INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
|---|---|
Dumps plugin data to the log directory. | def dump_data(logdir):
"""Dumps plugin data to the log directory."""
# Create a tfevents file in the logdir so it is detected as a run.
write_empty_event_file(logdir)
plugin_logdir = plugin_asset_util.PluginDirectory(
logdir, profile_plugin.ProfilePlugin.plugin_name)
_maybe_create_directory(plugin_logd... |
Calculate health pill of a tensor.
Args:
tensor: An instance of `np.array` (for initialized tensors) or
`tensorflow.python.debug.lib.debug_data.InconvertibleTensorProto`
(for unininitialized tensors).
Returns:
If `tensor` is an initialized tensor of numeric or boolean types:
the calculat... | def calc_health_pill(tensor):
"""Calculate health pill of a tensor.
Args:
tensor: An instance of `np.array` (for initialized tensors) or
`tensorflow.python.debug.lib.debug_data.InconvertibleTensorProto`
(for unininitialized tensors).
Returns:
If `tensor` is an initialized tensor of numeric o... |
Reads the config file from disk or creates a new one. | def _get_config(self):
'''Reads the config file from disk or creates a new one.'''
filename = '{}/{}'.format(self.PLUGIN_LOGDIR, CONFIG_FILENAME)
modified_time = os.path.getmtime(filename)
if modified_time != self.config_last_modified_time:
config = read_pickle(filename, default=self.previous_con... |
Writes the frame to disk as a tensor summary. | def _write_summary(self, session, frame):
'''Writes the frame to disk as a tensor summary.'''
summary = session.run(self.summary_op, feed_dict={
self.frame_placeholder: frame
})
path = '{}/{}'.format(self.PLUGIN_LOGDIR, SUMMARY_FILENAME)
write_file(summary, path) |
For limiting how often frames are computed. | def _enough_time_has_passed(self, FPS):
'''For limiting how often frames are computed.'''
if FPS == 0:
return False
else:
earliest_time = self.last_update_time + (1.0 / FPS)
return time.time() >= earliest_time |
Adds a frame to the current video output. | def _update_recording(self, frame, config):
'''Adds a frame to the current video output.'''
# pylint: disable=redefined-variable-type
should_record = config['is_recording']
if should_record:
if not self.is_recording:
self.is_recording = True
logger.info(
'Starting reco... |
Creates a frame and writes it to disk.
Args:
arrays: a list of np arrays. Use the "custom" option in the client.
frame: a 2D np array. This way the plugin can be used for video of any
kind, not just the visualization that comes with the plugin.
frame can also be a function, w... | def update(self, session, arrays=None, frame=None):
'''Creates a frame and writes it to disk.
Args:
arrays: a list of np arrays. Use the "custom" option in the client.
frame: a 2D np array. This way the plugin can be used for video of any
kind, not just the visualization that comes wit... |
A helper to get the gradients out at each step.
Args:
optimizer: the optimizer op.
loss: the op that computes your loss value.
Returns: the gradient tensors and the train_step op. | def gradient_helper(optimizer, loss, var_list=None):
'''A helper to get the gradients out at each step.
Args:
optimizer: the optimizer op.
loss: the op that computes your loss value.
Returns: the gradient tensors and the train_step op.
'''
if var_list is None:
var_list = tf.compa... |
Returns a key_func to be used in list.sort().
Returns a key_func to be used in list.sort() that sorts session groups
by the value extracted by extractor. 'None' extracted values will either
be considered largest or smallest as specified by the "none_is_largest"
boolean parameter.
Args:
extractor: An ext... | def _create_key_func(extractor, none_is_largest):
"""Returns a key_func to be used in list.sort().
Returns a key_func to be used in list.sort() that sorts session groups
by the value extracted by extractor. 'None' extracted values will either
be considered largest or smallest as specified by the "none_is_large... |
Creates extractors to extract properties corresponding to 'col_params'.
Args:
col_params: List of ListSessionGroupsRequest.ColParam protobufs.
Returns:
A list of extractor functions. The ith element in the
returned list extracts the column corresponding to the ith element of
_request.col_params | def _create_extractors(col_params):
"""Creates extractors to extract properties corresponding to 'col_params'.
Args:
col_params: List of ListSessionGroupsRequest.ColParam protobufs.
Returns:
A list of extractor functions. The ith element in the
returned list extracts the column corresponding to the i... |
Returns function that extracts a metric from a session group or a session.
Args:
metric_name: tensorboard.hparams.MetricName protobuffer. Identifies the
metric to extract from the session group.
Returns:
A function that takes a tensorboard.hparams.SessionGroup or
tensorborad.hparams.Session protobu... | def _create_metric_extractor(metric_name):
"""Returns function that extracts a metric from a session group or a session.
Args:
metric_name: tensorboard.hparams.MetricName protobuffer. Identifies the
metric to extract from the session group.
Returns:
A function that takes a tensorboard.hparams.Session... |
Returns the metric_value for a given metric in a session or session group.
Args:
session_or_group: A Session protobuffer or SessionGroup protobuffer.
metric_name: A MetricName protobuffer. The metric to search for.
Returns:
A MetricValue protobuffer representing the value of the given metric or
Non... | def _find_metric_value(session_or_group, metric_name):
"""Returns the metric_value for a given metric in a session or session group.
Args:
session_or_group: A Session protobuffer or SessionGroup protobuffer.
metric_name: A MetricName protobuffer. The metric to search for.
