From 1c5a950c6f244d78208c671283456ca89f07c822 Mon Sep 17 00:00:00 2001 From: vedran-kasalica Date: Wed, 11 Sep 2024 10:49:26 +0000 Subject: [PATCH] deploy: dc189cbbe0f5a5f2a054cf74e47e9ee97e5d0a5d --- 404.html | 131 - _modules/index.html | 136 + _modules/tsdf/file_utils.html | 163 ++ _modules/tsdf/legacy_tsdf_utils.html | 260 ++ _modules/tsdf/numpy_utils.html | 254 ++ _modules/tsdf/parse_metadata.html | 422 +++ _modules/tsdf/read_binary.html | 250 ++ _modules/tsdf/read_tsdf.html | 247 ++ _modules/tsdf/tsdfmetadata.html | 290 ++ _modules/tsdf/validator.html | 187 ++ _modules/tsdf/write_binary.html | 213 ++ _modules/tsdf/write_tsdf.html | 319 +++ _sources/autoapi/tsdf/constants/index.rst.txt | 48 + .../autoapi/tsdf/file_utils/index.rst.txt | 37 + _sources/autoapi/tsdf/index.rst.txt | 286 ++ .../tsdf/legacy_tsdf_utils/index.rst.txt | 87 + .../autoapi/tsdf/numpy_utils/index.rst.txt | 110 + .../autoapi/tsdf/parse_metadata/index.rst.txt | 142 + .../autoapi/tsdf/read_binary/index.rst.txt | 63 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- - - - - - - - diff --git a/_modules/index.html b/_modules/index.html new file mode 100644 index 0000000..3f62bb4 --- /dev/null +++ b/_modules/index.html @@ -0,0 +1,136 @@ + + + + + + Overview: module code — tsdf documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Overview: module code
    • +
  • +
    • +
+
+
+
+ +
+
+ +
+ +
+

© Copyright 2024, Netherlands eScience Center.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/tsdf/file_utils.html b/_modules/tsdf/file_utils.html new file mode 100644 index 0000000..069e94b --- /dev/null +++ b/_modules/tsdf/file_utils.html @@ -0,0 +1,163 @@ + + + + + + tsdf.file_utils — tsdf documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for tsdf.file_utils

+import json
+import os
+import glob
+from typing import Dict, Any
+
+
+

+[docs]
+def get_files_matching(directory: str,  criteria: str) -> list:
+    """
+    Get all files matching the criteria in the directory and its subdirectories.
+
+    :param directory: directory to search in.
+    :param criteria: criteria to match (e.g., `**meta.json`).
+
+    :return: list of files matching the criteria.
+    """
+    return glob.glob(os.path.join(directory, criteria), recursive=True)

+
+
+
+

+[docs]
+def write_to_file(dict: Dict[str, Any], dir_path: str, file_name: str) -> None:
+    """
+    Write a dictionary to a json file.
+
+    :param dict: Dictionary to be written.
+    :param dir_path: Path to the directory where the file will be saved.
+    :param file_name: Name of the file to be saved.
+    """
+    path = os.path.join(dir_path, file_name)
+    with open(path, "w") as convert_file:
+        convert_file.write(json.dumps(dict, indent=4))

+
+
+
+ +
+
+
+ +
+ +
+

© Copyright 2024, Netherlands eScience Center.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/tsdf/legacy_tsdf_utils.html b/_modules/tsdf/legacy_tsdf_utils.html new file mode 100644 index 0000000..59f934a --- /dev/null +++ b/_modules/tsdf/legacy_tsdf_utils.html @@ -0,0 +1,260 @@ + + + + + + tsdf.legacy_tsdf_utils — tsdf documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for tsdf.legacy_tsdf_utils

+import json
+from typing import Dict, Any
+from tsdf.constants import METADATA_NAMING_PATTERN
+from tsdf import file_utils 
+
+
+# the old (TSDB) and new field (TSDF) names
+TSDB_TSDF_KEY_MAP = {
+    "project_id": "study_id",
+    "quantities": "channels",
+    "datatype": "data_type",
+    "start_datetime_iso8601": "start_iso8601",
+    "end_datetime_iso8601": "end_iso8601",
+}
+
+# the field whose value should be an array
+TSDB_ARRAY_KEYS = {"channels", "units"}
+
+
+

+[docs]
+def _rename_keys_in_metadata(old_dict: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    This function renames the keys in a metadata file.
+    If a key in the metadata matches a key in the provided dictionary, it is renamed to the corresponding value in the dictionary.
+    It handles nested dictionaries and lists of dictionaries.
+
+    :param old_dict: The metadata file (dictionary) with keys to rename
+    :return: The updated metadata file (dictionary)
+    """
+    new_dict = {}
+    for key, value in old_dict.items():
+        new_key = TSDB_TSDF_KEY_MAP.get(key, key)
+        if isinstance(value, dict):
+            new_dict[new_key] = _rename_keys_in_metadata(value)
+        elif isinstance(value, list):
+            new_dict[new_key] = [
+                _rename_keys_in_metadata(v) if isinstance(v, dict) else v for v in value
+            ]
+        else:
+            new_dict[new_key] = value
+    return new_dict

+
+
+
+

+[docs]
+def _convert_to_array(data: Dict[str, Any], key: str) -> Dict[str, Any]:
+    """
+    This function converts the value of a specified key in a dictionary to an array if it's not already an array.
+    It handles nested dictionaries and lists of dictionaries.
+
+    :param data: The dictionary with a value to convert
+    :param key: The key in the dictionary whose value to convert
+    :return: The updated dictionary
+    """
+    for k, value in data.items():
+        if k == key and not isinstance(value, list):
+            data[k] = [str(value)]
+        elif isinstance(value, dict):
+            data[k] = _convert_to_array(value, key)
+        elif isinstance(value, list):
+            data[k] = [
+                _convert_to_array(v, key) if isinstance(v, dict) else v for v in value
+            ]
+    return data

+
+
+

+[docs]
+def convert_tsdb_to_tsdf(data: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Converts a data from TSDB (legacy) to TSDF (0.1) format.
+
+    :param data: The data in legacy (tsdb) format.
+    :return: The data in tsdf format.
+    """
+     # rename the keys in the dictionary
+    new_data = _rename_keys_in_metadata(data)
+    # convert the values of the specified keys to arrays
+    for key in TSDB_ARRAY_KEYS:
+        new_data = _convert_to_array(new_data, key)
+
+    return new_data

+
+
+
+

+[docs]
+def generate_tsdf_metadata_from_tsdb(filepath_existing: str, filepath_new: str) -> None:
+    """
+    This function creates a metadata file (JSON) file in TSDF (0.1) format from a TSDB (legacy) file.
+
+    :param filepath_existing: The path to the JSON file to process
+    :param filepath_new: The path to the new JSON file
+    """
+    with open(filepath_existing, "r") as f:
+        data = json.load(f)
+    new_data = convert_tsdb_to_tsdf(data)
+    with open(filepath_new, "w") as f:
+        json.dump(new_data, f)

+
+
+

+[docs]
+def convert_file_tsdb_to_tsdf(filepath: str) -> None:
+    """
+    This function converts a metadata file (JSON) from TSDB (legacy) to TSDF (0.1) format. It overwrites the original file.
+
+    :param filepath: The path to the JSON file to process
+    """
+    with open(filepath, "r") as f:
+        data = json.load(f)
+        new_data = convert_tsdb_to_tsdf(data)
+    with open(filepath, "w") as f:
+        json.dump(new_data, f)

+
+
+
+

+[docs]
+def convert_files_tsdb_to_tsdf(directory: str) -> None:
+    """
+    This function converts all metadata files in a directory (and its subdirectories) from TSDB (legacy) to TSDF (0.1) format.
+    It walks through all files in a directory (and its subdirectories),
+    and processes all files with a .json extension.
+
+    :param directory: The directory to process files in
+    """
+    
+    for filepath in file_utils.get_files_matching(directory, METADATA_NAMING_PATTERN):
+        convert_file_tsdb_to_tsdf(filepath)

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2024, Netherlands eScience Center.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/tsdf/numpy_utils.html b/_modules/tsdf/numpy_utils.html new file mode 100644 index 0000000..135f6b2 --- /dev/null +++ b/_modules/tsdf/numpy_utils.html @@ -0,0 +1,254 @@ + + + + + + tsdf.numpy_utils — tsdf documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for tsdf.numpy_utils

+import sys
+import numpy as np
+
+
+_map_from_numpy_types = {
+    "i": "int",
+    "f": "float",
+    # etc
+}
+""" Mapping of NumPy data types to their TSDF metadata annotations. """
+
+
+

+[docs]
+def data_type_numpy_to_tsdf(data: np.ndarray):
+    """Compute the TSDF metadata 'data_type' value, based on the NumPy data."""
+    return _map_from_numpy_types[data.dtype.kind]

+
+
+
+_map_to_numpy_types = {
+    "int": "i",
+    "float": "f",
+    # etc
+}
+""" Mapping of data types that are supported by TSDF to 
+    their NumPy representation used for parsing. """
+
+
+

+[docs]
+def data_type_tsdf_to_numpy(data_type: str) -> str:
+    """
+    Compute the the NumPy data type, based on the TSDF metadata 'data_type' value.
+
+    :param data_type: TSDF metadata 'data_type' value.
+
+    :return: NumPy data type (as a char).
+    """
+    return _map_to_numpy_types[data_type]

+
+
+
+

+[docs]
+def bits_numpy_to_tsdf(data: np.ndarray) -> int:
+    """
+    Compute TSDF metadata 'n_bits' value, based on the NumPy data.
+
+    :param data: NumPy data.
+
+    :return: TSDF metadata 'n_bits' value.
+    """
+    return data.dtype.itemsize * 8

+
+
+
+

+[docs]
+def bytes_tsdf_to_numpy(n_bits: int):
+    """
+    Compute the the NumPy byte number, based on the TSDF metadata 'n_bits' value.
+
+    :param n_bits: TSDF metadata 'n_bits' value.
+
+    :return: NumPy byte number.
+    """
+    return str(n_bits // 8)

+
+
+
+_map_from_numpy_endianness = {
+    "<": "little",
+    ">": "big",
+    "=": sys.byteorder,
+    "|": "not applicable",
+}
+""" Supported endianness values. """
+
+
+

+[docs]
+def endianness_numpy_to_tsdf(data: np.ndarray) -> str:
+    """
+    Compute TSDF metadata 'data_type' value, based on the NumPy data.
+
+    :param data: NumPy data.
+
+    :return: TSDF metadata 'data_type' value (as a string).
+    """
+    return _map_from_numpy_endianness[data.dtype.byteorder]

+
+
+
+_map_to_numpy_endianness = {
+    "little": "<",
+    "big": ">",
+    "not applicable": "|",
+}
+""" Supported endianness values. """
+
+
+

+[docs]
+def endianness_tsdf_to_numpy(endianness: str) -> str:
+    """
+    Compute TSDF metadata 'data_type' value, based on the NumPy data.
+
+    :param endianness: TSDF metadata 'data_type' value.
+
+    :return: NumPy data type (as a char)."""
+    return _map_to_numpy_endianness[endianness]

+
+
+
+

+[docs]
+def rows_numpy_to_tsdf(data: np.ndarray) -> int:
+    """
+    Compute TSDF metadata 'rows' value, based on the NumPy data.
+
+    :param data: NumPy data.
+
+    :return: TSDF metadata 'rows' value.
+    """
+    return data.shape[0]

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2024, Netherlands eScience Center.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/tsdf/parse_metadata.html b/_modules/tsdf/parse_metadata.html new file mode 100644 index 0000000..4311f89 --- /dev/null +++ b/_modules/tsdf/parse_metadata.html @@ -0,0 +1,422 @@ + + + + + + tsdf.parse_metadata — tsdf documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for tsdf.parse_metadata

+"""
+Module for parsing TSDF metadata files.
+
+Reference: https://arxiv.org/abs/2211.11294
+"""
+
+import os
+from typing import Any, Dict, List
+import re
+from dateutil import parser
+
+from tsdf import constants
+from tsdf import tsdfmetadata
+
+
+

+[docs]
+def read_data(data: Any, source_path: str) -> Dict[str, "tsdfmetadata.TSDFMetadata"]:
+    """
+    Function used to parse the JSON object containing TSDF metadata. It returns a
+    list of TSDFMetadata objects, where each object describes formatting of a binary file.
+
+    :param data: JSON object containing TSDF metadata.
+    :param source_path: path to the metadata file.
+
+    :return: list of TSDFMetadata objects.
+
+    :raises tsdf_metadata.TSDFMetadataFieldValueError: if the TSDF metadata file is missing a mandatory field.
+    """
+
+    # Check if the version is supported
+    version = data["metadata_version"]
+    if version not in constants.SUPPORTED_TSDF_VERSIONS:
+        raise tsdfmetadata.TSDFMetadataFieldValueError(
+            f"TSDF file version {version} not supported."
+        )
+
+    defined_properties: Dict[str, Any] = {}
+    return _read_struct(data, defined_properties.copy(), source_path, version)

+
+
+
+

+[docs]
+def _read_struct(
+    data: Any, defined_properties: Dict[str, Any], source_path, version: str
+) -> Dict[str, "tsdfmetadata.TSDFMetadata"]:
+    """
+    Recursive method used to parse the TSDF metadata in a hierarchical
+    order (from the root towards the leaves).
+
+    :param data: JSON object containing TSDF metadata.
+    :param defined_properties: dictionary containing all the properties defined at the current level of the TSDF structure.
+    :param source_path: path to the metadata file.
+    :param version: version of the TSDF used within the file.
+
+    :return: list of TSDFMetadata objects.
+
+    :raises tsdf_metadata.TSDFMetadataFieldError: if the TSDF metadata file is missing a mandatory field.
+    """
+    all_streams: Dict[str, "tsdfmetadata.TSDFMetadata"] = {}
+    remaining_data = {}
+    leaf: bool = True
+
+    # 1) Map all the values provided at the current level of the TSDF structure.
+    for key, value in data.items():
+        if is_mandatory_type(key, version):
+            defined_properties[key] = value
+        elif not _contains_file_name(value):
+            defined_properties[key] = value
+        else:
+            leaf = False
+            remaining_data[key] = value
+
+    # 2) If the current element is a leaf in the structure, convert it into a TSDFMetadata object.
+    if leaf:
+        try:
+            bin_file_name = defined_properties["file_name"]
+        except KeyError:
+            raise tsdfmetadata.TSDFMetadataFieldError.missing_field("file_name") 
+        path = os.path.split(source_path)
+        file_dir = os.path.join(path[0])
+        meta_file_name = path[1]
+        all_streams[bin_file_name] = tsdfmetadata.TSDFMetadata(
+            defined_properties, file_dir, meta_file_name
+        )
+
+    # 3) If the current element is not a leaf, `remaining_data`` will contain lower
+    # levels of the TSDF structure.
+    # Extend the mapping recursively with values provided at those levels.
+    for key, value in remaining_data.items():
+        if isinstance(value, list):
+            for each_value in value:
+                all_streams = all_streams | _read_struct(
+                    each_value, defined_properties.copy(), source_path, version
+                )
+        else:
+            all_streams = all_streams | _read_struct(
+                value, defined_properties.copy(), source_path, version
+            )
+
+    return all_streams

+
+
+
+

+[docs]
+def is_mandatory_type(key: str, version: str) -> bool:
+    """
+    Function returns True if the field that corresponds to the
+    key is mandatory for the given TSDF version, otherwise it returns False.
+
+    :param key: key of the TSDF metadata field.
+    :param version: version of the TSDF used within the file.
+
+    :return: True if the field is mandatory, otherwise False.
+    """
+    return True if key in constants.MANDATORY_TSDF_KEYS[version] else False

+
+
+
+

+[docs]
+def _contains_file_name(data: Any) -> bool:
+    """
+    Function return True if the data contains the "file_name" key,
+    and thus, represents nested data elements.
+    Otherwise it returns False.
+
+    :param data: data to be checked.
+
+    :return: True if the data contains the "file_name" key, otherwise False.
+    """
+
+    if isinstance(data, list):
+        for elem in data:
+            if _contains_file_name(elem):
+                return True
+
+    if not isinstance(data, dict):
+        return False
+
+    for key, value in data.items():
+        if key == "file_name":
+            return True
+        if _contains_file_name(value):
+            return True
+    return False

+
+
+
+

+[docs]
+def contains_tsdf_mandatory_fields(dictionary: Dict[str, Any]) -> bool:
+    """
+    Verifies that all the mandatory properties for TSDF metadata are provided,
+    and are in the right format.
+
+    :param dictionary: dictionary containing TSDF metadata.
+
+    :return: True if the metadata is well formatted.
+
+    :raises tsdf_metadata.TSDFMetadataFieldError: if the TSDF metadata file is missing a mandatory field.
+    :raises tsdf_metadata.TSDFMetadataFieldValueError: if the TSDF metadata file contains an invalid value.
+    """
+    version_key = "metadata_version"
+    if version_key not in dictionary.keys():
+        raise tsdfmetadata.TSDFMetadataFieldError.missing_field(version_key)
+
+    version = dictionary[version_key]
+    for key in constants.MANDATORY_TSDF_KEYS[version]:
+        if key not in dictionary.keys():
+            raise tsdfmetadata.TSDFMetadataFieldError.missing_field(key)
+    units = "units"
+    channels = "channels"
+    if len(dictionary[units]) != len(dictionary[channels]):
+        raise tsdfmetadata.TSDFMetadataFieldValueError(
+            f"TSDF metadata structure must specify equal number of {units} and {channels} for each binary file."
+        )
+
+    for key, value in dictionary.items():
+        _check_tsdf_property_format(key, value, version)
+
+    return True

+
+
+
+

+[docs]
+def _check_tsdf_property_format(key: str, value, version: str) -> None:
+    """
+    Function checks whether the value of the mandatory TSDF field specified by the key
+    is of the expected data format.\\
+    `Note: If the key is not mandatory the function does not perform any checks.`
+
+    :param key: key of the TSDF metadata field.
+    :param value: value of the TSDF metadata field.
+    :param version: version of the TSDF used within the file.
+
+    :raises tsdf_metadata.TSDFMetadataFieldValueError: if the TSDF metadata file contains an invalid value.
+    """
+    if not is_mandatory_type(key, version):
+        return
+
+    index = constants.MANDATORY_TSDF_KEYS[version].index(key)
+    type_name = constants.MANDATORY_TSDF_KEYS_VALUES[version][index]
+
+    if not isinstance(value, constants.KEY_VALUE_TYPES[type_name]):
+        raise tsdfmetadata.TSDFMetadataFieldValueError(
+            f"The given value for {key} is not in the expected ({type_name}) format."
+        )

+
+
+
+

+[docs]
+def get_file_metadata_at_index(
+    metadata: Dict[str, "tsdfmetadata.TSDFMetadata"], index: int
+) -> "tsdfmetadata.TSDFMetadata":
+    """
+    Returns the metadata object at the position defined by the index.
+
+    :param metadata: dictionary containing TSDF metadata.
+    :param index: index of the metadata object to be returned.
+
+    :return: metadata object at the position defined by the index.
+
+    :raises IndexError: if the index is out of range.
+    """
+    for _key, value in metadata.items():
+        if index == 0:
+            return value
+        index -= 1
+    raise IndexError("The index is out of range.")

+
+
+
+

+[docs]
+def confirm_dir_of_metadata(metadatas: List["tsdfmetadata.TSDFMetadata"]) -> None:
+    """
+    The method is used to confirm whether all the metadata files are expected in the same directory.
+
+    :param metadatas: list of metadata objects.
+
+    :raises tsdf_metadata.TSDFMetadataFieldValueError: if the metadata files are not in the same directory or describe the same binaries.
+    """
+    metadata_iter = iter(metadatas)
+    init_metadata = next(metadata_iter)
+
+    for curr_metadata in metadata_iter:
+        if init_metadata.file_dir_path != curr_metadata.file_dir_path:
+            raise tsdfmetadata.TSDFMetadataFieldValueError(
+                "Metadata files have to be in the same folder to be combined."
+            )
+        if init_metadata.file_name == curr_metadata.file_name:
+            raise tsdfmetadata.TSDFMetadataFieldValueError(
+                "Two metadata objects cannot reference the same binary file (file_name)."
+                #TODO: why not?
+            )

+
+
+

+[docs]
+def is_iso8601(date_string: str) -> bool:
+    """
+    Checks if the given date string is in ISO8601 format.
+
+    :param date_string: date string to be checked.
+    """
+    # Note that we need both the regex and the parser to validate the date string
+    # The regex only still allows for invalid dates, e.g. 2021-02-29
+    # The parser is too lenient in accepting different formats
+    iso8601_regex = r"^(-?(?:[1-9][0-9]*)?[0-9]{4})-(1[0-2]|0[1-9])-(3[01]|0[1-9]|[12][0-9])(T(2[0-3]|[01][0-9]):[0-5][0-9]:[0-5][0-9](?:\.[0-9]+)?(?:Z|[+-](?:2[0-3]|[01][0-9]):[0-5][0-9])?)?$"
+    if re.match(iso8601_regex, date_string):
+        try:
+            parser.parse(date_string)
+            return True
+        except tsdfmetadata.TSDFMetadataFieldValueError:
+            return False
+    return False

+
+
+

+[docs]
+def validate_datetimes(metadata: tsdfmetadata.TSDFMetadata) -> bool:
+    """
+    Validates the start and end date format of the TSDFMetaData object.
+    """
+    if not is_iso8601(metadata.start_iso8601):
+        raise tsdfmetadata.TSDFMetadataFieldValueError(f"Invalid start_iso8601: {metadata.start_iso8601}")
+    if not is_iso8601(metadata.end_iso8601):
+        raise tsdfmetadata.TSDFMetadataFieldValueError(f"Invalid end_iso8601: {metadata.end_iso8601}")
+    return True

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2024, Netherlands eScience Center.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/tsdf/read_binary.html b/_modules/tsdf/read_binary.html new file mode 100644 index 0000000..10a3564 --- /dev/null +++ b/_modules/tsdf/read_binary.html @@ -0,0 +1,250 @@ + + + + + + tsdf.read_binary — tsdf documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for tsdf.read_binary

+"""
+Module for reading and writing binary files associated with TSDF.
+
+Reference: https://arxiv.org/abs/2211.11294
+"""
+
+import os
+from typing import List, Union
+import numpy as np
+import pandas as pd
+from tsdf import numpy_utils
+from tsdf import tsdfmetadata
+from tsdf.constants import ConcatenationType
+
+
+

+[docs]
+def load_dataframe_from_binaries(
+    metadatas: List["tsdfmetadata.TSDFMetadata"],
+    concatenation: ConcatenationType = ConcatenationType.none,
+) -> Union[pd.DataFrame, List[pd.DataFrame]]:
+    """
+    Load content of binary files associated with TSDF into a pandas DataFrame. The data frames can be concatenated horizontally (ConcatenationType.columns), vertically (ConcatenationType.rows) or provided as a list of data frames (ConcatenationType.none).
+
+    :param metadatas: list of TSDFMetadata objects.
+    :param concatenation: concatenation rule, i.e., determines whether the data frames (content of binary files) should be concatenated horizontally (ConcatenationType.columns), vertically (ConcatenationType.rows) or provided as a list of data frames (ConcatenationType.none).
+
+    :return: pandas DataFrame containing the combined data.
+    """
+    # Load the data
+    data_frames = []
+    for metadata in metadatas:
+        data = load_ndarray_from_binary(metadata)
+        df = pd.DataFrame(data, columns=metadata.channels)
+        data_frames.append(df)
+
+    # Merge the data
+    if concatenation == ConcatenationType.rows:
+        return pd.concat(data_frames)
+    elif concatenation == ConcatenationType.columns:
+        return pd.concat(data_frames, axis=1)
+    elif concatenation == ConcatenationType.none:
+        return data_frames

+
+
+
+

+[docs]
+def load_ndarray_from_binary(
+    metadata: "tsdfmetadata.TSDFMetadata", start_row: int = 0, end_row: int = -1
+) -> np.ndarray:
+    """
+    Use metadata properties to load and return numpy array from a binary file (located the same directory where the metadata is saved).
+
+    :param metadata: TSDFMetadata object.
+    :param start_row: (optional) first row to load.
+    :param end_row: (optional) last row to load. If -1, load all rows.
+
+    :return: numpy array containing the data."""
+    metadata_dir = metadata.file_dir_path
+
+    bin_path = os.path.join(metadata_dir, metadata.file_name)
+    return _load_binary_file(
+        bin_path,
+        metadata.data_type,
+        metadata.bits,
+        metadata.endianness,
+        metadata.rows,
+        len(metadata.channels),
+        start_row,
+        end_row,
+    )

