"""zea H5 file functionality."""
import enum
from pathlib import Path
from typing import List, Tuple, Union
import h5py
import numpy as np
from keras.utils import pad_sequences
from zea import log
from zea.data.preset_utils import HF_PREFIX, _hf_resolve_path
from zea.internal.checks import (
_DATA_TYPES,
_NON_IMAGE_DATA_TYPES,
_REQUIRED_SCAN_KEYS,
get_check,
)
from zea.internal.core import DataTypes
from zea.internal.utils import reduce_to_signature
from zea.probes import Probe
from zea.scan import Scan
[docs]
def assert_key(file: h5py.File, key: str):
"""Asserts key is in a h5py.File."""
if key not in file.keys():
raise KeyError(f"{key} not found in file")
[docs]
class File(h5py.File):
"""h5py.File in zea format."""
def __init__(self, name, *args, **kwargs):
"""Initialize the file.
Args:
name (str, Path, HFPath): The path to the file.
Can be a string or a Path object. Additionally can be a string with
the prefix 'hf://', in which case it will be resolved to a
huggingface path.
*args: Additional arguments to pass to h5py.File.
**kwargs: Additional keyword arguments to pass to h5py.File.
"""
# Resolve huggingface path
if str(name).startswith(HF_PREFIX):
name = _hf_resolve_path(str(name))
# Disable locking for read mode by default
if "locking" not in kwargs and "mode" in kwargs and kwargs["mode"] == "r":
# If the file is opened in read mode, disable locking
kwargs["locking"] = False
# Initialize the h5py.File
super().__init__(name, *args, **kwargs)
@property
def path(self):
"""Return the path of the file."""
return Path(self.filename)
@property
def name(self):
"""Return the name of the file."""
return self.path.name
@property
def stem(self):
"""Return the stem of the file."""
return self.path.stem
@property
def event_keys(self):
"""Return all events in the file."""
return [key for key in self.keys() if "event" in key]
@property
def has_events(self):
"""Check if the file has events."""
return any("event" in key for key in self.keys())
# return self.attrs.get("event_structure", False)
@property
def n_frames(self):
"""Return number of frames in a file."""
if "scan" in self.file:
return int(self.file["scan"]["n_frames"][()])
else:
return sum(int(event["scan"]["n_frames"][()]) for event in self.file.values())
[docs]
def get_event_shapes(self, key):
"""Get the shapes of a key for all events."""
for event_key in self.event_keys:
yield self[event_key][key].shape
[docs]
def events_have_same_shape(self, key):
"""Check if all events have the same shape for a given key."""
if not self.has_events:
return True
shapes = list(self.get_event_shapes(key))
return len(np.unique(shapes)) == 1
def _simple_index(self, key):
return not self.has_events or "event" in key
[docs]
def shape(self, key) -> tuple:
"""Return shape of some key, or all events."""
key = self.format_key(key)
if self._simple_index(key):
return self[key].shape
else:
raise NotImplementedError
[docs]
def load_scan(self, event=None):
"""Alias for get_scan_parameters."""
return self.get_scan_parameters(event)
[docs]
def to_iterator(self, key):
"""Convert the data to an iterator over all frames."""
for frame_idx in range(self.n_frames):
yield self.load_data(key, frame_idx)
[docs]
@staticmethod
def key_to_data_type(key):
"""Convert the key to a data type."""
data_type = key.split("/")[-1]
return data_type
[docs]
def load_transmits(self, key, selected_transmits):
"""Load raw_data or aligned_data for a given list of transmits.
Args:
key (str): The type of data to load. Options are 'raw_data' and 'aligned_data'.
selected_transmits (list, np.ndarray): The transmits to load.
"""
key = self.format_key(key)
data_type = self.key_to_data_type(key)
assert data_type in ["raw_data", "aligned_data"], (
f"Cannot load transmits for {data_type}. Only raw_data and aligned_data are supported."
)
# First axis: all frames, second axis: selected transmits
indices = (slice(None), np.array(selected_transmits))
return self.load_data(key, indices)
[docs]
def load_data(
self,
data_type,
indices: Tuple[Union[list, slice, int], ...] | List[int] | int | None = None,
) -> np.ndarray:
"""Load data from the file.
