+import copy
+import itertools
+from dataclasses import dataclass, field
+from typing import Any, NamedTuple, TypeVar
+
+import numpy as np
+from typing_extensions import Self
+
+from eitprocessing.datahandling.mixins.equality import Equivalence
+from eitprocessing.datahandling.mixins.slicing import HasTimeIndexer
+
+
+
+
+
+
+[docs]
+class TimeRange(NamedTuple):
+ """A tuple containing the start time and end time of a time range."""
+
+
+
+
+
+
+
+
+@dataclass(eq=False)
+
+[docs]
+class IntervalData(Equivalence, HasTimeIndexer):
+ """Container for interval data existing over a period of time.
+
+ Interval data is data that constists for a given time interval. Examples are a ventilator setting (e.g.
+ end-expiratory pressure), the position of a patient, a maneuver (end-expiratory hold) being performed, detected
+ periods in the data, etc.
+
+ Interval data consists of a number of time range-value pairs or time ranges without associated values. E.g. interval
+ data with the label "expiratory_breath_hold" only requires time ranges for when expiratory breath holds were
+ performed. Other interval data, e.g. "set_driving_pressure" do have associated values.
+
+ Interval data can be selected by time through the `select_by_time(start_time, end_time)` method. Alternatively,
+ `t[start_time:end_time]` can be used. When the start or end time overlaps with a time range, the time range and its
+ associated value are included in the selection if `partial_inclusion` is `True`, but ignored if `partial_inclusion`
+ is `False`. If the time range is partially included, the start and end times are trimmed to the start and end time
+ of the selection.
+
+ A potential use case where `partial_inclusion` should be set to `True` is "set_driving_pressure": you might want to
+ keep the driving pressure that was set before the start of the selectioon. A use case where `partial_inclusion`
+ should be set to `False` is "detected_breaths": you might want to ignore partial breaths that started before or
+ ended after the selected period.
+
+ Note that when selecting by time, the end time is included in the selection.
+
+ Args:
+ label: a computer-readable name
+ name: a human-readable name
+ unit: the unit associated with the data
+ category: the category of data
+ time_ranges: a list of time ranges (tuples containing a start time and end time)
+ values: an optional list of values with the same length as time_ranges
+ parameters: parameters used to derive the data
+ derived_from: list of data sets this data was derived from
+ description: extended human readible description of the data
+ partial_inclusion: whether to include a trimmed version of a time range when selecting data
+ """
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+[docs]
+ def __post_init__(self) -> None:
+ self.time_ranges = [TimeRange._make(time_range) for time_range in self.time_ranges]
+ self._check_equivalence = ["unit", "category", "parameters", "partial_inclusion"]
+
+
+
+
+
+
+
+
+
+[docs]
+ def select_by_time( # noqa: C901
+ self,
+ start_time: float | None = None,
+ end_time: float | None = None,
+ partial_inclusion: bool | None = None,
+ newlabel: str | None = None,
+ ) -> Self:
+ """Return only period data that overlaps (partly) with start and end time.
+
+ Other types of data (e.g. ContinuousData and SparseData) support the start_inclusive and end_inclusive
+ arguments. PeriodData does not. That means that selection by time of PeriodData probably works slightly
+ different than other types of selecting/slicing data.
