def add_line_to_table(idx):
x = sort_radii[idx]
if self.tilecounts[x] < 3:
radial = (0,0)
tangential = (0,0)
rmean,tmean,rsigma,tsigma=(0,0,1,1)
else:
radial,tangential,rmean,tmean,rsigma,tsigma = get_radial_tangential_vectors(self,x,
self.post_mean_cv[x],
self.correction_vector_x, self.correction_vector_y,
self.model_calcx-self.refined_cntr_x,
self.model_calcy-self.refined_cntr_y)
table_data.append( [
format_value("%3d", x),
format_value("%7.2f", self.radii[x]),
format_value("%6d", self.tilecounts[x]),
format_value("%5.2f", self.asymmetric_tile_rmsd[x]),
format_value("%5.2f", self.tile_rmsd[x]),
format_value("%5.2f", self.post_mean_cv[x][0]),
format_value("%5.2f", self.post_mean_cv[x][1]),
format_value("%5.2f", matrix.col(self.post_mean_cv[x]).length()),
format_value("%6.2f", wtaveg[x]),
format_value("%6.2f", rsigma),
format_value("%6.2f", tsigma),
format_value("%5.2f", self.tile_translations.x[2*x]),
format_value("%5.2f", self.tile_translations.x[2*x+1]),
"",
format_value("%5.2f", self.tile_rotations.x[x])
])
def print_table(self):
from libtbx import table_utils
from libtbx.str_utils import format_value
table_header = ["Tile","Dist","Nobs","aRmsd","Rmsd","delx","dely","disp","rotdeg","Rsigma","Tsigma"]
table_data = []
table_data.append(table_header)
sort_radii = flex.sort_permutation(flex.double(self.radii))
tile_rmsds = flex.double()
radial_sigmas = flex.double(len(self.tiles) // 4)
tangen_sigmas = flex.double(len(self.tiles) // 4)
for idx in range(len(self.tiles) // 4):
x = sort_radii[idx]
if self.tilecounts[x] < 3:
wtaveg = 0.0
radial = (0,0)
tangential = (0,0)
rmean,tmean,rsigma,tsigma=(0,0,1,1)
else:
wtaveg = self.weighted_average_angle_deg_from_tile(x)
radial,tangential,rmean,tmean,rsigma,tsigma = get_radial_tangential_vectors(self,x)
radial_sigmas[x]=rsigma
tangen_sigmas[x]=tsigma
table_data.append( [
format_value("%3d", x),
format_value("%7.2f", self.radii[x]),
format_value("%6d", self.tilecounts[x]),
format_value("%5.2f", self.asymmetric_tile_rmsd[x]),
format_value("%5.2f", self.tile_rmsd[x]),
format_value("%5.2f", self.mean_cv[x][0]),
format_value("%5.2f", self.mean_cv[x][1]),
format_value("%5.2f", matrix.col(self.mean_cv[x]).length()),
format_value("%6.2f", wtaveg),
format_value("%6.2f", rsigma),
format_value("%6.2f", tsigma),
])
table_data.append([""]*len(table_header))
rstats = flex.mean_and_variance(radial_sigmas,self.tilecounts.as_double())
tstats = flex.mean_and_variance(tangen_sigmas,self.tilecounts.as_double())
table_data.append( [
format_value("%3s", "ALL"),
format_value("%s", ""),
format_value("%6d", self.overall_N),
format_value("%5.2f", math.sqrt(flex.mean(self.delrsq))),
format_value("%5.2f", self.overall_rmsd),
format_value("%5.2f", self.overall_cv[0]),
format_value("%5.2f", self.overall_cv[1]),
format_value("%5.2f", flex.mean(flex.double([matrix.col(cv).length() for cv in self.mean_cv]))),
format_value("%s", ""),
format_value("%6.2f", rstats.mean()),
format_value("%6.2f", tstats.mean()),
])
print
print table_utils.format(table_data,has_header=1,justify='center',delim=" ")
def print_table(self):
from libtbx import table_utils
