import copy
import numpy as np
import os
import pandas as pd
import re
import posixpath
import sys
from wfdb.io import download
from wfdb.io import _header
from wfdb.io import record
class Annotation(object):
"""
The class representing WFDB annotations.
Annotation objects can be created using the initializer, or by reading a
WFDB annotation file with `rdann`.
The attributes of the Annotation object give information about the
annotation as specified by:
https://www.physionet.org/physiotools/wag/annot-5.htm
Call `show_ann_labels()` to see the list of standard annotation codes. Any
text used to label annotations that are not one of these codes should go in
the 'aux_note' field rather than the 'sym' field.
The current annotation values organized as such:
AnnotationLabel(label_store (or subtype), symbol (or aux_note), short_description, description)
where the associated values are:
ann_labels = [
AnnotationLabel(0, " ", 'NOTANN', 'Not an actual annotation'),
AnnotationLabel(1, "N", 'NORMAL', 'Normal beat'),
AnnotationLabel(2, "L", 'LBBB', 'Left bundle branch block beat'),
AnnotationLabel(3, "R", 'RBBB', 'Right bundle branch block beat'),
AnnotationLabel(4, "a", 'ABERR', 'Aberrated atrial premature beat'),
AnnotationLabel(5, "V", 'PVC', 'Premature ventricular contraction'),
AnnotationLabel(6, "F", 'FUSION', 'Fusion of ventricular and normal beat'),
AnnotationLabel(7, "J", 'NPC', 'Nodal (junctional) premature beat'),
AnnotationLabel(8, "A", 'APC', 'Atrial premature contraction'),
AnnotationLabel(9, "S", 'SVPB', 'Premature or ectopic supraventricular beat'),
AnnotationLabel(10, "E", 'VESC', 'Ventricular escape beat'),
AnnotationLabel(11, "j", 'NESC', 'Nodal (junctional) escape beat'),
AnnotationLabel(12, "/", 'PACE', 'Paced beat'),
AnnotationLabel(13, "Q", 'UNKNOWN', 'Unclassifiable beat'),
AnnotationLabel(14, "~", 'NOISE', 'Signal quality change'),
AnnotationLabel(16, "|", 'ARFCT', 'Isolated QRS-like artifact'),
AnnotationLabel(18, "s", 'STCH', 'ST change'),
AnnotationLabel(19, "T", 'TCH', 'T-wave change'),
AnnotationLabel(20, "*", 'SYSTOLE', 'Systole'),
AnnotationLabel(21, "D", 'DIASTOLE', 'Diastole'),
AnnotationLabel(22, '"', 'NOTE', 'Comment annotation'),
AnnotationLabel(23, "=", 'MEASURE', 'Measurement annotation'),
AnnotationLabel(24, "p", 'PWAVE', 'P-wave peak'),
AnnotationLabel(25, "B", 'BBB', 'Left or right bundle branch block'),
AnnotationLabel(26, "^", 'PACESP', 'Non-conducted pacer spike'),
AnnotationLabel(27, "t", 'TWAVE', 'T-wave peak'),
AnnotationLabel(28, "+", 'RHYTHM', 'Rhythm change'),
AnnotationLabel(29, "u", 'UWAVE', 'U-wave peak'),
AnnotationLabel(30, "?", 'LEARN', 'Learning'),
AnnotationLabel(31, "!", 'FLWAV', 'Ventricular flutter wave'),
AnnotationLabel(32, "[", 'VFON', 'Start of ventricular flutter/fibrillation'),
AnnotationLabel(33, "]", 'VFOFF', 'End of ventricular flutter/fibrillation'),
AnnotationLabel(34, "e", 'AESC', 'Atrial escape beat'),
AnnotationLabel(35, "n", 'SVESC', 'Supraventricular escape beat'),
AnnotationLabel(36, "@", 'LINK', 'Link to external data (aux_note contains URL)'),
AnnotationLabel(37, "x", 'NAPC', 'Non-conducted P-wave (blocked APB)'),
AnnotationLabel(38, "f", 'PFUS', 'Fusion of paced and normal beat'),
AnnotationLabel(39, "(", 'WFON', 'Waveform onset'),
AnnotationLabel(40, ")", 'WFOFF', 'Waveform end'),
AnnotationLabel(41, "r", 'RONT', 'R-on-T premature ventricular contraction')
]
The current annotation classes are organized as such:
AnnotationClass(extension, description, human_reviewed)
where the associated values are:
ann_classes = [
AnnotationClass('atr', 'Reference ECG annotations', True),
AnnotationClass('blh', 'Human reviewed beat labels', True),
AnnotationClass('blm', 'Machine beat labels', False),
AnnotationClass('alh', 'Human reviewed alarms', True),
AnnotationClass('alm', 'Machine alarms', False),
AnnotationClass('qrsc', 'Human reviewed QRS detections', True),
AnnotationClass('qrs', 'Machine QRS detections', False),
AnnotationClass('bph', 'Human reviewed BP beat detections', True),
AnnotationClass('bpm', 'Machine BP beat detections', False)
]
Attributes
----------
record_name : str
The base file name (without extension) of the record that the
annotation is associated with.
extension : str
The file extension of the file the annotation is stored in.
sample : ndarray
A numpy array containing the annotation locations in samples relative to
the beginning of the record.
symbol : list, numpy array, optional
The symbols used to display the annotation labels. List or numpy array.
If this field is present, `label_store` must not be present.
subtype : ndarray, optional
A numpy array containing the marked class/category of each annotation.
chan : ndarray, optional
A numpy array containing the signal channel associated with each
annotation.
num : ndarray, optional
A numpy array containing the labelled annotation number for each
annotation.
aux_note : list, optional
A list containing the auxiliary information string (or None for
annotations without notes) for each annotation.
fs : int, float, optional
The sampling frequency of the record.
label_store : ndarray, optional
The integer value used to store/encode each annotation label
description : list, optional
A list containing the descriptive string of each annotation label.
custom_labels : pandas dataframe, optional
The custom annotation labels defined in the annotation file. Maps
the relationship between the three label fields. The data type is a
pandas DataFrame with three columns:
['label_store', 'symbol', 'description'].
contained_labels : pandas dataframe, optional
The unique labels contained in this annotation. Same structure as
`custom_labels`.
Examples
--------
>>> ann1 = wfdb.Annotation(record_name='rec1', extension='atr',
sample=[10,20,400], symbol=['N','N','['],
aux_note=[None, None, 'Serious Vfib'])
"""
def __init__(
self,
record_name,
extension,
sample,
symbol=None,
subtype=None,
chan=None,
num=None,
aux_note=None,
fs=None,
label_store=None,
description=None,
custom_labels=None,
contained_labels=None,
):
self.record_name = record_name
self.extension = extension
self.sample = sample
self.symbol = symbol
self.subtype = subtype
self.chan = chan
self.num = num
self.aux_note = aux_note
self.fs = fs
self.label_store = label_store
self.description = description
self.custom_labels = custom_labels
self.contained_labels = contained_labels
self.ann_len = len(self.sample)
# __label_map__: (storevalue, symbol, description) hidden attribute
def __eq__(self, other):
"""
Equal comparison operator for objects of this type.
Parameters
----------
other : object
The object that is being compared to self.
Returns
-------
bool
Determines if the objects are equal (True) or not equal (False).
"""
att1 = self.__dict__
att2 = other.__dict__
if set(att1.keys()) != set(att2.keys()):
print("keyset")
return False
for k in att1.keys():
v1 = att1[k]
v2 = att2[k]
if type(v1) != type(v2):
print(k)
return False
if isinstance(v1, np.ndarray):
if not np.array_equal(v1, v2):
print(k)
return False
elif isinstance(v1, pd.DataFrame):
if not v1.equals(v2):
print(k)
return False
else:
if v1 != v2:
print(k)
return False
return True
def apply_range(self, sampfrom=0, sampto=None):
"""
Filter the annotation attributes to keep only items between the
desired sample values.
Parameters
----------
sampfrom : int, optional
The minimum sample number for annotations to be returned.
sampto : int, optional
The maximum sample number for annotations to be returned.
"""
sampto = sampto or self.sample[-1]
kept_inds = np.intersect1d(
np.where(self.sample >= sampfrom), np.where(self.sample <= sampto)
)
for field in ["sample", "label_store", "subtype", "chan", "num"]:
setattr(self, field, getattr(self, field)[kept_inds])
self.aux_note = [self.aux_note[i] for i in kept_inds]
self.ann_len = len(self.sample)
def wrann(self, write_fs=False, write_dir=""):
"""
Write a WFDB annotation file from this object.
Parameters
----------
write_fs : bool, optional
Whether to write the `fs` attribute to the file.
write_dir : str, optional
The output directory in which the header is written.
Returns
-------
N/A
"""
for field in ["record_name", "extension"]:
if getattr(self, field) is None:
raise Exception(
"Missing required field for writing annotation file: ",
field,
)
present_label_fields = self.get_label_fields()
if not present_label_fields:
raise Exception(
"At least one annotation label field is required to write the annotation: ",
ann_label_fields,
)
# Check the validity of individual fields
self.check_fields()
# Standardize the format of the custom_labels field
self.standardize_custom_labels()
# Create the label map used in this annotaion
self.create_label_map()
# Check the cohesion of fields
self.check_field_cohesion(present_label_fields)
# Calculate the label_store field if necessary
if "label_store" not in present_label_fields:
self.convert_label_attribute(
source_field=present_label_fields[0], target_field="label_store"
)
# Calculate the symbol field if necessary
if "symbol" not in present_label_fields:
self.convert_label_attribute(
source_field=present_label_fields[0], target_field="symbol"
)
# Write the header file using the specified fields
self.wr_ann_file(write_fs=write_fs, write_dir=write_dir)
return
def get_label_fields(self):
"""
Get the present label fields in the object.
Parameters
----------
N/A
Returns
-------
present_label_fields : list
All of the present label fields in the object.
"""
present_label_fields = []
for field in ann_label_fields:
if getattr(self, field) is not None:
present_label_fields.append(field)
return present_label_fields
def check_fields(self):
"""
Check the set fields of the annotation object.
Parameters
----------
N/A
Returns
-------
N/A
"""
# Check all set fields
for field in ALLOWED_TYPES:
if getattr(self, field) is not None:
# Check the type of the field's elements
self.check_field(field)
return
def check_field(self, field):
"""
Check a particular annotation field.
