enhancements for small_cell powder plotting

xfel/small_cell/command_line/powder_from_spots.py

@@ -97,12 +97,20 @@
     .help = Instead of counts, plot the likelihood that a d-spacing is observed \
             together with a reference peak identified by filter.d_max and \
             filter.d_min.
-  d_max = None
-    .type = float
-    .help = Max resolution of the peak to filter on
-  d_min = None
-    .type = float
-    .help = Min resolution of the peak to filter on
+  d_vals = None
+    .type = floats
+    .help = Resolution ranges (pairs dmax, dmin) to filter on
+  select_mode = *any all
+    .type = choice
+    .help = Any: select frames where any of the given d-spacings is present. \
+            All: select frames where all of the given d-spacings are present.
+  plot_mode = *ratio simple
+    .type = choice
+    .help = Ratio: y-value is the "correlation" between the reference d-vals \
+            and the given d-val. \
+            Simple: Plot a powder pattern conditioned on the presence of the \
+            reference peak(s).
+
 }
 output {
   log = dials.powder_from_spots.log

xfel/small_cell/powder_util.py

@@ -36,8 +36,9 @@ def _process_pixel(self, i_panel, s0, panel, xy, value):
     res_inv = 1 / panel.get_resolution_at_pixel(s0, xy)
     res = 1/res_inv
     if self.params.filter.enable:
-      if self.params.filter.d_max > res > self.params.filter.d_min:
-        self.filter_counts += 1
+      for i, (dmax, dmin) in enumerate(zip(self.d_max_vals, self.d_min_vals)):
+        if dmax > res > dmin:
+          self.filter_counts[i] += 1
     n_bins = self.params.n_bins
     i_bin = int(
         n_bins * (res_inv - d_max_inv ) / (d_min_inv - d_max_inv)
@@ -99,9 +100,13 @@ def calculate(self):
       self.current_panelsums = [
           np.zeros(params.n_bins) for _ in range(self.n_panels)
       ]
-      self.filter_counts = 0
-      if self.params.filter.d_max is not None:
-        assert self.params.filter.d_min is not None
+      if self.params.filter.enable:
+        assert self.params.filter.d_vals and len(self.params.filter.d_vals)%2==0
+        self.filter_counts = [0 for _ in range(len(self.params.filter.d_vals)//2)]
+        self.d_max_vals = self.params.filter.d_vals[::2]
+        self.d_min_vals = self.params.filter.d_vals[1::2]
+
+      if self.params.filter.enable:
         self.use_current_expt = False
       else:
         self.use_current_expt = True
@@ -134,8 +139,10 @@ def calculate(self):
             self._process_pixel(i_panel, s0, panel, (x,y), value)
       for i in range(len(self.panelsums)):
         self.panelsums[i] = self.panelsums[i] + self.current_panelsums[i]
-      if self.params.filter.enable:
-        use_current_expt = self.filter_counts >= 1
+      if self.params.filter.enable and self.params.filter.select_mode=='any':
+        use_current_expt = any(self.filter_counts)
+      elif self.params.filter.enable and self.params.filter.select_mode=='all':
+        use_current_expt = all(self.filter_counts)
       else:
         use_current_expt = True
       if use_current_expt:
@@ -165,14 +172,17 @@ def plot(self):
       for i_sums, sums in enumerate(self.panelsums):
         yvalues = np.array(sums)
         plt.plot(xvalues, yvalues+0.5*i_sums*offset)
-    elif params.filter.enable:
+    elif params.filter.enable and params.filter.plot_mode=="ratio":
       for x in self.filtered_panelsums:
         x /= self.filtered_expt_count
       for x in self.antifiltered_panelsums:
         x /= self.antifiltered_expt_count
       yvalues = sum(self.filtered_panelsums) - sum(self.antifiltered_panelsums)
       plt.plot(xvalues, yvalues)
-
+    elif params.filter.enable and params.filter.plot_mode=="simple":
+      # same as below, but keeping this separate for flexibility
+      yvalues = sum(self.filtered_panelsums)
+      plt.plot(xvalues, yvalues)
     else:
       yvalues = sum(self.filtered_panelsums)
       plt.plot(xvalues, yvalues)