+ """
+ if partial_inclusion is None:
+ partial_inclusion = self.partial_inclusion
+ newlabel = newlabel or self.label
+
+ if start_time is None:
+ start_time = self.time_ranges[0].start_time
+ if end_time is None:
+ end_time = self.time_ranges[-1].end_time
+
+ def keep_starting_on_or_before_end(item: tuple[TimeRange, Any]) -> bool:
+ time_range, _ = item
+ return time_range.start_time <= end_time
+
+ def keep_ending_on_or_after_start(item: tuple[TimeRange, Any]) -> bool:
+ time_range, _ = item
+ return time_range.end_time >= start_time
+
+ def keep_fully_overlapping(item: tuple[TimeRange, Any]) -> bool:
+ time_range, _ = item
+ if time_range.start_time < start_time:
+ return False
+ if time_range.end_time > end_time:
+ return False
+ return True
+
+ def replace_start_end_time(time_range: TimeRange) -> TimeRange:
+ start_time_ = max(time_range.start_time, start_time)
+ end_time_ = min(time_range.end_time, end_time)
+ return TimeRange(start_time_, end_time_)
+
+ has_values = self.values is not None
+ iter_values = self.values or itertools.repeat(None)
+
+ time_range_value_pairs: Any = zip(self.time_ranges, iter_values, strict=True)
+ time_range_value_pairs = filter(keep_starting_on_or_before_end, time_range_value_pairs)
+ time_range_value_pairs = list(filter(keep_ending_on_or_after_start, time_range_value_pairs))
+
+ if not partial_inclusion:
+ time_range_value_pairs = list(filter(keep_fully_overlapping, time_range_value_pairs))
+
+ if len(time_range_value_pairs):
+ time_ranges, values = zip(*time_range_value_pairs, strict=True)
+ time_ranges = list(map(replace_start_end_time, time_ranges))
+ else:
+ time_ranges, values = [], []
+
+ return self.__class__(
+ label=newlabel,
+ name=self.name,
+ unit=self.unit,
+ category=self.category,
+ derived_from=[*self.derived_from, self],
+ time_ranges=list(time_ranges),
+ values=list(values) if has_values else None,
+ )
+
+
+
+[docs]
+ def concatenate(self: T, other: T, newlabel: str | None = None) -> T: # noqa: D102, will be moved to mixin in future
+ self.isequivalent(other, raise_=True)
+
+ # TODO: make proper copy functions
+ if not len(self):
+ return copy.deepcopy(other)
+ if not len(other):
+ return copy.deepcopy(self)
+
+ if other.time_ranges[0].start_time < self.time_ranges[-1].end_time:
+ msg = f"{other} (b) starts before {self} (a) ends."
+ raise ValueError(msg)
+
+ cls = type(self)
+ newlabel = newlabel or self.label
+
+ if isinstance(self.values, list | tuple) and isinstance(other.values, list | tuple):
+ new_values = self.values + other.values
+ elif isinstance(self.values, np.ndarray) and isinstance(other.values, np.ndarray):
+ new_values = np.concatenate((self.values, other.values))
+ elif self.values is None and other.values is None:
+ new_values = None
+ else:
+ msg = "self and other have different value types"
+ raise TypeError(msg)
+
+ return cls(
+ label=newlabel,
+ name=self.name,
+ unit=self.unit,
+ category=self.category,
+ description=self.description,
+ derived_from=[*self.derived_from, *other.derived_from, self, other],
+ time_ranges=self.time_ranges + other.time_ranges,
+ values=new_values,
+ )
+
+
+
+import copy
+from dataclasses import dataclass, field
+from typing import Any, TypeVar
+
+import numpy as np
+from typing_extensions import Self
+
+from eitprocessing.datahandling.mixins.equality import Equivalence
+from eitprocessing.datahandling.mixins.slicing import SelectByTime
+
+
+
+
+
+@dataclass(eq=False)
+class SparseData(Equivalence, SelectByTime):
+ """Container for data occuring at unpredictable time points.
+
+ In sparse data the time points are not necessarily evenly spaced. Data can consist time-value pairs or only time
+ points. Values generally are numeric values in arrays, but can also be lists of different types of object.
+
+ Sparse data differs from IntervalData in that each data points is associated with a single time point rather than a
+ time range.
+
+ Examples are data points at end of inspiration/end of expiration (e.g. tidal volume, end-expiratoy lung impedance)
+ or detected time points (e.g. QRS complexes).
+
+
+
+ Args:
+ label: Computer readable name.
+ name: Human readable name.
+ unit: Unit of the data, if applicable.
+ category: Category the data falls into, e.g. 'airway pressure'.
+ description: Human readible extended description of the data.
+ parameters: Parameters used to derive the data.
+ derived_from: Traceback of intermediates from which the current data was derived.
+ values: List or array of values. These van be numeric data, text or Python objects.
