def fp_distro(plt):
OX = GS_ROI(full_path("data_sherrell/pf-rd-ox_fftkk.out"))
RD = GS_ROI(full_path("data_sherrell/pf-rd-red_fftkk.out"))
MM = GS_ROI(full_path("data_sherrell/Fe_fake.dat"))
delta_fp = flex.double()
for i in range(1,100):
#delta_fp.append ( MM.fp[i]-MM.fp[i-1] );
delta_fp.append ( OX.fp[i]-OX.fp[i-1] ); delta_fp.append ( RD.fp[i]-RD.fp[i-1] )
STATS = flex.mean_and_variance(delta_fp)
mean = STATS.mean()
sigma = STATS.unweighted_sample_standard_deviation()
displaynorm = 3.0
plotx = flex.double([0.005*x for x in range(-100,100)])
ploty = flex.double([displaynorm *
(1./math.sqrt(2.*math.pi*sigma*sigma))*math.exp(-0.5*(math.pow(x-mean,2))/(sigma*sigma))
for x in plotx])
print("mean",mean,"sigma",sigma)
compute_functional_and_gradients_fp(FE1_fp=OX.fp,FE2_fp=RD.fp,mean=mean,sigma=sigma)
n,bins,patches = plt.hist(delta_fp, 50, normed=0, facecolor="orange", alpha=0.75)
plt.plot(plotx, ploty, "r-")
plt.xlabel("Delta fp")
plt.title("Histogram of Delta fp")
plt.axis([-1,1,0,40])
plt.show()
def restrain_II(plt):
OX = GS_ROI(full_path("data_sherrell/pf-rd-ox_fftkk.out"))
RD = GS_ROI(full_path("data_sherrell/pf-rd-red_fftkk.out"))
MT = GS_ROI(full_path("data_sherrell/Fe_fake.dat")) # with interpolated points
r_mean = flex.double(200)
r_sigma = flex.double(200)
for ichannel in range(100):
fp_pop = flex.mean_and_variance(flex.double([OX.fp[ichannel],RD.fp[ichannel],MT.fp[ichannel]]))
fdp_pop = flex.mean_and_variance(flex.double([OX.fdp[ichannel],RD.fdp[ichannel],MT.fdp[ichannel]]))
r_mean[ichannel] = fp_pop.mean(); r_mean[100+ichannel] = fdp_pop.mean()
r_sigma[ichannel] = fp_pop.unweighted_sample_standard_deviation()
r_sigma[100+ichannel] = fdp_pop.unweighted_sample_standard_deviation()
plt.stackplot(OX.energy,r_mean[0:100]-r_sigma[0:100],color=('lightgreen'))
plt.stackplot(OX.energy,r_mean[0:100]+r_sigma[0:100],color=('white'))
plt.stackplot(OX.energy,r_mean[100:200]+r_sigma[100:200],color=('lightgreen'))
plt.stackplot(OX.energy,r_mean[100:200]-r_sigma[100:200],color=('white'))
plt.plot(OX.energy,r_mean[0:100]+r_sigma[0:100],'g-')
plt.plot(OX.energy,r_mean[100:200]+r_sigma[100:200],'g-')
plt.plot(OX.energy,r_mean[0:100]-r_sigma[0:100],'g-')
plt.plot(OX.energy,r_mean[100:200]-r_sigma[100:200],'g-')
plt.plot(OX.energy,r_mean[0:100],'g-')
plt.plot(OX.energy,r_mean[100:200],'g-')
OX.plot_them(plt,f1="b.",f2="b.")
OX.plot_them(plt,f1="b-",f2="b-")
RD = GS_ROI(full_path("data_sherrell/pf-rd-red_fftkk.out"))
RD.plot_them(plt,f1="r.",f2="r.")
RD.plot_them(plt,f1="r-",f2="r-")
MT = GS_ROI(full_path("data_sherrell/Fe_fake.dat")) # with interpolated points
MT.plot_them(plt,f1="m-",f2="m-")
plt.axes().set_ylim((-8.3,4.2))
plt.show()
def XXX(plt):
GS = GS_ROI(full_path("data_sherrell/pf-rd-ox_fftkk.out"))
GS.plot_them(plt,f1="b.",f2="b.")
GS.plot_them(plt,f1="b-",f2="b-")
GS = GS_ROI(full_path("data_sherrell/pf-rd-red_fftkk.out"))
GS.plot_them(plt,f1="r.",f2="r.")
