def test_sample_stats(self):
fa_text = """>1
AGAGGUUCUAGCUACACCCUCUAUAAAAAACUAAGG
(((((............)))))..............
"""
cg = ftmc.CoarseGrainRNA()
cg.from_fasta(fa_text)
conf_stats = ftms.get_conformation_stats()
stats = conf_stats.sample_stats(cg, 't1')
fud.pv('cg.to_cg_string()')
fud.pv('stats')
def test_sample_stats(self):
fa_text = """>1
AGAGGUUCUAGCUACACCCUCUAUAAAAAACUAAGG
(((((............)))))..............
"""
cg = ftmc.CoarseGrainRNA()
cg.from_fasta(fa_text)
conf_stats = ftms.get_conformation_stats()
stats = conf_stats.sample_stats(cg, 't1')
fud.pv('cg.to_cg_string()')
fud.pv('stats')
cg = ftmc.CoarseGrainRNA(dotbracket_str=dotbracket,
seq=seq)
return cg
parser=generateParser()
if __name__=="__main__":
args = parser.parse_args()
energy = fbe.CombinedEnergy([fbe.RoughJunctionClosureEnergy()])
for link_length in range(0,4):
cutoff_distance = (link_length) * 6.22 + 14.0
print("\nLink Length {}".format(link_length))
for l in range(1, 30): #Sampling 30 times with different stem stats
cg = construct_test_graph(random.randrange(3,15),l,link_length)
sm=fbm.SpatialModel(cg, ftms.get_conformation_stats(data_file("stats/all_filtered.stats")))
sm.sample_stats()
possible_sstats=sm.conf_stats.sample_stats(cg, "s1")
for sstat in possible_sstats:
if sstat.pdb_name.startswith("ideal"):
break
maxdist=0
sm.elem_defs["s1"] = sstat
possible_stats=sm.conf_stats.sample_stats(cg, "m0")
for i, stat in enumerate(possible_stats):
#if i%1000 == 0:
# print(i, "/", len(possible_stats), end="")
sm.elem_defs["m0"] = stat
sm.traverse_and_build()
e=energy.eval_energy(sm)
import collections as col
import sklearn.cluster
def generateParser():
parser=argparse.ArgumentParser( description="Cluster angle stats.")
parser.add_argument("--stats-file", type=str, help="What stats file to use", default="stats/all_filtered.stats")
parser.add_argument("-p", "--plot", action="store_true", help="Plot some clusters")
parser.add_argument("-t", "--threshold", type=float, help="Threshold for the Birch clustering algorithm", default=0.3)
return parser
parser=generateParser()
if __name__=="__main__":
args = parser.parse_args()
#Get the stats object
conf_stats = ftms.get_conformation_stats(data_file(args.stats_file))
angle_stats = conf_stats.angle_stats
colors = plt.cm.Spectral(np.linspace(0, 1, 10))
colors=np.array(list(colors)*500)
markers="o"*10+"v"*10+"<"*10+">"*10+"."*10+"s"*10+"^"*10+"8"*10+"*"*10+"+"*10
markers=markers*500
for key in angle_stats.keys():
point_to=[]
to_clust=[]
angles=[]
for stat in angle_stats[key]:
stem_start = np.array(ftuv.spherical_polar_to_cartesian([stat.r1, stat.u1, stat.v1]))
#stem_vec = np.array(ftuv.spherical_polar_to_cartesian([1, stat.u, stat.v]))
#point1 = stem_start+stem_vec
stem_vec = np.array(ftuv.spherical_polar_to_cartesian([1, stat.u, stat.v]))
#point2 = stem_start+stem_vec
"--plot",
action="store_true",
help="Plot some clusters")
parser.add_argument("-t",
"--threshold",
type=float,