Parameters
----------
field : str
The annotation field to be checked.
Returns
-------
N/A
"""
item = getattr(self, field)
if not isinstance(item, ALLOWED_TYPES[field]):
raise TypeError(
"The " + field + " field must be one of the following types:",
ALLOWED_TYPES[field],
)
# Numerical integer annotation fields: sample, label_store, sub,
# chan, num
if ALLOWED_TYPES[field] == (np.ndarray):
record.check_np_array(
item=item,
field_name=field,
ndim=1,
parent_class=np.integer,
channel_num=None,
)
# Field specific checks
if field == "record_name":
if bool(re.search(r"[^-\w]", self.record_name)):
raise ValueError(
"record_name must only comprise of letters, digits, hyphens, and underscores."
)
elif field == "extension":
if bool(re.search("[^a-zA-Z]", self.extension)):
raise ValueError("extension must only comprise of letters.")
elif field == "fs":
if self.fs <= 0:
raise ValueError("The fs field must be a non-negative number")
elif field == "custom_labels":
# The role of this section is just to check the
# elements of this item, without utilizing
# any other fields. No format conversion
# or free value looksups etc are done.
# Check the structure of the subelements
if isinstance(item, pd.DataFrame):
column_names = list(item)
if "symbol" in column_names and "description" in column_names:
if "label_store" in column_names:
label_store = list(item["label_store"].values)
else:
label_store = None
symbol = item["symbol"].values
description = item["description"].values
else:
raise ValueError(
"".join(
[
"If the "
+ field
+ " field is pandas dataframe, its columns",
" must be one of the following:\n-[label_store, symbol, description]",
"\n-[symbol, description]",
]
)
)
else:
if set([len(i) for i in item]) == {2}:
label_store = None
symbol = [i[0] for i in item]
description = [i[1] for i in item]
elif set([len(i) for i in item]) == {3}:
label_store = [i[0] for i in item]
symbol = [i[1] for i in item]
description = [i[2] for i in item]
else:
raise ValueError(
"".join(
[
"If the "
+ field
+ " field is an array-like object, its subelements",
" must be one of the following:\n- tuple triplets storing: ",
"(label_store, symbol, description)\n- tuple pairs storing: ",
"(symbol, description)",
]
)
)
# Check the values of the subelements
if label_store:
if len(item) != len(set(label_store)):
raise ValueError(
"The label_store values of the "
+ field
+ " field must be unique"
)
if min(label_store) < 1 or max(label_store) > 49:
raise ValueError(
"The label_store values of the custom_labels field must be between 1 and 49"
)
if len(item) != len(set(symbol)):
raise ValueError(
"The symbol values of the "
+ field
+ " field must be unique"
)
for i in range(len(item)):
if label_store:
if not hasattr(label_store[i], "__index__"):
raise TypeError(
"The label_store values of the "
+ field
+ " field must be integer-like"
)
if not isinstance(symbol[i], str_types) or len(
symbol[i]
) not in [
1,
2,
3,
]:
raise ValueError(
"The symbol values of the "
+ field
+ " field must be strings of length 1 to 3"
)
if bool(re.search("[ \t\n\r\f\v]", symbol[i])):
raise ValueError(
"The symbol values of the "
+ field
+ " field must not contain whitespace characters"
)
if not isinstance(description[i], str_types):
raise TypeError(
"The description values of the "
+ field
+ " field must be strings"
)
# Would be good to enfore this but existing garbage annotations have tabs and newlines...
# if bool(re.search('[\t\n\r\f\v]', description[i])):
# raise ValueError('The description values of the '+field+' field must not contain tabs or newlines')
# The string fields
elif field in ["symbol", "description", "aux_note"]:
uniq_elements = set(item)
for e in uniq_elements:
if not isinstance(e, str_types):
raise TypeError(
"Subelements of the " + field + " field must be strings"
)
if field == "symbol":
for e in uniq_elements:
if len(e) not in [1, 2, 3]:
raise ValueError(
"Subelements of the "
+ field
+ " field must be strings of length 1 to 3"
)
if bool(re.search("[ \t\n\r\f\v]", e)):
raise ValueError(
"Subelements of the "
+ field
+ " field may not contain whitespace characters"
)
else:
for e in uniq_elements:
if bool(re.search("[\t\n\r\f\v]", e)):
raise ValueError(
"Subelements of the "
+ field
+ " field must not contain tabs or newlines"
)
elif field == "sample":
if len(self.sample) == 1:
sampdiffs = np.array([self.sample[0]])
elif len(self.sample) > 1:
sampdiffs = np.concatenate(
([self.sample[0]], np.diff(self.sample))
)
else:
raise ValueError(
"The 'sample' field must be a numpy array with length greater than 0"
)
if min(self.sample) < 0:
raise ValueError(
"The 'sample' field must only contain non-negative integers"
)
if min(sampdiffs) < 0:
raise ValueError(
"The 'sample' field must contain monotonically increasing sample numbers"
)
elif field == "label_store":
if min(item) < 1 or max(item) > 49:
raise ValueError(
"The label_store values must be between 1 and 49"
)
# The C WFDB library stores num/sub/chan as chars.
elif field == "subtype":
# signed character
if min(self.subtype) < -128 or max(self.subtype) > 127:
raise ValueError(
"The 'subtype' field must only contain integers from -128 to 127"
)
elif field == "chan":
# un_signed character
if min(self.chan) < 0 or max(self.chan) > 255:
raise ValueError(
"The 'chan' field must only contain non-negative integers up to 255"
)
elif field == "num":
# signed character
if min(self.num) < 0 or max(self.num) > 127:
raise ValueError(
"The 'num' field must only contain non-negative integers up to 127"
)
return
def check_field_cohesion(self, present_label_fields):
"""
Check that the content and structure of different fields are consistent
with one another.
Parameters
----------
present_label_fields : list
All of the present label fields in the object.
Returns
-------
N/A
"""
# Ensure all written annotation fields have the same length
nannots = len(self.sample)
for field in [
"sample",
"num",
"subtype",
"chan",
"aux_note",
] + present_label_fields:
if getattr(self, field) is not None:
if len(getattr(self, field)) != nannots:
raise ValueError(
"The lengths of the 'sample' and '"
+ field
+ "' fields do not match"
)
# Ensure all label fields are defined by the label map. This has to be checked because
# it is possible the user defined (or lack of) custom_labels does not capture all the
# labels present.
for field in present_label_fields:
defined_values = self.__label_map__[field].values
if set(getattr(self, field)) - set(defined_values) != set():
raise ValueError(
"\n".join(
[
"\nThe "
+ field
+ " field contains elements not encoded in the stardard WFDB annotation labels, or this object's custom_labels field",
"- To see the standard WFDB annotation labels, call: show_ann_labels()",
"- To transfer non-encoded symbol items into the aux_note field, call: self.sym_to_aux()",
"- To define custom labels, set the custom_labels field as a list of tuple triplets with format: (label_store, symbol, description)",
]
)
)
return
def standardize_custom_labels(self):
"""
Set the custom_labels field of the object to a standardized format:
3 column pandas df with ann_label_fields as columns.
Does nothing if there are no custom labels defined.
Does nothing if custom_labels is already a df with all 3 columns.
If custom_labels is an iterable of pairs/triplets, this
function will convert it into a df.
If the label_store attribute is not already defined, this
function will automatically choose values by trying to use:
1. The undefined store values from the standard WFDB annotation
label map.
2. The unused label store values. This is extracted by finding the
set of all labels contained in this annotation object and seeing
which symbols/descriptions are not used.
If there are more custom labels defined than there are enough spaces,
even in condition 2 from above, this function will raise an error.
This function must work when called as a standalone.
Parameters
----------
N/A
Returns
-------
N/A
"""
custom_labels = self.custom_labels
if custom_labels is None:
return
self.check_field("custom_labels")
# Convert to dataframe if not already
if not isinstance(custom_labels, pd.DataFrame):
if len(self.custom_labels[0]) == 2:
symbol = self.get_custom_label_attribute("symbol")
description = self.get_custom_label_attribute("description")
custom_labels = pd.DataFrame(
{"symbol": symbol, "description": description}
)
else:
label_store = self.get_custom_label_attribute("label_store")
symbol = self.get_custom_label_attribute("symbol")
description = self.get_custom_label_attribute("description")
custom_labels = pd.DataFrame(
{
"label_store": label_store,
"symbol": symbol,
"description": description,
}
)
# Assign label_store values to the custom labels if not defined
if "label_store" not in list(custom_labels):
undefined_label_stores = self.get_undefined_label_stores()
if len(custom_labels) > len(undefined_label_stores):
available_label_stores = self.get_available_label_stores()
else:
available_label_stores = undefined_label_stores
n_custom_labels = custom_labels.shape[0]
if n_custom_labels > len(available_label_stores):
raise ValueError(
"There are more custom_label definitions than storage values available for them."
)
custom_labels["label_store"] = available_label_stores[
:n_custom_labels
]
custom_labels.set_index(
custom_labels["label_store"].values, inplace=True
)
custom_labels = custom_labels[list(ann_label_fields)]
self.custom_labels = custom_labels
return
def get_undefined_label_stores(self):
"""
Get the label_store values not defined in the
standard WFDB annotation labels.
Parameters
----------
N/A
Returns
-------
list
The label_store values not found in WFDB annotation labels.
"""
return list(set(range(50)) - set(ann_label_table["label_store"]))
def get_available_label_stores(self, usefield="tryall"):
"""
Get the label store values that may be used
for writing this annotation.
Available store values include:
- the undefined values in the standard WFDB labels
- the store values not used in the current
annotation object.
- the store values whose standard WFDB symbols/descriptions
match those of the custom labels (if custom_labels exists)
Parameters
----------
usefield : str, optional
If 'usefield' is explicitly specified, the function will use that
field to figure out available label stores. If 'usefield'
is set to 'tryall', the function will choose one of the contained
attributes by checking availability in the order: label_store, symbol, description.
Returns
-------
available_label_stores : set
The available store values used for writing the annotation.
