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221,300 | dask/dask-ml | dask_ml/cluster/spectral.py | _slice_mostly_sorted | def _slice_mostly_sorted(array, keep, rest, ind=None):
"""Slice dask array `array` that is almost entirely sorted already.
We perform approximately `2 * len(keep)` slices on `array`.
This is OK, since `keep` is small. Individually, each of these slices
is entirely sorted.
Parameters
----------... | python | def _slice_mostly_sorted(array, keep, rest, ind=None):
"""Slice dask array `array` that is almost entirely sorted already.
We perform approximately `2 * len(keep)` slices on `array`.
This is OK, since `keep` is small. Individually, each of these slices
is entirely sorted.
Parameters
----------... | [
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221,301 | dask/dask-ml | dask_ml/datasets.py | make_counts | def make_counts(
n_samples=1000,
n_features=100,
n_informative=2,
scale=1.0,
chunks=100,
random_state=None,
):
"""
Generate a dummy dataset for modeling count data.
Parameters
----------
n_samples : int
number of rows in the output array
n_features : int
... | python | def make_counts(
n_samples=1000,
n_features=100,
n_informative=2,
scale=1.0,
chunks=100,
random_state=None,
):
"""
Generate a dummy dataset for modeling count data.
Parameters
----------
n_samples : int
number of rows in the output array
n_features : int
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number of features that are correlated with the outcome
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221,302 | dask/dask-ml | dask_ml/datasets.py | make_blobs | def make_blobs(
n_samples=100,
n_features=2,
centers=None,
cluster_std=1.0,
center_box=(-10.0, 10.0),
shuffle=True,
random_state=None,
chunks=None,
):
"""
Generate isotropic Gaussian blobs for clustering.
This can be used to generate very large Dask arrays on a cluster of
... | python | def make_blobs(
n_samples=100,
n_features=2,
centers=None,
cluster_std=1.0,
center_box=(-10.0, 10.0),
shuffle=True,
random_state=None,
chunks=None,
):
"""
Generate isotropic Gaussian blobs for clustering.
This can be used to generate very large Dask arrays on a cluster of
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221,303 | dask/dask-ml | dask_ml/datasets.py | make_regression | def make_regression(
n_samples=100,
n_features=100,
n_informative=10,
n_targets=1,
bias=0.0,
effective_rank=None,
tail_strength=0.5,
noise=0.0,
shuffle=True,
coef=False,
random_state=None,
chunks=None,
):
"""
Generate a random regression problem.
The input se... | python | def make_regression(
n_samples=100,
n_features=100,
n_informative=10,
n_targets=1,
bias=0.0,
effective_rank=None,
tail_strength=0.5,
noise=0.0,
shuffle=True,
coef=False,
random_state=None,
chunks=None,
):
"""
Generate a random regression problem.
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221,304 | dask/dask-ml | dask_ml/decomposition/truncated_svd.py | TruncatedSVD.fit_transform | def fit_transform(self, X, y=None):
"""Fit model to X and perform dimensionality reduction on X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data.
y : Ignored
Returns
-------
X_new : array, shape (n_samples, ... | python | def fit_transform(self, X, y=None):
"""Fit model to X and perform dimensionality reduction on X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data.
y : Ignored
Returns
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221,305 | dask/dask-ml | dask_ml/compose/_column_transformer.py | ColumnTransformer._hstack | def _hstack(self, Xs):
"""
Stacks X horizontally.
Supports input types (X): list of
numpy arrays, sparse arrays and DataFrames
"""
types = set(type(X) for X in Xs)
if self.sparse_output_:
return sparse.hstack(Xs).tocsr()
elif dd.Series in... | python | def _hstack(self, Xs):
"""
Stacks X horizontally.
Supports input types (X): list of
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"""
types = set(type(X) for X in Xs)
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221,306 | dask/dask-ml | dask_ml/decomposition/pca.py | PCA.transform | def transform(self, X):
"""Apply dimensionality reduction on X.
X is projected on the first principal components previous extracted
from a training set.
Parameters
----------
X : array-like, shape (n_samples, n_features)
New data, where n_samples in the numb... | python | def transform(self, X):
"""Apply dimensionality reduction on X.
X is projected on the first principal components previous extracted
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221,307 | dask/dask-ml | dask_ml/decomposition/pca.py | PCA.fit_transform | def fit_transform(self, X, y=None):
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Parameters
----------
X : array-like, shape (n_samples, n_features)
New data, where n_samples in the number of samples
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221,308 | dask/dask-ml | dask_ml/decomposition/pca.py | PCA.inverse_transform | def inverse_transform(self, X):
"""Transform data back to its original space.
Returns an array X_original whose transform would be X.
Parameters
----------
X : array-like, shape (n_samples, n_components)
New data, where n_samples in the number of samples
... | python | def inverse_transform(self, X):
"""Transform data back to its original space.
Returns an array X_original whose transform would be X.
Parameters
----------
X : array-like, shape (n_samples, n_components)
New data, where n_samples in the number of samples
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221,309 | dask/dask-ml | dask_ml/decomposition/pca.py | PCA.score_samples | def score_samples(self, X):
"""Return the log-likelihood of each sample.
See. "Pattern Recognition and Machine Learning"
by C. Bishop, 12.2.1 p. 574
or http://www.miketipping.com/papers/met-mppca.pdf
Parameters
----------
X : array, shape(n_samples, n_features)
... | python | def score_samples(self, X):
"""Return the log-likelihood of each sample.
See. "Pattern Recognition and Machine Learning"
by C. Bishop, 12.2.1 p. 574
or http://www.miketipping.com/papers/met-mppca.pdf
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X : array, shape(n_samples, n_features)
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221,310 | dask/dask-ml | dask_ml/utils.py | assert_estimator_equal | def assert_estimator_equal(left, right, exclude=None, **kwargs):
"""Check that two Estimators are equal
Parameters
----------
left, right : Estimators
exclude : str or sequence of str
attributes to skip in the check
kwargs : dict
Passed through to the dask `assert_eq` method.
... | python | def assert_estimator_equal(left, right, exclude=None, **kwargs):
"""Check that two Estimators are equal
Parameters
----------
left, right : Estimators
exclude : str or sequence of str
attributes to skip in the check
kwargs : dict
Passed through to the dask `assert_eq` method.
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221,311 | dask/dask-ml | dask_ml/utils.py | check_matching_blocks | def check_matching_blocks(*arrays):
"""Check that the partitioning structure for many arrays matches.
Parameters
----------
*arrays : Sequence of array-likes
This includes
* Dask Array
* Dask DataFrame
* Dask Series
"""
if len(arrays) <= 1:
return
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"""Check that the partitioning structure for many arrays matches.
Parameters
----------
*arrays : Sequence of array-likes
This includes
* Dask Array
* Dask DataFrame
* Dask Series
"""
if len(arrays) <= 1:
return
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221,312 | dask/dask-ml | dask_ml/utils.py | check_chunks | def check_chunks(n_samples, n_features, chunks=None):
"""Validate and normalize the chunks argument for a dask.array
Parameters
----------
n_samples, n_features : int
Give the shape of the array
chunks : int, sequence, optional, default None
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"""Validate and normalize the chunks argument for a dask.array
Parameters
----------
n_samples, n_features : int
Give the shape of the array
chunks : int, sequence, optional, default None
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221,313 | dask/dask-ml | dask_ml/metrics/scorer.py | get_scorer | def get_scorer(scoring, compute=True):
"""Get a scorer from string
Parameters
----------
scoring : str | callable
scoring method as string. If callable it is returned as is.
Returns
-------
scorer : callable
The scorer.
"""
# This is the same as sklearns, only we us... | python | def get_scorer(scoring, compute=True):
"""Get a scorer from string
Parameters
----------
scoring : str | callable
scoring method as string. If callable it is returned as is.
