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Per-Primitive Docs

Currently, the available primitives are as follows:

['sigpro.SigPro',
 'sigpro.aggregations.amplitude.statistical.crest_factor',
 'sigpro.aggregations.amplitude.statistical.kurtosis', 
 'sigpro.aggregations.amplitude.statistical.mean', 
 'sigpro.aggregations.amplitude.statistical.rms', 
 'sigpro.aggregations.amplitude.statistical.skew',  
 'sigpro.aggregations.amplitude.statistical.std',
 'sigpro.aggregations.amplitude.statistical.var', 
 'sigpro.aggregations.frequency.band.band_mean',
 'sigpro.transformations.amplitude.identity.identity', 
 'sigpro.transformations.amplitude.spectrum.power_spectrum',
 'sigpro.transformations.frequency.band.frequency_band', 
 'sigpro.transformations.frequency.fft.fft', 
 'sigpro.transformations.frequency.fft.fft_real', 
 'sigpro.transformations.frequency_time.stft.stft', 
 'sigpro.transformations.frequency_time.stft.stft_real']

sigpro.SigPro

path: sigpro.SigPro

description : Please see the SigPro page for more detailed documentation.

argument type description
parameters
See pipeline documentation
hyperparameters
keep_columns bool, list If bool, whether to keep non-feature columns. If list, keep the columns in the list.
values_column_name str Name of signal values column in input.
transformations list List of transformations to apply sequentially before applying aggregations in parallel.
aggregations list List of aggregations to apply in parallel after all transformations are applied (in sequence).
input_is_dataframe bool Whether the input is a pandas DataFrame. Defaults to True.
output
See Documentation

sigpro.transformations.amplitude.identity.identity

path: sigpro.transformations.amplitude.identity.identity

description : This primitive simply returns the input amplitude values as its output.

argument type description
parameters
amplitude_values numpy.ndarray Input signal amplitude values
hyperparameters
N/A
output
amplitude_values numpy.ndarray Output (identity) signal amplitude values 
import numpy as np
from sigpro.contributing import run_primitive

data = np.array([[1,2,3,4,5]])
transformed_data = run_primitive(
    'sigpro.transformations.amplitude.identity.identity',
    amplitude_values= data
)
transformed_data

sigpro.transformations.amplitude.spectrum.power_spectrum

path: sigpro.transformations.amplitude.spectrum.power_spectrum

description : This primitive applies an RFFT on the amplitude values and return the real components.

argument type description
parameters
amplitude_values numpy.ndarray Input signal amplitude values
sampling_frequency int, float Sampling frequency value passed in Hz.
hyperparameters
N/A
output
amplitude_values numpy.ndarray Output real components
frequency_values numpy.ndarray Frequency values 
import numpy as np
from sigpro.contributing import run_primitive

data = np.array([[1,2,3,4,5]])
frequency = 1000
transformed_data = run_primitive(
    'sigpro.transformations.amplitude.spectrum.power_spectrum',
    amplitude_values= data,
		sampling_frequency = frequency
)
transformed_data, freq_values

sigpro.transformations.frequency.band.frequency_band

path: sigpro.transformations.frequency.band.frequency_band

description : This primitive filters between a high and low band frequency and return the amplitude values and frequency values for those.

argument type description
parameters
amplitude_values numpy.ndarray Input signal amplitude values
frequency_values numpy.ndarray Input frequency values passed in Hz.
hyperparameters
low int Lower band frequency
high int Higher band frequency
output
amplitude_values numpy.ndarray Output real components
frequency_values numpy.ndarray Frequency values 
import numpy as np
from sigpro.contributing import run_primitive

data = np.array([[1,2,3,4,5]])
frequency_values = np.array([[70,140,210, 280, 350]])
transformed_data, freq_values = run_primitive(
    'sigpro.transformations.frequency.band.frequency_band',
    amplitude_values= data,
	frequency_values = frequency_values,
    low = 100, 
    high = 300
)
transformed_data, freq_values,

sigpro.transformations.frequency.fft.fft

path: sigpro.transformations.frequency.fft.fft

description : This primitive applies an FFT on the amplitude values using the discrete Fourier transform in numpy.