"""
# Figure out which field to use to get available labels stores.
if usefield == "tryall":
if self.label_store is not None:
usefield = "label_store"
elif self.symbol is not None:
usefield = "symbol"
elif self.description is not None:
usefield = "description"
else:
raise ValueError(
"No label fields are defined. At least one of the following is required: ",
ann_label_fields,
)
return self.get_available_label_stores(usefield=usefield)
# Use the explicitly stated field to get available stores.
else:
# If usefield == 'label_store', there are slightly fewer/different steps
# compared to if it were another option
contained_field = getattr(self, usefield)
# Get the unused label_store values
if usefield == "label_store":
unused_label_stores = (
set(ann_label_table["label_store"].values) - contained_field
)
else:
# the label_store values from the standard WFDB annotation labels
# whose symbols are not contained in this annotation
unused_field = (
set(ann_label_table[usefield].values) - contained_field
)
unused_label_stores = ann_label_table.loc[
ann_label_table[usefield] in unused_field, "label_store"
].values
# Get the standard WFDB label_store values overwritten by the
# custom_labels if any
if self.custom_symbols is not None:
custom_field = set(self.get_custom_label_attribute(usefield))
if usefield == "label_store":
overwritten_label_stores = set(custom_field).intersection(
set(ann_label_table["label_store"])
)
else:
overwritten_fields = set(custom_field).intersection(
set(ann_label_table[usefield])
)
overwritten_label_stores = ann_label_table.loc[
ann_label_table[usefield] in overwritten_fields,
"label_store",
].values
else:
overwritten_label_stores = set()
# The undefined values in the standard WFDB labels
undefined_label_stores = self.get_undefined_label_stores()
# Final available label stores = undefined + unused + overwritten
available_label_stores = (
set(undefined_label_stores)
.union(set(unused_label_stores))
.union(overwritten_label_stores)
)
return available_label_stores
def get_custom_label_attribute(self, attribute):
"""
Get a list of the custom_labels attribute i.e. label_store,
symbol, or description. The custom_labels variable could be in
a number of formats.
Parameters
----------
attribute : str
The selected attribute to generate the list.
Returns
-------
a : list
All of the custom_labels attributes.
"""
if attribute not in ann_label_fields:
raise ValueError("Invalid attribute specified")
if isinstance(self.custom_labels, pd.DataFrame):
if "label_store" not in list(self.custom_labels):
raise ValueError("label_store not defined in custom_labels")
a = list(self.custom_labels[attribute].values)
else:
if len(self.custom_labels[0]) == 2:
if attribute == "label_store":
raise ValueError("label_store not defined in custom_labels")
elif attribute == "symbol":
a = [l[0] for l in self.custom_labels]
elif attribute == "description":
a = [l[1] for l in self.custom_labels]
else:
if attribute == "label_store":
a = [l[0] for l in self.custom_labels]
elif attribute == "symbol":
a = [l[1] for l in self.custom_labels]
elif attribute == "description":
a = [l[2] for l in self.custom_labels]
return a
def create_label_map(self, inplace=True):
"""
Creates mapping df based on ann_label_table and self.custom_labels. Table
composed of entire WFDB standard annotation table, overwritten/appended
with custom_labels if any. Sets __label_map__ attribute, or returns value.
Parameters
----------
inplace : bool, optional
Determines whether to add the label map to the current
object (True) or as a return variable (False).
Returns
-------
label_map : pandas DataFrame
Mapping based on ann_label_table and self.custom_labels.
"""
label_map = ann_label_table.copy()
if self.custom_labels is not None:
self.standardize_custom_labels()
for i in self.custom_labels.index:
label_map.loc[i] = self.custom_labels.loc[i]
if inplace:
self.__label_map__ = label_map
else:
return label_map
def wr_ann_file(self, write_fs, write_dir=""):
"""
Calculate the bytes used to encode an annotation set and
write them to an annotation file.
Parameters
----------
write_fs : bool
Whether to write the `fs` attribute to the file.
write_dir : str, optional
The output directory in which the header is written.
Returns
-------
N/A
"""
# Calculate the fs bytes to write if present and desired to write
if write_fs:
fs_bytes = self.calc_fs_bytes()
else:
fs_bytes = []
# Calculate the custom_labels bytes to write if present
cl_bytes = self.calc_cl_bytes()
# Calculate the core field bytes to write
core_bytes = self.calc_core_bytes()
# Mark the end of the special annotation types if needed
if fs_bytes == [] and cl_bytes == []:
end_special_bytes = []
else:
end_special_bytes = [0, 236, 255, 255, 255, 255, 1, 0]
# Write the file
with open(
os.path.join(write_dir, self.record_name + "." + self.extension),
"wb",
) as f:
# Combine all bytes to write: fs (if any), custom annotations (if any), main content, file terminator
np.concatenate(
(
fs_bytes,
cl_bytes,
end_special_bytes,
core_bytes,
np.array([0, 0]),
)
).astype("u1").tofile(f)
return
def calc_fs_bytes(self):
"""
Calculate the bytes written to the annotation file for the fs field.
Parameters
----------
N/A
Returns
-------
list, ndarray
All of the bytes to be written to the annotation file.
"""
if self.fs is None:
return []
# Initial indicators of encoding fs
data_bytes = [
0,
88,
0,
252,
35,
35,
32,
116,
105,
109,
101,
32,
114,
101,
115,
111,
108,
117,
116,
105,
111,
110,
58,
32,
]
# Check if fs is close enough to int
if isinstance(self.fs, float):
if round(self.fs, 8) == float(int(self.fs)):
self.fs = int(self.fs)
fschars = str(self.fs)
ndigits = len(fschars)
for i in range(ndigits):
data_bytes.append(ord(fschars[i]))
# Fill in the aux_note length
data_bytes[2] = ndigits + 20
# odd number of digits
if ndigits % 2:
data_bytes.append(0)
return np.array(data_bytes).astype("u1")
def calc_cl_bytes(self):
"""
Calculate the bytes written to the annotation file for the
custom_labels field.
Parameters
----------
N/A
Returns
-------
list, ndarray
All of the bytes to be written to the annotation file.
"""
if self.custom_labels is None:
return []
# The start wrapper: '0 NOTE length aux_note ## annotation type definitions'
headbytes = [
0,
88,
30,
252,
35,
35,
32,
97,
110,
110,
111,
116,
97,
116,
105,
111,
110,
32,
116,
121,
112,
101,
32,
100,
101,
102,
105,
110,
105,
116,
105,
111,
110,
115,
]
# The end wrapper: '0 NOTE length aux_note ## end of definitions' followed by SKIP -1, +1
tailbytes = [
0,
88,
21,
252,
35,
35,
32,
101,
110,
100,
32,
111,
102,
32,
100,
101,
102,
105,
110,
105,
116,
105,
111,
110,
115,
0,
]
custom_bytes = []
for i in self.custom_labels.index:
custom_bytes += custom_triplet_bytes(
list(self.custom_labels.loc[i, list(ann_label_fields)])
)
# writecontent = []
# for i in range(len(self.custom_labels)):
# writecontent.append([freenumbers[i],list(custom_labels.keys())[i],list(custom_labels.values())[i]])
# custombytes = [customcode2bytes(triplet) for triplet in writecontent]
# custombytes = [item for sublist in custombytes for item in sublist]
return np.array(headbytes + custom_bytes + tailbytes).astype("u1")
def calc_core_bytes(self):
"""
Convert all used annotation fields into bytes to write.
Parameters
----------
N/A
Returns
-------
list, ndarray
All of the bytes to be written to the annotation file.
"""
# The difference sample to write
if len(self.sample) == 1:
sampdiff = np.array([self.sample[0]])
else:
sampdiff = np.concatenate(([self.sample[0]], np.diff(self.sample)))
# Create a copy of the annotation object with a
# compact version of fields to write
compact_annotation = copy.deepcopy(self)
compact_annotation.compact_fields()
# The optional fields to be written. Write if they are not None or all empty
extra_write_fields = []
for field in ["num", "subtype", "chan", "aux_note"]:
if not isblank(getattr(compact_annotation, field)):
extra_write_fields.append(field)
data_bytes = []
# Allow use of custom labels
label_table = ann_label_table
if self.custom_labels is not None:
label_table = pd.concat(
[label_table, self.custom_labels], ignore_index=True
)
# Generate typecodes from annotation label table
typecodes = {
label_table.iloc[i]["symbol"]: label_table.iloc[i]["label_store"]
for i in range(len(label_table))
}
# Iterate across all fields one index at a time
for i in range(len(sampdiff)):
# Process the samp (difference) and sym items
data_bytes.append(
field2bytes(
"samptype", [sampdiff[i], self.symbol[i]], typecodes
)
)
# Process the extra optional fields
for field in extra_write_fields:
value = getattr(compact_annotation, field)[i]
if value is not None:
data_bytes.append(field2bytes(field, value, typecodes))
# Flatten and convert to correct format
data_bytes = np.array(
[item for sublist in data_bytes for item in sublist]
).astype("u1")
return data_bytes
def compact_fields(self):
"""
Compact all of the object's fields so that the output
writing annotation file writes as few bytes as possible.
Parameters
----------
N/A
Returns
-------
N/A
"""
# Number of annotations
nannots = len(self.sample)
# Chan and num carry over previous fields. Get lists of as few
# elements to write as possible
self.chan = compact_carry_field(self.chan)
self.num = compact_carry_field(self.num)
# Elements of 0 (default) do not need to be written for subtype.
# num and sub are signed in original c package...
if self.subtype is not None:
if isinstance(self.subtype, list):
for i in range(nannots):
if self.subtype[i] == 0:
self.subtype[i] = None
if np.array_equal(self.subtype, [None] * nannots):
self.subtype = None
else:
zero_inds = np.where(self.subtype == 0)[0]
if len(zero_inds) == nannots:
self.subtype = None
else:
self.subtype = list(self.subtype)
for i in zero_inds:
self.subtype[i] = None
# Empty aux_note strings are not written
if self.aux_note is not None:
for i in range(nannots):
if self.aux_note[i] == "":
self.aux_note[i] = None
if np.array_equal(self.aux_note, [None] * nannots):
self.aux_note = None
def sym_to_aux(self):
"""
Move non-encoded symbol elements into the aux_note field.
Parameters
----------
N/A
Returns
-------
N/A
"""
self.check_field("symbol")
# Non-encoded symbols
label_table_map = self.create_label_map(inplace=False)
external_syms = set(self.symbol) - set(label_table_map["symbol"].values)
if external_syms == set():
return
if self.aux_note is None:
self.aux_note = [""] * len(self.sample)
for ext in external_syms:
for i in [i for i, x in enumerate(self.symbol) if x == ext]:
if not self.aux_note[i]:
self.aux_note[i] = self.symbol[i]
else:
self.aux_note[i] = self.symbol[i] + " " + self.aux_note[i]
self.symbol[i] = '"'
return
def get_contained_labels(self, inplace=True):
"""
Get the set of unique labels contained in this annotation.
