Source code for dclab.rtdc_dataset.writer

from __future__ import annotations

from import Mapping
import copy
import json
import os
import pathlib
from typing import Dict, List, Literal, Tuple
import warnings

import h5py
import hdf5plugin
import numpy as np

from .. import definitions as dfn
from ..util import hashobj
from .._version import version

from .feat_anc_plugin import PlugInFeature


#: Chunks size in bytes for storing HDF5 datasets
CHUNK_SIZE_BYTES = 1024**2  # 1MiB

[docs] class RTDCWriter: def __init__(self, path_or_h5file: str | pathlib.Path | h5py.File, mode: Literal['append', 'replace', 'reset'] = "append", compression_kwargs: Dict | Mapping = None, compression: str = "deprecated"): """RT-DC data writer classe Parameters ---------- path_or_h5file: str or pathlib.Path or h5py.Group Path to an HDF5 file or an HDF5 file opened in write mode mode: str Defines how the data are stored: - "append": append new feature data to existing h5py Datasets - "replace": replace existing h5py Datasets with new features (used for ancillary feature storage) - "reset": do not keep any previous data compression_kwargs: dict-like Dictionary with the keys "compression" and "compression_opts" which are passed to :func:`h5py.H5File.create_dataset`. The default is Zstandard compression with the lowest compression level `hdf5plugin.Zstd(clevel=1)`. To disable compression, use `{"compression": None}`. compression: str or None Compression method used for data storage; one of [None, "lzf", "gzip", "szip"]. .. deprecated:: 0.43.0 Use `compression_kwargs` instead. """ if mode not in ["append", "replace", "reset"]: raise ValueError(f"Invalid mode '{mode}'!") if compression != "deprecated": warnings.warn("The `compression` kwarg is deprecated in favor of " "`compression_kwargs`!", DeprecationWarning) if compression_kwargs is not None: raise ValueError("You may not specify `compression` and " "`compression_kwargs` at the same time!") # be backwards-compatible compression_kwargs = {"compression": compression} if compression_kwargs is None: compression_kwargs = hdf5plugin.Zstd(clevel=1) self.mode = mode self.compression_kwargs = compression_kwargs if isinstance(path_or_h5file, h5py.Group): self.owns_path = False self.path = pathlib.Path(path_or_h5file.file.filename) self.h5file = path_or_h5file if mode == "reset": raise ValueError("'reset' mode incompatible with h5py.Group!") else: self.owns_path = True self.path = pathlib.Path(path_or_h5file) self.h5file = h5py.File(path_or_h5file, mode=("w" if mode == "reset" else "a")) #: unfortunate necessity, as `len(h5py.Group)` can be really slow self._group_sizes = {} def __enter__(self): return self def __exit__(self, type, value, tb): # close the HDF5 file try: self.h5file.require_group("events") if len(self.h5file["events"]): self.rectify_metadata() self.version_brand() except BaseException: raise finally: # This is guaranteed to run if any exception is raised. self.close()
[docs] @staticmethod def get_best_nd_chunks(item_shape, item_dtype=np.float64): """Return best chunks for HDF5 datasets Chunking has performance implications. It’s recommended to keep the total size of dataset chunks between 10 KiB and 1 MiB. This number defines the maximum chunk size as well as half the maximum cache size for each dataset. """ # Note that ` == 1` event_size = * np.dtype(item_dtype).itemsize chunk_size = CHUNK_SIZE_BYTES / event_size # Set minimum chunk size to 10 so that we can have at least some # compression performance. chunk_size_int = max(10, int(np.floor(chunk_size))) return tuple([chunk_size_int] + list(item_shape))
[docs] def close(self): """Close the underlying HDF5 file if a path was given during init""" if self.owns_path: self.h5file.close()
[docs] def rectify_metadata(self): """Autocomplete the metadta of the RTDC-measurement The following configuration keys are updated: - experiment:event count - fluorescence:samples per event - imaging: roi size x (if image or mask is given) - imaging: roi size y (if image or mask is given) The following configuration keys are added if not present: - fluorescence:channel count """ # set event count feats = sorted(self.h5file.get("events", {}).keys()) if feats: self.h5file.attrs["experiment:event count"] = len( self.h5file["events"][feats[0]]) else: raise ValueError(f"No features in '{self.path}'!") # ignore empty features in the checks further below for feat in feats[:]: # iterate