help="Threshold for the Birch clustering algorithm",
default=0.3)
return parser
parser = generateParser()
if __name__ == "__main__":
args = parser.parse_args()
#Get the stats object
conf_stats = ftms.get_conformation_stats(data_file(args.stats_file))
angle_stats = conf_stats.angle_stats
colors = plt.cm.Spectral(np.linspace(0, 1, 10))
colors = np.array(list(colors) * 500)
markers = "o" * 10 + "v" * 10 + "<" * 10 + ">" * 10 + "." * 10 + "s" * 10 + "^" * 10 + "8" * 10 + "*" * 10 + "+" * 10
markers = markers * 500
for key in angle_stats.keys():
point_to = []
to_clust = []
angles = []
for stat in angle_stats[key]:
stem_start = np.array(
ftuv.spherical_polar_to_cartesian([stat.r1, stat.u1, stat.v1]))
#stem_vec = np.array(ftuv.spherical_polar_to_cartesian([1, stat.u, stat.v]))
#point1 = stem_start+stem_vec
stem_vec = np.array(
def compare(self, d, other_stats):
if not self.rmsd:
stats = self.ref_sm.elem_defs[d]
return is_similar(stats, other_stats, self.tol)
else:
self.ref_sm.elem_defs[d]=other_stats
self.ref_sm.traverse_and_build(start=d)
curr_vress=self.ref_sm.bg.get_ordered_virtual_residue_poss()
rmsd=ftur.rmsd(self.ref_vress, curr_vress)
return rmsd<self.tol
parser=generateParser()
if __name__=="__main__":
args = parser.parse_args()
cg=ftmc.CoarseGrainRNA(args.cg)
sm=fbm.SpatialModel(cg, ftms.get_conformation_stats(args.stats_file))
sm.load_sampled_elems()
maxNum=0
maxLoop=0
sumStats=0
comp = Comparison(sm.bg, args.tolerance, args.rmsd)
for define in cg.defines:
similar_stats=0
allstats = sm.conf_stats.sample_stats(cg, define)
numStats = len(allstats)
if define[0] in "mi" and numStats:
if args.cluster:
distance=np.full((len(allstats),len(allstats)),np.NAN)
for i, stat in enumerate(allstats):
if args.cluster:
for j, stat2 in enumerate(allstats):
from __future__ import (absolute_import, division,
print_function, unicode_literals)
from builtins import (ascii, bytes, chr, dict, filter, hex, input,
int, map, next, oct, open, pow, range, round,
str, super, zip)
import forgi.threedee.model.coarse_grain as ftmc
import fess.builder.models as fbm
import time
import forgi.threedee.model.stats as ftms
#This takes some time, which is why it is only executed once at startup of the server.
print(" * Loading default conformation stats")
default_conf_stats=ftms.get_conformation_stats()
class CgFileCache():
def __init__(self):
self.cached={}
self.time={}
self.max_size=64
def loadSM(self, filename):
if filename not in self.cached:
if len(self.cached)>self.max_size:
self.cleanup()
cg=ftmc.CoarseGrainRNA("user_files/"+filename)
sm=fbm.SpatialModel(cg, conf_stats=default_conf_stats)
self.cached[filename]=sm
self.time[filename]=time.time()
return self.cached[filename]
def removeSM(self, filename):
"""
def setup_deterministic(args):
"""
The part of the setup procedure that does not use any call to the random number generator.
:param args: An argparse.ArgumentParser object holding the parsed arguments.
