Source code for dclab.cli.task_join

"""Concatenate .rtdc files"""
import argparse
import time
import warnings

import hdf5plugin
import numpy as np

from ..rtdc_dataset import new_dataset, RTDCWriter
from .. import definitions as dfn
from .._version import version

from . import common

class FeatureSetNotIdenticalJoinWarning(UserWarning):

[docs]def join(path_out=None, paths_in=None, metadata=None): """Join multiple RT-DC measurements into a single .rtdc file""" cmp_kw = hdf5plugin.Zstd(clevel=5) if metadata is None: metadata = {"experiment": {"run index": 1}} if path_out is None or paths_in is None: parser = join_parser() args = parser.parse_args() paths_in = args.input path_out = args.output if len(paths_in) < 2: raise ValueError("At least two input files must be specified!") paths_in, path_out, path_temp = common.setup_task_paths( paths_in, path_out, allowed_input_suffixes=[".rtdc", ".tdms"]) # Order input files by date key_paths = [] for pp in paths_in: with new_dataset(pp) as ds: # sorting key key = "_".join([ds.config["experiment"]["date"], ds.config["experiment"]["time"], str(ds.config["experiment"]["run index"]) ]) key_paths.append((key, pp)) sorted_paths = [p[1] for p in sorted(key_paths, key=lambda x: x[0])] # Determine temporal offsets toffsets = np.zeros(len(sorted_paths), dtype=float) for ii, pp in enumerate(sorted_paths): with new_dataset(pp) as ds: etime = ds.config["experiment"]["time"] st = time.strptime(ds.config["experiment"]["date"] + etime[:8], "%Y-%m-%d%H:%M:%S") toffsets[ii] = time.mktime(st) if len(etime) > 8: # floating point time stored as well (HH:MM:SS.SS) toffsets[ii] += float(etime[8:]) toffsets -= toffsets[0] logs = {} # Determine features to export (based on first file) with warnings.catch_warnings(record=True) as w: # Catch all FeatureSetNotIdenticalJoinWarnings warnings.simplefilter("ignore") warnings.simplefilter("always", category=FeatureSetNotIdenticalJoinWarning) features = None for pp in sorted_paths: with new_dataset(pp) as ds: # features present if features is None: # The initial features are the innate features of the # first file (sorted by time). If we didn't use the innate # features, then the resulting file might become large # (e.g. if we included ancillary features). features = sorted(ds.features_innate) else: # Remove features from the feature list, if it is not in # this dataset, or cannot be computed on-the-fly. for feat in features: if feat not in ds.features: features.remove(feat) warnings.warn( f"Excluding feature '{feat}', because " + f"it is not present in '{pp}'!", FeatureSetNotIdenticalJoinWarning) # Warn the user if this dataset has an innate feature that # is being ignored, because it is not an innate feature of # the first dataset. for feat in ds.features_innate: if feat not in features: warnings.warn( f"Ignoring feature '{feat}' in '{pp}', " + "because it is not present in the " + "other files being joined!", FeatureSetNotIdenticalJoinWarning) if w: logs["dclab-join-feature-warnings"] = common.assemble_warnings(w) # Create initial output file with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") with new_dataset(sorted_paths[0]) as ds0: ds0.export.hdf5(path=path_temp, features=features, filtered=False, override=True, compression_kwargs=cmp_kw) if w: logs["dclab-join-warnings-#1"] = common.assemble_warnings(w) with RTDCWriter(path_temp, compression_kwargs=cmp_kw) as hw: ii = 1 # Append data from other files for pi, ti in zip(sorted_paths[1:], toffsets[1:]): ii += 1 # we start with the second dataset with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") with new_dataset(pi) as dsi: for feat in features: if feat == "time": # handle time offset fdata = dsi["time"] + ti elif feat == "frame": # handle frame offset fr = dsi.config["imaging"]["frame rate"] frame_offset = ti * fr fdata = dsi["frame"] + frame_offset elif feat == "index_online": if "events/index_online" in hw.h5file: # index_online is usually larger than index ido0 = hw.h5file["events/index_online"][-1] + 1 else: ido0 = 0 fdata = dsi["index_online"] + ido0 else: fdata = dsi[feat] hw.store_feature(feat=feat, data=fdata) if w: lkey = f"dclab-join-warnings-#{ii}" logs[lkey] = common.assemble_warnings(w) # Logs and configs from source files logs["dclab-join"] = common.get_command_log(paths=sorted_paths) for ii, pp in enumerate(sorted_paths): with new_dataset(pp) as ds: # data file logs for ll in ds.logs: logs[f"src-#{ii+1}_{ll}"] = ds.logs[ll] # configuration cfg = ds.config.tostring(sections=dfn.CFG_METADATA).split("\n") logs[f"cfg-#{ii+1}"] = cfg # Write logs and missing meta data for name in logs: hw.store_log(name, logs[name]) hw.store_metadata(metadata) # Finally, rename temp to out path_temp.rename(path_out)
def join_parser(): descr = "Join two or more RT-DC measurements. This will produce " \ + "one larger .rtdc file. The meta data of the dataset " \ + "that was recorded earliest will be used in the output " \ + "file. Please only join datasets that were recorded " \ + "in the same measurement run." parser = argparse.ArgumentParser(description=descr) parser.add_argument('input', metavar="INPUT", nargs="*", type=str, help='Input paths (.tdms or .rtdc files)') required_named = parser.add_argument_group('required named arguments') required_named.add_argument('-o', '--output', metavar="OUTPUT", type=str, help='Output path (.rtdc file)', required=True) parser.add_argument('--version', action='version', version=f'dclab-join {version}') return parser