def calc_deshaw_centroid_alpha_cartesian(ptlist=None):
""" Calc RMSD from list of trajectories
"""
if ptlist is None:
pts = deshaw.loadpts(skip=100, filt=deshaw.FILTER['alpha'])
else:
pts = ptlist
# sums = np.zeros(shape=(5, 58, 3))
# cnts = [0 for i in range(5)]
groupby = [[] for i in range(5)]
label = deshaw.loadlabels_aslist()
for idx, pt in enumerate(pts):
# idx = math.floor(i/10)
try:
state = label[idx]
if state == label[idx-2] == label[idx-1] == label[idx+1] == label[idx+2]:
# sums[state] += pt
# cnts[state] += 1
groupby[state].append(pt)
except IndexError as err:
pass # ignore idx errors due to tail end of DEShaw data
# cent = [sums[i] / cnts[i] for i in range(5)]
cent = np.zeros(shape=(5, 58,3))
for i in range(5):
cent[i] = ndimage.measurements.center_of_mass(np.array(groupby[i]))
return np.array(cent)
def calc_deshaw_centroid_alpha_cartesian(ptlist=None):
""" Calc RMSD from list of trajectories
"""
if ptlist is None:
pts = deshaw.loadpts(skip=100, filt=deshaw.FILTER['alpha'])
else:
pts = ptlist
# sums = np.zeros(shape=(5, 58, 3))
# cnts = [0 for i in range(5)]
groupby = [[] for i in range(5)]
label = deshaw.loadlabels_aslist()
for idx, pt in enumerate(pts):
# idx = math.floor(i/10)
try:
state = label[idx]
if state == label[idx - 2] == label[idx - 1] == label[
idx + 1] == label[idx + 2]:
# sums[state] += pt
# cnts[state] += 1
groupby[state].append(pt)
except IndexError as err:
pass # ignore idx errors due to tail end of DEShaw data
# cent = [sums[i] / cnts[i] for i in range(5)]
cent = np.zeros(shape=(5, 58, 3))
for i in range(5):
cent[i] = ndimage.measurements.center_of_mass(np.array(groupby[i]))
return np.array(cent)
cent = np.load(home+'/ddc/bpti-alpha-cart-centroid-well.npy')
r.delete('label:hist')
for size in ['sm', 'md', 'lg']:
r.delete('label:hist:%s'%size)
raw_obs = []
st = dt.datetime.now()
SM = 0.25
MD = .5
LG = 1
theta = {'sm':.25, 'md':.5, 'lg':1.}
raw_obs = {'sm':[], 'md':[], 'lg':[]}
missing = 0
points = deshaw.loadpts(skip=1000, filt=deshaw.FILTER['alpha'])
rmsd = []
for num, pt in enumerate(points):
# rmsd.append(np.array([np.sum([LA.norm(pt[a] - C[a]) for a in range(58)]) for C in cent]))
rmsd.append(np.array([LA.norm(pt - C) for C in cent]))
prox = [np.argsort(i) for i in rmsd]
pipe = r.pipeline()
for i, rm in enumerate(rmsd):
A = prox[i][0]
B = prox[i][1]
for size in ['sm', 'md', 'lg']:
if (rm[B] - rm[A]) > theta[size]:
raw_obs[size].append((A, A))
else:
raw_obs[size].append((A, B))