class Model:
""" Holds all the pertinent information regarding the signals.
It also caches the information so it isn't re-calculated unnecesarily (for example when re-dimensioning windows)
It communicates with the Controller by emiting messages.
"""
FFT_METHODS = ['process_gain', 'noise_floor', 'SFDR', 'SINAD', 'THD', 'SNR', 'ENOB']
def __init__(self):
self.signal = None
self.fft_signal = None
self.cache_signal()
self.cache_fft_signal()
def parse_file(self, path, max_peaks=0, dB = None, time_domain = None):
""" Invoked when a new file needs to be parsed. Will raise an exception if the file contains syntax errors
Emits the message 'SIGNAL CHANGED' if the file was cached succesfully
"""
self.signal = None
self.fft_signal = None
try:
config = ConfigParser.RawConfigParser()
config.read(path)
nbits = config.getint('SIGNAL', 'nbits')
rate = config.getint('SIGNAL', 'rate')
dataString = config.get('SIGNAL', 'data').split('\n')
data = map(string.atoi, dataString)
self.signal = Signal(nbits, rate, data)
self.fft_signal = self.signal.FFT(dB, time_domain)
finally:
self.cache_signal()
self.cache_fft_signal(max_peaks)
pub.sendMessage("SIGNAL CHANGED")
def reprocess_fft(self, window, slices, max_peaks):
""" Invoked when the FFT controls on tab 3 have been changed. re-calculates fft
Emits the message 'FFT CHANGED' when done calculating
"""
if self.signal is None:
self.fft_signal = None
else:
# TODO: figure out how window, slices and max_peax change the way fft is calculated
# maybe they define dB and time_domain?
dB = None
time_domain = None
self.fft_signal = self.signal.FFT(dB, time_domain)
self.cache_fft_signal(max_peaks)
pub.sendMessage("FFT CHANGED")
def cache_signal(self):
""" Invokes all methods in Signal and caches their result for later use (mainly window resizing)
Resets values to safe values (0, []) if there was an error while processing the signal definition file
"""
if self.signal is None:
self.data = []
self.INL, self.max_INL = [], 0
self.DNL, self.max_DNL = [], 0
self.histogram, self.ideal_histogram = [], []
else:
self.data = self.signal.data
self.INL, self.max_INL = self.signal.INL()
self.DNL, self.max_DNL = self.signal.DNL()
self.histogram, self.ideal_histogram = self.signal.histogram(), self.signal.ideal_histogram()
def cache_fft_signal(self, max_peaks=0):
""" Invokes all methods in FFTSignal and caches their result for later use (mainly window resizing)
Resets values to safe values (0, []) if there was an error while processing the signal definition file
"""
if self.fft_signal is None:
self.fft = []
self.harmonic_peaks = []
for name in Model.FFT_METHODS:
setattr(self, name, 0)
else:
self.fft = self.fft_signal.fft
self.harmonic_peaks = self.fft_signal.harmonic_peaks(max_peaks)
for name in Model.FFT_METHODS:
setattr( self, name, getattr(self.fft_signal, name)() )
