diff --git a/denoising-plots/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/denoising-plots/.ipynb_checkpoints/Untitled-checkpoint.ipynb
deleted file mode 100644
index 2d4751c9dd5597e0bc92c06c2ec65ce0bca0e323..0000000000000000000000000000000000000000
--- a/denoising-plots/.ipynb_checkpoints/Untitled-checkpoint.ipynb
+++ /dev/null
@@ -1,139 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f5605c04",
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import scipy\n",
- "import matplotlib.pyplot as plt\n",
- "import quaternion # load the quaternion module\n",
- "import bispy as bsp\n",
- "import torch\n",
- "import tqdm\n",
- "import time\n",
- "from sklearn.preprocessing import normalize\n",
- "from utils import STIS,optimize_loop,snr_bivariate,param_search,objective_STIS,objective_KReSP,KReSP\n",
- "from ray import tune,init\n",
- "from tempfile import TemporaryFile\n",
- "import pickle\n",
- "\n",
- "init(num_cpus=13)\n",
- "\n",
- "## PLOT AN EXAMPLE \n",
- "\n",
- "np.random.seed(5)\n",
- "N = 1024 # length of the signal\n",
- "t = np.linspace(0, 2*np.pi/4, N) # time vector\n",
- "dt = t[1]-t[0]\n",
- "\n",
- "# ellipse parameters - AM-FM-PM polarized \n",
- "theta1 = np.pi/4 - 2*t\n",
- "chi1 = np.pi/16 - t\n",
- "phi1 = 0 \n",
- "f0 = 25/N/dt \n",
- "S0 = bsp.utils.windows.hanning(N)\n",
- "\n",
- "x_quad = bsp.signals.bivariateAMFM(S0, theta1, chi1, 2*np.pi*f0*t+ phi1)\n",
- "x = quaternion.as_float_array(x_quad)[:,:2]\n",
- "\n",
- "bsp.utils.visual.plot2D(t,x_quad)\n",
- "plt.savefig(\"clean_sig.pdf\")\n",
- "\n",
- "sigma = 0.05\n",
- "n = np.zeros([N,4])\n",
- "noise_complex = np.random.randn(N,2)\n",
- "y = x + noise_complex\n",
- "\n",
- "uH = np.imag(scipy.signal.hilbert(noise_complex[:,0]))\n",
- "vH = np.imag(scipy.signal.hilbert(noise_complex[:,1]))\n",
- "n[:,0] = noise_complex[:,0]\n",
- "n[:,1] = noise_complex[:,1]\n",
- "n[:,2] = uH\n",
- "n[:,3] = vH\n",
- "n = quaternion.from_float_array(n)\n",
- "y_quad = sigma*n + x_quad # Noisy signal\n",
- "\n",
- "bsp.utils.visual.plot2D(t,y_quad)\n",
- "plt.savefig(\"noisy_sig.pdf\")\n",
- "n = np.random.randn(N,2)\n",
- "y = sigma*n + x # Noisy signal\n",
- "\n",
- "print(\"sigma: \" + str(sigma) + \" Noise SNR: \"+ str(snr_bivariate(x,y) ) )\n",
- "search_space = {\"x\":tune.grid_search([x]),\"t\":tune.grid_search([t]),\"y\":tune.grid_search([y]),\"lambdax\": tune.grid_search((0.1)**np.linspace(5,15,7)), \"lambdaS\": tune.grid_search((0.1)**np.linspace(5,10,7)) , \"beta1\":tune.grid_search((0.10)**np.linspace(-2,2,5)),\"beta2\":tune.grid_search((0.10)**np.linspace(-2,2,5)),\"sigma2\":tune.grid_search([sigma**2])}\n",
- "config = param_search(objective_STIS,search_space)\n",
- "model = STIS(t,y,lambdax=config[\"lambdax\"],lambdaS=config[\"lambdaS\"],beta1=config[\"beta1\"],beta2=config[\"beta2\"],sigma2=sigma**2,p=2)\n",
- "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
- "model = optimize_loop(model,optimizer,numit=1000)\n",
- "\n",
- "x_stis = quaternion.from_float_array(model.Xquad.detach().numpy())\n",
- "bsp.utils.visual.plot2D(t,x_stis)\n",
- "plt.savefig(\"denoised_via_all_terms.pdf\")\n",
- "\n",
- "print(\"sigma: \" + str(sigma) + \" STIS SNR: \"+ str(snr_bivariate(x,model.X.detach().numpy())))\n",
- "\n",
- "\n",
- "# NO STOKES REGULARIZATION \n",
- "search_space = {\"x\":tune.grid_search([x]),\"t\":tune.grid_search([t]),\"y\":tune.grid_search([y]),\"lambdax\": tune.grid_search((0.1)**np.linspace(5,15,7)), \"lambdaS\":tune.grid_search([0.0]) , \"beta1\":tune.grid_search([0.0]),\"beta2\":tune.grid_search([0.0]),\"sigma2\":tune.grid_search([sigma**2])}\n",
