From 717baf02b161305545bc4ac21f6059acdf4bff62 Mon Sep 17 00:00:00 2001 From: Koki Muraoka Date: Tue, 28 Feb 2023 19:23:50 +0900 Subject: [PATCH 1/6] test for se_a_ebd --- source/tests/test_model_se_a_ebd.py | 118 ++++++++++++++++++++++++++++ source/tests/water_se_a_ebd.json | 53 +++++++++++++ 2 files changed, 171 insertions(+) create mode 100644 source/tests/test_model_se_a_ebd.py create mode 100644 source/tests/water_se_a_ebd.json diff --git a/source/tests/test_model_se_a_ebd.py b/source/tests/test_model_se_a_ebd.py new file mode 100644 index 0000000000..7e0b38cfc9 --- /dev/null +++ b/source/tests/test_model_se_a_ebd.py @@ -0,0 +1,118 @@ +import dpdata,os,sys,unittest +import numpy as np +from deepmd.env import tf +from common import Data,gen_data, j_loader + +from deepmd.utils.data_system import DataSystem +from deepmd.descriptor import DescrptSeR +from deepmd.descriptor.se_a_ebd import DescrptSeAEbd +from deepmd.fit import EnerFitting +from deepmd.model import EnerModel +from deepmd.common import j_must_have + +GLOBAL_ENER_FLOAT_PRECISION = tf.float64 +GLOBAL_TF_FLOAT_PRECISION = tf.float64 +GLOBAL_NP_FLOAT_PRECISION = np.float64 + +class TestModel(tf.test.TestCase): + def setUp(self) : + gen_data() + + def test_model(self): + jfile = 'water_se_ebd.json' + jdata = j_loader(jfile) + + systems = j_must_have(jdata, 'systems') + set_pfx = j_must_have(jdata, 'set_prefix') + batch_size = j_must_have(jdata, 'batch_size') + test_size = j_must_have(jdata, 'numb_test') + batch_size = 1 + test_size = 1 + stop_batch = j_must_have(jdata, 'stop_batch') + rcut = j_must_have (jdata['model']['descriptor'], 'rcut') + + data = DataSystem(systems, set_pfx, batch_size, test_size, rcut, run_opt = None) + + test_data = data.get_test () + numb_test = 1 + + jdata['model']['descriptor'].pop('type', None) + descrpt = DescrptSeAEbd(**jdata['model']['descriptor'], ) + jdata['model']['fitting_net']['descrpt'] = descrpt + fitting = EnerFitting(**jdata['model']['fitting_net'], ) + # fitting = EnerFitting(jdata['model']['fitting_net'], descrpt) + model = EnerModel(descrpt, fitting) + + # model._compute_dstats([test_data['coord']], [test_data['box']], [test_data['type']], [test_data['natoms_vec']], [test_data['default_mesh']]) + input_data = {'coord' : [test_data['coord']], + 'box': [test_data['box']], + 'type': [test_data['type']], + 'natoms_vec' : [test_data['natoms_vec']], + 'default_mesh' : [test_data['default_mesh']] + } + model._compute_input_stat(input_data) + model.descrpt.bias_atom_e = data.compute_energy_shift() + + t_prop_c = tf.placeholder(tf.float32, [5], name='t_prop_c') + t_energy = tf.placeholder(GLOBAL_ENER_FLOAT_PRECISION, [None], name='t_energy') + t_force = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name='t_force') + t_virial = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name='t_virial') + t_atom_ener = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name='t_atom_ener') + t_coord = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name='i_coord') + t_type = tf.placeholder(tf.int32, [None], name='i_type') + t_natoms = tf.placeholder(tf.int32, [model.ntypes+2], name='i_natoms') + t_box = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None, 9], name='i_box') + t_mesh = tf.placeholder(tf.int32, [None], name='i_mesh') + is_training = tf.placeholder(tf.bool) + t_fparam = None + + model_pred\ + = model.build (t_coord, + t_type, + t_natoms, + t_box, + t_mesh, + t_fparam, + suffix = "se_r", + reuse = False) + energy = model_pred['energy'] + force = model_pred['force'] + virial = model_pred['virial'] + atom_ener = model_pred['atom_ener'] + + feed_dict_test = {t_prop_c: test_data['prop_c'], + t_energy: test_data['energy'] [:numb_test], + t_force: np.reshape(test_data['force'] [:numb_test, :], [-1]), + t_virial: np.reshape(test_data['virial'] [:numb_test, :], [-1]), + t_atom_ener: np.reshape(test_data['atom_ener'][:numb_test, :], [-1]), + t_coord: np.reshape(test_data['coord'] [:numb_test, :], [-1]), + t_box: test_data['box'] [:numb_test, :], + t_type: np.reshape(test_data['type'] [:numb_test, :], [-1]), + t_natoms: test_data['natoms_vec'], + t_mesh: test_data['default_mesh'], + is_training: False} + + sess = self.test_session().__enter__() + sess.run(tf.global_variables_initializer()) + [e, f, v] = sess.run([energy, force, virial], + feed_dict = feed_dict_test) + + e = e.reshape([-1]) + f = f.reshape([-1]) + v = v.reshape([-1]) + + + refe = [-4.0809183546731935] + reff = [-0.0009433080242679126, 0.0024540766182440917, 7.134654644656728e-05, 0.0012476799379696184, 0.002212567421017593, -0.0007091290731634111, 0.009875291762629728, -0.007876013249122177, -8.78061284553672e-05, -0.013879889764531257, 0.005100427326599536, 0.00027143866516841334, 0.003799286895370519, -0.007567683893582063, 0.00024200485149578332, -9.906080717069433e-05, 0.005676625776843024, 0.00021214513850801415] + refv = [0.034972620377374586, -0.01539771296182217, -0.0008639588474713173, -0.015397712961822166, 0.011757303581302702, 0.0005455501828665077, -0.0008639588474713181, 0.0005455501828665083, -6.180425284925767e-07] + refe = np.reshape(refe, [-1]) + reff = np.reshape(reff, [-1]) + refv = np.reshape(refv, [-1]) + + places = 6 + for ii in range(e.size) : + self.assertAlmostEqual(e[ii], refe[ii], places = places) + for ii in range(f.size) : + self.assertAlmostEqual(f[ii], reff[ii], places = places) + for ii in range(v.size) : + self.assertAlmostEqual(v[ii], refv[ii], places = places) diff --git a/source/tests/water_se_a_ebd.json b/source/tests/water_se_a_ebd.json new file mode 100644 index 0000000000..0702170148 --- /dev/null +++ b/source/tests/water_se_a_ebd.json @@ -0,0 +1,53 @@ +{ + "_comment": " model parameters", + "model" : { + "descriptor" : { + "type": "se_a_ebd", + "sel": [46, 92], + "rcut_smth": 5.80, + "rcut": 6.00, + "neuron": [25, 50, 100], + "resnet_dt": false, + "seed": 1 + }, + "fitting_net" : { + "neuron": [240, 240, 240], + "resnet_dt": true, + "seed": 1 + } + }, + + "_comment": " traing controls", + "systems": ["system"], + "set_prefix": "set", + "stop_batch": 100000, + "batch_size": 1, + "start_lr": 0.005, + "decay_steps": 5000, + "decay_rate": 0.95, + + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0, + + "seed": 1, + + "_comment": " display and restart", + "_comment": " frequencies counted in batch", + "disp_file": "lcurve.out", + "disp_freq": 100, + "numb_test": 1, + "save_freq": 1000, + "save_ckpt": "model.ckpt", + "load_ckpt": "model.ckpt", + "disp_training": true, + "time_training": true, + "profiling": false, + "profiling_file": "timeline.json", + + "_comment": "that's all" +} + From 371acb8ec308f422c840435514a083321eda5b62 Mon Sep 17 00:00:00 2001 From: Koki Muraoka Date: Tue, 28 Feb 2023 19:24:42 +0900 Subject: [PATCH 2/6] Fix typo --- source/tests/test_model_se_a_ebd.