tra-analysis/analysis-master/tra_analysis/Array.py

# Titan Robotics Team 2022: Array submodule
# Written by Arthur Lu
# Notes:
#    this should be imported as a python module using 'from tra_analysis import Array'
# setup:

__version__ = "1.0.4"

__changelog__ = """changelog:
	1.0.4:
		- fixed spelling of deprecate
	1.0.3:
		- fixed __all__
	1.0.2:
		- fixed several implementation bugs with magic methods
	1.0.1:
		- removed search and __search functions
	1.0.0:
		- ported analysis.Array() here
"""

__author__ = (
	"Arthur Lu <learthurgo@gmail.com>",
)

__all__ = [
	"Array",
]

import numpy as np
import warnings

class Array(): # tests on nd arrays independent of basic_stats

	def __init__(self, narray):

		self.array = np.array(narray)

	def __str__(self):

		return str(self.array)

	def __repr__(self):

		return str(self.array)
	
	def elementwise_mean(self, axis = 0): # expects arrays that are size normalized

		return np.mean(self.array, axis = axis)

	def elementwise_median(self, axis = 0):

		return np.median(self.array, axis = axis)

	def elementwise_stdev(self, axis = 0):

		return np.std(self.array, axis = axis)

	def elementwise_variance(self, axis = 0):

		return np.var(self.array, axis = axis)

	def elementwise_npmin(self, axis = 0):
		return np.amin(self.array, axis = axis)


	def elementwise_npmax(self, axis = 0):
		return np.amax(self.array, axis = axis)

	def elementwise_stats(self, axis = 0):

		_mean = self.elementwise_mean(axis = axis)
		_median = self.elementwise_median(axis = axis)
		_stdev = self.elementwise_stdev(axis = axis)
		_variance = self.elementwise_variance(axis = axis)
		_min = self.elementwise_npmin(axis = axis)
		_max = self.elementwise_npmax(axis = axis)

		return _mean, _median, _stdev, _variance, _min, _max

	def __getitem__(self, key):

		return self.array[key]

	def __setitem__(self, key, value):

		self.array[key] = value

	def __len__(self):

		return len(self.array)

	def normalize(self):

		a = np.atleast_1d(np.linalg.norm(self.array))
		a[a==0] = 1
		return Array(self.array / np.expand_dims(a, -1))

	def __add__(self, other):

		return Array(self.array + other.array)

	def __sub__(self, other):

		return Array(self.array - other.array)

	def __neg__(self):
		
		return Array(-self.array)

	def __abs__(self):

		return Array(abs(self.array))

	def __invert__(self):

		return Array(1/self.array)

	def __mul__(self, other):

		if(isinstance(other, Array)):
			return Array(self.array.dot(other.array))
		elif(isinstance(other, int)):
			return Array(other * self.array)
		else:
			raise Exception("unsupported multiplication between Array and " + str(type(other)))

	def __rmul__(self, other):

		return self.__mul__(other)

	def cross(self, other):

		return np.cross(self.array, other.array)

	def transpose(self):

		return Array(np.transpose(self.array))

	def sort(self, array): # deprecated
		warnings.warn("Array.sort has been deprecated in favor of Sort")
		array_length = len(array)
		if array_length <= 1:
			return array
		middle_index = int(array_length / 2)
		left = array[0:middle_index]
		right = array[middle_index:]
		left = self.sort(left)
		right = self.sort(right)
		return self.__merge(left, right)

	def __merge(self, left, right):
		sorted_list = []
		left = left[:]
		right = right[:]
		while len(left) > 0 or len(right) > 0:
			if len(left) > 0 and len(right) > 0:
				if left[0] <= right[0]:
					sorted_list.append(left.pop(0))
				else:
					sorted_list.append(right.pop(0))
			elif len(left) > 0:
				sorted_list.append(left.pop(0))
			elif len(right) > 0:
				sorted_list.append(right.pop(0))
		return sorted_list