def plot_statistics(statistics, prefix='', degrees_per_bin=5,
cutoff_anom=None, cutoff_non_anom=None):
range_width = 1
range_min = flex.min(statistics.dose) - range_width
range_max = flex.max(statistics.dose)
n_steps = 2 + int((range_max - range_min) - range_width)
x = flex.double_range(n_steps) * range_width + range_min
x *= degrees_per_bin
dpi = 300
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
try: plt.style.use('ggplot')
except AttributeError: pass
line1, = plt.plot(x, statistics.ieither_completeness, label='Unique reflections')
line2, = plt.plot(x, statistics.iboth_completeness, label='Bijvoet pairs')
if cutoff_non_anom is not None:
plt.plot([cutoff_non_anom, cutoff_non_anom], plt.ylim(), c=line1.get_color(), linestyle='dashed')
if cutoff_anom is not None:
plt.plot([cutoff_anom, cutoff_anom], plt.ylim(), c=line2.get_color(), linestyle='dotted')
plt.xlim(0, plt.xlim()[1])
plt.xlabel('Scan angle (degrees)')
plt.ylabel('Completeness (%)')
plt.ylim(0, 1)
plt.legend(loc='lower right', fontsize='small')
plt.savefig('%scompleteness_vs_scan_angle.png' %prefix, dpi=dpi)
plt.clf()
line1, = plt.plot(x[1:], 100 * statistics.frac_new_ref, label='Unique reflections')
line2, = plt.plot(x[1:], 100 * statistics.frac_new_pairs, label='Bijvoet pairs')
ylim = plt.ylim()
if cutoff_non_anom is not None:
plt.plot([cutoff_non_anom, cutoff_non_anom], ylim, c=line1.get_color(), linestyle='dashed')
if cutoff_anom is not None:
plt.plot([cutoff_anom, cutoff_anom], ylim, c=line2.get_color(), linestyle='dotted')
plt.ylim(ylim)
plt.xlim(0, plt.xlim()[1])
plt.xlabel('Scan angle (degrees)')
plt.ylabel('% new reflections per degree')
plt.legend(loc='upper right', fontsize='small')
plt.savefig('%spercent_new_reflections_vs_scan_angle.png' %prefix, dpi=dpi)
plt.clf()
def plot_statistics(statistics, prefix='', degrees_per_bin=5,
cutoff_anom=None, cutoff_non_anom=None):
range_width = 1
range_min = flex.min(statistics.dose) - range_width
range_max = flex.max(statistics.dose)
n_steps = 2 + int((range_max - range_min) - range_width)
x = flex.double_range(n_steps) * range_width + range_min
x *= degrees_per_bin
dpi = 300
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
try: plt.style.use('ggplot')
except AttributeError: pass
line1, = plt.plot(x, statistics.ieither_completeness, label='Unique reflections')
line2, = plt.plot(x, statistics.iboth_completeness, label='Bijvoet pairs')
if cutoff_non_anom is not None:
plt.plot([cutoff_non_anom, cutoff_non_anom], plt.ylim(), c=line1.get_color(), linestyle='dashed')
if cutoff_anom is not None:
plt.plot([cutoff_anom, cutoff_anom], plt.ylim(), c=line2.get_color(), linestyle='dotted')
plt.xlim(0, plt.xlim()[1])
plt.xlabel('Scan angle (degrees)')
plt.ylabel('Completeness (%)')
plt.ylim(0, 1)
plt.legend(loc='lower right', fontsize='small')
plt.savefig('%scompleteness_vs_scan_angle.png' %prefix, dpi=dpi)
plt.clf()
line1, = plt.plot(x[1:], 100 * statistics.frac_new_ref, label='Unique reflections')
line2, = plt.plot(x[1:], 100 * statistics.frac_new_pairs, label='Bijvoet pairs')
ylim = plt.ylim()
if cutoff_non_anom is not None:
plt.plot([cutoff_non_anom, cutoff_non_anom], ylim, c=line1.get_color(), linestyle='dashed')
if cutoff_anom is not None:
plt.plot([cutoff_anom, cutoff_anom], ylim, c=line2.get_color(), linestyle='dotted')
plt.ylim(ylim)
plt.xlim(0, plt.xlim()[1])
plt.xlabel('Scan angle (degrees)')
plt.ylabel('% new reflections per degree')
plt.legend(loc='upper right', fontsize='small')
plt.savefig('%spercent_new_reflections_vs_scan_angle.png' %prefix, dpi=dpi)
plt.clf()
def save_plots(params, raw, smoothed, suffix=""):
"""Create plots for the centroid analysis results for a single experiment.