+
+
+
+

+[docs]
+def _load_binary_file(
+    bin_file_path: str,
+    data_type: str,
+    n_bits: int,
+    endianness: str,
+    n_rows: int,
+    n_columns: int,
+    start_row: int = 0,
+    end_row: int = -1,
+) -> np.ndarray:
+    """
+    Use provided parameters to load and return a numpy array from a binary file.
+
+    :param bin_file_path: path to the binary file.
+    :param data_type: data type of the binary file.
+    :param n_bits: number of bits per value.
+    :param endianness: endianness of the binary file.
+    :param n_rows: number of rows in the binary file.
+    :param n_columns: number of columns in the binary file.
+    :param start_row: (optional) first row to load.
+    :param end_row: (optional) last row to load. If -1, load all rows.
+
+    :return: numpy array containing the data.
+    """
+
+    s_endianness = numpy_utils.endianness_tsdf_to_numpy(endianness)
+    s_type = numpy_utils.data_type_tsdf_to_numpy(data_type)
+    s_n_bytes = numpy_utils.bytes_tsdf_to_numpy(n_bits)
+    format_string = "".join([s_endianness, s_type, s_n_bytes])
+
+    # Load the data and reshape
+    with open(bin_file_path, "rb") as fid:
+        fid.seek(start_row * n_columns * n_bits // 8)
+        if end_row == -1:
+            end_row = n_rows
+        buffer = fid.read((end_row - start_row) * n_columns * n_bits // 8)
+        values = np.frombuffer(buffer, dtype=format_string)
+        if n_columns > 1:
+            values = values.reshape((-1, n_columns))
+
+    # Check whether the number of rows matches the metadata
+    if values.shape[0] != end_row - start_row:
+        raise Exception("Number of rows doesn't match file length.")
+
+    return values

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2024, Netherlands eScience Center.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/tsdf/read_tsdf.html b/_modules/tsdf/read_tsdf.html new file mode 100644 index 0000000..a99cde6 --- /dev/null +++ b/_modules/tsdf/read_tsdf.html @@ -0,0 +1,247 @@ + + + + + + tsdf.read_tsdf — tsdf documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for tsdf.read_tsdf

+"""
+Module for reading TSDF files.
+
+Reference: https://arxiv.org/abs/2211.11294
+"""
+
+import json
+import os
+from pathlib import Path
+from typing import Dict, List
+from tsdf import file_utils 
+from tsdf.constants import METADATA_NAMING_PATTERN
+from tsdf import parse_metadata 
+from tsdf import legacy_tsdf_utils 
+from tsdf.tsdfmetadata import TSDFMetadata
+
+
+

+[docs]
+def load_metadata_file(file) -> Dict[str, TSDFMetadata]:
+    """Loads a TSDF metadata file, returns a dictionary
+
+    :param file: file object containing the TSDF metadata.
+
+    :return: dictionary of TSDFMetadata objects.
+    """
+
+    # The data is isomorphic to a JSON
+    data = json.load(file)
+
+    abs_path = os.path.realpath(file.name)
+
+    # Parse the data and verify that it complies with TSDF requirements
+    return parse_metadata.read_data(data, abs_path)

+
+
+

+[docs]
+def load_metadata_legacy_file(file) -> Dict[str, TSDFMetadata]:
+    """Loads a TSDB metadata file, i.e., legacy format of the TSDF. It returns a dictionary representing the metadata.
+
+    :param file: file object containing the TSDF metadata.
+
+    :return: dictionary of TSDFMetadata objects.
+    """
+
+    # The data is isomorphic to a JSON
+    legacy_data = json.load(file)
+
+    abs_path = os.path.realpath(file.name)
+
+    tsdf_data = legacy_tsdf_utils.convert_tsdb_to_tsdf(legacy_data)
+
+    # Parse the data and verify that it complies with TSDF requirements
+    return parse_metadata.read_data(tsdf_data, abs_path)

+
+
+

+[docs]
+def load_metadatas_from_dir(
+    dir_path: str, naming_pattern=METADATA_NAMING_PATTERN
+) -> List[Dict[str, TSDFMetadata]]:
+    """
+    Loads all TSDF metadata files in a directory, returns a dictionary
+
+    :param dir_path: path to the directory containing the TSDF metadata files.
+    :param naming_pattern: (optional) naming pattern of the TSDF metadata files .
+
+    :return: dictionary of TSDFMetadata objects.
+    """
+    # Get all files in the directory
+    file_paths = file_utils.get_files_matching(dir_path, naming_pattern)
+
+    # Load all files
+    metadatas = []
+    for file_path in file_paths:
+        metadata = load_metadata_from_path(file_path)
+        metadatas.append(metadata)
+
+    return metadatas

+
+
+
+

+[docs]
+def load_metadata_from_path(path: Path) -> Dict[str, TSDFMetadata]:
+    """
+    Loads a TSDF metadata file, returns a dictionary
+
+    :param path: path to the TSDF metadata file.
+
+    :return: dictionary of TSDFMetadata objects.
+    """
+    # The data is isomorphic to a JSON
+    with open(path, "r") as file:
+        data = json.load(file)
+
+    abs_path = os.path.realpath(path)
+    # Parse the data and verify that it complies with TSDF requirements
+    return parse_metadata.read_data(data, abs_path)

+
+
+
+

+[docs]
+def load_metadata_string(json_str) -> Dict[str, TSDFMetadata]:
+    """
+    Loads a TSDF metadata string, returns a dictionary.
+
+    :param json_str: string containing the TSDF metadata.
+
+    :return: dictionary of TSDFMetadata objects.
+    """
+
+    # The data is isomorphic to a JSON
+    data = json.loads(json_str)
+
+    # Parse the data and verify that it complies with TSDF requirements
+    return parse_metadata.read_data(data, "")

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2024, Netherlands eScience Center.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/tsdf/tsdfmetadata.html b/_modules/tsdf/tsdfmetadata.html new file mode 100644 index 0000000..a7d9c1e --- /dev/null +++ b/_modules/tsdf/tsdfmetadata.html @@ -0,0 +1,290 @@ + + + + + + tsdf.tsdfmetadata — tsdf documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for tsdf.tsdfmetadata

+import copy
+from typing import Any, Dict, List
+from datetime import datetime
+from dateutil import parser
+
+from tsdf import parse_metadata
+
+

+[docs]
+class TSDFMetadataFieldError(Exception):
+    "Raised when the TSDFMetadata is missing an obligatory field."
+

+[docs]
+    @classmethod
+    def missing_field(cls, field_name: str):
+        message = f"Value for the obligatory TSDF field '{field_name}' is missing in the provided TSDF metadata file."
+        return cls(message)

+

+
+
+
+

+[docs]
+class TSDFMetadataFieldValueError(Exception):
+    "Raised when a TSDFMetadata field is wrongly annotated."
+    pass

+
+
+
+

+[docs]
+class TSDFMetadata:
+    """Structure that provides metadata needed for reading a data stream."""
+
+    metadata_version: str
+    """Version of the TSDF metadata file."""
+    study_id: str
+    """Study ID."""
+    subject_id: str
+    """Subject ID."""
+    device_id: str
+    """Device ID."""
+    start_iso8601: str
+    """Start time of the recording in ISO8601 format."""
+    end_iso8601: str
+    """End time of the recording in ISO8601 format."""
+    file_name: str
+    """Name of the binary file containing the data."""
+    rows: int
+    """Number of rows in the binary file."""
+    channels: List[str]
+    """List of channels in the binary file."""
+    units: List[str]
+    """List of units for each channel in the binary file."""
+    data_type: str
+    """Data type of the binary file."""
+    bits: int
+    """Number of bits per sample in the binary file."""
+    endianness: str
+    """Endianness of the binary file."""
+
+    file_dir_path: str
+    """ A reference to the directory path, so we don't need it again when reading associated binary files. """
+    metadata_file_name: str #TODO: do we need this?? / is it used?
+    """ A reference to the source path, so we don't need it again when reading associated binary files. """
+
+    def __init__(
+        self, dictionary: Dict[str, Any], dir_path: str, metadata_file_name: str = "", do_validate: bool = True
+    ) -> None:
+        """
+        The default constructor takes a dictionary as an argument and creates each
+        field as a separate property.\\
+        `Both, mandatory and non-mandatory fields are stored as object properties.`
+
+        :param dictionary: dictionary containing TSDF metadata.
+        :param dir_path: path to the directory where the metadata file is stored.
+        :param metadata_file_name: (optional) name of the metadata file.
+        :param do_validate: (optional) flag to validate the metadata.
+        """
+
+        # Copy the attributes from the dictionary to the object
+        for key, value in dictionary.items():
+            setattr(self, key, value)
+        self.file_dir_path = dir_path
+        self.metadata_file_name = metadata_file_name
+
+        # Validate the metadata
+        if do_validate:
+            if not self.validate():
+                raise TSDFMetadataFieldValueError("The provided metadata is invalid.")
+
+
+

+[docs]
+    def validate(self) -> bool:
+        isValid: bool = True
+
+        # Validate presence of mandatory fields
+        dict = self.get_plain_tsdf_dict_copy()
+        isValid = isValid and parse_metadata.contains_tsdf_mandatory_fields(dict)
+
+        # Validate datetimes
+        isValid = isValid and parse_metadata.validate_datetimes(self)
+
+        return isValid

+
+
+

+[docs]
+    def get_plain_tsdf_dict_copy(self) -> Dict[str, Any]:
+        """
+        Method returns the a copy of the dict containing fields needed for the TSDF file.
+
+        :return: a copy of the dict containing fields needed for the TSDF file.
+        """
+        simple_dict = copy.deepcopy(self.__dict__)
+        if simple_dict.get("file_dir_path") is not None:
+            simple_dict.pop("file_dir_path")
+        if simple_dict.get("metadata_file_name") is not None:
+            simple_dict.pop("metadata_file_name")
+        return simple_dict

+
+
+

+[docs]
+    def set_start_datetime(self, date_time: datetime) -> None:
+        """
+        Sets the start date of the recording in ISO8601 format.
+        :param date_time: datetime object containing the start date.
+        """
+        self.start_iso8601 = date_time.isoformat()

+
+
+

+[docs]
+    def get_start_datetime(self) -> datetime:
+        """
+        Returns the start date of the recording as a datetime object.
+        :return: datetime object containing the start date.
+        """
+        return parser.parse(self.start_iso8601)

+
+
+

+[docs]
+    def set_end_datetime(self, date_time: datetime) -> None:
+        """
+        Sets the end date of the recording in ISO8601 format.
+        :param date_time: datetime object containing the end date.
+        """
+        self.end_iso8601 = date_time.isoformat()

+
+
+

+[docs]
+    def get_end_datetime(self) -> datetime:
+        """
+        Returns the end date of the recording as a datetime object.
+        :return: datetime object containing the end date.
+        """
+        return parser.parse(self.end_iso8601)

+

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2024, Netherlands eScience Center.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/tsdf/validator.html b/_modules/tsdf/validator.html new file mode 100644 index 0000000..a51c3e0 --- /dev/null +++ b/_modules/tsdf/validator.html @@ -0,0 +1,187 @@ + + + + + + tsdf.validator — tsdf documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for tsdf.validator

+import os
+import argparse
+import traceback
+import json
+from tsdf import read_tsdf, read_binary
+
+
+

+[docs]
+def validate_tsdf_format(file_path):
+    try:
+        # Read the meta data (this will check for compulsory fields and such)
+        metadata = read_tsdf.load_metadata_from_path(file_path)
+
+        # Get the absolute path of the file and cut off the file name
+        abs_path = os.path.abspath(file_path)
+        abs_dir = os.path.dirname(abs_path)
+
+        # Loop through all the files in the metadata
+        for file_name, file_metadata in metadata.items():
+            # print the file_metadata as json
+            # print(json.dumps(file_metadata.get_plain_tsdf_dict_copy(), indent=4))
+
+            # Load the binary data
+            binary_data = read_binary.load_ndarray_from_binary(file_metadata)
+
+            # Success message
+            print(
+                f"Successfully loaded binary file {file_name}, resulting shape: {binary_data.shape}"
+            )
+
+        return True
+
+    except Exception as e:
+        print(f"Error while validating: {e}")
+        # traceback.print_exc()
+        return False

+
+
+
+

+[docs]
+def main():
+    # Parse the arguments
+    parser = argparse.ArgumentParser(
+        description="Validate a file content against the TSDF format."
+    )
+    parser.add_argument("file_path", help="Path to the file to validate")
+    args = parser.parse_args()
+
+    # Perform validation
+    is_valid = validate_tsdf_format(args.file_path)
+
+    # Exit with error code 1 if the validation failed
+    exit(0 if is_valid else 1)

+
+
+
+if __name__ == "__main__":
+    main()
+
+ +
+
+
+ +
+ +
+

© Copyright 2024, Netherlands eScience Center.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/tsdf/write_binary.html b/_modules/tsdf/write_binary.html new file mode 100644 index 0000000..b73d574 --- /dev/null +++ b/_modules/tsdf/write_binary.html @@ -0,0 +1,213 @@ + + + + + + tsdf.write_binary — tsdf documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for tsdf.write_binary

+"""
+Module for writing binary files associated with TSDF.
+
+Reference: https://arxiv.org/abs/2211.11294
+"""
+
+import os
+from typing import Any, Dict, List
+import numpy as np
+import pandas as pd
+from tsdf import numpy_utils
+
+from tsdf.tsdfmetadata import TSDFMetadata
+
+
+

+[docs]
+def write_dataframe_to_binaries(
+    file_dir: str, df: pd.DataFrame, metadatas: List[TSDFMetadata]
+) -> None:
+    """
+    Save binary file based on the provided pandas DataFrame.
+
+    :param file_dir:    path to the directory where the file will be saved.
+    :param df:          pandas DataFrame containing the data.
+    :param metadatas:   list of metadata objects to be saved, also contains
+                        channels to be retrieved from dataframe.
+    """
+    for metadata in metadatas:
+        file_name = metadata.file_name
+        path = os.path.join(file_dir, file_name)
+        
+        # Write
+        data = df[metadata.channels].to_numpy() # TODO: derive channels from dataframe or use specified in metadata? Also for file_name?
+        data.tofile(path)
+
+        # Update metadata with data properties
+        data_props = _get_metadata_from_ndarray(data)
+        for key in data_props:
+            metadata.__setattr__(key, data_props[key])

+
+
+
+

+[docs]
+def _get_metadata_from_ndarray(data: np.ndarray) -> Dict[str, Any]:
+    """
+    Retrieve metadata information encoded in the NumPy array.
+
+    :param data: NumPy array containing the data.
+
+    :return: dictionary containing the metadata.
+    """
+
+    metadata = {
+        "data_type": numpy_utils.data_type_numpy_to_tsdf(data),
+        "bits": numpy_utils.bits_numpy_to_tsdf(data),
+        "endianness": numpy_utils.endianness_numpy_to_tsdf(data),
+        "rows": numpy_utils.rows_numpy_to_tsdf(data),
+    }
+    return metadata

+
+
+
+

+[docs]
+def write_binary_file(
+    file_dir: str, file_name: str, data: np.ndarray, metadata: dict
+) -> TSDFMetadata:
+    """
+    Save binary file based on the provided NumPy array.
+
+    :param file_dir: path to the directory where the file will be saved.
+    :param file_name: name of the file to be saved.
+    :param data: NumPy array containing the data.
+    :param metadata: dictionary containing the metadata.
+
+    :return: TSDFMetadata object.
+    """
+    path = os.path.join(file_dir, file_name)
+    data.tofile(path)
+    metadata.update(_get_metadata_from_ndarray(data))
+    metadata.update({"file_name": file_name})
+
+    return TSDFMetadata(metadata, file_dir)

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2024, Netherlands eScience Center.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/tsdf/write_tsdf.html b/_modules/tsdf/write_tsdf.html new file mode 100644 index 0000000..ddba379 --- /dev/null +++ b/_modules/tsdf/write_tsdf.html @@ -0,0 +1,319 @@ + + + + + + tsdf.write_tsdf — tsdf documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for tsdf.write_tsdf

+"""
+Module for writing TSDF files.
+
+Reference: https://arxiv.org/abs/2211.11294
+"""
+
+from typing import Any, Dict, List
+from tsdf import file_utils
+from tsdf import parse_metadata
+from tsdf.tsdfmetadata import TSDFMetadata, TSDFMetadataFieldValueError
+
+
+

+[docs]
+def write_metadata(metadatas: List[TSDFMetadata], file_name: str) -> None:
+    """
+    Combine and save the TSDF metadata objects as a json file.
+
+    :param metadatas: List of TSDFMetadata objects to be saved.
+    :param file_name: Name of the file to be saved. The file will be saved in the directory of the first TSDFMetadata object in the list.
+
+    :raises TSDFMetadataFieldValueError: if the metadata files cannot be combined (e.g. they have no common fields) or if the list of TSDFMetadata objects is empty.
+    """
+    for meta in metadatas:
+        meta.validate()
+
+    if len(metadatas) == 0:
+        raise TSDFMetadataFieldValueError(
+            "Metadata cannot be saved, as the list of TSDFMetadata objects is empty."
+        )
+
+    if len(metadatas) == 1:
+        meta = metadatas[0]
+        file_utils.write_to_file(
+            meta.get_plain_tsdf_dict_copy(), meta.file_dir_path, file_name
+        )
+        return
+
+    # Ensure that the metadata files can be combined
+    parse_metadata.confirm_dir_of_metadata(metadatas)
+
+    plain_meta = [meta.get_plain_tsdf_dict_copy() for meta in metadatas]
+    overlap = _extract_common_fields(plain_meta)
+    if not overlap:
+        raise TSDFMetadataFieldValueError(
+            "Metadata files mist have at least one common field. Otherwise, they should be stored separately."
+        )
+
+    if len(plain_meta) > 0:
+        overlap["sensors"] = _calculate_overlaps_rec(plain_meta)
+    file_utils.write_to_file(overlap, metadatas[0].file_dir_path, file_name)

+
+
+
+

+[docs]
+def _extract_common_fields(metadatas: List[Dict[str, Any]]) -> Dict[str, Any]:
+    """
+    Extract the fields that are the same for all the metadata files.
+    A new dict is created and the fields are removed from the original dictionaries.
+
+    :param metadatas: List of dictionaries containing the metadata.
+
+    :return: Dictionary containing the common fields.
+    """
+    meta_overlap: dict = {}
+
+    # Return empty dict if metadatas is empty
+    if len(metadatas) == 0:
+        return meta_overlap
+    if len(metadatas) == 1:
+        return metadatas.pop(0)
+    init_metadata = metadatas[0]
+    for key, value in init_metadata.items():
+        key_in_all = True
+        for curr_meta in metadatas[1:]:
+            if key not in curr_meta.keys() or curr_meta[key] != value:
+                key_in_all = False
+        if key_in_all:
+            meta_overlap[key] = value
+    for key, _ in meta_overlap.items():
+        for meta_dict in metadatas:
+            meta_dict.pop(key)
+    return meta_overlap

+
+
+
+

+[docs]
+def _calculate_overlaps_rec(metadatas: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """
+    A recursive call that optimises the structure of the TSDF metadata, by grouping common values. For the input the list of dictionaries
+    corresponds to a list of "flat" metadata dictionaries. The output is a list of dictionaries (potentially of length 1) that contain
+    the metadata in a tree structure. The tree structure is created by grouping the common values in the metadata.
+    The grouping is done recursively, until no more grouping is possible.
+
+    :param metadatas: List of dictionaries containing the metadata.
+
+    :return: List of dictionaries containing the metadata in a tree structure.
+    """
+
+    if len(metadatas) == 0:
+        return []
+    if len(metadatas) == 1:
+        return metadatas
+
+    overlap_per_key: Dict[str, List[dict]] = {}  # Overlap for each key
+    final_metadata: List[dict] = []  # The metadata that is left to be processed
+
+    for key in _get_all_keys(metadatas):
+        overlap_per_key[key] = calculate_max_overlap(metadatas, key)
+
+    max_key = max_len_key(overlap_per_key)
+
+    first_group = overlap_per_key[max_key]
+    second_grop = [meta for meta in metadatas if meta not in first_group]
+
+    # Handle the first group
+    first_overlap = _extract_common_fields(first_group)
+    if len(first_group) > 0:
+        first_overlap["sensors"] = _calculate_overlaps_rec(first_group)
+    final_metadata.append(first_overlap)
+
+    # Handle the rest of the elements
+    second_overlap = _calculate_overlaps_rec(second_grop)
+    final_metadata.extend(second_overlap)
+
+    return final_metadata

+
+
+
+

+[docs]
+def _get_all_keys(metadatas: List[Dict[str, Any]]) -> List[str]:
+    """
+    Get all the keys from the metadata files.
+
+    :param metadatas: List of dictionaries containing the metadata.
+
+    :return: List of keys.
+    """
+    keys: List[str] = []
+    for meta in metadatas:
+        keys.extend(meta.keys())
+    return list(set(keys))

+
+
+
+

+[docs]
+def calculate_max_overlap(
+    meta_files: List[Dict[str, Any]], meta_key: str
+) -> List[Dict[str, Any]]:
+    """
+    Calculate the maximum overlap between the metadata files, for a specific key.
+    It returns the biggest group of dictionaries that contain the same value for the given meta_key.
+
+    :param meta_files: List of dictionaries containing the metadata.
+    :param meta_key: The key for which the overlap is calculated.
+
+    :return: List of dictionaries containing the metadata.
+    """
+    values: Dict[
+        str, List[Dict[str, Any]]
+    ] = (
+        {}
+    )  # Key: a value for the given meta_key, Value: list of metadata files that have that value
+    for meta in meta_files:
+        if meta_key in meta.keys():
+            curr_value = str(meta[meta_key])
+            if curr_value not in values.keys():
+                values[curr_value] = [meta]
+            else:
+                values[curr_value].append(meta)
+
+    max_key = max_len_key(values)
+    return values[max_key]

+
+
+
+

+[docs]
+def max_len_key(elements: Dict[str, List[Dict[str, Any]]]) -> str:
+    """
+    Return the key in a dictionary that has the longest list as a value.
+
+    :param elements: Dictionary containing the elements.
+
+    :return: The key that has the longest list as a value.
+    """
+    return max(elements, key=lambda x: len(elements[x]))

