unify illumination.photons functionality for model, ptyr and np.array…

… probe inits

ptypy/core/illumination.py

@@ -134,11 +134,13 @@
     userlevel = 0
 
     [photons]
-    type = int, float, None
-    default = None
+    type = int, float, str, None
+    default = 'maxdiff'
     help = Number of photons in the incident illumination
-    doc = A value specified here will take precedence over calculated statistics from the loaded data.
-    lowlim = 0
+    doc = A value specified here will take precedence over calculated statistics from the loaded data. Choices:
+         - ``None`` : modeled or loaded probe remains unscaled in intensity
+         - ``int`` or ``float`` : modeled or loaded probe intensity is scaled to that number of photons
+         - ``'maxdiff'`` : modeled or loaded probe intensity is scaled to match the brightest diffraction pattern
     userlevel = 2
 
     [propagation]
@@ -311,7 +313,8 @@ def init_storage(storage, pars, energy=None, **kwargs):
 
     p = DEFAULT.copy(depth=3)
     model = None
-    if hasattr(pars, 'items') or hasattr(pars, 'items'):
+
+    if hasattr(pars, 'items'):
         # This is a dict
         p.update(pars, in_place_depth=3)
 
@@ -366,7 +369,6 @@ def init_storage(storage, pars, energy=None, **kwargs):
             model *= np.sqrt(p.photons) / np.prod(s.shape)
     elif type(p.model) is np.ndarray:
         model = p.model
-        p.photons = None
     elif p.model in resources.probes:
         model = resources.probes[p.model](s.shape)
     elif str(p.model) == 'recon':
@@ -378,7 +380,6 @@ def init_storage(storage, pars, energy=None, **kwargs):
             'Attempt to load layer `%s` of probe storage with ID `%s` from `%s`'
             % (str(layer), str(ID), p.recon.rfile))
         model = u.load_from_ptyr(p.recon.rfile, 'probe', ID, layer)
-        p.photons = None
         # This could be more sophisticated,
         # i.e. matching the real space grids etc.
     elif str(p.model) == 'stxm':
@@ -476,7 +477,7 @@ def _process(model, aperture_pars=None, prop_pars=None, photons=1e7,
     model = prop(model)
 
     # apply photon count
-    if photons is not None:
+    if (type(photons) is int) or (type(photons) is float):
         model *= np.sqrt(photons / u.norm2(model))
 
     return model

ptypy/core/manager.py

@@ -1130,18 +1130,26 @@ def _initialize_probe(self, probe_ids):
             # Bypass additional tests if input is a string (previous reconstruction)
             if illu_pars != str(illu_pars):
 
-                # if photon count is None, assign a number from the stats.
                 phot = illu_pars.get('photons')
                 phot_max = self.diff.max_power
-
-                if phot is None:
+                if phot == 'maxdiff':
+                    # probe intensity to be scaled to the brightest diffraction pattern
                     logger.info(
-                        'Found no photon count for probe in parameters.\nUsing photon count %.2e from photon report' % phot_max)
+                        'Probe intensity is being rescaled to match the brightest diffraction pattern.\nUsing photon count %.2e from photon report' % phot_max)
                     illu_pars['photons'] = phot_max
-                elif np.abs(np.log10(phot) - np.log10(phot_max)) > 1:
-                    logger.warning(
-                        'Photon count from input parameters (%.2e) differs from statistics (%.2e) by more than a magnitude' % (
-                        phot, phot_max))
+                elif phot is None:
+                    # probe intensity to remain untouched
+                    pass
+                elif (type(phot) is int) or (type(phot) is float):
+                    # probe intensity to be scaled to a specific value
+                    if phot < 0:
+                        logger.warning(
+                            f'Given photon count is negative. Using the absolute of the given value: {-1 * phot:.2e}')
+                        phot = -1 * phot
+                    if np.abs(np.log10(phot) - np.log10(phot_max)) > 1:
+                        logger.warning(
+                            'Photon count from input parameters (%.2e) differs from statistics (%.2e) by more than a magnitude' % (
+                            phot, phot_max))
 
                 if (self.p.coherence.num_probe_modes > 1) and (type(illu_pars) is not np.ndarray):