Returns a pandas dataframe or sets the contained_labels
attribute of the object. Requires the label_store field to be set.
Function will try to use attributes contained in the order:
1. label_store
2. symbol
3. description
This function should also be called to summarize information about an
annotation after it has been read. Should not be a helper function
to others except rdann.
Parameters
----------
inplace : bool, optional
Determines whether to add the label map to the current
object (True) or as a return variable (False).
Returns
-------
contained_labels : pandas DataFrame
Mapping based on ann_label_table and self.custom_labels.
"""
if self.custom_labels is not None:
self.check_field("custom_labels")
# Create the label map
label_map = ann_label_table.copy()
# Convert the tuple triplets into a pandas dataframe if needed
if isinstance(self.custom_labels, (list, tuple)):
custom_labels = label_triplets_to_df(self.custom_labels)
elif isinstance(self.custom_labels, pd.DataFrame):
# Set the index just in case it doesn't already match the label_store
self.custom_labels.set_index(
self.custom_labels["label_store"].values, inplace=True
)
custom_labels = self.custom_labels
else:
custom_labels = None
# Merge the standard WFDB labels with the custom labels.
# custom labels values overwrite standard WFDB if overlap.
if custom_labels is not None:
for i in custom_labels.index:
label_map.loc[i] = custom_labels.loc[i]
# This doesn't work...
# label_map.loc[custom_labels.index] = custom_labels.loc[custom_labels.index]
# Get the labels using one of the features
if self.label_store is not None:
index_vals = set(self.label_store)
reset_index = False
counts = np.unique(self.label_store, return_counts=True)
elif self.symbol is not None:
index_vals = set(self.symbol)
label_map.set_index(label_map["symbol"].values, inplace=True)
reset_index = True
counts = np.unique(self.symbol, return_counts=True)
elif self.description is not None:
index_vals = set(self.description)
label_map.set_index(label_map["description"].values, inplace=True)
reset_index = True
counts = np.unique(self.description, return_counts=True)
else:
raise Exception("No annotation labels contained in object")
contained_labels = label_map.loc[index_vals, :]
# Add the counts
for i in range(len(counts[0])):
contained_labels.loc[counts[0][i], "n_occurrences"] = counts[1][i]
contained_labels["n_occurrences"] = pd.to_numeric(
contained_labels["n_occurrences"], downcast="integer"
)
if reset_index:
contained_labels.set_index(
contained_labels["label_store"].values, inplace=True
)
if inplace:
self.contained_labels = contained_labels
return
else:
return contained_labels
def set_label_elements(self, wanted_label_elements):
"""
Set one or more label elements based on at least one of the others.
Parameters
----------
wanted_label_elements : list
All of the desired label elements.
Returns
-------
N/A
"""
if isinstance(wanted_label_elements, str):
wanted_label_elements = [wanted_label_elements]
# Figure out which desired label elements are missing
missing_elements = [
e for e in wanted_label_elements if getattr(self, e) is None
]
contained_elements = [
e for e in ann_label_fields if getattr(self, e) is not None
]
if not contained_elements:
raise Exception("No annotation labels contained in object")
for e in missing_elements:
self.convert_label_attribute(contained_elements[0], e)
unwanted_label_elements = list(
set(ann_label_fields) - set(wanted_label_elements)
)
self.rm_attributes(unwanted_label_elements)
return
def rm_attributes(self, attributes):
"""
Remove attributes from object.
Parameters
----------
attributes : list
All of the desired attributes to remove.
Returns
-------
N/A
"""
if isinstance(attributes, str):
attributes = [attributes]
for a in attributes:
setattr(self, a, None)
return
def convert_label_attribute(
self, source_field, target_field, inplace=True, overwrite=True
):
"""
Convert one label attribute (label_store, symbol, or description) to
another. Creates mapping df on the fly based on ann_label_table and
self.custom_labels.
Parameters
----------
source_field : str
The label attribute to be converted.
target_field : str
The label attribute that will be converted to.
inplace : bool, optional
Determines whether to add the label map to the current
object (True) or as a return variable (False).
overwrite : bool, optional
If True, performs conversion and replaces target field attribute
even if the target attribute already has a value. If False, does
not perform conversion in the aforementioned case. Set to
True (do conversion) if inplace=False.
Returns
-------
target_item : list
All of the desired target fields.
"""
if inplace and not overwrite:
if getattr(self, target_field) is not None:
return
label_map = self.create_label_map(inplace=False)
label_map.set_index(label_map[source_field].values, inplace=True)
try:
target_item = label_map.loc[
getattr(self, source_field), target_field
].values
except KeyError:
target_item = label_map.reindex(
index=getattr(self, source_field), columns=[target_field]
).values.flatten()
if target_field != "label_store":
# Should already be int64 dtype if target is label_store
target_item = list(target_item)
if inplace:
setattr(self, target_field, target_item)
else:
return target_item
def label_triplets_to_df(triplets):
"""
Get a pd dataframe from a tuple triplets used to define annotation labels.
Parameters
----------
triplets : list
Should come in the form: (label_store, symbol, description).
Returns
-------
label_df : pandas DataFrame
Captures all of the tuple triplets used to define annotation labels.
"""
label_df = pd.DataFrame(
{
"label_store": np.array([t[0] for t in triplets], dtype="int"),
"symbol": [t[1] for t in triplets],
"description": [t[2] for t in triplets],
}
)
label_df.set_index(label_df["label_store"].values, inplace=True)
label_df = label_df[list(ann_label_fields)]
return label_df
def custom_triplet_bytes(custom_triplet):
"""
Convert triplet of [label_store, symbol, description] into bytes
for defining custom labels in the annotation file.
Parameters
----------
custom_triplet : list
Triplet of [label_store, symbol, description].
Returns
-------
annbytes : list
All the bytes converted from the tiplet.
"""
# Structure: 0, NOTE, len(aux_note), aux_note, codenumber, space, codesymbol, space, description, (0 null if necessary)
# Remember, aux_note string includes 'number(s)<space><symbol><space><description>''
annbytes = (
[0, 88, len(custom_triplet[2]) + 3 + len(str(custom_triplet[0])), 252]
+ [ord(c) for c in str(custom_triplet[0])]
+ [32]
+ [ord(custom_triplet[1])]
+ [32]
+ [ord(c) for c in custom_triplet[2]]
)
if len(annbytes) % 2:
annbytes.append(0)
return annbytes
def isblank(x):
"""
Tests whether the item is blank.
Parameters
----------
x : ndarray, list
The item to be checked.
Returns
-------
bool
Determines if the item was blank (True) or not (False).
"""
if x is None:
return True
elif isinstance(x, list):
if set(x) == set([None]):
return True
return False
def compact_carry_field(full_field):
"""
Return the compact list version of a list/array of an annotation
field that has previous values carried over (chan or num).
- The first sample is 0 by default. Only set otherwise if necessary.
- Only set fields if they are different from their prev field.
Parameters
----------
full_field : str
The annotation field to be converted to a list.
Returns
-------
compact_field : list
Compact list version of the annotation fields.
"""
# Keep in mind that the field may already be compact or None
if full_field is None:
return None
# List of same length. Place None where element
# does not need to be written
compact_field = [None] * len(full_field)
prev_field = 0
for i in range(len(full_field)):
current_field = full_field[i]
if current_field != prev_field:
compact_field[i] = current_field
prev_field = current_field
# May further simplify
if np.array_equal(compact_field, [None] * len(full_field)):
compact_field = None
return compact_field
def field2bytes(field, value, typecodes):
"""
Convert an annotation field into bytes to write.
Parameters
----------
field : str
The annotation field of the value to be converted to bytes.
value : list
The value to be converted to bytes.
typecodes : dict
The mapping between each annotation label an its corresponding typecode.
Returns
-------
data_bytes : list, ndarray
All of the bytes to be written to the annotation file.
"""
data_bytes = []
# samp and sym bytes come together
if field == "samptype":
# Numerical value encoding annotation symbol
typecode = typecodes[value[1]]
# sample difference
sd = value[0]
data_bytes = []
# Add SKIP element(s) if the sample difference is too large to
# be stored in the annotation type word.
#
# Each SKIP element consists of three words (6 bytes):
# - Bytes 0-1 contain the SKIP indicator (59 << 10)
# - Bytes 2-3 contain the high 16 bits of the sample difference
# - Bytes 4-5 contain the low 16 bits of the sample difference
# If the total difference exceeds 2**31 - 1, multiple skips must
# be used.
while sd > 1023:
n = min(sd, 0x7FFFFFFF)
data_bytes += [
0,
59 << 2,
(n >> 16) & 255,
(n >> 24) & 255,
(n >> 0) & 255,
(n >> 8) & 255,
]
sd -= n
# Annotation type itself is stored as a single word:
# - bits 0 to 9 store the sample difference (0 to 1023)
# - bits 10 to 15 store the type code
data_bytes += [sd & 255, ((sd & 768) >> 8) + 4 * typecode]
elif field == "num":
# First byte stores num
# second byte stores 60*4 indicator
data_bytes = [value, 240]
elif field == "subtype":
# First byte stores subtype
# second byte stores 61*4 indicator
data_bytes = [value, 244]
elif field == "chan":
# First byte stores num
# second byte stores 62*4 indicator
data_bytes = [value, 248]
elif field == "aux_note":
# - First byte stores length of aux_note field
# - Second byte stores 63*4 indicator
# - Then store the aux_note string characters
data_bytes = [len(value), 252] + [ord(i) for i in value]
# Zero pad odd length aux_note strings
if len(value) % 2:
data_bytes.append(0)
return data_bytes
def wrann(
record_name,
extension,
sample,
symbol=None,
subtype=None,
chan=None,
num=None,
aux_note=None,
label_store=None,
fs=None,
custom_labels=None,
write_dir="",
):
"""
Write a WFDB annotation file.
Specify at least the following:
- The record name of the WFDB record (record_name).
- The annotation file extension (extension).
- The annotation locations in samples relative to the beginning of
the record (sample).
- Either the numerical values used to store the labels.
(`label_store`), or more commonly, the display symbols of each
label (`symbol`).
