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#
# Copyright 2008 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio 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, or (at your option)
# any later version.
#
# GNU Radio 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.
#
# You should have received a copy of the GNU General Public License
# along with GNU Radio; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street,
# Boston, MA 02110-1301, USA.
#
#A macro to apply an index to a key
index_key = lambda key, i: "%s_%d"%(key, i+1)
def _register_access_method(destination, controller, key):
"""
Helper function for register access methods.
This helper creates distinct set and get methods for each key
and adds them to the destination object.
"""
def set(value): controller[key] = value
setattr(destination, 'set_'+key, set)
def get(): return controller[key]
setattr(destination, 'get_'+key, get)
def register_access_methods(destination, controller):
"""
Register setter and getter functions in the destination object for all keys in the controller.
@param destination the object to get new setter and getter methods
@param controller the pubsub controller
"""
for key in controller.keys(): _register_access_method(destination, controller, key)
##################################################
# Input Watcher Thread
##################################################
from gnuradio import gru
class input_watcher(gru.msgq_runner):
"""
Input watcher thread runs forever.
Read messages from the message queue.
Forward messages to the message handler.
"""
def __init__ (self, msgq, controller, msg_key, arg1_key='', arg2_key=''):
self._controller = controller
self._msg_key = msg_key
self._arg1_key = arg1_key
self._arg2_key = arg2_key
gru.msgq_runner.__init__(self, msgq, self.handle_msg)
def handle_msg(self, msg):
if self._arg1_key: self._controller[self._arg1_key] = msg.arg1()
if self._arg2_key: self._controller[self._arg2_key] = msg.arg2()
self._controller[self._msg_key] = msg.to_string()
##################################################
# Shared Functions
##################################################
import numpy
import math
def get_exp(num):
"""
Get the exponent of the number in base 10.
@param num the floating point number
@return the exponent as an integer
"""
if num == 0: return 0
return int(math.floor(math.log10(abs(num))))
def get_clean_num(num):
"""
Get the closest clean number match to num with bases 1, 2, 5.
@param num the number
@return the closest number
"""
if num == 0: return 0
sign = num > 0 and 1 or -1
exp = get_exp(num)
nums = numpy.array((1, 2, 5, 10))*(10**exp)
return sign*nums[numpy.argmin(numpy.abs(nums - abs(num)))]
def get_clean_incr(num):
"""
Get the next higher clean number with bases 1, 2, 5.
@param num the number
@return the next higher number
"""
num = get_clean_num(num)
exp = get_exp(num)
coeff = int(round(num/10**exp))
return {
-5: -2,
-2: -1,
-1: -.5,
1: 2,
2: 5,
5: 10,
}[coeff]*(10**exp)
def get_clean_decr(num):
"""
Get the next lower clean number with bases 1, 2, 5.
@param num the number
@return the next lower number
"""
num = get_clean_num(num)
exp = get_exp(num)
coeff = int(round(num/10**exp))
return {
-5: -10,
-2: -5,
-1: -2,
1: .5,
2: 1,
5: 2,
}[coeff]*(10**exp)
def get_min_max(samples):
"""
Get the minimum and maximum bounds for an array of samples.
@param samples the array of real values
@return a tuple of min, max
"""
scale_factor = 3
mean = numpy.average(samples)
rms = numpy.max([scale_factor*((numpy.sum((samples-mean)**2)/len(samples))**.5), .1])
min_val = mean - rms
max_val = mean + rms
return min_val, max_val
def get_min_max_fft(fft_samps):
"""
Get the minimum and maximum bounds for an array of fft samples.
@param samples the array of real values
@return a tuple of min, max
"""
#get the peak level (max of the samples)
peak_level = numpy.max(fft_samps)
#separate noise samples
noise_samps = numpy.sort(fft_samps)[:len(fft_samps)/2]
#get the noise floor
noise_floor = numpy.average(noise_samps)
#get the noise deviation
noise_dev = numpy.std(noise_samps)
#determine the maximum and minimum levels
max_level = peak_level
min_level = noise_floor - abs(2*noise_dev)
return min_level, max_level
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