Image¶
The documentation of the image module.
The pyts.image module includes imaging algorithms.
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class
pyts.image.GASF(image_size=32, overlapping=False, scale=-1)[source]¶ Gramian Angular Summation Field.
Parameters: - image_size : int (default = 32)
Determine the shape of the output images: (image_size, image_size)
- overlapping : bool (default = False)
If True, reduce the size of each time series using PAA with possible overlapping windows.
- scale : {-1, 0} (default = -1)
The lower bound of the scaled time series.
Methods
fit([X, y])Pass. fit_transform(X[, y])Fit to data, then transform it. get_params([deep])Get parameters for this estimator. set_params(**params)Set the parameters of this estimator. transform(X)Transform each time series into a GASF image.
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class
pyts.image.GADF(image_size=32, overlapping=False, scale=-1)[source]¶ Gramian Angular Difference Field.
Parameters: - image_size : int (default = 32)
Determine the shape of the output images: (image_size, image_size)
- overlapping : bool (default = False)
If True, reducing the size of the time series with PAA is done with possible overlapping windows.
- scale : {-1, 0} (default = -1)
The lower bound of the scaled time series.
Methods
fit([X, y])Pass. fit_transform(X[, y])Fit to data, then transform it. get_params([deep])Get parameters for this estimator. set_params(**params)Set the parameters of this estimator. transform(X)Transform each time series into a GADF image.
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class
pyts.image.MTF(image_size=32, n_bins=4, quantiles=u'empirical', overlapping=False)[source]¶ Markov Transition Field.
Parameters: - image_size : int (default = 32)
Determine the shape of the output images: (image_size, image_size)
- n_bins : int (default = 4)
Number of bins (also known as the size of the alphabet)
- quantiles : {‘gaussian’, ‘empirical’} (default = ‘gaussian’)
The way to compute quantiles. If ‘gaussian’, quantiles from a gaussian distribution N(0,1) are used. If ‘empirical’, empirical quantiles are used.
- overlapping : bool (default = False)
If False, reducing the image with the blurring kernel will be applied on non-overlapping rectangles. If True, it will be applied on eventually overlapping squares.
Methods
fit([X, y])Pass. fit_transform(X[, y])Fit to data, then transform it. get_params([deep])Get parameters for this estimator. set_params(**params)Set the parameters of this estimator. transform(X)Transform each time series into a MTF image.
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class
pyts.image.RecurrencePlots(dimension=1, epsilon=None, percentage=10)[source]¶ Recurrence Plots.
Parameters: - dimension : int (default = 1)
Dimension of the trajectory.
- epsilon : float, ‘percentage_points’, ‘percentage_distance’ or None
- (default = None)
Threshold for the minimum distance.
- percentage : float (default = 10)
Percentage of black points if
epsilon='percentage_points'or percentage of maximum distance for threshold ifepsilon='percentage_distance'.
Methods
fit([X, y])Pass. fit_transform(X[, y])Fit to data, then transform it. get_params([deep])Get parameters for this estimator. set_params(**params)Set the parameters of this estimator. transform(X)Transform each time series into a recurrence plot.