+
+
+ +
+
+
+ +
+ +
+

© Copyright 2024, Netherlands eScience Center.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/_sources/autoapi/tsdf/constants/index.rst.txt b/_sources/autoapi/tsdf/constants/index.rst.txt new file mode 100644 index 0000000..c6dc2a5 --- /dev/null +++ b/_sources/autoapi/tsdf/constants/index.rst.txt @@ -0,0 +1,48 @@ +tsdf.constants +============== + +.. py:module:: tsdf.constants + + +Attributes +---------- + +.. autoapisummary:: + + tsdf.constants.SUPPORTED_TSDF_VERSIONS + tsdf.constants.MANDATORY_TSDF_KEYS + tsdf.constants.MANDATORY_TSDF_KEYS_VALUES + tsdf.constants.KEY_VALUE_TYPES + tsdf.constants.METADATA_NAMING_PATTERN + tsdf.constants.ConcatenationType + + +Module Contents +--------------- + +.. py:data:: SUPPORTED_TSDF_VERSIONS + :value: ['0.1'] + + + List of currently supported versions. + +.. py:data:: MANDATORY_TSDF_KEYS + + Dictionary linking mandatory keys for different versions + +.. py:data:: MANDATORY_TSDF_KEYS_VALUES + + Dictionary linking mandatory keys to their values + +.. py:data:: KEY_VALUE_TYPES + + List of data types that are supported within the TSDF metadata file. + +.. py:data:: METADATA_NAMING_PATTERN + :value: '**meta.json' + + + Naming convention for the metadata files. ** allows for any prefix, including additional directories. + +.. py:data:: ConcatenationType + diff --git a/_sources/autoapi/tsdf/file_utils/index.rst.txt b/_sources/autoapi/tsdf/file_utils/index.rst.txt new file mode 100644 index 0000000..31ac1da --- /dev/null +++ b/_sources/autoapi/tsdf/file_utils/index.rst.txt @@ -0,0 +1,37 @@ +tsdf.file_utils +=============== + +.. py:module:: tsdf.file_utils + + +Functions +--------- + +.. autoapisummary:: + + tsdf.file_utils.get_files_matching + tsdf.file_utils.write_to_file + + +Module Contents +--------------- + +.. py:function:: get_files_matching(directory: str, criteria: str) -> list + + Get all files matching the criteria in the directory and its subdirectories. + + :param directory: directory to search in. + :param criteria: criteria to match (e.g., `**meta.json`). + + :return: list of files matching the criteria. + + +.. py:function:: write_to_file(dict: Dict[str, Any], dir_path: str, file_name: str) -> None + + Write a dictionary to a json file. + + :param dict: Dictionary to be written. + :param dir_path: Path to the directory where the file will be saved. + :param file_name: Name of the file to be saved. + + diff --git a/_sources/autoapi/tsdf/index.rst.txt b/_sources/autoapi/tsdf/index.rst.txt new file mode 100644 index 0000000..b847172 --- /dev/null +++ b/_sources/autoapi/tsdf/index.rst.txt @@ -0,0 +1,286 @@ +tsdf +==== + +.. py:module:: tsdf + + +Submodules +---------- + +.. toctree:: + :maxdepth: 1 + + /autoapi/tsdf/constants/index + /autoapi/tsdf/file_utils/index + /autoapi/tsdf/legacy_tsdf_utils/index + /autoapi/tsdf/numpy_utils/index + /autoapi/tsdf/parse_metadata/index + /autoapi/tsdf/read_binary/index + /autoapi/tsdf/read_tsdf/index + /autoapi/tsdf/tsdfmetadata/index + /autoapi/tsdf/validator/index + /autoapi/tsdf/write_binary/index + /autoapi/tsdf/write_tsdf/index + + +Classes +------- + +.. autoapisummary:: + + tsdf.TSDFMetadata + + +Functions +--------- + +.. autoapisummary:: + + tsdf.load_metadata_file + tsdf.load_metadata_from_path + tsdf.load_metadatas_from_dir + tsdf.load_metadata_string + tsdf.load_metadata_legacy_file + tsdf.write_metadata + tsdf.write_binary_file + tsdf.write_dataframe_to_binaries + tsdf.load_ndarray_from_binary + tsdf.load_dataframe_from_binaries + + +Package Contents +---------------- + +.. py:function:: load_metadata_file(file) -> Dict[str, tsdf.tsdfmetadata.TSDFMetadata] + + Loads a TSDF metadata file, returns a dictionary + + :param file: file object containing the TSDF metadata. + + :return: dictionary of TSDFMetadata objects. + + +.. py:function:: load_metadata_from_path(path: pathlib.Path) -> Dict[str, tsdf.tsdfmetadata.TSDFMetadata] + + Loads a TSDF metadata file, returns a dictionary + + :param path: path to the TSDF metadata file. + + :return: dictionary of TSDFMetadata objects. + + +.. py:function:: load_metadatas_from_dir(dir_path: str, naming_pattern=METADATA_NAMING_PATTERN) -> List[Dict[str, tsdf.tsdfmetadata.TSDFMetadata]] + + Loads all TSDF metadata files in a directory, returns a dictionary + + :param dir_path: path to the directory containing the TSDF metadata files. + :param naming_pattern: (optional) naming pattern of the TSDF metadata files . + + :return: dictionary of TSDFMetadata objects. + + +.. py:function:: load_metadata_string(json_str) -> Dict[str, tsdf.tsdfmetadata.TSDFMetadata] + + Loads a TSDF metadata string, returns a dictionary. + + :param json_str: string containing the TSDF metadata. + + :return: dictionary of TSDFMetadata objects. + + +.. py:function:: load_metadata_legacy_file(file) -> Dict[str, tsdf.tsdfmetadata.TSDFMetadata] + + Loads a TSDB metadata file, i.e., legacy format of the TSDF. It returns a dictionary representing the metadata. + + :param file: file object containing the TSDF metadata. + + :return: dictionary of TSDFMetadata objects. + + +.. py:function:: write_metadata(metadatas: List[tsdf.tsdfmetadata.TSDFMetadata], file_name: str) -> None + + Combine and save the TSDF metadata objects as a json file. + + :param metadatas: List of TSDFMetadata objects to be saved. + :param file_name: Name of the file to be saved. The file will be saved in the directory of the first TSDFMetadata object in the list. + + :raises TSDFMetadataFieldValueError: if the metadata files cannot be combined (e.g. they have no common fields) or if the list of TSDFMetadata objects is empty. + + +.. py:function:: write_binary_file(file_dir: str, file_name: str, data: numpy.ndarray, metadata: dict) -> tsdf.tsdfmetadata.TSDFMetadata + + Save binary file based on the provided NumPy array. + + :param file_dir: path to the directory where the file will be saved. + :param file_name: name of the file to be saved. + :param data: NumPy array containing the data. + :param metadata: dictionary containing the metadata. + + :return: TSDFMetadata object. + + +.. py:function:: write_dataframe_to_binaries(file_dir: str, df: pandas.DataFrame, metadatas: List[tsdf.tsdfmetadata.TSDFMetadata]) -> None + + Save binary file based on the provided pandas DataFrame. + + :param file_dir: path to the directory where the file will be saved. + :param df: pandas DataFrame containing the data. + :param metadatas: list of metadata objects to be saved, also contains + channels to be retrieved from dataframe. + + +.. py:function:: load_ndarray_from_binary(metadata: tsdf.tsdfmetadata.TSDFMetadata, start_row: int = 0, end_row: int = -1) -> numpy.ndarray + + Use metadata properties to load and return numpy array from a binary file (located the same directory where the metadata is saved). + + :param metadata: TSDFMetadata object. + :param start_row: (optional) first row to load. + :param end_row: (optional) last row to load. If -1, load all rows. + + :return: numpy array containing the data. + + +.. py:function:: load_dataframe_from_binaries(metadatas: List[tsdf.tsdfmetadata.TSDFMetadata], concatenation: tsdf.constants.ConcatenationType = ConcatenationType.none) -> Union[pandas.DataFrame, List[pandas.DataFrame]] + + Load content of binary files associated with TSDF into a pandas DataFrame. The data frames can be concatenated horizontally (ConcatenationType.columns), vertically (ConcatenationType.rows) or provided as a list of data frames (ConcatenationType.none). + + :param metadatas: list of TSDFMetadata objects. + :param concatenation: concatenation rule, i.e., determines whether the data frames (content of binary files) should be concatenated horizontally (ConcatenationType.columns), vertically (ConcatenationType.rows) or provided as a list of data frames (ConcatenationType.none). + + :return: pandas DataFrame containing the combined data. + + +.. py:class:: TSDFMetadata(dictionary: Dict[str, Any], dir_path: str, metadata_file_name: str = '', do_validate: bool = True) + + Structure that provides metadata needed for reading a data stream. + + + .. py:attribute:: metadata_version + :type: str + + Version of the TSDF metadata file. + + + .. py:attribute:: study_id + :type: str + + Study ID. + + + .. py:attribute:: subject_id + :type: str + + Subject ID. + + + .. py:attribute:: device_id + :type: str + + Device ID. + + + .. py:attribute:: start_iso8601 + :type: str + + Start time of the recording in ISO8601 format. + + + .. py:attribute:: end_iso8601 + :type: str + + End time of the recording in ISO8601 format. + + + .. py:attribute:: file_name + :type: str + + Name of the binary file containing the data. + + + .. py:attribute:: rows + :type: int + + Number of rows in the binary file. + + + .. py:attribute:: channels + :type: List[str] + + List of channels in the binary file. + + + .. py:attribute:: units + :type: List[str] + + List of units for each channel in the binary file. + + + .. py:attribute:: data_type + :type: str + + Data type of the binary file. + + + .. py:attribute:: bits + :type: int + + Number of bits per sample in the binary file. + + + .. py:attribute:: endianness + :type: str + + Endianness of the binary file. + + + .. py:attribute:: file_dir_path + :type: str + + A reference to the directory path, so we don't need it again when reading associated binary files. + + + .. py:attribute:: metadata_file_name + :type: str + + A reference to the source path, so we don't need it again when reading associated binary files. + + + .. py:method:: validate() -> bool + + + .. py:method:: get_plain_tsdf_dict_copy() -> Dict[str, Any] + + Method returns the a copy of the dict containing fields needed for the TSDF file. + + :return: a copy of the dict containing fields needed for the TSDF file. + + + + .. py:method:: set_start_datetime(date_time: datetime.datetime) -> None + + Sets the start date of the recording in ISO8601 format. + :param date_time: datetime object containing the start date. + + + + .. py:method:: get_start_datetime() -> datetime.datetime + + Returns the start date of the recording as a datetime object. + :return: datetime object containing the start date. + + + + .. py:method:: set_end_datetime(date_time: datetime.datetime) -> None + + Sets the end date of the recording in ISO8601 format. + :param date_time: datetime object containing the end date. + + + + .. py:method:: get_end_datetime() -> datetime.datetime + + Returns the end date of the recording as a datetime object. + :return: datetime object containing the end date. + + + diff --git a/_sources/autoapi/tsdf/legacy_tsdf_utils/index.rst.txt b/_sources/autoapi/tsdf/legacy_tsdf_utils/index.rst.txt new file mode 100644 index 0000000..5854ba9 --- /dev/null +++ b/_sources/autoapi/tsdf/legacy_tsdf_utils/index.rst.txt @@ -0,0 +1,87 @@ +tsdf.legacy_tsdf_utils +====================== + +.. py:module:: tsdf.legacy_tsdf_utils + + +Attributes +---------- + +.. autoapisummary:: + + tsdf.legacy_tsdf_utils.TSDB_TSDF_KEY_MAP + tsdf.legacy_tsdf_utils.TSDB_ARRAY_KEYS + + +Functions +--------- + +.. autoapisummary:: + + tsdf.legacy_tsdf_utils._rename_keys_in_metadata + tsdf.legacy_tsdf_utils._convert_to_array + tsdf.legacy_tsdf_utils.convert_tsdb_to_tsdf + tsdf.legacy_tsdf_utils.generate_tsdf_metadata_from_tsdb + tsdf.legacy_tsdf_utils.convert_file_tsdb_to_tsdf + tsdf.legacy_tsdf_utils.convert_files_tsdb_to_tsdf + + +Module Contents +--------------- + +.. py:data:: TSDB_TSDF_KEY_MAP + +.. py:data:: TSDB_ARRAY_KEYS + +.. py:function:: _rename_keys_in_metadata(old_dict: Dict[str, Any]) -> Dict[str, Any] + + This function renames the keys in a metadata file. + If a key in the metadata matches a key in the provided dictionary, it is renamed to the corresponding value in the dictionary. + It handles nested dictionaries and lists of dictionaries. + + :param old_dict: The metadata file (dictionary) with keys to rename + :return: The updated metadata file (dictionary) + + +.. py:function:: _convert_to_array(data: Dict[str, Any], key: str) -> Dict[str, Any] + + This function converts the value of a specified key in a dictionary to an array if it's not already an array. + It handles nested dictionaries and lists of dictionaries. + + :param data: The dictionary with a value to convert + :param key: The key in the dictionary whose value to convert + :return: The updated dictionary + + +.. py:function:: convert_tsdb_to_tsdf(data: Dict[str, Any]) -> Dict[str, Any] + + Converts a data from TSDB (legacy) to TSDF (0.1) format. + + :param data: The data in legacy (tsdb) format. + :return: The data in tsdf format. + + +.. py:function:: generate_tsdf_metadata_from_tsdb(filepath_existing: str, filepath_new: str) -> None + + This function creates a metadata file (JSON) file in TSDF (0.1) format from a TSDB (legacy) file. + + :param filepath_existing: The path to the JSON file to process + :param filepath_new: The path to the new JSON file + + +.. py:function:: convert_file_tsdb_to_tsdf(filepath: str) -> None + + This function converts a metadata file (JSON) from TSDB (legacy) to TSDF (0.1) format. It overwrites the original file. + + :param filepath: The path to the JSON file to process + + +.. py:function:: convert_files_tsdb_to_tsdf(directory: str) -> None + + This function converts all metadata files in a directory (and its subdirectories) from TSDB (legacy) to TSDF (0.1) format. + It walks through all files in a directory (and its subdirectories), + and processes all files with a .json extension. + + :param directory: The directory to process files in + + diff --git a/_sources/autoapi/tsdf/numpy_utils/index.rst.txt b/_sources/autoapi/tsdf/numpy_utils/index.rst.txt new file mode 100644 index 0000000..490b2c1 --- /dev/null +++ b/_sources/autoapi/tsdf/numpy_utils/index.rst.txt @@ -0,0 +1,110 @@ +tsdf.numpy_utils +================ + +.. py:module:: tsdf.numpy_utils + + +Attributes +---------- + +.. autoapisummary:: + + tsdf.numpy_utils._map_from_numpy_types + tsdf.numpy_utils._map_to_numpy_types + tsdf.numpy_utils._map_from_numpy_endianness + tsdf.numpy_utils._map_to_numpy_endianness + + +Functions +--------- + +.. autoapisummary:: + + tsdf.numpy_utils.data_type_numpy_to_tsdf + tsdf.numpy_utils.data_type_tsdf_to_numpy + tsdf.numpy_utils.bits_numpy_to_tsdf + tsdf.numpy_utils.bytes_tsdf_to_numpy + tsdf.numpy_utils.endianness_numpy_to_tsdf + tsdf.numpy_utils.endianness_tsdf_to_numpy + tsdf.numpy_utils.rows_numpy_to_tsdf + + +Module Contents +--------------- + +.. py:data:: _map_from_numpy_types + + Mapping of NumPy data types to their TSDF metadata annotations. + +.. py:function:: data_type_numpy_to_tsdf(data: numpy.ndarray) + + Compute the TSDF metadata 'data_type' value, based on the NumPy data. + + +.. py:data:: _map_to_numpy_types + + Mapping of data types that are supported by TSDF to + their NumPy representation used for parsing. + +.. py:function:: data_type_tsdf_to_numpy(data_type: str) -> str + + Compute the the NumPy data type, based on the TSDF metadata 'data_type' value. + + :param data_type: TSDF metadata 'data_type' value. + + :return: NumPy data type (as a char). + + +.. py:function:: bits_numpy_to_tsdf(data: numpy.ndarray) -> int + + Compute TSDF metadata 'n_bits' value, based on the NumPy data. + + :param data: NumPy data. + + :return: TSDF metadata 'n_bits' value. + + +.. py:function:: bytes_tsdf_to_numpy(n_bits: int) + + Compute the the NumPy byte number, based on the TSDF metadata 'n_bits' value. + + :param n_bits: TSDF metadata 'n_bits' value. + + :return: NumPy byte number. + + +.. py:data:: _map_from_numpy_endianness + + Supported endianness values. + +.. py:function:: endianness_numpy_to_tsdf(data: numpy.ndarray) -> str + + Compute TSDF metadata 'data_type' value, based on the NumPy data. + + :param data: NumPy data. + + :return: TSDF metadata 'data_type' value (as a string). + + +.. py:data:: _map_to_numpy_endianness + + Supported endianness values. + +.. py:function:: endianness_tsdf_to_numpy(endianness: str) -> str + + Compute TSDF metadata 'data_type' value, based on the NumPy data. + + :param endianness: TSDF metadata 'data_type' value. + + :return: NumPy data type (as a char). + + +.. py:function:: rows_numpy_to_tsdf(data: numpy.ndarray) -> int + + Compute TSDF metadata 'rows' value, based on the NumPy data. + + :param data: NumPy data. + + :return: TSDF metadata 'rows' value. + + diff --git a/_sources/autoapi/tsdf/parse_metadata/index.rst.txt b/_sources/autoapi/tsdf/parse_metadata/index.rst.txt new file mode 100644 index 0000000..36e61ad --- /dev/null +++ b/_sources/autoapi/tsdf/parse_metadata/index.rst.txt @@ -0,0 +1,142 @@ +tsdf.parse_metadata +=================== + +.. py:module:: tsdf.parse_metadata + +.. autoapi-nested-parse:: + + Module for parsing TSDF metadata files. + + Reference: https://arxiv.org/abs/2211.11294 + + + +Functions +--------- + +.. autoapisummary:: + + tsdf.parse_metadata.read_data + tsdf.parse_metadata._read_struct + tsdf.parse_metadata.is_mandatory_type + tsdf.parse_metadata._contains_file_name + tsdf.parse_metadata.contains_tsdf_mandatory_fields + tsdf.parse_metadata._check_tsdf_property_format + tsdf.parse_metadata.get_file_metadata_at_index + tsdf.parse_metadata.confirm_dir_of_metadata + tsdf.parse_metadata.is_iso8601 + tsdf.parse_metadata.validate_datetimes + + +Module Contents +--------------- + +.. py:function:: read_data(data: Any, source_path: str) -> Dict[str, tsdf.tsdfmetadata.TSDFMetadata] + + Function used to parse the JSON object containing TSDF metadata. It returns a + list of TSDFMetadata objects, where each object describes formatting of a binary file. + + :param data: JSON object containing TSDF metadata. + :param source_path: path to the metadata file. + + :return: list of TSDFMetadata objects. + + :raises tsdf_metadata.TSDFMetadataFieldValueError: if the TSDF metadata file is missing a mandatory field. + + +.. py:function:: _read_struct(data: Any, defined_properties: Dict[str, Any], source_path, version: str) -> Dict[str, tsdf.tsdfmetadata.TSDFMetadata] + + Recursive method used to parse the TSDF metadata in a hierarchical + order (from the root towards the leaves). + + :param data: JSON object containing TSDF metadata. + :param defined_properties: dictionary containing all the properties defined at the current level of the TSDF structure. + :param source_path: path to the metadata file. + :param version: version of the TSDF used within the file. + + :return: list of TSDFMetadata objects. + + :raises tsdf_metadata.TSDFMetadataFieldError: if the TSDF metadata file is missing a mandatory field. + + +.. py:function:: is_mandatory_type(key: str, version: str) -> bool + + Function returns True if the field that corresponds to the + key is mandatory for the given TSDF version, otherwise it returns False. + + :param key: key of the TSDF metadata field. + :param version: version of the TSDF used within the file. + + :return: True if the field is mandatory, otherwise False. + + +.. py:function:: _contains_file_name(data: Any) -> bool + + Function return True if the data contains the "file_name" key, + and thus, represents nested data elements. + Otherwise it returns False. + + :param data: data to be checked. + + :return: True if the data contains the "file_name" key, otherwise False. + + +.. py:function:: contains_tsdf_mandatory_fields(dictionary: Dict[str, Any]) -> bool + + Verifies that all the mandatory properties for TSDF metadata are provided, + and are in the right format. + + :param dictionary: dictionary containing TSDF metadata. + + :return: True if the metadata is well formatted. + + :raises tsdf_metadata.TSDFMetadataFieldError: if the TSDF metadata file is missing a mandatory field. + :raises tsdf_metadata.TSDFMetadataFieldValueError: if the TSDF metadata file contains an invalid value. + + +.. py:function:: _check_tsdf_property_format(key: str, value, version: str) -> None + + Function checks whether the value of the mandatory TSDF field specified by the key + is of the expected data format.\ + `Note: If the key is not mandatory the function does not perform any checks.` + + :param key: key of the TSDF metadata field. + :param value: value of the TSDF metadata field. + :param version: version of the TSDF used within the file. + + :raises tsdf_metadata.TSDFMetadataFieldValueError: if the TSDF metadata file contains an invalid value. + + +.. py:function:: get_file_metadata_at_index(metadata: Dict[str, tsdf.tsdfmetadata.TSDFMetadata], index: int) -> tsdf.tsdfmetadata.TSDFMetadata + + Returns the metadata object at the position defined by the index. + + :param metadata: dictionary containing TSDF metadata. + :param index: index of the metadata object to be returned. + + :return: metadata object at the position defined by the index. + + :raises IndexError: if the index is out of range. + + +.. py:function:: confirm_dir_of_metadata(metadatas: List[tsdf.tsdfmetadata.TSDFMetadata]) -> None + + The method is used to confirm whether all the metadata files are expected in the same directory. + + :param metadatas: list of metadata objects. + + :raises tsdf_metadata.TSDFMetadataFieldValueError: if the metadata files are not in the same directory or describe the same binaries. + + +.. py:function:: is_iso8601(date_string: str) -> bool + + Checks if the given date string is in ISO8601 format. + + :param date_string: date string to be checked. + + +.. py:function:: validate_datetimes(metadata: tsdf.tsdfmetadata.TSDFMetadata) -> bool + + Validates the start and end date format of the TSDFMetaData object. + + diff --git a/_sources/autoapi/tsdf/read_binary/index.rst.txt b/_sources/autoapi/tsdf/read_binary/index.rst.txt new file mode 100644 index 0000000..c8c2dce --- /dev/null +++ b/_sources/autoapi/tsdf/read_binary/index.rst.txt @@ -0,0 +1,63 @@ +tsdf.read_binary +================ + +.. py:module:: tsdf.read_binary + +.. autoapi-nested-parse:: + + Module for reading and writing binary files associated with TSDF. + + Reference: https://arxiv.org/abs/2211.11294 + + + +Functions +--------- + +.. autoapisummary:: + + tsdf.read_binary.load_dataframe_from_binaries + tsdf.read_binary.load_ndarray_from_binary + tsdf.read_binary._load_binary_file + + +Module Contents +--------------- + +.. py:function:: load_dataframe_from_binaries(metadatas: List[tsdf.tsdfmetadata.TSDFMetadata], concatenation: tsdf.constants.ConcatenationType = ConcatenationType.none) -> Union[pandas.DataFrame, List[pandas.DataFrame]] + + Load content of binary files associated with TSDF into a pandas DataFrame. The data frames can be concatenated horizontally (ConcatenationType.columns), vertically (ConcatenationType.rows) or provided as a list of data frames (ConcatenationType.none). + + :param metadatas: list of TSDFMetadata objects. + :param concatenation: concatenation rule, i.e., determines whether the data frames (content of binary files) should be concatenated horizontally (ConcatenationType.columns), vertically (ConcatenationType.rows) or provided as a list of data frames (ConcatenationType.none). + + :return: pandas DataFrame containing the combined data. + + +.. py:function:: load_ndarray_from_binary(metadata: tsdf.tsdfmetadata.TSDFMetadata, start_row: int = 0, end_row: int = -1) -> numpy.ndarray + + Use metadata properties to load and return numpy array from a binary file (located the same directory where the metadata is saved). + + :param metadata: TSDFMetadata object. + :param start_row: (optional) first row to load. + :param end_row: (optional) last row to load. If -1, load all rows. + + :return: numpy array containing the data. + + +.. py:function:: _load_binary_file(bin_file_path: str, data_type: str, n_bits: int, endianness: str, n_rows: int, n_columns: int, start_row: int = 0, end_row: int = -1) -> numpy.ndarray + + Use provided parameters to load and return a numpy array from a binary file. + + :param bin_file_path: path to the binary file. + :param data_type: data type of the binary file. + :param n_bits: number of bits per value. + :param endianness: endianness of the binary file. + :param n_rows: number of rows in the binary file. + :param n_columns: number of columns in the binary file. + :param start_row: (optional) first row to load. + :param end_row: (optional) last row to load. If -1, load all rows. + + :return: numpy array containing the data. + + diff --git a/_sources/autoapi/tsdf/read_tsdf/index.rst.txt b/_sources/autoapi/tsdf/read_tsdf/index.rst.txt new file mode 100644 index 0000000..501e0b2 --- /dev/null +++ b/_sources/autoapi/tsdf/read_tsdf/index.rst.txt @@ -0,0 +1,74 @@ +tsdf.read_tsdf +============== + +.. py:module:: tsdf.read_tsdf + +.. autoapi-nested-parse:: + + Module for reading TSDF files. + + Reference: https://arxiv.org/abs/2211.11294 + + + +Functions +--------- + +.. autoapisummary:: + + tsdf.read_tsdf.load_metadata_file + tsdf.read_tsdf.load_metadata_legacy_file + tsdf.read_tsdf.load_metadatas_from_dir + tsdf.read_tsdf.load_metadata_from_path + tsdf.read_tsdf.load_metadata_string + + +Module Contents +--------------- + +.. py:function:: load_metadata_file(file) -> Dict[str, tsdf.tsdfmetadata.TSDFMetadata] + + Loads a TSDF metadata file, returns a dictionary + + :param file: file object containing the TSDF metadata. + + :return: dictionary of TSDFMetadata objects. + + +.. py:function:: load_metadata_legacy_file(file) -> Dict[str, tsdf.tsdfmetadata.TSDFMetadata] + + Loads a TSDB metadata file, i.e., legacy format of the TSDF. It returns a dictionary representing the metadata. + + :param file: file object containing the TSDF metadata. + + :return: dictionary of TSDFMetadata objects. + + +.. py:function:: load_metadatas_from_dir(dir_path: str, naming_pattern=METADATA_NAMING_PATTERN) -> List[Dict[str, tsdf.tsdfmetadata.TSDFMetadata]] + + Loads all TSDF metadata files in a