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#!/usr/bin/env python
# * Copyright 2012 Alistair Buxton <a.j.buxton@gmail.com>
# *
# * License: This program is free software; you can redistribute it and/or
# * modify it under the terms of the GNU General Public License as published
# * by the Free Software Foundation; either version 3 of the License, or (at
# * your option) any later version. This program is distributed in the hope
# * that it will be useful, but WITHOUT ANY WARRANTY; without even the implied
# * warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# * GNU General Public License for more details.
import math
import numpy as np
from scipy.interpolate import interp1d
from scipy.ndimage import correlate1d
_le = np.arange(0, 8, 1)
_be = np.arange(7, -1, -1)
def calc_kernel(sigma):
sd = float(sigma)
# make the length of the filter equal to 4 times the standard
# deviations:
lw = int(4.0 * sd + 0.5)
weights = [0.0] * (2 * lw + 1)
weights[lw] = 1.0
sum = 1.0
sd = sd * sd
# calculate the kernel:
for ii in range(1, lw + 1):
tmp = math.exp(-0.5 * float(ii * ii) / sd)
weights[lw + ii] = tmp
weights[lw - ii] = tmp
sum += 2.0 * tmp
for ii in range(2 * lw + 1):
weights[ii] /= sum
return np.array(weights)
_kernel = calc_kernel(5.112)
_lk = len(_kernel)
_mask_bits = np.zeros(47*8, dtype=np.float32)
_mask_bits[0:32] += 1
class Guess(object):
def __init__(self, width=2048, bitwidth=5.112):
self._width = width
self._bitwidth = bitwidth
self.bytes = np.zeros(42, dtype=np.uint8)
self._bits = np.zeros(47*8, dtype=np.float32)
self._set_bits(1, 0x55)
self._set_bits(2, 0x55)
self._set_bits(3, 0x27)
self._interp_x = np.zeros(376, dtype=np.float32)
self._interp_x[:] = (np.arange(0,47*8,1.0) * bitwidth) - bitwidth*8
self._guess_x = np.zeros(2100, dtype=np.float32)
self._guess_scaler = interp1d(self._interp_x, self._bits,
kind='linear', copy=False,
bounds_error=False, fill_value=0)
self.convolved = np.zeros(width, dtype=np.float32)
self._mask_scaler = interp1d(self._interp_x, _mask_bits,
kind='linear', copy=False,
bounds_error=False, fill_value=1)
self.mask = np.zeros(width, dtype=np.float32)
def set_offset(self, offset):
self._offset = offset
self._guess_x[:] = np.arange(0,2100,1.0) - offset
def get_bit_pos(self, bit):
bit += 32
low = max(0, self._interp_x[bit] + self._offset - (self._bitwidth*0.5))
return (int(low)+1, int(low+self._bitwidth)+1)
def set_update_range(self, which, n):
which *= 8
self.low = int(self._interp_x[which] + self._offset) - _lk
self.high = int(self._interp_x[which+(n*8)] + self._offset) + _lk
self.olow = self.low - _lk
self.ohigh = self.high + _lk
return (self.low, self.high)
def update(self):
self.convolved[self.low:self.high] = correlate1d(self._guess_scaler(self._guess_x[self.olow:self.ohigh]), _kernel)[_lk:-_lk]
def update_cri(self, low, high):
self.convolved[low:high] = correlate1d(self._guess_scaler(self._guess_x[low:high]), _kernel)
self.mask[low:high] = self._mask_scaler(self._guess_x[low:high])
def update_all(self):
self.convolved[0:self._width] = correlate1d(self._guess_scaler(self._guess_x)[0:self._width], _kernel)
def _set_bits(self, which, value):
which *= 8
self._bits[which:which+8] = 1&(value>>_le)
def set_byte(self, which, value):
self.bytes[which] = value
which += 4
self._set_bits(which, value)
self.update()
def set_two_bytes(self, which, v1, v2):
self.bytes[which] = v1
self.bytes[which+1] = v2
which += 4
self._set_bits(which, v1)
self._set_bits(which+1, v2)
self.update()
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