argument type description
parameters
amplitude_values numpy.ndarray Input signal amplitude values
sampling_frequency int, float Sampling frequency value passed in Hz.
hyperparameters
N/A
output
amplitude_values numpy.ndarray Output all components
frequency_values numpy.ndarray Frequency values 
import numpy as np
from sigpro.contributing import run_primitive

data = np.array([[1,2,3,4,5]])
frequency = 1000
transformed_data, freq_values = run_primitive(
    'sigpro.transformations.frequency.fft.fft',
    amplitude_values= data,
		sampling_frequency = frequency

)
transformed_data, freq_values

sigpro.transformations.frequency.fft.fft_real

path: sigpro.transformations.frequency.fft.fft_real

description : This primitive applies an FFT on the amplitude values using the discrete Fourier transform in numpy and returns the real components.

argument type description
parameters
amplitude_values numpy.ndarray Input signal amplitude values
sampling_frequency int, float Sampling frequency value passed in Hz.
hyperparameters
N/A
output
amplitude_values numpy.ndarray Output real components of FFT
frequency_values numpy.ndarray Frequency values 
import numpy as np
from sigpro.contributing import run_primitive

data = np.array([[1,2,3,4,5]])
frequency = 1000
transformed_data, freq_values = run_primitive(
    'sigpro.transformations.frequency.fft.fft_real',
    amplitude_values= data,
		sampling_frequency = frequency

)
transformed_data, freq_values

sigpro.transformations.frequency_time.stft.stft

path: sigpro.transformations.frequency.stft.stft

description : This primitive computes and returns the short time Fourier transform.

argument type description
parameters
amplitude_values numpy.ndarray Input signal amplitude values
sampling_frequency int, float Sampling frequency value passed in Hz.
hyperparameters
N/A
output
amplitude_values numpy.ndarray Output all components of STFT
frequency_values numpy.ndarray Frequency values
time_values numpy.ndarray Time values 
import numpy as np
from sigpro.contributing import run_primitive

data = np.array([[1,2,3,4,5]])
frequency = 1000
transformed_data, freq_values, time_values = run_primitive(
    'sigpro.transformations.frequency_time.stft.stft', #note: this is inconsistent
    amplitude_values= data,
		sampling_frequency = frequency

)
transformed_data, freq_values, time_values

sigpro.transformations.frequency_time.stft.stft_real

path: sigpro.transformations.frequency.stft.stft_real

description : This primitive computes and returns the real part of the short time Fourier transform.

argument type description
parameters
amplitude_values numpy.ndarray Input signal amplitude values
sampling_frequency int, float Sampling frequency value passed in Hz.
hyperparameters
N/A
output
amplitude_values numpy.ndarray Output real components of STFT
frequency_values numpy.ndarray Frequency values
time_values numpy.ndarray Time values 
import numpy as np
from sigpro.contributing import run_primitive

data = np.array([[1,2,3,4,5]])
frequency = 1000
transformed_data, freq_values, time_values = run_primitive(
    'sigpro.transformations.frequency_time.stft.stft_real', #note: this is inconsistent
    amplitude_values= data,
		sampling_frequency = frequency

)
transformed_data, freq_values, time_values

sigpro.aggregation.amplitude.statistical.mean

path: sigpro.aggregation.amplitude.statistical.mean

description : This primitive computes and returns the arithmetic mean of the input values.

argument type description
parameters
amplitude_values numpy.ndarray Input signal amplitude values
hyperparameters
N/A
output
mean_value float Output mean of amplitude values 
import numpy as np
from sigpro.contributing import run_primitive

data = np.array([1,2,3,4,5])
output = run_primitive(
    'sigpro.aggregations.amplitude.statistical.mean', 
    amplitude_values= data,
)
output

sigpro.aggregation.amplitude.statistical.std

path: sigpro.aggregation.amplitude.statistical.std

description : This primitive computes and returns the standard deviation of the input values.