Returns
-------
scorer : callable
The scorer.
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scorer : callable
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221,314 | dask/dask-ml | dask_ml/model_selection/utils.py | to_indexable | def to_indexable(*args, **kwargs):
"""Ensure that all args are an indexable type.
Conversion runs lazily for dask objects, immediately otherwise.
Parameters
----------
args : array_like or scalar
allow_scalars : bool, optional
Whether to allow scalars in args. Default is False.
"""... | python | def to_indexable(*args, **kwargs):
"""Ensure that all args are an indexable type.
Conversion runs lazily for dask objects, immediately otherwise.
Parameters
----------
args : array_like or scalar
allow_scalars : bool, optional
Whether to allow scalars in args. Default is False.
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221,315 | dask/dask-ml | dask_ml/model_selection/utils.py | _index_param_value | def _index_param_value(num_samples, v, indices):
"""Private helper function for parameter value indexing.
This determines whether a fit parameter `v` to a SearchCV.fit
should be indexed along with `X` and `y`. Note that this differs
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... | python | def _index_param_value(num_samples, v, indices):
"""Private helper function for parameter value indexing.
This determines whether a fit parameter `v` to a SearchCV.fit
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221,316 | dask/dask-ml | dask_ml/model_selection/utils.py | DeprecationDict.add_warning | def add_warning(self, key, *args, **kwargs):
"""Add a warning to be triggered when the specified key is read
Parameters
----------
key : any hashable object
The key
"""
self._deprecations[key] = (args, kwargs) | python | def add_warning(self, key, *args, **kwargs):
"""Add a warning to be triggered when the specified key is read
Parameters
----------
key : any hashable object
The key
"""
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221,317 | dask/dask-ml | dask_ml/preprocessing/label.py | _construct | def _construct(x, categories):
"""Make a sparse matrix from an encoded array.
>>> construct(np.array([0, 1, 0]), np.array([0, 1])).toarray()
array([[1., 0.],
[0., 1.],
[1., 0.]])
"""
# type: (np.ndarray, np.ndarray) -> scipy.sparse.csr_matrix
data = np.ones(len(x))
row... | python | def _construct(x, categories):
"""Make a sparse matrix from an encoded array.
>>> construct(np.array([0, 1, 0]), np.array([0, 1])).toarray()
array([[1., 0.],
[0., 1.],
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221,318 | dask/dask-ml | dask_ml/preprocessing/label.py | _encode_dask_array | def _encode_dask_array(values, uniques=None, encode=False, onehot_dtype=None):
"""One-hot or label encode a dask array.
Parameters
----------
values : da.Array, shape [n_samples,]
unqiques : np.ndarray, shape [n_uniques,]
encode : bool, default False
Whether to encode the values (True) ... | python | def _encode_dask_array(values, uniques=None, encode=False, onehot_dtype=None):
"""One-hot or label encode a dask array.
Parameters
----------
values : da.Array, shape [n_samples,]
unqiques : np.ndarray, shape [n_uniques,]
encode : bool, default False
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221,319 | dask/dask-ml | dask_ml/model_selection/methods.py | pipeline | def pipeline(names, steps):
"""Reconstruct a Pipeline from names and steps"""
steps, times = zip(*map(_maybe_timed, steps))
fit_time = sum(times)
if any(s is FIT_FAILURE for s in steps):
fit_est = FIT_FAILURE
else:
fit_est = Pipeline(list(zip(names, steps)))
return fit_est, fit_t... | python | def pipeline(names, steps):
"""Reconstruct a Pipeline from names and steps"""
steps, times = zip(*map(_maybe_timed, steps))
fit_time = sum(times)
if any(s is FIT_FAILURE for s in steps):
fit_est = FIT_FAILURE
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221,320 | dask/dask-ml | dask_ml/model_selection/methods.py | feature_union | def feature_union(names, steps, weights):
"""Reconstruct a FeatureUnion from names, steps, and weights"""
steps, times = zip(*map(_maybe_timed, steps))
fit_time = sum(times)
if any(s is FIT_FAILURE for s in steps):
fit_est = FIT_FAILURE
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"""Reconstruct a FeatureUnion from names, steps, and weights"""
steps, times = zip(*map(_maybe_timed, steps))
fit_time = sum(times)
if any(s is FIT_FAILURE for s in steps):
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221,321 | dask/dask-ml | dask_ml/model_selection/methods.py | feature_union_concat | def feature_union_concat(Xs, nsamples, weights):
"""Apply weights and concatenate outputs from a FeatureUnion"""
if any(x is FIT_FAILURE for x in Xs):
return FIT_FAILURE
Xs = [X if w is None else X * w for X, w in zip(Xs, weights) if X is not None]
if not Xs:
return np.zeros((nsamples, 0... | python | def feature_union_concat(Xs, nsamples, weights):
"""Apply weights and concatenate outputs from a FeatureUnion"""
if any(x is FIT_FAILURE for x in Xs):
return FIT_FAILURE
Xs = [X if w is None else X * w for X, w in zip(Xs, weights) if X is not None]
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221,322 | dask/dask-ml | dask_ml/model_selection/_split.py | _generate_idx | def _generate_idx(n, seed, n_train, n_test):
"""Generate train, test indices for a length-n array.
Parameters
----------
n : int
The length of the array
seed : int
Seed for a RandomState
n_train, n_test : int, 0 < n_train, n_test < n
Number of samples to use for the trai... | python | def _generate_idx(n, seed, n_train, n_test):
"""Generate train, test indices for a length-n array.
Parameters
----------
n : int
The length of the array
seed : int
Seed for a RandomState
n_train, n_test : int, 0 < n_train, n_test < n
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221,323 | dask/dask-ml | dask_ml/model_selection/_split.py | _blockwise_slice | def _blockwise_slice(arr, idx):
"""Slice an array that is blockwise-aligned with idx.
Parameters
----------
arr : Dask array
idx : Dask array
Should have the following properties
* Same blocks as `arr` along the first dimension
* Contains only integers
* Each block'... | python | def _blockwise_slice(arr, idx):
"""Slice an array that is blockwise-aligned with idx.
Parameters
----------
arr : Dask array
idx : Dask array
Should have the following properties
* Same blocks as `arr` along the first dimension
* Contains only integers
* Each block'... | [
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221,324 | dask/dask-ml | dask_ml/feature_extraction/text.py | HashingVectorizer.transform | def transform(self, X):
"""Transform a sequence of documents to a document-term matrix.
Transformation is done in parallel, and correctly handles dask
collections.
Parameters
----------
X : dask.Bag of raw text documents, length = n_samples
Samples. Each sam... | python | def transform(self, X):
"""Transform a sequence of documents to a document-term matrix.
Transformation is done in parallel, and correctly handles dask
collections.
