def disturb(*args, glob_vars={}, printer=ConsolePrinter()):
var = save_eval(*args, glob_vars=glob_vars, printer=printer)
off = (random.random() - 0.5)*0.02
res = []
for val in var:
res.append(val + (random.random()-0.5)*0.009 + off)
return array(res)
def convolute_with_psf(*args, glob_vars={}, printer=ConsolePrinter()):
var = save_eval(*args, glob_vars=glob_vars, printer=printer)
if "sigma" in glob_vars:
printer.print("Variable sigma is defined. Taking it to scale point spread function...")
sigma = glob_vars["sigma"]
else:
printer.print("Variable sigma is undefined. Taking 1...")
sigma = 1
if not isinstance(var, ndarray):
printer.print("Convolution is only possible with matrices/vectors right now.", color="red")
dim = len(var.shape)
assert dim == 3
result = zeros(var.shape)
def convolute(*args, glob_vars={}, printer=ConsolePrinter()):
var = save_eval(*args, glob_vars=glob_vars, printer=printer)
if "sigma" in glob_vars:
printer.print("Variable sigma is defined. Taking it to scale point spread function...")
sigma = glob_vars["sigma"]
else:
printer.print("Variable sigma is undefined. Taking 1...")
sigma = 1
res = []
for i in range(0, len(var)):
this = 0
for j in range(0, len(var)):
this += point_spread_kernel(i, j, sigma, len(var)) * var[j]
res.append(this)
return array(res)
def mmult(*args, glob_vars={}, printer=ConsolePrinter()):
args = seperate_by_keywords(args, ["with"])
first=save_eval(args[None], glob_vars=glob_vars, printer=printer)
sec=save_eval(args["with"], glob_vars=glob_vars, printer=printer)
return array(matrix(first)*matrix(sec))