over a copy of the list obj = self.h5file["events"][feat] if ((isinstance(feat, h5py.Dataset) and obj.shape[0] == 0) # ds or len(obj) == 0): # groups feats.remove(feat) # set samples per event if "trace" in feats: traces = list(self.h5file["events"]["trace"].keys()) trsize = self.h5file["events"]["trace"][traces[0]].shape[1] self.h5file.attrs["fluorescence:samples per event"] = trsize # set channel count chcount = sum( ["fl1_max" in feats, "fl2_max" in feats, "fl3_max" in feats]) if chcount: if "fluorescence:channel count" not in self.h5file.attrs: self.h5file.attrs["fluorescence:channel count"] = chcount # set roi size x/y if "image" in feats: shape = self.h5file["events"]["image"][0].shape elif "mask" in feats: shape = self.h5file["events"]["mask"][0].shape else: shape = None if shape is not None: # update shape self.h5file.attrs["imaging:roi size x"] = shape[1] self.h5file.attrs["imaging:roi size y"] = shape[0]
[docs] def store_basin(self, basin_name: str, basin_type: Literal['file', 'remote'], basin_format: str, basin_locs: List[str | pathlib.Path], basin_descr: str | None = None, basin_feats: List[str] = None, basin_map: np.ndarray | Tuple[str, np.ndarray] = None, verify: bool = True, ): """Write basin information Parameters ---------- basin_name: str basin name; Names do not have to be unique. basin_type: str basin type (file or remote); Files are paths accessible by the operating system (including e.g. network shares) whereas remote locations normally require an active internet connection. basin_format: str The basin format must match the ``format`` property of an :class:`.RTDCBase` subclass (e.g. "hdf5" or "dcor") basin_locs: list location of the basin as a string or (optionally) a ``pathlib.Path`` basin_descr: str optional string describing the basin basin_feats: list of str list of features this basin provides; You may use this to restrict access to features for a specific basin. basin_map: np.ndarray or tuple of (str, np.ndarray) If this is an integer numpy array, it defines the mapping of event indices from the basin dataset to the referring dataset (the dataset being written to disk). Normally, the basinmap feature used for the storing the mapping information is inferred from the currently defined basinmap features. However, if you are incepting basins, then this might not be sufficient, and you have to specify explicitly which basinmap feature to use. In such a case, you may specify a tuple `(feature_name, mapping_array)` where `feature_name` is the explicit mapping name, e.g. `"basinmap3"`. verify: bool whether to verify the basin before storing it; You might have set this to False if you would like to write a basin that is e.g. temporarily not available Returns ------- basin_hash: str hash of the basin which serves as the name of the HDF5 dataset stored in the output file .. versionadded:: 0.58.0 """ # Expand optional tuple for basin_map if isinstance(basin_map, (list, tuple)) and len(basin_map) == 2: basin_map_name, basin_map = basin_map else: basin_map_name = None if verify: # We have to import this here to avoid circular imports from .load import new_dataset # Make sure the basin can be opened by dclab, verify its ID cur_id = self.h5file.attrs.get("experiment:run identifier") for loc in basin_locs: with new_dataset(loc) as ds: # We can open the file, which is great. if cur_id: # Compare the IDs. ds_id = ds.get_measurement_identifier() if not (ds_id == cur_id or (basin_map is not None and cur_id.startswith(ds_id))): raise ValueError( f"Measurement identifier mismatch between " f"{self.path} ({cur_id}) and {loc} ({ds_id})!") if basin_feats: for feat in basin_feats: if not dfn.feature_exists(feat): raise ValueError(f"Invalid feature: '{feat}'") if basin_map is not None: if (not isinstance(basin_map, np.ndarray) and basin_map.dtype != np.uint64): raise ValueError( "The array specified in `basin_map` argument must be " "a numpy array with the dtype `np.uint64`!") # determine the basinmap to use if basin_map is not None: self.h5file.require_group("events") if basin_map_name is None: # We have to determine the basin_map_name to use for this # mapped basin. for ii in range(10): # basinmap0 to basinmap9 bm_cand = f"basinmap{ii}" if bm_cand in self.h5file["events"]: # There is a basin mapping defined in the file. Check # whether it is identical to ours. if np.all(self.h5file["events"][bm_cand] == basin_map): # Great, we are done