Parameters
----------
X : dask.Bag of raw text documents, length = n_samples
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221,325 | MIT-LCP/wfdb-python | wfdb/processing/peaks.py | correct_peaks | def correct_peaks(sig, peak_inds, search_radius, smooth_window_size,
peak_dir='compare'):
"""
Adjust a set of detected peaks to coincide with local signal maxima,
and
Parameters
----------
sig : numpy array
The 1d signal array
peak_inds : np array
Array of ... | python | def correct_peaks(sig, peak_inds, search_radius, smooth_window_size,
peak_dir='compare'):
"""
Adjust a set of detected peaks to coincide with local signal maxima,
and
Parameters
----------
sig : numpy array
The 1d signal array
peak_inds : np array
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221,326 | MIT-LCP/wfdb-python | wfdb/processing/peaks.py | shift_peaks | def shift_peaks(sig, peak_inds, search_radius, peak_up):
"""
Helper function for correct_peaks. Return the shifted peaks to local
maxima or minima within a radius.
peak_up : bool
Whether the expected peak direction is up
"""
sig_len = sig.shape[0]
n_peaks = len(peak_inds)
# The ... | python | def shift_peaks(sig, peak_inds, search_radius, peak_up):
"""
Helper function for correct_peaks. Return the shifted peaks to local
maxima or minima within a radius.
peak_up : bool
Whether the expected peak direction is up
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sig_len = sig.shape[0]
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221,327 | MIT-LCP/wfdb-python | wfdb/plot/plot.py | get_plot_dims | def get_plot_dims(signal, ann_samp):
"Figure out the number of plot channels"
if signal is not None:
if signal.ndim == 1:
sig_len = len(signal)
n_sig = 1
else:
sig_len = signal.shape[0]
n_sig = signal.shape[1]
else:
sig_len = 0
... | python | def get_plot_dims(signal, ann_samp):
"Figure out the number of plot channels"
if signal is not None:
if signal.ndim == 1:
sig_len = len(signal)
n_sig = 1
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221,328 | MIT-LCP/wfdb-python | wfdb/plot/plot.py | create_figure | def create_figure(n_subplots, figsize):
"Create the plot figure and subplot axes"
fig = plt.figure(figsize=figsize)
axes = []
for i in range(n_subplots):
axes.append(fig.add_subplot(n_subplots, 1, i+1))
return fig, axes | python | def create_figure(n_subplots, figsize):
"Create the plot figure and subplot axes"
fig = plt.figure(figsize=figsize)
axes = []
for i in range(n_subplots):
axes.append(fig.add_subplot(n_subplots, 1, i+1))
return fig, axes | [
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221,329 | MIT-LCP/wfdb-python | wfdb/plot/plot.py | plot_signal | def plot_signal(signal, sig_len, n_sig, fs, time_units, sig_style, axes):
"Plot signal channels"
# Extend signal style if necesary
if len(sig_style) == 1:
sig_style = n_sig * sig_style
# Figure out time indices
if time_units == 'samples':
t = np.linspace(0, sig_len-1, sig_len)
... | python | def plot_signal(signal, sig_len, n_sig, fs, time_units, sig_style, axes):
"Plot signal channels"
# Extend signal style if necesary
if len(sig_style) == 1:
sig_style = n_sig * sig_style
# Figure out time indices
if time_units == 'samples':
t = np.linspace(0, sig_len-1, sig_len)
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221,330 | MIT-LCP/wfdb-python | wfdb/plot/plot.py | plot_annotation | def plot_annotation(ann_samp, n_annot, ann_sym, signal, n_sig, fs, time_units,
ann_style, axes):
"Plot annotations, possibly overlaid on signals"
# Extend annotation style if necesary
if len(ann_style) == 1:
ann_style = n_annot * ann_style
# Figure out downsample factor for ... | python | def plot_annotation(ann_samp, n_annot, ann_sym, signal, n_sig, fs, time_units,
ann_style, axes):
"Plot annotations, possibly overlaid on signals"
# Extend annotation style if necesary
if len(ann_style) == 1:
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221,331 | MIT-LCP/wfdb-python | wfdb/plot/plot.py | plot_ecg_grids | def plot_ecg_grids(ecg_grids, fs, units, time_units, axes):
"Add ecg grids to the axes"
if ecg_grids == 'all':
ecg_grids = range(0, len(axes))
for ch in ecg_grids:
# Get the initial plot limits
auto_xlims = axes[ch].get_xlim()
auto_ylims= axes[ch].get_ylim()
(major... | python | def plot_ecg_grids(ecg_grids, fs, units, time_units, axes):
"Add ecg grids to the axes"
if ecg_grids == 'all':
ecg_grids = range(0, len(axes))
for ch in ecg_grids:
# Get the initial plot limits
auto_xlims = axes[ch].get_xlim()
auto_ylims= axes[ch].get_ylim()
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221,332 | MIT-LCP/wfdb-python | wfdb/plot/plot.py | calc_ecg_grids | def calc_ecg_grids(minsig, maxsig, sig_units, fs, maxt, time_units):
"""
Calculate tick intervals for ecg grids
- 5mm 0.2s major grids, 0.04s minor grids
- 0.5mV major grids, 0.125 minor grids
10 mm is equal to 1mV in voltage.
"""
# Get the grid interval of the x axis
if time_units == ... | python | def calc_ecg_grids(minsig, maxsig, sig_units, fs, maxt, time_units):
"""
Calculate tick intervals for ecg grids
- 5mm 0.2s major grids, 0.04s minor grids
- 0.5mV major grids, 0.125 minor grids
10 mm is equal to 1mV in voltage.
"""
# Get the grid interval of the x axis
if time_units == ... | [
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221,333 | MIT-LCP/wfdb-python | wfdb/plot/plot.py | label_figure | def label_figure(axes, n_subplots, time_units, sig_name, sig_units, ylabel,
title):
"Add title, and axes labels"
if title:
axes[0].set_title(title)
# Determine y label
# Explicit labels take precedence if present. Otherwise, construct labels
# using signal names and units
... | python | def label_figure(axes, n_subplots, time_units, sig_name, sig_units, ylabel,
title):
"Add title, and axes labels"
if title:
axes[0].set_title(title)
# Determine y label
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221,334 | MIT-LCP/wfdb-python | wfdb/plot/plot.py | get_wfdb_plot_items | def get_wfdb_plot_items(record, annotation, plot_sym):
"""
Get items to plot from wfdb objects
"""
# Get record attributes
if record:
if record.p_signal is not None:
signal = record.p_signal
elif record.d_signal is not None:
signal = record.d_signal
el... | python | def get_wfdb_plot_items(record, annotation, plot_sym):
"""
Get items to plot from wfdb objects
"""
# Get record attributes
if record:
if record.p_signal is not None:
signal = record.p_signal
elif record.d_signal is not None:
signal = record.d_signal
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221,335 | MIT-LCP/wfdb-python | wfdb/io/download.py | _remote_file_size | def _remote_file_size(url=None, file_name=None, pb_dir=None):
"""
Get the remote file size in bytes
Parameters
----------
url : str, optional
The full url of the file. Use this option to explicitly
state the full url.
file_name : str, optional
The base file name. Use thi... | python | def _remote_file_size(url=None, file_name=None, pb_dir=None):
"""
Get the remote file size in bytes
Parameters
----------
url : str, optional
The full url of the file. Use this option to explicitly
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file_name : str, optional
The base file name. Use thi... | [
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221,336 | MIT-LCP/wfdb-python | wfdb/io/download.py | _stream_header | def _stream_header(file_name, pb_dir):
"""
Stream the lines of a remote header file.
Parameters
----------
file_name : str
pb_dir : str
The Physiobank database directory from which to find the
required header file. eg. For file '100.hea' in
'http://physionet.org/physiob... | python | def _stream_header(file_name, pb_dir):
"""
Stream the lines of a remote header file.
Parameters
----------
file_name : str
pb_dir : str
The Physiobank database directory from which to find the
required header file. eg. For file '100.hea' in
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221,337 | MIT-LCP/wfdb-python | wfdb/io/download.py | _stream_dat | def _stream_dat(file_name, pb_dir, byte_count, start_byte, dtype):
"""
Stream data from a remote dat file, into a 1d numpy array.
Parameters
----------
file_name : str
The name of the dat file to be read.
pb_dir : str
The physiobank directory where the dat file is located.
b... | python | def _stream_dat(file_name, pb_dir, byte_count, start_byte, dtype):
"""
Stream data from a remote dat file, into a 1d numpy array.