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/source/tests/test_model_se_a_ebd.py b/source/tests/test_model_se_a_ebd.py index 7e0b38cfc9..c55a007dd4 100644 --- a/source/tests/test_model_se_a_ebd.py +++ b/source/tests/test_model_se_a_ebd.py @@ -19,7 +19,7 @@ def setUp(self) : gen_data() def test_model(self): - jfile = 'water_se_ebd.json' + jfile = 'water_se_a_ebd.json' jdata = j_loader(jfile) systems = j_must_have(jdata, 'systems') From 3a47a9163d20f9a1642c7d2dfe1732be5da365c7 Mon Sep 17 00:00:00 2001 From: Koki Muraoka Date: Tue, 28 Feb 2023 19:37:09 +0900 Subject: [PATCH 3/6] fix typo --- source/tests/test_model_se_a_ebd.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/source/tests/test_model_se_a_ebd.py b/source/tests/test_model_se_a_ebd.py index c55a007dd4..7faa53995b 100644 --- a/source/tests/test_model_se_a_ebd.py +++ b/source/tests/test_model_se_a_ebd.py @@ -1,9 +1,8 @@ import dpdata,os,sys,unittest import numpy as np from deepmd.env import tf -from common import Data,gen_data, j_loader +from common import Data, gen_data, j_loader, DataSystem -from deepmd.utils.data_system import DataSystem from deepmd.descriptor import DescrptSeR from deepmd.descriptor.se_a_ebd import DescrptSeAEbd from deepmd.fit import EnerFitting From 87d783f2a154a67e369c55c7fe82df408f215a3d Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 28 Feb 2023 11:55:35 +0000 Subject: [PATCH 4/6] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- source/tests/test_model_se_a_ebd.py | 201 +++++++++++++++++----------- source/tests/water_se_a_ebd.json | 102 +++++++------- 2 files changed, 183 insertions(+), 120 deletions(-) diff --git a/source/tests/test_model_se_a_ebd.py b/source/tests/test_model_se_a_ebd.py index 7faa53995b..1d482e6c78 100644 --- a/source/tests/test_model_se_a_ebd.py +++ b/source/tests/test_model_se_a_ebd.py @@ -1,117 +1,168 @@ -import dpdata,os,sys,unittest -import numpy as np -from deepmd.env import tf -from common import Data, gen_data, j_loader, DataSystem -from deepmd.descriptor import DescrptSeR -from deepmd.descriptor.se_a_ebd import DescrptSeAEbd -from deepmd.fit import EnerFitting -from deepmd.model import EnerModel -from deepmd.common import j_must_have +import numpy as np +from common import ( + DataSystem, + gen_data, + j_loader, +) + +from deepmd.common import ( + j_must_have, +) +from deepmd.descriptor.se_a_ebd import ( + DescrptSeAEbd, +) +from deepmd.env import ( + tf, +) +from deepmd.fit import ( + EnerFitting, +) +from deepmd.model import ( + EnerModel, +) GLOBAL_ENER_FLOAT_PRECISION = tf.float64 GLOBAL_TF_FLOAT_PRECISION = tf.float64 GLOBAL_NP_FLOAT_PRECISION = np.float64 + class TestModel(tf.test.TestCase): - def setUp(self) : + def setUp(self): gen_data() def test_model(self): - jfile = 'water_se_a_ebd.json' + jfile = "water_se_a_ebd.json" jdata = j_loader(jfile) - systems = j_must_have(jdata, 'systems') - set_pfx = j_must_have(jdata, 'set_prefix') - batch_size = j_must_have(jdata, 'batch_size') - test_size = j_must_have(jdata, 'numb_test') + systems = j_must_have(jdata, "systems") + set_pfx = j_must_have(jdata, "set_prefix") + batch_size = j_must_have(jdata, "batch_size") + test_size = j_must_have(jdata, "numb_test") batch_size = 1 test_size = 1 - stop_batch = j_must_have(jdata, 'stop_batch') - rcut = j_must_have (jdata['model']['descriptor'], 'rcut') + stop_batch = j_must_have(jdata, "stop_batch") + rcut = j_must_have(jdata["model"]["descriptor"], "rcut") - data = DataSystem(systems, set_pfx, batch_size, test_size, rcut, run_opt = None) + data = DataSystem(systems, set_pfx, batch_size, test_size, rcut, run_opt=None) - test_data = data.get_test () + test_data = data.get_test() numb_test = 1 - jdata['model']['descriptor'].pop('type', None) - descrpt = DescrptSeAEbd(**jdata['model']['descriptor'], ) - jdata['model']['fitting_net']['descrpt'] = descrpt - fitting = EnerFitting(**jdata['model']['fitting_net'], ) + jdata["model"]["descriptor"].pop("type", None) + descrpt = DescrptSeAEbd( + **jdata["model"]["descriptor"], + ) + jdata["model"]["fitting_net"]["descrpt"] = descrpt + fitting = EnerFitting( + **jdata["model"]["fitting_net"], + ) # fitting = EnerFitting(jdata['model']['fitting_net'], descrpt) model = EnerModel(descrpt, fitting) # model._compute_dstats([test_data['coord']], [test_data['box']], [test_data['type']], [test_data['natoms_vec']], [test_data['default_mesh']]) - input_data = {'coord' : [test_data['coord']], - 'box': [test_data['box']], - 'type': [test_data['type']], - 'natoms_vec' : [test_data['natoms_vec']], - 'default_mesh' : [test_data['default_mesh']] + input_data = { + "coord": [test_data["coord"]], + "box": [test_data["box"]], + "type": [test_data["type"]], + "natoms_vec": [test_data["natoms_vec"]], + "default_mesh": [test_data["default_mesh"]], } model._compute_input_stat(input_data) model.descrpt.bias_atom_e = data.compute_energy_shift() - t_prop_c = tf.placeholder(tf.float32, [5], name='t_prop_c') - t_energy = tf.placeholder(GLOBAL_ENER_FLOAT_PRECISION, [None], name='t_energy') - t_force = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name='t_force') - t_virial = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name='t_virial') - t_atom_ener = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name='t_atom_ener') - t_coord = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name='i_coord') - t_type = tf.placeholder(tf.int32, [None], name='i_type') - t_natoms = tf.placeholder(tf.int32, [model.ntypes+2], name='i_natoms') - t_box = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None, 9], name='i_box') - t_mesh = tf.placeholder(tf.int32, [None], name='i_mesh') - is_training = tf.placeholder(tf.bool) + t_prop_c = tf.placeholder(tf.float32, [5], name="t_prop_c") + t_energy = tf.placeholder(GLOBAL_ENER_FLOAT_PRECISION, [None], name="t_energy") + t_force = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="t_force") + t_virial = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="t_virial") + t_atom_ener = tf.placeholder( + GLOBAL_TF_FLOAT_PRECISION, [None], name="t_atom_ener" + ) + t_coord = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name="i_coord") + t_type = tf.placeholder(tf.int32, [None], name="i_type") + t_natoms = tf.placeholder(tf.int32, [model.ntypes + 2], name="i_natoms") + t_box = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None, 9], name="i_box") + t_mesh = tf.placeholder(tf.int32, [None], name="i_mesh") + is_training = tf.placeholder(tf.bool) t_fparam = None - model_pred\ - = model.build (t_coord, - t_type, - t_natoms, - t_box, - t_mesh, - t_fparam, - suffix = "se_r", - reuse = False) - energy = model_pred['energy'] - force = model_pred['force'] - virial = model_pred['virial'] - atom_ener = model_pred['atom_ener'] - - feed_dict_test = {t_prop_c: test_data['prop_c'], - t_energy: test_data['energy'] [:numb_test], - t_force: np.reshape(test_data['force'] [:numb_test, :], [-1]), - t_virial: np.reshape(test_data['virial'] [:numb_test, :], [-1]), - t_atom_ener: np.reshape(test_data['atom_ener'][:numb_test, :], [-1]), - t_coord: np.reshape(test_data['coord'] [:numb_test, :], [-1]), - t_box: test_data['box'] [:numb_test, :], - t_type: np.reshape(test_data['type'] [:numb_test, :], [-1]), - t_natoms: test_data['natoms_vec'], - t_mesh: test_data['default_mesh'], - is_training: False} + model_pred = model.build( + t_coord, + t_type, + t_natoms, + t_box, + t_mesh, + t_fparam, + suffix="se_r", + reuse=False, + ) + energy = model_pred["energy"] + force = model_pred["force"] + virial = model_pred["virial"] + atom_ener = model_pred["atom_ener"] + + feed_dict_test = { + t_prop_c: test_data["prop_c"], + t_energy: test_data["energy"][:numb_test], + t_force: np.reshape(test_data["force"][:numb_test, :], [-1]), + t_virial: np.reshape(test_data["virial"][:numb_test, :], [-1]), + t_atom_ener: np.reshape(test_data["atom_ener"][:numb_test, :], [-1]), + t_coord: np.reshape(test_data["coord"][:numb_test, :], [-1]), + t_box: test_data["box"][:numb_test, :], + t_type: np.reshape(test_data["type"][:numb_test, :], [-1]), + t_natoms: test_data["natoms_vec"], + t_mesh: test_data["default_mesh"], + is_training: False, + } sess = self.test_session().__enter__() sess.run(tf.global_variables_initializer()) - [e, f, v] = sess.run([energy, force, virial], - feed_dict = feed_dict_test) + [e, f, v] = sess.run([energy, force, virial], feed_dict=feed_dict_test) e = e.reshape([-1]) f = f.reshape([-1]) v = v.reshape([-1]) - refe = [-4.0809183546731935] - reff = [-0.0009433080242679126, 0.0024540766182440917, 7.134654644656728e-05, 0.0012476799379696184, 0.002212567421017593, -0.0007091290731634111, 0.009875291762629728, -0.007876013249122177, -8.78061284553672e-05, -0.013879889764531257, 0.005100427326599536, 0.00027143866516841334, 0.003799286895370519, -0.007567683893582063, 0.00024200485149578332, -9.906080717069433e-05, 0.005676625776843024, 0.00021214513850801415] - refv = [0.034972620377374586, -0.01539771296182217, -0.0008639588474713173, -0.015397712961822166, 0.011757303581302702, 0.0005455501828665077, -0.0008639588474713181, 0.0005455501828665083, -6.180425284925767e-07] + reff = [ + -0.0009433080242679126, + 0.0024540766182440917, + 7.134654644656728e-05, + 0.0012476799379696184, + 0.002212567421017593, + -0.0007091290731634111, + 0.009875291762629728, + -0.007876013249122177, + -8.78061284553672e-05, + -0.013879889764531257, + 0.005100427326599536, + 0.00027143866516841334, + 0.003799286895370519, + -0.007567683893582063, + 0.00024200485149578332, + -9.906080717069433e-05, + 0.005676625776843024, + 0.00021214513850801415, + ] + refv = [ + 0.034972620377374586, + -0.01539771296182217, + -0.0008639588474713173, + -0.015397712961822166, + 0.011757303581302702, + 0.0005455501828665077, + -0.0008639588474713181, + 0.0005455501828665083, + -6.180425284925767e-07, + ] refe = np.reshape(refe, [-1]) reff = np.reshape(reff, [-1]) refv = np.reshape(refv, [-1]) places = 6 - for ii in range(e.size) : - self.assertAlmostEqual(e[ii], refe[ii], places = places) - for ii in range(f.size) : - self.assertAlmostEqual(f[ii], reff[ii], places = places) - for ii in range(v.size) : - self.assertAlmostEqual(v[ii], refv[ii], places = places) + for ii in range(e.size): + self.assertAlmostEqual(e[ii], refe[ii], places=places) + for ii in range(f.size): + self.assertAlmostEqual(f[ii], reff[ii], places=places) + for ii in