.. include:: ../common/file_indexing.rst
.. doctest::
>>> from zea import File
>>> path_to_file = (
... "hf://zeahub/picmus/database/experiments/contrast_speckle/"
... "contrast_speckle_expe_dataset_iq/contrast_speckle_expe_dataset_iq.hdf5"
... )
>>> with File(path_to_file, mode="r") as file:
... # data has shape (n_frames, n_tx, n_el, n_ax, n_ch)
... data = file.load_data("raw_data")
... data.shape
... # load first frame only
... data = file.load_data("raw_data", indices=0)
... data.shape
... # load frame 0 and transmits 0, 2 and 4
... data = file.load_data("raw_data", indices=(0, [0, 2, 4]))
... data.shape
(1, 75, 832, 128, 2)
(75, 832, 128, 2)
(3, 832, 128, 2)
Args:
data_type (str): The type of data to load. Options are 'raw_data', 'aligned_data',
'beamformed_data', 'envelope_data', 'image' and 'image_sc'.
indices (optional): The indices to load. Defaults to `None` in
which case all data is loaded.
"""
key = self.format_key(data_type)
if indices is None or (isinstance(indices, str) and indices == "all"):
indices = slice(None)
if self._simple_index(key):
data = self[key]
try:
data = data[indices]
except (OSError, IndexError) as exc:
raise ValueError(
f"Invalid indices {indices} for key {key}. {key} has shape {data.shape}."
) from exc
elif self.events_have_same_shape(key):
raise NotImplementedError
else:
raise NotImplementedError
return data
@property
def probe_name(self):
"""Reads the probe name from the data file and returns it."""
assert "probe" in self.attrs, (
"Probe name not found in file attributes. "
"Make sure you are using a zea file. "
f"Found attributes: {list(self.attrs)}"
)
probe_name = self.attrs["probe"]
return probe_name
@property
def description(self):
"""Reads the description from the data file and returns it."""
assert "description" in self.attrs, (
"Description not found in file attributes. "
"Make sure you are using a zea file. "
f"Found attributes: {list(self.attrs)}"
)
description = self.attrs["description"]
return description
[docs]
def get_parameters(self, event=None):
"""Returns a dictionary of parameters to initialize a scan
object that comes with the file (stored inside datafile).
If there are no scan parameters in the hdf5 file, returns
an empty dictionary.
Args:
event (int, optional): Event number. When specified, an event structure
is expected as follows::
event_0 / scan
event_1 / scan
...
Defaults to None. In that case no event structure is expected.
Returns:
dict: The scan parameters.
"""
scan_parameters = {}
if "scan" in self:
scan_parameters = self.recursively_load_dict_contents_from_group("scan")
elif "event" in list(self.keys())[0]:
if event is None:
raise ValueError(
log.error(
"Please specify an event number to read scan parameters "
"from a file with an event structure."
)
)
assert f"event_{event}/scan" in self, (
f"Could not find scan parameters for event {event} in file. "
f"Found number of events: {len(self.keys())}."
)
scan_parameters = self.recursively_load_dict_contents_from_group(f"event_{event}/scan")
else:
log.warning("Could not find scan parameters in file.")
scan_parameters = self._check_focus_distances(scan_parameters)
return scan_parameters
def _check_focus_distances(self, scan_parameters):
if "focus_distances" in scan_parameters:
focus_distances = scan_parameters["focus_distances"]
# check if focus distances are in wavelengths
if np.any(np.logical_and(focus_distances >= 1, focus_distances != np.inf)):
log.warning(
f"We have detected that focus distances in '{self.path}' are "
"(probably) stored wavelengths. Please update your file! "
"Converting to meters automatically for now, but this assumes that "
"`center_frequency` is the probe center frequency which is not always "
"the case!"
)
assert "sound_speed" in scan_parameters, (
"Cannot convert focus distances from wavelengths to meters "
"because sound_speed is not defined in the scan parameters."
)
assert "center_frequency" in scan_parameters, (
"Cannot convert focus distances from wavelengths to meters "
"because center_frequency is not defined in the scan parameters."
)
wavelength = scan_parameters["sound_speed"] / scan_parameters["center_frequency"]
focus_distances = focus_distances * wavelength
scan_parameters["focus_distances"] = focus_distances
return scan_parameters
[docs]
def get_scan_parameters(self, event=None) -> dict:
"""Returns a dictionary of scan parameters stored in the file."""
return self.get_parameters(event)
[docs]
def scan(self, event=None, safe=True, **kwargs) -> Scan:
"""Returns a Scan object initialized with the parameters from the file.
Args:
event (int, optional): Event number. When specified, an event structure
is expected as follows::
event_0 / scan
event_1 / scan
...
