Module wavelet.compression.compressor_peak
Compression using the difference of the max & min data in the signal
Expand source code
""" Compression using the difference of the max & min data in the signal """
import numpy as np
from wavelet.compression.compressor import Compressor
class CompressorPeak:
"""
The magnitude calculated will be used to threshold the data signal, the
output generated would be the compressed signal with zeros that was threhold
by the algorithm
Attributes
------------
__max: float
the maximum value in the data
__min: float
the minimum value in the data
__magnitude: float
the calculated threshold value
__compressor: Compressor
object that performs the compression with the calculated magnitude value
"""
def __init__(self):
self.__max = None
self.__min = None
self.__magnitude = 0.
self.__compressor = Compressor()
def compress(self, data):
"""
Perform calculation of the magnitude that is the difference of the highest and lowest
peaks on the input data, this magnitude is then use to the threshold the signal
Look at the Compressor to see the implementation
Parameters
----------
data: array_like
input signal, mostly output of the decomposition process
Returns
-------
array_like
thresholded signal
"""
data = np.asanyarray(data)
self.__max = np.max(data)
self.__min = np.min(data)
self.__magnitude = 0.5 * (self.__max - self.__min)
return self.__compressor.compress(data, self.__magnitude)
def getCompressionRate(self, data):
"""
Returns the compression rate of the final data that was thresholded
Parameters
----------
data: array_like
data to check the compression rate for
Returns
-------
float
compression rate in percentage
"""
return self.__compressor.calculateCompressionRate(data)
def getMagnitude(self):
return self.__magnitudeClasses
class CompressorPeak-
The magnitude calculated will be used to threshold the data signal, the output generated would be the compressed signal with zeros that was threhold by the algorithm
Attributes
__max:float- the maximum value in the data
__min:float- the minimum value in the data
__magnitude:float- the calculated threshold value
__compressor:Compressor- object that performs the compression with the calculated magnitude value
Expand source code
class CompressorPeak: """ The magnitude calculated will be used to threshold the data signal, the output generated would be the compressed signal with zeros that was threhold by the algorithm Attributes ------------ __max: float the maximum value in the data __min: float the minimum value in the data __magnitude: float the calculated threshold value __compressor: Compressor object that performs the compression with the calculated magnitude value """ def __init__(self): self.__max = None self.__min = None self.__magnitude = 0. self.__compressor = Compressor() def compress(self, data): """ Perform calculation of the magnitude that is the difference of the highest and lowest peaks on the input data, this magnitude is then use to the threshold the signal Look at the Compressor to see the implementation Parameters ---------- data: array_like input signal, mostly output of the decomposition process Returns ------- array_like thresholded signal """ data = np.asanyarray(data) self.__max = np.max(data) self.__min = np.min(data) self.__magnitude = 0.5 * (self.__max - self.__min) return self.__compressor.compress(data, self.__magnitude) def getCompressionRate(self, data): """ Returns the compression rate of the final data that was thresholded Parameters ---------- data: array_like data to check the compression rate for Returns ------- float compression rate in percentage """ return self.__compressor.calculateCompressionRate(data) def getMagnitude(self): return self.__magnitudeMethods
def compress(self, data)-
Perform calculation of the magnitude that is the difference of the highest and lowest peaks on the input data, this magnitude is then use to the threshold the signal
Look at the Compressor to see the implementation
Parameters
data:array_like- input signal, mostly output of the decomposition process
Returns
array_like- thresholded signal
Expand source code
def compress(self, data): """ Perform calculation of the magnitude that is the difference of the highest and lowest peaks on the input data, this magnitude is then use to the threshold the signal Look at the Compressor to see the implementation Parameters ---------- data: array_like input signal, mostly output of the decomposition process Returns ------- array_like thresholded signal """ data = np.asanyarray(data) self.__max = np.max(data) self.__min = np.min(data) self.__magnitude = 0.5 * (self.__max - self.__min) return self.__compressor.compress(data, self.__magnitude) def getCompressionRate(self, data)-
Returns the compression rate of the final data that was thresholded
Parameters
data:array_like- data to check the compression rate for
Returns
float- compression rate in percentage
Expand source code
def getCompressionRate(self, data): """ Returns the compression rate of the final data that was thresholded Parameters ---------- data: array_like data to check the compression rate for Returns ------- float compression rate in percentage """ return self.__compressor.calculateCompressionRate(data) def getMagnitude(self)-
Expand source code
def getMagnitude(self): return self.__magnitude