def run():
structure = random_structure.xray_structure(
sgtbx.space_group_info("P21/c"),
elements=["Si"] * 10,
volume_per_atom=18.6,
min_distance=1.2,
general_positions_only=False)
miller_set_f_obs = miller.build_set(crystal_symmetry=structure,
anomalous_flag=True,
d_min=0.8)
f_obs = miller_set_f_obs.structure_factors_from_scatterers(
xray_structure=structure, algorithm="direct").f_calc()
fft_map = f_obs.fft_map(symmetry_flags=maptbx.use_space_group_symmetry)
padded = fft_map.real_map()
unpadded = fft_map.real_map_unpadded() # copy
unpadded_1d = unpadded.as_1d() # 1D view => in-place
mmm = flex.min_max_mean_double(unpadded_1d)
for delta in ((mmm.min + mmm.mean) / 2, mmm.mean,
(mmm.mean + mmm.max) / 2):
# in-place charge flipping
ab_initio.ext.flip_charges_in_place(padded, delta)
# same but on an unpadded copy using the flex tools
flipped_selection = unpadded_1d < delta
flipped = unpadded_1d.select(flipped_selection)
flipped *= -1
unpadded_1d.set_selected(flipped_selection, flipped)
assert approx_equal(padded, unpadded, 1e-15)
print(format_cpu_times())
def run():
structure = random_structure.xray_structure(
sgtbx.space_group_info("P21/c"),
elements=["Si"]*10,
volume_per_atom=18.6,
min_distance=1.2,
general_positions_only=False)
miller_set_f_obs = miller.build_set(
crystal_symmetry=structure,
anomalous_flag=True,
d_min=0.8)
f_obs = miller_set_f_obs.structure_factors_from_scatterers(
xray_structure=structure,
algorithm="direct").f_calc()
fft_map = f_obs.fft_map(symmetry_flags=maptbx.use_space_group_symmetry)
padded = fft_map.real_map()
unpadded = fft_map.real_map_unpadded() # copy
unpadded_1d = unpadded.as_1d() # 1D view => in-place
mmm = flex.min_max_mean_double(unpadded_1d)
for delta in ((mmm.min + mmm.mean)/2, mmm.mean, (mmm.mean + mmm.max)/2):
# in-place charge flipping
ab_initio.ext.flip_charges_in_place(padded, delta)
# same but on an unpadded copy using the flex tools
flipped_selection = unpadded_1d < delta
flipped = unpadded_1d.select(flipped_selection)
flipped *= -1
unpadded_1d.set_selected(flipped_selection, flipped)
assert approx_equal(padded, unpadded, 1e-15)
print format_cpu_times()
def plot_uc_vs_detector_distance(
uc_params,
panel_distances,
outliers,
steps_per_angstrom=20,
filename="uc_vs_distance.png",
):
from matplotlib import pyplot as plt
plt.style.use("ggplot")
f = plt.figure(figsize=(12, 8))
ax1 = plt.subplot2grid((2, 3), (0, 0))
ax2 = plt.subplot2grid((2, 3), (0, 1), sharey=ax1)
ax3 = plt.subplot2grid((2, 3), (0, 2), sharey=ax1)
ax4 = plt.subplot2grid((2, 3), (1, 0), colspan=3)
a, b, c = uc_params[:3]
def hist2d(p1, p2, ax):
nbins = 100
H, xedges, yedges = np.histogram2d(p1, p2, bins=nbins)
H = np.rot90(H)
H = np.flipud(H)
Hmasked = np.ma.masked_where(H == 0, H)
ax.pcolormesh(xedges, yedges, Hmasked)
hist2d(a, panel_distances[0], ax1)
hist2d(b, panel_distances[0], ax2)
hist2d(c, panel_distances[0], ax3)
mmm = flex.min_max_mean_double(panel_distances[0])
steps_per_mm = 20
Amin = math.floor(mmm.min * steps_per_mm) / steps_per_mm
Amax = math.floor(mmm.max * steps_per_mm) / steps_per_mm
n_slots = max(1, int((Amax - Amin) * steps_per_mm))
if Amin == Amax:
eps = 0.05
Amin -= eps
Amax += eps
hist = flex.histogram(panel_distances[0], Amin, Amax, n_slots=n_slots)
ax4.bar(
hist.slot_centers(),
hist.slots(),
align="center",
width=hist.slot_width(),
zorder=10,
color="red",
edgecolor=None,
linewidth=0,
)
ax1.set_ylabel("Detector distance (mm)")
ax1.set_xlabel(r"a ($\AA$)")
ax2.set_xlabel(r"b ($\AA$)")
ax3.set_xlabel(r"c ($\AA$)")
ax4.set_xlabel("Detector distance (mm)")
f.savefig(filename)
plt.tight_layout()
f.clf()
def plot_uc_histograms(
uc_params, outliers, steps_per_angstrom=20, plot_name="uc_histograms.png"
):
from matplotlib import pyplot as plt
plt.style.use("ggplot")
f, ax = plt.subplots(nrows=2, ncols=3, figsize=(12, 8))
a, b, c = uc_params[:3]
def uc_param_hist2d(p1, p2, ax):
nbins = 100
H, xedges, yedges = np.histogram2d(p1, p2, bins=nbins)
H = np.rot90(H)
H = np.flipud(H)
Hmasked = np.ma.masked_where(H == 0, H)
ax.pcolormesh(xedges, yedges, Hmasked)
uc_param_hist2d(a, b, ax[0][0])
uc_param_hist2d(b, c, ax[0][1])
uc_param_hist2d(c, a, ax[0][2])
for i in range(3):
mmm = flex.min_max_mean_double(uc_params[i])
steps_per_A = steps_per_angstrom
Amin = math.floor(mmm.min * steps_per_A) / steps_per_A
Amax = math.floor(mmm.max * steps_per_A) / steps_per_A
n_slots = max(1, int((Amax - Amin) * steps_per_A))
if Amin == Amax:
eps = 0.05
Amin -= eps
Amax += eps
hist = flex.histogram(uc_params[i], Amin, Amax, n_slots=n_slots)
hist_inliers = flex.histogram(
uc_params[i].select(~outliers), Amin, Amax, n_slots=n_slots
)
ax[1][i].bar(
hist.slot_centers(),
hist.slots(),
align="center",
width=hist.slot_width(),
zorder=10,
color="black",
edgecolor=None,
linewidth=0,
)
ax[1][i].bar(
hist_inliers.slot_centers(),
hist_inliers.slots(),
align="center",
width=hist_inliers.slot_width(),
zorder=10,
color="red",
edgecolor=None,
linewidth=0,
)
ax[0][i].set_xlim(ax[1][i].get_xlim())
ax[0][0].set_ylabel(r"b ($\AA$)")
ax[0][1].set_ylabel(r"c ($\AA$)")
ax[0][2].set_ylabel(r"a ($\AA$)")
ax[1][0].set_xlabel(r"a ($\AA$)")
ax[1][1].set_xlabel(r"b ($\AA$)")
ax[1][2].set_xlabel(r"c ($\AA$)")
f.savefig(plot_name)
plt.tight_layout()
plt.close(f)