Overlay raw and smoothed periodograms"""
# draw vertical lines at frequencies corresponding to periods of 54, 36 and 18
# degrees
if params.plot.mark_periods is not None:
vlines = [1.0 / e for e in params.plot.mark_periods]
else:
vlines = []
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
nblocks = raw["nblocks"]
block_size = raw["block_size"]
phistart = raw["phi_range"][0]
block_centres = (block_size * flex.double_range(nblocks) + phistart +
block_size / 2.0)
# X residuals plot
plt.figure(1)
plt.subplot(211)
plt.plot(block_centres, 1000.0 * raw["av_x_resid_per_block"])
plt.xlabel("phi (degrees)")
plt.ylabel("x residuals per block (microns)")
# X periodogram
plt.subplot(212)
for dat in [raw, smoothed]: # overlay raw and smoothed periodogram plots
px = dat["x_periodogram"]
if px is None:
continue
sample_freq = 1.0 / dat["block_size"]
freq = px.freq * sample_freq
(line, ) = plt.semilogy(freq, px.spec)
for vline in vlines:
plt.axvline(x=vline, color="r")
x_interval = smoothed["x_interval"]
if x_interval:
x_freq = 1.0 / x_interval
plt.axvline(x=x_freq, color="c")
plt.text(
0.2,
0.9,
"interval width: {0:.3f}".format(x_interval),
transform=line.axes.transAxes,
color="c",
)
plt.xlabel("frequency")
plt.ylabel("spectrum")
# write them out
fname = "x-residual-analysis" + suffix + "." + params.output.format
print("Saving {0}".format(fname))
plt.savefig(fname)
# Y residuals plot
plt.figure(2)
plt.subplot(211)
plt.plot(block_centres, 1000.0 * raw["av_y_resid_per_block"])
plt.xlabel("phi (degrees)")
plt.ylabel("y residuals per block (microns)")
# Y periodogram
plt.subplot(212)
for dat in [raw, smoothed]: # overlay raw and smoothed periodogram plots
py = dat["y_periodogram"]
if py is None:
continue
sample_freq = 1.0 / dat["block_size"]
freq = py.freq * sample_freq
(line, ) = plt.semilogy(freq, py.spec)
for vline in vlines:
plt.axvline(x=vline, color="r")
y_interval = smoothed["y_interval"]
if y_interval:
y_freq = 1.0 / y_interval
plt.axvline(x=y_freq, color="c")
plt.text(
0.2,
0.9,
"interval width: {0:.3f}".format(y_interval),
transform=line.axes.transAxes,
color="c",
)
plt.xlabel("frequency")
plt.ylabel("spectrum")
# write them out
fname = "y-residual-analysis" + suffix + "." + params.output.format
print("Saving {0}".format(fname))
plt.savefig(fname)
# phi residuals plot
plt.figure(3)
plt.subplot(211)
plt.plot(block_centres, 1000.0 * raw["av_phi_resid_per_block"])
plt.xlabel("phi (degrees)")
plt.ylabel("phi residuals per block (mrad)")
# phi periodogram
plt.subplot(212)
for dat in [raw, smoothed]: # overlay raw and smoothed periodogram plots
pz = dat["phi_periodogram"]
if pz is None:
continue
sample_freq = 1.0 / dat["block_size"]
freq = pz.freq * sample_freq
(line, ) = plt.semilogy(freq, pz.spec)
for vline in vlines:
plt.axvline(x=vline, color="r")
phi_interval = smoothed["phi_interval"]
if phi_interval:
phi_freq = 1.0 / phi_interval
plt.axvline(x=phi_freq, color="c")
plt.text(
0.2,
0.9,
"interval width: {0:.3f}".format(phi_interval),
transform=line.axes.transAxes,
color="c",
)
plt.xlabel("frequency")
plt.ylabel("spectrum")
# write them out
fname = "phi-residual-analysis" + suffix + "." + params.output.format
print("Saving {0}".format(fname))
plt.savefig(fname)
return
def save_plots(params, raw, smoothed, suffix=''):
"""Create plots for the centroid analysis results for a single experiment.
