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[Cctbx-cvs] SF.net SVN: cctbx: [6302] trunk/cctbx/cctbx/regression/ tst_xray_minimization.py
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From: <luc...@us...> - 2007-09-26 20:26:26
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Revision: 6302
http://cctbx.svn.sourceforge.net/cctbx/?rev=6302&view=rev
Author: luc_j_bourhis
Date: 2007-09-26 13:26:22 -0700 (Wed, 26 Sep 2007)
Log Message:
-----------
normalise indentation
Modified Paths:
--------------
trunk/cctbx/cctbx/regression/tst_xray_minimization.py
Modified: trunk/cctbx/cctbx/regression/tst_xray_minimization.py
===================================================================
--- trunk/cctbx/cctbx/regression/tst_xray_minimization.py 2007-09-26 16:02:18 UTC (rev 6301)
+++ trunk/cctbx/cctbx/regression/tst_xray_minimization.py 2007-09-26 20:26:22 UTC (rev 6302)
@@ -6,17 +6,17 @@
from libtbx.test_utils import approx_equal
def shift_u_iso(structure, shift):
- for sc in structure.scatterers():
- if(sc.flags.use_u_iso() and sc.flags.grad_u_iso()):
- sc.u_iso += (shift * random.random())
+ for sc in structure.scatterers():
+ if(sc.flags.use_u_iso() and sc.flags.grad_u_iso()):
+ sc.u_iso += (shift * random.random())
def shift_u_aniso(structure, shift):
- for sc in structure.scatterers():
- if(sc.flags.use_u_aniso() and sc.flags.grad_u_aniso()):
- u_star = list(sc.u_star)
- for i in xrange(6):
- u_star[i] += (shift * random.random())
- sc.u_star = u_star
+ for sc in structure.scatterers():
+ if(sc.flags.use_u_aniso() and sc.flags.grad_u_aniso()):
+ u_star = list(sc.u_star)
+ for i in xrange(6):
+ u_star[i] += (shift * random.random())
+ sc.u_star = u_star
def exercise(target_functor, data_type, space_group_info, anomalous_flag,
gradient_flags, occupancy_penalty,
@@ -24,7 +24,7 @@
verbose=0,tan_u_iso=False, param = 0):
assert data_type == 'F' or data_type == 'F^2'
if (data_type == 'F^2'
- and not target_functor == xray.unified_least_squares_residual): return
+ and not target_functor == xray.unified_least_squares_residual): return
structure_ideal = random_structure.xray_structure(
space_group_info,
elements=(("O","N","C")*(n_elements))[:n_elements],#("Se",)*n_elements,
@@ -34,9 +34,9 @@
random_occupancy=True,
use_u_aniso = True)
random_structure.random_modify_adp_and_adp_flags(
- scatterers = structure_ideal.scatterers(),
- random_u_iso_scale = 0.3,
- random_u_iso_min = 0.0)
+ scatterers = structure_ideal.scatterers(),
+ random_u_iso_scale = 0.3,
+ random_u_iso_min = 0.0)
xray.set_scatterer_grad_flags(scatterers = structure_ideal.scatterers(),
site = gradient_flags.site,
u_iso = gradient_flags.u_iso,
@@ -58,12 +58,12 @@
algorithm="direct",
cos_sin_table=True).f_calc()
if data_type == "F":
- y_obs = abs(rnd_f_calc)
+ y_obs = abs(rnd_f_calc)
elif data_type == "F^2":
- y_obs = rnd_f_calc.norm()
- y_obs.set_observation_type_xray_intensity()
+ y_obs = rnd_f_calc.norm()
+ y_obs.set_observation_type_xray_intensity()
else:
- raise "Error: invalid data type: %s" % data_type
+ raise "Error: invalid data type: %s" % data_type
if (0 or verbose):
print "structure_ideal:"
structure_ideal.show_summary().show_scatterers()
@@ -85,7 +85,7 @@
if (gradient_flags.u_iso):
shift_u_iso(structure_shake, 0.1)
assert tuple(structure_ideal.special_position_indices()) \
- == tuple(structure_shake.special_position_indices())
+ == tuple(structure_shake.special_position_indices())
if (0 or verbose):
print "structure_shake:"
structure_shake.show_summary().show_scatterers()
@@ -96,9 +96,9 @@
occupancy_penalty=occupancy_penalty,
structure_factor_algorithm="direct")
if (0 or verbose):
- print "first:", minimizer.first_target_value
- print "final:", minimizer.final_target_value
- print
+ print "first:", minimizer.first_target_value
+ print "final:", minimizer.final_target_value
+ print
assert minimizer.final_target_value < minimizer.first_target_value
if (0 or verbose):
print "minimized structure_shake:"
@@ -109,9 +109,9 @@
algorithm="direct",
cos_sin_table=True).f_calc()
if data_type == 'F':
- f_final = abs(f_final)
+ f_final = abs(f_final)
elif data_type == 'F^2':
- f_final = f_final.norm()
+ f_final = f_final.norm()
c = flex.linear_correlation(y_obs.data(), f_final.data())
assert c.is_well_defined()
if (0 or verbose):
@@ -122,7 +122,7 @@
print
c_coefficient = c.coefficient()
if(c_coefficient <= 0.999):
- print c_coefficient
+ print c_coefficient
if data_type == 'F':
assert c_coefficient > 0.999
elif data_type == 'F^2':
@@ -134,10 +134,10 @@
for target_functor in xray.target_functors.registry().values():
for (fsite, fu_iso, foccupancy, fu_aniso) in options:
gradient_flags = xray.structure_factors.gradient_flags(
- site = fsite,
- u_iso = fu_iso,
- u_aniso = fu_aniso,
- occupancy = foccupancy)
+ site = fsite,
+ u_iso = fu_iso,
+ u_aniso = fu_aniso,
+ occupancy = foccupancy)
for anomalous_flag in (False, True)[:]: #SWITCH
u_penalty_types = [None]
tan_u_isos = [False]
@@ -149,30 +149,30 @@
xray.minimization.occupancy_penalty_exp())
for tan_u_iso in tan_u_isos:
if(tan_u_iso):
- param = 100
+ param = 100
else:
- param = 0
+ param = 0
for occupancy_penalty in occupancy_penalty_types:
if(0):
- print fsite,fu_iso,foccupancy,fu_aniso,anomalous_flag,tan_u_iso
+ print fsite,fu_iso,foccupancy,fu_aniso,anomalous_flag,tan_u_iso
do_exercise = lambda: exercise(
- target_functor,
- data_type,
- space_group_info,
- anomalous_flag,
- gradient_flags,
- occupancy_penalty=occupancy_penalty,
- verbose=flags.Verbose,
- tan_u_iso=tan_u_iso,
- param = param,
- d_min = 2.5)
+ target_functor,
+ data_type,
+ space_group_info,
+ anomalous_flag,
+ gradient_flags,
+ occupancy_penalty=occupancy_penalty,
+ verbose=flags.Verbose,
+ tan_u_iso=tan_u_iso,
+ param = param,
+ d_min = 2.5)
try:
- do_exercise()
+ do_exercise()
except AssertionError:
- print "Test did not pass: ruling out a random fluke..."
- do_exercise()
- do_exercise()
- print "Ruled out!"
+ print "Test did not pass: ruling out a random fluke..."
+ do_exercise()
+ do_exercise()
+ print "Ruled out!"
def run():
cmd_args = ['--F', '--F_sq', '--debugging']
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