Defaults to None. In that case no event structure is expected.
safe (bool, optional): If True, will only use parameters that are
defined in the Scan class. If False, will use all parameters
from the file. Defaults to True.
**kwargs: Additional keyword arguments to pass to the Scan object.
These will override the parameters from the file if they are
present in the file.
Returns:
Scan: The scan object.
"""
return Scan.merge(_reformat_waveforms(self.get_scan_parameters(event)), kwargs, safe=safe)
[docs]
def get_probe_parameters(self, event=None) -> dict:
"""Returns a dictionary of probe parameters to initialize a probe
object that comes with the file (stored inside datafile).
Returns:
dict: The probe parameters.
"""
file_scan_parameters = self.get_parameters(event)
probe_parameters = reduce_to_signature(Probe.__init__, file_scan_parameters)
return probe_parameters
[docs]
def probe(self, event=None) -> Probe:
"""Returns a Probe object initialized with the parameters from the file.
Args:
event (int, optional): Event number. When specified, an event structure
is expected as follows::
event_0 / scan
event_1 / scan
...
Defaults to None. In that case, no event structure is expected.
Returns:
Probe: The probe object.
"""
probe_parameters_file = self.get_probe_parameters(event)
return Probe.from_parameters(self.probe_name, probe_parameters_file)
[docs]
def recursively_load_dict_contents_from_group(self, path: str) -> dict:
"""Load dict from contents of group
Values inside the group are converted to numpy arrays
or primitive types (int, float, str).
Args:
path (str): path to group
Returns:
dict: dictionary with contents of group
"""
ans = {}
for key, item in self[path].items():
if isinstance(item, h5py.Dataset):
ans[key] = item[()]
elif isinstance(item, h5py.Group):
ans[key] = self.recursively_load_dict_contents_from_group(path + "/" + key + "/")
return ans
[docs]
def has_key(self, key: str) -> bool:
"""Check if the file has a specific key.
Args:
key (str): The key to check.
Returns:
bool: True if the key exists, False otherwise.
"""
try:
key = self.format_key(key)
except AssertionError:
return False
return True
[docs]
@classmethod
def get_shape(cls, path: str, key: str) -> tuple:
"""Get the shape of a key in a file.
Args:
path (str): The path to the file.
key (str): The key to get the shape of.
Returns:
tuple: The shape of the key.
"""
with cls(path, mode="r") as file:
return file.shape(key)
[docs]
def validate(self):
"""Validate the file structure.
Returns:
dict: A dictionary with the validation results.
"""
return validate_file(file=self)
def __repr__(self):
return (
f"<zea.data.file.File at 0x{id(self):x} "
f'("{Path(self.filename).name}" mode={self.mode})>'
)
def __str__(self):
return f"zea HDF5 File: '{self.path.name}' (mode={self.mode})"
[docs]
def copy_key(self, key: str, dst: "File"):
"""Copy a specific key to another file.
Will always copy the attributes and the scan data if it exists. Will warn if the key is
not in this file or if the key already exists in the destination file.
Args:
key (str): The key to copy.
dst (File): The destination file to copy the key to.
"""
key = self.format_key(key)
# Copy the key if it does not already exist in the destination file
if key in dst:
log.warning(f"Skipping key '{key}' because it already exists in dst file {dst.path}.")
elif key in self:
self.copy(key, dst, name=key)
else:
log.warning(f"Key '{key}' not found in src file {self.path}. Skipping copy.")
# Copy attributes from src to dst
for attr_key, attr_value in self.attrs.items():
dst[key].attrs[attr_key] = attr_value
# Copy scan data if requested
if "scan" in self and "scan" not in dst:
# Copy the scan data if it exists
self.copy("scan", dst)
[docs]
def summary(self):
"""Print the contents of the file."""
_print_hdf5_attrs(self)
[docs]
def load_file_all_data_types(
path,
indices: Tuple[Union[list, slice, int], ...] | List[int] | int | None = None,
scan_kwargs: dict = None,
):
"""Loads a zea data files (h5py file).
Returns all data types together with a scan object containing the parameters
of the acquisition and a probe object containing the parameters of the probe.
Additionally, it can load a specific subset of frames / transmits.
.. include:: ../common/file_indexing.rst
Args:
path (str, pathlike): The path to the hdf5 file.
indices (optional): The indices to load. Defaults to None in
which case all frames are loaded.
scan_kwargs (Config, dict, optional): Additional keyword arguments
to pass to the Scan object. These will override the parameters from the file
if they are present in the file. Defaults to None.