range(v.size): + self.assertAlmostEqual(v[ii], refv[ii], places=places) diff --git a/source/tests/water_se_a_ebd.json b/source/tests/water_se_a_ebd.json index 0702170148..528786c4ef 100644 --- a/source/tests/water_se_a_ebd.json +++ b/source/tests/water_se_a_ebd.json @@ -1,53 +1,65 @@ { - "_comment": " model parameters", - "model" : { - "descriptor" : { - "type": "se_a_ebd", - "sel": [46, 92], - "rcut_smth": 5.80, - "rcut": 6.00, - "neuron": [25, 50, 100], - "resnet_dt": false, - "seed": 1 - }, - "fitting_net" : { - "neuron": [240, 240, 240], - "resnet_dt": true, - "seed": 1 - } + "_comment": " model parameters", + "model": { + "descriptor": { + "type": "se_a_ebd", + "sel": [ + 46, + 92 + ], + "rcut_smth": 5.80, + "rcut": 6.00, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "seed": 1 }, + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1 + } + }, - "_comment": " traing controls", - "systems": ["system"], - "set_prefix": "set", - "stop_batch": 100000, - "batch_size": 1, - "start_lr": 0.005, - "decay_steps": 5000, - "decay_rate": 0.95, + "_comment": " traing controls", + "systems": [ + "system" + ], + "set_prefix": "set", + "stop_batch": 100000, + "batch_size": 1, + "start_lr": 0.005, + "decay_steps": 5000, + "decay_rate": 0.95, - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0, - "limit_pref_v": 0, + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0, - "seed": 1, + "seed": 1, - "_comment": " display and restart", - "_comment": " frequencies counted in batch", - "disp_file": "lcurve.out", - "disp_freq": 100, - "numb_test": 1, - "save_freq": 1000, - "save_ckpt": "model.ckpt", - "load_ckpt": "model.ckpt", - "disp_training": true, - "time_training": true, - "profiling": false, - "profiling_file": "timeline.json", + "_comment": " display and restart", + "_comment": " frequencies counted in batch", + "disp_file": "lcurve.out", + "disp_freq": 100, + "numb_test": 1, + "save_freq": 1000, + "save_ckpt": "model.ckpt", + "load_ckpt": "model.ckpt", + "disp_training": true, + "time_training": true, + "profiling": false, + "profiling_file": "timeline.json", - "_comment": "that's all" + "_comment": "that's all" } - From c770ac1ccb47cebe6ab81443fda81ebcaf2fbd5b Mon Sep 17 00:00:00 2001 From: Han Wang <92130845+wanghan-iapcm@users.noreply.github.com> Date: Tue, 7 Mar 2023 15:17:01 +0800 Subject: [PATCH 5/6] Update source/tests/test_model_se_a_ebd.py Co-authored-by: Jinzhe Zeng Signed-off-by: Han Wang <92130845+wanghan-iapcm@users.noreply.github.com> --- source/tests/test_model_se_a_ebd.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/source/tests/test_model_se_a_ebd.py b/source/tests/test_model_se_a_ebd.py index 1d482e6c78..43ac5d4766 100644 --- a/source/tests/test_model_se_a_ebd.py +++ b/source/tests/test_model_se_a_ebd.py @@ -93,7 +93,7 @@ def test_model(self): t_box, t_mesh, t_fparam, - suffix="se_r", + suffix="se_a_ebd", reuse=False, ) energy = model_pred["energy"] From 447d493471db3c6e8723b445d29df0bd1ef7f4e3 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 7 Mar 2023 07:17:23 +0000 Subject: [PATCH 6/6] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- source/tests/test_model_se_a_ebd.py | 1 - 1 file changed, 1 deletion(-) diff --git a/source/tests/test_model_se_a_ebd.py b/source/tests/test_model_se_a_ebd.py index 43ac5d4766..34832e2b5b 100644 --- a/source/tests/test_model_se_a_ebd.py +++ b/source/tests/test_model_se_a_ebd.py @@ -1,4 +1,3 @@ - import numpy as np from common import ( DataSystem,