def test_local_search(self):
ref_box=self.ref_proj.get_bounding_square(margin=30)
ref_img, _=self.ref_proj.rasterize(70, bounding_square=ref_box, rotate=0)
scale=ref_box[1]-ref_box[0]
distance, img, params = fph.locally_minimal_distance(ref_img, scale, self.cg, proj_dir=list(fph.to_polar([1,1.1,1.2]))[1:])
self.assertLessEqual(distance, 2)
self.assertLessEqual(abs(params[1]%360), 5)
nptest.assert_allclose(params[0], list(fph.to_polar([1,1.1,1.2]))[1:], atol=5)
def test_local_search(self):
ref_box=self.ref_proj.get_bounding_square(margin=30)
ref_img, _=self.ref_proj.rasterize(70, bounding_square=ref_box, rotate=0)
scale=ref_box[1]-ref_box[0]
distance, img, params = fph.locally_minimal_distance(ref_img, scale, self.cg, proj_dir=list(fph.to_polar([1,1.1,1.2]))[1:])
self.assertLessEqual(distance, 2)
self.assertLessEqual(abs(params[1]%360), 5)
nptest.assert_allclose(params[0], list(fph.to_polar([1,1.1,1.2]))[1:], atol=5)
def parallel_localOpt(im_and_box, numFiles, filenames):
(ref_img, ref_box) = im_and_box
try:
distances=[]
for j in range(numFiles):
cg=ftmc.CoarseGrainRNA(filenames[j])
distance, img, _ = fph.locally_minimal_distance(ref_img, ref_box[1]-ref_box[0], cg)
distances.append(distance)
return distances
except KeyboardInterrupt:
print("In worker {}: Keyboard Interrupt".format(id(ref_img)))
return
def main(args):
cgs = []
projs = []
for file_ in args.files:
cgs.append(ftmc.CoarseGrainRNA(file_))
try:
projs.append(ftmp.Projection2D(cgs[-1]))
except ValueError:
projs.append(None)
p_rmsds = OrderedDict()
cg_rmsds = OrderedDict()
p_rmsds[0] = 0.0
cg_rmsds[0] = 0.0
if "," in args.max_diff:
diffs = map(int, args.max_diff.split(","))
else:
diffs = range(1, int(args.max_diff) + 1)
for diff in diffs:
print ("diff {}".format(diff))
prmsd = 0
cgrmsd = 0
count = 0
pcount = 0
for i in range(0, len(cgs) - diff):
count += 1
try:
vrs1 = np.array([x for p in sorted(projs[i]._coords.keys()) for x in projs[i]._coords[p]])
vrs2 = np.array([x for p in sorted(projs[i + diff]._coords.keys()) for x in projs[i + diff]._coords[p]])
prmsd += ftms.rmsd(vrs1, vrs2)
pcount += 1
except:
pass
cgrmsd += ftms.cg_rmsd(cgs[i], cgs[i + diff])
if count:
cg_rmsds[diff] = cgrmsd / count
if pcount:
p_rmsds[diff] = prmsd / pcount
print "projection RMSDs:", p_rmsds
print "3D-RMSDs:", cg_rmsds
import matplotlib.pyplot as plt
if args.target_structure and args.hausdorff_reference:
fig, (ax, axT, axH) = plt.subplots(3)
elif args.target_structure:
fig, (ax, axT) = plt.subplots(2)
elif args.hausdorff_reference:
fig, (ax, axH) = plt.subplots(2)
else:
fig, ax = plt.subplots()
if args.target_structure:
target = ftmc.CoarseGrainRNA(args.target_structure)
target_rmsds = []
target_proj_rmsds = []
try:
target_proj = ftmp.Projection2D(target)
except ValueError:
pass
else:
target_vrs = np.array([x for p in sorted(target_proj._coords.keys()) for x in target_proj._coords[p]])
for proj in projs:
try:
vrs1 = np.array([x for p in sorted(proj._coords.keys()) for x in proj._coords[p]])
prmsd = ftms.rmsd(vrs1, target_vrs)
target_proj_rmsds.append(prmsd)
except:
target_proj_rmsds.append(float("nan"))
target_vrs = ftug.bg_virtual_residues(target)
for cg in cgs:
vrs1 = ftug.bg_virtual_residues(cg)
cgrmsd = ftms.rmsd(vrs1, target_vrs)
target_rmsds.append(cgrmsd)
xval = np.arange(len(cgs)) * args.step_size
if target_proj_rmsds:
axT.plot(xval, target_proj_rmsds, label="Projection", color="green")