Returns:
A MetricValue proto... |
Returns an extractor function that extracts an hparam from a session group.
Args:
hparam_name: str. Identies the hparam to extract from the session group.
Returns:
A function that takes a tensorboard.hparams.SessionGroup protobuffer and
returns the value, as a native Python object, of the hparam identi... | def _create_hparam_extractor(hparam_name):
"""Returns an extractor function that extracts an hparam from a session group.
Args:
hparam_name: str. Identies the hparam to extract from the session group.
Returns:
A function that takes a tensorboard.hparams.SessionGroup protobuffer and
returns the value,... |
Creates filters for the given col_params.
Args:
col_params: List of ListSessionGroupsRequest.ColParam protobufs.
extractors: list of extractor functions of the same length as col_params.
Each element should extract the column described by the corresponding
element of col_params.
Returns:
A ... | def _create_filters(col_params, extractors):
"""Creates filters for the given col_params.
Args:
col_params: List of ListSessionGroupsRequest.ColParam protobufs.
extractors: list of extractor functions of the same length as col_params.
Each element should extract the column described by the correspond... |
Creates a filter for the given col_param and extractor.
Args:
col_param: A tensorboard.hparams.ColParams object identifying the column
and describing the filter to apply.
extractor: A function that extract the column value identified by
'col_param' from a tensorboard.hparams.SessionGroup protobuf... | def _create_filter(col_param, extractor):
"""Creates a filter for the given col_param and extractor.
Args:
col_param: A tensorboard.hparams.ColParams object identifying the column
and describing the filter to apply.
extractor: A function that extract the column value identified by
'col_param' f... |
Returns a boolean function that filters strings based on a regular exp.
Args:
regex: A string describing the regexp to use.
Returns:
A function taking a string and returns True if any of its substrings
matches regex. | def _create_regexp_filter(regex):
"""Returns a boolean function that filters strings based on a regular exp.
Args:
regex: A string describing the regexp to use.
Returns:
A function taking a string and returns True if any of its substrings
matches regex.
"""
# Warning: Note that python's regex lib... |
Returns a function that checkes whether a number belongs to an interval.
Args:
interval: A tensorboard.hparams.Interval protobuf describing the interval.
Returns:
A function taking a number (a float or an object of a type in
six.integer_types) that returns True if the number belongs to (the closed)
... | def _create_interval_filter(interval):
"""Returns a function that checkes whether a number belongs to an interval.
Args:
interval: A tensorboard.hparams.Interval protobuf describing the interval.
Returns:
A function taking a number (a float or an object of a type in
six.integer_types) that returns Tr... |
Converts a google.protobuf.Value to a native Python object. | def _value_to_python(value):
"""Converts a google.protobuf.Value to a native Python object."""
assert isinstance(value, struct_pb2.Value)
field = value.WhichOneof('kind')
if field == 'number_value':
return value.number_value
elif field == 'string_value':
return value.string_value
elif field == 'boo... |
Sets the metrics for the group to be the average of its sessions.
The resulting session group metrics consist of the union of metrics across
the group's sessions. The value of each session group metric is the average
of that metric values across the sessions in the group. The 'step' and
'wall_time_secs' fields... | def _set_avg_session_metrics(session_group):
"""Sets the metrics for the group to be the average of its sessions.
The resulting session group metrics consist of the union of metrics across
the group's sessions. The value of each session group metric is the average
of that metric values across the sessions in t... |
Sets the metrics for session_group to those of its "median session".
The median session is the session in session_group with the median value
of the metric given by 'aggregation_metric'. The median is taken over the
subset of sessions in the group whose 'aggregation_metric' was measured
at the largest training... | def _set_median_session_metrics(session_group, aggregation_metric):
"""Sets the metrics for session_group to those of its "median session".
The median session is the session in session_group with the median value
of the metric given by 'aggregation_metric'. The median is taken over the
subset of sessions in th... |
Sets the metrics for session_group to those of its "extremum session".
The extremum session is the session in session_group with the extremum value
of the metric given by 'aggregation_metric'. The extremum is taken over the
subset of sessions in the group whose 'aggregation_metric' was measured
at the largest ... | def _set_extremum_session_metrics(session_group, aggregation_metric,
extremum_fn):
"""Sets the metrics for session_group to those of its "extremum session".
The extremum session is the session in session_group with the extremum value
of the metric given by 'aggregation_metric'. ... |
A generator for the values of the metric across the sessions in the group.
Args:
session_group: A SessionGroup protobuffer.
metric_name: A MetricName protobuffer.
Yields:
The next metric value wrapped in a _Measurement instance. | def _measurements(session_group, metric_name):
"""A generator for the values of the metric across the sessions in the group.
Args:
session_group: A SessionGroup protobuffer.
metric_name: A MetricName protobuffer.
Yields:
The next metric value wrapped in a _Measurement instance.
"""
for session_in... |
Handles the request specified on construction.
Returns:
A ListSessionGroupsResponse object. | def run(self):
"""Handles the request specified on construction.
Returns:
A ListSessionGroupsResponse object.
"""
session_groups = self._build_session_groups()
session_groups = self._filter(session_groups)
self._sort(session_groups)
return self._create_response(session_groups) |
Returns a list of SessionGroups protobuffers from the summary data. | def _build_session_groups(self):
"""Returns a list of SessionGroups protobuffers from the summary data."""