Overlay raw and smoothed periodograms"""
# draw vertical lines at frequencies corresponding to periods of 54, 36 and 18
# degrees
if params.plot.mark_periods is not None:
vlines = [1./e for e in params.plot.mark_periods]
else:
vlines = []
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
nblocks = raw['nblocks']
block_size = raw['block_size']
phistart = raw['phi_range'][0]
block_centres = block_size * flex.double_range(nblocks) + phistart + block_size/2.0
# X residuals plot
plt.figure(1)
plt.subplot(211)
plt.plot(block_centres, 1000. * raw['av_x_resid_per_block'])
plt.xlabel('phi (degrees)')
plt.ylabel('x residuals per block (microns)')
# X periodogram
plt.subplot(212)
for dat in [raw, smoothed]: # overlay raw and smoothed periodogram plots
px = dat['x_periodogram']
if px is None: continue
sample_freq = 1./dat['block_size']
freq = px.freq * sample_freq
line, = plt.semilogy(freq, px.spec)
for vline in vlines:
plt.axvline(x=vline, color='r')
x_interval = smoothed['x_interval']
if x_interval:
x_freq = 1./x_interval
plt.axvline(x=x_freq, color='c')
plt.text(0.2, 0.9, 'interval width: {0:.3f}'.format(x_interval),
transform=line.axes.transAxes, color='c')
plt.xlabel('frequency')
plt.ylabel('spectrum')
# write them out
fname = 'x-residual-analysis' + suffix + '.' + params.output.format
print("Saving {0}".format(fname))
plt.savefig(fname)
# Y residuals plot
plt.figure(2)
plt.subplot(211)
plt.plot(block_centres, 1000. * raw['av_y_resid_per_block'])
plt.xlabel('phi (degrees)')
plt.ylabel('y residuals per block (microns)')
# Y periodogram
plt.subplot(212)
for dat in [raw, smoothed]: # overlay raw and smoothed periodogram plots
py = dat['y_periodogram']
if py is None: continue
sample_freq = 1./dat['block_size']
freq = py.freq * sample_freq
line, = plt.semilogy(freq, py.spec)
for vline in vlines:
plt.axvline(x=vline, color='r')
y_interval = smoothed['y_interval']
if y_interval:
y_freq = 1./y_interval
plt.axvline(x=y_freq, color='c')
plt.text(0.2, 0.9, 'interval width: {0:.3f}'.format(y_interval),
transform=line.axes.transAxes, color='c')
plt.xlabel('frequency')
plt.ylabel('spectrum')
# write them out
fname = 'y-residual-analysis' + suffix + '.' + params.output.format
print("Saving {0}".format(fname))
plt.savefig(fname)
# phi residuals plot
plt.figure(3)
plt.subplot(211)
plt.plot(block_centres, 1000. * raw['av_phi_resid_per_block'])
plt.xlabel('phi (degrees)')
plt.ylabel('phi residuals per block (mrad)')
# phi periodogram
plt.subplot(212)
for dat in [raw, smoothed]: # overlay raw and smoothed periodogram plots
pz = dat['phi_periodogram']
if pz is None: continue
sample_freq = 1./dat['block_size']
freq = pz.freq * sample_freq
line, = plt.semilogy(freq, pz.spec)
for vline in vlines:
plt.axvline(x=vline, color='r')
phi_interval = smoothed['phi_interval']
if phi_interval:
phi_freq = 1./phi_interval
plt.axvline(x=phi_freq, color='c')
plt.text(0.2, 0.9, 'interval width: {0:.3f}'.format(phi_interval),
transform=line.axes.transAxes, color='c')
plt.xlabel('frequency')
plt.ylabel('spectrum')
# write them out
fname = 'phi-residual-analysis' + suffix + '.' + params.output.format
print("Saving {0}".format(fname))
plt.savefig(fname)
return
def plot_statistics(
statistics, prefix="", degrees_per_bin=5, cutoff_anom=None, cutoff_non_anom=None
):
range_width = 1
range_min = flex.min(statistics.dose) - range_width
range_max = flex.max(statistics.dose)
n_steps = 2 + int((range_max - range_min) - range_width)
x = flex.double_range(n_steps) * range_width + range_min
x *= degrees_per_bin
dpi = 300
import matplotlib
matplotlib.use("Agg")
from matplotlib import pyplot as plt
try:
plt.style.use("ggplot")
except AttributeError:
pass
(line1,) = plt.plot(x, statistics.ieither_completeness, label="Unique reflections")
(line2,) = plt.plot(x, statistics.iboth_completeness, label="Bijvoet pairs")
if cutoff_non_anom is not None:
plt.plot(
[cutoff_non_anom, cutoff_non_anom],
plt.ylim(),
c=line1.get_color(),
linestyle="dashed",
)
if cutoff_anom is not None:
plt.plot(
[cutoff_anom, cutoff_anom],
plt.ylim(),
c=line2.get_color(),
linestyle="dotted",
)
plt.xlim(0, plt.xlim()[1])
plt.xlabel("Scan angle (degrees)")
plt.ylabel("Completeness (%)")
plt.ylim(0, 1)
plt.legend(loc="lower right", fontsize="small")
plt.savefig("%scompleteness_vs_scan_angle.png" % prefix, dpi=dpi)
plt.clf()
(line1,) = plt.plot(
x[1:], 100 * statistics.frac_new_ref, label="Unique reflections"
)
(line2,) = plt.plot(x[1:], 100 * statistics.frac_new_pairs, label="Bijvoet pairs")
ylim = plt.ylim()
if cutoff_non_anom is not None:
plt.plot(
[cutoff_non_anom, cutoff_non_anom],
ylim,
c=line1.get_color(),
linestyle="dashed",
)
if cutoff_anom is not None:
plt.plot(
[cutoff_anom, cutoff_anom], ylim, c=line2.get_color(), linestyle="dotted"
)
plt.ylim(ylim)
plt.xlim(0, plt.xlim()[1])
plt.xlabel("Scan angle (degrees)")
plt.ylabel("% new reflections per degree")
plt.legend(loc="upper right", fontsize="small")
plt.savefig("%spercent_new_reflections_vs_scan_angle.png" % prefix, dpi=dpi)
plt.clf()
def save_plots(raw, smoothed, suffix='', vlines=None):
"""Create plots for the centroid analysis results for a single experiment.
