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How to use truncate_log method in localstack

Best Python code snippet using localstack_python

prepare_input.py

Source:prepare_input.py

1"""Functions responsible for preparing input."""2import os3from statistics import mean4from typing import List, Tuple, TypedDict, Union5from Bio.PDB import PDBParser, Polypeptide6import pyarrow7import tqdm8import vaex9from anu.constants.amino_acid import amino_acid10from anu.data.dataframe_operation import (11    read_dataframes_from_file,12    save_dataframe_to_file,13)14# Dictionary keys15col_name = [16    "seq",17    "x_pos",18    "y_pos",19    "z_pos",20    "hydropathy",21    "hydropathy_index",22    "acidity_basicity",23    "mass",24    "isoelectric_point",25    "charge",26]27class BuildMatrixDict(TypedDict):28    """Dictionary shape for build matrix class."""29    seq: List[List[int]]30    x_pos: List[List[int]]31    y_pos: List[List[int]]32    z_pos: List[List[int]]33    hydropathy: List[List[int]]34    hydropathy_index: List[List[int]]35    acidity_basicity: List[List[int]]36    mass: List[List[int]]37    isoelectric_point: List[List[int]]38    charge: List[List[int]]39def build_matrix(40    path: str, filename: str, truncate_log: Union[tqdm.tqdm, None] = None41) -> BuildMatrixDict:42    """Build the input matrix for one protein.43    Args:44        path: path of the pdb file.45        filename: name of the file (without extension).46        truncate_log: tqdm logger47    Returns:48        Build matrix dictionary49    """50    PROTEIN_SEQ_MAX_LEN = 400051    protein_matrix = [[0 for x in range(PROTEIN_SEQ_MAX_LEN)] for y in range(10)]52    protein_structure = PDBParser().get_structure(filename, path)53    protein_model = list(protein_structure.get_models())54    protein_chains = list(protein_model[0].get_chains())55    col = 056    try:57        for chain in protein_chains:58            protein_residues = list(chain.get_residues())59            for residue in protein_residues:60                if Polypeptide.is_aa(residue.get_resname(), standard=True):61                    atoms = list(residue.get_atoms())62                    x = []63                    y = []64                    z = []65                    for atom in atoms:66                        vec = atom.get_vector()67                        x.append(vec.__getitem__(0))68                        y.append(vec.__getitem__(1))69                        z.append(vec.__getitem__(2))70                    # calculate position of residue71                    x = round(mean(x))72                    y = round(mean(y))73                    z = round(mean(z))74                    # one letter code75                    code = Polypeptide.three_to_one(residue.get_resname())76                    aa = amino_acid[code]77                    protein_matrix[0][col] = aa["code"]78                    protein_matrix[1][col] = x79                    protein_matrix[2][col] = y80                    protein_matrix[3][col] = z81                    protein_matrix[4][col] = aa["hydropathy"]82                    protein_matrix[5][col] = aa["hydropathy_index"]83                    protein_matrix[6][col] = aa["acidity_basicity"]84                    protein_matrix[7][col] = aa["mass"]85                    protein_matrix[8][col] = aa["isoelectric_point"]86                    protein_matrix[9][col] = aa["charge"]87                # Even if the current residue is not amino acid we increase the col.88                # 0 is save at this position if it is not an amino acid.89                col = col + 190    except IndexError:91        if truncate_log is not None:92            truncate_log.set_description_str(f"Protein {filename} is truncated.")93    # Prepare dict so it can be load to vaex dataframe94    dic: BuildMatrixDict = {95        "seq": [[]],96        "x_pos": [[]],97        "y_pos": [[]],98        "z_pos": [[]],99        "hydropathy": [[]],100        "hydropathy_index": [[]],101        "acidity_basicity": [[]],102        "mass": [[]],103        "isoelectric_point": [[]],104        "charge": [[]],105    }106    for i in range(10):107        dic[col_name[i]] = pyarrow.array(108            [[protein_matrix[i][x] for x in range(PROTEIN_SEQ_MAX_LEN)]]109        )110    return dic111def build_df_from_dic(112    protein_a: BuildMatrixDict,113    protein_b: BuildMatrixDict,114    interaction_type: Union[bool, None] = None,115) -> vaex.dataframe.DataFrame:116    """Build dataframe using protein dict.117    Args:118        protein_a: Protein A in the form of BuildMatrixDict.119        protein_b: Protein B in the form of BuildMatrixDict.120        interaction_type: boolean, true if protein interacts.121    Returns:122        vaex dataframe.123    """124    if interaction_type is not None:125        interaction_array = (126            pyarrow.array([[1, 0]]) if interaction_type else pyarrow.array([[0, 1]])127        )128    else:129        interaction_array = [[]]130    return vaex.from_arrays(131        proteinA_seq=protein_a[col_name[0]],132        proteinB_seq=protein_b[col_name[0]],133        proteinA_x=protein_a[col_name[1]],134        proteinB_x=protein_b[col_name[1]],135        proteinA_y=protein_a[col_name[2]],136        proteinB_y=protein_b[col_name[2]],137        proteinA_z=protein_a[col_name[3]],138        proteinB_z=protein_b[col_name[3]],139        proteinA_hydropathy=protein_a[col_name[4]],140        proteinB_hydropathy=protein_b[col_name[4]],141        proteinA_hydropathy_index=protein_a[col_name[5]],142        proteinB_hydropathy_index=protein_b[col_name[5]],143        proteinA_acidity_basicity=protein_a[col_name[6]],144        