directory, returns a dictionary + + :param dir_path: path to the directory containing the TSDF metadata files. + :param naming_pattern: (optional) naming pattern of the TSDF metadata files . + + :return: dictionary of TSDFMetadata objects. + + +.. py:function:: load_metadata_from_path(path: pathlib.Path) -> Dict[str, tsdf.tsdfmetadata.TSDFMetadata] + + Loads a TSDF metadata file, returns a dictionary + + :param path: path to the TSDF metadata file. + + :return: dictionary of TSDFMetadata objects. + + +.. py:function:: load_metadata_string(json_str) -> Dict[str, tsdf.tsdfmetadata.TSDFMetadata] + + Loads a TSDF metadata string, returns a dictionary. + + :param json_str: string containing the TSDF metadata. + + :return: dictionary of TSDFMetadata objects. + + diff --git a/_sources/autoapi/tsdf/tsdfmetadata/index.rst.txt b/_sources/autoapi/tsdf/tsdfmetadata/index.rst.txt new file mode 100644 index 0000000..5a3aca3 --- /dev/null +++ b/_sources/autoapi/tsdf/tsdfmetadata/index.rst.txt @@ -0,0 +1,181 @@ +tsdf.tsdfmetadata +================= + +.. py:module:: tsdf.tsdfmetadata + + +Exceptions +---------- + +.. autoapisummary:: + + tsdf.tsdfmetadata.TSDFMetadataFieldError + tsdf.tsdfmetadata.TSDFMetadataFieldValueError + + +Classes +------- + +.. autoapisummary:: + + tsdf.tsdfmetadata.TSDFMetadata + + +Module Contents +--------------- + +.. py:exception:: TSDFMetadataFieldError + + Bases: :py:obj:`Exception` + + + Raised when the TSDFMetadata is missing an obligatory field. + + + .. py:method:: missing_field(field_name: str) + :classmethod: + + + +.. py:exception:: TSDFMetadataFieldValueError + + Bases: :py:obj:`Exception` + + + Raised when a TSDFMetadata field is wrongly annotated. + + +.. py:class:: TSDFMetadata(dictionary: Dict[str, Any], dir_path: str, metadata_file_name: str = '', do_validate: bool = True) + + Structure that provides metadata needed for reading a data stream. + + + .. py:attribute:: metadata_version + :type: str + + Version of the TSDF metadata file. + + + .. py:attribute:: study_id + :type: str + + Study ID. + + + .. py:attribute:: subject_id + :type: str + + Subject ID. + + + .. py:attribute:: device_id + :type: str + + Device ID. + + + .. py:attribute:: start_iso8601 + :type: str + + Start time of the recording in ISO8601 format. + + + .. py:attribute:: end_iso8601 + :type: str + + End time of the recording in ISO8601 format. + + + .. py:attribute:: file_name + :type: str + + Name of the binary file containing the data. + + + .. py:attribute:: rows + :type: int + + Number of rows in the binary file. + + + .. py:attribute:: channels + :type: List[str] + + List of channels in the binary file. + + + .. py:attribute:: units + :type: List[str] + + List of units for each channel in the binary file. + + + .. py:attribute:: data_type + :type: str + + Data type of the binary file. + + + .. py:attribute:: bits + :type: int + + Number of bits per sample in the binary file. + + + .. py:attribute:: endianness + :type: str + + Endianness of the binary file. + + + .. py:attribute:: file_dir_path + :type: str + + A reference to the directory path, so we don't need it again when reading associated binary files. + + + .. py:attribute:: metadata_file_name + :type: str + + A reference to the source path, so we don't need it again when reading associated binary files. + + + .. py:method:: validate() -> bool + + + .. py:method:: get_plain_tsdf_dict_copy() -> Dict[str, Any] + + Method returns the a copy of the dict containing fields needed for the TSDF file. + + :return: a copy of the dict containing fields needed for the TSDF file. + + + + .. py:method:: set_start_datetime(date_time: datetime.datetime) -> None + + Sets the start date of the recording in ISO8601 format. + :param date_time: datetime object containing the start date. + + + + .. py:method:: get_start_datetime() -> datetime.datetime + + Returns the start date of the recording as a datetime object. + :return: datetime object containing the start date. + + + + .. py:method:: set_end_datetime(date_time: datetime.datetime) -> None + + Sets the end date of the recording in ISO8601 format. + :param date_time: datetime object containing the end date. + + + + .. py:method:: get_end_datetime() -> datetime.datetime + + Returns the end date of the recording as a datetime object. + :return: datetime object containing the end date. + + + diff --git a/_sources/autoapi/tsdf/validator/index.rst.txt b/_sources/autoapi/tsdf/validator/index.rst.txt new file mode 100644 index 0000000..867d98e --- /dev/null +++ b/_sources/autoapi/tsdf/validator/index.rst.txt @@ -0,0 +1,22 @@ +tsdf.validator +============== + +.. py:module:: tsdf.validator + + +Functions +--------- + +.. autoapisummary:: + + tsdf.validator.validate_tsdf_format + tsdf.validator.main + + +Module Contents +--------------- + +.. py:function:: validate_tsdf_format(file_path) + +.. py:function:: main() + diff --git a/_sources/autoapi/tsdf/write_binary/index.rst.txt b/_sources/autoapi/tsdf/write_binary/index.rst.txt new file mode 100644 index 0000000..3343c7c --- /dev/null +++ b/_sources/autoapi/tsdf/write_binary/index.rst.txt @@ -0,0 +1,57 @@ +tsdf.write_binary +================= + +.. py:module:: tsdf.write_binary + +.. autoapi-nested-parse:: + + Module for writing binary files associated with TSDF. + + Reference: https://arxiv.org/abs/2211.11294 + + + +Functions +--------- + +.. autoapisummary:: + + tsdf.write_binary.write_dataframe_to_binaries + tsdf.write_binary._get_metadata_from_ndarray + tsdf.write_binary.write_binary_file + + +Module Contents +--------------- + +.. py:function:: write_dataframe_to_binaries(file_dir: str, df: pandas.DataFrame, metadatas: List[tsdf.tsdfmetadata.TSDFMetadata]) -> None + + Save binary file based on the provided pandas DataFrame. + + :param file_dir: path to the directory where the file will be saved. + :param df: pandas DataFrame containing the data. + :param metadatas: list of metadata objects to be saved, also contains + channels to be retrieved from dataframe. + + +.. py:function:: _get_metadata_from_ndarray(data: numpy.ndarray) -> Dict[str, Any] + + Retrieve metadata information encoded in the NumPy array. + + :param data: NumPy array containing the data. + + :return: dictionary containing the metadata. + + +.. py:function:: write_binary_file(file_dir: str, file_name: str, data: numpy.ndarray, metadata: dict) -> tsdf.tsdfmetadata.TSDFMetadata + + Save binary file based on the provided NumPy array. + + :param file_dir: path to the directory where the file will be saved. + :param file_name: name of the file to be saved. + :param data: NumPy array containing the data. + :param metadata: dictionary containing the metadata. + + :return: TSDFMetadata object. + + diff --git a/_sources/autoapi/tsdf/write_tsdf/index.rst.txt b/_sources/autoapi/tsdf/write_tsdf/index.rst.txt new file mode 100644 index 0000000..a8c5c94 --- /dev/null +++ b/_sources/autoapi/tsdf/write_tsdf/index.rst.txt @@ -0,0 +1,90 @@ +tsdf.write_tsdf +=============== + +.. py:module:: tsdf.write_tsdf + +.. autoapi-nested-parse:: + + Module for writing TSDF files. + + Reference: https://arxiv.org/abs/2211.11294 + + + +Functions +--------- + +.. autoapisummary:: + + tsdf.write_tsdf.write_metadata + tsdf.write_tsdf._extract_common_fields + tsdf.write_tsdf._calculate_overlaps_rec + tsdf.write_tsdf._get_all_keys + tsdf.write_tsdf.calculate_max_overlap + tsdf.write_tsdf.max_len_key + + +Module Contents +--------------- + +.. py:function:: write_metadata(metadatas: List[tsdf.tsdfmetadata.TSDFMetadata], file_name: str) -> None + + Combine and save the TSDF metadata objects as a json file. + + :param metadatas: List of TSDFMetadata objects to be saved. + :param file_name: Name of the file to be saved. The file will be saved in the directory of the first TSDFMetadata object in the list. + + :raises TSDFMetadataFieldValueError: if the metadata files cannot be combined (e.g. they have no common fields) or if the list of TSDFMetadata objects is empty. + + +.. py:function:: _extract_common_fields(metadatas: List[Dict[str, Any]]) -> Dict[str, Any] + + Extract the fields that are the same for all the metadata files. + A new dict is created and the fields are removed from the original dictionaries. + + :param metadatas: List of dictionaries containing the metadata. + + :return: Dictionary containing the common fields. + + +.. py:function:: _calculate_overlaps_rec(metadatas: List[Dict[str, Any]]) -> List[Dict[str, Any]] + + A recursive call that optimises the structure of the TSDF metadata, by grouping common values. For the input the list of dictionaries + corresponds to a list of "flat" metadata dictionaries. The output is a list of dictionaries (potentially of length 1) that contain + the metadata in a tree structure. The tree structure is created by grouping the common values in the metadata. + The grouping is done recursively, until no more grouping is possible. + + :param metadatas: List of dictionaries containing the metadata. + + :return: List of dictionaries containing the metadata in a tree structure. + + +.. py:function:: _get_all_keys(metadatas: List[Dict[str, Any]]) -> List[str] + + Get all the keys from the metadata files. + + :param metadatas: List of dictionaries containing the metadata. + + :return: List of keys. + + +.. py:function:: calculate_max_overlap(meta_files: List[Dict[str, Any]], meta_key: str) -> List[Dict[str, Any]] + + Calculate the maximum overlap between the metadata files, for a specific key. + It returns the biggest group of dictionaries that contain the same value for the given meta_key. + + :param meta_files: List of dictionaries containing the metadata. + :param meta_key: The key for which the overlap is calculated. + + :return: List of dictionaries containing the metadata. + + +.. py:function:: max_len_key(elements: Dict[str, List[Dict[str, Any]]]) -> str + + Return the key in a dictionary that has the longest list as a value. + + :param elements: Dictionary containing the elements. + + :return: The key that has the longest list as a value. + + diff --git a/_sources/basic_reading_and_writing_numpy.ipynb.txt b/_sources/basic_reading_and_writing_numpy.ipynb.txt new file mode 100644 index 0000000..225b57a --- /dev/null +++ b/_sources/basic_reading_and_writing_numpy.ipynb.txt @@ -0,0 +1,236 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Basic reading and writing (numpy)\n", + "\n", + "These are some examples on how to read and write TSDF data into and from a numpy array, using the `tsdf` library." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import tsdf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load some data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data type:\t int16\n", + "Data shape:\t (10, 3)\n" + ] + } + ], + "source": [ + "# The file names\n", + "metadata_path = \"data/example_meta.json\"\n", + "binary_filename = \"example_binary.bin\"\n", + "\n", + "# Multiple metadata files (one for each binary) are loaded into a dictionary\n", + "# mapping the binary file name to the metadata object\n", + "metadata_dict = tsdf.load_metadata_from_path(metadata_path)\n", + "\n", + "# Retrieve the metadata object we want, using the name of the binary as key\n", + "metadata = metadata_dict[binary_filename]\n", + "\n", + "# Load the data\n", + "data = tsdf.load_ndarray_from_binary(metadata)\n", + "\n", + "# Print some info\n", + "print(f\"Data type:\\t {data.dtype}\")\n", + "print(f\"Data shape:\\t {data.shape}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform basic data processing" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processed data type:\t float32\n", + "Data shape:\t\t (10, 3)\n" + ] + } + ], + "source": [ + "# Perform an operation, resulting in a different data type\n", + "processed_data_1 = (data / 10).astype('float32')\n", + "\n", + "# Print some info\n", + "print(f\"Processed data type:\\t {processed_data_1.dtype}\")\n", + "print(f\"Data shape:\\t\\t {processed_data_1.shape}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Write the processed data \n", + "Write the processed data in binary format. The call returns the corresponding metadata object." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File written to data/tmp_example_processed.bin\n" + ] + } + ], + "source": [ + "# The new name of the file\n", + "output_bin_filename = \"tmp_example_processed.bin\"\n", + "\n", + "# Write the data to a new binary file\n", + "processed_metadata_1 = tsdf.write_binary_file(\n", + " \"data\",\n", + " output_bin_filename,\n", + " processed_data_1,\n", + " metadata.get_plain_tsdf_dict_copy(),\n", + " )\n", + "\n", + "print(f\"File written to data/{output_bin_filename}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Write the TSDF metadata file" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File written to data/tmp_example_processed_meta.json\n" + ] + } + ], + "source": [ + "# Write new metadata file\n", + "output_meta_filename = \"tmp_example_processed_meta.json\"\n", + "tsdf.write_metadata([processed_metadata_1], output_meta_filename)\n", + "print(f\"File written to data/{output_meta_filename}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Write a metadata file that combines multiple binary files" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File written to data/tmp_example_processed_2.bin\n", + "File written to data/tmp_example_processed_2_meta.json\n" + ] + } + ], + "source": [ + "# Preprocess the original data to generate another data source\n", + "processed_data_2 = (data * 1000).astype(\"int32\")\n", + "\n", + "# Adjust the metadata slightly\n", + "updated_metadata = metadata.get_plain_tsdf_dict_copy()\n", + "updated_metadata.pop(\"scale_factors\") # remove the 'scale_factors'\n", + "\n", + "# Save the new binary file\n", + "output_bin_filename_2 = \"tmp_example_processed_2.bin\"\n", + "processed_metadata_2 = tsdf.write_binary_file(\n", + " \"data\",\n", + " output_bin_filename_2,\n", + " processed_data_2,\n", + " updated_metadata,\n", + ")\n", + "print(f\"File written to data/{output_bin_filename_2}\")\n", + "\n", + "# Write a metadata file that combines the two binary files\n", + "output_meta_filename_2 = \"tmp_example_processed_2_meta.json\"\n", + "tsdf.write_metadata([processed_metadata_1, processed_metadata_2],\n", + " output_meta_filename_2)\n", + "print(f\"File written to data/{output_meta_filename_2}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tsdf-zVA6tG---py3.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "d1e978847a77d4ff49203fd09f0f7925f58560bf1007438482d75cb657018d9b" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/basic_reading_and_writing_pandas.ipynb.txt b/_sources/basic_reading_and_writing_pandas.ipynb.txt new file mode 100644 index 0000000..8d95fad --- /dev/null +++ b/_sources/basic_reading_and_writing_pandas.ipynb.txt @@ -0,0 +1,393 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Basic reading and writing (pandas)\n", + "\n", + "These are some examples on how to read and write TSDF data into and from a numpy array, using the `tsdf` library." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import tsdf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load some data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data shape:\t (17, 7)\n" + ] + }, + { + "data": { + "text/html": [ + "
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The call updates the metadata object with the data attributes of the dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File written to data/tmp_pandas_example_processed.bin\n" + ] + } + ], + "source": [ + "# The new name of the file\n", + "output_bin_filename = \"tmp_pandas_example_processed.bin\"\n", + "metadata_samples.__setattr__(\"file_name\", output_bin_filename)\n", + "\n", + "# Write the data to a new binary file\n", + "tsdf.write_dataframe_to_binaries(\"data\", df_scaled, [metadata_time, metadata_samples])\n", + "\n", + "print(f\"File written to data/{output_bin_filename}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Write the TSDF metadata file" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File written to data/tmp_pandas_example_processed_meta.json\n" + ] + } + ], + "source": [ + "# Write new metadata file\n", + "output_meta_filename = \"tmp_pandas_example_processed_meta.json\"\n", + "tsdf.write_metadata([metadata_time, metadata_samples], output_meta_filename)\n", + "print(f\"File written to data/{output_meta_filename}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tsdf-zVA6tG---py3.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "d1e978847a77d4ff49203fd09f0f7925f58560bf1007438482d75cb657018d9b" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/contact.md.txt b/_sources/contact.md.txt new file mode 100644 index 0000000..e104928 --- /dev/null +++ b/_sources/contact.md.txt @@ -0,0 +1,10 @@ +# Contact + +More information about the TSDF format can be found in the [TSDF preprint](https://arxiv.org/abs/2211.11294). + +This package has been written by engineers from the [Netherlands eScience Center](https://esciencecenter.nl). +It is maintained by: + +- Peter Kok ([p.kok@esciencecenter.nl](mailto:p.kok@esciencecenter.nl)). +- Vedran Kasalica ([v.kasalica@esciencecenter.nl](mailto:v.kasalica@esciencecenter.nl)). +- Pablo Rodríguez-Sánchez ([p.rodriguez-sanchez@esciencecenter.nl](mailto:p.rodriguez-sanchez@esciencecenter.nl)). diff --git a/_sources/converting_legacy_data.ipynb.txt b/_sources/converting_legacy_data.ipynb.txt new file mode 100644 index 0000000..b500f3a --- /dev/null +++ b/_sources/converting_legacy_data.ipynb.txt @@ -0,0 +1,71 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Transform legacy format\n", + "\n", + "Transform one file (or all files within the given directory) from legacy TSDB to the current TSDF format (v0.1)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from tsdf.legacy_tsdf_utils import (\n", + " generate_tsdf_metadata_from_tsdb,\n", + " convert_file_tsdb_to_tsdf,\n", + " convert_files_tsdb_to_tsdf,\n", + ")\n", + "\n", + "data_dir = 'data'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Path to the metadata file\n", + "path_to_file = os.path.join(data_dir, \"ppp_format_meta_legacy.json\")\n", + "path_to_new_file = os.path.join(data_dir, \"tmp_ppp_format_meta.json\")\n", + "\n", + "# Generate a TSDF metadata file from TSDB\n", + "generate_tsdf_metadata_from_tsdb(path_to_file, path_to_new_file)\n", + "\n", + "# Convert a TSDB metadata file to TSDB format\n", + "# convert_metadata_tsdb_to_tsdf(path_to_file)\n", + "\n", + "# Convert all metadata files in the directory from TSDB to TSDF format\n", + "# convert_metadatas_tsdb_to_tsdf(path_to_dir)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tsdf-zVA6tG---py3.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 0000000..92799c5 --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,35 @@ + +.. toctree:: + :maxdepth: 2 + :caption: Getting Started + + introduction + installation + +.. toctree:: + :maxdepth: 2 + :caption: The format + + tsdf_fields_table + +.. toctree:: + :maxdepth: 2 + :caption: Usage + + basic_reading_and_writing_numpy + basic_reading_and_writing_pandas + writing_data_from_scratch + converting_legacy_data + validating_files + +.. toctree:: + :maxdepth: 2 + :caption: API Reference + + autoapi/tsdf/index + +.. toctree:: + :maxdepth: 2 + :caption: About + + contact diff --git a/_sources/installation.md.txt b/_sources/installation.md.txt new file mode 100644 index 0000000..8ae90fb --- /dev/null +++ b/_sources/installation.md.txt @@ -0,0 +1,28 @@ +# Installation + +The package is available in [PyPI](https://pypi.org/project/tsdf/). The latest stable release can be installed using: + +```bash +pip install tsdf +``` + +## Installing the development version + +The source code is available on [GitHub](https://github.com/biomarkersParkinson/tsdf). + +If you have `git` installed, the latest version of tsdf can be installed by typing: + +```bash +pip install 'tsdf @ git+https://github.com/biomarkersParkinson/tsdf.git' +``` + +Otherwise you can install it manually by following these steps: + +1. Download or clone the content from [our repository](https://github.com/biomarkersParkinson/tsdf). +2. Browse to the folder you just downloaded (typically: `cd tsdf/`). +3. Install using your package manager (typically: `pip install .`). + +## What now? + +Now you can import functions from the `tsdf` Python package. +See some examples in the [usage section](/basic_reading_and_writing_numpy). diff --git a/_sources/introduction.md.txt b/_sources/introduction.md.txt new file mode 100644 index 0000000..dc70a3f --- /dev/null +++ b/_sources/introduction.md.txt @@ -0,0 +1,77 @@ +# Introduction + +Welcome to the TSDF (Time Series Data Format) Python package! + +A package to work with TSDF data in Python. This implementation is based on the the TSDF format specification, which can be found in this [preprint](https://arxiv.org/abs/2211.11294). + +## What is TSDF data? + +TSDF provides a unified, user-friendly format for both numerical sensor data and metadata, utilizing raw binary data and JSON-format text files for measurements/timestamps and metadata, respectively. It defines essential metadata fields to enhance data interpretability and exchangeability, aiming to bolster scientific reproducibility in studies reliant on digital biosensor data as a critical evidence base across various disease domains. + + +## Example: TSDF Metadata + +This example demonstrates a TSDF metadata JSON file, showcasing the structured format used to easily interpret and read the corresponding binary data. For more intricate examples and detailed specifications, the paper serves as a comprehensive reference. + +```json +{ + "study_id": "voicedata", + "subject_id": "recruit089", + "device_id": "audiotechnica02", + "endianness": "little", + "metadata_version": "0.1", + "start_iso8601": "2016-08-09T10:31:00.000+00:00", + "end_iso8601": "2016-08-10T10:31:30.000+00:00", + "sampling_rate": 44100, + "rows": 1323000, + "channels": [ + "left", + "right" + ], + "units": [ + "unitless", + "unitless" + ], + "compression": "none", + "data_type": "int", + "bits": 16, + "file_name": "audio_voice_089.raw" +} +``` +**Explanation:** + +- `study_id`: Identifies the study as "voicedata". + +- `subject_id`: Specifies the subject as "recruit089". + +- `device_id`: Indicates the device used as "audiotechnica02". + +- `endianness`: Specifies the byte order as "little". + +- `metadata_version`: Denotes the metadata version as "0.1". + +- `start_iso8601` and `end_iso8601`: Define the start and end timestamps of data collection in ISO 8601 format. + +- `sampling_rate`: Represents the data sampling rate as 44,100 samples per second. + +- `rows`: Specifies the number of data rows as 1,323,000. + +- `channels`: Lists the data channels as "left" and "right". + +- `units`: Specifies the units for each channel as "unitless". + +- `compression`: Indicates that no compression has been applied to the data. + +- `data_type`: Defines the data type as "int". + +- `bits`: Specifies the bit length as 16. + +- `file_name`: Names the binary file "audio_voice_089.raw" that contains the described data. + + + +## The python library - `tsdf` +This Python library facilitates the manipulation of Time Series Data Format (TSDF) metadata and binary files, providing users with a familiar and structured interface. Leveraging the power of numpy arrays, it simplifies the process of working with TSDF data, allowing users to efficiently read, write, and manipulate both metadata and binary data. This approach enhances data management and analysis, making it a valuable tool for researchers and data scientists dealing with extensive physiological sensor data. + + +The package is available in [PyPI](https://pypi.org/project/tsdf/). diff --git a/_sources/tsdf_fields_table.md.txt b/_sources/tsdf_fields_table.md.txt new file mode 100644 index 0000000..322b5ad --- /dev/null +++ b/_sources/tsdf_fields_table.md.txt @@ -0,0 +1,42 @@ +# TSDF metadata fields + +TSDF metadata is represented as a dictionary (or a JSON object). In this section, we will list the fields within the TSDF format, as described in the [format specification](https://arxiv.org/abs/2211.11294). In addition to the mandatory fields, any custom fields and structures can be added. + + +## TSDF v0.1 mandatory fields + +| Field | Type | Description | +|------------------|--------------|-----------------------------------------------------------------------------| +| `study_id` | `str` | Unique identifier for the particular study. | +| `device_id` | `str` | Specifies the device used for data collection. | +| `subject_id` | `str` | Unique identifier for the subject or participant in the study. | +| `source_file_name` | `str` | Name of the original source file containing the data. | +| `endianness` | `str` | [Byte order](https://en.wikipedia.org/wiki/Endianness) for numerical values in binary data ("big" or "little"). | +| `metadata_version` | `str` | Version information for the metadata. | +| `start_iso8601` | `str` | [ISO 8601](https://en.wikipedia.org/wiki/ISO_8601) time stamp for the start of the recording, with ms precision. | +| `end_iso8601` | `str` | Same as `start_iso8601`, but for the end of the recording. | +| `file_name` | `str` | The name of the file in consideration, e.g., "eeee.bin". | +| `channels` | `str[]` | Labels for each data channel (_e.g.:_ `[time]` for time data or `[X, Y, Z]` for 3D accelerometry). | +| `time_encode` | `str` | Encoding type for time, e.g., "difference". | +| `units` | `str[]` | Units for each channel in the data, e.g., "ms" for milliseconds. | +| `data_type` | `str` | Number format of the measured data (_e.g.:_ `float`). | +| `bits` | `int` | Bit-length of the number format (e.g., 32-bit). | +| `rows` | `int` | Number of rows in the data matrix. | + + +## Legacy fields + +The following table lists the legacy fields from the time when the format was called TSDB, along with their updated counterparts: + +| Legacy field | Updated field | Updated data type | +|--------------------------|-----------------------------|---------------| +| `project_id` | `study_id` | - | +| `quantities` | `channels` | `str[]` | +| `units` | `units` | `str[]` | +| `datatype` | `data_type` | - | +| `start_datetime_iso8601` | `start_iso8601` | - | +| `end_datetime_iso8601` | `end_iso8601` | - | + +**Note** + +As presented in the table above, the `quantities` and `units` fields are required to be arrays within the TSDF standard. \ No newline at end of file diff --git a/_sources/validating_files.ipynb.txt b/_sources/validating_files.ipynb.txt new file mode 100644 index 0000000..09c5104 --- /dev/null +++ b/_sources/validating_files.ipynb.txt @@ -0,0 +1,76 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Validate TSDF file\n", + "\n", + "Files can be validated using the validator module, which is also callable from the command line. The validator checks the metadata file and inspects whether the binary file is consistent with the metadata.