"""RT-DC hdf5 format"""
import functools
import numbers
import pathlib
import h5py
import numpy as np
from .. import definitions as dfn
from ..external.packaging import parse as parse_version
from ..util import hashobj, hashfile
from .config import Configuration
from .core import RTDCBase
class OldFormatNotSupportedError(BaseException):
pass
class UnknownKeyWarning(UserWarning):
pass
class H5ContourEvent:
def __init__(self, h5group):
self.h5group = h5group
# for hashing in util.obj2bytes
self.identifier = (h5group.file.filename, h5group["0"].name)
def __getitem__(self, key):
if not isinstance(key, numbers.Integral):
# slicing!
indices = np.arange(len(self))[key]
output = []
# populate the output list
for evid in indices:
output.append(self.h5group[str(evid)][:])
return output
elif key < 0:
return self.__getitem__(key + len(self))
else:
return self.h5group[str(key)][:]
def __iter__(self):
for idx in range(len(self)):
yield self[idx]
@functools.lru_cache()
def __len__(self):
# computing the length of an H5Group is slow
return len(self.h5group)
@property
def shape(self):
return len(self), np.nan, 2
class H5Events:
def __init__(self, h5):
self._h5 = h5
self._features = sorted(self._h5["events"].keys())
# make sure that "trace" is not empty
if "trace" in self._features and len(self._h5["events"]["trace"]) == 0:
self._features.remove("trace")
def __contains__(self, key):
return key in self.keys()
def __getitem__(self, key):
# user-level checking is done in core.py
assert dfn.feature_exists(key), "Feature '{}' not valid!".format(key)
data = self._h5["events"][key]
if key == "contour":
return H5ContourEvent(data)
elif key == "mask":
return H5MaskEvent(data)
elif key == "trace":
return H5TraceEvent(data)
elif data.ndim == 1:
return H5ScalarEvent(data)
else:
# for features like "image", "image_bg" and other non-scalar
# ancillary features
return data
def __iter__(self):
# dict-like behavior
for key in self.keys():
yield key
def _is_defective_feature(self, feat):
"""Whether the stored feature is defective"""
defective = False
if feat in DEFECTIVE_FEATURES and feat in self._features:
# feature exists in the HDF5 file
# workaround machinery for sorting out defective features
defective = DEFECTIVE_FEATURES[feat](self._h5)
return defective
def keys(self):
"""Returns list of valid features
Checks for
- defective features
- existing feature names
"""
features = []
for key in self._features:
# check for defective features
if dfn.feature_exists(key) and not self._is_defective_feature(key):
features.append(key)
return features
class H5Logs:
def __init__(self, h5):
self._h5 = h5
def __getitem__(self, key):
if key in self.keys():
log = list(self._h5["logs"][key])
if isinstance(log[0], bytes):
log = [li.decode("utf") for li in log]
else:
raise KeyError(
f"Log '{key}' not found or empty in {self._h5.file.filename}!")
return log
def __iter__(self):
# dict-like behavior
for key in self.keys():
yield key
def __len__(self):
return len(self.keys())
@functools.lru_cache()
def keys(self):
names = []
if "logs" in self._h5:
for key in self._h5["logs"]:
if self._h5["logs"][key].size:
names.append(key)
return names
class H5MaskEvent:
"""Cast uint8 masks to boolean"""
def __init__(self, h5dataset):