Parameters
----------
record_name : str
The string name of the WFDB record to be written (without any file
extensions).
extension : str
The string annotation file extension.
sample : ndarray
A numpy array containing the annotation locations in samples relative to
the beginning of the record.
symbol : list, numpy array, optional
The symbols used to display the annotation labels. List or numpy array.
If this field is present, `label_store` must not be present.
subtype : ndarray, optional
A numpy array containing the marked class/category of each annotation.
chan : ndarray, optional
A numpy array containing the signal channel associated with each
annotation.
num : ndarray, optional
A numpy array containing the labelled annotation number for each
annotation.
aux_note : list, optional
A list containing the auxiliary information string (or None for
annotations without notes) for each annotation.
label_store : ndarray, optional
A numpy array containing the integer values used to store the
annotation labels. If this field is present, `symbol` must not be
present.
fs : int, float, optional
The numerical sampling frequency of the record to be written to the file.
custom_labels : pandas dataframe, optional
The map of custom defined annotation labels used for this annotation, in
addition to the standard WFDB annotation labels. Custom labels are
defined by two or three fields:
- The integer values used to store custom annotation labels in the file
(optional).
- Their short display symbols
- Their long descriptions.
This input argument may come in four formats:
1. A pandas.DataFrame object with columns:
['label_store', 'symbol', 'description']
2. A pandas.DataFrame object with columns: ['symbol', 'description']
If this option is chosen, label_store values are automatically chosen.
3. A list or tuple of tuple triplets, with triplet elements
representing: (label_store, symbol, description).
4. A list or tuple of tuple pairs, with pair elements representing:
(symbol, description). If this option is chosen, label_store values
are automatically chosen.
If the `label_store` field is given for this function, and
`custom_labels` is defined, `custom_labels` must contain `label_store`
in its mapping. ie. it must come in format 1 or 3 above.
write_dir : str, optional
The directory in which to write the annotation file
Returns
-------
N/A
Notes
-----
This is a gateway function, written as a simple way to write WFDB annotation
files without needing to explicity create an Annotation object. You may also
create an Annotation object, manually set its attributes, and call its
`wrann` instance method.
Each annotation stored in a WFDB annotation file contains a sample field and
a label field. All other fields may or may not be present.
Examples
--------
>>> # Read an annotation as an Annotation object
>>> annotation = wfdb.rdann('b001', 'atr', pn_dir='cebsdb')
>>> # Write a copy of the annotation file
>>> wfdb.wrann('b001', 'cpy', annotation.sample, annotation.symbol)
"""
# Create Annotation object
annotation = Annotation(
record_name=record_name,
extension=extension,
sample=sample,
symbol=symbol,
subtype=subtype,
chan=chan,
num=num,
aux_note=aux_note,
label_store=label_store,
fs=fs,
custom_labels=custom_labels,
)
# Find out which input field describes the labels
if symbol is None:
if label_store is None:
raise Exception(
"Either the 'symbol' field or the 'label_store' field must be set"
)
else:
if label_store is None:
annotation.sym_to_aux()
else:
raise Exception(
"Only one of the 'symbol' and 'label_store' fields may be input, for describing annotation labels"
)
# Perform field checks and write the annotation file
annotation.wrann(write_fs=True, write_dir=write_dir)
def show_ann_labels():
"""
Display the standard WFDB annotation label mapping.
Parameters
----------
N/A
Returns
-------
N/A
Examples
--------
>>> show_ann_labels()
"""
print(ann_label_table)
def show_ann_classes():
"""
Display the standard WFDB annotation classes.
Parameters
----------
N/A
Returns
-------
N/A
Examples
--------
>>> show_ann_classes()
"""
print(ann_class_table)
# todo: return as df option?
def rdann(
record_name,
extension,
sampfrom=0,
sampto=None,
shift_samps=False,
pn_dir=None,
return_label_elements=["symbol"],
summarize_labels=False,
):
"""
Read a WFDB annotation file record_name.extension and return an
Annotation object.
Parameters
----------
record_name : str
The record name of the WFDB annotation file. ie. for file '100.atr',
record_name='100'.
extension : str
The annotatator extension of the annotation file. ie. for file
'100.atr', extension='atr'.
sampfrom : int, optional
The minimum sample number for annotations to be returned.
sampto : int, optional
The maximum sample number for annotations to be returned.
shift_samps : bool, optional
Specifies whether to return the sample indices relative to `sampfrom`
(True), or sample 0 (False).
pn_dir : str, optional
Option used to stream data from Physionet. The PhysioNet database
directory from which to find the required annotation file. eg. For
record '100' in 'http://physionet.org/content/mitdb': pn_dir='mitdb'.
return_label_elements : list, optional
The label elements that are to be returned from reading the annotation
file. A list with at least one of the following options: 'symbol',
'label_store', 'description'.
summarize_labels : bool, optional
If True, assign a summary table of the set of annotation labels
contained in the file to the 'contained_labels' attribute of the
returned object. This table will contain the columns:
['label_store', 'symbol', 'description', 'n_occurrences'].
Returns
-------
annotation : Annotation
The Annotation object. Call help(wfdb.Annotation) for the attribute
descriptions.
Notes
-----
For every annotation sample, the annotation file explictly stores the
'sample' and 'symbol' fields, but not necessarily the others. When reading
annotation files using this function, fields which are not stored in the
file will either take their default values of 0 or None, or will be carried
over from their previous values if any.
Examples
--------
>>> ann = wfdb.rdann('sample-data/100', 'atr', sampto=300000)
"""
if (pn_dir is not None) and ("." not in pn_dir):
dir_list = pn_dir.split("/")
pn_dir = posixpath.join(
dir_list[0], download.get_version(dir_list[0]), *dir_list[1:]
)
return_label_elements = check_read_inputs(
sampfrom, sampto, return_label_elements
)
# Read the file in byte pairs
filebytes = load_byte_pairs(record_name, extension, pn_dir)
# Get WFDB annotation fields from the file bytes
(sample, label_store, subtype, chan, num, aux_note) = proc_ann_bytes(
filebytes, sampto
)
# Get the indices of annotations that hold definition information about
# the entire annotation file, and other empty annotations to be removed.
potential_definition_inds, rm_inds = get_special_inds(
sample, label_store, aux_note
)
# Try to extract information describing the annotation file
(fs, custom_labels) = interpret_defintion_annotations(
potential_definition_inds, aux_note
)
# Remove annotations that do not store actual sample and label information
(sample, label_store, subtype, chan, num, aux_note) = rm_empty_indices(
rm_inds, sample, label_store, subtype, chan, num, aux_note
)
# Convert lists to numpy arrays dtype='int'
(label_store, subtype, chan, num) = lists_to_int_arrays(
label_store, subtype, chan, num
)
# Convert sample numbers to a numpy array of 'int64'
sample = np.array(sample, dtype="int64")
# Try to get fs from the header file if it is not contained in the
# annotation file
if fs is None:
try:
rec = record.rdheader(record_name, pn_dir)
fs = rec.fs
except:
pass
# Create the annotation object
annotation = Annotation(
record_name=os.path.split(record_name)[1],
extension=extension,
sample=sample,
label_store=label_store,
subtype=subtype,
chan=chan,
num=num,
aux_note=aux_note,
fs=fs,
custom_labels=custom_labels,
)
# Apply the desired index range
if sampfrom > 0 and sampto is not None:
annotation.apply_range(sampfrom=sampfrom, sampto=sampto)
# If specified, obtain annotation samples relative to the starting
# index
if shift_samps and len(sample) > 0 and sampfrom:
annotation.sample = annotation.sample - sampfrom
# Get the set of unique label definitions contained in this
# annotation
if summarize_labels:
annotation.get_contained_labels(inplace=True)
# Set/unset the desired label values
annotation.set_label_elements(return_label_elements)
return annotation
def check_read_inputs(sampfrom, sampto, return_label_elements):
"""
Check if all the read inputs are valid.
Parameters
----------
sampfrom : int
The minimum sample number for annotations to be returned.
sampto : int
The maximum sample number for annotations to be returned.
return_label_elements : list, str
The label elements that are to be returned from reading the annotation
file. A list with at least one of the following options: 'symbol',
'label_store', 'description'.
Returns
-------
return_label_elements : list
The label elements that are to be returned from reading the annotation
file. A list with at least one of the following options: 'symbol',
'label_store', 'description'.
"""
if sampto and sampto <= sampfrom:
raise ValueError("sampto must be greater than sampfrom")
if sampfrom < 0:
raise ValueError("sampfrom must be a non-negative integer")
if isinstance(return_label_elements, str):
return_label_elements = [return_label_elements]
if set.union(set(ann_label_fields), set(return_label_elements)) != set(
ann_label_fields
):
raise ValueError(
"return_label_elements must be a list containing one or more of the following elements:",
ann_label_fields,
)
return return_label_elements
def load_byte_pairs(record_name, extension, pn_dir):
"""
Load the annotation file 1 byte at a time and arrange in pairs.
Parameters
----------
record_name : str
The record name of the WFDB annotation file. ie. for file '100.atr',
record_name='100'.
extension : str
The annotatator extension of the annotation file. ie. for file
'100.atr', extension='atr'.
pn_dir : str
Option used to stream data from Physionet. The PhysioNet database
directory from which to find the required annotation file. eg. For
record '100' in 'http://physionet.org/content/mitdb': pn_dir='mitdb'.
Returns
-------
filebytes : ndarray
The input filestream converted to an Nx2 array of unsigned bytes.
"""
# local file
if pn_dir is None:
with open(record_name + "." + extension, "rb") as f:
filebytes = np.fromfile(f, "<u1").reshape([-1, 2])
# PhysioNet file
else:
filebytes = download._stream_annotation(
record_name + "." + extension, pn_dir
).reshape([-1, 2])
return filebytes
def proc_ann_bytes(filebytes, sampto):
"""
Get regular annotation fields from the annotation bytes.
Parameters
----------
filebytes : ndarray
The input filestream converted to an Nx2 array of unsigned bytes.
sampto : int
The maximum sample number for annotations to be returned.
Returns
-------
sample : ndarray
A numpy array containing the annotation locations in samples relative to
the beginning of the record.
label_store : ndarray
A numpy array containing the integer values used to store the
annotation labels. If this field is present, `symbol` must not be
present.
subtype : ndarray
A numpy array containing the marked class/category of each annotation.
chan : ndarray
A numpy array containing the signal channel associated with each
annotation.
num : ndarray
A numpy array containing the labelled annotation number for each
annotation.
aux_note : list
A list containing the auxiliary information string (or None for
annotations without notes) for each annotation.