\n", + "\n", + "The validator can be called from the command line as follows:\n", + "\n", + "```bash\n", + "pip install tsdf # in case you didn't already do this\n", + "validate-tsdf file-to-check.json\n", + "```\n", + "\n", + "The snippet below shows how to use the validator from code." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from tsdf import validator\n", + "\n", + "data_dir = \"../tests/data\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully loaded binary file ppp_format_time.bin, resulting shape: (17,)\n", + "Successfully loaded binary file ppp_format_samples.bin, resulting shape: (17, 6)\n" + ] + } + ], + "source": [ + "# Verify the metadata file\n", + "path_to_metadata_file = os.path.join(data_dir, \"ppp_format_meta.json\")\n", + "result = validator.validate_tsdf_format(path_to_metadata_file)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tsdf-zVA6tG---py3.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_sources/writing_data_from_scratch.ipynb.txt b/_sources/writing_data_from_scratch.ipynb.txt new file mode 100644 index 0000000..9184df9 --- /dev/null +++ b/_sources/writing_data_from_scratch.ipynb.txt @@ -0,0 +1,95 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Writing data from scratch\n", + "\n", + "When your data is not based on existing data, you will have to create a metadata object yourself." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tsdf\n", + "\n", + "data_dir = \"data\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files written to data/\n" + ] + } + ], + "source": [ + "# Generate random data\n", + "rs = np.random.RandomState(seed=42)\n", + "data_1 = rs.rand(17, 1).astype(np.float32)\n", + "data_2 = rs.rand(15, 2).astype(np.int16)\n", + "data_3 = rs.rand(10, 3).astype(np.int16)\n", + "\n", + "# Define the metadata\n", + "basic_metadata = {\n", + " \"subject_id\": \"example\",\n", + " \"study_id\": \"example\",\n", + " \"device_id\": \"example\",\n", + " \"endianness\": \"little\",\n", + " \"metadata_version\": \"0.1\",\n", + " \"start_datetime_unix_ms\": 1571135957025,\n", + " \"start_iso8601\": \"2019-10-15T10:39:17.025000+00:00\",\n", + " \"end_datetime_unix_ms\": 1571168851826,\n", + " \"end_iso8601\": \"2019-10-15T19:47:31.826000+00:00\",\n", + " \"channels\": [\"x\", \"y\", \"z\"],\n", + " \"units\": [\"m/s/s\", \"m/s/s\", \"m/s/s\"]\n", + "}\n", + "\n", + "# Write the three binary files based on the provided metadata.\n", + "# The new_meta variables will contain the basic_metadata, combined\n", + "# with the fields derived from the binary data (data type, bit depth, etc.)\n", + "file_prefix = \"tmp_test\"\n", + "new_meta_1 = tsdf.write_binary_file(data_dir, f\"{file_prefix}_1.bin\", data_1, basic_metadata)\n", + "new_meta_2 = tsdf.write_binary_file(data_dir, f\"{file_prefix}_2.bin\", data_2, basic_metadata)\n", + "new_meta_3 = tsdf.write_binary_file(data_dir, f\"{file_prefix}_3.bin\", data_3, basic_metadata)\n", + "\n", + "# Write the metadata file, which combines the metadata, in turn referencing the three binary files\n", + "tsdf.write_metadata([new_meta_1, new_meta_2, new_meta_3], f\"{file_prefix}_meta.json\")\n", + "print(f\"Files written to {data_dir}/\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tsdf-zVA6tG---py3.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 0000000..8141580 --- /dev/null +++ b/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * small 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span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} diff --git a/_static/basic.css b/_static/basic.css new file mode 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translated ANSI escape sequences +Color values are copied from Jupyter Notebook +https://github.com/jupyter/notebook/blob/52581f8eda9b319eb0390ac77fe5903c38f81e3e/notebook/static/notebook/less/ansicolors.less#L14-L21 +Background colors from +https://nbsphinx.readthedocs.io/en/latest/code-cells.html#ANSI-Colors +*/ +div.highlight .-Color-Bold { + font-weight: bold; +} + +div.highlight .-Color[class*=-Black] { + color: #3E424D +} + +div.highlight .-Color[class*=-Red] { + color: #E75C58 +} + +div.highlight .-Color[class*=-Green] { + color: #00A250 +} + +div.highlight .-Color[class*=-Yellow] { + color: #DDB62B +} + +div.highlight .-Color[class*=-Blue] { + color: #208FFB +} + +div.highlight .-Color[class*=-Magenta] { + color: #D160C4 +} + +div.highlight .-Color[class*=-Cyan] { + color: #60C6C8 +} + +div.highlight .-Color[class*=-White] { + color: #C5C1B4 +} + +div.highlight .-Color[class*=-BGBlack] { + background-color: #3E424D +} + +div.highlight .-Color[class*=-BGRed] { + background-color: #E75C58 +} + +div.highlight .-Color[class*=-BGGreen] { + background-color: #00A250 +} + +div.highlight .-Color[class*=-BGYellow] { + background-color: #DDB62B +} + +div.highlight .-Color[class*=-BGBlue] { + background-color: #208FFB +} + +div.highlight .-Color[class*=-BGMagenta] { + background-color: #D160C4 +} + +div.highlight .-Color[class*=-BGCyan] { + background-color: #60C6C8 +} + +div.highlight .-Color[class*=-BGWhite] { + background-color: #C5C1B4 +} + +/* Font colors for 8-bit ANSI */ + +div.highlight .-Color[class*=-C0] { + color: #000000 +} + +div.highlight .-Color[class*=-BGC0] { + background-color: #000000 +} + +div.highlight .-Color[class*=-C1] { + color: #800000 +} + +div.highlight .-Color[class*=-BGC1] { + background-color: #800000 +} + +div.highlight .-Color[class*=-C2] { + color: #008000 +} + +div.highlight .-Color[class*=-BGC2] { + background-color: #008000 +} + +div.highlight .-Color[class*=-C3] { + color: #808000 +} + +div.highlight .-Color[class*=-BGC3] { + background-color: #808000 +} + +div.highlight .-Color[class*=-C4] { + color: #000080 +} + +div.highlight .-Color[class*=-BGC4] { + background-color: #000080 +} + +div.highlight .-Color[class*=-C5] { + color: #800080 +} + +div.highlight .-Color[class*=-BGC5] { + background-color: #800080 +} + +div.highlight .-Color[class*=-C6] { + color: #008080 +} + +div.highlight .-Color[class*=-BGC6] { + background-color: #008080 +} + +div.highlight .-Color[class*=-C7] { + color: #C0C0C0 +} + +div.highlight .-Color[class*=-BGC7] { + background-color: #C0C0C0 +} + +div.highlight .-Color[class*=-C8] { + color: #808080 +} + +div.highlight .-Color[class*=-BGC8] { + background-color: #808080 +} + +div.highlight .-Color[class*=-C9] { + color: #FF0000 +} + +div.highlight .-Color[class*=-BGC9] { + background-color: #FF0000 +} + +div.highlight .-Color[class*=-C10] { + color: #00FF00 +} + +div.highlight .-Color[class*=-BGC10] { + background-color: #00FF00 +} + +div.highlight .-Color[class*=-C11] { + color: #FFFF00 +} + +div.highlight .-Color[class*=-BGC11] { + background-color: #FFFF00 +} + +div.highlight .-Color[class*=-C12] { + color: #0000FF +} + +div.highlight .-Color[class*=-BGC12] { + background-color: #0000FF +} + +div.highlight .-Color[class*=-C13] { + color: #FF00FF +} + +div.highlight .-Color[class*=-BGC13] { + background-color: #FF00FF +} + +div.highlight .-Color[class*=-C14] { + color: #00FFFF +} + +div.highlight .-Color[class*=-BGC14] { + background-color: #00FFFF +} + +div.highlight .-Color[class*=-C15] { + color: #FFFFFF +} + +div.highlight .-Color[class*=-BGC15] { + background-color: #FFFFFF +} + +div.highlight .-Color[class*=-C16] { + color: #000000 +} + +div.highlight .-Color[class*=-BGC16] { + background-color: #000000 +} + +div.highlight .-Color[class*=-C17] { + color: #00005F +} + +div.highlight .-Color[class*=-BGC17] { + background-color: #00005F +} + +div.highlight .-Color[class*=-C18] { + color: #000087 +} + +div.highlight .-Color[class*=-BGC18] { + background-color: #000087 +} + +div.highlight .-Color[class*=-C19] { + color: #0000AF +} + +div.highlight .-Color[class*=-BGC19] { + background-color: #0000AF +} + +div.highlight .-Color[class*=-C20] { + color: #0000D7 +} + +div.highlight .-Color[class*=-BGC20] { + background-color: #0000D7 +} + +div.highlight .-Color[class*=-C21] { + color: #0000FF +} + +div.highlight .-Color[class*=-BGC21] { + background-color: #0000FF +} + +div.highlight .-Color[class*=-C22] { + color: #005F00 +} + +div.highlight .-Color[class*=-BGC22] { + background-color: #005F00 +} + +div.highlight .-Color[class*=-C23] { + color: #005F5F +} + +div.highlight .-Color[class*=-BGC23] { + background-color: #005F5F +} + +div.highlight .-Color[class*=-C24] { + color: #005F87 +} + +div.highlight .-Color[class*=-BGC24] { + background-color: #005F87 +} + +div.highlight .-Color[class*=-C25] { + color: #005FAF +} + +div.highlight .-Color[class*=-BGC25] { + background-color: #005FAF +} + +div.highlight .-Color[class*=-C26] { + color: #005FD7 +} + +div.highlight .-Color[class*=-BGC26] { + background-color: #005FD7 +} + +div.highlight .-Color[class*=-C27] { + color: #005FFF +} + +div.highlight .-Color[class*=-BGC27] { + background-color: #005FFF +} + +div.highlight .-Color[class*=-C28] { + color: #008700 +} + +div.highlight .-Color[class*=-BGC28] { + background-color: #008700 +} + +div.highlight .-Color[class*=-C29] { + color: #00875F +} + +div.highlight .-Color[class*=-BGC29] { + background-color: #00875F +} + +div.highlight .-Color[class*=-C30] { + color: #008787 +} + +div.highlight .-Color[class*=-BGC30] { + background-color: #008787 +} + +div.highlight .-Color[class*=-C31] { + color: #0087AF +} + +div.highlight .-Color[class*=-BGC31] { + background-color: #0087AF +} + +div.highlight .-Color[class*=-C32] { + color: #0087D7 +} + +div.highlight .-Color[class*=-BGC32] { + background-color: #0087D7 +} + +div.highlight .-Color[class*=-C33] { + color: #0087FF +} + +div.highlight .-Color[class*=-BGC33] { + background-color: #0087FF +} + +div.highlight .-Color[class*=-C34] { + color: #00AF00 +} + +div.highlight .-Color[class*=-BGC34] { + background-color: #00AF00 +} + +div.highlight .-Color[class*=-C35] { + color: #00AF5F +} + +div.highlight .-Color[class*=-BGC35] { + background-color: #00AF5F +} + +div.highlight .-Color[class*=-C36] { + color: #00AF87 +} + +div.highlight .-Color[class*=-BGC36] { + background-color: #00AF87 +} + +div.highlight .-Color[class*=-C37] { + color: #00AFAF +} + +div.highlight .-Color[class*=-BGC37] { + background-color: #00AFAF +} + +div.highlight .-Color[class*=-C38] { + color: #00AFD7 +} + +div.highlight .-Color[class*=-BGC38] { + background-color: #00AFD7 +} + +div.highlight .-Color[class*=-C39] { + color: #00AFFF +} + +div.highlight .-Color[class*=-BGC39] { + background-color: #00AFFF +} + +div.highlight .-Color[class*=-C40] { + color: #00D700 +} + +div.highlight .-Color[class*=-BGC40] { + background-color: #00D700 +} + +div.highlight .-Color[class*=-C41] { + color: #00D75F +} + +div.highlight .-Color[class*=-BGC41] { + background-color: #00D75F +} + +div.highlight .-Color[class*=-C42] { + color: #00D787 +} + +div.highlight .-Color[class*=-BGC42] { + background-color: #00D787 +} + +div.highlight .-Color[class*=-C43] { + color: #00D7AF +} + +div.highlight .-Color[class*=-BGC43] { + background-color: #00D7AF +} + +div.highlight .-Color[class*=-C44] { + color: #00D7D7 +} + +div.highlight .-Color[class*=-BGC44] { + background-color: #00D7D7 +} + +div.highlight .-Color[class*=-C45] { + color: #00D7FF +} + +div.highlight .-Color[class*=-BGC45] { + background-color: #00D7FF +} + +div.highlight .-Color[class*=-C46] { + color: #00FF00 +} + +div.highlight .-Color[class*=-BGC46] { + background-color: #00FF00 +} + +div.highlight .-Color[class*=-C47] { + color: #00FF5F +} + +div.highlight .-Color[class*=-BGC47] { + background-color: #00FF5F +} + +div.highlight .-Color[class*=-C48] { + color: #00FF87 +} + +div.highlight .-Color[class*=-BGC48] { + background-color: #00FF87 +} + +div.highlight .-Color[class*=-C49] { + color: #00FFAF +} + +div.highlight .-Color[class*=-BGC49] { + background-color: #00FFAF +} + +div.highlight .-Color[class*=-C50] { + color: #00FFD7 +} + +div.highlight .-Color[class*=-BGC50] { + background-color: #00FFD7 +} + +div.highlight .-Color[class*=-C51] { + color: #00FFFF +} + +div.highlight .-Color[class*=-BGC51] { + background-color: #00FFFF +} + +div.highlight .-Color[class*=-C52] { + color: #5F0000 +} + +div.highlight .-Color[class*=-BGC52] { + background-color: #5F0000 +} + +div.highlight .-Color[class*=-C53] { + color: #5F005F +} + +div.highlight .-Color[class*=-BGC53] { + background-color: #5F005F +} + +div.highlight .-Color[class*=-C54] { + color: #5F0087 +} + +div.highlight .-Color[class*=-BGC54] { + background-color: #5F0087 +} + +div.highlight .-Color[class*=-C55] { + color: #5F00AF +} + +div.highlight .-Color[class*=-BGC55] { + background-color: #5F00AF +} + +div.highlight .-Color[class*=-C56] { + color: #5F00D7 +} + +div.highlight .-Color[class*=-BGC56] { + background-color: #5F00D7 +} + +div.highlight .-Color[class*=-C57] { + color: #5F00FF +} + +div.highlight .-Color[class*=-BGC57] { + background-color: #5F00FF +} + +div.highlight .-Color[class*=-C58] { + color: #5F5F00 +} + +div.highlight .-Color[class*=-BGC58] { + background-color: #5F5F00 +} + +div.highlight .-Color[class*=-C59] { + color: #5F5F5F +} + +div.highlight .-Color[class*=-BGC59] { + background-color: #5F5F5F +} + +div.highlight .-Color[class*=-C60] { + color: #5F5F87 +} + +div.highlight .-Color[class*=-BGC60] { + background-color: #5F5F87 +} + +div.highlight .-Color[class*=-C61] { + color: #5F5FAF +} + +div.highlight .-Color[class*=-BGC61] { + background-color: #5F5FAF +} + +div.highlight .-Color[class*=-C62] { + color: #5F5FD7 +} + +div.highlight .-Color[class*=-BGC62] { + background-color: #5F5FD7 +} + +div.highlight .-Color[class*=-C63] { + color: #5F5FFF +} + +div.highlight .-Color[class*=-BGC63] { + background-color: #5F5FFF +} + +div.highlight .-Color[class*=-C64] { + color: #5F8700 +} + +div.highlight .-Color[class*=-BGC64] { + background-color: #5F8700 +} + +div.highlight .-Color[class*=-C65] { + color: #5F875F +} + +div.highlight .-Color[class*=-BGC65] { + background-color: #5F875F +} + +div.highlight .-Color[class*=-C66] { + color: #5F8787 +} + +div.highlight .-Color[class*=-BGC66] { + background-color: #5F8787 +} + +div.highlight .-Color[class*=-C67] { + color: #5F87AF +} + +div.highlight .-Color[class*=-BGC67] { + background-color: #5F87AF +} + +div.highlight .-Color[class*=-C68] { + color: #5F87D7 +} + +div.highlight .-Color[class*=-BGC68] { + background-color: #5F87D7 +} + +div.highlight .-Color[class*=-C69] { + color: #5F87FF +} + +div.highlight .-Color[class*=-BGC69] { + background-color: #5F87FF +} + +div.highlight .-Color[class*=-C70] { + color: #5FAF00 +} + +div.highlight .-Color[class*=-BGC70] { + background-color: #5FAF00 +} + +div.highlight .-Color[class*=-C71] { + color: #5FAF5F +} + +div.highlight .-Color[class*=-BGC71] { + background-color: #5FAF5F +} + +div.highlight .-Color[class*=-C72] { + color: #5FAF87 +} + +div.highlight .-Color[class*=-BGC72] { + background-color: #5FAF87 +} + +div.highlight .-Color[class*=-C73] { + color: #5FAFAF +} + +div.highlight .-Color[class*=-BGC73] { + background-color: #5FAFAF +} + +div.highlight .-Color[class*=-C74] { + color: #5FAFD7 +} + +div.highlight .-Color[class*=-BGC74] { + background-color: #5FAFD7 +} + +div.highlight .-Color[class*=-C75] { + color: #5FAFFF +} + +div.highlight .-Color[class*=-BGC75] { + background-color: #5FAFFF +} + +div.highlight .-Color[class*=-C76] { + color: #5FD700 +} + +div.highlight .-Color[class*=-BGC76] { + background-color: #5FD700 +} + +div.highlight .-Color[class*=-C77] { + color: #5FD75F +} + +div.highlight .-Color[class*=-BGC77] { + background-color: #5FD75F +} + +div.highlight .-Color[class*=-C78] { + color: #5FD787 +} + +div.highlight .-Color[class*=-BGC78] { + background-color: #5FD787 +} + +div.highlight .-Color[class*=-C79] { + color: #5FD7AF +} + +div.highlight .-Color[class*=-BGC79] { + background-color: #5FD7AF +} + +div.highlight .-Color[class*=-C80] { + color: #5FD7D7 +} + +div.highlight .-Color[class*=-BGC80] { + background-color: #5FD7D7 +} + +div.highlight .-Color[class*=-C81] { + color: #5FD7FF +} + +div.highlight .-Color[class*=-BGC81] { + background-color: #5FD7FF +} + +div.highlight .-Color[class*=-C82] { + color: #5FFF00 +} + +div.highlight .-Color[class*=-BGC82] { + background-color: #5FFF00 +} + +div.highlight .-Color[class*=-C83] { + color: #5FFF5F +} + +div.highlight .-Color[class*=-BGC83] { + background-color: #5FFF5F +} + +div.highlight .-Color[class*=-C84] { + color: #5FFF87 +} + +div.highlight .-Color[class*=-BGC84] { + background-color: #5FFF87 +} + +div.highlight .-Color[class*=-C85] { + color: #5FFFAF +} + +div.highlight .-Color[class*=-BGC85] { + background-color: #5FFFAF +} + +div.highlight .-Color[class*=-C86] { + color: #5FFFD7 +} + +div.highlight .-Color[class*=-BGC86] { + background-color: #5FFFD7 +} + +div.highlight .-Color[class*=-C87] { + color: #5FFFFF +} + +div.highlight .-Color[class*=-BGC87] { + background-color: #5FFFFF +} + +div.highlight .-Color[class*=-C88] { + color: #870000 +} + +div.highlight .-Color[class*=-BGC88] { + background-color: #870000 +} + +div.highlight .-Color[class*=-C89] { + color: #87005F +} + +div.highlight .-Color[class*=-BGC89] { + background-color: #87005F +} + +div.highlight .-Color[class*=-C90] { + color: #870087 +} + +div.highlight .-Color[class*=-BGC90] { + background-color: #870087 +} + +div.highlight .-Color[class*=-C91] { + color: #8700AF +} + +div.highlight .-Color[class*=-BGC91] { + background-color: #8700AF +} + +div.highlight .-Color[class*=-C92] { + color: #8700D7 +} + +div.highlight .-Color[class*=-BGC92] { + background-color: #8700D7 +} + +div.highlight .-Color[class*=-C93] { + color: #8700FF +} + +div.highlight .-Color[class*=-BGC93] { + background-color: #8700FF +} + +div.highlight .-Color[class*=-C94] { + color: #875F00 +} + +div.highlight .-Color[class*=-BGC94] { + background-color: #875F00 +} + +div.highlight .-Color[class*=-C95] { + color: #875F5F +} + +div.highlight .-Color[class*=-BGC95] { + background-color: #875F5F +} + +div.highlight .-Color[class*=-C96] { + color: #875F87 +} + +div.highlight .-Color[class*=-BGC96] { + background-color: #875F87 +} + +div.highlight .-Color[class*=-C97] { + color: #875FAF +} + +div.highlight .-Color[class*=-BGC97] { + background-color: #875FAF +} + +div.highlight .-Color[class*=-C98] { + color: #875FD7 +} + +div.highlight .-Color[class*=-BGC98] { + background-color: #875FD7 +} + +div.highlight .-Color[class*=-C99] { + color: #875FFF +} + +div.highlight .-Color[class*=-BGC99] { + background-color: #875FFF +} + +div.highlight .-Color[class*=-C100] { + color: #878700 +} + +div.highlight .-Color[class*=-BGC100] { + background-color: #878700 +} + +div.highlight .-Color[class*=-C101] { + color: #87875F +} + +div.highlight .-Color[class*=-BGC101] { + background-color: #87875F +} + +div.highlight .-Color[class*=-C102] { + color: #878787 +} + +div.highlight .-Color[class*=-BGC102] { + background-color: #878787 +} + +div.highlight .-Color[class*=-C103] { + color: #8787AF +} + +div.highlight .-Color[class*=-BGC103] { + background-color: #8787AF +} + +div.highlight .-Color[class*=-C104] { + color: #8787D7 +} + +div.highlight .-Color[class*=-BGC104] { + background-color: #8787D7 +} + +div.highlight .-Color[class*=-C105] { + color: #8787FF +} + +div.highlight .-Color[class*=-BGC105] { + background-color: #8787FF +} + +div.highlight .-Color[class*=-C106] { + color: #87AF00 +} + +div.highlight .-Color[class*=-BGC106] { + background-color: #87AF00 +} + +div.highlight .-Color[class*=-C107] { + color: #87AF5F +} + +div.highlight .-Color[class*=-BGC107] { + background-color: #87AF5F +} + +div.highlight .-Color[class*=-C108] { + color: #87AF87 +} + +div.highlight .-Color[class*=-BGC108] { + background-color: #87AF87 +} + +div.highlight .-Color[class*=-C109] { + color: #87AFAF +} + +div.highlight .-Color[class*=-BGC109] { + background-color: #87AFAF +} + +div.highlight .-Color[class*=-C110] { + color: #87AFD7 +} + +div.highlight .-Color[class*=-BGC110] { + background-color: #87AFD7 +} + +div.highlight .-Color[class*=-C111] { + color: #87AFFF +} + +div.highlight .-Color[class*=-BGC111] { + background-color: #87AFFF +} + +div.highlight .-Color[class*=-C112] { + color: #87D700 +} + +div.highlight .-Color[class*=-BGC112] { + background-color: #87D700 +} + +div.highlight .-Color[class*=-C113] { + color: #87D75F +} + +div.highlight .-Color[class*=-BGC113] { + background-color: #87D75F +} + +div.highlight .-Color[class*=-C114] { + color: #87D787 +} + +div.highlight .-Color[class*=-BGC114] { + background-color: #87D787 +} + +div.highlight .-Color[class*=-C115] { + color: #87D7AF +} + +div.highlight .-Color[class*=-BGC115] { + background-color: #87D7AF +} + +div.highlight .-Color[class*=-C116] { + color: #87D7D7 +} + +div.highlight .-Color[class*=-BGC116] { + background-color: #87D7D7 +} + +div.highlight .-Color[class*=-C117] { + color: #87D7FF +} + +div.highlight .-Color[class*=-BGC117] { + background-color: #87D7FF +} + +div.highlight .-Color[class*=-C118] { + color: #87FF00 +} + +div.highlight .-Color[class*=-BGC118] { + background-color: #87FF00 +} + +div.highlight .-Color[class*=-C119] { + color: #87FF5F +} + +div.highlight .-Color[class*=-BGC119] { + background-color: #87FF5F +} + +div.highlight .-Color[class*=-C120] { + color: #87FF87 +} + +div.highlight .-Color[class*=-BGC120] { + background-color: #87FF87 +} + +div.highlight .-Color[class*=-C121] { + color: #87FFAF +} + +div.highlight .-Color[class*=-BGC121] { + background-color: #87FFAF +} + +div.highlight .-Color[class*=-C122] { + color: #87FFD7 +} + +div.highlight .-Color[class*=-BGC122] { + background-color: #87FFD7 +} + +div.highlight .-Color[class*=-C123] { + color: #87FFFF +} + +div.highlight .-Color[class*=-BGC123] { + background-color: #87FFFF +} + +div.highlight .-Color[class*=-C124] { + color: #AF0000 +} + +div.highlight .-Color[class*=-BGC124] { + background-color: #AF0000 +} + +div.highlight .-Color[class*=-C125] { + color: #AF005F +} + +div.highlight .-Color[class*=-BGC125] { + background-color: #AF005F +} + +div.highlight .-Color[class*=-C126] { + color: #AF0087 +} + +div.highlight .-Color[class*=-BGC126] { + background-color: #AF0087 +} + +div.highlight .-Color[class*=-C127] { + color: #AF00AF +} + +div.highlight .-Color[class*=-BGC127] { + background-color: #AF00AF +} + +div.highlight .-Color[class*=-C128] { + color: #AF00D7 +} + +div.highlight .-Color[class*=-BGC128] { + background-color: #AF00D7 +} + +div.highlight .-Color[class*=-C129] { + color: #AF00FF +} + +div.highlight .-Color[class*=-BGC129] { + background-color: #AF00FF +} + +div.highlight .-Color[class*=-C130] { + color: #AF5F00 +} + +div.highlight .-Color[class*=-BGC130] { + background-color: #AF5F00 +} + +div.highlight .-Color[class*=-C131] { + color: #AF5F5F +} + +div.highlight .-Color[class*=-BGC131] { + background-color: #AF5F5F +} + +div.highlight .-Color[class*=-C132] { + color: #AF5F87 +} + +div.highlight .-Color[class*=-BGC132] { + background-color: #AF5F87 +} + +div.highlight .-Color[class*=-C133] { + color: #AF5FAF +} + +div.highlight .-Color[class*=-BGC133] { + background-color: #AF5FAF +} + +div.highlight .-Color[class*=-C134] { + color: #AF5FD7 +} + +div.highlight .-Color[class*=-BGC134] { + background-color: #AF5FD7 +} + +div.highlight .-Color[class*=-C135] { + color: #AF5FFF +} + +div.highlight .-Color[class*=-BGC135] { + background-color: #AF5FFF +} + +div.highlight .-Color[class*=-C136] { + color: #AF8700 +} + +div.highlight .-Color[class*=-BGC136] { + background-color: #AF8700 +} + +div.highlight .-Color[class*=-C137] { + color: #AF875F +} + +div.highlight .-Color[class*=-BGC137] { + 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LICENSE for details. + * + */ +"use strict"; + +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + "Search finished, found ${resultCount} page(s) matching the search query." + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlink", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + const score = Math.round(Scorer.title * queryLower.length / title.length); + const boost = titles[file] === title ? 1 : 0; // add a boost for document titles + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score + boost, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 0000000..8a96c69 --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/autoapi/tsdf/constants/index.html b/autoapi/tsdf/constants/index.html new file mode 100644 index 0000000..6a039d0 --- /dev/null +++ b/autoapi/tsdf/constants/index.html @@ -0,0 +1,222 @@ + + + + + + + tsdf.constants — tsdf documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