Returns:
(dict): A dictionary with all data types as keys and the corresponding data as values.
(Scan): A scan object containing the parameters of the acquisition.
(Probe): A probe object containing the parameters of the probe.
"""
# Define the additional keyword parameters from the scan object
if scan_kwargs is None:
scan_kwargs = {}
data_dict = {}
with File(path, mode="r") as file:
# Load the probe object from the file
probe = file.probe()
for data_type in DataTypes:
if not file.has_key(data_type.value):
data_dict[data_type.value] = None
continue
# Load the desired frames from the file
data_dict[data_type.value] = file.load_data(data_type.value, indices=indices)
# extract transmits from indices
# we only have to do this when the data has a n_tx dimension
# in that case we also have update scan parameters to match
# the number of selected transmits
if isinstance(indices, tuple) and len(indices) > 1:
scan_kwargs["selected_transmits"] = indices[1]
scan = file.scan(**scan_kwargs)
return data_dict, scan, probe
[docs]
def load_file(
path,
data_type="raw_data",
indices: Tuple[Union[list, slice, int], ...] | List[int] | int | None = None,
scan_kwargs: dict = None,
) -> Tuple[np.ndarray, Scan, Probe]:
"""Loads a zea data files (h5py file).
Returns the data together with a scan object containing the parameters
of the acquisition and a probe object containing the parameters of the probe.
Additionally, it can load a specific subset of frames / transmits.
.. include:: ../common/file_indexing.rst
Args:
path (str, pathlike): The path to the hdf5 file.
data_type (str, optional): The type of data to load. Defaults to
'raw_data'. Other options are 'aligned_data', 'beamformed_data',
'envelope_data', 'image' and 'image_sc'.
indices (optional): The indices to load. Defaults to None in
which case all frames are loaded.
scan_kwargs (Config, dict, optional): Additional keyword arguments
to pass to the Scan object. These will override the parameters from the file
if they are present in the file. Defaults to None.
Returns:
(np.ndarray): The raw data of shape (n_frames, n_tx, n_ax, n_el, n_ch).
(Scan): A scan object containing the parameters of the acquisition.
(Probe): A probe object containing the parameters of the probe.
"""
# Define the additional keyword parameters from the scan object
if scan_kwargs is None:
scan_kwargs = {}
with File(path, mode="r") as file:
# Load the probe object from the file
probe = file.probe()
# Load the desired frames from the file
data = file.load_data(data_type, indices=indices)
# extract transmits from indices
# we only have to do this when the data has a n_tx dimension
# in that case we also have update scan parameters to match
# the number of selected transmits
if data_type in ["raw_data", "aligned_data"]:
if isinstance(indices, tuple) and len(indices) > 1:
scan_kwargs["selected_transmits"] = indices[1]
scan = file.scan(**scan_kwargs)
return data, scan, probe
def _print_hdf5_attrs(hdf5_obj, prefix=""):
"""Recursively prints all keys, attributes, and shapes in an HDF5 file.
Args:
hdf5_obj (h5py.File, h5py.Group, h5py.Dataset): HDF5 object to print.
prefix (str, optional): Prefix to print before each line. This
parameter is used in internal recursion and should not be supplied
by the user.
"""
assert isinstance(hdf5_obj, (h5py.File, h5py.Group, h5py.Dataset)), (
"ERROR: hdf5_obj must be a File, Group, or Dataset object"
)
if isinstance(hdf5_obj, h5py.File):
name = "root" if hdf5_obj.name == "/" else hdf5_obj.name
print(prefix + name + "/")
prefix += " "
elif isinstance(hdf5_obj, h5py.Dataset):
shape_str = str(hdf5_obj.shape).replace(",)", ")")
print(prefix + "├── " + hdf5_obj.name + " (shape=" + shape_str + ")")
prefix += "│ "
# Print all attributes
for key, val in hdf5_obj.attrs.items():
print(prefix + "├── " + key + ": " + str(val))
# Recursively print all keys, attributes, and shapes in groups
if isinstance(hdf5_obj, h5py.Group):
for i, key in enumerate(hdf5_obj.keys()):
is_last = i == len(hdf5_obj.keys()) - 1
if is_last:
marker = "└── "
new_prefix = prefix + " "
else:
marker = "├── "
new_prefix = prefix + "│ "
print(prefix + marker + key + "/")
_print_hdf5_attrs(hdf5_obj[key], new_prefix)
[docs]
def validate_file(path: str = None, file: File = None):
"""Reads the hdf5 file at the given path and validates its structure.
