"""
__author__ = "John Kirkham <*****@*****.**>"
__date__ = "$May 01, 2014 14:23:45 EDT$"
import numpy
# Need in order to have logging information no matter what.
from nanshe.util import prof
# Get the logger
trace_logger = prof.getTraceLogger(__name__)
@prof.log_call(trace_logger)
def estimate_noise(input_array, significance_threshold=3.0):
"""
Estimates the noise in the given array.
Using the array finds what the standard deviation is of some values in
the array, which are within the standard deviation of the whole array
times the significance threshold.
Args:
input_array(numpy.ndarray): the array to estimate noise of.
significance_threshold(float): the number of standard
deviations (of the whole
import warnings
import numpy
import vigra
from nanshe.io import hdf5
from nanshe.util.iters import irange
from nanshe.util.xnumpy import binomial_coefficients
# Need in order to have logging information no matter what.
from nanshe.util import prof
# Get the logger
trace_logger = prof.getTraceLogger(__name__)
@prof.log_call(trace_logger)
def binomial_1D_array_kernel(i, n=4):
"""
Generates a 1D numpy array used to make the kernel for the wavelet
transform.
Args:
i(int): which scaling to use.
n(int): which row of Pascal's triangle to return.
Returns:
r(numpy.ndarray): a 1D numpy array to use as the wavelet
transform kernel.