from libtbx.str_utils import format_value
table_header = ["Tile","Dist","Nobs","aRmsd","Rmsd","delx","dely","disp","rotdeg","Rsigma","Tsigma"]
table_data = []
table_data.append(table_header)
sort_radii = flex.sort_permutation(flex.double(self.radii))
tile_rmsds = flex.double()
radial_sigmas = flex.double(len(self.tiles) // 4)
tangen_sigmas = flex.double(len(self.tiles) // 4)
for idx in range(len(self.tiles) // 4):
x = sort_radii[idx]
if self.tilecounts[x] < 3:
wtaveg = 0.0
radial = (0,0)
tangential = (0,0)
rmean,tmean,rsigma,tsigma=(0,0,1,1)
else:
wtaveg = self.weighted_average_angle_deg_from_tile(x)
radial,tangential,rmean,tmean,rsigma,tsigma = get_radial_tangential_vectors(self,x)
radial_sigmas[x]=rsigma
tangen_sigmas[x]=tsigma
table_data.append( [
format_value("%3d", x),
format_value("%7.2f", self.radii[x]),
format_value("%6d", self.tilecounts[x]),
format_value("%5.2f", self.asymmetric_tile_rmsd[x]),
format_value("%5.2f", self.tile_rmsd[x]),
format_value("%5.2f", self.mean_cv[x][0]),
format_value("%5.2f", self.mean_cv[x][1]),
format_value("%5.2f", matrix.col(self.mean_cv[x]).length()),
format_value("%6.2f", wtaveg),
format_value("%6.2f", rsigma),
format_value("%6.2f", tsigma),
])
table_data.append([""]*len(table_header))
rstats = flex.mean_and_variance(radial_sigmas,self.tilecounts.as_double())
tstats = flex.mean_and_variance(tangen_sigmas,self.tilecounts.as_double())
table_data.append( [
format_value("%3s", "ALL"),
format_value("%s", ""),
format_value("%6d", self.overall_N),
format_value("%5.2f", math.sqrt(flex.mean(self.delrsq))),
format_value("%5.2f", self.overall_rmsd),
format_value("%5.2f", self.overall_cv[0]),
format_value("%5.2f", self.overall_cv[1]),
format_value("%5.2f", flex.mean(flex.double([matrix.col(cv).length() for cv in self.mean_cv]))),
format_value("%s", ""),
format_value("%6.2f", rstats.mean()),
format_value("%6.2f", tstats.mean()),
])
print
print table_utils.format(table_data,has_header=1,justify='center',delim=" ")
def print_table(self):
from libtbx import table_utils
from libtbx.str_utils import format_value
table_header = ["Tile","Dist","Nobs","aRmsd","Rmsd","delx","dely","disp","rotdeg",
"Rsigma","Tsigma","Transx","Transy","DelRot","Rotdeg"]
table_data = []
table_data.append(table_header)
sort_radii = flex.sort_permutation(flex.double(self.radii))
tile_rmsds = flex.double()
radial_sigmas = flex.double(len(self.tiles) // 4)
tangen_sigmas = flex.double(len(self.tiles) // 4)
wtaveg = [0.]*(len(self.tiles) // 4)
for x in range(len(self.tiles) // 4):
if self.tilecounts[x] >= 3:
wtaveg[x] = self.weighted_average_angle_deg_from_tile(x, self.post_mean_cv[x], self.correction_vector_x,
self.correction_vector_y)
for idx in range(len(self.tiles) // 4):
x = sort_radii[idx]
if self.tilecounts[x] < 3:
radial = (0,0)
tangential = (0,0)
rmean,tmean,rsigma,tsigma=(0,0,1,1)
else:
radial,tangential,rmean,tmean,rsigma,tsigma = get_radial_tangential_vectors(self,x,
self.post_mean_cv[x],
self.correction_vector_x, self.correction_vector_y,
self.model_calcx-self.refined_cntr_x,
self.model_calcy-self.refined_cntr_y)