+ """
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+[docs]
+ def __post_init__(self) -> None:
+ self._check_equivalence = ["unit", "category", "parameters"]
+
+
+
+
+
+
+
+
+
+[docs]
+ def _sliced_copy(
+ self,
+ start_index: int,
+ end_index: int,
+ newlabel: str,
+ ) -> Self:
+ # TODO: check correct implementation
+ cls = self.__class__
+ time = self.time[start_index:end_index]
+ values = self.values[start_index:end_index] if self.values is not None else None
+ description = f"Slice ({start_index}-{end_index}) of <{self.description}>"
+
+ return cls(
+ label=newlabel,
+ name=self.name,
+ unit=self.unit,
+ category=self.category,
+ description=description,
+ derived_from=[*self.derived_from, self],
+ time=time,
+ values=values,
+ )
+
+
+
+[docs]
+ def concatenate(self: T, other: T, newlabel: str | None = None) -> T: # noqa: D102, will be moved to mixin in future
+ self.isequivalent(other, raise_=True)
+
+ # TODO: make proper copy functions
+ if not len(self):
+ return copy.deepcopy(other)
+ if not len(other):
+ return copy.deepcopy(self)
+
+ if np.min(other.time) <= np.max(self.time):
+ msg = f"{other} (b) starts before {self} (a) ends."
+ raise ValueError(msg)
+
+ cls = type(self)
+ newlabel = newlabel or self.label
+
+ new_values: Any
+ if isinstance(self.values, list) and isinstance(other.values, list):
+ new_values = self.values + other.values
+ elif isinstance(self.values, np.ndarray) and isinstance(other.values, np.ndarray):
+ new_values = np.concatenate((self.values, other.values))
+ elif self.values is None and other.values is None:
+ new_values = None
+ else:
+ msg = "Value types of self and other do not match."
+ raise TypeError(msg)
+
+ return cls(
+ label=newlabel,
+ name=self.name,
+ unit=self.unit,
+ category=self.category,
+ description=self.description,
+ derived_from=[*self.derived_from, *other.derived_from, self, other],
+ time=np.concatenate((self.time, other.time)),
+ values=new_values,
+ )
+Bases: NamedTuple
Represents a breath with a start, middle and end index.
+ + + + + + +Concatenate this collection with an equivalent collection.
Each item of self of concatenated with the item of other with the same key.
Return a DataCollection containing sliced copies of the items.
Return the path or paths as a list.
The path of any EITData object can be a single str/Path or a list of str/Path objects. This method returns a list of Path objects given either a str/Path or list of str/Paths.
@@ -516,35 +528,9 @@Return the global baseline, i.e. the minimum pixel impedance across all pixels.
-Return the pixel impedance with the global baseline removed.
-In the resulting array the minimum impedance across all pixels is set to 0.
-Return the lowest value in each individual pixel over time.
-Return the pixel impedance with the baseline of each individual pixel removed.
-Each pixel in the resulting array has a minimum value of 0.
-Return the global impedance, i.e. the sum of all pixels at each frame.
Bases: eitprocessing.datahandling.mixins.equality.Equivalence, eitprocessing.datahandling.mixins.slicing.HasTimeIndexer
Container for interval data existing over a period of time.
+Interval data is data that constists for a given time interval. Examples are a ventilator setting (e.g. +end-expiratory pressure), the position of a patient, a maneuver (end-expiratory hold) being performed, detected +periods in the data, etc.
+Interval data consists of a number of time range-value pairs or time ranges without associated values. E.g. interval +data with the label “expiratory_breath_hold” only requires time ranges for when expiratory breath holds were +performed. Other interval data, e.g. “set_driving_pressure” do have associated values.
+Interval data can be selected by time through the select_by_time(start_time, end_time) method. Alternatively, +t[start_time:end_time] can be used. When the start or end time overlaps with a time range, the time range and its +associated value are included in the selection if partial_inclusion is True, but ignored if partial_inclusion +is False. If the time range is partially included, the start and end times are trimmed to the start and end time +of the selection.
+A potential use case where partial_inclusion should be set to True is “set_driving_pressure”: you might want to +keep the driving pressure that was set before the start of the selectioon. A use case where partial_inclusion +should be set to False is “detected_breaths”: you might want to ignore partial breaths that started before or +ended after the selected period.
+Note that when selecting by time, the end time is included in the selection.