GS.plot_them(plt,f1="r-",f2="r-")
GS = GS_ROI(full_path("data_sherrell/Fe_fake.dat")) # with interpolated points
GS.plot_them(plt,f1="m-",f2="m-")
#plt.axes().set_xlim((7088,7152))
plt.axes().set_ylim((-8.3,4.2))
plt.show()
def plot_em(self, key, values):
self.x = values # XXX
if not self.plot_plt_imported:
from matplotlib import pyplot as plt
self.plt = plt
if self.params.LLG_evaluator.title is None:
self.plt.ion() # interactive - on
self.plot_plt_imported = True
if self.params.LLG_evaluator.title is None:
self.plt.cla() #clear last access
fine = self.params.LLG_evaluator.plot_interpolation # plot the non-modeled f values
fig = self.plt.figure()
# ground truth
from LS49.sim.step5_pad import full_path
GS = george_sherrell(full_path("data_sherrell/pf-rd-ox_fftkk.out"))
GS.plot_them(self.plt, f1="b-", f2="b-")
GS = george_sherrell(full_path("data_sherrell/pf-rd-red_fftkk.out"))
GS.plot_them(self.plt, f1="r-", f2="r-")
GS = george_sherrell(
full_path("data_sherrell/Fe_fake.dat")) # with interpolated points
GS.plot_them(self.plt, f1="m-", f2="m-")
# starting values
GS = george_sherrell_star(fp=self.starting_params_FE1[0:100],
fdp=self.starting_params_FE1[100:200])
GS.plot_them(fine, self.plt, f1="bx", f2="bx")
GS = george_sherrell_star(fp=self.starting_params_FE2[0:100],
fdp=self.starting_params_FE2[100:200])
GS.plot_them(fine, self.plt, f1="rx", f2="rx")
# current values
GS = george_sherrell_star(fp=self.x[0:100], fdp=self.x[100:200])
GS.plot_them(fine, self.plt, f1="b.", f2="b.")
GS = george_sherrell_star(fp=self.x[200:300], fdp=self.x[300:400])
GS.plot_them(fine, self.plt, f1="r.", f2="r.")
self.plt.axes().set_xlim((7102, 7137)) # XXX 7088,7152
self.plt.axes().set_ylim((-8.6, 4.5))
self.plt.title("Macrocycle %d Iteration %d" %
(self.macrocycle, self.iteration)) # XXX
if self.params.LLG_evaluator.title is not None:
macrocycle_tell = "" if self.macrocycle is None else "macrocycle_%02d_" % self.macrocycle
fig.savefig(
os.path.join(
self.params.LLG_evaluator.plot_outdir,
"replot_%s_%siteration_%02d.png" %
(self.params.LLG_evaluator.title, macrocycle_tell,
self.iteration)))
else:
self.plt.draw()
self.plt.pause(0.2)
fig.clf() # clear figure XXX
def restrain_II_values():
OX = GS_ROI(full_path("data_sherrell/pf-rd-ox_fftkk.out"))
RD = GS_ROI(full_path("data_sherrell/pf-rd-red_fftkk.out"))
MT = GS_ROI(full_path("data_sherrell/Fe_fake.dat")) # with interpolated points
r_mean = flex.double(200)
r_sigma = flex.double(200)
for ichannel in range(100):
fp_pop = flex.mean_and_variance(flex.double([OX.fp[ichannel],RD.fp[ichannel],MT.fp[ichannel]]))
fdp_pop = flex.mean_and_variance(flex.double([OX.fdp[ichannel],RD.fdp[ichannel],MT.fdp[ichannel]]))
r_mean[ichannel] = fp_pop.mean(); r_mean[100+ichannel] = fdp_pop.mean()
r_sigma[ichannel] = (1./SEVERITY_FACTOR)*fp_pop.unweighted_sample_standard_deviation()
r_sigma[100+ichannel] = (1./SEVERITY_FACTOR)*fdp_pop.unweighted_sample_standard_deviation()
return r_mean, r_sigma
def tst_analytical_fp():
from libtbx.test_utils import approx_equal
OX = GS_ROI(full_path("data_sherrell/pf-rd-ox_fftkk.out"))
RD = GS_ROI(full_path("data_sherrell/pf-rd-red_fftkk.out"))
fbase,g1base,g2base = compute_functional_and_gradients_fp(
FE1_fp=OX.fp,FE2_fp=RD.fp,mean=0.0,sigma=0.1)
EPS = 0.0001
for ichannel in range(100):
OX.fp[ichannel]+=EPS
fnew,g1new,g2new = compute_functional_and_gradients_fp(
FE1_fp=OX.fp,FE2_fp=RD.fp,mean=0.0,sigma=0.1)
assert approx_equal((fnew-fbase)/EPS , g1base[ichannel],eps = 1e-1)
OX.fp[ichannel]-=EPS
RD.fp[ichannel]+=EPS
fnew,g1new,g2new = compute_functional_and_gradients_fp(
FE1_fp=OX.fp,FE2_fp=RD.fp,mean=0.0,sigma=0.1)
assert approx_equal((fnew-fbase)/EPS , g2base[ichannel],eps = 1e-1)
RD.fp[ichannel]-=EPS
from __future__ import division, absolute_import, print_function
from LS49.sim.fdp_plot import george_sherrell
# %%% boilerplate context: specialize to packaged big data %%%
import os
ls49_big_data = os.environ["LS49_BIG_DATA"] # get absolute path from environment
from LS49.sim import step5_pad
step5_pad.big_data = ls49_big_data
from LS49.sim.step5_pad import full_path
# %%%%%%
if __name__=="__main__":
from matplotlib import pyplot as plt
GS = george_sherrell(full_path("data_sherrell/pf-rd-ox_fftkk.out"))
GS.plot_them(plt,f1="b.",f2="b.")