Parameters
----------
file_name : str
The name of the dat file to be read.
pb_dir : str
The physiobank directory where the dat file is located.
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221,338 | MIT-LCP/wfdb-python | wfdb/io/download.py | _stream_annotation | def _stream_annotation(file_name, pb_dir):
"""
Stream an entire remote annotation file from physiobank
Parameters
----------
file_name : str
The name of the annotation file to be read.
pb_dir : str
The physiobank directory where the annotation file is located.
"""
# Ful... | python | def _stream_annotation(file_name, pb_dir):
"""
Stream an entire remote annotation file from physiobank
Parameters
----------
file_name : str
The name of the annotation file to be read.
pb_dir : str
The physiobank directory where the annotation file is located.
"""
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221,339 | MIT-LCP/wfdb-python | wfdb/io/download.py | get_dbs | def get_dbs():
"""
Get a list of all the Physiobank databases available.
Examples
--------
>>> dbs = get_dbs()
"""
url = posixpath.join(config.db_index_url, 'DBS')
response = requests.get(url)
dbs = response.content.decode('ascii').splitlines()
dbs = [re.sub('\t{2,}', '\t', li... | python | def get_dbs():
"""
Get a list of all the Physiobank databases available.
Examples
--------
>>> dbs = get_dbs()
"""
url = posixpath.join(config.db_index_url, 'DBS')
response = requests.get(url)
dbs = response.content.decode('ascii').splitlines()
dbs = [re.sub('\t{2,}', '\t', li... | [
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221,340 | MIT-LCP/wfdb-python | wfdb/io/download.py | get_record_list | def get_record_list(db_dir, records='all'):
"""
Get a list of records belonging to a database.
Parameters
----------
db_dir : str
The database directory, usually the same as the database slug.
The location to look for a RECORDS file.
records : list, optional
A Option use... | python | def get_record_list(db_dir, records='all'):
"""
Get a list of records belonging to a database.
Parameters
----------
db_dir : str
The database directory, usually the same as the database slug.
The location to look for a RECORDS file.
records : list, optional
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221,341 | MIT-LCP/wfdb-python | wfdb/io/download.py | make_local_dirs | def make_local_dirs(dl_dir, dl_inputs, keep_subdirs):
"""
Make any required local directories to prepare for downloading
"""
# Make the local download dir if it doesn't exist
if not os.path.isdir(dl_dir):
os.makedirs(dl_dir)
print('Created local base download directory: %s' % dl_dir... | python | def make_local_dirs(dl_dir, dl_inputs, keep_subdirs):
"""
Make any required local directories to prepare for downloading
"""
# Make the local download dir if it doesn't exist
if not os.path.isdir(dl_dir):
os.makedirs(dl_dir)
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221,342 | MIT-LCP/wfdb-python | wfdb/io/download.py | dl_pb_file | def dl_pb_file(inputs):
"""
Download a file from physiobank.
The input args are to be unpacked for the use of multiprocessing
map, because python2 doesn't have starmap...
"""
basefile, subdir, db, dl_dir, keep_subdirs, overwrite = inputs
# Full url of file
url = posixpath.join(config... | python | def dl_pb_file(inputs):
"""
Download a file from physiobank.
The input args are to be unpacked for the use of multiprocessing
map, because python2 doesn't have starmap...
"""
basefile, subdir, db, dl_dir, keep_subdirs, overwrite = inputs
# Full url of file
url = posixpath.join(config... | [
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221,343 | MIT-LCP/wfdb-python | wfdb/io/download.py | dl_full_file | def dl_full_file(url, save_file_name):
"""
Download a file. No checks are performed.
Parameters
----------
url : str
The url of the file to download
save_file_name : str
The name to save the file as
"""
response = requests.get(url)
with open(save_file_name, 'wb') as... | python | def dl_full_file(url, save_file_name):
"""
Download a file. No checks are performed.
Parameters
----------
url : str
The url of the file to download
save_file_name : str
The name to save the file as
"""
response = requests.get(url)
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221,344 | MIT-LCP/wfdb-python | wfdb/io/download.py | dl_files | def dl_files(db, dl_dir, files, keep_subdirs=True, overwrite=False):
"""
Download specified files from a Physiobank database.
Parameters
----------
db : str
The Physiobank database directory to download. eg. For database:
'http://physionet.org/physiobank/database/mitdb', db='mitdb'.... | python | def dl_files(db, dl_dir, files, keep_subdirs=True, overwrite=False):
"""
Download specified files from a Physiobank database.
Parameters
----------
db : str
The Physiobank database directory to download. eg. For database:
'http://physionet.org/physiobank/database/mitdb', db='mitdb'.... | [
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221,345 | MIT-LCP/wfdb-python | wfdb/io/annotation.py | label_triplets_to_df | def label_triplets_to_df(triplets):
"""
Get a pd dataframe from a tuple triplets
used to define annotation labels.
The triplets should come in the
form: (label_store, symbol, description)
"""
label_df = pd.DataFrame({'label_store':np.array([t[0] for t in triplets],
... | python | def label_triplets_to_df(triplets):
"""
Get a pd dataframe from a tuple triplets
used to define annotation labels.
The triplets should come in the
form: (label_store, symbol, description)
"""
label_df = pd.DataFrame({'label_store':np.array([t[0] for t in triplets],
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221,346 | MIT-LCP/wfdb-python | wfdb/io/annotation.py | wrann | def wrann(record_name, extension, sample, symbol=None, subtype=None, chan=None,
num=None, aux_note=None, label_store=None, fs=None,
custom_labels=None, write_dir=''):
"""
Write a WFDB annotation file.
Specify at least the following:
- The record name of the WFDB record (record_name... | python | def wrann(record_name, extension, sample, symbol=None, subtype=None, chan=None,
num=None, aux_note=None, label_store=None, fs=None,
custom_labels=None, write_dir=''):
"""
Write a WFDB annotation file.
Specify at least the following:
- The record name of the WFDB record (record_name... | [
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221,347 | MIT-LCP/wfdb-python | wfdb/io/annotation.py | rdann | def rdann(record_name, extension, sampfrom=0, sampto=None, shift_samps=False,
pb_dir=None, return_label_elements=['symbol'],
summarize_labels=False):
"""
Read a WFDB annotation file record_name.extension and return an
Annotation object.
Parameters
----------
record_name : st... | python | def rdann(record_name, extension, sampfrom=0, sampto=None, shift_samps=False,
pb_dir=None, return_label_elements=['symbol'],
summarize_labels=False):
"""
Read a WFDB annotation file record_name.extension and return an
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Parameters
----------
record_name : st... | [
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221,348 | MIT-LCP/wfdb-python | wfdb/io/annotation.py | update_extra_fields | def update_extra_fields(subtype, chan, num, aux_note, update):
"""
Update the field if the current annotation did not
provide a value.
- aux_note and sub are set to default values if missing.
- chan and num copy over previous value if missing.
"""
if update['subtype']:
subtype.appe... | python | def update_extra_fields(subtype, chan, num, aux_note, update):
"""
Update the field if the current annotation did not
provide a value.
- aux_note and sub are set to default values if missing.
- chan and num copy over previous value if missing.
"""
if update['subtype']:
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221,349 | MIT-LCP/wfdb-python | wfdb/io/annotation.py | get_special_inds | def get_special_inds(sample, label_store, aux_note):
"""
Get the indices of annotations that hold definition information about
the entire annotation file, and other empty annotations to be removed.
Note: There is no need to deal with SKIP annotations (label_store=59)
which were already dealt ... | python | def get_special_inds(sample, label_store, aux_note):
"""
Get the indices of annotations that hold definition information about
the entire annotation file, and other empty annotations to be removed.