here. basin_map_name = bm_cand break else: # This mapping belongs to a different basin, # try the next mapping. continue else: # The mapping is not defined in the dataset, and we may # write it to a new feature. basin_map_name = bm_cand self.store_feature(feat=basin_map_name, data=basin_map) break else: raise ValueError( "You have exhausted the usage of mapped basins for " "the current dataset. Please revise your analysis " "pipeline.") else: if basin_map_name not in self.h5file["events"]: # Write the explicit basin mapping into the file. self.store_feature(feat=basin_map_name, data=basin_map) elif not np.all( self.h5file["events"][basin_map_name] == basin_map): # This is a sanity check that we have to perform. raise ValueError( f"The basin mapping feature {basin_map_name} you " f"specified explicitly already exists in " f"{self.h5file} and they do not match. I assume " f"you are trying explicitly write to a basinmap that " f"is already used elsewhere.") else: # Classic, simple case basin_map_name = "same" b_data = { "description": basin_descr, "format": basin_format, "name": basin_name, "type": basin_type, "features": basin_feats, "mapping": basin_map_name, } if basin_type == "file": flocs = [] for pp in basin_locs: pp = pathlib.Path(pp) if verify: flocs.append(str(pp.resolve())) # Also store the relative path for user convenience. # Don't use pathlib.Path.relative_to, because that # is deprecated in Python 3.12. # Also, just look in subdirectories which simplifies # path resolution. this_parent = str(self.path.parent) + os.sep path_parent = str(pp.parent) + os.sep if path_parent.startswith(this_parent): flocs.append(str(pp).replace(this_parent, "", 1)) else: # We already did (or did not upon user request) verify # the path. Just pass it on to the list. flocs.append(str(pp)) b_data["paths"] = flocs else: b_data["urls"] = [str(p) for p in basin_locs] b_lines = json.dumps(b_data, indent=2).split("\n") basins = self.h5file.require_group("basins") key = hashobj(b_lines) if key not in basins: self.write_text(basins, key, b_lines) return key
[docs] def store_feature(self, feat, data, shape=None): """Write feature data Parameters ---------- feat: str feature name data: np.ndarray or list or dict feature data shape: tuple of int For non-scalar features, this is the shape of the feature for one event (e.g. `(90, 250)` for an "image". Usually, you do not have to specify this value, but you do need it in case of plugin features that don't have the "feature shape" set or in case of temporary features. If you don't specify it, then the shape is guessed based on the data you provide and a UserWarning will be issued. """ if not dfn.feature_exists(feat): raise ValueError(f"Undefined feature '{feat}'!") events = self.h5file.require_group("events") # replace data? if feat in events and self.mode == "replace": if feat == "trace": for tr_name in data.keys(): if tr_name in events[feat]: del events[feat][tr_name] else: del events[feat] if feat == "index": # By design, the index must be a simple enumeration. # We enforce that by not trusting the user. If you need # a different index, please take a look at the index_online # feature. nev = len(data) if "index" in events: nev0 = len(events["index"]) else: nev0 = 0 self.write_ndarray(group=events, name="index", data=np.arange(nev0 + 1, nev0 + nev + 1)) elif dfn.scalar_feature_exists(feat): self.write_ndarray(group=events, name=feat, data=np.atleast_1d(data)) elif feat == "contour": self.write_ragged(group=events, name=feat, data=data) elif feat in ["image", "image_bg", "mask", "qpi_oah", "qpi_oah_bg"]: self.write_image_grayscale(group=events, name=feat, data=data, is_boolean=(feat == "mask")) elif feat in ["qpi_amp", "qpi_pha"]: self.write_image_float32(group=events, name=feat, data=data) elif feat == "trace": for tr_name in data.keys(): # verify trace names if tr_name not in dfn.FLUOR_TRACES: raise ValueError(f"Unknown trace key: '{tr_name}'!") # write trace self.write_ndarray(group=events.require_group("trace"), name=tr_name, data=np.atleast_2d(data[tr_name]) ) else: if not shape: # OK, so we are dealing with a plugin feature or a temporary # feature here. Now, we don't know the exact shape of that # feature, but we give the user the option to advertise # the shape of the feature in the plugin. # First, try to obtain the shape from the PluginFeature # (if that exists). for pf in PlugInFeature.get_instances(feat): if