GS.plot_them(plt,f1="b-",f2="b-")
GS = george_sherrell(full_path("data_sherrell/pf-rd-red_fftkk.out"))
GS.plot_them(plt,f1="r.",f2="r.")
GS.plot_them(plt,f1="r-",f2="r-")
GS = george_sherrell(full_path("data_sherrell/Fe_fake.dat")) # with interpolated points
#GS = george_sherrell(full_path("data_sherrell/Fe.dat"))
GS.plot_them(plt,f1="m-",f2="m-")
plt.axes().set_xlim((7088,7152))
plt.axes().set_ylim((-8.3,4.2))
print(list(GS.energy))
print(list(GS.fdp))
from __future__ import division, absolute_import, print_function
from LS49.sim.fdp_plot import george_sherrell
# %%% boilerplate context: specialize to packaged big data %%%
from LS49 import ls49_big_data
from LS49.sim import step5_pad
step5_pad.big_data = ls49_big_data
from LS49.sim.step5_pad import full_path
# %%%%%%
if __name__ == "__main__":
from matplotlib import pyplot as plt
GS = george_sherrell(full_path("data_sherrell/pf-rd-ox_fftkk.out"))
GS.plot_them(plt, f1="b.", f2="b.")
GS.plot_them(plt, f1="b-", f2="b-")
GS = george_sherrell(full_path("data_sherrell/pf-rd-red_fftkk.out"))
GS.plot_them(plt, f1="r.", f2="r.")
GS.plot_them(plt, f1="r-", f2="r-")
GS = george_sherrell(full_path("data_sherrell/Fe.dat"))
GS.plot_them(plt, f1="m-", f2="m-")
from scipy.interpolate import interp1d
fFe0fp = interp1d(GS.energy, GS.fp, kind="cubic")
fFe0fdp = interp1d(GS.energy, GS.fdp, kind="linear")
xnew = range(7070, 7180)
plt.plot(xnew, fFe0fp(xnew), "g+")
plt.plot(xnew, fFe0fdp(xnew), "g+")
for item in xnew:
print("%11.2f%15.7f%15.7f" %
def plot_em_broken(self, key, values):
if self.params.LLG_evaluator.plot_scope == "P1":
scope = dict(xlimits=(7102, 7138),
fdp_ylimits=(0.1, 4.5),
fp_ylimits=(-8.6, -5.1))
elif self.params.LLG_evaluator.plot_scope == "P2":
scope = dict(xlimits=(7068, 7172),
fdp_ylimits=(-2.1, 6.5),
fp_ylimits=(-10.6, -3.1))
self.x = values # XXX
cc = CC_to_ground_truth()
if not self.plot_plt_imported:
from matplotlib import pyplot as plt
self.plt = plt
if self.params.LLG_evaluator.title is None:
self.plt.ion() # interactive - on
self.plot_plt_imported = True
if self.params.LLG_evaluator.title is None:
self.plt.cla() #clear last access
fine = self.params.LLG_evaluator.plot_interpolation # plot the non-modeled f values
fig, (ax1, ax2) = self.plt.subplots(2, 1, sharex=True, squeeze=True)
# ground truth
from LS49.sim.step5_pad import full_path
GS = george_sherrell(full_path("data_sherrell/pf-rd-ox_fftkk.out"))
GS.plot_them(ax1, f1="b-", f2="b-")
GS.plot_them(ax2, f1="b-", f2="b-")
cc.get_gt(GS, imodel=0)
GS = george_sherrell(full_path("data_sherrell/pf-rd-red_fftkk.out"))
GS.plot_them(ax1, f1="r-", f2="r-")
GS.plot_them(ax2, f1="r-", f2="r-")
cc.get_gt(GS, imodel=1)
GS = george_sherrell(
full_path("data_sherrell/Fe_fake.dat")) # with interpolated points