Provide either the path or the file, but not both.
Args:
path (str, pathlike): The path to the hdf5 file.
file (File): The hdf5 file.
"""
assert (path is not None) ^ (file is not None), (
"Provide either the path or the file, but not both."
)
if path is not None:
path = Path(path)
with File(path, "r") as _file:
event_structure, num_events = _validate_hdf5_file(_file)
else:
event_structure, num_events = _validate_hdf5_file(file)
return {
"status": "success",
"event_structure": event_structure,
"num_events": num_events,
}
def _validate_hdf5_file(file: File):
all_keys = list(file.keys())
if file.has_events:
num_events = len(all_keys)
for event_no in range(num_events):
assert_key(file, f"event_{event_no}")
_validate_structure(file[f"event_{event_no}"])
else:
num_events = 0
_validate_structure(file)
return file.has_events, num_events
def _validate_structure(file: File):
# Validate the root group
assert_key(file, "data")
# Assert file["data"] is a group
assert isinstance(file["data"], h5py.Group), (
"The data group is not a group. Please check the file structure. "
"Maybe this is not a zea file?"
)
# Check if there is only image data
not_only_image_data = len([i for i in _NON_IMAGE_DATA_TYPES if i in file["data"].keys()]) > 0
# Only check scan group if there is non-image data
if not_only_image_data:
assert_key(file, "scan")
for key in _REQUIRED_SCAN_KEYS:
assert_key(file["scan"], key)
# validate the data group
for key in file["data"].keys():
assert key in _DATA_TYPES, "The data group contains an unexpected key."
# Validate data shape
data_shape = file["data"][key].shape
if key == "raw_data":
get_check(key)(shape=data_shape, with_batch_dim=True)
assert data_shape[0] == file["scan"]["n_frames"][()], (
"n_frames does not match the first dimension of raw_data."
)
assert data_shape[1] == file["scan"]["n_tx"][()], (
"n_tx does not match the second dimension of raw_data."
)
assert data_shape[2] == file["scan"]["n_ax"][()], (
"n_ax does not match the third dimension of raw_data."
)
assert data_shape[3] == file["scan"]["n_el"][()], (
"n_el does not match the fourth dimension of raw_data."
)
elif key == "aligned_data":
get_check(key)(shape=data_shape, with_batch_dim=True)
assert data_shape[0] == file["scan"]["n_frames"][()], (
"n_frames does not match the first dimension of aligned_data."
)
elif key == "beamformed_data":
get_check(key)(shape=data_shape, with_batch_dim=True)
assert data_shape[0] == file["scan"]["n_frames"][()], (
"n_frames does not match the first dimension of beamformed_data."
)
elif key == "envelope_data":
get_check(key)(shape=data_shape, with_batch_dim=True)
assert data_shape[0] == file["scan"]["n_frames"][()], (
"n_frames does not match the first dimension of envelope_data."
)
elif key == "image":
get_check(key)(shape=data_shape, with_batch_dim=True)
assert data_shape[0] == file["scan"]["n_frames"][()], (
"n_frames does not match the first dimension of image."
)
elif key == "image_sc":
get_check(key)(shape=data_shape, with_batch_dim=True)
assert data_shape[0] == file["scan"]["n_frames"][()], (
"n_frames does not match the first dimension of image_sc."
)
if not_only_image_data:
_assert_scan_keys_present(file)
_assert_unit_and_description_present(file)
def _assert_scan_keys_present(file: File):
"""Ensure that all required keys are present.
Args:
file (h5py.File): The file instance to check.
Raises:
AssertionError: If a required key is missing or does not have the right shape.
"""
for required_key in _REQUIRED_SCAN_KEYS:
assert required_key in file["scan"].keys(), (
f"The scan group does not contain the required key {required_key}."