isinstance(pf, PlugInFeature): shape = pf.plugin_feature_info.get("feature shape") if shape is not None: break # This is good. else: # Temporary features will have to live with this warning. warnings.warn( "There is no information about the shape of the " + f"feature '{feat}'. I am going out on a limb " + "for you and assume that you are storing " + "multiple events at a time. If this works, " + f"you could put the shape `{data[0].shape}` " + 'in the `info["feature shapes"]` key of ' + "your plugin feature.") shape = data.shape[1:] if shape == data.shape: data = data.reshape(1, *shape) elif shape == data.shape[1:]: pass else: raise ValueError(f"Bad shape for {feat}! Expeted {shape}, " + f"but got {data.shape[1:]}!") self.write_ndarray(group=events, name=feat, data=data)
[docs] def store_log(self, name, lines): """Write log data Parameters ---------- name: str name of the log entry lines: list of str or str the text lines of the log """ log_group = self.h5file.require_group("logs") self.write_text(group=log_group, name=name, lines=lines)
[docs] def store_metadata(self, meta): """Store RT-DC metadata Parameters ---------- meta: dict-like The metadata to store. Each key depicts a metadata section name whose data is given as a dictionary, e.g.:: meta = {"imaging": {"exposure time": 20, "flash duration": 2, ... }, "setup": {"channel width": 20, "chip region": "channel", ... }, ... } Only section key names and key values therein registered in dclab are allowed and are converted to the pre-defined dtype. Only sections from the :const:`dclab.definitions.CFG_METADATA` dictionary are stored. If you have custom metadata, you can use the "user" section. """ meta = copy.deepcopy(meta) # Ignore/remove tdms section meta.pop("fmt_tdms", None) # Check meta data for sec in meta: if sec == "user": # user-defined metadata are always written. # Any errors (incompatibilities with HDF5 attributes) # are the user's responsibility continue elif sec not in dfn.CFG_METADATA: # only allow writing of meta data that are not editable # by the user (not dclab.dfn.CFG_ANALYSIS) raise ValueError( f"Meta data section not defined in dclab: {sec}") for ck in meta[sec]: if not dfn.config_key_exists(sec, ck): raise ValueError( f"Meta key not defined in dclab: {sec}:{ck}") # update version old_version = meta.get("setup", {}).get("software version", "") new_version = self.version_brand( old_version=old_version or None, write_attribute=False ) meta.setdefault("setup", {})["software version"] = new_version # Write metadata for sec in meta: for ck in meta[sec]: idk = f"{sec}:{ck}" value = meta[sec][ck] if isinstance(value, bytes): # We never store byte attribute values. # In this case, `convfunc` should be `str` or `lcstr` or # somesuch. But we don't test that, because no other # datatype competes with str for bytes. value = value.decode("utf-8") if sec == "user": # store user-defined metadata as-is self.h5file.attrs[idk] = value else: # pipe the metadata through the hard-coded converter # functions convfunc = dfn.get_config_value_func(sec, ck) self.h5file.attrs[idk] = convfunc(value)
[docs] def store_table(self, name, cmp_array): """Store a compound array table Tables are semi-metadata. They may contain information collected during a measurement (but with a lower temporal resolution) or other tabular data relevant for a dataset. Tables have named columns. Therefore, they can be represented as a numy recarray, and they should be stored as such in an HDF5 file (compund dataset). Parameters ---------- name: str Name of the table cmp_array: np.recarray, h5py.Dataset, or dict If a np.recarray or h5py.Dataset are provided, then they are written as-is to the file. If a dictionary is provided, then the dictionary is converted into a numpy recarray. """ if isinstance(cmp_array, (np.recarray, h5py.Dataset)): # A table is a compound array (np.recarray). If we are here, # this means that the user passed an instance of np.recarray # or an instance h5py.Dataset (which we trust to be a proper # compound dataset at this point). No additional steps needed. pass elif isinstance(cmp_array, dict): # The user passed a dict which we now have to convert to a # compound dataset. We do this because we are user-convenient. # The user should not need to wade through these steps: columns = list(cmp_array.keys()) # Everything should be floats in a table. ds_dt = np.dtype({'names': columns, 