# paired rotations of two ASICS on the same sensor
if x%2==0:
# previous method: delrot = "%5.2f"%(wtaveg[x]-wtaveg[x+1])
delrot = "%5.2f"%(self.tile_rotations.x[x] - self.tile_rotations.x[1+x])
else:
delrot = ""
radial_sigmas[x]=rsigma
tangen_sigmas[x]=tsigma
table_data.append( [
format_value("%3d", x),
format_value("%7.2f", self.radii[x]),
format_value("%6d", self.tilecounts[x]),
format_value("%5.2f", self.asymmetric_tile_rmsd[x]),
format_value("%5.2f", self.tile_rmsd[x]),
format_value("%5.2f", self.post_mean_cv[x][0]),
format_value("%5.2f", self.post_mean_cv[x][1]),
format_value("%5.2f", matrix.col(self.post_mean_cv[x]).length()),
format_value("%6.2f", wtaveg[x]),
format_value("%6.2f", rsigma),
format_value("%6.2f", tsigma),
format_value("%5.2f", self.tile_translations.x[2*x]),
format_value("%5.2f", self.tile_translations.x[2*x+1]),
copy.copy(delrot),
format_value("%5.2f", self.tile_rotations.x[x])
])
table_data.append([""]*len(table_header))
rstats = flex.mean_and_variance(radial_sigmas,self.tilecounts.as_double())
tstats = flex.mean_and_variance(tangen_sigmas,self.tilecounts.as_double())
table_data.append( [
format_value("%3s", "ALL"),
format_value("%s", ""),
format_value("%6d", self.overall_N),
format_value("%5.2f", math.sqrt(flex.mean(self.delrsq))),
format_value("%5.2f", self.overall_rmsd),
format_value("%5.2f", self.overall_cv[0]),
format_value("%5.2f", self.overall_cv[1]),
format_value("%5.2f", flex.mean(flex.double([cv.length() for cv in self.post_mean_cv]))),
format_value("%s", ""),
format_value("%6.2f", rstats.mean()),
format_value("%6.2f", tstats.mean()),
format_value("%s", ""),
format_value("%s", ""),
#root mean squared difference in same-sensor (adjacent)-ASIC rotations, weighted by minimum # of observations on either ASIC of the sensor
format_value("%5.2f", math.sqrt(
flex.sum(
flex.double([
(min([self.tilecounts[2*isen],self.tilecounts[2*isen+1]])) *
(self.tile_rotations.x[2*isen] - self.tile_rotations.x[1+2*isen])**2
for isen in xrange(len(self.tiles) // 8)]
)
)/
flex.sum(
flex.double(
[(min([self.tilecounts[2*isen],self.tilecounts[2*isen+1]])) for isen in xrange(len(self.tiles) // 8)]
)
)
)),
format_value("%s", ""),
])
print
print table_utils.format(table_data,has_header=1,justify='center',delim=" ")
def run_correction_vector_plot(working_phil):
L = lines(working_phil)
for line in L.vectors():
pass # pull out the information, lines class does all the work
close_x = flex.double()
close_y = flex.double()
far_x = flex.double()
far_y = flex.double()
master_coords = L.master_coords
master_cv = L.master_cv
master_tiles = L.master_tiles
for idx in range(0, len(master_coords), 10):
if matrix.col(
master_cv[idx]).length() < L.tile_rmsd[master_tiles[idx]]:
pass
#close_x.append(master_coords[idx][0])
#close_y.append(master_coords[idx][1])
else:
far_x.append(master_coords[idx][0])
far_y.append(master_coords[idx][1])
close_x.append(master_coords[idx][0] + master_cv[idx][0])
close_y.append(master_coords[idx][1] + master_cv[idx][1])
if working_phil.show_plots is True:
from matplotlib import pyplot as plt
plt.plot(close_x, close_y, "r.")
plt.plot(far_x, far_y, "g.")
