mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-12-26 17:49:09 +00:00
166 lines
3.5 KiB
Python
166 lines
3.5 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__ = "1.0.4"
|
|
|
|
__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 |