def main(args):
with fuc.hide_traceback():
cg1, cg2 = fuc.cgs_from_args(args, rna_type="3d", enable_logging=True)
if not (args.acc or args.rmsd or args.pdb_rmsd):
showall = True
else:
showall = False
if showall or args.acc:
if cg1.defines != cg2.defines:
if args.acc:
print(
"Cannot compare two 3d structures that do not correspond to the same RNA."
)
sys.exit(1)
else:
adj = ftms.AdjacencyCorrelation(cg1)
print("ACC:\t{:.3f}".format(ftms.mcc(adj.evaluate(cg2))))
if showall or args.rmsd:
print("RMSD:\t{:.3f}".format(ftms.cg_rmsd(cg1, cg2)))
if showall or args.pdb_rmsd:
if not pdb_rmsd(cg1, cg2):
# If --pdb-rmsd was not given, just don't print it.
# If it was given, we exit with non-zero exit status.
if args.pdb_rmsd:
print(
"Cannot calculate PDB-RMSD: The two files do not contain the same chains."
)
sys.exit(1)
def __init__(self, reference_cg):
super(ACCStatistics, self).__init__()
try:
self._cm_calc = ftme.AdjacencyCorrelation(reference_cg)
except Exception as e:
log.exception(e)
warnings.warn("Cannot report ACC. {} in reference SM: {}".format(
type(e), str(e)))
self.silent = True
self.history = None
def test_new_confusionmatrix_is_like_old(self):
cg1 = ftmc.CoarseGrainRNA('test/forgi/threedee/data/1GID_A.cg')
cg2 = ftmc.CoarseGrainRNA('test/forgi/threedee/data/1GID_A_sampled.cg')
cm = confusion_matrix(cg1, cg2)
mcc = ftme.mcc(cm)
cm_new = ftme.AdjacencyCorrelation(cg1) #previousely named ConfusionMatrix
mcc_n = ftme.mcc(cm_new.evaluate(cg2))
self.assertAlmostEqual(mcc, mcc_n)
self.assertAlmostEqual(mcc_n, 0.6756639246921762)
cm = confusion_matrix(cg1, cg1)
mcc = ftme.mcc(cm)
mcc_n = ftme.mcc(cm_new.evaluate(cg1))
self.assertAlmostEqual(mcc, mcc_n)
self.assertAlmostEqual(mcc_n, 1.0)
def test_new_confusionmatrix_is_like_old(self):
cg1 = ftmc.CoarseGrainRNA('test/forgi/threedee/data/1GID_A.cg')
cg2 = ftmc.CoarseGrainRNA('test/forgi/threedee/data/1GID_A_sampled.cg')
cm = ftme.confusion_matrix(cg1, cg2)
mcc = ftme.mcc(cm)
cm_new = ftme.AdjacencyCorrelation(
cg1) #previousely named ConfusionMatrix
mcc_n = ftme.mcc(cm_new.evaluate(cg2))
self.assertAlmostEqual(mcc, mcc_n)
self.assertLess(abs(mcc_n - 0.0761),
0.0001) #0.086 for distance=30, 0.0761 for 25
cm = ftme.confusion_matrix(cg1, cg1)
mcc = ftme.mcc(cm)
mcc_n = ftme.mcc(cm_new.evaluate(cg1))
self.assertAlmostEqual(mcc, mcc_n)
self.assertAlmostEqual(mcc_n, 1.0)
def main(args):
if len(args.compareTo) == 1:
cg1 = ftmc.CoarseGrainRNA(args.reference[0])
cg2 = ftmc.CoarseGrainRNA(args.compareTo[0])
print(ftms.cg_rmsd(cg1, cg2))
else:
print("{:15}\t{:6}\t{:6}\t{:6}".format("filename", "RMSD", "dRMSD",
"ACC"))
ref_cg = ftmc.CoarseGrainRNA(args.reference[0])
reference = ref_cg.get_ordered_virtual_residue_poss()
acc_calc = ftms.AdjacencyCorrelation(ref_cg)
for filename in args.compareTo:
cg = ftmc.CoarseGrainRNA(filename)
curr_vress = cg.get_ordered_virtual_residue_poss()
rmsd = ftur.rmsd(reference, curr_vress)
drmsd = ftur.drmsd(reference, curr_vress)
acc = ftms.mcc(acc_calc.evaluate(cg))
print("{:15}\t{:6.3f}\t{:6.3f}\t{:6.3f}".format(
filename[-15:], rmsd, drmsd, acc))
def main():
usage = """
python cg_to_fornac_html.py file1.cg file2.cg
Convert coarse grain files to html files using fornac
to display a 2D version of the structure.
"""
num_args = 1
parser = OptionParser(usage=usage)
parser.add_option(
'-d',
'--distance',
dest='distance',
default=25,
help=
"Draw links between elements that are within a certain distance from each other",
type='float')
parser.add_option(
'-b',
'--bp-distance',
dest='bp_distance',
default=16,
help=
"Draw links only between nucleotides which are so many nucleotides apart",
type='int')
parser.add_option('-s',
'--sort-by',
dest='sort_by',
default='mcc',
help="What to sort by (options: mcc, pca)",
type='string')
parser.add_option('-n',
'--names',
dest='names',
default=False,
action='store_true',
help='Add the name of the structure to the display')
(options, args) = parser.parse_args()
if len(args) < num_args:
parser.print_help()
sys.exit(1)
structs = []
pair_bitmaps = []
cgs = []
all_links = []
mccs = []
cm = None
for filename in args:
cg = ftmc.CoarseGrainRNA(filename)
cgs += [cg]
if not cm:
cm = ftme.AdjacencyCorrelation(cg)
(links, pair_bitmap) = extract_extra_links(
cg,
options.distance,
options.bp_distance,
correct_links=None if len(all_links) == 0 else all_links[0])
all_links += [links]
pair_bitmaps += [pair_bitmap]
mcc = ftme.mcc(cm.evaluate(cg))
rmsd = ftme.cg_rmsd(cgs[0], cg)
seq_struct = {
"sequence": cg.seq,
"structure": cg.to_dotbracket_string(),
"extraLinks": links
}
fud.pv('options.names')
fud.pv('mcc, rmsd')
if options.names:
seq_struct['name'] = op.basename(
filename) + " ({:.2f},{:.1f})".format(mcc, rmsd)
else:
seq_struct['name'] = ''
structs += [seq_struct]
mccs += [mcc]
if options.sort_by == 'pca':
print("Sorting by pca", file=sys.stderr)
ix = reorder_structs(pair_bitmaps)
else:
print("Sorting by mcc", file=sys.stderr)
ix = np.argsort(-np.array(mccs))
new_array = [0 for i in range(len(ix))]
for i, x in enumerate(ix):
new_array[i] = structs[x]
print(output_template.format(json.dumps(new_array)))