tsdf.constants

+
+

Attributes

+ + + + + + + + + + + + + + + + + + + + + +

SUPPORTED_TSDF_VERSIONS

List of currently supported versions.

MANDATORY_TSDF_KEYS

Dictionary linking mandatory keys for different versions

MANDATORY_TSDF_KEYS_VALUES

Dictionary linking mandatory keys to their values

KEY_VALUE_TYPES

List of data types that are supported within the TSDF metadata file.

METADATA_NAMING_PATTERN

Naming convention for the metadata files. ** allows for any prefix, including additional directories.

ConcatenationType

+
+
+

Module Contents

+
+
+tsdf.constants.SUPPORTED_TSDF_VERSIONS = ['0.1']
+

List of currently supported versions.

+
+ +
+
+tsdf.constants.MANDATORY_TSDF_KEYS
+

Dictionary linking mandatory keys for different versions

+
+ +
+
+tsdf.constants.MANDATORY_TSDF_KEYS_VALUES
+

Dictionary linking mandatory keys to their values

+
+ +
+
+tsdf.constants.KEY_VALUE_TYPES
+

List of data types that are supported within the TSDF metadata file.

+
+ +
+
+tsdf.constants.METADATA_NAMING_PATTERN = '**meta.json'
+

Naming convention for the metadata files. ** allows for any prefix, including additional directories.

+
+ +
+
+tsdf.constants.ConcatenationType
+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/autoapi/tsdf/file_utils/index.html b/autoapi/tsdf/file_utils/index.html new file mode 100644 index 0000000..c8e537f --- /dev/null +++ b/autoapi/tsdf/file_utils/index.html @@ -0,0 +1,207 @@ + + + + + + + tsdf.file_utils — tsdf documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

tsdf.file_utils

+
+

Functions

+ + + + + + + + + +

get_files_matching(→ list)

Get all files matching the criteria in the directory and its subdirectories.

write_to_file(→ None)

Write a dictionary to a json file.

+
+
+

Module Contents

+
+
+tsdf.file_utils.get_files_matching(directory: str, criteria: str) → list[source]
+

Get all files matching the criteria in the directory and its subdirectories.

+
+
Parameters:
+
    +

  • directory – directory to search in.

    • +

  • criteria – criteria to match (e.g., **meta.json).

    • +
+
+
Returns:
+

list of files matching the criteria.

+
+
+
+ +
+
+tsdf.file_utils.write_to_file(dict: Dict[str, Any], dir_path: str, file_name: str) → None[source]
+

Write a dictionary to a json file.

+
+
Parameters:
+
    +

  • dict – Dictionary to be written.

    • +

  • dir_path – Path to the directory where the file will be saved.

    • +

  • file_name – Name of the file to be saved.

    • +
+
+
+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/autoapi/tsdf/index.html b/autoapi/tsdf/index.html new file mode 100644 index 0000000..cb7825e --- /dev/null +++ b/autoapi/tsdf/index.html @@ -0,0 +1,558 @@ + + + + + + + tsdf — tsdf documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

tsdf

+
+

Submodules

+
+ +
+
+
+

Classes

+ + + + + + +

TSDFMetadata

Structure that provides metadata needed for reading a data stream.

+
+
+

Functions

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

load_metadata_file(→ Dict[str, ...)

Loads a TSDF metadata file, returns a dictionary

load_metadata_from_path(→ Dict[str, ...)

Loads a TSDF metadata file, returns a dictionary

load_metadatas_from_dir(→ List[Dict[str, ...)

Loads all TSDF metadata files in a directory, returns a dictionary

load_metadata_string(→ Dict[str, ...)

Loads a TSDF metadata string, returns a dictionary.

load_metadata_legacy_file(→ Dict[str, ...)

Loads a TSDB metadata file, i.e., legacy format of the TSDF. It returns a dictionary representing the metadata.

write_metadata(→ None)

Combine and save the TSDF metadata objects as a json file.

write_binary_file(→ tsdf.tsdfmetadata.TSDFMetadata)

Save binary file based on the provided NumPy array.

write_dataframe_to_binaries(→ None)

Save binary file based on the provided pandas DataFrame.

load_ndarray_from_binary(→ numpy.ndarray)

Use metadata properties to load and return numpy array from a binary file (located the same directory where the metadata is saved).

load_dataframe_from_binaries(→ Union[pandas.DataFrame, ...)

Load content of binary files associated with TSDF into a pandas DataFrame. The data frames can be concatenated horizontally (ConcatenationType.columns), vertically (ConcatenationType.rows) or provided as a list of data frames (ConcatenationType.none).

+
+
+

Package Contents

+
+
+tsdf.load_metadata_file(file) → Dict[str, tsdf.tsdfmetadata.TSDFMetadata][source]
+

Loads a TSDF metadata file, returns a dictionary

+
+
Parameters:
+

file – file object containing the TSDF metadata.

+
+
Returns:
+

dictionary of TSDFMetadata objects.

+
+
+
+ +
+
+tsdf.load_metadata_from_path(path: pathlib.Path) → Dict[str, tsdf.tsdfmetadata.TSDFMetadata][source]
+

Loads a TSDF metadata file, returns a dictionary

+
+
Parameters:
+

path – path to the TSDF metadata file.

+
+
Returns:
+

dictionary of TSDFMetadata objects.

+
+
+
+ +
+
+tsdf.load_metadatas_from_dir(dir_path: str, naming_pattern=METADATA_NAMING_PATTERN) → List[Dict[str, tsdf.tsdfmetadata.TSDFMetadata]][source]
+

Loads all TSDF metadata files in a directory, returns a dictionary

+
+
Parameters:
+
    +

  • dir_path – path to the directory containing the TSDF metadata files.

    • +

  • naming_pattern – (optional) naming pattern of the TSDF metadata files .

    • +
+
+
Returns:
+

dictionary of TSDFMetadata objects.

+
+
+
+ +
+
+tsdf.load_metadata_string(json_str) → Dict[str, tsdf.tsdfmetadata.TSDFMetadata][source]
+

Loads a TSDF metadata string, returns a dictionary.

+
+
Parameters:
+

json_str – string containing the TSDF metadata.

+
+
Returns:
+

dictionary of TSDFMetadata objects.

+
+
+
+ +
+
+tsdf.load_metadata_legacy_file(file) → Dict[str, tsdf.tsdfmetadata.TSDFMetadata][source]
+

Loads a TSDB metadata file, i.e., legacy format of the TSDF. It returns a dictionary representing the metadata.

+
+
Parameters:
+

file – file object containing the TSDF metadata.

+
+
Returns:
+

dictionary of TSDFMetadata objects.

+
+
+
+ +
+
+tsdf.write_metadata(metadatas: List[tsdf.tsdfmetadata.TSDFMetadata], file_name: str) → None[source]
+

Combine and save the TSDF metadata objects as a json file.

+
+
Parameters:
+
    +

  • metadatas – List of TSDFMetadata objects to be saved.

    • +

  • file_name – Name of the file to be saved. The file will be saved in the directory of the first TSDFMetadata object in the list.

    • +
+
+
Raises:
+

TSDFMetadataFieldValueError – if the metadata files cannot be combined (e.g. they have no common fields) or if the list of TSDFMetadata objects is empty.

+
+
+
+ +
+
+tsdf.write_binary_file(file_dir: str, file_name: str, data: numpy.ndarray, metadata: dict) → tsdf.tsdfmetadata.TSDFMetadata[source]
+

Save binary file based on the provided NumPy array.

+
+
Parameters:
+
    +

  • file_dir – path to the directory where the file will be saved.

    • +

  • file_name – name of the file to be saved.

    • +

  • data – NumPy array containing the data.

    • +

  • metadata – dictionary containing the metadata.

    • +
+
+
Returns:
+

TSDFMetadata object.

+
+
+
+ +
+
+tsdf.write_dataframe_to_binaries(file_dir: str, df: pandas.DataFrame, metadatas: List[tsdf.tsdfmetadata.TSDFMetadata]) → None[source]
+

Save binary file based on the provided pandas DataFrame.

+
+
Parameters:
+
    +

  • file_dir – path to the directory where the file will be saved.

    • +

  • df – pandas DataFrame containing the data.

    • +

  • metadatas – list of metadata objects to be saved, also contains +channels to be retrieved from dataframe.

    • +
+
+
+
+ +
+
+tsdf.load_ndarray_from_binary(metadata: tsdf.tsdfmetadata.TSDFMetadata, start_row: int = 0, end_row: int = -1) → numpy.ndarray[source]
+

Use metadata properties to load and return numpy array from a binary file (located the same directory where the metadata is saved).

+
+
Parameters:
+
    +

  • metadata – TSDFMetadata object.

    • +

  • start_row – (optional) first row to load.

    • +

  • end_row – (optional) last row to load. If -1, load all rows.

    • +
+
+
Returns:
+

numpy array containing the data.

+
+
+
+ +
+
+tsdf.load_dataframe_from_binaries(metadatas: List[tsdf.tsdfmetadata.TSDFMetadata], concatenation: tsdf.constants.ConcatenationType = ConcatenationType.none) → pandas.DataFrame | List[pandas.DataFrame][source]
+

Load content of binary files associated with TSDF into a pandas DataFrame. The data frames can be concatenated horizontally (ConcatenationType.columns), vertically (ConcatenationType.rows) or provided as a list of data frames (ConcatenationType.none).

+
+
Parameters:
+
    +

  • metadatas – list of TSDFMetadata objects.

    • +

  • concatenation – concatenation rule, i.e., determines whether the data frames (content of binary files) should be concatenated horizontally (ConcatenationType.columns), vertically (ConcatenationType.rows) or provided as a list of data frames (ConcatenationType.none).

    • +
+
+
Returns:
+

pandas DataFrame containing the combined data.

+
+
+
+ +
+
+class tsdf.TSDFMetadata(dictionary: Dict[str, Any], dir_path: str, metadata_file_name: str = '', do_validate: bool = True)[source]
+

Structure that provides metadata needed for reading a data stream.

+
+
+metadata_version: str
+

Version of the TSDF metadata file.

+
+ +
+
+study_id: str
+

Study ID.

+
+ +
+
+subject_id: str
+

Subject ID.

+
+ +
+
+device_id: str
+

Device ID.

+
+ +
+
+start_iso8601: str
+

Start time of the recording in ISO8601 format.

+
+ +
+
+end_iso8601: str
+

End time of the recording in ISO8601 format.

+
+ +
+
+file_name: str
+

Name of the binary file containing the data.

+
+ +
+
+rows: int
+

Number of rows in the binary file.

+
+ +
+
+channels: List[str]
+

List of channels in the binary file.

+
+ +
+
+units: List[str]
+

List of units for each channel in the binary file.

+
+ +
+
+data_type: str
+

Data type of the binary file.

+
+ +
+
+bits: int
+

Number of bits per sample in the binary file.

+
+ +
+
+endianness: str
+

Endianness of the binary file.

+
+ +
+
+file_dir_path: str
+

A reference to the directory path, so we don’t need it again when reading associated binary files.

+
+ +
+
+metadata_file_name: str
+

A reference to the source path, so we don’t need it again when reading associated binary files.

+
+ +
+
+validate() → bool[source]
+
+ +
+
+get_plain_tsdf_dict_copy() → Dict[str, Any][source]
+

Method returns the a copy of the dict containing fields needed for the TSDF file.

+
+
Returns:
+

a copy of the dict containing fields needed for the TSDF file.

+
+
+
+ +
+
+set_start_datetime(date_time: datetime.datetime) → None[source]
+

Sets the start date of the recording in ISO8601 format. +:param date_time: datetime object containing the start date.

+
+ +
+
+get_start_datetime() → datetime.datetime[source]
+

Returns the start date of the recording as a datetime object. +:return: datetime object containing the start date.

+
+ +
+
+set_end_datetime(date_time: datetime.datetime) → None[source]
+

Sets the end date of the recording in ISO8601 format. +:param date_time: datetime object containing the end date.

+
+ +
+
+get_end_datetime() → datetime.datetime[source]
+

Returns the end date of the recording as a datetime object. +:return: datetime object containing the end date.

+
+ +
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/autoapi/tsdf/legacy_tsdf_utils/index.html b/autoapi/tsdf/legacy_tsdf_utils/index.html new file mode 100644 index 0000000..a43b4b5 --- /dev/null +++ b/autoapi/tsdf/legacy_tsdf_utils/index.html @@ -0,0 +1,297 @@ + + + + + + + tsdf.legacy_tsdf_utils — tsdf documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

tsdf.legacy_tsdf_utils

+
+

Attributes

+ + + + + + + + + +

TSDB_TSDF_KEY_MAP

TSDB_ARRAY_KEYS

+
+
+

Functions

+ + + + + + + + + + + + + + + + + + + + + +

_rename_keys_in_metadata(→ Dict[str, Any])

This function renames the keys in a metadata file.

_convert_to_array(→ Dict[str, Any])

This function converts the value of a specified key in a dictionary to an array if it's not already an array.

convert_tsdb_to_tsdf(→ Dict[str, Any])

Converts a data from TSDB (legacy) to TSDF (0.1) format.

generate_tsdf_metadata_from_tsdb(→ None)

This function creates a metadata file (JSON) file in TSDF (0.1) format from a TSDB (legacy) file.

convert_file_tsdb_to_tsdf(→ None)

This function converts a metadata file (JSON) from TSDB (legacy) to TSDF (0.1) format. It overwrites the original file.

convert_files_tsdb_to_tsdf(→ None)

This function converts all metadata files in a directory (and its subdirectories) from TSDB (legacy) to TSDF (0.1) format.

+
+
+

Module Contents

+
+
+tsdf.legacy_tsdf_utils.TSDB_TSDF_KEY_MAP
+
+ +
+
+tsdf.legacy_tsdf_utils.TSDB_ARRAY_KEYS
+
+ +
+
+tsdf.legacy_tsdf_utils._rename_keys_in_metadata(old_dict: Dict[str, Any]) → Dict[str, Any][source]
+

This function renames the keys in a metadata file. +If a key in the metadata matches a key in the provided dictionary, it is renamed to the corresponding value in the dictionary. +It handles nested dictionaries and lists of dictionaries.

+
+
Parameters:
+

old_dict – The metadata file (dictionary) with keys to rename

+
+
Returns:
+

The updated metadata file (dictionary)

+
+
+
+ +
+
+tsdf.legacy_tsdf_utils._convert_to_array(data: Dict[str, Any], key: str) → Dict[str, Any][source]
+

This function converts the value of a specified key in a dictionary to an array if it’s not already an array. +It handles nested dictionaries and lists of dictionaries.

+
+
Parameters:
+
    +

  • data – The dictionary with a value to convert

    • +

  • key – The key in the dictionary whose value to convert

    • +
+
+
Returns:
+

The updated dictionary

+
+
+
+ +
+
+tsdf.legacy_tsdf_utils.convert_tsdb_to_tsdf(data: Dict[str, Any]) → Dict[str, Any][source]
+

Converts a data from TSDB (legacy) to TSDF (0.1) format.

+
+
Parameters:
+

data – The data in legacy (tsdb) format.

+
+
Returns:
+

The data in tsdf format.

+
+
+
+ +
+
+tsdf.legacy_tsdf_utils.generate_tsdf_metadata_from_tsdb(filepath_existing: str, filepath_new: str) → None[source]
+

This function creates a metadata file (JSON) file in TSDF (0.1) format from a TSDB (legacy) file.

+
+
Parameters:
+
    +

  • filepath_existing – The path to the JSON file to process

    • +

  • filepath_new – The path to the new JSON file

    • +
+
+
+
+ +
+
+tsdf.legacy_tsdf_utils.convert_file_tsdb_to_tsdf(filepath: str) → None[source]
+

This function converts a metadata file (JSON) from TSDB (legacy) to TSDF (0.1) format. It overwrites the original file.

+
+
Parameters:
+

filepath – The path to the JSON file to process

+
+
+
+ +
+
+tsdf.legacy_tsdf_utils.convert_files_tsdb_to_tsdf(directory: str) → None[source]
+

This function converts all metadata files in a directory (and its subdirectories) from TSDB (legacy) to TSDF (0.1) format. +It walks through all files in a directory (and its subdirectories), +and processes all files with a .json extension.

+
+
Parameters:
+

directory – The directory to process files in

+
+
+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/autoapi/tsdf/numpy_utils/index.html b/autoapi/tsdf/numpy_utils/index.html new file mode 100644 index 0000000..d55493d --- /dev/null +++ b/autoapi/tsdf/numpy_utils/index.html @@ -0,0 +1,325 @@ + + + + + + + tsdf.numpy_utils — tsdf documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

tsdf.numpy_utils

+
+

Attributes

+ + + + + + + + + + + + + + + +

_map_from_numpy_types

Mapping of NumPy data types to their TSDF metadata annotations.

_map_to_numpy_types

Mapping of data types that are supported by TSDF to

_map_from_numpy_endianness

Supported endianness values.

_map_to_numpy_endianness

Supported endianness values.

+
+
+

Functions

+ + + + + + + + + + + + + + + + + + + + + + + + +

data_type_numpy_to_tsdf(data)

Compute the TSDF metadata 'data_type' value, based on the NumPy data.

data_type_tsdf_to_numpy(→ str)

Compute the the NumPy data type, based on the TSDF metadata 'data_type' value.

bits_numpy_to_tsdf(→ int)

Compute TSDF metadata 'n_bits' value, based on the NumPy data.

bytes_tsdf_to_numpy(n_bits)

Compute the the NumPy byte number, based on the TSDF metadata 'n_bits' value.

endianness_numpy_to_tsdf(→ str)

Compute TSDF metadata 'data_type' value, based on the NumPy data.

endianness_tsdf_to_numpy(→ str)

Compute TSDF metadata 'data_type' value, based on the NumPy data.

rows_numpy_to_tsdf(→ int)

Compute TSDF metadata 'rows' value, based on the NumPy data.

+
+
+

Module Contents

+
+
+tsdf.numpy_utils._map_from_numpy_types
+

Mapping of NumPy data types to their TSDF metadata annotations.

+
+ +
+
+tsdf.numpy_utils.data_type_numpy_to_tsdf(data: numpy.ndarray)[source]
+

Compute the TSDF metadata ‘data_type’ value, based on the NumPy data.

+
+ +
+
+tsdf.numpy_utils._map_to_numpy_types
+

Mapping of data types that are supported by TSDF to +their NumPy representation used for parsing.

+
+ +
+
+tsdf.numpy_utils.data_type_tsdf_to_numpy(data_type: str) → str[source]
+

Compute the the NumPy data type, based on the TSDF metadata ‘data_type’ value.

+
+
Parameters:
+

data_type – TSDF metadata ‘data_type’ value.

+
+
Returns:
+

NumPy data type (as a char).

+
+
+
+ +
+
+tsdf.numpy_utils.bits_numpy_to_tsdf(data: numpy.ndarray) → int[source]
+

Compute TSDF metadata ‘n_bits’ value, based on the NumPy data.

+
+
Parameters:
+

data – NumPy data.

+
+
Returns:
+

TSDF metadata ‘n_bits’ value.

+
+
+
+ +
+
+tsdf.numpy_utils.bytes_tsdf_to_numpy(n_bits: int)[source]
+

Compute the the NumPy byte number, based on the TSDF metadata ‘n_bits’ value.

+
+
Parameters:
+

n_bits – TSDF metadata ‘n_bits’ value.

+
+
Returns:
+

NumPy byte number.

+
+
+
+ +
+
+tsdf.numpy_utils._map_from_numpy_endianness
+

Supported endianness values.

+
+ +
+
+tsdf.numpy_utils.endianness_numpy_to_tsdf(data: numpy.ndarray) → str[source]
+

Compute TSDF metadata ‘data_type’ value, based on the NumPy data.

+
+
Parameters:
+

data – NumPy data.

+
+
Returns:
+

TSDF metadata ‘data_type’ value (as a string).

+
+
+
+ +
+
+tsdf.numpy_utils._map_to_numpy_endianness
+

Supported endianness values.

+
+ +
+
+tsdf.numpy_utils.endianness_tsdf_to_numpy(endianness: str) → str[source]
+

Compute TSDF metadata ‘data_type’ value, based on the NumPy data.

+
+
Parameters:
+

endianness – TSDF metadata ‘data_type’ value.

+
+
Returns:
+

NumPy data type (as a char).

