Merge branch 'multithread-testing' of https://github.com/titanscouting/red-alliance-analysis into multithread-testing

.devcontainer/devcontainer.json

@@ -24,5 +24,5 @@
 		"ms-python.python",
 		"waderyan.gitblame"
 	],
-	"postCreateCommand": "apt install vim -y ; pip install -r data-analysis/requirements.txt ; pip install -r analysis-master/requirements.txt ; pip install tra-analysis"
+	"postCreateCommand": "apt install vim -y ; pip install -r data-analysis/requirements.txt ; pip install -r analysis-master/requirements.txt ; pip install pylint ; pip install tra-analysis"
 }

SECURITY.md

@@ -0,0 +1,6 @@
+# Security Policy
+
+
+## Reporting a Vulnerability
+
+Please email `titanscout2022@gmail.com` to report a vulnerability.

analysis-master/test_analysis.py

@@ -1,8 +1,11 @@
 from tra_analysis import analysis as an
 from tra_analysis import metrics
+from tra_analysis import fits
 
 def test_():
 	test_data_linear = [1, 3, 6, 7, 9]
+	x_data_circular = []
+	y_data_circular = []
 	y_data_ccu = [1, 3, 7, 14, 21]
 	y_data_ccd = [1, 5, 7, 8.5, 8.66]
 	test_data_scrambled = [-32, 34, 19, 72, -65, -11, -43, 6, 85, -17, -98, -26, 12, 20, 9, -92, -40, 98, -78, 17, -20, 49, 93, -27, -24, -66, 40, 84, 1, -64, -68, -25, -42, -46, -76, 43, -3, 30, -14, -34, -55, -13, 41, -30, 0, -61, 48, 23, 60, 87, 80, 77, 53, 73, 79, 24, -52, 82, 8, -44, 65, 47, -77, 94, 7, 37, -79, 36, -94, 91, 59, 10, 97, -38, -67, 83, 54, 31, -95, -63, 16, -45, 21, -12, 66, -48, -18, -96, -90, -21, -83, -74, 39, 64, 69, -97, 13, 55, 27, -39]
@@ -29,3 +32,4 @@ def test_():
 	assert all(a == b for a, b in zip(an.Sort().bubblesort(test_data_scrambled), test_data_sorted))
 	assert all(a == b for a, b in zip(an.Sort().cyclesort(test_data_scrambled), test_data_sorted))
 	assert all(a == b for a, b in zip(an.Sort().cocktailsort(test_data_scrambled), test_data_sorted))
+	assert fits.CircleFit(x=[0,0,-1,1], y=[1, -1, 0, 0]).LSC() == (0.0, 0.0, 1.0, 0.0)

analysis-master/tra_analysis/fits.py

@@ -0,0 +1,85 @@
+# Titan Robotics Team 2022: CPU fitting models
+# Written by Dev Singh
+# Notes:
+#   this module is cuda-optimized (as appropriate) and vectorized (except for one small part)
+# setup:
+
+__version__ = "0.0.1"
+
+# changelog should be viewed using print(analysis.fits.__changelog__)
+__changelog__ = """changelog:
+	0.0.1:
+		- initial release, add circle fitting with LSC
+"""
+
+__author__ = (
+	"Dev Singh <dev@devksingh.com>"
+)
+
+__all__ = [
+	'CircleFit'
+]
+
+import numpy as np
+
+class CircleFit:
+	"""Class to fit data to a circle using the Least Square Circle (LSC) method"""
+	# For more information on the LSC method, see: 
+	# http://www.dtcenter.org/sites/default/files/community-code/met/docs/write-ups/circle_fit.pdf
+	def __init__(self, x, y, xy=None):
+		self.ournp = np #todo: implement cupy correctly
+		if type(x) == list:
+			x = np.array(x)
+		if type(y) == list:
+			y = np.array(y)
+		if type(xy) == list:
+			xy = np.array(xy)
+		if xy != None: 
+			self.coords = xy
+		else: 
+			# following block combines x and y into one array if not already done
+			self.coords = self.ournp.vstack(([x.T], [y.T])).T
+	def calc_R(x, y, xc, yc):
+		"""Returns distance between center and point"""
+		return self.ournp.sqrt((x-xc)**2 + (y-yc)**2)
+	def f(c, x, y):
+		"""Returns distance between point and circle at c"""
+		Ri = calc_R(x, y, *c)
+		return Ri - Ri.mean()
+	def LSC(self):
+		"""Fits given data to a circle and returns the center, radius, and variance"""
+		x = self.coords[:, 0]
+		y = self.coords[:, 1]
+		# guessing at a center
+		x_m = self.ournp.mean(x)
+		y_m = self.ournp.mean(y)
+
+		# calculation of the reduced coordinates
+		u = x - x_m
+		v = y - y_m
+
+		# linear system defining the center (uc, vc) in reduced coordinates:
+		#    Suu * uc +  Suv * vc = (Suuu + Suvv)/2
+		#    Suv * uc +  Svv * vc = (Suuv + Svvv)/2
+		Suv  = self.ournp.sum(u*v)
+		Suu  = self.ournp.sum(u**2)
+		Svv  = self.ournp.sum(v**2)
+		Suuv = self.ournp.sum(u**2 * v)
+		Suvv = self.ournp.sum(u * v**2)
+		Suuu = self.ournp.sum(u**3)
+		Svvv = self.ournp.sum(v**3)
+
+		# Solving the linear system
+		A = self.ournp.array([ [ Suu, Suv ], [Suv, Svv]])
+		B = self.ournp.array([ Suuu + Suvv, Svvv + Suuv ])/2.0
+		uc, vc = self.ournp.linalg.solve(A, B)
+
+		xc_1 = x_m + uc
+		yc_1 = y_m + vc
+
+		# Calculate the distances from center (xc_1, yc_1)
+		Ri_1     = self.ournp.sqrt((x-xc_1)**2 + (y-yc_1)**2)
+		R_1      = self.ournp.mean(Ri_1)
+		# calculate residual error
+		residu_1 = self.ournp.sum((Ri_1-R_1)**2)
+		return (xc_1, yc_1, R_1, residu_1)

analysis-master/tra_analysis/regression.py

@@ -1,8 +1,9 @@
 # Titan Robotics Team 2022: CUDA-based Regressions Module
+# Not actively maintained, may be removed in future release
 # Written by Arthur Lu & Jacob Levine
 # Notes:
 #   this module has been automatically inegrated into analysis.py, and should be callable as a class from the package
-#   this module is cuda-optimized and vectorized (except for one small part)
+#   this module is cuda-optimized (as appropriate) and vectorized (except for one small part)
 # setup:
 
 __version__ = "0.0.4"
@@ -25,7 +26,7 @@ __changelog__ = """
 
 __author__ = (
 	"Jacob Levine <jlevine@imsa.edu>",
-	"Arthur Lu <learthurgo@gmail.com>"
+	"Arthur Lu <learthurgo@gmail.com>",
 )
 
 __all__ = [
@@ -40,14 +41,15 @@ __all__ = [
 	'ExpRegKernel',
 	'SigmoidalRegKernelArthur',
 	'SGDTrain',
-	'CustomTrain'
+	'CustomTrain',
+	'CircleFit'
 ]
 
 import torch
 
 global device
 
-device = "cuda:0" if torch.torch.cuda.is_available() else "cpu"
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
 
 #todo: document completely