- "config = param_search(objective_STIS,search_space)\n",
- "model = STIS(t,y,lambdax=config[\"lambdax\"],lambdaS=config[\"lambdaS\"],beta1=config[\"beta1\"],beta2=config[\"beta2\"],sigma2=sigma**2,p=2)\n",
- "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
- "model = optimize_loop(model,optimizer,numit=1000)\n",
- "\n",
- "print(\"sigma: \" + str(sigma) + \" STIS SNR: \"+ str((snr_bivariate(x,model.X.detach().numpy()))))\n",
- "x_stis = quaternion.from_float_array(model.Xquad.detach().numpy())\n",
- "bsp.utils.visual.plot2D(t,x_stis)\n",
- "plt.savefig(\"denoised_via_no_stokes.pdf\")\n",
- "\n",
- "# ONLY SMOOTH STOKES\n",
- "search_space = {\"x\":tune.grid_search([x]),\"t\":tune.grid_search([t]),\"y\":tune.grid_search([y]),\"lambdax\": tune.grid_search([0.0]), \"lambdaS\": tune.grid_search((0.1)**np.linspace(5,10,7)) , \"beta1\":tune.grid_search((0.1)**np.linspace(-2,2,7)),\"beta2\":tune.grid_search((0.1)**np.linspace(-2,2,5)),\"sigma2\":tune.grid_search([sigma**2])}\n",
- "config = param_search(objective_STIS,search_space)\n",
- "model = STIS(t,y,lambdax=config[\"lambdax\"],lambdaS=config[\"lambdaS\"],beta1=config[\"beta1\"],beta2=config[\"beta2\"],sigma2=sigma**2,p=2)\n",
- "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
- "model = optimize_loop(model,optimizer,numit=1000)\n",
- "x_stis = quaternion.from_float_array(model.Xquad.detach().numpy())\n",
- "bsp.utils.visual.plot2D(t,x_stis)\n",
- "plt.savefig(\"denoised_via_no_signal_smoother.pdf\")\n",
- "\n",
- "print(\"sigma: \" + str(sigma) + \" STIS SNR: \"+ str((snr_bivariate(x,model.X.detach().numpy()))))\n",
- "\n",
- "# Kernel regression on normalized \n",
- "search_space = {\"x\":tune.grid_search([x]),\"t\":tune.grid_search([t]),\"y\":tune.grid_search([y]),\"alpha\": tune.grid_search((0.1)**np.linspace(5,15,7)),\"lambda_1\": tune.grid_search((0.1)**np.linspace(5,15,7)), \"lambda_s\": tune.grid_search((0.1)**np.linspace(5,10,7)) , \"beta\":tune.grid_search((0.10)**np.linspace(-2,2,7)),\"gamma\":tune.grid_search([0.2]),\"sigma2\":tune.grid_search([sigma**2])}\n",
- "config = param_search(objective_KReSP,search_space)\n",
- "model = KReSP(t,y,lambda_1=config[\"lambda_1\"],beta=config[\"beta\"],lambda_s=config[\"lambda_s\"],alpha=config[\"alpha\"],gamma=config[\"gamma\"],eps=10**-7,win_width=32,sigma2=sigma**2)\n",
- "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
- "model = optimize_loop(model,optimizer,numit=300)\n",
- "\n",
- "\n",
- "print(\"sigma: \" + str(sigma) + \" KReSP SNR: \"+ str(snr_bivariate(x,model.X.detach().numpy())))\n",
- "x_kerreg = quaternion.from_float_array(model.Xquad.detach().numpy())\n",
- "bsp.utils.visual.plot2D(t,x_kerreg)\n",
- "plt.savefig(\"denoised_via_kerreg.pdf\")"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/denoising-plots/.vscode/settings.json b/denoising-plots/.vscode/settings.json
deleted file mode 100644
index 9e26dfeeb6e641a33dae4961196235bdb965b21b..0000000000000000000000000000000000000000
--- a/denoising-plots/.vscode/settings.json
+++ /dev/null
@@ -1 +0,0 @@
-{}
\ No newline at end of file
diff --git a/denoising-plots/__pycache__/utils.cpython-310.pyc b/denoising-plots/__pycache__/utils.cpython-310.pyc
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diff --git a/denoising-plots/__pycache__/utils.cpython-311.pyc b/denoising-plots/__pycache__/utils.cpython-311.pyc
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