def reduce_raw_data(raw_data, qmax, bandwidth, level=0.05, q_background=None):
print "delta_q is ", bandwidth
if qmax > raw_data.q[-1]:
qmax = raw_data.q[-1]
### Get rid of noisy signall at very low q range ###
qmin_indx = flex.max_index(raw_data.i)
qmin = raw_data.q[qmin_indx]
new_data = get_q_array_uniform_body(raw_data,
q_min=qmin,
q_max=qmax,
level=level)
qmax = new_data.q[-1]
print "LEVEL=%f" % level, "and Q_MAX=%f" % qmax
raw_q = raw_data.q[qmin_indx:]
raw_i = raw_data.i[qmin_indx:]
raw_s = raw_data.s[qmin_indx:]
### Take care of the background (set zero at very high q) ###
if (q_background is not None):
cutoff = flex.bool(raw_q > q_background)
q_bk_indx = flex.last_index(cutoff, False)
if (q_bk_indx < raw_q.size()):
bkgrd = flex.mean(raw_i[q_bk_indx:])
print "Background correction: I=I-background, where background=", bkgrd
raw_i = flex.abs(raw_i - bkgrd)
q = flex.double(range(int(
(qmax - qmin) / bandwidth) + 1)) * bandwidth + qmin
raw_data.i = flex.linear_interpolation(raw_q, raw_i, q)
raw_data.s = flex.linear_interpolation(raw_q, raw_s, q)
raw_data.q = q
return raw_data
def reduce_raw_data(raw_data, qmax, bandwidth, level=0.05, q_background=None):
print
print " ==== Data reduction ==== "
print
print " Preprocessing of data increases efficiency of shape retrieval procedure."
print
print " - Interpolation stepsize : %4.3e" % bandwidth
print " - Uniform density criteria: level is set to : %4.3e" % level
print " maximum q to consider : %4.3e" % qmax
qmin_indx = flex.max_index(raw_data.i)
qmin = raw_data.q[qmin_indx]
new_data = get_q_array_uniform_body(raw_data,
q_min=qmin,
q_max=qmax,
level=level)
qmax = new_data.q[-1]
if qmax > raw_data.q[-1]:
qmax = raw_data.q[-1]
print " Resulting q range to use in search: q start : %4.3e" % qmin
print " q stop : %4.3e" % qmax
print
raw_q = raw_data.q[qmin_indx:]
raw_i = raw_data.i[qmin_indx:]
raw_s = raw_data.s[qmin_indx:]
### Take care of the background (set zero at very high q) ###
if (q_background is not None):
cutoff = flex.bool(raw_q > q_background)
q_bk_indx = flex.last_index(cutoff, False)
if (q_bk_indx < raw_q.size()):
bkgrd = flex.mean(raw_i[q_bk_indx:])
print "Background correction: I=I-background, where background=", bkgrd
raw_i = flex.abs(raw_i - bkgrd)
q = flex.double(range(int(
(qmax - qmin) / bandwidth) + 1)) * bandwidth + qmin
raw_data.i = flex.linear_interpolation(raw_q, raw_i, q)
raw_data.s = flex.linear_interpolation(raw_q, raw_s, q)
raw_data.q = q
return raw_data
def find_delta(rho_map, tol): """ Find delta as hinted on fig. 1 of ref. [1] in module charge_flipping """ rho = rho_map.real_map_unpadded().as_1d() max_rho = flex.max(rho) rho /= max_rho sorting = flex.sort_permutation(rho) sorted_rho = rho.select(sorting) n = len(sorted_rho) p,q = n//4, 3*n//4 indexes = flex.double_range(p,q) values = sorted_rho[p:q] c = flex.linear_correlation(indexes, values) assert c.is_well_defined() and c.coefficient() > 0.99 r = flex.linear_regression(indexes, values) a,b = r.y_intercept(), r.slope() deviation = flex.abs(a + b*flex.double_range(n) - sorted_rho) non_linear_sel = deviation > tol low = flex.first_index(non_linear_sel, False) high = flex.last_index(non_linear_sel, False) assert non_linear_sel[low:high].count(False)/(high-low+1) > 0.99 assert sorted_rho[low] < 0 and sorted_rho[high] > 0 return min(sorted_rho[high], -sorted_rho[low]), max_rho