feat: finding best parameters for SE Kernel

frank/fitExample/AS209_1mm_FrankFit.py

@@ -0,0 +1,44 @@
+import frank
+import numpy as np 
+import matplotlib.pyplot as plt
+
+from frank.geometry import SourceGeometry
+from frank.radial_fitters import FrankFitter, FourierBesselFitter
+
+# Huang 2018 
+inc = 34.97
+pa = 85.76
+dra = 1.9e-3
+ddec = -2.5e-3
+r_out = 1.9
+
+# Frank Parameters
+n_pts = 300
+alpha = 1.3
+w_smooth = 1e-1
+
+# UVtable AS209 at 1mm with removed visibilities between .
+dir = "./"
+data_file = dir + 'AS209_continuum_prom_1chan_30s_keepflagsFalse_removed1.txt'
+
+# Loading data
+u, v, Re, Im, Weights = np.loadtxt(data_file, unpack = True, skiprows = 1)
+vis = Re + Im*1j
+
+geom = SourceGeometry(inc= inc, PA= pa, dRA= dra, dDec= ddec)
+
+" Fitting with frank "
+#FF = FrankFitter(r_out, n_pts, geom, alpha = alpha, weights_smooth = w_smooth)
+FF = FourierBesselFitter(r_out, n_pts, geom)
+sol = FF.fit(u, v, vis, Weights)
+#setattr(sol, 'positive', sol.solve_non_negative())
+
+fig = plt.figure(num = 1, figsize = (10, 5))
+plt.plot(sol.r, sol.mean / 1e10)
+plt.xlabel('Radius ["]', size = 15)
+plt.ylabel(r'Brightness profile [$10^{10}$ Jy sr$^{-1}$]', size = 15)
+plt.title('Frank Fit AS209 1mm', size = 15)
+plt.xlim(0, 1.3)
+#plt.savefig('FrankFit_AS209_1mm.jpg', bbox_inches='tight')
+plt.show()
+plt.close()

frank/radial_fitters.py

@@ -510,6 +510,8 @@ def _build_matrices(self, mapping):
 
         self._M = mapping['M']
         self._j = mapping['j']
+        self._V = mapping['V']
+        self._Wvalues = mapping['W']
 
         self._H0 = mapping['null_likelihood']
 
@@ -574,7 +576,8 @@ def fit(self, u, v, V, weights=1):
     def _fit(self):
         """Fit step. Computes the best fit given the pre-processed data"""
         fit = GaussianModel(self._DHT, self._M, self._j,
-                            noise_likelihood=self._H0)
+                            noise_likelihood=self._H0,
+                            Wvalues= self._Wvalues, V = self._V)
 
         self._sol = FrankGaussianFit(self._vis_map, fit, self._info,
                                      geometry=self._geometry.clone())

frank/statistical_models.py

@@ -628,9 +628,12 @@ class GaussianModel:
     """
 
     def __init__(self, DHT, M, j, p=None, scale=None, guess=None,
-                 Nfields=None, noise_likelihood=0):
+                 Nfields=None, noise_likelihood=0,
+                 Wvalues = None, V = None):
 
         self._DHT = DHT
+        self._Wvalues = Wvalues
+        self._V = V
 
         # Correct shape of design matrix etc.        
         if len(M.shape) == 2:
@@ -687,7 +690,19 @@ def __init__(self, DHT, M, j, p=None, scale=None, guess=None,
 
                 self._Sinv[sn:en, sn:en] += Sj[n]
         else:
-            self._Sinv =  None
+            #self._Sinv =  None
+            q_array = self._DHT.q
+
+            def true_squared_exponential_kernel(q, p, l):
+
+                q1, q2 = np.meshgrid(q, q)
+                p1, p2 = np.meshgrid(p, p)
+                SE_Kernel = np.sqrt(p1 * p2) * np.exp(-0.5*(q1-q2)**2 / l**2)
+                return SE_Kernel
+
+            Ykm = self._DHT.coefficients(direction="backward")
+            # We continue after set M matrix because is needed to calculate
+            # best parameters for S matrix.
 
