How to use the dpdata.LabeledSystem function in dpdata

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github deepmodeling / dpgen / dpgen / generator / run.py View on Github external
def detect_batch_size(batch_size, system=None):
    if type(batch_size) == int:
        return batch_size
    elif batch_size == "auto":
        # automaticcaly set batch size, batch_size = 32 // atom_numb (>=1, <=fram_numb)
        s = dpdata.LabeledSystem(system, fmt='deepmd/npy')
        return int(min( np.ceil(32.0 / float(s["coords"].shape[1]) ), s["coords"].shape[0]))
    else:
        raise RuntimeError("Unsupported batch size")
github deepmodeling / dpgen / tests / generator / test_post_fp.py View on Github external
def setUp(self):
        self.places = 5
        self.e_places = 5
        self.f_places = 5
        self.v_places = 2
        assert os.path.isdir('out_data_post_fp_pwscf'), 'out data for post fp pwscf should exist'
        if os.path.isdir('iter.000000') :
            shutil.rmtree('iter.000000')
        shutil.copytree('out_data_post_fp_pwscf', 'iter.000000')
        with open (param_pwscf_file, 'r') as fp :
            jdata = json.load (fp)
        post_fp(0, jdata)
        self.system_1 = dpdata.LabeledSystem('iter.000000/orig', fmt = 'deepmd/raw')
        self.system_2 = dpdata.LabeledSystem('iter.000000/02.fp/data.000', fmt = 'deepmd/raw')
github deepmodeling / dpgen / tests / data / test_coll_vasp.py View on Github external
def test_coll(self):

        with open (param_file, 'r') as fp :
            jdata = json.load (fp)
        jdata['out_dir'] = self.odir
        coll_vasp_md(jdata)
        
        sys = dpdata.LabeledSystem(self.odir + '/02.md/sys-004/deepmd//', fmt = 'deepmd/raw')
        self.assertEqual(sys.get_nframes(), 2)
        
        if sys.data['coords'][0][1][0] < sys.data['coords'][1][1][0]:
            idx = [1, 0]
        else :
            idx = [0, 1]
        ref_coord = self.ref_coord[idx]
        ref_cell = self.ref_cell[idx]
        ref_e = self.ref_e[idx]
        ref_f = self.ref_f[idx]
        ref_v = self.ref_v[idx]
        ref_at = self.ref_at
            
        for ff in range(2) :
            self.assertAlmostEqual(ref_e[ff], sys.data['energies'][ff])
        for ii in range(2) :
github deepmodeling / dpgen / dpgen / collect / collect.py View on Github external
# goto input    
    cwd = os.getcwd()
    os.chdir(target_folder)
    jdata = json.load(open(param_file))
    sys_configs_prefix = jdata.get('sys_configs_prefix', '')
    sys_configs = jdata.get('sys_configs', [])
    if verbose :
        max_str_len = max([len(str(ii)) for ii in sys_configs])
        max_form_len = 16
        ptr_fmt = '%%%ds %%%ds natoms %%6d nframes %%6d' % (max_str_len+5, max_form_len)
    # init systems
    init_data = []
    init_data_prefix = jdata.get('init_data_prefix', '')
    init_data_sys = jdata.get('init_data_sys', [])
    for ii in init_data_sys:
        init_data.append(dpdata.LabeledSystem(os.path.join(init_data_prefix, ii), fmt='deepmd/npy'))        
    # collect systems from iter dirs    
    coll_data = {}
    numb_sys = len(sys_configs)
    model_devi_jobs = jdata.get('model_devi_jobs', {})
    numb_jobs = len(model_devi_jobs)
    iters = ['iter.%06d' % ii for ii in range(numb_jobs)]
    # loop over iters to collect data
    for ii in range(len(iters)) :
        iter_data = glob.glob(os.path.join(iters[ii], '02.fp', 'data.[0-9]*[0-9]'))
        iter_data.sort()
        for jj in iter_data :
            sys = dpdata.LabeledSystem(jj, fmt = 'deepmd/npy')
            if merge:
                sys_str = sys.formula
            else:
                sys_str = (os.path.basename(jj).split('.')[-1])
github deepmodeling / dpgen / dpgen / collect / collect.py View on Github external
init_data_prefix = jdata.get('init_data_prefix', '')
    init_data_sys = jdata.get('init_data_sys', [])
    for ii in init_data_sys:
        init_data.append(dpdata.LabeledSystem(os.path.join(init_data_prefix, ii), fmt='deepmd/npy'))        
    # collect systems from iter dirs    
    coll_data = {}
    numb_sys = len(sys_configs)
    model_devi_jobs = jdata.get('model_devi_jobs', {})
    numb_jobs = len(model_devi_jobs)
    iters = ['iter.%06d' % ii for ii in range(numb_jobs)]
    # loop over iters to collect data
    for ii in range(len(iters)) :
        iter_data = glob.glob(os.path.join(iters[ii], '02.fp', 'data.[0-9]*[0-9]'))
        iter_data.sort()
        for jj in iter_data :
            sys = dpdata.LabeledSystem(jj, fmt = 'deepmd/npy')
            if merge:
                sys_str = sys.formula
            else:
                sys_str = (os.path.basename(jj).split('.')[-1])            
            if sys_str in coll_data.keys():
                coll_data[sys_str].append(sys)
            else:
                coll_data[sys_str] = sys
    # print information
    if verbose:
        for ii in range(len(init_data)):
            print(ptr_fmt % (str(init_data_sys[ii]), 
                             init_data[ii].formula,
                             init_data[ii].get_natoms(),
                             init_data[ii].get_nframes() ))            
        keys = list(coll_data.keys())
github deepmodeling / dpgen / dpgen / generator / tools / relabel.py View on Github external
fp_jdata = json.load(open(fp_json))
    # fp settings
    mass_map = jdata['mass_map']
    type_map = jdata['type_map']
    fp_style = fp_jdata['fp_style']
    fp_pp_path = fp_jdata['fp_pp_path']
    fp_pp_files = fp_jdata['fp_pp_files']
    cwd_ = os.getcwd()
    os.chdir(target_folder)
    fp_pp_path = os.path.abspath(fp_pp_path)
    os.chdir(cwd_)
    # init data sys
    init_data_prefix = jdata['init_data_prefix']
    init_data_sys = jdata['init_data_sys']
    for idx,ii in enumerate(init_data_sys):        
        sys = dpdata.LabeledSystem(os.path.join(init_data_prefix, ii), fmt = 'deepmd/npy', type_map = type_map)
        nframes = sys.get_nframes()
        sys_dir = os.path.join(output, 'init_system.%03d' % idx)
        os.makedirs(sys_dir, exist_ok = True)
        if verbose :
            print('# working on ' + sys_dir)
        with open(os.path.join(sys_dir,'record'), 'w') as fp:
            fp.write(os.path.join(init_data_prefix, ii) + '\n')
        for ff in range(nframes) :
            task_dir = os.path.join(sys_dir, 'task.%06d' % ff)
            os.makedirs(task_dir, exist_ok = True)
            sys.to_vasp_poscar(os.path.join(task_dir, 'POSCAR'), frame_idx=ff)
            # make fp
            cwd_ = os.getcwd()
            os.chdir(task_dir)
            for pp in fp_pp_files :
                if os.path.lexists(pp) :
github deepmodeling / dpgen / dpgen / generator / run.py View on Github external
system_index.sort()
    set_tmp = set(system_index)
    system_index = list(set_tmp)
    system_index.sort()