Note: There is no need to deal with SKIP annotations (label_store=59)
which were already dealt ... | [
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221,350 | MIT-LCP/wfdb-python | wfdb/io/annotation.py | rm_empty_indices | def rm_empty_indices(*args):
"""
Remove unwanted list indices. First argument is the list
of indices to remove. Other elements are the lists
to trim.
"""
rm_inds = args[0]
if not rm_inds:
return args[1:]
keep_inds = [i for i in range(len(args[1])) if i not in rm_inds]
retu... | python | def rm_empty_indices(*args):
"""
Remove unwanted list indices. First argument is the list
of indices to remove. Other elements are the lists
to trim.
"""
rm_inds = args[0]
if not rm_inds:
return args[1:]
keep_inds = [i for i in range(len(args[1])) if i not in rm_inds]
retu... | [
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221,351 | MIT-LCP/wfdb-python | wfdb/io/annotation.py | Annotation.apply_range | def apply_range(self, sampfrom=0, sampto=None):
"""
Filter the annotation attributes to keep only items between the
desired sample values
"""
sampto = sampto or self.sample[-1]
kept_inds = np.intersect1d(np.where(self.sample>=sampfrom),
... | python | def apply_range(self, sampfrom=0, sampto=None):
"""
Filter the annotation attributes to keep only items between the
desired sample values
"""
sampto = sampto or self.sample[-1]
kept_inds = np.intersect1d(np.where(self.sample>=sampfrom),
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221,352 | MIT-LCP/wfdb-python | wfdb/io/annotation.py | Annotation.wrann | def wrann(self, write_fs=False, write_dir=''):
"""
Write a WFDB annotation file from this object.
Parameters
----------
write_fs : bool, optional
Whether to write the `fs` attribute to the file.
"""
for field in ['record_name', 'extension']:
... | python | def wrann(self, write_fs=False, write_dir=''):
"""
Write a WFDB annotation file from this object.
Parameters
----------
write_fs : bool, optional
Whether to write the `fs` attribute to the file.
"""
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221,353 | MIT-LCP/wfdb-python | wfdb/io/annotation.py | Annotation.get_label_fields | def get_label_fields(self):
"""
Get the present label fields in the object
"""
present_label_fields = []
for field in ann_label_fields:
if getattr(self, field) is not None:
present_label_fields.append(field)
return present_label_fields | python | def get_label_fields(self):
"""
Get the present label fields in the object
"""
present_label_fields = []
for field in ann_label_fields:
if getattr(self, field) is not None:
present_label_fields.append(field)
return present_label_fields | [
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221,354 | MIT-LCP/wfdb-python | wfdb/io/annotation.py | Annotation.check_field_cohesion | def check_field_cohesion(self, present_label_fields):
"""
Check that the content and structure of different fields are consistent
with one another.
"""
# Ensure all written annotation fields have the same length
nannots = len(self.sample)
for field in ['sample', ... | python | def check_field_cohesion(self, present_label_fields):
"""
Check that the content and structure of different fields are consistent
with one another.
"""
# Ensure all written annotation fields have the same length
nannots = len(self.sample)
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221,355 | MIT-LCP/wfdb-python | wfdb/io/annotation.py | Annotation.get_available_label_stores | def get_available_label_stores(self, usefield='tryall'):
"""
Get the label store values that may be used
for writing this annotation.
Available store values include:
- the undefined values in the standard wfdb labels
- the store values not used in the current
a... | python | def get_available_label_stores(self, usefield='tryall'):
"""
Get the label store values that may be used
for writing this annotation.
Available store values include:
- the undefined values in the standard wfdb labels
- the store values not used in the current
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221,356 | MIT-LCP/wfdb-python | wfdb/io/annotation.py | Annotation.get_custom_label_attribute | def get_custom_label_attribute(self, attribute):
"""
Get a list of the custom_labels attribute.
ie. label_store, symbol, or description.
The custom_labels variable could be in
a number of formats
"""
if attribute not in ann_label_fields:
raise ValueE... | python | def get_custom_label_attribute(self, attribute):
"""
Get a list of the custom_labels attribute.
ie. label_store, symbol, or description.
The custom_labels variable could be in
a number of formats
"""
if attribute not in ann_label_fields:
raise ValueE... | [
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221,357 | MIT-LCP/wfdb-python | wfdb/io/annotation.py | Annotation.create_label_map | def create_label_map(self, inplace=True):
"""
Creates mapping df based on ann_label_table and self.custom_labels.
Table composed of entire WFDB standard annotation table, overwritten/appended
with custom_labels if any. Sets __label_map__ attribute, or returns value.
"""
... | python | def create_label_map(self, inplace=True):
"""
Creates mapping df based on ann_label_table and self.custom_labels.
Table composed of entire WFDB standard annotation table, overwritten/appended
with custom_labels if any. Sets __label_map__ attribute, or returns value.
"""
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221,358 | MIT-LCP/wfdb-python | wfdb/io/annotation.py | Annotation.wr_ann_file | def wr_ann_file(self, write_fs, write_dir=''):
"""
Calculate the bytes used to encode an annotation set and
write them to an annotation file
"""
# Calculate the fs bytes to write if present and desired to write
if write_fs:
fs_bytes = self.calc_fs_bytes()
... | python | def wr_ann_file(self, write_fs, write_dir=''):
"""
Calculate the bytes used to encode an annotation set and
write them to an annotation file
"""
# Calculate the fs bytes to write if present and desired to write
if write_fs:
fs_bytes = self.calc_fs_bytes()
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221,359 | MIT-LCP/wfdb-python | wfdb/io/annotation.py | Annotation.calc_core_bytes | def calc_core_bytes(self):
"""
Convert all used annotation fields into bytes to write
"""
# The difference sample to write
if len(self.sample) == 1:
sampdiff = np.array([self.sample[0]])
else:
sampdiff = np.concatenate(([self.sample[0]], np.diff(se... | python | def calc_core_bytes(self):
"""
Convert all used annotation fields into bytes to write
"""
# The difference sample to write
if len(self.sample) == 1:
sampdiff = np.array([self.sample[0]])
else:
sampdiff = np.concatenate(([self.sample[0]], np.diff(se... | [
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221,360 | MIT-LCP/wfdb-python | wfdb/io/annotation.py | Annotation.get_contained_labels | def get_contained_labels(self, inplace=True):
"""
Get the set of unique labels contained in this annotation.
Returns a pandas dataframe or sets the contained_labels
attribute of the object.
Requires the label_store field to be set.
Function will try to use attributes c... | python | def get_contained_labels(self, inplace=True):
"""
Get the set of unique labels contained in this annotation.
Returns a pandas dataframe or sets the contained_labels
attribute of the object.
Requires the label_store field to be set.
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221,361 | MIT-LCP/wfdb-python | wfdb/io/annotation.py | Annotation.set_label_elements | def set_label_elements(self, wanted_label_elements):
"""
Set one or more label elements based on
at least one of the others
"""
if isinstance(wanted_label_elements, str):
wanted_label_elements = [wanted_label_elements]
# Figure out which desired label element... | python | def set_label_elements(self, wanted_label_elements):
"""
Set one or more label elements based on
at least one of the others
"""
if isinstance(wanted_label_elements, str):
wanted_label_elements = [wanted_label_elements]
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221,362 | MIT-LCP/wfdb-python | wfdb/io/_signal.py | _dat_read_params | def _dat_read_params(fmt, sig_len, byte_offset, skew, tsamps_per_frame,
sampfrom, sampto):
"""
Calculate the parameters used to read and process a dat file, given
its layout, and the desired sample range.
Parameters
----------
fmt : str
The format of the dat file
... | python | def _dat_read_params(fmt, sig_len, byte_offset, skew, tsamps_per_frame,
sampfrom, sampto):
"""
Calculate the parameters used to read and process a dat file, given
its layout, and the desired sample range.
