ltsm.prompt_reader.stat_prompt.tsfel.utils package
Submodules
ltsm.prompt_reader.stat_prompt.tsfel.utils.add_personal_features module
- ltsm.prompt_reader.stat_prompt.tsfel.utils.add_personal_features.add_feature_json(features_path, json_path)[source]
Adds new feature to features.json.
- Parameters:
features_path (string) – Personal Python module directory containing new features implementation.
json_path (string) – Personal .json file directory containing existing features from TSFEL. New customised features will be added to file in this directory.
ltsm.prompt_reader.stat_prompt.tsfel.utils.calculate_complexity module
- ltsm.prompt_reader.stat_prompt.tsfel.utils.calculate_complexity.compute_complexity(feature, domain, json_path, **kwargs)[source]
Computes the feature complexity.
- Parameters:
feature (string) – Feature name
domain (string) – Feature domain
json_path (json) – Features json file
**kwargs
below (See) –
- features_path (
string) – Directory of script with personal features
- features_path (
- Returns:
int – Feature complexity
Writes complexity in json file
- ltsm.prompt_reader.stat_prompt.tsfel.utils.calculate_complexity.find_best_curve(t, signal)[source]
Finds the best curve.
- Parameters:
t (nd-array) – Log space
signal (nd-array) – Mean execution time array
- Returns:
Best fit curve name
- Return type:
str
- ltsm.prompt_reader.stat_prompt.tsfel.utils.calculate_complexity.n_constant(x, no)[source]
The model function
- ltsm.prompt_reader.stat_prompt.tsfel.utils.calculate_complexity.n_linear(x, no)[source]
The model function
- ltsm.prompt_reader.stat_prompt.tsfel.utils.calculate_complexity.n_log(x, no)[source]
The model function
ltsm.prompt_reader.stat_prompt.tsfel.utils.progress_bar module
- ltsm.prompt_reader.stat_prompt.tsfel.utils.progress_bar.display_progress_bar(iteration, total, out)[source]
Displays progress bar according to python interface.
- Parameters:
iteration (int) – current iteration
total (int) – total iterations
out (progress bar notebook output)
- ltsm.prompt_reader.stat_prompt.tsfel.utils.progress_bar.progress_bar_notebook(iteration, total=100)[source]
Progress bar for notebooks.
- Parameters:
iteration (int) – current iteration
total (int) – total iterations
- Returns:
Progress bar for notebooks
- ltsm.prompt_reader.stat_prompt.tsfel.utils.progress_bar.progress_bar_terminal(iteration, total, prefix='', suffix='', decimals=0, length=100, fill='█', printend='\r')[source]
Call in a loop to create terminal progress bar.
- iteration: int
current iteration
- total: int
total iterations
- prefix: str
prefix string
- suffix: str
suffix string
- decimals: int
positive number of decimals in percent complete
- length: int
character length of bar
- fill: str
bar fill character
- printend: str
end character (e.g. “
“, ” “)
ltsm.prompt_reader.stat_prompt.tsfel.utils.signal_processing module
Compute pairwise correlation of features using pearson method
- Parameters:
features (DataFrame) – features
threshold – correlation value for removing highly correlated features
- Returns:
correlated features names
- Return type:
DataFrame
- ltsm.prompt_reader.stat_prompt.tsfel.utils.signal_processing.merge_time_series(data, fs_resample, time_unit)[source]
Time series data interpolation
- Parameters:
data (dict) – data to interpolate
fs_resample – resample sampling frequency
time_unit – time unit in seconds
- Returns:
Interpolated data
- Return type:
DataFrame
- ltsm.prompt_reader.stat_prompt.tsfel.utils.signal_processing.signal_window_splitter(signal, window_size, overlap=0)[source]
Splits the signal into windows :type signal: :param signal: input signal :type signal: nd-array or pandas DataFrame :type window_size: :param window_size: number of points of window size :type window_size: int :type overlap: :param overlap: percentage of overlap, value between 0 and 1 (exclusive)
Default: 0
- Returns:
list of signal windows
- Return type:
list