+
+
+
+ +
+
+tsdf.numpy_utils.rows_numpy_to_tsdf(data: numpy.ndarray) → int[source]
+

Compute TSDF metadata ‘rows’ value, based on the NumPy data.

+
+
Parameters:
+

data – NumPy data.

+
+
Returns:
+

TSDF metadata ‘rows’ value.

+
+
+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/autoapi/tsdf/parse_metadata/index.html b/autoapi/tsdf/parse_metadata/index.html new file mode 100644 index 0000000..ce570a4 --- /dev/null +++ b/autoapi/tsdf/parse_metadata/index.html @@ -0,0 +1,370 @@ + + + + + + + tsdf.parse_metadata — tsdf documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

tsdf.parse_metadata

+

Module for parsing TSDF metadata files.

+

Reference: https://arxiv.org/abs/2211.11294

+
+

Functions

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

read_data(→ Dict[str, tsdf.tsdfmetadata.TSDFMetadata])

Function used to parse the JSON object containing TSDF metadata. It returns a

_read_struct(→ Dict[str, tsdf.tsdfmetadata.TSDFMetadata])

Recursive method used to parse the TSDF metadata in a hierarchical

is_mandatory_type(→ bool)

Function returns True if the field that corresponds to the

_contains_file_name(→ bool)

Function return True if the data contains the "file_name" key,

contains_tsdf_mandatory_fields(→ bool)

Verifies that all the mandatory properties for TSDF metadata are provided,

_check_tsdf_property_format(→ None)

Function checks whether the value of the mandatory TSDF field specified by the key

get_file_metadata_at_index(...)

Returns the metadata object at the position defined by the index.

confirm_dir_of_metadata(→ None)

The method is used to confirm whether all the metadata files are expected in the same directory.

is_iso8601(→ bool)

Checks if the given date string is in ISO8601 format.

validate_datetimes(→ bool)

Validates the start and end date format of the TSDFMetaData object.

+
+
+

Module Contents

+
+
+tsdf.parse_metadata.read_data(data: Any, source_path: str) → Dict[str, tsdf.tsdfmetadata.TSDFMetadata][source]
+

Function used to parse the JSON object containing TSDF metadata. It returns a +list of TSDFMetadata objects, where each object describes formatting of a binary file.

+
+
Parameters:
+
    +

  • data – JSON object containing TSDF metadata.

    • +

  • source_path – path to the metadata file.

    • +
+
+
Returns:
+

list of TSDFMetadata objects.

+
+
Raises:
+

tsdf_metadata.TSDFMetadataFieldValueError – if the TSDF metadata file is missing a mandatory field.

+
+
+
+ +
+
+tsdf.parse_metadata._read_struct(data: Any, defined_properties: Dict[str, Any], source_path, version: str) → Dict[str, tsdf.tsdfmetadata.TSDFMetadata][source]
+

Recursive method used to parse the TSDF metadata in a hierarchical +order (from the root towards the leaves).

+
+
Parameters:
+
    +

  • data – JSON object containing TSDF metadata.

    • +

  • defined_properties – dictionary containing all the properties defined at the current level of the TSDF structure.

    • +

  • source_path – path to the metadata file.

    • +

  • version – version of the TSDF used within the file.

    • +
+
+
Returns:
+

list of TSDFMetadata objects.

+
+
Raises:
+

tsdf_metadata.TSDFMetadataFieldError – if the TSDF metadata file is missing a mandatory field.

+
+
+
+ +
+
+tsdf.parse_metadata.is_mandatory_type(key: str, version: str) → bool[source]
+

Function returns True if the field that corresponds to the +key is mandatory for the given TSDF version, otherwise it returns False.

+
+
Parameters:
+
    +

  • key – key of the TSDF metadata field.

    • +

  • version – version of the TSDF used within the file.

    • +
+
+
Returns:
+

True if the field is mandatory, otherwise False.

+
+
+
+ +
+
+tsdf.parse_metadata._contains_file_name(data: Any) → bool[source]
+

Function return True if the data contains the “file_name” key, +and thus, represents nested data elements. +Otherwise it returns False.

+
+
Parameters:
+

data – data to be checked.

+
+
Returns:
+

True if the data contains the “file_name” key, otherwise False.

+
+
+
+ +
+
+tsdf.parse_metadata.contains_tsdf_mandatory_fields(dictionary: Dict[str, Any]) → bool[source]
+

Verifies that all the mandatory properties for TSDF metadata are provided, +and are in the right format.

+
+
Parameters:
+

dictionary – dictionary containing TSDF metadata.

+
+
Returns:
+

True if the metadata is well formatted.

+
+
Raises:
+
    +

  • tsdf_metadata.TSDFMetadataFieldError – if the TSDF metadata file is missing a mandatory field.

    • +

  • tsdf_metadata.TSDFMetadataFieldValueError – if the TSDF metadata file contains an invalid value.

    • +
+
+
+
+ +
+
+tsdf.parse_metadata._check_tsdf_property_format(key: str, value, version: str) → None[source]
+

Function checks whether the value of the mandatory TSDF field specified by the key +is of the expected data format.Note: If the key is not mandatory the function does not perform any checks.

+
+
Parameters:
+
    +

  • key – key of the TSDF metadata field.

    • +

  • value – value of the TSDF metadata field.

    • +

  • version – version of the TSDF used within the file.

    • +
+
+
Raises:
+

tsdf_metadata.TSDFMetadataFieldValueError – if the TSDF metadata file contains an invalid value.

+
+
+
+ +
+
+tsdf.parse_metadata.get_file_metadata_at_index(metadata: Dict[str, tsdf.tsdfmetadata.TSDFMetadata], index: int) → tsdf.tsdfmetadata.TSDFMetadata[source]
+

Returns the metadata object at the position defined by the index.

+
+
Parameters:
+
    +

  • metadata – dictionary containing TSDF metadata.

    • +

  • index – index of the metadata object to be returned.

    • +
+
+
Returns:
+

metadata object at the position defined by the index.

+
+
Raises:
+

IndexError – if the index is out of range.

+
+
+
+ +
+
+tsdf.parse_metadata.confirm_dir_of_metadata(metadatas: List[tsdf.tsdfmetadata.TSDFMetadata]) → None[source]
+

The method is used to confirm whether all the metadata files are expected in the same directory.

+
+
Parameters:
+

metadatas – list of metadata objects.

+
+
Raises:
+

tsdf_metadata.TSDFMetadataFieldValueError – if the metadata files are not in the same directory or describe the same binaries.

+
+
+
+ +
+
+tsdf.parse_metadata.is_iso8601(date_string: str) → bool[source]
+

Checks if the given date string is in ISO8601 format.

+
+
Parameters:
+

date_string – date string to be checked.

+
+
+
+ +
+
+tsdf.parse_metadata.validate_datetimes(metadata: tsdf.tsdfmetadata.TSDFMetadata) → bool[source]
+

Validates the start and end date format of the TSDFMetaData object.

+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/autoapi/tsdf/read_binary/index.html b/autoapi/tsdf/read_binary/index.html new file mode 100644 index 0000000..bafbc35 --- /dev/null +++ b/autoapi/tsdf/read_binary/index.html @@ -0,0 +1,238 @@ + + + + + + + tsdf.read_binary — tsdf documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

tsdf.read_binary

+

Module for reading and writing binary files associated with TSDF.

+

Reference: https://arxiv.org/abs/2211.11294

+
+

Functions

+ + + + + + + + + + + + +

load_dataframe_from_binaries(→ Union[pandas.DataFrame, ...)

Load content of binary files associated with TSDF into a pandas DataFrame. The data frames can be concatenated horizontally (ConcatenationType.columns), vertically (ConcatenationType.rows) or provided as a list of data frames (ConcatenationType.none).

load_ndarray_from_binary(→ numpy.ndarray)

Use metadata properties to load and return numpy array from a binary file (located the same directory where the metadata is saved).

_load_binary_file(→ numpy.ndarray)

Use provided parameters to load and return a numpy array from a binary file.

+
+
+

Module Contents

+
+
+tsdf.read_binary.load_dataframe_from_binaries(metadatas: List[tsdf.tsdfmetadata.TSDFMetadata], concatenation: tsdf.constants.ConcatenationType = ConcatenationType.none) → pandas.DataFrame | List[pandas.DataFrame][source]
+

Load content of binary files associated with TSDF into a pandas DataFrame. The data frames can be concatenated horizontally (ConcatenationType.columns), vertically (ConcatenationType.rows) or provided as a list of data frames (ConcatenationType.none).

+
+
Parameters:
+
    +

  • metadatas – list of TSDFMetadata objects.

    • +

  • concatenation – concatenation rule, i.e., determines whether the data frames (content of binary files) should be concatenated horizontally (ConcatenationType.columns), vertically (ConcatenationType.rows) or provided as a list of data frames (ConcatenationType.none).

    • +
+
+
Returns:
+

pandas DataFrame containing the combined data.

+
+
+
+ +
+
+tsdf.read_binary.load_ndarray_from_binary(metadata: tsdf.tsdfmetadata.TSDFMetadata, start_row: int = 0, end_row: int = -1) → numpy.ndarray[source]
+

Use metadata properties to load and return numpy array from a binary file (located the same directory where the metadata is saved).

+
+
Parameters:
+
    +

  • metadata – TSDFMetadata object.

    • +

  • start_row – (optional) first row to load.

    • +

  • end_row – (optional) last row to load. If -1, load all rows.

    • +
+
+
Returns:
+

numpy array containing the data.

+
+
+
+ +
+
+tsdf.read_binary._load_binary_file(bin_file_path: str, data_type: str, n_bits: int, endianness: str, n_rows: int, n_columns: int, start_row: int = 0, end_row: int = -1) → numpy.ndarray[source]
+

Use provided parameters to load and return a numpy array from a binary file.

+
+
Parameters:
+
    +

  • bin_file_path – path to the binary file.

    • +

  • data_type – data type of the binary file.

    • +

  • n_bits – number of bits per value.

    • +

  • endianness – endianness of the binary file.

    • +

  • n_rows – number of rows in the binary file.

    • +

  • n_columns – number of columns in the binary file.

    • +

  • start_row – (optional) first row to load.

    • +

  • end_row – (optional) last row to load. If -1, load all rows.

    • +
+
+
Returns:
+

numpy array containing the data.

+
+
+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/autoapi/tsdf/read_tsdf/index.html b/autoapi/tsdf/read_tsdf/index.html new file mode 100644 index 0000000..cfe4837 --- /dev/null +++ b/autoapi/tsdf/read_tsdf/index.html @@ -0,0 +1,259 @@ + + + + + + + tsdf.read_tsdf — tsdf documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

tsdf.read_tsdf

+

Module for reading TSDF files.

+

Reference: https://arxiv.org/abs/2211.11294

+
+

Functions

+ + + + + + + + + + + + + + + + + + +

load_metadata_file(→ Dict[str, ...)

Loads a TSDF metadata file, returns a dictionary

load_metadata_legacy_file(→ Dict[str, ...)

Loads a TSDB metadata file, i.e., legacy format of the TSDF. It returns a dictionary representing the metadata.

load_metadatas_from_dir(→ List[Dict[str, ...)

Loads all TSDF metadata files in a directory, returns a dictionary

load_metadata_from_path(→ Dict[str, ...)

Loads a TSDF metadata file, returns a dictionary

load_metadata_string(→ Dict[str, ...)

Loads a TSDF metadata string, returns a dictionary.

+
+
+

Module Contents

+
+
+tsdf.read_tsdf.load_metadata_file(file) → Dict[str, tsdf.tsdfmetadata.TSDFMetadata][source]
+

Loads a TSDF metadata file, returns a dictionary

+
+
Parameters:
+

file – file object containing the TSDF metadata.

+
+
Returns:
+

dictionary of TSDFMetadata objects.

+
+
+
+ +
+
+tsdf.read_tsdf.load_metadata_legacy_file(file) → Dict[str, tsdf.tsdfmetadata.TSDFMetadata][source]
+

Loads a TSDB metadata file, i.e., legacy format of the TSDF. It returns a dictionary representing the metadata.

+
+
Parameters:
+

file – file object containing the TSDF metadata.

+
+
Returns:
+

dictionary of TSDFMetadata objects.

+
+
+
+ +
+
+tsdf.read_tsdf.load_metadatas_from_dir(dir_path: str, naming_pattern=METADATA_NAMING_PATTERN) → List[Dict[str, tsdf.tsdfmetadata.TSDFMetadata]][source]
+

Loads all TSDF metadata files in a directory, returns a dictionary

+
+
Parameters:
+
    +

  • dir_path – path to the directory containing the TSDF metadata files.

    • +

  • naming_pattern – (optional) naming pattern of the TSDF metadata files .

    • +
+
+
Returns:
+

dictionary of TSDFMetadata objects.

+
+
+
+ +
+
+tsdf.read_tsdf.load_metadata_from_path(path: pathlib.Path) → Dict[str, tsdf.tsdfmetadata.TSDFMetadata][source]
+

Loads a TSDF metadata file, returns a dictionary

+
+
Parameters:
+

path – path to the TSDF metadata file.

+
+
Returns:
+

dictionary of TSDFMetadata objects.

+
+
+
+ +
+
+tsdf.read_tsdf.load_metadata_string(json_str) → Dict[str, tsdf.tsdfmetadata.TSDFMetadata][source]
+

Loads a TSDF metadata string, returns a dictionary.

+
+
Parameters:
+

json_str – string containing the TSDF metadata.

+
+
Returns:
+

dictionary of TSDFMetadata objects.

+
+
+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/autoapi/tsdf/tsdfmetadata/index.html b/autoapi/tsdf/tsdfmetadata/index.html new file mode 100644 index 0000000..efd5cba --- /dev/null +++ b/autoapi/tsdf/tsdfmetadata/index.html @@ -0,0 +1,345 @@ + + + + + + + tsdf.tsdfmetadata — tsdf documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

tsdf.tsdfmetadata

+
+

Exceptions

+ + + + + + + + + +

TSDFMetadataFieldError

Raised when the TSDFMetadata is missing an obligatory field.

TSDFMetadataFieldValueError

Raised when a TSDFMetadata field is wrongly annotated.

+
+
+

Classes

+ + + + + + +

TSDFMetadata

Structure that provides metadata needed for reading a data stream.

+
+
+

Module Contents

+
+
+exception tsdf.tsdfmetadata.TSDFMetadataFieldError[source]
+

Bases: Exception

+

Raised when the TSDFMetadata is missing an obligatory field.

+
+
+classmethod missing_field(field_name: str)[source]
+
+ +
+ +
+
+exception tsdf.tsdfmetadata.TSDFMetadataFieldValueError[source]
+

Bases: Exception

+

Raised when a TSDFMetadata field is wrongly annotated.

+
+ +
+
+class tsdf.tsdfmetadata.TSDFMetadata(dictionary: Dict[str, Any], dir_path: str, metadata_file_name: str = '', do_validate: bool = True)[source]
+

Structure that provides metadata needed for reading a data stream.

+
+
+metadata_version: str
+

Version of the TSDF metadata file.

+
+ +
+
+study_id: str
+

Study ID.

+
+ +
+
+subject_id: str
+

Subject ID.

+
+ +
+
+device_id: str
+

Device ID.

+
+ +
+
+start_iso8601: str
+

Start time of the recording in ISO8601 format.

+
+ +
+
+end_iso8601: str
+

End time of the recording in ISO8601 format.

+
+ +
+
+file_name: str
+

Name of the binary file containing the data.

+
+ +
+
+rows: int
+

Number of rows in the binary file.

+
+ +
+
+channels: List[str]
+

List of channels in the binary file.

+
+ +
+
+units: List[str]
+

List of units for each channel in the binary file.

+
+ +
+
+data_type: str
+

Data type of the binary file.

+
+ +
+
+bits: int
+

Number of bits per sample in the binary file.

+
+ +
+
+endianness: str
+

Endianness of the binary file.

+
+ +
+
+file_dir_path: str
+

A reference to the directory path, so we don’t need it again when reading associated binary files.

+
+ +
+
+metadata_file_name: str
+

A reference to the source path, so we don’t need it again when reading associated binary files.

+
+ +
+
+validate() → bool[source]
+
+ +
+
+get_plain_tsdf_dict_copy() → Dict[str, Any][source]
+

Method returns the a copy of the dict containing fields needed for the TSDF file.

+
+
Returns:
+

a copy of the dict containing fields needed for the TSDF file.

+
+
+
+ +
+
+set_start_datetime(date_time: datetime.datetime) → None[source]
+

Sets the start date of the recording in ISO8601 format. +:param date_time: datetime object containing the start date.

+
+ +
+
+get_start_datetime() → datetime.datetime[source]
+

Returns the start date of the recording as a datetime object. +:return: datetime object containing the start date.

+
+ +
+
+set_end_datetime(date_time: datetime.datetime) → None[source]
+

Sets the end date of the recording in ISO8601 format. +:param date_time: datetime object containing the end date.

+
+ +
+
+get_end_datetime() → datetime.datetime[source]
+

Returns the end date of the recording as a datetime object. +:return: datetime object containing the end date.

+
+ +
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/autoapi/tsdf/validator/index.html b/autoapi/tsdf/validator/index.html new file mode 100644 index 0000000..07633c5 --- /dev/null +++ b/autoapi/tsdf/validator/index.html @@ -0,0 +1,185 @@ + + + + + + + tsdf.validator — tsdf documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

tsdf.validator

+
+

Functions

+ + + + + + + + + +

validate_tsdf_format(file_path)

main()

+
+
+

Module Contents

+
+
+tsdf.validator.validate_tsdf_format(file_path)[source]
+
+ +
+
+tsdf.validator.main()[source]
+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/autoapi/tsdf/write_binary/index.html b/autoapi/tsdf/write_binary/index.html new file mode 100644 index 0000000..c2192c9 --- /dev/null +++ b/autoapi/tsdf/write_binary/index.html @@ -0,0 +1,229 @@ + + + + + + + tsdf.write_binary — tsdf documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

tsdf.write_binary

+

Module for writing binary files associated with TSDF.

+

Reference: https://arxiv.org/abs/2211.11294

+
+

Functions

+ + + + + + + + + + + + +

write_dataframe_to_binaries(→ None)

Save binary file based on the provided pandas DataFrame.

_get_metadata_from_ndarray(→ Dict[str, Any])

Retrieve metadata information encoded in the NumPy array.

write_binary_file(→ tsdf.tsdfmetadata.TSDFMetadata)

Save binary file based on the provided NumPy array.

+
+
+

Module Contents

+
+
+tsdf.write_binary.write_dataframe_to_binaries(file_dir: str, df: pandas.DataFrame, metadatas: List[tsdf.tsdfmetadata.TSDFMetadata]) → None[source]
+

Save binary file based on the provided pandas DataFrame.

+
+
Parameters:
+
    +

  • file_dir – path to the directory where the file will be saved.

    • +

  • df – pandas DataFrame containing the data.

    • +

  • metadatas – list of metadata objects to be saved, also contains +channels to be retrieved from dataframe.

    • +
+
+
+
+ +
+
+tsdf.write_binary._get_metadata_from_ndarray(data: numpy.ndarray) → Dict[str, Any][source]
+

Retrieve metadata information encoded in the NumPy array.

+
+
Parameters:
+

data – NumPy array containing the data.

+
+
Returns:
+

dictionary containing the metadata.

+
+
+
+ +
+
+tsdf.write_binary.write_binary_file(file_dir: str, file_name: str, data: numpy.ndarray, metadata: dict) → tsdf.tsdfmetadata.TSDFMetadata[source]
+

Save binary file based on the provided NumPy array.

+
+
Parameters:
+
    +

  • file_dir – path to the directory where the file will be saved.

    • +

  • file_name – name of the file to be saved.

    • +

  • data – NumPy array containing the data.

    • +

  • metadata – dictionary containing the metadata.

    • +
+
+
Returns:
+

TSDFMetadata object.

+
+
+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/autoapi/tsdf/write_tsdf/index.html b/autoapi/tsdf/write_tsdf/index.html new file mode 100644 index 0000000..be786bc --- /dev/null +++ b/autoapi/tsdf/write_tsdf/index.html @@ -0,0 +1,284 @@ + + + + + + + tsdf.write_tsdf — tsdf documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

tsdf.write_tsdf

+

Module for writing TSDF files.

+

Reference: https://arxiv.org/abs/2211.11294

+
+

Functions

+ + + + + + + + + + + + + + + + + + + + + +

write_metadata(→ None)

Combine and save the TSDF metadata objects as a json file.

_extract_common_fields(→ Dict[str, Any])

Extract the fields that are the same for all the metadata files.

_calculate_overlaps_rec(→ List[Dict[str, Any]])

A recursive call that optimises the structure of the TSDF metadata, by grouping common values. For the input the list of dictionaries

_get_all_keys(→ List[str])

Get all the keys from the metadata files.

calculate_max_overlap(→ List[Dict[str, Any]])

Calculate the maximum overlap between the metadata files, for a specific key.

max_len_key(→ str)

Return the key in a dictionary that has the longest list as a value.

+
+
+

Module Contents

+
+
+tsdf.write_tsdf.write_metadata(metadatas: List[tsdf.tsdfmetadata.TSDFMetadata], file_name: str) → None[source]
+

Combine and save the TSDF metadata objects as a json file.

+
+
Parameters:
+
    +

  • metadatas – List of TSDFMetadata objects to be saved.

    • +

  • file_name – Name of the file to be saved. The file will be saved in the directory of the first TSDFMetadata object in the list.

    • +
+
+
Raises:
+

TSDFMetadataFieldValueError – if the metadata files cannot be combined (e.g. they have no common fields) or if the list of TSDFMetadata objects is empty.

+
+
+
+ +
+
+tsdf.write_tsdf._extract_common_fields(metadatas: List[Dict[str, Any]]) → Dict[str, Any][source]
+

Extract the fields that are the same for all the metadata files. +A new dict is created and the fields are removed from the original dictionaries.

+
+
Parameters:
+

metadatas – List of dictionaries containing the metadata.

+
+
Returns:
+

Dictionary containing the common fields.

+
+
+
+ +
+
+tsdf.write_tsdf._calculate_overlaps_rec(metadatas: List[Dict[str, Any]]) → List[Dict[str, Any]][source]
+

A recursive call that optimises the structure of the TSDF metadata, by grouping common values. For the input the list of dictionaries +corresponds to a list of “flat” metadata dictionaries. The output is a list of dictionaries (potentially of length 1) that contain +the metadata in a tree structure. The tree structure is created by grouping the common values in the metadata. +The grouping is done recursively, until no more grouping is possible.

+
+
Parameters:
+

metadatas – List of dictionaries containing the metadata.

+
+
Returns:
+

List of dictionaries containing the metadata in a tree structure.

+
+
+
+ +
+
+tsdf.write_tsdf._get_all_keys(metadatas: List[Dict[str, Any]]) → List[str][source]
+

Get all the keys from the metadata files.

+
+
Parameters:
+

metadatas – List of dictionaries containing the metadata.

+
+
Returns:
+

List of keys.

+
+
+
+ +
+
+tsdf.write_tsdf.calculate_max_overlap(meta_files: List[Dict[str, Any]], meta_key: str) → List[Dict[str, Any]][source]
+

Calculate the maximum overlap between the metadata files, for a specific key. +It returns the biggest group of dictionaries that contain the same value for the given meta_key.

+
+
Parameters:
+
    +

  • meta_files – List of dictionaries containing the metadata.

    • +

  • meta_key – The key for which the overlap is calculated.

    • +
+
+
Returns:
+

List of dictionaries containing the metadata.

+
+
+
+ +
+
+tsdf.write_tsdf.max_len_key(elements: Dict[str, List[Dict[str, Any]]]) → str[source]
+

Return the key in a dictionary that has the longest list as a value.

+
+
Parameters:
+

elements – Dictionary containing the elements.

+
+
Returns:
+

The key that has the longest list as a value.

+
+
+
+ +
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/basic_reading_and_writing_numpy.html b/basic_reading_and_writing_numpy.html new file mode 100644 index 0000000..797a524 --- /dev/null +++ b/basic_reading_and_writing_numpy.html @@ -0,0 +1,285 @@ + + + + + + + Basic reading and writing (numpy) — tsdf documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Basic reading and writing (numpy)

+

These are some examples on how to read and write TSDF data into and from a numpy array, using the tsdf library.