Parameters
----------
fmt : str
The format of the dat file
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221,363 | MIT-LCP/wfdb-python | wfdb/io/_signal.py | _required_byte_num | def _required_byte_num(mode, fmt, n_samp):
"""
Determine how many signal bytes are needed to read or write a
number of desired samples from a dat file.
Parameters
----------
mode : str
Whether the file is to be read or written: 'read' or 'write'.
fmt : str
The wfdb dat forma... | python | def _required_byte_num(mode, fmt, n_samp):
"""
Determine how many signal bytes are needed to read or write a
number of desired samples from a dat file.
Parameters
----------
mode : str
Whether the file is to be read or written: 'read' or 'write'.
fmt : str
The wfdb dat forma... | [
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221,364 | MIT-LCP/wfdb-python | wfdb/io/_signal.py | _rd_dat_file | def _rd_dat_file(file_name, dir_name, pb_dir, fmt, start_byte, n_samp):
"""
Read data from a dat file, either local or remote, into a 1d numpy
array.
This is the lowest level dat reading function (along with
`_stream_dat` which this function may call), and is called by
`_rd_dat_signals`.
P... | python | def _rd_dat_file(file_name, dir_name, pb_dir, fmt, start_byte, n_samp):
"""
Read data from a dat file, either local or remote, into a 1d numpy
array.
This is the lowest level dat reading function (along with
`_stream_dat` which this function may call), and is called by
`_rd_dat_signals`.
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221,365 | MIT-LCP/wfdb-python | wfdb/io/_signal.py | _skew_sig | def _skew_sig(sig, skew, n_sig, read_len, fmt, nan_replace, samps_per_frame=None):
"""
Skew the signal, insert nans and shave off end of array if needed.
Parameters
----------
sig : numpy array
The original signal
skew : list
List of samples to skew for each signal
n_sig : i... | python | def _skew_sig(sig, skew, n_sig, read_len, fmt, nan_replace, samps_per_frame=None):
"""
Skew the signal, insert nans and shave off end of array if needed.
Parameters
----------
sig : numpy array
The original signal
skew : list
List of samples to skew for each signal
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221,366 | MIT-LCP/wfdb-python | wfdb/io/_signal.py | _check_sig_dims | def _check_sig_dims(sig, read_len, n_sig, samps_per_frame):
"""
Integrity check of a signal's shape after reading.
"""
if isinstance(sig, np.ndarray):
if sig.shape != (read_len, n_sig):
raise ValueError('Samples were not loaded correctly')
else:
if len(sig) != n_sig:
... | python | def _check_sig_dims(sig, read_len, n_sig, samps_per_frame):
"""
Integrity check of a signal's shape after reading.
"""
if isinstance(sig, np.ndarray):
if sig.shape != (read_len, n_sig):
raise ValueError('Samples were not loaded correctly')
else:
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221,367 | MIT-LCP/wfdb-python | wfdb/io/_signal.py | _digi_bounds | def _digi_bounds(fmt):
"""
Return min and max digital values for each format type.
Accepts lists.
Parmeters
---------
fmt : str, or list
The wfdb dat format, or a list of them.
"""
if isinstance(fmt, list):
return [_digi_bounds(f) for f in fmt]
if fmt == '80':
... | python | def _digi_bounds(fmt):
"""
Return min and max digital values for each format type.
Accepts lists.
Parmeters
---------
fmt : str, or list
The wfdb dat format, or a list of them.
"""
if isinstance(fmt, list):
return [_digi_bounds(f) for f in fmt]
if fmt == '80':
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221,368 | MIT-LCP/wfdb-python | wfdb/io/_signal.py | _digi_nan | def _digi_nan(fmt):
"""
Return the wfdb digital value used to store nan for the format type.
Parmeters
---------
fmt : str, or list
The wfdb dat format, or a list of them.
"""
if isinstance(fmt, list):
return [_digi_nan(f) for f in fmt]
if fmt == '80':
return -... | python | def _digi_nan(fmt):
"""
Return the wfdb digital value used to store nan for the format type.
Parmeters
---------
fmt : str, or list
The wfdb dat format, or a list of them.
"""
if isinstance(fmt, list):
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221,369 | MIT-LCP/wfdb-python | wfdb/io/_signal.py | est_res | def est_res(signals):
"""
Estimate the resolution of each signal in a multi-channel signal in
bits. Maximum of 32 bits.
Parameters
----------
signals : numpy array, or list
A 2d numpy array representing a uniform multichannel signal, or
a list of 1d numpy arrays representing mul... | python | def est_res(signals):
"""
Estimate the resolution of each signal in a multi-channel signal in
bits. Maximum of 32 bits.
Parameters
----------
signals : numpy array, or list
A 2d numpy array representing a uniform multichannel signal, or
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221,370 | MIT-LCP/wfdb-python | wfdb/io/_signal.py | _np_dtype | def _np_dtype(bit_res, discrete):
"""
Given the bit resolution of a signal, return the minimum numpy dtype
used to store it.
Parameters
----------
bit_res : int
The bit resolution.
discrete : bool
Whether the dtype is to be int or float.
Returns
-------
dtype : ... | python | def _np_dtype(bit_res, discrete):
"""
Given the bit resolution of a signal, return the minimum numpy dtype
used to store it.
Parameters
----------
bit_res : int
The bit resolution.
discrete : bool
Whether the dtype is to be int or float.
Returns
-------
dtype : ... | [
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221,371 | MIT-LCP/wfdb-python | wfdb/io/_signal.py | _infer_sig_len | def _infer_sig_len(file_name, fmt, n_sig, dir_name, pb_dir=None):
"""
Infer the length of a signal from a dat file.
Parameters
----------
file_name : str
Name of the dat file
fmt : str
WFDB fmt of the dat file
n_sig : int
Number of signals contained in the dat file
... | python | def _infer_sig_len(file_name, fmt, n_sig, dir_name, pb_dir=None):
"""
Infer the length of a signal from a dat file.
Parameters
----------
file_name : str
Name of the dat file
fmt : str
WFDB fmt of the dat file
n_sig : int
Number of signals contained in the dat file
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221,372 | MIT-LCP/wfdb-python | wfdb/io/_signal.py | SignalMixin.adc | def adc(self, expanded=False, inplace=False):
"""
Performs analogue to digital conversion of the physical signal stored
in p_signal if expanded is False, or e_p_signal if expanded is True.
The p_signal/e_p_signal, fmt, gain, and baseline fields must all be
valid.
If inp... | python | def adc(self, expanded=False, inplace=False):
"""
Performs analogue to digital conversion of the physical signal stored
in p_signal if expanded is False, or e_p_signal if expanded is True.
The p_signal/e_p_signal, fmt, gain, and baseline fields must all be
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221,373 | MIT-LCP/wfdb-python | wfdb/io/_signal.py | SignalMixin.dac | def dac(self, expanded=False, return_res=64, inplace=False):
"""
Performs the digital to analogue conversion of the signal stored
in `d_signal` if expanded is False, or `e_d_signal` if expanded
is True.
The d_signal/e_d_signal, fmt, gain, and baseline fields must all be
... | python | def dac(self, expanded=False, return_res=64, inplace=False):
"""
Performs the digital to analogue conversion of the signal stored
in `d_signal` if expanded is False, or `e_d_signal` if expanded
is True.
The d_signal/e_d_signal, fmt, gain, and baseline fields must all be
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221,374 | MIT-LCP/wfdb-python | wfdb/io/_signal.py | SignalMixin.calc_adc_params | def calc_adc_params(self):
"""
Compute appropriate adc_gain and baseline parameters for adc
conversion, given the physical signal and the fmts.