"""
# Base annotation fields
sample, label_store, subtype, chan, num, aux_note = [], [], [], [], [], []
# Indexing Variables
# Total number of sample from beginning of record. Annotation bytes
# only store sample_diff
sample_total = 0
# Byte pair index
bpi = 0
# Process annotations. Iterate across byte pairs.
# Sequence for one ann is:
# - SKIP pair (if any)
# - samp + sym pair
# - other pairs (if any)
# The last byte pair of the file is 0 indicating eof.
while bpi < filebytes.shape[0] - 1:
# Get the sample and label_store fields of the current annotation
sample_diff, current_label_store, bpi = proc_core_fields(filebytes, bpi)
sample_total = sample_total + sample_diff
sample.append(sample_total)
label_store.append(current_label_store)
# Process any other fields belonging to this annotation
# Flags that specify whether the extra fields need to be updated
update = {"subtype": True, "chan": True, "num": True, "aux_note": True}
# Get the next label store value - it may indicate additional
# fields for this annotation, or the values of the next annotation.
current_label_store = filebytes[bpi, 1] >> 2
while current_label_store > 59:
subtype, chan, num, aux_note, update, bpi = proc_extra_field(
current_label_store,
filebytes,
bpi,
subtype,
chan,
num,
aux_note,
update,
)
current_label_store = filebytes[bpi, 1] >> 2
# Set defaults or carry over previous values if necessary
subtype, chan, num, aux_note = update_extra_fields(
subtype, chan, num, aux_note, update
)
if sampto and sampto < sample_total:
sample, label_store, subtype, chan, num, aux_note = rm_last(
sample, label_store, subtype, chan, num, aux_note
)
break
return sample, label_store, subtype, chan, num, aux_note
def proc_core_fields(filebytes, bpi):
"""
Get the sample difference and store fields of the current annotation.
Parameters
----------
filebytes : ndarray
The input filestream converted to an Nx2 array of unsigned bytes.
bpi : int
The index to start the conversion.
Returns
-------
sample_diff : int
The sample difference.
label_store : ndarray
A numpy array containing the integer values used to store the
annotation labels. If this field is present, `symbol` must not be
present.
bpi : int
The index to start the conversion.
"""
sample_diff = 0
# The current byte pair will contain either the actual d_sample + annotation store value,
# or 0 + SKIP.
while filebytes[bpi, 1] >> 2 == 59:
# 4 bytes storing dt
skip_diff = (
(int(filebytes[bpi + 1, 0]) << 16)
+ (int(filebytes[bpi + 1, 1]) << 24)
+ (int(filebytes[bpi + 2, 0]) << 0)
+ (int(filebytes[bpi + 2, 1]) << 8)
)
# Data type is long integer (stored in two's complement). Range -2**31 to 2**31 - 1
if skip_diff > 2147483647:
skip_diff = skip_diff - 4294967296
sample_diff += skip_diff
bpi = bpi + 3
# Not a skip - it is the actual sample number + annotation type store value
label_store = filebytes[bpi, 1] >> 2
sample_diff += int(filebytes[bpi, 0] + 256 * (filebytes[bpi, 1] & 3))
bpi = bpi + 1
return sample_diff, label_store, bpi
def proc_extra_field(
label_store, filebytes, bpi, subtype, chan, num, aux_note, update
):
"""
Process extra fields belonging to the current annotation. Potential
updated fields: subtype, chan, num, aux_note.
Parameters
----------
label_store : ndarray
A numpy array containing the integer values used to store the
annotation labels. If this field is present, `symbol` must not be
present.
filebytes : str
The input filestream converted to bytes.
bpi : int
The index to start the conversion.
subtype : ndarray
A numpy array containing the marked class/category of each annotation.
chan : ndarray
A numpy array containing the signal channel associated with each
annotation.
num : ndarray
A numpy array containing the labelled annotation number for each
annotation.
aux_note : list
A list containing the auxiliary information string (or None for
annotations without notes) for each annotation.
update : dict
The container of updated fields.
Returns
-------
subtype : ndarray
A numpy array containing the marked class/category of each annotation.
chan : ndarray
A numpy array containing the signal channel associated with each
annotation.
num : ndarray
A numpy array containing the labelled annotation number for each
annotation.
aux_note : list
A list containing the auxiliary information string (or None for
annotations without notes) for each annotation.
update : dict
The container of updated fields.
bpi : int
The index to start the conversion.
"""
# aux_note and sub are reset between annotations. chan and num copy over
# previous value if missing.
# SUB
if label_store == 61:
# sub is interpreted as signed char.
subtype.append(filebytes[bpi, 0].astype("i1"))
update["subtype"] = False
bpi = bpi + 1
# CHAN
elif label_store == 62:
# chan is interpreted as un_signed char
chan.append(filebytes[bpi, 0])
update["chan"] = False
bpi = bpi + 1
# NUM
elif label_store == 60:
# num is interpreted as signed char
num.append(filebytes[bpi, 0].astype("i1"))
update["num"] = False
bpi = bpi + 1
# aux_note
elif label_store == 63:
# length of aux_note string. Max 256? No need to check other bits of
# second byte?
aux_notelen = filebytes[bpi, 0]
aux_notebytes = filebytes[
bpi + 1 : bpi + 1 + int(np.ceil(aux_notelen / 2.0)), :
].flatten()
if aux_notelen & 1:
aux_notebytes = aux_notebytes[:-1]
# The aux_note string
aux_note.append("".join([chr(char) for char in aux_notebytes]))
update["aux_note"] = False
bpi = bpi + 1 + int(np.ceil(aux_notelen / 2.0))
return subtype, chan, num, aux_note, update, bpi
def update_extra_fields(subtype, chan, num, aux_note, update):
"""
Update the field if the current annotation did not
provide a value.
- aux_note and sub are set to default values if missing.
- chan and num copy over previous value if missing.
Parameters
----------
subtype : ndarray
A numpy array containing the marked class/category of each annotation.
chan : ndarray
A numpy array containing the signal channel associated with each
annotation.
num : ndarray
A numpy array containing the labelled annotation number for each
annotation.
aux_note : list
A list containing the auxiliary information string (or None for
annotations without notes) for each annotation.
update : dict
The container of updated fields.
Returns
-------
subtype : ndarray
A numpy array containing the marked class/category of each annotation.
chan : ndarray
A numpy array containing the signal channel associated with each
annotation.
num : ndarray
A numpy array containing the labelled annotation number for each
annotation.
aux_note : list
A list containing the auxiliary information string (or None for
annotations without notes) for each annotation.
"""
if update["subtype"]:
subtype.append(0)
if update["chan"]:
if chan == []:
chan.append(0)
else:
chan.append(chan[-1])
if update["num"]:
if num == []:
num.append(0)
else:
num.append(num[-1])
if update["aux_note"]:
aux_note.append("")
return subtype, chan, num, aux_note
rx_fs = re.compile(r"## time resolution: (?P<fs>\d+\.?\d*)")
rx_custom_label = re.compile(
r"(?P<label_store>\d+) (?P<symbol>\S+) (?P<description>.+)"
)
def get_special_inds(sample, label_store, aux_note):
"""
Get the indices of annotations that hold definition information about
the entire annotation file, and other empty annotations to be removed.
Note: There is no need to deal with SKIP annotations (label_store=59)
which were already dealt with in proc_core_fields and hence not
included here.
Parameters
----------
sample : ndarray
A numpy array containing the annotation locations in samples relative to
the beginning of the record.
label_store : ndarray
A numpy array containing the integer values used to store the
annotation labels. If this field is present, `symbol` must not be
present.
aux_note : list
A list containing the auxiliary information string (or None for
annotations without notes) for each annotation.
Returns
-------
potential_definition_inds : set
The indices to be used for annotation notes.
rm_inds : set
The indices to be removed.
"""
s0_inds = np.where(sample == np.int64(0))[0]
note_inds = np.where(label_store == np.int64(22))[0]
# sample = 0 with aux_note means there should be an fs or custom label definition.
# Either way, they are to be removed.
potential_definition_inds = set(s0_inds).intersection(note_inds)
# Other indices which are not actual annotations.
notann_inds = np.where(label_store == np.int64(0))[0]
rm_inds = potential_definition_inds.union(set(notann_inds))
return potential_definition_inds, rm_inds
def interpret_defintion_annotations(potential_definition_inds, aux_note):
"""
Try to extract annotation definition information from annotation notes.
Information that may be contained:
- fs - sample=0, label_state=22, aux_note='## time resolution: XXX'.
- custom annotation label definitions.
Parameters
----------
potential_definition_inds : set
The indices to be used for annotation notes.
aux_note : list
A list containing the auxiliary information string (or None for
annotations without notes) for each annotation.
Returns
-------
fs : int, float
The sampling frequency of the record.
custom_labels : pandas dataframe
The custom annotation labels defined in the annotation file. Maps
the relationship between the three label fields. The data type is a
pandas DataFrame with three columns:
['label_store', 'symbol', 'description'].
"""
fs = None
custom_labels = []
if len(potential_definition_inds) > 0:
i = 0
while i < len(potential_definition_inds):
if aux_note[i].startswith("## "):
if not fs:
search_fs = rx_fs.findall(aux_note[i])
if search_fs:
fs = float(search_fs[0])
if round(fs, 8) == float(int(fs)):
fs = int(fs)
i += 1
continue
if aux_note[i] == "## annotation type definitions":
i += 1
while aux_note[i] != "## end of definitions":
(
label_store,
symbol,
description,
) = rx_custom_label.findall(aux_note[i])[0]
custom_labels.append(
(int(label_store), symbol, description)
)
i += 1
i += 1
else:
i += 1
if not custom_labels:
custom_labels = None
return fs, custom_labels
def rm_empty_indices(*args):
"""
Remove unwanted list indices.
Parameters
----------
args : tuple
First argument is the list of indices to remove. Other elements
are the lists to trim.
Returns
-------
list
The remaining trimmed list.
"""
rm_inds = args[0]
if not rm_inds:
return args[1:]
keep_inds = [i for i in range(len(args[1])) if i not in rm_inds]
return [[a[i] for i in keep_inds] for a in args[1:]]
def lists_to_int_arrays(*args):
"""
Convert lists to numpy int arrays.
Parameters
----------
args : tuple
Any number of lists to be converted.
Returns
-------
numpy array
The converted input list.