plt.axes().set_aspect("equal")
plt.show()
sort_radii = flex.sort_permutation(flex.double(L.radii))
tile_rmsds = flex.double()
radial_sigmas = flex.double(64)
tangen_sigmas = flex.double(64)
for idx in range(64):
x = sort_radii[idx]
print(
"Tile %2d: radius %7.2f, %6d observations, delx %5.2f dely %5.2f, rmsd = %5.2f"
% (x, L.radii[x], L.tilecounts[x], L.mean_cv[x][0],
L.mean_cv[x][1], L.tile_rmsd[x]),
end=' ')
if L.tilecounts[x] < 3:
print()
radial = (0, 0)
tangential = (0, 0)
rmean, tmean, rsigma, tsigma = (0, 0, 1, 1)
else:
wtaveg = L.weighted_average_angle_deg_from_tile(x)
print("Tile rotation %6.2f deg" % wtaveg, end=' ')
radial, tangential, rmean, tmean, rsigma, tsigma = get_radial_tangential_vectors(
L, x)
print("%6.2f %6.2f" % (rsigma, tsigma))
radial_sigmas[x] = rsigma
tangen_sigmas[x] = tsigma
rstats = flex.mean_and_variance(radial_sigmas, L.tilecounts.as_double())
tstats = flex.mean_and_variance(tangen_sigmas, L.tilecounts.as_double())
print(
"\nOverall %8d observations, delx %5.2f dely %5.2f, rmsd = %5.2f"
% (L.overall_N, L.overall_cv[0], L.overall_cv[1], L.overall_rmsd))
print("Average tile rmsd %5.2f" % flex.mean(flex.double(L.tile_rmsd)))
print("Average tile displacement %5.2f" %
(flex.mean(flex.double([matrix.col(cv).length()
for cv in L.mean_cv]))))
print("Weighted average radial sigma %6.2f" % rstats.mean())
print("Weighted average tangential sigma %6.2f" % tstats.mean())
if working_phil.show_plots is True:
plt.plot([(L.tiles[4 * x + 0] + L.tiles[4 * x + 2]) / 2.
for x in range(64)],
[(L.tiles[4 * x + 1] + L.tiles[4 * x + 3]) / 2.
for x in range(64)], "go")
for x in range(64):
plt.text(10 + (L.tiles[4 * x + 0] + L.tiles[4 * x + 2]) / 2.,
10 + (L.tiles[4 * x + 1] + L.tiles[4 * x + 3]) / 2.,
"%d" % x)
plt.show()
for idx in range(64):
x = sort_radii[idx]
print(
"Tile %2d: radius %7.2f, %6d observations, delx %5.2f dely %5.2f, rmsd = %5.2f"
% (x, L.radii[x], L.tilecounts[x], L.mean_cv[x][0],
L.mean_cv[x][1], L.tile_rmsd[x]),
end=' ')
if L.tilecounts[x] < 3:
print()
radial = (0, 0)
tangential = (0, 0)
rmean, tmean, rsigma, tsigma = (0, 0, 1, 1)
else:
wtaveg = L.weighted_average_angle_deg_from_tile(x)
print("Tile rotation %6.2f deg" % wtaveg, end=' ')
radial, tangential, rmean, tmean, rsigma, tsigma = get_radial_tangential_vectors(
L, x)
print("%6.2f %6.2f" % (rsigma, tsigma))
if working_phil.colormap:
from pylab import imshow, axes, colorbar, show
import numpy
xcv, ycv = get_correction_vector_xy(L, x)
_min = min(min(xcv), min(ycv))
_max = max(max(xcv), max(ycv))
hist, xedges, yedges = numpy.histogram2d(xcv,
ycv,
bins=40,
range=[[_min, _max],
[_min, _max]])
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
imshow(hist.T,
extent=extent,
interpolation='nearest',
origin='lower')
from matplotlib.patches import Ellipse
ell = Ellipse(xy=(L.mean_cv[x][0], L.mean_cv[x][1]),
width=2. * rsigma,
height=2. * tsigma,
angle=math.atan2(-(radial[1]), -(radial[0])) *
180. / math.pi,
edgecolor="y",
linewidth=2,
fill=False,
zorder=100)
axes().add_artist(ell)
colorbar()
show()
else:
from matplotlib import pyplot as plt
xcv, ycv = get_correction_vector_xy(L, x)
if len(xcv) == 0 or len(ycv) == 0: continue
plt.plot(xcv, ycv, "r.")