# Algorithm: We keep a dict 'groups_by_name' mapping a SessionGroup name
# (str) to a SessionGroup protobuffer. We traverse the runs associated with
# the plugin--each representing a single sessio... |
Adds a new Session protobuffer to the 'groups_by_name' dictionary.
Called by _build_session_groups when we encounter a new session. Creates
the Session protobuffer and adds it to the relevant group in the
'groups_by_name' dict. Creates the session group if this is the first time
we encounter it.
A... | def _add_session(self, session, start_info, groups_by_name):
"""Adds a new Session protobuffer to the 'groups_by_name' dictionary.
Called by _build_session_groups when we encounter a new session. Creates
the Session protobuffer and adds it to the relevant group in the
'groups_by_name' dict. Creates the... |
Builds a session object. | def _build_session(self, name, start_info, end_info):
"""Builds a session object."""
assert start_info is not None
result = api_pb2.Session(
name=name,
start_time_secs=start_info.start_time_secs,
model_uri=start_info.model_uri,
metric_values=self._build_session_metric_values... |
Builds the session metric values. | def _build_session_metric_values(self, session_name):
"""Builds the session metric values."""
# result is a list of api_pb2.MetricValue instances.
result = []
metric_infos = self._experiment.metric_infos
for metric_info in metric_infos:
metric_name = metric_info.name
try:
metric... |
Sets the metrics of the group based on aggregation_type. | def _aggregate_metrics(self, session_group):
"""Sets the metrics of the group based on aggregation_type."""
if (self._request.aggregation_type == api_pb2.AGGREGATION_AVG or
self._request.aggregation_type == api_pb2.AGGREGATION_UNSET):
_set_avg_session_metrics(session_group)
elif self._request... |
Sorts 'session_groups' in place according to _request.col_params. | def _sort(self, session_groups):
"""Sorts 'session_groups' in place according to _request.col_params."""
# Sort by session_group name so we have a deterministic order.
session_groups.sort(key=operator.attrgetter('name'))
# Sort by lexicographical order of the _request.col_params whose order
# is no... |
recordAnalyzeAudio(duration, outputWavFile, midTermBufferSizeSec, modelName, modelType)
This function is used to record and analyze audio segments, in a fix window basis.
ARGUMENTS:
- duration total recording duration
- outputWavFile path of the output WAV file
- midTermBufferSizeSec (fix)segment length in... | def recordAnalyzeAudio(duration, outputWavFile, midTermBufferSizeSec, modelName, modelType):
'''
recordAnalyzeAudio(duration, outputWavFile, midTermBufferSizeSec, modelName, modelType)
This function is used to record and analyze audio segments, in a fix window basis.
ARGUMENTS:
- duration total recording dura... |
Break an audio stream to segments of interest,
defined by a csv file
- wavFile: path to input wavfile
- csvFile: path to csvFile of segment limits
Input CSV file must be of the format <T1>\t<T2>\t<Label> | def annotation2files(wavFile, csvFile):
'''
Break an audio stream to segments of interest,
defined by a csv file
- wavFile: path to input wavfile
- csvFile: path to csvFile of segment limits
Input CSV file must be of the format <T1>\t<T2>\t<Label>
... |
This function converts the MP3 files stored in a folder to WAV. If required, the output names of the WAV files are based on MP3 tags, otherwise the same names are used.
ARGUMENTS:
- dirName: the path of the folder where the MP3s are stored
- Fs: the sampling rate of the generated WAV files
... | def convertDirMP3ToWav(dirName, Fs, nC, useMp3TagsAsName = False):
'''
This function converts the MP3 files stored in a folder to WAV. If required, the output names of the WAV files are based on MP3 tags, otherwise the same names are used.
ARGUMENTS:
- dirName: the path of the folder where the MP3s... |
This function converts the WAV files stored in a folder to WAV using a different sampling freq and number of channels.
ARGUMENTS:
- dirName: the path of the folder where the WAVs are stored
- Fs: the sampling rate of the generated WAV files
- nC: the number of channesl of the ge... | def convertFsDirWavToWav(dirName, Fs, nC):
'''
This function converts the WAV files stored in a folder to WAV using a different sampling freq and number of channels.
ARGUMENTS:
- dirName: the path of the folder where the WAVs are stored
- Fs: the sampling rate of the generated WAV fil... |
This function returns a numpy array that stores the audio samples of a specified WAV of AIFF file | def readAudioFile(path):
'''
This function returns a numpy array that stores the audio samples of a specified WAV of AIFF file
'''
extension = os.path.splitext(path)[1]
try:
#if extension.lower() == '.wav':
#[Fs, x] = wavfile.read(path)
if extension.lower() == '.aif' or ... |
This function converts the input signal
(stored in a numpy array) to MONO (if it is STEREO) | def stereo2mono(x):
'''
This function converts the input signal
(stored in a numpy array) to MONO (if it is STEREO)
'''
if isinstance(x, int):
return -1
if x.ndim==1:
return x
elif x.ndim==2:
if x.shape[1]==1:
return x.flatten()
else:
i... |
This function computes the self-similarity matrix for a sequence
of feature vectors.
ARGUMENTS:
- featureVectors: a numpy matrix (nDims x nVectors) whose i-th column
corresponds to the i-th feature vector
RETURNS:
- S: the self-similarity matrix (nV... | def selfSimilarityMatrix(featureVectors):
'''
This function computes the self-similarity matrix for a sequence
of feature vectors.