"""
#Load the RNA from file
rnafile, = args.rna #Tuple unpacking
if rnafile[-3:] == '.fa':
cg = ftmc.CoarseGrainRNA()
try:
with open(rnafile) as fastafile:
cg.from_fasta(fastafile.read())
except IOError as e:
print("ERROR: Could not open file '{}' for reading:".format(rnafile),e, file=sys.stderr)
sys.exit(1)
else:
cg = ftmc.CoarseGrainRNA(rnafile)
#Output file and directory
ofilename=None
if not args.eval_energy:
if args.output_base_dir and not os.path.exists(args.output_base_dir):
os.makedirs(args.output_base_dir)
print ("INFO: Directory {} created.".format(args.output_base_dir), file=sys.stderr)
subdir=cg.name+args.output_dir_suffix
config.Configuration.sampling_output_dir = os.path.join(args.output_base_dir, subdir)
if not os.path.exists(config.Configuration.sampling_output_dir):
os.makedirs(config.Configuration.sampling_output_dir)
print ("INFO: Directory {} created. This folder will be used for all output "
"files.".format(config.Configuration.sampling_output_dir), file=sys.stderr)
if args.output_file:
ofilename=os.path.join(config.Configuration.sampling_output_dir, args.output_file)
#Initialize the spatial model
if args.clustered_angle_stats and args.jar3d:
print("ERROR: --clustered-angle-stats and --jar3d are mutually exclusive!", file=sys.stderr)
sys.exit(1)
if args.clustered_angle_stats:
sm=fbm.SpatialModel(cg, ftms.get_conformation_stats(args.stats_file, args.clustered_angle_stats))
elif args.jar3d:
jared_out = op.join(config.Configuration.sampling_output_dir, "filtered_stats")
jared_tmp = op.join(config.Configuration.sampling_output_dir, "jar3d")
motifs = fma.annotate_structure(cg, jared_tmp, cg.name.split('_')[0])
elems = fma.motifs_to_cg_elements(motifs, config.Configuration.sampling_output_dir, filename = jared_out)
filtered_stats = ftms.FilteredConformationStats(stats_file=args.stats_file,
filter_filename=jared_out)
ftms.set_conformation_stats(filtered_stats)
sm=fbm.SpatialModel(cg, ftms.get_conformation_stats())
else:
sm=fbm.SpatialModel(cg, ftms.get_conformation_stats(args.stats_file))
#Load the reference sm (if given)
if args.rmsd_to:
if args.rmsd_to.endswith(".pdb"):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
pdb = list(bp.PDBParser().get_structure('reference', args.rmsd_to).get_chains())[0]
original_sm = fbm.SpatialModel(ftmc.from_pdb(pdb))
else:
original_sm=fbm.SpatialModel(ftmc.CoarseGrainRNA(args.rmsd_to))
else:
original_sm=fbm.SpatialModel(copy.deepcopy(sm.bg))
#Initialize the requested energies
if args.energy=="D":
energy=fbe.CombinedEnergy([],getDefaultEnergies(cg))
else:
energy=parseCombinedEnergyString(args.energy, cg, original_sm.bg, args)
#Initialize energies to track
energies_to_track=[]
for track_energy_string in args.track_energies.split(":"):
if track_energy_string:
if track_energy_string=="D":
energies_to_track.append(fbe.CombinedEnergy([],getDefaultEnergies(cg)))
else:
energies_to_track.append(parseCombinedEnergyString(track_energy_string, cg,
original_sm.bg, args))
#Initialize the Constraint energies
junction_energy=None
clash_energy=None
if args.constraint_energy in ["D","B","J"]:
sm.junction_constraint_energy=fbe.CombinedEnergy([fbe.RoughJunctionClosureEnergy()])
if args.constraint_energy in ["D","B","C"]:
sm.constraint_energy=fbe.CombinedEnergy([fbe.StemVirtualResClashEnergy()])
return sm, original_sm, ofilename, energy, energies_to_track
cg = ftmc.CoarseGrainRNA(dotbracket_str=dotbracket, seq=seq)
return cg
parser = generateParser()
if __name__ == "__main__":
args = parser.parse_args()
energy = fbe.CombinedEnergy([fbe.RoughJunctionClosureEnergy()])
for link_length in range(0, 4):
cutoff_distance = (link_length) * 6.22 + 14.0
print("\nLink Length {}".format(link_length))
for l in range(1, 30): #Sampling 30 times with different stem stats
cg = construct_test_graph(random.randrange(3, 15), l, link_length)
sm = fbm.SpatialModel(
cg,
ftms.get_conformation_stats(
data_file("stats/all_filtered.stats")))
sm.sample_stats()
possible_sstats = sm.conf_stats.sample_stats(cg, "s1")
for sstat in possible_sstats:
if sstat.pdb_name.startswith("ideal"):
break
maxdist = 0
sm.elem_defs["s1"] = sstat
possible_stats = sm.conf_stats.sample_stats(cg, "m0")
for i, stat in enumerate(possible_stats):
#if i%1000 == 0:
# print(i, "/", len(possible_stats), end="")
sm.elem_defs["m0"] = stat
sm.traverse_and_build()
e = energy.eval_energy(sm)