self.h5dataset = h5dataset
# identifier required because "mask" is used for computation
# of ancillary feature "contour".
self.identifier = (self.h5dataset.file.filename, self.h5dataset.name)
self.dtype = np.dtype(bool)
def __getitem__(self, idx):
return np.asarray(self.h5dataset[idx], dtype=bool)
def __iter__(self):
for idx in range(len(self)):
yield self[idx]
def __len__(self):
return len(self.h5dataset)
@property
def attrs(self):
return self.h5dataset.attrs
@property
def shape(self):
return self.h5dataset.shape
class H5ScalarEvent(np.lib.mixins.NDArrayOperatorsMixin):
def __init__(self, h5ds):
"""Lazy access to a scalar feature with cache"""
self.h5ds = h5ds
# for hashing in util.obj2bytes
self.identifier = (self.h5ds.file.filename, self.h5ds.name)
self._array = None
self.ndim = 1 # matplotlib might expect this from an array
# attrs
self._ufunc_attrs = dict(self.h5ds.attrs)
def __array__(self, dtype=None):
if self._array is None:
self._array = np.asarray(self.h5ds, dtype=dtype)
return self._array
def __getitem__(self, idx):
return self.__array__()[idx]
def __len__(self):
return len(self.h5ds)
def _fetch_ufunc_attr(self, uname, ufunc):
"""A wrapper for calling functions on the scalar feature data
The ideas are:
1. If there is a ufunc (max/mean/min) value stored in the dataset
attributes, then use this one.
2. If the ufunc is computed, it is cached permanently in
self._ufunc_attrs
"""
val = self._ufunc_attrs.get(uname, None)
if val is None:
val = ufunc(self.__array__())
self._ufunc_attrs[uname] = val
return val
def max(self, *args, **kwargs):
return self._fetch_ufunc_attr("max", np.nanmax)
def mean(self, *args, **kwargs):
return self._fetch_ufunc_attr("mean", np.nanmean)
def min(self, *args, **kwargs):
return self._fetch_ufunc_attr("min", np.nanmin)
@property
def shape(self):
return self.h5ds.shape
class H5TraceEvent:
def __init__(self, h5group):
self.h5group = h5group
def __getitem__(self, idx):
return self.h5group[idx]
def __contains__(self, item):
return item in self.h5group
def __len__(self):
return len(self.h5group)
def __iter__(self):
for key in sorted(self.h5group.keys()):
yield key
def keys(self):
return self.h5group.keys()
@property
def shape(self):
atrace = list(self.h5group.keys())[0]
return tuple([len(self.h5group)] + list(self.h5group[atrace].shape))
[docs]class RTDC_HDF5(RTDCBase):
def __init__(self, h5path, *args, **kwargs):
"""HDF5 file format for RT-DC measurements
Parameters
----------
h5path: str or pathlib.Path
Path to a '.tdms' measurement file.
*args:
Arguments for `RTDCBase`
**kwargs:
Keyword arguments for `RTDCBase`
Attributes
----------
path: pathlib.Path
Path to the experimental HDF5 (.rtdc) file
"""
super(RTDC_HDF5, self).__init__(*args, **kwargs)
h5path = pathlib.Path(h5path)
self._hash = None
self.path = h5path
# Setup events
self._h5 = h5py.File(h5path, mode="r")
self._events = H5Events(self._h5)
# Parse configuration
self.config = RTDC_HDF5.parse_config(h5path)
# Override logs property with HDF5 data
self.logs = H5Logs(self._h5)
# check version
rtdc_soft = self.config["setup"]["software version"]
if rtdc_soft.startswith("dclab "):
rtdc_ver = parse_version(rtdc_soft.split(" ")[1])
if rtdc_ver < parse_version(MIN_DCLAB_EXPORT_VERSION):
msg = "The file {} was created ".format(self.path) \
+ "with dclab {} which is ".format(rtdc_ver) \
+ "not supported anymore! Please rerun " \
+ "dclab-tdms2rtdc / export the data again."
raise OldFormatNotSupportedError(msg)
self.title = "{} - M{}".format(self.config["experiment"]["sample"],
self.config["experiment"]["run index"])
# Set up filtering
self._init_filters()
def __enter__(self):
return self
def __exit__(self, type, value, tb):
# close the HDF5 file
self._h5.close()
@functools.lru_cache()
def __len__(self):
ec = self._h5.get("experiment:event count")
if ec is not None:
return ec
else:
return super(RTDC_HDF5, self).__len__()
[docs] @staticmethod
def can_open(h5path):
"""Check whether a given file is in the .rtdc file format"""
h5path = pathlib.Path(h5path)
if h5path.suffix == ".rtdc":
return True
else:
# we don't know the extension; check for the "events" group
canopen = False
try:
# This is a workaround for Python2 where h5py cannot handle
# unicode file names.
with h5path.open("rb") as fd:
h5 = h5py.File(fd, "r")
if "events" in h5:
canopen = True
except IOError:
# not an HDF5 file
pass
return canopen
[docs] @staticmethod
def parse_config(h5path):
"""Parse the RT-DC configuration of an HDF5 file"""
with h5py.File(h5path, mode="r") as fh5:
h5attrs = dict(fh5.attrs)
# Convert byte strings to unicode strings
# https://github.com/h5py/h5py/issues/379
for key in h5attrs:
if isinstance(h5attrs[key], bytes):
h5attrs[key] = h5attrs[key].decode("utf-8")
config = Configuration()
for key in h5attrs:
section, pname = key.split(":")
config[section][pname] = h5attrs[key]
return config
@property
def hash(self):
"""Hash value based on file name and content"""
if self._hash is None:
tohash = [self.path.name,
# Hash a maximum of ~1MB of the hdf5 file
hashfile(self.path, blocksize=65536, count=20)]
self._hash = hashobj(tohash)
return self._hash
def is_defective_feature_aspect(h5):
"""In Shape-In 2.0.6, there was a wrong variable cast"""
software_version = h5.attrs["setup:software version"]
if isinstance(software_version, bytes):
software_version = software_version.decode("utf-8")
return software_version in ["ShapeIn 2.0.6", "ShapeIn 2.0.7"]
def is_defective_feature_time(h5):
"""Shape-In stores the "time" feature as a low-precision float32
This makes time resolution for large measurements useless,
because times are only resolved with four digits after the
decimal point. Here, we first check whether the "frame" feature
and the [imaging]:"frame rate" configuration are set. If so,
then we can compute "time" as an ancillary feature which will
be more accurate than its float32 version.
"""
# This is a necessary requirement. If we cannot compute the
# ancillary feature, then we cannot ignore (even inaccurate) information.
has_ancil = "frame" in h5["events"] and h5.attrs.get("imaging:frame rate",
0) != 0
if not has_ancil:
return False
# If we have a 32 bit dataset, then things are pretty clear.
is_32float = h5["events/time"].dtype.char[-1] == "f"
if is_32float:
return True
# Consider the software
software_version = h5.attrs["setup:software version"]
if isinstance(software_version, bytes):
software_version = software_version.decode("utf-8")
# Only Shape-In stores false data, so we can ignore other recording
# software.
is_shapein = software_version.count("ShapeIn")
if not is_shapein:
return False
# The tricky part: dclab might have analyzed the dataset recorded by
# Shape-In, e.g. in a compression step. Since dclab appends its version
# string to the software_version, we just have to parse that and make
# sure that it is above 0.47.6.
last_version = software_version.split("|")[-1].strip()
if last_version.startswith("dclab"):
dclab_version = last_version.split()[1]
if parse_version(dclab_version) < parse_version("0.47.6"):
# written with an older version of dclab
return True
# We covered all cases:
# - ancillary information are available
# - it's not a float32 dataset
# - we excluded all non-Shape-In recording software
# - it was not written with an older version of dclab
return False
def is_defective_feature_volume(h5):
"""dclab computed volume wrong up until version 0.36.1"""
# first check if the scripted fix was applied
if "dclab_issue_141" in list(h5.get("logs", {}).keys()):
return False
# if that does not apply, check the software version
software_version = h5.attrs["setup:software version"]
if isinstance(software_version, bytes):
software_version = software_version.decode("utf-8")
if software_version:
last_version = software_version.split("|")[-1].strip()
if last_version.startswith("dclab"):
dclab_version = last_version.split()[1]
if parse_version(dclab_version) < parse_version("0.37.0"):
return True
return False
#: rtdc files exported with dclab prior to this version are not supported
MIN_DCLAB_EXPORT_VERSION = "0.3.3.dev2"
#: dictionary of defective features, defined by HDF5 attributes;
#: if a value matches the given HDF5 attribute, the feature is
#: considered defective
DEFECTIVE_FEATURES = {
# feature: [HDF5_attribute, matching_value]
"aspect": is_defective_feature_aspect,
"time": is_defective_feature_time,
"volume": is_defective_feature_volume,
}