+label – a computer-readable name
name – a human-readable name
unit – the unit associated with the data
category – the category of data
time_ranges – a list of time ranges (tuples containing a start time and end time)
values – an optional list of values with the same length as time_ranges
parameters – parameters used to derive the data
derived_from – list of data sets this data was derived from
description – extended human readible description of the data
partial_inclusion – whether to include a trimmed version of a time range when selecting data
Return only period data that overlaps (partly) with start and end time.
+Other types of data (e.g. ContinuousData and SparseData) support the start_inclusive and end_inclusive +arguments. PeriodData does not. That means that selection by time of PeriodData probably works slightly +different than other types of selecting/slicing data.
+Bases: PhaseIndicator
Automatically registered local maximum of an EIT measurement.
-Bases: eitprocessing.datahandling.mixins.equality.Equivalence, eitprocessing.datahandling.mixins.slicing.SelectByTime
Container for data occuring at unpredictable time points.
+In sparse data the time points are not necessarily evenly spaced. Data can consist time-value pairs or only time +points. Values generally are numeric values in arrays, but can also be lists of different types of object.
+Sparse data differs from IntervalData in that each data points is associated with a single time point rather than a +time range.
+Examples are data points at end of inspiration/end of expiration (e.g. tidal volume, end-expiratoy lung impedance) +or detected time points (e.g. QRS complexes).
+label – Computer readable name.
name – Human readable name.
unit – Unit of the data, if applicable.
category – Category the data falls into, e.g. ‘airway pressure’.
description – Human readible extended description of the data.
parameters – Parameters used to derive the data.
derived_from – Traceback of intermediates from which the current data was derived.
values – List or array of values. These van be numeric data, text or Python objects.
Bases: PhaseIndicator
Automatically registered local minimum of an EIT measurement.
+ + + + + + +Bases: PhaseIndicator
Automatically registered QRS mark an EIT measurement from a Timpel device.
+ + +Slicing method that must be implemented by all subclasses.
+Must return a copy of self object with all attached data within selected +indices.
Load Timpel EIT data from path.
Breath
+ContinuousDataContinuousData.labelContinuousData.nameEITData.timeEITData.framerateEITData.vendorEITData.phasesEITData.eventsEITData.labelEITData.nameEITData.pixel_impedanceEITData.concatenate()EITData._sliced_copy()EITData.__len__()EITData.global_baselineEITData.pixel_impedance_global_offsetEITData.pixel_baselineEITData.pixel_impedance_individual_offsetEITData.global_impedanceEITData._calculate_global_impedance()VendorVendor.DRÄGERIntervalDataIntervalData.labelIntervalData.nameIntervalData.unitIntervalData.categoryIntervalData.time_rangesIntervalData.valuesIntervalData.parametersIntervalData.derived_fromIntervalData.descriptionIntervalData.partial_inclusionIntervalData.__post_init__()IntervalData.__repr__()IntervalData.__len__()IntervalData.select_by_time()IntervalData.concatenate()load_eit_data()MaxValueMinValueQRSMarkSparseDataSparseDataSparseData.labelSparseData.nameSparseData.unitSparseData.categorySparseData.timeSparseData.descriptionSparseData.parametersSparseData.derived_fromSparseData.valuesSparseData.__post_init__()SparseData.__repr__()SparseData.__len__()SparseData._sliced_copy()SparseData.concatenate()_COLUMN_WIDTH_NAN_VALUETIMPEL_FRAMERATEload_timpel_dataload_from_single_path()_make_breaths()Compare two dataclasses for equality.
+EquivalenceEquivalence.__eq__()Equivalence._eq_dataclass()Equivalence._eq_userdict()Equivalence._array_safe_eq()Equivalence.isequivalent()Container for EIT data.
Container for interval data existing over a period of time.
Mixin class that adds an equality and equivalence check.
Gives access to a TimeIndexer object that can be used to slice by time.
Adds methods for slicing by time rather than index.
SparseData.
Container for data occuring at unpredictable time points.
Sequence of timepoints containing respiratory data.
Concatenate this collection with an equivalent collection.
Each item of self of concatenated with the item of other with the same key.
Return a DataCollection containing sliced copies of the items.
Return the path or paths as a list.
The path of any EITData object can be a single str/Path or a list of str/Path objects. This method returns a list of Path objects given either a str/Path or list of str/Paths.