Returns
-------
adc_gains : list
List of calculated `adc_gain` values for each channel.
baselines : list
... | python | def calc_adc_params(self):
"""
Compute appropriate adc_gain and baseline parameters for adc
conversion, given the physical signal and the fmts.
Returns
-------
adc_gains : list
List of calculated `adc_gain` values for each channel.
baselines : list
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221,375 | MIT-LCP/wfdb-python | wfdb/io/_signal.py | SignalMixin.wr_dat_files | def wr_dat_files(self, expanded=False, write_dir=''):
"""
Write each of the specified dat files
"""
# Get the set of dat files to be written, and
# the channels to be written to each file.
file_names, dat_channels = describe_list_indices(self.file_name)
# Get th... | python | def wr_dat_files(self, expanded=False, write_dir=''):
"""
Write each of the specified dat files
"""
# Get the set of dat files to be written, and
# the channels to be written to each file.
file_names, dat_channels = describe_list_indices(self.file_name)
# Get th... | [
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221,376 | MIT-LCP/wfdb-python | wfdb/io/_header.py | wfdb_strptime | def wfdb_strptime(time_string):
"""
Given a time string in an acceptable wfdb format, return
a datetime.time object.
Valid formats: SS, MM:SS, HH:MM:SS, all with and without microsec.
"""
n_colons = time_string.count(':')
if n_colons == 0:
time_fmt = '%S'
elif n_colons == 1:
... | python | def wfdb_strptime(time_string):
"""
Given a time string in an acceptable wfdb format, return
a datetime.time object.
Valid formats: SS, MM:SS, HH:MM:SS, all with and without microsec.
"""
n_colons = time_string.count(':')
if n_colons == 0:
time_fmt = '%S'
elif n_colons == 1:
... | [
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221,377 | MIT-LCP/wfdb-python | wfdb/io/_header.py | _read_header_lines | def _read_header_lines(base_record_name, dir_name, pb_dir):
"""
Read the lines in a local or remote header file.
Parameters
----------
base_record_name : str
The base name of the WFDB record to be read, without any file
extensions.
dir_name : str
The local directory loca... | python | def _read_header_lines(base_record_name, dir_name, pb_dir):
"""
Read the lines in a local or remote header file.
Parameters
----------
base_record_name : str
The base name of the WFDB record to be read, without any file
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dir_name : str
The local directory loca... | [
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221,378 | MIT-LCP/wfdb-python | wfdb/io/_header.py | _parse_record_line | def _parse_record_line(record_line):
"""
Extract fields from a record line string into a dictionary
"""
# Dictionary for record fields
record_fields = {}
# Read string fields from record line
(record_fields['record_name'], record_fields['n_seg'],
record_fields['n_sig'], record_fields[... | python | def _parse_record_line(record_line):
"""
Extract fields from a record line string into a dictionary
"""
# Dictionary for record fields
record_fields = {}
# Read string fields from record line
(record_fields['record_name'], record_fields['n_seg'],
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221,379 | MIT-LCP/wfdb-python | wfdb/io/_header.py | _parse_signal_lines | def _parse_signal_lines(signal_lines):
"""
Extract fields from a list of signal line strings into a dictionary.
"""
n_sig = len(signal_lines)
# Dictionary for signal fields
signal_fields = {}
# Each dictionary field is a list
for field in SIGNAL_SPECS.index:
signal_fields[field... | python | def _parse_signal_lines(signal_lines):
"""
Extract fields from a list of signal line strings into a dictionary.
"""
n_sig = len(signal_lines)
# Dictionary for signal fields
signal_fields = {}
# Each dictionary field is a list
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221,380 | MIT-LCP/wfdb-python | wfdb/io/_header.py | _read_segment_lines | def _read_segment_lines(segment_lines):
"""
Extract fields from segment line strings into a dictionary
"""
# Dictionary for segment fields
segment_fields = {}
# Each dictionary field is a list
for field in SEGMENT_SPECS.index:
segment_fields[field] = [None] * len(segment_lines)
... | python | def _read_segment_lines(segment_lines):
"""
Extract fields from segment line strings into a dictionary
"""
# Dictionary for segment fields
segment_fields = {}
# Each dictionary field is a list
for field in SEGMENT_SPECS.index:
segment_fields[field] = [None] * len(segment_lines)
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221,381 | MIT-LCP/wfdb-python | wfdb/io/_header.py | BaseHeaderMixin.get_write_subset | def get_write_subset(self, spec_type):
"""
Get a set of fields used to write the header; either 'record'
or 'signal' specification fields. Helper function for
`get_write_fields`. Gets the default required fields, the user
defined fields, and their dependencies.
Parameter... | python | def get_write_subset(self, spec_type):
"""
Get a set of fields used to write the header; either 'record'
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221,382 | MIT-LCP/wfdb-python | wfdb/io/_header.py | HeaderMixin.set_defaults | def set_defaults(self):
"""
Set defaults for fields needed to write the header if they have
defaults.
Notes
-----
- This is NOT called by `rdheader`. It is only automatically
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"""
Set defaults for fields needed to write the header if they have
defaults.
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221,383 | MIT-LCP/wfdb-python | wfdb/io/_header.py | HeaderMixin.get_write_fields | def get_write_fields(self):
"""
Get the list of fields used to write the header, separating
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required fields, the user defined fields,
and their dependencies.
Does NOT include `d_signal` or `e_d_signal`.
... | python | def get_write_fields(self):
"""
Get the list of fields used to write the header, separating
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Does NOT include `d_signal` or `e_d_signal`.
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221,384 | MIT-LCP/wfdb-python | wfdb/io/_header.py | HeaderMixin.set_default | def set_default(self, field):
"""
Set the object's attribute to its default value if it is missing
and there is a default.
Not responsible for initializing the
attribute. That is done by the constructor.
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# Record specification fields
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"""
Set the object's attribute to its default value if it is missing
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221,385 | MIT-LCP/wfdb-python | wfdb/io/_header.py | HeaderMixin.check_field_cohesion | def check_field_cohesion(self, rec_write_fields, sig_write_fields):
"""
Check the cohesion of fields used to write the header
"""
# If there are no signal specification fields, there is nothing to check.
if self.n_sig>0:
# The length of all signal specification fiel... | python | def check_field_cohesion(self, rec_write_fields, sig_write_fields):
"""
Check the cohesion of fields used to write the header
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# If there are no signal specification fields, there is nothing to check.
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221,386 | MIT-LCP/wfdb-python | wfdb/io/_header.py | HeaderMixin.wr_header_file | def wr_header_file(self, rec_write_fields, sig_write_fields, write_dir):
"""
Write a header file using the specified fields. Converts Record
attributes into appropriate wfdb format strings.
Parameters
----------
rec_write_fields : list
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"""
Write a header file using the specified fields. Converts Record
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221,387 | MIT-LCP/wfdb-python | wfdb/io/_header.py | MultiHeaderMixin.get_write_fields | def get_write_fields(self):
"""
Get the list of fields used to write the multi-segment header.
Returns the default required fields, the user defined fields,
and their dependencies.
"""
# Record specification fields
write_fields = self.get_write_subset('record')
... | python | def get_write_fields(self):
"""
Get the list of fields used to write the multi-segment header.
Returns the default required fields, the user defined fields,
and their dependencies.