"""
return [np.array(a, dtype="int") for a in args]
def rm_last(*args):
"""
Remove the last index from each list.
Parameters
----------
args : tuple
Any number of lists to be trimmed.
Returns
-------
list
The trimmed input list.
"""
if len(args) == 1:
return args[:-1]
else:
return [a[:-1] for a in args]
return
def mrgann(
ann_file1,
ann_file2,
out_file_name="merged_ann.atr",
merge_method="combine",
chan1=-1,
chan2=-1,
start_ann=0,
end_ann="e",
record_only=True,
verbose=False,
):
"""
This function reads a pair of annotation files (specified by `ann_file1`
and `ann_file2`) for the specified record and writes a third annotation
file (specified by `out_file_name`) for the same record. The header (.hea)
file should be included in the same directory as each annotation file so
that the sampling rate can be read. Typical applications of `mrgann`
include combining annotation files that apply to different signals within
a multi-signal record, and replacing a segment of an annotation file with
annotations from another file. For example, setting 'merge_method' to
'combine' will simply blindly merge the annotation files for the specified
'start_ann' and 'end_ann' range while setting 'merge_method' to 'replace1'
will replace the contents of the first file with the second in that
specified range. Setting 'merge_method' to 'replace2' will replace the
contents of the second file with the first in that specified range.
Parameters
----------
ann_file1 : string
The file path of the first annotation file (with extension included).
ann_file2 : string
The file path of the second annotation file (with extension included).
out_file_name : string
The name of the output file name (with extension included). The
default is 'merged_ann.atr'.
merge_method : string, optional
The method used to merge the two annotation files. The default is
'combine' which simply combines the two files along every attribute;
duplicates will be preserved. The other options are 'replace1' which
replaces attributes of the first annotation file with attributes of
the second for the desired time range, 'replace2' which does the
same thing except switched (first file replaces second), and 'delete'
which deletes all of the annotations in the desired time range.
chan1 : int, optional
Sets the value of `chan` for the first annotation file. The default is
-1 which means to keep it the same.
chan2 : int, optional
Sets the value of `chan` for the second annotation file. The default
is -1 which means to keep it the same.
start_ann : float, int, string, optional
The location (sample, time, etc.) to start the annotation filtering.
If float, it will be interpreted as time in seconds. If int, it will
be interpreted as sample number. If string, it will be interpreted
as time formatted in HH:MM:SS format (the same as that in `wfdbtime`).
The default is 0 to represent sample number 0. A value of 0.0 would
represent 0 seconds instead.
end_ann : float, int, string, optional
The location (sample, time, etc.) to stop the annotation filtering.
If float, it will be interpreted as time in seconds. If int, it will
be interpreted as sample number. If string, it will be interpreted
as time formatted in HH:MM:SS format (the same as that in `wfdbtime`).
The default is 'e' to represent the end of the annotation.
record_only : bool, optional
Whether to only return the annotation information (True) or not
(False). If False, this function will generate a WFDB-formatted
annotation file. If True, it will return the object returned if that
file was read with `rdann`.
verbose : bool, optional
Whether to print all the information read about each annotation file
and the methodology for merging them (True) or not (False).
Returns
-------
N/A : Annotation, optional
If 'record_only' is set to True, then return the new WFDB-formatted
annotation object which is the same as generated by the `rdann`
output. Else, create the WFDB-formatted annotation file.
"""
ann1 = rdann(ann_file1.split(".")[0], ann_file1.split(".")[1])
ann2 = rdann(ann_file2.split(".")[0], ann_file2.split(".")[1])
if ann1.fs != ann2.fs:
raise Exception(
"Annotation sample rates do not match up: samples "
"can be aligned but final sample rate can not be "
"determined"
)
# Apply the channel mapping if desired
if chan1 != -1:
if chan1 < -1:
raise Exception("Invalid value for `chan1`: must be >= 0")
ann1.chan = np.array([chan1] * ann1.ann_len)
if chan2 != -1:
if chan2 < -1:
raise Exception("Invalid value for `chan2`: must be >= 0")
ann2.chan = np.array([chan2] * ann2.ann_len)
if start_ann == "e":
raise Exception("Start time can not be set to the end of the record")
if end_ann == 0:
raise Exception("End time can not be set to the start of the record")
samples = []
for i, time in enumerate([start_ann, end_ann]):
if time == "e":
# End of annotation, set end sample to largest int, roughly
sample = sys.maxsize
else:
if type(time) is int:
# Sample number
sample = time
elif type(time) is float:
# Time in seconds
sample = int(time * ann1.fs)
else:
# HH:MM:SS format, loosely
time_split = [t if t != "" else "0" for t in time.split(":")]
if len(time_split) == 1:
seconds = float(time) % 60
minutes = int(float(time) // 60)
hours = int(float(time) // 60 // 60)
elif len(time_split) == 2:
seconds = float(time_split[1])
minutes = int(time_split[0])
hours = 0
elif len(time_split) == 3:
seconds = float(time_split[2])
minutes = int(time_split[1])
hours = int(time_split[0])
if seconds >= 60:
raise Exception("Seconds not in correct format")
if minutes >= 60:
raise Exception("Minutes not in correct format")
total_seconds = hours * 60 * 60 + minutes * 60 + seconds
if (i == 1) and (total_seconds == 0):
raise Exception(
"End time can not be set to the start of " "the record"
)
sample = int(total_seconds * ann1.fs)
if sample > max([max(ann1.sample), max(ann2.sample)]):
if i == 0:
raise Exception(
"Start time can not be set to the "
"end of the record"
)
else:
print(
"'end_ann' greater than the highest "
"annotation... reverting to the highest "
"annotation"
)
samples.append(sample)
start_sample = samples[0]
end_sample = samples[1]
if verbose:
print(f"Start sample: {start_sample}, end sample: {end_sample}")
if (merge_method == "combine") or (merge_method == "delete"):
if verbose:
print("Combining the two files together")
# The sample should never be empty but others can (though they
# shouldn't be)
both_sample = np.concatenate([ann1.sample, ann2.sample]).astype(
np.int64
)
# Generate a list of sorted indices then sort the array
sort_indices = np.argsort(both_sample)
both_sample = np.sort(both_sample)
# Find where to filter the array
if merge_method == "combine":
sample_range = (both_sample >= start_sample) & (
both_sample <= end_sample
)
if merge_method == "delete":
sample_range = (both_sample < start_sample) | (
both_sample > end_sample
)
index_range = np.where(sample_range)[0]
both_sample = both_sample[sample_range]
# Combine both annotation attributes
ann_attr = {}
blank_array = np.array([], dtype=np.int64)
for cat in [
"chan",
"num",
"subtype",
"label_store",
"symbol",
"aux_note",
]:
ann1_cat = ann1.__dict__[cat]
ann2_cat = ann2.__dict__[cat]
if cat in ["symbol", "aux_note"]:
ann1_cat = ann1_cat if ann1_cat is not None else []
ann2_cat = ann2_cat if ann2_cat is not None else []
temp_cat = ann1_cat
temp_cat.extend(ann2_cat)
if len(temp_cat) == 0:
ann_attr[cat] = None
else:
temp_cat = [temp_cat[i] for i in sort_indices]
ann_attr[cat] = [temp_cat[i] for i in index_range]
else:
ann1_cat = ann1_cat if ann1_cat is not None else blank_array
ann2_cat = ann2_cat if ann2_cat is not None else blank_array
temp_cat = np.concatenate([ann1_cat, ann2_cat]).astype(np.int64)
if temp_cat.shape[0] == 0:
ann_attr[cat] = None
else:
temp_cat = np.array([temp_cat[i] for i in sort_indices])
ann_attr[cat] = np.array([temp_cat[i] for i in index_range])
elif (merge_method == "replace1") or (merge_method == "replace2"):
if merge_method == "replace1":
if verbose:
print(
"Replacing the contents of the first file with the "
"contents of the second"
)
keep_ann = ann2
remove_ann = ann1
elif merge_method == "replace2":
if verbose:
print(
"Replacing the contents of the second file with the "
"contents of the first"
)
keep_ann = ann1
remove_ann = ann2
# Find where to filter the first array
keep_sample_range = (keep_ann.sample >= start_sample) & (
keep_ann.sample <= end_sample
)
keep_index_range = np.where(keep_sample_range)[0]
# Find where to filter the second array
remove_sample_range = (remove_ann.sample < start_sample) | (
remove_ann.sample > end_sample
)
remove_index_range = np.where(remove_sample_range)[0]
# The sample should never be empty but others can (though they
# shouldn't be)
keep_ann_sample = keep_ann.sample[keep_index_range]
remove_ann_sample = remove_ann.sample[remove_index_range]
both_sample = np.concatenate(
[keep_ann_sample, remove_ann_sample]
).astype(np.int64)
# Generate a list of sorted indices then sort the array
sort_indices = np.argsort(both_sample)
both_sample = np.sort(both_sample)
# Combine both annotation attributes
ann_attr = {}
blank_array = np.array([], dtype=np.int64)
for cat in [
"chan",
"num",
"subtype",
"label_store",
"symbol",
"aux_note",
]:
keep_cat = keep_ann.__dict__[cat]
remove_cat = remove_ann.__dict__[cat]
if cat in ["symbol", "aux_note"]:
keep_cat = (
[keep_cat[i] for i in keep_index_range]
if keep_cat is not None
else []
)
remove_cat = (
[remove_cat[i] for i in remove_index_range]
if remove_cat is not None
else []
)
temp_cat = keep_cat
temp_cat.extend(remove_cat)
if len(temp_cat) == 0:
ann_attr[cat] = None
else:
ann_attr[cat] = [temp_cat[i] for i in sort_indices]
else:
keep_cat = (
np.array([keep_cat[i] for i in keep_index_range])
if keep_cat is not None
else blank_array
)
remove_cat = (
np.array([remove_cat[i] for i in remove_index_range])
if remove_cat is not None
else blank_array
)
temp_cat = np.concatenate([keep_cat, remove_cat]).astype(
np.int64
)
if temp_cat.shape[0] == 0:
ann_attr[cat] = None
else:
ann_attr[cat] = np.array(
[temp_cat[i] for i in sort_indices]
)
else:
raise Exception(
"Invalid value for 'merge_method': options are "
"'combine', 'replace1', and 'replace2'"
)
if record_only:
if verbose:
print("Returning Annotation object")
return Annotation(
record_name=out_file_name.split(".")[0],
extension=out_file_name.split(".")[1],
sample=both_sample,
symbol=ann_attr["symbol"],
subtype=ann_attr["subtype"],
chan=ann_attr["chan"],
num=ann_attr["num"],
aux_note=ann_attr["aux_note"],
label_store=ann_attr["label_store"],
fs=ann1.fs,
)
else:
if verbose:
print(f"Creating annotation file called: {out_file_name}")
wrann(
out_file_name.split(".")[0],
out_file_name.split(".")[1],
sample=both_sample,
symbol=ann_attr["symbol"],
subtype=ann_attr["subtype"],
chan=ann_attr["chan"],
num=ann_attr["num"],
aux_note=ann_attr["aux_note"],
label_store=ann_attr["label_store"],
fs=ann1.fs,
)
def format_ann_from_df(df_in):
"""
Parameters
----------
df_in : Pandas dataframe
Contains all the information needed to create WFDB-formatted
annotations. Of the form:
onset,duration,description
onset_1,duration_1,description_1
onset_2,duration_2,description_2
...,...,...
