kmeans_common Namespace Reference

Functions
def	get_common_int_coords_2 (x, y, z)
def	get_common_int_coords (x, y, z)
def	load_centroid (fn, ncluster)
def	load_centroid_xyz (fn, ncluster)

Variables
float	cutoff = 1.6
	restype = sys.argv[1]
	ncluster = int(sys.argv[2])
string	inpfile = "./split/" + restype + ".dat"
	inp = open(inpfile,'r')
	lines = inp.readlines()
	skip = int(len(lines)/10000)
list	colorlist = ['r', 'b', 'g', 'y', 'c', 'm', 'k', 'w', 'r']
list	marklist = ['o', '^', '+', '>', (5,2), (5,0)]
list	xyzlist = []
list	bblist = []
	dats = line.split()
	dis = float(dats[0])
	ang = float(dats[1])
	dih = float(dats[2])
	psi = float(dats[3])
	phi = float(dats[4])
	x = dis*sin(ang)*cos(dih)
	y = dis*sin(ang)*sin(dih)
	z = dis*cos(ang)
	data = vstack(xyzlist)
	cluster, find the centroid More...
	save_data = data
string	fn = "refine/" + restype + ".xyz"
def	centroids = load_centroid_xyz(fn, ncluster)
	_
	idx
	assign More...
	dist
	nd = len(data)
	savend = nd
	fig = plt.figure()
	ax = fig.add_subplot(111, projection='3d')
float	R = 2.0
list	c = colorlist[i]
string	m = '.'
	flag = i
	xs = save_data[flag, 0]
	ys = save_data[flag, 1]
	zs = save_data[flag, 2]
	n = len(xs)
	s
	marker
	r = sqrt(x*x+y*y+z*z)
	t = arccos(z/r)
	p = arctan2(y,x)
	u
	v
	color

Function Documentation

get_common_int_coords()

def kmeans_common.get_common_int_coords	(		x,
			y,
			z
	)

References numeric.arccos(), and ObjexxFCL.len().

get_common_int_coords_2()

def kmeans_common.get_common_int_coords_2	(		x,
			y,
			z
	)

References numeric.arccos(), and ObjexxFCL.len().

load_centroid()

def kmeans_common.load_centroid	(		fn,
			ncluster
	)

References run_backbone.float, ObjexxFCL.len(), and basic::database.open().

load_centroid_xyz()

def kmeans_common.load_centroid_xyz	(		fn,
			ncluster
	)

References run_backbone.float, ObjexxFCL.len(), and basic::database.open().

Variable Documentation

_

kmeans_common._

private

ang

kmeans_common.ang = float(dats[1])

ax

kmeans_common.ax = fig.add_subplot(111, projection='3d')

bblist

list kmeans_common.bblist = []

c

kmeans_common.c = colorlist[i]

centroids

kmeans_common.centroids = load_centroid_xyz(fn, ncluster)

color

kmeans_common.color

colorlist

list kmeans_common.colorlist = ['r', 'b', 'g', 'y', 'c', 'm', 'k', 'w', 'r']

cutoff

kmeans_common.cutoff = 1.6

data

kmeans_common.data = vstack(xyzlist)

cluster, find the centroid

dats

kmeans_common.dats = line.split()

dih

kmeans_common.dih = float(dats[2])

dis

kmeans_common.dis = float(dats[0])

dist

kmeans_common.dist

fig

kmeans_common.fig = plt.figure()

flag

kmeans_common.flag = i

fn

string kmeans_common.fn = "refine/" + restype + ".xyz"

idx

kmeans_common.idx

assign

inp

kmeans_common.inp = open(inpfile,'r')

inpfile

string kmeans_common.inpfile = "./split/" + restype + ".dat"

lines

kmeans_common.lines = inp.readlines()

m

string kmeans_common.m = '.'

marker

kmeans_common.marker

marklist

list kmeans_common.marklist = ['o', '^', '+', '>', (5,2), (5,0)]

n

kmeans_common.n = len(xs)

ncluster

kmeans_common.ncluster = int(sys.argv[2])

nd

kmeans_common.nd = len(data)

p

kmeans_common.p = arctan2(y,x)

phi

kmeans_common.phi = float(dats[4])

psi

kmeans_common.psi = float(dats[3])

R

kmeans_common.R = 2.0

r

kmeans_common.r = sqrt(x*x+y*y+z*z)

restype

kmeans_common.restype = sys.argv[1]

s

kmeans_common.s

save_data

kmeans_common.save_data = data

savend

kmeans_common.savend = nd

skip

kmeans_common.skip = int(len(lines)/10000)

t

kmeans_common.t = arccos(z/r)

u

kmeans_common.u

v

kmeans_common.v

x

float kmeans_common.x = dis*sin(ang)*cos(dih)

xs

kmeans_common.xs = save_data[flag, 0]

xyzlist

list kmeans_common.xyzlist = []

y

float kmeans_common.y = dis*sin(ang)*sin(dih)

ys

kmeans_common.ys = save_data[flag, 1]

z

float kmeans_common.z = dis*cos(ang)

zs

kmeans_common.zs = save_data[flag, 2]