@@ -483,38 +476,131 @@Return the global baseline, i.e. the minimum pixel impedance across all pixels.
+Return the global impedance, i.e. the sum of all pixels at each frame.
Return the pixel impedance with the global baseline removed.
-In the resulting array the minimum impedance across all pixels is set to 0.
Return the lowest value in each individual pixel over time.
-Bases: eitprocessing.datahandling.mixins.equality.Equivalence, eitprocessing.datahandling.mixins.slicing.HasTimeIndexer
Container for interval data existing over a period of time.
+Interval data is data that constists for a given time interval. Examples are a ventilator setting (e.g. +end-expiratory pressure), the position of a patient, a maneuver (end-expiratory hold) being performed, detected +periods in the data, etc.
+Interval data consists of a number of time range-value pairs or time ranges without associated values. E.g. interval +data with the label “expiratory_breath_hold” only requires time ranges for when expiratory breath holds were +performed. Other interval data, e.g. “set_driving_pressure” do have associated values.
+Interval data can be selected by time through the select_by_time(start_time, end_time) method. Alternatively, +t[start_time:end_time] can be used. When the start or end time overlaps with a time range, the time range and its +associated value are included in the selection if partial_inclusion is True, but ignored if partial_inclusion +is False. If the time range is partially included, the start and end times are trimmed to the start and end time +of the selection.
+A potential use case where partial_inclusion should be set to True is “set_driving_pressure”: you might want to +keep the driving pressure that was set before the start of the selectioon. A use case where partial_inclusion +should be set to False is “detected_breaths”: you might want to ignore partial breaths that started before or +ended after the selected period.
+Note that when selecting by time, the end time is included in the selection.
+label – a computer-readable name
name – a human-readable name
unit – the unit associated with the data
category – the category of data
time_ranges – a list of time ranges (tuples containing a start time and end time)
values – an optional list of values with the same length as time_ranges
parameters – parameters used to derive the data
derived_from – list of data sets this data was derived from
description – extended human readible description of the data
partial_inclusion – whether to include a trimmed version of a time range when selecting data
Return the pixel impedance with the baseline of each individual pixel removed.
-Each pixel in the resulting array has a minimum value of 0.
+ + + + + + + + + + + + + + + + + + + + + -Return the global impedance, i.e. the sum of all pixels at each frame.
+ + +Return only period data that overlaps (partly) with start and end time.
+Other types of data (e.g. ContinuousData and SparseData) support the start_inclusive and end_inclusive +arguments. PeriodData does not. That means that selection by time of PeriodData probably works slightly +different than other types of selecting/slicing data.
SparseData.
+Bases: eitprocessing.datahandling.mixins.equality.Equivalence, eitprocessing.datahandling.mixins.slicing.SelectByTime
Container for data occuring at unpredictable time points.
+In sparse data the time points are not necessarily evenly spaced. Data can consist time-value pairs or only time +points. Values generally are numeric values in arrays, but can also be lists of different types of object.
+Sparse data differs from IntervalData in that each data points is associated with a single time point rather than a +time range.
+Examples are data points at end of inspiration/end of expiration (e.g. tidal volume, end-expiratoy lung impedance) +or detected time points (e.g. QRS complexes).
+label – Computer readable name.
name – Human readable name.
unit – Unit of the data, if applicable.
category – Category the data falls into, e.g. ‘airway pressure’.
description – Human readible extended description of the data.
parameters – Parameters used to derive the data.
derived_from – Traceback of intermediates from which the current data was derived.
values – List or array of values. These van be numeric data, text or Python objects.
Return a sliced version of the Sequence.
See SelectByTime.select_by_time().
