clean up and improved cell submodule, has also an helper for printing reflections on figure

xray/cell.py

@@ -1,4 +1,6 @@
 from __future__ import division,print_function
+import collections
+import itertools
 import numpy as np
 from numpy import sin,cos
 
@@ -34,35 +36,63 @@ class Triclinic(object):
     d    = self.V/np.sqrt(temp)
     return d
 
-  def q(self,h,k,l):
+  def Q(self,h,k,l):
     return 2*np.pi/self.d(h,k,l)
 
+  def reflection_list(self,maxQ=3,lim=10):
+    ret=dict()
+    # prepare hkl
+    i = range(-lim,lim+1)
+    prod = itertools.product( i,i,i )
+    hkl = np.asarray( list( itertools.product( i,i,i ) )  )
+    h,k,l = hkl.T
+    q = self.Q(h,k,l)
+
+    idx = q<maxQ;
+    q = q[idx]
+    hkl = hkl[idx]
+
+    qunique = np.unique(q)
+    ret = []
+    for qi in qunique:
+      reflec = hkl[ q == qi ]
+      ret.append( (qi,tuple(np.abs(reflec)[0]),len(reflec),reflec) )
+    return qunique,ret
+   
+#    for h in range(-lim,lim+1):
+#      for j in range(-lim,lim+1):
+
+
 class Orthorombic(Triclinic):
   def __init__(self,a=1,b=1,c=1):
     Triclinic.__init__(self,a=a,b=b,c=c,alpha=90,beta=90,gamma=90)
 
 class Monoclinic(object):
   def __init__(self,a=1,b=1,c=1,beta=90.):
-    self.a = a
+    Triclinic.__init__(self,a=a,b=b,c=c,alpha=90,beta=beta,gamma=90)
-    self.b = b
-    self.c = c
-    beta   = beta/np.pi*180
-    self.beta = beta
-
-    self.V    = (a*b*c)
-
-  def __call__(self,h,k,l):  return self.Q(h,k,l)
-
-  def Q(self,h,k,l):
-    temp = h**2/self.a**2 + (k*sin(self.beta))**2/self.b**2+l**2/self.c**2+2*h*l*cos(self.beta)/self.a/self.c
-    d    = 1/np.sqrt(temp)
-    print(d)
-    return 2*np.pi/d
 
 
+def plotReflections(cell_instance,ax=None,line_kw=dict(),text_kw=dict()):
+  import matplotlib.pyplot as plt
+  from matplotlib import lines
+  import matplotlib.transforms as transforms
+  _,refl_info = cell_instance.reflection_list()
+  if ax is None: ax = plt.gca()
+
+  # the x coords of this transformation are data, and the
+  # y coord are axes
+  trans = transforms.blended_transform_factory(ax.transData, ax.transAxes)
+  txt_kw = dict( horizontalalignment='center', rotation=45)
+  txt_kw.update(**text_kw)
+  for reflection in refl_info[1:]:
+    q,hkl,n,_ = reflection
+    line = lines.Line2D( [q,q],[1,1.1],transform=trans,**line_kw)
+    line.set_clip_on(False)
+    ax.add_line(line)
+    ax.text(q,1.15,str(hkl),transform=trans,**txt_kw)
 
 ti3o5_lambda = Triclinic(a = 9.83776, b = 3.78674, c = 9.97069, beta = 91.2567)
 ti3o5_beta   = Triclinic(a = 9.7382 , b = 3.8005 , c = 9.4333 , beta = 91.496)
 #ti3o5_beta   = Monoclinic(a = 9.7382 , b = 3.8005 , c = 9.4333 , beta = 91.496)
 ti3o5_alpha  = Triclinic(a = 9.8372,  b = 3.7921,  c = 9.9717)
-#ti3o5_alpha1  = Orthorombic(a = 9.8372,  b = 3.7921,  c = 9.9717)
+ti3o5_alpha1  = Orthorombic(a = 9.8372,  b = 3.7921,  c = 9.9717)