230 lines
8.5 KiB
Python
230 lines
8.5 KiB
Python
import unittest
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from copy import deepcopy
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import scgenerator.initialize as init
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import numpy as np
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import toml
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from scgenerator import defaults, utils, math
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from scgenerator.errors import *
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from scgenerator.physics import units
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def load_conf(name):
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with open("testing/configs/" + name + ".toml") as file:
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conf = toml.load(file)
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return conf
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def conf_maker(folder):
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def conf(name):
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return load_conf(folder + "/" + name)
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return conf
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class TestParamSequence(unittest.TestCase):
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def iterconf(self, files):
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conf = conf_maker("param_sequence")
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for path in files:
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yield init.ParamSequence(conf(path))
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def test_no_repeat_in_sub_folder_names(self):
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for param_seq in self.iterconf(["almost_equal", "equal", "no_variations"]):
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l = []
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s = []
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for vary_list, _ in utils.required_simulations(param_seq.config):
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self.assertNotIn(vary_list, l)
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self.assertNotIn(utils.format_variable_list(vary_list), s)
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l.append(vary_list)
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s.append(utils.format_variable_list(vary_list))
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def test_init_config_not_affected_by_iteration(self):
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for param_seq in self.iterconf(["almost_equal", "equal", "no_variations"]):
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config = deepcopy(param_seq.config)
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for _ in utils.required_simulations(param_seq.config):
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self.assertEqual(config.items(), param_seq.config.items())
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def test_no_variations_yields_only_num_and_id(self):
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for param_seq in self.iterconf(["no_variations"]):
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for vary_list, _ in utils.required_simulations(param_seq.config):
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self.assertEqual(vary_list[1][0], "num")
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self.assertEqual(vary_list[0][0], "id")
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self.assertEqual(2, len(vary_list))
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class TestInitializeMethods(unittest.TestCase):
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def test_validate_types(self):
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conf = lambda s: load_conf("validate_types/" + s)
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with self.assertRaisesRegex(TypeError, "belong"):
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init._validate_types(conf("bad1"))
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with self.assertRaisesRegex(TypeError, "valid list of behaviors"):
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init._validate_types(conf("bad2"))
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with self.assertRaisesRegex(TypeError, "single, real, non-negative number"):
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init._validate_types(conf("bad3"))
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with self.assertRaisesRegex(TypeError, "'parallel' is not a valid variable parameter"):
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init._validate_types(conf("bad4"))
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with self.assertRaisesRegex(TypeError, "Variable parameters should be specified in a list"):
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init._validate_types(conf("bad5"))
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with self.assertRaisesRegex(
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TypeError,
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"value '0' of type .*int.* for key 'repeat' is not valid, must be a strictly positive integer",
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):
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init._validate_types(conf("bad6"))
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with self.assertRaisesRegex(
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ValueError,
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r"Variable parameters lists should contain at least 1 element",
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):
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init._ensure_consistency(conf("bad7"))
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self.assertIsNone(init._validate_types(conf("good")))
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def test_ensure_consistency(self):
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conf = lambda s: load_conf("ensure_consistency/" + s)
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with self.assertRaisesRegex(
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MissingParameterError,
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r"1 of '\['t0', 'width'\]' is required and no defaults have been set",
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):
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init._ensure_consistency(conf("bad1"))
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with self.assertRaisesRegex(
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MissingParameterError,
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r"1 of '\['peak_power', 'mean_power', 'energy', 'width', 't0'\]' is required when 'soliton_num' is specified and no defaults have been set",
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):
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init._ensure_consistency(conf("bad2"))
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with self.assertRaisesRegex(
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MissingParameterError,
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r"2 of '\['dt', 't_num', 'time_window'\]' are required and no defaults have been set",
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):
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init._ensure_consistency(conf("bad3"))
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with self.assertRaisesRegex(
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DuplicateParameterError,
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r"got multiple values for parameter 'width'",
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):
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init._ensure_consistency(conf("bad4"))
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with self.assertRaisesRegex(
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MissingParameterError,
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r"'capillary_thickness' is a required parameter for fiber model 'hasan' and no defaults have been set",
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):