ARGUMENTS:
- featureVectors: a numpy matrix (nDims x nVectors) whose i-th column
corresponds to the i-th feature vector
RETURNS:
... |
ARGUMENTS:
- flags: a sequence of class flags (per time window)
- window: window duration (in seconds)
RETURNS:
- segs: a sequence of segment's limits: segs[i,0] is start and
segs[i,1] are start and end point of segment i
- classes: a sequence of class flags... | def flags2segs(flags, window):
'''
ARGUMENTS:
- flags: a sequence of class flags (per time window)
- window: window duration (in seconds)
RETURNS:
- segs: a sequence of segment's limits: segs[i,0] is start and
segs[i,1] are start and end point of segment i
... |
This function converts segment endpoints and respective segment
labels to fix-sized class labels.
ARGUMENTS:
- seg_start: segment start points (in seconds)
- seg_end: segment endpoints (in seconds)
- seg_label: segment labels
- win_size: fix-sized window (in seconds)
RETURNS... | def segs2flags(seg_start, seg_end, seg_label, win_size):
'''
This function converts segment endpoints and respective segment
labels to fix-sized class labels.
ARGUMENTS:
- seg_start: segment start points (in seconds)
- seg_end: segment endpoints (in seconds)
- seg_label: segment ... |
This function computes the precision, recall and f1 measures,
given a confusion matrix | def computePreRec(cm, class_names):
'''
This function computes the precision, recall and f1 measures,
given a confusion matrix
'''
n_classes = cm.shape[0]
if len(class_names) != n_classes:
print("Error in computePreRec! Confusion matrix and class_names "
"list must be of th... |
This function reads a segmentation ground truth file, following a simple CSV format with the following columns:
<segment start>,<segment end>,<class label>
ARGUMENTS:
- gt_file: the path of the CSV segment file
RETURNS:
- seg_start: a numpy array of segments' start positions
- seg_... | def readSegmentGT(gt_file):
'''
This function reads a segmentation ground truth file, following a simple CSV format with the following columns:
<segment start>,<segment end>,<class label>
ARGUMENTS:
- gt_file: the path of the CSV segment file
RETURNS:
- seg_start: a numpy array ... |
This function plots statistics on the classification-segmentation results produced either by the fix-sized supervised method or the HMM method.
It also computes the overall accuracy achieved by the respective method if ground-truth is available. | def plotSegmentationResults(flags_ind, flags_ind_gt, class_names, mt_step, ONLY_EVALUATE=False):
'''
This function plots statistics on the classification-segmentation results produced either by the fix-sized supervised method or the HMM method.
It also computes the overall accuracy achieved by the respectiv... |
This function computes the statistics used to train an HMM joint segmentation-classification model
using a sequence of sequential features and respective labels
ARGUMENTS:
- features: a numpy matrix of feature vectors (numOfDimensions x n_wins)
- labels: a numpy array of class indices (n_wins x... | def trainHMM_computeStatistics(features, labels):
'''
This function computes the statistics used to train an HMM joint segmentation-classification model
using a sequence of sequential features and respective labels
ARGUMENTS:
- features: a numpy matrix of feature vectors (numOfDimensions x n_wi... |
This function trains a HMM model for segmentation-classification using a single annotated audio file
ARGUMENTS:
- wav_file: the path of the audio filename
- gt_file: the path of the ground truth filename
(a csv file of the form <segment start in seconds>,<segment end ... | def trainHMM_fromFile(wav_file, gt_file, hmm_model_name, mt_win, mt_step):
'''
This function trains a HMM model for segmentation-classification using a single annotated audio file
ARGUMENTS:
- wav_file: the path of the audio filename
- gt_file: the path of the ground truth filename
... |
This function trains a HMM model for segmentation-classification using
a where WAV files and .segment (ground-truth files) are stored
ARGUMENTS:
- dirPath: the path of the data diretory
- hmm_model_name: the name of the HMM model to be stored
- mt_win: mid-term window size
-... | def trainHMM_fromDir(dirPath, hmm_model_name, mt_win, mt_step):
'''
This function trains a HMM model for segmentation-classification using
a where WAV files and .segment (ground-truth files) are stored
ARGUMENTS:
- dirPath: the path of the data diretory
- hmm_model_name: the name of ... |
This function performs mid-term classification of an audio stream.
Towards this end, supervised knowledge is used, i.e. a pre-trained classifier.
ARGUMENTS:
- input_file: path of the input WAV file
- model_name: name of the classification model
- model_type: svm or k... | def mtFileClassification(input_file, model_name, model_type,
plot_results=False, gt_file=""):
'''
This function performs mid-term classification of an audio stream.
Towards this end, supervised knowledge is used, i.e. a pre-trained classifier.