EITData.timeEITData.framerateEITData.vendorEITData.phasesEITData.eventsEITData.labelEITData.nameEITData.pixel_impedanceEITData.concatenate()EITData._sliced_copy()EITData.__len__()EITData.global_baselineEITData.pixel_impedance_global_offsetEITData.pixel_baselineEITData.pixel_impedance_individual_offsetEITData.global_impedanceEITData._calculate_global_impedance()IntervalDataIntervalData.labelIntervalData.nameIntervalData.unitIntervalData.categoryIntervalData.time_rangesIntervalData.valuesIntervalData.parametersIntervalData.derived_fromIntervalData.descriptionIntervalData.partial_inclusionIntervalData.__post_init__()IntervalData.__repr__()IntervalData.__len__()IntervalData.select_by_time()IntervalData.concatenate()Equivalence
HasTimeIndexer
SelectByTime
SparseDataSparseDataSparseData.labelSparseData.nameSparseData.unitSparseData.categorySparseData.timeSparseData.descriptionSparseData.parametersSparseData.derived_fromSparseData.valuesSparseData.__post_init__()SparseData.__repr__()SparseData.__len__()SparseData._sliced_copy()SparseData.concatenate()SequenceSequence.labelSequence.nameSequence.eit_dataSequence.continuous_dataSequence.sparse_dataSequence.interval_dataSequence.__post_init__()Sequence.timeSequence.__len__()| + | + |
| + | Mixin class that adds an equality and equivalence check. |
+|
| + | Adds methods for slicing by time rather than index. |
+|
| - | SparseData. |
+Container for data occuring at unpredictable time points. |
Mixin class that adds an equality and equivalence check.
+ + + + + + +Check if a and b are equal, even if they are numpy arrays containing nans.
+Test whether the data structure between two objects are equivalent.
+Equivalence, in this case means that objects are compatible e.g. to be +merged. Data content can vary, but e.g. the category of data (e.g. +airway pressure, flow, tidal volume) and unit, etc., must match.
+other – object that will be compared to self.
raise – sets this method’s behavior in case of non-equivalence. If +True, an EquivalenceError is raised, otherwise False is +returned.
EquivalenceError – if raise_ == True and the objects are not
equivalent. –
bool describing result of equivalence comparison.
+Bases: SelectByIndex, HasTimeIndexer
Adds methods for slicing by time rather than index.
+Get a slice from start to end time stamps.
+Given a start and end time stamp (i.e. its value, not its index), +return a slice of the original object, which must contain a time axis.
+start_time – first time point to include. Defaults to first frame of sequence.
end_time – last time point. Defaults to last frame of sequence.
(default (start_inclusive) – True), end_inclusive (default False): +these arguments control the behavior if the given time stamp +does not match exactly with an existing time stamp of the input. +if True: the given time stamp will be inside the sliced object. +if False: the given time stamp will be outside the sliced object.
label – Description. Defaults to None, which will create a label based +on the original object label and the frames by which it is sliced.
TypeError – if self does not contain a time attribute.
ValueError – if time stamps are not sorted.
Slice of self.
+SparseData.
+Bases: eitprocessing.datahandling.mixins.equality.Equivalence, eitprocessing.datahandling.mixins.slicing.SelectByTime
Container for data occuring at unpredictable time points.
+In sparse data the time points are not necessarily evenly spaced. Data can consist time-value pairs or only time +points. Values generally are numeric values in arrays, but can also be lists of different types of object.
+Sparse data differs from IntervalData in that each data points is associated with a single time point rather than a +time range.
+Examples are data points at end of inspiration/end of expiration (e.g. tidal volume, end-expiratoy lung impedance) +or detected time points (e.g. QRS complexes).
+label – Computer readable name.
name – Human readable name.
unit – Unit of the data, if applicable.
category – Category the data falls into, e.g. ‘airway pressure’.
description – Human readible extended description of the data.
parameters – Parameters used to derive the data.
derived_from – Traceback of intermediates from which the current data was derived.
values – List or array of values. These van be numeric data, text or Python objects.
SparseDataEquivalence
+SelectByTime
+TSparseDataSparseData.labelSparseData.nameSparseData.unitSparseData.categorySparseData.timeSparseData.descriptionSparseData.parametersSparseData.derived_fromSparseData.valuesSparseData.__post_init__()SparseData.__repr__()SparseData.__len__()SparseData._sliced_copy()SparseData.concatenate()
|
+
|
-
| ||
| + |
+ eitprocessing.datahandling.breath | + | ||
@@ -78,6 +83,11 @@ Python Module Index |
eitprocessing.datahandling.event | |||
| + |
+ eitprocessing.datahandling.intervaldata | + | ||
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