proteinB_acidity_basicity=protein_b[col_name[6]],145        proteinA_mass=protein_a[col_name[7]],146        proteinB_mass=protein_b[col_name[7]],147        proteinA_isoelectric_point=protein_a[col_name[8]],148        proteinB_isoelectric_point=protein_b[col_name[8]],149        proteinA_charge=protein_a[col_name[9]],150        proteinB_charge=protein_b[col_name[9]],151        interaction=interaction_array,152    )153def save_build_df(154    list_of_logs: List[tqdm.tqdm], df_base_path: str, save_path: str, index_path: str155) -> None:156    """Saving progress of build input from json.157    Args:158        list_of_logs: list of tqdm logs.159        df_base_path: path of last saved df.160        save_path: path to save new df.161        index_path: path of the file containing index information162    """163    for logger in list_of_logs:164        logger.clear()165        logger.close()166    length = 0167    with open(index_path) as fp:168        length = int(fp.read())169    if length != 0:170        expected_time = (length // 300) + 1171        print("Please don't press ctrl+c...")172        print("Trying to build and save dataframe...")173        print(f"On average machine, this will take around {expected_time}min")174        df = read_dataframes_from_file(175            [os.path.join(df_base_path, str(x)) for x in range(length + 1)]176        )177        save_dataframe_to_file(df, save_path)178def build_input_from_json_intermediate_step(179    protein_a: str,180    protein_b: str,181    pdb_file_path: str,182    current_log: tqdm.tqdm,183    truncate_log: tqdm.tqdm,184    interaction_type: bool,185) -> vaex.dataframe:186    """Intermediate step for build input from json.187    Args:188        protein_a: first protein id.189        protein_b: second protein id.190        pdb_file_path: root location of pdb files.191        current_log: tqdm logger for current status.192        truncate_log: tqdm logger for truncate status.193        interaction_type: interaction status of both protein194    Returns:195        vaex dataframe.196    """197    current_log.set_description_str(f"Processing  [{protein_a}, {protein_b}]")198    a = build_matrix(199        os.path.join(pdb_file_path, f"{protein_a}.pdb"), protein_a, truncate_log200    )201    b = build_matrix(202        os.path.join(pdb_file_path, f"{protein_b}.pdb"), protein_b, truncate_log203    )204    return build_df_from_dic(a, b, interaction_type)205def get_proteins_list_from_json(file_path: str) -> Tuple[List[str], List[str]]:206    """Get proteins list from json.207    Args:208        file_path: path of the json file.209    Returns:210        Tuple of protein list.211    """212    import json213    protein_json = {}214    with open(file_path) as fp:215        protein_json = json.load(fp)216    protein_list = []217    for _, value in protein_json.items():218        protein_list.append(value)219    return protein_list[0], protein_list[1]220def build_input_from_json(221    path: str, db_name: str, filename: str, interaction_type: bool222) -> None:223    """Build input from json file.224    Args:225        path: path of json file.226        db_name: name of the database.227        filename: name of the output file containing df.228        interaction_type: boolean, true if protein interacts.229    """230    import os231    import warnings232    warnings.simplefilter("ignore")233    BASE_DATA_DIR = os.path.realpath(234        os.path.abspath(235            os.path.join(os.path.dirname(__file__), "..", "..", "..", "..", "data")236        )237    )238    file_path = os.path.join(BASE_DATA_DIR, "processed", "protein_id", path)239    pdb_file_path = os.path.join(BASE_DATA_DIR, "raw", "pdb")240    protein_list_a, protein_list_b = get_proteins_list_from_json(file_path)241    total = min(len(protein_list_a), len(protein_list_b))242    current_log = tqdm.tqdm(total=0, position=1, bar_format="{desc}", leave=False)243    truncate_log = tqdm.tqdm(total=0, position=2, bar_format="{desc}", leave=False)244    loggers = [current_log, truncate_log]245    df_chunk_base_path = os.path.join("input", db_name, "df_chunks")246    save_df_path = os.path.join("input", db_name, filename)247    row_already_processed_path = os.path.join(248        BASE_DATA_DIR, "processed", "input", db_name, f"{filename}_processed_row.txt"249    )250    start = 0251    try:252        with open(row_already_processed_path) as fp:253            start = int(fp.read())254    except IOError:255        start = 0256    try:257        progress_log = tqdm.tqdm(total=total, position=0, leave=False, unit="pairs")258        progress_log.update(start)259        loggers.append(progress_log)260        for i in range(start, total):261            try:262                df = build_input_from_json_intermediate_step(263                    protein_list_a[i],264                    protein_list_b[i],265                    pdb_file_path,266                    current_log,267                    truncate_log,268                    interaction_type,269                )270                save_dataframe_to_file(df, os.path.join(df_chunk_base_path, str(i)))271                with open(row_already_processed_path, "w") as fp:272                    fp.write(str(i))273                progress_log.update(1)274            except KeyboardInterrupt:275                break276        print("Completed...")277        save_build_df(278            loggers, df_chunk_base_path, save_df_path, row_already_processed_path279        )280    except KeyboardInterrupt:281        save_build_df(282            loggers, df_chunk_base_path, save_df_path, row_already_processed_path...