ARGUMENTS:
- input_file: ... |
Event Detection (silence removal)
ARGUMENTS:
- x: the input audio signal
- fs: sampling freq
- st_win, st_step: window size and step in seconds
- smoothWindow: (optinal) smooth window (in seconds)
- weight: (optinal) weight f... | def silenceRemoval(x, fs, st_win, st_step, smoothWindow=0.5, weight=0.5, plot=False):
'''
Event Detection (silence removal)
ARGUMENTS:
- x: the input audio signal
- fs: sampling freq
- st_win, st_step: window size and step in seconds
- smoo... |
This function prints the cluster purity and speaker purity for
each WAV file stored in a provided directory (.SEGMENT files
are needed as ground-truth)
ARGUMENTS:
- folder_name: the full path of the folder where the WAV and
SEGMENT (ground-truth) fi... | def speakerDiarizationEvaluateScript(folder_name, ldas):
'''
This function prints the cluster purity and speaker purity for
each WAV file stored in a provided directory (.SEGMENT files
are needed as ground-truth)
ARGUMENTS:
- folder_name: the full path of the folder ... |
ARGUMENTS:
- filename: the name of the WAV file to be analyzed
- n_speakers the number of speakers (clusters) in the recording (<=0 for unknown)
- mt_size (opt) mid-term window size
- mt_step (opt) mid-term window step
- st_win (opt) short-term window size
... | def speakerDiarization(filename, n_speakers, mt_size=2.0, mt_step=0.2,
st_win=0.05, lda_dim=35, plot_res=False):
'''
ARGUMENTS:
- filename: the name of the WAV file to be analyzed
- n_speakers the number of speakers (clusters) in the recording (<=0 for unknown)
... |
This function detects instances of the most representative part of a
music recording, also called "music thumbnails".
A technique similar to the one proposed in [1], however a wider set of
audio features is used instead of chroma features.
In particular the following steps are followed:
- Extract s... | def musicThumbnailing(x, fs, short_term_size=1.0, short_term_step=0.5,
thumb_size=10.0, limit_1 = 0, limit_2 = 1):
'''
This function detects instances of the most representative part of a
music recording, also called "music thumbnails".
A technique similar to the one proposed in [... |
This function generates a 256 jet colormap of HTML-like
hex string colors (e.g. FF88AA) | def generateColorMap():
'''
This function generates a 256 jet colormap of HTML-like
hex string colors (e.g. FF88AA)
'''
Map = cm.jet(np.arange(256))
stringColors = []
for i in range(Map.shape[0]):
rgb = (int(255*Map[i][0]), int(255*Map[i][1]), int(255*Map[i][2]))
if (sys.vers... |
Distance between two strings | def levenshtein(str1, s2):
'''
Distance between two strings
'''
N1 = len(str1)
N2 = len(s2)
stringRange = [range(N1 + 1)] * (N2 + 1)
for i in range(N2 + 1):
stringRange[i] = range(i,i + N1 + 1)
for i in range(0,N2):
for j in range(0,N1):
if str1[j] == s2[i]:
... |
Generates a list of colors based on a list of names (strings). Similar strings correspond to similar colors. | def text_list_to_colors(names):
'''
Generates a list of colors based on a list of names (strings). Similar strings correspond to similar colors.
'''
# STEP A: compute strings distance between all combnations of strings
Dnames = np.zeros( (len(names), len(names)) )
for i in range(len(names)):
... |
Generates a list of colors based on a list of names (strings). Similar strings correspond to similar colors. | def text_list_to_colors_simple(names):
'''
Generates a list of colors based on a list of names (strings). Similar strings correspond to similar colors.
'''
uNames = list(set(names))
uNames.sort()
textToColor = [ uNames.index(n) for n in names ]
textToColor = np.array(textToColor)
textTo... |
Generates a d3js chordial diagram that illustrates similarites | def chordialDiagram(fileStr, SM, Threshold, names, namesCategories):
'''
Generates a d3js chordial diagram that illustrates similarites
'''
colors = text_list_to_colors_simple(namesCategories)
SM2 = SM.copy()
SM2 = (SM2 + SM2.T) / 2.0
for i in range(SM2.shape[0]):
M = Threshold
# ... |
Computes zero crossing rate of frame | def stZCR(frame):
"""Computes zero crossing rate of frame"""
count = len(frame)
countZ = numpy.sum(numpy.abs(numpy.diff(numpy.sign(frame)))) / 2
return (numpy.float64(countZ) / numpy.float64(count-1.0)) |
Computes entropy of energy | def stEnergyEntropy(frame, n_short_blocks=10):
"""Computes entropy of energy"""
Eol = numpy.sum(frame ** 2) # total frame energy
L = len(frame)
sub_win_len = int(numpy.floor(L / n_short_blocks))
if L != sub_win_len * n_short_blocks:
frame = frame[0:sub_win_len * n_short_blocks]
# ... |
Computes spectral centroid of frame (given abs(FFT)) | def stSpectralCentroidAndSpread(X, fs):
"""Computes spectral centroid of frame (given abs(FFT))"""
ind = (numpy.arange(1, len(X) + 1)) * (fs/(2.0 * len(X)))
Xt = X.copy()
Xt = Xt / Xt.max()
NUM = numpy.sum(ind * Xt)
DEN = numpy.sum(Xt) + eps
# Centroid:
C = (NUM / DEN)
# Spread:
... |
Computes the spectral entropy | def stSpectralEntropy(X, n_short_blocks=10):
"""Computes the spectral entropy"""
L = len(X) # number of frame samples
Eol = numpy.sum(X ** 2) # total spectral energy
sub_win_len = int(numpy.floor(L / n_short_blocks)) # length of sub-frame