axT.plot(xval, target_rmsds, label="3D", color="blue")
axT.set_title("RMSD to target structure")
axT.set_xlabel("step")
axT.set_ylabel("RMSD")
leg = axT.legend(framealpha=0.8, fancybox=True)
leg.get_frame().set_linewidth(0.0)
plt.subplots_adjust(hspace=0.4)
if args.hausdorff_reference:
ref_img = scipy.ndimage.imread(args.hausdorff_reference)
ref_quarter = scipy.ndimage.zoom(ref_img, 0.3) > 150
degrees = get_refimg_longest_axis(ref_img)
global_optima = []
local_optima = []
for cg in cgs:
score, img, params = fph.try_parameters(ref_img, args.hausdorff_scale, cg, degrees)
local_optima.append(score)
s, i, params = fph.globally_minimal_distance(
ref_quarter, args.hausdorff_scale, cg, start_points=40, starting_rotations=degrees, virtual_atoms=False
)
score, img, params = fph.locally_minimal_distance(
ref_img, args.hausdorff_scale, cg, params[1], params[2], params[0], maxiter=200
)
global_optima.append(score)
axH.plot(xval, global_optima, label="Global Minimization", color="red")
axH.plot(xval, local_optima, label="Local Minimization", color="lavender")
axH.set_title("Hausdorff Distance to reference image")
axH.set_xlabel("step")
axH.set_ylabel("Hausdorff Distance")
leg = axH.legend(framealpha=0.8, fancybox=True)
leg.get_frame().set_linewidth(0.0)
plt.subplots_adjust(hspace=0.4)
p_rmsds = np.array(p_rmsds.items())
cg_rmsds = np.array(cg_rmsds.items())
ax.plot(p_rmsds[:, 0] * args.step_size, p_rmsds[:, 1], label="Projection", color="green", marker="o")
ax.plot(cg_rmsds[:, 0] * args.step_size, cg_rmsds[:, 1], label="3D", color="blue", marker="o")
ax.set_title("Average RMSD between structures X steps apart.")
ax.set_xlabel("steps apart")
ax.set_ylabel("RMSD")
leg = ax.legend(framealpha=0.8, fancybox=True)
leg.get_frame().set_linewidth(0.0)
fig.patch.set_facecolor("white")
if args.save_fig:
with open(args.save_fig, "w") as f:
pickle.dump(fig, f)
plt.show()
def main(args):
cgs = []
projs = []
for file_ in args.files:
cgs.append(ftmc.CoarseGrainRNA(file_))
try:
projs.append(ftmp.Projection2D(cgs[-1]))
except ValueError:
projs.append(None)
p_rmsds = OrderedDict()
cg_rmsds = OrderedDict()
p_rmsds[0] = 0.
cg_rmsds[0] = 0.
if "," in args.max_diff:
diffs = map(int, args.max_diff.split(","))
else:
diffs = range(1, int(args.max_diff) + 1)
for diff in diffs:
print("diff {}".format(diff))
prmsd = 0
cgrmsd = 0
count = 0
pcount = 0
for i in range(0, len(cgs) - diff):
count += 1
try:
vrs1 = np.array([
x for p in sorted(projs[i]._coords.keys())
for x in projs[i]._coords[p]
])
vrs2 = np.array([
x for p in sorted(projs[i + diff]._coords.keys())
for x in projs[i + diff]._coords[p]
])
prmsd += ftms.rmsd(vrs1, vrs2)
pcount += 1
except:
pass
cgrmsd += ftms.cg_rmsd(cgs[i], cgs[i + diff])
if count:
cg_rmsds[diff] = cgrmsd / count
if pcount:
p_rmsds[diff] = prmsd / pcount
print("projection RMSDs:", p_rmsds)
print("3D-RMSDs:", cg_rmsds)
import matplotlib.pyplot as plt
if args.target_structure and args.hausdorff_reference:
fig, (ax, axT, axH) = plt.subplots(3)
elif args.target_structure:
fig, (ax, axT) = plt.subplots(2)
elif args.hausdorff_reference:
fig, (ax, axH) = plt.subplots(2)
else:
fig, ax = plt.subplots()
if args.target_structure:
target = ftmc.CoarseGrainRNA(args.target_structure)
target_rmsds = []
target_proj_rmsds = []
try:
target_proj = ftmp.Projection2D(target)
except ValueError:
pass
else:
target_vrs = np.array([
x for p in sorted(target_proj._coords.keys())
