added fibonacci frolov

Author: Vlad-Kor

model/distributions/cylinder/partially_wraped_normal/fibonacci_frolov.py

@@ -0,0 +1,42 @@
+import numpy as np
+from deterministic_gaussian_sampling_fibonacci import sample_gaussian_fibonacci
+
+from util.selectors.silder_log import LogSlider
+from model.distributions.cylinder.cylinder_sampling_schema import CylinderSamplingSchema
+from util.selectors.silder_manual_input_wrapper import SliderManualInputWrapper as MI
+from util.gaus_util import GausUtil as gu
+
+
+class FFrolovPWNSampling(CylinderSamplingSchema):
+	def __init__(self):
+		def _check_input(val):
+			return val >= 1 and val <= 100003 and isinstance(val, int)
+
+		self.sample_options = [
+			MI(LogSlider("Number of Samples", 10, 100, 10000))
+		]
+
+	def get_name(self):
+		return "Fibonacci Frolov"
+	
+	def sample(self, sample_options, distribution_options):
+		sample_count = sample_options[0].state
+
+		mean_x = distribution_options[0].state
+		mean_y = distribution_options[1].state
+		sigma_t = distribution_options[2].state
+		sigma_p = distribution_options[3].state
+		correlation = distribution_options[4].state
+
+		mu = np.array([mean_x, mean_y])
+
+		Cov = np.array([
+			[sigma_t**2, correlation * sigma_t * sigma_p],
+			[correlation * sigma_t * sigma_p, sigma_p**2]
+		])
+		
+		gaus_grid = sample_gaussian_fibonacci(mu, Cov, sample_count, "Fibonacci")
+
+		# wrapp
+		gaus_grid[:,0] = gaus_grid[:,0] % (2 * np.pi)
+		return gaus_grid

model/distributions/cylinder/partially_wraped_normal/partially_warpped_normal.py

@@ -10,6 +10,7 @@
 from model.distributions.cylinder.partially_wraped_normal.cartesian import CylinderFibCartPWNSampling
 from model.distributions.cylinder.partially_wraped_normal.frolov import CFrolovPWNSampling
 from model.distributions.cylinder.partially_wraped_normal.improved_frolov import IFrolovPWNSampling
+from model.distributions.cylinder.partially_wraped_normal.fibonacci_frolov import FFrolovPWNSampling
 
 from model.cylinder.cylinder import Cylinder
 
@@ -29,6 +30,7 @@ def __init__(self):
 			CylinderFibCartPWNSampling(),
 			CFrolovPWNSampling(),
 			IFrolovPWNSampling(),
+			FFrolovPWNSampling(),
 		]
 
 	def get_name(self):

model/distributions/torus/wrapped_normal/fibonacci_frolov.py

@@ -0,0 +1,43 @@
+import numpy as np
+from deterministic_gaussian_sampling_fibonacci import sample_gaussian_fibonacci
+
+from util.selectors.silder_log import LogSlider
+from model.distributions.torus.torus_sampling_schema import TorusSamplingSchema
+from util.selectors.silder_manual_input_wrapper import SliderManualInputWrapper as MI
+from util.gaus_util import GausUtil as gu
+
+
+class FFrolovWNSampling(TorusSamplingSchema):
+	def __init__(self):
+		def _check_input(val):
+			return val >= 1 and val <= 100003 and isinstance(val, int)
+
+		self.sample_options = [
+			MI(LogSlider("Number of Samples", 10, 100, 10000))
+		]
+
+	def get_name(self):
+		return "Fibonacci Frolov"
+	
+	def sample(self, sample_options, distribution_options):
+		sample_count = sample_options[0].state
+
+		mean_x = distribution_options[0].state
+		mean_y = distribution_options[1].state
+		sigma_t = distribution_options[2].state
+		sigma_p = distribution_options[3].state
+		correlation = distribution_options[4].state
+
+		mu = np.array([mean_x, mean_y])
+
+		Cov = np.array([
+			[sigma_t**2, correlation * sigma_t * sigma_p],
+			[correlation * sigma_t * sigma_p, sigma_p**2]
+		])
+		
+		gaus_grid = sample_gaussian_fibonacci(mu, Cov, sample_count, "Fibonacci")
+
+		# wrapp
+		gaus_grid[:,0] = gaus_grid[:,0] % (2 * np.pi)
+		gaus_grid[:,1] = gaus_grid[:,1] % (2 * np.pi)
+		return gaus_grid

model/distributions/torus/wrapped_normal/wrapped_normal.py

@@ -10,6 +10,7 @@
 from model.distributions.torus.wrapped_normal.cartesian import TorusFibCartWNSampling
 from model.distributions.torus.wrapped_normal.frolov import CFrolovWNSampling
 from model.distributions.torus.wrapped_normal.improved_frolov import IFrolovWNSampling
+from model.distributions.torus.wrapped_normal.fibonacci_frolov import FFrolovWNSampling
 from model.torus.torus import Torus
 class WrappedNormalTorusDistribution(TorusDistribution):
 	def __init__(self):
@@ -27,6 +28,7 @@ def __init__(self):
 			TorusFibCartWNSampling(),
 			CFrolovWNSampling(),
 			IFrolovWNSampling(),
+			FFrolovWNSampling(),
 		]