Overlay raw and smoothed periodograms"""
if vlines is None: vlines = []
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
nblocks = raw['nblocks']
block_size = raw['block_size']
phistart = raw['phi_range'][0]
block_centres = block_size * flex.double_range(nblocks) + phistart + block_size/2.0
# X residuals plot
plt.figure(1)
plt.subplot(211)
plt.plot(block_centres, 1000. * raw['av_x_resid_per_block'])
plt.xlabel('phi (degrees)')
plt.ylabel('x residuals per block (microns)')
# X periodogram
plt.subplot(212)
for dat in [raw, smoothed]: # overlay raw and smoothed periodogram plots
px = dat['x_periodogram']
sample_freq = 1./dat['block_size']
freq = px.freq * sample_freq
line, = plt.semilogy(freq, px.spec)
for vline in vlines:
plt.axvline(x=vline, color='r')
x_interval = smoothed['x_interval']
if x_interval:
x_freq = 1./x_interval
plt.axvline(x=x_freq, color='c')
plt.text(0.2, 0.9, 'interval width: {0:.3f}'.format(x_interval),
transform=line.axes.transAxes, color='c')
plt.xlabel('frequency')
plt.ylabel('spectrum')
# write them out
fname = 'x-residual-analysis' + suffix + '.png'
print "Saving {0}".format(fname)
plt.savefig(fname)
# Y residuals plot
plt.figure(2)
plt.subplot(211)
plt.plot(block_centres, 1000. * raw['av_y_resid_per_block'])
plt.xlabel('phi (degrees)')
plt.ylabel('y residuals per block (microns)')
# Y periodogram
plt.subplot(212)
for dat in [raw, smoothed]: # overlay raw and smoothed periodogram plots
py = dat['y_periodogram']
sample_freq = 1./dat['block_size']
freq = py.freq * sample_freq
line, = plt.semilogy(freq, py.spec)
for vline in vlines:
plt.axvline(x=vline, color='r')
y_interval = smoothed['y_interval']
if y_interval:
y_freq = 1./y_interval
plt.axvline(x=y_freq, color='c')
plt.text(0.2, 0.9, 'interval width: {0:.3f}'.format(y_interval),
transform=line.axes.transAxes, color='c')
plt.xlabel('frequency')
plt.ylabel('spectrum')
# write them out
fname = 'y-residual-analysis' + suffix + '.png'
print "Saving {0}".format(fname)
plt.savefig(fname)
# phi residuals plot
plt.figure(3)
plt.subplot(211)
plt.plot(block_centres, 1000. * raw['av_phi_resid_per_block'])
plt.xlabel('phi (degrees)')
plt.ylabel('phi residuals per block (mrad)')
# phi periodogram
plt.subplot(212)
for dat in [raw, smoothed]: # overlay raw and smoothed periodogram plots
pz = dat['phi_periodogram']
sample_freq = 1./dat['block_size']
freq = pz.freq * sample_freq
line, = plt.semilogy(freq, pz.spec)
for vline in vlines:
plt.axvline(x=vline, color='r')
phi_interval = smoothed['phi_interval']
if phi_interval:
phi_freq = 1./phi_interval
plt.axvline(x=phi_freq, color='c')
plt.text(0.2, 0.9, 'interval width: {0:.3f}'.format(phi_interval),
transform=line.axes.transAxes, color='c')
plt.xlabel('frequency')
plt.ylabel('spectrum')
# write them out
fname = 'phi-residual-analysis' + suffix + '.png'
print "Saving {0}".format(fname)
plt.savefig(fname)
return