plt.plot([L.mean_cv[x][0]], [L.mean_cv[x][1]], "go")
plt.plot([L.mean_cv[x][0] + radial[0]],
[L.mean_cv[x][1] + radial[1]], "yo")
plt.plot([L.mean_cv[x][0] + tangential[0]],
[L.mean_cv[x][1] + tangential[1]], "bo")
from matplotlib.patches import Ellipse
ell = Ellipse(xy=(L.mean_cv[x][0], L.mean_cv[x][1]),
width=2. * rsigma,
height=2. * tsigma,
angle=math.atan2(-(radial[1]), -(radial[0])) *
180. / math.pi,
edgecolor="y",
linewidth=2,
fill=False,
zorder=100)
plt.axes().add_artist(ell)
plt.axes().set_aspect("equal")
_min = min(min(xcv), min(ycv))
_max = max(max(xcv), max(ycv))
plt.axes().set_xlim(_min, _max)
plt.axes().set_ylim(_min, _max)
plt.show()
def print_table(self):
from libtbx import table_utils
from libtbx.str_utils import format_value
table_header = [
"Tile",
"Dist",
"Nobs",
"aRmsd",
"Rmsd",
"delx",
"dely",
"disp",
"rotdeg",
"Rsigma",
"Tsigma",
"Transx",
"Transy",
"DelRot",
]
table_data = []
table_data.append(table_header)
sort_radii = flex.sort_permutation(flex.double(self.radii))
tile_rmsds = flex.double()
radial_sigmas = flex.double(64)
tangen_sigmas = flex.double(64)
wtaveg = [0.0] * 64
for x in xrange(64):
if self.tilecounts[x] >= 3:
wtaveg[x] = self.weighted_average_angle_deg_from_tile(
x, self.post_mean_cv[x], self.correction_vector_x, self.correction_vector_y
)
for idx in xrange(64):
x = sort_radii[idx]
if self.tilecounts[x] < 3:
radial = (0, 0)
tangential = (0, 0)
rmean, tmean, rsigma, tsigma = (0, 0, 1, 1)
else:
radial, tangential, rmean, tmean, rsigma, tsigma = get_radial_tangential_vectors(self, x)
# paired rotations of two ASICS on the same sensor
if x % 2 == 0:
delrot = "%5.2f" % (wtaveg[x] - wtaveg[x + 1])
else:
delrot = ""
radial_sigmas[x] = rsigma
tangen_sigmas[x] = tsigma
table_data.append(
[
format_value("%3d", x),
format_value("%7.2f", self.radii[x]),
format_value("%6d", self.tilecounts[x]),
format_value("%5.2f", self.asymmetric_tile_rmsd[x]),
format_value("%5.2f", self.tile_rmsd[x]),
format_value("%5.2f", self.post_mean_cv[x][0]),
format_value("%5.2f", self.post_mean_cv[x][1]),
format_value("%5.2f", matrix.col(self.post_mean_cv[x]).length()),
format_value("%6.2f", wtaveg[x]),
format_value("%6.2f", rsigma),
format_value("%6.2f", tsigma),
format_value("%5.2f", self.x[2 * x]),
format_value("%5.2f", self.x[2 * x + 1]),
copy.copy(delrot),
]
)
table_data.append([""] * len(table_header))
rstats = flex.mean_and_variance(radial_sigmas, self.tilecounts.as_double())
tstats = flex.mean_and_variance(tangen_sigmas, self.tilecounts.as_double())
table_data.append(
[
format_value("%3s", "ALL"),
format_value("%s", ""),
format_value("%6d", self.overall_N),
format_value("%5.2f", math.sqrt(flex.mean(self.delrsq))),
format_value("%5.2f", self.overall_rmsd),
format_value("%5.2f", self.overall_cv[0]),
format_value("%5.2f", self.overall_cv[1]),
format_value("%5.2f", flex.mean(flex.double([cv.length() for cv in self.post_mean_cv]))),
format_value("%s", ""),
format_value("%6.2f", rstats.mean()),
format_value("%6.2f", tstats.mean()),
format_value("%s", ""),
format_value("%s", ""),
format_value("%s", ""),
]
)
print
print table_utils.format(table_data, has_header=1, justify="center", delim=" ")
def print_table(self):
from libtbx import table_utils