clear_log.py

Source:clear_log.py

...18	if os.path.getsize(file_path) >= FILE_SIZE:19		return True20	else:21		return False22def truncate_log(abs_path, flist):23	os.chdir(abs_path)24	for f in flist:25		if os.path.isfile(f):26			# logging.info('compress start [%s %s]', abs_path, f)27			logging.info('compress start  [%s]', f)28			reduce_files(f)29			remove_files(f)30			logging.info('compress end [%s]',  f)31def ret_abs_dir(f_path):32	ret = {}33	fs = []34	for root, dirs, files in os.walk(f_path):35		for name in files:36			if not 'tar.gz' in name and '.log.' in name:37				#ret.append(os.path.join(root, name))38				logging.debug('walk to path %s [] file %s', root, name)39				fs.append(name)40				ret[root] = fs41	logging.debug('return dic is %s', ret)42	return ret43def remove_files(file_name):44	if os.path.isfile(file_name):45		os.remove(file_name)46#not use47def ret_file_size(f_list):48	for f in range(len(f_list)):49		if get_file_size(f_list[f]):50			truncate_log(f_list[f])51	return 052def run(file_dic):53	"""54	input: file_dic is dictory55	"""56	for k,v in file_dic.items():57		logging.debug("input dic key:%s value:%s", k, v)58		truncate_log(k,v)59def main():60	for i in LOG_DIR:61		all_files = ret_abs_dir(i)62		run(all_files)63		#ret_file_size(all_files)64if __name__ == '__main__':...

sample.py

Source:sample.py

...11                    help='If specified, database mist be closed first', dest='vacuum')12args = parser.parse_args()13log = v8log(args.db_file)14if args.to_date == '':15    log.truncate_log()16else:17    log.truncate_log(datetime.strptime(args.to_date, '%Y-%m-%d'))18if args.vacuum:
...

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