def __init__(self, spots, targetResolution, fractionCalculator):
#Total rows by formula
Total_rows = int(math.pow(targetResolution / spots[len(spots) - 1],
3.)) + 1
Spreadsheet.__init__(self, rows=Total_rows)
self.addColumn('Limit')
self.addColumn('Fract')
self.addColumn('Population', 0)
self.addColumn('adjustPop',
Formula('self.Population[%row] / self.Fract[%row]'))
# the outer resolution limit of the lowest-resolution shell
self.Limit[0] = targetResolution
self.Fract[0] = 1.0
def BinRes(shellnumber):
return self.Limit[0] / math.pow(shellnumber + 1, 1.0 / 3.0)
#this little loop consumes 1.2 seconds of CPU time for a 1400-row spreadsheet
for xrow in range(1, Total_rows):
self.Limit[xrow] = BinRes(xrow)
self.Fract[xrow] = fractionCalculator(self.Limit[xrow])
bp = bin_populations(spots, self.Limit[0])
for c in range(min(Total_rows,
len(bp))): #reconcile inconsistent bin counts
self.Population[c] = bp[c]
self.Limit.format = "%.2f"
self.Fract.format = "%.2f"
self.Population.format = "%7d"
self.adjustPop.format = "%7.1f"
def __init__(self,spots,targetResolution,fractionCalculator):
#Total rows by formula
Total_rows = int(math.pow(
targetResolution/spots[len(spots)-1],3.))+1
Spreadsheet.__init__(self,rows=Total_rows)
self.addColumn('Limit')
self.addColumn('Fract')
self.addColumn('Population',0)
self.addColumn('adjustPop',
Formula('self.Population[%row] / self.Fract[%row]'))
# the outer resolution limit of the lowest-resolution shell
self.Limit[0] = targetResolution
self.Fract[0] = 1.0
def BinRes(shellnumber):
return self.Limit[0]/math.pow(shellnumber+1,1.0/3.0)
#this little loop consumes 1.2 seconds of CPU time for a 1400-row spreadsheet
for xrow in xrange(1,Total_rows):
self.Limit[xrow] = BinRes(xrow)
self.Fract[xrow] = fractionCalculator(self.Limit[xrow])
bp = bin_populations(spots,self.Limit[0])
for c in xrange(min(Total_rows,len(bp))): #reconcile inconsistent bin counts
self.Population[c]=bp[c]
self.Limit.format = "%.2f"
self.Fract.format = "%.2f"
self.Population.format = "%7d"
self.adjustPop.format = "%7.1f"
def __init__(self,spots,phil_params,targetResolution=None,wavelength=None,
max_total_rows=None,fractionCalculator=None,use_binning_of=None):
if len(spots) == 0: return # No spots, no binwise analysis
if use_binning_of is None or not hasattr(use_binning_of,"binning"):
self.original_binning=True
assert targetResolution is not None and wavelength is not None
self.wavelength = wavelength
#Total rows by formula
Total_rows = int(math.pow(
targetResolution/spots[len(spots)-1],3.))+1
if max_total_rows is not None:
Total_rows = min(Total_rows, max_total_rows)
Spreadsheet.__init__(self,rows=Total_rows)
self.addColumn('Limit')
self.addColumn('Fract')
self.addColumn('Missing') # fraction res shell remaining after missing cone is subtracted
self.addColumn('Population',0)
if fractionCalculator is not None and \
phil_params.distl.bins.corner:
last_d_star = 1./flex.min(fractionCalculator.corner_resolutions())
else:
last_d_star = 1./spots[-1]
volume_fraction = (1./Total_rows)*math.pow(last_d_star,3)
def BinRes(shellnumber):
return 1./math.pow((shellnumber+1)*volume_fraction,1.0/3.0)
#this little loop consumes 1.2 seconds of CPU time for a 1400-row spreadsheet
for xrow in xrange(0,Total_rows):