from libtbx.str_utils import format_value
table_header = ["Tile","Dist","Nobs","aRmsd","Rmsd","delx","dely","disp","rotdeg",
"Rsigma","Tsigma","Transx","Transy","DelRot"]
table_data = []
table_data.append(table_header)
sort_radii = flex.sort_permutation(flex.double(self.radii))
tile_rmsds = flex.double()
radial_sigmas = flex.double(64)
tangen_sigmas = flex.double(64)
wtaveg = [0.]*64
for x in range(64):
if self.tilecounts[x] >= 3:
wtaveg[x] = self.weighted_average_angle_deg_from_tile(x, self.post_mean_cv[x], self.correction_vector_x,
self.correction_vector_y)
for idx in range(64):
x = sort_radii[idx]
if self.tilecounts[x] < 3:
radial = (0,0)
tangential = (0,0)
rmean,tmean,rsigma,tsigma=(0,0,1,1)
else:
radial,tangential,rmean,tmean,rsigma,tsigma = get_radial_tangential_vectors(self,x)
# paired rotations of two ASICS on the same sensor
if x%2==0:
delrot = "%5.2f"%(wtaveg[x]-wtaveg[x+1])
else:
delrot = ""
radial_sigmas[x]=rsigma
tangen_sigmas[x]=tsigma
table_data.append( [
format_value("%3d", x),
format_value("%7.2f", self.radii[x]),
format_value("%6d", self.tilecounts[x]),
format_value("%5.2f", self.asymmetric_tile_rmsd[x]),
format_value("%5.2f", self.tile_rmsd[x]),
format_value("%5.2f", self.post_mean_cv[x][0]),
format_value("%5.2f", self.post_mean_cv[x][1]),
format_value("%5.2f", matrix.col(self.post_mean_cv[x]).length()),
format_value("%6.2f", wtaveg[x]),
format_value("%6.2f", rsigma),
format_value("%6.2f", tsigma),
format_value("%5.2f", self.x[2*x]),
format_value("%5.2f", self.x[2*x+1]),
copy.copy(delrot)
])
table_data.append([""]*len(table_header))
rstats = flex.mean_and_variance(radial_sigmas,self.tilecounts.as_double())
tstats = flex.mean_and_variance(tangen_sigmas,self.tilecounts.as_double())
table_data.append( [
format_value("%3s", "ALL"),
format_value("%s", ""),
format_value("%6d", self.overall_N),
format_value("%5.2f", math.sqrt(flex.mean(self.delrsq))),
format_value("%5.2f", self.overall_rmsd),
format_value("%5.2f", self.overall_cv[0]),
format_value("%5.2f", self.overall_cv[1]),
format_value("%5.2f", flex.mean(flex.double([cv.length() for cv in self.post_mean_cv]))),
format_value("%s", ""),
format_value("%6.2f", rstats.mean()),
format_value("%6.2f", tstats.mean()),
format_value("%s", ""),
format_value("%s", ""),
format_value("%s", ""),
])
print()
print(table_utils.format(table_data,has_header=1,justify='center',delim=" "))
def run_correction_vector_plot(working_phil):
L = lines(working_phil)
for line in L.vectors():
pass # pull out the information, lines class does all the work
close_x = flex.double()
close_y = flex.double()
far_x = flex.double()
far_y = flex.double()
master_coords = L.master_coords
master_cv = L.master_cv
master_tiles = L.master_tiles
for idx in xrange(0,len(master_coords),10):
if matrix.col(master_cv[idx]).length() < L.tile_rmsd[ master_tiles[idx] ]:
pass
#close_x.append(master_coords[idx][0])
#close_y.append(master_coords[idx][1])
else:
far_x.append(master_coords[idx][0])
far_y.append(master_coords[idx][1])
close_x.append(master_coords[idx][0]+master_cv[idx][0])
close_y.append(master_coords[idx][1]+master_cv[idx][1])
if working_phil.show_plots is True:
from matplotlib import pyplot as plt
plt.plot(close_x,close_y,"r.")
plt.plot(far_x,far_y,"g.")