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init._ensure_consistency(conf("bad5"))
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with self.assertRaisesRegex(
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MissingParameterError,
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r"1 of '\['capillary_spacing', 'capillary_outer_d'\]' is required for fiber model 'hasan' and no defaults have been set",
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):
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init._ensure_consistency(conf("bad6"))
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self.assertLessEqual(
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{"model": "pcf"}.items(), init._ensure_consistency(conf("good1"))["fiber"].items()
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)
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self.assertNotIn("gas", init._ensure_consistency(conf("good1")))
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self.assertNotIn("gamma", init._ensure_consistency(conf("good4"))["fiber"])
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self.assertLessEqual(
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{"raman_type": "agrawal"}.items(),
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init._ensure_consistency(conf("good2"))["simulation"].items(),
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)
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self.assertLessEqual(
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{"name": "no name"}.items(), init._ensure_consistency(conf("good3")).items()
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)
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self.assertLessEqual(
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{"capillary_nested": 0, "capillary_resonance_strengths": []}.items(),
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init._ensure_consistency(conf("good4"))["fiber"].items(),
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)
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self.assertLessEqual(
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dict(he_mode=(1, 1)).items(),
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init._ensure_consistency(conf("good5"))["fiber"].items(),
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)
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self.assertLessEqual(
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dict(temperature=300, pressure=1e5, gas_name="vacuum", plasma_density=0).items(),
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init._ensure_consistency(conf("good5"))["gas"].items(),
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)
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self.assertLessEqual(
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dict(
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t_num=16384,
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time_window=37e-12,
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lower_wavelength_interp_limit=defaults.default_parameters[
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"lower_wavelength_interp_limit"
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],
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upper_wavelength_interp_limit=defaults.default_parameters[
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"upper_wavelength_interp_limit"
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],
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).items(),
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init._ensure_consistency(conf("good6"))["simulation"].items(),
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)
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def test_setup_custom_field(self):
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d = np.load("testing/configs/custom_field/init_field.npz")
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t = d["time"]
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field = d["field"]
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conf = load_conf("custom_field/no_change")
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conf = init.build_sim_grid(conf)
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result = init.setup_custom_field(conf)
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self.assertAlmostEqual(conf["field_0"].real.max(), field.real.max(), 4)
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self.assertTrue(result)
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conf = load_conf("custom_field/peak_power")
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conf = init.build_sim_grid(conf)
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result = init.setup_custom_field(conf)
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self.assertAlmostEqual(math.abs2(conf["field_0"]).max(), 20000, 4)
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self.assertTrue(result)
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self.assertNotAlmostEqual(conf["wavelength"], 1593e-9)
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conf = load_conf("custom_field/mean_power")
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conf = init.build_sim_grid(conf)
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result = init.setup_custom_field(conf)
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self.assertAlmostEqual(np.trapz(math.abs2(conf["field_0"]), conf["t"]), 0.22 / 40e6, 4)
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self.assertTrue(result)
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conf = load_conf("custom_field/recover1")
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conf = init.build_sim_grid(conf)
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result = init.setup_custom_field(conf)
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self.assertAlmostEqual(math.abs2(conf["field_0"] - field).sum(), 0)
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self.assertTrue(result)
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conf = load_conf("custom_field/recover2")
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conf = init.build_sim_grid(conf)
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result = init.setup_custom_field(conf)
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self.assertAlmostEqual((math.abs2(conf["field_0"]) / 0.9 - math.abs2(field)).sum(), 0)
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self.assertTrue(result)
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conf = load_conf("custom_field/wavelength_shift1")
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result = init.compute_init_parameters(conf)
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self.assertAlmostEqual(
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units.m.inv(result["w"])[np.argmax(math.abs2(result["spec_0"]))], 1050e-9
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)
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conf = load_conf("custom_field/wavelength_shift1")
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conf["pulse"]["wavelength"] = 1593e-9
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result = init.compute_init_parameters(conf)
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conf = load_conf("custom_field/wavelength_shift2")
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for target, (variable, config) in zip(
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[1050e-9, 1321e-9, 1593e-9], init.ParamSequence(conf)
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):
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self.assertAlmostEqual(
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units.m.inv(config["w"])[np.argmax(math.abs2(config["spec_0"]))], target
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)
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print(config["wavelength"], target)
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if __name__ == "__main__":
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conf = conf_maker("validate_types")
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unittest.main()
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