self.Limit[xrow] = BinRes(xrow)
self.Fract[xrow] = fractionCalculator(self.Limit[xrow])
self.populate_missing_cone(Total_rows)
if use_binning_of is not None:
if not hasattr(use_binning_of,"binning"):
use_binning_of.binning = self #inject this spreadsheet into caller's space
else:
self.original_binning=False
self.wavelength = wavelength
Spreadsheet.__init__(self,rows=use_binning_of.binning.S_table_rows)
self.addColumn('Limit')
self.addColumn('Fract')
self.addColumn('Missing')
self.addColumn('Population',0)
for xrow in xrange(0,self.S_table_rows):
self.Limit[xrow] = use_binning_of.binning.Limit[xrow]
self.Fract[xrow] = use_binning_of.binning.Fract[xrow]
self.Missing[xrow] = use_binning_of.binning.Missing[xrow]
Total_rows = self.S_table_rows
bp = bin_populations(spots,self.Limit[0])
for c in xrange(min(Total_rows,len(bp))): #reconcile inconsistent bin counts
self.Population[c]=bp[c]
self.Limit.format = "%.2f"
self.Fract.format = "%.2f"
self.Missing.format = "%.2f"
self.Population.format = "%7d"
def __init__(self,
spots,
phil_params,
targetResolution=None,
wavelength=None,
max_total_rows=None,
fractionCalculator=None,
use_binning_of=None):
if len(spots) == 0: return # No spots, no binwise analysis
if use_binning_of is None or not hasattr(use_binning_of, "binning"):
self.original_binning = True
assert targetResolution is not None and wavelength is not None
self.wavelength = wavelength
#Total rows by formula
Total_rows = int(
math.pow(targetResolution / spots[len(spots) - 1], 3.)) + 1
if max_total_rows is not None:
Total_rows = min(Total_rows, max_total_rows)
Spreadsheet.__init__(self, rows=Total_rows)
self.addColumn('Limit')
self.addColumn('Fract')
self.addColumn(
'Missing'
) # fraction res shell remaining after missing cone is subtracted
self.addColumn('Population', 0)
if fractionCalculator is not None and \
phil_params.distl.bins.corner:
last_d_star = 1. / flex.min(
fractionCalculator.corner_resolutions())
else:
last_d_star = 1. / spots[-1]
volume_fraction = (1. / Total_rows) * math.pow(last_d_star, 3)
def BinRes(shellnumber):
return 1. / math.pow(
(shellnumber + 1) * volume_fraction, 1.0 / 3.0)
#this little loop consumes 1.2 seconds of CPU time for a 1400-row spreadsheet
for xrow in xrange(0, Total_rows):
self.Limit[xrow] = BinRes(xrow)
self.Fract[xrow] = fractionCalculator(self.Limit[xrow])
self.populate_missing_cone(Total_rows)
if use_binning_of is not None:
if not hasattr(use_binning_of, "binning"):
use_binning_of.binning = self #inject this spreadsheet into caller's space
else:
self.original_binning = False
self.wavelength = wavelength
Spreadsheet.__init__(self,
rows=use_binning_of.binning.S_table_rows)
self.addColumn('Limit')
self.addColumn('Fract')
self.addColumn('Missing')
self.addColumn('Population', 0)
for xrow in xrange(0, self.S_table_rows):
self.Limit[xrow] = use_binning_of.binning.Limit[xrow]
self.Fract[xrow] = use_binning_of.binning.Fract[xrow]
self.Missing[xrow] = use_binning_of.binning.Missing[xrow]
Total_rows = self.S_table_rows
bp = bin_populations(spots, self.Limit[0])
for c in xrange(min(Total_rows,
len(bp))): #reconcile inconsistent bin counts
self.Population[c] = bp[c]
self.Limit.format = "%.2f"
self.Fract.format = "%.2f"
self.Missing.format = "%.2f"
self.Population.format = "%7d"