plt.axes().set_aspect("equal")
plt.show()
sort_radii = flex.sort_permutation(flex.double(L.radii))
tile_rmsds = flex.double()
radial_sigmas = flex.double(64)
tangen_sigmas = flex.double(64)
for idx in xrange(64):
x = sort_radii[idx]
print "Tile %2d: radius %7.2f, %6d observations, delx %5.2f dely %5.2f, rmsd = %5.2f"%(
x, L.radii[x], L.tilecounts[x], L.mean_cv[x][0], L.mean_cv[x][1],
L.tile_rmsd[x]
),
if L.tilecounts[x] < 3:
print
radial = (0,0)
tangential = (0,0)
rmean,tmean,rsigma,tsigma=(0,0,1,1)
else:
wtaveg = L.weighted_average_angle_deg_from_tile(x)
print "Tile rotation %6.2f deg"%wtaveg,
radial,tangential,rmean,tmean,rsigma,tsigma = get_radial_tangential_vectors(L,x)
print "%6.2f %6.2f"%(rsigma,tsigma)
radial_sigmas[x]=rsigma
tangen_sigmas[x]=tsigma
rstats = flex.mean_and_variance(radial_sigmas,L.tilecounts.as_double())
tstats = flex.mean_and_variance(tangen_sigmas,L.tilecounts.as_double())
print "\nOverall %8d observations, delx %5.2f dely %5.2f, rmsd = %5.2f"%(
L.overall_N, L.overall_cv[0], L.overall_cv[1], L.overall_rmsd)
print "Average tile rmsd %5.2f"%flex.mean(flex.double(L.tile_rmsd))
print "Average tile displacement %5.2f"%(flex.mean(
flex.double([matrix.col(cv).length() for cv in L.mean_cv])))
print "Weighted average radial sigma %6.2f"%rstats.mean()
print "Weighted average tangential sigma %6.2f"%tstats.mean()
if working_phil.show_plots is True:
plt.plot([(L.tiles[4*x+0]+L.tiles[4*x+2])/2. for x in xrange(64)],[(L.tiles[4*x+1]+L.tiles[4*x+3])/2. for x in xrange(64)],"go")
for x in xrange(64):
plt.text(10+(L.tiles[4*x+0]+L.tiles[4*x+2])/2.,10+(L.tiles[4*x+1]+L.tiles[4*x+3])/2.,"%d"%x)
plt.show()
for idx in xrange(64):
x = sort_radii[idx]
print "Tile %2d: radius %7.2f, %6d observations, delx %5.2f dely %5.2f, rmsd = %5.2f"%(
x, L.radii[x], L.tilecounts[x], L.mean_cv[x][0], L.mean_cv[x][1],
L.tile_rmsd[x]
),
if L.tilecounts[x] < 3:
print
radial = (0,0)
tangential = (0,0)
rmean,tmean,rsigma,tsigma=(0,0,1,1)
else:
wtaveg = L.weighted_average_angle_deg_from_tile(x)
print "Tile rotation %6.2f deg"%wtaveg,
radial,tangential,rmean,tmean,rsigma,tsigma = get_radial_tangential_vectors(L,x)
print "%6.2f %6.2f"%(rsigma,tsigma)
if working_phil.colormap:
from pylab import imshow, axes, colorbar, show
import numpy
xcv,ycv = get_correction_vector_xy(L,x)
_min = min(min(xcv),min(ycv))
_max = max(max(xcv),max(ycv))
hist,xedges,yedges = numpy.histogram2d(xcv,ycv,bins=40,range=[[_min,_max],[_min,_max]])
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1] ]
imshow(hist.T,extent=extent,interpolation='nearest',origin='lower')
from matplotlib.patches import Ellipse
ell = Ellipse(xy=(L.mean_cv[x][0],L.mean_cv[x][1]),
width=2.*rsigma, height=2.*tsigma,
angle=math.atan2(-(radial[1]),-(radial[0]))*180./math.pi,
edgecolor="y", linewidth=2, fill=False, zorder=100)
axes().add_artist(ell)
colorbar()
show()
else:
from matplotlib import pyplot as plt
xcv,ycv = get_correction_vector_xy(L,x)
if len(xcv)==0 or len(ycv)==0: continue
plt.plot(xcv,ycv,"r.")
plt.plot([L.mean_cv[x][0]],[L.mean_cv[x][1]],"go")
plt.plot([L.mean_cv[x][0]+radial[0]],[L.mean_cv[x][1]+radial[1]],"yo")
plt.plot([L.mean_cv[x][0]+tangential[0]],[L.mean_cv[x][1]+tangential[1]],"bo")
from matplotlib.patches import Ellipse
ell = Ellipse(xy=(L.mean_cv[x][0],L.mean_cv[x][1]),
width=2.*rsigma, height=2.*tsigma,
angle=math.atan2(-(radial[1]),-(radial[0]))*180./math.pi,
edgecolor="y", linewidth=2, fill=False, zorder=100)
plt.axes().add_artist(ell)
plt.axes().set_aspect("equal")
_min = min(min(xcv),min(ycv))
_max = max(max(xcv),max(ycv))
plt.axes().set_xlim(_min,_max)
plt.axes().set_ylim(_min,_max)
plt.show()