Returns
-------
N/A : Pandas dataframe
The WFDB-formatted input dataframe.
"""
# Create two separate dataframes for the start and end annotation
# then remove them from the original
df_start = df_in[df_in["duration"] > 0]
df_end = df_in[df_in["duration"] > 0]
df_trunc = df_in[df_in["duration"] == 0]
# Append parentheses at the start for annotation start and end for
# annotation end
df_start["description"] = "(" + df_start["description"].astype(str)
df_end["description"] = df_end["description"].astype(str) + ")"
# Add the duration time to the onset for the end annotation to convert
# to single time annotations only
df_end["onset"] = df_end["onset"] + df_end["duration"]
# Concatenate all of the dataframes
df_out = pd.concat([df_trunc, df_start, df_end], ignore_index=True)
# Make sure the sorting is correct
df_out["col_index"] = df_out.index
return df_out.sort_values(["onset", "col_index"])
## ------------- Annotation Field Specifications ------------- ##
"""
WFDB field specifications for each field. The indexes are the field
names.
Notes
-----
In the original WFDB package, certain fields have default values, but
not all of them. Some attributes need to be present for core
functionality, ie. baseline, whereas others are not essential, yet have
defaults, ie. base_time.
This inconsistency has likely resulted in the generation of incorrect
files, and general confusion. This library aims to make explicit,
whether certain fields are present in the file, by setting their values
to None if they are not written in, unless the fields are essential, in
which case an actual default value will be set.
The read vs write default values are different for 2 reasons:
1. We want to force the user to be explicit with certain important
fields when writing WFDB records fields, without affecting
existing WFDB headers when reading.
2. Certain unimportant fields may be dependencies of other
important fields. When writing, we want to fill in defaults
so that the user doesn't need to. But when reading, it should
be clear that the fields are missing.
"""
# Allowed types of each Annotation object attribute.
ALLOWED_TYPES = {
"record_name": (str),
"extension": (str),
"sample": (np.ndarray,),
"symbol": (list, np.ndarray),
"subtype": (np.ndarray,),
"chan": (np.ndarray,),
"num": (np.ndarray,),
"aux_note": (list, np.ndarray),
"fs": _header.float_types,
"label_store": (np.ndarray,),
"description": (list, np.ndarray),
"custom_labels": (pd.DataFrame, list, tuple),
"contained_labels": (pd.DataFrame, list, tuple),
}
str_types = (str, np.str_)
# Elements of the annotation label
ann_label_fields = ("label_store", "symbol", "description")
class AnnotationClass(object):
"""
Describes the annotations.
Attributes
----------
extension : str
The file extension of the annotation.
description : str
The description provided with the annotation.
human_reviewed : bool
Whether the annotation was human-reviewed (True) or not (False).
"""
def __init__(self, extension, description, human_reviewed):
self.extension = extension
self.description = description
self.human_reviewed = human_reviewed
ann_classes = [
AnnotationClass("atr", "Reference ECG annotations", True),
AnnotationClass("blh", "Human reviewed beat labels", True),
AnnotationClass("blm", "Machine beat labels", False),
AnnotationClass("alh", "Human reviewed alarms", True),
AnnotationClass("alm", "Machine alarms", False),
AnnotationClass("qrsc", "Human reviewed QRS detections", True),
AnnotationClass("qrs", "Machine QRS detections", False),
AnnotationClass("bph", "Human reviewed BP beat detections", True),
AnnotationClass("bpm", "Machine BP beat detections", False),
# AnnotationClass('alh', 'Human reviewed BP alarms', True),
# AnnotationClass('alm', 'Machine BP alarms', False),
# separate ECG and other signal category alarms?
# Can we use signum to determine the channel it was triggered off?
# ppg alarms?
# eeg alarms?
]
ann_class_table = pd.DataFrame(
{
"extension": [ac.extension for ac in ann_classes],
"description": [ac.description for ac in ann_classes],
"human_reviewed": [ac.human_reviewed for ac in ann_classes],
}
)
ann_class_table.set_index(ann_class_table["extension"].values, inplace=True)
ann_class_table = ann_class_table[
["extension", "description", "human_reviewed"]
]
# Individual annotation labels
class AnnotationLabel(object):
"""
Describes the individual annotation labels.
Attributes
----------
label_store : int
The value used to store the labels.
symbol : str
The shortened version of the annotation label abbreviation.
short_description : str
The shortened version of the description provided with the annotation.
description : str
The description provided with the annotation.
"""
def __init__(self, label_store, symbol, short_description, description):
self.label_store = label_store
self.symbol = symbol
self.short_description = short_description
self.description = description
def __str__(self):
return (
str(self.label_store)
+ ", "
+ str(self.symbol)
+ ", "
+ str(self.short_description)
+ ", "
+ str(self.description)
)
is_qrs = [
False,
True,
True,
True,
True,
True,
True,
True,
True,
True, # 0 - 9
True,
True,
True,
True,
False,
False,
False,
False,
False,
False, # 10 - 19
False,
False,
False,
False,
False,
True,
False,
False,
False,
False, # 20 - 29
True,
True,
False,
False,
True,
True,
False,
False,
True,
False, # 30 - 39
False,
True,
False,
False,
False,
False,
False,
False,
False,
False, # 40 - 49
]
ann_labels = [
AnnotationLabel(0, " ", "NOTANN", "Not an actual annotation"),
AnnotationLabel(1, "N", "NORMAL", "Normal beat"),
AnnotationLabel(2, "L", "LBBB", "Left bundle branch block beat"),
AnnotationLabel(3, "R", "RBBB", "Right bundle branch block beat"),
AnnotationLabel(4, "a", "ABERR", "Aberrated atrial premature beat"),
AnnotationLabel(5, "V", "PVC", "Premature ventricular contraction"),
AnnotationLabel(6, "F", "FUSION", "Fusion of ventricular and normal beat"),
AnnotationLabel(7, "J", "NPC", "Nodal (junctional) premature beat"),
AnnotationLabel(8, "A", "APC", "Atrial premature contraction"),
AnnotationLabel(
9, "S", "SVPB", "Premature or ectopic supraventricular beat"
),
AnnotationLabel(10, "E", "VESC", "Ventricular escape beat"),
AnnotationLabel(11, "j", "NESC", "Nodal (junctional) escape beat"),
AnnotationLabel(12, "/", "PACE", "Paced beat"),
AnnotationLabel(13, "Q", "UNKNOWN", "Unclassifiable beat"),
AnnotationLabel(14, "~", "NOISE", "Signal quality change"),
# AnnotationLabel(15, None, None, None),
AnnotationLabel(16, "|", "ARFCT", "Isolated QRS-like artifact"),
# AnnotationLabel(17, None, None, None),
AnnotationLabel(18, "s", "STCH", "ST change"),
AnnotationLabel(19, "T", "TCH", "T-wave change"),
AnnotationLabel(20, "*", "SYSTOLE", "Systole"),
AnnotationLabel(21, "D", "DIASTOLE", "Diastole"),
AnnotationLabel(22, '"', "NOTE", "Comment annotation"),
AnnotationLabel(23, "=", "MEASURE", "Measurement annotation"),
AnnotationLabel(24, "p", "PWAVE", "P-wave peak"),
AnnotationLabel(25, "B", "BBB", "Left or right bundle branch block"),
AnnotationLabel(26, "^", "PACESP", "Non-conducted pacer spike"),
AnnotationLabel(27, "t", "TWAVE", "T-wave peak"),
AnnotationLabel(28, "+", "RHYTHM", "Rhythm change"),
AnnotationLabel(29, "u", "UWAVE", "U-wave peak"),
AnnotationLabel(30, "?", "LEARN", "Learning"),
AnnotationLabel(31, "!", "FLWAV", "Ventricular flutter wave"),
AnnotationLabel(
32, "[", "VFON", "Start of ventricular flutter/fibrillation"
),
AnnotationLabel(
33, "]", "VFOFF", "End of ventricular flutter/fibrillation"
),
AnnotationLabel(34, "e", "AESC", "Atrial escape beat"),
AnnotationLabel(35, "n", "SVESC", "Supraventricular escape beat"),
AnnotationLabel(
36, "@", "LINK", "Link to external data (aux_note contains URL)"
),
AnnotationLabel(37, "x", "NAPC", "Non-conducted P-wave (blocked APB)"),
AnnotationLabel(38, "f", "PFUS", "Fusion of paced and normal beat"),
AnnotationLabel(39, "(", "WFON", "Waveform onset"),
AnnotationLabel(40, ")", "WFOFF", "Waveform end"),
AnnotationLabel(
41, "r", "RONT", "R-on-T premature ventricular contraction"
),
# AnnotationLabel(42, None, None, None),
# AnnotationLabel(43, None, None, None),
# AnnotationLabel(44, None, None, None),
# AnnotationLabel(45, None, None, None),
# AnnotationLabel(46, None, None, None),
# AnnotationLabel(47, None, None, None),
# AnnotationLabel(48, None, None, None),
# AnnotationLabel(49, None, None, None),
]
ann_label_table = pd.DataFrame(
{
"label_store": np.array(
[al.label_store for al in ann_labels], dtype="int"
),
"symbol": [al.symbol for al in ann_labels],
"description": [al.description for al in ann_labels],
}
)
ann_label_table.set_index(ann_label_table["label_store"].values, inplace=True)
ann_label_table = ann_label_table[["label_store", "symbol", "description"]]