if L != sub_win_len * n... |
Computes the spectral flux feature of the current frame
ARGUMENTS:
X: the abs(fft) of the current frame
X_prev: the abs(fft) of the previous frame | def stSpectralFlux(X, X_prev):
"""
Computes the spectral flux feature of the current frame
ARGUMENTS:
X: the abs(fft) of the current frame
X_prev: the abs(fft) of the previous frame
"""
# compute the spectral flux as the sum of square distances:
sumX = numpy.sum... |
Computes spectral roll-off | def stSpectralRollOff(X, c, fs):
"""Computes spectral roll-off"""
totalEnergy = numpy.sum(X ** 2)
fftLength = len(X)
Thres = c*totalEnergy
# Ffind the spectral rolloff as the frequency position
# where the respective spectral energy is equal to c*totalEnergy
CumSum = numpy.cumsum(X ** 2) + ... |
Computes harmonic ratio and pitch | def stHarmonic(frame, fs):
"""
Computes harmonic ratio and pitch
"""
M = numpy.round(0.016 * fs) - 1
R = numpy.correlate(frame, frame, mode='full')
g = R[len(frame)-1]
R = R[len(frame):-1]
# estimate m0 (as the first zero crossing of R)
[a, ] = numpy.nonzero(numpy.diff(numpy.sign(R... |
Computes the triangular filterbank for MFCC computation
(used in the stFeatureExtraction function before the stMFCC function call)
This function is taken from the scikits.talkbox library (MIT Licence):
https://pypi.python.org/pypi/scikits.talkbox | def mfccInitFilterBanks(fs, nfft):
"""
Computes the triangular filterbank for MFCC computation
(used in the stFeatureExtraction function before the stMFCC function call)
This function is taken from the scikits.talkbox library (MIT Licence):
https://pypi.python.org/pypi/scikits.talkbox
"""
... |
Computes the MFCCs of a frame, given the fft mag
ARGUMENTS:
X: fft magnitude abs(FFT)
fbank: filter bank (see mfccInitFilterBanks)
RETURN
ceps: MFCCs (13 element vector)
Note: MFCC calculation is, in general, taken from the
scikits.talkbox library (MI... | def stMFCC(X, fbank, n_mfcc_feats):
"""
Computes the MFCCs of a frame, given the fft mag
ARGUMENTS:
X: fft magnitude abs(FFT)
fbank: filter bank (see mfccInitFilterBanks)
RETURN
ceps: MFCCs (13 element vector)
Note: MFCC calculation is, in general, taken fr... |
This function initializes the chroma matrices used in the calculation of the chroma features | def stChromaFeaturesInit(nfft, fs):
"""
This function initializes the chroma matrices used in the calculation of the chroma features
"""
freqs = numpy.array([((f + 1) * fs) / (2 * nfft) for f in range(nfft)])
Cp = 27.50
nChroma = numpy.round(12.0 * numpy.log2(freqs / Cp)).astype(int)
... |
Short-term FFT mag for spectogram estimation:
Returns:
a numpy array (nFFT x numOfShortTermWindows)
ARGUMENTS:
signal: the input signal samples
fs: the sampling freq (in Hz)
win: the short-term window size (in samples)
step: the short-term win... | def stChromagram(signal, fs, win, step, PLOT=False):
"""
Short-term FFT mag for spectogram estimation:
Returns:
a numpy array (nFFT x numOfShortTermWindows)
ARGUMENTS:
signal: the input signal samples
fs: the sampling freq (in Hz)
win: the short-term... |
This function extracts an estimate of the beat rate for a musical signal.
ARGUMENTS:
- st_features: a numpy array (n_feats x numOfShortTermWindows)
- win_len: window size in seconds
RETURNS:
- BPM: estimates of beats per minute
- Ratio: a confidence measure | def beatExtraction(st_features, win_len, PLOT=False):
"""
This function extracts an estimate of the beat rate for a musical signal.
ARGUMENTS:
- st_features: a numpy array (n_feats x numOfShortTermWindows)
- win_len: window size in seconds
RETURNS:
- BPM: estimates o... |
Short-term FFT mag for spectogram estimation:
Returns:
a numpy array (nFFT x numOfShortTermWindows)
ARGUMENTS:
signal: the input signal samples
fs: the sampling freq (in Hz)
win: the short-term window size (in samples)
step: the short-term win... | def stSpectogram(signal, fs, win, step, PLOT=False):
"""
Short-term FFT mag for spectogram estimation:
Returns:
a numpy array (nFFT x numOfShortTermWindows)
ARGUMENTS:
signal: the input signal samples
fs: the sampling freq (in Hz)
win: the short-term... |
This function implements the shor-term windowing process. For each short-term window a set of features is extracted.
This results to a sequence of feature vectors, stored in a numpy matrix.
ARGUMENTS
signal: the input signal samples
fs: the sampling freq (in Hz)
win: ... | def stFeatureExtraction(signal, fs, win, step):
"""
This function implements the shor-term windowing process. For each short-term window a set of features is extracted.
This results to a sequence of feature vectors, stored in a numpy matrix.
ARGUMENTS
signal: the input signal samples
... |
Mid-term feature extraction | def mtFeatureExtraction(signal, fs, mt_win, mt_step, st_win, st_step):
"""
Mid-term feature extraction
"""
mt_win_ratio = int(round(mt_win / st_step))
mt_step_ratio = int(round(mt_step / st_step))
mt_features = []
st_features, f_names = stFeatureExtraction(signal, fs, st_win, st_step)
... |
This function extracts the mid-term features of the WAVE files of a particular folder.
The resulting feature vector is extracted by long-term averaging the mid-term features.