'formats': [np.float64] * len(columns)}) # We trust the user to provide a dictionary with one-dimensional # lists or arrays of the same length. tabsize = len(cmp_array[columns[0]]) tab_data = np.zeros((tabsize, len(columns))) for ii, tab in enumerate(columns): tab_data[:, ii] = cmp_array[tab] # Now create a new compound array (discarding the old dict) cmp_array = np.rec.array(tab_data, dtype=ds_dt) else: raise NotImplementedError( f"Cannot convert {type(cmp_array)} to table!") group = self.h5file.require_group("tables") tab = group.create_dataset( name, data=cmp_array, fletcher32=True, **self.compression_kwargs) # Also store metadata if hasattr(cmp_array, "attrs"): for key in cmp_array.attrs: tab.attrs[key] = cmp_array.attrs[key]
[docs] def version_brand(self, old_version=None, write_attribute=True): """Perform version branding Append a " | dclab X.Y.Z" to the "setup:software version" attribute. Parameters ---------- old_version: str or None By default, the version string is taken from the HDF5 file. If set to a string, then this version is used instead. write_attribute: bool If True (default), write the version string to the "setup:software version" attribute """ if old_version is None: old_version = self.h5file.attrs.get("setup:software version", "") if isinstance(old_version, bytes): old_version = old_version.decode("utf-8") version_chain = [vv.strip() for vv in old_version.split("|")] version_chain = [vv for vv in version_chain if vv] cur_version = "dclab {}".format(version) if version_chain: if version_chain[-1] != cur_version: version_chain.append(cur_version) else: version_chain = [cur_version] new_version = " | ".join(version_chain) if write_attribute: self.h5file.attrs["setup:software version"] = new_version else: return new_version
[docs] def write_image_float32(self, group, name, data): """Write 32bit floating point image array This function wraps :func:`RTDCWriter.write_ndarray` and adds image attributes to the HDF5 file so HDFView can display the images properly. Parameters ---------- group: h5py.Group parent group name: str name of the dataset containing the text data: np.ndarray or list of np.ndarray image data """ if isinstance(data, (list, tuple)): # images may be in lists data = np.atleast_2d(data) if len(data.shape) == 2: # put single event in 3D array data = data[np.newaxis] dset = self.write_ndarray(group=group, name=name, data=data, dtype=np.float32) # Create and Set image attributes: # HDFView recognizes this as a series of images. # Use np.string_ as per # dset.attrs.create('CLASS', np.string_('IMAGE')) dset.attrs.create('IMAGE_VERSION', np.string_('1.2')) dset.attrs.create('IMAGE_SUBCLASS', np.string_('IMAGE_GRAYSCALE'))
[docs] def write_image_grayscale(self, group, name, data, is_boolean): """Write grayscale image data to and HDF5 dataset This function wraps :func:`RTDCWriter.write_ndarray` and adds image attributes to the HDF5 file so HDFView can display the images properly. Parameters ---------- group: h5py.Group parent group name: str name of the dataset containing the text data: np.ndarray or list of np.ndarray image data is_boolean: bool whether the input data is of boolean nature (e.g. mask data) - if so, data are converted to uint8 """ if isinstance(data, (list, tuple)): # images may be in lists data = np.atleast_2d(data) if len(data.shape) == 2: # put single event in 3D array data = data.reshape(1, data.shape[0], data.shape[1]) if is_boolean: # convert binary (mask) data to uint8 if data.__class__.__name__ == "H5MaskEvent": # (if we use `isinstance`, we get circular imports) # Be smart and directly write back the original data # (otherwise we would convert to bool and back to uint8). data = data.h5dataset elif data.dtype == bool: # Convert binary input mask data to uint8 with max range data = np.asarray(data, dtype=np.uint8) * 255 dset = self.write_ndarray(group=group, name=name, data=data, dtype=np.uint8) # Create and Set image attributes: # HDFView recognizes this as a series of images. # Use np.string_ as per # dset.attrs.create('CLASS', np.string_('IMAGE')) dset.attrs.create('IMAGE_VERSION', np.string_('1.2')) dset.attrs.create('IMAGE_SUBCLASS', np.string_('IMAGE_GRAYSCALE'))
