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PySpectrogram.py
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PySpectrogram.py
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#!/usr/bin/env python
import os
import time
import struct
import math
import numpy as np
import scipy as sp
import pymoos
import matplotlib.pyplot as plt
from matplotlib.mlab import window_hanning,specgram
from matplotlib.colors import LogNorm
class pPySpectrogram(object):
def __init__(self):
self.moos_app_name = 'PySpectrogram'
self.time_warp = 1
self.server_host = 'localhost'
self.server_port = 9000
self.freq_sampling = 37500.0
self.num_samples = 16000
self.num_hydrophones = 5
self.aco_data = np.zeros((self.num_samples, self.num_hydrophones)) #array that holds the acoustic data currently being processed
self.aco_data_latest = None #array that holds new acoustic data while it waits to be processed
self.aco_new = False #flag to indicate that new acoustic data has been read
self.extents = None
''' Initialize Python-MOOS Communications '''
self.mooscomms = pymoos.comms()
self.mooscomms.set_on_connect_callback(self.moos_on_connect)
self.mooscomms.add_active_queue('cbf_queue', self.moos_on_new_acoustic_binary_data) #let's use a queue callback instead to handle distinct messages
self.mooscomms.add_message_route_to_active_queue('cbf_queue', 'DAQ_BINARY_DATA') #route 'DAQ_BINARY_DATA' messages to moos_on_new_acoustic_binary_data function
self.mooscomms.run(self.server_host, self.server_port, self.moos_app_name)
pymoos.set_moos_timewarp(self.time_warp)
def moos_on_connect(self):
''' On connection to MOOSDB, register for desired MOOS variables (allows for * regex) e.g. register('variable', 'community', 'interval')
self.mooscomms.register('NODE_*_PING','NODE_*',0) '''
self.mooscomms.register('DAQ_BINARY_DATA', 0) #register for acoustic binary data
return True
def moos_on_new_acoustic_binary_data(self, msg):
''' Queue callback function to handle 'DAQ_BINARY_DATA' messages specifically; it has the same accessors a regular mail callback:
msg.trace(), msg.time(), msg.name(), msg.key(), msg.is_name(), msg.source(), msg.is_double(), msg.double(), msg.double_aux(),
msg.is_string(), msg.string(), msg.is_binary(), msg.binary_data(), msg.binary_data_size(), msg.mark_as_binary() '''
msg_binary = msg.binary_data() # remember - iMCC1608FS DAQ data is sent as binary float (32-bit)
try:
self.aco_data_latest = np.frombuffer(msg_binary, dtype=np.float32).reshape((self.num_samples, self.num_hydrophones)) # much faster than struct.unpack
self.aco_new = True
except Exception, e:
print 'Error occurred attempting to unpack acoustic binary data:'
print str(e)
return True
def get_specgram(self,signal):
arr2D,freqs,bins = specgram(signal,window=window_hanning,Fs=self.freq_sampling,NFFT=256,noverlap=128)
return arr2D,freqs,bins
def run(self):
while True:
if self.aco_new:
self.aco_data[:] = self.aco_data_latest
arr2D,freqs,bins = self.get_specgram(self.aco_data[:,0])
if self.extents is None:
self.extents = (freqs[0],freqs[-1],bins[0],bins[-1])
img.set_extent(self.extents)
ax.invert_yaxis()
img.set_array(arr2D.T)
img.set_norm(LogNorm(vmin=np.min(arr2D),vmax=np.max(arr2D)))
img.axes.figure.canvas.draw()
plt.show()
self.aco_new = False
time.sleep(0.01)
plt.ion()
plt.figure(figsize=(8, 6))
ax = plt.gca()
fig = plt.gcf()
img = ax.imshow(np.zeros((513,257)), aspect='auto', cmap='jet', interpolation='nearest', norm=LogNorm(vmin=1e-16,vmax=1e-5))
plt.xlabel('Frequency (Hz)')
plt.ylabel('Time (s)')
if __name__ == '__main__':
pyspec = pPySpectrogram()
pyspec.run()