本文整理汇总了Python中Analysis.loadmapping方法的典型用法代码示例。如果您正苦于以下问题：Python Analysis.loadmapping方法的具体用法？Python Analysis.loadmapping怎么用？Python Analysis.loadmapping使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Analysis的用法示例。
在下文中一共展示了Analysis.loadmapping方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: len
# 需要导入模块: import Analysis [as 别名]
# 或者: from Analysis import loadmapping [as 别名]
help = "Maximum allowed inter-color colocalization distance",
default = 100, type = "float")
parser.add_option('-q', '--quiet', dest = "quiet",
action = "store_true", default = False)
parser.add_option('-d', '--quiver-distance', dest="quiv_dist",
help = "Minimum distance between points on the quiver map",
default = 10)
opts, args = parser.parse_args()
if not opts.map:
if opts.x1 and opts.x2 and opts.y1 and opts.y2:
opts.xshift = opts.x2 - opts.x1
opts.yshift = opts.y2 - opts.y1
else:
mapping = Analysis.loadmapping(opts.map)
if opts.map2:
mapping2 = Analysis.loadmapping(opts.map2)
if len(args) == 1 and '*' in args[0]:
args = glob(args[0])
print "Found this many files", len(args)
xrs = []
xls = []
yrs = []
yls = []
varxrs = []
varxls = []
for fname in args:示例2: mapping
# 需要导入模块: import Analysis [as 别名]
# 或者: from Analysis import loadmapping [as 别名]
import Analysis, sys, math, numpy as np, pylab
mapping = Analysis.loadmapping(sys.argv[1])
num_rows = 256
num_cols = 512
colors = np.zeros((256,512,3))
starty = np.random.rand(1,100)*150 + 50
startx = np.random.rand(1,100)*400 + 50
endx, endy = mapping(startx, starty)
base_shift_x = 256 # (endx - startx).mean()
base_shift_y = 512 # (endy - starty).mean()
for x in range(num_cols):
for y in range(num_rows):
newx, newy = mapping(x,y)
sat = 1
hue = (90 + 180 / math.pi * math.atan2(newy - base_shift_y, newx - base_shift_x)) % 360
val = min(.001 * math.sqrt((newy - base_shift_y)**2 + (newx - base_shift_x)**2), 1)
hueprm = hue / 60
C = val * sat
X = C * (1 - abs((hueprm %2) -1 ))
if hueprm < 1: r1, g1, b1 = C, X, 0
elif hueprm < 2: r1, g1, b1 = X, C, 0
elif hueprm < 3: r1, g1, b1 = X, C, 0