for x in target_proj._coords[p]
])
for proj in projs:
try:
vrs1 = np.array([
x for p in sorted(proj._coords.keys())
for x in proj._coords[p]
])
prmsd = ftms.rmsd(vrs1, target_vrs)
target_proj_rmsds.append(prmsd)
except:
target_proj_rmsds.append(float("nan"))
target_vrs = target.get_ordered_virtual_residue_poss()
for cg in cgs:
vrs1 = cg.get_ordered_virtual_residue_poss()
cgrmsd = ftms.rmsd(vrs1, target_vrs)
target_rmsds.append(cgrmsd)
xval = np.arange(len(cgs)) * args.step_size
if target_proj_rmsds:
axT.plot(xval,
target_proj_rmsds,
label="Projection",
color="green")
axT.plot(xval, target_rmsds, label="3D", color="blue")
axT.set_title("RMSD to target structure")
axT.set_xlabel("step")
axT.set_ylabel("RMSD")
leg = axT.legend(framealpha=0.8, fancybox=True)
leg.get_frame().set_linewidth(0.0)
plt.subplots_adjust(hspace=0.4)
if args.hausdorff_reference:
ref_img = scipy.ndimage.imread(args.hausdorff_reference)
ref_quarter = (scipy.ndimage.zoom(ref_img, 0.3) > 150)
degrees = get_refimg_longest_axis(ref_img)
global_optima = []
local_optima = []
for cg in cgs:
score, img, params = fph.try_parameters(ref_img,
args.hausdorff_scale, cg,
degrees)
local_optima.append(score)
s, i, params = fph.globally_minimal_distance(
ref_quarter,
args.hausdorff_scale,
cg,
start_points=40,
starting_rotations=degrees,
virtual_atoms=False)
score, img, params = fph.locally_minimal_distance(
ref_img,
args.hausdorff_scale,
cg,
params[1],
params[2],
params[0],
maxiter=200)
global_optima.append(score)
axH.plot(xval, global_optima, label="Global Minimization", color="red")
axH.plot(xval,
local_optima,
label="Local Minimization",
color="lavender")
axH.set_title("Hausdorff Distance to reference image")
axH.set_xlabel("step")
axH.set_ylabel("Hausdorff Distance")
leg = axH.legend(framealpha=0.8, fancybox=True)
leg.get_frame().set_linewidth(0.0)
plt.subplots_adjust(hspace=0.4)
p_rmsds = np.array(list(p_rmsds.items()))
cg_rmsds = np.array(list(cg_rmsds.items()))
ax.plot(p_rmsds[:, 0] * args.step_size,
p_rmsds[:, 1],
label="Projection",
color="green",
marker="o")
ax.plot(cg_rmsds[:, 0] * args.step_size,
cg_rmsds[:, 1],
label="3D",
color="blue",
marker="o")
ax.set_title("Average RMSD between structures X steps apart.")
ax.set_xlabel("steps apart")
ax.set_ylabel("RMSD")
leg = ax.legend(framealpha=0.8, fancybox=True)
leg.get_frame().set_linewidth(0.0)
fig.patch.set_facecolor('white')
if args.save_fig:
with open(args.save_fig, "w") as f:
pickle.dump(fig, f)
plt.show()
distances=[]
for j in range(numFiles):
cg=ftmc.CoarseGrainRNA(filenames[j])
distance, img, _ = fph.globally_minimal_distance(ref_img, ref_box[1]-ref_box[0], cg)
distances.append(distance)
return distances
except KeyboardInterrupt:
print("In worker {}: Keyboard Interrupt".format(id(ref_img)))
return
def parallel_localOpt((ref_img, ref_box), numFiles, filenames):
try:
distances=[]
for j in range(numFiles):
cg=ftmc.CoarseGrainRNA(filenames[j])
distance, img, _ = fph.locally_minimal_distance(ref_img, ref_box[1]-ref_box[0], cg)
distances.append(distance)
return distances
except KeyboardInterrupt:
print("In worker {}: Keyboard Interrupt".format(id(ref_img)))
return
def get_parser():
"""
Here all commandline & help-messages arguments are defined.
:returns: an instance of argparse.ArgumentParser
"""
parser = argparse.ArgumentParser()
#Argument(s)
parser.add_argument('files', nargs="+", help='One or more "*.cg"/"*.coord" files.')