mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-12-27 09:59:10 +00:00
122 lines
2.5 KiB
Python
122 lines
2.5 KiB
Python
# 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__ = "1.0.1"
|
|
|
|
__changelog__ = """changelog:
|
|
1.0.1:
|
|
- removed search and __search functions
|
|
1.0.0:
|
|
- ported analysis.Array() here
|
|
"""
|
|
|
|
__author__ = (
|
|
"Arthur Lu <learthurgo@gmail.com>",
|
|
)
|
|
|
|
import numpy as np
|
|
|
|
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 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 normalize(self, array):
|
|
|
|
a = np.atleast_1d(np.linalg.norm(array))
|
|
a[a==0] = 1
|
|
return array / np.expand_dims(a, -1)
|
|
|
|
def __add__(self, other):
|
|
|
|
return self.array + other.array
|
|
|
|
def __sub__(self, other):
|
|
|
|
return self.array - other.array
|
|
|
|
def __neg__(self):
|
|
|
|
return -self.array
|
|
|
|
def __abs__(self):
|
|
|
|
return abs(self.array)
|
|
|
|
def __invert__(self):
|
|
|
|
return 1/self.array
|
|
|
|
def __mul__(self, other):
|
|
|
|
return self.array.dot(other.array)
|
|
|
|
def __rmul__(self, other):
|
|
|
|
return self.array.dot(other.array)
|
|
|
|
def cross(self, other):
|
|
|
|
return np.cross(self.array, other.array)
|
|
|
|
def sort(self, array): # depreciated
|
|
warnings.warn("Array.sort has been depreciated 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) |