Therefore ONE FEATURE VECTOR is extracted for each WAV file.
ARGUMENTS:
- dirName: the path of the WAVE directory
... | def dirWavFeatureExtraction(dirName, mt_win, mt_step, st_win, st_step,
compute_beat=False):
"""
This function extracts the mid-term features of the WAVE files of a particular folder.
The resulting feature vector is extracted by long-term averaging the mid-term features.
Ther... |
Same as dirWavFeatureExtraction, but instead of a single dir it
takes a list of paths as input and returns a list of feature matrices.
EXAMPLE:
[features, classNames] =
a.dirsWavFeatureExtraction(['audioData/classSegmentsRec/noise','audioData/classSegmentsRec/speech',
... | def dirsWavFeatureExtraction(dirNames, mt_win, mt_step, st_win, st_step, compute_beat=False):
'''
Same as dirWavFeatureExtraction, but instead of a single dir it
takes a list of paths as input and returns a list of feature matrices.
EXAMPLE:
[features, classNames] =
a.dirsWavFeatureExtrac... |
This function extracts the mid-term features of the WAVE
files of a particular folder without averaging each file.
ARGUMENTS:
- dirName: the path of the WAVE directory
- mt_win, mt_step: mid-term window and step (in seconds)
- st_win, st_step: short-term window and step (... | def dirWavFeatureExtractionNoAveraging(dirName, mt_win, mt_step, st_win, st_step):
"""
This function extracts the mid-term features of the WAVE
files of a particular folder without averaging each file.
ARGUMENTS:
- dirName: the path of the WAVE directory
- mt_win, mt_step: m... |
This function is used as a wrapper to:
a) read the content of a WAV file
b) perform mid-term feature extraction on that signal
c) write the mid-term feature sequences to a numpy file | def mtFeatureExtractionToFile(fileName, midTermSize, midTermStep, shortTermSize, shortTermStep, outPutFile,
storeStFeatures=False, storeToCSV=False, PLOT=False):
"""
This function is used as a wrapper to:
a) read the content of a WAV file
b) perform mid-term feature extract... |
[float] 市值 | def market_value(self):
"""
[float] 市值
"""
return sum(position.market_value for position in six.itervalues(self._positions)) |
[float] 总费用 | def transaction_cost(self):
"""
[float] 总费用
"""
return sum(position.transaction_cost for position in six.itervalues(self._positions)) |
买入开仓。
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
:param int amount: 下单手数
:param float price: 下单价格，默认为None，表示 :class:`~MarketOrder`, 此参数主要用于简化 `style` 参数。
:param style: 下单类型, 默认是市价单。目前支持的订单类型有 :class:`~LimitOrder` 和 :cla... | def buy_open(id_or_ins, amount, price=None, style=None):
"""
买入开仓。
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
:param int amount: 下单手数
:param float price: 下单价格，默认为None，表示 :class:`~MarketOrder`, 此参数主要用于简化 `style` 参数。
... |
平卖仓
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
:param int amount: 下单手数
:param float price: 下单价格，默认为None，表示 :class:`~MarketOrder`, 此参数主要用于简化 `style` 参数。
:param style: 下单类型, 默认是市价单。目前支持的订单类型有 :class:`~LimitOrder` 和 :class... | def buy_close(id_or_ins, amount, price=None, style=None, close_today=False):
"""
平卖仓
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
:param int amount: 下单手数
:param float price: 下单价格，默认为None，表示 :class:`~MarketOrder`, 此参数主要... |
卖出开仓
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
:param int amount: 下单手数
:param float price: 下单价格，默认为None，表示 :class:`~MarketOrder`, 此参数主要用于简化 `style` 参数。
:param style: 下单类型, 默认是市价单。目前支持的订单类型有 :class:`~LimitOrder` 和 :clas... | def sell_open(id_or_ins, amount, price=None, style=None):
"""
卖出开仓
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
:param int amount: 下单手数
:param float price: 下单价格，默认为None，表示 :class:`~MarketOrder`, 此参数主要用于简化 `style` 参数。
... |
平买仓
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
:param int amount: 下单手数
:param float price: 下单价格，默认为None，表示 :class:`~MarketOrder`, 此参数主要用于简化 `style` 参数。
:param style: 下单类型, 默认是市价单。目前支持的订单类型有 :class:`~LimitOrder` 和 :class... | def sell_close(id_or_ins, amount, price=None, style=None, close_today=False):
"""
平买仓
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
:param int amount: 下单手数
:param float price: 下单价格，默认为None，表示 :class:`~MarketOrder`, 此参数主... |
获取某一期货品种在策略当前日期的可交易合约order_book_id列表。按照到期月份，下标从小到大排列，返回列表中第一个合约对应的就是该品种的近月合约。
:param str underlying_symbol: 期货合约品种，例如沪深300股指期货为'IF'
:return: list[`str`]
:example:
获取某一天的主力合约代码（策略当前日期是20161201）:
.. code-block:: python
[In]
logger.info(get_future_contracts('IF'))