)
# Ensure that all keys have the correct shape
for key in file["scan"].keys():
if isinstance(file["scan"][key], h5py.Group):
shape_file = None
else:
shape_file = file["scan"][key].shape
if key == "probe_geometry":
correct_shape = (file["scan"]["n_el"][()], 3)
elif key == "t0_delays":
correct_shape = (
file["scan"]["n_tx"][()],
file["scan"]["n_el"][()],
)
elif key == "tx_apodizations":
correct_shape = (
file["scan"]["n_tx"][()],
file["scan"]["n_el"][()],
)
elif key == "focus_distances":
correct_shape = (file["scan"]["n_tx"][()],)
elif key == "transmit_origins":
correct_shape = (file["scan"]["n_tx"][()], 3)
elif key == "polar_angles":
correct_shape = (file["scan"]["n_tx"][()],)
elif key == "azimuth_angles":
correct_shape = (file["scan"]["n_tx"][()],)
elif key == "initial_times":
correct_shape = (file["scan"]["n_tx"][()],)
elif key == "time_to_next_transmit":
correct_shape = (
file["scan"]["n_frames"][()],
file["scan"]["n_tx"][()],
)
elif key == "tgc_gain_curve":
correct_shape = (file["scan"]["n_ax"][()],)
elif key == "tx_waveform_indices":
correct_shape = (file["scan"]["n_tx"][()],)
elif key in ("waveforms_one_way", "waveforms_two_way"):
correct_shape = None
elif key in (
"sampling_frequency",
"center_frequency",
"demodulation_frequency",
"n_frames",
"n_tx",
"n_el",
"n_ax",
"n_ch",
"sound_speed",
"bandwidth_percent",
"element_width",
"lens_correction",
):
correct_shape = ()
shape_file = file["scan"][key].shape
else:
correct_shape = None
log.debug(f"No validation has been defined for {log.orange(key)}.")
if correct_shape is not None:
assert shape_file == correct_shape, (
f"`{key}` does not have the correct shape. "
f"Expected shape: {correct_shape}, got shape: {shape_file}"
)
def _assert_unit_and_description_present(hdf5_file, _prefix=""):
"""Checks that all keys have a unit and description attribute.
Args:
hdf5_file (h5py.File): The hdf5 file to check.
Raises:
AssertionError: If a file does not have a unit or description attribute.
"""
for key in hdf5_file.keys():
if isinstance(hdf5_file[key], h5py.Group):
_assert_unit_and_description_present(hdf5_file[key], _prefix=_prefix + key + "/")
else:
assert "unit" in hdf5_file[key].attrs.keys(), (
f"The file {_prefix}/{key} does not have a unit attribute."
)
assert "description" in hdf5_file[key].attrs.keys(), (
f"The file {_prefix}/{key} does not have a description attribute."
)
def _reformat_waveforms(scan_kwargs: dict) -> dict:
"""Reformat waveforms from dict to array if needed. This is for backwards compatibility and will
be removed in a future version of zea.
Args:
scan_kwargs (dict): The scan parameters.
Returns:
scan_kwargs (dict): The scan parameters with the keys waveforms_one_way and
waveforms_two_way reformatted to arrays if they were stored as dicts.
"""
# TODO: remove this in a future version of zea
if "waveforms_one_way" in scan_kwargs and isinstance(scan_kwargs["waveforms_one_way"], dict):
log.warning(
"The waveforms_one_way parameter is stored as a dictionary in the file. "
"Converting to array. This will be deprecated in future versions of zea. "
"Please update your files to store waveforms as arrays of shape `(n_tx, n_samples)`."
)
scan_kwargs["waveforms_one_way"] = _waveforms_dict_to_array(
scan_kwargs["waveforms_one_way"]
)
if "waveforms_two_way" in scan_kwargs and isinstance(scan_kwargs["waveforms_two_way"], dict):
log.warning(
"The waveforms_two_way parameter is stored as a dictionary in the file. "
"Converting to array. This will be deprecated in future versions of zea. "
"Please update your files to store waveforms as arrays of shape `(n_tx, n_samples)`."
)
scan_kwargs["waveforms_two_way"] = _waveforms_dict_to_array(
scan_kwargs["waveforms_two_way"]
)
return scan_kwargs
def _waveforms_dict_to_array(waveforms_dict: dict):
"""Convert waveforms stored as a dictionary to a padded numpy array."""
waveforms = dict_to_sorted_list(waveforms_dict)
return pad_sequences(waveforms, dtype=np.float32, padding="post")
[docs]
def dict_to_sorted_list(dictionary: dict):
"""Convert a dictionary with sortable keys to a sorted list of values.
.. note::
This function operates on the top level of the dictionary only.
If the dictionary contains nested dictionaries, those will not be sorted.
Example:
.. doctest::
>>> from zea.data.file import dict_to_sorted_list
>>> input_dict = {"number_000": 5, "number_001": 1, "number_002": 23}
>>> dict_to_sorted_list(input_dict)
[5, 1, 23]
Args:
dictionary (dict): The dictionary to convert. The keys must be sortable.
Returns:
list: The sorted list of values.
"""
return [value for _, value in sorted(dictionary.items())]