"""
# Record specification fields
write_fields = self.get_write_subset('record')
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221,388 | MIT-LCP/wfdb-python | wfdb/io/_header.py | MultiHeaderMixin.wr_header_file | def wr_header_file(self, write_fields, write_dir):
"""
Write a header file using the specified fields
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record_line = ''
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221,389 | MIT-LCP/wfdb-python | wfdb/processing/basic.py | resample_ann | def resample_ann(resampled_t, ann_sample):
"""
Compute the new annotation indices
Parameters
----------
resampled_t : numpy array
Array of signal locations as returned by scipy.signal.resample
ann_sample : numpy array
Array of annotation locations
Returns
-------
re... | python | def resample_ann(resampled_t, ann_sample):
"""
Compute the new annotation indices
Parameters
----------
resampled_t : numpy array
Array of signal locations as returned by scipy.signal.resample
ann_sample : numpy array
Array of annotation locations
Returns
-------
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221,390 | MIT-LCP/wfdb-python | wfdb/processing/basic.py | resample_sig | def resample_sig(x, fs, fs_target):
"""
Resample a signal to a different frequency.
Parameters
----------
x : numpy array
Array containing the signal
fs : int, or float
The original sampling frequency
fs_target : int, or float
The target frequency
Returns
--... | python | def resample_sig(x, fs, fs_target):
"""
Resample a signal to a different frequency.
Parameters
----------
x : numpy array
Array containing the signal
fs : int, or float
The original sampling frequency
fs_target : int, or float
The target frequency
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221,391 | MIT-LCP/wfdb-python | wfdb/processing/basic.py | resample_singlechan | def resample_singlechan(x, ann, fs, fs_target):
"""
Resample a single-channel signal with its annotations
Parameters
----------
x: numpy array
The signal array
ann : wfdb Annotation
The wfdb annotation object
fs : int, or float
The original frequency
fs_target : ... | python | def resample_singlechan(x, ann, fs, fs_target):
"""
Resample a single-channel signal with its annotations
Parameters
----------
x: numpy array
The signal array
ann : wfdb Annotation
The wfdb annotation object
fs : int, or float
The original frequency
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221,392 | MIT-LCP/wfdb-python | wfdb/processing/basic.py | resample_multichan | def resample_multichan(xs, ann, fs, fs_target, resamp_ann_chan=0):
"""
Resample multiple channels with their annotations
Parameters
----------
xs: numpy array
The signal array
ann : wfdb Annotation
The wfdb annotation object
fs : int, or float
The original frequency
... | python | def resample_multichan(xs, ann, fs, fs_target, resamp_ann_chan=0):
"""
Resample multiple channels with their annotations
Parameters
----------
xs: numpy array
The signal array
ann : wfdb Annotation
The wfdb annotation object
fs : int, or float
The original frequency
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221,393 | MIT-LCP/wfdb-python | wfdb/processing/basic.py | normalize_bound | def normalize_bound(sig, lb=0, ub=1):
"""
Normalize a signal between the lower and upper bound
Parameters
----------
sig : numpy array
Original signal to be normalized
lb : int, or float
Lower bound
ub : int, or float
Upper bound
Returns
-------
x_normal... | python | def normalize_bound(sig, lb=0, ub=1):
"""
Normalize a signal between the lower and upper bound
Parameters
----------
sig : numpy array
Original signal to be normalized
lb : int, or float
Lower bound
ub : int, or float
Upper bound
Returns
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221,394 | MIT-LCP/wfdb-python | wfdb/processing/basic.py | smooth | def smooth(sig, window_size):
"""
Apply a uniform moving average filter to a signal
Parameters
----------
sig : numpy array
The signal to smooth.
window_size : int
The width of the moving average filter.
"""
box = np.ones(window_size)/window_size
return np.convolve(... | python | def smooth(sig, window_size):
"""
Apply a uniform moving average filter to a signal
Parameters
----------
sig : numpy array
The signal to smooth.
window_size : int
The width of the moving average filter.
"""
box = np.ones(window_size)/window_size
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221,395 | MIT-LCP/wfdb-python | wfdb/processing/basic.py | get_filter_gain | def get_filter_gain(b, a, f_gain, fs):
"""
Given filter coefficients, return the gain at a particular
frequency.
Parameters
----------
b : list
List of linear filter b coefficients
a : list
List of linear filter a coefficients
f_gain : int or float, optional
The ... | python | def get_filter_gain(b, a, f_gain, fs):
"""
Given filter coefficients, return the gain at a particular
frequency.
Parameters
----------
b : list
List of linear filter b coefficients
a : list
List of linear filter a coefficients
f_gain : int or float, optional
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221,396 | MIT-LCP/wfdb-python | wfdb/io/record.py | _check_item_type | def _check_item_type(item, field_name, allowed_types, expect_list=False,
required_channels='all'):
"""
Check the item's type against a set of allowed types.
Vary the print message regarding whether the item can be None.
Helper to `BaseRecord.check_field`.
Parameters
--------... | python | def _check_item_type(item, field_name, allowed_types, expect_list=False,
required_channels='all'):
"""
Check the item's type against a set of allowed types.
Vary the print message regarding whether the item can be None.
Helper to `BaseRecord.check_field`.
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Vary the print message regarding whether the item can be None.
Helper to `BaseRecord.check_field`.
Parameters
----------
item : any
The item to check.
field_name : str
The field name.
allowed_types : iterable
Iter... | [
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] | cc8c9e9e44f10af961b7a9d8ae03708b31ac8a8c | https://github.com/MIT-LCP/wfdb-python/blob/cc8c9e9e44f10af961b7a9d8ae03708b31ac8a8c/wfdb/io/record.py#L858-L909 |
221,397 | MIT-LCP/wfdb-python | wfdb/io/record.py | check_np_array | def check_np_array(item, field_name, ndim, parent_class, channel_num=None):
"""
Check a numpy array's shape and dtype against required
specifications.
Parameters
----------
item : numpy array
The numpy array to check
field_name : str
The name of the field to check
ndim :... | python | def check_np_array(item, field_name, ndim, parent_class, channel_num=None):
"""
Check a numpy array's shape and dtype against required
specifications.
Parameters
----------
item : numpy array
The numpy array to check
field_name : str
The name of the field to check
ndim :... | [
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The numpy array to check
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The name of the field to check
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The required number of dimensions
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] | cc8c9e9e44f10af961b7a9d8ae03708b31ac8a8c | https://github.com/MIT-LCP/wfdb-python/blob/cc8c9e9e44f10af961b7a9d8ae03708b31ac8a8c/wfdb/io/record.py#L912-L944 |
221,398 | MIT-LCP/wfdb-python | wfdb/io/record.py | rdheader | def rdheader(record_name, pb_dir=None, rd_segments=False):
"""
Read a WFDB header file and return a `Record` or `MultiRecord`
object with the record descriptors as attributes.
Parameters
----------
record_name : str
The name of the WFDB record to be read, without any file
extens... | python | def rdheader(record_name, pb_dir=None, rd_segments=False):
"""
Read a WFDB header file and return a `Record` or `MultiRecord`
object with the record descriptors as attributes.
Parameters
----------
record_name : str
The name of the WFDB record to be read, without any file
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221,399 | MIT-LCP/wfdb-python | wfdb/io/record.py | rdsamp | def rdsamp(record_name, sampfrom=0, sampto=None, channels=None, pb_dir=None,
channel_names=None, warn_empty=False):
"""
Read a WFDB record, and return the physical signals and a few important
descriptor fields.
Parameters
----------
record_name : str
The name of the WFDB reco... | python | def rdsamp(record_name, sampfrom=0, sampto=None, channels=None, pb_dir=None,
channel_names=None, warn_empty=False):
"""
Read a WFDB record, and return the physical signals and a few important
descriptor fields.
Parameters
----------
record_name : str
The name of the WFDB reco... | [
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] | cc8c9e9e44f10af961b7a9d8ae03708b31ac8a8c | https://github.com/MIT-LCP/wfdb-python/blob/cc8c9e9e44f10af961b7a9d8ae03708b31ac8a8c/wfdb/io/record.py#L1323-L1402 |
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