psffit takes an instance instead of a class

maoppy/example/fit_muse_simulated_psf.py

@@ -30,7 +30,7 @@ bckgd = 1.0
 
 #%% Initialize PSF model
 samp = muse_nfm.samp(wvl) # sampling (2.0 for Shannon-Nyquist)
-Pmodel = Psfao((npix,npix),system=muse_nfm,samp=samp)
+Pmodel = Psfao((npix,npix), system=muse_nfm, samp=samp)
 
 #%% Generate a MUSE-NFM PSF
 r0 = 0.15 # Fried parameter [m]
@@ -53,7 +53,7 @@ guess = [0.145,2e-2,1.2,0.08,ratio,theta,1.5]
 w = np.ones_like(image)/ron**2.0
 fixed = [False,False,False,False,True,True,False]
 
-out = psffit(image,Psfao,guess,weights=w,system=muse_nfm,samp=samp,fixed=fixed)
+out = psffit(image, Pmodel, guess, weights=w, fixed=fixed)
 
 flux_fit, bck_fit = out.flux_bck
 fitao = flux_fit*out.psf + bck_fit

maoppy/example/fit_muse_simulated_psf_small_fov.py

@@ -38,7 +38,7 @@ bckgd = 1.0
 
 #%% Initialize PSF model
 samp = muse_nfm.samp(wvl) # sampling (2.0 for Shannon-Nyquist)
-Pmodel = Psfao((npix,npix),system=muse_nfm,samp=samp)
+Pmodel = Psfao((npix,npix), system=muse_nfm, samp=samp)
 
 #%% Generate a MUSE-NFM PSF
 r0 = 0.15 # Fried parameter [m]
@@ -66,8 +66,7 @@ guess = [0.145,2e-2,1.2,0.08,ratio,theta,1.5]
 w = np.ones_like(im_crop)/ron**2.0
 fixed = [False,False,False,False,True,True,False]
 
-out = psffit(im_crop,Psfao,guess,weights=w,fixed=fixed,npixfit=npix, # fit keywords
-                                 system=muse_nfm,samp=samp) # Psfao keywords
+out = psffit(im_crop, Pmodel, guess, weights=w, fixed=fixed, npixfit=npix)
 
 flux_fit, bck_fit = out.flux_bck
 fitao = flux_fit*out.psf + bck_fit

maoppy/example/fit_zimpol_psf.py

@@ -21,7 +21,7 @@ from maoppy.instrument import zimpol
 # %% PARAMETERS TO MODIFY
 psf_path = "path/to/your/zimpol_psf.fits" # set the path to your PSF *.fits file
 filt = zimpol.filters["N_R"]  # ZIMPOL filter
-Npix = 512 # size of PSF for fitting [pixels]. The larger the better
+npix = 512 # size of PSF for fitting [pixels]. The larger the better
 
 r0 = 0.18 # Fried parameter [m]
 moff_C = 1e-2 # PSD constant [rad²m²]
@@ -36,7 +36,7 @@ fitsPSF = fits.open(psf_path)
 hdr = fitsPSF[0].header
 psf = fitsPSF[0].data
 
-psf = imcenter(psf, (Npix, Npix), maxi=True) * zimpol.gain
+psf = imcenter(psf, (npix,npix), maxi=True) * zimpol.gain
 
 # %% FIT PSFAO model
 wvl = filt[0]
@@ -48,7 +48,8 @@ fixed = [False,False,False,False,True,True,False]
 weights = np.ones_like(psf)
 
 print("PSFAO fitting (please wait)")
-out = psffit(psf,Psfao,x0,weights=weights,system=zimpol,samp=samp,fixed=fixed)
+model = Psfao((npix,npix), system=zimpol, samp=samp)
+out = psffit(psf, model, x0, weights=weights, fixed=fixed)
 
 out.x[5] = out.x[5]%np.pi # make angle between 0 and PI
 
@@ -111,4 +112,4 @@ plt.legend()
 plt.grid()
 plt.xlabel("Radius [pix]")
 plt.ylabel("PSF [photon]")
-plt.xlim(-Npix//2,Npix//2)
\ No newline at end of file
+plt.xlim(-npix//2,npix//2)
\ No newline at end of file

maoppy/psffit.py

@@ -50,7 +50,7 @@ def lsq_flux_bck(model, data, weights, background=True, positive_bck=False):
     return amp, bck
 
 
-def psffit(psf,Model,x0,weights=None,dxdy=(0.,0.),flux_bck=(True,True),
+def psffit(psf,model,x0,weights=None,dxdy=(0.,0.),flux_bck=(True,True),
            positive_bck=False,fixed=None,npixfit=None,**kwargs):
     """Fit a PSF with a parametric model solving the least-square problem
        epsilon(x) = SUM_pixel { weights * (amp * Model(x) + bck - psf)² }
@@ -59,8 +59,8 @@ def psffit(psf,Model,x0,weights=None,dxdy=(0.,0.),flux_bck=(True,True),
     ----------
     psf : numpy.ndarray
         The experimental image to be fitted
-    Model : class
-        The class representing the fitting model
+    model : instance of ParametricPSF (or its subclasses)
+        The model to fit
     x0 : tuple, list, numpy.ndarray
         Initial guess for parameters
     weights : numpy.ndarray
@@ -79,7 +79,7 @@ def psffit(psf,Model,x0,weights=None,dxdy=(0.,0.),flux_bck=(True,True),
     npixfit : int (default=None)
         Increased pixel size for improved fitting accuracy
     **kwargs :
-        All keywords used to instantiate your `Model`
+        All keywords used to call your `model`
     
     Returns
     -------
@@ -123,7 +123,6 @@ def psffit(psf,Model,x0,weights=None,dxdy=(0.,0.),flux_bck=(True,True),
         weights = wbig
     
     sqw = np.sqrt(weights)
-    model_inst = Model(np.shape(psf),**kwargs)
     
     class CostClass:
         """A cost function that can print iterations"""
@@ -134,9 +133,9 @@ def psffit(psf,Model,x0,weights=None,dxdy=(0.,0.),flux_bck=(True,True),
                 print("-",end="")
             self.iter += 1
             x, dxdy = mini2input(y)
-            mm = model_inst(x,dx=dxdy[0],dy=dxdy[1])
+            mm = model(x, dx=dxdy[0], dy=dxdy[1], **kwargs)
             amp, bck = lsq_flux_bck(mm, psf, weights, background=flux_bck[1], positive_bck=positive_bck)
-            return np.reshape(sqw * (amp * mm + bck - psf), np.size(psf))
+            return np.reshape(sqw * (amp*mm + bck - psf), np.size(psf))
     
     cost = CostClass()
     
@@ -174,13 +173,13 @@ def psffit(psf,Model,x0,weights=None,dxdy=(0.,0.),flux_bck=(True,True),
         b_up = np.concatenate((b_up,[np.inf,np.inf]))
         return (b_low,b_up)
     
-    result = least_squares(cost, input2mini(x0,dxdy), bounds=get_bound(model_inst))
+    result = least_squares(cost, input2mini(x0,dxdy), bounds=get_bound(model))
     
     print("") #finish line of "-"
     
     result.x, result.dxdy = mini2input(result.x) #split output between x and dxdy
 
-    m = model_inst(result.x,dx=result.dxdy[0],dy=result.dxdy[1])
+    m = model(result.x,dx=result.dxdy[0],dy=result.dxdy[1])
     amp, bck = lsq_flux_bck(m, psf, weights, background=flux_bck[1], positive_bck=positive_bck)
     
     result.flux_bck = (amp,bck)