[docs] def write_ndarray(self, group, name, data, dtype=None): """Write n-dimensional array data to an HDF5 dataset It is assumed that the shape of the array data is correct, i.e. that the shape of `data` is (number_events, feat_shape_1, ..., feat_shape_n). Parameters ---------- group: h5py.Group parent group name: str name of the dataset containing the text data: np.ndarray data dtype: dtype the dtype to use for storing the data (defaults to `data.dtype`) """ if len(data) == 0: raise ValueError(f"Empty data object for '{name}'") if name not in group: chunks = self.get_best_nd_chunks(item_shape=data.shape[1:], item_dtype=data.dtype) maxshape = tuple([None] + list(data.shape)[1:]) dset = group.create_dataset( name, shape=data.shape, dtype=dtype or data.dtype, maxshape=maxshape, chunks=chunks, fletcher32=True, **self.compression_kwargs) offset = 0 else: dset = group[name] offset = dset.shape[0] dset.resize(offset + data.shape[0], axis=0) if len(data.shape) == 1: # store scalar data in one go dset[offset:] = data # store ufunc data for min/max for uname, ufunc in [("min", np.nanmin), ("max", np.nanmax)]: val_a = dset.attrs.get(uname, None) if val_a is not None: val_b = ufunc(data) val = ufunc([val_a, val_b]) else: val = ufunc(dset) dset.attrs[uname] = val # store ufunc data for mean (weighted with size) mean_a = dset.attrs.get("mean", None) if mean_a is not None: num_a = offset mean_b = np.nanmean(data) num_b = data.size mean = (mean_a * num_a + mean_b * num_b) / (num_a + num_b) else: mean = np.nanmean(dset) dset.attrs["mean"] = mean else: chunk_size = dset.chunks[0] # populate higher-dimensional data in chunks # (reduces file size, memory usage, and saves time) num_chunks = len(data) // chunk_size for ii in range(num_chunks): start = ii * chunk_size stop = start + chunk_size dset[offset+start:offset+stop] = data[start:stop] # write remainder (if applicable) num_remain = len(data) % chunk_size if num_remain: start_e = num_chunks * chunk_size stop_e = start_e + num_remain dset[offset+start_e:offset+stop_e] = data[start_e:stop_e] return dset
[docs] def write_ragged(self, group, name, data): """Write ragged data (i.e. list of arrays of different lenghts) Ragged array data (e.g. contour data) are stored in a separate group and each entry becomes an HDF5 dataset. Parameters ---------- group: h5py.Group parent group name: str name of the dataset containing the text data: list of np.ndarray or np.ndarray the data in a list """ if isinstance(data, np.ndarray) and len(data.shape) == 2: # place single event in list data = [data] grp = group.require_group(name) # The following case is just a workaround for the very slow # `len(grp)` which makes things horrible if you are storing # contour data one-by-one. The only downside of this is that # we have to keep track of the length of the group. But I # think that is OK, since everything is very private here. # - Paul (2021-10-18) if grp not in self._group_sizes: self._group_sizes[grp] = len(grp) curid = self._group_sizes[grp] for ii, cc in enumerate(data): grp.create_dataset("{}".format(curid + ii), data=cc, fletcher32=True, chunks=cc.shape, **self.compression_kwargs) self._group_sizes[grp] += 1
[docs] def write_text(self, group, name, lines): """Write text to an HDF5 dataset Text data are written as a fixed-length string dataset. Parameters ---------- group: h5py.Group parent group name: str name of the dataset containing the text lines: list of str or str the text, line by line """ # replace text? if name in group and self.mode == "replace": del group[name] # handle strings if isinstance(lines, (str, bytes)): lines = [lines] lnum = len(lines) # Determine the maximum line length and use fixed-length strings, # because compression and fletcher32 filters won't work with # variable length strings. # # 100 is the recommended maximum and the default, because if # `mode` is e.g. "append", then this line may not be the longest. max_length = 100 lines_as_bytes = [] for line in lines: # convert lines to bytes if not isinstance(line, bytes): lbytes = line.encode("UTF-8") else: lbytes = line max_length = max(max_length, len(lbytes)) lines_as_bytes.append(lbytes) if name not in group: # Create the dataset txt_dset = group.create_dataset( name, shape=(lnum,), dtype=f"S{max_length}", maxshape=(None,), chunks=True, fletcher32=True, **self.compression_kwargs) line_offset = 0 else: # TODO: test whether fixed length is long enough! # Resize the dataset txt_dset = group[name] line_offset = txt_dset.shape[0] txt_dset.resize(line_offset + lnum, axis=0) # Write the text data line-by-line for ii, lbytes in enumerate(lines_as_bytes): txt_dset[line_offset + ii] = lbytes