... | def get_future_contracts(underlying_symbol):
"""
获取某一期货品种在策略当前日期的可交易合约order_book_id列表。按照到期月份，下标从小到大排列，返回列表中第一个合约对应的就是该品种的近月合约。
:param str underlying_symbol: 期货合约品种，例如沪深300股指期货为'IF'
:return: list[`str`]
:example:
获取某一天的主力合约代码（策略当前日期是20161201）:
.. code-block:: python
[In... |
[int] 订单数量 | def quantity(self):
"""
[int] 订单数量
"""
if np.isnan(self._quantity):
raise RuntimeError("Quantity of order {} is not supposed to be nan.".format(self.order_id))
return self._quantity |
[int] 订单已成交数量 | def filled_quantity(self):
"""
[int] 订单已成交数量
"""
if np.isnan(self._filled_quantity):
raise RuntimeError("Filled quantity of order {} is not supposed to be nan.".format(self.order_id))
return self._filled_quantity |
[float] 冻结价格 | def frozen_price(self):
"""
[float] 冻结价格
"""
if np.isnan(self._frozen_price):
raise RuntimeError("Frozen price of order {} is not supposed to be nan.".format(self.order_id))
return self._frozen_price |
[datetime.datetime] 当前快照数据的时间戳 | def datetime(self):
"""
[datetime.datetime] 当前快照数据的时间戳
"""
try:
dt = self._tick_dict['datetime']
except (KeyError, ValueError):
return datetime.datetime.min
else:
if not isinstance(dt, datetime.datetime):
if dt > 1000000... |
[float] 获得该持仓的实时市场价值在股票投资组合价值中所占比例，取值范围[0, 1] | def value_percent(self):
"""
[float] 获得该持仓的实时市场价值在股票投资组合价值中所占比例，取值范围[0, 1]
"""
accounts = Environment.get_instance().portfolio.accounts
if DEFAULT_ACCOUNT_TYPE.STOCK.name not in accounts:
return 0
total_value = accounts[DEFAULT_ACCOUNT_TYPE.STOCK.name].total_v... |
判断合约是否过期 | def is_de_listed(self):
"""
判断合约是否过期
"""
env = Environment.get_instance()
instrument = env.get_instrument(self._order_book_id)
current_date = env.trading_dt
if instrument.de_listed_date is not None:
if instrument.de_listed_date.date() > env.config.bas... |
[已弃用] | def bought_value(self):
"""
[已弃用]
"""
user_system_log.warn(_(u"[abandon] {} is no longer valid.").format('stock_position.bought_value'))
return self._quantity * self._avg_price |
[float] 交易盈亏，策略在当前交易日产生的盈亏中来源于当日成交的部分 | def trading_pnl(self):
"""
[float] 交易盈亏，策略在当前交易日产生的盈亏中来源于当日成交的部分
"""
last_price = self._data_proxy.get_last_price(self._order_book_id)
return self._contract_multiplier * (self._trade_quantity * last_price - self._trade_cost) |
[float] 昨仓盈亏，策略在当前交易日产生的盈亏中来源于昨仓的部分 | def position_pnl(self):
"""
[float] 昨仓盈亏，策略在当前交易日产生的盈亏中来源于昨仓的部分
"""
last_price = self._data_proxy.get_last_price(self._order_book_id)
if self._direction == POSITION_DIRECTION.LONG:
price_spread = last_price - self._last_price
else:
price_spread = s... |
注册事件 | def register_event(self):
"""
注册事件
"""
event_bus = Environment.get_instance().event_bus
event_bus.prepend_listener(EVENT.PRE_BEFORE_TRADING, self._pre_before_trading)
event_bus.prepend_listener(EVENT.POST_SETTLEMENT, self._post_settlement) |
[float] 实时净值 | def unit_net_value(self):
"""
[float] 实时净值
"""
if self._units == 0:
return np.nan
return self.total_value / self._units |
[float] 当前最新一天的日收益 | def daily_returns(self):
"""
[float] 当前最新一天的日收益
"""
if self._static_unit_net_value == 0:
return np.nan
return 0 if self._static_unit_net_value == 0 else self.unit_net_value / self._static_unit_net_value - 1 |
[float]总权益 | def total_value(self):
"""
[float]总权益
"""
return sum(account.total_value for account in six.itervalues(self._accounts)) |
[dict] 持仓 | def positions(self):
"""
[dict] 持仓
"""
if self._mixed_positions is None:
self._mixed_positions = MixedPositions(self._accounts)
return self._mixed_positions |
[float] 可用资金 | def cash(self):
"""
[float] 可用资金
"""
return sum(account.cash for account in six.itervalues(self._accounts)) |
[float] 市值 | def market_value(self):
"""
[float] 市值
"""
return sum(account.market_value for account in six.itervalues(self._accounts)) |
[float] 买方向当日持仓盈亏 | def buy_holding_pnl(self):
"""
[float] 买方向当日持仓盈亏
"""
return (self.last_price - self.buy_avg_holding_price) * self.buy_quantity * self.contract_multiplier |
[float] 卖方向当日持仓盈亏 | def sell_holding_pnl(self):
"""
[float] 卖方向当日持仓盈亏
"""
return (self.sell_avg_holding_price - self.last_price) * self.sell_quantity * self.contract_multiplier |
[float] 买方向累计盈亏 | def buy_pnl(self):
"""
[float] 买方向累计盈亏
"""
return (self.last_price - self._buy_avg_open_price) * self.buy_quantity * self.contract_multiplier |
[float] 卖方向累计盈亏 | def sell_pnl(self):
"""
[float] 卖方向累计盈亏
"""
return (self._sell_avg_open_price - self.last_price) * self.sell_quantity * self.contract_multiplier |
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