argument type description
parameters
amplitude_values numpy.ndarray Input signal amplitude values
hyperparameters
N/A
output
std_value float Output standard deviation of amplitude values 
import numpy as np
from sigpro.contributing import run_primitive

data = np.array([1,2,3,4,5])
output = run_primitive(
    'sigpro.aggregations.amplitude.statistical.std', 
    amplitude_values= data,
)
output

sigpro.aggregation.amplitude.statistical.kurtosis

path: sigpro.aggregation.amplitude.statistical.std

description : This primitive computes and returns the kurtosis of the input values.

argument type description
parameters
amplitude_values numpy.ndarray Input signal amplitude values
hyperparameters
fisher bool If True (default), use Fisher definition (normal 0.0). If False, use Pearson definition (normal 3.0).
bias bool If False, correct calculations for statistical bias. Defaults to True.
output
kurtosis_value float Output kurtosis of amplitude values 
import numpy as np
from sigpro.contributing import run_primitive

data = np.array([1,2,3,4,5])
output = run_primitive(
    'sigpro.aggregations.amplitude.statistical.kurtosis', 
    amplitude_values= data,
		fisher = True,
		bias = True
)
output

sigpro.aggregation.amplitude.statistical.var

path: sigpro.aggregation.amplitude.statistical.var

description : This primitive computes and returns the variance of the input values.

argument type description
parameters
amplitude_values numpy.ndarray Input signal amplitude values
hyperparameters
N/A
output
var_value float Output variance of amplitude values 
import numpy as np
from sigpro.contributing import run_primitive

data = np.array([1,2,3,4,5])
output = run_primitive(
    'sigpro.aggregations.amplitude.statistical.var', 
    amplitude_values= data,
)
output

sigpro.aggregation.amplitude.statistical.rms

path: sigpro.aggregation.amplitude.statistical.rms

description : This primitive computes and returns the root mean square (RMS) of the input values.

argument type description
parameters
amplitude_values numpy.ndarray Input signal amplitude values
hyperparameters
N/A
output
rms_value float Output RMS of amplitude values 
import numpy as np
from sigpro.contributing import run_primitive

data = np.array([1,2,3,4,5])
output = run_primitive(
    'sigpro.aggregations.amplitude.statistical.rms', 
    amplitude_values= data,
)
output

sigpro.aggregation.amplitude.statistical.skew

path: sigpro.aggregation.amplitude.statistical.skew

description : This primitive computes and returns the skew of the input values.

argument type description
parameters
amplitude_values numpy.ndarray Input signal amplitude values
hyperparameters
N/A
output
skew_value float Output skew of amplitude values 
import numpy as np
from sigpro.contributing import run_primitive

data = np.array([1,2,3,4,5])
output = run_primitive(
    'sigpro.aggregations.amplitude.statistical.skew', 
    amplitude_values= data,
)
output

sigpro.aggregation.amplitude.statistical.crest_factor

path: sigpro.aggregation.amplitude.statistical.crest_factor

description : This primitive computes and returns the crest factor (ratio of peak to RMS) of the input values.

argument type description
parameters
amplitude_values numpy.ndarray Input signal amplitude values
hyperparameters
N/A
output
crest_factor_value float Output crest factor of amplitude values 
import numpy as np
from sigpro.contributing import run_primitive

data = np.array([1,2,3,4,5])
output = run_primitive(
    'sigpro.aggregations.amplitude.statistical.crest_factor', 
    amplitude_values= data,
)
output

sigpro.aggregations.frequency.band.band_mean

path: sigpro.aggregations.frequency.band.band_mean

description : This primitive filters between a high and low band and computes the mean value for this specific band.

argument type description
parameters
amplitude_values numpy.ndarray Input signal amplitude values
frequency_values numpy.ndarray Input frequency values passed in Hz.
hyperparameters
min_frequency float Lower band threshold.
max_frequency float Upper band threshold.
output
value float Output mean of amplitude values within frequency band. 
import numpy as np
from sigpro.contributing import run_primitive

data = np.array([[1,2,3,4,5]])
frequency_values = np.array([[70,140,210, 280, 350]])
output = run_primitive(
    'sigpro.aggregations.frequency.band.band_mean',
    amplitude_values= data,
	frequency_values = frequency_values,
    min_frequency = 100, 
    max_frequency = 300
)
output