value=32)]127        )128    )129)130def run_vertical_advection_dycore(backend, id_version, domain):131    validate_field_names = ["utens_stage"]132    origins = {133        "utens_stage": (0, 0, 0),134        "u_stage": (0, 0, 0),135        "wcon": (0, 0, 0),136        "u_pos": (0, 0, 0),137        "utens": (0, 0, 0),138    }139    shapes = {140        "utens_stage": domain,141        "u_stage": domain,142        "wcon": tuple(d + 1 if i == 0 else d for i, d in enumerate(domain)),143        "u_pos": domain,144        "utens": domain,145    }146    name = "vertical_advection_dycore"147    arg_fields = get_reference(name, backend, domain, origins, shapes)148    validate_fields = {149        name + "_reference": arg_fields.pop(name + "_reference") for name in validate_field_names150    }151    testmodule = generate_test_module(152        "vertical_advection_dycore", backend, id_version=id_version, rebuild=False153    )154    testmodule.run(155        **arg_fields,156        _domain_=domain,157        _origin_=origins,158        # _origin_={159        #    k: [oo[0] if isinstance(oo, tuple) else oo for oo in o] for k, o in origins.items()160        # },161        exec_info=None,162    )163    for k in validate_field_names:164        np.testing.assert_allclose(165            cpu_copy(arg_fields[k]), cpu_copy(validate_fields[k + "_reference"])166        )167@register168@hyp.given(169    domain=hyp_st.tuples(170        *(171            [hyp_st.integers(min_value=1, max_value=32)] * 2172            + [hyp_st.integers(min_value=16, max_value=32)]173        )174    )175)176def run_large_k_interval(backend, id_version, domain):177    """Test stencils with large static and potentially zero-length intervals."""178    validate_field_names = ["out_field"]179    origins = {"in_field": (0, 0, 0), "out_field": (0, 0, 0)}180    shapes = {181        name: tuple(domain[i] + 2 * origin[i] for i in range(3)) for name, origin in origins.items()182    }183    name = "large_k_interval"184    arg_fields = get_reference(name, backend, domain, origins, shapes)185    validate_fields = {186        name + "_reference": arg_fields.pop(name + "_reference") for name in validate_field_names187    }188    testmodule = generate_test_module(189        "large_k_interval", backend, id_version=id_version, rebuild=False190    )191    testmodule.run(192        **arg_fields,193        _domain_=domain,194        _origin_=origins,195        exec_info=None,196    )197    for k in validate_field_names:198        if hasattr(arg_fields[k], "synchronize"):199            arg_fields[k].device_to_host(force=True)200        np.testing.assert_allclose(201            cpu_copy(arg_fields[k]), cpu_copy(validate_fields[k + "_reference"])202        )203@pytest.mark.parametrize("backend", ALL_BACKENDS)204@pytest.mark.parametrize("function", REGISTRY)205def test_cpp_regression(backend, id_version, function):...

function_applier.py

Source:function_applier.py

...11        :param field_name_to_func: a dict of mappings, where values are12                                  functions of the form: x -> y.13        """14        try:15            validate_field_names(list(field_name_to_func.keys()))16        except Exception as e:17            raise e18        for f in field_name_to_func.values():19            if not callable(f):20                raise ValueError("Please provide all valid callable functions.")21        super(FunctionApplier, self).__init__(**kwargs)22        self.field_name_to_func = field_name_to_func23    def _transform(self, data_chunk):24        """25        :param data_chunk: self-explanatory.26        :return data_chunk: same as input but with modified values.27        """28        for field_name, func in self.field_name_to_func.items():29            data_chunk[field_name] = func(data_chunk[field_name])...

field_selector.py

Source:field_selector.py

...11                            fields that should be selected from data-chunks.12                            Other fields are discarded.13        """14        try:15            validate_field_names(fnames)16        except Exception as e:17            raise e18        super(FieldSelector, self).__init__(**kwargs)19        self.fnames = listify(fnames)20    def _transform(self, data_chunk):21        fn_set = set(self.fnames)22        for fn in fn_set:23            if fn not in data_chunk:24                raise ValueError(25                    "The data-chunk does not have the `%s` field." % fn)26        fnames_to_del = [fn for fn in data_chunk if fn not in fn_set]27        for fn in fnames_to_del:28            del data_chunk[fn]29        return data_chunk

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