         # Compute the design matrix
         self._M = np.zeros([Nr*Nfields, Nr*Nfields], dtype='f8')
@@ -707,7 +722,89 @@ def __init__(self, DHT, M, j, p=None, scale=None, guess=None,
 
         self._like_noise = noise_likelihood
 
+        # M is already defined, so we find best parameters for S matrix and use it.
+        m, c , l = self.minimizeS()
+        pI =  np.exp(m*np.log(q_array) + c)
+        S_fspace = true_squared_exponential_kernel(q_array, pI, l)
+        S_real = np.dot(np.transpose(Ykm), np.dot(S_fspace, Ykm))
+        S_real_inv = np.linalg.inv(S_real)
+        self._Sinv =  S_real_inv
+
         self._fit()
+
+    def minimizeS(self):
+        from scipy.optimize import minimize
+        from scipy.special import gamma
+        V = self._V
+
+        def calculate_S(m, c, l):
+            q_array = self._DHT.q
+            p_array = c*(q_array**m)
+            def true_squared_exponential_kernel(q, p, l):
+                q1, q2 = np.meshgrid(q, q)
+                p1, p2 = np.meshgrid(p, p)
+                SE_Kernel = np.sqrt(p1 * p2) * np.exp(-0.5*(q1-q2)**2 / l**2)
+                return SE_Kernel
+
+            Ykm = self._DHT.coefficients(direction="backward")
+            S_fspace = true_squared_exponential_kernel(q_array, p_array, l)
+            S_real = np.dot(np.transpose(Ykm), np.dot(S_fspace, Ykm))
+            return S_real
+
+        def calculate_D(S):
+            S_real_inv = np.linalg.inv(S)
+            Dinv = self._M + S_real_inv
+            D = np.linalg.inv(Dinv)
+            return [Dinv, D]
+
+        def calculate_mu(Dinv):
+            try: 
+                Dchol = scipy.linalg.cho_factor(Dinv)
+                mu =  scipy.linalg.cho_solve(Dchol, self._j)
+
+            except np.linalg.LinAlgError:
+                U, s, V = scipy.linalg.svd(Dinv, full_matrices=False)
+                s1 = np.where(s > 0, 1. / s, 0)
+                mu = np.dot(V.T, np.multiply(np.dot(U.T, self._j), s1))
+            return mu
+
+        def likelihood(param, data):
+            m, c, l = param
+            Wvalues = self._Wvalues
+            N = np.diag(1/Wvalues)
+
+            alpha = 1.3
+            l0 = 1e7
+
+            # Create an Inverse Gamma distribution function
+            def inv_gamma_function(l, alpha, beta):
+                return ((gamma(alpha)*beta)**(-1))*((beta/l)**(alpha + 1))*np.exp(-beta/l) 
+
+            S  = calculate_S(m,c, l)
+            [Dinv, D] = calculate_D(S)
+            mu = calculate_mu(Dinv)
+            logdetS = np.linalg.slogdet(S)[1]
+            logdetD = np.linalg.slogdet(D)[1]
+            logdetN = np.linalg.slogdet(N)[1]
+            factor = np.log(2*np.pi)
+
+            log_likelihood =  2*np.log(np.abs((1/m)*(1/c))) \
+            + 2*np.log(inv_gamma_function(l, alpha, l0)) \
+            - 0.5*(factor + logdetN) \
+            - 0.5*(factor + logdetS) \
+            + 0.5*(factor + logdetD) \
+            + 0.5*np.dot(np.transpose(self._j), mu) \
+            - 0.5*np.dot(np.transpose(data), np.dot(np.diag(Wvalues), data)) 
+            return -log_likelihood
+
+        result = minimize(likelihood, x0=np.array([-5, 60, 5e4]), args=(V,),
+                           bounds=[(-6, 6), (1, 70), (1e3, 1e6)],
+                           method="Powell", tol=1e-6,
+                           )
+        m, c, l = result.x
+        print("Result: ", "m: ", m, "c: ", c, "l: ", "{:e}".format(l))
+        return [m, c, l]
+
 
     def _fit(self):
         """Compute the mean and variance"""