emmap_1 = emmap1.copy()
emmap_2 = emmap2.copy()
#if a soft mask has to be applied to both maps
if msk:
print 'Applying soft mask'
emmap1.fullMap = emmap1._soft_mask(c1)
emmap2.fullMap = emmap2._soft_mask(c2)
#print datetime.now().time()
sc = ScoringFunctions()
if flag_scale:
print 'scaling'
if refsc: print 'Using second model/map amplitudes as reference'
# amplitude scaling independant of the grid
emmap_1.fullMap,emmap_2.fullMap = sc._amplitude_match(emmap1,emmap2,0,0,sw,0,0,max(r1,r2),lpfiltb=flag_filt,lpfilta=False,ref=refsc)
#resample scaled maps to the common grid
if apix is None: spacing = max(r1,r2)*0.33
else: spacing = apix
apix_ratio = emmap_1.apix/spacing
diff1 = emmap_1._interpolate_to_grid1(grid_shape,spacing,new_ori,1)
diff2 = emmap_2._interpolate_to_grid1(grid_shape,spacing,new_ori,1)
# get mask inside contour for the initial maps
emmap_1.fullMap = (emmap1.fullMap>c1)*1.0
emmap_2.fullMap = (emmap2.fullMap>c2)*1.0
mask1 = emmap_1._interpolate_to_grid1(grid_shape,spacing,new_ori,1,'zero')
mask2 = emmap_2._interpolate_to_grid1(grid_shape,spacing,new_ori,1,'zero')
mask1.fullMap = mask1.fullMap > 0.8
def genmap(session, map0 = None, map1 = None, rez1 = None, rez2 = None, c1 = None, c2 = None):
""" Generate our new map."""
m0 = chimera_to_tempy_map(map0)
m1 = chimera_to_tempy_map(map1)
# What do we do with the contours? We may already have them?
# TODO - pull contours from m0,m1
#MAIN CALCULATION
#whether to shift density to positive values
if c1 == None:
c1 = map_contour(m0,t=1.5)
if c2 == None:
c2 = map_contour(m1,t=1.5)
c1 = (c1 - m0.min())
c2 = (c2 - m1.min())
m0.fullMap = (m0.fullMap - m0.min())
m1.fullMap = (m1.fullMap - m1.min())
#find a common box to hold both maps
spacing = max(m0.apix,m1.apix)
grid_shape, new_ori = m0._alignment_box(m1,spacing)
emmap_1 = m0.copy()
emmap_2 = m1.copy()
#resample scaled maps to the common grid
spacing = max(rez1,rez2)*0.33
# Not sure we should do scaling here?
sc = ScoringFunctions()
emmap_1.fullMap,emmap_2.fullMap = sc._amplitude_match(m0,m1,0,0,0.02,0,0,max(rez1,rez2),lpfiltb=True,lpfilta=False,ref=False)
apix_ratio = emmap_1.apix/spacing
diff1 = emmap_1._interpolate_to_grid(grid_shape,spacing,new_ori,1)
diff2 = emmap_2._interpolate_to_grid(grid_shape,spacing,new_ori,1)
# get mask inside contour for the initial maps
emmap_1.fullMap = (m0.fullMap>c1)*1.0
emmap_2.fullMap = (m1.fullMap>c2)*1.0
#interpolate masks into common grid
mask1 = emmap_1._interpolate_to_grid(grid_shape,spacing,new_ori,1,'zero')
mask2 = emmap_2._interpolate_to_grid(grid_shape,spacing,new_ori,1,'zero')
mask1.fullMap = mask1.fullMap > 0.1
mask2.fullMap = mask2.fullMap > 0.1
#min of minimums in the two scaled maps
min1 = diff1.min()
min2 = diff2.min()
min_scaled_maps = min(min1,min2)
#shift to positive values
diff1.fullMap = diff1.fullMap - min_scaled_maps
diff2.fullMap = diff2.fullMap - min_scaled_maps
#range of values in the scaled maps
min1 = np.amin(diff1.fullMap[mask1.fullMap])
diffc1 = min1+0.10*(np.amax(diff1.fullMap)-min1)
min2 = np.amin(diff2.fullMap[mask2.fullMap])
diffc2 = min2+0.10*(np.amax(diff2.fullMap)-min2)
#calculate difference
diff_map = diff1.copy()
#calculate difference
diff1.fullMap = (diff1.fullMap - diff2.fullMap)
diff2.fullMap = (diff2.fullMap - diff_map.fullMap)
diff1.fullMap = diff1.fullMap*(mask1.fullMap)
diff2.fullMap = diff2.fullMap*(mask2.fullMap)
#interpolate back to original grids
#mask1 = diff1._interpolate_to_grid1(m0.fullMap.shape,m0.apix,m0.origin,1,'zero')
mask1 = diff1._interpolate_to_grid(m0.fullMap.shape,m0.apix,m0.origin,1,'zero')
mask2 = diff2._interpolate_to_grid(m1.fullMap.shape,m1.apix,m1.origin,1,'zero')
# for assigning differences (see below), use positive differences
mask1.fullMap = mask1.fullMap*(mask1.fullMap>0.)
mask2.fullMap = mask2.fullMap*(mask2.fullMap>0.)
nm0 = tempy_to_chimera_map(session, mask1)
nm1 = tempy_to_chimera_map(session, mask2)
session.models.add([nm0,nm1])