GS.plot_them(ax1, f1="m-", f2="m-")
GS.plot_them(ax2, f1="m-", f2="m-")
# starting values
GS = george_sherrell_star(fp=self.starting_params_FE1[0:100],
fdp=self.starting_params_FE1[100:200])
GS.plot_them(fine, ax1, f1="bx", f2="bx")
GS.plot_them(fine, ax2, f1="bx", f2="bx")
GS = george_sherrell_star(fp=self.starting_params_FE2[0:100],
fdp=self.starting_params_FE2[100:200])
GS.plot_them(fine, ax1, f1="rx", f2="rx")
GS.plot_them(fine, ax2, f1="rx", f2="rx")
# current values
GS = george_sherrell_star(fp=self.x[0:100], fdp=self.x[100:200])
GS.plot_them(fine, ax1, f1="b.", f2="b.")
GS.plot_them(fine, ax2, f1="b.", f2="b.")
cc.get_data(GS, imodel=0)
GS = george_sherrell_star(fp=self.x[200:300], fdp=self.x[300:400])
GS.plot_them(fine, ax1, f1="r.", f2="r.")
GS.plot_them(fine, ax2, f1="r.", f2="r.")
cc.get_data(GS, imodel=1)
#self.plt.axes().set_xlim((7102,7137)) # XXX 7088,7152
ax1.set_xlim(scope["xlimits"])
ax2.set_xlabel("Energy (eV)")
ax1.set_ylabel("∆ f ′′")
ax2.set_ylabel("∆ f ′")
ax2.set_ylim(scope["fp_ylimits"])
ax1.set_ylim(scope["fdp_ylimits"])
ax1.set_title("Macrocycle %d Iteration %d" %
(self.macrocycle, self.iteration)) # XXX
ax1.spines['bottom'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax1.xaxis.tick_top()
ax1.tick_params(labeltop='off') # don't put tick labels at the top
ax2.xaxis.tick_bottom()
d = .015 # how big to make the diagonal lines in axes coordinates
# arguments to pass to plot, just so we don't keep repeating them
kwargs = dict(transform=ax1.transAxes,
color='k',
clip_on=False,
linewidth=1)
ax1.plot((-d, +d), (-d, +d), **kwargs) # top-left diagonal
ax1.plot((1 - d, 1 + d), (-d, +d), **kwargs) # top-right diagonal
kwargs.update(transform=ax2.transAxes) # switch to the bottom axes
ax2.plot((-d, +d), (1 - d, 1 + d), **kwargs) # bottom-left diagonal
ax2.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs) # bottom-right diagonal
if self.params.LLG_evaluator.title is not None:
macrocycle_tell = "" if self.macrocycle is None else "macrocycle_%02d_" % self.macrocycle
fig.savefig(
os.path.join(
self.params.LLG_evaluator.plot_outdir,
"replot_%s_%siteration_%02d.png" %
(self.params.LLG_evaluator.title, macrocycle_tell,
self.iteration)))
if self.macrocycle in [
1, 2, 3
] and self.iteration == self.params.LLG_evaluator.max_calls:
fig.savefig(
os.path.join(
self.params.LLG_evaluator.plot_outdir,
"replot_%s_%siteration_%02d.pdf" %
(self.params.LLG_evaluator.title, macrocycle_tell,
self.iteration)))
print(
"%s_%siteration_%02d CC=%6.3f%%" %
(self.params.LLG_evaluator.title, macrocycle_tell, self.iteration,
100 * cc.get_cc()), "CC_fp = %6.3f%% CC_fdp = %6.3f%%" %
(100 * cc.get_cc_fp(), 100 * cc.get_cc_fdp()),
"rmsd fp: %6.4f, rmsd fdp %6.4f" %
(cc.get_rmsd_fp(), cc.get_rmsd_fdp()))
else:
self.plt.draw()
self.plt.pause(0.2)
