made zero mode prior params optional in SLAmplitude

nifty5/library/smooth_linear_amplitude.py

@@ -118,7 +118,7 @@ def CepstrumOperator(target, a, k0):
     return sym @ qht @ makeOp(cepstrum.sqrt())
 
 
-def SLAmplitude(*, target, n_pix, a, k0, sm, sv, im, iv, za, zq,
+def SLAmplitude(*, target, n_pix, a, k0, sm, sv, im, iv, za=None, zq=None,
                 keys=['tau', 'phi', 'zero_mode']):
     '''Operator for parametrizing smooth amplitudes (square roots of power
     spectra).
@@ -168,10 +168,10 @@ def SLAmplitude(*, target, n_pix, a, k0, sm, sv, im, iv, za, zq,
         LogRGSpace (see :class:`ExpTransform`).
     iv : float
         Prior standard deviation of y-intercept of power law.
-    za : float
+    za : float, optional
         The alpha-parameter of the inverse-gamma distribution.
         Setting the a seperate prior on the zeroGmode of the amplitude model.
-    zq : float
+    zq : float, optional
         The q-parameter of the inverse-gamma distribution.
 
     Returns
@@ -186,10 +186,17 @@ def SLAmplitude(*, target, n_pix, a, k0, sm, sv, im, iv, za, zq,
 
     a, k0 = float(a), float(k0)
     sm, sv, im, iv = float(sm), float(sv), float(im), float(iv)
-    za, zq = float(za), float(zq)
     if sv <= 0 or iv <= 0:
         raise ValueError
 
+    if za != None and zq != None:
+        separate_zero_mode_prior = True
+        za, zq = float(za), float(zq)
+    else:
+        separate_zero_mode_prior = False
+        if za != None or zq != None:
+            raise ValueError("za and zq have to be given together")
+
     et = ExpTransform(target, n_pix)
     dom = et.domain[0]
 
@@ -208,16 +215,19 @@ def SLAmplitude(*, target, n_pix, a, k0, sm, sv, im, iv, za, zq,
     # Combine linear and smooth component
     loglog_ampl = 0.5*(smooth + linear)
 
-    zero_mode = ValueInserter(et.target, (0,)*len(et.target.shape))
-
-    mask = np.ones(et.target.shape)
-    mask[(0,)*len(et.target.shape)] = 0.
-    mask = from_global_data(et.target, mask)
-    mask = DiagonalOperator(mask)
-    zero_mode = zero_mode @ InverseGammaOperator(
-        zero_mode.domain, za, zq) @ FieldAdapter(
-            zero_mode.domain, keys[2])
-    # Go from loglog-space to linear-linear-space
-    print(keys[2] + " mean: ", (zq/(1+za)))
-
-    return (mask @ (et @ loglog_ampl.exp()) + zero_mode)
+    if not separate_zero_mode_prior:
+        # Go from loglog-space to linear-linear-space
+        return et @ loglog_ampl.exp()
+    else:
+        zero_mode = ValueInserter(et.target, (0,)*len(et.target.shape))
+
+        mask = np.ones(et.target.shape)
+        mask[(0,)*len(et.target.shape)] = 0.
+        mask = from_global_data(et.target, mask)
+        mask = DiagonalOperator(mask)
+        zero_mode = zero_mode @ InverseGammaOperator(
+            zero_mode.domain, za, zq) @ FieldAdapter(
+                zero_mode.domain, keys[2])
+        print(keys[2] + " mean: ", (zq/(1+za)))
+
+        return mask @ (et @ loglog_ampl.exp()) + zero_mode