    cwd = os.getcwd()
    for ss in system_index :
        sys_output = glob.glob(os.path.join(work_path, "task.%s.*/output"%ss))
        sys_input = glob.glob(os.path.join(work_path, "task.%s.*/input"%ss))
        sys_output.sort()
        sys_input.sort()

        flag=True
        for ii,oo in zip(sys_input,sys_output) :
            if flag:
                _sys = dpdata.LabeledSystem(oo, fmt = 'qe/pw/scf', type_map = jdata['type_map'])
                if len(_sys)>0:
                   all_sys=_sys
                   flag=False
                else:
                   pass
            else:
                _sys = dpdata.LabeledSystem(oo, fmt = 'qe/pw/scf', type_map = jdata['type_map'])
                if len(_sys)>0:
                   all_sys.append(_sys)

        sys_data_path = os.path.join(work_path, 'data.%s'%ss)
        all_sys.to_deepmd_raw(sys_data_path)
        all_sys.to_deepmd_npy(sys_data_path, set_size = len(sys_output))
github deepmodeling / dpgen / dpgen / data / gen.py View on Github external
flag=True
        if ("type_map" in jdata) and isinstance(jdata["type_map"], list):
            type_map = jdata["type_map"]
        else:
            type_map = None 
        for oo in valid_outcars :
            if flag:
                _sys = dpdata.LabeledSystem(oo, type_map= type_map)
                if len(_sys)>0:
                   all_sys=_sys
                   flag=False
                else:
                   pass
            else:
                _sys = dpdata.LabeledSystem(oo, type_map= type_map)
                if len(_sys)>0:
                   all_sys.append(_sys)
        # create deepmd data
        if all_sys.get_nframes() >= coll_ndata :
            all_sys = all_sys.sub_system(np.arange(coll_ndata))
        all_sys.to_deepmd_raw('deepmd')
        all_sys.to_deepmd_npy('deepmd', set_size = all_sys.get_nframes())
        os.chdir(path_md)
    os.chdir(cwd)
github deepmodeling / dpgen / dpgen / simplify / simplify.py View on Github external
def get_system_cls(jdata):
    if jdata.get("labeled", False):
        return dpdata.LabeledSystem
    return dpdata.System

dpdata

Manipulating data formats of DeePMD-kit, VASP, QE, PWmat, and LAMMPS, etc.

LGPL-3.0
Latest version published 2 months ago

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