+
+
+
import tsdf
+
+
+
+
+
+

Load some data

+
+
+
# The file names
+metadata_path = "data/example_meta.json"
+binary_filename = "example_binary.bin"
+
+# Multiple metadata files (one for each binary) are loaded into a dictionary
+# mapping the binary file name to the metadata object
+metadata_dict = tsdf.load_metadata_from_path(metadata_path)
+
+# Retrieve the metadata object we want, using the name of the binary as key
+metadata = metadata_dict[binary_filename]
+
+# Load the data
+data = tsdf.load_ndarray_from_binary(metadata)
+
+# Print some info
+print(f"Data type:\t {data.dtype}")
+print(f"Data shape:\t {data.shape}")
+
+
+
+
+
Data type:	 int16
+Data shape:	 (10, 3)
+
+
+
+
+
+
+

Perform basic data processing

+
+
+
# Perform an operation, resulting in a different data type
+processed_data_1 = (data / 10).astype('float32')
+
+# Print some info
+print(f"Processed data type:\t {processed_data_1.dtype}")
+print(f"Data shape:\t\t {processed_data_1.shape}")
+
+
+
+
+
Processed data type:	 float32
+Data shape:		 (10, 3)
+
+
+
+
+
+
+

Write the processed data

+

Write the processed data in binary format. The call returns the corresponding metadata object.

+
+
+
# The new name of the file
+output_bin_filename = "tmp_example_processed.bin"
+
+# Write the data to a new binary file
+processed_metadata_1 = tsdf.write_binary_file(
+        "data",
+        output_bin_filename,
+        processed_data_1,
+        metadata.get_plain_tsdf_dict_copy(),
+    )
+
+print(f"File written to data/{output_bin_filename}")
+
+
+
+
+
File written to data/tmp_example_processed.bin
+
+
+
+
+
+
+

Write the TSDF metadata file

+
+
+
# Write new metadata file
+output_meta_filename = "tmp_example_processed_meta.json"
+tsdf.write_metadata([processed_metadata_1], output_meta_filename)
+print(f"File written to data/{output_meta_filename}")
+
+
+
+
+
File written to data/tmp_example_processed_meta.json
+
+
+
+
+
+
+

Write a metadata file that combines multiple binary files

+
+
+
# Preprocess the original data to generate another data source
+processed_data_2 = (data * 1000).astype("int32")
+
+# Adjust the metadata slightly
+updated_metadata = metadata.get_plain_tsdf_dict_copy()
+updated_metadata.pop("scale_factors")  # remove the 'scale_factors'
+
+# Save the new binary file
+output_bin_filename_2 = "tmp_example_processed_2.bin"
+processed_metadata_2 = tsdf.write_binary_file(
+    "data",
+    output_bin_filename_2,
+    processed_data_2,
+    updated_metadata,
+)
+print(f"File written to data/{output_bin_filename_2}")
+
+# Write a metadata file that combines the two binary files
+output_meta_filename_2 = "tmp_example_processed_2_meta.json"
+tsdf.write_metadata([processed_metadata_1, processed_metadata_2],
+    output_meta_filename_2)
+print(f"File written to data/{output_meta_filename_2}")
+
+
+
+
+
File written to data/tmp_example_processed_2.bin
+File written to data/tmp_example_processed_2_meta.json
+
+
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/basic_reading_and_writing_numpy/index.html b/basic_reading_and_writing_numpy/index.html deleted file mode 100644 index c53a010..0000000 --- a/basic_reading_and_writing_numpy/index.html +++ /dev/null @@ -1,1680 +0,0 @@ - - - - - - - - Basic reading and writing (numpy) - tsdf - - - - - - - - - - - - - -
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- - - - - - - - diff --git a/basic_reading_and_writing_pandas.html b/basic_reading_and_writing_pandas.html new file mode 100644 index 0000000..c55060a --- /dev/null +++ b/basic_reading_and_writing_pandas.html @@ -0,0 +1,517 @@ + + + + + + + Basic reading and writing (pandas) — tsdf documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Basic reading and writing (pandas)

+

These are some examples on how to read and write TSDF data into and from a numpy array, using the tsdf library.

+
+
+
import tsdf
+
+
+
+
+
+

Load some data

+
+
+
# Load the metadata
+metadata_path = "data/ppp_format_meta.json"
+metadata_dict = tsdf.load_metadata_from_path(metadata_path)
+metadata_time = metadata_dict["ppp_format_time.bin"]
+metadata_samples = metadata_dict["ppp_format_samples.bin"]
+
+# Load the data
+df = tsdf.load_dataframe_from_binaries([metadata_time, metadata_samples], tsdf.constants.ConcatenationType.columns)
+
+# Print some info
+print(f"Data shape:\t {df.shape}")
+df.head()
+
+
+
+
+
Data shape:	 (17, 7)
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
timeacceleration_xacceleration_yacceleration_zrotation_xrotation_yrotation_z
00.374540613253
10.950714791460
20.731994922671
30.598659743272
40.156019742055
+
+
+
+
+

Perform basic data processing

+
+
+
scale_factors = getattr(metadata_samples, "scale_factors")
+print(f"Scale factors: {scale_factors}")
+
+# Perform scaling
+df_scaled = df.copy()
+df_scaled.iloc[:, 1:] = df_scaled.iloc[:, 1:].multiply(scale_factors, axis=1)
+
+# Print some info
+print(f"Data shape:\t\t {df.shape}")
+df_scaled.head()
+
+
+
+
+
Scale factors: [0.00469378, 0.00469378, 0.00469378, 0.06097561, 0.06097561, 0.06097561]
+Data shape:		 (17, 7)
+
+
+
/tmp/ipykernel_2035/4007094660.py:6: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '0     0.028163
+1     0.032856
+2     0.042244
+3     0.032856
+4     0.032856
+5     0.018775
+6     0.023469
+7     0.000000
+8     0.028163
+9     0.009388
+10    0.042244
+11    0.042244
+12    0.037550
+13    0.037550
+14    0.014081
+15    0.037550
+16    0.042244
+Name: acceleration_x, dtype: float64' has dtype incompatible with int16, please explicitly cast to a compatible dtype first.
+  df_scaled.iloc[:, 1:] = df_scaled.iloc[:, 1:].multiply(scale_factors, axis=1)
+/tmp/ipykernel_2035/4007094660.py:6: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '0     0.004694
+1     0.042244
+2     0.009388
+3     0.018775
+4     0.018775
+5     0.000000
+6     0.004694
+7     0.014081
+8     0.014081
+9     0.042244
+10    0.028163
+11    0.028163
+12    0.009388
+13    0.028163
+14    0.028163
+15    0.018775
+16    0.037550
+Name: acceleration_y, dtype: float64' has dtype incompatible with int16, please explicitly cast to a compatible dtype first.
+  df_scaled.iloc[:, 1:] = df_scaled.iloc[:, 1:].multiply(scale_factors, axis=1)
+/tmp/ipykernel_2035/4007094660.py:6: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '0     0.014081
+1     0.004694
+2     0.009388
+3     0.014081
+4     0.009388
+5     0.004694
+6     0.014081
+7     0.009388
+8     0.037550
+9     0.023469
+10    0.004694
+11    0.018775
+12    0.018775
+13    0.037550
+14    0.032856
+15    0.014081
+16    0.042244
+Name: acceleration_z, dtype: float64' has dtype incompatible with int16, please explicitly cast to a compatible dtype first.
+  df_scaled.iloc[:, 1:] = df_scaled.iloc[:, 1:].multiply(scale_factors, axis=1)
+/tmp/ipykernel_2035/4007094660.py:6: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '0     0.121951
+1     0.243902
+2     0.365854
+3     0.121951
+4     0.000000
+5     0.426829
+6     0.060976
+7     0.548780
+8     0.426829
+9     0.121951
+10    0.060976
+11    0.182927
+12    0.243902
+13    0.243902
+14    0.060976
+15    0.182927
+16    0.365854
+Name: rotation_x, dtype: float64' has dtype incompatible with int16, please explicitly cast to a compatible dtype first.
+  df_scaled.iloc[:, 1:] = df_scaled.iloc[:, 1:].multiply(scale_factors, axis=1)
+/tmp/ipykernel_2035/4007094660.py:6: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '0     0.304878
+1     0.365854
+2     0.426829
+3     0.426829
+4     0.304878
+5     0.243902
+6     0.243902
+7     0.304878
+8     0.000000
+9     0.548780
+10    0.060976
+11    0.304878
+12    0.304878
+13    0.426829
+14    0.304878
+15    0.060976
+16    0.243902
+Name: rotation_y, dtype: float64' has dtype incompatible with int16, please explicitly cast to a compatible dtype first.
+  df_scaled.iloc[:, 1:] = df_scaled.iloc[:, 1:].multiply(scale_factors, axis=1)
+/tmp/ipykernel_2035/4007094660.py:6: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '0     0.182927
+1     0.000000
+2     0.060976
+3     0.121951
+4     0.304878
+5     0.243902
+6     0.182927
+7     0.304878
+8     0.426829
+9     0.487805
+10    0.121951
+11    0.487805
+12    0.121951
+13    0.487805
+14    0.182927
+15    0.182927
+16    0.060976
+Name: rotation_z, dtype: float64' has dtype incompatible with int16, please explicitly cast to a compatible dtype first.
+  df_scaled.iloc[:, 1:] = df_scaled.iloc[:, 1:].multiply(scale_factors, axis=1)
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
timeacceleration_xacceleration_yacceleration_zrotation_xrotation_yrotation_z
00.3745400.0281630.0046940.0140810.1219510.3048780.182927
10.9507140.0328560.0422440.0046940.2439020.3658540.000000
20.7319940.0422440.0093880.0093880.3658540.4268290.060976
30.5986590.0328560.0187750.0140810.1219510.4268290.121951
40.1560190.0328560.0187750.0093880.0000000.3048780.304878
+
+
+
+
+

Write the processed data

+

Write the processed data in binary format. The call updates the metadata object with the data attributes of the dataframe.

+
+
+
# The new name of the file
+output_bin_filename = "tmp_pandas_example_processed.bin"
+metadata_samples.__setattr__("file_name", output_bin_filename)
+
+# Write the data to a new binary file
+tsdf.write_dataframe_to_binaries("data", df_scaled, [metadata_time, metadata_samples])
+
+print(f"File written to data/{output_bin_filename}")
+
+
+
+
+
File written to data/tmp_pandas_example_processed.bin
+
+
+
+
+
+
+

Write the TSDF metadata file

+
+
+
# Write new metadata file
+output_meta_filename = "tmp_pandas_example_processed_meta.json"
+tsdf.write_metadata([metadata_time, metadata_samples], output_meta_filename)
+print(f"File written to data/{output_meta_filename}")
+
+
+
+
+
File written to data/tmp_pandas_example_processed_meta.json
+
+
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/basic_reading_and_writing_pandas/index.html b/basic_reading_and_writing_pandas/index.html deleted file mode 100644 index a698de2..0000000 --- a/basic_reading_and_writing_pandas/index.html +++ /dev/null @@ -1,1744 +0,0 @@ - - - - - - - - Basic reading and writing (pandas) - tsdf - - - - - - - - - - - - - -
- - -
- -
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- -
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- - - - - - - - diff --git a/contact.html b/contact.html new file mode 100644 index 0000000..77b04e9 --- /dev/null +++ b/contact.html @@ -0,0 +1,141 @@ + + + + + + + Contact — tsdf documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Contact

+

More information about the TSDF format can be found in the TSDF preprint.

+

This package has been written by engineers from the Netherlands eScience Center. +It is maintained by:

+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/contact/index.html b/contact/index.html deleted file mode 100644 index 0d23265..0000000 --- a/contact/index.html +++ /dev/null @@ -1,152 +0,0 @@ - - - - - - - - Contact - tsdf - - - - - - - - - - - - - -
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Contact

-

More information about the TSDF format can be found in the TSDF preprint.

-

This package has been written by engineers from the Netherlands eScience Center. -It is maintained by:

- - -
-
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-
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- -
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- - - - - - - - diff --git a/converting_legacy_data.html b/converting_legacy_data.html new file mode 100644 index 0000000..3173e0e --- /dev/null +++ b/converting_legacy_data.html @@ -0,0 +1,168 @@ + + + + + + + Transform legacy format — tsdf documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Transform legacy format

+

Transform one file (or all files within the given directory) from legacy TSDB to the current TSDF format (v0.1).

+
+
+
import os
+from tsdf.legacy_tsdf_utils import (
+    generate_tsdf_metadata_from_tsdb,
+    convert_file_tsdb_to_tsdf,
+    convert_files_tsdb_to_tsdf,
+)
+
+data_dir = 'data'
+
+
+
+
+
+
+
# Path to the metadata file
+path_to_file = os.path.join(data_dir, "ppp_format_meta_legacy.json")
+path_to_new_file = os.path.join(data_dir, "tmp_ppp_format_meta.json")
+
+# Generate a TSDF metadata file from TSDB
+generate_tsdf_metadata_from_tsdb(path_to_file, path_to_new_file)
+
+# Convert a TSDB metadata file to TSDB format
+# convert_metadata_tsdb_to_tsdf(path_to_file)
+
+# Convert all metadata files in the directory from TSDB to TSDF format
+# convert_metadatas_tsdb_to_tsdf(path_to_dir)
+
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/converting_legacy_data/index.html b/converting_legacy_data/index.html deleted file mode 100644 index 0bb9440..0000000 --- a/converting_legacy_data/index.html +++ /dev/null @@ -1,1292 +0,0 @@ - - - - - - - - Converting legacy data - tsdf - - - - - - - - - - - - - -
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- - - - - - - - diff --git a/css/theme_extra.css b/css/theme_extra.css deleted file mode 100644 index 9f4b063..0000000 --- a/css/theme_extra.css +++ /dev/null @@ -1,191 +0,0 @@ -/* - * Wrap inline code samples otherwise they shoot of the side and - * can't be read at all. - * - * https://github.com/mkdocs/mkdocs/issues/313 - * https://github.com/mkdocs/mkdocs/issues/233 - * https://github.com/mkdocs/mkdocs/issues/834 - */ -.rst-content code { - white-space: pre-wrap; - word-wrap: break-word; - padding: 2px 5px; -} - -/** - * Make code blocks display as blocks and give them the appropriate - * font size and padding. - * - * https://github.com/mkdocs/mkdocs/issues/855 - * https://github.com/mkdocs/mkdocs/issues/834 - * https://github.com/mkdocs/mkdocs/issues/233 - */ -.rst-content pre code { - white-space: pre; - word-wrap: normal; - display: block; - padding: 12px; - font-size: 12px; -} - -/** - * Fix code colors - * - * https://github.com/mkdocs/mkdocs/issues/2027 - */ -.rst-content code { - color: #E74C3C; -} - -.rst-content pre code { - color: #000; - background: #f8f8f8; -} - -/* - * Fix link colors when the link text is inline code. - * - * https://github.com/mkdocs/mkdocs/issues/718 - */ -a code { - color: #2980B9; -} -a:hover code { - color: #3091d1; -} -a:visited code { - color: #9B59B6; -} - -/* - * The CSS classes from highlight.js seem to clash with the - * ReadTheDocs theme causing some code to be incorrectly made - * bold and italic. - * - * https://github.com/mkdocs/mkdocs/issues/411 - */ -pre .cs, pre .c { - font-weight: inherit; - font-style: inherit; -} - -/* - * Fix some issues with the theme and non-highlighted code - * samples. Without and highlighting styles attached the - * formatting is broken. - * - * https://github.com/mkdocs/mkdocs/issues/319 - */ -.rst-content .no-highlight { - display: block; - padding: 0.5em; - color: #333; -} - - -/* - * Additions specific to the search functionality provided by MkDocs - */ - -.search-results { - margin-top: 23px; -} - -.search-results article { - border-top: 1px solid #E1E4E5; - padding-top: 24px; -} - -.search-results article:first-child { - border-top: none; -} - -form .search-query { - width: 100%; - border-radius: 50px; - padding: 6px 12px; /* csslint allow: box-model */ - border-color: #D1D4D5; -} - -/* - * Improve inline code blocks within admonitions. - * - * https://github.com/mkdocs/mkdocs/issues/656 - */ - .rst-content .admonition code { - color: #404040; - border: 1px solid #c7c9cb; - border: 1px solid rgba(0, 0, 0, 0.2); - background: #f8fbfd; - background: rgba(255, 255, 255, 0.7); -} - -/* - * Account for wide tables which go off the side. - * Override borders to avoid weirdness on narrow tables. - * - * https://github.com/mkdocs/mkdocs/issues/834 - * https://github.com/mkdocs/mkdocs/pull/1034 - */ -.rst-content .section .docutils { - width: 100%; - overflow: auto; - display: block; - border: none; -} - -td, th { - border: 1px solid #e1e4e5 !important; /* csslint allow: important */ - border-collapse: collapse; -} - -/* - * Without the following amendments, the navigation in the theme will be - * slightly cut off. This is due to the fact that the .wy-nav-side has a - * padding-bottom of 2em, which must not necessarily align with the font-size of - * 90 % on the .rst-current-version container, combined with the padding of 12px - * above and below. These amendments fix this in two steps: First, make sure the - * .rst-current-version container has a fixed height of 40px, achieved using - * line-height, and then applying a padding-bottom of 40px to this container. In - * a second step, the items within that container are re-aligned using flexbox. - * - * https://github.com/mkdocs/mkdocs/issues/2012 - */ - .wy-nav-side { - padding-bottom: 40px; -} - -/* - * The second step of above amendment: Here we make sure the items are aligned - * correctly within the .rst-current-version container. Using flexbox, we - * achieve it in such a way that it will look like the following: - * - * [No repo_name] - * Next >> // On the first page - * << Previous Next >> // On all subsequent pages - * - * [With repo_name] - * Next >> // On the first page - * << Previous Next >> // On all subsequent pages - * - * https://github.com/mkdocs/mkdocs/issues/2012 - */ -.rst-versions .rst-current-version { - padding: 0 12px; - display: flex; - font-size: initial; - justify-content: space-between; - align-items: center; - line-height: 40px; -} - -/* - * Please note that this amendment also involves removing certain inline-styles - * from the file ./mkdocs/themes/readthedocs/versions.html. - * - * https://github.com/mkdocs/mkdocs/issues/2012 - */ -.rst-current-version span { - flex: 1; - text-align: center; -} diff --git a/data/example_binary.bin b/data/example_binary.bin deleted file mode 100644 index be558becf19bead136a8365764e20d361759af65..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 60 tcmW-X2@U`t2*U~@#Q%TTYL+d#nqZM^bvUM?L6@TKLe~F`&GYHL#RU&80DAxc diff --git a/data/example_meta.json b/data/example_meta.json deleted file mode 100644 index 1c0bd7d..0000000 --- a/data/example_meta.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "subject_id": "dummy", - "study_id": "dummy", - "device_id": "dummy", - "endianness": "little", - "metadata_version": "0.1", - "start_datetime_unix_ms": 1571135957025, - "start_iso8601": "2019-10-15T10:39:17.025000+00:00", - "end_datetime_unix_ms": 1571168851826, - "end_iso8601": "2019-10-15T19:47:31.826000+00:00", - "file_name": "example_binary.bin", - "channels": [ - "x", - "y", - "z" - ], - "units": [ - "m/s/s", - "m/s/s", - "m/s/s" - ], - "scale_factors": [ - 0.00469378, - 0.00469378, - 0.00469378 - ], - "data_type": "int", - "bits": 16, - "rows": 10 -} \ No newline at end of file diff --git a/data/ppp_format_meta.json b/data/ppp_format_meta.json deleted file mode 100644 index 18cbd78..0000000 --- a/data/ppp_format_meta.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "study_id": "PPP", - "device_id": "Verily Study Watch", - "subject_id": "77", - "source_file_name": "WatchData.IMU.Week10.raw", - "endianness": "little", - "metadata_version": "0.1", - "start_datetime_unix_ms": 1571135957025, - "start_iso8601": "2019-10-15T10:39:17.025000+00:00", - "end_datetime_unix_ms": 1571168851826, - "end_iso8601": "2019-10-15T19:47:31.826000+00:00", - "datasets": [ - { - "file_name": "ppp_format_time.bin", - "channels": [ - "time" - ], - "time_encode": "difference", - "units": [ - "ms" - ], - "scale_factors": [ - 1 - ], - "data_type": "float", - "bits": 32, - "columns": 1, - "rows": 17 - }, - { - "file_name": "ppp_format_samples.bin", - "channels": [ - "acceleration_x", - "acceleration_y", - "acceleration_z", - "rotation_x", - "rotation_y", - "rotation_z" - ], - "units": [ - "m/s/s", - "m/s/s", - "m/s/s", - "deg/s", - "deg/s", - "deg/s" - ], - "scale_factors": [ - 0.00469378, - 0.00469378, - 0.00469378, - 0.06097561, - 0.06097561, - 0.06097561 - ], - "data_type": "int", - "bits": 16, - "columns": 6, - "rows": 17 - } - ] -} \ No newline at end of file diff --git a/data/ppp_format_meta_legacy.json b/data/ppp_format_meta_legacy.json deleted file mode 100644 index cf238b5..0000000 --- a/data/ppp_format_meta_legacy.json +++ /dev/null @@ -1,58 +0,0 @@ -{ - "project_id": "PPP", - "device_id": "Verily Study Watch", - "subject_id": "77", - "source_file_name": "WatchData.IMU.Week10.raw", - "endianness": "little", - "metadata_version": "0.1", - "start_datetime_unix_ms": 1571135957025, - "start_datetime_iso8601": "2019-10-15T10:39:17.025000+00:00", - "end_datetime_unix_ms": 1571168851826, - "end_datetime_iso8601": "2019-10-15T19:47:31.826000+00:00", - "datasets": [ - { - "file_name": "ppp_format_time.bin", - "quantities": "time", - "time_encode": "difference", - "units": "ms", - "scale_factors": [ - 1 - ], - "datatype": "float", - "bits": 32, - "columns": 1, - "rows": 17 - }, - { - "file_name": "ppp_format_samples.bin", - "quantities": [ - "acceleration_x", - "acceleration_y", - "acceleration_z", - "rotation_x", - "rotation_y", - "rotation_z" - ], - "units": [ - "m/s/s", - "m/s/s", - "m/s/s", - "deg/s", - "deg/s", - "deg/s" - ], - "scale_factors": [ - 0.00469378, - 0.00469378, - 0.00469378, - 0.06097561, - 0.06097561, - 0.06097561 - ], - "datatype": "int", - "bits": 16, - "columns": 6, - "rows": 17 - } - ] -} \ No newline at end of file diff --git a/data/ppp_format_samples.bin b/data/ppp_format_samples.bin deleted file mode 100644 index e9142bd5b995f2fbee4a328e3172bbdf3459115b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 204 zcmXwz2@=2{2m_-a;{QL~jcq3bB;nA~P-tqAw+<>*5^RFP8${ljjblFPFObx?n<(G$ tcHsKrxibZCwnV(1a}&k1VO@7`;Ws+LqBefRH#xq)_@DLy_{INLq8_aV0mlFU diff --git a/data/ppp_format_time.bin b/data/ppp_format_time.bin deleted file mode 100644 index 10c24db..0000000 --- a/data/ppp_format_time.bin +++ /dev/null @@ -1 +0,0 @@ -ÿ>bs?c;?A?Z>>m=]???D5?Р<Lx?U?moY>S0:>d;>ś> \ No newline at end of file diff --git a/genindex.html b/genindex.html new file mode 100644 index 0000000..095a033 --- /dev/null +++ b/genindex.html @@ -0,0 +1,682 @@ + + + + + + Index — tsdf documentation + + + + + + + + + + + + + + + + + + + + +
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Index

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+ + + + \ No newline at end of file diff --git a/img/favicon.ico b/img/favicon.ico deleted file mode 100644 index e85006a3ce1c6fd81faa6d5a13095519c4a6fc96..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1150 zcmd6lF-yZh9L1kl>(HSEK`2y^4yB6->f+$wD)=oNY!UheIt03Q=;qj=;8*Bap_4*& za8yAl;wmmx5Yyi^7dXN-WYdJ-{qNqpcez|5t#Fr0qTSYcPTG`I2PBk8r$~4kg^0zN zCJe(rhix3do!L$bZ+IuZ{i08x=JR3=e+M4pv0KsKA??{u_*EFfo|`p&t`Vf=jn{)F z1fKk9hWsmYwqWAP^JO*5u*R;*L&dX3H$%S7oB$f0{ISh{QVXuncnzN67WQH2`lip7 zhX+VI$6x$1+$8gMjh4+1l0N#8_0Fh=N#EwpKk{SeE!)SHFB@xQFX3y+8sF#_@!bDW eIdI-IC`$c%>bk?KbPeN9RHtL<1^)v~#xMt8oB^@` diff --git a/index.html b/index.html index 1fc8f43..f8659dc 100644 --- a/index.html +++ b/index.html @@ -1,231 +1,197 @@ - + - - - - - tsdf - - - - - - - - - - + + + + <no title> — tsdf documentation + + + + - + + + + + + + + + + + + + + +
-